Deviations from normal bedtimes are associated with short-term increases in resting heart rate

An exploration into the sleep of workers on block-calving, pasture-based dairy farms

The benefits of sufficient and high-quality sleep for people are well documented. Insufficient sleep increases the risk of accidents, injuries, and negative health implications for people. This is especially relevant for farmers, as they work with large animals and machinery. Dairy farming often requires early start times and long days, particularly over the high workload calving period in block calving, pasture-based systems. However, there is little published data quantifying the sleep quantity and quality of farmers over this period. In this study, the sleep patterns of workers (n = 33) on 10 New Zealand dairy farms was measured for 90 d over the spring calving period using a sleep measuring device (OuraTM ring, Oura Health Ltd., Oulu, Finland). Total sleep time (TST) averaged 6 h 15 min, lower than the required 7 to 9 h for optimal wellbeing and cognitive functioning. TST decreased over the calving period and was significantly correlated with both sleep start and wake times. Factors such as work start time, farm location, and role on farm influenced sleep quantity and quality; indicating adjusting these on-farm factors could positively impact TST. Further research is required to better understand sleep and its effect on dairy farmers, over both the calving period and the remaining months of the year.

Associations of Subjective Sleep Quality with Wearable Device-Derived Resting Heart Rate During REM Sleep and Non-REM Sleep in a Cohort of Japanese Office Workers

Background

Associations between subjective sleep quality and stage-specific heart rate (HR) may have important clinical relevance when aiming to optimize sleep and overall health. The majority of previously studies have been performed during short periods under laboratory-based conditions. The aim of this study was to investigate the associations of subjective sleep quality with heart rate during REM sleep (HR REMS) and non-REM sleep (HR NREMS) using a wearable device (Fitbit Versa).

Methods

This is a secondary analysis of data from the intervention group of a randomized controlled trial (RCT) performed between December 3, 2018, and March 2, 2019, in Tokyo, Japan. The intervention group consisted of 179 Japanese office workers with metabolic syndrome (MetS), Pre-MetS or a high risk of developing MetS. HR was collected with a wearable device and sleep quality was assessed with a mobile application where participants answered The St. Mary's Hospital Sleep Questionnaire. Both HR and sleep quality was collected daily for a period of 90 days. Associations of between-individual and within-individual sleep quality with HR REMS and HR NREMS were analyzed with multi-level model regression in 3 multivariate models.

Results

The cohort consisted of 92.6% men (n=151) with a mean age (± standard deviation) of 44.1 (±7.5) years. A non-significant inverse between-individual association was observed for sleep quality with HR REMS (HR REMS -0.18; 95% CI -0.61, 0.24) and HR NREMS (HR NREMS -0.23; 95% CI -0.66, 0.21), in the final multivariable adjusted models; a statistically significant inverse within-individual association was observed for sleep quality with HR REMS (HR REMS -0.21 95% CI -0.27, -0.15) and HR NREMS (HR NREMS -0.21 95% CI -0.27, -0.14) after final adjustments for covariates.

Conclusion

The present study shows a statistically significant within-individual association of subjective sleep quality with HR REMS and HR NREMS. These findings emphasize the importance of considering sleep quality on the individual level. The results may contribute to early detection and prevention of diseases associated with sleep quality which may have important implications on public health given the high prevalence of sleep disturbances in the population.

Associations between resting heart rate and cognitive decline in Chinese oldest old individuals: a longitudinal cohort study

BACKGROUND

The trajectories of cognitive function in the oldest old individuals is unclear, and the relationship between resting heart rate (RHR) and cognitive decline is controversial.

METHODS

3300 participants who had cognitive function repeatedly measured 4 ~ 8 times were included, and latent class growth mixed models were used to identified the cognitive function trajectories. Cognitive decline was defined by the trajectory shapes, considering level and slope. After excluding individuals with sinus rhythm abnormal, 3109 subjects were remained and were divided into five groups by their RHR. Logistic regression models were used to estimate the relationship between RHR and cognitive decline.

