PERSPECTIVE: A Path to Value-Based Insurance Design for Mental Health Services.

BACKGROUND: Aligning cost of mental health care with expected clinical and functional benefits of that care would incentivize the delivery of high value treatments and services. In turn, ineffective or untested care could still be offered but at costs high enough to offset the delivery of high value care. AIMS: The authors comment on Benson and Fendrick's paper on Value-Based Insurance Design (VBID) for mental health in the September 2023 special issue of this journal. The authors also present a preliminary framework of key ingredients needed to consider VBID for mental health treatments and services. METHODS: The authors briefly review current and past efforts to contain costs and improve quality of mental health care, which include (for example) use of carve-out and carve-in programs, evaluation of cost sharing models, impact of accountable care organizations, and studying other benefit designs and impact of federal and state policies. RESULTS: Using PTSD as an example, key ingredients of VBID for mental health services were identified and include the following: tools for case identification and monitoring progress over time at the population level; specific treatments and services with evidence of clinical effectiveness, cost-effectiveness, and health equity; and an approach to document the specific treatment or service was delivered (versus another treatment or service that may lack evidence). DISCUSSION: The inability to afford mental health care is a top barrier to treatment seeking. People who do elect to spend time and money on mental health care are further disadvantaged by accessing care that is not well regulated and the quality at best is questionable. VBID could be an important lever for increasing access to and use of high value mental health care. Partnerships among the research, practice, and policy communities can help ensure research solutions meet needs of these two communities. IMPLICATIONS FOR HEALTH CARE: VBID holds promise to make high value mental health care more affordable while discouraging low value treatments and services. IMPLICATIONS FOR HEALTH POLICIES: While evidence gaps remain, these gaps can be filled concurrently with pursuit of VBID for mental health services. IMPLICATIONS FOR FUTURE RESEARCH: This paper identifies important research opportunities to help make VBID a reality for mental health care.

Comparative Effectiveness of Clinician- Versus Peer-Supported Problem-Solving Therapy for Rural Older Adults With Depression.

OBJECTIVE: Self-guided and peer-supported treatments for depression among rural older adults may address some common barriers to treatment. This pilot study compared the effect on depression of peer-supported, self-guided problem-solving therapy (SG-PST) with case management problem-solving therapy (CM-PST) among older adults in rural California. METHODS: Older adults with depression (N=105) received an introductory PST session with a clinician, followed by 11 sessions of CM-PST with a clinician (N=85) or SG-PST with a peer counselor (N=20). RESULTS: Both interventions resulted in clinically significant improvement in depression by week 12. Depression scores in the CM-PST group dropped by 4.1 points more than in the SG-PST group between baseline and week 12 (95% CI=0.99-7.22, p<0.001, Hedges's g=1.08). CONCLUSIONS: The results suggest that peer-supported SG-PST is a viable, acceptable option for rural older adults with depression as a second-line treatment if access to clinicians is limited.

Behavioral Health Workforce Development in Washington State: Addition of a Behavioral Health Support Specialist.

The mental and behavioral health workforce shortage has hindered access to care in the United States, resulting in long waitlists for persons who need behavioral health care. Global models for task sharing, combined with U.S.-led studies of nonspecialists delivering interventions for depression and anxiety, support the development of this workforce in a stepped care system. This Open Forum highlights an innovative effort in Washington State to initiate a bachelor's-level behavioral health support specialist curriculum leading to credentialing to expand the mental health workforce and improve access to care for people with depression and anxiety.

Use and Engagement With Low-Intensity Cognitive Behavioral Therapy Techniques Used Within an App to Support Worry Management: Content Analysis of Log Data.

