Training, supervision, and experience of coaches offering digital guided self-help for mental health concerns

Accessible, low-cost intervention options are necessary to address the rise in mental health problems among college students. Digital guided self-help, or coached, programs have been developed to provide such services, with many commercially available. As such, there are a large and growing number of individuals coaching these programs. However, an unmet need is to evaluate and assess best practices for training and supervising individuals in these positions. To this end, we describe how we recruited, trained, and supervised coaches as part of a large randomized controlled trial using a widely available digital commercial platform. Coaches were trained to provide digital guided self-help for depression, anxiety, and/or eating disorders for college students. Coaches initially attended three live training sessions over 2-3 weeks, viewed multiple training videos, and read a detailed coaching manual developed by our team. Thereafter, they attended weekly supervision. Following their term, coaches completed an exit survey to assess their supervision and training experiences. A total of 37 of 70 (53%) graduate-level student coaches completed the survey. The experience was reported as very positive (95%). In particular, the majority reported feeling well prepared, more confident, and felt they had developed useful skills for their own practice

Examining the Theoretical Framework of Behavioral Activation for Major Depressive Disorder: Smartphone-Based Ecological Momentary Assessment Study

Background: Behavioral activation (BA), either as a stand-alone treatment or as part of cognitive behavioral therapy, has been shown to be effective for treating depression. The theoretical underpinnings of BA derive from Lewinsohn et al's theory of depression. The central premise of BA is that having patients engage in more pleasant activities leads to them experiencing more pleasure and elevates their mood, which, in turn, leads to further (behavioral) activation. However, there is a dearth of empirical evidence about the theoretical framework of BA.Objective: This study aims to examine the assumed (temporal) associations of the 3 constructs in the theoretical framework of BA.Methods: Data were collected as part of the "European Comparative Effectiveness Research on Internet-based Depression Treatment versus treatment-as-usual" trial among patients who were randomly assigned to receive blended cognitive behavioral therapy (bCBT). As part of bCBT, patients completed weekly assessments of their level of engagement in pleasant activities, the pleasure they experienced as a result of these activities, and their mood over the course of the treatment using a smartphone-based ecological momentary assessment (EMA) application. Longitudinal cross-lagged and cross-sectional associations of 240 patients were examined using random intercept cross-lagged panel models.Results: The analyses did not reveal any statistically significant cross-lagged coefficients (all P>.05). Statistically significant cross-sectional positive associations between activities, pleasure, and mood levels were identified. Moreover, the levels of engagement in activities, pleasure, and mood slightly increased over the duration of the treatment. In addition, mood seemed to carry over, over time, while both levels of engagement in activities and pleasurable experiences did not.Conclusions: The results were partially in accordance with the theoretical framework of BA, insofar as the analyses revealed cross-sectional relationships between levels of engagement in activities, pleasurable experiences deriving from these activities, and enhanced mood. However, given that no statistically significant temporal relationships were revealed, no conclusions could be drawn about potential causality. A shorter measurement interval (eg, daily rather than weekly EMA reports) might be more attuned to detecting potential underlying temporal pathways. Future research should use an EMA methodology to further investigate temporal associations, based on theory and how treatments are presented to patients.</p

A Data-Driven Clustering Method for Discovering Profiles in the Dynamics of Major Depressive Disorder Using a Smartphone-Based Ecological Momentary Assessment of Mood

BackgroundAlthough major depressive disorder (MDD) is characterized by a pervasive negative mood, research indicates that the mood of depressed patients is rarely entirely stagnant. It is often dynamic, distinguished by highs and lows, and it is highly responsive to external and internal regulatory processes. Mood dynamics can be defined as a combination of mood variability (the magnitude of the mood changes) and emotional inertia (the speed of mood shifts). The purpose of this study is to explore various distinctive profiles in real-time monitored mood dynamics among MDD patients in routine mental healthcare. MethodsEcological momentary assessment (EMA) data were collected as part of the cross-European E-COMPARED trial, in which approximately half of the patients were randomly assigned to receive the blended Cognitive Behavioral Therapy (bCBT). In this study a subsample of the bCBT group was included (n = 287). As part of bCBT, patients were prompted to rate their current mood (on a 1-10 scale) using a smartphone-based EMA application. During the first week of treatment, the patients were prompted to rate their mood on three separate occasions during the day. Latent profile analyses were subsequently applied to identify distinct profiles based on average mood, mood variability, and emotional inertia across the monitoring period. ResultsOverall, four profiles were identified, which we labeled as: (1) "very negative and least variable mood" (n = 14) (2) "negative and moderate variable mood" (n = 204), (3) "positive and moderate variable mood" (n = 41), and (4) "negative and highest variable mood" (n = 28). The degree of emotional inertia was virtually identical across the profiles. ConclusionsThe real-time monitoring conducted in the present study provides some preliminary indications of different patterns of both average mood and mood variability among MDD patients in treatment in mental health settings. Such varying patterns were not found for emotional inertia

