Background: Studying the effects of mobile health (m-health) apps is crucial for providing accessible mental healthcare to people at high risk for psychopathology, such as adolescents during the...Show moreBackground: Studying the effects of mobile health (m-health) apps is crucial for providing accessible mental healthcare to people at high risk for psychopathology, such as adolescents during the COVID-19 pandemic. Therefore, investigating elements such as real-time personalized feedback are essential to understand what underpins the effectiveness of m-health. The Grow It! app is a multiplayer serious gaming m-health app for adolescents aged 12 to 25 aimed at identification of emotional problems and improving well-being. This study aimed to improve suboptimal app activity (i.e., amount of experience sampling method questionnaires completed) by incorporating feedback in the form of an emotion overview chart into the app. Furthermore, this study wanted to investigate changes in affective and cognitive wellbeing and assess how users rate the emotion overview. Method: Adolescents (N = 143) played Grow It! for three weeks during the COVID-19 pandemic. Participants filled in a questionnaire before and after playing the app, to measure differences in affective (measured on a 7-point Likert scale) and cognitive (measured on a 10-point Likert scale) well-being. The emotion overview was evaluated with four questions. Results: After conducting two paired samples t-tests, I found that affective well-being significantly increased by 0.29 points, t(142) = 3.30, p < .001 (one-tailed), d = .28. Forty percent (40%) of individuals experienced increases. Cognitive well-being significantly increased with 0.44 points, t(142) = 3.17, p < .001 (one-tailed), d = .27. Forty-nine percent (49%) of individuals experienced increases. After conducting a one-way ANOVA, I found that app activity was significantly higher for users playing Grow It! with the emotion overview included, F(2,1335) = 53.13, p < .001. User evaluations were overall positive with the emotion overview being a welcomed addition. Conclusion: The results of this study demonstrate successful replication of previous studies and support gamification theories on the usefulness of real-time personalized feedback. The addition of the emotion overview seems valuable for increasing app activity and positive app evaluations. Further research with a control group is recommended to be able to make any substantial claims on the effectiveness of Grow It! and what added value the emotion overview provides.Show less
Research master thesis | Psychology (research) (MSc)
open access
Ecological Momentary Assessment (EMA) is a data collection method that utilizes phone apps to gather data in daily life. EMA has many advantages, such as ecological validity. However, data...Show moreEcological Momentary Assessment (EMA) is a data collection method that utilizes phone apps to gather data in daily life. EMA has many advantages, such as ecological validity. However, data collection protocols are often intense, with multiple measurements per day, which can interrupt participants’ everyday activities and place a burden on them. This can reduce compliance. One way to tackle this is to provide participants with personalized data reports as an intrinsic reward. However, current frameworks to generate such reports are focused on single individuals in treatment, and not suitable for large-scale studies. Here we introduce a software to fill this gap, FRED (Feedback Reports on EMA Data), and showcase FRED by generating reports for 428 participants who took part in the WARN-D study. Participants were followed for 85 consecutive days, and received four daily and one weekly survey, resulting in up to 352 observations. We provided feedback to participants in the form of downloadable HTML-files, which were generated using the R programing environment. Reports included descriptive statistics, timeseries visualizations, and network analyses on selected variables. Furthermore, we assessed participants’ perceptions of the created reports (n=54), who judged reports mostly as understandable, insightful, and that reports resonated well with them. Given that FRED is flexible and can be adjusted to the needs of a particular research project, it provides a good basis to generate large numbers of personalized data reports.Show less
