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Longitudinal Study | Definition, Approaches & Examples

Longitudinal Study | Definition, Approaches & Examples

Published on May 8, 2020 by Lauren Thomas . Revised on June 22, 2023.

In a longitudinal study, researchers repeatedly examine the same individuals to detect any changes that might occur over a period of time.

Longitudinal studies are a type of correlational research in which researchers observe and collect data on a number of variables without trying to influence those variables.

While they are most commonly used in medicine, economics, and epidemiology, longitudinal studies can also be found in the other social or medical sciences.

How long is a longitudinal study, longitudinal vs cross-sectional studies, how to perform a longitudinal study, advantages and disadvantages of longitudinal studies, other interesting articles, frequently asked questions about longitudinal studies.

No set amount of time is required for a longitudinal study, so long as the participants are repeatedly observed. They can range from as short as a few weeks to as long as several decades. However, they usually last at least a year, oftentimes several.

One of the longest longitudinal studies, the Harvard Study of Adult Development , has been collecting data on the physical and mental health of a group of Boston men for over 80 years!

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The opposite of a longitudinal study is a cross-sectional study. While longitudinal studies repeatedly observe the same participants over a period of time, cross-sectional studies examine different samples (or a “cross-section”) of the population at one point in time. They can be used to provide a snapshot of a group or society at a specific moment.

Both types of study can prove useful in research. Because cross-sectional studies are shorter and therefore cheaper to carry out, they can be used to discover correlations that can then be investigated in a longitudinal study.

If you want to implement a longitudinal study, you have two choices: collecting your own data or using data already gathered by somebody else.

Using data from other sources

Many governments or research centers carry out longitudinal studies and make the data freely available to the general public. For example, anyone can access data from the 1970 British Cohort Study, which has followed the lives of 17,000 Brits since their births in a single week in 1970, through the UK Data Service website .

These statistics are generally very trustworthy and allow you to investigate changes over a long period of time. However, they are more restrictive than data you collect yourself. To preserve the anonymity of the participants, the data collected is often aggregated so that it can only be analyzed on a regional level. You will also be restricted to whichever variables the original researchers decided to investigate.

If you choose to go this route, you should carefully examine the source of the dataset as well as what data is available to you.

Collecting your own data

If you choose to collect your own data, the way you go about it will be determined by the type of longitudinal study you choose to perform. You can choose to conduct a retrospective or a prospective study.

In a retrospective study , you collect data on events that have already happened.
In a prospective study , you choose a group of subjects and follow them over time, collecting data in real time.

Retrospective studies are generally less expensive and take less time than prospective studies, but are more prone to measurement error.

Like any other research design , longitudinal studies have their tradeoffs: they provide a unique set of benefits, but also come with some downsides.

Longitudinal studies allow researchers to follow their subjects in real time. This means you can better establish the real sequence of events, allowing you insight into cause-and-effect relationships.

Longitudinal studies also allow repeated observations of the same individual over time. This means any changes in the outcome variable cannot be attributed to differences between individuals.

Prospective longitudinal studies eliminate the risk of recall bias , or the inability to correctly recall past events.

Disadvantages

Longitudinal studies are time-consuming and often more expensive than other types of studies, so they require significant commitment and resources to be effective.

Since longitudinal studies repeatedly observe subjects over a period of time, any potential insights from the study can take a while to be discovered.

Attrition, which occurs when participants drop out of a study, is common in longitudinal studies and may result in invalid conclusions.

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Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Longitudinal study	Cross-sectional study
observations	Observations at a in time
Observes the multiple times	Observes (a “cross-section”) in the population
Follows in participants over time	Provides of society at a given point

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

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Thomas, L. (2023, June 22). Longitudinal Study | Definition, Approaches & Examples. Scribbr. Retrieved August 20, 2024, from https://www.scribbr.com/methodology/longitudinal-study/

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Longitudinal Study | Definition, Approaches & Examples

Longitudinal Study | Definition, Approaches & Examples

Published on 5 May 2022 by Lauren Thomas . Revised on 24 October 2022.

In a longitudinal study, researchers repeatedly examine the same individuals to detect any changes that might occur over a period of time.

Longitudinal studies are a type of correlational research in which researchers observe and collect data on a number of variables without trying to influence those variables.

While they are most commonly used in medicine, economics, and epidemiology, longitudinal studies can also be found in the other social or medical sciences.

How long is a longitudinal study, longitudinal vs cross-sectional studies, how to perform a longitudinal study, advantages and disadvantages of longitudinal studies, frequently asked questions about longitudinal studies.

One of the longest longitudinal studies, the Harvard Study of Adult Development , has been collecting data on the physical and mental health of a group of men in Boston, in the US, for over 80 years.

Prevent plagiarism, run a free check.

The opposite of a longitudinal study is a cross-sectional study. While longitudinal studies repeatedly observe the same participants over a period of time, cross-sectional studies examine different samples (or a ‘cross-section’) of the population at one point in time. They can be used to provide a snapshot of a group or society at a specific moment.

If you want to implement a longitudinal study, you have two choices: collecting your own data or using data already gathered by somebody else.

Using data from other sources

Many governments or research centres carry out longitudinal studies and make the data freely available to the general public. For example, anyone can access data from the 1970 British Cohort Study, which has followed the lives of 17,000 Brits since their births in a single week in 1970, through the UK Data Service website .

These statistics are generally very trustworthy and allow you to investigate changes over a long period of time. However, they are more restrictive than data you collect yourself. To preserve the anonymity of the participants, the data collected is often aggregated so that it can only be analysed on a regional level. You will also be restricted to whichever variables the original researchers decided to investigate.

If you choose to go down this route, you should carefully examine the source of the dataset as well as what data are available to you.

Collecting your own data

In a retrospective study , you collect data on events that have already happened.
In a prospective study , you choose a group of subjects and follow them over time, collecting data in real time.

Retrospective studies are generally less expensive and take less time than prospective studies, but they are more prone to measurement error.

Like any other research design , longitudinal studies have their trade-offs: they provide a unique set of benefits, but also come with some downsides.

Longitudinal studies also allow repeated observations of the same individual over time. This means any changes in the outcome variable cannot be attributed to differences between individuals.

Prospective longitudinal studies eliminate the risk of recall bias , or the inability to correctly recall past events.

Disadvantages

Longitudinal studies are time-consuming and often more expensive than other types of studies, so they require significant commitment and resources to be effective.

Since longitudinal studies repeatedly observe subjects over a period of time, any potential insights from the study can take a while to be discovered.

Attrition, which occurs when participants drop out of a study, is common in longitudinal studies and may result in invalid conclusions.

Longitudinal study	Cross-sectional study
observations	Observations at a in time
Observes the multiple times	Observes (a ‘cross-section’) in the population
Follows in participants over time	Provides of society at a given point

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Cite this Scribbr article

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Thomas, L. (2022, October 24). Longitudinal Study | Definition, Approaches & Examples. Scribbr. Retrieved 19 August 2024, from https://www.scribbr.co.uk/research-methods/longitudinal-study-design/

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Developing a Methodological Research Program for Longitudinal Studies

National Academies of Sciences, Engineering, and Medicine ; Division of Behavioral and Social Sciences and Education ; Committee on National Statistics .

August 2017

One of the strategic objectives of the National Institute on Aging (NIA) is to “support the development of population-based data sets, especially from longitudinal studies, suitable for analysis of biological, behavioral, and social factors affecting health, well-being, and functional status through the life course.” 1 To contribute to that objective and to inform the development of a methodological research program for longitudinal studies, the Committee on National Statistics held a public workshop in June 2017. The discussion focused on challenges that are specific to the types of longitudinal studies supported by NIA and aimed to identify areas of methodological research that could be pursued in order to benefit from emerging methods, new techniques, or other opportunities to enhance the data and increase data collection efficiency. This document summarizes the presentations and discussion.

More information about the workshop is available at http://nas.edu/Longitudinal-Methods-Workshop .

See https://www .nia.nih.gov /about/strategic-directions-2016 /goal-g-support-infrastructure-resources-needed-promote-high-quality-research [July 2017].

LONG-STANDING ISSUES

As background about the motivation for the workshop, John Haaga (National Institute on Aging) said that the types of surveys funded by NIA are complex and the data needs require increasingly more complex methodological solutions. Moreover, longitudinal studies of aging have unique characteristics, so that the findings from the wealth of methodological research generated by the survey industry—primarily focused on cross-sectional studies—are not always applicable.

Graham Kalton (Westat) underscored that there are several different types of longitudinal studies, including cohort studies of individuals (such as the National Longitudinal Surveys or the National Health and Aging Trends Study) and household panel surveys (such as the Panel Study of Income Dynamics and Understanding Society and the UK Household Longitudinal Study). Rotating panel surveys (such as the Medical Expenditure Panel Survey, the Survey of Income and Program Participation) can also be considered a type of longitudinal survey, though they are typically used for cross-sectional analyses. Kalton provided an overview of long-standing methodological issues that are unique, or are of particular importance, to cohort studies of individuals and household panel surveys, the types of studies of primary interest for the workshop.

Kalton said that longitudinal studies need to be designed with a recognition that objectives will change over time. With that in mind, there are several basic design considerations. One is whether the survey is focused on a national or a local population, which has implications for the extent to which mobility in and out of the area will be a factor. Another is the types of characteristics used for oversampling, which are usually fixed characteristics, such as age and race, rather than characteristics that can change, such as poverty. And a third is a plan for replenishing the sample to add new entrants to the population.

Additional considerations emphasized by Kalton as particularly important for the design of longitudinal surveys include: (1) rules that determine which sample members are followed from one wave to another, (2) sample loss from one wave to another, (3) strategies for maintaining high response rates, (4) use of administrative records, (5) use of adaptive design, (6) sample replenishment, (7) intervals between waves, (8) data collection modes, (9) panel conditioning effects, (10) implications for longitudinal analyses, (11) statistical disclosure control, and (12) management of a survey as a national resource.

Kalton highlighted several general priority areas for which new or continuing research is needed to inform the design of longitudinal surveys: research on how to obtain high initial response rates and maintain high retention rates; the effects of changing modes from one wave to another on longitudinal analyses; ways of reducing respondent burden; and opportunities for the use of administrative records and contextual variables to reduce burden and expand analysis capabilities.

LESSONS FROM THE UNITED KINGDOM

To provide a perspective based on the experience of a longitudinal survey that has a formal, large-scale mechanism built in for methodological research and experimentation, Annette Jäckle (University of Essex) described the UK's Understanding Society Study and its Innovation Panel. The Innovation Panel is a survey of approximately 1,500 households and has been conducted annually since 2008. The survey includes the core Understanding Society questions and core methodological research experiments that have been developed to inform the design of the main survey. There is an open competition for researchers who would like to add their own methodological research to the Innovation Panel. On average, there are 11 experiments in each wave, and some experiments are conducted across multiple waves. Sometimes additional studies are conducted between the annual waves, using the Innovation Panel sample.

Jäckle said that the main strength of the Innovation Panel is that it is designed the same way and uses the same procedures as the main Understanding Society Study. However, she acknowledged, the large number of experiments raises questions about whether the findings can be generalized to the main survey. In particular, it is possible that the experiments could lead to differential errors related to attrition, context effects, and panel conditioning. Based on the research that has been conducted to date to examine these issues, Jäckle said it appears that the findings from the Innovation Panel have good external and internal validity. The experiments have been useful in informing decisions about such issues as mixed mode designs and a mobile app to measure monthly household expenditures.

OPTIMIZING PERIODICITY AND CONTENT

One of the workshop sessions focused on the research needs to inform decisions about the frequency and timing of data collection waves in longitudinal studies. Vicki Freedman (University of Michigan) emphasized that decisions about periodicity need to be considered in the context of the primary scientific focus of the study, which can be grouped into three broad categories of topics: (1) population-level trends, (2) life-course influences on later life, and (3) individual-level trajectories.

Freedman said that research is needed to better understand how wider periodicities affect costs, including interviewer recruiting, training, and retention costs, and the costs associated with sample member tracking and contacts between follow-ups. Life-course studies in particular would benefit from research on the relevant windows of vulnerability for different types of predictors of interest. It would also be useful to conduct research on the relevant ages for measuring outcomes of interest, for example, by identifying periods of steep change.

Freedman noted that secondary analysis of small-scale epidemiological studies with rich detail could shed further light on this question, even if the predictors in the available studies are not exactly the predictors of interest. In studies that aim to understand individual-level trajectories, it may be possible to assign a subset of the sample to more frequent data collections, some of which align with the data collections in a broader interviewer-assisted study. Secondary analysis of the dynamics could shed light on the extent of the bias as the interval is widened, she said.

Randall Olsen (Ohio State University) shared Freedman's view that the ideal periodicity for a survey ultimately depends on its research goals. He argued that existing longitudinal surveys are a rich source of underutilized data that could be used to research periodicity. For example, the National Longitudinal Surveys have been conducted with varying periodicity since their beginning more than four decades ago, and several experiments were embedded into them over the years. He noted that sample members who do not respond during each follow-up wave introduce additional variation, but that some of the missing data are recovered during subsequent rounds using bounded interviewing techniques. He argued that the most valuable experiments in the near future will likely be focused on fieldwork strategies, particularly on evaluating strategies that reduce nonresponse.

Marco Angrisani (University of Southern California) discussed a project that evaluated the feasibility of linking survey data from the Understanding America Study to data from real-time electronic financial transactions. Linking to those types of data is challenging because respondents not only have to consent to provide access but also have to take several steps to enable the linking: create an account with the financial management Website used for the study, add a financial institution to the account, and keep the account up to date. Due to the burden of this process, only a subset of sample members who initially agree to participate actually complete all the steps. He said that additional research is needed to understand how to overcome these barriers and promote participation through the use of incentives, visual aids, a help line, or other methods. Studies that advance understanding on how to implement these linkages would be particularly valuable because access to data of this type could represent a paradigm shift from fixed-period survey designs to continuous or event-triggered designs.

ALTERNATIVES AND ENHANCEMENTS TO SELF-REPORTED DATA

One of the workshop sessions focused on the growing area of methodological research to identify and evaluate new sources and forms of data that can be linked to self-reported survey data. Pamela Herd (University of Wisconsin) discussed the feasibility of expanding biological data collections in population-based longitudinal studies to include data on the gut microbiome. These types of data collections involve logistical challenges and an increased burden on respondents, but a pilot study conducted as part of the Wisconsin Longitudinal Study had high response rates. She noted that the qualitative research conducted in preparation for the study was helpful in identifying the messages that were able to overcome respondent concerns and encourage participation. For example, the advance letter for the study included a Time magazine article about the gut microbiome. Herd underscored the benefits of interdisciplinary collaborations for these types of studies, particularly the greatly expanded usefulness of the data when biological data can be linked to population-based survey data.

Turning to two areas that are especially difficult to measure using self-reports, activity and sleep, particularly sleep quality, Phil Schumm (University of Chicago) described advances in using actigraphy to more directly measure activity and sleep in population-based longitudinal studies. He said that the latest generation of actigraphy devices is very capable and cost efficient and can be shared across studies. Work on new methods for extracting features is progressing quickly across many disciplines, he said, but more integration is needed with the types of research conducted by survey methodologists. In addition, Schumm said, there is a need to focus on new functional analysis approaches to the study of activity (and, possibly, sleep) throughout the entire day.

Jeffrey Kaye (Oregon Health and Science University) discussed other remote sensing methodologies for unobtrusive data collection. He noted that detecting meaningful change is of particular interest for longitudinal studies, but it is difficult to accomplish using traditional data collection methods. Technologies that can provide continuous data—such as passive sensors installed in people's homes—can detect mobility, walking speed, sleep, and night-time behavior and can detect changes in variability often missed with sparsely spaced periodic data collections.

In addition to more frequent measurement, remote-sensing technologies can also provide more objective, reliable, and ecologically valid data than self-reports. Consequently, deeper analysis is possible because of greater time domain precision and the possibility of integrating uniformly time-stamped data across multiple domains. Although there are major challenges associated with the scalability of using remote-sensing technologies in large longitudinal studies, it is not impossible to do, particularly for studies that begin with an in-person interview. Kaye argued that smaller studies can continue to provide valuable information that helps researchers understand how to take advantage of these new sources of data on a larger scale in the near future.

Nancy Bates (U.S. Census Bureau) provided an overview of consent and confidentiality considerations when linking survey data to data from other sources. These considerations are becoming increasingly important as more and more data from different sources are integrated. She said that research is needed to build on studies that have begun to examine such questions as how the mode, framing, and placement of the request affects consent and the most effective ways for communicating the request. Of particular importance to studies on aging is to better understand how older people interpret consent. Bates noted that vast amounts of data are available from existing studies, which can be used to conduct analyses about the characteristics of people who do not provide consent.

Kelly Peters (American Institutes for Research) described the record linkage activities in Project Talent, a longitudinal survey that began with a sample of high school students in 1960. The study team has worked on expanding the usefulness of the survey data with linkages to data from the Social Security Administration and the Centers for Medicare & Medicaid Services. Among several questions, methodological research as part of these projects has evaluated ways of obtaining consent. She noted that, overall, record linkages reduced cost and respondent burden for Project Talent.

Jennifer Ailshire (University of Southern California) discussed opportunities for expanding longitudinal studies of aging through linkages to contextual data, such as information about the socioeconomic and demographic characteristics of a place, the built environment, physical environment, and availability of health care. Although these types of linked datasets are not easy to manage and use, and data user access needs to be balanced with considerations of confidentiality and administrative burden, the datasets create valuable new research opportunities. To move forward, Ailshire said, it would be useful to establish a central data warehouse that could bring together linked data and facilitate their storage and distribution. Developing an infrastructure that would enable researchers to input respondent household or workplace coordinates and receive spatially linked data for these cases would be of great benefit to researchers, she said.

Building on work in the fields of cognitive neuroscience, psychology, and empirical social science, Seth Sanders (Duke University) described research on the possible uses of response-time data to model cognition and cognitive decline. Acknowledging that survey response-time data can conflate several different processes (such as reading and answering speed or interviewer and respondent behavior) and that researchers do not have the same control over the environment during a survey interview as neuroscientists in a laboratory experiment, these types of data are readily available as a byproduct of computer-assisted data collections and could be particularly useful in longitudinal surveys to compare two data points. Sanders reported that he and his colleagues used response time on the Montreal Cognitive Assessment screening test, but future research could evaluate whether other sets of survey questions could also be used to model cognitive decline. This research also raises the question of whether other neuroscience methods (such as eye tracking or functional magnetic resonance imaging) could also be used to improve the usefulness of response-time data in surveys and to investigate whether response-time data could improve measurement and modeling in other research.

As a wrap-up to the session on alternatives to self-reported data, Brian Harris-Kojetin (National Academies of Sciences, Engineering, and Medicine) summarized the work of a Committee on National Statistics panel that was asked to provide recommendations to increase the use of combinations of multiple data sources in federal statistical programs. The panel's first report discussed the use of both government and private-sector data and recommended the development of a framework for combining data from multiple sources. 2 The report also discussed the increased privacy and confidentiality concerns when combining datasets. The panel's more comprehensive second report is scheduled to be released in late summer 2017.

REDUCING RESPONDENT BURDEN, INCREASING PARTICIPATION, AND IMPROVING DATA QUALITY

Mick Couper (University of Michigan) turned to the issues of respondent burden, participation rates, and data quality, which are complex, interconnected, and often involve tradeoffs. He provided an overview of the opportunities and challenges associated with the use of mixed-mode data collections and new interview modes. Research is needed to better understand how to target mixed-mode designs to maximum effect, he said, for example, by predicting who is most likely to respond through the Web and targeting requests accordingly. More work is also needed to identify the best ways for addressing differential Internet and smartphone coverage, he said.

Couper noted that longitudinal surveys collect an increasing variety of measurements, in addition to self-reported data, and there are questions about how to most efficiently integrate the collection of these types of data into the overall survey process. He offered several examples, including: What types of biological samples is it feasible to ask respondents to mail in? What are the implications of different modes of administration for tests of cognitive ability? He agreed with Bates that research is needed on how to increase consent rates for administrative data linkages, particularly on the Web. A related question is how to increase the use of new technologies that facilitate additional forms of measurement in longitudinal surveys, such as accelerometers or global positioning systems. Couper said that an underlying goal has to be to develop research designs that can answer questions related to mixed-mode data collections and new technologies without negatively affecting the core data collection processes in ongoing longitudinal surveys.

Chris Chapman (National Center for Education Statistics [NCES]) described efforts to increase response rates and reduce nonresponse bias in the Beginning Postsecondary Student Longitudinal Study (BPS). He argued that longitudinal studies are particularly well suited for adaptive design strategies that rely on modeling response propensity and potential response bias because they benefit from the availability of data from the sampling frame or administrative records, paradata, substantive information from prior interviews, and, sometimes, mode flexibility. The BPS experiments allowed NCES to target incentives in ways that were most effective in improving data quality. Specifically, NCES was able to substantially reduce nonresponse bias by focusing on nonrespondents who had a high likelihood of contributing to nonresponse bias and had a high response propensity score, and by identifying the incentive amount ($45) that led to reduction in bias in the largest number of estimates. Chapman noted that studying intervention options with a sub-sample prior to implementation of a full survey is prudent when it is feasible.

Rob Warren (University of Minnesota) turned to the subject of panel conditioning effects, the idea that the act of responding to survey questions can, over time, change attitudes, behaviors, or at least the quality of the reports on those attitudes and behaviors. This challenge is unique to longitudinal studies. Warren said that although it is generally well understood that these types of effects may occur, they rarely receive adequate attention. More research is needed to understand the circumstances and respondent characteristics that increase the likelihood of panel conditioning effects, he said. Building methodological experiments into data collections can improve understanding of these issues and help improve survey design in subsequent waves. Although it is not always practical to undertake elaborate experiments, rotating panels can usually be easily accommodated and can provide a mechanism for evaluating panel conditioning effects in a particular survey.

COST CONSIDERATIONS AND COST-EFFECTIVENESS MEASURES

Michaela Benzeval (University of Essex) talked about survey costs in the context of the Understanding Society Study described earlier. She said that funding for the study comes from a variety of sources, including the UK Economic and Social Research Council and government agencies. She noted that there is increasing interest in the ability of a study to demonstrate its value and a general trend toward tighter budgets. In recent years, the sponsors pushed for a switch to a sequential mixed-mode design for Understanding Society. In response, Benzeval and her team had to become more creative in managing the uncertainties and risks involved with this shift, in an environment in which the data collection contractors' actual costs are not sufficiently transparent. Because only prices (no direct cost information) are available from the survey's current contractor, an “open book” accounting framework was developed: that framework includes a detailed spreadsheet of variable prices associated with different survey modes, linked to predicted responses for those modes. If either the budget or the response rates differ substantially from what was anticipated, the detailed variable price information can inform decisions about how to vary activities in order to maximize response rates within the fixed budget.

The initial Understanding Society experience with introducing mixed-mode data collection was that the variable costs declined modestly, but the fixed costs and the overall cost per household increased substantially. Whether the fixed costs could be reduced in subsequent waves is an open question. In particular, the research team will want to investigate whether it is possible to encourage the Web response mode before other modes for the entire sample, whether it is possible to reduce face-to-face contact without a drop in response rates over waves, and whether there are other ways of engaging respondents—for example, through newsletters—that would reduce costs.

Stephen Smith (NORC at the University of Chicago) discussed survey costs, primarily based on his experience with the National Social Life, Health, and Aging Project. He noted that there are many differences among contractors in the metrics used to track data collection costs. These differences make comparisons difficult even when cost data might be available. Although there are cost implications associated with all of the methodological issues that have been discussed throughout the workshop, Smith argued that there are several areas that deserve particular attention from a cost perspective: (1) exploring different options for periodicity, including continuous data collections; (2) understanding the actual cost implications of “cheaper” modes; (3) chasing high response rates; (4) considering cost versus the quality of mail data collection; (5) assessing interviewer training approaches; (6) staying connected with respondents between data collection waves; (7) leveraging new technologies for respondent contact and response mode; (8) considering incentive levels; (9) and leveraging bulk purchasing power across studies (e.g., for specimen equipment).

Brad Edwards (Westat), using the example of the National Health and Aging Trends Study, pointed out that longitudinal studies have unique and complex cost implications that differ from cross-sectional studies. However, he said, there are some basic metrics related to variable costs that tend to be measured the same way across contractors, such as response rates, incentives per sample unit, and interviewer hours per completed case.

Edwards noted that there are several additional cost metrics that have the potential to be shared more openly. One set of metrics includes total project costs divided by years in funding vehicle, sample size, total number of interviews, number of rounds, and design type. A second set of metrics for fixed costs would be months from inception to launch, number of variables, and months from the end of data collection to the public release of data. A third set of metrics for variable costs could come from paradata on contact attempts and successes; incentive protocol and payouts; response rate and level-of-effort effects by incentives, as well as the total cost by sample unit and by incentives; and for Web and mail responses, the effects of reminders over time.

In terms of research needs, Edwards highlighted the question of what makes participation of value to respondents. In particular, he said, it would be useful to better understand how engagement, saliency, and gamification influence respondent retention and survey costs.

PERSPECTIVES ON KEY THEMES AND RESEARCH NEEDS

During the final session of the workshop, four of the workshop planning committee members offered their perspectives on the key themes and research needs that emerged from the discussion. Robert Hauser (University of Wisconsin) began by highlighting panel conditioning effects and consent for data linkage as two areas for which more research is needed. With new rules going into effect in 2018 for the protection of research participants, there will be increased emphasis on the informed aspects of consent and a better understanding of what facilitates informed consent will become particularly important. Hauser argued that the tradeoffs associated with nonresponse need to be better understood, building on research on the bias implications associated with nonresponse, but also remembering the importance of maintaining sufficient statistical power for longitudinal analyses. He noted that the discussion of survey costs was very useful and urged the workshop participants to maintain a focus as part of the cost research on how methodological changes may affect subsequent waves of data collections in longitudinal studies.

Hauser suggested that more research may be needed to understand how questionnaire length relates to respondents' willingness to complete a survey, and, in particular, the differences between the two self-administered modes, paper-and-pencil questionnaires and Web surveys. He also argued that the way researchers communicate with participants between survey waves deserves more attention and that new research in the area of science communication may be able to provide ideas.

Another area of research that Hauser said is promising is the use of paradata, such as contact records. The question of how interviewer characteristics affect interview outcomes is not new, but the discussion about continuous data collection designs raises this question in a new light. Finally, an important question that emerged from the workshop is whether it would be possible to develop a vehicle dedicated to methodological research, similar to the Innovation Panel of the Understanding Society Study. Hauser said that a panel that could simultaneously serve the needs of several longitudinal surveys may not be feasible, but whether individual surveys could include a methodological research panel deserves consideration.

Maria Glymour (University of California, San Francisco) argued that one of the most urgent research needs is related to the representativeness of data that may be available for potential linkages, and, in particular, how this affects the generalizability of research based on linked data. In addition to selective participation, selective survival of the records is also a concern. Glymour agreed with others who pointed out that the related question of what motivates people to consent to data linkage is an important area of research to pursue, and some of this research could be based on qualitative studies. She argued that a better understanding of what drives participation could be used to develop weighting models for nonrepresentative samples.

In terms of research on aging, Glymour said that pursuing continuous data collection, which could be a passive form of data collection, in combination with adaptive designs that could trigger targeted followups during the last months of a person's life, could provide invaluable information during a critical period in the life course. She noted that it is not clear which variables would be most useful to monitor, but the issue of representativeness is, again, an important consideration.

Another priority area for research highlighted by Glymour related to opportunities for expanding the use of existing data from nonsurvey sources. Clinical data are highly relevant for aging studies, and therefore the ability to link to electronic medical records needs to be explored. The usefulness of these types of data could further be expanded through more ambitious projects, she said, such as the use of machine learning and natural language processing techniques to extract detailed information from medical records and the potential addition of imaging data. Glymour also agreed that the use of paradata, such as response time or interviewer effects, needs to be explored and that longitudinal studies, particularly those that are interested in cognition, present unique opportunities for expanding the use of these types of data. Glymour argued that interdisciplinary collaborations could make an especially powerful contribution to advancing research in this area. A final general area of research that Glymour emphasized as a priority is evaluating and reporting data quality, ideally using standardized metrics. As noted throughout the workshop, this topic becomes especially important with the increasing use of data from multiple sources.

Jäckle agreed with Hauser that implementing a survey similar to the Understanding Society Innovation Panel for multiple longitudinal surveys is unlikely to be feasible, due to the many differences in data collection methods among surveys. However, it may be worthwhile to pursue whether individual surveys could carve out subsets of their samples for methodological research, which could enable in-depth research that not only shows an effect (or the lack of an effect) but also provides insights into why a certain phenomenon occurs.

Jäckle agreed with others that research on consent is important. Increasingly more complicated data linkages call for a better understanding of the factors that influence people's willingness to provide consent, and in a broader sense of the barriers to participation, as illustrated by Angrisani's research, described earlier. Jäckle echoed Glymour's argument that it is also important to pursue research on the implications for data quality of linking information from different sources, particularly the implications in terms of representativeness.

Jäckle noted that the question of panel conditioning effects deserves renewed attention with the increasing interest in the use of data associated with technologies that were originally designed specifically to change behavior, such as actigraphy devices and financial aggregators. Jäckle pointed out that another characteristic of new technologies is that they are rapidly evolving, which could have implications for the comparability of data collected over several waves in a longitudinal study. She argued that research similar to the mode-effects studies may be needed to understand these potential effects.

Colm O'Muircheartaigh (University of Chicago) noted that the discussion of costs revealed that the challenges that stand in the way of meaningful exchanges about costs are not simply due to differences in accounting systems and concerns about the proprietary nature of the data; rather, much more work is needed to develop the types of metrics that would be most useful to have in order to inform the design of longitudinal studies. He argued that the general goal of increasing synergies across longitudinal surveys is worth exploring and that synergies can take many forms, but, he acknowledged, these types of collaborations are challenging and often involve compromises. He noted that involving survey methodologists and survey operations staff in discussions about particular surveys could make a substantial difference in researchers' ability to identify opportunities to improve the designs.

