Effect of sleep on weight loss and adherence to diet and physical activity recommendations during an 18-month behavioral weight loss intervention.

BACKGROUND/OBJECTIVES: To examine the association between indices of sleep quantity and quality with dietary adherence, physical activity adherence, and weight loss during a behavioral weight loss intervention. METHODS: Adults (n = 156) with overweight and obesity (40 ± 9 years, 84% female, BMI: 34.4 ± 4.2 kg/m2) participated in an 18-month behavioral weight loss intervention which prescribed a reduced calorie diet (1200-1800 kcal/d) and increased physical activity (300 min/wk). Body weight, indices of sleep (SenseWear armband; SWA), energy intake (EI, 3-day food records), and moderate-to-vigorous physical activity (SWA) were measured at baseline, 6, 12, and 18 months. Linear mixed effects models examined the association between sleep and weight change over time. Additional models were adjusted for covariates including age, BMI, sex, race, ethnicity, study completion, randomization, EI, and physical activity. Secondary analyses examined the association between sleep and adherence to diet and physical activity recommendations. RESULTS: Mean weight loss was 7.7 ± 5.4, 8.4 ± 7.9, and 7.1 ± 9.0 kg at 6, 12, and 18 months, respectively. Lower sleep efficiency, higher wake after sleep onset (WASO), more awakenings, and higher sleep onset latency (SOL) were significantly associated with attenuated weight loss (p < 0.05). Lower sleep efficiency, more awakenings, and higher SOL remained significantly associated with blunted weight loss after adjustment for covariates (p < 0.05). Later waketime, longer time in bed, longer sleep duration, higher WASO, more awakenings, and higher SOL were associated with lower odds of achieving ≥300 min/wk of moderate-to-vigorous physical activity, adjusted for covariates (FDR p < 0.05). CONCLUSIONS: Future studies should evaluate whether incorporating strategies to improve sleep health within a behavioral weight loss intervention leads to improved adherence to diet and physical activity recommendations and enhanced weight loss. CLINICAL TRIALS IDENTIFIER: NCT01985568.

Predicting energy intake in adults who are dieting and exercising.

BACKGROUND: When a lifestyle intervention combines caloric restriction and increased physical activity energy expenditure (PAEE), there are two components of energy balance, energy intake (EI) and physical activity energy expenditure (PAEE), that are routinely misreported and expensive to measure. Energy balance models have successfully predicted EI if PAEE is known. Estimating EI from an energy balance model when PAEE is not known remains an open question. OBJECTIVE: The objective was to evaluate the performance of an energy balance differential equation model to predict EI in an intervention that includes both calorie restriction and increases in PAEE. DESIGN: The Antonetti energy balance model that predicts body weight trajectories during weight loss was solved and inverted to estimate EI during weight loss. Using data from a calorie restriction study that included interventions with and without prescribed PAEE, we tested the validity of the Antonetti weight predictions against measured weight and the Antonetti EI model against measured EI using the intake-balance method at 168 days. We then evaluated the predicted EI from the model against measured EI in a study that prescribed both calorie restriction and increased PAEE. RESULTS: Compared with measured body weight at 168 days, the mean (±SD) model error was 1.30 ± 3.58 kg. Compared with measured EI at 168 days, the mean EI (±SD) model error in the intervention that prescribed calorie restriction and did not prescribe increased PAEE, was -84.9 ± 227.4 kcal/d. In the intervention that prescribed calorie restriction combined with increased PAEE, the mean (±SD) EI model error was -155.70 ± 205.70 kcal/d. CONCLUSION: The validity of the newly developed EI model was supported by experimental observations and can be used to determine EI during weight loss.

Cancer-related fatigue mediates the relationships between physical fitness and attendance and quality of life after participation in a clinical exercise program for survivors of cancer.

