Jonathan George Hakun

Background: Physical activity (PA) is a protective factor of brain health. Existing literature has predominately examined total PA volume or sedentary time in relation to cognitive health, with limited attention to the distributional features of the movement activity bouts. Variability in the duration of daily movement activity bouts may influence cognitive function beyond total exposure to PA or sedentary time. This study investigates whether daily PA and sedentary bout distributions are associated with daily cognitive function in older adults, and further explores whether these associations differ across the days of the week. Methods: Older adults ( n = 87, age = 68.5 ± 7.0 years, 38% male, 21% non-White) with elevated dementia risk completed 14 days of ecological momentary assessment (EMA). Participants wore an accelerometer (activPAL) and completed smartphone-based cognitive assessments of processing speed four times each day. The number and duration of sedentary, light (LPA), or moderate-to-vigorous physical activity (MVPA) bouts (≥ 1 min) were calculated from the activPAL data. Median response time (RT) and number of correct trials from the symbol search task were aggregated at the day level. Two distributional indicators (alpha and Gini) were each calculated for daily sedentary, LPA, and MVPA bouts to investigate their associations with the symbol search outcomes. Functional regression models examined the associations between the distributional indicators and cognitive outcomes by each day of the week (Sunday-Saturday). Results: On most weekdays, higher Gini (greater variations) in daily MVPA bout length was associated with faster RT ( p <.001, R ² =0.138); higher Gini of daily LPA bout length was also associated with more accurate trials ( p <.05, R ² =0.022). Conversely, higher Gini in daily sedentary bout length was associated with slower RT ( p <.05, R ² =0.164). Conclusions: This study provides proof-of-concept that the distribution of daily movement behavior bouts is associated with variations in daily cognition. It also illustrates how integrating accelerometry with repeated ambulatory cognitive measures can capture within-person dynamics. The findings can support future research to examine whether specific daily movement patterns may represent appropriate targets for interventions aimed at promoting cognitive health in older adults.

Objective: To describe the degree of correspondence between ecological momentary assessment (EMA) symptom ratings and cognitive performance derived from the Mobile Neurocognitive Health Project (MNCH) and clinic-based standard-of-care assessments in adolescents and adults with concussion. Environmental exposures, avoidance behavior, and symptom reactivity data were documented, and the clinical utility of the MNCH protocol for predicting protracted recovery was examined. Method: 89 patients (13–25 yrs) with concussion enrolled in the MNCH study on their smartphones within one week of injury. Participants completed four brief surveys per day for a 7-day period. The surveys included assessments of environmental exposures, avoidance of symptom-provoking environments, symptom ratings, ultra-brief performance-based cognitive assessments, and ratings of symptom reactivity to the surveys and cognitive assessments. The primary criteria used for determining the utility of EMA-based data were the degree of correspondence with in-clinic symptom ratings and cognitive performance, and the ability of EMA-based measures to predict risk for protracted recovery. Results: Symptom scores and cognitive performance outcomes obtained via EMA were significantly associated with in-clinic symptom ratings (PCSS) and computerized neurocognitive testing (ImPACT). Multiple logistic modeling results indicated that EMA total symptom scores were stronger predictors of protracted recovery than in-clinic symptom ratings. Additionally, ratings of avoidance behavior and reactivity to the EMA surveys and assessments were significantly associated with protracted recovery. Conclusions: Valid estimates of concussion symptoms may be obtained via remote, unsupervised, high-frequency assessment. In addition to EMA-based symptom scores, avoidance behavior and symptom reactivity may yield prognostic information about risk for protracted recovery.

Background: Lack of physical activity is a major risk factor for Cardiovascular diseases (CVDs). Wearables and smartphones offer potential advantages in cost, accessibility, and scale to change physical activity This systematic review aims to determine if interventions comprising smartphones and wearables are effective in enhancing physical activity among individuals with CVD. Methods: PubMed, Cochrane, and Web of Science databases were searched for RCTs, published since January 2000, on wearables and smartphone applications to enhance physical activity among CVD patients. Non-randomized trials, interventions without a smartphone/wearable component were excluded. Risk of bias was assessed by the Cochrane Collaboration tool. Meta-analyses were performed to assess the pooled effect on steps per day, distance, VO2, and (moderate-to-vigorous) physical activity in minutes per day. Results: Fourteen RCTs were included. Interventions comprising smartphones and wearables resulted in a mean difference of 1097.4 (95% CI = [409.2-1785.6]; p=0.0018) steps per day, and 3.9 (95% CI = [0.2-7.6]; p=0.0413) minutes of moderate-vigorous physical activity per day compared to control groups. Conclusion: This meta-analysis showed that interventions comprising smartphones and wearable devices are effective at increasing physical activity among CVD patients. Wearables and smartphones could provide accessible and tailored interventions to enhance physical activity.

