Faculty Bibliography

INTRODUCTION:Intensive weight management programs are effective but often have low enrollment and high attrition. Lack of motivation is a key psychological barrier to enrollment, engagement, and weight loss. Mental Contrasting with Implementation Intentions (MCII) is a unique imagery technique that increases motivation for behavior change. We describe our study protocol to assess the efficacy and implementation of MCII to enhance the effectiveness of VA's MOVE! or TeleMOVE! weight management programs using a procedure called "WOOP" (Wish, Outcome, Obstacle, Plan) for Veterans. We hypothesize that WOOP+MOVE! or TeleMOVE! (intervention) will lead to greater MOVE!/TeleMOVE! program engagment and consequently weight loss than MOVE!/TeleMOVE! alone (control). METHOD:Veterans are randomized to either the intervention or control. Both arms receive the either MOVE! or TeleMOVE! weight management programs. The intervention group receives an hour long WOOP training while the control group receives patient education. Both groups receive telephone follow up calls at 3 days, 4 weeks, and 2 months post-baseline. Eligible participants are Veterans (ages 18-70 years) with either obesity (BMI ≥ 30 kg/m2) or overweight (BMI ≥ 25 kg/m2) and an obesity-associated co-morbidity. At baseline, 6 and 12 months, we assess weight, diet, physical activity in both groups. The primary outcome is mean percent weight change at 6 months. Secondary outcomes include changes in waist circumference, diet, physical activity, and dieting self-efficacy and engagement in regular physical activity. We assess implementation using the RE-AIM framework. CONCLUSION:If WOOP VA is found to be efficacious, it will be an important tool to facilitate weight management and improve weight outcomes. CLINICAL TRIAL REGISTRATION:NCT05014984.

OBJECTIVES:This study evaluated whether a range of demographic, social and geographic factors had an influence on glycaemic control longitudinally after an initial diagnosis of diabetes. DESIGN, SETTING AND PARTICIPANTS:We used the US Veterans Administration Diabetes Risk national cohort to track glycaemic control among patients 20-79-year old with a new diagnosis of type 2 diabetes. PRIMARY OUTCOME AND METHODS:We modelled associations between glycaemic control at follow-up clinical assessments and geographic factors including neighbourhood race/ethnicity, socioeconomic, land use and food environment measures. We also adjusted for individual demographics, comorbidities, haemoglobin A1c (HbA1c) at diagnosis and duration of follow-up. These factors were analysed within strata of community type: high-density urban, low-density urban, suburban/small town and rural areas. RESULTS:We analysed 246 079 Veterans who developed a new type 2 diabetes diagnosis in 2008-2018 and had at least 2 years of follow-up data available. Across all community types, we found that lower baseline HbA1c and female sex were strongly associated with a higher likelihood of within-range HbA1c at follow-up. Surprisingly, patients who were older or had more documented comorbidities were more likely to have within-range follow-up HbA1c results. While there was variation by community type, none of the geographic measures analysed consistently demonstrated significant associations with glycaemic control across all community types.

INTRODUCTION:Inequitable access to leisure-time physical activity (LTPA) resources may explain geographic disparities in type 2 diabetes (T2D). We evaluated whether the neighborhood socioeconomic environment (NSEE) affects T2D through the LTPA environment. RESEARCH DESIGN AND METHODS:We conducted analyses in three study samples: the national Veterans Administration Diabetes Risk (VADR) cohort comprising electronic health records (EHR) of 4.1 million T2D-free veterans, the national prospective cohort REasons for Geographic and Racial Differences in Stroke (REGARDS) (11 208 T2D free), and a case-control study of Geisinger EHR in Pennsylvania (15 888 T2D cases). New-onset T2D was defined using diagnoses, laboratory and medication data. We harmonized neighborhood-level variables, including exposure, confounders, and effect modifiers. We measured NSEE with a summary index of six census tract indicators. The LTPA environment was measured by physical activity (PA) facility (gyms and other commercial facilities) density within street network buffers and population-weighted distance to parks. We estimated natural direct and indirect effects for each mediator stratified by community type. RESULTS:The magnitudes of the indirect effects were generally small, and the direction of the indirect effects differed by community type and study sample. The most consistent findings were for mediation via PA facility density in rural communities, where we observed positive indirect effects (differences in T2D incidence rates (95% CI) comparing the highest versus lowest quartiles of NSEE, multiplied by 100) of 1.53 (0.25, 3.05) in REGARDS and 0.0066 (0.0038, 0.0099) in VADR. No mediation was evident in Geisinger. CONCLUSIONS:PA facility density and distance to parks did not substantially mediate the relation between NSEE and T2D. Our heterogeneous results suggest that approaches to reduce T2D through changes to the LTPA environment require local tailoring.