RESULTS

Three distinct cognitive function trajectory groups were identified: high-stable (n = 1226), medium-decreasing (n = 1526), and rapid-decreasing (n = 357). Individuals of medium/rapid-decreasing group were defined as cognitive decline. Adjusting for covariates, the odds ratios (95% confidence intervals) of RHR sub-groups were 1.19 (0.69, 2.05), 1.27 (1.03, 1.56), 1.30 (1.01, 1.67) and 1.62 (1.07, 2.47) for those RHR < 60 bpm, 70 ~ 79 bpm, 80 ~ 89 bpm and > 90 bpm respectively, compared with those RHR 60 ~ 69 bpm. The interaction effect between RHR and physical activity (PA) on cognitive decline was found, and stratification analysis was presented that higher RHR would only show risk effects on cognitive decline in those with physical inactivity (P < 0.05 for all).

CONCLUSIONS

Our study demonstrates RHR more than 70 bpm present significant risk effect on cognitive decline, and this relationship is modified by PA. Elder population with physical inactivity and higher RHR should be paid more attention to prevent cognitive decline.

The importance of sleep regularity: a consensus statement of the National Sleep Foundation sleep timing and variability panel

OBJECTIVE

To develop and present consensus findings of the National Sleep Foundation sleep timing and variability panel regarding the impact of sleep timing variability on health and performance.

METHODS

The National Sleep Foundation assembled a panel of sleep and circadian experts to evaluate the scientific evidence and conduct a formal consensus and voting procedure. A systematic literature review was conducted using the NIH National Library of Medicine PubMed database, and panelists voted on the appropriateness of 3 questions using a modified Delphi RAND/UCLA Appropriateness Method with 2 rounds of voting.

RESULTS

The literature search and panel review identified 63 full text publications to inform consensus voting. Panelists achieved consensus on each question: (1) is daily regularity in sleep timing important for (a) health or (b) performance? and (2) when sleep is of insufficient duration during the week (or work days), is catch-up sleep on weekends (or non-work days) important for health? Based on the evidence currently available, panelists agreed to an affirmative response to all 3 questions.

CONCLUSIONS

Consistency of sleep onset and offset timing is important for health, safety, and performance. Nonetheless, when insufficient sleep is obtained during the week/work days, weekend/non-work day catch-up sleep may be beneficial.

Multifractal foundations of biomarker discovery for heart disease and stroke

Any reliable biomarker has to be specific, generalizable, and reproducible across individuals and contexts. The exact values of such a biomarker must represent similar health states in different individuals and at different times within the same individual to result in the minimum possible false-positive and false-negative rates. The application of standard cut-off points and risk scores across populations hinges upon the assumption of such generalizability. Such generalizability, in turn, hinges upon this condition that the phenomenon investigated by current statistical methods is ergodic, i.e., its statistical measures converge over individuals and time within the finite limit of observations. However, emerging evidence indicates that biological processes abound with nonergodicity, threatening this generalizability. Here, we present a solution for how to make generalizable inferences by deriving ergodic descriptions of nonergodic phenomena. For this aim, we proposed capturing the origin of ergodicity-breaking in many biological processes: cascade dynamics. To assess our hypotheses, we embraced the challenge of identifying reliable biomarkers for heart disease and stroke, which, despite being the leading cause of death worldwide and decades of research, lacks reliable biomarkers and risk stratification tools. We showed that raw R-R interval data and its common descriptors based on mean and variance are nonergodic and non-specific. On the other hand, the cascade-dynamical descriptors, the Hurst exponent encoding linear temporal correlations, and multifractal nonlinearity encoding nonlinear interactions across scales described the nonergodic heart rate variability more ergodically and were specific. This study inaugurates applying the critical concept of ergodicity in discovering and applying digital biomarkers of health and disease.

