Background Mental illness is a pervasive worldwide public health issue. Residentially vulnerable populations, such as those living in rural medically underserved areas (MUAs) or mental health provider shortage areas (MHPSAs), face unique access barriers to mental health care. Despite the growth of digital mental health interventions using relational agent technology, little is known about their use patterns, efficacy, and favorability among residentially vulnerable populations. Objective This study aimed to explore differences in app use, therapeutic alliance, mental health outcomes, and satisfaction across residential subgroups (metropolitan, nonmetropolitan, or rural), MUAs (yes or no), and MHPSAs (yes or no) among users of a smartphone-based, digital mental health intervention, Woebot LIFE (WB-LIFE). WB-LIFE was designed to help users better understand and manage their moods and features a relational agent, Woebot, that converses through text-based messages. Methods We used an exploratory study that examined data from 255 adults enrolled in an 8-week, single-arm trial of WB-LIFE. Analyses compared levels of app use and therapeutic alliance total scores as well as subscales (goal, task, and bond), mental health outcomes (depressive and anxiety symptoms, stress, resilience, and burnout), and program satisfaction across residential subgroups. Results Few study participants resided in nonmetropolitan (25/255, 10%) or rural (3/255, 1%) areas, precluding estimates across this variable. Despite a largely metropolitan sample, nearly 39% (99/255) resided in an MUA and 55% (141/255) in an MHPSA. There were no significant differences in app use or satisfaction by MUA or MHPSA status. There also were no differences in depressive symptoms, anxiety, stress, resilience, or burnout, with the exception of MUA participants having higher baseline depressive symptoms among those starting in the moderate range or higher (Patient Health Questionnaire-8 item scale≥10) than non-MUA participants (mean 16.50 vs 14.41, respectively; P=.01). Although working alliance scores did not differ by MHPSA status, those who resided in an MUA had higher goal (2-tailed t203.47=2.21; P=.03), and bond (t203.47=1.94; P=.05) scores at day 3 (t192.98=2.15; P=.03), and higher goal scores at week 8 (t186.19=2.28; P=.02) as compared with those not living in an MUA. Conclusions Despite the study not recruiting many participants from rural or nonmetropolitan populations, sizable proportions resided in an MUA or an MHPSA. Analyses revealed few differences in app use, therapeutic alliance, mental health outcomes (including baseline levels), or satisfaction across MUA or MHPSA status over the 8-week study. Findings suggest that vulnerable residential populations may benefit from using digital agent–guided cognitive behavioral therapy. Trial Registration ClinicalTrials.gov NCT05672745; https://clinicaltrials.gov/study/NCT05672745

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Background: The ability for digital mental health interventions (DMHI) to reduce mental health disparities relies on recruitment of research participants with diverse sociodemographic and self-identity characteristics. Despite its importance, sociodemographic reporting in research is often limited, and the state of reporting practices in DMHI research in particular has not been comprehensively reviewed. Objectives: To characterize the state of sociodemographic data reported in randomized controlled trials (RCTs) of app-based DMHIs published globally from 2007 to 2022. Methods: A scoping review of RCTs of app-based DMHIs examined reporting frequency for 16 sociodemographic domains (i.e., Gender) and common category options within each domain (i.e., woman). The search queried five electronic databases. 5079 records were screened and 299 articles were included. Results: On average, studies reported 4.64 (SD = 1.79; range 0 - 9) of 16 sociodemographic domains. The most common were Age (97%) and Education (67%). The least common were Housing Situation (6%), Residency/Location (5%), Veteran Status (4%), Number of Children (3%), Sexual Orientation (2%), Disability Status (2%), and Food Security (<1%). Gender or Sex was reported in 98% of studies: Gender only (51%), Sex only (28%), both (<1%), Gender/Sex reported but unspecified (18%). Race or Ethnicity was reported in 48% of studies: Race only (14%), Ethnicity only (14%), both (10%), Race/Ethnicity reported but unspecified (10%). Conclusions: This review describes widespread underreporting of sociodemographic information in RCTs of app-based DMHIs published from 2007 to 2022. Reporting was often incomplete (i.e., % female only), unclear (i.e., conflation of Gender/Sex), and limited (i.e., only options representing majority groups were reported). Trends suggest reporting somewhat improved in recent years. Diverse participant populations must be welcomed and described in DMHI research to broaden learnings and generalizability of results; a prerequisite of DMHIs potential to reduce disparities in mental healthcare.

