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Published on in Vol 6, No 11 (2022): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39511, first published .
A Hybrid Ecological Momentary Compassion–Focused Intervention for Enhancing Resilience in Help-Seeking Young People: Prospective Study of Baseline Characteristics in the EMIcompass Trial

A Hybrid Ecological Momentary Compassion–Focused Intervention for Enhancing Resilience in Help-Seeking Young People: Prospective Study of Baseline Characteristics in the EMIcompass Trial

A Hybrid Ecological Momentary Compassion–Focused Intervention for Enhancing Resilience in Help-Seeking Young People: Prospective Study of Baseline Characteristics in the EMIcompass Trial

Authors of this article:

Isabell Paetzold1 Author Orcid Image ;   Anita Schick1 Author Orcid Image ;   Christian Rauschenberg1 Author Orcid Image ;   Dusan Hirjak2 Author Orcid Image ;   Tobias Banaschewski3 Author Orcid Image ;   Andreas Meyer-Lindenberg2 Author Orcid Image ;   Sebastian Butz1 Author Orcid Image ;   Chiara Floesser1 Author Orcid Image ;   Leonie Schueltke1 Author Orcid Image ;   Jan Rasmus Boehnke4 Author Orcid Image ;   Benjamin Boecking5 Author Orcid Image ;   Ulrich Reininghaus1, 6, 7 Author Orcid Image
Article Authors Cited by (4) Tweetations (11) Metrics

    Original Paper

    1Department of Public Mental Health, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

    2Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

    3Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

    4School of Health Sciences, University of Dundee, Dundee, United Kingdom

    5Tinnitus Center, Charité Universitätsmedizin Berlin, Berlin, Germany

    6ESRC Centre for Society and Mental Health and Social Epidemiology Research Group, King's College London, London, United Kingdom

    7Health Service and Population Research Department, Centre for Epidemiology and Public Health, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom

    Corresponding Author:

    Ulrich Reininghaus, PhD

    Department of Public Mental Health

    Central Institute of Mental Health

    Medical Faculty Mannheim, Heidelberg University

    J 5 1

    Mannheim, 68159

    Germany

    Phone: 49 62117031930

    Email: ulrich.reininghaus@zi-mannheim.de


    Background: Young people are a target population for mental health–related early intervention and prevention. Although evidence for early intervention is promising, availability of and access to youth mental health services remain limited. Therefore, the development of an evidence-based hybrid intervention is urgently needed.

    Objective: This study aimed to present a manual for a hybrid intervention, combining an ecological momentary intervention and face-to-face sessions aimed for enhancing resilience in help-seeking young people based on compassion-focused interventions, and explore whether participants’ baseline characteristics are associated with putative mechanisms and outcomes of the EMIcompass intervention. Specifically, we aimed to explore initial signals as to whether participants’ sociodemographic, clinical, and functional characteristics at baseline are associated with putative mechanisms (ie, change in self-compassion, change in emotion regulation, working alliance, training frequency); and whether participants’ sociodemographic, clinical, and functional characteristics, self-compassion, and emotion regulation at baseline are associated with clinical outcomes (ie, psychological distress and general psychopathology at postintervention and 4-week follow-ups) in the experimental condition and obtain first parameter estimates.

    Methods: We recruited young people aged 14 to 25 years, with psychological distress, Clinical High At-Risk Mental State, or first episodes of severe mental disorder for an exploratory randomized controlled trial with assessments at baseline and postintervention and 4-week follow-ups. A structured manual was developed and optimized based on a pilot study’s manual, a scoping review of existing literature and manuals, exchange with experts, the team’s clinical experience of working with compassion-focused interventions, and the principles of ecological momentary interventions. This analysis focuses on the experimental condition receiving the EMIcompass intervention.

    Results: A total of 46 young individuals were randomized to the experimental condition. There was evidence for initial signals of effects of age (B=0.11, 95% CI 0.00-0.22), general psychopathology (B=0.08, 95% CI −0.01 to 0.16), and clinical stage (B=1.50, 95% CI 0.06-2.93) on change in momentary self-compassion and change in emotion regulation from baseline to postintervention assessments. There was no evidence for associations of other baseline characteristics (eg, gender, minority status, and level of functioning) and putative mechanisms (eg, overall self-compassion, working alliance, and training frequency). In addition, except for an initial signal for an association of momentary self-compassion at baseline and psychological distress (B=−2.83, 95% CI −5.66 to 0.00), we found no evidence that baseline characteristics related to clinical outcomes.

    Conclusions: The findings indicated the reach of participants by the intervention largely independent of sociodemographic, clinical, and functional baseline characteristics. The findings need to be confirmed in a definitive trial.

    Trial Registration: German Clinical Trials Register NDRKS00017265; https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00017265

    International Registered Report Identifier (IRRID): RR2-10.2196/27462

    JMIR Form Res 2022;6(11):e39511

    doi:10.2196/39511

    Keywords

    mobile health intervention; mHealth intervention; digital intervention; just-in-time adaptive intervention; JITAI; blended care; public mental health; inclusiveness; transdiagnostic; clinical staging; intervention manual; mobile phone 


    Background

    Young people constitute a priority target population for mental health–related prevention and early intervention, as they are particularly affected by mental health problems. Mental disorders primarily emerge in adolescence and young adulthood, and >60% of all lifetime cases have their onset before the age of 25 years [1]. With a worldwide pooled prevalence of 21% of mental disorders in adolescents aged 12 to 18 years [2], mental health problems contribute substantially to the disease burden [3,4]. Addressing the co-occurrence and overlap of subclinical and clinical experiences and symptoms [5-8], especially in the early stages of psychopathology, dimensional classification frameworks [9,10] cutting across traditional diagnostic boundaries, including the Hierarchical Taxonomy of Psychopathology (HiTOP) [11], have been proposed. Clinical staging models take early, overlapping, and nonspecific psychopathological symptoms and transitional staging processes into account [12,13].

    There is convincing evidence on risk factors that are modifiable, on mental health problems that can be changed, and on protective factors that can be strengthened to enhance resilience [14-16]. Traditional psychotherapeutic interventions, including standard cognitive behavioral therapy, as well as third-wave approaches, show moderate to high effect sizes in randomized controlled trials (RCTs) and meta-analyses [17-20]. However, there is considerable room for improvement, as—even after successful treatment—many service users show significant residual symptoms or relapse [21]. In addition, the availability of and access to youth mental health services remain limited [22,23]. More downstream, this may result in a longer duration of untreated illness, an important marker of poor prognosis and complex course and outcome [24,25].

    Some of these problems of standard care might be caused by difficulties transferring preventive and therapeutic strategies developed in face-to face sessions to service users’ daily life. Mobile health (mHealth) may be a promising approach to address these challenges by improving access to mental health care for young people by using mobile devices for the delivery of prevention and intervention [26-30]. With ecological momentary assessment (EMA), often also referred to as experience sampling methodology (ESM) [26,31], a structured diary method, momentary fluctuations in experience and behavior can be assessed in real time and real life. Ecological momentary interventions (EMIs) [26,29,32-34] offer the opportunity to deliver adaptive and personalized intervention components in daily life. The digital approach may help to lower the threshold for young people to access interventions meeting their needs and preferences and facilitates the ecological translation of techniques learned into service users’ everyday lives [29]. A recent nationally representative survey indicated that young people do frequently use mHealth apps and are even more likely to do so when feeling distressed [30].

    However, digital approaches are also confronted with challenges: most apps currently available in major app stores are not evidence based, and some even include potentially harmful content [28,35]. In addition, the reach of digital interventions has been subject to controversial debate, as concerns have been expressed that barriers to treatment may be created rather than removed [36,37]. A review indicated that studies of the effectiveness of mHealth apps mostly include samples of predominantly female, White participants with an average age of 30 to 45 years [38], and the degree of generalizability of findings to service users with other characteristics remains largely unexplored. Therefore, the development of evidence-based, low-threshold interventions that specifically target established candidate mechanisms that have been linked to the development and persistence of mental health conditions across various groups and settings is urgently needed. In addition, it is crucial to explore the association of participants’ baseline characteristics with putative mechanisms and outcomes to examine the reach of the intervention.

    Extensive research identified stress reactivity as a putative transdiagnostic mechanism in the development of psychopathology and a promising target for prevention and early intervention [26]. Stress reactivity (ie, increases in negative affect in response to minor daily stressors) is thought to be a behavioral marker of stress sensitization, positing that frequent or chronic experiences of adversity may gradually increase individuals’ stress response to subsequent adversities and minor stressors in everyday life [26,39,40].

    Compassion-focused interventions (CFIs) may be a promising approach to target stress reactivity in daily life. Building on a combination of evolutionary psychology, attachment theory, and social mentality theory, the compassion-focused approach claims that various psychological problems are caused by unhelpful loops among distressing emotions, defensive behaviors, and cognitive processes such as rumination, worry, and self-criticism [41]. A model with 3 interrelated major emotional systems is suggested [41-43]: threat, drive, and soothing. Many people experience an overactive threat system, an overactive or somehow blocked drive system, and an underactive soothing system [41]. Therefore, CFIs focus on strengthening the soothing system, as it is thought to be an antagonist to an overactive threat system and a good basis for a well-functioning drive system. CFIs are not symptom specific, and previous studies demonstrated that they are an effective treatment for various mental health problems [19,44-46]. Positive imagery, a key component of CFIs, has been shown to effectively reduce a wide range of mental health problems and increases positive affect, optimism, and behavioral activation [46-50]. In laboratory studies, the application of compassion-focused techniques has been shown to reduce state negative affect and paranoia in moments of high stress [49,51].

