RESUMO
BACKGROUND: Digital mental health interventions (DMHIs) represent a promising solution to address the growing unmet mental health needs and increase access to care. Integrating DMHIs into clinical and community settings is challenging and complex. Frameworks that explore a wide range of factors, such as the Exploration, Preparation, Implementation, Sustainment (EPIS) framework, can be useful for examining multilevel factors related to DMHI implementation efforts. OBJECTIVE: This paper aimed to identify the barriers to, facilitators of, and best practice recommendations for implementing DMHIs across similar organizational settings, according to the EPIS domains of inner context, outer context, innovation factors, and bridging factors. METHODS: This study stems from a large state-funded project in which 6 county behavioral health departments in California explored the use of DMHIs as part of county mental health services. Our team conducted interviews with clinical staff, peer support specialists, county leaders, project leaders, and clinic leaders using a semistructured interview guide. The development of the semistructured interview guide was informed by expert input regarding relevant inner context, outer context, innovation factors, and bridging factors in the exploration, preparation, and implementation phases of the EPIS framework. We followed a recursive 6-step process to conduct qualitative analyses using inductive and deductive components guided by the EPIS framework. RESULTS: On the basis of 69 interviews, we identified 3 main themes that aligned with the EPIS framework: readiness of individuals, readiness of innovations, and readiness of organizations and systems. Individual-level readiness referred to the extent to which clients had the necessary technological tools (eg, smartphones) and knowledge (digital literacy) to support the DMHI. Innovation-level readiness pertained to the accessibility, usefulness, safety, and fit of the DMHI. Organization- and system-level readiness concerned the extent to which providers and leadership collectively held positive views about DMHIs as well as the extent to which infrastructure (eg, staffing and payment model) was appropriate. CONCLUSIONS: The successful implementation of DMHIs requires readiness at the individual, innovation, and organization and system levels. To improve individual-level readiness, we recommend equitable device distribution and digital literacy training. To improve innovation readiness, we recommend making DMHIs easier to use and introduce, clinically useful, and safe and adapting them to fit into the existing client needs and clinical workflow. To improve organization- and system-level readiness, we recommend supporting providers and local behavioral health departments with adequate technology and training and exploring potential system transformations (eg, integrated care model). Conceptualizing DMHIs as services allows the consideration of both the innovation characteristics of DMHIs (eg, efficacy, safety, and clinical usefulness) and the ecosystem around DMHIs, such as individual and organizational characteristics (inner context), purveyors and intermediaries (bridging factor), client characteristics (outer context), as well as the fit between the innovation and implementation settings (innovation factor).
RESUMO
BACKGROUND: Mental health concerns are a significant issue among the deaf and hard of hearing (D/HH) community, but community members can face several unique challenges to accessing appropriate resources. OBJECTIVE: The aim of this study was to investigate the mental health needs of the D/HH community and how mental health apps may be able to support these needs. METHODS: A total of 10 members of the D/HH community participated in a focus group and survey to provide their perspectives and experiences. Participants were members of the Center on Deafness Inland Empire team, which comprises people with lived experience as members of and advocates for the D/HH community. RESULTS: Findings identified a spectrum of needs for mental health apps, including offering American Sign Language and English support, increased education of mental health to reduce stigma around mental health, direct communication with a Deaf worker, and apps that are accessible to a range of community members in terms of culture, resources required, and location. CONCLUSIONS: These findings can inform the development of digital mental health resources and outreach strategies that are appropriate for the D/HH community.
RESUMO
OBJECTIVE: Given the increasing number of publicly available mental health apps, we need independent advice to guide adoption. This paper discusses the challenges and opportunities of current mental health app rating systems and describes the refinement process of one prominent system, the One Mind PsyberGuide Credibility Rating Scale (PGCRS). METHODS: PGCRS Version 1 was developed in 2013 and deployed for 7 years, during which time a number of limitations were identified. Version 2 was created through multiple stages, including a review of evaluation guidelines and consumer research, input from scientific experts, testing, and evaluation of face validity. We then re-reviewed 161 mental health apps using the updated rating scale, investigated the reliability and discrepancy of initial scores, and updated ratings on the One Mind PsyberGuide public app guide. RESULTS: Reliabilities across the scale's 9 items ranged from -0.10 to 1.00, demonstrating that some characteristics of apps are more difficult to rate consistently. The average overall score of the 161 reviewed mental health apps was 2.51/5.00 (range 0.33-5.00). Ratings were not strongly correlated with app store star ratings, suggesting that credibility scores provide different information to what is contained in star ratings. CONCLUSION: PGCRS summarizes and weights available information in 4 domains: intervention specificity, consumer ratings, research, and development. Final scores are created through an iterative process of initial rating and consensus review. The process of updating this rating scale and integrating it into a procedure for evaluating apps demonstrates one method for determining app quality.
