research on cognitive technology

Applied Cognitive Science and Technology

Implications of Interactions Between Human Cognition and Technology

• © 2023
• Sumitava Mukherjee 0 ,
• Varun Dutt 1 ,
• Narayanan Srinivasan 2

Department of Humanities and Social Sciences, Indian Institute of Technology Delhi, New Delhi, India

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School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Kamand, India

Department of cognitive science, indian institute of technology kanpur, kanpur, india.

• Offers an interdisciplinary review of cutting-edge concepts in technology and cognitive science
• Presents diverse methods and research from an applied cognitive science perspective
• Appeals to readers from a broad range of disciplines, including engineering and technology

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• Human technology interaction

Behavioral Cybersecurity

• Social Media analytics
• Natural language Processing
• Psychology of Virtual reality
• Human centric Artificial Intelligence
• Technology Acceptance

Table of contents (15 chapters)

Front matter, artificial intelligence and agents, toward behavioral ai: cognitive factors underlying the public psychology of artificial intelligence.

• Sumitava Mukherjee, Deeptimayee Senapati, Isha Mahajan

Defining the Relationship Between the Level of Autonomy in a Computer and the Cognitive Workload of Its User

• Thom Hawkins, Daniel N. Cassenti

Cognitive Effects of the Anthropomorphization of Artificial Agents in Human–Agent Interactions

• Bas Vegt, Roy de Kleijn

Decision Support and Assistance Systems

Psychological factors impacting adoption of decision support tools.

• Thom Hawkins

Model-Based Operator Assistance: How to Match Engineering Models with Humans’ Cognitive Representations of Their Actions?

• Romy Müller, Leon Urbas

Behavioral Game Theory in Cyber Security: The Influence of Interdependent Information's Availability on Cyber-Strike and Patching Processes

• Zahid Maqbool, V. S. Chandrasekhar Pammi, Varun Dutt

Exploring Cybercriminal Activities, Behaviors, and Profiles

• Maria Bada, Jason R. C. Nurse

Neural Networks and Machine Learning

Computer vision technology: do deep neural networks model nonlinear compositionality in the brain’s representation of human–object interactions.

• Aditi Jha, Sumeet Agarwal

Assessment of Various Deep Reinforcement Learning Techniques in Complex Virtual Search-and-Retrieve Environments Compared to Human Performance

• Shashank Uttrani, Akash K. Rao, Bhavik Kanekar, Ishita Vohra, Varun Dutt

Cognate Identification to Augment Lexical Resources for NLP

• Shantanu Kumar, Ashwini Vaidya, Sumeet Agarwal

Human Factors

Psychophysiological monitoring to improve human–computer collaborative tasks.

• Daniel N. Cassenti, Chou P. Hung

Human–Technology Interfaces: Did ‘I’ do it? Agency, Control, and why it matters

• Devpriya Kumar

Engineering Design

Do analogies and analogical distance influence ideation outcomes in engineering design.

• V. Srinivasan, Binyang Song, Jianxi Luo, Karupppasamy Subburaj, Mohan Rajesh Elara, Lucienne Blessing et al.

Editors and Affiliations

Sumitava Mukherjee

Narayanan Srinivasan

About the editors

Bibliographic information.

Book Title : Applied Cognitive Science and Technology

Book Subtitle : Implications of Interactions Between Human Cognition and Technology

Editors : Sumitava Mukherjee, Varun Dutt, Narayanan Srinivasan

DOI : https://doi.org/10.1007/978-981-99-3966-4

Publisher : Springer Singapore

eBook Packages : Behavioral Science and Psychology , Behavioral Science and Psychology (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023

Hardcover ISBN : 978-981-99-3965-7 Published: 24 August 2023

Softcover ISBN : 978-981-99-3968-8 Due: 06 September 2024

eBook ISBN : 978-981-99-3966-4 Published: 23 August 2023

Edition Number : 1

Number of Pages : XVIII, 259

Number of Illustrations : 22 b/w illustrations, 13 illustrations in colour

Topics : Cognitive Psychology , Personality and Social Psychology

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• Open access
• Published: 15 August 2024

Dose–response relationship between computerized cognitive training and cognitive improvement

• Liyang Liu 1 , 2   na1 ,
• Haibo Wang   ORCID: orcid.org/0000-0003-4726-8939 3 , 4   na1 ,
• Yi Xing 1 , 2 ,
• Ziheng Zhang 5 ,
• Qingge Zhang 5 ,
• Ming Dong 5 ,
• Zhujiang Ma 5 ,
• Longjun Cai 5 ,
• Xiaoyi Wang 5 &
• Yi Tang   ORCID: orcid.org/0000-0002-8052-065X 1 , 2  

npj Digital Medicine volume  7 , Article number:  214 ( 2024 ) Cite this article

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• Outcomes research

Although computerized cognitive training (CCT) is an effective digital intervention for cognitive impairment, its dose-response relationship is understudied. This retrospective cohort study explores the association between training dose and cognitive improvement to find the optimal CCT dose. From 2017 to 2022, 8,709 participants with subjective cognitive decline, mild cognitive impairment, and mild dementia were analyzed. CCT exposure varied in daily dose and frequency, with cognitive improvement measured weekly using Cognitive Index. A mixed-effects model revealed significant Cognitive Index increases across most dose groups before reaching the optimal dose. For participants under 60 years, the optimal dose was 25 to <30 min per day for 6 days a week. For those 60 years or older, it was 50 to <55 min per day for 6 days a week. These findings highlight a dose-dependent effect in CCT, suggesting age-specific optimal dosing for cognitive improvement.

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Introduction.

Computerized cognitive training (CCT) aims at improving or maintaining cognitive abilities by transforming the conventional cognitive training tasks into digital form. In CCT, patients are trained with computerized cognitive tasks which are designed from the classic psychiatric paradigms that can stimulate certain cognitive domains 1 . Under the assumption of maintaining or improving cognitive abilities, clinical trials have been designed to prove the effects of cognitive training in patients with cognitive impairment caused by diverse neurological or psychiatric diseases 2 , 3 , 4 , 5 . Due to the benefits of applying cognitive training in the early stages of dementia, cognitive training is recommended as a non-pharmacological treatment in the guideline of MCI 6 . However, despite lots of discussions about the efficacy of cognitive training 7 , 8 , 9 , 10 , few studies focused on the methodologies, especially on the training dose that could substantially affect the efficacy of cognitive training 11 , 12 .

A meta-analysis has summarized the types, delivery methods, and training dose of CCT to explore the optimal training plan for old people 12 . According to this study, training less than 30 min might be ineffective and training efficacy might decline when training more than 3 times a week 12 . However, the results were limited by the small number of literature and there is currently lack of clinical research focusing on the dose-response relationship of CCT. Consequently, studies always chose training doses according to training task contents or clinical practice rather than an evidence-based guideline, which ranged from 15 min to 100 min per day 7 , 11 . The “best guess” approach for dose selection would impede the findings in research and compromising the application of CCT in a evidence-based approach.

The dose-dependent characteristic has been observed in neuroplasticity that underlying the basis of cognitive training 13 , 14 . The short-term synaptic plasticity and long-term potentiation occur in milliseconds to minutes, but the formation of new neural fibers requires several days to months 15 , 16 . Changes in gray matters spread to the bilateral cortex after 3-week training of juggler, while one-week training only leads to changes in occipito-temporal junction 17 . Learning effects may also vary significantly when the learning session is prolonged 18 . Given that, we hypothesized that cognitive training might have dose-dependent effects on patients suffering from cognitive impairment.

Therefore, we aimed to explore the dose-response relationship of CCT and estimate the optimal dose for patients with cognitive impairment in this retrospective cohort study using the real-world data. By comparing the weekly changes in Cognitive Index 19 (WCCI) in different dose groups (Fig. 1 ), we illustrated the dose-response relationship of CCT and found age substantially affecting the dose-response relationship. Specifically, the optimal doses were different in participants younger or older than 60 years. The estimation of the optimal dose of CCT could not only help physicians with suggestions on treatment doses for patients with cognitive impairment, but also make the CCT a more robust evidence-based therapy in cognitive disorders.

The Cognitive Index was measured repeatedly every week of every patient. The weekly change in Cognitive Index was compared among different dose groups using the repeatedly measuring data.

Demographic characteristics of study population

From 2017 to 2022 we enrolled 21,845 eligible participants in total and the details of the recruitment process are presented in a flowchart (Fig. 2 ). The demographic characteristics of the final 8709 participants are presented in Table 1 . The average (SD) age of the participants was 63.2 (12.3) years. And 4752 participants (54.6%) had less than a high school education; 3957 participants (45.4%) had high school or higher education. Of the participants included, 5301 (60.9%) were diagnosed with mild cognitive impairment (MCI), 2735 (31.4%) were diagnosed with mild dementia, and 673 (7.7%) were diagnosed with subjective cognitive decline (SCD). Participants were grouped into two age groups, in which 3691 participants were under 60 years and 5018 participants were 60 years or older.

SCD subjective cognitive decline, MCI mild cognitive impairment.

Association of the training frequency with cognitive improvement

The relationship between the training frequency and WCCI showed a trend that the training effect would reach the threshold when training for 6 days per week (Fig. 3a ). And training for 7 days per week would cause a sharp decline in WCCI. The trend was verified in the mixed effect model in both age groups. In the participants aged under 60 years old, an increasing trend was found in WCCI ( p value for trend: <0.001) and reach a maximum of 1.2 (95% CI, [0.7, 1.8], p  < 0.001; Fig. 3b ; Supplementary Table 3 ) when taking training for 6 days per week. In participants aged 60 years or older, we also observed an increasing trend in WCCI ( p value for trend: 0.04) for training days. The increasing WCCI reached a threshold of 0.9 (95% CI, [0.4, 1.4], p  < 0.001; Fig. 3c ; Supplementary Table 4 ) when taking training for 6 days per week.

Dose-response relationship of training frequency was first explored by comparing the absolute WCCI among various dose groups in the entire study population ( a ) with black circles indicating mean value of WCCI. For patients aged under 60 years ( b ), or aged 60 years or older ( c ), the dose-response relationship of training frequency was elucidated through estimated cognitive improvement with (red circles) or without (blue circles) statistical significance based on mixed effects model. The error bar depicted the 95% confidence interval.

Association of the daily training dose with cognitive improvement

Next, we examined the dose-response relationship between daily dose and WCCI (Fig. 4 ). We also observed a trend of increasing WCCI with the extension of daily dose and the WCCI reached the maximum when training for 45 to <50 min/day. When the training duration exceeded 55 min/day, there was a sharp decline in WCCI (Fig. 4a ). The mixed effect model revealed an age-dependent dose-response relationship in daily dose. In the participants aged under 60 years old, an increasing trend was observed in WCCI ( p value for trend: <0.001) among the participants taking 1–6 times the baseline daily dose. And the optimal daily dose was 5–6 times the baseline daily dose (25 to <30 min/day: adjusted effect estimate, 1.9 (95% CI, [0.8, 3.0]; p  < 0.001; Fig. 4b ; Supplementary Table 5 ). This estimated dose is in accordance with the first peak of WCCI in Fig. 4a . In participants aged 60 years or older, we observed a positive trend in WCCI ( p value for trend: <0.001) for the dose range from 1 to 11 times the baseline daily dose (5 to <55 min/day; Supplementary Table 6 ). The optimal dose was 10–11 times the baseline dose (50 to <55 min/day: adjusted effect estimate, 3.9 (95% CI, [1.4, 6.4]; p  = 0.002); Fig. 4c ; Supplementary Table 6 ) and training for 60 min and above did not confer further increases in WCCI (adjusted effect estimate, 2.0 (95% CI, [0.2, 3.9]; p  = 0.03)).

Dose-response relationship of daily dose was first explored by comparing the absolute WCCI among various dose groups in the entire study population ( a ) with black circles indicating mean value of WCCI. For patients aged under 60 years ( b ), or aged 60 years or older ( c ), the dose-response relationship of daily dose was elucidated through estimated cognitive improvement with (red circles) or without (blue circles) statistical significance based on mixed effects model. The error bar depicted the 95% confidence interval.

Ultimately, we investigated the variations in the dose-response relationship across SCD, MCI, or mild dementia participants with our subgroup analysis demonstrating consistent trends across the three subgroups (Supplementary Figure 1 ). Significant trends ( p  < 0.05) were observed in WCCI with increased daily dose in all subgroups and with training frequency in SCD or MCI subgroups. The exception was the mild dementia subgroup, in which the p value for trend in training frequency did not reach significance (Supplementary Tables 7 – 12 ).

Using the large cohort of computerized cognitive training in the real world, we analyzed the dose-response relationship between training doses and changes in cognitive abilities which were measured by Cognitive Index. Our findings give several contributions to the application of CCT: (1) showed dose-dependent effects in cognitive training and suggested the optimal doses for self-adapted CCT, (2) the results also suggested that higher dose would not certainly lead to the increase in WCCI, (3) further, our results show that age has an impact on the dose-response relationship. The optimal daily dose was doubled in the older participants (age ≥60 years) compared to the younger participants (age <60 years). The optimal training frequency was 6 days per week in both age groups.

Our findings revealed that the optimal doses were estimated as 25 to <30 min training per day in participants under 60 years old. Some studies chose the daily doses closed to the optimal daily dose found in our study and showed a significant improvement in cognitive abilities with the intervention of CCT 20 , 21 , 22 , 23 . Our findings also revealed the optimal daily dose of 50 to <55 min/day in participants aged 60 years or older. Thus, in future clinical trials, higher dose may be considered for administration when recruiting participants aged 60 years or older. Meanwhile, our findings revealed the optimal training frequency was 6 days/week. However, most studies on CCT set the training frequency to less than 6 days per week 7 , indicating that the training effect may not reach the maximum.

CCT beyond the optimal dose does not yield incremental cognitive benefits according to our results. A meta-analysis has mentioned there may be a maximal dose of cognitive training after which the training effects would decline, although it concluded that the optimal training frequency was 3 days a week 12 . Factors such as cognitive fatigue may be responsible for declining training effects 24 . Similar non-linear dose-response models were found in other non-pharmacological interventions such as walking steps and moderate-to-vigorous physical activities (MVPA) 25 , 26 , 27 .

The dose-response relationship and training efficacy were differentiated by age groups (<60 years and ≥60 years). The older group got the highest cognitive gains when training for almost double dose than the younger group. In a study investigating the association between daily steps and all-cause mortality, different results were also observed in two age groups (<60 years and ≥60 years) 27 . However, the older people required a small number of steps to get similar health benefits compared to the younger people. The decreasing metabolic rate, aerobic capacity and biochemical inefficiencies may explain the difference in walking steps 27 . The decreasing volume of brain structure, neural excitability and plasticity could impair the learning ability in older people 28 , 29 . Therefore, the older people might need more time to gain the optimal training effect.

Investigating the dose-response relationship is a crucial prerequisite for establishing non- pharmacological intervention as an robust evidence-based therapy 30 . A dearth of research on treatment dose has impeded CCT’s application as a broadly acceptable therapy for cognitive disorders, although there has been substantial exploration of the effectiveness of CCT 7 . It is hard to study the dose-response relationship of cognitive training using inflexible and time-limited randomized controlled trials which cannot satisfy the patients’ needs 11 . Meta-analysis may find clues for the dose-response associations, but the results are limited by sparse data and diverse types of training 12 . Although a few clinical trials reported the effect of training doses, they mainly study the cumulative dose through the clinical trials, and the results were limited by small sample size and trial design 31 , 32 . Specifically, Lampit et al. reported the relationship between training weeks and cognitive abilities 32 . They found the global cognitive abilities reaching the maximum at the end of training (36 weeks) but the optimal daily or weekly dose was unknown. Some research of other non-pharmacological interventions studies the dose-response relationship by analyzing the average dose of the individual (i.e., one patient under one exposure) 25 , 27 which would neglect the variation of dose through the study.

