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Who uses mHealth apps? Identifying user archetypes of mHealth apps

OBJECTIVE: This study aims to explore the user archetypes of health apps based on average usage and psychometrics. METHODS: The study utilized a dataset collected through a dedicated smartphone application and contained usage data, i.e. the timestamps of each app session from October 2020 to April 2...

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Autores principales: Aziz, Maryam, Erbad, Aiman, Belhaouari, Samir B, Almourad, Mohamed B, Altuwairiqi, Majid, Ali, Raian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9880587/
https://www.ncbi.nlm.nih.gov/pubmed/36714545
http://dx.doi.org/10.1177/20552076231152175
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author Aziz, Maryam
Erbad, Aiman
Belhaouari, Samir B
Almourad, Mohamed B
Altuwairiqi, Majid
Ali, Raian
author_facet Aziz, Maryam
Erbad, Aiman
Belhaouari, Samir B
Almourad, Mohamed B
Altuwairiqi, Majid
Ali, Raian
author_sort Aziz, Maryam
collection PubMed
description OBJECTIVE: This study aims to explore the user archetypes of health apps based on average usage and psychometrics. METHODS: The study utilized a dataset collected through a dedicated smartphone application and contained usage data, i.e. the timestamps of each app session from October 2020 to April 2021. The dataset had 129 participants for mental health apps usage and 224 participants for physical health apps usage. Average daily launches, extraversion, neuroticism, and satisfaction with life were the determinants of the mental health apps clusters, whereas average daily launches, conscientiousness, neuroticism, and satisfaction with life were for physical health apps. RESULTS: Two clusters of mental health apps users were identified using k-prototypes clustering: help-seeking and maintenance users and three clusters of physical health apps users were identified: happy conscious occasional, happy neurotic occasional, and unhappy neurotic frequent users. CONCLUSION: The findings from this study helped to understand the users of health apps based on the frequency of usage, personality, and satisfaction with life. Further, with these findings, apps can be tailored to optimize user experience and satisfaction which may help to increase user retention. Policymakers may also benefit from these findings since understanding the populations’ needs may help to better invest in effective health technology.
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spelling pubmed-98805872023-01-28 Who uses mHealth apps? Identifying user archetypes of mHealth apps Aziz, Maryam Erbad, Aiman Belhaouari, Samir B Almourad, Mohamed B Altuwairiqi, Majid Ali, Raian Digit Health Original Research OBJECTIVE: This study aims to explore the user archetypes of health apps based on average usage and psychometrics. METHODS: The study utilized a dataset collected through a dedicated smartphone application and contained usage data, i.e. the timestamps of each app session from October 2020 to April 2021. The dataset had 129 participants for mental health apps usage and 224 participants for physical health apps usage. Average daily launches, extraversion, neuroticism, and satisfaction with life were the determinants of the mental health apps clusters, whereas average daily launches, conscientiousness, neuroticism, and satisfaction with life were for physical health apps. RESULTS: Two clusters of mental health apps users were identified using k-prototypes clustering: help-seeking and maintenance users and three clusters of physical health apps users were identified: happy conscious occasional, happy neurotic occasional, and unhappy neurotic frequent users. CONCLUSION: The findings from this study helped to understand the users of health apps based on the frequency of usage, personality, and satisfaction with life. Further, with these findings, apps can be tailored to optimize user experience and satisfaction which may help to increase user retention. Policymakers may also benefit from these findings since understanding the populations’ needs may help to better invest in effective health technology. SAGE Publications 2023-01-22 /pmc/articles/PMC9880587/ /pubmed/36714545 http://dx.doi.org/10.1177/20552076231152175 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research
Aziz, Maryam
Erbad, Aiman
Belhaouari, Samir B
Almourad, Mohamed B
Altuwairiqi, Majid
Ali, Raian
Who uses mHealth apps? Identifying user archetypes of mHealth apps
title Who uses mHealth apps? Identifying user archetypes of mHealth apps
title_full Who uses mHealth apps? Identifying user archetypes of mHealth apps
title_fullStr Who uses mHealth apps? Identifying user archetypes of mHealth apps
title_full_unstemmed Who uses mHealth apps? Identifying user archetypes of mHealth apps
title_short Who uses mHealth apps? Identifying user archetypes of mHealth apps
title_sort who uses mhealth apps? identifying user archetypes of mhealth apps
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9880587/
https://www.ncbi.nlm.nih.gov/pubmed/36714545
http://dx.doi.org/10.1177/20552076231152175
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