    Combining digital approaches and CFIs in a hybrid intervention using imagery-based techniques may be particularly well-suited to target stress reactivity in the daily life of young people. Previous research indicated higher acceptability and larger effect sizes for hybrid interventions in comparison with stand-alone internet- and mobile-based interventions [52,53]. Therefore, EMIcompass was developed as a hybrid intervention combining an EMI with guided face-to-face sessions. A pilot study provided initial evidence for feasibility, safety, and beneficial effects of a compassion-focused EMI for enhancing resilience in help-seeking young people [54]. Feasibility and initial signals of efficacy of the intervention have been investigated in a registered exploratory RCT in Germany [55], comparing treatment as usual (TAU) with TAU+EMIcompass in young people with early mental health problems.

    Objectives

    This paper aims to (1) present the intervention manual for EMIcompass, a hybrid intervention combining an EMI and face-to-face sessions aiming at enhancing resilience in help-seeking young people based on compassion-focused principles [41-43] and (2) explore whether participants’ baseline characteristics are associated with putative mechanisms and outcomes of the EMIcompass intervention. To this end, we aimed to obtain first parameter estimates and explore initial signals as to whether sociodemographic, clinical, and functional characteristics at baseline (ie, clinical stage, psychological distress, general psychopathology, level of functioning, age, gender, and minority status) are associated with putative mechanisms (ie, change in self-compassion, change in emotion regulation, working alliance, and training frequency); and sociodemographic, clinical, and functional characteristics (ie, clinical stage, psychological distress, general psychopathology, level of functioning, age, gender, and minority status) as well as self-compassion and emotion regulation at baseline are associated with clinical outcomes (ie, psychological distress and general psychopathology at postintervention and 4-week follow-ups) in the experimental condition and obtain 95% CIs.


    Study Design

    In our exploratory RCT, participants were randomly allocated to a control condition of TAU or an experimental condition of TAU+EMIcompass in a 50:50 ratio. For this analysis, data from the experimental condition were used to examine the impact of participants’ baseline characteristics on the putative mechanisms and outcomes of the intervention. In the RCT, candidate mechanisms (primary: stress reactivity; secondary: resilience, interpersonal sensitivity, threat anticipation, and negative affective appraisals) and outcomes (primary: psychological distress; secondary: primary psychiatric symptoms, general psychopathology, and quality of life) were assessed at baseline (ie, before randomization), at the end of the intervention, and at the 4-week follow-up. Observer ratings were performed by blinded assessors. The sample size was based on a power simulation for the primary outcome of the trial [56]. The RCT was conducted between August 2019 and September 2021. Appointments were held in person or via video calls (owing to the COVID-19 pandemic). Further details on study procedures are described in the study protocol [56].

    Ethics Approval

    The trial has received ethical approval from the local ethics committee of the Medical Faculty Mannheim, Heidelberg University (2017-602N-MA). All participants and, in case of minors, parents or legal guardians, provided written informed consent before inclusion in the study.

    Manual for the EMIcompass Intervention

    To ensure consistent delivery of the intervention, a structured manual was developed and refined building on the manual from the pilot study (Multimedia Appendix 1 provides changes to the pilot version) [54]. The development and optimization process comprised a scoping review of available literature and existing manuals. In addition, local CFI experts were consulted, and the team’s clinical experience of working with these approaches was considered. The intervention was designed based on principles of EMIs [26,27,29,34].

    The development and optimization process resulted in a structured manual for a 6-week intervention combining 4 individual sessions with daily training via a dedicated smartphone app. The manual is reported in the Multimedia Appendix 2 in line with state-of-the art guidelines such as World Health Organization guidelines for reporting health interventions using mobile phones [57] as well as the Template for Intervention Description and Replication Checklist [58]. An overview of the intervention structure and the types of tasks is provided in Figure 1. Figure 2 displays a summary of the intervention content. The intervention can be aligned to participants’ personal needs; for example, sessions or training weeks can be repeated if necessary. Moreover, the intervention provides 2 different study tracks with varying foci and demand levels. On the basis of the trained psychologists’ impression and the participants’ experiences in the first 2 weeks of the intervention, participants were allocated to the basic or the elaborate track of the intervention. The basic study track focused on creating feelings of safeness and calmness by introducing breathing techniques and soothing imagery. The elaborate track extended breathing exercises and soothing imagery by introducing self-compassionate imagery and writing.

    The intervention comprised 3 guided sessions to introduce compassion-focused principles and practical tasks to activate participants’ soothing system and to provide feedback on their current progress and a short review session. The content was presented on the smartphone and was discussed with the trained psychologist. All sessions could be delivered in person or via video calls. The in-person sessions were delivered in dedicated treatment and assessment rooms. For sessions delivered via video call, participants attended the sessions at home. Psychologists were trained in delivering the EMIcompass intervention and supervised by an expert in CFIs (BB) to ensure intervention quality.

    ‎
    Figure 1. Overview of the intervention structure and the types of tasks. EMA: ecological momentary assessment.
    View this figure

    To facilitate real-time and real-world translation of techniques into participants’ daily lives, an EMI was administered through an mHealth app (movisensXS) on a study smartphone that they received in the first guided session. To learn new techniques, participants were asked to complete one enhancing task per week, which was subsequently extended over the intervention. In the weeks with sessions, the new task was introduced in contact with the trained psychologists; in the weeks without session, participants familiarized with the new enhancing task autonomously. Short consolidating tasks were offered to practice the techniques previously introduced in enhancing tasks. Once a day, at a time set by the participants, a signal was prompted to offer participants a consolidating task. In addition, on-demand consolidating tasks were available at any time during the intervention. Furthermore, participants could decide whether they also wanted to allow for interactive tasks. To present interactive tasks, the smartphone prompted a signal 6 times per day on 3 consecutive days per week at random within set blocks of time. At each signal, participants were asked to complete a short EMA questionnaire on momentary stress and affect. If participants indicated high stress or negative affect in the EMA, they were offered an interactive task. Thereby, the interactive tasks guided participants to use previously learned compassion-focused techniques in moments of distress, which is an essential element of CFIs [42]. A gamification element was used to provide feedback on the progress made. If appropriate, participants could choose between reading the instructions on the smartphone’s screen and a guided audio version of the tasks.

    Between sessions, participants received weekly feedback on their progress and were offered email and phone contact to discuss questions and technical problems. At the beginning of weeks without scheduled session (ie, weeks 2, 4, and 6), participants were contacted to notify them about a new enhancing task becoming available for them to try out autonomously. To proceed with the subsequent study week, participants had to complete at least one consolidating task per week. If this was not the case, the intervention week was repeated.

    ‎
    Figure 2. Summary of the intervention content.
    View this figure

    Participants

    In line with a modified version of the clinical staging model [12,56], the EMIcompass study recruited young individuals aged 14 to 25 with current psychological distress, Clinical High At-Risk Mental State (CHARMS), or a first treated episode of severe mental disorder (for a detailed description of the modified criteria, see Multimedia Appendix 3 [12,56,59-64]; age range based on suggestions of the youth mental health reform and local regulations [65]). Participants were recruited from mental health services at the Central Institute of Mental Health, Mannheim, Germany, via local registries and advertisements on the institute’s webpage and social media. Self-reported and observer-rated measures were used to assess eligibility to participate. All participants (including caregivers for minors) provided informed consent and were reimbursed for their time and travel expenses. Further details on inclusion and exclusion criteria are provided in the study protocol [56].

    Measures

    Multimedia Appendix 4 [12,59,60,66-78] provides an overview of the measures used and the time points of administration. We used self-reports and, in the case of ethnicity, family assessments to collect data on sociodemographic characteristics. Clinical characteristics (ie, clinical stage, psychological distress, general psychopathology, and level of functioning) were assessed using self-report questionnaires, observer ratings, and standardized interviews. Self-report questionnaires were used to assess overall self-compassion, emotion regulation, and working alliance. Momentary self-compassion was assessed using EMA. The total number of training tasks completed in the EMI was used as an indicator of training frequency. Multimedia Appendix 5 displays a correlation table of the measures used.

    Statistical Analysis

    The study was registered on the open science framework prior to accessing the data [79]. To obtain parameter estimates for the effect of sociodemographic, clinical, and functional characteristics on putative mechanisms and processes, we fitted linear regression models with change in self-compassion (δpostintervention−baseline), change in adaptive and maladaptive emotion regulation (δpostintervention−baseline), working alliance (patient and therapist ratings and total scores), and training frequency (total score) as dependent variables. Independent variables in the models were clinical stage (stage 1a, stage 1b, and stage 2), psychological distress, general psychopathology, level of functioning, age, gender (female and male), and ethnic minority status (minority and majority). Parameter estimates (95% CIs) were obtained for the main effects of baseline characteristics on change in self-compassion, change in adaptive and maladaptive emotion regulation, working alliance, and training frequency. We computed partial η2 as estimators of effect size for the predictors.

    To obtain parameter estimates for the effect of sociodemographic, clinical, and functional characteristics and baseline level of self-compassion, adaptive, and maladaptive emotion regulation on clinical outcomes, we fitted mixed effects regression models with psychological distress and general psychopathology at postintervention or at follow-up as the dependent variables. Independent variables in these models were time (postintervention and follow-up), clinical stage (stage 1a, stage 1b, and stage 2), level of functioning at baseline, age, gender (female and male), ethnic minority status (minority and majority), momentary and overall self-rated self-compassion at baseline, adaptive and maladaptive emotion regulation at baseline, psychological distress at baseline (as independent variable in the model with general psychopathology at postintervention or follow-up as outcome and as control variable with psychological distress at postintervention or follow-up as outcome), and general psychopathology at baseline (as independent variable in the model with psychological distress at postintervention or follow-up as outcome and as control variable with general psychopathology at postintervention or follow-up as outcome). We took into account the within-subject clustering of repeated measures by adding a level-2 random intercept. The model was fitted using restricted maximum likelihood estimation. Parameter estimates (95% CIs) were obtained for the main effects of baseline characteristics on outcomes across the 2 follow-up (ie, postintervention and 4-week follow-ups). In the next step, given the exploratory nature of this trial, 95% CIs for the 2 time-specific contrasts were obtained. For this, the aforementioned model was extended by time×characteristic interactions (time×clinical stage, time×psychological distress, time×general psychopathology, time×level of functioning, time×age, time×gender, time×self-compassion, time×adaptive emotion regulation, and time×maladaptive emotion regulation). The “margins” command was used for each interaction to obtain predicted means for both time points and all manifestations of categorical variables (eg, “margins time point #clinical stage”). For continuous variables, the “margins” command was used with z-standardized continuous variables to obtain predicted means for both time points and low (mean−1 SD), mean, and high (mean+1 SD) levels of the given continuous variable (eg, “margins, at [z_age = (−1 0 1)] over [time]”).

