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Wearable and mobile devices that capture multimodal data have the potential to identify risk factors for high stress and poor mental health and to provide information to improve health and well-being. 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. The aim of this study was to examine, using machine learning, how accurately these measures could identify conditions of self-reported high stress and poor mental health and which of the underlying modalities and measures were most accurate in identifying those conditions. We designed and conducted the 1-month SNAPSHOT study that investigated how daily behaviors and social networks influence self-reported stress, mood, and other health or well-being-related factors. We collected over 145,000 hours of data from 201 college students (age: 18-25 years, male:female=1.8:1) at one university, all recruited within self-identified social groups. Each student filled out standardized pre- and postquestionnaires on stress and mental health; during the month, each student completed twice-daily electronic diaries (e-diaries), wore two wrist-based sensors that recorded continuous physical activity and autonomic physiology, and installed an app on their mobile phone that recorded phone usage and geolocation patterns. We developed tools to make data collection more efficient, including data-check systems for sensor and mobile phone data and 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, which reduced the time taken to clean e-diary data by 69%. We constructed features and applied machine learning to the multimodal data to identify factors associated with self-reported poststudy stress and mental health, including behaviors that can be possibly modified by the individual to improve these measures. We identified the physiological sensor, phone, mobility, and modifiable behavior features that were best predictors for stress and mental health classification. In general, wearable sensor features showed better classification performance than mobile phone or modifiable behavior features. Wearable sensor features, including skin conductance and temperature, reached 78.3% (148/189) accuracy for classifying students into high or low stress groups and 87% (41/47) accuracy for classifying high or low mental health groups. Modifiable behavior features, including number of naps, studying duration, calls, mobility patterns, and phone-screen-on time, reached 73.5% (139/189) accuracy for stress classification and 79% (37/47) accuracy for mental health classification. New semiautomated tools improved the efficiency of long-term ambulatory data collection from wearable and mobile devices. Applying machine learning to the resulting data revealed a set of both objective features and modifiable behavioral features that could classify self-reported high or low stress and mental health groups in a college student population better than previous studies and showed new insights into digital phenotyping.

The association of irregular sleep schedules with circadian timing and academic performance has not been systematically examined. We studied 61 undergraduates for 30 days using sleep diaries, and quantified sleep regularity using a novel metric, the sleep regularity index (SRI). In the most and least regular quintiles, circadian phase and light exposure were assessed using salivary dim-light melatonin onset (DLMO) and wrist-worn photometry, respectively. DLMO occurred later (00:08 ± 1:54 vs. 21:32 ± 1:48; p < 0.003); the daily sleep propensity rhythm peaked later (06:33 ± 0:19 vs. 04:45 ± 0:11; p < 0.005); and light rhythms had lower amplitude (102 ± 19 lux vs. 179 ± 29 lux; p < 0.005) in Irregular compared to Regular sleepers. A mathematical model of the circadian pacemaker and its response to light was used to demonstrate that Irregular vs. Regular group differences in circadian timing were likely primarily due to their different patterns of light exposure. A positive correlation (r = 0.37; p < 0.004) between academic performance and SRI was observed. These findings show that irregular sleep and light exposure patterns in college students are associated with delayed circadian rhythms and lower academic performance. Moreover, the modeling results reveal that light-based interventions may be therapeutically effective in improving sleep regularity in this population.