Depression deconstructed: Wearables and passive digital phenotyping for analyzing individual symptoms

Wearable technology enables unobtrusive collection of longitudinally dense data, allowing for continuous monitoring of physiology and behavior. These digital phenotypes, or device-based indicators, are frequently leveraged to study depression. However, they are usually considered alongside questionnaire sum-scores which collapse the symptomatic gamut into a general representation of severity. To explore the contributions of passive sensing streams more precisely, associations of nine passive sensing-derived features with self-report responses to Center for Epidemiologic Studies Depression (CES-D) items were modeled. Using data from the NetHealth study on N=469 college students, this work generated mixed ordinal logistic regression models to summarize contributions of pulse, movement, and sleep data to depression symptom detection. Emphasizing the importance of the college context, wearable features displayed unique and complementary properties in their heterogeneously significant associations with CES-D items. This work provides conceptual and exploratory blueprints for a reductionist approach to modeling depression within passive sensing research.

Adolescent sleep health in Africa: a scoping review protocol

INTRODUCTION

Problematic sleep is a major threat to health and quality of life among adolescents. Hence, to provide directions for research and interventions, there is a need to examine the literature on adolescent sleep health in Africa. However, available studies on adolescent sleep health in Africa have not been properly mapped. Thus, this scoping review aims to investigate the extent and type of available evidence concerning sleep health among adolescents in Africa and to highlight the relationship of adolescent sleep health with adverse mental health outcomes and cardiometabolic risk factors. The review will further highlight areas of agreement and controversies on adolescent sleep health, and identify evidence gaps that require research attention across the continent.

METHODS AND ANALYSIS

This scoping review will be conducted using Arksey and O'Malley's six-step procedure. Thus, we have prepared this protocol according to the framework for scoping reviews developed by the Joanna Briggs Institute. To identify eligible studies, we will search MEDLINE, Scopus, PsycINFO, AJOL, JSTOR, HINARI and Google Scholar. The review will include all published articles in English, French, Spanish, Portuguese and Italian languages on adolescent sleep health in Africa from the inception of the databases, while relevant information will be extracted from included studies using an adapted data extraction tool. The results will be presented using tables and charts as appropriate.

ETHICS AND DISSEMINATION

The scoping review does not require ethical approval because the publications to be used for the review are publicly available and the study does not involve contact with humans or other animals as research participants. Furthermore, clinical records will not be used for the study. Upon completion, findings from the study will be disseminated through presentations at scientific meetings and publication in a relevant peer-reviewed journal.

SCOPING REVIEW REGISTRATION

Open Science Framework (https://osf.io/5sjwq/).

Knowledge Discovery in Ubiquitous and Personal Sleep Tracking: Scoping Review

BACKGROUND

Over the past few decades, there has been a rapid increase in the number of wearable sleep trackers and mobile apps in the consumer market. Consumer sleep tracking technologies allow users to track sleep quality in naturalistic environments. In addition to tracking sleep per se, some sleep tracking technologies also support users in collecting information on their daily habits and sleep environments and reflecting on how those factors may contribute to sleep quality. However, the relationship between sleep and contextual factors may be too complex to be identified through visual inspection and reflection. Advanced analytical methods are needed to discover new insights into the rapidly growing volume of personal sleep tracking data.

OBJECTIVE

This review aimed to summarize and analyze the existing literature that applies formal analytical methods to discover insights in the context of personal informatics. Guided by the problem-constraints-system framework for literature review in computer science, we framed 4 main questions regarding general research trends, sleep quality metrics, contextual factors considered, knowledge discovery methods, significant findings, challenges, and opportunities of the interested topic.

METHODS

Web of Science, Scopus, ACM Digital Library, IEEE Xplore, ScienceDirect, Springer, Fitbit Research Library, and Fitabase were searched to identify publications that met the inclusion criteria. After full-text screening, 14 publications were included.

