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 (ß: 0.20; 95% CI: -0.02, 0.42) and decreased calories (ß: -0.19; 95% CI: -0.33, -0.04), fat levels (ß: -0.22; 95% CI: -0.39, -0.06), and high-sugar food intake (ß: -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 (ß: 0.32; 95% CI: -0.93, 1.57) and fruit and vegetable intake (ß: 0.38; 95% CI: -0.75, 1.50) and decreased calories (ß: -0.37; 95% CI: -1.29, 0.54), fat levels (ß: -0.37; 95% CI: -1.50, 0.76), sodium concentrations (ß: -0.36; 95% CI: -1.32, 0.60), and alcohol intake (ß: -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.

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.

Association of physical activity levels and brain white matter in older Latino adults.

OBJECTIVE: Investigate the associations between self-reported physical activity (PA) engagement and white matter (WM) health (i.e. volume, integrity, and hyperintensities) in older Latinos. DESIGN: Cross-sectional study with community-dwelling older adults from predominantly Latino neighborhoods. Participants: Thirty-four cognitively healthy older Latinos from two different cohorts. Measurements: Participants self-reported demographic information, PA engagement [Community Healthy Activities Model Program for Seniors (CHAMPS) Physical Activity Questionnaire for Older Adults] and magnetic resonance imaging (MRI). We used high-resolution three-dimensional T1- and T2-FLAIR weighted images and diffusion tensor imaging acquired via 3 T MRI. We performed a series of hierarchical linear regression models with the addition of relevant covariates to examine the associations between self-reported PA levels and WM volume, integrity, and hyperintensities (separately). We adjusted p-values with the use of the Benjamini-Hochberg's false discovery rate procedure. RESULTS: Higher reported levels of leisure-time moderate-to-vigorous PA were significantly associated with higher WM volume of the posterior cingulate (ß = 0.220, SE = 0.125, 95% CI 0.009-0.431, p = 0.047) and isthmus cingulate (ß = 0.212, SE = 0.110, 95% CI 0.001-0.443, p = 0.044) after controlling for intracranial volume. Higher levels of total PA were significantly associated with higher overall WM volume of these same regions (posterior cingulate: ß = 0.220, SE = 0.125, CI 0.024-0.421, p = 0.046; isthmus cingulate: ß = 0.220, SE = 0.125, 95% CI 0.003-0.393; p = 0.040). Significant p-values did not withstand Benjamini-Hochberg's adjustment. PA was not significantly associated with WM integrity or WM hyperintensities. CONCLUSION: Higher levels of PA, particularly higher leisure-time moderate-to-vigorous PA, might be associated with greater WM volume in select white matter regions key to brain network integration for physical and cognitive functioning in older Latinos. More research is needed to further confirm these associations.

Sex Moderates Treatment Effects of Integrated Collaborative Care for Comorbid Obesity and Depression: The RAINBOW RCT.

BACKGROUND: Sex influences health and related behaviors due to biological and psychosocial/socioeconomic factors. Assessing sex-specific responses to integrated treatment for comorbid obesity and depression could inform intervention targeting. PURPOSE: To test (a) whether sex moderates the effects of integrated collaborative care on weight and depression outcomes through 24 months and (b) whether treatment response at 6 months predicts 12 and 24 month outcomes by sex. METHODS: Secondary data analyses on weight and depression severity (SCL-20) measured over 24 months among 409 adults with obesity and depression in the Research Aimed at Improving Both Mood and Weight trial. RESULTS: Men achieved significantly greater weight reductions in intervention versus usual care than women, whereas women achieved significantly greater percentage reductions in SCL-20 than men at both 12 and 24 months. In logistic models, at 80% specificity for correctly identifying participants not achieving clinically significant long-term outcomes, women who lost <3.0% weight and men who lost <4.1% weight at 6 months had ≥84% probability of not meeting 5% weight loss at 24 months. Similarly, at 80% specificity, women who reduced SCL-20 by <39.5% and men who reduced by <53.0% at 6 months had ≥82% probability of not meeting 50% decrease in SCL-20 at 24 months. CONCLUSIONS: Sex modified the integrated treatment effects for obesity and depression. Sex-specific responses at 6 months predicted clinically significant weight loss and depression outcomes through 24 months. Based on early responses, interventions may need to be tailored to address sex-specific barriers and facilitators to achieving healthy weight and depression outcomes at later time points. CLINICAL TRIAL REGISTRATION: NCT02246413 (https://clinicaltrials.gov/ct2/show/NCT02246413).