BACKGROUND: Low-intensity cognitive behavioral therapy (LICBT) has been implemented by the Improving Access to Psychological Therapies services across England to manage excessive worry associated with generalized anxiety disorder and support emotional well-being. However, barriers to access limit scalability. A solution has been to incorporate LICBT techniques derived from an evidence-based protocol within the Iona Mind Well-being app for Worry management (IMWW) with support provided through an algorithmically driven conversational agent. OBJECTIVE: This study aims to examine engagement with a mobile phone app to support worry management with specific attention directed toward interaction with specific LICBT techniques and examine the potential to reduce symptoms of anxiety. METHODS: Log data were examined with respect to a sample of "engaged" users who had completed at least 1 lesson related to the Worry Time and Problem Solving in-app modules that represented the "minimum dose." Paired sample 2-tailed t tests were undertaken to examine the potential for IMWW to reduce worry and anxiety, with multivariate linear regressions examining the extent to which completion of each of the techniques led to reductions in worry and anxiety. RESULTS: There was good engagement with the range of specific LICBT techniques included within IMWW. The vast majority of engaged users were able to interact with the cognitive behavioral therapy model and successfully record types of worry. When working through Problem Solving, the conversational agent was successfully used to support the user with lower levels of engagement. Several users engaged with Worry Time outside of the app. Forgetting to use the app was the most common reason for lack of engagement, with features of the app such as completion of routine outcome measures and weekly reflections having lower levels of engagement. Despite difficulties in the collection of end point data, there was a significant reduction in severity for both anxiety (t53=5.5; P<.001; 95% CI 2.4-5.2) and low mood (t53=2.3; P=.03; 95% CI 0.2-3.3). A statistically significant linear model was also fitted to the Generalized Anxiety Disorder-7 (F2,51=6.73; P<.001), while the model predicting changes in the Patient Health Questionnaire-8 did not reach significance (F2,51=2.33; P=.11). This indicates that the reduction in these measures was affected by in-app engagement with Worry Time and Problem Solving. CONCLUSIONS: Engaged users were able to successfully interact with the LICBT-specific techniques informed by an evidence-based protocol although there were lower completion rates of routine outcome measures and weekly reflections. Successful interaction with the specific techniques potentially contributes to promising data, indicating that IMWW may be effective in the management of excessive worry. A relationship between dose and improvement justifies the use of log data to inform future developments. However, attention needs to be directed toward enhancing interaction with wider features of the app given that larger improvements were associated with greater engagement.

Digital and precision clinical trials: innovations for testing mental health medications, devices, and psychosocial treatments.

Mental health treatment advances - including neuropsychiatric medications and devices, psychotherapies, and cognitive treatments - lag behind other fields of clinical medicine such as cardiovascular care. One reason for this gap is the traditional techniques used in mental health clinical trials, which slow the pace of progress, produce inequities in care, and undermine precision medicine goals. Newer techniques and methodologies, which we term digital and precision trials, offer solutions. These techniques consist of (1) decentralized (i.e., fully-remote) trials which improve the speed and quality of clinical trials and increase equity of access to research, (2) precision measurement which improves success rate and is essential for precision medicine, and (3) digital interventions, which offer increased reach of, and equity of access to, evidence-based treatments. These techniques and their rationales are described in detail, along with challenges and solutions for their utilization. We conclude with a vignette of a depression clinical trial using these techniques.

Understanding the impact of the COVID-19 pandemic on U.S. older adults: self-reported pandemic-related concerns and consequences in a cross-sectional survey study.

Background and objectives: The purpose of this study was to explore COVID-19 pandemic-related concerns among a racially and ethnically representative sample of older adults in the U.S. Research design and methods: Participants were 501 English-speaking adults 60 years and older recruited online nationally across the U.S. from Amazon Mechanical Turk (mTurk) and Prolific Research Platforms during June of 2020. Data comes from a larger cross-sectional survey. We content analyzed open-ended responses about pandemic-related concerns and observed responses to a checklist of items created by the research team to assess for specific physical, social, and financial consequences experienced due to the pandemic. Results: A majority of the sample (92%) reported at least one pandemic-related concern, with the highest percentage expressing concerns coded as Concern for Others (28%), Physical Health (27%), Socializing (24%), Finance (15%) and Socio-Political-Economic (14%). Participants reported high concern severity (M = 4.03, SD = 1.04) about their concerns mentioned in response to the open-ended concerns question. When prompted with a checklist of items, participants frequently endorsed disruption in social activities as a consequence of the pandemic (83%), disruptions that could impact physical health (45%), and concern over finances as a consequence of the pandemic (41%). Discussion and implications: Older adults most frequently mentioned concerns about the well-being and behavior of others, one's own physical health, and the impacts of the pandemic and social distancing policies on social activities. Findings align with the Socioemotional Selectivity Theory and point to the importance of supporting older adults to maintain meaningful social engagement under conditions of a pandemic and social distancing policies.

