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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. 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. 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. 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. 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.

Although digital technology represents a growing field aiming to revolutionize early Alzheimer disease risk prediction and monitoring, the perspectives of older adults on an integrated digital brain health platform have not been investigated. This study aims to understand the perspectives of older adults on a digital brain health platform by conducting semistructured interviews and analyzing their transcriptions by natural language processing. The study included 28 participants from the Boston University Alzheimer's Disease Research Center, all of whom engaged with a digital brain health platform over an initial assessment period of 14 days. Semistructured interviews were conducted to collect data on participants' experiences with the digital brain health platform. The transcripts generated from these interviews were analyzed using natural language processing techniques. The frequency of positive and negative terms was evaluated through word count analysis. A sentiment analysis was used to measure the emotional tone and subjective perceptions of the participants toward the digital platform. Word count analysis revealed a generally positive sentiment toward the digital platform, with \"like,\" \"well,\" and \"good\" being the most frequently mentioned positive terms. However, terms such as \"problem\" and \"hard\" indicated certain challenges faced by participants. Sentiment analysis showed a slightly positive attitude with a median polarity score of 0.13 (IQR 0.08-0.15), ranging from -1 (completely negative) to 1 (completely positive), and a median subjectivity score of 0.51 (IQR 0.47-0.53), ranging from 0 (completely objective) to 1 (completely subjective). These results suggested an overall positive attitude among the study cohort. The study highlights the importance of understanding older adults' attitudes toward digital health platforms amid the comprehensive evolution of the digitalization era. Future research should focus on refining digital solutions to meet the specific needs of older adults, fostering a more personalized approach to brain health.

Background Alzheimer's disease (AD) precision medicine will advance through the application of two key technological advances: 1) digital technologies that can more deeply characterize clinically relevant symptoms and 2) machine learning (ML) approaches the can classify subgroups with shared characteristics that could align with specific treatment plants. This study leverages a digital data collection platform for enhanced characterization and NetraAI, an artificial intelligence (AI) platform to analyze multimodal data to differentiate causal and non‐causal subpopulations within a cohort and integrates a “No Call” system to exclude ambiguous data points. Method We analyzed data from 98 Boston University Alzheimer's Disease Research Center participants and 453 variables derived from digital tasks administered over two months. Eight participants were clinically diagnosed as mild cognitive impairment. Digital measures included sleep metrics (57 measures), clinical scales (324 measures), and cognitive performance assessments (72 GoNoGo and Code Substitution measures). Of the 98 subjects, 81 were cognitively unimpaired and 17 transitioned to MCI during the course of study enrollment. Result Sleep‐derived metrics, including 3% and 4% desaturation thresholds (p = 4×10−5, p = 7×10−5), and periodicity (eLFCnb) (p = 0.008) characterized one population of 8 participants, 7 of whom had been diagnosed with MCI. Incorporating maximum heart rate, another sleep metric, distinguished another subpopulation of 8 subjects (6/8 were diagnosed MCI) with elevated heart rate (p = 10−10). We examined 81 cognitively intact (e.g., non‐transitioners; Class 0) and 17 MCI transitioners (Class 1) related to Go/No‐Go and Code Substitution tasks. Go/No‐Go Inter‐Trial Intervals (ITI), REM sleep percentage, and maximum apnea duration were key predictors. Shorter, more stable ITI times (inter‐trial intervals between tasks), higher REM sleep percentage, and shorter apnea durations were strongly correlated with non‐transitioners. A 10‐fold cross‐validation yielded an average accuracy of 80.89%. Conclusion Our findings present an ongoing effort on the potential of explainable AI to validate digital measures to identify those with MCI. While the current model effectively identifies prevalent non‐transitioners, it remains limited in identifying prevalent transitioners. Future research will focus on refining model sensitivity and balancing classification performance.