RESULTS

The research on knowledge discovery in sleep tracking is limited. More than half of the studies (8/14, 57%) were conducted in the United States, followed by Japan (3/14, 21%). Only a few of the publications (5/14, 36%) were journal articles, whereas the remaining were conference proceeding papers. The most used sleep metrics were subjective sleep quality (4/14, 29%), sleep efficiency (4/14, 29%), sleep onset latency (4/14, 29%), and time at lights off (3/14, 21%). Ratio parameters such as deep sleep ratio and rapid eye movement ratio were not used in any of the reviewed studies. A dominant number of the studies applied simple correlation analysis (3/14, 21%), regression analysis (3/14, 21%), and statistical tests or inferences (3/14, 21%) to discover the links between sleep and other aspects of life. Only a few studies used machine learning and data mining for sleep quality prediction (1/14, 7%) or anomaly detection (2/14, 14%). Exercise, digital device use, caffeine and alcohol consumption, places visited before sleep, and sleep environments were important contextual factors substantially correlated to various dimensions of sleep quality.

CONCLUSIONS

This scoping review shows that knowledge discovery methods have great potential for extracting hidden insights from a flux of self-tracking data and are considered more effective than simple visual inspection. Future research should address the challenges related to collecting high-quality data, extracting hidden knowledge from data while accommodating within-individual and between-individual variations, and translating the discovered knowledge into actionable insights.

The impact of the covid-19 pandemic on lifestyle behaviors in U.S. college students

OBJECTIVE

To investigate COVID-19's impact on college student health behaviors.

PARTICIPANTS

189 college students.

METHODS

Participants completed an online survey on behaviors relating to sleep, sedentary activities, and physical activity before and during the COVID-19 pandemic. Comparisons utilized Students' dependent t-test or Wilcoxon signed-rank tests.

RESULTS

There was an increase in time to fall asleep (before: 23.4 ± 18.0 vs. during: 42.8 ± 44.3 min·day^-1, p < 0.001), time spent in bed (before: 7.8 ± 1.5 vs. during: 8.5 ± 1.5 hr·day^-1, p < 0.001), as well as shifts in later bed and awake time (p < 0.001). Total sedentary time increased during the pandemic (before: 9.0 ± 3.8 vs. during: 9.9 ± 4.1 hr·day^-1, p = 0.016); and time spent using a TV, computer, or phone (before: 3.1 ± 1.9 vs. during: 4.2 ± 2.3 hr·day^-1, p < 0.001). There was a significant decrease in moderate-vigorous activity (before: 123.8 ± 96.0 vs. during: 108.9 ± 75.5 min·week^-1, p = 0.028) and resistance training days (before: 2.4 ± 2.1 vs. during: 1.7 ± 2.1 days·week^-1, p < 0.001).

CONCLUSIONS

COVID-19 negatively influenced health behaviors in college students.

Heart Rate Variability and Chronotype in Young Adult Men

Whether morning heart rate variability (HRV) predicts the magnitude of its circadian variation in the absence of disease or is influenced by chronotype is unclear. We aimed to quantify associations between (1) morning HRV and its diurnal change, and (2) morning HRV and a Morningness−Eveningness Questionnaire (MEQ)-derived chronotype. Resting electrocardiograms were obtained in the morning and evening on separate days in a counterbalanced order to determine the mean RR interval, root mean square of successive differences (RMSSD), and standard deviation of normal-to-normal RR intervals (SDNN) in 23 healthy men (24.6 ± 3.4 yrs; body mass index: 25.3 ± 2.8 kg/m2). The MEQ was completed during the first laboratory visit. Morning RMSSD and SDNN were significantly higher (Ps < 0.05) than evening values. Morning RMSSD and SDNN were associated with their absolute (Ps < 0.0001), and relative diurnal changes (Ps < 0.01). No associations were observed between HRV parameters and the MEQ chronotypes (Ps > 0.09). Morning HRV was a stronger determinant of its evening change than chronotype. Greater diurnal variation in HRV was dependent on higher morning values. Strategies to improve basal HRV may therefore support healthier cardio-autonomic circadian profiles in healthy young men.