Beyond non-inferior: how telepsychiatry technologies can lead to superior care.

Telepsychiatry refers to the use of technology to support the remote provision of psychiatric services. Discussions of this technology have often focussed on the use of video conferencing in place of in-person visits and how such care is found to be non-inferior to traditional care. New developments in the fields of remote-sensing and digital phenotyping have the potential to overcome the limitations inherent in remote visits as well as the limitations of current outpatient care models more generally. Such technologies may enable the collection of more relevant, objective clinical data which could lead to improved care quality and transformed care delivery models. The development and implementation of these new technologies raise important ethical questions.

A novel biomarker selection method using multimodal neuroimaging data.

Identifying biomarkers is essential to obtain the optimal therapeutic benefit while treating patients with late-life depression (LLD). We compare LLD patients with healthy controls (HC) using resting-state functional magnetic resonance and diffusion tensor imaging data to identify neuroimaging biomarkers that may be potentially associated with the underlying pathophysiology of LLD. We implement a Bayesian multimodal local false discovery rate approach for functional connectivity, borrowing strength from structural connectivity to identify disrupted functional connectivity of LLD compared to HC. In the Bayesian framework, we develop an algorithm to control the overall false discovery rate of our findings. We compare our findings with the literature and show that our approach can better detect some regions never discovered before for LLD patients. The Hub of our discovery related to various neurobehavioral disorders can be used to develop behavioral interventions to treat LLD patients who do not respond to antidepressants.

Using a Novel Digital Go/No-Go to Dissociate Intra-subject Temporal Fluctuations in Reaction Time and Accuracy.

Impulsivity can be a risk factor for serious complications for those with mood disorders. To understand intra-individual impulsivity variability, we analyzed longitudinal data of a novel gamified digital Go/No-Go (GNG) task in a clinical sample (n=43 mood disorder participants, n=17 healthy controls) and an open-science sample (n=121, self-reported diagnoses). With repeated measurements within-subject, we disentangled two aspects of GNG: reaction time and accuracy in response inhibition (i.e., incorrect No-Go trials) with respect to diurnal and potential learning effects. Mixed-effects models showed diurnal effects in reaction time but not accuracy, with a significant effect of hour on reaction time in the clinical sample and the open-science sample. Moreover, subjects improved on their response inhibition but not reaction time. Additionally, significant interactions emerged between depression symptom severity and time-of-day in both samples, supporting that repeated administration of our GNG task can yield mood-dependent circadian rhythm-aware biomarkers of neurocognitive function.

Study of a PST-trained voice-enabled artificial intelligence counselor for adults with emotional distress (SPEAC-2): Design and methods.

