key: cord-0873001-k6yau1l1
authors: Kringle, Emily A.; Lv, Nan; Ronneberg, Corina R.; Wittels, Nancy; Rosas, Lisa G.; Steinman, Lesley E.; Smyth, Joshua M.; Gerber, Ben S.; Xiao, Lan; Venditti, Elizabeth M.; Ajilore, Olusola A.; Williams, Leanne M.; Ma, Jun
title: Association of COVID-19 impact with outcomes of an integrated obesity and depression intervention: Posthoc analysis of an RCT
date: 2022-05-20
journal: Obes Res Clin Pract
DOI: 10.1016/j.orcp.2022.05.005
sha: 5cc3d2a26ddb0328b3ca9ca196bdc65ab4c8e436
doc_id: 873001
cord_uid: k6yau1l1

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.

During the COVID-19 pandemic, stay-at-home orders and social distancing impacted health behaviors, including physical activity and eating habits, and exacerbated the widespread obesity pandemic. Global estimates suggest that 12.8% to 48.6% of community-dwelling adults reported weight gain associated with lifestyle changes during the COVID-19 pandemic, with higher odds of weight gain among those with elevated baseline BMI [1] . Furthermore, the global prevalence of depression, which commonly co-occurs with obesity [2] , grew from 4% in 2017 [3] to 28% in 2021 [4] . Simultaneous growth of the prevalence of obesity and depression during the COVID-19 pandemic heightened the risk for future health consequences and highlighted the urgent need for effective interventions that support people with this comorbid pattern.

The effectiveness of interventions that leverage lifestyle modification to treat people with obesity and comorbid depression during the post-COVID-19 era may be impacted by societal shifts that occurred during the pandemic. Disparate influences of the COVID-19 pandemic on daily and instrumental activities (e.g., childcare, healthcare access) [5, 6] , stress [7] , and health behaviors (i.e., physical activity, diet, sleep) [8] may impede lifestyle modification efforts, especially among individuals with comorbid depression. Investigating the impact of the COVID-19 pandemic on the effectiveness of behavioral interventions for people with obesity and comorbid depression is important as this may inform tailoring of interventions that address the different needs of people among this high-risk population during the ongoing pandemic.

We leveraged data from a pilot randomized clinical trial (RCT) that took place during the COVID-19 stay-at-home order in Illinois [9] . The objectives of this posthoc analysis were to: identify clusters of pilot RCT participants who reported common patterns of COVID-19 pandemic impact, examine associations between cluster membership and baseline characteristics, and examine intervention effects on 6-month clinical outcomes stratified by cluster. Our hypothesis was that the intervention effects would be smaller among clusters of J o u r n a l P r e -p r o o f participants that experienced more negative impacts of COVID-19 relative to those who experienced fewer negative impacts.

Data were collected between March 7, 2019 and August 19, 2020. The trial aimed to elucidate neurobiological mechanisms of behavior change associated with an integrated collaborative care intervention for comorbid obesity and depression (ClinicalTrials.gov NCT03841682). Participants (N=106) were randomized (2:1 ratio) to receive an integrated collaborative care intervention (I-CARE2) or usual care over 6 months.

Adults (≥18 years) with obesity (body mass index [BMI] ≥30.0, or ≥27.0 if Asian) and clinically significant depressive symptoms (Patient Health Questionnaire-9 [PHQ-9] score ≥10) were included. People with significant comorbid physical or mental health conditions (including pre-existing cardiovascular disease, diabetes, cancer, psychotic or bipolar disorders) or circumstances that would preclude completion of the study procedures were excluded. The inclusion and exclusion criteria were described in detail in the trial protocol [9] . All 105 out of 106 participants who remained enrolled in the trial in March 2020 were invited to complete the COVID-19 Impact Survey, and 75 participants (71%) who completed the survey were included in this posthoc study.

The I-CARE2 intervention integrates the Diabetes Prevention Program-based Group Lifestyle Balance TM (GLB) video program for weight loss [10] and Problem-Solving Therapy (PST) as first-line, plus antidepressant medications as needed for depression care management [11] . A trained health coach delivered 6 one-on-one in-person PST sessions over 2 months followed by 3 PST sessions and 11 home-viewed GLB videos over 4 months.