One major strength of our study is using the repeated measurement data from a large cohort in the real world, in contrast to the other studies. CCT enables us to monitor the training effects from the beginning to the end of training and makes it possible to analyze the dose-response relationship by measuring the exposures (dose) and outcomes (changes in cognitive abilities) every week. Similarly, longitudinal real-world data from a cognitive training platform has been used to analyze the learning trajectories of cognitive training tasks 33 . However, this study focused on predicting the next performance in cognitive training and the effect of training dose has yet to be elucidated.

Our study has some limitations and results should be interpreted with caution. The study results were based on the repeated measurement data of a retrospective cohort. Thus, the results are limited by selection bias and confounding bias such as history bias, testing bias, and test-retest effect. However, the large sample size and mixed effects model can reduce these biases. The second limitation concerns the validity of Cognitive Index. Although the validity of the Cognitive Index has been examined in a previous study in patients with MCI and dementia 19 , it should be further tested with longitudinal data to ensure that the changes in Cognitive Index can reflect cognitive abilities. A third limitation pertains to the limited sample size for subgroup analyses. Although subgroup analyses revealed consistent trends across different cognitive statuses, it was notable that more participants are required to make the findings more robust for specific type of population. Another limitation of our study concerns potential cognitive improvement due to the practice effect from continuous cognitive testing. However, the varied content of cognitive assessments each week helped mitigate the practice effect. Furthermore, as the practice effect impacted all participants simultaneously, leading to improvement in everyone’s Cognitive Index, the impact of this practice effect on estimating optimal dose was limited.

In conclusion, we revealed the dose-response relationship of CCT with multilevel variables and found the optimal doses in two age groups (<60 and ≥60 years). Our results provided the evidence for dose selection in CCT and can contribute to the future guidelines for making the CCT a broadly acceptable treatment.

Study design and participants

In this retrospective cohort study conducted in a real-world setting, we screened individuals with cognitive complaints or impairments who were users of the reported computerized cognitive training platform 34 from 2017 to 2022 in Beijing. This cognitive training platform recruited participants from community residents and hospital outpatients. Before initiating cognitive training, it was mandatory to assess the cognitive status of each registered participant. Qualified assessors conducted cognitive assessment (i.e., MoCA and CDR) in both community settings and hospitals, and cognitive status was diagnosed based on specific diagnostic criteria. The diagnosis of SCD was based on cognitive complaints without evidence of objective cognitive decline. MCI was diagnosed using criteria derived from Petersen criteria: (1) cognitive complaints (2) Montreal Cognitive Assessment (MoCA) 35 with a score of ≥18 to ≤26 (adjusted for education level), Clinical Dementia Rating Scale global score (CDR-GS) ≤ 0.5 (at least one cognitive domain ≥0.5) (3) maintaining independence in activities of daily living 36 , 37 (4) not demented. Diagnosis of dementia was based on DSM-V criteria for major neurocognitive disorder 38 , with dementia severity rated using CDR-GS (mild: 0.5–1, moderate: 2, severe: 3) 39 . Diagnoses including cognitive status and other medical conditions were documented in the user profiles after confirmation by neurologists, geriatricians, or certified general practitioners. When documenting diagnoses on the platform, doctors selected diagnoses from a list of options or manually entered them if not listed.

During the initial screening, 21,845 participants were recruited. The inclusion criteria were participants with SCD, MCI, or mild dementia, age ≥40 years, and training duration ≥2 weeks. The exclusion criteria included: Cognitive status included moderate to severe dementia; diagnoses encompassed cancer, unstable systemic disease, or psychiatric disorders. The final analysis included 8709 participants diagnosed with SCD, MCI, or mild dementia. The study was approved by the ethics committee of Xuanwu Hospital (NO.2023027) with a waiver of informed consent due to the retrospective nature of the study using de-identified data. The study protocol has been registered at Clinicaltrial.gov (NCT05922319).

Computerized cognitive training

Participants diagnosed with SCD, MCI, or mild dementia were suggested to take the CCT as therapy. The demographic information and diagnosis were accessible on the CCT platform 34 and the data extraction was permitted by the platform users. Time and scores of training tasks were automatically recorded by the CCT platform. The CCT procedure was proven to be effective in improving cognitive function and was described in the Supplementary Materials 34 . Briefly, the training tasks target cognitive abilities of human intelligence (defined according to Cattell–Horn–Carroll (CHC) intelligence theory 40 ). Every task was designed to target specific cognitive abilities based on the psychological paradigms. Participants were encouraged to take the cognitive training at home for at least 3 days per week, for at least 20 min per training day. The cognitive function was assessed by the Cognitive Index which was calculated based on the performance in the CCT with a strong correlation with MoCA (r = 0.8) and the Mini-Mental State Examination (MMSE) (r = 0.7) 19 .

Procedure of CCT

Computerized cognitive training (CCT) used in the research was designed to exercise multiple cognitive domains (Memory, executive function, thinking, perception, attention, calculation etc.) of people 34 . The training tasks were designed based on the psychological paradigms such as Corsi block-tapping task, Delayed match to sample, Face-name task, Spatial monitoring task, Size matching task, Visual search task, Pursuit tracking task, Continuous performance test, Flanker task, Stroop task, Mental arithmetic task and so on. For each paradigm, multiple training tasks were designed, and each task was set into different levels of difficulty. We recommended users to complete a minimum of 7 specific cognitive training tasks each day, with each task lasting 2 min, after finishing 6- minute warm-up exercise. At the beginning of the training, every user was given the similar training tasks with the same level of difficulty. The software will adjust the content of the next training task based on the user’s performance on the initial round of training to ensure that the training effectively targets impaired cognitive functions. The difficulty of tasks aimed at improving the same cognitive function will also gradually increase in response to the user’s performance.

Cognitive Index

The performance in every task was recorded as task scores and was normalized based on the percentile in the population. The cognitive ability of a specific cognitive domain was obtained by averaging all the task scores targeting the same cognitive domain. The Cognitive Index was calculated by averaging the scores for each cognitive domain 19 .

Exposures and outcomes

The exposure was CCT with different training doses. The training dose of CCT was usually defined as training frequency (e.g., the number of training days per week) or training duration (e.g., the minutes of training) in a certain period 11 , 12 . Specifically, we assessed the effect of number of training days per week (frequency) and average training duration per training day (daily dose) in this study. The relationship between the training frequency and daily dose can be illustrated with this formula:

The daily dose was divided into 13 categories with an interval of 5 min and the training frequency has 7 categories according to the number of training days per week.

The outcome was the change in cognitive abilities from last week. As shown in the Fig. 1 , the exposures and outcomes were measured repeatedly during the training process. At the end of each week, the CCT platform recorded variables such as training duration in a week, number of training days in a week, and the real-time Cognitive Index. The Cognitive Index was used to monitor cognitive abilities through the training process, and the weekly change in Cognitive Index between adjacent weeks can present the fluctuation in cognitive status. Consequently, the outcome of this research was the change in Cognitive Index between adjacent weeks which was defined as weekly changes in Cognitive Index.

Statistical analysis

Participants’ characteristics were summarized by the mean and standard deviation for quantitative variables (i.e., age) and as the frequency and percentage for categorical variables (i.e., sex, education level, and cognitive status). We first explored the effect of training frequency or daily dose on WCCI, separately. We calculated the average WCCI of various dose groups to investigate the dose-response relationship, along with the corresponding standard error. Then, we applied the linear mixed effects model for analysis and treated the training frequency and daily dose as fixed effects in the model. With the linear mixed effects model, we were able to (1) account for the random effects due to the individual variability, for the cognitive training record was measured repeatedly of each participant; (2) assess the effect of training frequency and daily dose simultaneously. With the mixed effects model, we aimed to find the optimal dose (i.e., the training frequency or daily dose at which the maximum WCCI was observed), in terms of the maximum fixed effect of training frequency or daily dose estimated by the model. The covariates contained the education level, age, and sex. In the process of modeling, we selected the proper model based on the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and the interpretation of the model. To ensure the balance between the complexity and interpretability, we finally chose to apply random intercept fixed slope model for assessing the dose-response relationship of CCT.

According to the definition of old people by the World Health Organization 41 , sixty years old is the cut-off point to separate whether people could be defined as “old people”, and the cognitive ability is largely affected by age. Thus, we applied the random intercept fixed slope model to participants aged 60 years or older, or participants under 60 years old, separately. The dose-response relationship was explored separately in the two age groups. To investigate differences among participants with various cognitive statuses, we conducted subgroup analysis to examine the dose-response relationship in individuals with SCD, MCI, or mild dementia. To assess the association between the training effects (illustrated by WCCI) and increasing training dose, we conducted a trend analysis. We calculated the p value for trend by using a quasi-continuous variable in the model. A p value for trend less than 0.05 was considered indicative of a significant association.

Modeling process

Before the final models were built, four potential models were built to assess the balance between complexity and interpretability. The four models were assessed based on the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and the interpretation of the model. The AIC and BIC are both penalized-likelihood information criteria and the smaller values indicate the better model. The four models included: a random intercept model (model 1), a random intercept fixed slope model (model 2), a random intercept random slope model (model 3), and a random intercept random slope with fixed quadratic term model (model 4). As presented in Supplementary Table 1 , model 4 conveys the smallest AIC and BIC. However, the quadratic term in model 4 can bring the complexity of interpretation, and the AIC and BIC index did not present a large decrease comparing to model 3. Furthermore, as presented in Supplementary Table 2 , by freeing the slope term as model 3 did not improve the model performance significantly. Therefore, the final decision is to apply random intercept fixed slope model (model 2) for the rest of the modeling process.

Data availability

The datasets analyzed during the current study are not publicly available due to patient privacy purposes but are available from the corresponding author on reasonable request.

Code availability

The underlying code for this study is not publicly available but may be made available to qualified researchers on reasonable request from the corresponding author.

Turnbull, A., Seitz, A., Tadin, D. & Lin, F. V. Unifying framework for cognitive training interventions in brain aging. Ageing Res. Rev. 81 , 101724 (2022).

Article   PubMed   PubMed Central   Google Scholar  

Edwards, J. D. et al. Randomized trial of cognitive speed of processing training in Parkinson disease. Neurology 81 , 1284–1290 (2013).

Bowie, C. R. et al. Cognitive remediation for treatment-resistant depression: effects on cognition and functioning and the role of online homework. J. Nerv. Ment. Dis. 201 , 680–685 (2013).

Article   PubMed   Google Scholar  

Hagovska, M., Takac, P. & Dzvonik, O. Effect of a combining cognitive and balanced training on the cognitive, postural and functional status of seniors with a mild cognitive deficit in a randomized, controlled trial. Eur. J. Phys. Rehabil. Med. 52 , 101–109 (2016).

PubMed   Google Scholar  

Subramaniam, K. et al. Computerized Cognitive Training Restores Neural Activity within the Reality Monitoring Network in Schizophrenia. Neuron 73 , 842–853 (2012).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Petersen, R. C. et al. Practice guideline update summary: Mild cognitive impairment. Neurology 90 , 126–135 (2018).

Hill, N. T. et al. Computerized Cognitive Training in Older Adults With Mild Cognitive Impairment or Dementia: A Systematic Review and Meta-Analysis. Am. J. Psychiatry 174 , 329–340 (2017).

Gates, N. J. et al. Computerised cognitive training for preventing dementia in people with mild cognitive impairment. Cochrane Database Syst. Rev . https://doi.org/10.1002/14651858.cd012279.pub2 (2019).

Willis, S. L. et al. Long-term Effects of Cognitive Training on Everyday Functional Outcomes in Older Adults. JAMA 296 , 2805 (2006).

Rosen, A. C., Sugiura, L., Kramer, J. H., Whitfield-Gabrieli, S. & Gabrieli, J. D. Cognitive Training Changes Hippocampal Function in Mild Cognitive Impairment: A Pilot Study. J. Alzheimers Dis. 26 , 349–357 (2011).

Hampstead, B. M., Gillis, M. M. & Stringer, A. Y. Cognitive rehabilitation of memory for mild cognitive impairment: a methodological review and model for future research. J. Int. Neuropsychol. Soc. 20 , 135–151 (2014).

Lampit, A., Hallock, H. & Valenzuela, M. Computerized cognitive training in cognitively healthy older adults: a systematic review and meta-analysis of effect modifiers. PLoS Med. 11 , e1001756 (2014).

Loriette, C., Ziane, C. & Ben Hamed, S. Neurofeedback for cognitive enhancement and intervention and brain plasticity. Rev. Neurol. 177 , 1133–1144 (2021).

Article   CAS   PubMed   Google Scholar  

Lüscher, C., Nicoll, R. A., Malenka, R. C. & Muller, D. Synaptic plasticity and dynamic modulation of the postsynaptic membrane. Nat. Neurosci. 3 , 545–550 (2000).

Tetzlaff, C., Kolodziejski, C., Markelic, I. & Wörgötter, F. Time scales of memory, learning, and plasticity. Biol. Cybern. 106 , 715–726 (2012).

Zucker, R. S. & Regehr, W. G. Short-term synaptic plasticity. Annu. Rev. Physiol. 64 , 355–405 (2002).

Driemeyer, J., Boyke, J., Gaser, C., Büchel, C. & May, A. Changes in Gray Matter Induced by Learning—Revisited. PLoS One 3 , e2669 (2008).

Shibata, K. et al. Overlearning hyperstabilizes a skill by rapidly making neurochemical processing inhibitory-dominant. Nat. Neurosci. 20 , 470–475 (2017).

Liu, L.-Y. et al. Validation of a computerized cognitive training tool to assess cognitive impairment and enable differentiation between mild cognitive impairment and dementia. J. Alzheimers Dis. https://doi.org/10.3233/JAD-230416 (2023).

Tarraga, L. A randomised pilot study to assess the efficacy of an interactive, multimedia tool of cognitive stimulation in Alzheimer’s disease. J. Neurol. Neurosurg. Psychiatry 77 , 1116–1121 (2006).

Sheng, C. et al. Advances in Non-Pharmacological Interventions for Subjective Cognitive Decline: A Systematic Review and Meta-Analysis. J. Alzheimers Dis. JAD 77 , 903–920 (2020).

Mingming, Y., Bolun, Z., Zhijian, L., Yingli, W. & Lanshu, Z. Effectiveness of computer-based training on post-stroke cognitive rehabilitation: A systematic review and meta-analysis. Neuropsychol. Rehabil. 32 , 481–497 (2022).

Bakheit, A. M. O. et al. A prospective, randomized, parallel group, controlled study of the effect of intensity of speech and language therapy on early recovery from poststroke aphasia. Clin. Rehabil. 21 , 885–894 (2007).

Holtzer, R., Shuman, M., Mahoney, J. R., Lipton, R. & Verghese, J. Cognitive Fatigue Defined in the Context of Attention Networks. Aging Neuropsychol. Cognit. 18 , 108–128 (2010).

Article   Google Scholar  

Del Pozo Cruz, B., Ahmadi, M., Naismith, S. L. & Stamatakis, E. Association of Daily Step Count and Intensity With Incident Dementia in 78 430 Adults Living in the UK. JAMA Neurol 79 , 1059–1063 (2022).

Ekelund, U. et al. Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality: systematic review and harmonised meta-analysis. BMJ , l4570, https://doi.org/10.1136/bmj.l4570 (2019).

Paluch, A. E. et al. Daily steps and all-cause mortality: a meta-analysis of 15 international cohorts. The Lancet Public Health 7 , e219–e228 (2022).

Burke, S. N. & Barnes, C. A. Neural plasticity in the ageing brain. Nat. Rev. Neurosci. 7 , 30–40 (2006).

Kirov, I. I. et al. Global brain volume and N-acetyl-aspartate decline over seven decades of normal aging. Neurobiol. Aging 98 , 42–51 (2021).

Sikkes, S. A. M. et al. Toward a theory‐based specification of non‐pharmacological treatments in aging and dementia: Focused reviews and methodological recommendations. Alzheimers Dementia 17 , 255–270 (2021).

