Aziz Maryam, Erbad Aiman, Belhaouari Samir B, Almourad Mohamed B, Altuwairiqi Majid, Ali Raian
College of Science and Engineering, Hamad Bin Khalifa University, Qatar.
College of Technological Innovation, Zayed University, United Arab Emirates.
Digit Health. 2023 Jan 22;9:20552076231152175. doi: 10.1177/20552076231152175. eCollection 2023 Jan-Dec.
This study aims to explore the user archetypes of health apps based on average usage and psychometrics.
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.
Two clusters of mental health apps users were identified using k-prototypes clustering: and users and three clusters of physical health apps users were identified: , , and users.
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.
本研究旨在基于平均使用情况和心理测量学探索健康应用程序的用户原型。
该研究使用了通过一款专用智能手机应用程序收集的数据集,其中包含使用数据,即2020年10月至2021年4月每个应用程序会话的时间戳。该数据集有129名心理健康应用程序使用者和224名身体健康应用程序使用者。心理健康应用程序集群的决定因素是平均每日启动次数、外向性、神经质和生活满意度,而身体健康应用程序的决定因素是平均每日启动次数、尽责性、神经质和生活满意度。
使用k-原型聚类法识别出两类心理健康应用程序用户: 类用户和 类用户,以及三类身体健康应用程序用户: 类用户、 类用户和 类用户。
本研究结果有助于基于使用频率、个性和生活满意度来了解健康应用程序的用户。此外,基于这些发现,可以对应用程序进行定制,以优化用户体验和满意度,这可能有助于提高用户留存率。政策制定者也可能从这些发现中受益,因为了解人群的需求可能有助于更好地投资于有效的健康技术。