Stephen M. Schueller’s research while affiliated with University of California, Irvine and other places

Objective: Loneliness is a global health issue, but current loneliness interventions are not scalable enough to reach many who might benefit from them. Brief online interventions could greatly expand access to evidence-based loneliness interventions. Method: We conducted a pre-registered three-armed trial (masked ClinicalTrials.gov and OSF: https://bit.ly/47s2E8c) (N=908, ages 16-78) to compare three self-guided online interventions: a single-session intervention (SSI) for loneliness, a three-week, three-session loneliness intervention, and an active control supportive therapy SSI. Results: Loneliness decreased between baseline and week eight across all conditions (b = -5.80; d = -0.55; 95% CI, -0.62, -0.47; p < 0.01), but did not decrease significantly more in those assigned to either the loneliness SSI (b = -1.27; d = -0.12; 95% CI, -0.30, 0.06; p = 0.20) or the three-week intervention (b = -0.93; d = -0.09; 95% CI, -0.27, 0.09; p = 0.34) than those assigned to the control SSI. Participants found all three interventions acceptable but rated both loneliness interventions as more acceptable than the control (ps < 0.01). Far more participants completed the 10-minute control SSI (86.6%) and 20-minute loneliness SSI (69.4%) than the 60-minute three-week intervention (14.9%). Conclusions: An SSI for loneliness was not significantly less effective than a longer loneliness intervention and had a much higher completion rate. Yet, neither loneliness intervention reduced loneliness more than an active control SSI did, against our primary and secondary hypotheses. Future work should aim to design more effective SSIs for loneliness and identify populations for which SSIs might be most helpful.

BACKGROUND It is difficult for families to navigate and access services for their children with autism. Barriers to service access are compounded among families from low-resourced backgrounds. OBJECTIVE The purpose of our study was to explore the development of an app to facilitate access to services among families of children with autism from low-resourced backgrounds. Our specific aims were to explore feedback from an advisory board about the app and to explore feedback from navigators about the app. METHODS Via a multistage codevelopment process, we elicited feedback from 5 key parties: the research team, a community organization, the app development team, the advisory board, and family navigators. Collectively, 36 individuals provided feedback about the development of the app via individual interviews, focus groups, observations, and surveys. The key features of the app included a dashboard showing the service needs of the family and related resources, a messaging feature between the family, the navigator, and the supervisor, and a fidelity checklist and evaluation feature. RESULTS The advisory board provided feedback about the app to increase its user-friendliness, include the ability to develop an action plan, improve the identification of needed services, and add information about service providers. Navigators suggested that the app should connect navigators to one another, have a clearer purpose for the notes section, and reflect an easier log-in process. Navigators also wanted training to role-play using the app. After participating in a role play using the app, navigators reported significantly more satisfaction with the app and greater usefulness ( P <.001). CONCLUSIONS Our work sheds light on the importance of eliciting feedback from end users, especially users who are often overlooked by the research community and app developers. Further, it is important to elicit feedback in multiple ways to improve the app.

Background It is difficult for families to navigate and access services for their children with autism. Barriers to service access are compounded among families from low-resourced backgrounds. Objective The purpose of our study was to explore the development of an app to facilitate access to services among families of children with autism from low-resourced backgrounds. Our specific aims were to explore feedback from an advisory board about the app and to explore feedback from navigators about the app. Methods Via a multistage codevelopment process, we elicited feedback from 5 key parties: the research team, a community organization, the app development team, the advisory board, and family navigators. Collectively, 36 individuals provided feedback about the development of the app via individual interviews, focus groups, observations, and surveys. The key features of the app included a dashboard showing the service needs of the family and related resources, a messaging feature between the family, the navigator, and the supervisor, and a fidelity checklist and evaluation feature. Results The advisory board provided feedback about the app to increase its user-friendliness, include the ability to develop an action plan, improve the identification of needed services, and add information about service providers. Navigators suggested that the app should connect navigators to one another, have a clearer purpose for the notes section, and reflect an easier log-in process. Navigators also wanted training to role-play using the app. After participating in a role play using the app, navigators reported significantly more satisfaction with the app and greater usefulness (P<.001). Conclusions Our work sheds light on the importance of eliciting feedback from end users, especially users who are often overlooked by the research community and app developers. Further, it is important to elicit feedback in multiple ways to improve the app.