Hampstead, B. M., Sathian, K., Moore, A. B., Nalisnick, C. & Stringer, A. Y. Explicit memory training leads to improved memory for face-name pairs in patients with mild cognitive impairment: results of a pilot investigation. J. Int. Neuropsychol. Soc. 14 , 883–889, (2008).

Lampit, A. et al. The Timecourse of Global Cognitive Gains from Supervised Computer-Assisted Cognitive Training: A Randomised, Active-Controlled Trial in Elderly with Multiple Dementia Risk Factors. J. Prev. Alzheimers Dis. 1 , 33–39 (2014).

CAS   PubMed   Google Scholar  

Steyvers, M. & Schafer, R. J. Inferring latent learning factors in large-scale cognitive training data. Nat. Hum. Behav. 4 , 1145–1155 (2020).

Tang, Y. et al. The effects of 7‐week cognitive training in patients with vascular cognitive impairment, no dementia (the Cog‐VACCINE study): A randomized controlled trial. Alzheimers Dementia 15 , 605–614 (2019).

Nasreddine, Z. S. et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J. Am. Geriatr. Soc. 53 , 695–699 (2005).

Lawton, M. P. & Brody, E. M. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist 9 , 179–186 (1969).

Rai, G. S., Gluck, T., Wientjes, H. J. & Rai, S. G. The Functional Autonomy Measurement System (SMAF): a measure of functional change with rehabilitation. Arch. Gerontol. Geriatr. 22 , 81–85 (1996).

Association, A. P. Diagnostic and Statistical Manual for Mental Disorders , 5th ed. https://doi.org/10.1176/appi.books.9780890425596 (2013).

Berg, L. Clinical Dementia Rating. Br. J. Psychiatry 145 , 339–339, https://doi.org/10.1192/s0007125000118082 (1984).

McGrew, K. S. in Contemporary Intellectual Assessment: Theories, Tests, and Issues . (eds Flanagan, D. P. & Harrison, P. L.) Ch. The Cattell-Horn-Carroll Theory of Cognitive Abilities: Past, Present, and Future, 136–181 (The Guilford Press, 2005).

World Health Organization. China country assessment report on ageing and health (WHO, 2016).

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (2022YFC3602600), National Natural Science Foundation of China (82220108009, 81970996). The funder played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript.

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These authors contributed equally: Liyang Liu, Haibo Wang.

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Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China

Liyang Liu, Yi Xing & Yi Tang

Neurodegenerative Laboratory of Ministry of Education of the People’s Republic of China, Beijing, China

Clinical Research Institute, Institute of Advanced Clinical Medicine, Peking University, 100191, Beijing, China

Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, 38 Xueyuan St, Haidian district, 100191, Beijing, China

Beijing Wispirit Technology Co., Ltd., Beijing, China

Ziheng Zhang, Qingge Zhang, Ming Dong, Zhujiang Ma, Longjun Cai & Xiaoyi Wang

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Y.T. acquired funding, designed the study, and supervised the study’s implementation. L.L. handled the implementation of the study, data analysis and manuscript writing. H.W. designed the study and statistical analysis methods. L.L. and H.W. contributed equally to this study and shared co-first authorship. Z.Z., Q.Z. and M.D. collected, managed and analyzed the data. Y.X., Z.M., L.C. and X.W. participated in the study design and manuscript revisions.

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Liu, L., Wang, H., Xing, Y. et al. Dose–response relationship between computerized cognitive training and cognitive improvement. npj Digit. Med. 7 , 214 (2024). https://doi.org/10.1038/s41746-024-01210-9

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How to get started with cognitive technology

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Best practices for putting cognitive technology to work

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In this article...

• Adopting a new technology starts with education.
• Adoption should support a business case.
• There are various approaches to implementing cognitive technology.

Putting cognitive technology to work is easier than you might think, but how?

First, some background. The cognitive era is an ongoing movement of sweeping technological transformation. The impetus of this movement is the emerging field of cognitive technology, radically disruptive systems that understand unstructured data, reason to form hypotheses, learn from experience and interact with humans naturally. Success in the cognitive era will depend on the ability to derive intelligence from all forms of data with this technology.

Cognitive computing is perhaps most unique in that it upends the established IT doctrine that a technology’s value diminishes over time; because cognitive systems improve as they learn, they actually become more valuable. This quality among others makes cognitive technology highly desirable for business, and many early adopters are leveraging the competitive advantage it affords.

In a recently conducted IBM market study, we investigated more than 600 early adopters who are putting cognitive technology to work. Our report, The Cognitive advantage: Insights from early adopters on driving business value , examines emerging patterns of early adoption. These patterns reveal a blueprint of sorts for future adopters.

The question then for you and your organization is simple: How do I get started?

Adopting a new technology starts with education

Cognitive initiatives come in all shapes and sizes, from transformational to tactical and everything in between. What the most successful projects have in common, no matter how ambitious, is they begin with a clear view of what the technology can do. Therefore, your first task is to gain a firm understanding of cognitive capabilities.

The cognitive era is here not only because the technology has come of age, but also because the phenomenon of big data requires it. Computing systems of the past can capture, move and store unstructured data, but they cannot understand it. Cognitive systems can. The application of this breakthrough is ideally suited to address business challenges like scaling human expertise and augmenting human intelligence.

Becoming a cognitive business looks different for almost everyone. Although a common perception is cognitive technology is complex and difficult, that is not necessarily true. While some early adopters start with ambitions to transform their organization or industry, most start relatively small. Talk to many successful early adopters and you will hear some variation on the theme of “I want to improve one specific operational process.”

The point is, it is helpful to avoid assumptions regarding what adoption will look like for you. It is better to keep an open mind during this information gathering phase. Here are resources that will help give you a solid foundation:

Welcome to the era of cognitive business

What is cognitive computing?

Watson Business Coach

The cognitive advantage report

Envision the possible and define your ideal outcomes

Judging by the success of early adopters, it's no surprise more and more organizations are looking to adopt. Many are grappling with how and when, but why is most important.

No one starts down this path expressly to adopt cognitive technology; the whole point is to improve the organization. Adopting cognitive technology above all else should align to business priorities. Successful early adopters identify a problem, then build a case for how solving that problem will support specific outcomes like saving money, gaining customers or increasing revenue.

Good planning will result in the selection of a specific and strategic use case. Usage patterns tend to fall into four major categories that play to the strengths of cognitive technology. First, cognitive technology is often used to enable innovation and discovery by understanding new patterns, insights and opportunities. Second, it is often used to optimize operations to provide better awareness, continuous learning, better forecasting and optimization. Third, to augment and scale expertise by capturing and sharing the collective knowledge of the organization. Finally, to create adaptive, personalized experiences, including individualized products and services, to better engage customers and meet their needs.

One temptation, however, is to pursue cognitive technology for the technology’s sake. "Most of the failures we've seen are when you start with the technology instead of the business case," according to an IBM cognitive technology architect. "There are so many things you can do with cognitive technology, and people get really excited. But you need to focus on what impacts your bottom line.”

Conversely, overthinking can lead to inaction. According to a CEO that leverages cognitive technology, “a lot of companies are over-analyzing what they should be doing. They want a fully detailed design and guaranteed quality of output, but it doesn’t work that way. It’s better to start small with a good idea, and from there scale out and scale up. There is no universal template for success, but focus on persistence is a proven formula.”

One IBM expert described this strategy as preventing the perfect from becoming the enemy of the good. In some cases, the best advice is to select a use case quickly to overcome the inertia created by a misguided desire for perfection. Adoption can mean something as basic as tapping a pre-built cognitive application. Starting small does not prohibit future expansion, and strategy can evolve over time.

"Often what’s difficult is the trade-off of fixing current pain points and doing something that aligns with long-term vision,” according to an IBM cognitive strategy specialist. “This is where people can struggle. It’s easy to be short-term focused. The challenge is to marry fixing the current problem with making sure it is the right move for the long term. So prioritizing the right use case that balances these things is the big challenge, and it’s where we can help the client the most."

As you develop your strategy, share ideas with other forward thinkers within your organization—their support is essential—or brainstorm with a member of the IBM team .

Choose the best implementation approach for you

Once you gain a realistic understanding of what cognitive technology can do, and specifically how it will help your business, it's time to choose your approach.

1. Deploy cognitive solutions and apps.

Many early adopters know exactly where they want to install cognitive technology, so they embed readily available cognitive offerings into existing workflows. The lever of this approach is a pre-built cognitive solution, like Watson Virtual Agent or Watson Explorer . These products are already coded, and only require installation and integration with data sources up front.

2. Build your own cognitive apps.

Developers can build their own cognitive apps through Bluemix , IBM’s cloud platform. More than 40,000 developers are building with APIs (application programming interfaces). The Watson Developer Cloud offers common language descriptions, demonstrations, case studies and starter kits for each API. “It’s good to let developers get in and play around,” said an IBM cognitive expert. “Because the technology is so new, it’s almost impossible to explain everything up front. You learn a lot by doing.”

Watch: What is a cognitive API?

3. Collaborate to create cognitive solutions.

If your strategy is ambitious and transformational, you will likely need to collaborate on unique and customized solutions. IBM offers various advisory programs designed to support these types of initiatives in which the adopter aims to change whole business functions or ways of working and competing. These programs often deliver prototypes, or proofs-of-concept, that simulate your desired cognitive-enabled state using your own data.

Checklist for adoption:

• What are my desired outcomes?
• How will cognitive technology help me achieve these outcomes?
• What is my long-term vision with this technology?
• Do I have strong executive support?
• Can my organization adapt existing processes and roles?
• Do I have the necessary skills within my organization?
• Do I have the IT environment I need to get started?
• Which path is right for me: build, deploy or collaborate?
• How will value be measured?

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Global market report: Explore how cognitive computing drives competitive advantage

Doing good in the cognitive era: Learn how augmented intelligence benefits us all

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IBM Watson Business Coach: Get a personalized roadmap for your cognitive journey

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Implementing Cognitive Technologies

Tom Davenport  and  John Houston

​A systematic approach to engaging with and implementing cognitive technologies may seem to require more effort than the “do something cognitive” approach, but it is more likely to achieve expected results 

Many of our clients and research sites report a set of behaviors relative to cognitive technology in their firms that represent both an opportunity and a challenge. The behaviors are these: Senior executives and members of boards of directors hear about the potential of cognitive technology to transform business. They encourage the company’s leaders to “do something cognitive.” The leaders, feeling the pressure, engage with prominent vendors of these technologies. The leaders do a high-level deal with the vendors—typically for a pilot application. The vendor contract often includes services work to develop the pilot—in part because the organization lacks the necessary skills. The pilot becomes highly visible within the organization. There is optimism around the transformative nature of the technology, but often a lack of consensus on the risks and goals of the pilot.

The opportunity here is that senior managers are interested in and engaged with a new technology with the potential to transform their businesses. They are displaying openness to innovation and a desirable urge to take advantage of an exciting capability. And we know that when senior executives aren’t engaged, technology projects often fail.

The challenge is that projects that start this way often fail for various reasons. Often, teams struggle to define a good starting set of use cases. Perhaps they don’t use the right technology for the problem, or the pilot is overly ambitious for the envisioned time and cost. “Transformative” projects are high-risk and high-reward. So it’s not surprising that they often fail even at the pilot project level. Some of the projects impact the organization’s existing technology architecture, but IT groups may not be involved in these initial cognitive projects, making it difficult for them to be integrated into an existing architecture. Finally, pilots that are not designed with humans as the end user in mind often lack adoption and acceptance within vital constituencies.

In any case, there are a number of negative outcomes from such a process. The failure of the project sets back the organization’s use of cognitive technology for some time. To use a Gartner term, the technology prematurely enters the “trough of despair.” And because the project was done largely outside the organization, it doesn’t improve internal capabilities and builds a layer of cynicism among the ultimate users.

A better way to approach cognitive

We believe there is a better way to get started with cognitive technology than the “do something cognitive” approach. It harnesses the potential enthusiasm of senior managers while preventing some of the current problems. The steps below require that there is some group or individual within an organization who can exercise at least a minimal level of coordination during the early stages of these technologies.

• Educate senior management on cognitive technologies and their likely impact.  Executives shouldn’t just hear about these technologies from newspapers and magazines, or from technology vendors. Someone in their organization should structure education on the different types of cognitive technologies and what can be accomplished with them. They should know, for example, the difference between robotic process automation and deep learning, and what business use cases can be done with each. In one consumer products company we worked with, the chief data officer offered one-on-one meetings with senior managers to provide this sort of education.
• Select the right technology for your business problem.  There are at least five key types of cognitive technologies (robotic process automation, traditional machine learning, deep learning, natural language processing, rule-based expert systems). Multiple types may need to be combined for a particular application. It’s important that an organization understands the proper uses of each technology and the best way to employ it. For example, a sophisticated user of technology may want to employ the growing number of free open-source tools, but that would be a big mistake for a company without a cadre of capable data scientists.
• Form a “community of practice” of interested and involved employees.  In many cases, it may be too early to form a centralized organization to manage cognitive projects, but executives who are sponsoring or considering sponsoring cognitive projects need to learn from each other. A “community of practice” with regular meetings is a way to create such learning. At an investments firm, for example, regular “cognitive summits” were used to share knowledge about projects, to learn from outside speakers, and to offer cognitive technology components in an informal market exchange.
• Recognize that “low hanging fruit” projects tend to have a much greater chance of succeeding, even though they have less potential business value.  In our experience, highly ambitious projects that push the limits of cognitive technology are the most likely to fail. Projects that perform a limited task, that combine human and machine-based expertise, and that automate a structured digital task are much more likely to achieve results, at least at this point in time. As cognitive technologies mature, more ambitious projects will be more likely to achieve results.
• Build in expectations for learning and adaptation.   By definition, many cognitive systems need to be trained and improved over time. Rarely does the initial “go live” mean that the pilot works at an optimal level. Often, the best measure of success is based on the ability of both the team and the cognitive system to adapt and improve over time.
• Get a portfolio of projects going.  As with any new technology—or collection of them, as is true of cognitive—it’s important to gain experience quickly with a group of small pilots or proofs of concept. They should represent several different types of technology and different use-case categories. All should be developed with agile, “minimum viable product” approaches.
• Discontinue some projects, scale up others.  Since it’s a new set of technologies, some will undoubtedly fail. Discontinue those projects, and scale up the ones that seem to be working well into production applications. In many cases, this will require integration with existing systems and other types of technologies.
• Follow the changes in technology, and continue to educate leaders.  Cognitive technologies are improving quickly in their capabilities, and new vendors emerge almost daily. Senior executives with an interest in the topic should get at least an annual update on new options.

This approach to engaging with technology may seem to require more effort than the “do something cognitive” approach, but it is more likely to achieve expected results and may require less time and money over the long run. Most importantly, it avoids the “trough of disillusionment” that can influence an organization’s thinking about a new technology for years.

This blog first appeared in the Deloitte University Press on April, 19, 2017, here .

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Testing the effectiveness of computerized cognitive training on an at-risk student population.

