Collective sleep and activity patterns of college students from wearable devices

TL;DR

This study investigates collective sleep and activity patterns among first-year college students (N=582) using high-temporal-resolution data from the Oura Gen3 ring over an 8-week period. The authors quantify chronotype and social jetlag via the sleep midpoint metrics ($MSF$, $MSW$, and $SJL = MSF - MSW$) and analyze activity with 5-minute MET-based levels, employing SP1D? (SPM) and linear mixed-effects models with random effects for participant and week. Key findings include a median sleep midpoint around $MSF \approx 5{:}00$, pronounced social jetlag during the in-session weeks, and synchronized weekday activity spikes aligned with class schedules; active calories are higher on weekdays and linked to the timing of activities rather than solely to circadian phase. The results demonstrate that regular sleep and activity routines can be inferred from consumer wearables when data are high-resolution and longitudinal, informing potential campus wellness interventions and broader applications to group-level behavior monitoring.

Abstract

To optimize interventions for improving wellness, it is essential to understand habits, which wearable devices can measure with greater precision. Using high temporal resolution biometric data taken from the Oura Gen3 ring, we examine daily and weekly sleep and activity patterns of a cohort of young adults (N=582) in their first semester of college. A high compliance rate is observed for both daily and nightly wear, with slight dips in wear compliance observed shortly after waking up and also in the evening. Most students have a late-night chronotype with a median midpoint of sleep at 5AM, with males and those with mental health impairment having more delayed sleep periods. Social jetlag, or the difference in sleep times between free days and school days, is prevalent in our sample. While sleep periods generally shift earlier on weekdays and later on weekends, sleep duration on both weekdays and weekends is shorter than during prolonged school breaks, suggesting chronic sleep debt when school is in session. Synchronized spikes in activity consistent with class schedules are also observed, suggesting that walking in between classes is a widespread behavior in our sample that substantially contributes to physical activity. Lower active calorie expenditure is associated with weekends and a delayed but longer sleep period the night before, suggesting that for our cohort, active calorie expenditure is affected less by deviations from natural circadian rhythms and more by the timing associated with activities. Our study shows that regular sleep and activity routines may be inferred from consumer wearable devices if high temporal resolution and long data collection periods are available.

Figures (7)

• Figure 1: Ring wear compliance of users over the study period. We observe at least 70% of the participants with sleep and physical activity data for most of the study period. Declines in participation are observed during the Thanksgiving break and the final exam period indicated in the figure by vertical lines.
• Figure 2: Ring wear compliance over the course of a day. For each user, we take the percentage of weekdays and weekends (excluding the week of Thanksgiving break) that they wear the ring at a given time. The solid curves show the average wear compliance of the time series across all users, and the shaded areas indicate the 95% confidence interval.
• Figure 3: Bedtimes and get-up times. The heatmap shows the number of recorded start and end times of sleep periods at a 1-minute resolution. Note the presence of bright vertical lines for get-up times that are absent in bedtimes, suggesting the use of alarm clocks. The delay in bedtimes and get-up times during the weekends is also evident. Thanksgiving break is excluded in the plot.
• Figure 4: Weekly patterns in sleep and activity. The solid lines give the mean of the per-user means of the Oura activity level time series, disregarding non-wear periods. The ordinal activity level categories based on METs are reported as integers: 1=rest, 2=inactive, 3=low intensity, 4=medium intensity, 5=high intensity. The dashed and dash-dotted lines indicate the distribution of the mean bedtimes and get-up times, respectively, per user per day of the week.
• Figure 5: Comparison of activity time series of males and females. Periods in the activity time series with differences statistically significant at $\alpha=0.05$ according to statistical parametric mapping (SPM) are shaded. For males and females, these differences are concentrated around bedtime. Regardless, the daytime weekday spikes in the activity time series are at the same times for both males and females.