PURPOSE: Cancer-related fatigue (CRF) is a common and limiting symptom reported by survivors of cancer, negatively impacting health-related quality of life (HRQoL). Exercise improves CRF, HRQoL, and physical fitness in survivors. Prospective research trials have shown that exercise-associated fitness improvements effects on HRQoL are mediated by CRF; however, this has not been investigated in a pragmatic real-world setting. This study utilizes data from a large heterogenous population of survivors participating in a clinical exercise program to investigate this mediation effect, as well as effects of program attendance. METHODS: Data were collected from 194 survivors completing the BfitBwell Cancer Exercise Program (July 2016-February 2020). Changes in HRQoL, CRF, and fitness were calculated and program attendance collected. Basic correlation analyses were performed. Linear regression analyses were performed to assess mediation by CRF. RESULTS: All measures of CRF, HRQoL, and physical fitness significantly improved following the exercise program. Improvements in physical fitness were significantly correlated with improvements in HRQoL (r = 0.15-0.18), as was program attendance (r = 0.26) and CRF (r = 0.59). The effects of physical fitness and program attendance on HRQoL were at least partially mediated by the effects of CRF. CONCLUSION: This study extends research findings on how exercise programs improve HRQoL in survivors of cancer to a real-world setting. Results indicate that clinical exercise programs should target reductions in CRF in survivors (during or after treatment) through improvements in physical fitness to improve HRQoL and that high attendance should be encouraged regardless of fitness changes.

Temporal patterns of physical activity in successful weight loss maintainers.

BACKGROUND/OBJECTIVES: Individuals successful at weight loss maintenance engage in high amounts of physical activity (PA). Understanding how and when weight loss maintainers accumulate PA within a day and across the week may inform PA promotion strategies and recommendations for weight management. METHODS: We compared patterns of PA in a cohort of weight loss maintainers (WLM, n = 28, maintaining ≥13.6 kg weight loss for ≥1 year, BMI 23.6 ± 2.3 kg/m2), controls without obesity (NC, n = 30, BMI similar to current BMI of WLM, BMI 22.8 ± 1.9 kg/m2), and controls with overweight/obesity (OC, n = 26, BMI similar to pre-weight loss BMI of WLM, 33.6 ± 5.1 kg/m2). PA was assessed during 7 consecutive days using the activPALTM activity monitor. The following variables were quantified; sleep duration, sedentary time (SED), light-intensity PA (LPA), moderate-to-vigorous intensity PA (MVPA), and steps. Data were examined to determine differences in patterns of PA across the week and across the day using mixed effect models. RESULTS: Across the week, WLM engaged in ≥60 min of MVPA on 73% of days, significantly more than OC (36%, p < 0.001) and similar to NC (59%, p = 0.10). Across the day, WLM accumulated more MVPA in the morning (i.e., within 3 h of waking) compared to both NC and OC (p < 0.01). WLM engaged in significantly more MVPA accumulated in bouts ≥10 min compared to NC and OC (p < 0.05). Specifically, WLM engaged in more MVPA accumulated in bouts of ≥60 min compared to NC and OC (p < 0.05). CONCLUSIONS: WLM engage in high amounts of MVPA (≥60 min/d) on more days of the week, accumulate more MVPA in sustained bouts, and accumulate more MVPA in the morning compared to controls. Future research should investigate if these distinct patterns of PA help to promote weight loss maintenance.

Motivational profiles and change in physical activity during a weight loss intervention: a secondary data analysis.

BACKGROUND: High levels of moderate-to-vigorous intensity physical activity (MVPA) are strongly associated with sustained weight loss, however the majority of adults are unsuccessful in maintaining high levels of MVPA long-term. Our goal was to identify profiles based on exercise motives, and examine the association between motivational profile and longitudinal changes in MVPA during a weight loss intervention. METHODS: Adults with overweight or obesity (n = 169, mean ± SE; age 39 ± 0.7 years, BMI 34.4 ± 0.3 kg/m2, 83% female) underwent an 18-month behavioral weight loss program, including 6 months of supervised exercise, followed by 6 months of unsupervised exercise. Participants self-reported behavioral regulations for exercise at baseline (BREQ-2). Latent profile analysis identified subgroups from external, introjected, identified, and intrinsic regulations measured at baseline. Mean differences in device-measured total MVPA were compared across motivational profiles at baseline, after 6 months of supervised exercise and after a subsequent 6 months of unsupervised exercise. RESULTS: Three motivational profiles emerged: high autonomous (high identified and intrinsic, low external regulations; n = 52), high combined (high scores on all exercise regulations; n = 25), and moderate combined (moderate scores on all exercise regulations; n = 92). Motivational profile was not associated with baseline level of MVPA or the increase in MVPA over the 6-month supervised exercise intervention (high autonomous: 21 ± 6 min/d; high combined: 20 ± 9 min/d; moderate combined: 33 ± 5 min/d; overall P > 0.05). However, during the transition from supervised to unsupervised exercise, MVPA decreased, on average, within all three profiles, but the high autonomous profile demonstrated the least attenuation in MVPA (- 3 ± 6 min/d) compared to the moderate combined profile (- 20 ± 5 min/d; P = 0.043). CONCLUSIONS: Results were in alignment with the Self-Determination Theory. Adults motivated by autonomous reasons (value benefits of exercise, intrinsic enjoyment) may be more likely to sustain increases in MVPA once support is removed, whereas participants with moderate-to-high scores on all types of exercise regulations may need additional long-term support in order to sustain initial increases in MVPA. CLINICAL TRIAL REGISTRATION: NCT01985568. Registered 24 October 2013.