    To transform the results into an effect size, the model was run including only a random intercept for participants, the estimated target relationship, and the baseline control to obtain the conditional and pooled variance across both assessment time points [78,80,81]. The resulting estimate of variance therefore approximates the variation in the dependent variable at any cross-section in postintervention and follow-up. The resulting estimate is on a similar scale as other typical d-type effect sizes (at “0” of any random slopes, if included), and if additional random effects were strong, these variances are underestimations, and the effect sizes in the following likely are at the upper possible limit.

    The analysis was conducted according to intention-to-treat principles, with data from all participants entered into the analysis, including those who have low adherence to or who dropped out of the intervention. To screen for potential collinearity problems, we computed variance inflation factors and tolerance values (Multimedia Appendix 6).


    Basic Sample and Clinical Characteristics

    An overview of basic sample and clinical characteristics is displayed in Table 1. The sample of those randomized to the experimental condition comprised 46 individuals (50% of the total sample in the exploratory RCT of N=92), with a mean age of 21.30 (SD 2.84; range 14-25) years. Most participants (35/46, 76%) identified as girls or women, 24% (11/46) of the participants identified as boys or men, and no participant identified as nonbinary. We identified 70% (32/46) of participants as White majority (German), 9% (4/46) as White other, and 22% (10/46) as other or mixed ethnicity. Most participants were classified as stage 1a (psychological distress, 26/46, 57%), 28% (13/46) of the participants met criteria for stage 1b (CHARMS), and 15% (7/46) of the participants were classified as stage 2 (first episode of severe mental disorder). The mean level of psychological distress at baseline was 28.20 (SD 5.08), and the mean level of general psychopathology at baseline was 24.55 (SD 9.94). The average level of functioning was 71.83 (SD 9.89). Participants showed comparable levels of overall self-rated self-compassion (P=.33) and adaptive (P=.57) and maladaptive emotion regulation (P=.21) at baseline and postintervention. We observed increases in momentary self-compassion at postintervention (P=.02).

    Table 1. Basic sample and clinical characteristics.

    		Baseline (nmax=46)aPostintervention (nmax=45)Follow-up (nmax=45)Baseline v postintervention




    		t test (df)P value
    Age at baseline (years), mean (SD)21.30 (2.84)—b———
    Gender, n (%)————

    		Female35 (76)




    		Male11 (24)




    		Nonbinary0 (0)



    Ethnicity, n (%)————

    		White majority32 (70)




    		Minority


    		Mixed White majority or White other3 (7)





    		White other4 (9)





    		Turkish3 (6)





    		Mixed other2 (4)





    		Middle East1 (2)





    		Asian1 (2)



    Level of education, n (%)————

    		School: General Certificate of Secondary Education7 (15)




    		Further: A levels14 (30)




    		Higher: university25 (54)



    Employment status, n (%)————

    		Student39 (85)




    		School4 (9)




    		Vocational training or university35 (76)




    		Employed4 (9)




    		Unemployed3 (6)



    Clinical stage at baseline, n (%)————

    		1a26 (57)




    		1b13 (28)




    		27 (15)



    Level of functioning at baseline, mean (SD)71.83 (9.89)————
    Psychological distress, mean (SD)28.20 (5.08)24.11 (6.55)22.73 (7.16)——
    General psychopathology, mean (SD)24.55 (9.94)18.0 (12.03)16.20 (10.68)——
    Self-compassion,mean (SD)

    		Overall self-rating18.34 (2.77)18.70 (2.06)—−0.99 (42).33

    		Momentary rating3.89 (0.87)4.30 (1.06)—−2.35 (44).02
    Emotion regulation, mean (SD)

    		Adaptive5.51 (1.45)5.61 (1.57)—−0.57 (42).57

    		Maladaptive5.97 (1.46)5.64 (1.45)—1.27 (42).21
    Training frequency, mean (SD)—75.84 (85.09)———
    Working alliance, mean (SD)

    		Patient rating—48.07 (8.37)———

    		Therapist rating—46.74 (6.47)———

    aSample sizes varied owing to missing values at baseline (nmax=46; nmin=45).

    bNot available.

    Sociodemographic, Clinical, and Functional Characteristics at Baseline Associated With Putative Mechanisms and Processes of Change

    Table 2 presents the associations of sociodemographic, clinical, and functional characteristics at baseline with change in self-compassion and emotion regulation. There was no evidence for initial signals that participants’ characteristics at baseline were associated with change in overall self-rated self-compassion (δpostintervention−baseline). For change in momentary self-compassion, we observed a tendency for an association with age (B=0.11, 95% CI 0.00-0.22): older participants tended to show more pronounced change in momentary self-compassion from baseline to postintervention. Clinical stage was associated with change in adaptive emotion regulation such that participants in stage 2 showed more pronounced positive changes in adaptive emotion regulation compared with participants in stage 1a (B=1.50, 95% CI 0.06-2.93). For change in maladaptive emotion regulation, we found a tendency for an association with general psychopathology such that participants with lower levels of psychopathology at baseline tended to show more pronounced reductions in maladaptive emotion regulation (B=0.08, 95% CI −0.01 to 0.16).

    Table 3 presents the associations of sociodemographic, clinical, and functional characteristics at baseline with working alliance and training frequency. We found no evidence for initial signals of associations of working alliance and training frequency with baseline characteristics.

    Table 2. Associations of sociodemographic, clinical, and functional characteristics at baseline with change in self-compassion and emotion regulation.

    		Putative mechanisms of change

    		Change in overall self-rated self-compassion (n=43)Change in momentary self-compassion (n=45)Change in adaptive emotion regulation (n=43)Change in maladaptive emotion regulation (n=43)

    		B (95% CI)Effect sizeaB (95% CI)Effect sizeB (95% CI)Effect sizeB (95% CI)Effect size
    Age−0.05 (−0.40 to 0.29)0.000.11 (0.00 to 0.22)0.10−0.10 (−0.23 to 0.04)0.05−0.09 (−0.26 to 0.09)0.03
    Gender0.81 (−1.68 to 3.29)0.01−0.07 (−0.85 to 0.71)0.00−0.70 (−1.70 to 0.30)0.060.45 (−0.81 to 1.72)0.02
    Ethnic minority status0.91 (−1.28 to 3.10)0.02−0.20 (−0.89 to 0.49)0.010.07 (−0.81 to 0.95)0.000.51 (−0.60 to 1.62)0.02
    Clinical stageb
    		0.01
    		0.04
    		0.15
    		0.00

    		Stage 1b0.57 (−1.66 to 2.81)
    		−0.31 (−1.03 to 0.40)
    		0.70 (−0.20 to 1.60)
    		0.18 (−0.95 to 1.32)

    		Stage 2−0.34 (−3.91 to 3.23)
    		0.27 (−0.81 to 1.34)
    		1.50 (0.06 to 2.93)
    		0.13 (−1.68 to 1.94)
    Psychological distress−0.01 (−0.38 to 0.35)0.000.03 (−0.09 to 0.14)0.01−0.02 (−0.16 to 0.13)0.00−0.15 (−0.34 to 0.03)0.08
    General psychopathology−0.01 (−0.18 to 0.16)0.000.03 (−0.02 to 0.09)0.04−0.04 (−0.11 to 0.03)0.030.08 (−0.01 to 0.16)0.09
    Level of functioning−0.01 (−0.11 to 0.10)0.00−0.02 (−0.06 to 0.01)0.040.00 (−0.04 to 0.05)0.000.01 (−0.05 to 0.07)0.00

    aEffect size partial η2.

    bStage 1a (individuals with psychological distress) as reference category.

    Table 3. Associations of sociodemographic, clinical, and functional characteristics at baseline with working alliance and training frequency.

    		Putative mechanisms of change

    		Working alliance—patient rating (n=44)Working alliance—therapist rating (n=43)Training frequency (n=45)

    		B (95% CI)Effect sizeaB (95% CI)Effect sizeB (95% CI)Effect size
    Age0.57 (−0.33 to 1.46)0.050.17 (−0.59 to 0.94)0.012.69 (−7.38 to 12.77)0.01
    Gender2.55 (−3.93 to 9.03)0.022.72 (−2.93 to 8.37)0.0312.80 (−85.02 to 9.43)0.00
    Ethnic minority status1.89 (−3.65 to 7.44)0.011.54 (−3.18 to 6.26)0.01−26.31 (88.48 to 35.86)0.02
    Clinical stageb
    		0.09
    		0.04
    		0.05

    		Stage 1b5.03 (−0.68 to 10.74)
    		−1.22 (−5.93 to 3.49)
    		26.07 (−38.45 to 90.60)

    		Stage 2−0.41 (−9.47 to 8.65)
    		3.51 (−4.00 to 11.02)
    		−41.07 (−141.05 to 58.91)
    Psychological distress0.70 (−0.24 to 1.65)0.060.33 (−0.46 to 1.12)0.024.83 (−5.80 to 15.47)0.02
    General psychopathology0.00 (−0.44 to 0.44)0.00−0.13 (−0.49 to 0.23)0.02−1.82 (−6.74 to 3.11)0.02
    Level of functioning0.01 (−0.28 To 0.29)0.000.11 (−0.14 To 0.36)0.02−0.31 (−3.50 to 2.87)0.00

    aEffect size partial η2.

    bStage 1a (individuals with psychological distress) as reference category.

