A circadian pacemaker within the central nervous system regulates the approximately 24-h physiologic rhythms in sleep cycles, hormone secretion, and other physiologic functions. Because the pacemaker cannot be examined directly in humans, markers of pacemaker function must be used to study the pacemaker and its response to environmental stimuli. Core body temperature (CBT), plasma cortisol, and plasma melatonin are three marker variables frequently used to estimate the phase of the human pacemaker. Measurements of circadian phase using markers can contain variability due to the circadian pacemaker itself, the intrinsic variability of the marker relative to the pacemaker, the method of analysis of the marker, and the marker assay. For this report, we compared the mathematical variability of a number of methods of identifying circadian phase from CBT, plasma cortisol, and plasma melatonin data collected in a protocol in which pacemaker variability was minimized using low light levels and regular timing of both the light pattern and the rest/activity schedule. We hoped to assess the relative variabilities of the different physiological markers and the analysis methods. Methods were based on the crossing of an absolute threshold, on the crossing of a relative threshold, or on fitting a curve to all data points. All methods of calculating circadian phase from plasma melatonin data were less variable than those calculated using CBT or cortisol data. The standard deviation for the phase estimates using CBT data was 0.78 h, using cortisol data was 0.65 h, and for the eight analysis methods using melatonin data was 0.23 to 0.35 h. While the variability for these markers might be different for other subject populations and/or less stringent study conditions, assessment of the intrinsic variability of the different calculations of circadian phase can be applied to allow inference of the statistical significance of phase and phase shift calculations, as well as estimation of sample size or statistical power for the number of subjects within an experimental protocol.

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 Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or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.

Non-invasive Gamma ENtrainment Using Sensory stimulation (GENUS) at 40Hz reduces Alzheimer’s disease (AD) pathology such as amyloid and tau levels, prevents cerebral atrophy, and improves behavioral testing performance in mouse models of AD. Here, we report data from (1) a Phase 1 feasibility study (NCT04042922, ClinicalTrials.gov) in cognitively normal volunteers (n = 25), patients with mild AD dementia (n = 16), and patients with epilepsy who underwent intracranial electrode monitoring (n = 2) to assess safety and feasibility of a single brief GENUS session to induce entrainment and (2) a single-blinded, randomized, placebo-controlled Phase 2A pilot study (NCT04055376) in patients with mild probable AD dementia (n = 15) to assess safety, compliance, entrainment, and exploratory clinical outcomes after chronic daily 40Hz sensory stimulation for 3 months. Our Phase 1 study showed that 40Hz GENUS was safe and effectively induced entrainment in both cortical regions and other cortical and subcortical structures such as the hippocampus, amygdala, insula, and gyrus rectus. Our Phase 2A study demonstrated that chronic daily 40Hz light and sound GENUS was well-tolerated and that compliance was equally high in both the control and active groups, with participants equally inaccurate in guessing their group assignments prior to unblinding. Electroencephalography recordings show that our 40Hz GENUS device safely and effectively induced 40Hz entrainment in participants with mild AD dementia. After 3 months of daily stimulation, the group receiving 40Hz stimulation showed (i) lesser ventricular dilation and hippocampal atrophy, (ii) increased functional connectivity in the default mode network as well as with the medial visual network, (iii) better performance on the face-name association delayed recall test, and (iv) improved measures of daily activity rhythmicity compared to the control group. These results support further evaluation of GENUS in a pivotal clinical trial to evaluate its potential as a novel disease-modifying therapeutic for patients with AD.

Study Objectives: Sleep regularity predicts many health-related outcomes. Currently, however, there is no systematic approach to measuring sleep regularity. Traditionally, metrics have assessed deviations in sleep patterns from an individual’s average; these traditional metrics include intra-individual standard deviation (StDev), interdaily stability (IS), and social jet lag (SJL). Two metrics were recently proposed that instead measure variability between consecutive days: composite phase deviation (CPD) and sleep regularity index (SRI). Using large-scale simulations, we investigated the theoretical properties of these five metrics.Methods: Multiple sleep–wake patterns were systematically simulated, including variability in daily sleep timing and/or duration. Average estimates and 95% confidence intervals were calculated for six scenarios that affect the measurement of sleep regularity: “scrambling” the order of days; daily vs. weekly variation; naps; awakenings; “all-nighters”; and length of study.Results: SJL measured weekly but not daily changes. Scrambling did not affect StDev or IS, but did affect CPD and SRI; these metrics, therefore, measure sleep regularity on multi-day and day-to-day timescales, respectively. StDev and CPD did not capture sleep fragmentation. IS and SRI behaved similarly in response to naps and awakenings but differed markedly for all-nighters. StDev and IS required over a week of sleep–wake data for unbiased estimates, whereas CPD and SRI required larger sample sizes to detect group differences.Conclusions: Deciding which sleep regularity metric is most appropriate for a given study depends on a combination of the type of data gathered, the study length and sample size, and which aspects of sleep regularity are most pertinent to the research question.

What can wearable sensors and usage of smart phones tell us about academic performance, self-reported sleep quality, stress and mental health condition? To answer this question, we collected extensive subjective and objective data using mobile phones, surveys, and wearable sensors worn day and night from 66 participants, for 30 days each, totaling 1,980 days of data. We analyzed daily and monthly behavioral and physiological patterns and identified factors that affect academic performance (GPA), Pittsburg Sleep Quality Index (PSQI) score, perceived stress scale (PSS), and mental health composite score (MCS) from SF-12, using these month-long data. We also examined how accurately the collected data classified the participants into groups of high/low GPA, good/poor sleep quality, high/low self-reported stress, high/low MCS using feature selection and machine learning techniques. We found associations among PSQI, PSS, MCS, and GPA and personality types. Classification accuracies using the objective data from wearable sensors and mobile phones ranged from 67-92%.