Snoozing: an examination of a common method of waking

Study Objectives

Snoozing was defined as using multiple alarms to accomplish waking, and considered as a method of sleep inertia reduction that utilizes the stress system. Surveys measured snoozing behavior including who, when, how, and why snoozing occurs. In addition, the physiological effects of snoozing on sleep were examined via wearable sleep staging and heart rate (HR) activity, both over a long time scale, and on the days that it occurs. We aimed to establish snoozing as a construct in need of additional study.

Methods

A novel survey examined snoozing prevalence, how snoozing was accomplished, and explored possible contributors and motivators of snoozing behavior in 450 participants. Trait- and day-level surveys were combined with wearable data to determine if snoozers sleep differently than nonsnoozers, and how snoozers and nonsnoozers differ in other areas, such as personality.

Results

57% of participants snoozed. Being female, younger, having fewer steps, having lower conscientiousness, having more disturbed sleep, and being a more evening chronotype increased the likelihood of being a snoozer. Snoozers had elevated resting HR and showed lighter sleep before waking. Snoozers did not sleep less than nonsnoozers nor did they feel more sleepiness or nap more often.

Conclusions

Snoozing is a common behavior associated with changes in sleep physiology before waking, both in a trait- and state-dependent manner, and is influenced by demographic and behavioral traits. Additional research is needed, especially in detailing the physiology of snoozing, its impact on health, and its interactions with observational studies of sleep.

Physiological markers of sleep quality: A scoping review

Understanding the associations between adequate sleep, performance and health outcomes is vital, yet a major limitation in the design and interpretation of studies of sleep and performance is the variability of subjective and objective markers used to assess sleep quality. The aim of this scoping review is to investigate how various physiological signals recorded during sleep or wakefulness relate to objective measures of cognitive or physical performance and subjectively perceived sleep quality to inform conceptual understanding of the elusive, amorphous, and multi-dimensional construct of sleep quality. We also aimed to suggest priorities for future areas of research in sleep quality and performance. We searched six databases ultimately yielding 439 studies after duplicate removal. Sixty-five studies were selected for full review. In general, correlations between objectively measured sleep and objective performance or subjectively assessed sleep quality were weak to moderate. Slow wave sleep was moderately correlated with better performance on tasks of vigilance, motor speed, and executive function as well as better subjective sleep quality and feeling well-rested, suggesting that slow wave sleep may be important for sleep quality and optimal daytime performance. However, these findings were inconsistent across studies. Increased sleep fragmentation was associated with poorer subjective sleep quality in both polysomnographic and actigraphic studies. Studies which simultaneously assessed physiologic sleep measures, performance measures and subjective sleep perception were few, limiting the ability to evaluate correlations between subjective and objective outcomes concurrently in the same individuals. Factors influencing the relationship between sleep quality and performance include circadian variability, sleep inertia, and mismatch between sleep stages studied and outcome measures of choice. Ultimately, the determination of "quality sleep" remains largely subjective and inconsistently quantifiable by current measures. Methods evaluating sleep as a continuous measure rather than traditional sleep stages may provide an intriguing approach to future studies of sleep and performance. Future well-designed studies using novel measures of sleep or multimodal ambulatory wearables assessing the three domains of sleep and performance (objective sleep physiology, objective performance, and subjective sleep quality) are needed to better define quality sleep.