    Sociodemographic, Clinical, and Functional Characteristics; Self-compassion; and Emotion Regulation at Baseline Associated With Clinical Outcomes

    Table 4 presents findings on associations of psychological distress with participants’ characteristics and level of putative mechanisms at baseline and predicted marginal means. There was some evidence for a main effect of momentary self-compassion such that higher momentary self-compassion at baseline tended to be associated with, on average, lower levels of psychological distress across postintervention and follow-up assessments (B=−2.83, 95% CI −5.66 to 0.00). There was no evidence for main effects of sociodemographic or clinical characteristics, overall self-rated self-compassion, and emotion regulation on psychological distress.

    Table 5 presents findings on associations of general psychopathology with participants’ characteristics and level of putative mechanisms at baseline and predicted marginal means. There was no evidence for initial signals of main effects of sociodemographic, clinical, and functional characteristics on general psychopathology.

    Cross-differences between high and low levels of baseline characteristics at the time points are presented in Multimedia Appendix 7 [82].

    Table 4. Associations of psychological distress with participants’ characteristics and level of putative mechanisms and processes at baseline and predicted marginal meansa.

    		PostinterventionFollow-upAdjusted B (95% CI)Effect sizeb

    		Predicted marginal mean (95% CI)SEPredicted marginal mean (95% CI)SE

    TimeN/AcN/AN/AN/A−7.46 (−37.20 to 22.29)−1.16
    Age−0.31 (−1.04 to 0.42)−0.05

    		Lowd24.92 (22.26 to 27.57)1.3624.99 (22.33 to 27.65)1.36


    		Mean24.03 (22.33 to 25.73)0.8722.62 (20.93 to 24.32)0.87


    		Highe23.15 (20.44 to 25.86)1.3820.26 (17.55 to 22.96)1.38

    Gender−0.51 (−5.26 to 4.23)−0.08

    		Female24.16 (22.15 to 26.17)1.0323.90 (21.89 to 25.91)1.03


    		Male23.65 (19.61 to 27.69)2.0618.45 (14.41 to 22.49)2.06

    Ethnic minority status2.39 (−4.40 to 9.18)0.37

    		White majority23.83 (22.02 to 25.64)0.9222.71 (20.91 to 24.52)0.92


    		Minority26.22 (19.81 to 32.62)3.2722.11 (15.70 to 28.52)3.27

    Clinical stagef−0.19 (−3.62 to 3.24)−0.03

    		Stage 1a25.42 (23.12 to 27.73)1.1823.75 (21.44 to 26.05)1.18


    		Stage 1b21.84 (18.58 to 25.10)1.6620.21 (16.95 to 23.46)1.66


    		Stage 222.62 (16.44 to 28.79)3.1523.37 (17.19 to 29.55)3.15

    General psychopathology at baseline0.04 (−0.29 to 0.38)0.01

    		Low23.62 (19.88 to 27.37)1.9118.79 (15.05 to 22.54)1.91


    		Mean24.03 (22.33 to 25.74)0.8722.56 (20.86 to 24.26)0.87


    		High24.45 (20.85 to 28.04)1.8326.33 (22.73 to 29.92)1.83

    Level of functioning at baseline−0.10 (−0.30 to 0.11)-0.02

    		Low25.02 (22.34 to 27.70)1.3722.27 (19.59 to 24.95)1.37


    		Mean24.07 (22.37 to 25.77)0.8722.65 (20.95 to 24.35)0.87


    		High23.12 (20.52 to 25.72)1.3323.03 (20.43 to 25.63)1.33

    Overall self-rated self-compassion at baseline0.06 (−0.83 to 0.94)0.01

    		Low23.81 (19.66 to 27.95)2.1121.28 (17.14 to 25.43)2.11


    		Mean24.02 (22.29 to 25.76)0.8922.53 (20.79 to 24.26)0.89


    		High24.24 (20.74 to 27.74)1.7923.77 (20.27 to 27.28)1.79

    Momentary self-compassion at baseline−2.83 (−5.66 to 0.00)-0.37

    		Low26.60 (23.53 to 29.68)1.5723.05 (19.97 to 26.12)1.57


    		Mean24.17 (22.47 to 25.88)0.8722.68 (20.98 to 24.38)0.87


    		High21.74 (18.88 to 24.60)1.4622.31 (19.45 to 25.17)1.46

    Adaptive emotion regulation at baseline−0.36 (−1.96 to 1.24)−0.06

    		Low24.57 (21.68 to 27.45)1.4724.01 (21.13 to 26.90)1.47


    		Mean24.01 (22.35 to 25.74)0.8722.66 (20.96 to 24.63)0.87


    		High23.52 (20.64 to 26.41)1.4721.30 (18.42 to 24.19)1.47

    Maladaptive emotion regulation at baseline0.03 (−1.74 to 1.80)0.00

    		Low24.00 (20.92 to 27.08)1.5720.53 (17.45 to 23.62)1.57


    		Mean24.05 (22.35 to 25.74)0.8722.66 (20.96 to 24.36)0.87


    		High24.09 (21.01 to 27.17)1.5724.78 (21.70 to 27.86)1.57

    aAdjusted for baseline levels of psychological distress.

    bd-type effect size.

    cN/A: not applicable.

    dLow = mean − 1 SD.

    eHigh = mean + 1 SD.

    fStage 1a (individuals with psychological distress) is used as the reference category.

    Table 5. Associations of general psychopathology with participants’ characteristics and level of putative mechanisms and processes at baseline and predicted marginal meansa.

    		PostinterventionFollow-upAdjusted B (95% CI)Effect sizeb

    		Predicted marginal mean (95% CI)SEPredicted marginal mean (95% CI)SE

    Time—c———−25.10 (−56.83 to 6.63)−2.38
    Age−0.79 (−2.05 to 0.46)−0.08

    		Lowd19.75 (15.20 to 24.29)2.3218.27 (13.72 to 22.81)2.32


    		Mean17.49 (14.58 to 20.39)1.4816.06 (13.15 to 18.96)1.48


    		Highe15.23 (10.61 to 19.86)2.3613.85 (9.22 to 18.48)2.36

    Gender−2.14 (−10.25 to 5.97)−0.20

    		Female18.01 (14.57 to 21.45)1.7617.44 (14.00 to 20.88)1.76


    		Male15.87 (8.96 to 22.78)3.5311.49 (4.58 to 8.40)3.53

    Ethnic minority status1.40 (−10.21 to 13.02)0.13

    		White majority17.40 (14.30 to 20.49)1.5815.69 (12.60 to 18.78)1.58


    		Minority18.80 (7.84 to 29.75)5.5920.10 (9.15 to 31.06)5.59

    Clinical stagef−0.97 (−6.84 to 4.89)−0.09

    		Stage 1a19.07 (15.13 to 23.01)2.0118.29 (14.35 to 22.23)2.01


    		Stage 1b14.32 (8.75 to 19.88)2.8411.60 (6.03 to 17.17)2.84


    		Stage 217.82 (7.26 to 28.39)5.3916.34 (5.77 to 26.91)5.39

    Psychological distress at baseline−0.27 (1.55 to 1.01)−0.03

    		Low18.83 (12.03 to 25.63)3.4719.44 (12.64 to 26.25)3.47


    		Mean17.52 (14.61 to 20.42)1.4816.07 (13.17 to 18.98)1.48


    		High16.20 (9.35 to 23.06)3.5012.71 (5.85 to 19.56)3.50

    Level of functioning at baseline−0.13 (−0.48 to 0.22)−0.01

    		Low18.84 (14.26 to 23.43)2.3415.01 (10.43 to 19.60)2.34


    		Mean17.56 (14.65 to 20.46)1.4816.06 (13.16 to 18.97)1.48


    		High16.27 (11.82 to 20.72)2.2717.11 (12.66 to 21.57)2.27

    Overall self-rated self-compassion at baseline0.31 (−1.20 to 1.83)0.03

    		Low16.18 (9.09 to 23.27)3.6210.90 (3.81 to 17.99)3.62


    		Mean17.39 (14.43 to 20.36)1.5115.60 (12.63 to 18.57)1.51


    		High18.61 (12.62 to 24.60)3.0520.29 (14.31 to 26.28)3.05

    Momentary self-compassion at baseline−3.24 (−8.08 to 1.61)−0.31

    		Low20.45 (15.19 to 25.70)2.6815.26 (10.01 to 20.52)2.68


    		Mean17.67 (14.76 to 20.58)1.4916.05 (13.14 to 18.96)1.49


    		High14.89 (9.99 to 19.79)2.5016.84 (11.94 to 21.73)2.50

    Adaptive emotion regulation at baseline0.32 (−2.42 to 3.06)0.03

    		Low17.06 (12.13 to 22.00)2.5219.47 (14.53 to 24.41)2.52


    		Mean17.52 (14.62 to 20.43)1.4816.09 (13.19 to 19.00)1.48


    		High17.98 (13.05 to 22.92)2.5212.71 (7.78 to 17.65)2.52

    Maladaptive emotion regulation at baseline0.97 (−2.42 to 3.06)0.09

    		Low16.11 (10.84 to 21.38)2.6914.42 (9.15 to 19.69)2.69


    		Mean17.52 (14.62 to 20.43)1.4816.09 (13.19 to 19.00)1.48


    		High18.93 (13.67 to 24.20)2.6917.76 (12.49 to 23.03)2.69

    aAdjusted for baseline levels of general psychopathology.

    bd-type effect size.

    cNot available.

    dLow = mean – 1 SD.

    eHigh = mean + 1 SD.

    fStage 1a (individuals with psychological distress) is used as the reference category.