Sleep deprivation and fatigue are common subjective complaints among astronauts. Previous studies of sleep and hypnotic drug use in space have been limited to post-flight subjective survey data or in-flight objective data collection from a small number of crew members. We aimed to characterise representative sleep patterns of astronauts on both short-duration and long-duration spaceflight missions. For this observational study, we recruited crew members assigned to Space Transportation System shuttle flights with in-flight experiments between July 12, 2001, and July 21, 2011, or assigned to International Space Station (ISS) expeditions between Sept 18, 2006, and March 16, 2011. We assessed sleep–wake timing objectively via wrist actigraphy, and subjective sleep characteristics and hypnotic drug use via daily logs, in-flight and during Earth-based data-collection intervals: for 2 weeks scheduled about 3 months before launch, 11 days before launch until launch day, and for 7 days upon return to Earth. We collected data from 64 astronauts on 80 space shuttle missions (26 flights, 1063 in-flight days) and 21 astronauts on 13 ISS missions (3248 in-flight days), with ground-based data from all astronauts (4014 days). Crew members attempted and obtained significantly less sleep per night as estimated by actigraphy during space shuttle missions (7·35 h [SD 0·47] attempted, 5·96 h [0·56] obtained), in the 11 days before spaceflight (7·35 h [0·51], 6·04 h [0·72]), and about 3 months before spaceflight (7·40 h [0·59], 6·29 h [0·67]) compared with the first week post-mission (8·01 h [0·78], 6·74 h [0·91]; p<0·0001 for both measures). Crew members on ISS missions obtained significantly less sleep during spaceflight (6·09 h [0·67]), in the 11 days before spaceflight (5·86 h [0·94]), and during the 2-week interval scheduled about 3 months before spaceflight (6·41 h [SD 0·65]) compared with in the first week post-mission (6·95 h [1·04]; p<0·0001). 61 (78%) of 78 shuttle-mission crew members reported taking a dose of sleep-promoting drug on 500 (52%) of 963 nights; 12 (75%) of 16 ISS crew members reported using sleep-promoting drugs. Sleep deficiency in astronauts was prevalent not only during space shuttle and ISS missions, but also throughout a 3 month preflight training interval. Despite chronic sleep curtailment, use of sleep-promoting drugs was pervasive during spaceflight. Because chronic sleep loss leads to performance decrements, our findings emphasise the need for development of effective countermeasures to promote sleep. The National Aeronautics and Space Administration.

Sleep tracking by consumers is becoming increasingly prevalent; yet, few studies have evaluated the accuracy of such devices. We sought to evaluate the accuracy of three devices (Oura Ring Gen3, Fitbit Sense 2, and Apple Watch Series 8) compared to the gold standard sleep assessment (polysomnography (PSG)). Thirty-five participants (aged 20–50 years) without a sleep disorder were enrolled in a single-night inpatient study, during which they wore the Oura Ring, Fitbit, and Apple Watch, and were monitored with PSG. For detecting sleep vs. wake, the sensitivity was ≥95% for all devices. For discriminating between sleep stages, the sensitivity ranged from 50 to 86%, as follows: Oura ring sensitivity 76.0–79.5% and precision 77.0–79.5%; Fitbit sensitivity 61.7–78.0% and precision 72.8–73.2%; and Apple sensitivity 50.5–86.1% and precision 72.7–87.8%. The Oura ring was not different from PSG in terms of wake, light sleep, deep sleep, or REM sleep estimation. The Fitbit overestimated light (18 min; p < 0.001) sleep and underestimated deep (15 min; p < 0.001) sleep. The Apple underestimated the duration of wake (7 min; p < 0.01) and deep (43 min; p < 0.001) sleep and overestimated light (45 min; p < 0.001) sleep. In adults with healthy sleep, all the devices were similar to PSG in the estimation of sleep duration, with the devices also showing moderate to substantial agreement with PSG-derived sleep stages.

The coronavirus disease 2019 (COVID-19) pandemic has been associated with mental health challenges related to the morbidity and mortality caused by the disease and to mitigation activities, including the impact of physical distancing and stay-at-home orders.* Symptoms of anxiety disorder and depressive disorder increased considerably in the United States during April-June of 2020, compared with the same period in 2019 (1,2). To assess mental health, substance use, and suicidal ideation during the pandemic, representative panel surveys were conducted among adults aged ≥18 years across the United States during June 24-30, 2020. Overall, 40.9% of respondents reported at least one adverse mental or behavioral health condition, including symptoms of anxiety disorder or depressive disorder (30.9%), symptoms of a trauma- and stressor-related disorder (TSRD) related to the pandemic (26.3%), and having started or increased substance use to cope with stress or emotions related to COVID-19 (13.3%). The percentage of respondents who reported having seriously considered suicide in the 30 days before completing the survey (10.7%) was significantly higher among respondents aged 18-24 years (25.5%), minority racial/ethnic groups (Hispanic respondents [18.6%], non-Hispanic black [black] respondents [15.1%]), self-reported unpaid caregivers for adults (30.7%), and essential workers (21.7%). Community-level intervention and prevention efforts, including health communication strategies, designed to reach these groups could help address various mental health conditions associated with the COVID-19 pandemic.