In terms of the usefulness of a potential panel set aside for methodological research, O'Muircheartaigh agreed with Jäckle that this would be most useful if the test panel mirrors the design of the main survey. He said this goal could be accomplished by simply partitioning the sample into two parts: a core sample and a subset that could be in the field in parallel, or possibly 6 to 12 months ahead of the main sample. From the perspective of obtaining funding for such a panel, he said, an ideal structure may be one that is flexible enough to allow for the possibility of combining data from the subset with the data from the main survey, when the nature and the outcome of the experiments allow this kind of approach.

Available: https://www .nap.edu/catalog /24652/innovations-in-federal-statistics-combining-data-sources-while-protecting-privacy [July 2017].

WORKSHOP PLANNING COMMITTEE:

James Jackson ( Chair ), Russell Sage Foundation, New York, and Institute for Social Research, University of Michigan; Maria Glymour, School of Medicine, University of California, San Francisco; Robert Hauser, Department of Sociology (emeritus), University of Wisconsin—Madison; Annette Jäckle, Institute for Social and Economic Research, University of Essex, United Kingdom; Colm O'Muircheartaigh, Harris School of Public Policy, University of Chicago.

DISCLAIMER:

This Proceedings of a Workshop—in Brief was prepared by Krisztina Marton, rapporteur, as a factual summary of what occurred at the meeting. The statements made are those of the rapporteur or individual meeting participants and do not necessarily represent the views of all meeting participants; the planning committee; the Committee on National Statistics; or the National Academies of Sciences, Engineering, and Medicine.

To ensure that it meets institutional standards for quality and objectivity, this Proceedings of a Workshop—in Brief was reviewed by Maria Glymour, School of Medicine, University of California, San Francisco; Richard Jones, Department of Psychiatry and Human Behavior, Brown University; and Stephen Smith, NORC at the University of Chicago. Kirsten Sampson Snyder, National Academies of Sciences, Engineering, and Medicine, served as review coordinator.

This workshop was supported by the National Institute on Aging of the National Institutes of Health. For additional information regarding the meeting, visit http://nas.edu/Longitudinal-Methods-Workshop .

Suggested citation: National Academies of Sciences, Engineering, and Medicine. (2017). Developing a Methodological Program for Longitudinal Studies: Proceedings of a Workshop—in Brief . Washington, DC: The National Academies Press. doi: https://doi.org/10.17226/24844 .

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Cite this Page National Academies of Sciences, Engineering, and Medicine; Division of Behavioral and Social Sciences and Education; Committee on National Statistics. Developing a Methodological Research Program for Longitudinal Studies: Proceedings of a Workshop—in Brief. Washington (DC): National Academies Press (US); 2017 Aug 4. doi: 10.17226/24844
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Longitudinal Study Design

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

A longitudinal study is a type of observational and correlational study that involves monitoring a population over an extended period of time. It allows researchers to track changes and developments in the subjects over time.

What is a Longitudinal Study?

In longitudinal studies, researchers do not manipulate any variables or interfere with the environment. Instead, they simply conduct observations on the same group of subjects over a period of time.

These research studies can last as short as a week or as long as multiple years or even decades. Unlike cross-sectional studies that measure a moment in time, longitudinal studies last beyond a single moment, enabling researchers to discover cause-and-effect relationships between variables.

They are beneficial for recognizing any changes, developments, or patterns in the characteristics of a target population. Longitudinal studies are often used in clinical and developmental psychology to study shifts in behaviors, thoughts, emotions, and trends throughout a lifetime.

For example, a longitudinal study could be used to examine the progress and well-being of children at critical age periods from birth to adulthood.

The Harvard Study of Adult Development is one of the longest longitudinal studies to date. Researchers in this study have followed the same men group for over 80 years, observing psychosocial variables and biological processes for healthy aging and well-being in late life (see Harvard Second Generation Study).

When designing longitudinal studies, researchers must consider issues like sample selection and generalizability, attrition and selectivity bias, effects of repeated exposure to measures, selection of appropriate statistical models, and coverage of the necessary timespan to capture the phenomena of interest.

Panel Study

A panel study is a type of longitudinal study design in which the same set of participants are measured repeatedly over time.
Data is gathered on the same variables of interest at each time point using consistent methods. This allows studying continuity and changes within individuals over time on the key measured constructs.
Prominent examples include national panel surveys on topics like health, aging, employment, and economics. Panel studies are a type of prospective study .

Cohort Study

A cohort study is a type of longitudinal study that samples a group of people sharing a common experience or demographic trait within a defined period, such as year of birth.
Researchers observe a population based on the shared experience of a specific event, such as birth, geographic location, or historical experience. These studies are typically used among medical researchers.
Cohorts are identified and selected at a starting point (e.g. birth, starting school, entering a job field) and followed forward in time.
As they age, data is collected on cohort subgroups to determine their differing trajectories. For example, investigating how health outcomes diverge for groups born in 1950s, 1960s, and 1970s.
Cohort studies do not require the same individuals to be assessed over time; they just require representation from the cohort.

Retrospective Study

In a retrospective study , researchers either collect data on events that have already occurred or use existing data that already exists in databases, medical records, or interviews to gain insights about a population.
Appropriate when prospectively following participants from the past starting point is infeasible or unethical. For example, studying early origins of diseases emerging later in life.
Retrospective studies efficiently provide a “snapshot summary” of the past in relation to present status. However, quality concerns with retrospective data make careful interpretation necessary when inferring causality. Memory biases and selective retention influence quality of retrospective data.

Allows researchers to look at changes over time

Because longitudinal studies observe variables over extended periods of time, researchers can use their data to study developmental shifts and understand how certain things change as we age.

High validation

Since objectives and rules for long-term studies are established before data collection, these studies are authentic and have high levels of validity.

Eliminates recall bias

Recall bias occurs when participants do not remember past events accurately or omit details from previous experiences.

Flexibility

The variables in longitudinal studies can change throughout the study. Even if the study was created to study a specific pattern or characteristic, the data collection could show new data points or relationships that are unique and worth investigating further.

Limitations

Costly and time-consuming.

Longitudinal studies can take months or years to complete, rendering them expensive and time-consuming. Because of this, researchers tend to have difficulty recruiting participants, leading to smaller sample sizes.

Large sample size needed

Longitudinal studies tend to be challenging to conduct because large samples are needed for any relationships or patterns to be meaningful. Researchers are unable to generate results if there is not enough data.

Participants tend to drop out

Not only is it a struggle to recruit participants, but subjects also tend to leave or drop out of the study due to various reasons such as illness, relocation, or a lack of motivation to complete the full study.

This tendency is known as selective attrition and can threaten the validity of an experiment. For this reason, researchers using this approach typically recruit many participants, expecting a substantial number to drop out before the end.

Report bias is possible

Longitudinal studies will sometimes rely on surveys and questionnaires, which could result in inaccurate reporting as there is no way to verify the information presented.

Data were collected for each child at three-time points: at 11 months after adoption, at 4.5 years of age and at 10.5 years of age. The first two sets of results showed that the adoptees were behind the non-institutionalised group however by 10.5 years old there was no difference between the two groups. The Romanian orphans had caught up with the children raised in normal Canadian families.
The role of positive psychology constructs in predicting mental health and academic achievement in children and adolescents (Marques Pais-Ribeiro, & Lopez, 2011)
The correlation between dieting behavior and the development of bulimia nervosa (Stice et al., 1998)
The stress of educational bottlenecks negatively impacting students’ wellbeing (Cruwys, Greenaway, & Haslam, 2015)
The effects of job insecurity on psychological health and withdrawal (Sidney & Schaufeli, 1995)
The relationship between loneliness, health, and mortality in adults aged 50 years and over (Luo et al., 2012)
The influence of parental attachment and parental control on early onset of alcohol consumption in adolescence (Van der Vorst et al., 2006)
The relationship between religion and health outcomes in medical rehabilitation patients (Fitchett et al., 1999)

Goals of Longitudinal Data and Longitudinal Research

The objectives of longitudinal data collection and research as outlined by Baltes and Nesselroade (1979):

Identify intraindividual change : Examine changes at the individual level over time, including long-term trends or short-term fluctuations. Requires multiple measurements and individual-level analysis.
Identify interindividual differences in intraindividual change : Evaluate whether changes vary across individuals and relate that to other variables. Requires repeated measures for multiple individuals plus relevant covariates.
Analyze interrelationships in change : Study how two or more processes unfold and influence each other over time. Requires longitudinal data on multiple variables and appropriate statistical models.
Analyze causes of intraindividual change: This objective refers to identifying factors or mechanisms that explain changes within individuals over time. For example, a researcher might want to understand what drives a person’s mood fluctuations over days or weeks. Or what leads to systematic gains or losses in one’s cognitive abilities across the lifespan.
Analyze causes of interindividual differences in intraindividual change : Identify mechanisms that explain within-person changes and differences in changes across people. Requires repeated data on outcomes and covariates for multiple individuals plus dynamic statistical models.

How to Perform a Longitudinal Study

When beginning to develop your longitudinal study, you must first decide if you want to collect your own data or use data that has already been gathered.

Using already collected data will save you time, but it will be more restricted and limited than collecting it yourself. When collecting your own data, you can choose to conduct either a retrospective or prospective study .

In a retrospective study, you are collecting data on events that have already occurred. You can examine historical information, such as medical records, in order to understand the past. In a prospective study, on the other hand, you are collecting data in real-time. Prospective studies are more common for psychology research.

Once you determine the type of longitudinal study you will conduct, you then must determine how, when, where, and on whom the data will be collected.

A standardized study design is vital for efficiently measuring a population. Once a study design is created, researchers must maintain the same study procedures over time to uphold the validity of the observation.

A schedule should be maintained, complete results should be recorded with each observation, and observer variability should be minimized.

Researchers must observe each subject under the same conditions to compare them. In this type of study design, each subject is the control.

Methodological Considerations

Important methodological considerations include testing measurement invariance of constructs across time, appropriately handling missing data, and using accelerated longitudinal designs that sample different age cohorts over overlapping time periods.

Testing measurement invariance

Testing measurement invariance involves evaluating whether the same construct is being measured in a consistent, comparable way across multiple time points in longitudinal research.

This includes assessing configural, metric, and scalar invariance through confirmatory factor analytic approaches. Ensuring invariance gives more confidence when drawing inferences about change over time.

Missing data

Missing data can occur during initial sampling if certain groups are underrepresented or fail to respond.

Attrition over time is the main source – participants dropping out for various reasons. The consequences of missing data are reduced statistical power and potential bias if dropout is nonrandom.

Handling missing data appropriately in longitudinal studies is critical to reducing bias and maintaining power.

It is important to minimize attrition by tracking participants, keeping contact info up to date, engaging them, and providing incentives over time.

Techniques like maximum likelihood estimation and multiple imputation are better alternatives to older methods like listwise deletion. Assumptions about missing data mechanisms (e.g., missing at random) shape the analytic approaches taken.

Accelerated longitudinal designs

Accelerated longitudinal designs purposefully create missing data across age groups.

Accelerated longitudinal designs strategically sample different age cohorts at overlapping periods. For example, assessing 6th, 7th, and 8th graders at yearly intervals would cover 6-8th grade development over a 3-year study rather than following a single cohort over that timespan.

This increases the speed and cost-efficiency of longitudinal data collection and enables the examination of age/cohort effects. Appropriate multilevel statistical models are required to analyze the resulting complex data structure.

In addition to those considerations, optimizing the time lags between measurements, maximizing participant retention, and thoughtfully selecting analysis models that align with the research questions and hypotheses are also vital in ensuring robust longitudinal research.

So, careful methodology is key throughout the design and analysis process when working with repeated-measures data.

Cohort effects

A cohort refers to a group born in the same year or time period. Cohort effects occur when different cohorts show differing trajectories over time.

Cohort effects can bias results if not accounted for, especially in accelerated longitudinal designs which assume cohort equivalence.

Detecting cohort effects is important but can be challenging as they are confounded with age and time of measurement effects.

Cohort effects can also interfere with estimating other effects like retest effects. This happens because comparing groups to estimate retest effects relies on cohort equivalence.

Overall, researchers need to test for and control cohort effects which could otherwise lead to invalid conclusions. Careful study design and analysis is required.

Retest effects

Retest effects refer to gains in performance that occur when the same or similar test is administered on multiple occasions.

For example, familiarity with test items and procedures may allow participants to improve their scores over repeated testing above and beyond any true change.

Specific examples include:

Memory tests – Learning which items tend to be tested can artificially boost performance over time
Cognitive tests – Becoming familiar with the testing format and particular test demands can inflate scores
Survey measures – Remembering previous responses can bias future responses over multiple administrations
Interviews – Comfort with the interviewer and process can lead to increased openness or recall

To estimate retest effects, performance of retested groups is compared to groups taking the test for the first time. Any divergence suggests inflated scores due to retesting rather than true change.

If unchecked in analysis, retest gains can be confused with genuine intraindividual change or interindividual differences.

This undermines the validity of longitudinal findings. Thus, testing and controlling for retest effects are important considerations in longitudinal research.

Data Analysis

Longitudinal data involves repeated assessments of variables over time, allowing researchers to study stability and change. A variety of statistical models can be used to analyze longitudinal data, including latent growth curve models, multilevel models, latent state-trait models, and more.

Latent growth curve models allow researchers to model intraindividual change over time. For example, one could estimate parameters related to individuals’ baseline levels on some measure, linear or nonlinear trajectory of change over time, and variability around those growth parameters. These models require multiple waves of longitudinal data to estimate.

Multilevel models are useful for hierarchically structured longitudinal data, with lower-level observations (e.g., repeated measures) nested within higher-level units (e.g., individuals). They can model variability both within and between individuals over time.

Latent state-trait models decompose the covariance between longitudinal measurements into time-invariant trait factors, time-specific state residuals, and error variance. This allows separating stable between-person differences from within-person fluctuations.

There are many other techniques like latent transition analysis, event history analysis, and time series models that have specialized uses for particular research questions with longitudinal data. The choice of model depends on the hypotheses, timescale of measurements, age range covered, and other factors.

In general, these various statistical models allow investigation of important questions about developmental processes, change and stability over time, causal sequencing, and both between- and within-person sources of variability. However, researchers must carefully consider the assumptions behind the models they choose.

Longitudinal vs. Cross-Sectional Studies

Longitudinal studies and cross-sectional studies are two different observational study designs where researchers analyze a target population without manipulating or altering the natural environment in which the participants exist.

Yet, there are apparent differences between these two forms of study. One key difference is that longitudinal studies follow the same sample of people over an extended period of time, while cross-sectional studies look at the characteristics of different populations at a given moment in time.

Longitudinal studies tend to require more time and resources, but they can be used to detect cause-and-effect relationships and establish patterns among subjects.

On the other hand, cross-sectional studies tend to be cheaper and quicker but can only provide a snapshot of a point in time and thus cannot identify cause-and-effect relationships.

Both studies are valuable for psychologists to observe a given group of subjects. Still, cross-sectional studies are more beneficial for establishing associations between variables, while longitudinal studies are necessary for examining a sequence of events.

1. Are longitudinal studies qualitative or quantitative?

Longitudinal studies are typically quantitative. They collect numerical data from the same subjects to track changes and identify trends or patterns.

However, they can also include qualitative elements, such as interviews or observations, to provide a more in-depth understanding of the studied phenomena.

2. What’s the difference between a longitudinal and case-control study?

Case-control studies compare groups retrospectively and cannot be used to calculate relative risk. Longitudinal studies, though, can compare groups either retrospectively or prospectively.

In case-control studies, researchers study one group of people who have developed a particular condition and compare them to a sample without the disease.

Case-control studies look at a single subject or a single case, whereas longitudinal studies are conducted on a large group of subjects.

3. Does a longitudinal study have a control group?

Yes, a longitudinal study can have a control group . In such a design, one group (the experimental group) would receive treatment or intervention, while the other group (the control group) would not.

Both groups would then be observed over time to see if there are differences in outcomes, which could suggest an effect of the treatment or intervention.

However, not all longitudinal studies have a control group, especially observational ones and not testing a specific intervention.

Baltes, P. B., & Nesselroade, J. R. (1979). History and rationale of longitudinal research. In J. R. Nesselroade & P. B. Baltes (Eds.), (pp. 1–39). Academic Press.

Cook, N. R., & Ware, J. H. (1983). Design and analysis methods for longitudinal research. Annual review of public health , 4, 1–23.

Fitchett, G., Rybarczyk, B., Demarco, G., & Nicholas, J.J. (1999). The role of religion in medical rehabilitation outcomes: A longitudinal study. Rehabilitation Psychology, 44, 333-353.

Harvard Second Generation Study. (n.d.). Harvard Second Generation Grant and Glueck Study. Harvard Study of Adult Development. Retrieved from https://www.adultdevelopmentstudy.org.

Le Mare, L., & Audet, K. (2006). A longitudinal study of the physical growth and health of postinstitutionalized Romanian adoptees. Pediatrics & child health, 11 (2), 85-91.

Luo, Y., Hawkley, L. C., Waite, L. J., & Cacioppo, J. T. (2012). Loneliness, health, and mortality in old age: a national longitudinal study. Social science & medicine (1982), 74 (6), 907–914.

Marques, S. C., Pais-Ribeiro, J. L., & Lopez, S. J. (2011). The role of positive psychology constructs in predicting mental health and academic achievement in children and adolescents: A two-year longitudinal study. Journal of Happiness Studies: An Interdisciplinary Forum on Subjective Well-Being, 12( 6), 1049–1062.

Sidney W.A. Dekker & Wilmar B. Schaufeli (1995) The effects of job insecurity on psychological health and withdrawal: A longitudinal study, Australian Psychologist, 30: 1,57-63.

Stice, E., Mazotti, L., Krebs, M., & Martin, S. (1998). Predictors of adolescent dieting behaviors: A longitudinal study. Psychology of Addictive Behaviors, 12 (3), 195–205.

Tegan Cruwys, Katharine H Greenaway & S Alexander Haslam (2015) The Stress of Passing Through an Educational Bottleneck: A Longitudinal Study of Psychology Honours Students, Australian Psychologist, 50:5, 372-381.

Thomas, L. (2020). What is a longitudinal study? Scribbr. Retrieved from https://www.scribbr.com/methodology/longitudinal-study/

Van der Vorst, H., Engels, R. C. M. E., Meeus, W., & Deković, M. (2006). Parental attachment, parental control, and early development of alcohol use: A longitudinal study. Psychology of Addictive Behaviors, 20 (2), 107–116.

Further Information

Schaie, K. W. (2005). What can we learn from longitudinal studies of adult development?. Research in human development, 2 (3), 133-158.
Caruana, E. J., Roman, M., Hernández-Sánchez, J., & Solli, P. (2015). Longitudinal studies. Journal of thoracic disease, 7 (11), E537.

Open access
Published: 01 October 2022

Qualitative longitudinal research in health research: a method study

Åsa Audulv 1 ,
Elisabeth O. C. Hall 2 , 3 ,
Åsa Kneck 4 ,
Thomas Westergren 5 , 6 ,
Liv Fegran 5 ,
Mona Kyndi Pedersen 7 , 8 ,
Hanne Aagaard 9 ,
Kristianna Lund Dam 3 &
Mette Spliid Ludvigsen 10 , 11

BMC Medical Research Methodology volume 22 , Article number: 255 ( 2022 ) Cite this article

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Qualitative longitudinal research (QLR) comprises qualitative studies, with repeated data collection, that focus on the temporality (e.g., time and change) of a phenomenon. The use of QLR is increasing in health research since many topics within health involve change (e.g., progressive illness, rehabilitation). A method study can provide an insightful understanding of the use, trends and variations within this approach. The aim of this study was to map how QLR articles within the existing health research literature are designed to capture aspects of time and/or change.

This method study used an adapted scoping review design. Articles were eligible if they were written in English, published between 2017 and 2019, and reported results from qualitative data collected at different time points/time waves with the same sample or in the same setting. Articles were identified using EBSCOhost. Two independent reviewers performed the screening, selection and charting.

A total of 299 articles were included. There was great variation among the articles in the use of methodological traditions, type of data, length of data collection, and components of longitudinal data collection. However, the majority of articles represented large studies and were based on individual interview data. Approximately half of the articles self-identified as QLR studies or as following a QLR design, although slightly less than 20% of them included QLR method literature in their method sections.

Conclusions

QLR is often used in large complex studies. Some articles were thoroughly designed to capture time/change throughout the methodology, aim and data collection, while other articles included few elements of QLR. Longitudinal data collection includes several components, such as what entities are followed across time, the tempo of data collection, and to what extent the data collection is preplanned or adapted across time. Therefore, there are several practices and possibilities researchers should consider before starting a QLR project.

Peer Review reports

Health research is focused on areas and topics where time and change are relevant. For example, processes such as recovery or changes in health status. However, relating time and change can be complicated in research, as the representation of reality in research publications is often collected at one point in time and fixed in its presentation, although time and change are always present in human life and experiences. Qualitative longitudinal research (QLR; also called longitudinal qualitative research, LQR) has been developed to focus on subjective experiences of time or change using qualitative data materials (e.g., interviews, observations and/or text documents) collected across a time span with the same participants and/or in the same setting [ 1 , 2 ]. QLR within health research may have many benefits. Firstly, human experiences are not fixed and consistent, but changing and diverse, therefore people’s experiences in relation to a health phenomenon may be more comprehensively described by repeated interviews or observations over time. Secondly, experiences, behaviors, and social norms unfold over time. By using QLR, researchers can collect empirical data that represents not only recalled human conceptions but also serial and instant situations reflecting transitions, trajectories and changes in people’s health experiences, personal development or health care organizations [ 3 , 4 , 5 ].

Key features of QLR

Whether QLR is a methodological approach in its own right or a design element of a particular study within a traditional methodological approach (e.g., ethnography or grounded theory) is debated [ 1 , 6 ]. For example, Bennett et al. [ 7 ] describe QLR as untied to methodology, giving researchers the flexibility to develop a suitable design for each study. McCoy [ 6 ] suggests that epistemological and ontological standpoints from interpretative phenomenological analysis (IPA) align with QLR traditions, thus making longitudinal IPA a suitable methodology. Plano-Clark et al. [ 8 ] described how longitudinal qualitative elements can be used in mixed methods studies, thus creating longitudinal mixed methods. In contrast, several researchers have argued that QLR is an emerging methodology [ 1 , 5 , 9 , 10 ]. For example, Thomson et al. [ 9 ] have stated “What distinguishes longitudinal qualitative research is the deliberate way in which temporality is designed into the research process, making change a central focus of analytic attention” (p. 185). Tuthill et al. [ 5 ] concluded that some of the confusion might have arisen from the diversity of data collection methods and data materials used within QLR research. However, there are no investigations showing to what extent QLR studies use QLR as a distinct methodology versus using a longitudinal data collection as a more flexible design element in combination with other qualitative methodologies.

QLR research should focus on aspects of temporality, time and/or change [ 11 , 12 , 13 ]. The concepts of time and change are seen as inseparable since change is happening with the passing of time [ 13 ]. However, time can be conceptualized in different ways. Time is often understood from a chronological perspective, and is viewed as fixed, objective, continuous and measurable (e.g., clock time, duration of time). However, time can also be understood from within, as the experience of the passing of time and/or the perspective from the current moment into the constructed conception of a history or future. From this perspective, time is seen as fluid, meaning that events, contexts and understandings create a subjective experience of time and change. Both the chronological and fluid understanding of time influence QLR research [ 11 ]. Furthermore, there is a distinction between over-time, which constitutes a comparison of the difference between points in time, often with a focus on the latter point or destination, and through-time, which means following an aspect across time while trying to understand the change that occurs [ 11 ]. In this article, we will mostly use the concept of across time to include both perspectives.

Some authors assert that QLR studies should include a qualitative data collection with the same sample across time [ 11 , 13 ], whereas Thomson et al. [ 9 ] also suggest the possibility of returning to the same data collection site with the same or different participants. When a QLR study involves data collection in shorter engagements, such as serial interviews, these engagements are often referred to as data collection time points. Data collection in time waves relates to longer engagements, such as field work/observation periods. There is no clear-cut definition for the minimum time span of a QLR study; instead, the length of the data collection period must be decided based upon what processes or changes are the focus of the study [ 13 ].

Most literature describing QLR methods originates from the social sciences, where the approach has a long tradition [ 1 , 10 , 14 ]. In health research, one-time-data collection studies have been the norm within qualitative methods [ 15 ], although health research using QLR methods has increased in recent years [ 2 , 5 , 16 , 17 ]. However, collecting and managing longitudinal data has its own sets of challenges, especially regarding how to integrate perspectives of time and/or change in the data collection and subsequent analysis [ 1 ]. Therefore, a study of QLR articles from the health research literature can provide an insightful understanding of the use, trends and variations of how methods are used and how elements of time/change are integrated in QLR studies. This could, in turn, provide inspiration for using different possibilities of collecting data across time when using QLR in health research. The aim of this study was to map how QLR articles within the existing health research literature are designed to capture aspects of time and/or change.

More specifically, the research questions were:

What methodological approaches are described to inform QLR research?

What methodological references are used to inform QLR research?

How are longitudinal perspectives articulated in article aims?

How is longitudinal data collection conducted?

In this method study, we used an adapted scoping review method [ 18 , 19 , 20 ]. Method studies are research conducted on research studies to investigate how research design elements are applied across a field [ 21 ]. However, since there are no clear guidelines for method studies, they often use adapted versions of systematic reviews or scoping review methods [ 21 ]. The adaptations of the scoping review method consisted of 1) using a large subsample of studies (publications from a three-year period) instead of including all QLR articles published, and 2) not including grey literature. The reporting of this study was guided by the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist [ 20 , 22 ] (see Additional file 1 ). A (unpublished) protocol was developed by the research team during the spring of 2019.

Eligibility criteria

In line with method study recommendations [ 21 ], we decided to draw on a manageable subsample of published QLR research. Articles that were eligible for inclusion were health research primary studies written in English, published between 2017 and 2019, and with a longitudinal qualitative data collection. Our operating definition for qualitative longitudinal data collection was data collected at different time points (e.g., repeated interviews) or time waves (e.g., periods of field work) involving the same sample or conducted in the same setting(s). We intentionally selected a broad inclusion criterion for QLR since we wanted a wide variety of articles. The selected time period was chosen because the first QLR method article directed towards health research was published in 2013 [ 1 ] and during the following years the methodological resources for QLR increased [ 3 , 8 , 17 , 23 , 24 , 25 ], thus we could expect that researchers publishing QLR in 2017–2019 should be well-grounded in QLR methods. Further, we found that from 2012 to 2019 the rate of published QLR articles were steady at around 100 publications per year, so including those from a three-year period would give a sufficient number of articles (~ 300 articles) for providing an overview of the field. Published conference abstracts, protocols, articles describing methodological issues, review articles, and non-research articles (e.g., editorials) were excluded.

Search strategy

Relevant articles were identified through systematic searches in EBSCOhost, including biomedical and life science research and nursing and allied health literature. A librarian who specialized in systematic review searches developed and performed the searches, in collaboration with the author team (LF, TW & ÅA). In the search, the term “longitudinal” was combined with terms for qualitative research (for the search strategy see Additional file 2 ). The searches were conducted in the autumn of 2019 (last search 2019-09-10).

Study selection

All identified citations were imported into EndNote X9 ( www.endnote.com ) and further imported into Rayyan QCRI online software [ 26 ], and duplicates were removed. All titles and abstracts were screened against the eligibility criteria by two independent reviewers (ÅA & EH), and conflicting decisions were discussed until resolved. After discussions by the team, we decided to include articles published between 2017 and 2019, that selection alone included 350 records with diverse methods and designs. The full texts of articles that were eligible for inclusion were retrieved. In the next stage, two independent reviewers reviewed each full text article to make final decisions regarding inclusion (ÅA, EH, Julia Andersson). In total, disagreements occurred in 8% of the decisions, and were resolved through discussion. Critical appraisal was not assessed since the study aimed to describe the range of how QLR is applied and not aggregate research findings [ 21 , 22 ].

Data charting and analysis

A standardized charting form was developed in Excel (Excel 2016). The charting form was reviewed by the research team and pretested in two stages. The tests were performed to increase internal consistency and reduce the risk of bias. First, four articles were reviewed by all the reviewers, and modifications were made to the form and charting instructions. In the next stage, all reviewers used the charting form on four other articles, and the convergence in ratings was 88%. Since the convergence was under 90%, charting was performed in duplicate to reduce errors in the data. At the end of the charting process, the convergence among the reviewers was 95%. The charting was examined by the first author, who revised the charting in cases of differences.

Data items that were charted included 1) the article characteristics (e.g., authors, publication year, journal, country), 2) the aim and scope (e.g., phenomenon of interest, population, contexts), 3) the stated methodology and analysis method, 4) text describing the data collection (e.g., type of data material, number of participants, time frame of data collection, total amount of data material), and 5) the qualitative methodological references used in the methods section. Extracted text describing data collection could consist of a few sentences or several sections from the articles (and sometimes figures) concerning data collection practices, rational for time periods and research engagement in the field. This was later used to analyze how the longitudinal data collection was conducted and elements of longitudinal design. To categorize the qualitative methodology approaches, a framework from Cresswell [ 27 ] was used (including the categories for grounded theory, phenomenology, ethnography, case study and narrative research). Overall, data items needed to be explicitly stated in the articles in order to be charted. For example, an article was categorized as grounded theory if it explicitly stated “in this grounded theory study” but not if it referred to the literature by Glaser and Strauss without situating itself as a grounded theory study (See Additional file 3 for the full instructions for charting).

All charting forms were compiled into a single Microsoft Excel spreadsheet (see Supplementary files for an overview of the articles). Descriptive statistics with frequencies and percentages were calculated to summarize the data. Furthermore, an iterative coding process was used to group the articles and investigate patterns of, for example, research topics, words in the aims, or data collection practices. Alternative ways of grouping and presenting the data were discussed by the research team.

