Background: Weight gain and obesity have reached alarming levels. Eating at a later clock hour is a newly described risk factor for adverse metabolic

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Wearable and mobile devices that capture multi-modal data have the potential to identify risk factors for self-reported high stress and poor mental health and provide information to improve health and wellbeing. Objective: We developed new tools that provide objective, physiological, and behavioral measures using wearable sensors and mobile phones, together with methods that improve their data integrity. Our goal was to examine, using machine learning, how accurately these measures can identify conditions of self-reported high stress and poor mental health, and which of the underlying modalities and measures are most accurate in identifying the conditions. Methods: We designed and conducted the one month SNAPSHOT study whose goal was to investigate how daily behaviors and social networks influence stress, mood, and other health/wellbeing-related factors. We collected over 180,000 hours of data from 251 college students, all recruited within self-identified social groups. For approximately one month, each student filled out standardized pre- and post-questionnaires on mental health and stress; completed twice-daily e-diaries; wore two wrist-based sensors that recorded continuous physical activity, light exposure and autonomic physiology; and installed an app on their phone that recorded call and texting behaviors, and geolocation patterns. We developed tools to make data collection more efficient including (i) automatic sensor and mobile phone data-check systems and (ii) an e-diary administrative module for study investigators to locate possible errors in the e-diaries and communicate with participants to correct their entries promptly that reduced the time taken to clean e-diary data by 69%. We constructed features and applied machine learning techniques to the multimodal data to identify factors associated with self-reported stress and mental health reported at the end of the study, including behaviors that can be possibly modified by the individual to improve these measures. Results: We identified physiological sensor, phone, and mobility features that were best predictors for classifying self-reported high/low stress and mental health. In general, features derived from wearable wrist-worn sensors performed better than features derived from mobile phone or modifiable behaviors. Features derived from wearable sensors, including skin conductance and temperature, reached 78% accuracy for classifying students into high/low stress groups and 86% accuracy for classifying high/low mental health groups. Features derived from modifiable behaviors, including number of naps, studying duration, phone calls, mobility patterns, and phone-screen-on time, reached 74% accuracy for classifying students into high/low stress groups and 78% accuracy for classifying high/low mental health groups. Conclusions: New semi-automated tools improved the efficiency of long-term ambulatory data collection using mobile and wearable devices. Applying machine learning to the resulting data revealed a set of both objective features and modifiable behavioral features that can classify self-reported high/low stress and mental health groups in a college student population better than previous studies and showed new insights into digital phenotyping.

Sleep inertia, or the grogginess felt upon awakening, is associated with significant cognitive performance decrements that dissipate as time awake increases. This impairment in cognitive performance has been observed in both tightly controlled in-laboratory studies and in real-world scenarios. Further, these decrements in performance are exaggerated by prior sleep loss and the time of day in which a person awakens. This review will examine current insights into the causes of sleep inertia, factors that may positively or negatively influence the degree of sleep inertia, the consequences of sleep inertia both in the laboratory and in real-world settings, and lastly discuss potential countermeasures to lessen the impact of sleep inertia.

Significance: Millions of individuals obtain insufficient sleep on a daily basis, which leads to impaired performance. Whether these decrements are caused by short sleep duration or extended wakefulness is unknown. In this study, healthy volunteers were randomized into either a chronically sleep-restricted or control protocol while living on a 20-h “day,” thus enabling short sleep without extended wakefulness. We demonstrate that chronic insufficient sleep, even without extended wakefulness, leads to neurobehavioral performance decrements at all times of the day, even when the circadian system is promoting arousal. These findings have implications for the understanding of basic physiology, the substantial population who chronically obtains insufficient sleep, and all of us who depend on sleep-restricted individuals working in safety-sensitive occupations.