Rekola Hanna, Tolmunen Tommi, Mattila Elina, Strömmer Juho, Lakka Timo A, Länsimies Helena, Mäki-Opas Tomi
Department of Social Sciences, Faculty of Social Sciences and Business Studies, University of Eastern Finland, PO Box 1627, Kuopio, 70211, Finland, 358 469214359.
Social, Wellbeing, and Rescue Research Centre, Wellbeing Services County of North Savo, Kuopio, Finland.
JMIR Mhealth Uhealth. 2025 Aug 18;13:e68982. doi: 10.2196/68982.
A healthy lifestyle is associated with mental well-being, and digital lifestyle interventions can be effective in promoting a healthy lifestyle. However, they do not appear to work for all, and we have limited knowledge of how users' background characteristics affect their tendency to adopt well-being-promoting digital apps and actively use them.
This study aimed to explore the association of the study participants' characteristics and current well-being with their likelihood of using a well-being-promoting mobile app.
The BitHabit web app (Wellpro Impact Solutions Ltd) was available for a 2-month trial in spring 2023 after completing a short cross-sectional digital questionnaire with questions about well-being, life satisfaction, and lifestyle. Individuals aged 15 years or younger were excluded from the analysis. We used logistic regression to assess how individual characteristics were associated with the initiation of BitHabit app use. To assess user archetypes among those who initiated app use, and k-means clustering analysis and multinomial logistic regression to assess user archetypes among those who initiated app use.
A total of 1646 eligible individuals responded to the questionnaire, and 863 initiated app use. Lower odds of initiating app use were detected among males (odds ratio [OR] 0.66, 95% CI 0.51-0.85; P<.001), the unemployed (OR 0.68, 95% CI 0.48-0.97; P=.03), those with higher general life satisfaction (OR 0.94, 95% CI 0.89-1.00; P=.04), and those reporting fewer life challenges (OR 1.13, 95% CI 1.02-1.24; P=.02). We identified (1) thriving non-active users, (2) struggling non-active users, and (3) active users as archetypes based on app use activity, life satisfaction, and reported life challenges. Older participants had lower odds of being thriving nonactive (OR 0.96, 95% CI 0.94-0.99; P=.01) or struggling nonactive users (OR 0.93, 95% CI 0.90-0.96; P<.001) than active users. Retired participants had higher odds of being struggling nonactive than active users (OR 4.06, 95% CI 1.44-11.42; P=.01) and unemployed lower odds of being thriving nonactive than active users (OR 0.2, 95% CI 0.08-0.51; P<.001). Those who were physically more active had higher odds of being thriving nonactive than active users (OR 2.71, 95% CI 1.00-7.32; P=.05). Participants with higher alcohol consumption had higher odds of being struggling nonactive users than active users (OR 3.22, 95% CI 1.16-8.99; P=.03).
While lower general life satisfaction and less favorable health behavior appeared to increase the likelihood of trying the app, those who eventually actively used the app were more satisfied with their lives at baseline. In addition, among nonactive users, there were recognizable user profiles of thriving and struggling nonactive users, which were associated with various individual characteristics. Further research is needed to develop digital apps to attract more potential users and meet the needs of those with an unhealthy lifestyle and poor mental health.
健康的生活方式与心理健康相关，数字生活方式干预在促进健康生活方式方面可能有效。然而，它们似乎并非对所有人都有效，而且我们对用户的背景特征如何影响其采用促进健康的数字应用程序并积极使用它们的倾向了解有限。
本研究旨在探讨研究参与者的特征和当前幸福感与其使用促进健康的移动应用程序的可能性之间的关联。
在完成一份关于幸福感、生活满意度和生活方式的简短横断面数字问卷后，BitHabit网络应用程序（Wellpro Impact Solutions Ltd）于2023年春季提供为期2个月的试用。15岁及以下的个体被排除在分析之外。我们使用逻辑回归来评估个体特征与开始使用BitHabit应用程序之间的关联。为了评估开始使用应用程序的用户原型，以及使用k均值聚类分析和多项逻辑回归来评估开始使用应用程序的用户原型。
共有1646名符合条件的个体回复了问卷，其中863人开始使用应用程序。在男性（优势比[OR]0.66，95%置信区间0.51 - 0.85；P <.001）、失业者（OR 0.68，95%置信区间0.48 - 0.97；P = 0.03）、总体生活满意度较高的人（OR 0.94，95%置信区间0.89 - 1.0；P = 0.04）以及报告生活挑战较少的人（OR 1.13，95%置信区间1.02 - 1.24；P = 0.02）中，开始使用应用程序的几率较低。根据应用程序使用活动、生活满意度和报告的生活挑战，我们确定了（1）蓬勃发展的非活跃用户、（2）挣扎中的非活跃用户和（3）活跃用户作为原型。年龄较大的参与者成为蓬勃发展的非活跃用户（OR 0.96，95%置信区间0.94 - 0.99；P = 0.01）或挣扎中的非活跃用户（OR 0.93，95%置信区间0.90 – 0.96；P <.001）的几率低于活跃用户。退休参与者成为挣扎中的非活跃用户的几率高于活跃用户（OR 4.06，95%置信区间1.44 - 11.42；P = 0.01），失业者成为蓬勃发展的非活跃用户的几率低于活跃用户（OR 0.2，95%置信区间0.08 - 0.51；P <.001）。身体活动较多的人成为蓬勃发展的非活跃用户的几率高于活跃用户（OR 2.71，95%置信区间1.00 - 7.32；P = 0.05）。饮酒量较高的参与者成为挣扎中的非活跃用户比活跃用户的几率更高（OR 3.22，95%置信区间1.16 - 8.99；P = 0.03）。
虽然总体生活满意度较低和健康行为较差似乎会增加尝试该应用程序的可能性，但最终积极使用该应用程序的人在基线时对自己的生活更满意。此外，在非活跃用户中，有可识别的蓬勃发展的非活跃用户和挣扎中的非活跃用户档案，这与各种个体特征相关。需要进一步研究来开发数字应用程序，以吸引更多潜在用户并满足那些生活方式不健康和心理健康不佳的人的需求。