    Principal Findings

    First, we developed a hybrid 6-week CFI comprising 2 intervention tracks with varying foci and demand levels. Second, we observed initial signals of effects of age, general psychopathology, and clinical stage on change in momentary self-compassion and change in emotion regulation. Older participants tended to show greater differences in momentary self-compassion comparing baseline and postintervention assessments. Participants classified as stage 2 were found to show greater differences in adaptive emotion regulation comparing baseline and postintervention assessments. In addition, participants with lower levels of psychopathology at baseline showed more pronounced reductions in maladaptive emotion regulation from baseline to postintervention assessments. There was no evidence for associations of other baseline characteristics (eg, gender, minority status, and level of functioning) and putative mechanisms (ie, overall self-rated self-compassion, working alliance, and training frequency). Third, there was some evidence that higher momentary self-compassion at baseline tended to be associated with, on average, lower levels of psychological distress across postintervention and follow-up assessments. We observed no other initial signals that clinical or functional characteristics at baseline impacted clinical outcomes.

    Methodological Considerations

    The reported results should be interpreted in light of several methodological considerations and limitations. First, sample size and selection as well as the exploratory nature of the analyses need to be critically appraised. Although the analyses were prospectively registered, they reflect secondary analyses with an increased risk of type 1 error. As noted, our findings reflect initial signals of associations of participants’ baseline characteristics with putative mechanisms, processes, and outcomes. Moreover, it should be taken into account that boys or men, individuals identifying as nonbinary, and participants from stage 2 (first episode of severe mental disorder) were considerably underrepresented in the sample. However, the gender difference in recruitment may partly be explained by higher prevalence of depressive and anxiety disorders in women and adolescent girls [83,84] and the exclusion of mental health problems that are especially prevalent in men and adolescent boys (eg, primary substance abuse disorder [66]). Randomization in a future definitive trial may therefore need to stratify by gender to rule out potential confounding by this factor. In addition, we assessed ethnicity by considering participants’ self-report of citizenship, country of birth, first language, and information provided in participants’ family assessment. Grouping participants into broad categories of ethnicity inevitably implies that some participants may have been assigned to a category that they do not consider belonging to and, hence, misclassification. In general, the concept of using categories, for example, with regard to ethnicity or gender, may be criticized as—of course—there is considerable heterogeneity within groups, which needs to be further explored in qualitative analyses [85,86]. These limitations can be tolerated at the exploratory stage of developing a complex intervention but should be addressed in future, definitive trials.

    Second, operationalizations of putative mechanisms were not measured at multiple time points during the intervention, and difference scores were used as proxies for change in self-compassion and emotion regulation. While proxies are acceptable in this exploratory study, a future definitive trial may use multiple assessments during the intervention to yield more fine-grained data on potential changes in mechanisms.

    Third, the assessment of self-compassion needs to be critically appraised: in our analyses, overall self-rated self-compassion and momentary self-compassion were not correlated, indexing low convergent validity (Multimedia Appendix 5). Similar phenomena have been observed before, for example, for negative symptoms measured with EMA and interviewer-rated measures, which may tap distinct but related constructs [87]. This may be viewed as underscoring the relevance of assessment under real-time and real-world conditions, which is supported by moderate to large correlations of momentary self-compassion with clinical characteristics (ie, clinical stage, psychological distress, general psychopathology, and level of functioning), indicating high concurrent validity. However, as the items for assessing momentary self-compassion were used for the first time in this study, they may also not fully capture the construct of self-compassion as operationalized by the subscales in the Self-Compassion Scale (ie, they are more similar in content to items from the self-kindness than mindfulness subscale) [73]. In addition, we aggregated EMA data on momentary self-compassion at the person level, which led to a loss of information in comparison with the level of EMA observations, given the repeated measurement and temporal variability EMA captures as an intensive longitudinal data collection method (Schick A, unpublished data, 2022). Nonetheless, aggregated experience sampling measures may still capture the target constructs with less noise and greater sensitivity than recall measures [88], so this may not reduce this study’s informative value substantially.

    Fourth, potential influences of the COVID-19 pandemic have not been statistically accounted for in current analyses and should be considered when interpreting the findings. Owing to local regulations (eg, lockdowns and contact restrictions), the intervention sessions were shifted from face-to-face contact to video calls. Recent systematic reviews and meta-analyses indicated no differences in telehealth and in-person psychotherapy [89], but generalizability to settings in which both in-person sessions and a video call format are used flexibly remains unclear, and the impact cannot be determined with certainty without further research.

    Comparison With Previous Research

    To our knowledge, the EMIcompass intervention is the first hybrid CFI blending an EMI and face-to-face sessions designed to enhance self-compassion and resilience in young people with nonspecific psychological distress, CHARMS, and first episode of severe mental disorder. Building on principles of EMIs [26,27,29,34], EMIcompass combined different intervention elements: enhancing tasks provided participants with new CFI strategies. Consolidating tasks facilitated training in different contexts and translation into daily life increasing the chances of generalization. Elements of experience sampling were used to increase reflective processing improving insight and awareness of own cognitive and emotional processes [90]. This may be further improved by incorporating elements of feedback into future versions of the intervention [91]. In addition, assessing stress and affect in daily life allows the EMI to offer useful techniques in moments of high distress (ie, interactive tasks), providing participants with support in challenging life situations.

    For the EMIcompass intervention, the results from an uncontrolled pilot study [54] indicated a reduction of stress reactivity at postintervention and follow-up and reduced clinical symptoms at follow-up when compared with baseline. A recent exploratory RCT [56] indicated that all feasibility criteria were met and a reduction of stress reactivity in the experimental condition as the primary candidate mechanism in comparison with a control condition of TAU. In addition, it suggests initial signals that the EMIcompass intervention may have beneficial effects on resilience in daily life and quality of life. Detailed findings on feasibility and initial signals of efficacy are described elsewhere [56].

    Apart from an association of age and change in momentary self-compassion, participants’ sociodemographic characteristics were not associated with putative processes, mechanisms, and outcomes of the EMIcompass intervention. This is at variance with findings in traditional psychotherapy for depression and psychosis, where reviews indicate differential treatment effects for various sociodemographic characteristics (eg, age, gender, and marital status) [92,93]. In an Acceptance and Commitment Therapy–based EMI in individuals at ultra–high risk for psychosis and with a first episode of psychosis, ethnic minority status was associated with lower compliance and higher app usefulness, whereas being female predicted lower usefulness of the app’s metaphor images (van Aubel E, unpublished data, August 2022).

    When examining the impact of clinical and functional characteristics, we observed associations of clinical stage and general psychopathology with putative mechanisms and processes (ie, change in momentary self-compassion and change in emotion regulation). Interestingly, later clinical stage was associated with a more pronounced increase in adaptive emotion regulation, whereas lower levels of general psychopathology tended to be associated with a more pronounced reduction of maladaptive emotion regulation. However, the findings on clinical stage must be interpreted with caution, given the small number of participants from stage 2 included in the study. The possibility of ceiling effects for a particular clinical stage could be ruled out, as the mean levels of adaptive emotion regulation were in the middle range of the scale for all clinical stages. An RCT of cognitive behavioral therapy in patients with psychotic disorders investigating predictors of improvement and dropout indicated that higher symptom severity and poor level of functioning do not pose a barrier to improvement [94]. The findings from an Acceptance and Commitment Therapy–based EMI in individuals at ultra–high risk for psychosis and with a first episode of psychosis show a differentiated perspective on symptom severity: the severity of affective symptoms was associated with higher perceived usefulness and that of negative symptoms was associated with lower perceived usefulness of the intervention (van Aubel E, unpublished data, August 2022). Besides sociodemographic, clinical, and functional characteristics at baseline, we moved beyond these previous studies and examined potential associations of baseline levels of self-compassion and emotion regulation with outcomes of the intervention. We found some evidence that higher levels of momentary self-compassion at baseline were associated with, on average, lower levels of psychological distress across assessment time points. By showing this in a longitudinal intervention study, the current findings extend evidence from a meta-analysis indicating associations of self-compassion and psychological distress in general [95]. However, in this study, this did not hold true for overall self-compassion. Apart from the effects delineated earlier, there were no initial signals of associations, tentatively suggesting that participants’ sociodemographic, clinical, and functional characteristics had little influence on their response to the EMIcompass intervention. This may indicate—within the limits of the variables assessed—that the EMIcompass intervention is relatively inclusive and reach of participants is largely independent from their sociodemographic, clinical, and functional baseline characteristics.

    The role of digital approaches in improving the reach of those in need within broader conceptualizations has been subject to controversial debate: qualitative studies with health professionals and service users indicate that digital approaches were viewed as having the potential to improve inclusion but also as having the risk of digital exclusion [36,37,96]. Concerns have been raised that digital approaches and the digital divide may further reinforce health inequalities (ie, systematic, avoidable, and unfair differences in health outcomes [97]) in marginalized and underserved populations; for example, in racial and ethnic minorities [98]. Digital inequalities are suggested to comprise multiple continuous dimensions; for example, socioeconomic and educational background, migrant and ethnic minority status, and health literacy [99-101]. To further improve our understanding of the consequences of digital inequalities for individuals’ response to the EMIcompass intervention, future studies may broaden their perspective by including further aspects of marginalized and underserved populations (eg, sexual minority status and socioeconomic background) and examining other criteria (eg, level of functioning, satisfaction with the intervention, goal attainment, and quality of life) in addition to those considered so far.

    To address digital exclusion of marginalized and underserved populations, demands for evidence-based digital inclusion strategies have been articulated [102], and potential pathways for improving inclusion in digital approaches have been discussed. On the one hand, adaptations of interventions have been suggested; for example, feasibility and beneficial effects of cultural adaptation of interventions have already been demonstrated [103]. In addition to adapting interventions for specific groups, the needs and perspectives of individual participants should be taken into account in process evaluations combining quantitative and qualitative data [104]. In line with this, we conducted a qualitative study incorporating realist methodology [105] examining what works for whom under which circumstances in the EMIcompass study, the findings of which are reported elsewhere (Paetzold I, unpublished data, 2022). An emerging research field targets the adaptation of digital interventions on an individual level aiming at personalizing assessment and intervention [27,29,34]. On the other hand, the creation of interventions for diverse populations has been suggested, for example, in the REACT recommendations [98]. In line with this approach, a recent review of digital mental health interventions specifically designed for marginalized populations indicated promising results on feasibility and acceptability in pilot studies but also a lack of larger-scale examinations [106].