Adapting an Efficacious Peer-Delivered Physical Activity Program for Survivors of Breast Cancer for Web Platform Delivery: Protocol for a 2-Phase Study.

BACKGROUND: Interventions promoting physical activity (PA) among survivors of cancer improve their functioning, reduce fatigue, and offer other benefits in cancer recovery and risk reduction for future cancer. There is a need for interventions that can be implemented on a wider scale than that is possible in research settings. We have previously demonstrated that a 3-month peer-delivered PA program (Moving Forward Together [MFT]) significantly increased the moderate to vigorous PA (MVPA) of survivors of breast cancer. OBJECTIVE: Our goal is to scale up the MFT program by adapting an existing peer mentoring web platform, Mentor1to1. InquistHealth's web platform (Mentor1to1) has demonstrated efficacy in peer mentoring for chronic disease management. We will partner with InquisitHealth to adapt their web platform for MFT. The adaptation will allow for automating key resource-intensive components such as matching survivors with a coach via the web-based peer mentoring platform and collecting key indexes to prepare for large-scale implementation. The aim is to streamline intervention delivery, assure fidelity, and improve survivor outcomes. METHODS: In phase 1 of this 2-phase study, we will interview 4 peer mentors or coaches with experience in delivering MFT and use their feedback to create Mentor1to1 web platform adapted for MFT (webMFT). Next, another 4 coaches will participate in rapid, iterative user-centered testing of webMFT. In phase 2, we will conduct a randomized controlled trial by recruiting and training 10 to 12 coaches from cancer organizations to deliver webMFT to 56 survivors of breast cancer, who will be assigned to receive either webMFT or MVPA tracking (control) for 3 months. We will assess effectiveness with survivors' accelerometer-measured MVPA and self-reported psychosocial well-being at baseline and 3 months. We will assess implementation outcomes, including acceptability, feasibility, and program costs from the perspective of survivors, coaches, and collaborating organizations, as guided by the expanded Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework. RESULTS: As of September 2023, phase 1 of the study was completed, and 61 survivors were enrolled in phase 2. Using newer technologies for enhanced intervention delivery, program management, and automated data collection has the exciting promise of facilitating effective implementation by organizations with limited resources. Adapting evidence-based MFT to a customized web platform and collecting data at multiple levels (coaches, survivors, and organizations) along with costs will provide a strong foundation for a robust multisite implementation trial to increase MVPA and its benefits among many more survivors of breast cancer. CONCLUSIONS: The quantitative and qualitative data collected from survivors of cancer, coaches, and organizations will be analyzed to inform a future larger-scale trial of peer mentoring for PA delivered by cancer care organizations to survivors. TRIAL REGISTRATION: ClinicalTrials.gov NCT05409664; https://clinicaltrials.gov/study/NCT05409664. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52494.

Impact of the built, social, and food environment on long-term weight loss within a behavioral weight loss intervention.

