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(A) TITLE: Assessing the Effects of Behavioral Circadian Rhythm Disruption in
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Shift-Working Police Academy Trainees
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(B) AUTHORS: Melissa L. Erickson
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*Ph.D., Will Wang
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*B.A., B.S., Julie Counts
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M.S., R.D., Leanne M. Redman
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Ph.D, Daniel Parker
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M.D., Janet L. Huebner
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M.S., Jessilyn Dunn
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Ph.D, and William E. Kraus
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M.D.
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*Indicates co-first authorship
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(C) AFFILIATIONS:
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1: Duke Molecular Physiology Institute, Duke University
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2: Translational Research Institute, Advent Health
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3: Pennington Biomedical Research Center, Louisiana State University
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4: Department of Biomedical Engineering, Duke University
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5: Department of Biostatistics & Bioinformatics, Duke University
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(D) CORRESPONDING AUTHOR: Melissa Erickson (301 E. Princeton St., Orlando,
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FL 32804; Melissa.L.Erickson@AdventHealth.com)
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(E) RUNNING TITLE: Assessing circadian disruption in police trainees.
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(F) CONFLICT OF INTEREST: The authors have declared no conflict of interest.
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FUNDING: This study was supported by NIH IP2CHD086851 to the Rehabilitation
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Research Resource to Enhance Clinical Trials Center at the University of Alabama,
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Birmingham. MLE was supported in part by NIH T32DK064584. This project has been
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made possible in part by grant number 2020-218599 from the Chan Zuckerberg
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Initiative DAF, an advised fund of Silicon Valley Community Foundation.
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ABSTRACT
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Night shift work, characterized by behavioral circadian disruption, increases
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cardiometabolic disease risk. Our long-term goal is to develop a novel methodology to
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quantify behavioral circadian disruption in field-based settings and to explore relations
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to four metabolic salivary biomarkers of circadian rhythm. This pilot study enrolled 36
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police academy trainees to test the feasibility of using wearable activity trackers to
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assess changes in behavioral patterns. Using a two-group observational study design,
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participants completed in-class training during dayshift for six weeks followed by either
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dayshift or nightshift field-training for six weeks. We developed a novel data-post
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processing step that improves sleep detection accuracy of sleep episodes that occur
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during daytime. We next assessed changes to resting heart rate (RHR) and sleep
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regularity index (SRI) during dayshift versus nightshift field training. Secondarily, we
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examined changes in field-based assessments of salivary cortisol, uric acid,
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testosterone, and melatonin during dayshift versus nightshift. Compared to dayshift,
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nightshift workers experienced larger changes to resting heart rate, sleep regularity
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index (indicating reduced sleep regularity), and alternations to sleep/wake activity
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patterns accompanied by blunted salivary cortisol. Salivary uric acid, testosterone, and
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melatonin did not change. These findings show that nightshift work—a form of
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behavioral circadian rhythm disruption—was detectable in police trainees using activity
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trackers alone and in combination with a specialized data analysis methodology.
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KEY WORDS: circadian rhythm, circadian disruption, circadian misalignment, shift
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work, cortisol
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(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprintthis version posted July 29, 2021. ; https://doi.org/10.1101/2021.07.23.21261052doi: medRxiv preprint
NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.
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KEY POINTS
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• Night shift work increases cardiometabolic disease risk and this may be a
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consequence of behavioral circadian misalignment.
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• To advance this hypothesis, methodologies to quantify behavioral irregularities
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during nightshift in field-based settings are needed.
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• In this pilot study, commercially available activity trackers combined with a novel
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data processing step were used to assess alterations in sleep/wake patterns in
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police trainees during dayshift versus nightshift.
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• We also explored relations with four metabolic salivary biomarkers of circadian
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rhythm during dayshift versus nightshift.
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• Compared to dayshift, nightshift resulted in larger perturbations of resting heart
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rate, sleep regularity index (indicating reduced regularity), and alterations in
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sleep and activity patterns; this was accompanied by blunted cortisol.
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• This novel data processing step extends commercially available technology for
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successful application in real-world shift work settings.
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All rights reserved. No reuse allowed without permission.
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprintthis version posted July 29, 2021. ; https://doi.org/10.1101/2021.07.23.21261052doi: medRxiv preprint
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INTRODUCTION
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Diverse occupational sectors—transportation, healthcare, manufacturing, and
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public safety—rely on shiftwork schedules in order to meet work sector demands.