1. Individual Differences in Cognitive Abilities

2. the malleability of cognitive abilities via computerized cognitive training, 3. computerized cognitive training in the school setting, 4. current study, participants, 6. materials, 7. procedure, 8. design and statistical analysis, 9.1. working memory, 9.2. task switching, 10. processing speed, 10.1. coding, 10.2. symbol search, 11. discussion, 11.1. the centrality of wm, ts, and ps, 11.2. integrating cct into the school-day, 11.3. limitations, 11.4. future directions, 12. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Baddeley, A.D.; Hitch, G.J. Working Memory. In The Psychology of Learning and Motivation ; Bower, G.H., Ed.; Academic Press: New York, NY, USA, 1974; Volume 8, pp. 47–89. [ Google Scholar ]
• Daneman, M.; Carpenter, P.A. Individual Differences in Working Memory and Reading. J. Verbal Learn. Verbal Behav. 1980 , 19 , 450–466. [ Google Scholar ] [ CrossRef ]
• Swanson, H.L.; Orosco, M.J.; Lussier, C.M. Cognition and Literacy in English Language Learners at Risk for Reading Disabilities. J. Educ. Psychol. 2012 , 104 , 302–320. [ Google Scholar ] [ CrossRef ]
• Passolunghi, M.C.; Caviola, S.; De Agostini, R.; Perin, C.; Mammarella, I.C. Mathematics Anxiety, Working Memory, and Mathematics Performance in Secondary-School Children. Front. Psychol. 2016 , 7 , 42. [ Google Scholar ] [ CrossRef ]
• Atkinson, R.C.; Shiffrin, R.M. Reprint of: Human Memory: A Proposed System and Its Control Processes. J. Mem. Lang. 2024 , 136 , 104479. [ Google Scholar ] [ CrossRef ]
• Kane, M.J.; Hambrick, D.Z.; Tuholski, S.W.; Wilhelm, O.; Payne, T.W.; Engle, R.W. The Generality of Working Memory Capacity: A Latent-Variable Approach to Verbal and Visuospatial Memory Span and Reasoning. J. Exp. Psychol. Gen. 2004 , 133 , 189–217. [ Google Scholar ] [ CrossRef ]
• Alloway, T.P.; Alloway, R.G. Investigating the Predictive Roles of Working Memory and IQ in Academic Attainment. J. Exp. Child Psychol. 2010 , 106 , 20–29. [ Google Scholar ] [ CrossRef ]
• Kane, M.J.; Bleckley, M.K.; Conway, A.R.A.; Engle, R.W. A Controlled-Attention View of Working-Memory Capacity. J. Exp. Psychol. Gen. 2001 , 130 , 169–183. [ Google Scholar ] [ CrossRef ]
• Engle, R.W. Working Memory Capacity as Executive Attention. Curr. Dir. Psychol. Sci. 2002 , 11 , 19–23. [ Google Scholar ] [ CrossRef ]
• Engle, R.W.; Kane, M.J. Executive Attention, Working Memory Capacity, and a Two-Factor Theory of Cognitive Control. In Psychology of Learning and Motivation ; Elsevier: Amsterdam, The Netherlands, 2003; Volume 44, pp. 145–199. ISBN 978-0-12-543344-0. [ Google Scholar ]
• Kane, M.J.; Engle, R.W. Working-Memory Capacity and the Control of Attention: The Contributions of Goal Neglect, Response Competition, and Task Set to Stroop Interference. J. Exp. Psychol. Gen. 2003 , 132 , 47–70. [ Google Scholar ] [ CrossRef ]
• Alloway, T.P. How Does Working Memory Work in the Classroom? Educ. Res. Rev. 2006 , 1 , 134–139. [ Google Scholar ]
• Gathercole, S.E.; Pickering, S.J. Working Memory Deficits in Children with Low Achievements in the National Curriculum at 7 Years of Age. Br. J. Educ. Psychol. 2000 , 70 , 177–194. [ Google Scholar ] [ CrossRef ]
• Gersten, R.; Jordan, N.C.; Flojo, J.R. Early Identification and Interventions for Students With Mathematics Difficulties. J. Learn. Disabil. 2005 , 38 , 293–304. [ Google Scholar ] [ CrossRef ]
• Hoskyn, M.; Swanson, H.L. The Relationship between Working Memory and Writing in Younger and Older Adults. Read. Writ. 2003 , 16 , 759–784. [ Google Scholar ] [ CrossRef ]
• Geary, D.C. Cognitive Predictors of Achievement Growth in Mathematics: A 5-Year Longitudinal Study. Dev. Psychol. 2011 , 47 , 1539–1552. [ Google Scholar ] [ CrossRef ]
• De Oliveira, D.G.; Da Silva, P.B.; Dias, N.M.; Seabra, A.G.; Macedo, E.C. Reading Component Skills in Dyslexia: Word Recognition, Comprehension and Processing Speed. Front. Psychol. 2014 , 5 , 1339. [ Google Scholar ] [ CrossRef ]
• Peng, P.; Barnes, M.; Wang, C.; Wang, W.; Li, S.; Swanson, H.L.; Dardick, W.; Tao, S. A Meta-Analysis on the Relation between Reading and Working Memory. Psychol. Bull. 2018 , 144 , 48–76. [ Google Scholar ] [ CrossRef ]
• Miyake, A.; Friedman, N.P.; Emerson, M.J.; Witzki, A.H.; Howerter, A.; Wager, T.D. The Unity and Diversity of Executive Functions and Their Contributions to Complex “Frontal Lobe” Tasks: A Latent Variable Analysis. Cognit. Psychol. 2000 , 41 , 49–100. [ Google Scholar ] [ CrossRef ]
• Diamond, A. Executive Functions. Annu. Rev. Psychol. 2013 , 64 , 135–168. [ Google Scholar ] [ CrossRef ]
• Wright, B.C. What Stroop Tasks Can Tell Us about Selective Attention from Childhood to Adulthood. Br. J. Psychol. 2017 , 108 , 583–607. [ Google Scholar ] [ CrossRef ]
• Monsell, S. Task Switching. Trends Cogn. Sci. 2003 , 7 , 134–140. [ Google Scholar ] [ CrossRef ]
• Ackerman, P.L.; Beier, M.E.; Boyle, M.O. Working Memory and Intelligence: The Same or Different Constructs? Psychol. Bull. 2005 , 131 , 30–60. [ Google Scholar ] [ CrossRef ]
• Wang, Y.; Zhou, X.; Peng, X.; Hu, X. Task Switching Involves Working Memory: Evidence from Neural Representation. Front. Psychol. 2022 , 13 , 1003298. [ Google Scholar ] [ CrossRef ]
• Peng, A.; Kirkham, N.Z.; Mareschal, D. Task Switching Costs in Preschool Children and Adults. J. Exp. Child Psychol. 2018 , 172 , 59–72. [ Google Scholar ] [ CrossRef ]
• Yeniad, N.; Malda, M.; Mesman, J.; Van IJzendoorn, M.H.; Pieper, S. Shifting Ability Predicts Math and Reading Performance in Children: A Meta-Analytical Study. Learn. Individ. Differ. 2013 , 23 , 1–9. [ Google Scholar ] [ CrossRef ]
• Ones, D.S.; Dilchert, S.; Viswesvaran, C. Cognitive Abilities. In The Oxford Handbook of Personnel Assessment and Selection ; Schmitt, N., Ed.; Oxford University Press: Oxford, UK, 2012; pp. 179–224. ISBN 978-0-19-973257-9. [ Google Scholar ]
• Schirmbeck, K.; Rao, N.; Maehler, C. Similarities and Differences across Countries in the Development of Executive Functions in Children: A Systematic Review. Infant Child Dev. 2020 , 29 , e2164. [ Google Scholar ] [ CrossRef ]
• Klingberg, T.; Fernell, E.; Olesen, P.J.; Johnson, M.; Gustafsson, P.; Dahlström, K.; Gillberg, C.G.; Forssberg, H.; Westerberg, H. Computerized Training of Working Memory in Children With ADHD-A Randomized, Controlled Trial. J. Am. Acad. Child Adolesc. Psychiatry 2005 , 44 , 177–186. [ Google Scholar ] [ CrossRef ]
• Melby-Lervåg, M.; Redick, T.S.; Hulme, C. Working Memory Training Does Not Improve Performance on Measures of Intelligence or Other Measures of “Far Transfer”: Evidence From a Meta-Analytic Review. Perspect. Psychol. Sci. 2016 , 11 , 512–534. [ Google Scholar ] [ CrossRef ]
• Zelazo, P.D.; Blair, C.B.; Willoughby, M.T. Executive Function: Implications for Education ; NCER 2017–2000; National Center for Education Research, Institute of Education Sciences, U.S. Department of Education: Washington, DC, USA, 2016. Available online: https://eric.ed.gov/?id=ED570880 (accessed on 15 June 2024).
• Wiest, D.J.; Wong, E.H.; Bacon, J.M.; Rosales, K.P.; Wiest, G.M. The Effectiveness of Computerized Cognitive Training on Working Memory in a School Setting. Appl. Cogn. Psychol. 2020 , 34 , 465–471. [ Google Scholar ] [ CrossRef ]
• Wong, E.H.; Rosales, K.P.; Looney, L. Improving Cognitive Abilities in School-Age Children via Computerized Cognitive Training: Examining the Effect of Extended Training Duration. Brain Sci. 2023 , 13 , 1618. [ Google Scholar ] [ CrossRef ]
• Looney, L.; Wong, E.H.; Rosales, K.P.; Bacon, J.M.; Wiest, D.J. Supporting Learning Differences: Effects of Cognitive Training on Cognitive Abilities in a School-Based Sample. Educ. Sci. 2024 , 14 , 89. [ Google Scholar ] [ CrossRef ]
• Rabiner, D.L.; Murray, D.W.; Skinner, A.T.; Malone, P.S. A Randomized Trial of Two Promising Computer-Based Interventions for Students with Attention Difficulties. J. Abnorm. Child Psychol. 2010 , 38 , 131–142. [ Google Scholar ] [ CrossRef ]
• Wechsler, D. Wechsler Intelligence Scale for Children-V ; Pearson Publishing: Hoboken, NJ, USA, 2014. [ Google Scholar ]
• Wechsler, D. Wechsler Adult Intelligence Scale-IV ; Pearson Publishing: Hoboken, NJ, USA, 2008. [ Google Scholar ]
• Delis, D.; Kaplan, E.; Kramer, J.H. Delis Kaplan Executive Function System ; Pearson Publishing: Hoboken, NJ, USA, 2001. [ Google Scholar ]
• Adams, W.; Sheslow, D. Wide Range Assessment of Memory and Learning , 3rd ed.; Pearson Publishing: Hoboken, NJ, USA, 2021. [ Google Scholar ]
• Kovacs, K.; Conway, A.R.A. Process Overlap Theory: A Unified Account of the General Factor of Intelligence. Psychol. Inq. 2016 , 27 , 151–177. [ Google Scholar ] [ CrossRef ]
• Jaeggi, S.M.; Buschkuehl, M.; Shah, P.; Jonides, J. The Role of Individual Differences in Cognitive Training and Transfer. Mem. Cognit. 2014 , 42 , 464–480. [ Google Scholar ] [ CrossRef ]

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Wong, E.H.; Rosales, K.P.; Looney, L. Testing the Effectiveness of Computerized Cognitive Training on an At-Risk Student Population. Behav. Sci. 2024 , 14 , 711. https://doi.org/10.3390/bs14080711

Wong EH, Rosales KP, Looney L. Testing the Effectiveness of Computerized Cognitive Training on an At-Risk Student Population. Behavioral Sciences . 2024; 14(8):711. https://doi.org/10.3390/bs14080711

Wong, Eugene H., Kevin P. Rosales, and Lisa Looney. 2024. "Testing the Effectiveness of Computerized Cognitive Training on an At-Risk Student Population" Behavioral Sciences 14, no. 8: 711. https://doi.org/10.3390/bs14080711

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Cognitive Technology Research Laboratory

Welcome to the CTRLab

The Cognitive Technology Research Lab at Washington University in St. Louis

The CTRLab at Washington University is run by Jason Hassenstab, PhD and his team. We explore cognition through various technological avenues and are happy to connect with research participants, collaborators, students, and the general public.

Want to join our team?

The Cognitive Technology Research Laboratory (CTRLab) in the Knight Alzheimer Disease Research Center at Washington University in St. Louis is seeking post-doctoral fellows to work in a friendly, flexible, and intellectually engaging lab on projects investigating the impact of normal aging and Alzheimer’s disease (AD) on cognition.

What do we do?

The CTRLab uses technology to improve the assessment of cognition and to increase engagement in clinical studies. We primarily focus on approaches using smartphones and web-based assessments in clinical populations including Alzheimer’s disease and other neurodegenerative diseases. We are based in the Knight Alzheimer’s Disease Research Center (Knight ADRC) and also lead the Cognition Cores for the Dominantly Inherited Alzheimer Network (DIAN) and the DIAN-Trials Unit (DIAN-TU) . Additionally, we lead a worldwide study that uses ecological momentary assessment in Down syndrome and Alzheimer disease.

The CTRLab develops, tests and implements: 

Our online testing has been developed by Drs. Jason Hassenstab and Andy Aschenbrenner, with help from the lab team. This is currently being used in two large cohorts – DIAN and the Knight ADRC. See our Web-based Testing page. 

Our smartphone-based cognitive testing platform has been iteratively developed over the past two years and is in-use in several studies around the world, including DIAN, DIAN-TU, and the Knight ADRC research cohorts. We are also developing a custom-made version for use with individuals who have Down syndrome. For more information, see the ARC smartphone app page. 

There are at least 20,000 different models of smartphones globally. A vexing issue for cognitive studies is whether differences in devices, operating systems, and operating conditions can bias data collected  from smartphones. We have designed and developed several iterations of robotic assessment technologies to rigorously evaluate performance characteristics of smartphones. See Smartphone Latency Testing for more information. 

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Check out Dr. Hassenstab’s Twitter feed for up-to-date information. 

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Collaborators

The Charles F. and Joanne Knight Alzheimer’s Disease Research Center was started as the Memory and Aging Project (MAP) in 1979 by Leonard Berg, MD. It is currently led by John Morris, MD. For more information.

The Dominantly Inherited Alzheimer Network (DIAN) is an international research study that looks at a rare genetic form of Alzheimer’s disease (AD) that causes cognitive problems and dementia in younger people who are in their 30s-50s. DIAN was established in 2008 and is directed by Randall Bateman, MD.

August 20, 2024

Many Older People Maintain and Even Gain Cognitive Skills

Contrary to stereotypes of the doddering elderly, research shows that half of people older than age 70 stay mentally sharp

By Lydia Denworth

As I watched my parents’ generation reach their 80s, I was struck by the dramatic dif­fer­ences among them. A handful suffered from dementia, but many others remained cognitively sharp—even if their knees and hips didn’t quite keep up with the speed of their thoughts.

That observation runs counter to prejudices about aging, which were high­lighted early in the 2024 presidential race between elderly candidates, but these biases permeate society in general. “The belief about old people is that they’re all kind of the same, they’re doddering, and that aging is this steady downward slope,” says psychologist Laura Car­sten­sen, founding director of the Stanford Center on Longevity. That view, she says, is a great misunderstanding.

Instead research highlights the very differences I noticed. In our 40s, most people are cognitively similar. Divergences in cognition appear around age 60. By 80 “it’s quite dramatically splayed out,” says physician John Rowe, a professor of health policy and aging at Columbia University’s Mailman School of Public Health. Yes, there will be a group diminished by dementia and cognitive decline, but in general the 80-somethings “include the wisest people on the planet,” Carstensen says.

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Focusing on only those with poor brain health misses more than half the population. Rowe led research showing that in the six years after turning 75, about half of people showed little to no change in their physical, biological, hormonal and cognitive functioning, whereas the other half changed quite a lot. A longer-term study followed more than 2,000 individuals with an average age of 77 for up to 16 years. It showed that the three quarters who did not develop dementia showed little to no cognitive decline.

Some of this is related to genetics. Studies of successful aging have shown that genes account for 30 to 50 percent of physical and cognitive changes. But factors like a healthy way of life and good self-­esteem are also consequential. So to an extent, Rowe says, “this is really good news because it means that you are, in fact, in control of your old age.”

Research has also busted the myth that there is no upside to aging past 70 or so. “We have found very clearly that there are things that improve with age,” Rowe says. The ability to resolve conflicts strengthens, for instance. Aging is also associated with more positive overall emotional well-being, which means older adults are more emotionally stable than younger adults, as well as better at regulating desires.

