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Examining young adults daily perspectives on usage of anxiety apps: A user study
Andreas Balaskas1, Stephen M Schueller2, Anna L Cox3
1School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland.
Abstract:
The growing number of mental health smartphone applications has led to increased interest in how these tools might support users in different models of care. However, research on the use of these interventions in real-world settings has been scarce. It is important to understand how apps are used in a deployment setting, especially among populations where such tools might add value to current models of care. The objective of this study is to explore the daily use of commercially-available mobile apps for anxiety that integrate CBT, with a focus on understanding reasons for and barriers for app use and engagement. This study recruited 17 young adults (age M = 24.17 years) while on a waiting list to receive therapy in a Student Counselling Service. Participants were asked to select up to two of a list of three selected apps (Wysa, Woebot, and Sanvello) and instructed to use the apps for two weeks. Apps were selected because they used techniques from cognitive behavioral therapy, and offer diverse functionality for anxiety management. Qualitative and quantitative data were gathered through daily questionnaires to capture participants' experiences with the mobile apps. In addition, eleven semi-structured interviews were conducted at the end of the study. We used descriptive statistics to analyze participants' interaction with different app features and used a general inductive approach to analyze the collected qualitative data. The results highlight that users form opinions about the apps during the first days of app use. A number of barriers to sustained use are identified including cost-related issues, inadequate content to support long-term use, and a lack of customization options for different app functions. The app features used differ among participants with self-monitoring and treatment elements being the most used features.
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