Background Limited English Proficiency (LEP) Latinxs experience a longer duration of untreated depression and anxiety. LEP Latinxs have difficulty accessing mental healthcare due to insufficient Spanish-speaking behavioral/mental health clinicians to meet demand. These under-resourced healthcare systems are less likely to be the site for the implementation of innovations. Digital interventions can provide an effective option for overcoming these barriers; yet, when digital evidence-based treatments are available, uptake and engagement is often low. This manuscript presents the protocol for the SUPERA (SUpport from PEeRs to expand Access) study which will evaluate the implementation of an evidence-based, Spanish language, digital cognitive-behavioral therapy (dCBT) intervention (i.e., SilverCloud) in safety-net primary care clinics for LEP Latinx patients with depression or anxiety. Methods We will conduct an effectiveness-implementation hybrid trial (Type 2) design comparing engagement and clinical outcomes in two modalities of dCBT delivery (peer-supported vs. unsupported). We will also compare provider-level outreach (using a clinic patient registry) versus inreach (traditional provider referral) to compare rates of initiation, completion, and cost. Participants will be 426 LEP Latinx adults ≥18 years of age, PHQ-9 ≥ 10 or GAD-7 ≥ 8, with access to the internet via smartphone, and not currently receiving individual psychotherapy. We will collect baseline, post-intervention (8 weeks), and follow up (3 months) data. Conclusion The long-term goal of this research is to aid in the implementation of digital mental health interventions that can be sustainably implemented in low-resourced settings, while reducing the reliance on professionals, overcoming workforce deficits, and increasing relevance for diverse populations.

Opinion statement Digital mental health interventions (DMHIs) offer a modern solution to increase access to mental health services, by supporting existing services or providing new services to those who are not as accessible to traditional mental healthcare. DMHIs, however, often face challenges transitioning from research to practice. Implementation science aims to enhance and establish frameworks to help identify barriers and facilitators towards implementing DMHIs in different communities. We analyze the barriers and facilitators towards successful implementation using the Consolidated Framework for Implementation Research (CFIR) domains and summarize the implementation strategies used to enhance the adoption, implementation, and sustainment of DMHIs. Finally, we address the ethical and safety principles that should be followed to protect the consumers/patients. With the growing amount of evidence supporting the effectiveness of DMHIs, this paper provides an overview of implementation considerations for DMHIs and its role in improving mental healthcare delivery. However, the role of DMHIs and their implementation need to be better defined in various settings. Implementation science and its frameworks can offer us a better understanding of determinants to better identify, create, and evaluate implementation strategies.

Technology-enabled services (TESs) are clinical interventions that combine technological and human components to provide health services. TESs for mental health are efficacious in the treatment of anxiety and depression and are currently being offered as frontline treatments around the world. It is hoped that these interventions will be able to reach diverse populations across a range of identities and ultimately decrease disparities in mental health treatment. However, this hope is largely unrealized. TESs include both technology and human service components, and we argue that cultural responsivity must be considered in each of these components to help address existing treatment disparities. To date, there is limited guidance on how to consider cultural responsivity within these interventions, including specific targets for the development, tailoring, or design of the technologies and services within TESs. In response, we propose a framework that provides specific recommendations for targets based on existing models, both at the technological component level (informed by the Behavioral Intervention Technology Model) and the human support level (informed by the Efficiency Model of Support). We hope that integrating culturally responsive considerations into these existing models will facilitate increased attention to cultural responsivity within TESs to ensure they are ethical and responsive for everyone.

In this article, Schueller and Morris discuss the recent advances made from large language models (LLMs) and generative artificial intelligence (AI). These advances include supporting humans to provide better interventions, understanding processes in clinical interventions, and providing ethical considerations for the use of generative AI in clinical research and practice. (PsycInfo Database Record (c) 2023 APA, all rights reserved).