Search and selection

A total of 2179 titles and abstracts were screened against the eligibility criteria (see Fig. 1 ). The full text of one article could not be found and the article was excluded [ 28 ]. Fifty full text articles were excluded. Finally, 299 articles, representing 271 individual studies, were included in this study (see additional files 4 and 5 respectively for tables of excluded and included articles).

PRISMA diagram of study selection]

General characteristics and research areas of the included articles

The articles were published in many journals ( n = 193), and 138 of these journals were represented with one article each. BMJ Open was the most prevalent journal ( n = 11), followed by the Journal of Clinical Nursing ( n = 8). Similarly, the articles represented many countries ( n = 41) and all the continents; however, a large part of the studies originated from the US or UK ( n = 71, 23.7% and n = 70, 23.4%, respectively). The articles focused on the following types of populations: patients, families−/caregivers, health care providers, students, community members, or policy makers. Approximately 20% ( n = 63, 21.1%) of the articles collected data from two or more of these types of population(s) (see Table 1 ).

Approximately half of the articles ( n = 158, 52.8%) articulated being part of a larger research project. Of them, 95 described a project with both quantitative and qualitative methods. They represented either 1) a qualitative study embedded in an intervention, evaluation or implementation study ( n = 66, 22.1%), 2) a longitudinal cohort study collecting both quantitative and qualitative material ( n = 23, 7.7%), or 3) qualitative longitudinal material collected together with a cross sectional survey (n = 6, 2.0%). Forty-eight articles (16.1%) described belonging to a larger qualitative project presented in several research articles.

Methodological traditions

Approximately one-third ( n = 109, 36.5%) of the included articles self-identified with one of the qualitative traditions recognized by Cresswell [ 27 ] (case study: n = 36, 12.0%; phenomenology: n = 35, 11.7%; grounded theory: n = 22, 7.4%; ethnography: n = 13, 4.3%; narrative method: n = 3, 1.0%). In nine articles, the authors described using a mix of two or more of these qualitative traditions. In addition, 19 articles (6.4%) self-identified as mixed methods research.

Every second article self-identified as having a qualitative longitudinal design ( n = 156, 52.2%); either they self-identified as “a longitudinal qualitative study” or “using a longitudinal qualitative research design”. However, in some articles, this was stated in the title and/or abstract and nowhere else in the article. Fifty-two articles (17.4%) self-identified both as having a QLR design and following one of the methodological approaches (case study: n = 8; phenomenology: n = 23; grounded theory: n = 9; ethnography: n = 6; narrative method: n = 2; mixed methods: n = 4).

The other 143 articles used various terms to situate themselves in relation to a longitudinal design. Twenty-seven articles described themselves as a longitudinal study (9.0%) or a longitudinal study within a specific qualitative tradition (e.g., a longitudinal grounded theory study or a longitudinal mixed method study) ( n = 64, 21.4%). Furthermore, 36 articles (12.0%) referred to using longitudinal data materials (e.g., longitudinal data or longitudinal interviews). Nine of the articles (3.0%) used the term longitudinal in relation to the data analysis or aim (e.g., the aim was to longitudinally describe), used terms such as serial or repeated in relation to the data collection design ( n = 2, 0.7%), or did not use any term to address the longitudinal nature of their design ( n = 5, 1.7%).

Use of methodological references

The mean number of qualitative method references in the methods sections was 3.7 (range 0 to 16), and 20 articles did not have any qualitative method reference in their methods sections. Footnote 1 Commonly used method references were generic books on qualitative methods, seminal works within qualitative traditions, and references specializing in qualitative analysis methods (see Table 2 ). It should be noted that some references were comprehensive books and thus could include sections about QLR without being focused on the QLR method. For example, Miles et al. [ 31 ] is all about analysis and coding and includes a chapter regarding analyzing change.

Only approximately 20% ( n = 58) of the articles referred to the QLR method literature in their methods sections. Footnote 2 The mean number of QLR method references (counted for articles using such sources) was 1.7 (range 1 to 6). Most articles using the QLR method literature also used other qualitative methods literature (except two articles using one QLR literature reference each [ 39 , 40 ]). In total, 37 QLR method references were used, and 24 of the QLR method references were only referred to by one article each.

Longitudinal perspectives in article aims

In total, 231 (77.3%) articles had one or several terms related to time or change in their aims, whereas 68 articles (22.7%) had none. Over one hundred different words related to time or change were identified. Longitudinally oriented terms could focus on changes across time (process, trajectory, transition, pathway or journey), patterns of how something changed (maintenance, continuity, stability, shifts), or phenomena that by nature included change (learning or implementation). Other types of terms emphasized the data collection time period (e.g., over 6 months) or a specific changing situation (e.g., during pregnancy, through the intervention period, or moving into a nursing home). The most common terms used for the longitudinal perspective were change ( n = 63), over time ( n = 52), process ( n = 36), transition ( n = 24), implementation ( n = 14), development ( n = 13), and longitudinal (n = 13). Footnote 3

Furthermore, the articles varied in what ways their aims focused on time/change, e.g., the longitudinal perspectives in the aims (see Table 3 ). In 71 articles, the change across time was the phenomenon of interest of the article : for example, articles investigating the process of learning or trajectories of diseases. In contrast, 46 articles investigated change or factors impacting change in relation to a defined outcome : for example, articles investigating factors influencing participants continuing in a physical activity trial. The longitudinal perspective could also be embedded in an article’s context . In such cases, the focus of the article was on experiences that happened during a certain time frame or in a time-related context (e.g., described experiences of the patient-provider relationship during 6 months of rehabilitation).

Types of data and length of data collection

The QLR articles were often large and complex in their data collection methods. The median number of participants was 20 (range from one to 1366, the latter being an article with open-ended questions in questionnaires [ 46 ]). Most articles used individual interviews as the data material ( n = 167, 55.9%) or a combination of data materials ( n = 98, 32.8%) (e.g., interviews and observations, individual interviews and focus group interviews, or interviews and questionnaires). Forty-five articles (15.1%) presented quantitative and qualitative results. The median number of interviews was 46 (range three to 507), which is large in comparison to many qualitative studies. The observation materials were also comprehensive and could include several hundred hours of observations. Documents were often used as complementary material and included official documents, newspaper articles, diaries, and/or patient records.

The articles’ time spans Footnote 4 for data collection varied between a few days and over 20 years, with 60% of the articles’ time spans being 1 year or shorter ( n = 180) (see Fig. 2 ). The variation in time spans might be explained by the different kinds of phenomena that were investigated. For example, Jensen et al. [ 47 ] investigated hospital care delivery and followed each participant, with observations lasting between four and 14 days. Smithbattle [ 48 ] described the housing trajectories of teen mothers, and collected data in seven waves over 28 years.

Number of articles in relation to the time span of data collection. The time span of data collection is given in months

Three components of longitudinal data collection

In the articles, the data collection was conducted in relation to three different longitudinal data collection components (see Table 4 ).

Entities followed across time

Four different types of entities were followed across time: 1) individuals, 2) individual cases or dyads, 3) groups, and 4) settings. Every second article ( n = 170, 56.9%) followed individuals across time, thus following the same participants through the whole data collection period. In contrast, when individual cases were followed across time, the data collection was centered on the primary participants (e.g., people with progressive neurological conditions) who were followed over time, and secondary participants (e.g., family caregivers) might provide complementary data at several time points or only at one-time point. When settings were followed over time, the participating individuals were sometimes the same, and sometimes changed across the data collection period. Typical settings were hospital wards, hospitals, smaller communities or intervention trials. The type of collected data corresponded with what kind of entities were followed longitudinally. Individuals were often followed with serial interviews, whereas groups were commonly followed with focus group interviews complemented with individual interviews, observations and/or questionnaires. Overall, the lengths of data collection periods seemed to be chosen based upon expected changes in the chosen entities. For example, the articles following an intervention setting were structured around the intervention timeline, collecting data before, after and sometimes during the intervention.

Tempo of data collection

The data collection tempo differed among the articles (e.g., the frequency and mode of the data collection). Approximately half ( n = 154, 51.5%) of the articles used serial time points, collecting data at several reoccurring but shorter sequences (e.g., through serial interviews or open-ended questions in questionnaires). When data were collected in time waves ( n = 50, 16.7%), the periods of data collection were longer, usually including both interviews and observations; often, time waves included observations of a setting and/or interviews at the same location over several days or weeks.

When comparing the tempo with the type of entities, some patterns were detected (see Fig. 3 ). When individuals were followed, data were often collected at time points, mirroring the use of individual interviews and/or short observations. For research in settings, data were commonly collected in time waves (e.g., observation periods over a few weeks or months). In studies exploring settings across time, time waves were commonly used and combined several types of data, particularly from interviews and observations. Groups were the least common studied entity ( n = 9, 3.0%), so the numbers should be interpreted with caution, but continuous data collection was used in five of the nine studies. The continuous data collection mode was, for example, collecting electronic diaries [ 62 ] or minutes from committee meetings during a time period [ 63 ].

Tempo of data collection in relation to entities followed over time

Preplanned or adapted data collection

A large majority ( n = 224, 74.9%) of the articles used preplanned data collection (e.g., in preplanned data collection, all participants were followed across time according to the same data collection plan). For example, all participants were interviewed one, six and twelve months’ post-diagnosis. In contrast to the preplanned data collection approach, 44 articles had a participant-adapted data collection (14.7%), and participants were followed at different frequencies and/or over various lengths of time depending on each participant’s situation. Participant-adapted data collection was more common among articles following individuals or individual cases (see Fig. 4 ). To adapt the data collection to the participants, the researchers created strategies to reach participants when crucial events were happening. Eleven articles used a participant entry approach to data collection ( n = 11, 6.7%), and the whole or parts of the data were independently sent in by participants in the form of diaries, questionnaires, or blogs. Another approach to data collection was using theoretical or analysis-driven ideas to guide the data collection ( n = 19, 6.4%). In these articles, the analysis and data collection were conducted simultaneously, and ideas arising in the analysis could be followed up, for example, returning to some participants, recruiting participants with specific experiences, or collecting complementary types of data materials. This approach was most common in the articles following settings across time, which often included observations and interviews with different types of populations. Articles using theoretical or analysis driven data collection were not associated with grounded theory to a greater extent than the other articles in the sample (e.g., did not self-identify as grounded theory or referred to methodological literature within grounded theory traditions to a greater proportion).

Preplanned or adapted data collection in relation to entities followed over time

According to our results, some researchers used QLR as a methodological approach and other researchers used a longitudinal qualitative data collection without aiming to investigate change. Adding to the debate on whether QLR is a methodological approach in its own right or a design element in a particular study we suggest that the use of QLR can be described as layered (see Fig. 5 ). Namely, articles must fulfill several criteria in order to use QLR as a methodological approach, and that is done in some articles. In those articles QLR method references were used, the aim was to investigate change of a phenomenon and the longitudinal elements of the data collection were thoroughly integrated into the method section. On the other hand, some articles using a longitudinal qualitative data collection were just collecting data over time, without addressing time and/or change in the aim. These articles can still be interesting research studies with valuable results, but they are not using the full potential of QLR as a methodological approach. In all, around 40% of the articles had an aim that focused on describing or understanding change (either as phenomenon or outcome); but only about 24% of the articles set out to investigate change across time as their phenomenon of interest.

The QLR onion. The use of QLR design can be described as layered, where researchers use more or less elements of a QLR design. The two inmost layers represents articles using QLR as a methodological approach

Regarding methodological influences, about one-third of the articles self-identify with any of the traditional qualitative methodologies. Using a longitudinal qualitative data collection as an element integrated with another methodological tradition can therefore be seen as one way of working with longitudinal qualitative materials. In our results, the articles referring to methodologies other than QLR preferably used case study, phenomenology and grounded theory methodologies. This was surprising since Neale [ 10 ] identified ethnography, case studies and narrative methods as the main methodological influences on QLR. Our findings might mirror the profound impacts that phenomenology and grounded theory have had on the qualitative field of health research. Regarding phenomenology, the findings can also be influenced by more recent discussions of combining interpretative phenomenological analysis with QLR [ 6 ].

Half of the articles self-identified as QLR studies, but QLR method references were used in less than 20% of the identified articles. This is both surprising and troublesome since use of appropriate method literature might have supported researchers who were struggling with for example a large quantity of materials and complex analysis. A possible explanation for the lack of use of QLR method literature is that QLR as a methodological approach is not well known, and authors might not be aware that method literature exists. It is quite understandable that researchers can describe a qualitative project with longitudinal data collection as a qualitative longitudinal study, without being aware that QLR is a specific form of study. Balmer [ 64 ] described how their group conducted serial interviews with medical students over several years before they became aware of QLR as a method of study. Within our networks, we have met researchers with similar experiences. Likewise, peer reviewers and editorial boards might not be accustomed to evaluating QLR manuscripts. In our results, 138 journals published one article between 2017 and 2019, and that might not be enough for editorial boards and peer reviewers to develop knowledge to enable them to closely evaluate manuscripts with a QLR method.

In 2007, Holland and colleagues [ 65 ] mapped QLR in the UK and described the following four categories of QLR: 1) mixed methods approaches with a QLR component; 2) planned prospective longitudinal studies; 3) follow-up studies complementing a previous data collection with follow-up; and 4) evaluation studies. Examples of all these categories can be found among the articles in this method study; however, our results do paint a more complex picture. According to our results, Holland’s categories are not multi-exclusive. For example, studies with intentions to evaluate or implement practices often used a mixed methods design and were therefore eligible for both categories one and four described above. Additionally, regarding the follow-up studies, it was seldom clearly described if they were planned as a two-time-point study or if researchers had gained an opportunity to follow up on previous data collection. When we tried to categorize QLR articles according to the data collection design, we could not identify multi-exclusive categories. Instead, we identified the following three components of longitudinal data collection: 1) entities followed across time; 2) tempo; and 3) preplanned or adapted data collection approaches. However, the most common combination was preplanned studies that followed individuals longitudinally with three or more time points.

The use of QLR differs between disciplines [ 14 ]. Our results show some patterns for QLR within health research. Firstly, the QLR projects were large and complex; they often included several types of populations and various data materials, and were presented in several articles. Secondly, most studies focused upon the individual perspective, following individuals across time, and using individual interviews. Thirdly, the data collection periods varied, but 53% of the articles had a data collection period of 1 year or shorter. Finally, patients were the most prevalent population, even though topics varied greatly. Previously, two other reviews that focused on QLR in different parts of health research (e.g., nursing [ 4 ] and gerontology [ 66 ]) pointed in the same direction. For example, individual interviews or a combination of data materials were commonly used, and most studies were shorter than 1 year but a wide range existed [ 4 , 66 ].

Considerations when planning a QLR project

Based on our results, we argue that when health researchers plan a QLR study, they should reflect upon their perspective of time/change and decide what part change should play in their QLR study. If researchers decide that change should play the main role in their project, then they should aim to focus on change as the phenomenon of interest. However, in some research, change might be an important part of the plot, without having the main role, and change in relation to the outcomes might be a better perspective. In such studies, participants with change, no change or different kinds of change are compared to explore possible explanations for the change. In our results, change in relation to the outcomes was often used in relation to intervention studies where participants who reached a desired outcome were compared to individuals who did not. Furthermore, for some research studies, change is part of the context in which the research takes place. This can be the case when certain experiences happen during a period of change; for example, when the aim is to explore the experience of everyday life during rehabilitation after stroke. In such cases a longitudinal data collection could be advisable (e.g., repeated interviews often give a deep relationship between interviewer and participants as well as the possibility of gaining greater depth in interview answers during follow-up interviews [ 15 ]), but the study might not be called a QLR study since it does not focus upon change [ 13 ]. We suggest that researchers make informed decisions of what kind of longitudinal perspective they set out to investigate and are transparent with their sources of methodological inspiration.

We would argue that length of data collection period, type of entities, and data materials should be in accordance with the type of change/changing processes that a study focuses on. Individual change is important in health research, but researchers should also remember the possibility of investigating changes in families, working groups, organizations and wider communities. Using these types of entities were less common in our material and could probably grant new perspectives to many research topics within health. Similarly, using several types of data materials can complement the insights that individual interviews can give. A large majority of the articles in our results had a preplanned data collection. Participant-adapted data collection can be a way to work in alignment with a “time-as-fluid” conceptualization of time because the events of subjective importance to participants can be more in focus and participants (or other entities) change processes can differ substantially across cases. In studies with lengthy and spaced-out data collection periods and/or uncertainty in trajectories, researchers should consider participant-adapted or participant entry data collection. For example, some participants can be followed for longer periods and/or with more frequency.

Finally, researchers should consider how to best publish and disseminate their results. Many QLR projects are large, and the results are divided across several articles when they are published. In our results, 21 papers self-identified as a mixed methods project or as part of a larger mixed methods project, but most of these did not include quantitative data in the article. This raises the question of how to best divide a large research project into suitable pieces for publication. It is an evident risk that the more interesting aspects of a mixed methods project are lost when the qualitative and quantitative parts are analyzed and published separately. Similar risks occur, for example, when data have been collected from several types of populations but are then presented per population type (e.g., one article with patient data and another with caregiver data). During the work with our study, we also came across studies where data were collected longitudinally, but the results were divided into publications per time point. We do not argue that these examples are always wrong, there are situations when these practices are appropriate. However, it often appears that data have been divided without much consideration. Instead, we suggest a thematic approach to dividing projects into publications, crafting the individual publications around certain ideas or themes and thus using the data that is most suitable for the particular research question. Combining several types of data and/or several populations in an analysis across time is in fact what makes QLR an interesting approach.

Strengths and limitations

This method study intended to paint a broad picture regarding how longitudinal qualitative methods are used within the health research field by investigating 299 published articles. Method research is an emerging field, currently with limited methodological guidelines [ 21 ], therefore we used scoping review method to support this study. In accordance with scoping review method we did not use quality assessment as a criterion for inclusion [ 18 , 19 , 20 ]. This can be seen as a limitation because we made conclusions based upon a set of articles with varying quality. However, we believe that learning can be achieved by looking at both good and bad examples, and innovation may appear when looking beyond established knowledge, or assessing methods from different angles. It should also be noted that the results given in percentages hold no value for what procedures that are better or more in accordance with QLR, the percentages simply state how common a particular procedure was among the articles.

As described, the included articles showed much variation in the method descriptions. As the basis for our results, we have only charted explicitly written text from the articles, which might have led to an underestimation of some results. The researchers might have had a clearer rationale than described in the reports. Issues, such as word restrictions or the journal’s scope, could also have influenced the amount of detail that was provided. Similarly, when charting how articles drew on a traditional methodology, only data from the articles that clearly stated the methodologies they used (e.g., phenomenology) were charted. In some articles, literature choices or particular research strategies could implicitly indicate that the researchers had been inspired by certain methodologies (e.g., referring to grounded theory literature and describing the use of simultaneous data collection and analysis could indicate that the researchers were influenced by grounded theory), but these were not charted as using a particular methodological tradition. We used the articles’ aims and objectives/research questions to investigate their longitudinal perspectives. However, as researchers have different writing styles, information regarding the longitudinal perspectives could have been described in surrounding text rather than in the aim, which might have led to an underestimation of the longitudinal perspectives.

The experience and diversity of the research team in our study was a strength. The nine authors on the team represent ten universities and three countries, and have extensive experience in different types of qualitative research, QLR and review methods. The different level of experiences with QLR within the team (some authors have worked with QLR in several projects and others have qualitative experience but no experience in QLR) resulted in interesting discussions that helped drive the project forward. These experiences have been useful for understanding the field.

Based on a method study of 299 articles, we can conclude that QLR in health research articles published between 2017 and 2019 often contain comprehensive complex studies with a large variation in topics. Some research was thoroughly designed to capture time/change throughout the methodology, focus and data collection, while other articles included a few elements of QLR. Longitudinal data collection included several components, such as what entities were followed across time, the tempo of data collection, and to what extent the data collection was preplanned or adapted across time. In sum, health researchers need to be considerate and make informed choices when designing QLR projects. Further research should delve deeper into what kind of research questions go well with QLR and investigate the best practice examples of presenting QLR findings.

Availability of data and materials

The datasets used and analyzed in this current study are available in supplementary file 6 .

Qualitative method references were defined as a journal article or book with a title that indicated an aim to guide researchers in qualitative research methods and/or research theories. Primary studies, theoretical works related to the articles’ research topics, protocols, and quantitative method literature were excluded. References written in a language other than English was also excluded since the authors could not evaluate their content.

QLR method references were defined as a journal article or book that 1) focused on qualitative methodological questions, 2) used terms such as ‘longitudinal’ or ‘time’ in the title so it was evident that the focus was on longitudinal qualitative research. Referring to another original QLR study was not counted as using QLR method literature.

Words were charted depending on their word stem, e.g., change, changes and changing were all charted as change.

It should be noted that here time span refers to the data collection related to each participant or case. Researchers could collect data for 2 years but follow each participant for 6 months.

Calman L, Brunton L, Molassiotis A. Developing longitudinal qualitative designs: lessons learned and recommendations for health services research. BMC Med Res Methodol. 2013;13:14.

Article PubMed PubMed Central Google Scholar

Solomon P, Nixon S, Bond V, Cameron C, Gervais N. Two approaches to longitudinal qualitative analyses in rehabilitation and disability research. Disabil Rehabil. 2020;42:3566–72.

Article PubMed Google Scholar

Grossoehme D, Lipstein E. Analyzing longitudinal qualitative data: the application of trajectory and recurrent cross-sectional approaches. BMC Res Notes. 2016;9:136.

SmithBattle L, Lorenz R, Reangsing C, Palmer JL, Pitroff G. A methodological review of qualitative longitudinal research in nursing. Nurs Inq. 2018;25:e12248.

Tuthill EL, Maltby AE, DiClemente K, Pellowski JA. Longitudinal qualitative methods in health behavior and nursing research: assumptions, design, analysis and lessons learned. Int J Qual Methods. 2020;19:10.

Article PubMed Central Google Scholar

McCoy LK. Longitudinal qualitative research and interpretative phenomenological analysis: philosophical connections and practical considerations. Qual Res Psychol. 2017;14:442–58.

Article Google Scholar

Bennett D, Kajamaa A, Johnston J. How to... Do longitudinal qualitative research. Clin Teach. 2020;17:489–92.

Plano Clark V, Anderson N, Wertz JA, Zhou Y, Schumacher K, Miaskowski C. Conceptualizing longitudinal mixed methods designs: a methodological review of health sciences research. J Mix Methods Res. 2014;23:1–23.

Google Scholar

Thomson R, Plumridge L, Holland J. Longitudinal qualitative research: a developing methodology. Int J Soc Res Methodol. 2003;6:185–7.

Neale B. The craft of qualitative longitudinal research: thousand oaks. Sage. 2021.

Balmer DF, Varpio L, Bennett D, Teunissen PW. Longitudinal qualitative research in medical education: time to conceptualise time. Med Educ. 2021;55:1253–60.

Smith N. Cross-sectional profiling and longitudinal analysis: research notes on analysis in the longitudinal qualitative study, 'Negotiating transitions to Citizenship'. Int J Soc Res Methodol. 2003;6:273–7.

Saldaña J. Longitudinal qualitative research - analyzing change through time. Walnut Creek: AltaMira Press; 2003.

Corden A, Millar J. Time and change: a review of the qualitative longitudinal research literature for social policy. Soc Policy Soc. 2007;6:583–92.

Thorne S. Interpretive description: qualitative research for applied practice (2nd ed): Routledge; 2016.

Book Google Scholar

Kneck Å, Audulv Å. Analyzing variations in changes over time: development of the pattern-oriented longitudinal analysis approach. Nurs Inq. 2019;26:e12288.

Whiffin CJ, Bailey C, Ellis-Hill C, Jarrett N. Challenges and solutions during analysis in a longitudinal narrative case study. Nurse Res. 2014;21:20–62.

Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8:19–32.

Levac D, Colquhoun H, O’Brien KK. Scoping studies: advancing the methodology. Implement Sci. 2010;5:69.

Peters MDJ, Marnie C, Tricco AC, Pollock D, Munn Z, Alexander L, et al. Updated methodological guidance for the conduct of scoping reviews. JBI Evidence Implementation. 2021;19:3–10.

Mbuagbaw L, Lawson DO, Puljak L, Allison DB, Thabane L. A tutorial on methodological studies: the what, when, how and why. BMC Med Res Methodol. 2020;20:226.

Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern. 2018;169:467–73.

Neale B. Adding time into the mix: stakeholder ethics in qualitative longitudinal research. Methodological Innovations Online. 2013;8:6–20.

Henderson S, Holland J, McGrellis S, Sharpe S, Thomson R. Storying qualitative longitudinal research: sequence, voice and motif. Qual Res. 2016;12:16–34.

Balmer DF, Richards BF. Longitudinal qualitative research in medical education. Perspect Med Educ. 2017;6:306–10.

Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016;5:210.

Creswell JW. Qualitative inquiry and research design: choosing among five approaches. 3rd ed: SAGE Publications; 2012.

McIntyre H, Fraser D. 'Hands-off' breastfeeding skill development in a UK, UNICEF baby friendly initiative pre-registration midwifery programme. MIDIRS Midwifery Digest. 2018;28:98–102.

Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3:77–101.

Patton MQ. Qualitative research and evaluation methods: integrating theory and practice. Sage. 2015.

Miles M, Huberman A, Saldaña J. Qualitative data analysis: a methods sourcebook. 4th ed: Sage Publications; 2020.

Miles MB, Huberman AM. Qualitative data analysis: an expanded sourcebook (2nd ed): Sage Publications; 1994.

Smith JA, Flowers P, Larkin M. Interpretative phenomenological analysis: theory, method and research. Sage. 2009.

Hsieh HF, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 2005;15:1277–88.

Glaser B, Strauss A. The discovery of grounded theory: strategies for qualitative research. Aldine De Gruyter. 1967.

Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19:349–57.

Murray SA, Kendall M, Carduff E, Worth A, Harris FM, Lloyd A, et al. Use of serial qualitative interviews to understand patients' evolving experiences and needs. BMJ. 2009;339:b3702.

Thomson R, Holland J. Hindsight, foresight and insight: the challenges of longitudinal qualitative research. Int J Soc Res Methodol. 2003;6:233–44.

Morrow V, Tafere Y, Chuta N, Zharkevich I. "I started working because I was hungry": the consequences of food insecurity for children's well-being in rural Ethiopia. Soc Sci Med. 2017;182:1–9.

Solomon P, O'Brien KK, Nixon S, Letts L, Baxter L, Gervais N. Qualitative longitudinal study of episodic disability experiences of older women living with HIV in Ontario, Canada. BMJ Open. 2018;8:e021507.

Coombs MA, Parker R, de Vries K. Managing risk during care transitions when approaching end of life: a qualitative study of patients’ and health care professionals’ decision making. Palliat Med. 2017;31:617–24.

Vaghefi I, Tulu B. The continued use of mobile health apps: insights from a longitudinal study. JMIR Mhealth And Uhealth. 2019;7:e12983.

Andersen IC, Thomsen TG, Bruun P, Bødtger U, Hounsgaard L. Patients' and their family members' experiences of participation in care following an acute exacerbation in chronic obstructive pulmonary disease: a phenomenological-hermeneutic study. J Clin Nurs. 2017;26:4877–89.

Albrecht TA, Keim-Malpass J, Boyiadzis M, Rosenzweig M. Psychosocial experiences of young adults diagnosed with acute leukemia during hospitalization for induction chemotherapy treatment. JHPN. 2019;21:167–73.

PubMed Google Scholar

Corepal R, Best P, O'Neill R, Tully MA, Edwards M, Jago R, et al. Exploring the use of a gamified intervention for encouraging physical activity in adolescents: a qualitative longitudinal study in Northern Ireland. BMJ Open. 2018;8:e019663.

PubMed PubMed Central Google Scholar

Malin H, Liauw I, Damon W. Purpose and character development in early adolescence. J Youth Adolesc. 2017;46:1200–15.

Jensen AM, Pedersen BD, Olsen RB, Hounsgaard L. Medication and care in Alzheimer's patients in the acute care setting: a qualitative analysis. Dementia. 2019;18:2173–88.

SmithBattle L. Housing trajectories of teen mothers and their families over 28 years. Am J Orthop. 2019;89:258–67.

Denney-Koelsch EM, Côté-Arsenault D, Jenkins Hall W. Feeling cared for versus experiencing added burden: Parents' interactions with health-care providers in pregnancy with a lethal fetal diagnosis. Illn Crisis Loss. 2018;26:293–315.

Pyörälä E, Mäenpää S, Heinonen L, Folger D, Masalin T, Hervonen H. The art of note taking with mobile devices in medical education. BMC Med Educ. 2019;19:96.

Lindberg K, Mørk BE, Walter L. Emergent coordination and situated learning in a hybrid OR: the mixed blessing of using radiation. Soc Science Med. 2019;228:232–9.

Frost J, Wingham J, Britten N, Greaves C, Abraham C, Warren FC, et al. Home-based rehabilitation for heart failure with reduced ejection fraction: mixed methods process evaluation of the REACH-HF multicentre randomised controlled trial. BMJ Open. 2019;9:e026039.

Young JL, Werner-Lin A, Mueller R, Hoskins L, Epstein N, Greene MH. Longitudinal cancer risk management trajectories of BRCA1/2 mutation-positive reproductive-age women. J Psych Oncology. 2017;35:393–408.

Lewis M, Jones A, Hunter B. Women's experience of trust within the midwife-mother relationship. Int J Childbirth. 2017;7:40–52.

Mozaffar H, Cresswell KM, Williams R, Bates DW, Sheikh A. Exploring the roots of unintended safety threats associated with the introduction of hospital ePrescribing systems and candidate avoidance and/or mitigation strategies: a qualitative study. BMJ Qual Saf. 2017;26:722–33.

