The ENGAGE-2 study: Engaging self-regulation targets to understand the mechanisms of behavior change and improve mood and weight outcomes in a randomized controlled trial (Phase 2)

A qualitative exploration of young adults' perceptions of a new intervention for alcohol use disorder

BACKGROUND

Due to the rising prevalence of alcohol use disorders among young adults, the need for effective and accessible interventions has become increasingly imperative. In acknowledgment of this issue, we developed a novel intervention known as contingency management plus problem solving therapy (CM-PST). The aim of the current study was to gain insight into the perspectives on the effectiveness of the newly developed CM-PST using focus group discussion among young adults who consume alcohol regularly.

MATERIALS AND METHODS

The study employed a qualitative research design, utilizing focus group discussions as the primary data collection method. Participants described their perceptions regarding the newly developed CM-PST. Semi-structured focus group sessions were conducted via Zoom in November 2022. A total of 19 young adults, aged 18-24 years old, participated in five focus group sessions. Data were analyzed using deductive content analysis.

RESULTS

Participants demonstrated overall positive attitudes toward the novel intervention, recognizing the potential benefits, it could offer in terms of alcohol use reduction and emotional well-being. They emphasized the importance of incentives in motivating behavioral changes, as well as the practicality of problem-solving techniques in addressing everyday challenges. Additionally, participants provided valuable insights into potential barriers and implementation challenges, highlighting the need for flexible and personalized approaches to accommodate individual preferences and needs.

CONCLUSIONS

The results of this study contribute to the growing body of literature on innovative intervention approaches for young adults facing alcohol use issues. The findings shed light on the acceptability and perceived effectiveness of the CM-PST intervention from the perspective of the target population.

Mediating Effects of Neural Targets on Depression, Weight, and Anxiety Outcomes of an Integrated Collaborative Care Intervention: The ENGAGE-2 Mechanistic Pilot Randomized Clinical Trial

Background

Integrated treatments for comorbid depression (often with anxiety) and obesity are lacking; mechanisms are poorly investigated.

Methods

In a mechanistic pilot trial, adults with body mass index ≥30 and Patient Health Questionnaire-9 scores ≥10 were randomized to usual care (n = 35) or an integrated behavioral intervention (n = 71). Changes at 6 months in body mass index and Depression Symptom Checklist-20 scores were co-primary outcomes, and Generalized Anxiety Disorder Scale-7 score was a secondary outcome. Changes at 2 months in the activation and functional connectivity of regions of interest in the negative affect circuit were primary neural targets, and secondary targets were in the cognitive control, default mode, and positive affect circuits.

Results

Participants were 47.0 years (SD = 11.9 years), 76% women, 55% Black, and 20% Latino. Depression Symptom Checklist-20 (between-group difference, -0.3 [95% CI: -0.6 to -0.1]) and Generalized Anxiety Disorder Scale-7 (-2.9 [-4.7 to -1.1]) scores, but not body mass index, decreased significantly at 6 months in the intervention versus usual care groups. Only Generalized Anxiety Disorder Scale-7 score changes at 6 months significantly correlated with neural target changes at 2 months in the negative affect (anterior insula, subgenual/pregenual anterior cingulate cortex, amygdala) and cognitive control circuits (dorsal lateral prefrontal cortex, dorsal anterior cingulate cortex). Effects were medium to large (0.41-1.18 SDs). Neural target changes at 2 months in the cognitive control circuit only differed by treatment group. Effects were medium (0.58-0.79 SDs).

Conclusions

Compared with usual care, the study intervention led to significantly improved depression but not weight loss, and the results on neural targets were null for both outcomes. The significant intervention effect on anxiety might be mediated through changes in the cognitive control circuit, but this warrants replication.

