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.

Demographic and clinical characteristics associated with anxiety and depressive symptom outcomes in users of a digital mental health intervention incorporating a relational agent.

BACKGROUND: Digital mental health interventions (DMHIs) may reduce treatment access issues for those experiencing depressive and/or anxiety symptoms. DMHIs that incorporate relational agents may offer unique ways to engage and respond to users and to potentially help reduce provider burden. This study tested Woebot for Mood & Anxiety (W-MA-02), a DMHI that employs Woebot, a relational agent that incorporates elements of several evidence-based psychotherapies, among those with baseline clinical levels of depressive or anxiety symptoms. Changes in self-reported depressive and anxiety symptoms over 8 weeks were measured, along with the association between each of these outcomes and demographic and clinical characteristics. METHODS: This exploratory, single-arm, 8-week study of 256 adults yielded non-mutually exclusive subsamples with either clinical levels of depressive or anxiety symptoms at baseline. Week 8 Patient Health Questionnaire-8 (PHQ-8) changes were measured in the depressive subsample (PHQ-8 ≥ 10). Week 8 Generalized Anxiety Disorder-7 (GAD-7) changes were measured in the anxiety subsample (GAD-7 ≥ 10). Demographic and clinical characteristics were examined in association with symptom changes via bivariate and multiple regression models adjusted for W-MA-02 utilization. Characteristics included age, sex at birth, race/ethnicity, marital status, education, sexual orientation, employment status, health insurance, baseline levels of depressive and anxiety symptoms, and concurrent psychotherapeutic or psychotropic medication treatments during the study. RESULTS: Both the depressive and anxiety subsamples were predominantly female, educated, non-Hispanic white, and averaged 38 and 37 years of age, respectively. The depressive subsample had significant reductions in depressive symptoms at Week 8 (mean change =-7.28, SD = 5.91, Cohen's d = -1.23, p < 0.01); the anxiety subsample had significant reductions in anxiety symptoms at Week 8 (mean change = -7.45, SD = 5.99, Cohen's d = -1.24, p < 0.01). No significant associations were found between sex at birth, age, employment status, educational background and Week 8 symptom changes. Significant associations between depressive and anxiety symptom outcomes and sexual orientation, marital status, concurrent mental health treatment, and baseline symptom severity were found. CONCLUSIONS: The present study suggests early promise for W-MA-02 as an intervention for depression and/or anxiety symptoms. Although exploratory in nature, this study revealed potential user characteristics associated with outcomes that can be investigated in future studies. TRIAL REGISTRATION: This study was retrospectively registered on ClinicalTrials.gov (#NCT05672745) on January 5th, 2023.

User Engagement Clusters of an 8-Week Digital Mental Health Intervention Guided by a Relational Agent (Woebot): Exploratory Study.

BACKGROUND: With the proliferation of digital mental health interventions (DMHIs) guided by relational agents, little is known about the behavioral, cognitive, and affective engagement components associated with symptom improvement over time. Obtaining a better understanding could lend clues about recommended use for particular subgroups of the population, the potency of different intervention components, and the mechanisms underlying the intervention's success. OBJECTIVE: This exploratory study applied clustering techniques to a range of engagement indicators, which were mapped to the intervention's active components and the connect, attend, participate, and enact (CAPE) model, to examine the prevalence and characterization of each identified cluster among users of a relational agent-guided DMHI. METHODS: We invited adults aged 18 years or older who were interested in using digital support to help with mood management or stress reduction through social media to participate in an 8-week DMHI guided by a natural language processing-supported relational agent, Woebot. Users completed assessments of affective and cognitive engagement, working alliance as measured by goal and task working alliance subscale scores, and enactment (ie, application of therapeutic recommendations in real-world settings). The app passively collected data on behavioral engagement (ie, utilization). We applied agglomerative hierarchical clustering analysis to the engagement indicators to identify the number of clusters that provided the best fit to the data collected, characterized the clusters, and then examined associations with baseline demographic and clinical characteristics as well as mental health outcomes at week 8. RESULTS: Exploratory analyses (n=202) supported 3 clusters: (1) "typical utilizers" (n=81, 40%), who had intermediate levels of behavioral engagement; (2) "early utilizers" (n=58, 29%), who had the nominally highest levels of behavioral engagement in week 1; and (3) "efficient engagers" (n=63, 31%), who had significantly higher levels of affective and cognitive engagement but the lowest level of behavioral engagement. With respect to mental health baseline and outcome measures, efficient engagers had significantly higher levels of baseline resilience (P<.001) and greater declines in depressive symptoms (P=.01) and stress (P=.01) from baseline to week 8 compared to typical utilizers. Significant differences across clusters were found by age, gender identity, race and ethnicity, sexual orientation, education, and insurance coverage. The main analytic findings remained robust in sensitivity analyses. CONCLUSIONS: There were 3 distinct engagement clusters found, each with distinct baseline demographic and clinical traits and mental health outcomes. Additional research is needed to inform fine-grained recommendations regarding optimal engagement and to determine the best sequence of particular intervention components with known potency. The findings represent an important first step in disentangling the complex interplay between different affective, cognitive, and behavioral engagement indicators and outcomes associated with use of a DMHI incorporating a natural language processing-supported relational agent. TRIAL REGISTRATION: ClinicalTrials.gov NCT05672745; https://classic.clinicaltrials.gov/ct2/show/NCT05672745.