Comparing Message-Based Psychotherapy to Once-Weekly, Video-Based Psychotherapy for Moderate Depression: Randomized Controlled Trial.

BACKGROUND: Despite the high prevalence of major depressive disorder and the related societal burden, access to effective traditional face-to-face or video-based psychotherapy is a challenge. An alternative that offers mental health care in a flexible setting is asynchronous messaging therapy. To date, no study has evaluated its efficacy and acceptability in a randomized controlled trial for depression. OBJECTIVE: The aim of this study was to compare the efficacy and acceptability of message-based psychotherapy for depression to once-weekly video-based psychotherapy. METHODS: In this 2-armed randomized controlled trial, individuals (N=83) with depressive symptomatology (Patient Health Questionnaire-9 ≥10) were recruited on the internet and randomly assigned to either a message-based intervention group (n=46) or a once-weekly video-based intervention group (n=37). Patients in the message-based treatment condition exchanged asynchronous messages with their therapist following an agreed-upon schedule. Patients in the video-based treatment condition met with their therapist once each week for a 45-minute video teletherapy session. Self-report data for depression, anxiety, and functional impairment were collected at pretreatment, weekly during treatment, at posttreatment, and at a 6-month follow-up. Self-reported treatment expectancy and credibility for the assigned intervention were assessed at pretreatment and therapeutic alliance at posttreatment. RESULTS: Findings from multilevel modeling indicated significant, medium-to-large improvements in depression (d=1.04; 95% CI 0.60-1.46), anxiety (d=0.61; 95% CI 0.22-0.99), and functional impairment (d=0.66; 95% CI 0.27-1.05) for patients in the message-based treatment condition. Changes in depression (d=0.11; 95% CI -0.43 to 0.66), anxiety (d=-0.01; 95% CI -0.56 to 0.53), and functional impairment (d=0.25; 95% CI -0.30 to 0.80) in the message-based treatment condition were noninferior to those in the video-based treatment condition. There were no significant differences in treatment credibility (d=-0.09; 95% CI -0.64 to 0.45), therapeutic alliance (d=-0.15; 95% CI -0.75 to 0.44), or engagement (d=0.24; 95% CI -0.20 to 0.67) between the 2 treatment conditions. CONCLUSIONS: Message-based psychotherapy could present an effective and accessible alternative treatment modality for patients who might not be able to engage in traditional scheduled services such as face-to-face or video-based psychotherapy. TRIAL REGISTRATION: ClinicalTrials.gov NCT05467787; https://www.clinicaltrials.gov/ct2/show/NCT05467787.

Course of Subtypes of Late-Life Depression Identified by Bipartite Network Analysis During Psychosocial Interventions.