Participants who demonstrated signs of poor engagement or progress toward depression J o u r n a l P r e -p r o o f and weight outcomes at sessions 4 and 7, respectively, received a focused or motivational interviewing-enhanced PST session to address barriers. Participants self-monitored their weight and diet and synchronized their activity tracker data via the Fitbit application throughout the intervention. Participants in the usual care control group received information about behavioral health and weight management services available at UI Health and a Fitbit Alta HR (Fitbit, Inc, San Francisco, CA). The full intervention protocol was previously published [9] .

Participant characteristics and health outcomes measured at baseline and 6 months were utilized in this study. On March 16, 2020, the state of Illinois instituted stay-at-home orders in response to the COVID-19 pandemic. All participants completed baseline data collection before this date. Outcome data collection continued until August 31, 2020. An ad hoc assessment, the COVID-19 Impact Survey, was sent to participants to complete during follow-up between May and August in 2020.

The COVID-19 Impact Survey was adapted from the Epidemic-Pandemic Impacts Inventory (EPII) [12] . A total of 54 of the original 92 items from the EPII were used to assess impacts on work and employment (6 items), education and training (1 item), home life (7 items), social activities (7 items), economic (4 items), emotional health and well-being (6 items), physical health problems (6 items), physical distancing and quarantine combined with COVID-19 infection history (5 items), and positive changes experienced during or as a result of the pandemic (12 items). Specific items are listed the Supplementary Material Table S1 . Participants indicated if each item was true in their life (yes/no).

J o u r n a l P r e -p r o o f Self-reported sociodemographic variables included age, sex, race/ethnicity, education, and income. Diet was assessed by interview using multiple multi-pass 24-hour recalls (2 on weekdays and 1 on weekend) to determine adherence to the DASH (Dietary Approach to Stop Hypertension) diet, fruit and vegetable intake, total fat intake, and total calorie intake [13] . Physical activity was assessed using the 7-Day Physical Activity Recall interview to determine metabolic equivalent task (MET) minutes per week of leisure-time moderate-intensity activity and average energy expenditure in kilocalories per kilogram per day [14] . The 25-item Social Problem Solving Inventory-Revised: Short Form was used to generate an overall problem solving ability score and scores on 5 subscales: positive problem orientation (PPO), negative problem orientation (NPO), rational problem solving (RPS), impulsive/careless style (ICS), and avoidance style (AS) [15] . The Coping Orientation to Problems Experienced (COPE) Inventory characterized the use of 14 cognitive coping strategies [16] . The Emotional Regulation Questionnaire characterized emotional regulation strategies [17] . The 15-item Brief Risk-Resilience Index for Screening (BRISC) characterized negativity bias, emotional resilience, and coping skills [18] . Baseline health variables included blood pressure and the health outcomes below.

Changes in BMI and SCL-20 scores at 6 months were the primary outcomes. Changes in GAD-7 scores and other health measures at 6 months were secondary outcomes.

Trained research staff measured height (baseline only) and body weight following standard protocols. BMI was computed as weight (kg) divided by height squared (m 2 ) [19] . Depressive symptoms were measured using the 20-item Symptom Checklist Depression Scale (SCL-20), with possible scores between 0 (best) and 4 (worst) [20] .

Anxiety was measured using the 7-item Generalized Anxiety Disorder questionnaire (GAD-7), with possible scores between 0 (best) and 21 (worst) [21] . Sleep was J o u r n a l P r e -p r o o f measured using the PROMIS 8-item Sleep Disturbance and Sleep-Related Impairment questionnaires, with higher T scores indicating more sleep disturbance and impairment [22] . Psychosocial problems related to weight were measured using the Obesity-Related Problems Scale [23] , with higher scores indicating more obesity-related psychosocial problems. Functional impairments in work/school, social, and family life was measured using the Sheehan Disability Scale [24] , with higher scores indicating more functional impairments. Health-related quality of life was measured using the Medical Outcomes Survey Short Form 8-item questionnaire (SF-8) [25] , with higher scores indicating higher physical or mental quality of life.

We conducted 3 analyses to 1) identify clusters of participants by COVID-19 Impact; 2) examine the association between baseline characteristics and COVID-19 Impact clusters; and 3) explore the intervention effects stratified by COVID-19 Impact clusters.

Clusters of participants who reported common patterns of impact on the COVID-19 Impact Scale were identified using Latent Class Analysis (LCA). This model-based approach uses participant responses on a set of indicators to identify profiles based on an underlying categorical latent variable. Similar COVID-19 Impact Survey items were combined resulting in 28 items (Supplementary Table S1 ). The final model was selected based on the model fit (e.g., Log Likelihood, AIC, BIC, CAIC, Adjusted BIC, entropy), relative cluster size, and utility of the clusters [26] .