BACKGROUND: Novel and scalable psychotherapies are urgently needed to address the depression and anxiety epidemic. Leveraging artificial intelligence (AI), a voice-based virtual coach named Lumen was developed to deliver problem solving treatment (PST). The first pilot trial showed promising changes in cognitive control measured by functional neuroimaging and improvements in depression and anxiety symptoms. METHODS: To further validate Lumen in a 3-arm randomized clinical trial, 200 participants with mild-to-moderate depression and/or anxiety will be randomly assigned in a 2:1:1 ratio to receive Lumen-coached PST, human-coached PST as active treatment comparison, or a waitlist control condition where participants can receive Lumen after the trial period. Participants will be assessed at baseline and 18 weeks. The primary aim is to confirm neural target engagement by testing whether compared with waitlist controls, Lumen participants will show significantly greater improvements from baseline to 18 weeks in the a priori neural target for cognitive control, right dorsal lateral prefrontal cortex engaged by the go/nogo task (primary superiority hypothesis). A secondary hypothesis will test whether compared with human-coached PST participants, Lumen participants will show equivalent improvements (i.e., noninferiority) in the same neural target from baseline to 18 weeks. The second aim is to examine (1) treatment effects on depression and anxiety symptoms, psychosocial functioning, and quality of life outcomes, and (2) relationships of neural target engagement to these patient-reported outcomes. CONCLUSIONS: This study offers potential to improve the reach and impact of psychotherapy, mitigating access, cost, and stigma barriers for people with depression and/or anxiety. CLINICALTRIALS: gov #: NCT05603923.

Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studies.

In this article, we developed a Bayesian multimodal model to detect biomarkers (or neuromarkers) using resting-state functional and structural data while comparing a late-life depression group with a healthy control group. Biomarker detection helps determine a target for treatment intervention to get the optimal therapeutic benefit for treatment-resistant patients. The borrowing strength of the structural connectivity has been quantified for functional activity while detecting the biomarker. In the biomarker searching process, thousands of hypotheses are generated and tested simultaneously using our novel method to control the false discovery rate for small samples. Several existing statistical approaches, frequently used in analyzing neuroimaging data have been investigated and compared via simulation with the proposed approach to show its excellent performance. Results are illustrated with a live data set generated in a late-life depression study. The role of detected biomarkers in terms of cognitive function has been explored.

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.

Association Between User Interaction and Treatment Response of a Voice-Based Coach for Treating Depression and Anxiety: Secondary Analysis of a Pilot Randomized Controlled Trial.

BACKGROUND: The quality of user interaction with therapeutic tools has been positively associated with treatment response; however, no studies have investigated these relationships for voice-based digital tools. OBJECTIVE: This study evaluated the relationships between objective and subjective user interaction measures as well as treatment response on Lumen, a novel voice-based coach, delivering problem-solving treatment to patients with mild to moderate depression or anxiety or both. METHODS: In a pilot trial, 42 adults with clinically significant depression (Patient Health Questionnaire-9 [PHQ-9]) or anxiety (7-item Generalized Anxiety Disorder Scale [GAD-7]) symptoms or both received Lumen, a voice-based coach delivering 8 problem-solving treatment sessions. Objective (number of conversational breakdowns, ie, instances where a participant's voice input could not be interpreted by Lumen) and subjective user interaction measures (task-related workload, user experience, and treatment alliance) were obtained for each session. Changes in PHQ-9 and GAD-7 scores at each ensuing session after session 1 measured the treatment response. RESULTS: Participants were 38.9 (SD 12.9) years old, 28 (67%) were women, 8 (19%) were Black, 12 (29%) were Latino, 5 (12%) were Asian, and 28 (67%) had a high school or college education. Mean (SD) across sessions showed breakdowns (mean 6.5, SD 4.4 to mean 2.3, SD 1.8) decreasing over sessions, favorable task-related workload (mean 14.5, SD 5.6 to mean 17.6, SD 5.6) decreasing over sessions, neutral-to-positive user experience (mean 0.5, SD 1.4 to mean 1.1, SD 1.3), and high treatment alliance (mean 5.0, SD 1.4 to mean 5.3, SD 0.9). PHQ-9 (Ptrend=.001) and GAD-7 scores (Ptrend=.01) improved significantly over sessions. Treatment alliance correlated with improvements in PHQ-9 (Pearson r=-0.02 to -0.46) and GAD-7 (r=0.03 to -0.57) scores across sessions, whereas breakdowns and task-related workload did not. Mixed models showed that participants with higher individual mean treatment alliance had greater improvements in PHQ-9 (ß=-1.13, 95% CI -2.16 to -0.10) and GAD-7 (ß=-1.17, 95% CI -2.13 to -0.20) scores. CONCLUSIONS: The participants had fewer conversational breakdowns and largely favorable user interactions with Lumen across sessions. Conversational breakdowns were not associated with subjective user interaction measures or treatment responses, highlighting how participants adapted and effectively used Lumen. Individuals experiencing higher treatment alliance had greater improvements in depression and anxiety. Understanding treatment alliance can provide insights on improving treatment response for this new delivery modality, which provides accessibility, flexibility, comfort with disclosure, and cost-related advantages compared to conventional psychotherapy. TRIAL REGISTRATION: ClinicalTrials.gov NCT04524104; https://clinicaltrials.gov/study/NCT04524104.

