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Digital circadian and sleep health in individual hospital shift workers: A cross sectional telemonitoring study
BACKGROUND: Telemonitoring of circadian and sleep cycles could identify shift workers at increased risk of poor health, including cancer and cardiovascular diseases, thus supporting personalized prevention. METHODS: The Circadiem cross-sectional study aimed at determining early warning signals of ri...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9253495/ https://www.ncbi.nlm.nih.gov/pubmed/35772217 http://dx.doi.org/10.1016/j.ebiom.2022.104121 |
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author | Zhang, Yiyuan Cordina-Duverger, Emilie Komarzynski, Sandra Attari, Amal M. Huang, Qi Aristizabal, Guillen Faraut, Brice Léger, Damien Adam, René Guénel, Pascal Brettschneider, Julia A. Finkenstädt, Bärbel F. Lévi, Francis |
author_facet | Zhang, Yiyuan Cordina-Duverger, Emilie Komarzynski, Sandra Attari, Amal M. Huang, Qi Aristizabal, Guillen Faraut, Brice Léger, Damien Adam, René Guénel, Pascal Brettschneider, Julia A. Finkenstädt, Bärbel F. Lévi, Francis |
author_sort | Zhang, Yiyuan |
collection | PubMed |
description | BACKGROUND: Telemonitoring of circadian and sleep cycles could identify shift workers at increased risk of poor health, including cancer and cardiovascular diseases, thus supporting personalized prevention. METHODS: The Circadiem cross-sectional study aimed at determining early warning signals of risk of health alteration in hospital nightshifters (NS) versus dayshifters (DS, alternating morning and afternoon shifts). Circadian rhythmicity in activity, sleep, and temperature was telemonitored on work and free days for one week. Participants wore a bluetooth low energy thoracic accelerometry and temperature sensor that was wirelessly connected to a GPRS gateway and a health data hub server. Hidden Markov modelling of activity quantified Rhythm Index, rest quality (probability, p1-1, of remaining at rest), and rest duration. Spectral analyses determined periods in body surface temperature and accelerometry. Parameters were compared and predictors of circadian and sleep disruption were identified by multivariate analyses using information criteria-based model selection. Clusters of individual shift work response profiles were recognized. FINDINGS: Of 140 per-protocol participants (133 females), there were 63 NS and 77 DS. Both groups had similar median rest amount, yet NS had significantly worse median rest-activity Rhythm Index (0·38 [IQR, 0·29-0·47] vs. 0·69 [0·60-0·77], p<0·0001) and rest quality p1-1 (0·94 [0·94-0·95] vs 0·96 [0·94-0·97], p<0·0001) over the whole study week. Only 48% of the NS displayed a circadian period in temperature, as compared to 70% of the DS (p=0·026). Poor p1-1 was associated with nightshift work on both work (p<0·0001) and free days (p=0·0098). The number of years of past night work exposure predicted poor rest-activity Rhythm Index jointly with shift type, age and chronotype on workdays (p= 0·0074), and singly on free days (p=0·0005). INTERPRETATION: A dedicated analysis toolbox of streamed data from a wearable device identified circadian and sleep rhythm markers, that constitute surrogate candidate endpoints of poor health risk in shift-workers. FUNDING: French Agency for Food, Environmental and Occupational Health & Safety (EST-2014/1/064), University of Warwick, Medical Research Council (United Kingdom, MR/M013170), Cancer Research UK(C53561/A19933). |
format | Online Article Text |
id | pubmed-9253495 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-92534952022-07-06 Digital circadian and sleep health in individual hospital shift workers: A cross sectional telemonitoring study Zhang, Yiyuan Cordina-Duverger, Emilie Komarzynski, Sandra Attari, Amal M. Huang, Qi Aristizabal, Guillen Faraut, Brice Léger, Damien Adam, René Guénel, Pascal Brettschneider, Julia A. Finkenstädt, Bärbel F. Lévi, Francis eBioMedicine Articles BACKGROUND: Telemonitoring of circadian and sleep cycles could identify shift workers at increased risk of poor health, including cancer and cardiovascular diseases, thus supporting personalized prevention. METHODS: The Circadiem cross-sectional study aimed at determining early warning signals of risk of health alteration in hospital nightshifters (NS) versus dayshifters (DS, alternating morning and afternoon shifts). Circadian rhythmicity in activity, sleep, and temperature was telemonitored on work and free days for one week. Participants wore a bluetooth low energy thoracic accelerometry and temperature sensor that was wirelessly connected to a GPRS gateway and a health data hub server. Hidden Markov modelling of activity quantified Rhythm Index, rest quality (probability, p1-1, of remaining at rest), and rest duration. Spectral analyses determined periods in body surface temperature and accelerometry. Parameters were compared and predictors of circadian and sleep disruption were identified by multivariate analyses using information criteria-based model selection. Clusters of individual shift work response profiles were recognized. FINDINGS: Of 140 per-protocol participants (133 females), there were 63 NS and 77 DS. Both groups had similar median rest amount, yet NS had significantly worse median rest-activity Rhythm Index (0·38 [IQR, 0·29-0·47] vs. 0·69 [0·60-0·77], p<0·0001) and rest quality p1-1 (0·94 [0·94-0·95] vs 0·96 [0·94-0·97], p<0·0001) over the whole study week. Only 48% of the NS displayed a circadian period in temperature, as compared to 70% of the DS (p=0·026). Poor p1-1 was associated with nightshift work on both work (p<0·0001) and free days (p=0·0098). The number of years of past night work exposure predicted poor rest-activity Rhythm Index jointly with shift type, age and chronotype on workdays (p= 0·0074), and singly on free days (p=0·0005). INTERPRETATION: A dedicated analysis toolbox of streamed data from a wearable device identified circadian and sleep rhythm markers, that constitute surrogate candidate endpoints of poor health risk in shift-workers. FUNDING: French Agency for Food, Environmental and Occupational Health & Safety (EST-2014/1/064), University of Warwick, Medical Research Council (United Kingdom, MR/M013170), Cancer Research UK(C53561/A19933). Elsevier 2022-06-27 /pmc/articles/PMC9253495/ /pubmed/35772217 http://dx.doi.org/10.1016/j.ebiom.2022.104121 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Articles Zhang, Yiyuan Cordina-Duverger, Emilie Komarzynski, Sandra Attari, Amal M. Huang, Qi Aristizabal, Guillen Faraut, Brice Léger, Damien Adam, René Guénel, Pascal Brettschneider, Julia A. Finkenstädt, Bärbel F. Lévi, Francis Digital circadian and sleep health in individual hospital shift workers: A cross sectional telemonitoring study |
title | Digital circadian and sleep health in individual hospital shift workers: A cross sectional telemonitoring study |
title_full | Digital circadian and sleep health in individual hospital shift workers: A cross sectional telemonitoring study |
title_fullStr | Digital circadian and sleep health in individual hospital shift workers: A cross sectional telemonitoring study |
title_full_unstemmed | Digital circadian and sleep health in individual hospital shift workers: A cross sectional telemonitoring study |
title_short | Digital circadian and sleep health in individual hospital shift workers: A cross sectional telemonitoring study |
title_sort | digital circadian and sleep health in individual hospital shift workers: a cross sectional telemonitoring study |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9253495/ https://www.ncbi.nlm.nih.gov/pubmed/35772217 http://dx.doi.org/10.1016/j.ebiom.2022.104121 |
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