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Accepted for/Published in: JMIR mHealth and uHealth

Date Submitted: Jan 17, 2019
Open Peer Review Period: Jan 21, 2019 - Mar 6, 2019
Date Accepted: Mar 24, 2019
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Validation of the Mobile App–Recorded Circadian Rhythm by a Digital Footprint

Lin YH, Wong BY, Pan YC, Chiu YC, Lee YH

Validation of the Mobile App–Recorded Circadian Rhythm by a Digital Footprint

JMIR Mhealth Uhealth 2019;7(5):e13421

DOI: 10.2196/13421

PMID: 31099340

PMCID: 6542252

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Validation of the Mobile App–Recorded Circadian Rhythm by a Digital Footprint

  • Yu-Hsuan Lin; 
  • Bo-Yu Wong; 
  • Yuan-Chien Pan; 
  • Yu-Chuan Chiu; 
  • Yang-Han Lee

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.


 Citation

Please cite as:

Lin YH, Wong BY, Pan YC, Chiu YC, Lee YH

Validation of the Mobile App–Recorded Circadian Rhythm by a Digital Footprint

JMIR Mhealth Uhealth 2019;7(5):e13421

DOI: 10.2196/13421

PMID: 31099340

PMCID: 6542252

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© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.