Individuals with serious mental illness experience changes in their clinical states over time that are difficult to assess and that result in increased disease burden and care utilization. It is not known if features derived from speech can serve as a transdiagnostic marker of these clinical states. This study evaluates the feasibility of collecting speech samples from people with serious mental illness and explores the potential utility for tracking changes in clinical state over time. Patients (n = 47) were recruited from a community-based mental health clinic with diagnoses of bipolar disorder, major depressive disorder, schizophrenia or schizoaffective disorder. Patients used an interactive voice response system for at least 4 months to provide speech samples. Clinic providers (n = 13) reviewed responses and provided global assessment ratings. We computed features of speech and used machine learning to create models of outcome measures trained using either population data or an individual’s own data over time. The system was feasible to use, recording 1101 phone calls and 117 hours of speech. Most (92%) of the patients agreed that it was easy to use. The individually-trained models demonstrated the highest correlation with provider ratings (rho = 0.78, p<0.001). Population-level models demonstrated statistically significant correlations with provider global assessment ratings (rho = 0.44, p<0.001), future provider ratings (rho = 0.33, p<0.05), BASIS-24 summary score, depression sub score, and self-harm sub score (rho = 0.25,0.25, and 0.28 respectively; p<0.05), and the SF-12 mental health sub score (rho = 0.25, p<0.05), but not with other BASIS-24 or SF-12 sub scores. This study brings together longitudinal collection of objective behavioral markers along with a transdiagnostic, personalized approach for tracking of mental health clinical state in a community-based clinical setting.

Background: Distorted perception of one’s body and appearance, in general, is a core feature of several psychiatric disorders including anorexia nervosa and body dysmorphic disorder and is operative to varying degrees in nonclinical populations. Yet, body image perception is challenging to assess, given its subjective nature and variety of manifestations. The currently available methods have several limitations including restricted ability to assess perceptions of specific body areas. To address these limitations, we created Somatomap, a mobile tool that enables individuals to visually represent their perception of body-part sizes and shapes as well as areas of body concerns and record the emotional valence of concerns. Objective: This study aimed to develop and pilot test the feasibility of a novel mobile tool for assessing 2D and 3D body image perception. Methods: We developed a mobile 2D tool consisting of a manikin figure on which participants outline areas of body concern and indicate the nature, intensity, and emotional valence of the concern. We also developed a mobile 3D tool consisting of an avatar on which participants select individual body parts and use sliders to manipulate their size and shape. The tool was pilot tested on 103 women: 65 professional fashion models, a group disproportionately exposed to their own visual appearance, and 38 nonmodels from the general population. Acceptability was assessed via a usability rating scale. To identify areas of body concern in 2D, topographical body maps were created by combining assessments across individuals. Statistical body maps of group differences in body concern were subsequently calculated using the formula for proportional z-score. To identify areas of body concern in 3D, participants’ subjective estimates from the 3D avatar were compared to corresponding measurements of their actual body parts. Discrepancy scores were calculated based on the difference between the perceived and actual body parts and evaluated using multivariate analysis of covariance. Results: Statistical body maps revealed different areas of body concern between models (more frequently about thighs and buttocks) and nonmodels (more frequently about abdomen/waist). Models were more accurate at estimating their overall body size, whereas nonmodels tended to underestimate the size of individual body parts, showing greater discrepancy scores for bust, biceps, waist, hips, and calves but not shoulders and thighs. Models and nonmodels reported high ease-of-use scores (8.4/10 and 8.5/10, respectively), and the resulting 3D avatar closely resembled their actual body (72.7% and 75.2%, respectively). Conclusions: These pilot results suggest that Somatomap is feasible to use and offers new opportunities for assessment of body image perception in mobile settings. Although further testing is needed to determine the applicability of this approach to other populations, Somatomap provides unique insight into how humans perceive and represent the visual characteristics of their body.

Background: Mobile technologies hold potential for improving the quality of care and engagement of patients. However, there are considerable challenges in ensuring that technologies are relevant, useful, and engaging. While end users such as patients and providers are increasingly involved in the design of health technologies, there are limited examples of their involvement in directly creating technologies for their personal use. Objective: We aim to evaluate the feasibility and acceptability of patients and providers creating mobile texting apps to support treatment goals. Methods: In an 11-month usability study, we enrolled 4 providers and 28 patients in an intensive outpatient program for obsessive-compulsive disorder. Patients and providers created their own mobile texting apps using a visual app development platform. A subsample of 10 patients and 4 providers completed a usability measure. Results: Participants created a total of 360 unique mobile text messages (1787 total messages sent). There were 4 types of messages identified, including personalized reminders, clinical exposures, interactive prompts, and encouraging/informational messages. A total of 9 out of 10 (90%) patients agreed that the messages were relevant to their recovery, and 8 out of 10 (80%) agreed that the messages were effective at helping complete treatment plans. Conclusions: Enabling patients and providers to cocreate apps for their own use by using a visual application platform is feasible and holds potential for increasing the relevance, sustainability, and effectiveness of digital health technologies.

