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Validation of the Mobile App–Recorded Circadian Rhythm by a Digital Footprint
BACKGROUND: Modern smartphone use is pervasive and could be an accessible method of evaluating the circadian rhythm and social jet lag via a mobile app. OBJECTIVE: This study aimed to validate the app-recorded sleep time with daily self-reports by examining the consistency of total sleep time (TST),...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6542252/ https://www.ncbi.nlm.nih.gov/pubmed/31099340 http://dx.doi.org/10.2196/13421 |
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author | Lin, Yu-Hsuan Wong, Bo-Yu Pan, Yuan-Chien Chiu, Yu-Chuan Lee, Yang-Han |
author_facet | Lin, Yu-Hsuan Wong, Bo-Yu Pan, Yuan-Chien Chiu, Yu-Chuan Lee, Yang-Han |
author_sort | Lin, Yu-Hsuan |
collection | PubMed |
description | BACKGROUND: Modern smartphone use is pervasive and could be an accessible method of evaluating the circadian rhythm and social jet lag via a mobile app. OBJECTIVE: This study aimed to validate the app-recorded sleep time with daily self-reports by examining the consistency of total sleep time (TST), as well as the timing of sleep onset and wake time, and to validate the app-recorded circadian rhythm with the corresponding 30-day self-reported midpoint of sleep and the consistency of social jetlag. METHODS: The mobile app, Rhythm, recorded parameters and these parameters were hypothesized to be used to infer a relative long-term pattern of the circadian rhythm. In total, 28 volunteers downloaded the app, and 30 days of automatically recorded data along with self-reported sleep measures were collected. RESULTS: No significant difference was noted between app-recorded and self-reported midpoint of sleep time and between app-recorded and self-reported social jetlag. The overall correlation coefficient of app-recorded and self-reported midpoint of sleep time was .87. CONCLUSIONS: The circadian rhythm for 1 month, daily TST, and timing of sleep onset could be automatically calculated by the app and algorithm. |
format | Online Article Text |
id | pubmed-6542252 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-65422522019-06-07 Validation of the Mobile App–Recorded Circadian Rhythm by a Digital Footprint Lin, Yu-Hsuan Wong, Bo-Yu Pan, Yuan-Chien Chiu, Yu-Chuan Lee, Yang-Han JMIR Mhealth Uhealth Original Paper BACKGROUND: Modern smartphone use is pervasive and could be an accessible method of evaluating the circadian rhythm and social jet lag via a mobile app. OBJECTIVE: This study aimed to validate the app-recorded sleep time with daily self-reports by examining the consistency of total sleep time (TST), as well as the timing of sleep onset and wake time, and to validate the app-recorded circadian rhythm with the corresponding 30-day self-reported midpoint of sleep and the consistency of social jetlag. METHODS: The mobile app, Rhythm, recorded parameters and these parameters were hypothesized to be used to infer a relative long-term pattern of the circadian rhythm. In total, 28 volunteers downloaded the app, and 30 days of automatically recorded data along with self-reported sleep measures were collected. RESULTS: No significant difference was noted between app-recorded and self-reported midpoint of sleep time and between app-recorded and self-reported social jetlag. The overall correlation coefficient of app-recorded and self-reported midpoint of sleep time was .87. CONCLUSIONS: The circadian rhythm for 1 month, daily TST, and timing of sleep onset could be automatically calculated by the app and algorithm. JMIR Publications 2019-05-16 /pmc/articles/PMC6542252/ /pubmed/31099340 http://dx.doi.org/10.2196/13421 Text en ©Yu-Hsuan Lin, Bo-Yu Wong, Yuan-Chien Pan, Yu-Chuan Chiu, Yang-Han Lee. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 16.05.2019. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Lin, Yu-Hsuan Wong, Bo-Yu Pan, Yuan-Chien Chiu, Yu-Chuan Lee, Yang-Han Validation of the Mobile App–Recorded Circadian Rhythm by a Digital Footprint |
title | Validation of the Mobile App–Recorded Circadian Rhythm by a Digital Footprint |
title_full | Validation of the Mobile App–Recorded Circadian Rhythm by a Digital Footprint |
title_fullStr | Validation of the Mobile App–Recorded Circadian Rhythm by a Digital Footprint |
title_full_unstemmed | Validation of the Mobile App–Recorded Circadian Rhythm by a Digital Footprint |
title_short | Validation of the Mobile App–Recorded Circadian Rhythm by a Digital Footprint |
title_sort | validation of the mobile app–recorded circadian rhythm by a digital footprint |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6542252/ https://www.ncbi.nlm.nih.gov/pubmed/31099340 http://dx.doi.org/10.2196/13421 |
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