Affective neural circuits and inflammatory markers linked to depression and anxiety symptoms in patients with comorbid obesity

Although we have effective treatments for depression and anxiety, we lack mechanistic understanding or evidence-based strategies to tailor these treatments in the context of major comorbidities such as obesity. The current feasibility study uses functional neuroimaging and biospecimen data to determine if changes in inflammatory markers, fecal short-chain fatty acids, and neural circuit-based targets can predict depression and anxiety outcomes among participants with comorbid obesity. Blood and stool samples and functional magnetic resonance imaging data were obtained at baseline and 2 months, during the parent ENGAGE-2 trial. From 30 participants with both biospecimen and fMRI data, this subsample study explored the relationship among changes in inflammatory markers and fecal short-chain fatty acids and changes in neural targets, and their joint relationship with depression and anxiety symptoms. Bivariate and partial correlation, canonical correlation, and partial least squares analyses were conducted, with adjustments for age, sex, and treatment group. Initial correlation analyses revealed three inflammatory markers (IL-1RA, IL-6, and TNF-α) and five neural targets (in Negative Affect, Positive Affect, and Default Mode Circuits) with significantly associated changes at 2 months. Partial least squares analyses then showed that changes in IL-1RA and TNF-α and changes in three neural targets (in Negative Affect and Positive Affect Circuits) at 2 months were associated with changes in depression and anxiety symptoms at 6 months. This study sheds light on the plausibility of incorporation of inflammatory and gastrointestinal biomarkers with neural targets as predictors of depression and comorbid anxiety outcomes among patients with obesity.

Associations between fecal short-chain fatty acids, plasma inflammatory cytokines, and dietary markers with depression and anxiety: Post hoc analysis of the ENGAGE-2 pilot trial

BACKGROUND

The microbiome-gut-brain-axis (MGBA) is emerging as an important mechanistic link between diet and mental health. The role of significant modifiers of the MGBA, including gut microbial metabolites and systemic inflammation, in individuals comorbid with obesity and mental disorders, is under-investigated.

OBJECTIVES

This exploratory analysis examined associations among microbial metabolites-fecal SCFAs, plasma inflammatory cytokines, and diet with depression and anxiety scores in adults comorbid with obesity and depression.

METHODS

Stool and blood were obtained from a subsample (n = 34) of participants enrolled in an integrated behavioral intervention for weight loss and depression. Pearson partial correlation and multivariate analyses determined associations among changes in fecal SCFAs (propionic, butyric, acetic, and isovaleric acids), plasma cytokines [C-reactive protein, interleukin 1 beta, interleukin 1 receptor antagonist (IL-1RA), interleukin 6, and TNF-α], and 35 dietary markers over 2 mo, and changes in SCL-20 (Depression Symptom Checklist 20-item) and GAD-7 (Generalized Anxiety Disorder 7-Item) scores over 6 mo.

RESULTS

Changes in the SCFAs and TNF-α at 2 mo were positively associated (standardized coefficients: 0.06-0.40; 0.03-0.34) with changes in depression and anxiety scores at 6 mo, whereas changes in IL-1RA at 2 mo were inversely associated (standardized coefficients: -0.24; -0.05). After 2 mo, changes in 12 dietary markers, including animal protein, were associated with changes in SCFAs, TNF-α, or IL-1RA at 2 mo (standardized coefficients: -0.27 to 0.20). Changes in 11 dietary markers, including animal protein, at 2 mo were associated with changes in depression or anxiety symptom scores at 6 mo (standardized coefficients: -0.24 to 0.20; -0.16 to 0.15).

CONCLUSIONS

Gut microbial metabolites and systemic inflammation may be biomarkers of importance within the MGBA, linking dietary markers, such as animal protein intake, to depression and anxiety for individuals with comorbid obesity. These findings are exploratory and warrant replication.

Association of Changes in Neural Targets and Dietary Outcomes among Patients with Comorbid Obesity and Depression: Post hoc Analysis of ENGAGE-2 Mechanistic Clinical Trial

BACKGROUND

Disruptions in brain circuits that regulate cognition and emotion can hinder dietary change and weight loss among individuals with obesity and depression.

OBJECTIVE

The study aimed to investigate whether changes in brain targets in the cognitive control, negative affect, and positive affect circuits after 2-mo problem-solving therapy (PST) predict changes in dietary outcomes at 2 and 6 mo.

METHODS

Adults with obesity and depression from an academic health system were randomly assigned to receive PST (7-step problem-solving and behavioral activation strategies) over 2 mo or usual care. Seventy participants (mean age = 45.9 ± 11.6 y; 75.7% women, 55.7% Black, 17.1% Hispanic, 20.0% White; mean BMI = 36.5 ± 5.3 kg/m2; mean Patient Health Questionnaire-9 depression score = 12.7 ± 2.8) completed functional MRI and 24-h food recalls. Ordinary least square regression analyses were performed.