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Mounting evidence suggests circadian disruptions caused by shiftwork schedules result
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in increased chronic disease risk (Antunes et al., 2010; Pan et al., 2011; Lieu et al.,
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2012; Barbadoro et al., 2013; Depner et al., 2014; Vetter et al., 2016; Manohar et al.,
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2017; Shan et al., 2018; Gao et al., 2019; Dutheil et al., 2020; Rivera et al., 2020;
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Schilperoort et al., 2020; Maidstone et al., 2021). For example, shiftwork is associated
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with obesity, type 2 diabetes (Antunes et al., 2010; Shan et al., 2018; Gao et al., 2019),
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hypertension (Manohar et al., 2017), dyslipidaemia (Dutheil et al., 2020), asthma
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(Maidstone et al., 2021), as well as increased breast cancer risk and stroke (Rivera et
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al., 2020). While the relationship between shiftwork and chronic disease susceptibility is
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likely complex, it is hypothesized that temporal misalignment between the internal
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circadian clock and worktimes play a role.
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To advance our understanding of the relationship between circadian disruption
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introduced by shiftwork and increased chronic disease risk, a feasible, straightforward
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methodology for assessing field-based behavioral circadian disruption is needed. This
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requisite was recently highlighted in a white paper summarizing discussions at the 2018
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Sleep Research Society’s sponsored workshop, “International Biomarkers Workshop
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and Wearables in Sleep and Circadian Science” (Depner et al., 2020). The widespread
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development of commercially available activity trackers affords researchers new
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opportunities to survey novel behavioral patterns in community settings that can be
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All rights reserved. No reuse allowed without permission.
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprintthis version posted July 29, 2021. ; https://doi.org/10.1101/2021.07.23.21261052doi: medRxiv preprint
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linked to key health indicators (Shcherbina et al., 2017). Wrist-worn smart watches
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provide information on behavioral regularity of when an individual sleeps and exercises.
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Current activity tracker technology is optimized for use in settings when “typical”
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sleep/wake behaviors occur, in that devices are more likely to accurately detect activity
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during daytime hours and sleep during nighttime hours. However, this may be
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problematic used in the shift work setting. Shiftwork requires an individual to be active
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during the nighttime hours and sleep during daytime hours. These misaligned
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behaviors are likely to go undetected, leading to inaccurate quantification. This
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shortcoming may be overcome by developing a novel data post-processing step that
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removes external clock time bias, thereby increasing sleep label detection accuracy in
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the shiftwork setting.
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We anticipated that proprietary sleep algorithms originally developed for use by
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consumers with regular sleep patterns might perform poorly during night shiftwork:
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daytime sleep episodes would go undetected. Therefore, the first aim of this study was
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to develop a novel algorithm for sleep detection that is not biased by external clock time,
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in a sample of shift working police trainees. The second aim was to assess the
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feasibility of concurrent field-based salivary sampling to detect changes in known
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biomarkers of circadian patterns. We hypothesized that our novel algorithm would
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accurately detect daytime sleep episodes that are missed by commercial technology;
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and, secondly, that nightshift work would be reflected by aberrations in biological
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samples (cortisol, uric acid, testosterone, and melatonin).
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All rights reserved. No reuse allowed without permission.
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprintthis version posted July 29, 2021. ; https://doi.org/10.1101/2021.07.23.21261052doi: medRxiv preprint
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METHODS
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Study Design
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This was a two-group observational, repeated measures study design, leveraging
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the established schedule followed by 36 police recruits. Schedules of police recruits
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involve 24 weeks of in-class training followed by 14 weeks of field-training. This pilot
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study lasted approximately twelve weeks and occurred during the last six weeks of in-
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class training (baseline phase) and the first six weeks of field-training. During in-class
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training, classes were held Monday through Friday during daytime (7:30 AM-5:00 PM)
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hours; this represented normal circadian alignment. This baseline phase was
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subsequently followed by six weeks of field training. During the field-training phase, 13
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participants maintained a normal daytime schedule, representing circadian alignment
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and 14 participants switched to night shift work, representing circadian misalignment
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(Figure 1). During the second phase (circadian misalignment), trainees were assigned
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to one of the following four shift work schedules:
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Schedule A: 6 AM-5 PM (circadian alignment)
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Schedule B: 10 AM-9 PM (circadian alignment)
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Schedule C: 4 PM-3 AM (circadian misalignment)
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Schedule D: 8 PM-7 AM (circadian misalignment)
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Two of these four field-training schedules (A and B) align with the 24h day/night
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cycle and represented a maintenance of behavioral circadian alignment. One
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participant engaged in office work continued to follow a 8 AM-5 PM schedule. The other
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two schedules (C and D) were misaligned with the day/night cycle and represented
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acute circadian misalignment. Work schedules were maintained for four consecutive
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All rights reserved. No reuse allowed without permission.
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprintthis version posted July 29, 2021. ; https://doi.org/10.1101/2021.07.23.21261052doi: medRxiv preprint