Alzheimer's disease (AD) precision medicine will advance through the application of two key technological advances: 1) digital technologies that can more deeply characterize clinically relevant symptoms and 2) machine learning (ML) approaches the can classify subgroups with shared characteristics that could align with specific treatment plants. This study leverages a digital data collection platform for enhanced characterization and NetraAI, an artificial intelligence (AI) platform to analyze multimodal data to differentiate causal and non-causal subpopulations within a cohort and integrates a \"No Call\" system to exclude ambiguous data points. We analyzed data from 98 Boston University Alzheimer's Disease Research Center participants and 453 variables derived from digital tasks administered over two months. Eight participants were clinically diagnosed as mild cognitive impairment. Digital measures included sleep metrics (57 measures), clinical scales (324 measures), and cognitive performance assessments (72 GoNoGo and Code Substitution measures). Of the 98 subjects, 81 were cognitively unimpaired and 17 transitioned to MCI during the course of study enrollment. Sleep-derived metrics, including 3% and 4% desaturation thresholds (p = 4×10 , p = 7×10 ), and periodicity (eLFCnb) (p = 0.008) characterized one population of 8 participants, 7 of whom had been diagnosed with MCI. Incorporating maximum heart rate, another sleep metric, distinguished another subpopulation of 8 subjects (6/8 were diagnosed MCI) with elevated heart rate (p = 10 ). We examined 81 cognitively intact (e.g., non-transitioners; Class 0) and 17 MCI transitioners (Class 1) related to Go/No-Go and Code Substitution tasks. Go/No-Go Inter-Trial Intervals (ITI), REM sleep percentage, and maximum apnea duration were key predictors. Shorter, more stable ITI times (inter-trial intervals between tasks), higher REM sleep percentage, and shorter apnea durations were strongly correlated with non-transitioners. A 10-fold cross-validation yielded an average accuracy of 80.89%. Our findings present an ongoing effort on the potential of explainable AI to validate digital measures to identify those with MCI. While the current model effectively identifies prevalent non-transitioners, it remains limited in identifying prevalent transitioners. Future research will focus on refining model sensitivity and balancing classification performance.

Background Self‐administered mobile cognitive assessment tools such as the Defense Automated Neurobehavioral Assessment (DANA) have recently emerged as promising solutions for the efficient monitoring of cognitive health. This study investigated the association of DANA with the risk of mild cognitive impairment (MCI). Method The study sample included participants enrolled in the Boston University Alzheimer's Disease Research Center (BU ADRC) who completed six DANA tasks on their smartphone, yielding five digital cognitive measures per task (four response time metrics and cognitive efficiency). Participants were categorized as either cognitively intact or diagnosed with MCI based on consensus diagnostic meetings at the BU ADRC, adhering to the criteria set by the National Alzheimer's Coordinating Center Uniform Data Set. Digital measures were standardized to have a mean of zero and a standard deviation of one. Logistic regression analyses, adjusted for age, sex, and education, related digital cognitive measures to cognitive status. Result A total of 132 participants were included in the study (mean age 71.9 ± 10.2 years, 57.6% female), among which, 17 were diagnosed as MCI. All five digital measurements from the code substitution task were associated with MCI. Each standard deviation increase in cognitive efficiency in the code substitution task was associated with a 76% reduction in the odds of MCI (OR = 0.24, 95% CI = 0.09‐0.51, P < 0.001). All except standard deviation of response time for all test trials from the procedural response time task were associated with MCI. However, none of the digital measurements from the go/no‐go task, match‐to‐sample, spatial processing, and simple response time were associated with MCI. Conclusion Digital measures associated with executive function appear to be most sensitive to the identification of MCI in this pilot study. These findings suggest that self‐administered smartphone applications provide an alternative tool for cognition monitoring and early detection of cognitive impairment.

Background The Montreal Cognitive Assessment (MoCA) is a commonly used screening tool for cognitive impairment. Despite translation into multiple languages to facilitate broader use globally, there are inherent education and cultural biases that result in variations in cognitive screening accuracies. Acoustic voice features are emerging as a more education, language and culturally agnostic indicator of cognitive status, but as a surrogate to the MoCA has not been adequately explored. This pilot study aimed to examine the association between spectral acoustic features extracted by two functionals and MoCA total scores. Method We included 80 participants from the Boston University Alzheimer's Disease Research Center, whose responses to a picture‐description task were digitally recorded and administered the MoCA. Using openSMILE, each recording was divided into 20‐ms frames, using a sliding window that advanced by 10 ms for every segment. For each segment, we extracted 26 RASTA‐style filtered auditory spectrum (bands 1‐26) low‐level descriptors (LLD). Two functionals were applied to the 26 LLD to summarize information across each recording: the mean (mean value of LLD for all segments in each recording) and “upleveltime75” (percentage of time the signal exceeds 75% of the feature range above the minimum). Linear regression models were used to assess the association of these acoustic features with MoCA total scores, adjusting for age, sex, and education. Result Participant demographics included age: mean 68.7, SD 9.27 years; 81.25% college graduate or higher; 60.0% women. Among these participants, 29 were cognitively impaired. The mean MoCA score was 26.49 (SD = 2.54). Among the 26 upleveltime75 functional‐based spectral features, 5 showed significant negative associations with MoCA. The strongest effect was observed for band 25 (beta = –0.77, SE = 0.26, p = 0.0038). In contrast, no significant associations were observed for the acoustic features generated by the mean functional. Conclusion These results suggest that digital voice contains cognitive‐related signals and show promise as a potential globally appropriate cognitive screening tool given the ease in which it can be collected and the availability of automated open‐source tools for analysis. Further exploration analyzing voice recordings across different languages and cultural/education strata are warranted.

