Working-from-home persistently influences sleep and physical activity 2 years after the Covid-19 pandemic onset: a longitudinal sleep tracker and electronic diary-based study.

Objective: Working from home (WFH) has become common place since the Covid-19 pandemic. Early studies observed population-level shifts in sleep patterns (later and longer sleep) and physical activity (reduced PA), during home confinement. Other studies found these changes to depend on the proportion of days that individuals WFH (vs. work from office; WFO). Here, we examined the effects of WFH on sleep and activity patterns in the transition to normality during the later stages of the Covid-19 pandemic (Aug 2021-Jan 2022). Methods: Two-hundred and twenty-five working adults enrolled in a public health study were followed for 22 weeks. Sleep and activity data were collected with a consumer fitness tracker (Fitbit Versa 2). Over three 2-week periods (Phase 1/week 1-2: August 16-29, 2021; Phase 2/week 11-12: October 25-November 7, 2021; Phase 3/week 21-22: January 3-16, 2022), participants provided daily Fitbit sleep and activity records. Additionally, they completed daily phone-based ecological momentary assessment (EMA), providing ratings of sleep quality, wellbeing (mood, stress, motivation), and information on daily work arrangements (WFH, WFO, no work). Work arrangement data were used to examine the effects of WFH vs. WFO on sleep, activity, and wellbeing. Results: The proportion of WFH vs. WFO days fluctuated over the three measurement periods, mirroring evolving Covid restrictions. Across all three measurement periods WFH days were robustly associated with later bedtimes (+14.7 min), later wake times (+42.3 min), and longer Total Sleep Time (+20.2 min), compared to WFO days. Sleep efficiency was not affected. WFH was further associated with lower daily step count than WFO (-2,471 steps/day). WFH was associated with higher wellbeing ratings compared to WFO for those participants who had no children. However, for participants with children, these differences were not present. Conclusion: Pandemic-initiated changes in sleep and physical activity were sustained during the later stage of the pandemic. These changes could have longer term effects, and conscious effort is encouraged to harness the benefits (i.e., longer sleep), and mitigate the pitfalls (i.e., less physical activity). These findings are relevant for public health as hybrid WHF is likely to persist in a post-pandemic world.

A randomized-controlled trial of a digital, small incentive-based intervention for working adults with short sleep.

STUDY OBJECTIVES: We evaluated the efficacy of a digitally delivered, small and scalable incentive-based intervention program on sleep and wellbeing in short-sleeping, working adults. METHODS: A 22-week, parallel-group, randomized-controlled trial was conducted on 21-40 y participants gifted with FitbitTM devices to measure sleep for ≥2 years, as part of a broader healthy lifestyle study. About 225 short sleepers (141 males; average time-in-bed, TIB < 7h) were randomly assigned in a 2:1 ratio to Goal-Setting or Control groups. The Goal-Setting group received health vouchers (~USD 0.24) for meeting each sleep goal (i.e. increasing weeknight TIB by 30 min/sleeping before midnight).The study spanned three phases: (1) 2-week Baseline, (2) 10-week Intervention, and (3) 10-week Follow-Up. Wellbeing questionnaires were administered on Weeks 1-2, 11-12, and 21-22. RESULTS: Baseline weeknight TIB (mean ±â€…SD) was 387 ±â€…43 min (Goal-Setting) and 399 ±â€…44 min (Control), while bedtime was 00:53 ±â€…01:13 (Goal-Setting), and 00:38 ±â€…00:56 (Control). No difference in sleep outcomes was observed at study endpoints, but exploratory week-by-week analysis showed that on Weeks 3-5, TIB in the Goal-Setting group increased (9-18 min; ps < 0.05) while on Week 5, bedtimes shifted earlier (15 min; p < 0.01) compared to Baseline. Morning sleepiness was reduced in the Goal-Setting group (mean[SEM] = -3.17(1.53); p = 0.04) compared to Baseline, although between-group differences were not significant (p = 0.62). Main barriers to sleeping longer were work hours (35%), followed by leisure activities (23%) and family commitments (22%). CONCLUSION: Our program resulted in encouraging subjective sleep improvements and short-term sleep extension, but sustained transformation of sleep will probably require structural measures to overcome significant obstacles to sleep. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04878380 (hiSG Sleep Health Study (hiSG-SHS); https://clinicaltrials.gov/ct2/show/NCT04878380).

Minimum number of nights for reliable estimation of habitual sleep using a consumer sleep tracker.

Study Objectives: To determine the minimum number of nights required to reliably estimate weekly and monthly mean sleep duration and sleep variability measures from a consumer sleep technology (CST) device (Fitbit). Methods: Data comprised 107 144 nights from 1041 working adults aged 21-40 years. Intraclass correlation (ICC) analyses were conducted on both weekly and monthly time windows to determine the number of nights required to achieve ICC values of 0.60 and 0.80, corresponding to "good" and "very good" reliability thresholds. These minimum numbers were then validated on data collected 1-month and 1-year later. Results: Minimally, 3 and 5 nights were required to obtain "good" and "very good" mean weekly total sleep time (TST) estimates, while 5 and 10 nights were required for monthly TST estimates. For weekday-only estimates, 2 and 3 nights were sufficient for weekly time windows while 3 and 7 nights sufficed for monthly time windows. Weekend-only estimates of monthly TST required 3 and 5 nights. TST variability required 5 and 6 nights for weekly time windows, and 11 and 18 nights for monthly time windows. Weekday-only weekly variability required 4 nights for both "good" and "very good" estimates while monthly variability required 9 and 14 nights. Weekend-only estimates of monthly variability required 5 and 7 nights. Error estimates made using data collected 1-month and 1-year later with these parameters were comparable to those associated with the original dataset. Conclusions: Studies should consider the metric, measurement window of interest, and desired reliability threshold to decide on the minimum number of nights required to assess habitual sleep using CST devices.