Using Fitbit data to monitor the heart rate evolution patterns of college students

ObjectiveTo investigate what social, psychological, personality, and behavioral factors affect overtime heart rate changes of college students. Participants: The daily heart rates of over 600 undergraduates at the University of Notre Dame were unobtrusively recorded via Fitbit devices from August 16, 2015, to May 13, 2017. Method: Latent Growth-Curve modeling strategy is utilized to examine how daily mean heart rate and its standard deviation change over time, and what foregoing factors predict observed changes. Results: The mean heart rate increased and its standard deviation stayed the same over the 637 days. Heart rate levels go up with that of social contacts, an indicator of peer influence. Both daily heart rate levels and changes are also affected by multiple external factors. Conclusion: Human heart rate is not only a physiological phenomenon but also a social-psychological one, as it is systematically affected by peer networks, social contexts, and human activities.

The Impact of Wearable Technologies in Health Research: Scoping Review

Background

Wearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen substantially in recent years. However, there is a shortage of aggregated insights into how wearables have been used in health research.

Objective

In this review, we aim to broadly overview and categorize the current research conducted with affordable wearable devices for health research.

Methods

We performed a scoping review to understand the use of affordable, consumer-grade wearables for health research from a population health perspective using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. A total of 7499 articles were found in 4 medical databases (PubMed, Ovid, Web of Science, and CINAHL). Studies were eligible if they used noninvasive wearables: worn on the wrist, arm, hip, and chest; measured vital signs; and analyzed the collected data quantitatively. We excluded studies that did not use wearables for outcome assessment and prototype studies, devices that cost >€500 (US $570), or obtrusive smart clothing.

Results

We included 179 studies using 189 wearable devices covering 10,835,733 participants. Most studies were observational (128/179, 71.5%), conducted in 2020 (56/179, 31.3%) and in North America (94/179, 52.5%), and 93% (10,104,217/10,835,733) of the participants were part of global health studies. The most popular wearables were fitness trackers (86/189, 45.5%) and accelerometer wearables, which primarily measure movement (49/189, 25.9%). Typical measurements included steps (95/179, 53.1%), heart rate (HR; 55/179, 30.7%), and sleep duration (51/179, 28.5%). Other devices measured blood pressure (3/179, 1.7%), skin temperature (3/179, 1.7%), oximetry (3/179, 1.7%), or respiratory rate (2/179, 1.1%). The wearables were mostly worn on the wrist (138/189, 73%) and cost <€200 (US $228; 120/189, 63.5%). The aims and approaches of all 179 studies revealed six prominent uses for wearables, comprising correlations—wearable and other physiological data (40/179, 22.3%), method evaluations (with subgroups; 40/179, 22.3%), population-based research (31/179, 17.3%), experimental outcome assessment (30/179, 16.8%), prognostic forecasting (28/179, 15.6%), and explorative analysis of big data sets (10/179, 5.6%). The most frequent strengths of affordable wearables were validation, accuracy, and clinical certification (104/179, 58.1%).

Conclusions

Wearables showed an increasingly diverse field of application such as COVID-19 prediction, fertility tracking, heat-related illness, drug effects, and psychological interventions; they also included underrepresented populations, such as individuals with rare diseases. There is a lack of research on wearable devices in low-resource contexts. Fueled by the COVID-19 pandemic, we see a shift toward more large-sized, web-based studies where wearables increased insights into the developing pandemic, including forecasting models and the effects of the pandemic. Some studies have indicated that big data extracted from wearables may potentially transform the understanding of population health dynamics and the ability to forecast health trends.

What possibly affects nighttime heart rate? Conclusions from N-of-1 observational data

Background

Heart rate (HR), especially at nighttime, is an important biomarker for cardiovascular health. It is known to be influenced by overall physical fitness, as well as daily life physical or psychological stressors like exercise, insufficient sleep, excess alcohol, certain foods, socialization, or air travel causing physiological arousal of the body. However, the exact mechanisms by which these stressors affect nighttime HR are unclear and may be highly idiographic (i.e. individual-specific). A single-case or “ n-of-1” observational study (N1OS) is useful in exploring such suggested effects by examining each subject's exposure to both stressors and baseline conditions, thereby characterizing suggested effects specific to that individual.