Castro A, Andrews G. Nursing lives in the blogosphere: a thematic analysis of anonymous online nursing narratives. J Adv Nurs. 2018;74:329–38.

Jensen AM, Pedersen BD, Olsen RB, Wilson RL, Hounsgaard L. "if only they could understand me!" acute hospital care experiences of patients with Alzheimer's disease. Dementia. 2018;19:2332–53.

Nash BH, Mitchell AW. Longitudinal study of changes in occupational therapy students' perspectives on frames of reference. Am J Occup Ther. 2017;71:7105230010p1–7.

Bright FAS, Kayes NM, McPherson KM, Worrall LE. Engaging people experiencing communication disability in stroke rehabilitation: a qualitative study. Int J Lang Commun Disord. 2018;53:981–94.

Superdock AK, Barfield RC, Brandon DH, Docherty SL. Exploring the vagueness of religion & spirituality in complex pediatric decision-making: a qualitative study. BMC Palliat. 2018;17:107.

Gordon L, Jindal-Snape D, Morrison J, Muldoon J, Needham G, Siebert S, et al. Multiple and multidimensional transitions from trainee to trained doctor: a qualitative longitudinal study in the UK. BMJ Open. 2017;7:e018583.

Cain CL, Frazer M, Kilaberia TR. Identity work within attempts to transform healthcare: invisible team processes. Hum Relat. 2019;72:370–96.

Klinga C, Hasson H, Andreen Sachs M, Hansson J. Understanding the dynamics of sustainable change: a 20-year case study of integrated health and social care. BMC Health Serv Res. 2018;18:400.

Balmer DF, Richards BF. Conducting qualitative research through time: how might theory be useful in longitudinal qualitative research? Adv Health Sci Educ Theory Pract. 2021;27:277–88.

Holland J. Qualitative longitudinal research: exploring ways of researching lives through time. ESRC National Centre for Research Methods Workshop; London: London South Bank University; 2007.

Nevedal AL, Ayalon L, Briller SH. A qualitative evidence synthesis review of longitudinal qualitative research in gerontology. Gerontologist. 2019;59:e791–801.

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Acknowledgments

The authors wish to acknowledge Ellen Sejersted, librarian at the University of Agder, Kristiansand, Norway, who conducted the literature searches and Julia Andersson, research assistant at the Department of Nursing, Umeå University, Sweden, who supported the data management and took part in the initial screening phases of the project.

Open access funding provided by Umea University. This project was conducted within the authors’ positions and did not receive any specific funding.

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ÅA conceived the study. ÅA, EH, TW, LF, MKP, HA, and MSL designed the study. ÅA, TW, and LF were involved in literature searches together with the librarian. ÅA and EH performed the screening of the articles. All authors (ÅA, EH, TW, LF, ÅK, MKP, KLD, HA, MSL) took part in the data charting. ÅA performed the data analysis and discussed the preliminary results with the rest of the team. ÅA wrote the 1st manuscript draft, and ÅK, MSL and EH edited. All authors (ÅA, EH, TW, LF, ÅK, MKP, KLD, HA, MSL) contributed to editing the 2nd draft. MSL and LF provided overall supervision. All authors read and approved the final manuscript.

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All authors represent the nursing discipline, but their research topics differ. ÅA and ÅK have previously worked together with QLR method development. ÅA, EH, TW, LF, MKP, HA, KLD and MSL work together in the Nordic research group PRANSIT, focusing on nursing topics connected to transition theory using a systematic review method, preferably meta synthesis. All authors have extensive experience with qualitative research but various experience with QLR.

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Additional file 1..

PRISMA-ScR checklist.

Additional file 2.

Data base searches.

Additional file 3.

Guidelines for data charting

Additional file 4.

List of excluded articles

Additional file 5.

Table of included articles (author(s), year of publication, reference, country, aims and research questions, methodology, type of data material, length of data collection period, number of participants)

Additional file 6.

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Audulv, Å., Hall, E.O.C., Kneck, Å. et al. Qualitative longitudinal research in health research: a method study. BMC Med Res Methodol 22 , 255 (2022). https://doi.org/10.1186/s12874-022-01732-4

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What (Exactly) Is A Longitudinal Study?

A plain-language explanation & definition (with examples).

By: Derek Jansen (MBA) | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably feeling a bit overwhelmed by all the technical lingo that’s hitting you. If you’ve landed here, chances are one of these terms is “longitudinal study”, “longitudinal survey” or “longitudinal research”.

Worry not – in this post, we’ll explain exactly:

What a longitudinal study is (and what the alternative is)
What the main advantages of a longitudinal study are
What the main disadvantages of a longitudinal study are
Whether to use a longitudinal or cross-sectional study for your research

What is a longitudinal study?

A longitudinal study or a longitudinal survey (both of which make up longitudinal research) is a study where the same data are collected more than once, at different points in time . The purpose of a longitudinal study is to assess not just what the data reveal at a fixed point in time, but to understand how (and why) things change over time.

Longitudinal research involves a study where the same data are collected more than once, at different points in time

Example: Longitudinal vs Cross-Sectional

Here are two examples – one of a longitudinal study and one of a cross-sectional study – to give you an idea of what these two approaches look like in the real world:

Longitudinal study: a study which assesses how a group of 13-year old children’s attitudes and perspectives towards income inequality evolve over a period of 5 years, with the same group of children surveyed each year, from 2020 (when they are all 13) until 2025 (when they are all 18).

Cross-sectional study: a study which assesses a group of teenagers’ attitudes and perspectives towards income equality at a single point in time. The teenagers are aged 13-18 years and the survey is undertaken in January 2020.

From this example, you can probably see that the topic of both studies is still broadly the same (teenagers’ views on income inequality), but the data produced could potentially be very different . This is because the longitudinal group’s views will be shaped by the events of the next five years, whereas the cross-sectional group all have a “2020 perspective”.

Additionally, in the cross-sectional group, each age group (i.e. 13, 14, 15, 16, 17 and 18) are all different people (obviously!) with different life experiences – whereas, in the longitudinal group, each the data at each age point is generated by the same group of people (for example, John Doe will complete a survey at age 13, 14, 15, and so on).

There are, of course, many other factors at play here and many other ways in which these two approaches differ – but we won’t go down that rabbit hole in this post.

There are many differences between longitudinal and cross-sectional studies

What are the advantages of a longitudinal study?

Longitudinal studies and longitudinal surveys offer some major benefits over cross-sectional studies. Some of the main advantages are:

Patterns – because longitudinal studies involve collecting data at multiple points in time from the same respondents, they allow you to identify emergent patterns across time that you’d never see if you used a cross-sectional approach.

Order – longitudinal studies reveal the order in which things happened, which helps a lot when you’re trying to understand causation. For example, if you’re trying to understand whether X causes Y or Y causes X, it’s essential to understand which one comes first (which a cross-sectional study cannot tell you).

Bias – because longitudinal studies capture current data at multiple points in time, they are at lower risk of recall bias . In other words, there’s a lower chance that people will forget an event, or forget certain details about it, as they are only being asked to discuss current matters.

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What are the disadvantages of a longitudinal study?

As you’ve seen, longitudinal studies have some major strengths over cross-sectional studies. So why don’t we just use longitudinal studies for everything? Well, there are (naturally) some disadvantages to longitudinal studies as well.

Cost – compared to cross-sectional studies, longitudinal studies are typically substantially more expensive to execute, as they require maintained effort over a long period of time.

Slow – given the nature of a longitudinal study, it takes a lot longer to pull off than a cross-sectional study. This can be months, years or even decades. This makes them impractical for many types of research, especially dissertations and theses at Honours and Masters levels (where students have a predetermined timeline for their research)

Drop out – because longitudinal studies often take place over many years, there is a very real risk that respondents drop out over the length of the study. This can happen for any number of reasons (for examples, people relocating, starting a family, a new job, etc) and can have a very detrimental effect on the study.

Some disadvantages to longitudinal studies include higher cost, longer execution time and higher dropout rates.

Which one should you use?

Choosing whether to use a longitudinal or cross-sectional study for your dissertation, thesis or research project requires a few considerations. Ultimately, your decision needs to be informed by your overall research aims, objectives and research questions (in other words, the nature of the research determines which approach you should use). But you also need to consider the practicalities. You should ask yourself the following:

Do you really need a view of how data changes over time, or is a snapshot sufficient?
Is your university flexible in terms of the timeline for your research?
Do you have the budget and resources to undertake multiple surveys over time?
Are you certain you’ll be able to secure respondents over a long period of time?

If your answer to any of these is no, you need to think carefully about the viability of a longitudinal study in your situation. Depending on your research objectives, a cross-sectional design might do the trick. If you’re unsure, speak to your research supervisor or connect with one of our friendly Grad Coaches .

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What is a longitudinal study?

Last updated

20 February 2023

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Longitudinal studies are common in epidemiology, economics, and medicine. People also use them in other medical and social sciences, such as to study customer trends. Researchers periodically observe and collect data from the variables without manipulating the study environment.

A company may conduct a tracking study, surveying a target audience to measure changes in attitudes and behaviors over time. The collected data doesn't change, and the time interval remains consistent. This longitudinal study can measure brand awareness, customer satisfaction , and consumer opinions and analyze the impact of an advertising campaign.

Analyze longitudinal studies

Dovetail streamlines longitudinal study data to help you uncover and share actionable insights

Types of longitudinal studies

There are two types of longitudinal studies: Cohort and panel studies.

Panel study

A panel study is a type of longitudinal study that involves collecting data from a fixed number of variables at regular but distant intervals. Researchers follow a group or groups of people over time. Panel studies are designed for quantitative analysis but are also usable for qualitative analysis .

A panel study may research the causes of age-related changes and their effects. Researchers may measure the health markers of a group over time, such as their blood pressure, blood cholesterol, and mental acuity. Then, they can compare the scores to understand how age positively or negatively correlates with these measures.

Cohort study

A cohort longitudinal study involves gathering information from a group of people with something in common, such as a specific trait or experience of the same event. The researchers observe behaviors and other details of the group over time. Unlike panel studies, you can pick a different group to test in cohort studies.

An example of a cohort study could be a drug manufacturer studying the effects on a group of users taking a new drug over a period. A drinks company may want to research consumers with common characteristics, like regular purchasers of sugar-free sodas. This will help the company understand trends within its target market.

Benefits of longitudinal research

If you want to study the relationship between variables and causal factors responsible for certain outcomes, you should adopt a longitudinal approach to your investigation.

The benefits of longitudinal research over other research methods include the following:

Insights over time

It gives insights into how and why certain things change over time.

Better information

Researchers can better establish sequences of events and identify trends.

No recall bias

The participants won't have recall bias if you use a prospective longitudinal study. Recall bias is an error that occurs in a study if respondents don't wholly or accurately recall the details of their actions, attitudes, or behaviors.

Because variables can change during the study, researchers can discover new relationships or data points worth further investigation.

Small groups

Longitudinal studies don't need a large group of participants.

Potential pitfalls

The challenges and potential pitfalls of longitudinal studies include the following:

A longitudinal survey takes a long time, involves multiple data collections , and requires complex processes, making it more expensive than other research methods.

Unpredictability

Because they take a long time, longitudinal studies are unpredictable. Unexpected events can cause changes in the variables, making earlier data potentially less valuable.

Slow insights

Researchers can take a long time to uncover insights from the study as it involves multiple observations.

Participants can drop out of the study, limiting the data set and making it harder to draw valid conclusions from the results.

Overly specific data

If you study a smaller group to reduce research costs, results will be less generalizable to larger populations versus a study with a larger group.

Despite these potential pitfalls, you can still derive significant value from a well-designed longitudinal study by uncovering long-term patterns and relationships.

Longitudinal study designs

Longitudinal studies can take three forms: Repeated cross-sectional, prospective, and retrospective.

Repeated cross-sectional studies

Repeated cross-sectional studies are a type of longitudinal study where participants change across sampling periods. For example, as part of a brand awareness survey , you ask different people from the same customer population about their brand preferences.

Prospective studies

A prospective study is a longitudinal study that involves real-time data collection, and you follow the same participants over a period. Prospective longitudinal studies can be cohort, where participants have similar characteristics or experiences. They can also be panel studies, where you choose the population sample randomly.

Retrospective studies

Retrospective studies are longitudinal studies that involve collecting data on events that some participants have already experienced. Researchers examine historical information to identify patterns that led to an outcome they established at the start of the study. Retrospective studies are the most time and cost-efficient of the three.

How to perform a longitudinal study

When developing a longitudinal study plan, you must decide whether to collect your data or use data from other sources. Each choice has its benefits and drawbacks.

Using data from other sources

You can freely access data from many previous longitudinal studies, especially studies conducted by governments and research institutes. For example, anyone can access data from the 1970 British Cohort Study on the UK Data Service website .

Using data from other sources saves the time and money you would have spent gathering data. However, the data is more restrictive than the data you collect yourself. You are limited to the variables the original researcher was investigating, and they may have aggregated the data, obscuring some details.

If you can't find data or longitudinal research that applies to your study, the only option is to collect it yourself.

Collecting your own data

Collecting data enhances its relevance, integrity, reliability, and verifiability. Your data collection methods depend on the type of longitudinal study you want to perform. For example, a retrospective longitudinal study collects historical data, while a prospective longitudinal study collects real-time data.

The only way to ensure relevant and reliable data is to use an effective and versatile data collection tool. It can improve the speed and accuracy of the information you collect.

What is a longitudinal study in research?

A longitudinal study is a research design that involves studying the same variables over time by gathering data continuously or repeatedly at consistent intervals.

What is an example of a longitudinal study?

An excellent example of a longitudinal study is market research to identify market trends. The organization's researchers collect data on customers' likes and dislikes to assess market trends and conditions. An organization can also conduct longitudinal studies after launching a new product to understand customers' perceptions and how it is doing in the market.

Why is it called a longitudinal study?

It’s a longitudinal study because you collect data over an extended period. Longitudinal data tracks the same type of information on the same variables at multiple points in time. You collect the data over repeated observations.

What is a longitudinal study vs. a cross-sectional study?

A longitudinal study follows the same people over an extended period, while a cross-sectional study looks at the characteristics of different people or groups at a given time. Longitudinal studies provide insights over an extended period and can establish patterns among variables.

Cross-sectional studies provide insights about a point in time, so they cannot identify cause-and-effect relationships.

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Administrative data

Administrative data is the term used to describe everyday data about individuals collected by government departments and agencies. Examples include exam results, benefit receipt and National Insurance payments.

Age effects

Age effects relates to changes in an outcome as a result of getting older.

Anonymisation

Anonymisation refers to the removal of study participants ’ identifying information (e.g., name, address) in order to preserve their privacy.

Attrition is the discontinued participation of study participants in a longitudinal study. Attrition can reflect a range of factors, from the study participant not being traceable to them choosing not to take part when contacted. Attrition is problematic both because it can lead to bias in the study findings (if the attrition is higher among some groups than others) and because it reduces the size of the sample .

Baseline refers to the start of a study when initial information is collected on participation (however, in longitudinal studies , researchers may adopt an alternative ‘baseline’ for the purposes of analysis).

Biological samples

Biological samples is the term used for specimens collected from human subjects from which biological information, such as genetic markers, can be extracted for analysis. Common examples include blood, saliva or hair.

Body mass index

Body mass index is a measure used to assess if an individual is a healthy weight for their height. It is calculated by dividing the individual’s weight by the square of their height, and it is typically represented in units of kg/m 2 .

Boosted samples

Boosted samples are used to overcome sample bias due to attrition or to supplement the representation of smaller sub-groups within the sample . Inclusion of boosted samples must be accompanied by appropriate survey weights .

Computer-assisted personal interviewing (CAPI) is a technique for collecting data from participants using computers to eliminate common errors such as questionnaire routing and data entry mistakes. The use of computers take place within the context of a face-to-face interview.

Computer-assisted self-interviewing (CASI) is a technique for collecting data from participants using computers to eliminate common errors such as questionnaire routing and data entry mistakes. The use of computers take place within the context of a self-completion questionnaire.

Categorical variable

A categorical variable is a variable that can take one of a limited number of discrete values. They can be either nominal – they contain no inherent order of categories (e.g. sex; marital status) – or ordinal – they can be ranked in some meaningful order (e.g. level of satisfaction with a service).

Computer-assisted telephone interviewing (CATI) is a technique for collecting data from participants using computers to eliminate common errors such as questionnaire routing and data entry mistakes. The use of computers take place within the context of a telephone interview.

For some study participants the exact time of an event will not be known because either: the study ends (or the analysis is carried out) before they have had the event, or the participant drops out of the study before experiencing the event. It is therefore, only known that the event has not occurred up to the time that they were last observed in the study.

Census refers to a universal and systematic collection of data from all individuals within a population . In the UK, the government conducts a census every ten years with the next one due in 2021.

A codebook is a document (online or hard-copy) that contains all the information about how a dataset has been coded, such that it can be deciphered by a researcher not familiar with the original coding frame.

Coding is the process of converting survey responses into numerical codes to facilitate data analysis. All potential responses (as well as possible reasons for non-response) for each variable are assigned numerical values according to a coding frame.

Cognitive assessments

Cognitive assessments are exercises used to measure thinking abilities, such as memory, reasoning and language. Longitudinal studies collecting data in this way can track the extent to which someone’s cognitive abilities change (develop or decline) over time.

Cohort studies

Cohort studies are concerned with charting the lives of groups of individuals who experience the same life events within a given time period. The best known examples are birth cohort studies, which follow a group of people born in a particular period.

Complete case analysis

Complete case analysis is the term used to describe a statistical analysis that only includes participants for which we have no missing data on the variables of interest. Participants with any missing data are excluded.

Conditioning

Conditioning refers to the process whereby participants’ answers to some questions may be influenced by their participation in the study – in other words, their responses are ‘conditioned’ by their being members of a longitudinal study. Examples would include study respondents answering questions differently or even behaving differently as a result of their participation in the study.

Confounding

Confounding occurs where the relationship between independent and dependent variables is distorted by one or more additional, and sometimes unmeasured, variables . A confounding variable must be associated with both the independent and dependent variables but must not be an intermediate step in the relationship between the two (i.e. not on the causal pathway).

For example, we know that physical exercise (an independent variable) can reduce a person’s risk of cardiovascular disease (a dependent variable ). We can say that age is a confounder of that relationship as it is associated with, but not caused by, physical activity and is also associated with coronary health. See also ‘ unobserved heterogeneity ’, below.

Continuous variable

A continuous variable is a variable that has an infinite number of uncountable values e.g. time, temperature. They are also known as quantitative variables or scale variables .

Cohort effects

Cohort effects relates to changes in an outcome associated with being a member of a specific cohort of people (e.g. born in the same year; or starting school at the same time).

In metadata management, coverage refers to the temporal, spatial and topical aspects of the data collection to describe the comprehensiveness of a dataset. For longitudinal studies , this can relate to the topics that are covered across waves, the population to which one can generalise or the geographic extent of the dataset.

Cross-sectional

Cross-sectional surveys involve interviewing a fresh sample of people each time they are carried out. Some cross-sectional studies are repeated regularly and can include a large number of repeat questions (questions asked on each survey round).

Data access agreement

Within the context of data protection , a data access agreement specifies the terms under which users are provided access to specified datasets. This usually forms part of the application process to the data controller to ensure that researchers adhere to a set of terms regarding data confidentiality , sensitivity and dissemination before accessing the data. See also: research ethics

Data cleaning

Data cleaning is an important preliminary step in the data analysis process and involves preparing a dataset so that it can be correctly analysed. ‘Cleaning’ the data usually involves identifying data input errors, assessing the completeness of the dataset and verifying any anomalies (e.g. outliers).

Data confidentiality

Within the context of data protection , data confidentiality is the process of protecting participants’ data from being accessed or disclosed by those unauthorised to do so. Key methods employed in data confidentiality include anonymisation of responses (removal of personal identifying information) and data encryption (protecting the data using codes and/or passwords).

Data harmonisation

Data harmonisation involves retrospectively adjusting data collected by different surveys to make it possible to compare the data that was collected. This enables researchers to make comparisons both within and across studies. Repeating the same longitudinal analysis across a number of studies allows researchers to test whether results are consistent across studies, or differ in response to changing social conditions.

Data imputation

Data imputation is a technique for replacing missing data with an alternative estimate. There are a number of different approaches, including mean substitution and model-based multivariate approaches.

Data linkage

Data linkage simply means connecting two or more sources of administrative, educational, geographic, health or survey data relating to the same individual for research and statistical purposes. For example, linking housing or income data to exam results data could be used to investigate the impact of socioeconomic factors on educational outcomes.

Data protection

Data protection refers to the broad suite of rules governing the handling and access of information about people. Data protection principles include confidentiality of responses, informed consent of participants and security of data access. These principles are legally protected by the Data Protection Act (DPA) and the General Data Protection Regulation (GDPR).

Data structure

Data structure refers to the way in which data are organised and formatting in advance of data analysis.

Dependent variable

In analysis, the dependent variable is the variable you expect to change in response to different values of your independent (or predictor) variables . For example, a students’ test results may be (partially) explained by the number of hours spent on revision. In this case, the dependent variable is students’ test score, which you expect to be different according to the amount of time spent revising.

Derived variable

A derived variable is a variable that is calculated from the values of other variables and not asked directly of the participants. It can involve a mathematical calculation (e.g. deriving monthly income from annual income by dividing by 12) or a recategorisation of one or more existing variables (e.g. categorising monthly income into £500 bands – £0 to £500, £501 to £1,000, etc.)

Diaries are a data collection instrument that is particularly useful in recording information about time use or other regular activity, such as food intake. They have the benefit of collecting data from participants as and when an activity occurs. As such, they can minimise recall bias and provide a more accurate record of activities than a retrospective interview.

Dissemination

Dissemination is the process of sharing information – particularly research findings – to other researchers, stakeholders, policy makers, and practitioners through various avenues and channels, including online, written publications and events. Dissemination is a planned process that involves consideration of target audiences in ways that will facilitate research uptake in decision-making processes and practice.

Dummy variables

Dummy variables , also called indicator variables , are sets of dichotomous (two-category) variables we create to enable subgroup comparisons when we are analysing a categorical variable with three or more categories.

Empirical data

Empirical data refers to data collected through observation or experimentation. Analysis of empirical data can provide evidence for how a theory or assumption works in practice.

In metadata management, fields are the elements of a database which describes the attributes of items of data.

General ability

General ability is a term used to describe cognitive ability, and is sometimes used as a proxy for intelligent quotient (IQ) scores.

Growth curve modelling

Growth curve modelling is used to analyse trajectories of longitudinal change over time allowing us to model the way participants change over time, and then to explore what characteristics or circumstances influence these patterns of longitudinal change.

Hazard rate

Hazard rate refers to the probability that an event of interest occurs at a given time point, given that it has not occurred before.

Health assessments

Health assessments refers to the assessments carried out on research participants in relation to their physical characteristics or function. These can include measurements of height and weight, blood pressure or lung function.

Heterogeneity

Heterogeneity is a term that refers to differences, most commonly differences in characteristics between study participants or samples. It is the opposite of homogeneity, which is the term used when participants share the same characteristics. Where there are differences between study designs, this is sometimes referred to as methodological heterogeneity. Both participant or methodological differences can cause divergences between the findings of individual studies and if these are greater than chance alone, we call this statistical heterogeneity. See also: unobserved heterogeneity .

Household panel surveys

Household panel surveys collect information about the whole household at each wave of data collection, to allow individuals to be viewed in the context of their overall household. To remain representative of the population of households as a whole, studies will typically have rules governing how new entrants to the household are added to the study.

Incentives and rewards

As a way of encouraging participants to take part in research, they may be offered an incentive or a reward. These may be monetary or, more commonly, non-monetary vouchers or tokens. Incentives are advertised beforehand and can act as an aid to recruitment; rewards are a token of gratitude to the participants for giving their time.

Independent variable

In analysis, an independent variable is any factor that may be associated with an outcome or dependent variable . For example, the number of hours a student spends on revision may influence their test result. In this case, the independent variable, revision time (at least partially) ‘explains’ the outcome of the test.

Informed consent

A key principle of research ethics , informed consent refers to the process of providing full details of the research to participants so that they are sufficiently able to choose whether or not to consent to taking part.

Kurtosis is sometimes described as a measure of ‘tailedness’. It is a characteristic of the distribution of observations on a variable and denotes the heaviness of the distribution’s tails. To put it another way, it is a measure of how thin or fat the lower and upper ends of a distribution are.

Life course

A person’s life course refers to the experiences and stages an individual passes through during their life. It centres on the individual and emphasises the changing social and contextual processes that influence their life over time.

Longitudinal studies

Longitudinal studies gather data about the same individuals (‘ study participants ’) repeatedly over a period of time, in some cases from birth until old age. Many longitudinal studies focus upon individuals, but some look at whole households or organisations.

Metadata refers to data about data, which provides the contextual information that allows you to interpret what data mean.

Missing data

Missing data refers to values that are missing and do not appear in a dataset. This may be due to item non-response, participant drop-out (or attrition ) or, in longitudinal studies , some data (e.g. date of birth) may be collected only in some waves. Large amounts of missing data can be a problem and lead researchers to make erroneous inferences from their analysis. There are several ways to deal with the issue of missing data, from casewise deletion to complex multiple imputation models.

Multi-level modelling

Multi-level modelling refers to statistical techniques used to analyse data that is structured in a hierarchical or nested way. For example. study participants who belong to the same household, or students who attend the same school may be expected to be more similar to each other than to participants in other households or schools (such as sharing similar contextual influences). This similarity means that the data from participants within these households/schools are not independent. Multi-level models can account for variability at both the individual level and the group (e.g. household or school) level.

Non-response bias

Non-response bias is a type of bias introduced when those who participate in a study differ to those who do not in a way that is not random (for example, if attrition rates are particularly high among certain sub-groups). Non-random attrition over time can mean that the sample no longer remains representative of the original population being studied. Two approaches are typically adopted to deal with this type of missing data : weighting survey responses to re-balance the sample , and imputing values for the missing information.

Observational studies

Observational studies focus on observing the characteristics of a particular sample without attempting to influence any aspects of the participants’ lives. They can be contrasted with experimental studies, which apply a specific ‘treatment’ to some participants in order to understand its effect.

Panel studies

Panel studies follow the same individuals over time. They vary considerably in scope and scale . Examples include online opinion panels and short-term studies whereby people are followed up once or twice after an initial interview.

Peer review

Peer review is a method of quality control in the process of academic publishing, whereby research is appraised (usually anonymously) by one or more independent academic with expertise in the subject.

Period effects

Period effects relate to changes in an outcome associated with living during a particular time, regardless of age or cohort membership (e.g. living through times of war, economic recession or global pandemic).

Piloting is the process of testing your research instruments and procedures to identify potential problems or issues before implementing them in the full study. A pilot study is usually conducted on a small subset of eligible participants who are encouraged to provide feedback on the length, comprehensibility and format of the process and to highlight any other potential issues.

Population refers to all the people of interest to the study and to whom the findings will be able to be generalized (e.g. a study looking into rates of recidivism may have a [target] population encompassing everyone with a criminal conviction). Owing to the size of the population, a study will usually select a sample from which to make inferences. See also: sample , representiveness.

Percentiles

A percentile is a measure that allows us to explore the distribution of data on a variable. It denotes the percentage of individuals or observations that fall below a specified value on a variable. The value that splits the number of observations evenly, i.e. 50% of the observations on a variable fall below this value and 50% above, is called the 50th percentile or more commonly, the median.

Primary research

Primary research refers to original research undertaken by researchers collecting new data. It has the benefit that researchers can design the study to answer specific questions and hypotheses rather than relying on data collected for similar but not necessarily identical purposes. See also: secondary research

Prospective study

In prospective studies, individuals are followed over time and data about them is collected as their characteristics or circumstances change.

Qualitative data

Qualitative data are non-numeric – typically textual, audio or visual. Qualitative data are collected through interviews, focus groups or participant observation. Qualitative data are often analysed thematically to identify patterns of behaviour and attitudes that may be highly context-specific.

Quantitative data

Quantitative data can be counted, measured and expressed numerically. They are collected through measurement or by administering structured questionnaires . Quantitative data can be analysed using statistical techniques to test hypotheses and make inferences to a population .

Questionnaires

Questionnaires are research instruments used to elicit information from participants in a structured way. They might be administered by an interviewer (either face-to-face or over the phone), or completed by the participants on their own (either online or using a paper questionnaire. Questions can cover a wide range of topics and often include previously-validated instruments and scales (e.g. the Rosenberg Self-Esteem Scale ).

Recall error or bias

Recall error or bias describes the errors that can occur when study participants are asked to recall events or experiences from the past. It can take a number of forms – participants might completely forget something happened, or misremember aspects of it, such as when it happened, how long it lasted, or other details. Certain questions are more susceptible to recall bias than others. For example, it is usually easy for a person to accurately recall the date they got married, but it is much harder to accurately recall how much they earned in a particular job, or how their mood at a particular time.

Record linkage

Record linkage studies involve linking together administrative records (for example, benefit receipts or census records) for the same individuals over time.

Reference group

A reference group is a category on a categorical variable to which we compare other values. It is a term that is commonly used in the context of regression analyses in which categorical variables are being modelled.

Regression analysis

Regression analysis refers to analytical techniques that use a mathematical ‘model’ to predict values of a dependent variable from values of one or many independent variable (s).

Repeated measures

Repeated measures are measurements of the same variable at multiple time points on the same participants, allowing researchers to study change over time.

Representativeness

Representativeness is the extent to which a sample is representative of the population from which it is selected. Representative samples can be achieved through, for example, random sampling, systematic sampling, stratified sampling or cluster sampling.

Research ethics

Research ethics relates to the fundamental codes of practice associated with conducting research. Ethical issues that need to be considered include providing informed consent to participants, non-disclosure of sensitive information, confidentiality and anonymity safeguarding of vulnerable groups , and respect for participants’ well-being. Academic research proposals need be approved by an ethics committee before any actual research (either primary or secondary) can begin.