Relations between negative emodiversity (NED; the variety and relative abundance of negative emotions) with depression and anxiety were examined before and during the COVID-19 pandemic. Forty-five individuals (ages 25-65) participated in two ecological momentary assessments (EMA): pre-pandemic and during-pandemic (Fall, 2020). Participants reported how much they felt 6 negative emotions several times each day for 10 days (resulting up to 91 EMA “events”). Each event’s NED was computed and then averaged using an adaptation of Shannon’s entropy. Participants with higher levels of average NED had higher levels of concurrent depression and anxiety. When adjusting for average levels of negative emotion and other covariates, NED was a significant predictor of depression and anxiety only during the pandemic. These findings, which did not vary by age, suggest that having more diverse negative emotions on a moment-to-moment basis may hold greater significance for mental illness outcomes during times of extreme chronic stress.

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BACKGROUND Mental illness is a pervasive worldwide public health issue. Residentially vulnerable populations such as those living in rural, medically underserved, or mental health provider shortage areas face unique access barriers to mental health care. Despite the growth of digital mental health interventions utilizing relational agent technology, little is known about their use patterns, efficacy, and favorability in residentially vulnerable populations. OBJECTIVE To explore differences in application (app) utilization, therapeutic alliance, mental health outcomes, and satisfaction across residential subgroups (metropolitan/nonmetropolitan/rural, medically underserved area (MUA) (yes/no), and mental health provider shortage areas (MHPSA) (yes/no) among users of a smartphone-based, digital mental health intervention, Woebot Life (WB-LIFE). WB-LIFE was designed to help users better understand and manage their moods and features a relational agent, Woebot, that converses through text-based messages. METHODS This exploratory study examined data from 255 adults enrolled in an 8-week, single-arm trial of WB-LIFE. Analyses compared levels of app utilization and therapeutic alliance Total scores as well as subscales Goal, Task, and Bond, mental health outcomes (depressive and anxiety symptoms, stress, resilience, burnout), and program satisfaction across residential subgroups. RESULTS Few study participants resided in non-metropolitan (n=25, 10%) or rural (n=3, 1%) areas, precluding estimates across this variable. Despite a largely metropolitan sample, nearly 39% (n=99) resided in an MUA and 55% (n=141) in an MHPSA. There were no significant differences in app utilization or satisfaction by MUA or MHPSA status. There also were no differences in depression, anxiety, stress, resilience or burnout, with the exception of MUA participants having higher baseline depressive symptoms among those starting in the moderate range or higher (Patient Health Questionnaire-8 item ([PHQ-8]≥10) than non-MUA participants (mean=16.50 versus 14.41, respectively, p=0.01). Although working alliance scores did not differ by MHPSA status, those who resided in an MUA had higher Goal (t=2.21, p=0.03), Bond (t=1.94, p=0.05) and Total therapeutic alliance scores at Day 3 (t=2.15, p=0.03), and higher Goal scores at Week 8 (t=2.28, p=0.02) as compared to those not living in an MUA. CONCLUSIONS Despite the study not recruiting many participants from rural or nonmetropolitan populations, sizable proportions resided in an MUA or an MHPSA. Analyses revealed few differences in app utilization, therapeutic alliance, mental health outcomes (including baseline levels), or satisfaction across MUA or MHPSA status over the 8-week study. Findings suggest that vulnerable residential populations may benefit from using digital agent-guided cognitive behavioral therapy.