Percentages and means (standard deviations [SD]) were used to describe baseline characteristics among participants who did and did not complete the COVID-19 Impact Survey, and by cluster. We examined the bivariate associations between identified clusters and baseline characteristics using the 2-step Fisher's least significant difference method.

After comparing baseline characteristics across clusters (analysis of variance, Chi-square), we examined pairwise comparisons (Student's t, Chi-square) for variables significant at J o u r n a l P r e -p r o o f α<.05. We then used canonical discriminant analysis to identify dimensions representing linear combinations of baseline characteristics that significantly differentiate the COVID-19 impact clusters by including baseline characteristics with P values ≤0.10 from the bivariate analyses [27] . Canonical discriminant analysis is a multivariate dimension-reduction technique that derives linear combinations of explanatory variables that have the highest possible multiple correlation, the canonical correlation, with the groups of a classification Analyses used all available data for each outcome, and missing data were handled directly through maximum-likelihood estimation via mixed modeling. In the case of missing studymeasured weight, the closest electronic health record (EHR) weight within 3 months of the due date of a missed study visit or self-reported weight (if no EHR weight) was used. All analyses were conducted in SAS version 9.4 (SAS Institute Inc., Cary, North Carolina).

Demographic and baseline health outcomes did not significantly differ between participants who did (n=75) and did not (n=31) respond to the COVID-19 Impact Survey (Table 1) .

Responding participants were primarily females (76.0%) with a mean (SD) age of 47.8 

The 3-class model provided the best overall fit relative to the 4-and 5-class models indicated by the lowest information criterion values (see Electronic Supplementary, Table   S2 ). Although the 2-class model had lower information criterion values, the 3-class model was selected due to its domain usefulness and lower log likelihood. Additionally, the entropy index (0.92) indicated good classification relative to the 2-class model and the smallest cluster contained over 5% of the sample [26] . Members of all 3 clusters had a high probability of reporting social separation and cancelled activities including family events, travel, and religious or spiritual activities (see Electronic Supplementary Material, Figure S1 ).

Members of all 3 clusters were also likely to report engagement in fewer enjoyable activities, worse health behaviors, and less medical care. Members of Cluster 1 (mental health and sleep impacts, n=37, 49.3%) were more likely to report negative mental health and sleep health impacts than members of clusters 2 and 3. Members of Cluster 2 (economic impacts, n=18, 24.0%) were more likely to report negative impacts on meeting basic needs (e.g., getting enough food or healthy food, paying important bills like rent or utilities, and getting needed medications) and transportation than members of clusters 1 and 3. Members of Cluster 3 (less overall impacts, n=20, 26.7%) were the least likely to report overall negative impacts of COVID and more time with family and friends relative to members of the other 2 clusters.

Significant bivariate associations were observed between clusters and age, systolic blood pressure, and a subset of coping strategies (i.e., acceptance and religion) (P<0.05) ( Table 2 ). Participants in cluster 1 were significantly younger (mean [SD], 43.5 [9.8] ), had lower systolic blood pressure (116.6 [12.7] mm Hg), and had lower scores for acceptance (4.7 [1.7] ) and religion (4.5 [2.3] ) relative to clusters 2 and 3.

Canonical discriminant analysis identified 2 orthogonal dimensions representing statistically significant combinations of baseline characteristics. The canonical variates of dimensions 1 and 2 explained 40% and 26%, respectively, of the total variance of the 3 clusters. Participants in clusters 1 and 2 were characterized by the most extreme mean scores (-0.81 vs. 1.00, P<0.001) on dimension 1 (Figure 1 ). Participants in clusters 2 and 3 had the most extreme mean scores (-0.73 vs. 0.86, P<0.001) on dimension 2 ( Figure   1 ). According to characteristics with the highest positive or negative correlation coefficients (Table 3) , participants with lower systolic blood pressure, lower calorie J o u r n a l P r e -p r o o f intake, lower acceptance coping score, lower self-blame coping score and more obesityrelated problems had a higher probability to be in cluster 1 versus cluster 2. Participants who were younger, had lower income, more obesity-related problems, higher religion coping score, lower self-blame coping score, and lower avoidance problem solving style score had a higher probability to be in cluster 2 versus cluster 3.

Standardized mean differences (95% CI) between the intervention and control groups on health outcomes over 6 months stratified by COVID-19 impact cluster are depicted in Figure   2 . Compared with the usual care control group, the intervention had medium mean effects Table   S3 .