    Conclusions

    We developed the first hybrid CFI combining an EMI and face-to-face sessions with 2 intervention tracks and varying foci and demand levels to enhance resilience in young people with early mental health problems. We aimed at exploring whether participants’ characteristics at baseline were associated with putative mechanisms and outcomes of the EMIcompass intervention. The findings indicated reach of participants by the intervention largely independent of sociodemographic, clinical, and functional baseline characteristics. The findings need to be confirmed in a definitive trial.

    Acknowledgments

    The authors are indebted to all individuals who participated in the EMIcompass study. The authors are grateful to Professor Stefan Priebe, Professor Stefan Wellek, Professor Nicolas Ruesch, and Dr Thomas Vaessen as well as Professor Maria Blettner, Professor Sebastian von Peter, and Professor Georg Schomerus for their being a member of the trial steering committee and data monitory and ethics committee, respectively. The authors also thank all trained psychologists and research assistants. This work was funded by a Deutsche Forschungsgemeinschaft / German Research Fundation (DFG) project grant (no. 389626655) and German research foundation Heisenberg professorship (number 389624707) to UR.

    Conflicts of Interest

    TB served in an advisory or consultancy role for ADHS digital, Infectopharm, Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg GmbH, Roche, and Takeda. He received conference support or speaker’s fee from Medice and Takeda. He received royalties from Hogrefe, Kohlhammer, CIP Medien, and Oxford University Press; this work is unrelated to these relationships. The other authors declare that they have no competing interests.

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    Multimedia Appendix 1

    Changes to the pilot version.

    DOCX File , 28 KB
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    Multimedia Appendix 2

    EMIcompass intervention manual.

    DOCX File , 16 KB
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    Multimedia Appendix 3

    Modified criteria of the clinical staging model.

    DOCX File , 699 KB
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    Multimedia Appendix 4

    Measures.

    DOCX File , 29 KB
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    Multimedia Appendix 5

    Correlation table.

    DOCX File , 18 KB
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    Multimedia Appendix 6

    Variance inflation factors and tolerance.

    DOCX File , 19 KB
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    Multimedia Appendix 7

    Cross-differences.