The normal aging process does bring changes to the brain, says Denise Park, a neuroscientist at the University of Texas at Dallas. There is some shrinkage in the frontal lobes and some damage to neurons and their connections. Cognitive processing slows down. Yet that slowdown is usually on the order of milliseconds and doesn’t always make a meaningful difference in daily life. And to compensate, older people activate more of the brain for tasks such as reading. “Older adults will often forge additional pathways” for particular activities, Park says. “Those pathways may not be as efficient as the pathways that younger adults use, but they nonetheless work.”

The cliché that age brings wisdom is also backed up by science. “Where older adults really shine is in their knowledge,” Park says. If you think of the brain as a computer, “there’s a lot more on the hard disk,” she says. Older adults can draw on their experience and often have much better solutions to problems than younger adults. “Frequently that can give them an edge that is unexpected,” Park says.

That edge shows up in decision-making and conflict resolution. One study asked several hundred people to read stories about personal and group conflicts. The study, published in 2010 in the Proceedings of the National Academy of Sciences USA , found that participants older than 60 were more likely to emphasize multiple perspectives, to compromise, and to recognize the limits of one’s own knowledge. Car­stensen’s observations reinforce these conclusions. “The decisions that people make as they get older tend to be ones that take into consideration multiple factors and multiple stakeholders,” she says. Older adults are less likely than younger people to see the world in stark black-and-white terms. Car­sten­sen says that when responses in such studies are rated by observers who don’t know how old participants are, the older people’s answers are seen as wiser.

Such wisdom may be the result of a gradual shift in perspective, Carstensen says. As we age and become more aware that time is short, we focus more on the positive. A meta-­analysis combining data on more than 7,000 older adults found they were significantly more likely than younger adults to lean toward the positive versus the negative when processing information.

The COVID pandemic has showcased this contrast. In a 2020 survey of nearly 1,000 adults, Carstensen and her colleagues found that the older adults were better able to cope with the stresses of the pandemic, despite being one of the groups at highest risk of health complications and death.

The fact is that different parts of the body can age at different rates in the same person. Someone who stumbles on stairs may do so because of creaky knees, not cognitive decline. If someone has a healthy brain, age alone might be considered a definite asset. “If you were to take the kinds of decisions presidents make and compare them to the kinds of skills older people have versus younger people, I put my money on older people,” Cars­ten­­sen says.

This is an opinion and analysis article, and the views expressed by the author or authors are not necessarily those of Scientific American.

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Brain health consequences of digital technology use


Consecuencias para la salud del cerebro del empleo de la tecnología digital, conséquences sur la santé cérébrale de l’usage des technologies numériques, gary w. small.

Department of Psychiatry and Biobehavioral Sciences and Semel Institute for Neuroscience and Human Behavior, the UCLA Longevity Center, David Geffen School of Medicine at the University of California, Los Angeles, California, US

Jooyeon Lee

Aaron kaufman, jason jalil, prabha siddarth, himaja gaddipati, teena d. moody, susan y. bookheimer.

Emerging scientific evidence indicates that frequent digital technology use has a significant impact—both negative and positive—on brain function and behavior. Potential harmful effects of extensive screen time and technology use include heightened attention-deficit symptoms, impaired emotional and social intelligence, technology addiction, social isolation, impaired brain development, and disrupted sleep. However, various apps, videogames, and other online tools may benefit brain health. Functional imaging scans show that internet-naive older adults who learn to search online show significant increases in brain neural activity during simulated internet searches. Certain computer programs and videogames may improve memory, multitasking skills, fluid intelligence, and other cognitive abilities. Some apps and digital tools offer mental health interventions providing self-management, monitoring, skills training, and other interventions that may improve mood and behavior. Additional research on the positive and negative brain health effects of technology is needed to elucidate mechanisms and underlying causal relationships.


La evidencia científica que está surgiendo muestra que el empleo frecuente de la tecnología digital tiene un impacto significativo, tanto negativo como positivo, en la función cerebral y en el comportamiento. Los posibles efectos nocivos del tiempo prolongado frente a la pantalla y del empleo de la tecnología incluyen síntomas como marcado déficit de atención, deterioro de la inteligencia emocional y social, adicción a la tecnología, aislamiento social, deterioro del desarrollo cerebral y alteraciones del sueño. Sin embargo, hay varias aplicaciones, videojuegos y otras herramientas en línea que pueden beneficiar la salud del cerebro. En las imágenes cerebrales funcionales se ha observado que los adultos mayores vírgenes a internet que aprenden a buscar en línea, muestran aumentos significativos en la actividad neuronal cerebral durante las búsquedas simuladas en internet. Ciertos programas computacionales y videojuegos pueden mejorar la memoria, las destrezas en tareas múltiples, la fluidez de la inteligencia y otras habilidades cognitivas. Hay varias aplicaciones y herramientas digitales que ofrecen intervenciones en salud mental y que proporcionan automanejo, monitoreo, capacitación junto a otras intervenciones que pueden mejorar el estado de ánimo y el comportamiento. Se require de investigación adicional acerca de los efectos positivos y negativos de la tecnología sobre la salud del cerebro para dilucidar los mecanismos y las relaciones causales subyacentes.

D’après de nouvelles données scientifiques, l’usage fréquent des technologies numériques influe significativement sur le comportement et le fonctionnement cérébral, de façon aussi bien négative que positive. Une pratique excessive des écrans et des technologies numériques peut avoir des effets néfastes comme des symptômes de déficit d'attention, une intelligence émotionnelle et sociale altérée, une dépendance à la technologie, un isolement social, un développement cérébral dégradé et des troubles du sommeil. Cependant, certaines applications, jeux vidéo et autres outils en ligne peuvent avoir des effets bénéfiques sur le cerveau. L'imagerie fonctionnelle montre une activité neuronale significativement augmentée chez des personnes âgées jamais exposées à Internet et qui apprennent à faire des recherches en ligne. Certains programmes informatiques et jeux vidéo peuvent améliorer la mémoire, les compétences multitâches, l'agilité de l’intelligence et d'autres capacités cognitives. Dans le domaine de la santé mentale, différents outils et applications numériques permettant l'autogestion, le suivi, l'acquisition de compétences et d'autres techniques sont susceptibles d'améliorer l'humeur et le comportement du patient. Les effets positifs et négatifs de la technologie sur la santé cérébrale nécessitent d’être encore étudiés afin d’en mieux comprendre les mécanismes et les relations de cause à effet.

Introduction


During the past three decades, digital technology has transformed our daily lives. People at every age are now taking advantage of the vast amounts of available online information and communication platforms that connect them with others. This technology helps us to generate, store, and process enormous amounts of information and interact with each other rapidly and efficiently.


Most adults use the internet daily, and nearly one out of four report being online most of the time. 1 Because of this transformation to an online world, neuroscientists have begun focusing their attention on how digital technology may be changing our brains and behavior. The emerging data suggest that constant technology use impacts brain function and behavior in both positive and negative ways. For example, older individuals suffering from cognitive decline could use the internet to access information to help them remain independent longer; however, many seniors with cognitive complaints are reluctant or unable to adopt new technologies. 2 Our group’s functional magnetic resonance imaging (MRI) research tracking neural activity during simulated internet searches suggests that simply searching online may represent a form of mental exercise that can strengthen neural circuits. 3 By contrast, the persistent multitasking that is characteristic of most technology users impairs cognitive performance. 4 In this review, we highlight some of the research suggesting potential benefits and possible risks of using digital technology.


Potential harmful effects of digital technology use


Reduced attention
.

Multiple studies have drawn a link between computer use or extensive screen time (eg, watching television, playing videogames) and symptoms of attention-deficit hyperactivity disorder (ADHD). A 2014 meta-analysis indicated a correlation between media use and attention problems. 5 A recent survey of adolescents without symptoms of ADHD at the start of the study indicated a significant association between more frequent use of digital media and symptoms of ADHD after 24 months of follow-up. 6 Although most of the research linking technology use and ADHD symptoms has involved children and adolescents, this association has been identified in people at any age. 7 


The reason for the link between technology use and attention problems is uncertain, but might be attributed to repetitive attentional shifts and multitasking, which can impair executive functioning. 8 Moreover, when people are constantly using their technology, they have fewer opportunities to interact offline and allow their brain to rest in its default mode. 9 


Impaired emotional and social intelligence


Because of concern that a young, developing brain may be particularly sensitive to chronic exposure to computers, smartphones, tablets, or televisions, the American Academy of Pediatrics has recommended that parents limit screen time for children aged 2 years or younger, when the brain is particularly malleable. 10 Spending extensive periods of time with digital media translates to spending less time communicating face to face. 11 


Kirsh and Mounts 12 explored the hypothesis that playing videogames would interfere with the ability to recognize emotions conveyed through facial expressions. They examined the effects of playing videogames on recognition of facial expressions of emotions in 197 students (ages 17 to 23 years). Participants played violent videogames before watching a series of calm faces morph into either angry or happy faces. Participants were asked to quickly identify the emotion while the facial expression changed. The authors found that happy faces were identified faster than angry faces, and that playing violent videogames delayed happy-face recognition time. 


Our team at the University of California, Los Angeles (UCLA) 13 hypothesized that preteens restricted from screen-based media would have more opportunities for face-to-face interactions, which would improve their ability to recognize nonverbal emotional and social cues. We studied 51 schoolchildren who spent five days at an overnight nature camp where television, computers, and smartphones were forbidden, and compared them with 54 school-based matched controls who continued their usual media practices (4 hours of screen time per day). At baseline and after 5 days, participants were assessed for their ability to recognize emotions from photographs of facial expressions and videotaped scenes of social interactions (without verbal cues). After 5 days, the nature camp participants restricted from screen time demonstrated significantly better recognition of nonverbal emotional and social cues than participants who continued their usual daily screen time. These findings suggest that time away from screen-based media and digital communication tools improves both emotional and social intelligence.


Technology addiction


Although not formally included in the Diagnostic and Statistical Manual of Mental Disorders , 14 excessive and pathological internet use has been recognized as an internet addiction, which shares features with substance-use disorders or pathological gambling. Common features include preoccupations, mood changes, development of tolerance, withdrawal, and functional impairment. 15 , 16 The global prevalence of internet addiction is estimated at 6%, but in some regions such as the Middle East the prevalence is as high as 11%. 17 Students with internet addiction are more likely to suffer from ADHD symptoms than from other psychiatric disorders. 18 You and colleagues 16 reported that schoolchildren with internet addiction experienced significantly greater symptoms of inattention, hyperactivity, and impulsivity than non–internet-addicted students. Panagiotidi and Overton 19 reported greater ADHD symptoms in adults aged 18 to 70 years with internet addiction: predictors of addiction included younger age, playing massively multiplayer online role-playing games, and spending more time online. Despite consistent associations between ADHD symptoms and internet addiction, a causal relationship has not been confirmed. It is possible that people with ADHD symptoms have a greater risk for developing technology addiction, but an alternative explanation is that extensive technology use from addictive behavior causes ADHD symptoms.


Social isolation


Ninety percent of young adults in the United States use social media platforms such as Facebook, Twitter, Snapchat, and Instagram, and most visit these sites at least daily. 20 Paradoxically, social media use is linked to social isolation (ie, a lack of social connections and quality relationships with others), 21 which is associated with poor health outcomes and increased mortality. 1 


Primack and colleagues 20 studied 1787 young adults (ages 19 to 32 years) and found that using social media 2 or more hours each day dou- bled the odds for perceived social isolation compared with use less than 30 minutes each day. Similar associations between perceived social isolation and social media use were observed in 213 middle-aged and older adults. 22 Possible explanations for such findings include reduced offline social experiences and the tendency to make upward social comparisons based on highly curated social media feeds that produce unrealistic expectations of oneself. 1 Future research should explore casual explanations for such relationships and seek ways to address the needs of people who may benefit from social media–based interventions, such as geographically isolated individuals.


Adverse impact on cognitive and brain development


Screen time may also adversely impact cognitive and brain development. In a recent review, children under age 2 were reported to spend over 1 hour each day in front of a screen; by age 3, that number exceeded 3 hours. 23 Increased screen time (and less reading time) has been associated with poorer language development and executive functioning, particularly in very young children, 24 as well as poorer language development in a large cohort of minority children. 25 In infants, increased screen time was one of several factors that predicted behavioral problems. 26 For infants 6 to 12 months, increased screen time was linked to poorer early language development. 27 In children of preschool age and older, digital media directed toward active learning can be educational, but only when accompanied by parental interaction. 23 


Recent research has examined the effects of media exposure on brain development. In a study of children aged 8 to 12 years, more screen and less reading time were associated with decreased brain connectivity between regions controlling word recognition and both language and cognitive control. 24 Such connections are considered important for reading comprehension and suggest a negative impact of screen time on the developing brain. Structurally, increased screen time relates to decreased integrity of white-matter pathways necessary for reading and language. 28 Given the growing prominence of screen use among even very young children at stages when brain plasticity is greatest, there is significant concern about the cognitive and brain development of the current generation of screen-exposed children that requires greater understanding


Sleep


Recent studies indicate that screen exposure disrupts sleep, which can have a negative effect on cognition and behavior. Daily touch-screen use among infants and toddlers was shown to negatively impact sleep onset, sleep duration, and nighttime awakenings. 29 In adolescents, more time using smartphones and touch screens was associated with greater sleep disturbances, and tablet time was associated with poor sleep quality and increased awakenings after sleep onset. 30 In adults, increased smartphone use was associated with shorter sleep duration and less efficient sleep. 31 Poor sleep quality is associated with brain changes, such as reduced functional connectivity and decreased gray-matter volume, as well as an increased risk for age-associated cognitive impairment and Alzheimer disease. 32 , 33 


It is unclear whether the act of looking at screens or media content disrupts sleep; however, it is well-known that the wavelength of light exposure affects the circadian rhythms that govern sleep. Computer and phone light-emitting diode (LED) screens emit slow wave, blue light that interferes with circadian rhythms. Exposure to LED versus non-LED screens has been shown to produce changes in melatonin levels and sleep quality, and such exposure decreases cognitive performance. 34 Thus, it is important to recognize the effects of screen time on sleep as a moderator of various negative effects on cognition and brain function.


Brain-health benefits of digital technology


Despite these potential harmful brain-health effects of digital technology, emerging evidence points to several benefits for the aging brain in particular, including opportunities for brain-strengthening neural exercise, cognitive training, and the online delivery of mental-health interventions and support ( Table I

Neural exercise


Internet-savvy versus internet-naive adults
.

Functional neuroimaging allows scientists to observe regional neural activity during various mental tasks. Our group was the first to explore neural activity using functional MRI while research volunteers performed simulated internet searching. 3 Previous studies suggested that mentally challenging tasks, such as searching online, may benefit brain health and even delay cognitive decline. 35 , 36 We focused on internet searching because it is so common among people of all ages. 37 


We assessed patterns of brain neural activation in 24 cognitively normal middle-aged and older adults (ages 55 to 76 years): 12 of them had minimal internet search experience (net-naive group), and 12 had extensive experience (net-savvy group). In addition to the internet-search task, we used a control task of reading text on a computer screen formatted to simulate a printed book layout.


We found that text reading activated brain regions controlling language, reading, memory, and visual abilities (left inferior frontal, temporal, posterior cingulate, parietal, and occipital regions), and the magnitude and extent of activation were similar in the net-naive and net-savvy groups. During internet searching, net-naive subjects displayed activation patterns similar to those observed while reading text. However, net-savvy subjects demonstrated significant activity in neural signal intensity in additional regions controlling decision-making, complex reasoning, and vision (frontal pole, anterior temporal region, anterior and posterior cingulate, and hippocampus). During the internet-search task, the net-savvy group displayed a more than twofold increase in the extent of activation in the major regional clusters compared with the net-naive group (21 782 versus 8646 total activated voxels).