Importance: Approximately half of older adults with depression remain symptomatic at treatment end. Identifying discrete clinical profiles associated with treatment outcomes may guide development of personalized psychosocial interventions. Objective: To identify clinical subtypes of late-life depression and examine their depression trajectory during psychosocial interventions in older adults with depression. Design, Setting, and Participants: This prognostic study included older adults aged 60 years or older who had major depression and participated in 1 of 4 randomized clinical trials of psychosocial interventions for late-life depression. Participants were recruited from the community and outpatient services of Weill Cornell Medicine and the University of California, San Francisco, between March 2002 and April 2013. Data were analyzed from February 2019 to February 2023. Interventions: Participants received 8 to 14 sessions of (1) personalized intervention for patients with major depression and chronic obstructive pulmonary disease, (2) problem-solving therapy, (3) supportive therapy, or (4) active comparison conditions (treatment as usual or case management). Main Outcomes and Measures: The main outcome was the trajectory of depression severity, assessed using the Hamilton Depression Rating Scale (HAM-D). A data-driven, unsupervised, hierarchical clustering of HAM-D items at baseline was conducted to detect clusters of depressive symptoms. A bipartite network analysis was used to identify clinical subtypes at baseline, accounting for both between- and within-patient variability across domains of psychopathology, social support, cognitive impairment, and disability. The trajectories of depression severity in the identified subtypes were compared using mixed-effects models, and time to remission (HAM-D score ≤10) was compared using survival analysis. Results: The bipartite network analysis, which included 535 older adults with major depression (mean [SD] age, 72.7 [8.7] years; 70.7% female), identified 3 clinical subtypes: (1) individuals with severe depression and a large social network; (2) older, educated individuals experiencing strong social support and social interactions; and (3) individuals with disability. There was a significant difference in depression trajectories (F2,2976.9 = 9.4; P < .001) and remission rate (log-rank χ22 = 18.2; P < .001) across clinical subtypes. Subtype 2 had the steepest depression trajectory and highest likelihood of remission regardless of the intervention, while subtype 1 had the poorest depression trajectory. Conclusions and Relevance: In this prognostic study, bipartite network clustering identified 3 subtypes of late-life depression. Knowledge of patients' clinical characteristics may inform treatment selection. Identification of discrete subtypes of late-life depression may stimulate the development of novel, streamlined interventions targeting the clinical vulnerabilities of each subtype.

An Integrative Engagement Model of Digital Psychotherapy: Exploratory Focus Group Findings.

BACKGROUND: Digital mental health interventions, such as 2-way and asynchronous messaging therapy, are a growing part of the mental health care treatment ecosystem, yet little is known about how users engage with these interventions over the course of their treatment journeys. User engagement, or client behaviors and therapeutic relationships that facilitate positive treatment outcomes, is a necessary condition for the effectiveness of any digital treatment. Developing a better understanding of the factors that impact user engagement can impact the overall effectiveness of digital psychotherapy. Mapping the user experience in digital therapy may be facilitated by integrating theories from several fields. Specifically, health science's Health Action Process Approach and human-computer interaction's Lived Informatics Model may be usefully synthesized with relational constructs from psychotherapy process-outcome research to identify the determinants of engagement in digital messaging therapy. OBJECTIVE: This study aims to capture insights into digital therapy users' engagement patterns through a qualitative analysis of focus group sessions. We aimed to synthesize emergent intrapersonal and relational determinants of engagement into an integrative framework of engagement in digital therapy. METHODS: A total of 24 focus group participants were recruited to participate in 1 of 5 synchronous focus group sessions held between October and November 2021. Participant responses were coded by 2 researchers using thematic analysis. RESULTS: Coders identified 10 relevant constructs and 24 subconstructs that can collectively account for users' engagement and experience trajectories in the context of digital therapy. Although users' engagement trajectories in digital therapy varied widely, they were principally informed by intrapsychic factors (eg, self-efficacy and outcome expectancy), interpersonal factors (eg, the therapeutic alliance and its rupture), and external factors (eg, treatment costs and social support). These constructs were organized into a proposed Integrative Engagement Model of Digital Psychotherapy. Notably, every participant in the focus groups indicated that their ability to connect with their therapist was among the most important factors that were considered in continuing or terminating treatment. CONCLUSIONS: Engagement in messaging therapy may be usefully approached through an interdisciplinary lens, linking constructs from health science, human-computer interaction studies, and clinical science in an integrative engagement framework. Taken together, our results suggest that users may not view the digital psychotherapy platform itself as a treatment so much as a means of gaining access to a helping provider, that is, users did not see themselves as engaging with a platform but instead viewed their experience as a healing relationship. The findings of this study suggest that a better understanding of user engagement is crucial for enhancing the effectiveness of digital mental health interventions, and future research should continue to explore the underlying factors that contribute to engagement in digital mental health interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT04507360; https://clinicaltrials.gov/ct2/show/NCT04507360.

Acute and intensive care nurses' perspectives on suicide prevention with medically hospitalized patients: Exploring barriers, facilitators, interests, and training opportunities.

