Common practices for sociodemographic data reporting in digital mental health intervention research: a scoping review.

BACKGROUND: The ability of digital mental health interventions (DMHIs) to reduce mental health disparities relies on the 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 characterise the state of sociodemographic data reported in randomised 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 (eg, gender) and common category options within each domain (eg, 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%) and gender/sex reported but unspecified (18%). Race or ethnicity was reported in 48% of studies: race only (14%), ethnicity only (14%), both (10%) and race/ethnicity reported but unspecified (10%). CONCLUSIONS: This review describes the widespread underreporting of sociodemographic information in RCTs of app-based DMHIs published from 2007 to 2022. Reporting was often incomplete (eg, % female only), unclear (eg, the conflation of gender/sex) and limited (eg, only options representing majority groups were reported). Trends suggest reporting has somewhat improved in recent years. Diverse participant populations must be welcomed and described in DMHI research to broaden learning and the generalisability of results, a prerequisite of DMHI's potential to reduce disparities in mental healthcare.

Engagement, Satisfaction, and Mental Health Outcomes Across Different Residential Subgroup Users of a Digital Mental Health Relational Agent: Exploratory Single-Arm Study.

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.

Changes in stress, burnout, and resilience associated with an 8-week intervention with relational agent "Woebot".

Background: Research investigating the potential for digital mental health interventions with integrated relational agents to improve mental health outcomes is in its infancy. By delivering evidence-based mental health interventions through tailored, empathic conversations, relational agents have the potential to help individuals manage their stress and mood, and increase positive mental health. Aims: The aims of this study were twofold: 1) to assess whether a smartphone app delivering mental health support through a relational agent, Woebot, is associated with changes in stress, burnout, and resilience over 8 weeks, and 2) to identify demographic and clinical factors associated with changes in these outcomes. Method: This exploratory, non-randomized, single-armed, open-labeled trial was conducted from May to July 2022. A total of 256 adults (mean age 39 ± 13.35; 72 % females) recruited through social media advertising enrolled in the study. Participants completed an 8-week intervention period during which they were invited to use a smartphone app called Woebot-LIFE that delivers cognitive behavioral therapy through a relational agent called "Woebot". Participant-reported measures of stress, burnout, and resilience were collected at Baseline, and Week 8. Changes in these outcomes during the study period were assessed. Bivariate and stepwise multiple regression modeling was used to identify sociodemographic and clinical factors associated with observed changes over the 8-week study period. Results: Exposure to Woebot-LIFE was associated with significant reductions in perceived stress and burnout and significantly increased resilience over the 8-week study period. A greater reduction in stress was observed among those with clinically elevated mood symptoms (i.e., Patient Health Questionnaire-8 or Generalized Anxiety Disorder 7-item scores ≥10) at baseline compared to those without; however, the differences in the improvements in resilience scores and burnout between the two groups were not statistically significant. Although a difference in the magnitude of change in stress was observed for participants with and without clinically elevated mood symptoms at baseline, significant improvements in stress, burnout, and resilience over the 8-week study period were observed for both groups. Bivariate analyses showed that race, insurance type, and baseline level of resilience were associated with changes in each of the outcomes, though baseline resilience was the only factor that remained significantly associated with changes in the outcomes in the stepwise multiple regression analyses. Conclusion: Results of this single-arm, exploratory study suggest that conversational agent-guided mental health interventions such as Woebot-LIFE may be associated with reduced stress and burnout and increased resilience in both clinical and non-clinical populations.

Sleep and occupational well-being in active duty special operations soldiers: A replication and expansion.

OBJECTIVE: To assess the relationship between sleep quality and occupational well-being in active duty military Service Members. DESIGN: Longitudinal prospective analysis. SETTING: An annual military training event. PARTICIPANTS: US Army special operations Soldiers (n = 60; 100% male; age 25.41 ± 3.74). INTERVENTION: None. MEASUREMENTS: The Pittsburgh Sleep Quality Index (PSQI) was administered prior to the training event, and the Emotional Exhaustion Scale, the Role Overload Scale, the Walter Reed Army Institute of Research Soldier-Specific Functional Impairment Scale, and the Perceived Stress Scale were administered after the event. Linear regression models were used to assess the relationship between sleep and occupational wellness measures, and the outcome measures of "good" and "poor" sleepers (per the PSQI scoring criteria) were compared with Student's t tests. RESULTS: Higher (poorer) PSQI Global Scores predicted poorer occupational wellness of all measures (emotional exhaustion: B = 1.60, P < .001, R2 = 0.25; functional impairment: B = 0.29, P = .03, R2 = 0.14; role overload: B = 0.28, P = .008, R2 = 0.12; and perceived stress: B = 0.34, P = .004, R2 = 0.20). There were additional relationships between specific PSQI component scores and occupational wellness measures, which is a replication of This team's previous work. Furthermore, emotional exhaustion (t(58) = -4.18, P < .001), functional impairment (t(59)= -3.68, P = .001), role overload (t(58) = -3.20, P = .002), and perceived stress (t(58) = -2.43, P = .02) were all higher in poor sleepers. CONCLUSIONS: The findings of this study suggest that US Army special operations Soldiers who have poorer sleep quality may be at increased risk for having poorer occupational well-being.