These findings suggest that searching online may be a form of brain neural exercise. Other research indicates that after several months, daily computer-game playing leads to reduced cortical neural activity. 38 Our other research indicates that memory training, along with healthy lifestyle behaviors (eg, physical exercise, healthy diet), leads to reduced dorsal prefrontal cortical metabolism after 2 weeks. 36 Such findings suggest that task repetition over time leads to lower neural activity during the task, which could reflect greater cognitive efficiency after mental training.


One model that could explain such findings is that novel and stimulating mental experiences, such as searching on the internet, initially lead to minimal activation before the internet user discovers strategies for solving the unfamiliar mental challenge. After such insights, a broader neural network is engaged. After repeated sessions, the initially novel mental task becomes routine and repetitive, no longer posing a mental challenge. The lower activity observed may thus reflect a more efficient neural response. These results also suggest that previous internet-search experience may alter the brain’s responsiveness in neural circuits controlling decision-making and complex reasoning. The net-savvy volunteers showed increased activation during the internet-search task, which suggests that internet searching may remain a novel and mentally stimulating process even after continued practice.


Internet training and brain function


We also used functional MRI to record brain neural activity during simulated internet-search tasks in 12 net-naive and 12 net-savvy subjects before and after internet training. 39 Based on our previous findings, we hypothesized that net-naive volunteers would recruit a larger frontal lobe network after internet training and that net-savvy volunteers would show either no increase or a decrease in activation after training because of greater cognitive efficiency due to training.


The training consisted of brief instructions on how to search online along with practice sessions (1 hour per day for a week). To increase motivation, participants were told that they would be quizzed on their knowledge of assigned search topics after the experiment.


During their first session, net-naive subjects recruited a neural network that included the superior, middle, and inferior frontal gyri, as well as the lateral occipital cortex and occipital pole. During the second session (after internet training), additional regions in the middle and inferior frontal gyri were recruited only in the net-naive group. By contrast, during their first scan session, the net-savvy subjects recruited a cortical network that, though overlapping with that of the net-naive subjects, showed more extensive regions of activation ( Figures 1 and 2 ). This cortical network included regions that control mental activities supporting tasks required for internet searches, including decision-making, working memory, and the ability to suppress nonrelevant information. Moreover, net-savvy participants showed a pattern of activation that was reduced after the training. This reduction is consistent with our hypothesis that the brain becomes more efficient and possibly habituates to the internet task over time. Overall, these findings suggest that internet searching for relatively short periods of time can change brain-activity patterns in middle-aged and older adults.


Other groups have explored the effects of internet-search training on brain structure and function. Dong and associates 40 studied the influence of short-term internet-search training on white-matter microstructure via diffusion tensor imaging. After 6 training days, they found that the 59 participants (mean age 21 years) showed increased fractional anisotropy (diffusion tensor imaging scans) in the right superior longitudinal fasciculus and within that region, decreased radial diffusivity. These findings suggest that short-term internet-search training may increase white-matter integrity in the right superior longitudinal fasciculus, which could result from increased myelination. 


Shapira and colleagues 41 assessed the psychological effects of learning computer and internet-search methods. They offered a course to 22 older adults (mean age 80 years), who were compared with 26 participants engaged in other activities. The investigators reported significant improvements in the intervention group in measures of life satisfaction, depression, loneliness, and self-control after 4 months, whereas the control group showed declines in each of these measures. These findings suggest that computer and internet training contribute to older adults’ well-being and sense of empowerment.


White and associates 42 performed a randomized controlled trial assessing the psychosocial impact of internet access to older adults during a 5-month period. The intervention group (n=29) received 9 hours of training (6 sessions over 2 weeks) and experienced less loneliness, less depression, and more positive attitudes toward computers than controls (n=19) who were not regular internet users.


Cognitive training


Memory ability
.

Findings showing that mental stimulation and cognitive training improve memory in older adults 43 , 44 have led to the development of several memory apps and computer games. Miller and associates 45 explored whether computerized brain-training exercises (Dakim Brain Fitness) improved cognitive performance in older adults without dementia (mean age of 82 years). Subjects were randomized into an intervention group (n=36) that used a computer program 5 days a week for 20 to 25 minutes each day, or a wait-list control group (n=33). Neuropsychological testing at baseline, 2 months, and 6 months showed that the intervention group improved significantly in delayed memory, and the control group did not. Moreover, participants who played the computer program for at least 40 sessions over 6 months improved in immediate memory, delayed memory, and language. These findings point to the potential benefit of cognitive training using a computerized, self-paced program. 


In a meta-analysis of computerized cognitive training, investigators found an overall moderate effect on cognition in mild cognitive impairment across 17 trials. 46 Small to moderate effects were reported for global cognition, attention, working memory, and learning abilities.


Multitasking skills


Multitasking has been defined as performing two simultaneous tasks, which is only possible when the tasks are automatic, but it can also refer to rapid switching between tasks. Research has shown that such task switching increases error rates. 47 Multitasking is common thanks to widespread technology use, and multiple studies point to its negative impact on cognitive performance. 48 However, certain computer games may enhance multitasking, one of the cognitive domains that declines in a linear fashion across the lifespan. 48 


Anguera and colleagues 49 trained volunteers (ages 60 to 85 years) over 4 weeks using a videogame called NeuroRacer, in which players control a car on a winding road while responding to signs that randomly appear. Out of 46 participants, 16 were trained in multitasking (both driving and sign reading), 15 in single-tasking mode (active controls; either sign reading or driving), and 15 received no training (no-contact controls). Only the multitasking training group showed significant improvements in performance scores, which not only exceeded that of untrained individuals in their twenties but was maintained for 6 months without additional training. Moreover, the multitasking training improved other cognitive skills, including working memory and divided and sustained attention.


Working memory and fluid intelligence


Fluid intelligence refers to the capacity to reason and think flexibly and requires working memory, the ability to retain information over a brief period of time. Investigators have found that training in working memory may improve fluid intelligence. 50 , 51 Jaeggi and associates 52 used a training program (n-back task) to investigate the effects of working-memory training on fluid intelligence. Healthy subjects (n=70) were randomized into working-memory training groups that were further randomized according to number of training sessions (8, 12, 17, or 19 days), or a control group that received no training. All subjects received pre- and post-testing on a measure of fluid intelligence at the same time intervals. The four groups not only showed significant improvements in working memory, but also on tests of fluid intelligence. Moreover, results demonstrated that the longer the training period, the greater the improvement in fluid intelligence. These results indicated successful transfer of improved working memory to improved fluid intelligence measures with a dose-dependent training effect. 


Visual attention and reaction time


Videogames have been popular for decades, and many gamers who began playing in the 1980s have continued to play through adulthood. Despite potential negative health effects of excessive playing (eg, attention deficits, social withdrawal, increased risk of obesity), recent research suggests potential benefits, such as improved visual attention processing, spatial visualization, reaction time, and mental rotation. Green and Bavelier 53 have shown that playing action videogames more than 4 days per week (at least 1 hour each day) for 6 months enhances visual attention (ie, the ability to recognize and process visual information), spatial attention over the visual field, and task-switching abilities.


Rosser and colleagues 54 examined a potential link between action videogaming and laparoscopic surgical skills and suturing. Surgeons who played videogames more than 3 hours each week made 37% fewer surgical errors, were 27% faster in response times, and scored 42% better in measures of laparoscopic and suturing skills than surgeons who do not play videogames. Moreover, the most experienced players in specific videogames (Super Monkey Ball 2, Star Wars Racer Revenge, and Silent Scope) made 47% fewer errors and performed 39% faster. These findings suggest that playing action videogames can improve cognitive and motor skills that improve surgical skills and lower error rates in the operating room.


Other mental health interventions


Technological advances have brought about novel approaches for delivering mental health support and interventions in the form of apps for smartphones or tablets, as well as through telepsychiatry. Internet-based mental health interventions offer the advantages of accessibility, cost-effectiveness, and anonymity. Between 2009 and 2015, the National Institute of Mental Health awarded more than 400 grants totaling $445 million for technology-enhanced mental-health interventions to further investigate roles for technology in preventing and treating mental disorders. 55 


Investigators have studied the efficacy of various online mental health interventions. For example, Peter and colleagues 56 found that an online, 4-week intervention using cognitive behavioral therapy for insomnia reduced depression and insomnia ratings at levels comparable to traditional face-to-face interventions. Segal and associates 57 evaluated the effectiveness of treating residual depressive symptoms with a web-based program that delivers mindfulness-based cognitive therapy. They found that use of this program in addition to usual depression care significantly improved depression and functional outcomes compared with usual depression care alone.


Several digital mental health applications have been developed or are in development, such as self-management apps that provide user feedback (eg, medication reminders, stress management tips, heart rate, and breathing patterns). Other programs provide skills training using educational videos on anxiety management or the importance of social support. Some applications have the capacity to collect data using smartphone sensors that record movement patterns, social interactions (eg, number of texts and phone calls), and other behaviors throughout the day.


Despite some promising early research, systematic studies demonstrating the efficacy of these emerging apps are limited. A recent review 58 indicated that only 3% of downloadable apps had research to justify their effectiveness claims, and most of that research was performed by the program developers. Another recent survey 59 of online-technology use to support mental health and well-being indicated that smartphone apps were the most commonly used technology: 78% of respondents used them either alone or in combination with other technologies. The apps that are being used provide guided activities, relaxation, and tracking; social media and discussion forums; and web-based programs to assist in the management of daily stress and anxiety.


Conclusions


Research on the brain-health consequences of digital technology is beginning to elucidate how these novel devices and programs can both help and harm brain function. Their frequent use heightens ADHD symptoms, interferes with emotional and social intelligence, can lead to addictive behaviors, increases social isolation, and interferes with brain development and sleep. However, specific programs, videogames, and other online tools may provide mental exercises that activate neural circuitry, improve cognitive functioning, reduce anxiety, increase restful sleep, and offer other brain-health benefits. Future research needs to elucidate underlying mechanisms and causal relationships between technology use and brain health, with a focus on both the positive and negative impact of digital technology use.


Acknowledgments

The University of California, Los Angeles, owns a US patent (6,274,119) entitled “Methods for Labeling β-Amyloid Plaques and Neurofibrillary Tangles,” which has been licensed to Ceremark Pharma, LLC. Dr Small is among the inventors and is a cofounder of Ceremark Pharma, LLC. Dr Small also reports having served as an advisor to and/or having received lecture fees from AARP, Acadia, Avanir, Genentech, Handok, Herbalife, Medscape, RB Health, Roche, Theravalues, and WebMD, and having received research funds from The Wonderful Company. Supported in part by the Parlow-Solomon Professorship on Aging

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Exercise Improves Cognitive Function, But Only When You Move by Choice

Voluntary exercise improves cognitive performance, while forced muscle movement via electrical stimulation does not..

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A study exploring the mechanisms behind why cognitive performance improves in response to exercise, has revealed forced muscle movement doesn’t have the same effect as voluntary.

Electrical muscle stimulation (EMS) is often used in physiotherapy and rehabilitation to help loosen tight muscles so they can recover. Devices trigger nerves that make muscles contract, ultimately relaxing and loosening tight spots.

Many gyms have also introduced EMS Training - which involves a person wearing similar devices during a workout - to help recruit more muscle fibres.

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Scientists have now investigated whether electrical muscle stimulation results in improved cognitive function, in the same way voluntary movement does.

As part of the study, experiments were carried out with 24 young, healthy male participants. Individuals were asked to complete cognitive tasks at rest and while cycling. They then had to do the same tasks with and without EMS being applied to the lower limb muscles.

Participants completed the tasks quicker when voluntary moderate-intensity exercise was done. This was not the case though during low-intensity exercise, and when only forced electrical stimulation was used.

Our results suggest that the relationship between exercise and brain activity is crucial for faster reaction time. Dr Joe Costello, University’s School of Psychology, Sport and Health Sciences

Co-author  Dr Joe Costello , from the  University’s School of Psychology, Sport and Health Sciences , said: “Our results suggest that the relationship between exercise and brain activity is crucial for faster reaction time. Forcing the muscles to move using an electrical current takes away this connection, and as a result participants didn’t experience an increase in cognitive performance like they did while cycling.”

The new study is part of ongoing research exploring what mechanism or mechanisms cause exercise to improve cognitive function in extreme environments. This discovery could help establish a new therapeutic pathway for cognitive health.

“Not everyone is able to reap the benefits of physical activity - like faster reaction times - because of injury or disability”, explained Associate Professor Costello.

“If we figure out exactly what it is that causes cardiovascular exercise to improve cognitive performance then we can potentially replicate this and remove the need to do moderate-intensity exercise.”

The latest findings support previous research by the authors which suggests  dopamine has a significant role in the relationship between exercise and cognitive function .

The “feel good” neurotransmitter and hormone - which is tied to pleasure, satisfaction and motivation – is known to increase when you work out. It plays a significant role in several conditions including Parkinson’s disease, schizophrenia, ADHD, addiction, and depression.

The team have also previously demonstrated that  20 minutes of exercise can boost your brain after a bad night’s sleep .

Soichi Ando,  Associate Professor in the Health & Sports Science Laboratory at the University of Electro-Communications in Japan, said: “These latest findings suggest that standard central neural activity - which happens during low-intensity and forced movement - isn’t enough to cause improved reaction time.

“Instead it may be - at least in part - the result of enhanced sympathetic nervous system activity, which happens during moderate-intensity exercise. Your sympathetic nervous system is best known for its role in responding to dangerous or stressful situations, where it activates to speed up your heart rate and deliver more blood to areas of your body to help you get out of danger.”

The paper,  published in the European Journal of Applied Physiology , says further studies are urgently needed to fully understand how our sympathetic nervous system is linked to cognitive performance following exercise.

The authors also recognise limitations to the sample size being relatively small, and recommend more participants are needed in future experiments, from a range of populations including women and older individuals, over a longer period of time.

Reference:  Sudo M, Kitajima D, Takagi Y, et al. Effects of voluntary exercise and electrical muscle stimulation on reaction time in the Go/No-Go task. Eur J Appl Physiol . 2024. doi:  10.1007/s00421-024-05562-8

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Published on 19.8.2024 in Vol 26 (2024)

The Dual Task Ball Balancing Test and Its Association With Cognitive Function: Algorithm Development and Validation

Authors of this article:

Original Paper

• Barry Greene 1, 2 , PhD   ; 
• Sean Tobyne 1 , PhD   ; 
• Ali Jannati 1 , PhD   ; 
• Killian McManus 1, 2 , PhD   ; 
• Joyce Gomes Osman 1 , PhD   ; 
• Russell Banks 1 , PhD   ; 
• Ranjit Kher 1   ; 
• John Showalter 1 , MD   ; 
• David Bates 1 , PhD   ; 
• Alvaro Pascual-Leone 1, 3 , MD, PhD  

1 Linus Health, Boston, MA, United States

2 Linus Health Europe, Dublin, Ireland

3 Hinda and Arthur Marcus Institute for Aging Research, Deanna and Sidney Wolk Center for Memory Health, Hebrew SeniorLife, Boston, MA, United States

Corresponding Author:

Killian McManus, PhD

Linus Health

280 Summer St

Boston, MA, 02210

United States

Phone: 1 851682046

Email: [email protected]

Background: Dual task paradigms are thought to offer a quantitative means to assess cognitive reserve and the brain’s capacity to allocate resources in the face of competing cognitive demands. The most common dual task paradigms examine the interplay between gait or balance control and cognitive function. However, gait and balance tasks can be physically challenging for older adults and may pose a risk of falls.

Objective: We introduce a novel, digital dual-task assessment that combines a motor-control task (the “ball balancing” test), which challenges an individual to maintain a virtual ball within a designated zone, with a concurrent cognitive task (the backward digit span task [BDST]).

Methods: The task was administered on a touchscreen tablet, performance was measured using the inertial sensors embedded in the tablet, conducted under both single- and dual-task conditions. The clinical use of the task was evaluated on a sample of 375 older adult participants (n=210 female; aged 73.0, SD 6.5 years).

