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User Reviews of Depression App Features: Sentiment Analysis
Julien Meyer1, Senanu Okuboyejo2
1School of Health Services Management, Ted Rogers School of Management, Ryerson University, Toronto, ON, Canada.
JMIR Formative Research
|December 14, 2021
Summary
Mobile health (mHealth) apps for depression can evoke varied user emotions. Features like medical assessments may cause distress, while supportive resources and entertainment are safer engagement tools.
Area of Science:
- Digital mental health
- Mobile health applications
- Sentiment analysis in healthcare
Background:
- Depression and mental health conditions are often undertreated.
- Mobile health (mHealth) apps present a scalable solution to improve access to mental healthcare.
- Understanding user emotions is crucial for the effectiveness of mHealth interventions.
Purpose of the Study:
- To investigate the emotional responses of individuals using depression mHealth apps.
- To analyze user sentiments towards various features within depression apps.
- To identify how different app functionalities impact user experience and emotional well-being.
Main Methods:
- Systematic analysis of 3,261 user reviews from depression mHealth apps.
- Categorization of 61 apps based on features: psychoeducation, medical assessment, therapeutic treatment, supportive resources, and entertainment.
- Utilized Linguistic Inquiry Word Count (LIWC) 2015 for sentiment and linguistic analysis of reviews.
Main Results:
- Significant variations in user sentiment were observed across different app features.
- Medical assessment features generated strong negative emotions and lower ratings.
- Therapeutic treatment features yielded more positive emotions but less authenticity; supportive resources and entertainment showed lower negative emotions.
Conclusions:
- App developers should carefully consider feature selection, as medical assessments may pose risks.
- Psychoeducation, supportive resources, and entertainment features appear to be safer engagement strategies.
- Assessing mHealth apps requires feature-specific evaluation, prioritizing user perception for adoption and safety.
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