Field-Based Assessments of Behavioral Patterns During Shiftwork in Police Academy Trainees Using Wearable Technology. Issue 3 (June 2022)

• Record Type: Journal Article
• Authors: Erickson, Melissa L.; Wang, Will; Counts, Julie; Redman, Leanne M.; Parker, Daniel; Huebner, Janet L.; Dunn, Jessilyn; Kraus, William E.
• Is Part Of: Journal of biological rhythms. Volume 37:Issue 3(2022)
• Page Start: 260
• Page End: 271
• Publication Date: 2022-06
• Subjects: circadian rhythm -- wearables -- field-based study -- circadian misalignment -- shiftwork -- cortisol; Biological rhythms -- Periodicals; Circadian rhythms -- Periodicals; 571.77
• DOI: 10.1177/07487304221087068
• Languages: English
• ISSNs: 0748-7304

Abstract

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.