Objective To determine the prevalence of depression and burnout among residents in paediatrics and to establish if a relation exists between these disorders and medication errors.Design Prospective cohort study.Setting Three urban freestanding children’s hospitals in the United States.Participants 123 residents in three paediatric residency programmes.Main outcome measures Prevalence of depression using the Harvard national depression screening day scale, burnout using the Maslach burnout inventory, and rate of medication errors per resident month.Results 24 (20%) of the participating residents met the criteria for depression and 92 (74%) met the criteria for burnout. Active surveillance yielded 45 errors made by participants. Depressed residents made 6.2 times as many medication errors per resident month as residents who were not depressed: 1.55 (95% confidence interval 0.57 to 4.22) compared with 0.25 (0.14 to 0.46, P<0.001). Burnt out residents and non-burnt out residents made similar rates of errors per resident month: 0.45 (0.20 to 0.98) compared with 0.53 (0.21 to 1.33, P=0.2).Conclusions Depression and burnout are major problems among residents in paediatrics. Depressed residents made significantly more medical errors than their non-depressed peers; however, burnout did not seem to correlate with an increased rate of medical errors.

Background Governments worldwide recommended unprecedented measures to contain the coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As pressure mounted to scale back measures, understanding public priorities was critical. We assessed initial public adherence with and support for stay-at-home orders in nations and cities with different SARS-CoV-2 infection and COVID-19 death rates. Methods Cross-sectional surveys were administered to representative samples of adults aged ≥18 years from regions with different SARS-CoV-2 prevalences from April 2–8, 2020. Regions included two nations [the United States (US—high prevalence) and Australia (AU—low prevalence)] and two US cities [New York City (NY—high prevalence) and Los Angeles (LA—low prevalence)]. Regional SARS-CoV-2 and COVID-19 prevalence (cumulative SARS-CoV-2 infections, COVID-19 deaths) as of April 8, 2020: US (363,321, 10,845), AU (5956, 45), NY (81,803, 4571), LA (7530, 198). Of 8718 eligible potential respondents, 5573 (response rate, 63.9%) completed surveys. Median age was 47 years (range, 18–89); 3039 (54.5%) were female. Results Of 5573 total respondents, 4560 (81.8%) reported adherence with recommended quarantine or stay-at-home policies (range of samples, 75.5–88.2%). Additionally, 29.1% of respondents screened positive for anxiety or depression symptoms (range of samples, 28.6–32.0%), with higher prevalences among those of younger age, female gender, and those in quarantine or staying at home most of the time versus those who did not report these behaviours. Despite elevated prevalences of adverse mental health symptoms and significant life disruptions, 5022 respondents (90.1%) supported government-imposed stay-at-home orders (range of samples, 88.9–93.1%). Of these, 90.8% believed orders should last at least three more weeks or until public health or government officials recommended, with support spanning the political spectrum. Conclusions Public adherence with COVID-19 mitigation policies was highly prevalent, in both highly-affected (US, NY) and minimally-affected regions (AU, LA). Despite disruption of respondents’ lives, the vast majority supported continuation of extended stay-at-home orders. Despite common support, these two countries diverged in stringent mitigation implementation, which may have contributed to subsequent outcomes. These results reveal the importance of surveillance of public support for and adherence with such policies during the COVID-19 pandemic and for future infectious disease outbreaks.

ObjectivesTo examine associations between shift work characteristics and schedules on burnout in police and whether sleep duration and sleepiness were associated with burnout.MethodsPolice officers (n=3140) completed the Maslach Burnout Inventory (emotional exhaustion, depersonalisation, personal accomplishment) and self-reported shift schedules (irregular, rotating, fixed), shift characteristics (night, duration, frequency, work hours), sleep duration and sleepiness.ResultsIrregular schedules, long shifts (≥11 hours), mandatory overtime, short sleep and sleepiness were associated with increased risk of overall burnout in police. Police working a greater frequency of long shifts were more likely to have emotional exhaustion (adjusted OR 1.91, 95% CI 1.35 to 2.72) than those not working long shifts. Night shifts were associated with depersonalisation (1.32, 1.05 to 1.66) compared with not working nights. Police working mandatory overtime had increased risk of emotional exhaustion (1.37, 1.14 to 1.65) than those who did not. Compared with fixed schedules, irregular schedules were associated with emotional exhaustion and depersonalisation (1.91, 1.44 to 2.54 and 1.39, 1.02 to 1.89, respectively). Police sleeping <6 hours were more likely to have emotional exhaustion (1.60, 1.33 to 1.93) than those sleeping longer, and excessive sleepiness was associated with emotional exhaustion (1.81, 1.50 to 2.18).ConclusionsIrregular schedules and increased night shifts, sleep disturbances and work hours were related to higher burnout risk in police. Future research should evaluate work schedules in law enforcement that optimise shift duration and frequency, and increase consistency in scheduling and control over work hours to limit burnout in police.