RESULTS

Among intervention participants, increased left dorsal lateral prefrontal cortex (dLPFC) activity of the cognitive control circuit at 2 mo was associated with increased diet quality (β: 0.20; 95% CI: -0.02, 0.42) and decreased calories (β: -0.19; 95% CI: -0.33, -0.04), fat levels (β: -0.22; 95% CI: -0.39, -0.06), and high-sugar food intake (β: -0.18; 95% CI: -0.37, 0.01) at 6 mo. For the negative affect circuit, increased right dLPFC-amygdala connectivity at 2 mo was associated with increased diet quality (β: 0.32; 95% CI: -0.93, 1.57) and fruit and vegetable intake (β: 0.38; 95% CI: -0.75, 1.50) and decreased calories (β: -0.37; 95% CI: -1.29, 0.54), fat levels (β: -0.37; 95% CI: -1.50, 0.76), sodium concentrations (β: -0.36; 95% CI: -1.32, 0.60), and alcohol intake (β: -0.71; 95% CI: -2.10, 0.68) at 2 but not at 6 mo. The usual care group showed opposing associations. The 95% CIs of all between-group differences did not overlap the null, suggesting a significant treatment effect.

CONCLUSIONS

Among adults with obesity and depression who underwent PST compared with those under usual care, improved dLPFC-amygdala regulation of negative affective brain states predicted dietary improvements at 2 mo, whereas improvements in dLPFC-based cognitive control predicted dietary improvements at 6 mo. These findings warrant confirmatory studies. This trial was at clinicaltrials.gov as NCT03841682.

Associations between daily step count trajectories and clinical outcomes among adults with comorbid obesity and depression

Purpose

To examine the relationship between features of daily measured step count trajectories and clinical outcomes among people with comorbid obesity and depression in the ENGAGE-2 Trial.

Methods

This post hoc analysis used data from the ENGAGE-2 trial where adults (n=106) with comorbid obesity (BMI ≥30.0 or 27.0 if Asian) and depressive symptoms (Patient Health Questionnaire-9 score ≥10) were randomized (2:1) to receive the experimental intervention or usual care. Daily step count trajectories over the first 60 days (Fitbit Alta HR) were characterized using functional principal component analyses. 7-day and 30-day trajectories were also explored. Functional principal component scores that described features of step count trajectories were entered into linear mixed models to predict weight (kg), depression (Symptom Checklist-20), and anxiety (Generalized Anxiety Disorder Questionnaire-7) at 2-months (2M) and 6-months (6M).

Results

Features of 60-day step count trajectories were interpreted as overall sustained high, continuous decline, and disrupted decline. Overall sustained high step count was associated with low anxiety (2M, β=-0.78, p<.05; 6M, β=-0.80, p<.05) and low depressive symptoms (6M, β=-0.15, p<.05). Continuous decline in step count was associated with high weight (2M, β=0.58, p<.05). Disrupted decline was not associated with clinical outcomes at 2M or 6M. Features of 30-day step count trajectories were also associated with weight (2M, 6M), depression (6M), and anxiety (2M, 6M); Features of 7-day step count trajectories were not associated with weight, depression, or anxiety at 2M or 6M.

Conclusions

Features of step count trajectories identified using functional principal component analysis were associated with depression, anxiety, and weight outcomes among adults with comorbid obesity and depression. Functional principal component analysis may be a useful analytic method that leverages daily measured physical activity levels to allow for precise tailoring of future behavioral interventions.

Integrated collaborative care intervention for depression and obesity in primary care: translation from research to practice

The objective of this study was to present lessons learned about engagement, delivery modality and pandemic impact while delivering a collaborative care intervention with a socioeconomically, racially and ethnically diverse sample. Participants completed a post-intervention survey (n = 41) on experiences and preferred intervention delivery modality, coronavirus 2019 (COVID-19) Impact Survey (n = 50) and provided open-ended feedback about the intervention (n = 27). Intervention process data included attendance, modality, and withdrawals. Data were analyzed using descriptive statistics and inductive content analyses. Of 71 intervention participants, 6 (8%) withdrew before session 1. Completers adhered to intervention timeline better than withdrawals. Participants liked the in-person interaction, efficient coach support, accountability of in-person and Zoom vs. phone sessions and the flexibility and convenience of phone and Zoom vs. in-person sessions. A majority of participants reported experiencing pandemic impacts such as heightened emotional distress, decreased activity engagement, poorer eating behaviors and being unable to meet basic needs. Participants deviating from intervention timelines may be re-engaged by targeted outreach attempts. Videoconference has the potential for providing as-needed coaching. Future interventions may be optimized to account for and address areas impacted by the pandemic. Findings revealed specific strategies that can be implemented in future interventions to improve emotional and physical health among diverse populations.

