Development of a mobile application (App) to delineate “digital chronotype” and the effects of delayed chronotype by bedtime smartphone use

Lin, Yu Hsuan; Wong, Bo Yu; Lin, Sheng‐Hsuan; Chiu, Yu‐Chuan; Pan, Yuan-Chien; Lee, Yang Han

doi:10.1016/j.jpsychires.2018.12.012

Cited by 32 publications

(31 citation statements)

References 43 publications

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“…Roenneberg et al., 2003; Roenneberg et al., 2007), we followed Lin et al. (2019) and used smartphone sensing data to determine a smartphone equivalent for the Roenneberg chronotype. We compared our resulting measure with the findings reported by Roenneberg et al.…”

Section: Discussionmentioning

confidence: 99%

To Challenge the Morning Lark and the Night Owl: Using Smartphone Sensing Data to Investigate Day–Night Behaviour Patterns

Schoedel

Pargent

Au³

et al. 2020

Eur J Pers

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For decades, day–night patterns in behaviour have been investigated by asking people about their sleep–wake timing, their diurnal activity patterns, and their sleep duration. We demonstrate that the increasing digitalization of lifestyle offers new possibilities for research to investigate day–night patterns and related traits with the help of behavioural data. Using smartphone sensing, we collected in vivo data from 597 participants across several weeks and extracted behavioural day–night pattern indicators. Using this data, we explored three popular research topics. First, we focused on individual differences in day–night patterns by investigating whether ‘morning larks’ and ‘night owls’ manifest in smartphone‐sensed behavioural indicators. Second, we examined whether personality traits are related to day–night patterns. Finally, exploring social jetlag, we investigated whether traits and work weekly day–night behaviours influence day–night patterns on weekends. Our findings highlight that behavioural data play an essential role in understanding daily routines and their relations to personality traits. We discuss how psychological research can integrate new behavioural approaches to study personality.

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Section: Discussionmentioning

confidence: 99%

To Challenge the Morning Lark and the Night Owl: Using Smartphone Sensing Data to Investigate Day–Night Behaviour Patterns

Schoedel

Pargent

Au³

et al. 2020

Eur J Pers

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“…Second, the exclusion of frequent notifications elaborated the differentiation from proactive and reactive use and delineated the sleep time more precisely. These 2 improvements in the present algorithm promoted the accuracy of TST estimation from 83.0% [19] to 90.4%. Owing to the extended tracing of the circadian rhythm and much precise sleep time identification, the correlation coefficient of app-recorded and self-reported 1-month circadian rhythm was .87.…”

Section: Discussionmentioning

confidence: 93%

“…Only a couple of mobile apps compute the sleep time and circadian rhythm solely based on smartphone usage patterns. The pilot study we performed identified proactive smartphone screen-on and screen-off patterns to estimate sleep time and achieved 83% accuracy [19]. UbiComp [27] similarly showed that smartphone usage patterns were able to detect sleep duration as well as symptoms of sleep deprivation.…”

Section: Discussionmentioning

confidence: 99%

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

Wong

Pan

Chiu

et al. 2019

JMIR Mhealth Uhealth

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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.

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“…In our previous study, we delineated the compulsive smartphone use parameters by 1 nonuse episode corresponded to 3 successive use episodes. This is because the use/nonuse parameter (ie, SI) was the most consistent parameter with psychiatrists’ clinical diagnosis [9]. The CI is an updated version of the SI, which may be a more representative index for the control ability of smartphone use.…”

Section: Methodsmentioning

confidence: 99%

Temporal Stability of Smartphone Use Data: Determining Fundamental Time Unit and Independent Cycle

Pan

Lin

Chiu

et al. 2019

JMIR Mhealth Uhealth

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Background Assessing human behaviors via smartphone for monitoring the pattern of daily behaviors has become a crucial issue in this century. Thus, a more accurate and structured methodology is needed for smartphone use research. Objective The study aimed to investigate the duration of data collection needed to establish a reliable pattern of use, how long a smartphone use cycle could perpetuate by assessing maximum time intervals between 2 smartphone periods, and to validate smartphone use and use/nonuse reciprocity parameters. Methods Using the Know Addiction database, we selected 33 participants and passively recorded their smartphone usage patterns for at least 8 weeks. We generated 4 parameters on the basis of smartphone use episodes, including total use frequency, total use duration, proactive use frequency, and proactive use duration. A total of 3 additional parameters (root mean square of successive differences, Control Index, and Similarity Index) were calculated to reflect impaired control and compulsive use. Results Our findings included (1) proactive use duration correlated with subjective smartphone addiction scores, (2) a 2-week period of data collection is required to infer a 2-month period of smartphone use, and (3) smartphone use cycles with a time gap of 4 weeks between them are highly likely independent cycles. Conclusions This study validated temporal stability for smartphone use patterns recorded by a mobile app. The results may provide researchers an opportunity to investigate human behaviors with more structured methods.

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Development of a mobile application (App) to delineate “digital chronotype” and the effects of delayed chronotype by bedtime smartphone use

Cited by 32 publications

References 43 publications

To Challenge the Morning Lark and the Night Owl: Using Smartphone Sensing Data to Investigate Day–Night Behaviour Patterns

To Challenge the Morning Lark and the Night Owl: Using Smartphone Sensing Data to Investigate Day–Night Behaviour Patterns

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

Temporal Stability of Smartphone Use Data: Determining Fundamental Time Unit and Independent Cycle

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