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Who is Tracking Health on Mobile Devices: Behavioral Logfile Analysis in Hong Kong
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 o...
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/PMC6552450/ https://www.ncbi.nlm.nih.gov/pubmed/31120429 http://dx.doi.org/10.2196/13679 |
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author | Guan, Lu Peng, Tai-Quan Zhu, Jonathan JH |
author_facet | Guan, Lu Peng, Tai-Quan Zhu, Jonathan JH |
author_sort | Guan, Lu |
collection | PubMed |
description | 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 (B(female) vs B(male)=–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. |
format | Online Article Text |
id | pubmed-6552450 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-65524502019-06-19 Who is Tracking Health on Mobile Devices: Behavioral Logfile Analysis in Hong Kong Guan, Lu Peng, Tai-Quan Zhu, Jonathan JH JMIR Mhealth Uhealth Original Paper 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 (B(female) vs B(male)=–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. JMIR Publications 2019-05-23 /pmc/articles/PMC6552450/ /pubmed/31120429 http://dx.doi.org/10.2196/13679 Text en ©Lu Guan, Tai-Quan Peng, Jonathan JH Zhu. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 23.05.2019. 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 Guan, Lu Peng, Tai-Quan Zhu, Jonathan JH Who is Tracking Health on Mobile Devices: Behavioral Logfile Analysis in Hong Kong |
title | Who is Tracking Health on Mobile Devices: Behavioral Logfile Analysis in Hong Kong |
title_full | Who is Tracking Health on Mobile Devices: Behavioral Logfile Analysis in Hong Kong |
title_fullStr | Who is Tracking Health on Mobile Devices: Behavioral Logfile Analysis in Hong Kong |
title_full_unstemmed | Who is Tracking Health on Mobile Devices: Behavioral Logfile Analysis in Hong Kong |
title_short | Who is Tracking Health on Mobile Devices: Behavioral Logfile Analysis in Hong Kong |
title_sort | who is tracking health on mobile devices: behavioral logfile analysis in hong kong |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6552450/ https://www.ncbi.nlm.nih.gov/pubmed/31120429 http://dx.doi.org/10.2196/13679 |
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