Research impact

Research impact is the demonstrable contribution that research makes to society and the economy that can be realised through engagement with other researchers and academics, policy makers, stakeholders and members of the general public. It includes influencing policy development, improving practice or service provision, or advancing skills and techniques.

Residuals are the difference between your observed values (the constant and predictors in the model) and expected values (the error), i.e. the distance of the actual value from the estimated value on the regression line.

Respondent burden

Respondent burden is a catch all phrase that describes the perceived burden faced by participants as a result of their being involved in a study. It could include time spent taking part in the interview and inconvenience this may cause, as well as any difficulties faced as a result of the content of the interview.

Response rate

Response rate refers to the proportion of participants in the target sample who completed the survey. Longitudinal surveys are designed with the expectation that response rates will decline over time so will typically seek to recruit a large initial sample in order to compensate for likely attrition of participants.

Retrospective study

In retrospective studies, individuals are sampled and information is collected about their past. This might be through interviews in which participants are asked to recall important events, or by identifying relevant administrative data to fill in information on past events and circumstances.

Sample is a subset of a population that is used to represent the population as a whole. This reflects the fact that it is often not practical or necessary to survey every member of a particular population . In the case of birth cohort studies , the larger ‘ population ’ from which the sample is drawn comprises those born in a particular period. In the case of a household panel study like Understanding Society, the larger population from which the sample was drawn comprised all residential addresses in the UK.

Sample size

Sample size refers to the number of data units contained within a dataset. It most frequently refers to the number of respondents who took part in your study and for whom there is usable data. However, it could also relate to households, countries or other institutions. The size of a sample , relative to the size of the population , will have consequences for analysis: the larger a sample is, the smaller the margin of error of its estimates, the more reliable the results of the analysis and the greater statistical power of the study.

Sampling frame

A sampling frame is a list of the target population from which potential study participants can be selected.

Scales are frequently used as part of a research instrument seeking to measure specific concepts in a uniform and replicable way. Typically, they are composed of multiple items that are aggregated into one or more composite scores. Examples of standardised scales include the British Ability Scale (BAS); the Malaise Inventory; and the Rosenberg Self-Esteem Scale.

Scatterplot

A scatterplot is a way of visualising the relationship between two continuous variables by plotting the value of each associated with a single case on a set of X-Y coordinates. For example, students’ test scores in English and maths can be represented as point on a graph, with each point representing a single student’s English (x-axis) and maths (y-axis) score. Looking at data for many students allows us to build up a visualisation of the relationship between students’ scores in maths and English.

Secondary research

Secondary research refers to new research undertaken using data previously collected by others. It has the benefit of being more cost-effective than primary research whilst still providing important insights into research questions under investigation.

Skewness is the measure of how assymetrical the distribution of observations are on a variable. If the distribution has a more pronounced/longer tail at the upper end of the distribution (right-hand side), we say that the distribution is negatively skewed. If it is more pronounced/longer at the lower end (left-hand side), we say that it is positively skewed.

Statistical model

A statistical model is a mathematical representation of the relationship between variables .

Statistical software

Statistical software packages are specifically designed to carry out statistical analysis; these can either be open-source (e.g. R ) or available through institutional or individual subscription (e.g. SPSS ; Stata ).

Structured metadata

Structured metadata define the relationship between data items/objects to enable computer systems to understand the contextual meaning of the data. It uses standardised content to facilitate the use of metadata for data discovery and sharing, and the relationship between metadata elements.

Study participants

Study participants are the individuals who are interviewed as part of a longitudinal study.

Survey logic

Also called conditional routing (sometimes called ‘filters’), survey logic refers to the flow that takes respondents through a survey. Respondents may be required to answer some questions only if they had provided a relevant response to a previous question. E.g. Only respondents who are currently at university may be asked to answer a question relating to their degree subject. This is important when considering missing data .

Survey weights

Survey weights can be used to adjust a survey sample so it is representative of the survey population as a whole. They may be used to reduce the impact of attrition on the sample , or to correct for certain groups being over-sampled.

Survival analysis

Survival analysis is an analytical technique that uses time-to-event data to statistically model the probability of experiencing an event by a given time point. For example, time to retirement, disease onset or length of periods of unemployment.

The term used to refer to a round of data collection in a particular longitudinal study (for example, the age 7 sweep of the National Child Development Study refers to the data collection that took place in 1965 when the participants were aged 7). Note that the term wave often has the same meaning.

Target population

The population of people that the study team wants to research, and from which a sample will be drawn.

Time to event

Time to event refers to the duration of time (e.g. in hours, days, months, etc.) from a defined baseline to the time of occurrence of an event of interest (e.g. diagnosis of an illness, first re-offence following release from prison). Survival analysis can be used to analyse such data.

Tracing (or tracking)

Tracing (or tracking) describes the process by which study teams attempt to locate participants who have moved from the address at which they were last interviewed.

Unobserved heterogeneity

Unobserved heterogeneity is a term that describes the existence of unmeasured (unobserved) differences between study participants or samples that are associated with the (observed) variables of interest. The existence of unobserved variables means that statistical findings based on the observed data may be incorrect.

Part of the documentation that is usually provided with statistical datasets, user guides are an invaluable resource for researchers. The guides contain information about the study, including the sample , data collection procedures, and data processing. Use guides may also provide information about how to analyse the data, whether there are missing data due to survey logic , and advice on how to analyse the data such the application of survey weights .

Variables is the term that tends to be used to describe data items within a dataset. So, for example, a questionnaire might collect information about a participant’s job (its title, whether it involves any supervision, the type of organisation they work for and so on). This information would then be coded using a code-frame and the results made available in the dataset in the form of a variable about occupation. In data analysis variables can be described as ‘dependent’ and ‘independent’, with the dependent variable being a particular outcome of interest (for example, high attainment at school) and the independent variables being the variables that might have a bearing on this outcome (for example, parental education, gender and so on).

Vulnerable groups

Vulnerable groups refers to research participants who may be particularly susceptible to risk or harm as a result of the research process. Different groups might be considered vulnerable in different settings. The term can encompass children and minors, adults with learning difficulties, refugees, the elderly and infirm, economically disadvantaged people, or those in institutional care. Additional consideration and mitigation of potential risk is usually required before research is carried out with vulnerable groups.

The term used to refer to a round of data collection in a particular longitudinal study (for example, the age 7 wave of the National Child Development Study refers to the data collection that took place in 1965 when the participants were aged 7). Note that the term sweep often has the same meaning.

Learning Hub

What are longitudinal studies, what are longitudinal studies and how do they work.

What information do they collect?
Where do the data come from?
Data collection methods

A longitudinal study is a prospective observational study that follows the same subjects repeatedly over a period of time.

The UK is home to the largest and longest-running portfolio of longitudinal studies in the world.

The UK is most well-known for its birth cohort studies , which each follow a group of people born at a particular point in time throughout their entire lives. The UK is also home to Understanding Society , one of the largest household panel studies of its kind anywhere in the world. Instead of following individuals, this study follows whole households of people through time.

There are also other kinds of longitudinal studies , such as those following a group of people with a particular disease, or a cohort of students leaving university. In this module, we will mostly draw on examples from longitudinal birth cohorts and household panels.

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10 Famous Examples of Longitudinal Studies

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Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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longitudinal studies examples and definition, explained below

A longitudinal study is a study that observes a subject or subjects over an extended period of time. They may run into several weeks, months, or years. An examples is the Up Series which has been going since 1963.

Longitudinal studies are deployed most commonly in psychology and sociology, where the intention is to observe the changes in the subject over years, across a lifetime, and sometimes, even across generations.

There have been several famous longitudinal studies in history. Some of the most well-known examples are listed below.

Examples of Longitudinal Studies

1. up series.

Duration: 1963 to Now

The Up Series is a continuing longitudinal study that studies the lives of 14 subjects in Britain at 7-year intervals.

The study is conducted in the form of interviews in which the subjects report the changes that have occurred in their lives in the last 7 years since the last interview.

The interviews are filmed and form the subject matter of the critically acclaimed Up series of documentary films directed by Michael Apsted.

When it was first conceived, the aim of the study was to document the life progressions of a cross-section of British children through the second half of the 20th century in light of the rapid social, economic, political, and demographic changes occuring in Britain.

14 children were selected from different socio-economic backgrounds for the first study in 1963 in which all were 7 years old.

The latest installment was filmed in 2019 by which time the participants had reached 63 years of age.

The study noted that life outcomes of subjects were determined to a large extent by their socio-economic and demographic circumstances, and that chances for upward mobility remained limited in late 20th century Britain (Pearson, 2012).

2. Minnesota Twin Study

Duration: 1979 to 1990 (11 years)

Siblings who are twins not only look alike but often display similar behavioral and personality traits.

This raises an oft-asked question: how much of this similarity is genetic and how much of it is the result of the twins growing up together in a similar environment.

The Minnesota twin study was a longitudinal study that set out to find an answer to this question by studying a group of twins from 1979 to 1990 under the supervision of Thomas J Bouchard.

The study found that identical twins who were reared apart in different environments did not display any greater chances of being different from each other than twins that were raised in the same environment.

The study concluded that the similarities and differences between twins are genetic in nature, rather than being the result of their environment (Bouchard et. al., 1990).

3. Grant Study

Duration: 1942 – Present

The Grant Study is one of the most ambitious longitudinal studies. It attempts to answer a philosophical question that has been central to human existence since the beginning of time – what is the secret to living a good life? (Shenk, 2009).

It does so by studying the lives of 268 male Harvard graduates who are interrogated at least every two years with the help of questionnaires, personal interviews, and gleaning information about their physical and mental well-being from their physicians.

Begun in 1942, the study continues to this day.

The study has provided researchers with several interesting insights into what constitutes the human quality of life.

For instance:

It reveals that the quality of our relationships is more influential than IQ when it comes to our financial success.
It suggests that our relationships with our parents during childhood have a lasting impact on our mental and physical well-being until late into our lives.

In short, the results gleaned from the study (so far) strongly indicate that the quality of our relationships is one of the biggest factors in determining our quality of life.

4. Terman Life Cycle Study

Duration: 1921 – Present

The Terman Life-Cycle Study, also called the Genetic Studies of Genius, is one of the longest studies ever conducted in the field of psychology.

Commenced in 1921, it continues to this day, over 100 years later!

The objective of the study at its commencement in 1921 was to study the life trajectories of exceptionally gifted children, as measured by standardized intelligence tests.

Lewis Terman, the principal investigator of the study, wanted to dispel the then-prevalent notion that intellectually gifted children tended to be:

socially inept, and
physically deficient

To this end, Terman selected 1528 students from public schools in California based on their scores on several standardized intelligence tests such as the Stanford-Binet Intelligence scales, National Intelligence Test, and the Army Alpha Test.

It was discovered that intellectually gifted children had the same social skills and the same level of physical development as other children.

As the study progressed, following the selected children well into adulthood and in their old age, it was further discovered that having higher IQs did not affect outcomes later in life in a significant way (Terman & Oden, 1959).

5. National Food Survey

Duration: 1940 to 2000 (60 years)

The National Food Survey was a British study that ran from 1940 to 2000. It attempted to study food consumption, dietary patterns, and household expenditures on food by British citizens.

Initially commenced to measure the effects of wartime rationing on the health of British citizens in 1940, the survey was extended and expanded after the end of the war to become a comprehensive study of British dietary consumption and expenditure patterns.

After 2000, the survey was replaced by the Expenditure and Food Survey, which lasted till 2008. It was further replaced by the Living Costs and Food Survey post-2008.

6. Millennium Cohort Study

Duration: 2000 to Present

The Millennium Cohort Study (MCS) is a study similar to the Up Series study conducted by the University of London.

Like the Up series, it aims to study the life trajectories of a group of British children relative to the socio-economic and demographic changes occurring in Britain.

However, the subjects of the Millenium Cohort Study are children born in the UK in the year 2000-01.

Also unlike the Up Series, the MCS has a much larger sample size of 18,818 subjects representing a much wider ethnic and socio-economic cross-section of British society.

7. The Study of Mathematically Precocious Youths

Duration: 1971 to Present

The Study of Mathematically Precocious Youths (SMPY) is a longitudinal study initiated in 1971 at the Johns Hopkins University.

At the time of its inception, the study aimed to study children who were exceptionally gifted in mathematics as evidenced from their Scholastic Aptitude Test (SAT) scores.

Later the study shifted to Vanderbilt University and was expanded to include children who scored exceptionally high in the verbal section of the SATs as well.

The study has revealed several interesting insights into the life paths, career trajectories, and lifestyle preferences of academically gifted individuals. For instance, it revealed:

Children with exceptionally high mathematical scores tended to gravitate towards academic, research, or corporate careers in the STEM fields.
Children with better verbal abilities went into academic, research, or corporate careers in the social sciences and humanities .

8. Baltimore Longitudinal Study of Aging

Duration: 1958 to Present

The Baltimore Longitudinal Study of Aging (BLSA) was initiated in 1958 to study the effects of aging, making it the longest-running study on human aging in America.

With a sample size of over 3200 volunteer subjects, the study has revealed crucial information about the process of human aging.

For instance, the study has shown that:

The most common ailments associated with the elderly such as diabetes, hypertension, and dementia are not an inevitable outcome of growing old, but rather result from genetic and lifestyle factors.
Aging does not proceed uniformly in humans, and all humans age differently.

9. Nurses’ Health Study

Duration: 1976 to Present

The Nurses’ Health Study began in 1976 to study the effects of oral contraceptives on women’s health.

The first commercially available birth control pill was approved by the Food and Drug Administration (FDA) in 1960, and the use of such pills rapidly spread across the US and the UK.

At the same time, a lot of misinformation prevailed about the perceived harmful effects of using oral contraceptives.

The nurses’ health study aimed to study the long-term effects of the use of these pills by researching a sample composed of female nurses.

Nurses were specially chosen for the study because of their medical awareness and hence the ease of data collection that this enabled.

Over time, the study expanded to include not just oral contraceptives but also smoking, exercise, and obesity within the ambit of its research.

As its scope widened, so did the sample size and the resources required for continuing the research.

As a result, the study is now believed to be one of the largest and the most expensive observational health studies in history.

10. The Seattle 500 Study

Duration: 1974 to Present

The Seattle 500 Study is a longitudinal study being conducted by the University of Washington.

It observes a cohort of 500 individuals in the city of Seattle to determine the effects of prenatal habits on human health.

In particular, the study attempts to track patterns of substance abuse and mental health among the subjects and correlate them to the prenatal habits of the parents.

From the examples above, it is clear that longitudinal studies are essential because they provide a unique perspective into certain issues which can not be acquired through any other method .

Especially in research areas that study developmental or life span issues, longitudinal studies become almost inevitable.

A major drawback of longitudinal studies is that because of their extended timespan, the results are likely to be influenced by epochal events.

For instance, in the Genetic Studies of Genius described above, the life prospects of all the subjects would have been impacted by events such as the Great Depression and the Second World War.

The female participants in the study, despite their intellectual precocity, spent their lives as home makers because of the cultural norms of the era. Thus, despite their scale and scope, longitudinal studies do not always succeed in controlling background variables.

Bouchard, T. J. Jr, Lykken, D. T., McGue, M., Segal, N. L., & Tellegen, A. (1990). Sources of human psychological differences: the Minnesota study of twins reared apart. Science , 250 (4978), 223–228. doi: https://doi.org/10.1126/science.2218526

Pearson, A. (2012, May) Seven Up!: A tale of two Englands that, shamefully, still exist The Telegraph https://www.telegraph.co.uk/comment/columnists/allison-pearson/9269805/Seven-Up-A-tale-of-two-Englands-that-shamefully-still-exist.html

Shenk, J.W. (2009, June) What makes us happy? The Atlantic https://www.theatlantic.com/magazine/archive/2009/06/what-makes-us-happy/307439/

Terman, L. M. & Oden, M. (1959). The Gifted group at mid-Life: Thirty-five years’ follow-up of the superior child . Genetic Studies of Genius Volume V . Stanford University Press.

Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Green Flags in a Relationship
Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Signs you're Burnt Out, Not Lazy
Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Toxic Things Parents Say to their Children
Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Red Flags Early in a Relationship

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Open access
Published: 02 August 2024

Longitudinal study on the multifactorial public health risks associated with sewage reclamation

Inés Girón-Guzmán 1 ,
Santiago Sánchez-Alberola 1 , 2 ,
Enric Cuevas-Ferrando ORCID: orcid.org/0000-0002-0799-009X 1 ,
Irene Falcó 1 , 3 ,
Azahara Díaz-Reolid 1 ,
Pablo Puchades-Colera ORCID: orcid.org/0009-0009-5692-3406 1 ,
Sandra Ballesteros 4 ,
Alba Pérez-Cataluña 1 ,
José María Coll 1 ,
Eugenia Núñez ORCID: orcid.org/0000-0002-1852-3374 1 , 2 ,
María José Fabra 1 , 2 ,
Amparo López-Rubio 1 , 2 na1 &
Gloria Sánchez 1 na1

npj Clean Water volume 7 , Article number: 72 ( 2024 ) Cite this article

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Environmental sciences
Water resources

This year-long research analyzed emerging risks in influent, effluent wastewaters and biosolids from six wastewater treatment plants in Spain’s Valencian Region. Specifically, it focused on human enteric and respiratory viruses, bacterial and viral faecal contamination indicators, extended-spectrum beta-lactamases-producing Escherichia coli , and antibiotic-resistance genes. Additionally, particles and microplastics in biosolid and wastewater samples were assessed. Human enteric viruses were prevalent in influent wastewater, with limited post-treatment reduction. Wastewater treatment effectively eliminated respiratory viruses, except for low levels of SARS-CoV-2 in effluent and biosolid samples, suggesting minimal public health risk. Antibiotic resistance genes and microplastics were persistently found in effluent and biosolids, thus indicating treatment inefficiencies and potential environmental dissemination. This multifaced research sheds light on diverse contaminants present after water reclamation, emphasizing the interconnectedness of human, animal, and environmental health in wastewater management. It underscores the need for a One Health approach to address the United Nations Sustainable Development Goals.

Rethinking wastewater risks and monitoring in light of the COVID-19 pandemic

Realising a global One Health disease surveillance approach: insights from wastewater and beyond

Research progress on the origin, fate, impacts and harm of microplastics and antibiotic resistance genes in wastewater treatment plants

Introduction.

Water is a fundamental resource for human life, being also essential for crops and livestock production. However, the increasing global population and limited freshwater resources pose significant challenges to meeting the demands of various sectors, including agriculture. Water reuse has emerged as a sustainable solution to preserve freshwater resources and reduce environmental pressure. Reclaimed water, also known as recycled water or effluent from wastewater treatment plants (WWTPs), refers to the treated wastewater that undergoes a series of physical, chemical, and biological processes to remove contaminants and pathogens. The reclaimed water is then suitable for non-potable uses, such as irrigation, industrial processes, and groundwater recharge according to national regulations 1 .

Water reuse has become increasingly important in agriculture due to the limited freshwater resources and the growing demand for food production. Agriculture accounts for approximately 70% of global freshwater withdrawals and the water demand for crops and livestock is projected to increase in the coming decades 2 . Reclaimed water offers a sustainable solution to reduce the demand for freshwater resources and ensure the availability of water for irrigation while reducing the discharge of treated wastewater into the environment and the cost of water supply. However, water reuse also poses several challenges, particularly in terms of microbiological and chemical safety. Reclaimed water may contain a variety of contaminants, including bacteria, viruses, protozoa, and emerging pollutants, such as microplastics (MPs), antibiotic resistant genes (ARGs), and pharmaceuticals 3 .

In particular, human enteric viruses are responsible for causing viral gastroenteritis, hepatitis, and various illnesses primarily transmitted through the faecal-oral route 4 . The spread of these viruses is primarily linked to person-to-person contact and the consumption of contaminated food and water. Enteric viruses are excreted in substantial quantities, up to 10 13 particles per gram of stool, by both symptomatic and asymptomatic individuals 5 , 6 . Major causative agents of waterborne viral gastroenteritis and hepatitis outbreaks worldwide include rotaviruses (RVs), norovirus genogroups I (HuNoV GI) and II (HuNoV GII), hepatitis A and E viruses (HAV and HEV), and human astroviruses 5 (HAstVs). In this context, and related to microbiological risks dissemination, a new European regulation (EC, 2020/741) on minimum quality criteria (MQR) for water reuse is in place since June 2023, outlining the guidelines for the use of reclaimed water for agricultural irrigation 7 . However, questions have arisen concerning potential non-compliance scenarios in European water reuse systems 8 , 9 , 10 , 11 , 12 . According to EC 2020/741 regulation, validation monitoring needs to assess whether the performance targets reductions are met. Monitoring of pathogen elimination in the water reclamation process is necessary to assess the suitability of reclaimed water in its secondary uses. In this respect, the WHO has suggested that another problem to be tackled in the framework of “One Health” is the rise of antibiotic resistance (AR) 13 . AR is frequent in places where antibiotics are employed, but antibiotic resistant bacteria (ARB) and ARGs are also widely prevalent in water environments 14 , 15 . According to several reports, surface water and reclaimed wastewater used for irrigation are significant sources of ARBs and ARGs 16 . Due to inadequate removal of ARGs, which are crucial in the growth of extremely unfavourable drug-resistant superbugs, reuse of WWTP effluents may be harmful to human health 17 .

On the other hand, plastic pollution is currently one of the most important environmental problems that humanity must face. The exponential growth of plastic production since 1950s (up to 368 million of tons were produced in 2019) and the massive use of plastics, together with insufficient/inadequate waste management/disposal strategies, are the main causes of the global presence of plastics in every environmental compartment 18 . The European Commission has recently published an amending Annex to Regulation (EC) No 1907/2006 concerning the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) as regards synthetic polymer microparticles, where the intentional use of microplastics in commercial products is prohibited 19 .

Current research is showing that one of the main concerns about plastics, apart from the fact that they persist in the environment for an extremely long time, is their constant fragmentation into even smaller particles called microplastics (MPs, 1 μm–5 mm) or nanoplastics (< 1 μm), depending on their final dimensions, though they are also released as such 20 .

MPs are emerging global threats as they can end up in our bodies through water and food ingestion or by air inhalation 21 . The larger MPs can cause mechanical damage to the intestinal epithelium, while the smaller particles can cross the epithelial barrier 22 and end up in the lung 23 , colon 24 , placenta 25 , and even blood 26 .

MPs can transport pathogens over long distances, due to their ability to harbor biofilms on the surface, which can lead to the spread of pathogenic viruses and bacteria to new areas where they were not previously found 27 . Another of the main risks associated with MPs is that plastic materials include approximately 4% by weight of additives 28 , some of them declared as possible human carcinogens, and most of them considered endocrine disruptors 29 . In addition, MPs also contain traces of persistent organic pollutants (COPs), such as polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), and organochlorine pesticides 22 .

It is important to highlight that depending on the performance of WWTPs high amounts of pathogens, MPs and ARGs can be released on a daily basis into rivers, lakes, and oceans 9 , 14 , 30 . On the other hand, the sludge generated as well as the effluent water from the WWTPs are generally used in agriculture as a fertilizer and for irrigation respectively, and, therefore, the presence of emerging contaminants in these biosolids and reclaimed waters can favour the propagation of plastic particles, emerging pathogens, and ARGs through agricultural soils which could reach cultivated vegetables and ultimately the human body through the trophic chain.

In overall terms, understanding the distinct risk factors involved in the water reclamation process is critical to ensuring the safety of water reuse in agriculture and other sectors, and the analysis of the water reclamation process can serve as an important risk assessment tool. Moreover, by analysing wastewater, we gain valuable insights into the collective health of a community, as it contains traces of chemical pollutants, pathogens, and biomarkers from human and animal sources. Thus, monitoring wastewater helps identifying trends in the prevalence of diseases, antibiotic resistance patterns, zoonotic pathogens, and exposure to environmental pollutants as MPs, providing early warning and valuable data for public health interventions. This integration of environmental, human, and animal health data underscores the significance of wastewater analysis in promoting a comprehensive and proactive “One Health” approach to public health and the well-being of both the planet and its inhabitants.

Incidence of human enteric viruses, respiratory viruses, and viral faecal indicators in influent and effluent wastewater samples

The presence of human enteric viruses, including HuNoV GI, HuNoV GII, HAstV, HAV, HEV, and RV, was analysed, along with novel viral faecal contamination indicators pepper mild mottle virus (PMMoV), crAssphage and somatic coliphages in influent, effluent and biosolid samples from six different WWTPs in the Valencian region of Spain (Figs. 1 and 2 ).

HEV hepatitis E virus, HAV hepatitis A virus, HAstV human astrovirus, RV rotavirus, PMMoV pepper mild mottle virus.

Whiskers in box are drawn min. to max., box extends from the 25th to 75th percentiles, and line within the box represents the median. Coloured circles above a box indicate significant differences between that box and the box with that same colour ( p < 0.05). GC genome copies, PFU plate forming units, RV rotavirus, HuNoV human norovirus, HAstV human astrovirus, HAV hepatitis A virus, HEV hepatitis E virus, PMMoV pepper mild mottle virus.

In influent wastewater samples, the mean highest levels of viruses were observed for RV (8.55 log genome copies, GC/L), followed by HuNoV GII (7.80 log GC/L) and HAstV (7.72 log GC/L). The lowest concentration levels were detected for HuNoV GI (4.46 log GC/L), HEV (4.13 log GC/L), and HAV (3.47 log GC/L) (Fig. 2 ). HAV was only detected in 4 out of 72 influent wastewater samples (Fig. 1 ). PMMoV and crAssphage were detected in all influent samples, with mean levels of 5.95 log GC/L and 8.44 log GC/L, respectively.

In the effluent wastewater samples, the titres of all viruses decreased after the water reclamation process. HuNoV GI, HuNoV GII, HAstV, and RV showed mean concentrations titers of 3.51, 6.25, 6.35, and 7.69 Log GC/L when detected, respectively (Fig. 2 ). On the contrary, HEV was not detected in any of the effluent samples. In the case of faecal viral indicators, PMMoV (4.72 Log GC/L) and crAssphage (6.23 Log GC/L) were present in all effluent samples. The highest reduction in virus levels were observed for HEV, with a reduction of 4 Log GC/L, even though the vast majority of viruses’ reduction levels were below 2 Logs GC/L (Supplementary Fig. 1 ). Interestingly, viable somatic coliphages were found at levels of 4.73 Log plaque forming units (PFU)/100 mL in effluent waters, with a mean reduction of 1.83 Log PFU/100 mL compared to the influent waters (6.54 Log PFU/100 mL) when testing positive.

As for biosolid samples, HuNoV GI, HuNoV GII, HAstV, and RV showed the highest mean concentrations, with titers ranging from 5.37 (HuNoV GI) to 7.27 Log GC/L (RV) when detected (Fig. 2 ). HAV and HEV rendered lower mean concentrations of 3.24 and 3.91 Log GC/L, respectively. Besides, proposed viral faecal indicators yielded mean concentrations levels of 7.06 Log GC/L for crAssphage, 4.85 Log GC/L for PMMoV. and 5.63 Log PFU/100 mL for somatic coliphages (Fig. 2 ).

Regarding respiratory viruses, respiratory syncytial virus (RSV) showed a remarkable seasonality, with almost all positive samples being collected on November and December 2022 (Fig. 3 ). Influenza A virus (IAV) was intermittently detected over the year, with the most noteworthy peaks taking place in spring and winter (Fig. 3 ). Finally, SARS-CoV-2 was present in 99% and 32% of the influent and effluent samples, respectively. When testing positive, mean concentration values for RSV, IAV, and SARS-CoV-2 were 4.57, 6.20, and 5.27 Log GC/L, respectively. Notably, any of the analysed effluent wastewater samples tested positive for either RSV or IAV.

Nd not detected, GC genome copies, SARS-CoV-2 severe acute respiratory syndrome coronavirus 2, RSV respiratory syncytial virus, IA Influenza A virus.

Regarding biosolid samples, SARS-CoV-2 was found positive in 71% of the samples at a mean concentration of 4.44 Log GC/L, while RSV and IAV only tested positive in three biosolid samples.

In general, no significant differences were found among the six different WWTPs analysed neither for enteric or respiratory viruses.

Quantification of Escherichia coli , Extended Spectrum Beta-Lactamases-producing E. coli , and ARGs in wastewater and biosolids samples

In influent wastewater samples, the mean concentration of E. coli and ESBL- E. coli were 7.08 Log colony forming units (CFU)/100 mL and 6.19 Log CFU/100 mL, respectively (Fig. 4 ). After the wastewater treatment process, the mean concentrations of E. coli , and ESBL- E. coli in the effluent wastewater samples were significantly reduced, with mean concentrations of 5.43 Log CFU/100 mL, and 4.76 Log CFU/100 mL, respectively.

Whiskers in box are drawn min. to max., box extends from the 25th to 75th percentiles, and the line within the box represents the median. Coloured circles above a box indicate significant differences between that box and the box with that same colour ( p < 0.05). CFU colony forming unit ESBL- E. coli extended spectrum beta-lactamases producing Escherichia coli .

Regarding biosolid samples, the mean concentration of E. coli was 5.64 Log CFU/100 mL, while ESBL- E. coli yielded a mean concentration of 4.89 Log CFU/100 mL.

Furthermore, a deeper analysis of the ARGs present in effluent and biosolids samples was performed due to the high levels of ESBL- E. coli in biosolids and the observed low performance of the water reclamation process (less than 2 log reduction; Fig. 4 ). ARGs including tetPB_3 , tetA_1 , and qacA_1 were not detected in effluent wastewater and biosolids. ARG sul1_1 , sul2_1 , pbp2b , bla CTX-M , cmlA_2 , nimE , and ermB were detected in effluent samples at mean concentrations of 9.20, 8.78, 8.57, 8.42, 8.31, 8,24, and 8.39 Log GC/100 mL, respectively (Fig. 5 ).