Exposure to repetitive head impacts (RHI) are increasingly recognized for their association with neurodegenerative diseases. However, limited research has explored the impact of RHI on digital neuropsychological measures in cognitively intact older adults. This study aims to address this gap by comparing the cognitive performance assessed using the Defense Automated Neurobehavioral Assessment (DANA), a digital cognitive tool, between cognitively intact older adults with and without a history of RHI. This study utilized data from community-based older participants longitudinally evaluated by the Boston University Alzheimer's Disease Research Center (BU ADRC) Clinical Core for long-term clinical outcomes associated with RHI. Participants take part in annual assessments that incorporate the Uniform Data Set. RHI classification is determined according to the 2021 National Institute of Neurological Disorders and Stroke (NINDS) Traumatic Encephalopathy Syndrome (TES) Research Diagnostic Criteria. In June 2021, participants engaged with a precision brain health monitoring digital platform that included six DANA tasks, generating five digital cognitive measures. Adjusted means and standard errors were calculated for each measure controlling for age and sex. To identify significant differences in digital measures associated with RHI exposure, Mann-Whitney U tests were conducted on residuals of these measures adjusted for age and sex, derived from linear regression models. This study included 97 cognitively intact participants from the BU ADRC (mean age: 74.4± 10.3 years; 63.9% women; mean education years: 17±2; 91.8% White) (Table 1). Of these, 22 were RHI, with football as the primary sport for 8, soccer for 5, rugby for 2, and other sports for 7. Table 2 shows the age- and sex-adjusted mean values of digital measures for both groups. Participants with RHI exhibited longer average response times in the Code Substitution (2091.26 vs. 1915.36, P = 0.017) and Procedural Response Time (739.55 vs. 688.69, P = 0.026) tasks compared to those without RHI. No significant differences were observed in the Simple Response Time and Match-to-Sample tasks. Our findings indicate that RHI are associated with specific deficits in response speed among cognitively intact older adults. Results indicate subtle cognitive alterations associated with frontal mediated pathways linked to RHI exposure.

Background Exposure to repetitive head impacts (RHI) from contact and collision sports, military service, and other sources have been linked to cognitive decline and neurodegenerative disorders. Traditional neuropsychological measures often miss the subtle cognitive deficits that can occur in individuals exposed to RHI. Digital measures might be more sensitive alternatives. However, little is known about how RHI influences the stability of digital neuropsychological measures in cognitively intact older adults. Method This pilot study analyzed data from participants enrolled in the Boston University Alzheimer's Disease Research Center. RHI status is based on the 2021 NINDS TES Research Diagnostic Criteria. Participants complete annual study visits that includes Uniform Data Set. In 2021, Defense Automated Neurobehavioral Assessment (DANA) was introduced. The sample included cognitively intact older adults with at least three days of engagement with DANA. Intraindividual variability was quantified using the coefficient of variation (CV), and Mann‐Whitney U tests compared CVs between RHI and non‐RHI groups. Day‐to‐day variations and group differences were further examined with a linear mixed‐effects model, adjusting for age, sex, the number of usage days and RHI group, plus their interaction term. Result This study included 58 participants (mean age: 75.4±9.3 years; 67.2% women; mean education: 17±2 years; 93.1% White) (Table 1). Of these, 10 were RHI, with soccer as the primary sport for 4, football for 2, rugby for 1, and other sports for 3. Overall, day‐to‐day fluctuations in DANA performance were small, yet individuals with RHI showed significantly greater variability across some tasks (e.g., Go/No‐Go, Code Substitution, Simple Response Time) than those without RHI (Table 2). Notably, in the Procedural Response Time task, each additional day of DANA usage was significantly associated with a 21.6 ms faster average response time for all test trials (P = 0.007). However, a significant interaction effect (beta = 38, P = 0.012) was observed for the RHI group. Conclusion Although digital measures were generally stable across days, older adults with RHI exhibited more pronounced variability and appeared to benefit less from repeated practice. These findings underscore the need to consider RHI history when interpreting longitudinal cognitive assessments in aging populations.