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.

Experimental Therapeutics: Opportunities and Challenges Stemming From the National Institute of Mental Health Workshop on Novel Target Discovery and Psychosocial Intervention Development.

There has been slow progress in the development of interventions that prevent and/or reduce mental-health morbidity and mortality. The National Institute of Mental Health (NIMH) launched an experimental-therapeutics initiative with the goal of accelerating the development of effective interventions. The emphasis is on interventions designed to engage a target mechanism. A target mechanism is a process (e.g., behavioral, neurobiological) proposed to underlie change in a defined clinical endpoint and through change in which an intervention exerts its effect. This article is based on discussions from an NIMH workshop conducted in February 2020 and subsequent conversations among researchers using this approach. We discuss the components of an experimental-therapeutics approach such as clinical-outcome selection, target definition and measurement, intervention design and selection, and implementation of a team-science strategy. We emphasize the important contributions of different constituencies (e.g., patients, caregivers, providers) in deriving hypotheses about novel target mechanisms. We highlight strategies for target-mechanism identification using published and hypothetical examples. We consider the decision-making dilemmas that arise with different patterns of results in purported mechanisms and clinical outcomes. We end with considerations of the practical challenges of this approach and the implications for future directions of this initiative.

Effects of a virtual voice-based coach delivering problem-solving treatment on emotional distress and brain function: a pilot RCT in depression and anxiety.

Consumer-based voice assistants have the ability to deliver evidence-based treatment, but their therapeutic potential is largely unknown. In a pilot trial of a virtual voice-based coach, Lumen, delivering problem-solving treatment, adults with mild-to-moderate depression and/or anxiety were randomized to the Lumen intervention (n = 42) or waitlist control (n = 21). The main outcomes included changes in neural measures of emotional reactivity and cognitive control, and Hospital Anxiety and Depression Scale [HADS] symptom scores over 16 weeks. Participants were 37.8 years (SD = 12.4), 68% women, 25% Black, 24% Latino, and 11% Asian. Activation of the right dlPFC (neural region of interest in cognitive control) decreased in the intervention group but increased in the control group, with an effect size meeting the prespecified threshold for a meaningful effect (Cohen's d = 0.3). Between-group differences in the change in activation of the left dlPFC and bilateral amygdala were observed, but were of smaller magnitude (d = 0.2). Change in right dlPFC activation was also meaningfully associated (r ≥ 0.4) with changes in self-reported problem-solving ability and avoidance in the intervention. Lumen intervention also led to decreased HADS depression, anxiety, and overall psychological distress scores, with medium effect sizes (Cohen's d = 0.49, 0.51, and 0.55, respectively), compared with the waitlist control group. This pilot trial showed promising effects of a novel digital mental health intervention on cognitive control using neuroimaging and depression and anxiety symptoms, providing foundational evidence for a future confirmatory study.

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.

The Effects of the BAILAMOSTM Dance Program on Physical Activity Levels and Cognition of Older Latino Adults: A Pilot Study.

