Patterns of engagement in digital mental health intervention for LGBTQ+ youth: a latent profile analysis

Engagement is a key metric that researchers can use to assess whether participants received the intended dose of a digital health intervention. However, the prevailing approach has predominantly focused on individual paradata metrics, resulting in a fragmented understanding of overall engagement. To address this limitation, our study utilizes person-centered approaches that allow for the simultaneous capture of multiple engagement metrics within imi–a web application specifically designed to support the mental health of lesbian, gay, bisexual, transgender, queer, and other sexual and gender minority youth (LGBTQ+ youth). This person-centered approach enabled us to explore the association between engagement patterns and stress appraisal outcomes within the imi intervention arm. Utilizing latent profile analysis, we classified users into two engagement forms: overall engagement (total number of sessions, pages visited, and external links clicked and their cumulative time spent using imi) and content engagement (number of pages viewed across imi's four core guides: gender, stress, queerness, and stigma). We identified two profiles for each form: a “high engagement” profile and an “average engagement” profile, with the majority of participants assigned to the “average engagement” profile. Our analyses revealed a significant association between overall engagement profiles and stress appraisals, with the “high engagement” profile demonstrating higher challenge appraisals and marginal improvements in threat appraisals compared to the “average engagement” profile. However, no such associations were observed for content engagement profiles and stress appraisal outcomes. The two person-centered approaches used were consistent with prior results utilizing a variable-centered approach, indicating a stronger intervention effect among individuals who exhibit higher engagement in digital health interventions. Although both methods yielded comparable findings, the person-centered approach mitigates concerns related to multi-collinearity and adds additional nuance and context to the study of digital engagement

This Too Shall Pass: Temporal Distance and the Regulation of Emotional Distress

Does the time perspective people adopt when reflecting on stressful events influence how they respond emotionally to these events? If so, through what cognitive pathway(s) does it have this effect? Part 1 of my dissertation examined these questions in a series of seven studies. Studies 1a, 1b, and 2 tested the hypothesis that adopting a distant-future perspective on recent stressors (relative to a near-future or control perspective) reduces emotional distress, examining four potential mediators of this effect. Study 3 built upon the prior studies by investigating whether their findings apply to a new domain and affect longer-term outcomes. Studies 4-6 centered on a key cognitive mechanism that helped to account for the distress-reducing properties of temporal distancing across our first three studies—impermanence focus. Studies 4 and 5 examined whether individual differences in impermanence focus predicted emotional reactions to negative events in a manner similar to adopting a distant-future perspective. Study 6 manipulated impermanence focus to test whether it affected emotional reactions to stressors in a manner parallel to adopting a distant-future perspective. Part 2 of my dissertation examined boundary conditions of the buffering effects of temporal distancing—exploring conditions under which temporal distancing might amplify rather than reduce emotional distress. However, very few moderators of the link between temporal distancing and reduced distress were found, and those that were identified were weak. Taken together, these studies demonstrate that temporal distancing plays an important role in emotional coping with negative events, and that it does so by directing individuals’ attention to the impermanent aspects of these events. Moreover, temporal distancing appears to be a strategy that effectively reduces the distress associated with a wide variety of stressors, for a broad range of individuals

Mobile Phone-Based Mood Ratings Prospectively Predict Psychotherapy Attendance.

Psychotherapy nonattendance is a costly and pervasive problem. While prior research has identified stable patient-level predictors of attendance, far less is known about dynamic (i.e., time-varying) factors. Identifying dynamic predictors can clarify how clinical states relate to psychotherapy attendance and inform effective "just-in-time" interventions to promote attendance. The present study examines whether daily mood, as measured by responses to automated mobile phone-based text messages, prospectively predicts attendance in group cognitive-behavioral therapy (CBT) for depression. Fifty-six Spanish-speaking Latino patients with elevated depressive symptoms (46 women, mean age=50.92years, SD=10.90years), enrolled in a manualized program of group CBT, received daily automated mood-monitoring text messages. Patients' daily mood ratings, message response rate, and delay in responding were recorded. Patients' self-reported mood the day prior to a scheduled psychotherapy session significantly predicted attendance, even after controlling for patients' prior attendance history and age (OR=1.33, 95% CI [1.04, 1.70], p=.02). Positive mood corresponded to a greater likelihood of attendance. Our results demonstrate the clinical utility of automated mood-monitoring text messages in predicting attendance. These results underscore the value of text messaging, and other mobile technologies, as adjuncts to psychotherapy. Future work should explore the use of such monitoring to guide interventions to increase attendance, and ultimately the efficacy of psychotherapy

