Using wearable device-based machine learning models to autonomously identify older adults with poor cognition

Conducting cognitive tests is time-consuming for patients and clinicians. Wearable device-based prediction models allow for continuous health monitoring under normal living conditions and could offer an alternative to identifying older adults with cognitive impairments for early interventions. In this study, we first derived novel wearable-based features related to circadian rhythms, ambient light exposure, physical activity levels, sleep, and signal processing. Then, we quantified the ability of wearable-based machine-learning models to predict poor cognition based on outcomes from the Digit Symbol Substitution Test (DSST), the Consortium to Establish a Registry for Alzheimers Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). We found that the wearable-based models had significantly higher AUCs when predicting all three cognitive outcomes compared to benchmark models containing age, sex, education, marital status, household income, diabetic status, depression symptoms, and functional independence scores. In addition to uncovering previously unidentified wearable-based features that are predictive of poor cognition such as the standard deviation of the midpoints of each persons most active 10-hour periods and least active 5-hour periods, our paper provides proof-of-concept that wearable-based machine learning models can be used to autonomously screen older adults for possible cognitive impairments. Such models offer cost-effective alternatives to conducting initial screenings manually in clinical settings

Associations between wearable device-measured sleep variability and cognition among older adults

Healthy sleep habits are protective against adverse health outcomes, but it is unclear how strongly sleep intraindividual variability is associated with cognitive function among older adults. In this study we aimed to examine how accelerometer-derived intraindividual variability in sleep duration, efficiency, onset timing, and offset timing is associated with cognition using cross-sectional data from the 2011-2014 waves of the National Health and Nutrition Examination Survey (NHANES). Cognition was assessed by creating a composite measure derived by summing z-scores from the Digit Symbol Substitution Test (DSST), Consortium to Establish a Registry for Alzheimers Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). A final cohort of 2508 older adults aged 60+ with at least three days of accelerometer wear time who completed all three cognitive tests were included in this study. After centering all sleep intraindividual variability metrics and adjusting for demographic factors, the presence of diabetes, depressive symptoms, and measures of functional independence, we found that increased intraindividual variability in sleep onset timing was associated with worse cognition (Beta, -0.12; 95% CI, -0.19 to -0.05), as was increased intraindividual variability in sleep efficiency (Beta, -0.12; 95% CI, -0.20 to -0.05), and increased intraindividual variability in sleep duration (Beta, -0.10; 95% CI, -0.17 to -0.03). This study suggests that sleep guidance aimed at preserving cognition among older adults could be revised to include a focus on sleep consistency regarding onset timing, quality, and duration.Comment: Typo corrected in the abstrac

Identifying the most predictive risk factors for future cognitive impairment among elderly Chinese

