Development of a Mental Health Apps Recommender Platform.

Abstract

The aim of the paper is to establish the requirements and methodology for the development and implementation of a recommender system for mental health apps to support patients in self-managing their mental health while awaiting formal treatment. The system was developed using an algorithm-based approach, including: (1) user needs assessment through literature review and interviews with various stakeholders, (2) software modelling and prototype creation, and (3) bench testing of the prototype with health experts and users. Based on initial exploration of users' requirements, relevant standards and regulations, a library of trusted mental health apps was compiled and a recommendation engine was built to generate accurate user profiles and deliver personalised health recommendations, which will be further tested to ensure quality. Developing a constructive mental health recommendation system requires the establishment of clear and comprehensive requirements, as well as a robust methodology adressing concerns related to data security, confidentiality, safety, and reliability. Subsequent research may compare various indicators of mental health outcomes at the start and end of patients' waiting period to gain more insights into how the recommender system could be further improved to enhance user experience and their overall well-being.