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Designing High-Fidelity Mobile Health for Depression in Indonesian Adolescents Using Design Science Research: Mixed
Mila Shania1, Putu Wuri Handayani1, Sali Asih1
1Faculty of Psychology, University of Indonesia, Depok, Indonesia.
Background:
COVID-19 mitigation protocols, enacted to control the pandemic, have also been shown to have a negative impact on mental health, including the mental health of adolescents. The threat of being infected by SARS-CoV-2 and substantial changes in lifestyle, including limited social interaction due to stay-at-home orders, led to loneliness as well as depressive symptoms. However, offline psychological assistance is restricted, as psychologists are bounded by mitigation protocols. Further, not all adolescents' guardians are open to their children attending or have the means to pay for psychological service; thus, adolescents remain untreated. Having a mobile health (mHealth) app for mental health that uses monitoring, provides social networks, and delivers psychoeducation may provide a solution, especially in countries that have limited health facilities and mental health workers.
Objective:
This study aimed to design an mHealth app to help prevent and monitor depression in adolescents. The design of this mHealth app was carried out as a high-fidelity prototype.
Methods:
We used a design science research (DSR) methodology with 3 iterations and 8 golden rule guidelines. The first iteration used interviews, and the second and third iterations used mixed method approaches. The DSR stages include the following: (1) identify the problem; (2) define the solution; (3) define the solution objective; (4) develop, demonstrate, and evaluate the solution; and (5) communicate the solution. This study involved students and medical experts.
Results:
The first iteration resulted in a wireframe and prototype for the next iteration. The second iteration resulted in a System Usability Scale score of 67.27, indicating a good fit. In the third iteration, the system usefulness, information quality, interface quality, and overall values were 2.416, 2.341, 2.597, and 2.261, respectively, indicating a good design. Key features of this mHealth app include a mood tracker, community, activity target, and meditation, and supporting features that complement the design include education articles and early detection features.
Conclusions:
Our findings provide guidance for health facilities and to design and implement future mHealth apps to help treat adolescent depression.