Background: Multiple independent lines of research on self-regulation point to executive cognitive ability (executive function/cognitive control) as a factor that underlies the capacity to successfully regulate one’s thoughts, emotions, and behavior. Critically, while leading theories point to the role of executive control in modifying “in-the-moment” regulatory processes (eg, enacting physical activity, resisting a poor dietary choice, reflecting on a stressful experience), few studies have tested the executive hypothesis at this timescale. Moreover, given the normative changes in executive control across the adult lifespan, it is essential to understand how cognitive aging might impact these processes. Objective: The Physical Activity, Imaging, and Ambulatory Testing (PHIAT) project was designed to test how variation in executive control at multiple timescales (from moment-to-moment within days to age differences across decades) influences self-regulation across the adult lifespan. Methods: A 14-day, high-frequency, ambulatory assessment protocol was designed for this study. The study was conducted in a measurement burst design and included a 14-day ecological momentary assessment (EMA) protocol involving 6 assessments per day. Ultra-brief ambulatory cognitive assessments of multiple domains of cognition were included in the EMA protocol. Throughout the measurement burst, participants also wore 3 activity monitors on the hip, thigh, and wrist to measure physical activity/exercise, measure sedentary behavior, and self-monitor physical activity behavior, respectively. Results: A total of 221 participants ranging from 18 to 89 years of age completed the PHIAT protocol over the course of 2021-2024, and data collection is complete. EMA data were collected from participants reflecting a wide range of psychosocial factors surrounding participation in health-promoting behaviors (motivation, intention, stress, built environment, and social cognitive factors). This EMA data stream is complemented by data from high-frequency, ambulatory cognitive assessments measuring processing speed, working memory capacity, inhibitory control, and divided/sustained attention administered 5 times per day throughout the 14-day burst. In addition, health-promoting behaviors, including sleep, diet, hydration, physical activity, and exercise, were assessed throughout the 14-day burst through a combination of EMA self-report and continuous activity monitoring. Conclusions: A rich, high-frequency dataset was generated by the PHIAT project that will provide a range of novel insights into the motivational factors, information processing, and environmental factors that surround self-regulation of health-promoting behavior.

Background: Recent advances in cognitive digital assessment methodology, including high-frequency, ambulatory assessments, promise to improve the detection of subtle cognitive changes. Computational modeling approaches may further improve the sensitivity of digital cognitive assessments to detect subtle cognitive changes by capturing features that map onto core cognitive processes. Objective: We explored the validity of a brief smartphone-based adaptation of a visual working memory task that has shown sensitivity for detecting preclinical Alzheimer disease risk. We aimed to optimize properties of the task for computational cognitive feature extraction with drift diffusion modeling. Methods: We analyzed data from 68 participants (n=47, 69% women; n=55, 81% White; mean age 49, SD 14; range 24-80 years) who completed 60 trials for each of 16 variations of a visual working memory binding task (the Color Shapes task) on smartphones, over an 8-day period. A drift diffusion model was fit to the response time and accuracy data from the task. We experimentally manipulated 3 properties of the Color Shapes task (study time, probability of change, and choice urgency) to test how they yielded differences in key drift diffusion model parameters (drift rate, initial bias toward a response option, and caution in decision-making). We also evaluated how an additional task property, the test array size, impacted responses across all conditions. For array size, we tested a whole display of 3 shapes against a single probe of 1 shape only. Results: The 3 task property manipulations yielded the following results: (1) increasing the ratio of different responses was credibly associated with higher initial bias toward the different response (mean 0.06, SD 0.02 for the whole display; mean 0.15, SD 0.02, for the single probe condition); (2) increasing the choice urgency during the test phase was credibly associated with decreased caution in decision-making in the single probe condition (mean −0.04, SD 0.02) but not in the whole display (mean −0.01, SD 0.02); and (3) contrary to expectation, longer study times did not yield a credibly faster drift rate but produced credibly slower ones for the whole display condition (mean −0.28, SD 0.05) and a null effect for the single probe condition (mean 0.01, SD 0.05). In addition, as expected, we found that individual differences in drift rate were associated with age in both array sizes (r=−0.45 with Bayes factor=191), with older participants having a slower drift rate. Older participants also showed higher caution (r=0.42 with Bayes factor=80.76) in the single probe condition. Conclusions: We identified a version of the Color Shapes task optimized for smartphone-based cognitive assessments in real-world settings, with data designed for analysis through computational cognitive modeling. Our proposed approach can advance the development of tools for efficient and effective early detection and monitoring of risk for Alzheimer disease.