Background: Behavioral weight loss interventions can lead to an average weight loss of 5%-10% of initial body weight, however there is wide individual variability in treatment response. Although built, social, and community food environments can have potential direct and indirect influences on body weight (through their influence on physical activity and energy intake), these environmental factors are rarely considered as predictors of variation in weight loss. Objective: Evaluate the association between built, social, and community food environments and changes in weight, moderate-to-vigorous physical activity (MVPA), and dietary intake among adults who completed an 18-month behavioral weight loss intervention. Methods: Participants included 93 adults (mean ± SD; 41.5 ± 8.3 years, 34.4 ± 4.2 kg/m2, 82% female, 75% white). Environmental variables included urbanicity, walkability, crime, Neighborhood Deprivation Index (includes 13 social economic status factors), and density of convenience stores, grocery stores, and limited-service restaurants at the tract level. Linear regressions examined associations between environment and changes in body weight, waist circumference (WC), MVPA (SenseWear device), and dietary intake (3-day diet records) from baseline to 18 months. Results: Grocery store density was inversely associated with change in weight (ß = -0.95; p = 0.02; R 2 = 0.062) and WC (ß = -1.23; p < 0.01; R 2 = 0.109). Participants living in tracts with lower walkability demonstrated lower baseline MVPA and greater increases in MVPA versus participants with higher walkability (interaction p = 0.03). Participants living in tracts with the most deprivation demonstrated greater increases in average daily steps (ß = 2048.27; p = 0.02; R 2 = 0.039) versus participants with the least deprivation. Limited-service restaurant density was associated with change in % protein intake (ß = 0.39; p = 0.046; R 2 = 0.051). Conclusion: Environmental factors accounted for some of the variability (<11%) in response to a behavioral weight loss intervention. Grocery store density was positively associated with weight loss at 18 months. Additional studies and/or pooled analyses, encompassing greater environmental variation, are required to further evaluate whether environment contributes to weight loss variability.

The Microbiome, Epigenome, and Diet in Adults with Obesity during Behavioral Weight Loss.

Obesity has been linked to the gut microbiome, epigenome, and diet, yet these factors have not been studied together during obesity treatment. Our objective was to evaluate associations among gut microbiota (MB), DNA methylation (DNAme), and diet prior to and during a behavioral weight loss intervention. Adults (n = 47, age 40.9 ± 9.7 years, body mass index (BMI) 33.5 ± 4.5 kg/m2, 77% female) with data collected at baseline (BL) and 3 months (3 m) were included. Fecal MB was assessed via 16S sequencing and whole blood DNAme via the Infinium EPIC array. Food group and nutrient intakes and Healthy Eating Index (HEI) scores were calculated from 7-day diet records. Linear models were used to test for the effect of taxa relative abundance on DNAme and diet cross-sectionally at each time point, adjusting for confounders and a false discovery rate of 5%. Mean weight loss was 6.2 ± 3.9% at 3 m. At BL, one MB taxon, Ruminiclostridium, was associated with DNAme of the genes COL20A1 (r = 0.651, p = 0.029), COL18A1 (r = 0.578, p = 0.044), and NT5E (r = 0.365, p = 0.043). At 3 m, there were 14 unique MB:DNAme associations, such as Akkermansia with DNAme of GUSB (r = -0.585, p = 0.003), CRYL1 (r = -0.419, p = 0.007), C9 (r = -0.439, p = 0.019), and GMDS (r = -0.559, p = 0.046). Among taxa associated with DNAme, no significant relationships were seen with dietary intakes of relevant nutrients, food groups, or HEI scores. Our findings indicate that microbes linked to mucin degradation, short-chain fatty acid production, and body weight are associated with DNAme of phenotypically relevant genes. These relationships offer an initial understanding of the possible routes by which alterations in gut MB may influence metabolism during weight loss.

New insights in the mechanisms of weight-loss maintenance: Summary from a Pennington symposium.

Obesity is a chronic disease that affects more than 650 million adults worldwide. Obesity not only is a significant health concern on its own, but predisposes to cardiometabolic comorbidities, including coronary heart disease, dyslipidemia, hypertension, type 2 diabetes, and some cancers. Lifestyle interventions effectively promote weight loss of 5% to 10%, and pharmacological and surgical interventions even more, with some novel approved drugs inducing up to an average of 25% weight loss. Yet, maintaining weight loss over the long-term remains extremely challenging, and subsequent weight gain is typical. The mechanisms underlying weight regain remain to be fully elucidated. The purpose of this Pennington Biomedical Scientific Symposium was to review and highlight the complex interplay between the physiological, behavioral, and environmental systems controlling energy intake and expenditure. Each of these contributions were further discussed in the context of weight-loss maintenance, and systems-level viewpoints were highlighted to interpret gaps in current approaches. The invited speakers built upon the science of obesity and weight loss to collectively propose future research directions that will aid in revealing the complicated mechanisms involved in the weight-reduced state.