Cross-trial prediction of depression remission using problem-solving therapy: A machine learning approach

BACKGROUND

Psychotherapy is a standard depression treatment; however, determining a patient's prognosis with therapy relies on clinical judgment that is subject to trial-and-error and provider variability.

PURPOSE

To develop machine learning (ML) algorithms to predict depression remission for patients undergoing 6 months of problem-solving therapy (PST).

METHOD

Using data from the treatment arm of 2 randomized trials, ML models were trained and validated on ENGAGE-2 (ClinicalTrials.gov, #NCT03841682) and tested on RAINBOW (ClinicalTrials.gov, #NCT02246413) for predictions at baseline and at 2-months. Primary outcome was depression remission using the Depression Symptom Checklist (SCL-20) score < 0.5 at 6 months. Predictor variables included baseline characteristics (sociodemographic, behavioral, clinical, psychosocial) and intervention engagement through 2-months.

RESULTS

Of the 26 candidate variables, 8 for baseline and 11 for 2-months were predictive of depression remission, and used to train the models. The best-performing model predicted remission with an accuracy significantly greater than chance in internal validation using the ENGAGE-2 cohort, at baseline [72.6% (SD = 3.6%), p < 0.0001] and at 2-months [72.3% (5.1%), p < 0.0001], and in external validation with the RAINBOW cohort at baseline [58.3% (0%), p < 0.0001] and at 2-months [62.3% (0%), p < 0.0001]. Model-agnostic explanations highlighted key predictors of depression remission at the cohort and patient levels, including female sex, lower self-reported sleep disturbance, lower sleep-related impairment, and lower negative problem orientation.

CONCLUSIONS

ML models using clinical and patient-reported data can predict depression remission for patients undergoing PST, affording opportunities for prospective identification of likely responders, and for developing personalized early treatment optimization along the patient care trajectory.

Association of COVID-19 impact with outcomes of an integrated obesity and depression intervention: Posthoc analysis of an RCT

OBJECTIVE

To examine the association between COVID-19 impact and clinical outcomes of an integrated collaborative care intervention for adults with obesity and comorbid depression.

METHODS

Latent class analysis identified clusters of self-reported COVID-19 impact. Cluster characteristics were examined using Fishers' least significant difference method and canonical discriminant analysis. Intervention vs. usual care effects on primary (body mass index [BMI], depressive symptoms) and secondary (anxiety symptoms and other psychosocial) outcomes stratified by cluster were examined using linear mixed models.

RESULTS

Three clusters were identified: mental health and sleep impact (cluster 1, n = 37), economic impact (cluster 2, n = 18), and less overall impact (cluster 3, n = 20). Clusters differed in age, income, diet, and baseline coping skills. The intervention led to improvements across several health outcomes compared with usual care, with medium to large effects on functional impairments (standardized mean difference, -0.7 [95% CI: -1.3, -0.1]) in cluster 1, depressive symptoms (-1.1 [95% CI: -2.0, -0.1]) and obesity-related problems (-1.6 [95% CI: -2.8, -0.4]) in cluster 2, and anxiety (-1.1 [95% CI: -1.9, -0.3]) in cluster 3.

CONCLUSIONS

People with obesity and comorbid depression may have varied intervention responses based on COVID-19 impact. Interventions tailored to specific COVID-19 impact clusters may restore post-pandemic health.

Problem-solving therapy-induced amygdala engagement mediates lifestyle behavior change in obesity with comorbid depression: a randomized proof-of-mechanism trial

BACKGROUND

Depression hinders obesity treatment; elucidating mechanisms may enable treatment enhancements.

