Early Weight Loss in Behavioral Treatment Predicts Later Rate of Weight Loss and Response to Pharmacotherapy

The Effectiveness of Nonsurgical Interventions for Weight Loss Maintenance in Adults: An Updated, GRADE-Assessed Systematic Review and Meta-Analysis of Randomized Clinical Trials

CONTEXT

Today, there are many discussions about the best way to maintain weight and prevent weight regain after a period of weight loss.

OBJECTIVES

The aim of this study was to summarize, based on data from randomized clinical trials (RCTs), the impact of nonsurgical interventions for adults' weight loss maintenance.

DATA SOURCES

The Medline (PubMed), Scopus, and Web of Science databases were reviewed during June 2023.

DATA EXTRACTION

Meta-analyses assessing the impacts of nonsurgical interventions for weight loss maintenance were conducted. Effect sizes of nutritional interventions were recalculated by applying a random-effects model. The Grading of Recommendations, Assessment, Development, and Evaluation framework was implemented to determine evidence certainty.

RESULTS

Meta-analysis of data from a total of 56 RCTs (n = 13 270 participants) represented a significant weight reduction after behavior and lifestyle interventions (mean difference [MD], -0.64 kg [95% CI, -1.18 to -0.09]; I2 = 89.5%; P < .001 for heterogeneity). Pharmacological interventions had also a significant effect on weight change during the weight maintenance phase (MD, -2.57 kg [95% CI, -3.12 to -2.02]; I2 = 91.6%; P < .001 for heterogeneity). The weight loss reduction from pharmacological interventions was greater with sibutramine (MD, -2.57; 95% CI: -3.12 to -2.02). Additionally, diet intervention and dietary and physical activity strategies were associated with a negligible trending decrease in weigh regain (respectively: MD, -0.91 kg [95% CI, -2.18 to 0.36], I2 = 55.7%, P = .016 for heterogeneity; and MD, -0.3 kg [95% CI, -4.13 to 3.52], I2 =  94.1%, P < .001).

CONCLUSION

The findings of this review indicate there is a favorable impact of behavior-based interventions and antiobesity medications on weight maintenance.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration no CRD42023468056.

Early safety of endoscopic sleeve gastroplasty in super obesity (body mass index > 50)

BACKGROUND

The prevalence of super obesity (body mass index [BMI] > 50) continues to rise. However, the adoption of bariatric surgery in this population remains very low. There are limited studies evaluating the utility of endoscopic sleeve gastroplasty (ESG) in super obesity.

OBJECTIVES

The purpose of this study is to evaluate the short-term safety profile of ESG in patients with super obesity using data from the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program database.

SETTING

United States.

METHODS

We retrospectively analyzed patients who underwent ESG and sleeve gastrectomy (SG) from 2016 to 2021. Patients with BMI >50 who underwent ESG were compared to ESG patients with BMI <50 and also SG patients with BMI >50. Primary outcomes included the incidence of severe adverse events (AEs), hospital readmission, reintervention, and reoperation within 30 days of the primary procedure. Secondary outcomes included procedure time, hospital length of stay, and total body weight loss at 30 days.

RESULTS

There were no significant differences in AE, reoperations, hospital readmissions, or reinterventions for patients with super obesity undergoing ESG, compared to patients with BMI below 50. Mean total body weight loss was greater in patients with super obesity. There were no significant differences in AEs for patients with super obesity who underwent ESG versus SG, although ESG patients had more hospital readmissions, reinterventions, and reoperations.

CONCLUSIONS

ESG may be performed safely, with comparable safety to SG, in patients with BMI as high as 70. However, further studies are needed to validate the feasibility and long-term efficacy prior to clinical implementation.

Importance of early weight loss and other predictors of lower weight loss in a commercial program: A secondary data analysis

Objective

There is substantial inter-individual variability in response to weight loss interventions and emerging evidence suggests that weight loss during the early weeks of an intervention may be predictive of longer-term weight loss. This secondary analysis of data from a commercial program therefore examined 1) the associations between early weight loss (i.e., week 4) with final visit weight loss and duration on the program, and 2) other predictors of lower weight loss at final visit.

Methods

Client charts of adults with overweight or obesity (N = 748) were analyzed. Clients were stratified into categories of weight loss at the week 4 (< and ≥2%, 3% and 4%) and final visits (< and ≥5% and 10%). Multivariate logistic regression was used to assess predictors of <5% and <10% final visit weight loss.

Results

The odds ratios for losing <5% or <10% of weight at the final visit were higher (49.0 (95% CI: 13.84, 173.63) and 20.1 (95% CI: 6.96, 58.06)) for clients who lost <2% or <3% compared to those who lost ≥2% or ≥3% at week 4. Other predictors of not losing a clinically relevant amount of weight included female sex, use of higher calorie meal plans and shorter time in the program, among others. Those who lost ≥2% at week 4 also had a significantly greater percent program completion (109.2 ± 75.2% vs. 82.3 ± 82.4, p < 0.01) compared with those who did not meet the 2% threshold.

Conclusions

Lower 4-week weight loss was identified as a strong predictor of not losing a clinically relevant amount of weight. These results may be useful for the early identification of individuals who can be targeted for additional counseling and support to aid in attaining weight loss goals.

Characterization of early non-responders within behavioral weight loss treatment

Objective

Given that low early (4 weeks) weight loss (WL) predicts longer-term WL, the purpose of this study was to identify factors associated with poor early WL.

Methods

438 adults with overweight/obesity participating in an Internet-delivered behavioral WL program provided weights at baseline and 4 weeks. Participants were stratified by percent WL at 4 weeks: LOW: <2% WL, MEDIUM: 2 to <4% WL, HIGH: ≥4% WL and groups were compared on baseline variables (demographics, physical activity, and psychosocial measures) and 4-week intervention adherence.