Each different symbol type represents a different WWTP. ND Not detected, MLSB Macrolide-lincosamide-streptogramin B group antibiotics, GC genome copies.

ARGs were identified in biosolids, with the following values: 9.87, 9.25, 8.58, 8.42, 8.50, 8.64, 8.28 Log GC/100 mL for sul1_1 , sul2_1 , pbp2b , bla CTX-M , cmlA_2 , erm B, and ermA , respectively. Notably, nimE was not found in any of analysed biosolids.

Quantification of particles and microplastics present in biosolids and reclaimed water samples

The presence of solid particles and microplastics was bi-monthly analysed in both influent and effluent wastewater samples. In general, a great reduction in both the number of particles between 1 μm and 5 mm or (T)-P and particles larger than 300 µm or (S)-P was observed after the wastewater treatment process (Fig. 6 ). Although there was not a clear effect derived from seasonality, WWTPs were slightly less efficient in removing (T)-P in January and March.

Concentration (log P/L) of total particles (T)-P and sieved particles (>300 μm, (S)-P) in influent and effluent wastewater samples in even months over a one-year period in six different WWTPs (P1-P6).

The efficiency of each WWTPs regarding the reduction of (T)-P and (S)-P particles was determined considering the average number of particles in the influent and effluent wastewater samples (Fig. 7 ). At the WWTP level, the calculated efficiency in (T)-P reduction was approximately 84, 68, 69, 46, 80 and 71%, for the different WWTPs (P1-P6) samples analysed. Notably, the efficiency in removing (S)-P was higher than in removing (T)-P, with the most noteworthy reduction taking place for P2 and P6 wastewater samples (91 and 93% approximately, and respectively), while the lowest efficiency in (T)-P reduction was approximately 40% for P5.

Removal efficiency (%) of all solid particles (P) and microplastics (MPs) between influent and effluent samples collected from six different WWTPs (P1-P6) after both pre-treatment protocols. Total Particles (T) and Sieved > 300 µm (S).

Once (T)-P and (S)-P particles were quantified, all samples were spectroscopically characterized in order to identify the presence of MPs derived from synthetic polymer particles, fibres, and films. In general terms, the highest reduction was observed in (S)-MPs as compared to (T)-MPs, thus suggesting the lower efficiency of wastewater treatments in removing microplastics smaller than 300 μm (Fig. 7 ). It should be highlighted that the efficiency of WWTPs for removing MPs of smaller particle size or (T)-MPs was lower than for removing all solid particles or (T)-P, being 59% the highest (T)-MPs efficiency (sample P6). In general, a higher efficiency in reducing (S)-MPs was observed (around 98-100%) in all samples, except in P2 (77%) (Fig. 7 ).

Considering the pre-treatment (T), the annual average MPs concentration in influent samples was around 1816 MPs/L which was slightly reduced in effluent samples (1724 MPs/L). In contrast, the annual average concentration of (S)-MPs (larger than 300 µm) in influent samples was 198 MPs/L and it was significantly reduced in effluent wastewater samples until 11 MPs/L in average (Fig. 8 ).

Average concentration (mean + standard deviation) of MPs in influent (I) and effluent (E) after (T) (left) and (S) (right) protocols collected from six different WWTPs. T total particles, S Sieved > 300 µm.

The annual average percentage of MPs with respect to all solid particles in influent and effluent wastewater samples and biosolids was also determined (Supplementary Fig. 2 ). It is worth mentioning that, regarding the particles larger than 300 μm, the MPs/all solid particles ratio in biosolid samples was similar to the MPs/all solid particles ratio in influent wastewater samples, reaching values up to 35 in some of the WWTPs (Supplementary Fig. 2 ).

In all the analysed biosolid samples a significant number of (S)-P was also detected, and no significant effects due to seasonality were found (Fig. 9 ). The average highest concentration of (S)-MPs was 122 MPs/g and 99 MPs/g for P1 and P2, respectively. In contrast, the lowest level of MPs was detected for P3 (23 MPs/g) (Supplementary Fig. 3 ).

Concentration (in log/g) of (S)-P and (S)-MPs in biosolids in even months over a one-year period in six different WWTPs (P1-P6).

Analysing the morphology and type of MPs identified in the WWTPs samples may help to understand the origin of water pollution (Supplementary Figs. 5 and 6 ). As depicted in Fig. 10 , the majority of MPs existing in influent wastewater samples had the shape of fragments ( ∼ 86%), percentage that was further increased in effluent wastewater samples. The percentage of particles identified as films was negligible both in influent or effluent samples. Most of the MPs found in influent samples were between 0 and 100 µm ( ∼ 61%) in size, percentage that was increased in effluents (up to 73%), and a small fraction of MPs ( ∼ 3-5%) were larger than 300 µm in size, in agreement with the results commented above (Fig. 8 ). It is hypothesized that, during sieving, particles smaller than 300 µm may aggregate and become retained, but following oxidative digestion, they break down into smaller particles. The composition of the MPs was dominated by common polymers, whereas the PS, PA, PVC, and PET were greatly decreased in effluent samples (Fig. 10 ). It is worth mentioning that the distribution of polymer type was quite different when comparing wastewater and biosolids samples. PE was dominant in all samples, accounting for 56, 46 and 57% of the total MPs, for wastewater (T)-MPs and (S)-MPs, and for biosolids (S)-MPs, respectively (Supplementary Fig. 4 ). The amount of PA was more than two-fold higher in (T)-MPs samples from wastewater than in (S)-MPs from biosolids (31% vs. 12%, respectively). PET represented around 21–28% of the (S)-MPs in wastewater and biosolid samples. Other polymers such as PS, polytetrafluoroethylene PTFE, PVC, and PS were detected in lower amounts.

PE polyethylene, PET polyethylene terephthalate, PA polyamide, PP polypropylene, PS polystyrene, PVC polyvinyl chloride, PTFE polytetrafluoroethylene, PAM polyacrylamide.

Reuse of effluent wastewater and biosolids in agriculture is essential to face the increasing demand of water and agricultural products in combination with global warming and water scarcity 31 . Effluent wastewater and biosolids, however, are sources of emerging contaminants of concern such as viral pathogens, antibiotic resistance genes, and microplastics. The reuse of water and the release of reclaimed water into the environment may compromise public health due to the combination of several risk factors. In recent years, several publications have pointed out the low efficiency of WWTPs in removing viral pathogens 9 . While decay rates of human enteric viruses in effluents wastewater samples are frequently studied, very few studies have reported the incidence of respiratory viruses, MPs, and ARGs in effluent wastewater and biosolids, with the potential of being used in agriculture.

The present study investigated the presence of human enteric viruses, including HuNoV GI and GII, HAstV, HEV, and RV, as well as ARBs, ARGs, MPs and two novel viral faecal contamination indicators (PMMoV and crAssphage) in influent, effluent and biosolids samples. Consistent with findings from earlier research, influent wastewater samples exhibited elevated concentrations of human enteric viruses, MPs and ARBs 14 , 32 (Figs. 1 , 2 , 4 , 6 , and 8 ).

Following the water reclamation process, the concentrations of all analysed viruses decreased in the effluent samples. However, it is worth noting that the reductions for HuNoV GI, HuNoV GII, HAstV, and RV (when detected in effluent) were below 2 Logs, suggesting the persistence of these viruses to a relevant extent after being exposed to either UV or chlorination treatments. Only HEV was not detected in any of the analysed effluent samples thus resulting in higher reductions (> 4 Log GC). The reductions observed for human enteric viruses along the year substantially differ from current European legislation (Regulation (EU) 2020/741, 2020) on water reuse, which indicates the need for ≥ 6 Log decreases on the presence of these pathogens 7 . Even though enteric viruses’ presence detected by RT-qPCR in this study might not correspond with infectious particles, several publications have pointed out the presence of infectious enteric viruses in reclaimed waters by capsid-integrity or cell culture approaches 8 , 9 , 10 , 11 , 33 .

Owing to the microbiological risk that the presence of enteric viruses in these waters could entail, this study also aimed to assess the levels of somatic coliphages and E. coli in influent and effluent wastewater samples, as well as biosolid samples. Coliphages have been found in locations where faecal contamination is present 34 , 35 , and numerous studies have suggested utilizing coliphages as markers for enteric viruses’ presence 34 , 35 , 36 , 37 , 38 , 39 . Following the water treatment process, reductions of 1.83 Log PFU and 1.65 Log CFU were observed for somatic coliphages and E. coli , respectively. These reductions, which are far from those stipulated by the legislation EU 2020/741, 2020, highlight the low performance of the investigated WWTPs in decreasing the microbial load and mitigating the potential risks associated with these pathogens (pathogenicity and antibiotic resistance transmission) 7 . The high prevalence of viruses in reclaimed waters and biosolids, attributed to their high stability, poses a significant risk when applied to agricultural fields, particularly for products such as leafy greens and berries, which are often consumed raw and are unlikely to undergo extensive processing 40 . Shellfish are highly susceptible to viral contamination due to their efficient water filtration capacity, and they are commonly consumed raw or with minimal processing, making them a potential source of viral outbreak.

For somatic coliphages and E. coli , obtained counts in biosolids were similar to those obtained in effluent wastewater samples, pointing out the risk of using biosolids without any further treatment in agriculture. Besides, in recent years, both crAssphage and PMMoV have been proposed as viral indicators of faecal contamination in water bodies and as a virus model to assess the performance of WWTPs 41 , 42 , 43 , 44 , 45 , 46 , 47 . Regarding effluent samples, the mean concentration of crAssphage detected in reclaimed waters was 6.25 Log GC/L, which consistently matches the reported mean concentrations of 6.5 Log GC/L in high-income countries as reviewed by Adnan et al. 48 . PMMoV concentrations in effluent wastewater samples are in line with existing bibliography, which reports mean concentration values of ~ 4 Log GC/L 49 , 50 , 51 . Notably, obtained mean concentrations of PMMoV in influent wastewaters (5.95 Log GC/L) are slightly under-average when compared with previously reported data, as the common concentration values of PMMoV published in influent wastewater samples range from 6 to 10 Log GC/L 49 , 50 , 51 , 52 , 53 , 54 , 55 . Interestingly, to our knowledge, this study includes the first report on PMMoV levels in biosolid samples. This finding suggests a potential risk for the dissemination of this plant pathogen, which can infect solanaceous plants, ultimately leading to reduced productivity.

As for respiratory viruses, SARS-CoV-2, and IAV were detected at mean titres similar to those reported in the US, Canada, Australia, and other regions in Spain covering the same time period, while RSV levels were at least one Log GC/L over the reported in the aforementioned studies 56 , 57 , 58 , 59 , 60 , 61 . In recent years, the possibility of transmission of various respiratory viruses through food and water consumption has been discussed 62 . The absence of RSV and IAV in all effluent samples analysed in this study indicates an almost non-existent risk of transmissibility caused by ineffective water treatment, a finding of significant relevance, especially given the current situation where IAV H5N1 has been detected in sewage 63 . Nevertheless, the high presence of SARS-CoV-2 in effluent samples, together with the presence of these respiratory viruses in several of the analysed biosolids samples and the lack of studies regarding non-respiratory routes of transmission, warrant the need for further studies to assess public health risks.

Recently, a new proposal by The Urban Wastewater Treatment Directive (UWWTD), requested that member states should monitor antibiotic resistance at WWTPs serving over 100,000 individuals 19 . As this monitoring has been proposed to be performed for both influent and effluent wastewater samples, it should tackle both environmental transmission risks arising from WWTPs and provide insights into resistance patterns within specific regional areas.

In this study, ESBL- E. coli levels in influent samples were very high, with 6.63 Log CFU /100 mL on average, with no statistical differences among the different WWTPs and along the year. When analysing the reclamation treatment applied by the WWTPs, only mean reductions of 1.43 Log were observed for ESBL- E. coli , with 4.30 Log counts on average in effluent samples, which surpasses by 3 Logs the levels reported in other studies, suggesting the important role of effluent water in the dissemination of ARB in the food chain if used for irrigation and the need to improve water reclamation processes 14 , 64 , 65 . Similarly, the high levels of ESBL- E. coli in biosolids, suggest the need for further treatments before application in agriculture.

As well as resistant bacteria, the spread of ARGs needs to be addressed worldwide 13 . Thus, it is important to understand and mitigate their occurrence in different ecological systems. This study has shown the prevalence of 11 different ARGs belonging to 7 of the most widely used antibiotic groups in effluent water and biosolids 66 . Our study revealed that sulfonamide ARGs ( sul1 and sul2 ) were the genes with higher concentrations in effluents and biosolid samples. In line with previous studies, levels of sulfonamide resistance genes in effluent samples were higher than macrolide, tetracycline, and quinolone resistance genes 66 , 67 . Furthermore, sulfonamide gene levels were higher in biosolids than effluents (Fig. 5 ) as in the Mao et al. 2015 aforementioned study, highlighting the risk of biosolids as carriers of ARGs 64 . Levels of bla CTX-M , ARG that confer resistance to beta-lactamase, were 4 Log higher than levels of viable ESBL- E. coli , which could be explained by the longer persistence of DNA 68 , the presence of extracellular genetic material with bacterial surfaces, colloids, and bacteriophages, which shields it from nucleases 69 , 70 , 71 , 72 . This fact supports the idea that the dissemination of ARGs is not only carried out by viable bacteria but also by being found free in the environment or carried by other microorganisms such as bacteriophages 73 .

ARGs profiles were comparable in effluents and biosolids despite gene concentration differences except for cmlA_2 and ermB_1 . The cmlA_2 gene, which confer resistance to phenicol, was not found in any effluent samples indicating that environmental conditions, microbial populations, or the presence of contaminants in water treatment facilities may have impacted effluents but not biosolids. In March–May 2022, the ermB_1 gene was only detected in effluent samples, whereas the ermA gene, conferring resistance to macrolide-lincosamide-streptogramin B group antibiotic, was only detected in biosolid samples collected in January, consistent with previously reported data, whereas erm genes were only detected in biosolids 74 . Cold stress, which is linked with low temperatures, may increase horizontal gene transfer of ARGs, explaining this fluctuation along the year 75 . The significant presence of the ARGs and ESBL- E. coli supports assertions that land application of biosolids may disseminate ARGs to soil bacteria and demonstrate their potential introduction to food products via both irrigation and amendment 76 . Furthermore, from a One Health perspective, the dissemination of ARGs in aquatic environments may have implications for both animal and human health, underscoring the importance of enhancing reclamation processes through innovative strategies such as membrane bioreactors.

The extensive presence of MPs in wastewater sources significantly contributes to environmental contamination and poses considerable risks. In this sense, WWTPs play an important role in hindering MPs from entering water environments 77 . As observed in this work, the concentration of MPs in wastewater decreased in effluent samples as compared to influent samples, being the water treatment more efficient in removing higher size particles. The number of MPs found in the different samples agreed with those reported in the literature. Previous works investigated the abundance of MPs in urban WWTPs, with ranges of 0.28 to 3.14 × 10 4 particles/L in the influent, which significant differed from 0.01 to 2.97 × 10 2 particles/L in the effluent 78 . However, they did not refer to the removal efficiency depending on the particle size. In this work, a higher efficiency in reducing MPs (between 77-100%) of higher particle size (S)-MPs has been observed, which was similar to the 88–94% efficiency of municipal WWTPs previously reported 79 . However, this value was significantly reduced for MPs with smaller particle size (S)-MPs and presented a great variability depending on the WWTP studied (4-59%). Deng et al. (2023) reported that the removal efficiency of MPs in a petrochemical WWTPs reached ̴ 92% and highlighted that the primary treatment removed most of the MPs 80 (87.5%). Talvitie et al. (2015) also stated that the primary treatment could remove most of the MPs, although they did not refer to their particle size 81 . They reported that the major part of the fibers can be removed already in primary sedimentation process, which agreed with the lower proportion of fibers (as compared to fragments) found in these samples. While some authors have indicated that removing MPs from wastewater is technically feasible and cost-effective, suggesting that membrane bioreactors and sludge incineration are the best options, further research is necessary to enhance processes within a circular economy framework 82 .

Concerning the type of polymers detected, there is a higher prevalence of PE, PET, PS, and PA, as it has been previously reported for drinking water and petrochemical and urban WWTPs 80 , 83 , 84 , 85 . Furthermore, WWTPs were more efficient in removing polymers with higher density such as PA and PET, probably during the density separation step, favouring a significant reduction of these polymers in the effluent wastewater. Furthermore, the size of more than 90% of microplastic particles detected in WWTPs ranged between 1 and 300 μm and fragments were found to be the most prevalent shape of microplastics, in agreement with other works 86 .

Within this context, MPs release into the environment through sludge and effluent wastewater can also pose another risk, since MPs can accumulate/transport harmful pollutants, posing concerns about their role in treatment resistance and disease spread 87 . Bacteria and viruses have been reported to adsorb onto MPs, forming plastispheres 88 . Pathogenic bacteria, including those harmful to humans and fish, have also been found in communities of MPs 89 , 90 , 91 . Regarding viruses, the primary interaction with MPs involves electrostatic adhesion, increasing the risk of waterborne viral transmission. These viral or bacterial plastispheres not only resist UV treatment but can also promote infections, as shown for polystyrene MPs, which have been observed to facilitate IAV infection of host cells 91 , 92 . Additionally, the persistence of pathogen-carrying MPs in aquatic environments raises concerns about reverse zoonosis, where these plastispheres might be ingested by aquatic organisms, potentially endangering human populations through the food chain 93 . In summary, MPs can act as carriers for pathogenic bacteria and viruses in municipal sewage, intensifying concerns about public health and the environment.

The wide distribution of MPs in wastewater sources and the capability of some viruses to remain intact after traditional tertiary treatment disinfection processes (UV and chlorination) undoubtedly bring about environmental pollution and risk. Regarding MPs, their removal before reaching environmental water courses is highly recommended. To overcome these problems, several researchers are focused on finding cutting-edge methods to improve the efficiency of microplastic removal rates in WWTPs, although the literature is still scarce. Nasir et al. 2024, have recently reviewed innovative technologies for the removal of microplastics, highlighting the use of a membrane bioreactor system which combines biological treatments (aerobic, anaerobic) with membrane technology, thus improving sludge separation and effluent quality as compared to traditional methods 94 . Al-Amir et al., 2024 proposed the use of ultrafiltration in WWTP. It consists on a low-pressure (1–10 bar) method that removes particles using perforated asymmetric membranes up to 1–100 μm 95 . In the case of viruses, over the past few decades, as reviewed by Ibrahim et al. 2021 and Al-Hazmi et al. 2022, several efforts have been made to employ membrane-based and other hybrid technologies to effectively eliminate waterborne enteric viruses 96 , 97 . Technologies such as microfiltration (MF), ultrafiltration (UF), and membrane bioreactors (MBR) have been widely applied. The major concerns with these technologies are the factors impacting membrane performance regarding virus removal efficiency and sustainable operation, including physical sieving, adsorption, cake layer formation, and changes in membrane fouling. Additionally, microalgae-based approaches have emerged as a biological alternative to energy-intensive and expensive disinfection techniques 98 . Utilising microalgal processes, in conjunction with natural temperature, pH, or light conditions in treatment systems, may facilitate the complete removal of viruses from wastewater. Also, enhancing systems to filter out particles of extremely small sizes, such as MPs or viruses, from reclaimed water increases protection against other potentially harmful contaminants, including pathogenic bacteria. Finally, despite these treatment methods having various advantages and disadvantages, combining these systems aims to overcome their known technical and economic limitations

Overall, the findings of this research underscore the potential threats to public health associated with the reuse and release of reclaimed water, particularly concerning microbiological pathogens and environmental pollutants like microplastics, as well as the release of emerging contaminants into the environment and food chain through the use of biosolids in agriculture. These risk factors, including the persistence of enteric viruses, the inadequate reduction of microbial load and antibiotic resistance genes, and the prevalent presence of microplastics, emphasize the need for a holistic approach in addressing health concerns. Integrating these insights from wastewater analysis as well as human epidemic respiratory viruses monitoring into the broader One Health framework is crucial for devising effective policies, improving water treatment processes, and safeguarding both human and ecosystem health in a sustainable manner.

Methods for viruses and ARGs in wastewater and biosolid samples

Grab influent ( n = 72) and effluent ( n = 72) wastewater samples were collected monthly along with dehydrated biosolid samples ( n = 72) from 6 different urban WWTPs over a one-year period (January 2022–December 2022). Samples were grabbed early in the morning (8 am) by collecting ~500 mL of wastewater in sterile HDPE plastic containers (Labbox Labware, Spain). Collected samples were transferred on ice to the laboratory, kept refrigerated at 4 °C, and concentrated within 24 h. Samples were artificially contaminated with 10 6 PCR units (PCRU) of porcine epidemic diarrhea virus (PEDV) strain CV777, serving as a coronavirus model. Additionally, 10 6 PCRU of mengovirus (MgV) vMC 0 (CECT 100,000) were used as a non-enveloped counterpart for recovery efficiency assessment. Effluent wastewater samples were concentrated through a previously validated aluminium-based adsorption-precipitation method 11 , 99 . Briefly, 200 mL of sample was adjusted to pH 6.0 and Al(OH) 3 precipitate formed by adding 1 part 0.9 N AlCl 3 solution to 100 parts of sample. Then, pH was readjusted to 6.0 and sample mixed using an orbital shaker at 150 rpm for 15 min at room temperature. Next, viruses and ARGs were collected by centrifugation at 1700 × g for 20 min. The pellet was resuspended in 10 mL of 3% beef extract pH 7.4, and samples were shaken for 10 min at 150 rpm. Finally, the concentrate was recovered by centrifugation at 1900 × g for 30 min and the pellet was resuspended in 1 ml of phosphate buffer saline (PBS) and stored at −80 °C. Alternatively, 40 mL of influent wastewater samples were processed with the Enviro Wastewater TNA Kit (Promega Corp., Spain) vacuum concentration system following the manufacturer’s instructions 100 . For biosolid samples, 0.1 g of biosolid were resuspended in 900 µL PBS for nucleic acid extraction prior to PCR analyses.

Nucleic acid extraction from influent and effluent wastewater concentrates and biosolid suspensions was performed by using the Maxwell® RSC Instrument (Promega, Spain) with the Maxwell RSC Pure Food GMO for viral and ARG extraction. Specific programs, namely ‘Maxwell RSC Viral Total Nucleic Acid’ and ‘PureFood GMO and Authentication,’ were employed for viral and ARG extractions, respectively.

Virus detection and quantification

The detection of process control viruses, PEDV and MgV, was carried out through RT-qPCR using the One Step PrimeScript™ RT-PCR Kit (Perfect Real Time) (Takara Bio Inc., USA) as detailed elsewhere 101 . Levels of HuNoV GI and GII, HAstV, RV, HAV, and HEV were determined using the RNA UltraSense One-Step kit (Invitrogen, USA), following previously described procedures 9 , 11 . The occurrence of crAssphage was established using the qPCR Premix Ex Taq™ kit (Takara Bio Inc) 102 . PMMoV detection was determined using the PMMoV Fecal Indicator RT-qPCR Kit (Promega, Spain) following the manufacturer’s instructions. SARS-CoV-2 detection was performed by targeting the N1 region of the nucleocapsid gene. The One Step PrimeScript™ RT-PCR Kit (Perfect Real Time) was used with N1 primers and conditions described by CDC 103 . IAV detection followed the protocol described by CDC (2009) using primers from CDC (2020) and the One Step PrimeScript™ RT-PCR Kit (Perfect Real Time) 104 .

Different controls were used in all assays: negative process control consisting of PBS; whole process control to monitor the process efficiency of each sample (spiked with PEDV and MgV); and positive (targeted gene reference material) and negative (RNase-free water) RT-qPCR controls. The recoveries of PEDV and MgV, spiked as enveloped and non-enveloped viral process controls, respectively, ranged between 6.31 and 59.65% (data not included). The validation of results for targeted viruses adhered the criteria specified in ISO 15216-1:2017, where a recovery of the process control of ≥1% is required 105 .

Commercially available gBlock synthetic gene fragments (Integrated DNA Technologies, Inc., USA) of HuNoVs GI and GII, HAstV, RV, HAV, HEV, and crAssphage were used to prepare standard curves for quantification. For IAV and RSV quantification, Twist Synthetic InfluenzaV H1N1 RNA control (Twist BioScience, South San Francisco, CA, USA), and purified RNA of RSV (Vircell, S.L., Spain) were used. The PMMoV Fecal Indicator RT-qPCR Kit (Promega) provided PMMoV RNA for generating a standard curve. A table, featuring primers, probes, PCR conditions, limit of quantification (LOQ/L), and limit of detection (LOD/L) for all targeted viruses in this work is available in the Supplementary materials (Supplementary Table 1 ).

Quantification of viable somatic coliphages, E. coli , and Extended Spectrum Beta-Lactamases producing E. coli

One mL of influent and effluent samples was filtered through sterile 0.45 μm pore syringe filters (Labbox Labware, S.L., Spain) to remove bacteria and fungus 106 . Phage enumeration was performed by plaque counting using the commercial Bluephage Easy Kit for Enumeration of Somatic Coliphages (Bluephage S.L., Spain), following manufacturer’s instructions. For biosolid samples, 1 g of biosolid was resuspended in 100 mL PBS for both somatic coliphages and E. coli enumeration.

For all water and biosolid samples, E. coli and ESBL- E. coli enumeration was assessed by using selective culture media Chromocult coliform agar (Merck, Darmstadt, Germany) and CHROMagar ESBL (CHROMagar, Paris, France), respectively. Spread plating (0.1 mL) or membrane filtration (200 mL) was used depending on the anticipated bacterial concentration. Influent wastewater samples were diluted serially, and 0.1 mL aliquots were spread-plated. Effluent samples were filtered through a 0.45 μm cellulose nitrate membrane filter (Sartorius, Madrid, Spain). Following incubation at 37 °C for 24 hours, results were interpreted, with. dark blue-violet colonies considered positive for E. coli and dark pink-reddish colonies considered positive for ESBL- E. coli . The analysis was performed in duplicate, and the results were expressed as CFU/100 mL. The detection limit (LOD) for E. coli and ESBL- E. coli counts in the influent and biosolid samples was 2.0 Log CFU/100 mL (100 CFU/100 mL), while in the effluents, the LOD was 0 Log CFU/100 mL (1 CFU/100 mL).

Detection and quantification of antimicrobial resistance genes in effluent waters and biosolids

In this study, 11 ARGs that confer resistance to Sulfonamides ( sul1 , sul2_ 1), beta-lactamase ( pbp2b , bla CTX- M ), phenicols ( cmlA_2 ), nitroimidazoles ( nimE ), MLSB ( ermB_ 1, ermA ), tetracyclines ( tetPB_3 , tetA_1 ) and fluoroquinolones ( qacA_1 ), were only detected in effluent waters and biosolids. The 16 S rRNA gene was used as positive control for qPCR measurement. Quantification of the 12 selected genes was performed by high-throughput quantitative PCR (HT-qPCR) using the SmartChip™ Real-Time PCR system (TakaraBio, CA, USA) by Resistomap Oy (Helsinki, Finland). qPCR cycling conditions and processing of raw data were described elsewhere 107 , 108 , 109 , 110 . Each DNA sample was analysed in duplicate. Data processing and analysis were performed by using a python-based script by Resistomap Oy (Helsinki, Finland) 100 , 111 .

Digestion of organic material and isolation of MPs

Initial steps consisted on optimizing the protocol for the removal of organic material and the isolation of the maximum number of MPs from wastewater and biosolid samples. Different volumes of water, amounts of biosolids and digestion strategies for organic biomass removal were tested to remove the greatest amount of organic material without compromising the integrity of the MPs. Avoiding filter clogging was a requirement during the methodology development, to facilitate further identification of MPs. To reduce the risk of external contamination by MPs, laboratory consumables made of glass were used, the reagents were purified by filtering through a 0.2 µm pore size nitrocellulose filter (Whatman, Maidstone, UK), 100% cotton lab aprons were used, samples were processed in a laminar flow cabinet, the beakers were covered with a watch glass, disposable nitrile gloves were used and, before and after using the material, all used materials were rinsed thoroughly with deionized water. In order to assure that the isolation of MPs was effective and external contamination did not occur, a negative control (NC) was included every month and a positive control (PC) was carried out every 3 months. The positive control was made with fluorescent polystyrene microspheres (Invitrogen, Waltham, USA) of 1 µm in diameter. Specifically, a solution of 1000 beads/20 µL was prepared and 20 µL of this solution was incorporated before the pre-treatment and, the number of remaining microbeads after the digestion protocol was determined to calculate the percentage of recovery. The average value of particle recovery was 93.9%.

Two different pre-treatment protocols were finally defined:

(1) Sieved > 300 µm or (S): With this pre-treatment, all solid particles (including MPs) larger than 300 µm were isolated from 2 L of wastewater or 5 g of biosolid samples after sieving, oxidative digestion, and filtration steps.

(2) Total Particles or (T): With this pre-treatment all solid particles (including MPs) with a size between 1 µm and 5 mm were isolated from a 10 mL aliquot of wastewater after oxidative digestion, density separation, and filtration steps.

Through protocol (S), a larger and more representative amount of wastewater was treated, but particles smaller than 300 µm were lost. In the other hand, protocol (T) allowed the analysis of particles down to 1 µm in size, but the amount of analysed wastewater was much smaller to avoid filter clogging.

In both protocols (S) and (T), oxidative digestion was performed to remove organic material, adapting the method described by the National Oceanic and Atmospheric Administration (NOAA) 112 .

In the case of the Sieved 300 µm or (S) protocol (Figs. 11 ), 2L of wastewater or 5 g of biosolids were treated. The 5 g of biosolids were previously dispersed in 100 mL of ultrapure MilliQ water by applying stirring and heat during 30 minutes at 30 °C. The wastewater or biosolid dispersion were subsequently poured through a 300 µm mesh stainless steel sieve. The retained particles were collected by washing with MilliQ water into a beaker and digested by adding an equivalent volume of NaClO (14%, VWR chemical, USA). After heating at 75 °C for 3 h under stirring, the sample was sieved again to remove the disaggregated smallest particles. The particles retained on the sieve were collected by washing with MilliQ water on a 0.8 µm pore size nitrocellulose filter (Whatman, USA). The filter was protected from external contamination between a microscope glass slide and a glass cover, and finally dried at 40 °C for 24 h in a convection oven.

