Mobile health apps: An exploration of user-generated reviews in Google Play Store on a physical activity application

Predictors of sustained use of mobile health applications: Content analysis of user perspectives from a fever management app

Objectives

Mobile health applications could be means of educating and changing behaviours of their users. Their features and qualities determine the sustainability of use. The FeverApp with two main features of information and documentation is a research-based app. In this observational cohort study, to evaluate the influential predictors of use, users' feedback on the FeverApp, were analyzed.

Methods

Feedback is given using a structured questionnaire, four Likert items and two open questions regarding positive and negative impressions, available via app menu. Conventional content analysis (inductive approach) on the two open questions was performed. Comments were grouped into 12 codes. These codes were grouped hierarchically in an iterative process into nine subcategories and lastly into two main categories 'format' and 'content'. Descriptive and quantitative analysis were performed.

Results

Out of 8243 users, 1804 of them answered the feedback questionnaire. The features of the app (N  =  344), followed by the information aspect (N  =  330) were most frequently mentioned. Documentation process (N  =  226), request for new features or improvement of the current ones (N  =  193), and functioning (N  =  132) were also highlighted in users' feedback. App's ease of use, design and being informative were important for the users. The first impression of the app seems important as the majority of feedback were given during the first month of using the app.

Conclusion

In-app feedback function could highlight shortcomings and strengths of mobile health apps. Taking users' feedback into consideration could increase the chance of sustained use. Besides ease of use and clear, likeable designs, users want apps that serve their needs while saving time.

Family Needs Checklist: Development of a Mobile Application for Parents with Children to Assess the Risk for Child Maltreatment

Child maltreatment (CM) has been enormously studied. However, a preventive practice still requires comprehensive and effective instruments to assess the risks for CM in a family context. The aim of this study is to describe the development process of an evidence-based CM risk assessment instrument (Family Needs Checklist, FNC) for primary prevention online utilization. This article reports the development process of the checklist and its mobile application, consisting of a systematic literature review, identification of known risk factors using the content analysis method, and generation of the checklist, including a multidisciplinary group in the design and feedback. As a result, a comprehensive and compact checklist was developed to be used by parents or caregivers as a self-referral instrument with an option to be used with professionals as a basis for joint conversations. The FNC consists of parental, family-, and child-related risk factors. Based on the international evidence, the online application consists of knowledge about different CM types, information about risk factors and protective factors as well as recommendations and guidance to support services. The FNC is based on robust evidence on known risk factors causing CM in families. It can be used for primary prevention utilization in the general population.

Use of Machine Learning to Mine User-Generated Content From Mobile Health Apps for Weight Loss to Assess Factors Correlated With User Satisfaction

IMPORTANCE

The effectiveness of mobile health (mHealth) apps for reducing obesity is not ideal in daily life. Therefore, it would be useful to explore factors associated with user satisfaction with weight loss apps. Currently, research on these factors from the perspective of user-generated content is lacking.

OBJECTIVE

To mine the themes and topics frequently discussed in user-generated content in mHealth apps for weight loss, explore correlations of the topics with user satisfaction and dissatisfaction, and assess whether these correlations were asymmetric.

DESIGN, SETTING, AND PARTICIPANTS

In this population-based cross-sectional study, unsupervised machine learning was used to identify themes and topics in online discussions generated between January 1, 2019, and May 20, 2021, by Chinese users of mHealth apps for weight loss.

MAIN OUTCOMES AND MEASURES

Based on the 2-factor theory, a tobit regression model was used to explore the correlation of various app discussion topics with user satisfaction and dissatisfaction. Differences of the coefficients in models of positive rating deviation (PD) and negative rating deviation (ND), defined as the difference between the users' rating of the app and the app's comprehensive rating in the app stores, were analyzed by the Wald test.

RESULTS

In total, 191 619 reviews and ratings from unique usernames were collected for 2139 weight loss apps; 86 423 reviews (45.1%) from 339 apps (15.8%) were included in the study. Most users (65 249 [75.5%]) were satisfied with the mHealth app. Eighteen topics were identified and summarized into 9 themes. Nine topics had significant positive correlations with the PD of user satisfaction, and 6 had significant negative correlations. The factor with the strongest positive correlation with the PD was celebrity effect (β = 0.307; 95% CI, 0.290-0.323), and the factor with the weakest correlation was economic cost (β = -0.426; 95% CI, -0.447 to -0.406). Nine topics had significant positive correlations with the ND of user satisfaction, whereas 7 topics had significant negative correlations. The factor with the strongest positive correlation with the ND was fitness effect (β = 1.369; 95% CI, 1.283-1.455), and the factor with the strongest negative correlation was economic cost (β = -2.813; 95% CI, -2.875 to -2.751). There were significant differences in the PD and ND of user satisfaction. Nine motivation factors (ie, value-added attributes) and 7 hygiene factors (ie, user-expected attributes) for mHealth apps were identified.

CONCLUSIONS AND RELEVANCE

In this cross-sectional study, 16 factors had asymmetric correlations with user satisfaction and dissatisfaction with weight loss apps; 7 were related to basic expected attributes of the apps and 9 to value-added attributes. By distinguishing between expected and value-added factors, the use of weight loss apps may be improved.