Adults with obesity and comorbid depression who completed the COVID-19 Impact Survey during the first part of the COVID-19 pandemic were characterized by 3 clusters, highlighting mental health and sleep impact, economic impact, and less overall impact. differed in age, J o u r n a l P r e -p r o o f income, diet, and baseline coping skills. The intervention led to improvements across several health outcomes compared with usual care, specifically with medium to large effects on functional impairments in cluster 1, on depressive symptoms and obesity-related problems in cluster 2, and on anxiety in cluster 3. These exploratory findings may have important implications for the tailoring and implementation of behavioral interventions designed to restore health among adults with comorbid depression and obesity as society navigates the ongoing pandemic.

Participants in cluster 1 were likely to report detrimental impacts on mental health and sleep. These participants were the youngest among all 3 clusters. This aligns with evidence that younger people reported worse mental health during the COVID-19 pandemic [14] . Participants in cluster 1 also had medium effect sizes on functional impairments in the intervention versus the control group. This effect size is similar to that reported in prior PST studies [28] , suggesting that PST may be a viable treatment for adults who experienced pandemic-related mental health and sleep impacts. It is also worth noting that although intervention effects (95% CI) on depression, anxiety, sleep-related impairments, obesity-related psychosocial problems, and mental health quality of life overlapped the null in cluster 1, the effect was in the expected direction. This may be, in part, related to challenges adhering to behavioral health recommendations reported by adults who experienced mental health impacts during the COVID-19 pandemic [29] . Augmenting PST with additional support or increasing the intensity of intervention may be required to bolster effects on important health outcomes. Further research specifying treatment needs of adults who report mental health and sleep impacts during the pandemic will inform tailoring of behavioral interventions that support this vulnerable group.

Participants in cluster 2 were likely to report difficulty meeting basic needs such as accessing food, medication, transportation, and inability to pay bills. These participants had low income relative to those in the less overall impact cluster. The economic toll of the COVID-19 pandemic was associated with elevated depressive symptoms among people with low income J o u r n a l P r e -p r o o f [30] . Participants in cluster 2 also had large effects on obesity-related problems and depression in the intervention versus the control group. PST, which was a key part of the integrated intervention, previously demonstrated efficacy for reducing depressive symptoms and improving mental health among low income populations [31] . The problem solving skills and coping strategies developed during the intervention may have supported participants as they navigated economic problems associated with the pandemic. Enhancing coping strategies may be particularly important for people with obesity and comorbid depression who experienced high economic impact associated with the COVID-19 pandemic. Further research that elucidates the impact of increasing positive coping strategies and reducing negative coping strategies on health outcomes should guide the tailoring of interventions for members of this cluster. Participants in cluster 3 were least likely to report negative or positive impacts related to the COVID-19 pandemic. These participants were the oldest and had the highest income among all 3 clusters. Interestingly, among all clusters, these participants also had the highest scores on self-blame coping and avoidance problem solving style, which are maladaptive strategies that were associated with worse anxiety, depression, and stress outcomes during the pandemic [32]. However, the demographic characteristics of this cluster are consistent with prior findings that age and income were protective of mental health [33] . This aligns with evidence suggesting that social determinants of health (i.e., access to resources such as income) may play a greater role in health outcomes than individual psychological factors [34] . Members of the less overall impact cluster demonstrated improvements on anxiety, but no other health outcomes. Future studies may investigate whether improving maladaptive coping strategies in this less overall impact cluster may lead to improvements in weight, sleep, and depression outcomes.

This clinical trial was ongoing at the onset of the COVID-19 pandemic. Consequently, it was not prospectively designed or adequately powered to evaluate the impact of COVID-19 on J o u r n a l P r e -p r o o f intervention outcomes. Although the small sample size used to conduct LCA was compensated for by the large number of indicators (COVID-19 Impact Survey questions), the clusters should be viewed as exploratory and replicated in larger samples [35] .

Additionally, 29% of trial participants did not respond to the COVID-19 Impact survey.

Although responders and nonresponders did not differ in baseline characteristics, generalizability of the findings may be limited. Finally, the EPII is a new tool which was designed in tandem with the ongoing COVID-19 pandemic and is currently undergoing validation to establish psychometric properties [12] . The present analysis supports and advances that work.

Despite these limitations, this posthoc study takes an important step toward characterizing the impact of the COVID-19 pandemic on the effectiveness of interventions within a uniquely high-risk and underserved population with obesity and comorbid depression. Participants in 3 distinct COVID-19 impact clusters differed in baseline characteristics (e.g., age, income, diet, and baseline coping skills) and may vary in their response to behavioral interventions.