Editorial Introduction to Technological Approaches for the Treatment of Mental Health in Youth

Excerpt: [Note: In lieu of an abstract, this is an excerpt from the first page.] According to the World Health Organization (WHO), 10–20% of adolescents (10–19 years old) worldwide suffer from mental health conditions, with 50% starting at the age of 14 (World Health Organization 2020). Traditionally, mental health problems among youth have been addressed with psychotherapy conducted via face-to-face methods. However, many youth are actively seeking resources online for mental health support (Stephens et al. 2020; Rideout et al. 2018). Therefore, digital interventions can provide alternative methods to support youth patients while addressing and improving the limitations of face-to-face delivery formats. This has become more evident during the COVID-19 pandemic, where clinicians have been forced to use creative strategies, such as telehealth to reach their patients remotely. [...

Editorial Introduction to Technological Approaches for the Treatment of Mental Health in Youth

According to the World Health Organization (WHO), 10–20% of adolescents (10–19 years old) worldwide suffer from mental health conditions, with 50% starting at the age of 14 (World Health Organization 2020) [...]</jats:p

Transdiagnostic and tailored internet intervention to improve mental health among university students: Research protocol for a randomized controlled trial

Background: Emerging adulthood is associated with mental health problems. About one in three university students shows symptoms of depression and anxiety that can negatively affect their developmental trajectory in work, intimate relationships and health, and interfere with academic performance. Mood and anxiety disorders are key predictors of dropout from higher education. A treatment gap exists, where a considerable proportion of students with such disorders do not seek help. Offering internet interventions to students with mental health problems could reduce the treatment gap, increase mental health and improve academic performance. Meta-analysis of internet interventions for university students has shown small effects for depression and none for anxiety. Larger trials are recommended to further explore effects of guidance, transdiagnostic approaches, and individual treatment components. Methods: This study offers 1200 university students in Sweden participation in a three-armed randomized controlled trial (RCT) evaluating a guided or unguided transdiagnostic internet intervention for mild to moderate depression and anxiety, where the waitlist control group accesses the intervention at 6-month follow-up. Students reporting suicidal ideation/behaviors are excluded and referred to treatment within the healthcare system. An embedded study within a trial (SWAT), aiming to minimize the risk of treatment failure, randomizes participants in the guided and unguided groups, identified at week 3 of 8 as being at higher risk of failing to benefit from treatment, to an adaptive treatment strategy, or to continue the treatment as originally randomized. Primary outcomes are symptoms of depression and anxiety. Follow-ups occur post-treatment and at 6-, 12- and 24-months post-randomization. Between-group outcome analyses and within-group analyses of clinically significant change will be reported. Qualitative interviews about treatment experiences are planned. Discussion: This study investigates the effects of a transdiagnostic internet intervention among university students in Sweden, with an adaptive treatment strategy employed during the course of treatment to minimize the risk of treatment failure. The study will contribute knowledge about longitudinal trajectories of mental health and well-being following treatment, taking into account possible gender differences in responsiveness to treatment. With time, effective internet interventions could make treatment for mental health issues more widely accessible to the student group. Trial registration: NCT05085756, October 20, 2021, https://classic.clinicaltrials.gov/ct2/show/NCT05085756, Prospectively registered Trial registration: NCT05085756, October20, 2021, https://classic.clinicaltrials.gov/ct2/show/NCT05085756, Prospectively registered</p