Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Sep 21, 2022
Date Accepted: Nov 28, 2022
Mining the Influencing Factors and their Asymmetry Effects of Sleep mHealth App User Satisfaction from Real World User-Generated Reviews: Content Analysis and Topic Modeling
ABSTRACT
Background:
Sleep disorders are a global challenge affecting a quarter of the worldwide population. Sleep mobile Health apps are a potential solution, but 25% of users stop using them after just one time. User satisfaction exerts a significant impact on continued use intentions.
Objective:
This Sino-US comparison study aims to mine the topics discussed in user-generated reviews of sleep mHealth apps and assess the effects of the topics on user satisfaction and dissatisfaction with these apps, then provide suggestions for improving users’ intentions to continue usage of the sleep mHealth apps.
Methods:
An unsupervised clustering technique was used to identify the topics discussed in user reviews of sleep mHealth apps. Based on Herzberg’s two-factor theory, the Tobit model was used to explore the effects of each topic on user satisfaction and dissatisfaction, and the differences in the effects were analyzed through the Wald test.
Results:
A total of 488,071 user reviews of ten mainstream sleep apps were collected, including 267,589 (54.8%) American user reviews and 220,482 (45.2%) Chinese user reviews. The user satisfaction rates of sleep apps were poor (China: 56.58% vs US: 45.87%). We identified 14 topics in the user-generated reviews of each country. In Chinese data, 13 topics had a significant effect on the positive deviation (PD) and negative deviation (ND) of user satisfaction. The two variables (PD and ND) were defined by the difference between the user rating and the overall rating of the app in the app store. Among those topics, the app's sound recording function (β=1.026; P<.001) had the largest positive effect on the PD of user satisfaction, and the topic with the largest positive effect on the ND of user satisfaction was the sleep improvement effect of the app (β=1.185; P<.001). In American data, all 14 topics had a significant effect on the PD and ND of user satisfaction. Among those, the topic with the largest positive effect on the ND of user satisfaction was the app's sleep promotion effect (β=1.389; P<.001), whereas the app's sleep improvement effect (β=1.168; P<.001) had the largest positive effect on the PD of user satisfaction. The Wald test showed that there were significant differences in the models of PD and ND of user satisfaction in both two countries (all P<.05), indicating that the influencing factors of user satisfaction with sleep mHealth apps were asymmetric. By Sino-US comparing, 4 hygiene factors (i.e., stability, compatibility, cost, sleep monitoring function) and 2 motivation factors (i.e., sleep suggestion function and sleep promotion effects) of sleep apps were identified.
Conclusions:
By distinguishing the hygiene and motivation factors, the use of sleep apps in the real world can be effectively promoted.
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