Exposure to repetitive head impacts (RHI) from contact and collision sports, military service, and other sources have been linked to cognitive decline and neurodegenerative disorders. Traditional neuropsychological measures often miss the subtle cognitive deficits that can occur in individuals exposed to RHI. Digital measures might be more sensitive alternatives. However, little is known about how RHI influences the stability of digital neuropsychological measures in cognitively intact older adults. This pilot study analyzed data from participants enrolled in the Boston University Alzheimer's Disease Research Center. RHI status is based on the 2021 NINDS TES Research Diagnostic Criteria. Participants complete annual study visits that includes Uniform Data Set. In 2021, Defense Automated Neurobehavioral Assessment (DANA) was introduced. The sample included cognitively intact older adults with at least three days of engagement with DANA. Intraindividual variability was quantified using the coefficient of variation (CV), and Mann-Whitney U tests compared CVs between RHI and non-RHI groups. Day-to-day variations and group differences were further examined with a linear mixed-effects model, adjusting for age, sex, the number of usage days and RHI group, plus their interaction term. This study included 58 participants (mean age: 75.4±9.3 years; 67.2% women; mean education: 17±2 years; 93.1% White) (Table 1). Of these, 10 were RHI, with soccer as the primary sport for 4, football for 2, rugby for 1, and other sports for 3. Overall, day-to-day fluctuations in DANA performance were small, yet individuals with RHI showed significantly greater variability across some tasks (e.g., Go/No-Go, Code Substitution, Simple Response Time) than those without RHI (Table 2). Notably, in the Procedural Response Time task, each additional day of DANA usage was significantly associated with a 21.6 ms faster average response time for all test trials (P = 0.007). However, a significant interaction effect (beta = 38, P = 0.012) was observed for the RHI group. Although digital measures were generally stable across days, older adults with RHI exhibited more pronounced variability and appeared to benefit less from repeated practice. These findings underscore the need to consider RHI history when interpreting longitudinal cognitive assessments in aging populations.

Background Exposure to repetitive head impacts (RHI) are increasingly recognized for their association with neurodegenerative diseases. However, limited research has explored the impact of RHI on digital neuropsychological measures in cognitively intact older adults. This study aims to address this gap by comparing the cognitive performance assessed using the Defense Automated Neurobehavioral Assessment (DANA), a digital cognitive tool, between cognitively intact older adults with and without a history of RHI. Method This study utilized data from community‐based older participants longitudinally evaluated by the Boston University Alzheimer's Disease Research Center (BU ADRC) Clinical Core for long‐term clinical outcomes associated with RHI. Participants take part in annual assessments that incorporate the Uniform Data Set. RHI classification is determined according to the 2021 National Institute of Neurological Disorders and Stroke (NINDS) Traumatic Encephalopathy Syndrome (TES) Research Diagnostic Criteria. In June 2021, participants engaged with a precision brain health monitoring digital platform that included six DANA tasks, generating five digital cognitive measures. Adjusted means and standard errors were calculated for each measure controlling for age and sex. To identify significant differences in digital measures associated with RHI exposure, Mann‐Whitney U tests were conducted on residuals of these measures adjusted for age and sex, derived from linear regression models. Result This study included 97 cognitively intact participants from the BU ADRC (mean age: 74.4± 10.3 years; 63.9% women; mean education years: 17±2; 91.8% White) (Table 1). Of these, 22 were RHI, with football as the primary sport for 8, soccer for 5, rugby for 2, and other sports for 7. Table 2 shows the age‐ and sex‐adjusted mean values of digital measures for both groups. Participants with RHI exhibited longer average response times in the Code Substitution (2091.26 vs. 1915.36, P = 0.017) and Procedural Response Time (739.55 vs. 688.69, P = 0.026) tasks compared to those without RHI. No significant differences were observed in the Simple Response Time and Match‐to‐Sample tasks. Conclusion Our findings indicate that RHI are associated with specific deficits in response speed among cognitively intact older adults. Results indicate subtle cognitive alterations associated with frontal mediated pathways linked to RHI exposure.