Objective: To investigate the effects of the BAILAMOSTM dance program on physical activity (PA), cardiorespiratory, and cognitive health. Methods: A parallel, two-armed pilot study was conducted with 57 older Latinos randomized to the BAILAMOSTM dance program (n = 28) or health education (HE) (n = 29). We conducted two- and three-way repeated-measures ANOVAs. Results: BAILAMOSTM participants increased participation in leisure moderate-to-vigorous PA (LMVPA) (F[1,53] = 3.17, p = .048, η2G = .01) and performance in global cognition relative to HE participants (F[1,52] = 4.19, p = .045, η2G = .01). Attendance moderated increases in moderate PA, MVPA, LMVPA, and total PA (p < .05). Participants of both groups with ≥75% attendance increased participation in PA. Among participants with <75% attendance, BAILAMOSTM participants increased PA relative to HE. Discussion: BAILAMOSTM positively impacted self-reported PA and global cognition in older Latinos. Even smaller doses of dance appear to impact self-reported PA levels.

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.

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.

The Impact of the BAILAMOS™ Dance Program on Brain Functional Connectivity and Cognition in Older Latino Adults: A Pilot Study.

Dance is a culturally salient form of physical activity (PA) for older Latinos. Resting-state functional connectivity (FC) is a putative biomarker for age-related cognitive decline. We aimed to investigate the impact of the BAILAMOS™ dance program on FC in three brain functional networks (Default Mode [DMN], Frontoparietal [FPN], and Salience [SAL] networks), and cognition. Ten cognitively healthy older Latinos participated in the four-month BAILAMOS™ dance program. We assessed PA levels (self-reported and device-assessed) and estimated cardiorespiratory fitness, cognition, and resting-state FC via functional magnetic resonance imaging at baseline and post-intervention. We performed paired t-tests and Pearson correlations. Given the pilot nature of the study, significance levels were set at p < 0.05 and effect sizes are reported. We observed a significant increase in self-reported moderate leisure-time PA from pre- to post-intervention (t(9) = 3.16, p = 0.011, d = 0.66). FC within-FPN regions of interest (ROIs) significantly increased pre- to post-intervention (t(9) = 2.35, p = 0.043, d = 0.70). DMN ROIs showed an increase, with a moderate effect size, in the integration with other networks' ROIs (t(9) = 1.96, p = 0.081, d = 0.64) post-intervention. Increases in moderate leisure-time PA at post-intervention were associated with increases in the FC within-FPN (R = 0.79, p = 0.006). Our results suggest that dance might be a promising approach for improving age-related disruption of FC within- and between-networks commonly associated with cognitive decline.

The factor structure of depressive symptoms in patients with obesity enrolled in the RAINBOW clinical trial.

BACKGROUND: Examining variability in the presenting symptoms of depression may be particularly important in characterizing depression in patients with comorbid conditions such as obesity. Identifying the underlying constructs of depression in such patients may produce phenotypic information to aid diagnosis and treatment decisions. OBJECTIVE: To examine the latent factors of symptoms using the depression Symptom Checklist (SCL-20) and the Patient Health Questionnaire (PHQ-9), separately, in patients with obesity and elevated depressive symptoms. METHODS: Exploratory factor analysis (EFA) was performed on baseline data from 409 patients with obesity and elevated depressive symptoms recruited in primary care. Bootstrap analysis was performed to estimate the precision and potential replicability of identified latent factors. RESULTS: Participants (70% women, mean age of 51.0 ± 12.1 years) had moderate depression. EFA of the SCL-20 suggested two reliable factors: dysphoric mood (71% of the variance) and anhedonia (15% of the variance). EFA of the PHQ-9 yielded one factor: dysphoric mood (87% of the variance). Bootstrapped results supported the replicability of these results. The top most endorsed symptoms were feeling low energy, overeating and disturbed sleep. LIMITATIONS: The generalizability of these findings to severe depression may be limited. CONCLUSIONS: Patients with elevated depressive symptoms and obesity present with heterogeneous symptoms. The SCL-20 seems more sensitive than the PHQ-9 for differentiating symptom profiles in this population. Some possible reasons include: 1) differences in number of scale items, and 2) differences in the aspects of depression they tap into; the SCL-20 measures the severity of symptoms, whereas the PHQ-9 measures the frequency of symptoms.