Matching science to reality: how to deploy a participant-driven digital brain health platform

De Anda-Duran, Ileana; Hwang, Phillip H.; Popp, Zachary Thomas; Low, Spencer; Ding, Huitong; Rahman, Salman; Igwe, Akwaugo; Kolachalama, Vijaya B.; Lin, Honghuang; Au, Rhoda

doi:10.3389/frdem.2023.1135451

Cited by 6 publications

(4 citation statements)

References 73 publications

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“…Participants will be asked to complete these tasks on a tablet provided to them by study staff during the in-clinic study visit. The details of study design and participation rate can be found in prior study ( 2 ). All touch screen and stylus responses will be recorded through the Linus Health app installed on the tablet.…”

Section: Methodsmentioning

confidence: 99%

“…Traditional neuropsychological (NP) testing has been the primary method for measuring cognitive function, but digital tools have emerged as a promising and convenient alternative ( 2 ). Among these tools, the Defense Automated Neurobehavioral Assessment (DANA) stands out as an innovative mobile application developed for the evaluation of neurobehavioral functioning in military personnel ( 3 ).…”

Section: Introductionmentioning

confidence: 99%

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Digital neuropsychological measures by defense automated neurocognitive assessment: reference values and clinical correlates

Ding,

Kim,

Searls

et al. 2024

Front. Neurol.

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IntroductionAlthough the growth of digital tools for cognitive health assessment, there’s a lack of known reference values and clinical implications for these digital methods. This study aims to establish reference values for digital neuropsychological measures obtained through the smartphone-based cognitive assessment application, Defense Automated Neurocognitive Assessment (DANA), and to identify clinical risk factors associated with these measures.MethodsThe sample included 932 cognitively intact participants from the Framingham Heart Study, who completed at least one DANA task. Participants were stratified into subgroups based on sex and three age groups. Reference values were established for digital cognitive assessments within each age group, divided by sex, at the 2.5th, 25th, 50th, 75th, and 97.5th percentile thresholds. To validate these values, 57 cognitively intact participants from Boston University Alzheimer’s Disease Research Center were included. Associations between 19 clinical risk factors and these digital neuropsychological measures were examined by a backward elimination strategy.ResultsAge- and sex-specific reference values were generated for three DANA tasks. Participants below 60 had median response times for the Go-No-Go task of 796 ms (men) and 823 ms (women), with age-related increases in both sexes. Validation cohort results mostly aligned with these references. Different tasks showed unique clinical correlations. For instance, response time in the Code Substitution task correlated positively with total cholesterol and diabetes, but negatively with high-density lipoprotein and low-density lipoprotein cholesterol levels, and triglycerides.DiscussionThis study established and validated reference values for digital neuropsychological measures of DANA in cognitively intact white participants, potentially improving their use in future clinical studies and practice.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digital neuropsychological measures by defense automated neurocognitive assessment: reference values and clinical correlates

Ding,

Kim,

Searls

et al. 2024

Front. Neurol.

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“…Detailed descriptions of all technologies and collection protocols are described elsewhere. 81 The multimodal, multiengagement data collection at the BU Alzheimer's Disease Research Center (ADRC) are conducted in tandem with data analysis and sharing initiatives. Analysis goals will include developing measures that encompass multiple technologies, multiple participant use schedules, and varying levels of task adherence and selection.…”

Section: Bridging the Gap Between Active And Passivementioning

confidence: 99%

“…Critically, those who engaged with passive typing–based technologies had the greatest adherence longitudinally, suggesting that additional passive‐sensing platforms are of great interest. 81 …”

Section: Bridging the Gap Between Active And Passivementioning

confidence: 99%

Shifting From Active to Passive Monitoring of Alzheimer Disease: The State of the Research

Popp,

Low,

Igwe

et al. 2024

JAHA

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Most research using digital technologies builds on existing methods for staff‐administered evaluation, requiring a large investment of time, effort, and resources. Widespread use of personal mobile devices provides opportunities for continuous health monitoring without active participant engagement. Home‐based sensors show promise in evaluating behavioral features in near real time. Digital technologies across these methodologies can detect precise measures of cognition, mood, sleep, gait, speech, motor activity, behavior patterns, and additional features relevant to health. As a neurodegenerative condition with insidious onset, Alzheimer disease and other dementias (AD/D) represent a key target for advances in monitoring disease symptoms. Studies to date evaluating the predictive power of digital measures use inconsistent approaches to characterize these measures. Comparison between different digital collection methods supports the use of passive collection methods in settings in which active participant engagement approaches are not feasible. Additional studies that analyze how digital measures across multiple data streams can together improve prediction of cognitive impairment and early‐stage AD are needed. Given the long timeline of progression from normal to diagnosis, digital monitoring will more easily make extended longitudinal follow‐up possible. Through the American Heart Association–funded Strategically Focused Research Network, the Boston University investigative team deployed a platform involving a wide range of technologies to address these gaps in research practice. Much more research is needed to thoroughly evaluate limitations of passive monitoring. Multidisciplinary collaborations are needed to establish legal and ethical frameworks for ensuring passive monitoring can be conducted at scale while protecting privacy and security, especially in vulnerable populations.

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Assessment of Wearable Device Adherence for Monitoring Physical Activity in Older Adults: Pilot Cohort Study

Ding,

Ho,

Searls

et al. 2024

JMIR Aging

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Background Physical activity has emerged as a modifiable behavioral factor to improve cognitive function. However, research on adherence to remote monitoring of physical activity in older adults is limited. Objective This study aimed to assess adherence to remote monitoring of physical activity in older adults within a pilot cohort from objective user data, providing insights for the scalability of such monitoring approaches in larger, more comprehensive future studies. Methods This study included 22 participants from the Boston University Alzheimer’s Disease Research Center Clinical Core. These participants opted into wearing the Verisense watch as part of their everyday routine during 14-day intervals every 3 months. Eighteen continuous physical activity measures were assessed. Adherence was quantified daily and cumulatively across the follow-up period. The coefficient of variation was used as a key metric to assess data consistency across participants over multiple days. Day-to-day variability was estimated by calculating intraclass correlation coefficients using a 2-way random-effects model for the baseline, second, and third days. Results Adherence to the study on a daily basis outperformed cumulative adherence levels. The median proportion of adherence days (wearing time surpassed 90% of the day) stood at 92.1%, with an IQR spanning from 86.9% to 98.4%. However, at the cumulative level, 32% (7/22) of participants in this study exhibited lower adherence, with the device worn on fewer than 4 days within the requested initial 14-day period. Five physical activity measures have high variability for some participants. Consistent activity data for 4 physical activity measures might be attainable with just a 3-day period of device use. Conclusions This study revealed that while older adults generally showed high daily adherence to the wearable device, consistent usage across consecutive days proved difficult. These findings underline the effectiveness of wearables in monitoring physical activity in older populations and emphasize the ongoing necessity to simplify usage protocols and enhance user engagement to guarantee the collection of precise and comprehensive data.

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Matching science to reality: how to deploy a participant-driven digital brain health platform

Cited by 6 publications

References 73 publications

Digital neuropsychological measures by defense automated neurocognitive assessment: reference values and clinical correlates

Digital neuropsychological measures by defense automated neurocognitive assessment: reference values and clinical correlates

Shifting From Active to Passive Monitoring of Alzheimer Disease: The State of the Research

Assessment of Wearable Device Adherence for Monitoring Physical Activity in Older Adults: Pilot Cohort Study

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