Objective

Our objective was to test and generate individual-specific N1OS hypotheses of the suggested effects of daily life stressors on nighttime HR. As an N1OS, this study provides conclusions for each participant, thus not requiring a representative population.

Methods

We studied three healthy, nonathlete individuals, collecting the data for up to four years. Additionally, we evaluated model-twin randomization (MoTR), a novel Monte Carlo method facilitating the discovery of personalized interventions on stressors in daily life.

Results

We found that physical activity can increase the nighttime heart rate amplitude, whereas there were no strong conclusions about its suggested effect on total sleep time. Self-reported states such as exercise, yoga, and stress were associated with increased (for the first two) and decreased (last one) average nighttime heart rate.

Conclusions

This study implemented the MoTR method evaluating the suggested effects of daily stressors on nighttime heart rate, sleep time, and physical activity in an individualized way: via the N-of-1 approach. A Python implementation of MoTR is freely available.

HRS White Paper on Clinical Utilization of Digital Health Technology

This collaborative statement from the Digital Health Committee of the Heart Rhythm Society provides everyday clinical scenarios in which wearables may be utilized by patients for cardiovascular health and arrhythmia management. We describe herein the spectrum of wearables that are commercially available for patients, and their benefits, shortcomings and areas for technological improvement. Although wearables for rhythm diagnosis and management have not been examined in large randomized clinical trials, undoubtedly the usage of wearables has quickly escalated in clinical practice. This document is the first of a planned series in which we will update information on wearables as they are revised and released to consumers.

A longitudinal analysis of COVID-19 lockdown stringency on sleep and resting heart rate measures across 20 countries

Lockdowns imposed to stem the spread of COVID-19 massively disrupted the daily routines of many worldwide, but studies to date have been mostly confined to observations within a limited number of countries, based on subjective reports and surveys from specific time periods during the pandemic. We investigated associations between lockdown stringency and objective sleep and resting-heart rate measures in ~ 113,000 users of a consumer sleep tracker across 20 countries from Jan to Jul 2020, compared to an equivalent period in 2019. With stricter lockdown measures, midsleep times were universally delayed, particularly on weekdays, while midsleep variability and resting heart rate declined. These shifts (midsleep: + 0.09 to + 0.58 h; midsleep variability: − 0.12 to − 0.26 h; resting heart rate: − 0.35 to − 2.08 bpm) correlated with the severity of lockdown across different countries (all Ps < 0.001) and highlight the graded influence of stringency lockdowns on human physiology.

Trait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data

Using polysomnography over multiple weeks to characterize an individual’s habitual sleep behavior while accurate, is difficult to upscale. As an alternative, we integrated sleep measurements from a consumer sleep-tracker, smartphone-based ecological momentary assessment, and user-phone interactions in 198 participants for 2 months. User retention averaged >80% for all three modalities. Agreement in bed and wake time estimates across modalities was high (rho = 0.81–0.92) and were adrift of one another for an average of 4 min, providing redundant sleep measurement. On the ~23% of nights where discrepancies between modalities exceeded 1 h, k-means clustering revealed three patterns, each consistently expressed within a given individual. The three corresponding groups that emerged differed systematically in age, sleep timing, time in bed, and peri-sleep phone usage. Hence, contrary to being problematic, discrepant data across measurement modalities facilitated the identification of stable interindividual differences in sleep behavior, underscoring its utility to characterizing population sleep and peri-sleep behavior.

Intensity of usual care physical therapy during inpatient rehabilitation for people with neurologic diagnoses

BACKGROUND

Early, intense rehabilitation is essential to promote recovery after stroke, spinal cord injury (SCI), and traumatic brain injury (TBI). However, intensity of usual care rehabilitation interventions during inpatient rehabilitation are poorly characterized.

OBJECTIVE

To describe the intensity of usual care rehabilitation interventions completed during the subacute phase of recovery from neurologic injury.

