Who is Tracking Health on Mobile Devices : Behavioral Logfile Analysis in Hong Kong

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

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Original languageEnglish
Article numbere13679
Journal / PublicationJMIR mHealth and uHealth
Volume7
Issue number5
Early online date23 May 2019
Publication statusPublished - May 2019

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Abstract

Background:  Health apps on mobile devices provide an unprecedented opportunity for ordinary people to develop social connections revolving around health issues. With increasing penetration of mobile devices and well-recorded behavioral data on such devices, it is desirable to employ digital traces on mobile devices rather than self-reported measures to capture the behavioral patterns underlying the use of mobile health (mHealth) apps in a more direct and valid way.
Objective:  The objectives of this study were to (1) assess the demographic predictors of the adoption of mHealth apps; (2) investigate the temporal pattern underlying the use of mHealth apps; and (3) explore the impacts of demographic variables, temporal features, and app genres on the use of mHealth apps.
Methods:  Logfile data of mobile devices were collected from a representative panel of about 2500 users in Hong Kong. Users’ mHealth app activities were analyzed. We first conducted a binary logistic regression analysis to uncover demographic predictors of users’ adoption status. Then we utilized a multilevel negative binomial regression to examine the impacts of demographic characteristics, temporal features, and app genres on mHealth app use.
Results:  It was found that 27.5% of mobile device users in Hong Kong adopt at least one genre of mHealth app. Adopters of mHealth apps tend to be female and better educated. However, demographic characteristics did not showcase the predictive powers on the use of mHealth apps, except for the gender effect (Bfemale vs Bmale=–0.18; P=.006). The use of mHealth apps demonstrates a significant temporal pattern, which is found to be moderately active during daytime and intensifying at weekends and at night. Such temporal patterns in mHealth apps use are moderated by individuals’ demographic characteristics. Finally, demographic characteristics were also found to condition the use of different genres of mHealth apps.
Conclusions:  Our findings suggest the importance of dynamic perspective in understanding users’ mHealth app activities. mHealth app developers should consider more the demographic differences in temporal patterns of mHealth apps in the development of mHealth apps. Furthermore, our research also contributes to the promotion of mHealth apps by emphasizing the differences of usage needs for various groups of users.

Research Area(s)

  • mobile apps, mHealth, circadian rhythm