Predictors of Disengagement and Symptom Improvement Among Adults With Depression Enrolled in Talkspace, a Technology-Mediated Psychotherapy Platform: Naturalistic Observational Study

Abstract

Background Depression is a common psychiatric condition with an estimated lifetime prevalence for major depression of 16.6% in the US adult population and is effectively treated through psychotherapy. The widespread availability of the internet and personal devices such as smartphones are changing the landscape of delivery of psychotherapy; however, little is known about whether and for whom this type of therapy is beneficial, and whether having synchronous video-based sessions provides additional benefits to clients above and beyond messaging-based therapy. Objective This study examined the outcomes associated with the use of a digital platform (Talkspace) for technology-mediated psychotherapy. We examined the duration of client engagement in therapy and client depression score trajectories over 16 weeks. We explored the association of client characteristics, therapist characteristics, and service plan type with time-to-disengagement and trajectories of change in depression scores. Methods This naturalistic observational study assessed data collected routinely by the platform between January 2016 and January 2018 and examined psychotherapy outcomes among a large representative sample of adult clients with clinically significant depression. Treatment disengagement was defined as a lack of client-initiated communication for more than 4 weeks. Clients completed the Patient Health Questionnaire-8 item (PHQ-8) at intake and every 3 weeks via an in-app survey. Cox regression analysis was used to examine the time until and predictors of disengagement. Changes in depression scores and predictors of change over time were examined using mixed-effects regression. Results The study included 5890 clients and 1271 therapists. Client scores on the PHQ-8 declined over time, with the average client improving from a score of 15 to below the clinical cutoff of 10 by week 6. At the same time point, 37% of clients had disengaged from the therapy. When combined into a final Cox regression model, those who were more likely to disengage were clients aged 18 to 25 years versus those aged ≥50 years (odds ratio [OR] 0.82, 95% CI 0.74-0.9; P<.001), had higher education (OR 1.14, 95% CI 1.06-1.22; P<.001), had been in therapy before (OR 1.09, 95% CI 1.02-1.17; P=.01), and were living with a partner but unmarried versus single (OR 1.14, 95% CI 1.02-1.27; P=.02). Having a therapist with >10 years of experience was related to lower odds of disengagement (OR 0.87, 95% CI 0.8-0.94; P=.01). When combined into a final regression model predicting improvement in depression scores over time, clients showing more improvement were those with an associate’s degree or higher (linear estimate=−0.07, P=.002) and higher intake PHQ-8 scores (estimate=3.73, P<.001). There were no differences based on the plan type. Conclusions Our findings add to the growing literature showing the benefits of technology-mediated psychotherapy over a relatively brief period (16 weeks).

Outcomes in Mental Health Counseling From Conversational Content With Transformer-Based Machine Learning

“…Diagnoses are established by the client’s online and mobile therapy app provider, and the population is similar to that of those seeking care within outpatient private therapy practice. Large-scale effectiveness studies to date suggest that the platform is beneficial for most clients . The information captured through the platform is intended to support quality monitoring, treatment planning, and auditing of services.…”

“…Mental Health Counseling From Conversational Content With Transformer-Based Machine Learning platform is beneficial for most clients. 6,26 The information captured through the platform is intended to support quality monitoring, treatment planning, and auditing of services. When registering to use the service, clients and therapists agree to the use of their anonymized data for quality assurance and for research.…”

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

“…Less than 1% of consumers who access clinician-assisted DMH complete all sessions [ 27 ]. Our research on text-based therapy found that over a third of clients disengaged within 6 weeks of initiating treatment [ 28 ], with disengagement rates higher for clients who were younger, had higher education, and had been in therapy before. Aside from these simplistic demographic determinants, we know very little about why a client might engage in DMH and how to tailor treatment to ensure optimal engagement.…”