Future research that clarifies the unique needs of patients who experienced substantial mental health, sleep, and economic impacts of the COVID-19 pandemic is required to advance intervention tailoring and implementation to restore health and well-being among at-risk populations as society continues to navigate the pandemic. Each symbol represents an individual participant. Individual symbols (participants) of one shape belong to the ellipse of the same shape denoting each cluster: circle, cluster 1 (mental health and sleep impacts, n=37); cross, cluster 2 (economic impacts, n=18); multiplication, cluster 3 (less overall impacts, n=20). Each ellipse indicates an 80% confidence ellipse for the mean of each cluster.

Forest plot depicting the overall and stratified standardized mean effects of the I-CARE2 intervention on health outcomes. Intervention effects in the overall sample for each outcome are represented by the triangle symbol. Intervention effects within each cluster are represented by: circle, cluster 1 (mental health and sleep impacts); cross, cluster 2 (economic impacts); multiplication, cluster 3 (less overall impacts). Error bars indicate 95% confidence intervals. Bolded text indicates that the 95% confidence interval does not contain null. Asterisk indicates outcomes in which expected direction of improvement is to the right of 0. For all other outcomes, expected direction of improvement is to the left of 0. BMI = Body Mass Index; SCL-20 = Symptom Checklist-20; GAD-7 = Generalized Anxiety Disorder Scale-7; SF-8 = Short Form 8-item Health Survey; ORPS = Obesity-Related Problems Scale.

Global impact of COVID-19 on weight and weight-related behaviors in the adult population: a scoping review

Depression and obesity in the U.S. adult household population

Depression and Other Common Mental Disorders: Global Health Estimates. Geneva: World Health Organization

Global prevalence of mental health issues among the general population during the coronavirus disease-2019 pandemic: a systematic review and meta-analysis

Women's and men's work, housework and childcare, before and during COVID-19

Delay or avoidance of medical care because of COVID-19-related concerns-United States

Racial and ethnic disparities in the prevalence of stress and worry, mental health conditions, and increased substance use among adults during the COVID-19 pandemic-United States

Physical activity, dietary habits and sleep quality before and during COVID-19 lockdown: A longitudinal study

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)

Translating the Diabetes Prevention Program: A comprehensive model for prevention training and program delivery

Community-integrated home-based depression treatment in older adults: A randomized controlled trial

The Epidemic-Pandemic Impacts Inventory (EPII): University of Connecticut School of Medicine

Deteriorating dietary habits among adults with hypertension: DASH dietary accordance

Assessment of habitual physical activity by a seven-day recall in a community survey and controlled experiments

Social problem-solving deficits and hopelessness, depression, and suicidal risk in college students and psychiatric inpatients

Assessing coping strategies: A theoretically based approach

Individual differences in two emotion regulation processes: implications for affect, relationships, and well-being

Sensitivity, specificity, and predictive power of the "Brief Risk-resilience Index for SC reening," a brief pan-diagnostic web screen for emotional health

PhenX (consensus measures of Phenotypes and eXposures) is supported by NHGRI award No

A comparison of two psychiatric screening tests

A brief measure for assessing generalized anxiety disorder: the GAD-7

Development of short forms from the PROMIS™ sleep disturbance and sleep-related impairment item banks

Psychosocial functioning in the obese before and after weight reduction: construct validity and responsiveness of the Obesity-Related Problems Scale

Assessing treatment effects in clinical trials with the discan metric of the Sheehan Disability Scale

How to score and interpret single-item health status measures: a manual for users of the SF-8 health survey

Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study

Purposeful selection of variables in logistic regression

Meta-analysis of problem solving therapy for the treatment of major depressive disorder in older adults

COVID-19 impacts mental health outcomes and ability/desire to participate in research among current research participants

Socioeconomic status and well-being during COVID-19: A resource-based examination

declare the following financial interests/personal relationships which may be considered as potential competing interests: Adherence to ethical standards: This research was conducted in accordance with the Declaration of Helsinki and with the approval and oversight of the University of Illinois at Chicago Institutional Review Board. A copy of the Institutional Review Board documentation and approval is available upon request

Dr. Leanne M. Williams is on the Scientific Advisory Board for One Mind Psyberguide and the External Advisory Board for the Laureate Institute for Brain Research

Keywise AI and serves on the advisory boards for Blueprint Health and Embodied Labs