BACKGROUND:While emerging studies suggest that the COVID-19 pandemic caused disruptions in routine healthcare utilization, the full impact of the pandemic on healthcare utilization among diverse group of patients with type 2 diabetes is unclear. The purpose of this study is to examine trends in healthcare utilization, including in-person and telehealth visits, among U.S. veterans with type 2 diabetes before, during and after the onset of the COVID-19 pandemic, by demographics, pre-pandemic glycemic control, and geographic region. METHODS:We longitudinally examined healthcare utilization in a large national cohort of veterans with new diabetes diagnoses between January 1, 2008 and December 31, 2018. The analytic sample was 733,006 veterans with recently-diagnosed diabetes, at least 1 encounter with veterans administration between March 2018-2020, and followed through March 2021. Monthly rates of glycohemoglobin (HbA1c) measurements, in-person and telehealth outpatient visits, and prescription fills for diabetes and hypertension medications were compared before and after March 2020 using interrupted time-series design. Log-linear regression model was used for statistical analysis. Secular trends were modeled with penalized cubic splines. RESULTS:In the initial 3 months after the pandemic onset, we observed large reductions in monthly rates of HbA1c measurements, from 130 (95%CI,110-140) to 50 (95%CI,30-80) per 1000 veterans, and in-person outpatient visits, from 1830 (95%CI,1640-2040) to 810 (95%CI,710-930) per 1000 veterans. However, monthly rates of telehealth visits doubled between March 2020-2021 from 330 (95%CI,310-350) to 770 (95%CI,720-820) per 1000 veterans. This pattern of increases in telehealth utilization varied by community type, with lowest increase in rural areas, and by race/ethnicity, with highest increase among non-hispanic Black veterans. Combined in-person and telehealth outpatient visits rebounded to pre-pandemic levels after 3 months. Despite notable changes in HbA1c measurements and visits during that initial window, we observed no changes in prescription fills rates. CONCLUSIONS:Healthcare utilization among veterans with diabetes was substantially disrupted at the onset of the pandemic, but rebounded after 3 months. There was disparity in uptake of telehealth visits by geography and race/ethnicity.

BACKGROUND:The prevalence of sleep disturbances among prostate cancer (PCa) survivors, and extent of urologist involvement in sleep care are not well-studied. METHODS:PCa survivors (n = 167) and urologists (n = 145) were surveyed about sleep disturbances and survivorship care practices. RESULTS:Most PCa survivors had sleep disturbances, including 50.9% with poor sleep quality, 18.0% with clinical/severe insomnia, and 36.5% at high-risk for sleep apnea. Few urologists routinely screened for sleep disturbances, as recommended in national cancer survivorship guidelines. CONCLUSIONS:Optimal PCa survivorship care should incorporate screening for sleep disturbances, addressing comorbid factors affecting sleep and referring to sleep medicine when appropriate.

IMPORTANCE/UNASSIGNED:Financial incentives for weight management may increase use of evidence-based strategies while addressing obesity-related economic disparities in low-income populations. OBJECTIVE/UNASSIGNED:To examine the effects of 2 financial incentive strategies developed using behavioral economic theory when added to provision of weight management resources. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:Three-group, randomized clinical trial conducted from November 2017 to May 2021 at 3 hospital-based clinics in New York City, New York, and Los Angeles, California. A total of 1280 adults with obesity living in low-income neighborhoods were invited to participate, and 668 were enrolled. INTERVENTIONS/UNASSIGNED:Participants were randomly assigned to goal-directed incentives, outcome-based incentives, or a resources-only group. The resources-only group participants were given a 1-year commercial weight-loss program membership, self-monitoring tools (digital scale, food journal, and physical activity monitor), health education, and monthly one-on-one check-in visits. The goal-directed group included resources and linked financial incentives to evidence-based weight-loss behaviors. The outcome-based arm included resources and linked financial incentives to percentage of weight loss. Participants in the incentive groups could earn up to $750. MAIN OUTCOMES AND MEASURES/UNASSIGNED:Proportion of patients achieving 5% or greater weight loss at 6 months. RESULTS/UNASSIGNED:The mean (SD) age of the 668 participants enrolled was 47.7 (12.4) years; 541 (81.0%) were women, 485 (72.6%) were Hispanic, and 99 (14.8%) were Black. The mean (SD) weight at enrollment was 98.96 (20.54) kg, and the mean body mass index (calculated as weight in kilograms divided by height in meters squared) was 37.95 (6.55). At 6 months, the adjusted proportion of patients who lost at least 5% of baseline weight was 22.1% in the resources-only group, 39.0% in the goal-directed group, and 49.1% in the outcome-based incentive group (difference, 10.08 percentage points [95% CI, 1.31-18.85] for outcome based vs goal directed; difference, 27.03 percentage points [95% CI, 18.20-35.86] and 16.95 percentage points [95% CI, 8.18-25.72] for outcome based or goal directed vs resources only, respectively). However, mean percentage of weight loss was similar in the incentive arms. Mean earned incentives was $440.44 in the goal-directed group and $303.56 in the outcome-based group, but incentives did not improve financial well-being. CONCLUSIONS AND RELEVANCE/UNASSIGNED:In this randomized clinical trial, outcome-based and goal-directed financial incentives were similarly effective, and both strategies were more effective than providing resources only for clinically significant weight loss in low-income populations with obesity. Future studies should evaluate cost-effectiveness and long-term outcomes. TRIAL REGISTRATION/UNASSIGNED:ClinicalTrials.gov Identifier: NCT03157713.