Improving lifestyle obesity treatment during the COVID-19 pandemic and beyond: New challenges for weight management.

Objective: The COVID-19 pandemic has resulted in significant changes to daily life and many health-related behaviors. The objective of this study was to examine how the stay-at-home/safer-at-home mandates issued in Colorado (March 2020-May 2020) impacted lifestyle behaviors and mental health among individuals with overweight or obesity participating in two separate behavioral weight loss trials (n = 82). Methods: Questionnaires were used to collect qualitative and quantitative data on challenges to weight loss presented by the COVID-19 pandemic, including changes in dietary intake, physical activity, sedentary behavior, and mental health during the stay-at-home/safer-at-home mandates. Results: Using a convergent mixed method approach integrating qualitative and quantitative data, the greatest challenge experienced by participants was increased stress and anxiety, which led to more unhealthy behaviors. The majority perceived it to be harder to adhere to the prescribed diet (81%) and recommended physical activity (68%); however, self-reported exercise on weekdays increased significantly and 92% of participants lost weight or maintained weight within ±1% 5-6 weeks following the stay-at-home mandate. Conclusion: Study results suggest that obesity treatment programs should consider and attempt to address the burden of stress and anxiety stemming from the COVID-19 pandemic and other sources due to the negative effects they can have on weight management and associated behaviors.

Creating an algorithm to identify indices of sleep quantity and quality from a wearable armband in adults.

Objective: To develop an algorithm to quantify indices of sleep quantity and quality using the SenseWear armband (SWA) and to compare indices of sleep from this novel algorithm to standard wrist actigraphy (Actiwatch 2; AW2) under free-living conditions. Material and Methods: Thirty participants (47±10 years; 33.0±4.8kg/m2) wore the SWA and AW2 for seven consecutive days. Participants self-reported bedtime and waketime across these 7 days. Bedtime, sleep onset, sleep offset, waketime, total sleep time (TST), time in bed (TIB), sleep effciency (SE), sleep onset latency (SOL), wake after sleep onset (WASO), sleep fragmentations (SF), sleep regularity (calculated as SD of waketime), and mid-point of sleep were calculated using each device. Results: There was significant evidence for equivalence of means (or mean ranks) for bedtime, sleep onset, sleep offset, waketime, TST, TIB, SOL, WASO, and midpoint of sleep measured by the SWA and AW2 (p<0.05). There was insuffcient evidence for equivalence of means in SF (SW: 25±6 vs. AW2: 10±3 events; p=1.0), mean ranks in sleep regularity (SW: 58±33 vs. AW2: 68±40 min; p=0.11), and mean ranks in SE (SW: 84.7±5.1% vs. AW2: 86.3±5.5%; p=0.05). When comparing minute-by-minute sleep/wake status, the sensitivity and specificity of the SWA were 0.94 (95%CI: 0.93, 0.95) and 0.88 (95%CI: 0.85, 0.90), respectively, using AW2 as the criterion measure. Conclusion: The algorithm developed for the SWA produced relatively accurate and consistent measurements of sleep quantity, timing, and quality compared to the AW2 under free-living conditions. Thus, the SWA is a viable alternative to standard wrist actigraphy.

Proteomics, dietary intake, and changes in cardiometabolic health within a behavioral weight-loss intervention: A pilot study.

OBJECTIVE: Identifying associations among circulating proteins, dietary intakes, and clinically relevant indicators of cardiometabolic health during weight loss may elucidate biologically relevant pathways affected by diet, allowing for an incorporation of precision nutrition approaches when designing future interventions. This study hypothesized that plasma proteins would be associated with diet and cardiometabolic health indicators within a behavioral weight-loss intervention. METHODS: This secondary data analysis included participants (n = 20, mean [SD], age: 40.1 [9.5] years, BMI: 34.2 [4.0] kg/m2 ) who completed a 1-year behavioral weight-loss intervention. Cardiovascular disease-related plasma proteins, diet, and cardiometabolic health indicators were evaluated at baseline and 3 months. Associations were determined via linear regression and integrated networks created using Visualization Of LineAr Regression Elements (VOLARE). RESULTS: A total of 16 plasma proteins were associated with ≥1 diet or health indicator at baseline (p < 0.001); changes in 42 proteins were associated with changes in diet or health indicators from baseline to 3 months (p < 0.005). Baseline tumor necrosis factor receptor superfamily member 10C (TNFRSF10C) was associated with intakes of dark green vegetables (r = -0.712), and fatty acid-binding protein 4 (FABP4) was associated with intakes of unsweetened coffee (r = -0.689). Changes in refined-grain intakes were associated with changes in scavenger receptor cysteine-rich type 1 protein M130 (CD163; r = 0.725), interleukin-1 receptor type 1 (IL1R-T1; r = 0.624), insulin (r = 0.656), and triglycerides (r = 0.648). CONCLUSIONS: Circulating cardiovascular disease-related proteins were associated with diet and cardiometabolic health indicators prior to and in response to weight loss.