Importance: In China,&nbsp;the&nbsp;societal burden of&nbsp;cognitive&nbsp;impairments&nbsp;continues to increase as&nbsp;the&nbsp;country ages, but our knowledge remains limited regarding how to accurately&nbsp;predict&nbsp;future&nbsp;cognitive&nbsp;impairment&nbsp;at&nbsp;the&nbsp;individual level&nbsp;for&nbsp;preventative interventions.&nbsp;Identifying&nbsp;the&nbsp;most&nbsp;predictive&nbsp;risk&nbsp;factors&nbsp;and socioeconomic groups where&nbsp;predictions&nbsp;are less accurate would provide a foundation&nbsp;for&nbsp;developing targeted&nbsp;prediction&nbsp;models that can&nbsp;identify&nbsp;elderly&nbsp;at high&nbsp;risks&nbsp;of&nbsp;future&nbsp;cognitive&nbsp;impairments. Objectives: To quantify how well demographics, instrumental activities of daily living, activities of daily living,&nbsp;cognitive&nbsp;tests, social&nbsp;factors, psychological&nbsp;factors, diet, exercise and sleep, chronic diseases, and three recently published&nbsp;prediction&nbsp;models&nbsp;predict&nbsp;future&nbsp;cognitive&nbsp;impairments&nbsp;in&nbsp;the&nbsp;general&nbsp;Chinese&nbsp;population and&nbsp;among&nbsp;male, female, rural, urban, educated, and uneducated&nbsp;elderly. Design:&nbsp;The&nbsp;Chinese&nbsp;Longitudinal Healthy Longevity Survey (CLHLS) is a prospective cohort study of&nbsp;elderly&nbsp;Chinese&nbsp;from 23 provinces. Individual information from&nbsp;the&nbsp;2011 CLHLS survey was used to&nbsp;predict&nbsp;if participants would become&nbsp;cognitively&nbsp;impaired&nbsp;by follow-up in 2014. Setting: Population-based. Participants: 4047 CLHLS participants 60 years of age or older without&nbsp;cognitive&nbsp;impairments&nbsp;at baseline were included. Main Outcome:&nbsp;Cognitive&nbsp;impairment&nbsp;was&nbsp;identified&nbsp;through&nbsp;the&nbsp;Chinese&nbsp;language version of&nbsp;the&nbsp;Mini Mental State Examination (MMSE).&nbsp;Predictive&nbsp;ability was quantified using&nbsp;the&nbsp;AUC, sensitivity, and specificity across 20 repeats of 10-fold cross validation where&nbsp;the&nbsp;target variable was an indicator of&nbsp;cognitive&nbsp;impairment&nbsp;3 years from&nbsp;the&nbsp;baseline survey. Results: A total of 337 (8.3%)&nbsp;elderly&nbsp;Chinese&nbsp;became&nbsp;cognitively&nbsp;impaired&nbsp;by&nbsp;the&nbsp;follow up survey.&nbsp;The&nbsp;risk&nbsp;factor&nbsp;groups with&nbsp;the&nbsp;most&nbsp;predictive&nbsp;ability in&nbsp;the&nbsp;general population were demographics (AUC, 0.78, 95% CI, 0.77-0.78),&nbsp;cognitive&nbsp;tests (AUC, 0.72, 95% CI, 0.72-0.73), and instrumental activities of daily living (AUC, 0.71, 95% CI, 0.70-0.71). Demographics,&nbsp;cognitive&nbsp;tests, instrumental activities of daily living, and all three re-created&nbsp;prediction&nbsp;models had significantly higher AUCs when making&nbsp;predictions&nbsp;among&nbsp;women compared to men and&nbsp;among&nbsp;the&nbsp;uneducated compared to&nbsp;the&nbsp;educated. Dietary&nbsp;factors, which have yet to be included in&nbsp;prediction&nbsp;models in China, had more&nbsp;predictive&nbsp;power (AUC, 0.59, 95% CI, 0.58-0.60) than activities of daily living (AUC, 0.57, 95% CI, 0.56-0.57), psychological&nbsp;factors&nbsp;(AUC, 0.58, 95% CI, 0.57-0.59), and chronic diseases (AUC, 0.53, 95% CI, 0.52-0.53). Conclusion and relevance: This study suggests that demographics,&nbsp;cognitive&nbsp;tests, and instrumental activities of daily living are&nbsp;the&nbsp;most&nbsp;useful&nbsp;risk&nbsp;factors&nbsp;for&nbsp;predicting&nbsp;future&nbsp;cognitive&nbsp;impairment&nbsp;among&nbsp;elderly&nbsp;Chinese. However,&nbsp;the&nbsp;most&nbsp;useful&nbsp;risk&nbsp;factors&nbsp;and existing models have lower&nbsp;predictive&nbsp;power&nbsp;among&nbsp;male, urban, and educated&nbsp;elderly&nbsp;Chinese. More efforts are needed to ensure that equally accurate&nbsp;risk&nbsp;assessments can be conducted across different socioeconomic groups in China.</p

Associations between wearable device-measured sleep variability and cognition among older adults