Scheme summary of the methodology used for the isolation, quantification, and identification of microplastics (MPs).

In the case of the Total Particles or (T) protocol, an oxidative digestion (Fenton reaction) was performed on a 10 mL wastewater sample by adding 20 mL of a H 2 O 2 (30%, Sigma- Aldrich, USA) solution and 20 mL of a 0.05 M Fe (II) solution prepared by mixing FeSO 4 (Sigma- Aldrich, USA), H 2 SO 4 (96%, PanReac AppliChem, ITW Reagents, USA) and deionized water. The sample was then heated at 75 °C for 30 min under stirring. The digestion step was repeated if any remaining organic material was visually. Thereafter, a density separation was performed after adding NaCl (99.5%, Sigma- Aldrich, USA) until saturation. Subsequently, the sample was left to sediment for 30 min in a separatory funnel and the supernatant was filtered through a 0.8 µm pore size nitrocellulose filter (Whatman, USA) under vacuum. The filter was also protected between glass slide and coverslip and dried at 40 °C for 24 hours.

Characterization of particles present in biosolid and wastewater samples

Filters obtained after pre-treatment protocols (S) and (T) were photographed using an EVOCAM II macrophotography equipment (Vision Engineering, Woking, UK) and the ViPlus software (2018, Vision Engineering). Two partially overlapping 2MPx color photos were taken for each filter, always at 20x magnification, with half of the filter appearing in each photo. These images were fused by digital stitching techniques using the mosaic J command of the FIJI software (ImageJ 1.49q Software, National Institutes of Health, USA). Each image showed a 25*15 mm field of view. The pixel size was 13.3 microns, obtaining an image to calibrate in each photo session to have precise external calibration data. A rough quantification was performed, and all particles, including MPs, were characterized using the Nis Elements BR 3.2 software (Nikon Corporation, Japan). To achieve this, a macro of programmed actions was designed in which, firstly, the pixel size was calibrated in the complete image of the filter, then a matrix-iterative detection tool for particles less bright than the filter was applied, which facilitated a binary segmentation by brightness levels and achieve the selection of the particles of each filter in an automated way, only in the filtration zone. Finally, the data of all the particles were exported to obtain the count and the different morphological values of numerous parameters and perform the statistical calculations.

For the characterization, the particles were classified into 3 size ranges of 1–100 µm, 100–300 µm and 300-5000 µm. The particles were also classified according to their circularity, calculated from the measured perimeter and area of each particle according to Eq. 1 , in 3 ranges: 0-0.4, 0.4–0.8, and 0.8-1. A circularity value of 1.0 indicates a perfect circle. As the value approaches 0.0, it indicates an increasingly elongated polygon. Particles with a circularity less than 0.4 were considered as fibers.

In addition, the efficiency of WWTPs in removing particles was calculated according to the following equation:

Where: Efficiency = particle removal efficiency (%); influent = number of particles detected at the WWTP influent; effluent = number of particles detected at the WWTP effluent.

Quantification of microplastics present in biosolid and wastewater samples

Quantification, identification, and characterization of MPs was carried out only on samples from the odd months. The analysis was performed using an automated Raman microscope Alpha300 apyron (Witec, Ulm, Germany). First, each filter was mapped by acquiring a total of 1089 images, which after reconstruction represented a 27% of the filter area or 1 cm 2 . The present particles were detected and selected by performing image analysis using the ParticleScout 6.0 software in automatic mode.

After particle selection, analysis on each particle by Raman spectroscopy and subsequent identification were carried out. The optimal conditions for Raman spectra acquisition were as follows: 785 nm laser which facilitates to identify fluorescent particles, 300 lines/mm diffraction grating opening, spectral range between 0 and 3000 cm –1 , 10 accumulations, 0.2 second acquisition time, and 40 mW laser power. The spectrum of each particle was registered and compared with an in-house build spectral library of polymers. The reference polymer materials included in the spectral library were polyethylene (PE), polyethylene terephthalate (PET), polyamide (PA), polypropylene (PP), polystyrene (PS), polyvinyl chloride (PVC), polytetrafluoroethylene (PTFE), polyacrylamide (PAM), Polyarylsulfones (PSU), Polymethylmethacrylate (PMMA), nitrile rubber (NBR), Cellophane and Melamine. Particles that had a 75% or better match (HQI) between the sample and reference spectra were identified as composed of the same material or of a similar chemical nature. In addition, a visual inspection was carried out and the spectrum acquisition was repeated on the particles where a clear identification was not initially possible. Three rules were considered to discriminate between plastics and non-plastics and to prioritize the particles to be analysed: (i) the object must not show cellular or natural organic structures; (ii) the fibre thickness must be uniform along the entire length; (iii) the colour of the particles must be clear and homogeneous 113 . The MPs already identified were classified based on material type, size, morphology, and area.

Statistical analysis

Results were statistically analysed and significance of differences was determined on the ranks with a one-way analysis of variance (ANOVA) and Tukey’s multiple comparison tests. In all cases, a value of p < 0.05 (confidence interval 95%) was deemed significant.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Barcelo, D., & Petrovic, M. The Handbook of Environmental Chemistry (D. Barcel & Andrey G. Kostianoy, Eds.; Springer) (2011).

FAO. Water for Sustainable Food and Agriculture A report produced for the G20 Presidency of Germany. Retrieved 17 October 2023, from www.fao.org/publications (2017).

K Mishra, R., Mentha, S. S., Misra, Y., & Dwivedi, N. Emerging pollutants of severe environmental concern in water and wastewater: A comprehensive review on current developments and future research. https://doi.org/10.1016/j.wen.2023.08.002 (2023).

Oude Munnink, B. B., & van der Hoek, L. Viruses causing gastroenteritis: the known, the new and those beyond. Viruses, 8 . https://doi.org/10.3390/v8020042 (2016).

Bosch, A., Guix, S., Sano, D. & Pintó, R. M. New tools for the study and direct surveillance of viral pathogens in water. Curr. Opin. Biotechnol. 19 , 295–301 (2008).

Article CAS Google Scholar

Okoh, A. I., Sibanda, T. & Gusha, S. S. Inadequately treated wastewater as a source of human enteric viruses in the environment. Int. J. Environ. Res. Public Health 7 , 2620–2637 (2010).

EC, E. C. Regulation (EU) 2020/741 of The European Parliament and of the Council of 25 May 2020 on minimum requirements for water reuse (Text with EEA relevance). Off. J. Eur. Union , L, 177 , 32–55(63) (2020).

Canh, V. D., Torii, S., Furumai, H. & Katayama, H. Application of Capsid Integrity (RT-)qPCR to assessing occurrence of intact viruses in surface water and tap water in Japan. Water Res. 189 , 116674 (2021).

Cuevas-Ferrando, E., Pérez-Cataluña, A., Falcó, I., Randazzo, W., & Sánchez, G. Monitoring human viral pathogens reveals potential hazard for treated wastewater discharge or reuse. Front. Microbiol., 13 . https://doi.org/10.3389/FMICB.2022.836193 (2022).

Gyawali, P. & Hewitt, J. Detection of infectious noroviruses from wastewater and seawater using PEMAXTM treatment combined with RT-qPCR. Water 10 , 841 (2018).

Article Google Scholar

Randazzo, W. et al. Interlaboratory comparative study to detect potentially infectious human enteric viruses in influent and effluent waters. Food Environ. Virol. 11 , 350–363 (2019).

Truchado, P. et al. Monitoring of human enteric virus and coliphages throughout water reuse system of wastewater treatment plants to irrigation endpoint of leafy greens. Sci. Total Environ. 782 , 146837 (2021).

One Health Initiative (n.d.). Retrieved 17 October 2023, from www.archive.onehealthinitiative.com/index.php .

Oliveira, M. et al. Surveillance on ESBL- Escherichia coli and Indicator ARG in wastewater and reclaimed water of four regions of Spain: Impact of different disinfection treatments. Antibiotics , 12. https://doi.org/10.3390/ANTIBIOTICS12020400 (2023).

Schwartz, T., Kohnen, W., Jansen, B. & Obst, U. Detection of antibiotic-resistant bacteria and their resistance genes in wastewater, surface water, and drinking water biofilms. FEMS Microbiol. Ecol. 43 , 325–335 (2003).

Koutsoumanis, K. et al. Role played by the environment in the emergence and spread of antimicrobial resistance (AMR) through the food chain. EFSA J. 19 , e06651 (2021).

CAS Google Scholar

Gajdoš, S. et al. Synergistic removal of pharmaceuticals and antibiotic resistance from ultrafiltered WWTP effluent: Free-floating ARGs exceptionally susceptible to degradation. J. Environ. Manag. 340 , 117861 (2023).

PlasticsEurope. Plastics-the Facts 2020. An analysis of European plastics production, demand, and waste data. Retrieved 12 September 2023. https://plasticseurope.org/wp-content/uploads/2021/09/Plastics_the_facts-WEB-2020_versionJun21_final.pdf (2020).

EC. Proposal for a revised Urban Wastewater Treatment Directive. Retrieved 10 November 2023, from https://environment.ec.europa.eu/publications/proposal-revised-urban-wastewater-treatment-directive_en (2022).

Wang, L. et al. Environmental fate, toxicity and risk management strategies of nanoplastics in the environment: Current status and future perspectives. J. Hazard. Mater. 401 , 123415 (2021).

Pironti, C., et al. Microplastics in the environment: intake through the food web, human exposure and toxicological effects. Toxics , 9 . https://doi.org/10.3390/TOXICS9090224 (2021).

Fackelmann, G. & Sommer, S. Microplastics and the gut microbiome: How chronically exposed species may suffer from gut dysbiosis. Mar. Pollut. Bull. 143 , 193–203 (2019).

Jenner, L. C. et al. Detection of microplastics in human lung tissue using μFTIR spectroscopy. Sci. Total Environ. 831 , 154907 (2022).

Ibrahim, Y. S. et al. Detection of microplastics in human colectomy specimens. JGH Open 5 , 116–121 (2021).

Zhu, L. et al. Identification of microplastics in human placenta using laser direct infrared spectroscopy. Sci. Total Environ. 856 , 159060 (2023).

Leslie, H. A. et al. Discovery and quantification of plastic particle pollution in human blood. Environ. Int. 163 , 107199 (2022).

Bowley, J., Baker-Austin, C., Porter, A., Hartnell, R. & Lewis, C. Oceanic Hitchhikers – assessing pathogen risks from marine microplastic. Trends Microbiol. 29 , 107–116 (2021).

Bouwmeester, H., Hollman, P. C. & Peters, R. J. Potential health impact of environmentally released micro- and nanoplastics in the human food production chain: experiences from nanotoxicology. Environ. Sci. Technol. 49 , 8932–8947 (2015).

Grindler, N. M. et al. Exposure to Phthalate, an endocrine disrupting chemical, alters the first trimester placental methylome and transcriptome in women. Sci. Rep 8 , 1–9 (2018).

Sadia, M. et al. Microplastics pollution from wastewater treatment plants: A critical review on challenges, detection, sustainable removal techniques and circular economy. Environ. Technol. Innov. 28 , 102946 (2022).

Zhu, L. et al. Quantifying health risks of plastisphere antibiotic resistome and deciphering driving mechanisms in an urbanizing watershed. Water Res. 245 , 120574 (2023).

Vermi, M., et al. Viruses in wastewater: occurrence, abundance and detection methods. https://doi.org/10.1016/j.scitotenv.2020.140910 (2020).

Simmons, F. J. & Xagoraraki, I. Release of infectious human enteric viruses by full-scale wastewater utilities. Water Res. 45 , 3590–3598 (2011).

AWPRC. Bacteriophages as model viruses in water quality controlag. Water Res. 25 , 529–545 (1991).

Grabow, W. O. K. Bacteriophages: update on application as models for viruses in water. Water SA 27 , 251–268 (2001).

Google Scholar

Funderburg, S. W. & Sorber, C. A. Coliphages as indicators of enteric viruses in activated sludge. Water Res. 19 , 547–555 (1985).

Kott1, Y. Estimation of low numbers of Escherichia coli bacteriophage by use of the most probable number method. Appl. Microbiol . https://journals.asm.org/journal/am (1966).

Lucena, F. et al. Reduction of bacterial indicators and bacteriophages infecting faecal bacteria in primary and secondary wastewater treatments. J. Appl. Microbiol. 97 , 1069–1076 (2004).

Ueda, T. & Horan, N. J. Fate of indigenous bacteriophage in a membrane bioreactor. Water Res. 34 , 2151–2159 (2000).

Sánchez G., Bosch A. Survival of enteric viruses in the environment and food. Viruses Foods . 26 :367–392. https://doi.org/10.1007/978-3-319-30723-7_13 (2016).

Bivins, A. et al. Cross-assembly phage and pepper mild mottle virus as viral water quality monitoring tools—potential, research gaps, and way forward. Curr. Opin. Environ. Sci. Health 16 , 54–61 (2020).

Farkas, K. et al. Critical evaluation of CrAssphage as a molecular marker for human-derived wastewater contamination in the aquatic environment. Food Environ. Virol. 11 , 113–119 (2019).

García-Aljaro, C., Ballesté, E., Muniesa, M. & Jofre, J. Determination of crAssphage in water samples and applicability for tracking human faecal pollution. Microb. Biotechnol. 10 , 1775–1780 (2017).

Kitajima, M., Iker, B. C., Pepper, I. L. & Gerba, C. P. Relative abundance and treatment reduction of viruses during wastewater treatment processes - Identification of potential viral indicators. Sci. Total Environ. 488–489 , 290–296 (2014).

Symonds, E. M., Rosario, K. & Breitbart, M. Pepper mild mottle virus: Agricultural menace turned effective tool for microbial water quality monitoring and assessing (waste)water treatment technologies. PLOS Pathog. 15 , e1007639 (2019).

Tandukar, S., Sherchan, S. P. & Haramoto, E. Applicability of crAssphage, pepper mild mottle virus, and tobacco mosaic virus as indicators of reduction of enteric viruses during wastewater treatment. Sci. Rep. 10 , 3616 (2020).

Wu, Z., Greaves, J., Arp, L., Stone, D. & Bibby, K. Comparative fate of CrAssphage with culturable and molecular fecal pollution indicators during activated sludge wastewater treatment. Environ. Int. 136 , 105452 (2020).

Sabar, M. A., Honda, R. & Haramoto, E. CrAssphage as an indicator of human-fecal contamination in water environment and virus reduction in wastewater treatment. Water Res 221 , 118827 (2022).

Kitajima, M., Sassi, H. P. & Torrey, J. R. Pepper mild mottle virus as a water quality indicator. Npj Clean. Water 2018 1 , 1–9 (2018).

Rosario, K., Symonds, E. M., Sinigalliano, C., Stewart, J., & Breitbart, M. Pepper Mild Mottle Virus as an indicator of fecal pollution. Appl. Environ. Microbiol. , 75 , 7261. https://doi.org/10.1128/AEM.00410-09 (2009a).

Symonds, E. M., Nguyen, K. H., Harwood, V. J. & Breitbart, M. Pepper mild mottle virus: A plant pathogen with a greater purpose in (waste)water treatment development and public health management. Water Res. 144 , 1–12 (2018).

Hamza, I. A., Jurzik, L., Überla, K. & Wilhelm, M. Evaluation of pepper mild mottle virus, human picobirnavirus and Torque teno virus as indicators of fecal contamination in river water. Water Res. 45 , 1358–1368 (2011).

Gyawali, P., Croucher, D., Ahmed, W., Devane, M. & Hewitt, J. Evaluation of pepper mild mottle virus as an indicator of human faecal pollution in shellfish and growing waters. Water Res. 154 , 370–376 (2019).

Kuroda, K. et al. Pepper mild mottle virus as an indicator and a tracer of fecal pollution in water environments: Comparative evaluation with wastewater-tracer pharmaceuticals in Hanoi, Vietnam. Sci. Total Environ. 506–507 , 287–298 (2015).

Schmitz, B. W., Kitajima, M., Campillo, M. E., Gerba, C. P. & Pepper, I. L. Virus Reduction during Advanced Bardenpho and Conventional Wastewater Treatment Processes. Environ. Sci. Technol. 50 , 9524–9532 (2016) .

Ando, H. et al. Impact of the COVID-19 pandemic on the prevalence of influenza A and respiratory syncytial viruses elucidated by wastewater-based epidemiology. Sci. Total Environ. , 880 . https://doi.org/10.1016/J.SCITOTENV.2023.162694 (2023).

Boehm, A. B. et al. More than a Tripledemic: Influenza A Virus, Respiratory Syncytial Virus, SARS-CoV-2, and human metapneumovirus in wastewater during winter 2022-2023. Environ. Sci. Technol. Lett. 10 , 622–627 (2023).

Hughes, B. et al. Respiratory Syncytial Virus (RSV) RNA in Wastewater Settled Solids Reflects RSV Clinical Positivity Rates. Environ. Sci. Technol. Lett. 9 , 173–178 (2022).

Mercier, E. et al. (123 C.E.). Municipal and neighbourhood level wastewater surveillance and subtyping of an influenza virus outbreak. Sci. Rep. , 12 , 15777.

Toribio-Avedillo, D. et al. Monitoring influenza and respiratory syncytial virus in wastewater. Beyond COVID-19. Sci. Total Environ. 892 , 164495 (2023).

Wolfe, M. K. et al. Wastewater-based detection of two influenza outbreaks. Environ. Sci. Technol. Lett. 2022 , 687–692 (2022).

Guo, M., Tao, W., Flavell, R. A. & Zhu, S. Potential intestinal infection and faecal-oral transmission of SARS-CoV-2. Nat. Rev. Gastroenterol. Hepatol. 18 , 269–283 (2021).

Detection of hemagglutinin H5 influenza A virus sequence in municipal wastewater solids at wastewater treatment plants with increases in influenza A in spring, 2024. Marlene K. Wolfe, Dorothea Duong, Bridgette Shelden, View ORCID ProfileElana M. G. Chan, Vikram Chan-Herur, Stephen Hilton, Abigail Harvey Paulos, Alessandro Zulli, Bradley J. White, View ORCID ProfileAlexandria B. Boehm. https://doi.org/10.1101/2024.04.26.24306409 .

Nzima, B. et al. Resistotyping and extended-spectrum beta-lactamase genes among Escherichia coli from wastewater treatment plants and recipient surface water for reuse in South Africa. https://doi.org/10.1016/j.nmni.2020.100803 (2020).

Raven, K. E., et al. Genomic surveillance of Escherichia coli in municipal wastewater treatment plants as an indicator of clinically relevant pathogens and their resistance genes. Microb. Genomics , 5. https://doi.org/10.1099/mgen.0.000267 (2019).

Mao, D. et al. Prevalence and proliferation of antibiotic resistance genes in two municipal wastewater treatment plants. Water Res. 85 , 458–466 (2015).

Christgen, B. et al. Metagenomics shows that low-energy anaerobic−aerobic treatment reactors reduce antibiotic resistance gene levels from domestic wastewater. https://doi.org/10.1021/es505521w (2015).

Gekenidis, M. T., Rigotti, S., Hummerjohann, J., Walsh, F. & Drissner, D. Long-term persistence of blaCTX-M-15 in soil and lettuce after introducing extended-Spectrum β-Lactamase (ESBL)-producing Escherichia coli via manure or water. Microorganisms 2020 8 , 1646 (2020).

Bryzgunova, O. E. et al. Redistribution of free- and cell-surface-bound DNA in blood of benign and malignant prostate tumor patients. Acta Nat. 7 , 115 (2015). /pmc/articles/PMC4463421/.

Laktionov, P. P. et al. Cell-surface-bound nucleic acids: Free and cell-surface-bound nucleic acids in blood of healthy donors and breast cancer patients. Ann. N. Y. Acad. Sci. 1022 , 221–227 (2004).

Nagler, M., Insam, H., Pietramellara, G. & Ascher-Jenull, J. Extracellular DNA in natural environments: features, relevance and applications. Appl. Microbiol. Biotechnol. 2018 102 , 6343–6356 (2018).

Zhang, Y., Snow, D. D., Parker, D., Zhou, Z. & Li, X. Intracellular and extracellular antimicrobial resistance genes in the sludge of livestock waste management structures. Environ. Sci. Technol. 47 , 10206–10213 (2013).

Muniesa, M., Colomer-Lluch, M. & Jofre, J. Potential impact of environmental bacteriophages in spreading antibiotic resistance genes. Future Microbiol 8 , 739–751 (2013).

Szczepanowski, R. et al. Detection of 140 clinically relevant antibiotic-resistance genes in the plasmid metagenome of wastewater treatment plant bacteria showing reduced susceptibility to selected antibiotics. Microbiology 155 , 2306–2319 (2009).

Miller, J. H., Novak, J. T., Knocke, W. R. & Pruden, A. Elevation of antibiotic resistance genes at cold temperatures: implications for winter storage of sludge and biosolids. Lett. Appl. Microbiol. 59 , 587–593 (2014).

Xu, S., Liu, Y., Wang, R., Zhang, T. & Lu, W. Behaviors of antibiotic resistance genes (ARGs) and metal resistance genes (MRGs) during the pilot-scale biophysical drying treatment of sewage sludge: Reduction of ARGs and enrichment of MRGs. Sci. Total Environ. 809 , 152221 (2022).

Enfrin, M., Dumée, L. F. & Lee, J. Nano/microplastics in water and wastewater treatment processes – Origin, impact and potential solutions. Water Res. 161 , 621–638 (2019).

Liu, W. et al. A review of the removal of microplastics in global wastewater treatment plants: Characteristics and mechanisms. Environ. Int. 146 , 106277 (2021).

Iyare, P. U., Ouki, S. K. & Bond, T. Microplastics removal in wastewater treatment plants: a critical review. Environ. Sci.: Water Res. Technol. 6 , 2664–2675 (2020).

Deng, L. et al. The destiny of microplastics in one typical petrochemical wastewater treatment plant. Sci. Total Environ. 896 , 165274 (2023).

Talvitie, J. et al. Do wastewater treatment plants act as a potential point source of microplastics? Preliminary study in the coastal Gulf of Finland, Baltic Sea. Water Sci. Technol. 72 , 1495–1504 (2015).

Larissa VuoriMarkku Ollikainen How to remove microplastics in wastewater? A cost-effectiveness analysis. Ecol. Econ. 192 , 107246 (2022).

Koelmans, A. A. et al. Microplastics in freshwaters and drinking water: Critical review and assessment of data quality. Water Res. 155 , 410–422 (2019).

Schymanski, D., Goldbeck, C., Humpf, H. U. & Fürst, P. Analysis of microplastics in water by micro-Raman spectroscopy: Release of plastic particles from different packaging into mineral water. Water Res. 129 , 154–162 (2018).

Senathirajah, K. et al. Estimation of the mass of microplastics ingested – A pivotal first step towards human health risk assessment. J. Hazard. Mater. 404 , 124004 (2021).

Lee, J. H. et al. Detection of microplastic traces in four different types of municipal wastewater treatment plants through FT-IR and TED-GC-MS. Environ. Pollut. 333 , 122017 (2023).

Issac, M. N. & Kandasubramanian, B. Effect of microplastics in water and aquatic systems. Environ. Sci. Pollut. Res. 2021 28 , 19544–19562 (2021).

Reeves, A. et al. Potential transmission of SARS-CoV-2 through microplastics in sewage: A wastewater-based epidemiological review ☆ . https://doi.org/10.1016/j.envpol.2023.122171 (2023).

Kruglova, A. et al. The dangerous transporters: A study of microplastic-associated bacteria passing through municipal wastewater treatment. https://doi.org/10.1016/j.envpol.2022.120316 (2023).

Lai, K. P. et al. Microplastics act as a carrier for wastewater-borne pathogenic bacteria in sewage. https://doi.org/10.1016/j.chemosphere.2022.134692 (2022).

Manoli, K. et al. Investigation of the effect of microplastics on the UV inactivation of antibiotic-resistant bacteria in water. Water Res. 222 , 43–1354 (2022).

Wang, C., et al. Polystyrene microplastics significantly facilitate influenza A virus infection of host cells. https://doi.org/10.1016/j.jhazmat.2022.130617 (2022).

Zhong, H. et al. The hidden risk of microplastic-associated pathogens in aquatic environments. Ecol. Environ. Health 2 , 142–151 (2023).

Nasir, M. S. et al. Innovative technologies for removal of micro plastic: A review of recent advances. Heliyon 10 , e25883 (2024).

Amri, A., Yavari, Z., Reza Nikoo, M. & Karimi, M. Microplastics removal efficiency and risk analysis of wastewater treatment plants in Oman. Chemosphere 359 , 142206 (2024).

Ibrahim, Y. et al. Detection and removal of waterborne enteric viruses from wastewater: A comprehensive review. J. Environ. Chem. Eng. 9 , 105613 (2021).

Al-Hazmi, H. E. et al. Recent advances in aqueous virus removal technologies. Chemosphere 305 , 135441 (2022).

Bhatt, A., Arora, P. & Prajapati, S. K. Occurrence, fates and potential treatment approaches for removal of viruses from wastewater: A review with emphasis on SARS-CoV-2. J. Environ. Chem. Eng. 8 , 104429 (2020) .

Pérez-Cataluña, A. et al. Comparing analytical methods to detect SARS-CoV-2 in wastewater. Sci. Total Environ. 758 , 143870 (2021).

Girón-Guzmán, I. et al. Evaluation of two different concentration methods for surveillance of human viruses in sewage and their effects on SARS-CoV-2 sequencing. Sci. Total Environ. 862 , 160914 (2023).

Puente, H., Randazzo, W., Falcó, I., Carvajal, A. & Sánchez, G. Rapid selective detection of potentially infectious porcine epidemic diarrhea coronavirus exposed to heat treatments using viability RT-qPCR. Front. Microbiol. 11 , 1911 (2020).

Stachler, E. et al. Quantitative CrAssphage PCR Assays for Human Fecal Pollution Measurement. Environ. Sci. Technol. 51 , 9146–9154 (2017).

CDC. CDC 2019-novel coronavirus (2019-nCoV) real-time RT-PCR diagnostic panel. https://www.Fda.Gov/Media/134922/Download . Accessed October 2020.

Sanghavi, S. K., Bullotta, A., Husain, S. & Rinaldo, C. R. Clinical evaluation of multiplex real-time PCR panels for rapid detection of respiratory viral infections. J. Med. Virol. 84 , 162–169 (2012).

Haramoto, E. et al. A review on recent progress in the detection methods and prevalence of human enteric viruses in water. Water Res 135 , 168–186 (2018).

Girón-Guzmán, I. et al. Urban wastewater-based epidemiology for multi-viral pathogen surveillance in the Valencian region, Spain. Water Res 255 , 121463 (2024).

Muurinen, J., et al. Influence of manure application on the environmental resistome under finnish agricultural practice with restricted antibiotic use. https://doi.org/10.1021/acs.est.7b00551 (2017).

Muziasari, W. I., et al. Aquaculture changes the profile of antibiotic resistance and mobile genetic element associated genes in Baltic Sea sediments. FEMS Microbiol. Ecol. , 92 . https://doi.org/10.1093/FEMSEC/FIW052 (2016).

Muziasari, W.I., et al. The resistome of farmed fish feces contributes to the enrichment of antibiotic resistance genes in sediments below baltic sea fish farms. Front. Microbiol. , 7 , 229367. https://doi.org/10.3389/FMICB.2016.02137/BIBTEX (2017).

Wang, F. H. et al. High throughput profiling of antibiotic resistance genes in urban park soils with reclaimed water irrigation. Environ. Sci. Technol. 48 , 9079–9085 (2014).

Yin Lai, F., Muziasari, W., Virta, M., Wiberg, K., & Ahrens, L. Profiles of environmental antibiotic resistomes in the urban aquatic recipients of Sweden using high-throughput quantitative PCR analysis ☆ . https://doi.org/10.1016/j.envpol.2021.117651 (2021).

Masura, J., Baker, J. E., 1959-, Foster, G. D. (Gregory D., Arthur, C., & Herring, C). Laboratory methods for the analysis of microplastics in the marine environment: recommendations for quantifying synthetic particles in waters and sediments. https://doi.org/10.25923/4X5W-8Z02 (2015).

Hidalgo-Ruz, V., Gutow, L., Thompson, R. F. & Thiel, M. Microplastics in the marine environment: A review of the methods used for identification and quantification. Environ. Sci. Technol. 46 , 3060–3075 (2012).

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Acknowledgements

This research was supported by project the Lagoon project (PROMETEO/2021/044) and MCEC WATER (PID 2020 116789 RB C 42 AEI/FEDER, UE). IATA-CSIC is a Centre of Excellence Severo Ochoa (CEX2021-001189-S MCIN/AEI / 10.13039/ 501100011033). IF (MS21-006) and SB were supported by a postdoctoral contract grant for the requalification of the Spanish university system from the Ministry of Universities of the Government of Spain, financed by the European Union (NextGeneration EU).IG-G is recipient of a predoctoral contract from the Generalitat Valenciana (ACIF/2021/181), EC-F is recipient of a postdoctoral contract from the MICINN Call 2018 (PRE2018-083753) and AP-C is recipient of the contract Juan de la Cierva – Incorporación (IJC2020-045382-I) which is financed by MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU/PRTR”. The authors thank Andrea López de Mota, Arianna Pérez, Agustín Garrido Fernández, Mercedes Reyes Sanz, José Miguel Pedra Tellols, and Alcira Reyes Rovatti for their technical support.

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These authors jointly supervised this work: Amparo López-Rubio, Gloria Sánchez.