Characterizing Patient Representations for Computational Phenotyping.

Patient representation learning methods create rich representations of complex data and have potential to further advance the development of computational phenotypes (CP). Currently, these methods are either applied to small predefined concept sets or all available patient data, limiting the potential for novel discovery and reducing the explainability of the resulting representations. We report on an extensive, data-driven characterization of the utility of patient representation learning methods for the purpose of CP development or automatization. We conducted ablation studies to examine the impact of patient representations, built using data from different combinations of data types and sampling windows on rare disease classification. We demonstrated that the data type and sampling window directly impact classification and clustering performance, and these results differ by rare disease group. Our results, although preliminary, exemplify the importance of and need for data-driven characterization in patient representation-based CP development pipelines.

Comparison of weight loss induced by daily caloric restriction versus intermittent fasting (DRIFT) in individuals with obesity: study protocol for a 52-week randomized clinical trial.

BACKGROUND: The standard of care for treating overweight and obesity is daily caloric restriction (DCR). While this approach produces modest weight loss, adherence to DCR declines over time and weight regain is common. Intermittent fasting (IMF) is an alternative dietary strategy for reducing energy intake (EI) that involves >60% energy restriction on 2-3 days per week, or on alternate days, with habitual intake on fed days. While numerous studies have evaluated IMF as a weight loss strategy, there are several limitations including lack of a standard-of-care DCR control, failure to provide guideline-based behavioral support, and failure to rigorously evaluate dietary and PA adherence using objective measures. To date, only three longer-term (52-week) trials have evaluated IMF as a weight loss strategy. None of these longer-duration studies reported significant differences between IMF and DCR in changes in weight. However, each of these studies has limitations that prohibit drawing generalizable conclusions about the relative long-term efficacy of IMF vs. DCR for obesity treatment. METHODS: The Daily Caloric Restriction vs. Intermittent Fasting Trial (DRIFT) is a two-arm, 52-week block randomized (1:1) clinical weight loss trial. The two intervention arms (DCR and IMF) are designed to prescribe an equivalent average weekly energy deficit from baseline weight maintenance energy requirements. Both DCR and IMF will be provided guideline-based behavioral support and a PA prescription. The primary outcome is change in body weight at 52 weeks. Secondary outcomes include changes in body composition (dual-energy x-ray absorptiometry (DXA)), metabolic parameters, total daily energy expenditure (TDEE, doubly labeled water (DLW)), EI (DLW intake-balance method, 7-day diet diaries), and patterns of physical activity (PA, activPAL device). DISCUSSION: Although DCR leads to modest weight loss success in the short-term, there is wide inter-individual variability in weight loss and poor long-term weight loss maintenance. Evidence-based dietary approaches to energy restriction that are effective long-term are needed to provide a range of evidence-based options to individuals seeking weight loss. The DRIFT study will evaluate the long-term effectiveness of IMF vs. DCR on changes in objectively measured weight, EI, and PA, when these approaches are delivered using guideline-based behavioral support and PA prescriptions.

Environmental data and methods from the Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) core measures environmental working group.