Results: All older adults, including those with mild cognitive impairment (MCI) and Alzheimer disease–related dementia (ADRD), and those with poor balance and gait problems due to diabetes, osteoarthritis, peripheral neuropathy, and other causes, were able to complete the task comfortably and safely while seated. As expected, task performance significantly decreased under dual task conditions compared to single task conditions. We show that performance was significantly associated with cognitive impairment; significant differences were found among healthy participants, those with MCI, and those with ADRD. Task results were significantly associated with functional impairment, independent of diagnosis, degree of cognitive impairment (as indicated by the Mini Mental State Examination [MMSE] score), and age. Finally, we found that cognitive status could be classified with >70% accuracy using a range of classifier models trained on 3 different cognitive function outcome variables (consensus clinical judgment, Rey Auditory Verbal Learning Test [RAVLT], and MMSE).

Conclusions: Our results suggest that the dual task ball balancing test could be used as a digital cognitive assessment of cognitive reserve. The portability, simplicity, and intuitiveness of the task suggest that it may be suitable for unsupervised home assessment of cognitive function.

Introduction

Recent research has suggested that up to 40% of dementia cases [ 1 ] can be delayed or prevented through early identification of impairment and adherence to recommended lifestyle modifications [ 2 ]. Furthermore, recent developments in pharmaceutical intervention suggest that the progression of Alzheimer dementia can be delayed through amyloid plaque removal [ 3 ].

An individual's cognitive and behavioral performance is a combination of brain activity and cognitive reserve. Cognitive reserve can be conceptualized as a property of the brain that allows for better than expected performance, given the degree of life-course related brain changes and brain injury or disease [ 4 ]. Cognitive reserve can be influenced by multiple genetic and environmental factors, operating at various points or continuously across the lifespan. In the presence of disease, for example, a neurodegenerative disease such as Alzheimer disease, cognitive reserve is engaged to sustain function for as long as possible and minimize symptoms and disability. Thus, individuals with more cognitive reserve manifest symptoms or disability later than those with lower cognitive reserve; symptoms are less prominent or severe than might be expected for a given amount of pathology. Low cognitive reserve makes individuals with underlying brain pathology prone to episodes of confusion, delirium, and other acute decompensations when exposed to a stressor or insult, for example, elective surgery, infection, sleep, and deprivation. Individuals with mild cognitive impairment (MCI) and higher cognitive reserve can delay the development of dementia. Thus, assessment of cognitive reserve is important to predict an individual’s functional state and prognosis. In addition, cognitive reserve can be a powerful therapeutic target, as increasing cognitive reserve might reduce disability.

The brain’s resource allocation capacity has been studied extensively and is thought to provide insight into cognitive reserve and depend on prefrontal function. However, the nature and causality of this relationship is not as well understood. Dual task paradigms have long been thought to unlock deficits in the allocation of prefrontal resources [ 5 ]. Recent studies [ 6 - 8 ] have examined the impact of a cognitive task (eg, backward counting) on a participant’s gait or balance, and thus, are dependent on peripheral nerve and musculoskeletal factors often affected in older adults. Furthermore, gait and balance analysis may not be suitable or safe for use with older adults or those with comorbidities such as osteoarthritis, neuropathies, etc. A validated tool that can support objective characterization and quantitative evaluation of cognitive reserve safely and reliably in older adults, as well as early identification of cognitive decline in nonclinical settings, could be of clinical benefit in more accurately identifying those patients who would benefit most from early and targeted intervention.

We introduce a novel test of motor control, coordination, and attention—the “ball balancing” test, in which an individual is asked to maintain the position of a virtual ball in the center of a circular target area. Task performance is measured by examining the position of a virtual ball on the screen of a touchscreen tablet, estimated using the inertial sensors embedded in the tablet. The test can be easily adapted to a dual task condition, for example, by asking the individual to balance the ball while simultaneously doing a different, attention demanding task. The test can be completed comfortably and safely in a sitting position. In an initial version of a dual task paradigm, an individual’s ball balancing test performance was assessed while simultaneously conducting a backward digit span test (BDST).

We aimed to examine the use of the ball balancing test under single and dual conditions [as quantified using the inertial measurement unit (IMU) sensors embedded in the target device] in assessment of cognitive reserve and identification of cognitive impairment. While this task (and other dual task paradigms) is not primarily aimed at serving as a means to classify cognitive function, one may predict there should be a loss of cognitive reserve between MCI and dementia, given that cognitive reserve would be “used up” to sustain cognitive function and ultimately be no longer sufficient to prevent progression of deficit, impact on activities of daily living (ADL), and thus transition from MCI to dementia. We report the performance of the task in classifying cognitive status according to 3 different outcome measures (consensus clinical judgment, Rey Auditory Verbal Learning, and Mini Mental State Examination). Given that the outcome measures are imperfectly mutually correlated, it can be assumed that they may contain complementary information pertinent to assessment of cognitive function, which can be leveraged to examine cognitive reserve deficits.

Ball Balancing Task

Participants were seated in a comfortable and supportive chair and asked to hold a touchscreen tablet device (iPad Pro, Apple) parallel to the ground and tilt the screen as needed to keep a virtual ball within a target area—the ball was not perturbed during the test unless the tablet was moved by the participant.

Participants were asked to balance a virtual ball on a touchscreen tablet screen, the subsequent movement is measured by the IMU sensors embedded in the tablet and used to calculate the position of the virtual ball on the tablet screen. The ball balancing test was completed under both single task (ball balancing alone) as well as under dual task conditions, with participants completing a single trial of each. The dual task involved asking the participant to complete the ball balancing test while simultaneously completing a BDST. In the BDST, the participant is played an audio sequence of 4 digits and is prompted to repeat them in reverse order. The single task was 20 seconds in duration while the dual task was 45 seconds in duration.

A custom iOS application (Swift, iOS) was developed to conduct the ball balancing test, supporting single and dual tasks. IMU and ball position data were stored within the application in JSON format and exported for offline analysis. All analyses were conducted using MATLAB (R2017b, MathWorks; Figure 1 ).

The following two distinct approaches were used to analyze the data, that is, by examining: (1) the ball position on the screen (estimated from IMU data); and (2) the IMU data from the movement of the tablet during the test.

Signal Processing: Ball Position Metrics

The position of the ball on the screen was calculated using the inertial sensor data as input to a kinematic model, which derives the ball placement on the screen using Newtonian mechanics and allowed plotting of ball displacement on the screen. The following parameters were calculated from the virtual ball displacement (values in parenthesis indicate variants of the calculated feature) ( Textbox 1 ).

For all ball position metrics, the displacement is normalized to the range [ 9 ], where the outer edge is the radius of the outer circle, while the radius of the inner circle is calculated based on the ratio of the inner circle radius to the outer circle. The percentage of time spent within the inner circle is calculated as the proportion of time where the resultant displacement is less than the radius of the inner circle less the radius of the ball. The radial symmetry is calculated as the sum of the first difference values of the resultant displacement from the center of the circle. It is intended to measure quadrant placement of the ball within the outer circle. To examine learning effects and changes in performance over the course of each test, the percentage of time within the inner circle is calculated for each 5-s epoch within the test. The mean, standard deviation, and first difference were then calculated across all epochs per test to provide a measure of intratest performance. A number of standard center of pressure measures [ 10 , 11 ] were also calculated based on time and frequency domain analysis of the ball displacement. Each ball position metric was calculated for each participant under single task (ST) and dual task (DT) conditions; the dual task cost was calculated as the percentage difference between the parameter value under DT conditions and the parameter value under ST conditions and can be expressed mathematically as –100*(DT-ST)/ST [ 12 ].

A “perfect score” was achieved when the ball was found to lie within the inner circle for 100% of the test. As it was possible to achieve a perfect score by placing the tablet flat on a table, we examined if perfect score tests had any effect on the overall results to rule out the possibility that certain participants were engaging less with the task but achieving a perfect score.

• Percentage of test time spent within inner circle
• Radial symmetry
• Percentage of time spent in the inner circle per 5-second epoch (mean, SD, and first difference)
• Median frequency of ball displacement (mean, X, and Y)
• 95% spectral edge frequency of ball displacement (mean, X, and Y)
• Sway area of ball displacement
• Mean sway frequency (mean, X, and Y)
• Mean sway distance (mean, X, and Y)
• Resultant sway distance (mean, X, and Y)
• Sway length of ball displacement (mean, X, and Y))
• Sway velocity (mean, X, and Y)

Inertial Sensor Parameters

Inertial sensor data from the tablet device under both ST and DT conditions were processed using an adapted version of a previously reported algorithm [ 13 , 14 ]; this approach treats the IMU data as arising from motion about a rigid plane. Figure 2 below shows the IMU (triaxial accelerometer and triaxial gyroscope) data for a dual task ball balancing test.

For each test, 1 second of data was excluded from the start and end of each recording to remove artifacts due to tablet positioning. Any recordings less than 10 seconds were discarded. IMU data were resampled to 100 Hz as iPad IMU data can be unevenly sampled, leading to distortion in frequency domain signal features [ 15 ]. Signals were bandpass filtered using a fourth order Butterworth IIR filter, in the range 0.1-40 Hz and calibrated using a standard method [ 16 ].

The following parameters were calculated from the IMU data for each ball balancing test ( Textbox 2 ).

For each calculated parameter, the dual task cost was calculated as the percentage difference between the parameter value under dual task conditions and the parameter value under single task conditions. Figure 2 provides a 3D representation of the ball balancing test signal relative to the rigid plane.

• Root mean square (RMS) acceleration (m/s 2 )
• RMS acceleration—x-axis (m/s 2 )
• RMS acceleration—y-axis (m/s 2 )
• RMS acceleration—z-axis (m/s 2 )
• RMS angular velocity (°/s)
• Median frequency acceleration (Hz)
• RMS angular velocity—x-axis (°/s)
• RMS angular velocity-y-axis (°/s)
• Spectral edge frequency acceleration (Hz)
• Spectral entropy acceleration
• Median frequency angular velocity (Hz)
• Spectral edge frequency angular velocity (Hz)
• Spectral entropy angular velocity
• Sway path length of acceleration—x-axis (m/s 2 )
• Sway path length of acceleration—z-axis (m/s 2 )
• Sway area of the acceleration
• Sway jerk of the acceleration
• Area of 95% confidence ellipse of acceleration

Statistical Analysis

To examine the association between the calculated ball balancing test parameters and cognitive function, we considered the 3 available neurocognitive measures (cohort status, Mini Mental State Examination [MMSE], and Rey Auditory Verbal Learning Test [RAVLT]), treated as either continuous variables or binary labels (eg, impaired or not impaired). Cohort status was treated as a 3-category label (healthy, MCI, and Alzheimer disease–related dementia [ADRD]). Similarly, the differences between the healthy and impaired subgroups (MCI and ADRD) were also examined using a Wilcoxon rank sum test. A Wilcoxon signed rank test was used to test for significant differences across task conditions.

The MMSE (total score) and RAVLT (long recall delay score) data were dichotomized into cognitively impaired and cognitively intact with values below a threshold of 28 for the MMSE [ 17 ] and age group thresholds for the RAVLT [ 18 ] used to identify impaired cognition.

Spearman rank correlation was used to examine the relationship between each feature with the MMSE and RAVLT, while the Wilcoxon rank sum test was used to test for differences between impaired and nonimpaired groups for each feature. A confusion matrix was calculated for each set of binary labels (impaired/nonimpaired) to see how well cohort status, MMSE-, and RAVLT-based categorization agree with each other.

To examine the association of each variable with cognitive function and allow for the effect of age, a linear mixed effects model analysis was conducted with age as a within-subjects’ factor and cohort status as a categorical response variable. ANOVA was then used to examine the effect of each factor on cohort status, while controlling for age. This analysis was repeated for each variant with binary cohort status as well as impaired and nonimpaired labels obtained from MMSE and RAVLT.

In addition, we aimed to examine if any of the calculated ball balancing parameters were associated with functional impairment, independent of cognitive impairment. We conducted a one-way ANOVA for each ball balancing parameter with functional impairment (as measured by the Functional Activity Questionnaire [FAQ], with a threshold greater than or equal to 6 denoting functional impairment), controlling for MMSE and age. This analysis was then repeated when controlling for RAVLT and age.

To determine how well ball balancing parameters (features) could classify “unseen” participants according to binary cognitive status (cognitively impaired or cognitively intact), we used a logistic regression classifier model with a sequential forward feature selection procedure [ 19 ] validated using 10-fold cross-validation. Interaction terms were included in the candidate feature set and separate models were produced for each condition and feature set (ST, DT, dual task cost, all features as well as age only).

A sample of 375 older adults (n=210 female; aged 73.0, SD 6.5 years). Completed a battery of cognitive and motor function tests as part of wider study on brain health. The Bio-Hermes research study is managed by the Global Alzheimer Platform (GAP) and seeks new solutions to monitor and maintain brain health. Each participant received a clinical examination, which included the MMSE [ 20 ], the RAVLT [ 21 ] and “cohort status,” which classified participants into 3 clinical categories (healthy, MCI, and ADRD), as determined by a panel of qualified clinicians. For RAVLT, 2 summary scores were examined: the RAVLT total score and the RAVLT long recall delay score. In addition, each participant completed an FAQ [ 9 ] to examine functional status including ADL.

Ethical Considerations

The Bio-Hermes research study is managed by the GAP. The study was performed in accordance with the Declaration of Helsinki and its later amendments. The study procedures were explained to participants verbally and through written informed consent that was approved by the local IRB of each site participating in the GAP consortium (see the Bio-Hermes study website [ 22 ] for a list of study sites). If, in the opinion of the site principal investigator, the participant did not have the capacity to sign the informed consent form, a legally authorized representative was used to grant consent on behalf of the participant. Ethical approval was granted by each institution participating in the GAP consortium (reference number: Pro00046018). Inclusion criteria for the study were adults 60-85 years of age, fluent in the language of the tests used and the test site, and with an MMSE score of 20-30 at Screening. Exclusion criteria were extensive and based on underlying conditions. All data collected as part of this study were deidentified to confidentiality protection. Participants in the study were compensated in order to cover any time or expense they incurred as a result of completing the study.

Age was significantly different ( P <.001) across cohort status groups. The mean total MMSE scores for the sample was 26.3 (SD 3.0), mean total adjusted RAVLT score was 38.8 (SD 14.3), while mean RAVLT long delay score was 5.4 (3.5). According to cohort status, 132 participants were deemed cognitively normal, 116 were considered to have MCI and 126 had probable AD (ADRD), 1 participant did not have a valid cohort status label. Combining the MCI and ADRD classes to produce 2 classes (Impaired and Intact) produced 242 participants with cognitive impairment and 132 deemed intact. Using MMSE and FAQ cut-offs of 28 and 6, respectively, along with RAVLT age group thresholds [ 18 ] to categorize participants as impaired or unimpaired, allowed a comparison of these labels against binary cohort status. MMSE agreed with cohort status with 73.8% (277/375) accuracy, RAVLT total score agreed with cohort status 45.7% (171/375), while RAVLT long recall delay score agreed with 84.8% (318/375) accuracy. Pearson's correlation coefficient between MMSE and RAVLT total score was 0.43, while correlation coefficient between MMSE and RAVLT long delay recall score was 0.60 (see Figure 3 ).

Ball Balancing Task Performance

All participants were able to complete the task under ST and DT conditions. The main metric of task performance was the percentage of time the virtual ball spent within the inner circle (“percentage time in circle”). Mean percentage time spent in the inner circle was 86.0% (SD 23.0%) and 66.1% (SD 35.8%) under ST and DT conditions, respectively, while the mean DT cost was 21.0% (SD 34.1%). Task performance was significantly different ( P <.05) across cognitive status groups and between ST and DT conditions ( Table 1 ). As expected, participants achieved lower performance under DT conditions with a higher mean percentage time within the inner circle and a lower proportion of “perfect score” tests (see Figures 4 and 5 ). Removing tests with “perfect” task performance (percentage time is circle equal to 100%) did not change this finding. Performance in the task declined with increased cognitive impairment, with best mean performance observed in the healthy group for both ST and DT and worst task performance in the ADRD group.