Mobile Phone-Based Mood Ratings Prospectively Predict Psychotherapy Attendance.

Psychotherapy nonattendance is a costly and pervasive problem. While prior research has identified stable patient-level predictors of attendance, far less is known about dynamic (i.e., time-varying) factors. Identifying dynamic predictors can clarify how clinical states relate to psychotherapy attendance and inform effective "just-in-time" interventions to promote attendance. The present study examines whether daily mood, as measured by responses to automated mobile phone-based text messages, prospectively predicts attendance in group cognitive-behavioral therapy (CBT) for depression. Fifty-six Spanish-speaking Latino patients with elevated depressive symptoms (46 women, mean age=50.92years, SD=10.90years), enrolled in a manualized program of group CBT, received daily automated mood-monitoring text messages. Patients' daily mood ratings, message response rate, and delay in responding were recorded. Patients' self-reported mood the day prior to a scheduled psychotherapy session significantly predicted attendance, even after controlling for patients' prior attendance history and age (OR=1.33, 95% CI [1.04, 1.70], p=.02). Positive mood corresponded to a greater likelihood of attendance. Our results demonstrate the clinical utility of automated mood-monitoring text messages in predicting attendance. These results underscore the value of text messaging, and other mobile technologies, as adjuncts to psychotherapy. Future work should explore the use of such monitoring to guide interventions to increase attendance, and ultimately the efficacy of psychotherapy

Implementing Group CBT for Depression Among Latinos in a Primary Care Clinic.

Depression in low-income Latino populations can be treated using group cognitive behavioral therapy (GCBT). However, effective delivery of GCBT for depression in primary care settings is often impeded by high dropout rates and poor homework adherence. In this study, we describe the structure, processes, and outcomes (including attendance, homework completion, and symptom measures) of GCBT for Spanish-speaking Latino patients with depression in an urban public sector primary care setting. For this study, 96 Latino patients in a primary care clinic participated in at least 1 session of GCBT. Although depressive symptoms among these patients, as measured by the PHQ-9, significantly decreased during treatment, attendance and homework completion were limited. Even with a strategy in place to allow patients to continue in treatment after missing several sessions, 23% of patients dropped out of therapy following their initial session, and approximately half of all patients completed less than 50% (or 8) therapy sessions. Homework was only completed 23% of the time it was checked. Greater session attendance prospectively predicted lower depressive symptoms over time. We discuss potential strategies to increase engagement, treatment effects, and symptom reduction for depression in primary care settings

Automated Text Messaging as an Adjunct to Cognitive Behavioral Therapy for Depression: A Clinical Trial