AIMS: To explore opportunities for acute and intensive care nurses to engage in suicide prevention activities with patients hospitalized for medical, surgical or traumatic injury reasons. DESIGN: A qualitative descriptive study. METHODS: We conducted two studies consisting of 1-h focus groups with nurses. Study 1 occurred prior to the onset of the COVID-19 pandemic during January and February of 2020 and identified barriers and facilitators of engaging in an eLearning training in suicide safety planning and engaging patients on their units in suicide safety planning. Study 2 occurred in December of 2020 and explored nurses' perspectives on their role in suicide prevention with patients on their units and training needs related to this. The research took place at an urban level 1 trauma center and safety net hospital where nurses universally screen all admitted patients for suicide risk. We conducted a rapid analysis of the focus group transcripts using a top-down, framework-driven approach to identify barriers, facilitators, strategies around barriers, and training interests mentioned. RESULTS: Twenty-seven registered nurses participated. Nurses indicated they serve a population in need of suicide prevention and that the nursing role is an important part of suicide care. A primary barrier was having adequate uninterrupted time for suicide prevention activities and training; however, nurses identified various strategies around barriers and offered suggestions to make training successful. CONCLUSION: Findings suggest training in suicide prevention is important for nurses in this context and there are opportunities for nurses to engage patients in interventions beyond initial screening; however, implementation will require tailoring interventions and training to accommodate nurses' workload in the hospital context. IMPACT: Acute and intensive care nurses play a key role in the public health approach to suicide prevention. Understanding perspectives of bedside nurses is critical for guiding development and deployment of effective brief interventions. NO PUBLIC OR PATIENT INVOLVEMENT: This study is focused on eliciting and exploring perspectives of acute and intensive care nurses.

A low-intensity behavioral intervention for depression in older adults delivered by lay coaches: proof-of-concept trial.

OBJECTIVES: A broader workforce is necessary to expand U.S. geriatric mental health services. We examined (1) feasibility of training undergraduate students to deliver Do More, Feel Better (DMFB), an evidence-informed program for depression; and (2) feasibility, acceptability, and outcomes in a single-arm proof-of-concept trial. METHOD: In Study 1, we taught DMFB to 18 upper-level undergraduate students and assessed fidelity using role plays. In Study 2, four students delivered six weekly DMFB sessions to 12 community-dwelling older adults (M = 66.83 years old, SD = 10.39) with depression (PHQ ≥ 10). Patient outcomes were change in pre- to post-treatment depressive symptoms, disability, and the target mechanism of increased activity. RESULTS: Fidelity was high in the course (Study 1; 82.4% of role plays rated as 'passing') and the trial (Study 2; 100% of 24 sessions rated as 'passing'). The majority (83.3%) of patients were retained and evidenced statistically and clinically significant improvement in depressive symptoms (Hamilton Rating Scale for Depression [HAM-D]), disability (World Health Organization's Disability Assessment Schedule 2.0 [WHODAS 2.0], and activity (Behavioral Activation for Depression Scale [BADS]). CONCLUSION: It is feasible to train bachelor's-level students to deliver a brief, structured intervention for depression. Future research should consider implementation strategies and stakeholder feedback.

Differential response of positive valence systems to psychotherapy for late-life depression: A pilot study.

BACKGROUND: Depression is characterized by deficits in the positive valence systems (PVS), which also decline with age. However, few studies have examined changes in PVS as a mechanism of treatment for depression, and none have done so using reward-focused interventions in older adults. AIM: The aim of this proof-of-concept study was to investigate changes in two event-related potential measures of PVS function, the late positive potential and the reward positivity, during psychotherapy designed to treat late-life depression by increasing rewarding experiences. METHODS: Eighteen adults age ≥ 60 with major depressive disorder recruited for a larger randomized controlled trial received 9 weeks of Problem-Solving Therapy or Engage therapy. The late positive potential and the reward positivity were recorded at baseline and week 6 of treatment. RESULTS: The late positive potential was larger for rewarding compared to neutral stimuli and increased from baseline to week 6. Exploratory analyses found that this increase was specific to rewarding stimuli. There were no significant effects for the reward positivity. LIMITATIONS: The small sample size limited power to detect associations with clinical measures or evaluate moderating effects of treatment modality, age, or gender. CONCLUSIONS: This study provides preliminary evidence that distinct facets of the PVS respond differently to psychotherapy in older adults with major depression. The late positive potential may be a dynamic marker of depressive state, whereas the reward positivity may constitute a vulnerability index for late-life depression.

