Circadian misalignment, as occurs in shiftwork, is associated with numerous negative health outcomes. Here, we sought to improve data labeling accuracy from wearable technology using a novel data pre-processing algorithm in 27 police trainees during shiftwork. Secondarily, we explored changes in four metabolic salivary biomarkers of circadian rhythm during shiftwork. Using a two-group observational study design, participants completed in-class training during dayshift for 6 weeks followed by either dayshift or nightshift field-training for 6 weeks. Using our novel algorithm, we imputed labels of circadian misaligned sleep episodes that occurred during daytime, which were previously were mislabeled as non-sleep by Garmin, supported by algorithm performance analysis. We next assessed changes to resting heart rate and sleep regularity index during dayshift versus nightshift field-training. We also examined changes in field-based assessments of salivary cortisol, uric acid, testosterone, and melatonin during dayshift versus nightshift. Compared to dayshift, nightshift workers experienced larger changes to resting heart rate, sleep regularity index (indicating reduced sleep regularity), and alterations in sleep/wake activity patterns accompanied by blunted salivary cortisol. Salivary uric acid and testosterone did not change. These findings show wearable technology combined with specialized data pre-processing can be used to monitor changes in behavioral patterns during shiftwork.

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Study Objective: Shiftwork increases risk for numerous chronic diseases, which is hypothesized to be linked to disruption of circadian timing of lifestyle behaviors. However, empirical data on timing of lifestyle behaviors in real-world shift workers are lacking. To address this, we characterized the regularity of timing of lifestyle behaviors in shift-working police trainees. Methods: Using a two-group observational study design (N=18), we compared lifestyle behavior timing during 6 weeks of in-class training during dayshift, followed by 6 weeks of field-based training during either dayshift or nightshift. Lifestyle behavior timing, including sleep/wake patterns, physical activity, and meals, was captured using wearable activity trackers and mobile devices. The regularity of lifestyle behavior timing was quantified as an index score, which reflects day-to-day stability on a 24h time scale: Sleep Regularity Index (SRI), Physical Activity Regularity Index (PARI) and Mealtime Regularity Index (MRI). Logistic regression was applied to these indices to develop a composite score, termed the Behavior Regularity Index (BRI). Results: Transitioning from dayshift to nightshift significantly worsened the BRI, relative to maintaining a dayshift schedule. Specifically, nightshift led to more irregular sleep/wake timing and meal timing; physical activity timing was not impacted. In contrast, maintaining a dayshift schedule did not impact regularity indices. Conclusion: Nightshift imposed irregular timing of lifestyle behaviors, which is consistent with the hypothesis that circadian disruption contributes to chronic disease risk in shift workers. How to mitigate the negative impact of shiftwork on human health as mediated by irregular timing of sleep/wake patterns and meals deserves exploration.