Background: Open design formats for mobile apps help clinicians and stakeholders bring their needs to direct, co-creative solutions. Palliative care for patients with advanced cancers requires intensive monitoring and support and remains an area in high need for innovation. Objective: This study aims to use community-partnered participatory research to co-design and pretest a mobile app that focuses on palliative care priorities of clinicians and patients with advanced cancer. Methods: In-person and teleconference workshops were held with patient and family stakeholders, researchers, and clinicians in palliative care and oncology. Question prompts, written feedback, semistructured interviews, and facilitated group discussions identified the core palliative care needs. Using Chorus, a no-code app-building platform, a mobile app was co-designed with the stakeholders. A pretest with 11 patients was conducted, with semistructured interviews of clinician and patient users for feedback. Results: Key themes identified from the focus groups included needs for patient advocacy and encouragement, access to vetted information, patient-clinician communication support, and symptom management. The initial prototype, My Wellness App, contained a weekly wellness journal to track patient-reported symptoms, goals, and medication use; information on self-management of symptoms; community resources; and patient and caregiver testimonial videos. Initial pretesting identified value in app-based communication for clinicians, patients, and caregivers, with suggestions for improving user interface, feedback and presentation of symptom reports, and gamification and staff coordinators to support patient app engagement. Conclusions: The development of a mobile app using community-partnered participatory research is a low-technology and feasible intervention for palliative care. Iterative redesign and user interface expertise may improve implementation.

This manuscript presents the protocol and participatory planning process for implementing the Community Resilience Learning Collaborative and Research Network (C-LEARN) study. C-LEARN is designed to determine how to build a service program and individual client capacity to improve mental health-related quality of life among individuals at risk for depression, with exposure to social risk factors or concerns about environmental hazards in areas of Southern Louisiana at risk for events such as hurricanes and storms. The study uses a Community Partnered Participatory Research (CPPR) framework to incorporate community priorities into study design and implementation. The first phase of C-LEARN is assessment of community priorities, assets, and opportunities for building resilience through key informant interviews and community agency outreach. Findings from this phase will inform the implementation of a two-level (program-level and individual client level) randomized study in up to four South Louisiana communities. Within communities, health and social-community service programs will be randomized to Community Engagement and Planning (CEP) for multi-sector coalition support or Technical Assistance (TA) for individual program support to implement evidence-based and community-prioritized intervention toolkits, including an expanded version of depression collaborative care and resources (referrals, manuals) to address social risk factors such as financial or housing instability and for a community resilience approach to disaster preparedness and response. Within each arm, the study will randomize individual adult clients to one of two mobile applications that provide informational resources on services for depression, social risk factors, and disaster response or also provide psychoeducation on Cognitive Behavioral Therapy to enhance coping with stress and mood. Planned data collection includes baseline, six-month and brief monthly surveys for clients, and baseline and 12-month surveys for administrators and staff.

Background: The COVID-19 pandemic increased disparities for communities burdened by structural barriers such as reduced affordable housing, with mental health consequences. Limited data are available on digital resources for public mental health prevention during the COVID-19 pandemic. Objective: The study aim was to evaluate engagement in and impact of free digital resources on the Together for Wellness/Juntos por Nuestro Bienestar (T4W/Juntos) website during COVID-19 in California. Methods: A pilot evaluation of T4W/Juntos was performed, with partner agencies inviting providers, clients, and partners to visit the website and complete surveys at baseline (September 20, 2021, to April 4, 2022) and at 4-6–week follow-up (October 22, 2021, to May 17, 2022). Website use was assessed by three engagement items (ease of use, satisfaction, relevance), comfort in use, and use of six resource categories. Primary outcomes at follow-up were depression and anxiety (scores≥3 on Patient Health Questionnaire-2 item [PHQ2] and Generalized Anxiety Disorder-2 item [GAD2] scales). Secondary outcomes were post-pre differences in PHQ2 and GAD2 scores, and use of behavioral health hotlines and services the month before follow-up. Results: Of 366 eligible participants, 315 (86.1%) completed baseline and 193 (61.3%) completed follow-up surveys. Of baseline participants, 72.6% identified as female, and 21.3% identified as lesbian, gay, bisexual, transgender, queer/questioning, and others (LGBTQ+). In terms of ethnicity, 44.0% identified as Hispanic, 17.8% as African American, 26.9% as non-Hispanic white, and 11.4% as other ethnicity. Overall, 32.7% had moderate anxiety or depression (GAD2/PHQ2≥3) at baseline. Predictors of baseline website engagement included being Hispanic versus other race/ethnicity (β=.27, 95% CI .10-.44; P=.002) and number of COVID-19–related behavior changes (β=.09, 95% CI .05-.13; P<.001). Predictors of comfort using the website were preferring English for website use (odds ratio [OR] 5.57, 95% CI 2.22-13.96; P<.001) and COVID-19–related behavior changes (OR 1.37, 95% CI 1.12-1.66; P=.002); receiving overnight behavioral health treatment in the prior 6 months (OR 0.15, 95% CI 0.03-0.69, P=.015) was associated with less comfort in website use. The main predictor of depression at follow-up (PHQ2≥3) was baseline depression (OR 6.24, 95% CI 2.77-14.09; P<.001). Engagement in T4W/Juntos was associated with lower likelihood of depression (OR 0.54, 95% CI 0.34-0.86; P=.01). Website use the month before follow-up was associated with a post-pre reduction in PHQ2 score (β=–.62, 95% CI –1.04 to –0.20; P=.004). The main predictor of GAD2≥3 at follow-up was baseline GAD2≥3 (OR 13.65, 95% CI 6.06-30.72; P<.001). Greater baseline website engagement predicted reduced hotline use (OR 0.36, 95% CI 0.18-0.71; P=.004). Conclusions: Ethnicity/language and COVID-19–related behavior changes were associated with website engagement; engagement and use predicted reduced follow-up depression and behavioral hotline use. Findings are based on participants recommended by community agencies with moderate follow-up rates; however, significance was similar when weighting for nonresponse. This study may inform research and policy on digital mental health prevention resources.