Background Mobile health (mHealth) platforms show promise in the management of mental health conditions such as anxiety and depression. This has resulted in an abundance of mHealth platforms available for research or commercial use. Objective The objective of this review is to characterize the current state of mHealth platforms designed for anxiety or depression that are available for research, commercial use, or both. Methods A systematic review was conducted using a two-pronged approach: searching relevant literature with prespecified search terms to identify platforms in published research and simultaneously searching 2 major app stores—Google Play Store and Apple App Store—to identify commercially available platforms. Key characteristics of the mHealth platforms were synthesized, such as platform name, targeted condition, targeted group, purpose, technology type, intervention type, commercial availability, and regulatory information. Results The literature and app store searches yielded 169 and 179 mHealth platforms, respectively. Most platforms developed for research purposes were designed for depression (116/169, 68.6%), whereas the app store search reported a higher number of platforms developed for anxiety (Android: 58/179, 32.4%; iOS: 27/179, 15.1%). The most common purpose of platforms in both searches was treatment (literature search: 122/169, 72.2%; app store search: 129/179, 72.1%). With regard to the types of intervention, cognitive behavioral therapy and referral to care or counseling emerged as the most popular options offered by the platforms identified in the literature and app store searches, respectively. Most platforms from both searches did not have a specific target age group. In addition, most platforms found in app stores lacked clinical and real-world evidence, and a small number of platforms found in the published research were available commercially. Conclusions A considerable number of mHealth platforms designed for anxiety or depression are available for research, commercial use, or both. The characteristics of these mHealth platforms greatly vary. Future efforts should focus on assessing the quality—utility, safety, and effectiveness—of the existing platforms and providing developers, from both commercial and research sectors, a reporting guideline for their platform description and a regulatory framework to facilitate the development, validation, and deployment of effective mHealth platforms.

Objective Health behaviors before, during and after pregnancy can have lasting effects on maternal and infant health outcomes. Although a digital health intervention (DHI) has potential as a pertinent avenue to deliver mechanisms for a healthy behavior change, its success is reliant on addressing the user needs, without creating apprehension that may lead to attrition. Accordingly, the current study aimed to understand DHI needs and expectations of women before, during and after pregnancy to inform and optimize future DHI developments, specifically ‘the do’s and the don’ts’ for sustainable engagement and efficient intervention. Methods Forty-four women (13 pre-, 16 during and 15 post-pregnancy; age range = 21–40 years) completed a 60-minute, semi-structured, qualitative interview exploring participant’s experience in their current phase, experience with, and attitude towards digital health tools, and their needs and expectations of DHIs. Interviews were audio-recorded, transcribed verbatim and thematically analyzed. Results From the interviews, two core concepts emerged – personalized journey and embedding within the local ecosystem. Between both concepts, five themes and 12 sub-themes were identified. Themes and sub-themes within personalization cover ideas of two-way interactivity, journey organization based on phases and circumstances, and privacy trade-off. Themes and sub-themes within localization cover ideas of access to local health-related resources and information, and connecting to local communities through anecdotal stories. Conclusion The findings captured - through understanding user needs and expectations - the key elements for the development and optimization of a successful DHI for women before, during and after pregnancy. To potentially empower downstream DHI implementation and adoption, these insights can serve as a foundation in the initial innovation process for DHI developers and be further built upon through a continued co-design process.

BACKGROUND Mobile health (mHealth) platforms show promise in the management of mental health conditions such as anxiety and depression. This has resulted in an abundance of mHealth platforms available for research or commercial use. OBJECTIVE This study aimed to characterize the current state of mHealth platforms designed for anxiety and/or depression that are available for research, commercial use or both. METHODS A systematic review was conducted using a two-pronged approach. (i) A systematic literature search of PubMed, EMBASE, CINAHL and PsycINFO was conducted to identify platforms available for research purposes. (ii) A simultaneous search of two major mobile app stores – Apple App Store and Google Play Store – to identify commercially available platforms. Key characteristics of the mHealth platforms such as platform name, targeted condition, targeted group, purpose, technology type, intervention type, commercial availability, regulatory information were synthesized. RESULTS The literature and app stores searches yielded 169 and 179 mHealth platforms respectively. Most platforms developed for research purposes were designed for depression (n=113) whereas the app stores search reported a higher number of platforms were developed for anxiety (n=58 and n=27 for Android and iOS operating systems, respectively). The most common purpose of platforms in both searches was treatment (n=122 and n=129 for the literature and app stores searches, respectively). In regard to the types of intervention, cognitive behavioral therapy and referral to care/counselling emerged as the most popular options offered by the platforms identified in the literature and app stores searches, respectively. Most platforms from both searches did not have a specific target age group. Additionally, most platforms found in the app stores lacked clinical and real-world evidence, while only small number of platforms found in published research were available commercially. CONCLUSIONS A considerable number of mHealth platforms designed for anxiety and/or depression are available for research, commercial use or both. Characteristics of these mHealth platforms vary greatly. Future effort should focus on accessing the quality – utility, safety and effectiveness – of the existing platforms and providing developers, from both commercial and research sectors alike, a reporting guideline for their platform description as well as a regulatory framework to facilitate the development, validation and deployment of effective mHealth platforms. CLINICALTRIAL CRD42020193956

BACKGROUND Doctors play a key role in integrating new clinical technology into care practices through their user feedback and growth propositions to developers of the technology. As doctors are stakeholders involved through the technology iteration process, understanding their roles as users can provide nuanced insights into the workings of these technologies that are currently being explored. Understanding doctor perceptions therefore can be critical towards clinical validation, implementation, and downstream adoption. Given the increasing prevalence of clinical decision support systems (CDSS), there remains a need to gain in-depth understandings of doctor perceptions and expectations towards their downstream implementation. As such, this article explores doctor perceptions of integrating CURATE.AI, a novel AI-based and clinical-stage personalised dosing CDSS, into clinical practice. OBJECTIVE Understanding doctors perceptions of adopting a novel clinical decision support system METHODS 13 participants completed a 60-minute semi structured interview examining their knowledge, experience, attitudes, risks and future course of the personalized combination therapy dosing platform, CURATE. AI. Interviews were audio recorded, transcribed verbatim and coded manually. Data was thematically analysed. RESULTS Three broad themes and 9 sub-themes were identified using thematic analysis. The themes covered considerations doctors perceived as significant across various stages of new technology development including trial, clinical implementation and mass adoption. CONCLUSIONS The study laid out the various ways doctors interpreted an AI-based personalised dosing CDSS, CURATE.AI, for their clinical practice. The research pointed out that doctors’ expectations during the different stages of technology exploration can be nuanced and layered with expectations of implementation relevant for technology developers and researchers.