Sleep deficiency is a hidden cost of our 24–7 society, with 70% of adults in the US admitting that they routinely obtain insufficient sleep. Further, it is estimated that 50–70 million adults in the US have a sleep disorder. Undiagnosed and untreated sleep disorders are associated with diminished health for the individual and increased costs for the employer. Research has shown that adverse impacts on employees and employers can be mitigated through sleep health education and sleep disorder screening and treatment programs. Smartphone applications (app) are increasingly commonplace and represent promising, scalable modalities for such programs. The dayzz app is a personalized sleep training program that incorporates assessment of sleep disorders and offers a personalized comprehensive sleep improvement solution. Using a sample of day workers affiliated with a large institution of higher education, we will conduct a single-site, parallel-group, randomized, waitlist control trial. Participants will be randomly assigned to either use the dayzz app throughout the study or receive the dayzz app at the end of the study. We will collect data on feasibility and acceptability of the dayzz app; employee sleep, including sleep behavioral changes, sleep duration, regularity, and quality; employee presenteeism, absenteeism, and performance; employee mood; adverse and safety outcomes; and healthcare utilization on a monthly basis throughout the study, as well as collect more granular daily data from the employee during pre-specified intervals. Our results will illuminate whether a personalized smartphone app is a viable approach for improving employee sleep, health, and productivity. Trial registration : ClinicalTrials.gov Identifier: NCT04224285 .

Study Objectives: Heart attacks and motor vehicle crashes are the leading causes of death in US firefighters. Given that sleep disorders are an independent risk factor for both of these, we examined the prevalence of common sleep disorders in a national sample of firefighters and their association with adverse health and safety outcomes. Methods: Firefighters (n = 6,933) from 66 US fire departments were assessed for common sleep disorders using validated screening tools, as available. Firefighters were also surveyed about health and safety, and documentation was collected for reported motor vehicle crashes. Results: A total of 37.2% of firefighters screened positive for any sleep disorder including obstructive sleep apnea (OSA), 28.4%; insomnia, 6.0%; shift work disorder, 9.1%; and restless legs syndrome, 3.4%. Compared with those who did not screen positive, firefighters who screened positive for a sleep disorder were more likely to report a motor vehicle crash (adjusted odds ratio 2.00, 95% CI 1.29–3.12, p = 0.0021) and were more likely to self-report falling asleep while driving (2.41, 2.06–2.82, p < 0.0001). Firefighters who screened positive for a sleep disorder were more likely to report having cardiovascular disease (2.37, 1.54–3.66, p < 0.0001), diabetes (1.91, 1.31–2.81, p = 0.0009), depression (3.10, 2.49–3.85, p < 0.0001), and anxiety (3.81, 2.87–5.05, p < 0.0001), and to report poorer health status (p < 0.0001) than those who did not screen positive. Adverse health and safety associations persisted when OSA and non-OSA sleep disorders were examined separately. Conclusions: Sleep disorders are prevalent in firefighters and are associated with increased risk of adverse health and safety outcomes. Future research is needed to assess the efficacy of occupational sleep disorders prevention, screening, and treatment programs in fire departments to reduce these safety and health risks. Citation: Barger LK, Rajaratnam SM, Wang W, O'Brien CS, Sullivan JP, Qadri S, Lockley SW, Czeisler CA, Harvard Work Hours, Health and Safety Group. Common sleep disorders increase risk of motor vehicle crashes and adverse health outcomes in firefighters. J Clin Sleep Med 2015;11(3):233–240.