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.

Overall and Differential Attrition in Mindfulness-Based Interventions: A Meta-Analysis.

Objectives: Attrition is very common in longitudinal research, including randomized controlled trials (RCTs) testing psychological interventions. Establishing rates and predictors of attrition in mindfulness-based interventions (MBIs) can assist clinical trialists and intervention developers. Differential attrition in RCTs that compared MBIs with structure and intensity matched active control conditions also provides an objective metric of relative treatment acceptability. Methods: We aimed to evaluate rates and predictors of overall and differential attrition in RCTs of MBIs compared with matched active control conditions. Attrition was operationalized as loss to follow-up at post-test. Six online databases were searched. Results: Across 114 studies (n = 11,288), weighted mean attrition rate was 19.1% (95% CI [.16, .22]) in MBIs and 18.6% ([.16, .21]) in control conditions. In the primary model, no significant difference was found in attrition between MBIs and controls (i.e., differential attrition; odds ratio [OR] = 1.05, [0.92, 1.19]). However, in sensitivity analyses with trim-and-fill adjustment, without outliers, and when using different estimation methods (Peto and Mantel-Haenszel), MBIs yielded slightly higher attrition (ORs = 1.10 to 1.25, ps < .050). Despite testing numerous moderators of overall and differential attrition, very few significant predictors emerged. Conclusions: Results support efforts to increase the acceptability of MBIs, active controls, and/or RCTs, and highlight the possibility that for some individuals, MBIs may be less acceptable than alternative interventions. Further research including individual patient data meta-analysis is warranted to identify predictors of attrition and to characterize instances where MBIs may or may not be recommended. Meta-Analysis Review Registration: Open Science Framework (https://osf.io/c3u7a/).

Perceived stress associations with hippocampal-dependent behavior and hippocampal subfield volume.

Background: Individual differences in stress appraisals influence trajectories of risk and resilience following exposure to chronic and acute stressors. Smaller hippocampal volume may contribute to elevated stress appraisals via deficient pattern separation, a process depending on dentate gyrus (DG)/CA3 hippocampal subfields. Here, we investigated links between perceived stress, DG/CA3 volume, and behavioral pattern separation to test hypothesized mechanisms underlying stress-related psychopathology. Methods: We collected the Perceived Stress Scale (PSS) and ratings of subjective stress reactivity during the Trier Social Stress Test (TSST) from 71 adult community participants. We obtained high-resolution T2 MRI scans and used Automatic Segmentation of Hippocampal Subfields to estimate DG/CA3 volume in 56 of these participants. Participants completed the mnemonic similarity task, which provides a behavioral index of pattern separation. Analyses investigated associations between perceived stress, DG/CA3 volume, and behavioral pattern separation, controlling for age, gender, hemisphere, and intracranial volume. Results: Greater PSS scores and TSST subjective stress reactivity were each independently related to poorer behavioral pattern separation, together accounting for 15% of variance in behavioral performance in a simultaneous regression. Contrary to hypotheses, DG/CA3 volume was not associated with either stress measure, although exploratory analyses suggested a link between hippocampal volume asymmetry and PSS scores. Conclusions: We observed novel associations between laboratory and questionnaire measures of perceived stress and a behavioral assay of pattern separation. Additional work is needed to clarify the involvement of the hippocampus in this stress-behavior relationship and determine the relevance of behavioral pattern separation for stress-related disorders.

A biobehavioral intervention to enhance recovery following hematopoietic cell transplantation: Protocol for a feasibility and acceptability randomized control trial.

Background: Insomnia, fatigue, and depression are among the most persistent and distressing concerns for hematologic cancer patients recovering from hematopoietic cell transplantation (HCT). This study will evaluate a novel behavioral intervention, Restoring Sleep and Energy after Transplant (ReSET), designed to alleviate insomnia, fatigue, and depression by improving rest-activity patterns. Evidence-based behavioral strategies to improve nighttime sleep and increase non-sedentary daytime activity will be combined to optimize 24-h rest-activity patterns. Methods: The protocol herein evaluates the feasibility and acceptability of ReSET by conducting a pilot randomized controlled trial to compare the intervention with usual care. Adults undergoing HCT will be randomly assigned to ReSET or usual care. The ReSET arm will receive 3 face-to-face sessions and telephone coaching delivered in an individual format tailored to each patient. Patient-reported insomnia, fatigue, and depression will be the primary outcome measures. Actigraphy will be used to objectively quantify rest-activity patterns. Semi-structured interviews will evaluate participant satisfaction with ReSET. The goals are to determine: (1) participant satisfaction with and acceptability of the behavioral techniques; (2) facilitator fidelity and participant uptake of key intervention components; (3) ability to recruit, retain, and collect complete data from participants; (4) participant willingness to be randomized and acceptability of the control condition; and (5) validity and acceptability of the assessment strategy. Conclusion: The overarching goal is to optimize recovery following HCT with a brief, non-invasive intervention that can be implemented as a part of routine clinical care.