    DOCX File , 19 KB
    1. Solmi M, Radua J, Olivola M, Croce E, Soardo L, Salazar de Pablo G, et al. Age at onset of mental disorders worldwide: large-scale meta-analysis of 192 epidemiological studies. Mol Psychiatry 2022 Jan;27(1):281-295 [FREE Full text] [CrossRef] [Medline]
    2. Polanczyk GV, Salum GA, Sugaya LS, Caye A, Rohde LA. Annual research review: a meta-analysis of the worldwide prevalence of mental disorders in children and adolescents. J Child Psychol Psychiatry 2015 Mar;56(3):345-365. [CrossRef] [Medline]
    3. Erskine HE, Moffitt TE, Copeland WE, Costello EJ, Ferrari AJ, Patton G, et al. A heavy burden on young minds: the global burden of mental and substance use disorders in children and youth. Psychol Med 2015 May;45(7):1551-1563 [FREE Full text] [CrossRef] [Medline]
    4. Global Burden of Disease Child and Adolescent Health Collaboration, Kassebaum N, Kyu HH, Zoeckler L, Olsen HE, Thomas K, et al. Child and adolescent health from 1990 to 2015: findings from the global burden of diseases, injuries, and risk factors 2015 study. JAMA Pediatr 2017 Jun 01;171(6):573-592 [FREE Full text] [CrossRef] [Medline]
    5. Reininghaus U, Priebe S, Bentall RP. Testing the psychopathology of psychosis: evidence for a general psychosis dimension. Schizophr Bull 2013 Jul;39(4):884-895 [FREE Full text] [CrossRef] [Medline]
    6. Shevlin M, McElroy E, Bentall RP, Reininghaus U, Murphy J. The psychosis continuum: testing a bifactor model of psychosis in a general population sample. Schizophr Bull 2017 Jan;43(1):133-141 [FREE Full text] [CrossRef] [Medline]
    7. van Os J, Reininghaus U. Psychosis as a transdiagnostic and extended phenotype in the general population. World Psychiatry 2016 Jun;15(2):118-124 [FREE Full text] [CrossRef] [Medline]
    8. Reininghaus U, Böhnke JR, Hosang G, Farmer A, Burns T, McGuffin P, et al. Evaluation of the validity and utility of a transdiagnostic psychosis dimension encompassing schizophrenia and bipolar disorder. Br J Psychiatry 2016 Aug;209(2):107-113. [CrossRef] [Medline]
    9. Forbes MK, Tackett JL, Markon KE, Krueger RF. Beyond comorbidity: toward a dimensional and hierarchical approach to understanding psychopathology across the life span. Dev Psychopathol 2016 Nov;28(4pt1):971-986 [FREE Full text] [CrossRef] [Medline]
    10. Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry 2010 Jul;167(7):748-751. [CrossRef] [Medline]
    11. Kotov R, Krueger RF, Watson D, Achenbach TM, Althoff RR, Bagby RM, et al. The Hierarchical Taxonomy of Psychopathology (HiTOP): a dimensional alternative to traditional nosologies. J Abnorm Psychol 2017 May;126(4):454-477. [CrossRef] [Medline]
    12. Hartmann JA, Nelson B, Spooner R, Paul Amminger G, Chanen A, Davey CG, et al. Broad clinical high-risk mental state (CHARMS): methodology of a cohort study validating criteria for pluripotent risk. Early Interv Psychiatry 2019 Jun;13(3):379-386. [CrossRef] [Medline]
    13. McGorry PD, Purcell R, Hickie IB, Yung AR, Pantelis C, Jackson HJ. Clinical staging: a heuristic model for psychiatry and youth mental health. Med J Aust 2007 Oct 01;187(S7):S40-S42. [CrossRef] [Medline]
    14. Forbes MK, Rapee RM, Krueger RF. Opportunities for the prevention of mental disorders by reducing general psychopathology in early childhood. Behav Res Ther 2019 Aug;119:103411 [FREE Full text] [CrossRef] [Medline]
    15. Bayer J, Hiscock H, Scalzo K, Mathers M, McDonald M, Morris A, et al. Systematic review of preventive interventions for children's mental health: what would work in Australian contexts? Aust N Z J Psychiatry 2009 Aug;43(8):695-710. [CrossRef] [Medline]
    16. Patel V, Flisher AJ, Hetrick S, McGorry P. Mental health of young people: a global public-health challenge. Lancet 2007 Apr 14;369(9569):1302-1313. [CrossRef] [Medline]
    17. Cuijpers P, Berking M, Andersson G, Quigley L, Kleiboer A, Dobson KS. A meta-analysis of cognitive-behavioural therapy for adult depression, alone and in comparison with other treatments. Can J Psychiatry 2013 Jul;58(7):376-385. [CrossRef] [Medline]
    18. Hofmann SG, Smits JA. Cognitive-behavioral therapy for adult anxiety disorders: a meta-analysis of randomized placebo-controlled trials. J Clin Psychiatry 2008 Apr;69(4):621-632 [FREE Full text] [CrossRef] [Medline]
    19. Kirby JN, Tellegen CL, Steindl SR. A meta-analysis of compassion-based interventions: current state of knowledge and future directions. Behav Ther 2017 Nov;48(6):778-792. [CrossRef] [Medline]
    20. Tai S, Turkington D. The evolution of cognitive behavior therapy for schizophrenia: current practice and recent developments. Schizophr Bull 2009 Sep;35(5):865-873 [FREE Full text] [CrossRef] [Medline]
    21. Fava GA, Ruini C, Belaise C. The concept of recovery in major depression. Psychol Med 2007 Mar;37(3):307-317. [CrossRef] [Medline]
    22. McGorry PD, Mei C. Early intervention in youth mental health: progress and future directions. Evid Based Ment Health 2018 Nov;21(4):182-184. [CrossRef] [Medline]
    23. Malla A, Shah J, Iyer S, Boksa P, Joober R, Andersson N, et al. Youth mental health should be a top priority for health care in Canada. Can J Psychiatry 2018 Apr;63(4):216-222 [FREE Full text] [CrossRef] [Medline]
    24. Ghio L, Gotelli S, Marcenaro M, Amore M, Natta W. Duration of untreated illness and outcomes in unipolar depression: a systematic review and meta-analysis. J Affect Disord 2014 Jan;152-154:45-51. [CrossRef] [Medline]
    25. Marshall M, Lewis S, Lockwood A, Drake R, Jones P, Croudace T. Association between duration of untreated psychosis and outcome in cohorts of first-episode patients: a systematic review. Arch Gen Psychiatry 2005 Sep;62(9):975-983. [CrossRef] [Medline]
    26. Myin-Germeys I, Kasanova Z, Vaessen T, Vachon H, Kirtley O, Viechtbauer W, et al. Experience sampling methodology in mental health research: new insights and technical developments. World Psychiatry 2018 Jun;17(2):123-132 [FREE Full text] [CrossRef] [Medline]
    27. Myin-Germeys I, Klippel A, Steinhart H, Reininghaus U. Ecological momentary interventions in psychiatry. Curr Opin Psychiatry 2016 Jul;29(4):258-263. [CrossRef] [Medline]
    28. Rauschenberg C, Schick A, Hirjak D, Seidler A, Paetzold I, Apfelbacher C, et al. Evidence synthesis of digital interventions to mitigate the negative impact of the COVID-19 pandemic on public mental health: rapid meta-review. J Med Internet Res 2021 Mar 10;23(3):e23365 [FREE Full text] [CrossRef] [Medline]
    29. Reininghaus U. Ambulatorische Interventionen in der Psychiatrie: das Momentum für Veränderung im alltäglichen sozialen. Psychiatr Prax 2018 Mar;45(2):59-61. [CrossRef] [Medline]
    30. Rauschenberg C, Schick A, Goetzl C, Roehr S, Riedel-Heller SG, Koppe G, et al. Social isolation, mental health, and use of digital interventions in youth during the COVID-19 pandemic: a nationally representative survey. Eur Psychiatry 2021 Mar 09;64(1):e20 [FREE Full text] [CrossRef] [Medline]
    31. Csikszentmihalyi M, Larson R. Validity and reliability of the experience-sampling method. J Nerv Ment Dis 1987 Sep;175(9):526-536. [CrossRef] [Medline]
    32. Myin-Germeys I, Birchwood M, Kwapil T. From environment to therapy in psychosis: a real-world momentary assessment approach. Schizophr Bull 2011 Mar;37(2):244-247 [FREE Full text] [CrossRef] [Medline]
    33. Heron KE, Smyth JM. Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments. Br J Health Psychol 2010 Feb;15(Pt 1):1-39 [FREE Full text] [CrossRef] [Medline]
    34. Reininghaus U, Depp CA, Myin-Germeys I. Ecological interventionist causal models in psychosis: targeting psychological mechanisms in daily life. Schizophr Bull 2016 Mar;42(2):264-269 [FREE Full text] [CrossRef] [Medline]
    35. Larsen ME, Huckvale K, Nicholas J, Torous J, Birrell L, Li E, et al. Using science to sell apps: evaluation of mental health app store quality claims. NPJ Digit Med 2019 Mar 22;2:18 [FREE Full text] [CrossRef] [Medline]
    36. Greer B, Robotham D, Simblett S, Curtis H, Griffiths H, Wykes T. Digital exclusion among mental health service users: qualitative investigation. J Med Internet Res 2019 Jan 09;21(1):e11696 [FREE Full text] [CrossRef] [Medline]
    37. Bucci S, Berry N, Morris R, Berry K, Haddock G, Lewis S, et al. "They are not hard-to-reach clients. We have just got hard-to-reach services." Staff views of digital health tools in specialist mental health services. Front Psychiatry 2019 May 10;10:344 [FREE Full text] [CrossRef] [Medline]
    38. Lui JH, Marcus DK, Barry CT. Evidence-based apps? A review of mental health mobile applications in a psychotherapy context. Prof Psychol Res Pr 2017 Jun;48(3):199-210. [CrossRef]
    39. Collip D, Myin-Germeys I, Van Os J. Does the concept of "sensitization" provide a plausible mechanism for the putative link between the environment and schizophrenia? Schizophr Bull 2008 Mar;34(2):220-225 [FREE Full text] [CrossRef] [Medline]
    40. Wichers M, Schrijvers D, Geschwind N, Jacobs N, Myin-Germeys I, Thiery E, et al. Mechanisms of gene-environment interactions in depression: evidence that genes potentiate multiple sources of adversity. Psychol Med 2009 Jul;39(7):1077-1086. [CrossRef] [Medline]
    41. Gilbert P. The origins and nature of compassion focused therapy. Br J Clin Psychol 2014 Mar;53(1):6-41. [CrossRef] [Medline]
    42. Gilbert P. Introducing compassion-focused therapy. Adv Psychiatr Treat 2009;15(3):199-208. [CrossRef]
    43. Gilbert P. Compassion Focused Therapy: Distinctive Features. London, UK: Routledge; 2010.
    44. Cuppage J, Baird K, Gibson J, Booth R, Hevey D. Compassion focused therapy: exploring the effectiveness with a transdiagnostic group and potential processes of change. Br J Clin Psychol 2018 Jun;57(2):240-254. [CrossRef] [Medline]
    45. Heriot-Maitland C, McCarthy-Jones S, Longden E, Gilbert P. Compassion focused approaches to working with distressing voices. Front Psychol 2019 Feb 1;10:152 [FREE Full text] [CrossRef] [Medline]
    46. Leaviss J, Uttley L. Psychotherapeutic benefits of compassion-focused therapy: an early systematic review. Psychol Med 2015 Apr;45(5):927-945 [FREE Full text] [CrossRef] [Medline]
    47. Holmes EA, Blackwell SE, Burnett Heyes S, Renner F, Raes F. Mental imagery in depression: phenomenology, potential mechanisms, and treatment implications. Annu Rev Clin Psychol 2016;12:249-280. [CrossRef] [Medline]
    48. Holmes EA, Mathews A. Mental imagery in emotion and emotional disorders. Clin Psychol Rev 2010 Apr;30(3):349-362. [CrossRef] [Medline]
    49. Pearson J, Naselaris T, Holmes EA, Kosslyn SM. Mental imagery: functional mechanisms and clinical applications. Trends Cogn Sci 2015 Oct;19(10):590-602 [FREE Full text] [CrossRef] [Medline]
    50. Renner F, Ji JL, Pictet A, Holmes EA, Blackwell SE. Effects of engaging in repeated mental imagery of future positive events on behavioural activation in individuals with major depressive disorder. Cognit Ther Res 2017;41(3):369-380 [FREE Full text] [CrossRef] [Medline]
    51. Lincoln TM, Hohenhaus F, Hartmann M. Can paranoid thoughts be reduced by targeting negative emotions and self-esteem? An experimental investigation of a brief compassion-focused intervention. Cogn Ther Res 2012 Aug 14;37(2):390-402. [CrossRef]
    52. Topooco N, Riper H, Araya R, Berking M, Brunn M, Chevreul K, E-COMPARED consortium. Attitudes towards digital treatment for depression: a European stakeholder survey. Internet Interv 2017 Jun;8:1-9 [FREE Full text] [CrossRef] [Medline]
    53. Baumeister H, Reichler L, Munzinger M, Lin J. The impact of guidance on Internet-based mental health interventions — a systematic review. Internet Interv 2014 Oct;1(4):205-215. [CrossRef]