Task Sharing to Expand Access to Care: Development of a Behavioral Health Support Specialist.

Task sharing improves access to mental health care in many countries, but little formative research has examined uptake in the United States. This Open Forum proposes the development of nonspecialist professional roles to deliver low-intensity behavioral interventions for common mental health conditions in U.S. settings such as primary care. Using data from a multilevel stakeholder assessment, the authors discuss findings and challenges associated with such a role. Key themes from stakeholder surveys concerned scope of practice, competencies, pragmatic concerns, and training needs. Although stakeholders generally found this role to be acceptable and feasible, the themes raised will be critical to developing and implementing such a role.

Deep Representations of First-person Pronouns for Prediction of Depression Symptom Severity.

Prior work has shown that analyzing the use of first-person singular pronouns can provide insight into individuals' mental status, especially depression symptom severity. These findings were generated by counting frequencies of first-person singular pronouns in text data. However, counting doesn't capture how these pronouns are used. Recent advances in neural language modeling have leveraged methods generating contextual embeddings. In this study, we sought to utilize the embeddings of first-person pronouns obtained from contextualized language representation models to capture ways these pronouns are used, to analyze mental status. De-identified text messages sent during online psychotherapy with weekly assessment of depression severity were used for evaluation. Results indicate the advantage of contextualized first-person pronoun embeddings over standard classification token embeddings and frequency-based pronoun analysis results in predicting depression symptom severity. This suggests contextual representations of first-person pronouns can enhance the predictive utility of language used by people with depression symptoms.

Recruitment and Retention in Remote Research: Learnings From a Large, Decentralized Real-world Study.

BACKGROUND: Smartphones are increasingly used in health research. They provide a continuous connection between participants and researchers to monitor long-term health trajectories of large populations at a fraction of the cost of traditional research studies. However, despite the potential of using smartphones in remote research, there is an urgent need to develop effective strategies to reach, recruit, and retain the target populations in a representative and equitable manner. OBJECTIVE: We aimed to investigate the impact of combining different recruitment and incentive distribution approaches used in remote research on cohort characteristics and long-term retention. The real-world factors significantly impacting active and passive data collection were also evaluated. METHODS: We conducted a secondary data analysis of participant recruitment and retention using data from a large remote observation study aimed at understanding real-world factors linked to cold, influenza, and the impact of traumatic brain injury on daily functioning. We conducted recruitment in 2 phases between March 15, 2020, and January 4, 2022. Over 10,000 smartphone owners in the United States were recruited to provide 12 weeks of daily surveys and smartphone-based passive-sensing data. Using multivariate statistics, we investigated the potential impact of different recruitment and incentive distribution approaches on cohort characteristics. Survival analysis was used to assess the effects of sociodemographic characteristics on participant retention across the 2 recruitment phases. Associations between passive data-sharing patterns and demographic characteristics of the cohort were evaluated using logistic regression. RESULTS: We analyzed over 330,000 days of engagement data collected from 10,000 participants. Our key findings are as follows: first, the overall characteristics of participants recruited using digital advertisements on social media and news media differed significantly from those of participants recruited using crowdsourcing platforms (Prolific and Amazon Mechanical Turk; P<.001). Second, participant retention in the study varied significantly across study phases, recruitment sources, and socioeconomic and demographic factors (P<.001). Third, notable differences in passive data collection were associated with device type (Android vs iOS) and participants' sociodemographic characteristics. Black or African American participants were significantly less likely to share passive sensor data streams than non-Hispanic White participants (odds ratio 0.44-0.49, 95% CI 0.35-0.61; P<.001). Fourth, participants were more likely to adhere to baseline surveys if the surveys were administered immediately after enrollment. Fifth, technical glitches could significantly impact real-world data collection in remote settings, which can severely impact generation of reliable evidence. CONCLUSIONS: Our findings highlight several factors, such as recruitment platforms, incentive distribution frequency, the timing of baseline surveys, device heterogeneity, and technical glitches in data collection infrastructure, that could impact remote long-term data collection. Combined together, these empirical findings could help inform best practices for monitoring anomalies during real-world data collection and for recruiting and retaining target populations in a representative and equitable manner.

