Association between recovery of frailty state and the nonparametric rest-activity rhythm patterns in the elderly community-dwellers: A 6-month follow-up study during Covid-19 pandemic.

Reversibility of frailty in the elderly has been discussed comprehensively and but association between recovery of frail state and rest-activity rhythm (RAR) patterns remains unclear. The aim of the current study was to examine a predictor of RAR patterns at the baseline against change of frail state after the intervention in the elderly community-dwellers. This study was performed during Covid-19 pandemic, at the period from April in 2020 to March in 2022. Participants were publicly recruited from senior's exercise program hosted by Akita city or Yurihonjo city. The revised Japanese version of the Cardiovascular Health Study criteria (revised J-CHS criteria) was used to assess frail state in each participant before and after the 6-month intervention. To measure the nonparametric RAR parameters consisting of interdaily stability (IS), intra-daily variability (IV), relative amplitude (RA) and average physical activity for the most active 10-h span (M10) or for the least active 5-h span (L5) over the average 24-h profile, an Actiwatch Spectrum Plus device was worn on each participant's non-dominant wrist for seven continuous days. The final samples were 75 participants except for persons with uncompleted data, classified into the improved group (n = 12), the maintained group (n = 53) and the deteriorated group (n = 10) according to frail alteration after the six-month intervention. As a result of the multinomial logistic regression analysis with the reference of the maintained group, the improvement of frail state associated with a low value of IS and total night-sleep time at the baseline, and M10 and L5 at the initial time were also able to predict worsening of frail state after the six-months intervention. A result of this follow-up study provides grounds for our proposal that alterations of RAR patterns in the elderly could be observed in association with recovery or worsening of frail state after the intervention. The potential finding, however, warrants further longitudinal investigation.

Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling.

BACKGROUND: Depression is a prevalent mental disorder that is undiagnosed and untreated in half of all cases. Wearable activity trackers collect fine-grained sensor data characterizing the behavior and physiology of users (ie, digital biomarkers), which could be used for timely, unobtrusive, and scalable depression screening. OBJECTIVE: The aim of this study was to examine the predictive ability of digital biomarkers, based on sensor data from consumer-grade wearables, to detect risk of depression in a working population. METHODS: This was a cross-sectional study of 290 healthy working adults. Participants wore Fitbit Charge 2 devices for 14 consecutive days and completed a health survey, including screening for depressive symptoms using the 9-item Patient Health Questionnaire (PHQ-9), at baseline and 2 weeks later. We extracted a range of known and novel digital biomarkers characterizing physical activity, sleep patterns, and circadian rhythms from wearables using steps, heart rate, energy expenditure, and sleep data. Associations between severity of depressive symptoms and digital biomarkers were examined with Spearman correlation and multiple regression analyses adjusted for potential confounders, including sociodemographic characteristics, alcohol consumption, smoking, self-rated health, subjective sleep characteristics, and loneliness. Supervised machine learning with statistically selected digital biomarkers was used to predict risk of depression (ie, symptom severity and screening status). We used varying cutoff scores from an acceptable PHQ-9 score range to define the depression group and different subsamples for classification, while the set of statistically selected digital biomarkers remained the same. For the performance evaluation, we used k-fold cross-validation and obtained accuracy measures from the holdout folds. RESULTS: A total of 267 participants were included in the analysis. The mean age of the participants was 33 (SD 8.6, range 21-64) years. Out of 267 participants, there was a mild female bias displayed (n=170, 63.7%). The majority of the participants were Chinese (n=211, 79.0%), single (n=163, 61.0%), and had a university degree (n=238, 89.1%). We found that a greater severity of depressive symptoms was robustly associated with greater variation of nighttime heart rate between 2 AM and 4 AM and between 4 AM and 6 AM; it was also associated with lower regularity of weekday circadian rhythms based on steps and estimated with nonparametric measures of interdaily stability and autocorrelation as well as fewer steps-based daily peaks. Despite several reliable associations, our evidence showed limited ability of digital biomarkers to detect depression in the whole sample of working adults. However, in balanced and contrasted subsamples comprised of depressed and healthy participants with no risk of depression (ie, no or minimal depressive symptoms), the model achieved an accuracy of 80%, a sensitivity of 82%, and a specificity of 78% in detecting subjects at high risk of depression. CONCLUSIONS: Digital biomarkers that have been discovered and are based on behavioral and physiological data from consumer wearables could detect increased risk of depression and have the potential to assist in depression screening, yet current evidence shows limited predictive ability. Machine learning models combining these digital biomarkers could discriminate between individuals with a high risk of depression and individuals with no risk.