Field-Based Assessments of Behavioral Patterns During Shiftwork in Police Academy Trainees Using Wearable Technology.

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.

Department

Description

Provenance

Citation

Published Version (Please cite this version)

10.1177/07487304221087068

Publication Info

Erickson, Melissa L, Will Wang, Julie Counts, Leanne M Redman, Daniel Parker, Janet L Huebner, Jessilyn Dunn, William E Kraus, et al. (2022). Field-Based Assessments of Behavioral Patterns During Shiftwork in Police Academy Trainees Using Wearable Technology. Journal of biological rhythms, 37(3). pp. 260–271. 10.1177/07487304221087068 Retrieved from https://hdl.handle.net/10161/25719.

This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.

Scholars@Duke

Parker

Daniel Christopher Parker

Assistant Professor of Medicine

Biological aging is the greatest risk factor for most chronic diseases, including frailty and dementia. The geroscience hypothesis posits that interventions that target aspects of biological aging may delay or prevent many of these diseases simultaneously. My research centers on understanding the mechanisms of interventions that affect biological aging, like exercise training and caloric restriction and using that understanding to develop therapies that maintain cognitive and physical function in older adults.

Dunn

Jessilyn Dunn

Assistant Professor of Biomedical Engineering

Developing new tools and infrastructure for multi-modal biomedical data integration to drive precision/personalized methods for early detection, intervention, and prevention of disease.


Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.