Task performance was statistically significantly different ( P <.05) across group and between conditions.

Exploratory Results for Cohort Status

Age is significantly different between impaired and nonimpaired groups. When controlling for age using ANOVA, a large number of calculated parameters below were significantly ( P <.05) different on the basis of 3 category cohort status.

Similarly, when using ANOVA with a binary cohort label and correcting for age, a large number of parameters were significantly ( P <.05) different on the basis of binary cognitive status. Figure 6 below details 2 IMU parameters where there were significant differences across groups when corrected for age.

Classification Using Cohort Status

A linear logistic regression classifier model based on ball balancing measures from the DT condition (including age and gender) compared against a model obtained from age only found that ball balancing parameters could classify cognitive status with 70.5% accuracy compared to 62.3% accuracy for age alone ( Table 2 ).

a Results are shown for a model based on ball balancing inertial measurement unit (IMU) parameters and a model based on age only. The best result per group is italicized. Results for separate gender stratified male and female models are reported as well as models based on All available data.

Exploratory Results of the FAQ

A small number of ball balancing parameters under both single and dual task conditions were significantly associated with functional impairment (as measured by the FAQ with a threshold of 6), independent of cognitive function (as measured by MMSE total score) and age. These parameters included dual task cost of task performance (percent time in circle), dual task median frequency acceleration and single task radial frequency. A similar analysis controlling for age and RAVLT long recall delay score found that several ball balancing parameters including median frequency acceleration and single task radial frequency were significantly associated with functional impairment.

RAVLT Exploratory Results

A number of ball balancing parameters under both single and dual task conditions were significantly different on the basis of cognitive status (using RAVLT long recall delay score, with age bucketed thresholds) to define cognitive impairment) and correcting for age.

Pearson correlation coefficient was used to calculate the correlation between the RAVLT (long recall delay score) and each of the calculated ball balancing parameters per task condition. Weak correlations were observed for a number of parameters.

Classification Using RAVLT Long Delay

A linear logistic regression using RAVLT long recall delay score with age bucket thresholds to denote impairment yielded a mean classification accuracy of 70.43% compared to 57.03% for age alone ( Table 3 ).

Classification results for a model using age alone trained with the RAVLT long delay are also supplied. Results for separate genders stratified as male and female models are reported as well as models based on all available data.

a Italics are used to highlight the values most indicative of the true model accuracy.

Exploratory Results of the MMSE

A number of parameters under both single and dual task conditions were significantly different (using ANOVA and correcting for age) on the basis of cognitive status using MMSE, with a threshold of 28 to classify participants as cognitively impaired or cognitively unimpaired.

Pearson correlation coefficient was used to calculate the correlation between the MMSE (total score) and each of the calculated ball balancing parameters per task condition. Weak correlations were observed for a number of parameters.

Classification Using the MMSE

A linear logistic regression using the MMSE total score with a threshold of 28 to denote impairment yielded a mean classification accuracy of 72.8% compared to 69.6% for age alone ( Table 4 ).

Classification results for a model using age alone trained with the MMSE are also supplied. Results for separate genders stratified as male and female models are reported as well as models based on all available data.

We introduce a novel dual task paradigm to evaluate cognitive reserve and prefrontal resource allocation that does not rely on gait and balance metrics and can, thus, be safely completed by older adults and those with falls risk. We found that older adults were able to complete the task regardless of their age or level of cognitive impairment. Even those with MCI and ADRD, as well as those with peripheral neuropathy, osteoarthritis, frailty, and other potential sources of gait and balance problems were able to complete the task reliably and safely.

A sample of 375 participants completed the dual task ball balancing test protocol. Participants ranged in age from 60 to 85 years and exhibited a wide range of cognitive ability. As predicted, participants achieved significantly higher ball balancing test performance under ST conditions (as measured by the percentage of test time, the ball was within the inner circle) compared to DT performance. Thus, along with the higher proportion of perfect tests under ST conditions, the findings confirm that participants were more challenged by the test under DT conditions and that task performance decreased with increasing cognitive impairment. We found that task performance was significantly improved in healthy individuals compared to those with MCI and that performance was worse again in those with ADRD.

A number of significant differences were observed between cognitively intact (unimpaired) and cognitively impaired participants for ball positioning and IMU parameters calculated during a ball balancing test, when correcting statistics for the effect of age, using cohort status, RAVLT long delay score, and MMSE to determine cognitive status.

Significantly decreased performance in the ball balancing test was observed during the DT compared to the ST. Similarly, decreased performance was observed for increasing levels of cognitive impairment. An interpretation of this result is that with increasing impairment, there needs to be greater reliance on cognitive reserve to sustain (or attempt to sustain) cognitive and functional performance. These results are in line with results reported in the literature for other DT paradigms, which suggest that task performance reduced during a DT as compared to an ST and that the reduction in task performance is increased with increased impairment [ 6 , 22 ]. As such, DT performance across different tasks becomes increasingly altered and with that increasingly correlated, while before the high DT cost suggesting impaired reserve (if present at all) might be detectable for some but not all DT conditions. Importantly, a number of ball balancing parameters, measured under both ST and DT conditions were found to be significantly associated with functional impairment (as measured by the FAQ score) independent of MMSE, RAVLT, and age. This suggests that differences observed between MCI to ADRD groups under dual task conditions are consistent with loss of cognitive reserve contributing to progression of clinical manifestation and impact on ADL [ 4 , 23 ]. The ball balancing dual task paradigm may, thus, offer a valuable, objective means to evaluate the risk of ADL impact and enable early detection of MCI-to-dementia transition risk [ 24 ].

Moderate classification performance (>70%) was also observed in classifying binary cognitive status using a logistic regression classifier model trained on each of the cognitive function outcome measures. This compared favorably to models based on age alone, which distinguished between impaired and unimpaired groups with ~60% accuracy. A simple linear classifier model (logistic regression) was used to obtain a baseline of classification performance; improved performance may be achieved through the addition of nonlinear interaction terms or the use of higher order classification methods (eg, support vector machines), given the wider data set and potential nonlinear statistical relationships between features. To provide an indication of how well the ball balancing test can distinguish cognitively impaired participants from cognitively intact participants, cross-validation and wrapper-based feature selection was used. This method ensures unbiased estimate of classifier performance on previously unseen participants [ 25 ].

Three cognitive function outcome measures were considered in analyzing the use of the ball balancing test in classifying cognitive status. Each outcome measure (MMSE, RAVLT, and cohort status) contains differing and potentially complementary information about cognitive status (as evidenced by the modest mutual correlation observed between each outcome measure). In future work, we will examine the ability of a model based on the weighted combination of the 3 outcomes in longitudinally predicting cognitive impairment on a statistically independent data set. Furthermore, future work may also seek to examine the relationship of the ball balancing test parameters with blood biomarkers [ 26 ] and brain structure and pathology [ 5 ].

A limitation of this implementation of the ball balancing test is that the virtual ball is not perturbed during the test (other than by the movement of the tablet). This means that placement of the tablet on a flat, stable surface would allow the participant to achieve “perfect” task performance. However, it should be noted that the presence of “perfect score” tests were not found to affect the group-wise findings. An additional limitation is potential usability issues in using this task with an older adult population, particularly those with cognitive fine motor or visual impairments. While the current study involved participants conducting the task under supervised conditions to ensure adequate adherence to the task protocol, there may have been participants in the cognitively impaired groups who struggled to understand the instructions even with the support of the research assistant. Furthermore, impairment to fine motor skills may have prevented some participants from performing to their full capacity. Such usability issues may be exacerbated if the task were to be conducted under unsupervised conditions and would need to be carefully considered in the protocol for future studies.

The ball balancing test is a novel dual task paradigm that may have use in assessment of cognitive reserve and identification of cognitive impairment. Participants with mild or severe cognitive impairment performed less well on the task than healthy participants, particularly when a DT was introduced. A simple cross-validated classifier model used inertial sensor derived parameters obtained during the task to distinguish between cognitively impaired and cognitively intact participants with 70% accuracy. As the ball balancing test can be delivered entirely through a touchscreen tablet device, does not require a controlled environment, and is relatively simple to understand, the task may be suitable for administration by nonexpert users or for unsupervised use in the home environment and could support remote, longitudinal assessment of cognitive function.

Acknowledgments

The data that support the findings of this study were collected as part of the Bio-Hermes study and are governed by the Global Alzheimer Platform (GAP) consortium agreement. We would like to thank Dante Smith for his help in determining kinematic position of the virtual ball during the task. We would also like to thank the patients and clinicians involved in the study.

Data Availability

The data sets generated and analyzed during this study are not publicly available due to the terms of the Global Alzheimer Platform (GAP) consortium agreement. However, the data are available from the corresponding author on reasonable request.

Authors' Contributions

Data analysis was carried out by BG. All authors contributed to manuscript preparation and analysis plan.

Conflicts of Interest

APL is a co-founder and Chief Medical Officer of Linus Health and declares ownership of shares or share options in the company. APL serves as a paid member of the scientific advisory boards for Neuroelectrics, Magstim Inc, TetraNeuron, Skin2Neuron, MedRhythms, and Hearts Radiant. All other authors are employees of Linus Health and declare ownership of shares or share options in the company.

• Lee M, Whitsel E, Avery C, Hughes TM, Griswold ME, Sedaghat S, et al. Variation in population attributable fraction of dementia associated with potentially modifiable risk factors by race and ethnicity in the US. JAMA Netw Open. 2022;5(7):e2219672. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Ngandu T, Lehtisalo J, Solomon A, Levälahti E, Ahtiluoto S, Antikainen R, et al. A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): a randomised controlled trial. Lancet. 2015;385(9984):2255-2263. [ CrossRef ] [ Medline ]
• van Dyck CH, Swanson CJ, Aisen P, Bateman RJ, Chen C, Gee M, et al. Lecanemab in early Alzheimer's disease. N Engl J Med. 2023;388(1):9-21. [ CrossRef ] [ Medline ]
• Stern Y, Albert M, Barnes CA, Cabeza R, Pascual-Leone A, Rapp PR. A framework for concepts of reserve and resilience in aging. Neurobiol Aging. 2023;124:100-103. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hupfeld KE, Geraghty JM, McGregor HR, Hass CJ, Pasternak O, Seidler RD. Differential relationships between brain structure and dual task walking in young and older adults. Front Aging Neurosci. 2022;14:809281. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Smith E, Walsh L, Doyle J, Greene B, Blake C. Effect of a dual task on quantitative timed up and go performance in community-dwelling older adults: a preliminary study. Geriatr Gerontol Int. 2017;17(8):1176-1182. [ CrossRef ] [ Medline ]
• Yang Q, Tian C, Tseng B, Zhang B, Huang S, Jin S, et al. Gait change in dual task as a behavioral marker to detect mild cognitive impairment in elderly persons: a systematic review and meta-analysis. Arch Phys Med Rehabil. 2020;101(10):1813-1821. [ CrossRef ] [ Medline ]
• Zhou J, Cattaneo G, Yu W, Lo O, Gouskova NA, Delgado-Gallén S, et al. The age-related contribution of cognitive function to dual-task gait in middle-aged adults in Spain: observations from a population-based study. Lancet Healthy Longev. 2023;4(3):e98-e106. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Mayo AM. Use of the functional activities questionnaire in older adults with dementia. Hartford Inst Geriatr Nurs. 2016;13:2. [ FREE Full text ]
• Weed LL. Medical records that guide and teach. N Engl J Med. 1968;278(12):652-657. [ CrossRef ] [ Medline ]
• Prieto T, Myklebust J, Hoffmann R, Lovett E, Myklebust B. Measures of postural steadiness: differences between healthy young and elderly adults. IEEE Trans Biomed Eng. 1996;43(9):956-966. [ CrossRef ] [ Medline ]
• Somberg BL, Salthouse TA. Divided attention abilities in young and old adults. J Exp Psychol Hum Percept Perform. 1982;8(5):651-663. [ CrossRef ] [ Medline ]
• McManus K, Greene BR, Ader LGM, Caulfield B. Development of data-driven metrics for balance impairment and fall risk assessment in older adults. IEEE Trans Biomed Eng. 2022;69(7):2324-2332. [ CrossRef ] [ Medline ]
• Greene BR, McManus K, Ader LGM, Caulfield B. Unsupervised assessment of balance and falls risk using a smartphone and machine learning. Sensors (Basel). 2021;21(14):4770. [ FREE Full text ] [ CrossRef ] [ Medline ]
• VanderPlas JT. Understanding the lomb–scargle periodogram. ApJS. 2018;236(1):16. [ CrossRef ]
• Ferraris F, Grimaldi U, Parvis M. Procedure for effortless in-field calibration of three-axis rate gyros and accelerometers. Sens. Mater. 1995;7(5):311-330.
• Kukull W, Larson E, Teri L, Bowen J, McCormick W, Pfanschmidt M. The mini-mental state examination score and the clinical diagnosis of dementia. J Clin Epidemiol. 1994;47(9):1061-1067. [ CrossRef ] [ Medline ]
• Mitrushina M, Boone KB, Razani J, D'Elia LF. Handbook of Normative Data for Neuropsychological Assessment, 2nd ed (Handbook of Normative Data for Neuropsychological Assessment, 2nd ed.). New York, NY, US. Oxford University Press; 2005:xxii-1029-xxii, 1029.
• Kohavi R, John GH. Wrappers for feature subset selection. Artificial Intelligence. 1997;97(1-2):273-324. [ FREE Full text ] [ CrossRef ]
• Folstein MF, Folstein SE, McHugh PR. "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189-198. [ CrossRef ] [ Medline ]
• A R. The Clinical Examination in Psychology. Paris. Presses Universitaires de France; 1964.
• Bio-hermes study. Global Alzheimers Platform. URL: https://globalalzplatform.org/biohermesstudy/ [accessed 2022-05-11]
• O'Brien C, Holtzer R. Cognitive reserve moderates associations between walking performance under single- and dual-task conditions and incident mobility impairment in older adults. J Gerontol A Biol Sci Med Sci. 2021;76(10):e314-e320. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Bahureksa L, Najafi B, Saleh A, Sabbagh M, Coon D, Mohler MJ, et al. The impact of mild cognitive impairment on gait and balance: a systematic review and meta-analysis of studies using instrumented assessment. Gerontology. 2017;63(1):67-83. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hastie T, Tibshirani R, Friedman JH. The Elements of Statistical Learning. USA. Springer; 2009.
• Montero-Odasso MM, Sarquis-Adamson Y, Speechley M, Borrie MJ, Hachinski VC, Wells J, et al. Association of dual-task gait with incident dementia in mild cognitive impairment: results from the gait and brain study. JAMA Neurol. 2017;74(7):857-865. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by A Mavragani; submitted 09.06.23; peer-reviewed by H-F Hsieh, J Rider, B Poston; comments to author 21.11.23; revised version received 23.11.23; accepted 13.05.24; published 19.08.24.

©Barry Greene, Sean Tobyne, Ali Jannati, Killian McManus, Joyce Gomes Osman, Russell Banks, Ranjit Kher, John Showalter, David Bates, Alvaro Pascual-Leone. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 19.08.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Global Issues Are Taking a Major Toll on Young People’s Mental Health

Economic, climate and technology woes are weighing on young adults, a report finds. It recommends overhauling how we approach mental health care.

By Christina Caron

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The mental health of adolescents and young adults has been on the decline and it’s partly because of “harmful megatrends” like financial inequality, according to a new report published on Tuesday in the scientific journal The Lancet Psychiatry. The global trends affecting younger generations also include wage theft , unregulated social media , job insecurity and climate change , all of which are creating “a bleak present and future for young people in many countries,” according to the authors.

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The report was produced over the course of five years by a commission of more than 50 people, including mental health and economic policy experts from several continents and young people who have experienced mental illness.

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