DESIGN

Observational.

SETTING

Inpatient rehabilitation facility.

INTERVENTIONS

Twenty-two usual care physical therapy interventions were grouped into six categories: gait (four activities), functional (two), strengthening (four), aerobic (six), balance (four), and wheelchair (two).

PATIENTS

Patients admitted to inpatient rehabilitation with a primary diagnosis of stroke, SCI or TBI within 6 months of injury.

MAIN OUTCOME MEASURE(S)

Cardiovascular intensity (physiological and perceived) was recorded during rehabilitation activity sessions. Physiological intensity was assessed by heart rate reserve (HRR) via a Polar A370 Fitness Watch and characterized as very light (<30%), light (30-39%), moderate (40-59%), vigorous (60-89%), and near maximal (≥90%). Perceived intensity was assessed using the Rating of Perceived Exertion scale.

RESULTS

Patients (stroke n = 16 [number of activity sessions = 388/average session duration = 15.1 min]; SCI n = 15 [299/27.3 min]; TBI n = 15 [340/13.4 min]) participated. For patients with stroke, moderate-to-vigorous HRR was attained between 42% (aerobic exercise) to 55% (wheelchair propulsion) of activity sessions. For patients with SCI, moderate-to-vigorous HRR was attained between 29% (strength training) to 46% (gait training) of activity sessions. For patients with TBI, moderate-to-vigorous HRR was attained between 29% (balance activities) to 47% (gait training) of activity sessions. Associations between HRR and rate of perceived exertion were very weak across stroke (r = 0.12), SCI (r = 0.18), and TBI (r = 0.27).

CONCLUSIONS

Patients with stroke, SCI, and TBI undergoing inpatient rehabilitation achieve moderate-to-vigorous intensity during some usual care activities such as gait training. Patient perception of intensity was dissimilar to physiological response.

Day-to-day variability in sleep parameters and depression risk: a prospective cohort study of training physicians

While 24-h total sleep time (TST) is established as a critical driver of major depression, the relationships between sleep timing and regularity and mental health remain poorly characterized because most studies have relied on either self-report assessments or traditional objective sleep measurements restricted to cross-sectional time frames and small cohorts. To address this gap, we assessed sleep with a wearable device, daily mood with a smartphone application and depression through the 9-item Patient Health Questionnaire (PHQ-9) over the demanding first year of physician training (internship). In 2115 interns, reduced TST (b = -0.11, p < 0.001), later bedtime (b = 0.068, p = 0.015), along with increased variability in TST (b = 0.4, p = 0.0012) and in wake time (b = 0.081, p = 0.005) were associated with more depressive symptoms. Overall, the aggregated impact of sleep variability parameters and of mean sleep parameters on PHQ-9 were similar in magnitude (both r² = 0.01). Within individuals, increased TST (b = 0.06, p < 0.001), later wake time (b = 0.09, p < 0.001), earlier bedtime (b = - 0.07, p < 0.001), as well as lower day-to-day shifts in TST (b = -0.011, p < 0.001) and in wake time (b = -0.004, p < 0.001) were associated with improved next-day mood. Variability in sleep parameters substantially impacted mood and depression, similar in magnitude to the mean levels of sleep parameters. Interventions that target sleep consistency, along with sleep duration, hold promise to improve mental health.

Big data-driven precision medicine: Starting the custom-made era of iatrology

Precision medicine is a new therapeutic concept and method emerging in recent years. The rapid development of precision medicine is driven by the development of omics related technology, biological information and big data science. Precision medicine is provided to implement precise and personalized treatment for diseases and specific patients. Precision medicine is commonly used in the diagnosis, treatment and prevention of various diseases. This review introduces the application of precision medicine in eight systematic diseases of the human body, and systematically presenting the current situation of precision medicine. At the same time, the shortcomings and limitations of precision medicine are pointed out. Finally, we prospect the development of precision medicine.