Background: Cognitive Behavioral Therapy (CBT) for depression is efficacious, but effectiveness is limited when implemented in low-income settings due to engagement difficulties including nonadherence with skill-building homework and early discontinuation of treatment. Automated messaging can be used in clinical settings to increase dosage of depression treatment and encourage sustained engagement with psychotherapy.Objectives: The aim of this study was to test whether a text messaging adjunct (mood monitoring text messages, treatment-related text messages, and a clinician dashboard to display patient data) increases engagement and improves clinical outcomes in a group CBT treatment for depression. Specifically, we aim to assess whether the text messaging adjunct led to an increase in group therapy sessions attended, an increase in duration of therapy attended, and reductions in Patient Health Questionnaire-9 item (PHQ-9) symptoms compared with the control condition of standard group CBT in a sample of low-income Spanish speaking Latino patients.Methods: Patients in an outpatient behavioral health clinic were assigned to standard group CBT for depression (control condition; n=40) or the same treatment with the addition of a text messaging adjunct (n=45). The adjunct consisted of a daily mood monitoring message, a daily message reiterating the theme of that week’s content, and medication and appointment reminders. Mood data and qualitative responses were sent to a Web-based platform (HealthySMS) for review by the therapist and displayed in session as a tool for teaching CBT skills.Results: Intent-to-treat analyses on therapy attendance during 16 sessions of weekly therapy found that patients assigned to the text messaging adjunct stayed in therapy significantly longer (median of 13.5 weeks before dropping out) than patients assigned to the control condition (median of 3 weeks before dropping out; Wilcoxon-Mann-Whitney z=−2.21, P=.03). Patients assigned to the text messaging adjunct also generally attended more sessions (median=6 sessions) during this period than patients assigned to the control condition (median =2.5 sessions), but the effect was not significant (Wilcoxon-Mann-Whitney z=−1.65, P=.10). Both patients assigned to the text messaging adjunct (B=−.29, 95% CI −0.38 to −0.19, z=−5.80, P<.001) and patients assigned to the control conditions (B=−.20, 95% CI −0.32 to −0.07, z=−3.12, P=.002) experienced significant decreases in depressive symptom severity over the course of treatment; however, the conditions did not significantly differ in their degree of symptom reduction.Conclusions: This study provides support for automated text messaging as a tool to sustain engagement in CBT for depression over time. There were no differences in depression outcomes between conditions, but this may be influenced by low follow-up rates of patients who dropped out of treatment

Automated Text Messaging as an Adjunct to Cognitive Behavioral Therapy for Depression: A Clinical Trial

Background: Cognitive Behavioral Therapy (CBT) for depression is efficacious, but effectiveness is limited when implemented in low-income settings due to engagement difficulties including nonadherence with skill-building homework and early discontinuation of treatment. Automated messaging can be used in clinical settings to increase dosage of depression treatment and encourage sustained engagement with psychotherapy.Objectives: The aim of this study was to test whether a text messaging adjunct (mood monitoring text messages, treatment-related text messages, and a clinician dashboard to display patient data) increases engagement and improves clinical outcomes in a group CBT treatment for depression. Specifically, we aim to assess whether the text messaging adjunct led to an increase in group therapy sessions attended, an increase in duration of therapy attended, and reductions in Patient Health Questionnaire-9 item (PHQ-9) symptoms compared with the control condition of standard group CBT in a sample of low-income Spanish speaking Latino patients.Methods: Patients in an outpatient behavioral health clinic were assigned to standard group CBT for depression (control condition; n=40) or the same treatment with the addition of a text messaging adjunct (n=45). The adjunct consisted of a daily mood monitoring message, a daily message reiterating the theme of that week’s content, and medication and appointment reminders. Mood data and qualitative responses were sent to a Web-based platform (HealthySMS) for review by the therapist and displayed in session as a tool for teaching CBT skills.Results: Intent-to-treat analyses on therapy attendance during 16 sessions of weekly therapy found that patients assigned to the text messaging adjunct stayed in therapy significantly longer (median of 13.5 weeks before dropping out) than patients assigned to the control condition (median of 3 weeks before dropping out; Wilcoxon-Mann-Whitney z=−2.21, P=.03). Patients assigned to the text messaging adjunct also generally attended more sessions (median=6 sessions) during this period than patients assigned to the control condition (median =2.5 sessions), but the effect was not significant (Wilcoxon-Mann-Whitney z=−1.65, P=.10). Both patients assigned to the text messaging adjunct (B=−.29, 95% CI −0.38 to −0.19, z=−5.80, P<.001) and patients assigned to the control conditions (B=−.20, 95% CI −0.32 to −0.07, z=−3.12, P=.002) experienced significant decreases in depressive symptom severity over the course of treatment; however, the conditions did not significantly differ in their degree of symptom reduction.Conclusions: This study provides support for automated text messaging as a tool to sustain engagement in CBT for depression over time. There were no differences in depression outcomes between conditions, but this may be influenced by low follow-up rates of patients who dropped out of treatment

Implementing Group CBT for Depression Among Latinos in a Primary Care Clinic.