Background and Objectives: Physical inactivity and excessive sedentary time (ST) are associated with poor cognitive health in older adults. However, current literature largely relies on cross-sectional designs or in-lab cognitive assessments, which do not adequately reflect cognitive function in naturalistic settings. Further, existing studies have largely overlooked the variability or the instability in daily cognitive function, which represents a critical marker of cognitive decline. This ambulatory assessment study examined the temporal associations of daily movement behaviors with the mean levels and the variability of cognition among older adults at risk of dementia. Research Design and Methods: 96 older adults from the community (68.3 ± 7.1 years) participated in this 14-day study. They wore an accelerometer and completed smartphone-based cognitive tests up to 4 times per day. The cognitive tests assessed both performance-based and subjective cognition. The movement behaviors collected from the accelerometers include daily light-intensity physical activity (LPA), moderate-to-vigorous physical activity (MVPA), and ST. Mixed-effects location-scale models were applied to estimate the within- and between-person associations of movement behaviors and cognitive outcomes in terms of the mean levels and the degree of variability. A total of 1,269 day-level observations were analyzed. Results: Older adults’ between-person levels of daily MVPA and steps were associated with better mean cognitive performance and lower variability across cognitive measures. Older adults’ daily LPA was positively associated with subjective cognition in both mean levels and variability. The increases in within-person levels of ST were negatively associated with older adults’ variability of all cognitive outcomes. Discussion and Implications: Study results suggest that moving more and sitting less in day-to-day life may sustain proximal cognitive health. Applying ambulatory assessments can advance aging research by examining the temporal dynamics between daily movement activities and within-person variability of cognition to inform strategies for promoting healthy aging in daily life.

OBJECTIVES: Cognitive change is a complex phenomenon encompassing both retest-related performance gains and potential cognitive decline. Disentangling these dynamics is necessary for effective tracking of subtle cognitive change and risk factors for ADRD.METHOD: We applied a computational cognitive model of learning and forgetting to data from Einstein Aging Study (n = 316). EAS participants completed multiple bursts of ultra-brief, high-frequency cognitive assessments on smartphones. Analyzing response time data from a measure of visual short-term working memory, the Color Shapes task, and from a measure of processing speed, the Symbol Search task, we extracted several key cognitive markers: short-term intraindividual variability in performance, within-burst retest learning and asymptotic (peak) performance, across-burst change in asymptote and forgetting of retest gains.RESULTS: Asymptotic performance was related to both MCI and age, and there was evidence of asymptotic slowing over time. Long-term forgetting, learning rate, and within-person variability uniquely signified MCI, irrespective of age.DISCUSSION: Computational cognitive markers hold promise as sensitive and specific indicators of preclinical cognitive change, aiding risk identification and targeted interventions.

Objectives: Cognitive change is a complex phenomenon encompassing both retest-related performance gains and potential cognitive decline. Disentangling these dynamics is necessary for effective tracking of subtle cognitive change and risk factors for Alzheimer’s Disease and Related Dementias (ADRD). Method: We applied a computational cognitive model of learning and forgetting to data from Einstein Aging Study (EAS; n = 316). EAS participants completed multiple bursts of ultra-brief, high-frequency cognitive assessments on smartphones. Analyzing response time data from a measure of visual short-term working memory, the Color Shapes task, and from a measure of processing speed, the Symbol Search task, we extracted several key cognitive markers: short-term intraindividual variability in performance, within-burst retest learning and asymptotic (peak) performance, across-burst change in asymptote and forgetting of retest gains. Results: Asymptotic performance was related to both mild cognitive impairment (MCI) and age, and there was evidence of asymptotic slowing over time. Long-term forgetting, learning rate, and within-person variability uniquely signified MCI, irrespective of age. Discussion: Computational cognitive markers hold promise as sensitive and specific indicators of preclinical cognitive change, aiding risk identification and targeted interventions.

Perceived discrimination has been linked with neurocognitive disparities between Black and White adults. Yet, cognitive assessments outside of laboratory settings and the relevance of perceived reasons for discrimination require additional attention. The present work addressed associations between discrimination and ambulatory cognitive performance (i.e., spatial working memory, short-term memory binding, processing speed) in ecological settings among older Black and White adults enrolled in the Einstein Aging Study. Consistent with past laboratory-based research, Black adults exhibited worse ambulatory cognitive performance and reported more frequent discrimination compared to White adults. Racially stratified analyses examined characteristics (i.e., number and type) of the perceived reasons for discrimination as moderators in relation to discrimination frequency and cognition. For Black adults who endorsed zero or one reason for discrimination compared to those who endorsed multiple reasons, discrimination frequency was associated with worse spatial working memory. Additionally, among Black participants who did not attribute discrimination to their race compared to those who endorsed racial discrimination, discrimination frequency related to worse spatial working memory. For White adults, cognitive performance was largely unrelated to discrimination frequency and characteristics. Findings highlight the value of examining discrimination and cognition in daily life, and the importance of assessing characteristics of discriminatory experiences within racial groups.