This article describes geospatial datasets and exemplary data across five environmental domains (walkability, socioeconomic deprivation, urbanicity, personal safety, and food outlet accessibility). The environmental domain is one of four domains (behavioral, biological, environmental and psychosocial) in which the Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures Project suggested measures to help explain variation in responses to weight loss interventions. These data are intended to facilitate additional research on potential environmental moderators of responses to weight loss, physical activity, or diet related interventions. These data represent a mix of publicly and commercially available pre-existing data that were downloaded, cleaned, restructured and analyzed to create datasets at the United States (U.S.) block group and/or census tract level for the five domains. Additionally, the resource includes detailed methods for obtaining, cleaning and summarizing two datasets concerning safety and the food environment that are only available commercially. Across the five domains considered, we include component as well as derived variables for three of the five domains. There are two versions of the National Walkability Index Dataset (one based on 2013 data and one on 2019 data) consisting of 15 variables. The Neighborhood Deprivation Index dataset contains 18 variables and is based on the US Census Bureau's 5-year American Community Survey (ACS) data for 2013-2017. The urbanicity dataset contains 11 variables and is based on USDA rural-urban commuting (RUCA) codes and Census Bureau urban/rural population data from 2010. Personal safety and food outlet accessibility data were purchased through commercial vendors and are not in the public domain. Thus, only exemplary figures and detailed instructions are provided. The website housing these datasets and examples should serve as a valuable resource for researchers who wish to examine potential environmental moderators of responses to weight loss and related interventions in the U.S.

Underreporting of energy intake in weight loss maintainers.

BACKGROUND: Individuals with overweight or obesity commonly underreport energy intake (EI), but it is unknown if the tendency to underreport persists in formerly obese individuals who lose significant weight and maintain their weight loss over long periods of time. OBJECTIVE: Assess the accuracy of self-reported EI in successful weight loss maintainers (WLM) compared with controls of normal body weight (NC) and controls with overweight/obesity (OC). METHODS: Participants for this case-controlled study were recruited in 3 groups: WLM [n = 26, BMI (in kg/m2) 24.1 ± 2.3; maintaining ≥13.6 kg weight loss for ≥1 y], NC (n = 33, BMI 22.7 ± 1.9; similar to current BMI of WLM), and OC (n = 32, BMI 34.0 ± 4.6; similar to pre-weight loss BMI of WLM). Total daily energy expenditure (TDEE) was measured over 7 d using the doubly labeled water (DLW) method, and self-reported EI was concurrently measured from 3-d diet diaries. DLW TDEE and self-reported EI were compared to determine accuracy of self-reported EI. RESULTS: WLM underreported EI (median, interquartile range) (-605, -915 to -314 kcal/d) to a greater degree than NC (-308, -471 to -68 kcal/d; P < 0.01) but not more than OC (-310, -970 to 18 kcal/d; P = 0.21). WLM also showed a greater degree of relative underreporting (-25.3%, -32.9% to -12.5%) compared with NC (-14.3%, -19.6% to -3.1%; P = 0.02) but not OC (-11.2%, -34.1% to -0.7%; P = 0.16). A greater proportion of WLM was classified as underreporters (30.8%) than NC (9.1%; P = 0.05) but not OC (28.1%; P = 1.00). CONCLUSIONS: WLM underreported EI in both absolute and relative terms to a greater extent than NC but not OC. These findings call into question the accuracy of self-reported EI in WLM published in previous studies and align with recent data suggesting that WLM rely less on chronic EI restriction and more on high levels of physical activity to maintain weight loss. This trial was registered at clinicaltrials.gov as NCT03422380.

Predictors of long-term weight loss trajectories during a behavioral weight loss intervention: An exploratory analysis.

BACKGROUND: Substantial interindividual variability in response to behavioral weight loss interventions remains a critical challenge in obesity treatment. An improved understanding of the complex factors that contribute to this variability may improve obesity treatment outcomes. OBJECTIVE: To identify weight change trajectories during a behavioral weight loss intervention and to explore differences between trajectory groups in sociodemographic, biologic, behavioral, and psychosocial factors. METHODS: Adults (n = 170, 40 ± 9 years, BMI 34 ± 4 kg/m2, 84% female) participated in an 18-month behavioral weight loss intervention. Weight was measured at 0, 3, 6, 9, 12, 15, 18, and 24 months. Among participants with at least two weights after baseline (n = 140), clusters of longitudinal trajectories of changes in weight were identified using a latent class growth mixture model. The association between baseline factors or changes in factors over time and trajectory group was examined. RESULTS: Two weight change trajectories were identified: "weight regainers" (n = 91) and "weight loss maintainers" (n = 49). Black participants (90%, 19/21) were more likely than non-Black participants to be regainers versus maintainers (p < 0.01). Maintainers demonstrated greater increases in device-measured physical activity, autonomous motivation for exercise, diet self-efficacy, cognitive restraint, and engagement in weight management behaviors and greater reductions in barriers for exercise, disinhibition, and depressive symptoms over 24 months versus regainers (p < 0.05). CONCLUSION: Maintainers and regainers appear to be distinct trajectories that are associated with specific sociodemographic, behavioral, and psychosocial factors. Study results suggest potential targets for more tailored, multifaceted interventions to improve obesity treatment outcomes.