Depression in low-income Latino populations can be treated using group cognitive behavioral therapy (GCBT). However, effective delivery of GCBT for depression in primary care settings is often impeded by high dropout rates and poor homework adherence. In this study, we describe the structure, processes, and outcomes (including attendance, homework completion, and symptom measures) of GCBT for Spanish-speaking Latino patients with depression in an urban public sector primary care setting. For this study, 96 Latino patients in a primary care clinic participated in at least 1 session of GCBT. Although depressive symptoms among these patients, as measured by the PHQ-9, significantly decreased during treatment, attendance and homework completion were limited. Even with a strategy in place to allow patients to continue in treatment after missing several sessions, 23% of patients dropped out of therapy following their initial session, and approximately half of all patients completed less than 50% (or 8) therapy sessions. Homework was only completed 23% of the time it was checked. Greater session attendance prospectively predicted lower depressive symptoms over time. We discuss potential strategies to increase engagement, treatment effects, and symptom reduction for depression in primary care settings

Self-talk as a regulatory mechanism: How you do it matters

Does the language people use to refer to the self during introspection influence how they think, feel, and behave under social stress? If so, do these effects extend to socially anxious people who are particularly vulnerable to such stress? Seven studies explored these questions (total N = 585). Studies 1a and 1b were proof-of-principle studies. They demonstrated that using non-first-person pronouns and one\u27s own name (rather than first-person pronouns) during introspection enhances self-distancing. Studies 2 and 3 examined the implications of these different types of self-talk for regulating stress surrounding making good first impressions (Study 2) and public speaking (Study 3). Compared with the first-person group, the non-first-person group performed better according to objective raters in both studies. They also displayed less distress (Studies 2 and 3) and engaged in less maladaptive postevent processing (Study 3). Studies 4 and 5 examined how these different forms of self-talk influence the way people appraise social-anxiety-provoking events. They demonstrated that non-first-person language use (compared with first-person language use) leads people to appraise future stressors in more challenging and less threatening terms. Finally, a meta-analysis (Study 6) indicated that none of these findings were moderated by trait social anxiety, highlighting their translational potential. Together, these findings demonstrate that small shifts in the language people use to refer to the self during introspection consequentially influence their ability to regulate their thoughts, feelings, and behavior under social stress, even for vulnerable individuals

Self-talk as a regulatory mechanism: How you do it matters.

Does the language people use to refer to the self during introspection influence how they think, feel, and behave under social stress? If so, do these effects extend to socially anxious people who are particularly vulnerable to such stress? Seven studies explored these questions (total N! 585). Studies 1a and 1b were proof-of-principle studies. They demonstrated that using non-first-person pronouns and one’s own name (rather than first-person pronouns) during introspection enhances self-distancing. Studies 2 and 3 examined the implications of these different types of self-talk for regulating stress surrounding making good first impressions (Study 2) and public speaking (Study 3). Compared with the first-person group, the non-first-person group performed better according to objective raters in both studies. They also displayed less distress (Studies 2 and 3) and engaged in less maladaptive postevent processing (Study 3). Studies 4 and 5 examined how these different forms of self-talk influence the way people appraise social-anxiety-provoking events. They demonstrated that non-first-person language use (compared with first-person language use) leads people to appraise future stressors in more challenging and less threatening terms. Finally, a meta-analysis (Study 6) indicated that none of these findings were moderated by trait social anxiety, highlighting their translational potential. Together, these findings demonstrate that small shifts in the language people use to refer to the self during introspection consequentially influence their ability to regulate their thoughts, feelings, and behavior under social stress, even for vulnerable individuals