Effectiveness of Mental Health Apps for Distress During COVID-19 in US Unemployed and Essential Workers: Remote Pragmatic Randomized Clinical Trial.

BACKGROUND: During the COVID-19 pandemic, the general public was concerned about the mental health impacts of unemployment due to COVID-19 and the stress essential workers experienced during this time. Several reports indicated that people in distress were turning to digital technology, but there was little evidence about the impact of these tools on mitigating distress. OBJECTIVE: This study seeks to determine the acceptability, feasibility, usability, and effectiveness of mobile mental health apps for decreasing mental health symptoms in essential workers and unemployed individuals with suicide risk. METHODS: We recruited participants who indicated that they were unemployed because of COVID-19 or were COVID-19-designated essential workers. Participants were randomized to 1 of 4 free commercial mobile apps for managing distress that were (1) highly rated by PsyberGuide and (2) met the criteria for intervention features these participants indicated were desirable in a previous survey. Participants used the apps for 4 weeks and completed baseline and 4-week self-assessments of depression, anxiety emotional regulation, and suicide risk. RESULTS: We found no differences between the apps in any outcome but did find significant changes in depression and anxiety over time (Patient Health Questionnaire [PHQ]-9: estimate=-1.5, SE 0.2, 95% CI -1.1 to -1.8, P<.001; Generalized Anxiety Disorder Scale [GAD]-7: estimate=-1.3, SE 0.2, 95% CI -1.0 to -1.6, P<.001). We found no significant changes in suicidal behavior (Suicide Behaviors Questionnaire-Revised [SBQ-R]) or emotional regulation (Difficulties in Emotion Regulation Scale - Short Form [DERS-SF]) for the 4 weeks. We did find a significant dose-response pattern for changes in depression and anxiety. Using the app at least once a week resulted in greater improvements in treatment conditions over time on depression (estimate=-0.6, SE 0.2, 95% CI 1.0-0.2, P=.003) and anxiety (estimate=0.1, SE 0.2, 95% CI 0.4-0.6, P=.78). There was no association between app frequency and changes in suicidal behavior (SBQ-R) or emotional regulation (DERS-SF). We further found a significant difference between the conditions with regard to app usability, with the control app being the most usable (meanBeautiful Mood 72.9, SD 16.7; meanCOVID Coach 71.2, SD 15.4; meanCalm 66.8, SD 17.3; mean7 Cups 65.2, SD 17.7). We found no significant differences for app acceptability or appropriateness. CONCLUSIONS: Few studies have evaluated prospectively the utility and usability of commercial apps for mood. This study found that free, self-guided commercial mobile mental health apps are seen as usable, but no one app is superior to the other. Although we found that regular use is indicated for effects on depression and anxiety to occur in those who are more symptomatic, regression to the mean cannot be ruled out. TRIAL REGISTRATION: ClinicalTrials.gov NCT04536935; https://tinyurl.com/mr36zx3s.

Participant retention in a fully remote trial of digital psychotherapy: Comparison of incentive types.