Authors and Affiliations

Institute of Agrochemistry and Food Technology, IATA-CSIC, Paterna, Valencia, Spain

Inés Girón-Guzmán, Santiago Sánchez-Alberola, Enric Cuevas-Ferrando, Irene Falcó, Azahara Díaz-Reolid, Pablo Puchades-Colera, Alba Pérez-Cataluña, José María Coll, Eugenia Núñez, María José Fabra, Amparo López-Rubio & Gloria Sánchez

Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy—Spanish National Research Council (SusPlast), CSIC, Madrid, Spain

Santiago Sánchez-Alberola, Eugenia Núñez, María José Fabra & Amparo López-Rubio

Department of Microbiology and Ecology, University of Valencia, Burjassot, Valencia, Spain

Irene Falcó

Department of Genetics and Microbiology, Faculty of Biosciences, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Barcelona, Spain

Sandra Ballesteros

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Girón-Guzmán, I., Sánchez-Alberola, S., Cuevas-Ferrando, E. et al. Longitudinal study on the multifactorial public health risks associated with sewage reclamation. npj Clean Water 7 , 72 (2024). https://doi.org/10.1038/s41545-024-00365-y

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Icssr promotes collaborative research on longitudinal studies in social and human sciences.

The ICSSR has given a special call for submitting collaborative research proposals on longitudinal studies pertaining to the field of social and human sciences. In this way, this call invites researchers to come up with such research aimed at unravelling the mysteries of change in society, which takes place over time, and it allows for a deep understanding of different phenomena in society. The present call for proposals provides scholars with an important opportunity to contribute to adding to furtherance of knowledge in these disciplines.

In this way, longitudinal studies deal with the collection and analysis of data from subjects over a long period of time. In this case, changes and patterns that rough out in this period can be fruitfully observed and analyzed to give useful insight into the dynamism of social, economic, and cultural phenomena. The studies will empower us with insight into how individual entities, communities, and societies change and adapt to various influences and circumstances.

The call for collaborative research sub-projects under the headship of Indian investigators willing to commit themselves to a longitudinal mode of inquiry in the social and human sciences falls under this general framework. The call for proposals is thus addressed to scholars from various intellectual traditions and institutions to pool their energies in undertaking innovative research. In doing so, the approach is expected to remain interdisciplinary, increasing the quality and overall relevance.

It encourages attention to major research questions and themes of the social and human sciences. Proposals are encouraged for projects covering a vast range of themes like:

Socio-economic transformation and its impacts on communities and individuals.
Cultural change and their dynamics in the society.
Effects of policy interventions and initiatives at the long run.
The interplay between technology, society, and human behaviour.
Changing patterns of social inequality and their consequences.
Evolution of social institutions and their place in shaping society.

Proposals can be submitted through the online application process by visiting the ICSSR’s website. This inherently invites multi-institutional and multidisciplinary approaches toward research proposals. Principal Investigator/Programme Director requires a Ph.D. degree of high quality, coupled with research experience and publication record. It will also encourage applications from civil servants, officers belonging to defence services, and other professionals with a social science perspective in collaboration with regular faculty members from known institutions.

The ICSSR will finance research projects that are selected for their conduct. Such projects, especially, can be of variable duration, ranging from half a year in duration for short-term projects to several years for long-term projects. The grants would be for the research itself, the gathering and analysis of data, and the printing and dissemination of the research findings.

The call for collaborative research proposals with the ICSSR on longitudinal studies in Social and Human Sciences opens the challenges for scholars to contribute toward understanding changes in society over time. Researchers would be able to provide articulation of the complexities of social phenomena and their consequences upon individuals and communities by bridling research and analysis. With this initiative, ICSSR aims to encourage more inter-disciplinary research and develop a greater understanding of the Social and Human Sciences.

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How to Write a Research Proposal: (with Examples & Templates)

Table of Contents

Before conducting a study, a research proposal should be created that outlines researchers’ plans and methodology and is submitted to the concerned evaluating organization or person. Creating a research proposal is an important step to ensure that researchers are on track and are moving forward as intended. A research proposal can be defined as a detailed plan or blueprint for the proposed research that you intend to undertake. It provides readers with a snapshot of your project by describing what you will investigate, why it is needed, and how you will conduct the research.

Your research proposal should aim to explain to the readers why your research is relevant and original, that you understand the context and current scenario in the field, have the appropriate resources to conduct the research, and that the research is feasible given the usual constraints.

This article will describe in detail the purpose and typical structure of a research proposal , along with examples and templates to help you ace this step in your research journey.

What is a Research Proposal ?

A research proposal¹ ,² can be defined as a formal report that describes your proposed research, its objectives, methodology, implications, and other important details. Research proposals are the framework of your research and are used to obtain approvals or grants to conduct the study from various committees or organizations. Consequently, research proposals should convince readers of your study’s credibility, accuracy, achievability, practicality, and reproducibility.

With research proposals , researchers usually aim to persuade the readers, funding agencies, educational institutions, and supervisors to approve the proposal. To achieve this, the report should be well structured with the objectives written in clear, understandable language devoid of jargon. A well-organized research proposal conveys to the readers or evaluators that the writer has thought out the research plan meticulously and has the resources to ensure timely completion.

Purpose of Research Proposals

A research proposal is a sales pitch and therefore should be detailed enough to convince your readers, who could be supervisors, ethics committees, universities, etc., that what you’re proposing has merit and is feasible . Research proposals can help students discuss their dissertation with their faculty or fulfill course requirements and also help researchers obtain funding. A well-structured proposal instills confidence among readers about your ability to conduct and complete the study as proposed.

Research proposals can be written for several reasons:³

To describe the importance of research in the specific topic
Address any potential challenges you may encounter
Showcase knowledge in the field and your ability to conduct a study
Apply for a role at a research institute
Convince a research supervisor or university that your research can satisfy the requirements of a degree program
Highlight the importance of your research to organizations that may sponsor your project
Identify implications of your project and how it can benefit the audience

What Goes in a Research Proposal?

Research proposals should aim to answer the three basic questions—what, why, and how.

The What question should be answered by describing the specific subject being researched. It should typically include the objectives, the cohort details, and the location or setting.

The Why question should be answered by describing the existing scenario of the subject, listing unanswered questions, identifying gaps in the existing research, and describing how your study can address these gaps, along with the implications and significance.

The How question should be answered by describing the proposed research methodology, data analysis tools expected to be used, and other details to describe your proposed methodology.

Research Proposal Example

Here is a research proposal sample template (with examples) from the University of Rochester Medical Center. 4 The sections in all research proposals are essentially the same although different terminology and other specific sections may be used depending on the subject.

Structure of a Research Proposal

If you want to know how to make a research proposal impactful, include the following components:¹

1. Introduction

This section provides a background of the study, including the research topic, what is already known about it and the gaps, and the significance of the proposed research.

2. Literature review

This section contains descriptions of all the previous relevant studies pertaining to the research topic. Every study cited should be described in a few sentences, starting with the general studies to the more specific ones. This section builds on the understanding gained by readers in the Introduction section and supports it by citing relevant prior literature, indicating to readers that you have thoroughly researched your subject.

3. Objectives

Once the background and gaps in the research topic have been established, authors must now state the aims of the research clearly. Hypotheses should be mentioned here. This section further helps readers understand what your study’s specific goals are.

4. Research design and methodology

Here, authors should clearly describe the methods they intend to use to achieve their proposed objectives. Important components of this section include the population and sample size, data collection and analysis methods and duration, statistical analysis software, measures to avoid bias (randomization, blinding), etc.

5. Ethical considerations

This refers to the protection of participants’ rights, such as the right to privacy, right to confidentiality, etc. Researchers need to obtain informed consent and institutional review approval by the required authorities and mention this clearly for transparency.

6. Budget/funding

Researchers should prepare their budget and include all expected expenditures. An additional allowance for contingencies such as delays should also be factored in.

7. Appendices

This section typically includes information that supports the research proposal and may include informed consent forms, questionnaires, participant information, measurement tools, etc.

8. Citations

Important Tips for Writing a Research Proposal

Writing a research proposal begins much before the actual task of writing. Planning the research proposal structure and content is an important stage, which if done efficiently, can help you seamlessly transition into the writing stage. 3,5

The Planning Stage

Manage your time efficiently. Plan to have the draft version ready at least two weeks before your deadline and the final version at least two to three days before the deadline.
What is the primary objective of your research?
Will your research address any existing gap?
What is the impact of your proposed research?
Do people outside your field find your research applicable in other areas?
If your research is unsuccessful, would there still be other useful research outcomes?

The Writing Stage

Create an outline with main section headings that are typically used.
Focus only on writing and getting your points across without worrying about the format of the research proposal , grammar, punctuation, etc. These can be fixed during the subsequent passes. Add details to each section heading you created in the beginning.
Ensure your sentences are concise and use plain language. A research proposal usually contains about 2,000 to 4,000 words or four to seven pages.
Don’t use too many technical terms and abbreviations assuming that the readers would know them. Define the abbreviations and technical terms.
Ensure that the entire content is readable. Avoid using long paragraphs because they affect the continuity in reading. Break them into shorter paragraphs and introduce some white space for readability.
Focus on only the major research issues and cite sources accordingly. Don’t include generic information or their sources in the literature review.
Proofread your final document to ensure there are no grammatical errors so readers can enjoy a seamless, uninterrupted read.
Use academic, scholarly language because it brings formality into a document.
Ensure that your title is created using the keywords in the document and is neither too long and specific nor too short and general.
Cite all sources appropriately to avoid plagiarism.
Make sure that you follow guidelines, if provided. This includes rules as simple as using a specific font or a hyphen or en dash between numerical ranges.
Ensure that you’ve answered all questions requested by the evaluating authority.

Key Takeaways

Here’s a summary of the main points about research proposals discussed in the previous sections:

A research proposal is a document that outlines the details of a proposed study and is created by researchers to submit to evaluators who could be research institutions, universities, faculty, etc.
Research proposals are usually about 2,000-4,000 words long, but this depends on the evaluating authority’s guidelines.
A good research proposal ensures that you’ve done your background research and assessed the feasibility of the research.
Research proposals have the following main sections—introduction, literature review, objectives, methodology, ethical considerations, and budget.

Frequently Asked Questions

Q1. How is a research proposal evaluated?

A1. In general, most evaluators, including universities, broadly use the following criteria to evaluate research proposals . 6

Significance —Does the research address any important subject or issue, which may or may not be specific to the evaluator or university?
Content and design —Is the proposed methodology appropriate to answer the research question? Are the objectives clear and well aligned with the proposed methodology?
Sample size and selection —Is the target population or cohort size clearly mentioned? Is the sampling process used to select participants randomized, appropriate, and free of bias?
Timing —Are the proposed data collection dates mentioned clearly? Is the project feasible given the specified resources and timeline?
Data management and dissemination —Who will have access to the data? What is the plan for data analysis?

Q2. What is the difference between the Introduction and Literature Review sections in a research proposal ?

A2. The Introduction or Background section in a research proposal sets the context of the study by describing the current scenario of the subject and identifying the gaps and need for the research. A Literature Review, on the other hand, provides references to all prior relevant literature to help corroborate the gaps identified and the research need.

Q3. How long should a research proposal be?

A3. Research proposal lengths vary with the evaluating authority like universities or committees and also the subject. Here’s a table that lists the typical research proposal lengths for a few universities.


	Arts programs	1,000-1,500
University of Birmingham	Law School programs	2,500
	PhD	2,500
		2,000
	Research degrees	2,000-3,500

Q4. What are the common mistakes to avoid in a research proposal ?

A4. Here are a few common mistakes that you must avoid while writing a research proposal . 7

No clear objectives: Objectives should be clear, specific, and measurable for the easy understanding among readers.
Incomplete or unconvincing background research: Background research usually includes a review of the current scenario of the particular industry and also a review of the previous literature on the subject. This helps readers understand your reasons for undertaking this research because you identified gaps in the existing research.
Overlooking project feasibility: The project scope and estimates should be realistic considering the resources and time available.
Neglecting the impact and significance of the study: In a research proposal , readers and evaluators look for the implications or significance of your research and how it contributes to the existing research. This information should always be included.
Unstructured format of a research proposal : A well-structured document gives confidence to evaluators that you have read the guidelines carefully and are well organized in your approach, consequently affirming that you will be able to undertake the research as mentioned in your proposal.
Ineffective writing style: The language used should be formal and grammatically correct. If required, editors could be consulted, including AI-based tools such as Paperpal , to refine the research proposal structure and language.

Thus, a research proposal is an essential document that can help you promote your research and secure funds and grants for conducting your research. Consequently, it should be well written in clear language and include all essential details to convince the evaluators of your ability to conduct the research as proposed.

This article has described all the important components of a research proposal and has also provided tips to improve your writing style. We hope all these tips will help you write a well-structured research proposal to ensure receipt of grants or any other purpose.

References

Sudheesh K, Duggappa DR, Nethra SS. How to write a research proposal? Indian J Anaesth. 2016;60(9):631-634. Accessed July 15, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5037942/
Writing research proposals. Harvard College Office of Undergraduate Research and Fellowships. Harvard University. Accessed July 14, 2024. https://uraf.harvard.edu/apply-opportunities/app-components/essays/research-proposals
What is a research proposal? Plus how to write one. Indeed website. Accessed July 17, 2024. https://www.indeed.com/career-advice/career-development/research-proposal
Research proposal template. University of Rochester Medical Center. Accessed July 16, 2024. https://www.urmc.rochester.edu/MediaLibraries/URMCMedia/pediatrics/research/documents/Research-proposal-Template.pdf
Tips for successful proposal writing. Johns Hopkins University. Accessed July 17, 2024. https://research.jhu.edu/wp-content/uploads/2018/09/Tips-for-Successful-Proposal-Writing.pdf
Formal review of research proposals. Cornell University. Accessed July 18, 2024. https://irp.dpb.cornell.edu/surveys/survey-assessment-review-group/research-proposals
7 Mistakes you must avoid in your research proposal. Aveksana (via LinkedIn). Accessed July 17, 2024. https://www.linkedin.com/pulse/7-mistakes-you-must-avoid-your-research-proposal-aveksana-cmtwf/

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How to Write Your Research Paper in APA Format

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Health and Ageing Research Team study pathways to wellbeing

Understanding the factors that contribute to wellbeing in later life is the aim of a Health, Work and Retirement longitudinal study led by Te Kunenga ki Pūrehuroa Massey University’s Health and Ageing Research Team (HART).

HART’s 2024 Health, Work and Retirement (HWR) survey will be sent out this month.

The growing number of older people in society reflects gains in public health and should be a source of pride for our community. However, not all groups have benefited equally from increases in healthy lifespan. To understand the pathways to healthy ageing, a study led by Te Kunenga ki Pūrehuroa Massey University’s Health and Ageing Research Team (HART) will explore early and mid-life predictors of frailty. The results from this study will be used to develop policies and practices that improve wellbeing for all. Improving healthy lifespan not only has benefits for older people, but also for their families and communities.

HART’s 2024 Health, Work and Retirement (HWR) survey will be sent out this month. The survey focuses on understanding the social and environmental factors that predict trajectories of wellbeing in later life.

The 2024 survey builds on 18 years of work by the HART team. The team’s longitudinal study of older people (over 55 years) began in 2006 and, to date, more than 14,000 people have contributed data on ageing in Aotearoa New Zealand. The study has examined health and access to healthcare, employment, retirement, the built environment, caregiving, COVID-19 and housing. It has provided valuable insights into the lives of older people as they age in Aotearoa and contributed to the development of government policies to improve the lives of older New Zealanders.

The 2024 survey will be sent to 6,626 participants who have contributed to the study before. An additional 8,570 people will also be invited to complete the survey. These new recruits have been identified through the electoral roll. Adding new people to the study is essential to ensure the study continues to represent those aged over-55 year as the original study participants age.

The Health and Ageing Research Team.

Principal Investigator and HART lead Professor Fiona Alpass ONZM says the focus of most research on ageing has been on how to respond when people are already frail.

“We provide care and support either through the community, unpaid family care, hospitalisation or aged residential care. We would prefer to support people to age in place in the community and age with good health. To achieve this, we need to focus on earlier life predictors to prevent frailty later.”

Dr Brendan Stevenson, who is also a Principal Investigator for this study, says he is looking forward to better understanding trajectories of healthy ageing for older Māori.

“We know that older Māori have very different outcomes in later life, which reflects poorer access to resources across the lifespan. Using this study, we can understand these differences and propose solutions to improve outcomes for future cohorts of older Māori.”

HART comprises researchers from seven institutions across Aotearoa who have expertise in ageing research, statistical modelling and data science. Researchers from Māori, Pacific and Chinese communities lead key components of the study, ensuring that the benefits of a long and healthy life are available for all members of our community.

The HART team acknowledge the generosity of the participants of the HWR longitudinal study who have contributed to the study for nearly two decades. Their commitment has created a powerful tool for understanding predictors of healthy ageing and possible solutions to improve health and wellbeing for all older people.

HART has received funding from the Ministry of Business, Innovation and Employment for 2023-2028 to continue this work.

Related news

Massey professors receive onzm for pioneering work in health psychology and ageing studies.

Two Professors from the School of Psychology have been included in this year’s King’s Birthday Honours List.

Insights from longest-running study of ageing released in new book

Researchers from Massey's Health and Ageing Research Team (HART) are releasing a book of the findings to date on 7 June.

Senior entrepreneurship an unrealised opportunity, new research says

The research report highlights the unique challenges and opportunities faced by people starting a business later in life, and how they could be better supported.

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Dr. Joshua Parsons Finds Research Grant Success Through Fellow Research Mentoring

Joshua Parsons, MD, credits his involvement with the Duke Department of Medicine’s Fellow Research Academy (FRA) as the key factor in his successful application for the National Institute of Health (NIH) K08: Clinical Investigator Award.

Dr. Parsons, a former clinical investigator pathway fellow and current assistant professor of medicine with the Division of Infectious Diseases has a PhD in biochemistry, which led him to his interest in the research topic of why some patients do well on antibiotics while others do poorly.

“Most of the work today is focused on how different antibiotic choice effects efficacy, but my work focuses on how differences in the bacteria shape how well people do when they’re on antibiotics,” said Parsons. “There are two aims: one is to examine how differences in bacteria genome affect susceptibility to antibiotics, and the second is to find out how changes in the Staphlyococcus aureus genome are related to clinical outcomes in humans.”

Dr. Parsons explained the importance of the research, saying that if it’s possible to detect in the micro-lab which strains of Staphylococcus aureus are likely to cause worse disease, practitioners can identify patients at risk of poor outcomes and adjust clinical management accordingly.

Inspired by the myriad of resources available to him at Duke, Dr. Parsons found interest in Duke’s immense S. aureus bacteremia group – prospective cohort study” (SABG-PCS) biorepository, which is a collection of S. aureus clinical bloodstream isolates from patients at Duke. “It’s the largest biorepositiory of S. aureus bloodstream isolates in the world that’s not available anywhere else, so that’s a real gold mine,” said Parsons. His mentor, Vance Fowler, MD, created this resource over the past 30 years. Parsons said his mentor makes that resource available to anybody with a good clinical question.

The K08 award is provided to individuals with a clinical doctoral degree and an intensive, supervised, research career development experience. According to the NIH, the grant supports individuals on a path to a productive, independent research career. Dr. Parsons received a score of 13 on his initial submission. With 10 being a perfect score, his grant will likely be funded.

The FRA aims to support the goals of aspiring researchers through three interrelated components, one of which is longitudinal grant development

The process that goes into writing a research grant isn’t openly taught to trainees, making it hard for most to find a resource that can guide them if they do not take advantage of the resources offered, such as the FRA.

“One of the unique things about the FRA is that it breaks things down into certain stages. Each component of the grant is broken down and systematically taught to you. It’s really good to have an expert to guide you,” said Parsons. “I did the FRA three years in a row, and it was the first time I’d been exposed to the systematic analysis of how you write grants and how you approach them.”

The FRA grant development workshop allows fellows to develop their own grants through large and small group work. Fellows who are ready to begin preparing career development award proposals participate in a fellow-focused grant incubator program led by the DOM Research Development Council team.

An additional benefit of the FRA is that it complements each research fellow’s existing mentor team by providing tailored training and a sense of community with other research fellows across the department.

“The amount of time Drs. Irina Mokrova and John Williams spent going through my grant and helping improve it was just astonishing,” said Parsons. “Sometimes, you give people things to read, and you can tell they just skimmed over it and didn’t really think about it, but they both gave me thoughtful comments and kept me encouraged and on target with dates.”

Fellows are encouraged to participate in the FRA throughout their designated research years, with no duration limit. Dr. Parsons encourages fellows to take advantage of the many tools offered through the FRA. “It’s an amazing program that the department has built over the last few years.”

Applications for the 2024-25 edition of the FRA will be opening in mid to late August 2024. For any additional details or questions, please email Saini Pillai, MBA .

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Incidence, antimicrobial prescribing practice and associated healthcare costs of paediatric otorrhoea in primary care in the UK: A longitudinal population study

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ORCID record for Elliot Heward
For correspondence: [email protected]

Background Paediatric otorrhoea (PO) is a symptom-based diagnosis encompassing acute and chronic ear infections which cause otorrhoea in children and young people (CYP). Aim To understand the burden of PO on primary care services. Design and Setting A longitudinal population study in UK primary care. Methods Data from the Clinical Practice Research Datalink (CPRD Aurum), January 2005 to December 2019, was analysed. CYP under 17 years of age with otorrhoea were included. Standardised annual incidence and presentation rates were estimated. Poisson regression modelling was used to determine risk ratios comparing sex, age and IMD. A probabilistic simulation scaled-up estimates for the UK population. Results The cohort included 6,605,193 CYP, observed over 32,942,594 person-years. There were 80,454 incident cases and 106,318 presentations of PO during the 15-year period, equating to standardised annual incidence and presentation rates per 1000 patient-years of 2.42 (95% CI: 2.40-2.44) and 3.15 (3.13-3.17) respectively. In the UK this equates to 41,141 primary care appointments per year. Incidence was higher in males, those aged 0-2 years, and those living in the least deprived quintile. Treatment involved oral antibiotics (57.1%), no prescription (28.1%), topical antibiotics (9.7%), or combination (4.9%). The cost to NHS primary care is estimated at £1.97 million per year. Conclusions This is the first longitudinal population-based study investigating PO which demonstrates the burden on primary care. Antimicrobial prescribing predominantly follows NICE guidelines using oral amoxicillin. Aminoglycosides are the most frequently prescribed topical antibiotic despite the concern of ototoxicity.

Received January 26, 2024.
Accepted June 21, 2024.

This article is Open Access: CC BY license ( https://creativecommons.org/licenses/by/4.0/ )

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Early Humans Migrated Out of Africa Several Times, DNA Study Suggests

Homo sapiens interbred with Neanderthals as early as 250,000 years ago and may have ultimately bred them out of existence, according to new research

Margherita Bassi

Daily Correspondent

a woman stands behind a model of a neanderthal man, looking over his shoulder

Currently, the widely accepted story of human origins suggests that early members of our species left Africa in a single wave of migration about 50,000 years ago and interbred with Neanderthals in Europe and Asia.

But now, in a study published in July in the journal Science , researchers suggest Homo sapiens migrated from the African continent in several waves, interbreeding with Neanderthal populations as early as 250,000 years ago.

“This is the first time that geneticists have identified multiple waves of modern human-Neanderthal admixture,” first author Liming Li , an associate research scholar at Princeton University, says in a statement .

“It wasn’t a single out-of-Africa migration,” Sarah Tishkoff , a geneticist at the University of Pennsylvania who was not involved in the new study, adds to the New York Time s ’ Carl Zimmer. “There have been lots of migrations out of Africa at different time periods.”

According to Tishkoff, who has also studied interbreeding between early modern humans and Neanderthals, previous research largely didn’t recognize these earlier waves of migration, because they didn’t leave clear fossil records or DNA traces in living people.

Researchers have sequenced the genomes of hundreds of thousands of living humans, but they only have three known complete Neanderthal genomes, collected from one site in Croatia and two in Russia. As a result, scientists have an understanding of how interbreeding affected Homo sapiens , but a much murkier picture of how it affected Neanderthals.

“We know much less about how these encounters impacted the genomes of Neanderthals,” Joshua Akey , a population geneticist at Princeton University and senior author of the study, tells Live Science ’s Charles Q. Choi.

This time, Akey’s team decided to look for human DNA in Neanderthal genomes, comparing the three available ones to those of 2,000 modern humans. They mapped the gene flow between the Neanderthal and modern human populations with a genetic tool called IBDmix, which uses machine learning to decode genomes. The researchers identified two separate instances of interbreeding: between 200,000 and 250,000 years ago and between 100,000 and 120,000 years ago.

Coupled with past research that supports a migration from Africa between 50,000 and 60,000 years ago, the study finds early modern humans left the continent several times and interbred with Neanderthals. Their results suggest the Neanderthal genome derived 2.5 percent to 3.7 percent of its DNA from Homo sapiens .

“Because we can now incorporate the Neanderthal component into our genetic studies, we are seeing these earlier dispersals in ways that we weren’t able to before,” Akey says in the statement.

This supports a separate study published in Current Biology last year in which another group of researchers, including Tishkoff, compared the genome of a Neanderthal fossil with the genomes of 180 humans from across Africa. This team also concluded that Homo sapiens and Neanderthals had contact 250,000 years ago.

Katerina Harvati , a paleoanthropologist at the University of Tübingen in Germany who was not involved in either study, tells the New York Times that “some mysterious human fossils from Europe and the Middle East might belong to these early waves.”

For one, skulls found in two Israeli caves seem to belong to modern humans, but they have a few confusing features, such as larger brows, that might have come from Neanderthal genes. These skulls date to about 100,000 years ago, during one of the newly proposed periods of migration. And another skull fragment found in Greece is roughly 210,000 years old—aligning with the first proposed exodus from Africa—and displays some human anatomical traits.

“We now know that for the vast majority of human history, we’ve had a history of contact between modern humans and Neanderthals,” Akey says in the statement.

His team’s research also hypothesizes, based on genetic data, that the Neanderthal population was even smaller than previously thought—on the order of 2,400 breeding individuals, rather than 3,400. As such, the team suggests Neanderthals’ mysterious disappearance had to do with them becoming absorbed into the population of modern humans through interbreeding.

“Modern humans were essentially like waves crashing on a beach, slowly but steadily eroding the beach away,” Akey says in the statement. “Eventually, we just demographically overwhelmed Neanderthals and incorporated them into modern human populations.”

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Margherita Bassi | READ MORE

Margherita Bassi is a trilingual storyteller and freelance journalist with a particular interest in ancient history, astronomy and human interest stories.

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A mentor can sponsor you and help with submitting your research proposal. Research with longitudinal cohort studies can be performed independently, requesting data from repositories such as the National Heart, Lung, and Blood Institute's Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) 4 or the National Center ...
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A review and summary of studies on panel conditioning. In Menard S. (Ed.), Handbook of longitudinal research: Designs, measurement and analysis (pp. 123-138). New York: Academic Press. Common Cause & Lokniti—Centre for the Study of Developing Societies (2018).
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Guidelines for Award of ICSSR Longitudinal Studies in Social and Human
The potential applicants applying for these longitudinal studies shall explore the research areas identified by ICSSR and develop their proposals in collaboration with researchers from different institutions. 1.3 Team for the longitudinal studies shall comprise four to six scholars.
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reliable information,ra. of the Study:This is a proposal for a longitudinal study desi. and examine the effects on children raised by lesbian mothers. Male. and female children between 5 to 7 years old of single lesbian women, single heterosexual women, and married heterosexual women, will be.
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Welcome to the Research Proposal System. Login on the left hand side of this page, or click on the ' Create new account ' link to request a user account. Please make sure you have read and understood the Access Policy before submitting a proposal. The data dictionary and variable catalogue will help you decide which data you want to apply for.
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Before conducting a study, a research proposal should be created that outlines researchers' plans and methodology and is submitted to the concerned evaluating organization or person. Creating a research proposal is an important step to ensure that researchers are on track and are moving forward as intended. A research proposal can be defined as a detailed plan or blueprint for the proposed ...
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Methods: We used data from the Venda Health Examination of Mothers, Babies and their Environment (VHEMBE), a birth cohort study conducted in Limpopo, South Africa. BLLs were measured in whole blood collected at age 1 year and IgG titers for measles, tetanus and Haemophilus influenzae type B (Hib) were determined at age 3.5 years among 425 fully ...
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Joshua Parsons, MD, credits his involvement with the Duke Department of Medicine's Fellow Research Academy (FRA) as the key factor in his successful application for the National Institute of Health (NIH) K08: Clinical Investigator Award.. Dr. Parsons, a former clinical investigator pathway fellow and current assistant professor of medicine with the Division of Infectious Diseases has a PhD ...
Incidence, antimicrobial prescribing practice and associated healthcare
Background Paediatric otorrhoea (PO) is a symptom-based diagnosis encompassing acute and chronic ear infections which cause otorrhoea in children and young people (CYP). Aim To understand the burden of PO on primary care services. Design and Setting A longitudinal population study in UK primary care. Methods Data from the Clinical Practice Research Datalink (CPRD Aurum), January 2005 to ...
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Coupled with past research that supports a migration from Africa between 50,000 and 60,000 years ago, the study finds early modern humans left the continent several times and interbred with ...

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Longitudinal Study Design

What is a Longitudinal Study?

Panel Study

Cohort Study

Retrospective Study

Allows researchers to look at changes over time

High validation

Eliminates recall bias

Flexibility

Limitations

Large sample size needed

Participants tend to drop out

Report bias is possible

Goals of Longitudinal Data and Longitudinal Research

How to Perform a Longitudinal Study

Methodological Considerations

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Missing data

Accelerated longitudinal designs

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Longitudinal vs. Cross-Sectional Studies

1. Are longitudinal studies qualitative or quantitative?

2. What’s the difference between a longitudinal and case-control study?

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Qualitative longitudinal research in health research: a method study

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Methodological traditions

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Types of data and length of data collection

Three components of longitudinal data collection

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