OBJECTIVES

The aim was to investigate whether changes in neural targets in the negative affect circuit following psychotherapy mediate subsequent changes in weight and behaviors.

METHODS

Adults (n = 108) with obesity and depression were randomly assigned to usual care or an intervention that delivered problem-solving therapy (PST) for depression over 2 mo. fMRI for brain imaging was performed at baseline and 2 mo. BMI, physical activity, and diet were measured at baseline and 12 mo. Mediation analysis assessed between-group differences in neural target changes using t test and correlations between neural target changes and outcome changes (simple and interaction effect) using ordinary least-squares regression.

RESULTS

Compared with usual care, PST led to reductions in left amygdala activation (-0.75; 95% CI: -1.49, -0.01) and global scores of the negative affect circuit (-0.43; -0.81, -0.06), engaged by threat stimuli. Increases in amygdala activation and global circuit scores at 2 mo correlated with decreases in physical activity outcomes at 12 mo in the usual-care group; these relations were altered by PST. In relation to change in leisure-time physical activity, standardized β-coefficients were -0.67 in usual care and -0.01 in the intervention (between-group difference: 0.66; 0.02, 1.30) for change in left amygdala activation and -2.02 in usual care and -0.11 in the intervention (difference: 1.92; 0.64, 3.20) for change in global circuit scores. In relation to change in total energy expenditure, standardized β-coefficients were -0.65 in usual care and 0.08 in the intervention (difference: 0.73; 0.29, 1.16) for change in left amygdala activation and -1.65 in usual care and 0.08 in the intervention (difference: 1.74; 0.85, 2.63) for change in global circuit scores. Results were null for BMI and diet.

CONCLUSIONS

Short-term changes in the negative affect circuit engaged by threat stimuli following PST for depression mediated longer-term changes in physical activity. This trial was registered at www.clinicaltrials.gov as NCT02246413 (https://clinicaltrials.gov/ct2/show/NCT02246413).

Seeking a pot of gold with integrated behavior therapy and research to improve health equity: insights from the RAINBOW trial for obesity and depression

More than one third of adults in the United States (U.S.) live with multiple chronic conditions that affect their physical and mental health, functional outcomes, independence, and mortality. The COVID-19 pandemic has exposed not only an increased risk for infection, morbidity, and mortality among those with chronic conditions but long-standing health inequities by age, race, sex, and other social determinants. Obesity plus depression represent one such prevalent comorbidity for which few effective integrated interventions exist, prompting concern about the potential for secondary physical and mental health pandemics post COVID-19. Translational behavioral medicine research can play an important role in studying integrated collaborative healthcare approaches and advancing scientific understanding on how to engage and more effectively treat diverse populations with physical and mental health comorbidities. The RAINBOW (Research Aimed at Improving Both Mood and Weight) clinical trial experience offers a wealth of insights into the potential of collaborative care interventions to advance behavior therapy research and practice. Primary care patients with co-occurring obesity and depression were assigned to either Integrated Coaching for Mood and Weight (I-CARE), which blended Group Lifestyle Balance (GLB) for weight management and the Program to Encourage Active Rewarding Lives (PEARLS) for depression, or usual care, to examine clinical, cost-effectiveness, and implementation outcomes. This commentary highlights the empirical findings of eight RAINBOW research papers and discusses implications for future studies, including their relevance in the U.S. COVID-19 context. Organized by key principles of translational behavioral medicine research, the commentary aims to examine and embrace the heterogeneity of baseline and intervention response differences among those living with multiple chronic conditions. We conclude that to prevent health and healthcare disparities from widening further, tailored engagement, dissemination, and implementation strategies and flexible delivery formats are essential to improve treatment access and outcomes among underrepresented populations.

Future directions of the National Institutes of Health Science of Behavior Change Program

Background

The National Institutes of Health Science of Behavior Change Common Fund Program has accelerated the investigation of mechanisms of behavior change applicable to multiple health behaviors and outcomes and facilitated the use of the experimental medicine approach to behavior change research.

Purpose

This commentary provides a brief background of the program, plans for its next phase, and thoughts about how the experimental medicine approach to behavior change research can inform future directions in two areas of science—reproductive health and COVID-19 vaccine uptake.

Conclusions

The incorporation of a mechanisms-based approach into behavior intervention research offers new opportunities for improving health.