Weight and body composition changes affect resting energy expenditure predictive equations during a 12-month weight-loss intervention.

OBJECTIVE: Mathematical equations that predict resting energy expenditure (REE) are widely used to derive calorie prescriptions during weight-loss interventions. Although such equations are known to introduce group- and individual-level error into REE prediction, their validity has largely been assessed in weight-stable populations. Therefore, this study sought to characterize how weight change affects the validity of commonly used REE predictive models throughout a 12-month weight-loss intervention. METHODS: Changes in predictive error of four models (Mifflin-St-Jeor, Harris-Benedict, Owen, and World Health Organization/Food and Agriculture) were assessed at 1-, 6-, and 12-month time points in adults (n = 66, 76% female, aged 18-55 years, BMI = 27-45 kg/m2 ) enrolled in a randomized clinical weight-loss trial. RESULTS: All equations experienced significant negative shifts in bias (measured - predicted REE) toward overprediction from baseline to 1 month (p < 0.05). Three equations showed reversal of bias in the positive direction (toward underprediction) from baseline to 12 months (p < 0.05). Early changes in bias were correlated with decreased fat-free mass (p ≤ 0.01). CONCLUSIONS: Changes in body composition and mass during a 12-month weight-loss intervention significantly affected REE predictive error in adults with overweight and obesity. Weight history should be considered when using mathematical models to predict REE during periods of weight fluctuation.

The Gut Microbiota during a Behavioral Weight Loss Intervention.

Altered gut microbiota has been linked to obesity and may influence weight loss. We are conducting an ongoing weight loss trial, comparing daily caloric restriction (DCR) to intermittent fasting (IMF) in adults who are overweight or obese. We report here an ancillary study of the gut microbiota and selected obesity-related parameters at the baseline and after the first three months of interventions. During this time, participants experienced significant improvements in clinical health measures, along with altered composition and diversity of fecal microbiota. We observed significant associations between the gut microbiota features and clinical measures, including weight and waist circumference, as well as changes in these clinical measures over time. Analysis by intervention group found between-group differences in the relative abundance of Akkermansia in response to the interventions. Our results provide insight into the impact of baseline gut microbiota on weight loss responsiveness as well as the early effects of DCR and IMF on gut microbiota.

Multiomic Predictors of Short-Term Weight Loss and Clinical Outcomes During a Behavioral-Based Weight Loss Intervention.

OBJECTIVE: Identifying predictors of weight loss and clinical outcomes may increase understanding of individual variability in weight loss response. We hypothesized that baseline multiomic features, including DNA methylation (DNAme), metabolomics, and gut microbiome, would be predictive of short-term changes in body weight and other clinical outcomes within a comprehensive weight loss intervention. METHODS: Healthy adults with overweight or obesity (n = 62, age 18-55 years, BMI 27-45 kg/m2 , 75.8% female) participated in a 1-year behavioral weight loss intervention. To identify baseline omic predictors of changes in clinical outcomes at 3 and 6 months, whole-blood DNAme, plasma metabolites, and gut microbial genera were analyzed. RESULTS: A network of multiomic relationships informed predictive models for 10 clinical outcomes (body weight, waist circumference, fat mass, hemoglobin A1c , homeostatic model assessment of insulin resistance, total cholesterol, triglycerides, C-reactive protein, leptin, and ghrelin) that changed significantly (P < 0.05). For eight of these, adjusted R2 ranged from 0.34 to 0.78. Our models identified specific DNAme sites, gut microbes, and metabolites that were predictive of variability in weight loss, waist circumference, and circulating triglycerides and that are biologically relevant to obesity and metabolic pathways. CONCLUSIONS: These data support the feasibility of using baseline multiomic features to provide insight for precision nutrition-based weight loss interventions.