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

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

doi:10.2196/13421

Cited by 23 publications

(22 citation statements)

References 27 publications

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“…We developed an iOS and Android smartphone app called "Staff Hours" that automatically calculates users' work hours through GPS data. This passive data collection by "Staff Hours" is similar to that of our previous apps, "Know Addiction" and "Rhythm," which collect and calculate smartphone screen time and sleep time, respectively [13][14][15][16][17][18]. However, these two apps were experimentally used for months, and user retention of an app collecting passive data in a natural setting is still unknown.…”

Section: Introductionmentioning

confidence: 91%

Assessing User Retention of a Mobile App: Survival Analysis (Preprint)

Chen¹,

Lin²,

Tai³

et al. 2019

Preprint

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BACKGROUND A mobile application (app) generates passive data without any direct involvement from the subject, such as Global Positioning System (GPS) data traces. These passive data transformed the manner of traditional assessment, which requires active participation from the subject. Passive data collection is one of the most important core techniques for mobile health development because it may promote user retention, which is a unique characteristic of a software medical device. OBJECTIVE The primary aim of this study was to quantify user retention for “Staff Hours” app by using survival analysis. The secondary aim was to compare user retention between passive data and active data, as well as associated factors of their survival rates of user retention. METHODS We developed an app “Staff Hours” to automatically calculate users’ work hours through GPS data (passive data). “Staff Hours” not only continuously collects these passive data but also sends an 11-item mental health survey to users monthly (active data). We applied survival analysis to compare user retention in passive and active data among 342 office workers from the “Staff Hours” database. We also compared user retention on Android and iOS platforms and examined the moderators of user retention. RESULTS A total of 342 volunteers (224 men; age: 33.8 ± 7.0 years) were included in this study. Passive data had higher user retention than active data (p = 0.011). In addition, user retention for Android in passive data was higher than that for iOS (p = 0.015). Trainee physicians had higher user retention than trainees from other occupations in active data, whereas no significant differences between these two groups were observed in passive data (p = 0.700). CONCLUSIONS Our findings demonstrated that passive data in Android had the best user retention in this app recording GPS-based work hours.

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Section: Introductionmentioning

confidence: 91%

Assessing User Retention of a Mobile App: Survival Analysis (Preprint)

Chen¹,

Lin²,

Tai³

et al. 2019

Preprint

Self Cite

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“…In the field of health care research, these fundamental results about digital rhythms suggest that telephones may be used to monitor daily social activity. In recent publications, this sort of exploitation of telephones has provided promising results for monitoring the health status and biological timeframe of individuals [37][38][39][40].…”

Section: Prior Studiesmentioning

confidence: 99%

Exploitation of Outgoing and Incoming Telephone Calls in the Context of Circadian Rhythms of Social Activity Among Elderly People: Observational Descriptive Study (Preprint)

Aubourg¹,

Demongeot²,

Provost³

et al. 2019

Preprint

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UNSTRUCTURED Circadian rhythms of social activity constitute important temporal markers of the individual’s overall social dynamic and offer relevant opportunities in a large variety of applications including health monitoring. Indeed, analyzing circadian rhythms of social activity can help in monitoring the elderly, disabled, and chronically ill person at home by supervising their persistence through time, as well as their synchronization regarding how the individual acts and reacts with his social network across the day. Recent works have convincingly demonstrated the ability of measuring such rhythms on a digital way by the use of new technologies and the exploitation of their generated data. In particular, studies focusing on phone call detail records (CDRs) datasets have provided relevant results for exhibiting persistent circadian rhythms of phone call activity in young healthy adults. However, whether and how persistence and synchronization of circadian rhythms of phone telecommunication activity could also be observed in older adults still remains unclear. The present paper is specifically designed to address this issue. To this end, we use a longitudinal 12-successive-month dataset that combines CDRs and questionnaire data of 21 volunteers older than 65 years. In this study, we use CDRs from which we operate four specific telecommunication parameters: (1) recipient (alter), (2) time, (3) duration and (4) direction of phone call. We specifically focus on two issues: (1) the existence of persistent daily rhythms in the older individual regarding his outgoing and incoming phone call activity, and (2) the existence of synchronization in the way the individual phones and responds to phone calls across the day. At individual level, our results demonstrate, for outgoing and incoming phone calls separately, that older adults do present persistent circadian rhythms of phone call activity. Then, we point towards these outgoing and incoming rhythms are also synchronized together. Indeed, despite the existence of some differences in their features, the overall distribution of the outgoing and incoming phone calls’ ratios across the day belong to the same family law at individual level. Interestingly, the present findings suggest that combining outgoing and incoming phone calls together without distinguishing their direction in CDRs analysis also can produce persistent circadian patterns of phone call activity in our older population. The significance and limitations of this study are discussed and a future research direction is proposed.

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“…We developed an iOS and Android smartphone app called “Staff Hours” that automatically calculates users’ work hours through GPS data. This passive data collection by “Staff Hours” is similar to that of our previous apps, “Know Addiction” and “Rhythm,” which collect and calculate smartphone screen time and sleep time, respectively [ 13 - 18 ]. However, these two apps were experimentally used for months, and user retention of an app collecting passive data in a natural setting is still unknown.…”

Section: Introductionmentioning

confidence: 99%

Assessing User Retention of a Mobile App: Survival Analysis

Lin¹,

Chen²,

Lin³

et al. 2020

JMIR Mhealth Uhealth

Self Cite

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Background A mobile app generates passive data, such as GPS data traces, without any direct involvement from the user. These passive data have transformed the manner of traditional assessments that require active participation from the user. Passive data collection is one of the most important core techniques for mobile health development because it may promote user retention, which is a unique characteristic of a software medical device. Objective The primary aim of this study was to quantify user retention for the “Staff Hours” app using survival analysis. The secondary aim was to compare user retention between passive data and active data, as well as factors associated with the survival rates of user retention. Methods We developed an app called “Staff Hours” to automatically calculate users’ work hours through GPS data (passive data). “Staff Hours” not only continuously collects these passive data but also sends an 11-item mental health survey to users monthly (active data). We applied survival analysis to compare user retention in the collection of passive and active data among 342 office workers from the “Staff Hours” database. We also compared user retention on Android and iOS platforms and examined the moderators of user retention. Results A total of 342 volunteers (224 men; mean age 33.8 years, SD 7.0 years) were included in this study. Passive data had higher user retention than active data (P=.011). In addition, user retention for passive data collected via Android devices was higher than that for iOS devices (P=.015). Trainee physicians had higher user retention for the collection of active data than trainees from other occupations, whereas no significant differences between these two groups were observed for the collection of passive data (P=.700). Conclusions Our findings demonstrated that passive data collected via Android devices had the best user retention for this app that records GPS-based work hours.

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

Cited by 23 publications

References 27 publications

Assessing User Retention of a Mobile App: Survival Analysis (Preprint)

Assessing User Retention of a Mobile App: Survival Analysis (Preprint)

Exploitation of Outgoing and Incoming Telephone Calls in the Context of Circadian Rhythms of Social Activity Among Elderly People: Observational Descriptive Study (Preprint)

Assessing User Retention of a Mobile App: Survival Analysis

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