Importance: Healthy sleep habits are protective against adverse health outcomes, but it is unclear how strongly sleep intraindividual variability is associated with cognitive function among older adults. Objective: To examine how intraindividual variability in sleep duration, efficiency, onset timing, and offset timing is associated with cognition among older adults in the United States. Design: Cross-sectional Setting: 2011-2014 waves of the National Health and Nutrition Examination Survey (NHANES) Participants: Older adults aged 60+ with valid accelerometer and cognitive test data Exposures: Accelerometer-derived variability in sleep duration, efficiency, onset timing, and offset timing. Average metrics were also considered for comparison purposes. Main Outcome and Measures: A composite cognitive measure derived by summing z-scores from the Digit Symbol Substitution Test (DSST), Consortium to Establish a Registry for Alzheimer&rsquo;s Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). Results: A final cohort of 2508 older adults aged 60+ with at least three days of accelerometer wear time who completed all three cognitive tests in the NHANES 2011-2014 waves were included in this study. After adjusting for demographic factors, the presence of diabetes, depressive symptoms, and measures of functional independence, we found that increased intraindividual variability in sleep onset timing was associated with worse cognition (&beta;, -0.12; 95% CI, -0.19 to -0.05), as was increased intraindividual variability in sleep efficiency (&beta;, -0.12; 95% CI, -0.20 to -0.05), and increased intraindividual variability in sleep duration (&beta;, -0.10; 95% CI, -0.17 to -0.03). Conclusion and Relevance: This study found that greater intraindividual variability in sleep duration, efficiency, and onset timing were significantly associated with worse cognition among older adults. Sleep variability metrics can be useful targets for interventions seeking to decrease the risk of cognitive impairments.</p

Using wearable device-based machine learning models to autonomously identify older adults with poor cognition

Conducting cognitive tests is time-consuming for patients and clinicians. Wearable device-based prediction models allow for continuous health monitoring under normal living conditions and could offer an alternative to identifying older adults with cognitive impairments for early interventions. In this study, we first derived novel wearable-based features related to circadian rhythms, ambient light exposure, physical activity levels, sleep, and signal processing. Then, we quantified the ability of wearable-based machine-learning models to predict poor cognition based on outcomes from the Digit Symbol Substitution Test (DSST), the Consortium to Establish a Registry for Alzheimer&rsquo;s Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). We found that the wearable-based models had significantly higher AUCs when predicting all three cognitive outcomes compared to benchmark models containing age, sex, education, marital status, household income, diabetic status, depression symptoms, and functional independence scores. In addition to uncovering previously unidentified wearable-based features that are predictive of poor cognition such as the standard deviation of the midpoints of each person&rsquo;s most active 10-hour periods and least active 5-hour periods, our paper provides proof-of-concept that wearable-based machine learning models can be used to autonomously screen older adults for possible cognitive impairments. Such models offer cost-effective alternatives to conducting initial screenings manually in clinical settings.</p

Development and validation of the Chinese Geriatric Depression Risk calculator (CGD-risk): A screening tool to identify elderly Chinese with depression

Background: The prevalence of depression among China's elderly is high, but stigma surrounding mental illness and a shortage of psychiatrists limit widespread screening and diagnosis of geriatric depression. We sought to develop a screening tool using easy-to-obtain and minimally sensitive predictors to identify elderly Chinese with depressive symptoms (depression hereafter) for referral to mental health services and determine the most important factors for effective screening.Methods: Using nationally representative survey data, we developed and externally validated the Chinese Geri-atric Depression Risk calculator (CGD-Risk). CGD-Risk, a gradient boosting machine learning model, was eval-uated based on discrimination (Concordance (C) statistic), calibration, and through a decision curve analysis. We conducted a sensitivity analysis on a cohort of middle-aged Chinese, a sub-group analysis using three data sets, and created predictor importance and partial dependence plots to enhance interpretability.Results: A total of 5681 elderly Chinese were included in the development data and 12,373 in the external validation data. CGD-Risk showed good discrimination during internal validation (C: 0.81, 95 % CI 0.79 to 0.84) and external validation (C: 0.77, 95 % CI: 0.76, 0.78). Compared to an alternative screening strategy CGD-Risk would correctly identify 17.8 more elderly with depression per 100 people screened. Limitations: We were only able to externally validate a partial version of CGD-Risk due to differences between the internal and external validation data.Conclusions: CGD-Risk is a clinically viable, minimally sensitive screening tool that could identify elderly Chinese at high risk of depression while circumventing issues of response bias from stigma surrounding emotional openness