Numerous studies have found that long term retention is very low in remote clinical studies (>4 weeks) and to date there is limited information on the best methods to ensure retention. The ability to retain participants in the completion of key assessments periods is critical to all clinical research, and to date little is known as to what methods are best to encourage participant retention. To study incentive-based retention methods we randomized 215 US adults (18+ years) who agreed to participate in a sequential, multiple assignment randomized trial to either high monetary incentive (HMI, $125 USD) and combined low monetary incentive ($75 USD) plus alternative incentive (LMAI). Participants were asked to complete daily and weekly surveys for a total of 12 weeks, which included a tailoring assessment around week 5 to determine who should be stepped up and rerandomized to one of two augmentation conditions. Key assessment points were weeks 5 and 12. There was no difference in participant retention at week 5 (tailoring event), with approximately 75% of the sample completing the week-5 survey. By week 10, the HMI condition retained approximately 70% of the sample, compared to 60% of the LMAI group. By week 12, all differences were attenuated. Differences in completed measures were not significant between groups. At the end of the study, participants were asked the impressions of the incentive condition they were assigned and asked for suggestions for improving engagement. There were no significant differences between conditions on ratings of the fairness of compensation, study satisfaction, or study burden, but study burden, intrinsic motivation and incentive fairness did influence participation. Men were also more likely to drop out of the study than women. Qualitative analysis from both groups found the following engagement suggestions: desire for feedback on survey responses and an interest in automated sharing of individual survey responses with study therapists to assist in treatment. Participants in the LMAI arm indicated that the alternative incentives were engaging and motivating. In sum, while we were able to increase engagement above what is typical for such study, more research is needed to truly improve long term retention in remote trials.

Acceptability of virtual therapy for postpartum women during COVID-19: A national mixed methods study.

Background: Postpartum depression (PPD) affects one in eight women in the U.S., with rates increasing due to the COVID-19 pandemic. Given the unique circumstances of COVID-19, virtual therapy might be a unique way to overcome barriers to mental health services. The study sought to explore the acceptability of virtual therapy among women in the postpartum period. Methods: Using an online recruitment mixed methods approach, we collected data from a U.S. national cross-sectional sample of women (N = 479) who gave birth in the last 12 months. Findings: Results show that 66% of women endorsed items consistent with possible depression during the COVID-19 pandemic. Only 27% accessed therapy services during the postpartum period. While 88% were open to engaging in virtual therapy services, 12% identified several major concerns with virtual therapy, namely: (1) preference for in-person therapy (2) no perceived need for therapy (3) uncomfortable with virtual therapy, and (4) lack of privacy. Of note, 36% more Latinas reported dissatisfaction with quality of care received during virtual therapy compared to non-Latina participants. Despite a major shift to virtual care with COVID-19, future work is needed to make virtual mental health services more accessible for women with PPD.

Message-based psychotherapy for older adults: A cohort comparison study.

Objective: Digital Mental Health Interventions (DMHI) can diminish inequities in mental health care provision. As DMHIs increase in popularity, however, older adults may be unintentionally excluded due to barriers such as lack of awareness, internet access, digital tools, technological socialization and education, physiological accessibility, and communication technology infrastructure. The aim of this study was to examine longitudinal treatment engagement patterns and 15-week clinical outcomes of depressed and anxious older adults compared to a matched cohort of younger adults seeking treatment from a large asynchronous telemedicine provider. Methods: The 2,470 older adults (55+ years) and a matched cohort of younger adults (26-35 years) diagnosed with depression or anxiety were treated by licensed therapists via messaging 5 days a week. Patterns of treatment engagement on the platform were compared across groups by examining total number of days in treatment, days actively messaging on the platform, and average words and messages per week sent by patients over the entire period they remained in treatment. Symptoms were assessed every 3 weeks using the Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder Scale (GAD-7), and changes were compared across age groups over 15 weeks. Results: Older patients attended more days in treatment than younger patients, but there were no differences in number of days actively messaging on the platform, number of messages per week, or word count per week. The two age groups did not differ in their final anxiety or depressive symptoms when controlling for total number of weeks attended. Patients in the younger age group experienced a quicker rate of reduction than older adults in their anxiety, but not depressive symptoms. Conclusions: Among individuals willing to initiate care through a DMHI, older adults had overall similar engagement as younger adults and they showed similar improvement in symptoms of depression and anxiety. Given the advantages of message-based care for aiding a mental health workforce in serving larger numbers of individuals in need and the expected growth of the aging population, these findings could help healthcare systems in evaluating a variety of treatment options and delivery media for meeting the healthcare needs of the future.