    54. Rauschenberg C, Boecking B, Paetzold I, Schruers K, Schick A, van Amelsvoort T, et al. A compassion-focused ecological momentary intervention for enhancing resilience in help-seeking youth: uncontrolled pilot study. JMIR Ment Health 2021 Aug 05;8(8):e25650 [FREE Full text] [CrossRef] [Medline]
    55. Efficacy of a novel, accessible, transdiagnostic, compassion-focused ecological momentary intervention for enhancing resilience in youth. Deutsches Register Klinischer Studien. 2019.   URL: https://www.drks.de/drks_web/navigate.do?navigation Id=trial.HTML&TRIAL_ID=DRKS00017265 [accessed 2022-09-20]
    56. Schick A, Paetzold I, Rauschenberg C, Hirjak D, Banaschewski T, Meyer-Lindenberg A, et al. Effects of a novel, transdiagnostic, hybrid ecological momentary intervention for improving resilience in youth (EMIcompass): protocol for an exploratory randomized controlled trial. JMIR Res Protoc 2021 Dec 03;10(12):e27462 [FREE Full text] [CrossRef] [Medline]
    57. Agarwal S, LeFevre AE, Lee J, L'Engle K, Mehl G, Sinha C, WHO mHealth Technical Evidence Review Group. Guidelines for reporting of health interventions using mobile phones: mobile health (mHealth) evidence reporting and assessment (mERA) checklist. BMJ 2016 Mar 17;352:i1174. [CrossRef] [Medline]
    58. Hoffmann TC, Glasziou PP, Boutron I, Milne R, Perera R, Moher D, et al. Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. BMJ 2014 Mar 07;348:g1687. [CrossRef] [Medline]
    59. Kessler RC, Andrews G, Colpe LJ, Hiripi E, Mroczek DK, Normand SL, et al. Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol Med 2002 Aug;32(6):959-976. [CrossRef] [Medline]
    60. Goldman HH, Skodol AE, Lave TR. Revising axis V for DSM-IV: a review of measures of social functioning. Am J Psychiatry 1992 Sep;149(9):1148-1156. [CrossRef] [Medline]
    61. First MB, Williams JB, Karg RS, Spitzer RL. Structured clinical interview for DSM-5—Research version (SCID-5 for DSM-5, research version; SCID-5-RV). Arlington, VA, USA: American Psychiatric Association; 2015.
    62. Hamilton M. The assessment of anxiety states by rating. Br J Med Psychol 1959;32(1):50-55. [CrossRef] [Medline]
    63. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry 1960 Feb;23(1):56-62. [CrossRef] [Medline]
    64. Yung AR, Yuen HP, McGorry PD, Phillips LJ, Kelly D, Dell'Olio M, et al. Mapping the onset of psychosis: the Comprehensive Assessment of At-Risk Mental States. Aust N Z J Psychiatry 2005;39(11-12):964-971. [CrossRef] [Medline]
    65. Malla A, Iyer S, McGorry P, Cannon M, Coughlan H, Singh S, et al. From early intervention in psychosis to youth mental health reform: a review of the evolution and transformation of mental health services for young people. Soc Psychiatry Psychiatr Epidemiol 2016 Mar 19;51(3):319-326. [CrossRef] [Medline]
    66. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry 2005 Jun;62(6):593-602. [CrossRef] [Medline]
    67. Derogatis LR. BSI Brief Symptom Inventory. Administration, Scoring, and Procedures Manual. 4th edition. Minneapolis, MN, USA: National Computer Systems; 1993.
    68. Derogatis L, Fitzpatrick M. The SCL-90-R, the Brief Symptom Inventory (BSI), and the BSI-18. In: Maruish ME, editor. Instruments for adults: the use of psychological testing for treatment planning and outcomes assessment. Mahwah, NJ, USA: Lawrence Erlbaum Associates; 2004:1-41.
    69. Shrout PE, Fleiss JL. Intraclass correlations: uses in assessing rater reliability. Psychol Bull 1979 Mar;86(2):420-428. [CrossRef] [Medline]
    70. JASP Team. JASP (Version 0.16.1). JASP. 2022.   URL: https://jasp-stats.org/download/ [accessed 2022-10-03]
    71. Hilsenroth MJ, Ackerman SJ, Blagys MD, Baumann BD, Baity MR, Smith SR, et al. Reliability and validity of DSM-IV axis V. Am J Psychiatry 2000 Nov;157(11):1858-1863. [CrossRef] [Medline]
    72. Hupfeld J, Ruffieux N. Validierung einer deutschen Version der Self-Compassion Scale (SCS-D). Z Klin Psychol Psychother 2011 Apr;40(2):115-123. [CrossRef]
    73. Neff KD. The development and validation of a scale to measure self-compassion. Self Identity 2003 Jul;2(3):223-250. [CrossRef]
    74. Garnefski N, Kraaij V. Cognitive emotion regulation questionnaire – development of a short 18-item version (CERQ-short). Pers Individ Dif 2006 Oct;41(6):1045-1053. [CrossRef]
    75. Garnefski N, Kraaij V, Spinhoven P. CERQ - Manual for the use of the Cognitive Emotion Regulation Questionnaire. Leiderdorp, The Netherlands: DATEC; 2002.
    76. Martins EC, Freire M, Ferreira-Santos F. Examination of adaptive and maladaptive cognitive emotion regulation strategies as transdiagnostic processes: associations with diverse psychological symptoms in college students. Stud Psychol 2016;58(1):59-73.
    77. Horvath AO, Greenberg LS. Development and validation of the Working Alliance Inventory. J Couns Psychol 1989;36(2):223-233. [CrossRef]
    78. Bijleveld CC, van der Kamp LJ, Mooijaart A, Van Der Van Der Kloot WA, Van Der Leeden R, Van Der Burg E. Longitudinal Data Analysis: Designs, Models and Methods. Thousand Oaks, CA, USA: Sage Publications; 1998.
    79. Paetzold I, Schick A, Rauschenberg C, Hirjak D, Banaschewski T, Meyer-Lindenberg A, et al. A hybrid ecological momentary compassion-focused intervention for enhancing resilience in help-seeking youths: findings on baseline characteristics from the EMIcompass trial. Open Science Framework. 2021 Nov 18.   URL: https://osf.io/yxadg [accessed 2022-09-20]
    80. Xiao Z, Kasim A, Higgins S. Same difference? Understanding variation in the estimation of effect sizes from educational trials. Int J Educ Res 2016;77:1-14.
    81. Hoffman L, Stawski RS. Persons as contexts: evaluating between-person and within-person effects in longitudinal analysis. Res Hum Dev 2009;6(2-3):97-120. [CrossRef]
    82. Puhani PA. The treatment effect, the cross difference, and the interaction term in nonlinear “difference-in-differences” models. Econ Lett 2012 Apr;115(1):85-87 [FREE Full text] [CrossRef]
    83. Parker G, Roy K. Adolescent depression: a review. Aust N Z J Psychiatry 2001 Oct;35(5):572-580. [CrossRef] [Medline]
    84. Afifi M. Gender differences in mental health. Singapore Med J 2007 May;48(5):385-391 [FREE Full text] [Medline]
    85. Morgan C, Kirkbride J, Leff J, Craig T, Hutchinson G, McKenzie K, et al. Parental separation, loss and psychosis in different ethnic groups: a case-control study. Psychol Med 2007 Apr;37(4):495-503. [CrossRef] [Medline]
    86. Bhopal R. Is research into ethnicity and health racist, unsound, or important science? BMJ 1997 Jun 14;314(7096):1751-1756 [FREE Full text] [CrossRef] [Medline]
    87. Paetzold I, Hermans KS, Schick A, Nelson B, Velthorst E, Schirmbeck F, EU-GEI High Risk Study, et al. Momentary manifestations of negative symptoms as predictors of clinical outcomes in people at high risk for psychosis: experience sampling study. JMIR Ment Health 2021 Nov 19;8(11):e30309 [FREE Full text] [CrossRef] [Medline]
    88. Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol 2008;4:1-32. [CrossRef] [Medline]
    89. Carlbring P, Andersson G, Cuijpers P, Riper H, Hedman-Lagerlöf E. Internet-based vs. face-to-face cognitive behavior therapy for psychiatric and somatic disorders: an updated systematic review and meta-analysis. Cogn Behav Ther 2018 Jan;47(1):1-18. [CrossRef] [Medline]
    90. Telford C, McCarthy-Jones S, Corcoran R, Rowse G. Experience Sampling Methodology studies of depression: the state of the art. Psychol Med 2012 Jun;42(6):1119-1129. [CrossRef] [Medline]
    91. Kramer I, Simons CJ, Hartmann JA, Menne-Lothmann C, Viechtbauer W, Peeters F, et al. A therapeutic application of the experience sampling method in the treatment of depression: a randomized controlled trial. World Psychiatry 2014 Feb 04;13(1):68-77. [CrossRef] [Medline]
    92. O'Keeffe J, Conway R, McGuire B. A systematic review examining factors predicting favourable outcome in cognitive behavioural interventions for psychosis. Schizophr Res 2017 May;183:22-30. [CrossRef] [Medline]
    93. Hamilton KE, Dobson KS. Cognitive therapy of depression: pretreatment patient predictors of outcome. Clin Psychol Rev 2002 Jul;22(6):875-893. [CrossRef] [Medline]
    94. Lincoln TM, Rief W, Westermann S, Ziegler M, Kesting ML, Heibach E, et al. Who stays, who benefits? Predicting dropout and change in cognitive behaviour therapy for psychosis. Psychiatry Res 2014 May 15;216(2):198-205. [CrossRef] [Medline]
    95. Marsh IC, Chan SW, MacBeth A. Self-compassion and psychological distress in adolescents—a meta-analysis. Mindfulness 2018;9(4):1011-1027. [CrossRef] [Medline]
    96. Berry N, Bucci S, Lobban F. Use of the internet and mobile phones for self-management of severe mental health problems: qualitative study of staff views. JMIR mental health 2017 Nov 1;4(4):e52. [CrossRef] [Medline]
    97. McCartney G, Popham F, McMaster R, Cumbers A. Defining health and health inequalities. Public Health 2019 Jul;172:22-30 [FREE Full text] [CrossRef] [Medline]
    98. Friis-Healy EA, Nagy GA, Kollins SH. It is time to REACT: opportunities for digital mental health apps to reduce mental health disparities in racially and ethnically minoritized groups. JMIR mental health. 2021 2021 Jan 26;8(1):e25456. [CrossRef] [Medline]
    99. Cruz-Jesus F, Vicente M, Bacao F, Oliveira T. The education-related digital divide: an analysis for the EU-28. Comput Human Behav 2016 Mar;56:72-82. [CrossRef]
    100. Haight M, Quan-Haase A, Corbett B. Revisiting the digital divide in Canada: the impact of demographic factors on access to the Internet, level of online activity, and social networking site usage. Inf Commun Soc 2014;17(4):503-519. [CrossRef]
    101. Bailey SC, O'Conor R, Bojarski EA, Mullen R, Patzer RE, Vicencio D, et al. Literacy disparities in patient access and health-related use of Internet and mobile technologies. Health Expect 2015 Dec;18(6):3079-3087 [FREE Full text] [CrossRef] [Medline]
    102. Robotham D, Satkunanathan S, Doughty L, Wykes T. Do we still have a digital divide in mental health? A five-year survey follow-up. J Med Internet Res 2016 Nov 22;18(11):e309. [CrossRef] [Medline]
    103. Rathod S, Phiri P, Harris S, Underwood C, Thagadur M, Padmanabi U. Cognitive behaviour therapy for psychosis can be adapted for minority ethnic groups: a randomised controlled trial. Schizophr Res. (2-3) 2013 Feb;143(2-3):319-326. [CrossRef] [Medline]
    104. Moore GF, Audrey S, Barker M, Bond L, Bonell C, Hardeman W, et al. Process evaluation of complex interventions: Medical Research Council guidance. BMJ 2015 Mar 19;350:h1258. [CrossRef] [Medline]
    105. Wong G, Westhorp G, Manzano A, Greenhalgh J, Jagosh J, Greenhalgh T. RAMESES II reporting standards for realist evaluations. BMC Med 2016 Jun 24;14(1):96 [FREE Full text] [CrossRef] [Medline]
    106. Schueller SM, Hunter JF, Figueroa C, Aguilera A. Use of digital mental health for marginalized and underserved populations. Curr Treat Options Psych 2019 Jul 5;6(3):243-255. [CrossRef]

    ‎
    CFI: compassion-focused intervention
    CHARMS: Clinical High At-Risk Mental State
    EMA: ecological momentary assessment
    EMI: ecological momentary intervention
    ESM: experience sampling methodology
    HiTOP: Hierarchical Taxonomy of Psychopathology
    mHealth: mobile health
    RCT: randomized controlled trial
    TAU: treatment as usual

    Edited by A Mavragani; submitted 13.05.22; peer-reviewed by S Mukherjee, K Abela , J Huber; comments to author 27.06.22; revised version received 19.07.22; accepted 09.08.22; published 04.11.22

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    ©Isabell Paetzold, Anita Schick, Christian Rauschenberg, Dusan Hirjak, Tobias Banaschewski, Andreas Meyer-Lindenberg, Sebastian Butz, Chiara Floesser, Leonie Schueltke, Jan Rasmus Boehnke, Benjamin Boecking, Ulrich Reininghaus. Originally published in JMIR Formative Research (https://formative.jmir.org), 04.11.2022.

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