Adherence of the #Here4U App – Military Version to Criteria for the Development of Rigorous Mental Health Apps

Abstract

Background Over the past several years, the emergence of mobile mental health apps has increased as a potential solution for populations who may face logistical and social barriers to traditional service delivery, including individuals connected to the military. Objective The goal of the #Here4U App – Military Version is to provide evidence-informed mental health support to members of Canada’s military community, leveraging artificial intelligence in the form of IBM Canada’s Watson Assistant to carry on unique text-based conversations with users, identify presenting mental health concerns, and refer users to self-help resources or recommend professional health care where appropriate. Methods As the availability and use of mental health apps has increased, so too has the list of recommendations and guidelines for efficacious development. We describe the development and testing conducted between 2018 and 2020 and assess the quality of the #Here4U App against 16 criteria for rigorous mental health app development, as identified by Bakker and colleagues in 2016. Results The #Here4U App – Military Version met the majority of Bakker and colleagues’ criteria, with those unmet considered not applicable to this particular product or out of scope for research conducted to date. Notably, a formal evaluation of the efficacy of the app is a major priority moving forward. Conclusions The #Here4U App – Military Version is a promising new mental health e-solution for members of the Canadian Armed Forces community, filling many of the gaps left by traditional service delivery.

Leveraging conversational technology to answer common COVID-19 questions

“…Although not specific to the healthcare domain, this platform has previously been applied to medication prescribing, mental health, and Parkinson's disease. [12][13][14] Core natural language capabilities include: (1) understanding input, (2) classifying topics, (3) state management and maintaining a structured dialog (eg, functions to support dynamically collecting multiple pieces of information, digressions for allowing users to change topics without losing their place in the conversation, and disambiguation to clarify when users say something for which the system has multiple relevant responses 15 ); and (4) retrieving information from a knowledge base through search. WA uses NLP and machine learning in the intent understanding, entity extraction, query expansion, and finding answers through estimating document relevancy.…”