Results

37.4%, 40.9%, and 21.7% of participants had LOW, MEDIUM, and HIGH early WL respectively. LOW was more likely to be female compared to HIGH and less likely to be non-Hispanic White compared to MEDIUM and HIGH (p's<0.05). After controlling for demographic differences, LOW had lower baseline physical activity compared to HIGH and watched fewer video lessons, self-monitored calorie intake and weight on fewer days, and were less likely to achieve the exercise goal compared to MEDIUM and HIGH (p's<0.05).

Conclusion

Findings can inform future adaptive interventions which tailor treatment based upon early WL to improve WL outcomes for more individuals.

Differential mechanisms affecting weight loss and weight loss maintenance

In most lifestyle, pharmacological and surgical interventions, weight loss occurs over an approximately 6- to 9-month period and is followed by a weight plateau and then weight regain. Overall, only about 15% of individuals can sustain a 10% or greater non-surgical, non-pharmacological, weight loss. A key question is the degree to which the genotypes, phenotypes and environmental correlates of success in weight loss and weight loss maintenance are continuous or dichotomous. This Perspective is a comparison of the interactions of weight loss and maintenance with genetic, behavioural, physiological and environmental homeostatic systems and a discussion of the implications of these findings for research in, and treatment of, obesity. Data suggest that weight loss and weight loss maintenance are physiologically and psychologically different in many ways. Consequently, individuals may require different interventions designed for temporarily sustaining a negative energy balance during weight loss versus permanently maintaining energy balance after weight loss.

Health Coaching for Weight Loss Among Overweight and Obese Individuals in Saudi Arabia: A Retrospective Analysis

Background Health coaching is an increasingly used strategy to help in adopting lifestyle changes for weight loss. While Saudi Arabia has one of the highest obesity prevalences worldwide, research on lifestyle interventions for weight loss is limited. Aim We aimed to investigate the effectiveness of health coaching for weight loss among the Saudi population in real-world primary healthcare settings. Methods This is a retrospective observational study. Secondary data from the health coach national program in the Eastern Health Cluster were retrieved. Obese and overweight individuals aged 15 years or older with weight-related goals who completed at least 12 weeks of coaching were included in the analysis. The primary outcomes are weight change (kg) and weight change percent (%) of the initial weight. We further compared the weight change% between different follow-up methods (i.e., physical, virtual, and hybrid) and studied the factors associated with -5% weight loss. Results In total, 465 participants were included in the analysis, with a female predominance (66.2%) and a median initial weight of 90 kg (interquartile range (IQR): 77, 101). The median follow-up duration was 127 days (IQR: 101, 157), and the median total number of coaching sessions was three (IQR: 2, 5). The mean weight change was -2.68 kg (95% confidence interval (CI): -3.12, -2.24), p<0.001. Comparing each follow-up group, no statistically significant difference was found when controlling for number of visits (p=0.059). The adjusted means for weight change% were -3.77%, -2.59%, and -2.54% for hybrid, physical, and virtual visits, respectively. Factors that were associated with achieving at least -5% weight loss were male sex (adjusted odds ratio (aOR)=1.87, 95% CI: 1.16, 3.02), five or more total coaching visits (aOR=5.23, 95% CI: 2.88, 9.50), longer follow-up duration (aOR=1.09, 95% CI: 1.03, 1.15), and having a weight management goal (aOR=4.5, 95% CI: 1.63, 12.45) as the reason for initial coaching visit. Conclusion We found statistically significant weight change among clients who completed 12 weeks of coaching in primary care settings. The findings in this paper contribute to the importance of lifestyle interventions for weight loss among the Saudi population. However, stronger controlled studies are needed to confirm this finding.

A very low-carbohydrate diabetes prevention program for veterans with prediabetes: a single-arm mixed methods pilot study

Introduction

All Veterans Affairs (VA) Medical Centers offer the MOVE! Weight Management Program to help patients achieve and maintain a healthy weight through a calorie-restricted, low-fat diet and increased physical activity. Yet, most MOVE! participants do not achieve clinically significant weight loss of ≥5%. A carbohydrate-restricted diet may help more Veterans to achieve ≥5% weight loss.

Methods

This was a single-arm explanatory sequential mixed methods pilot study conducted in one VA health care system. Veterans with prediabetes and body mass index ≥25 kg/m2 were invited to participate in a group-based, virtual, very low-carbohydrate Diabetes Prevention Program (VLC-DPP) consisting of 23 sessions over 12 months. Participants were taught to follow a very low-carbohydrate eating pattern, defined as 20-35 grams of net carbohydrates per day. The primary outcomes were measures of feasibility and acceptability, including program uptake and session attendance. Secondary outcomes included change in weight, hemoglobin A1c, lipids, and patient-reported measures of food cravings, stress eating, perceived health status, and motivation. Interviews were conducted at 6 months to identify factors that facilitated or hindered participants' achievement of ≥5% weight loss.

Results

Among 108 screened Veterans, 21 enrolled in the study (19%), and 18 were included in the analytic cohort. On average, participants attended 12.4/16 weekly sessions and 3.6/8 bimonthly or monthly sessions. At 12 months, mean percent weight loss was 9.4% (SD = 10.7) with 9 participants (50%) achieving ≥5% weight loss. Three factors facilitated achievement of ≥5% weight loss among 10/16 interviewees: (1) enjoyment of low-carbohydrate foods; (2) careful monitoring of carbohydrate intake; and (3) reduced hunger and food cravings. Three factors hindered achievement of ≥5% weight loss among 6/16 interviewees: (1) food cravings, particularly for sweets; (2) challenges with maintaining a food log; and (3) difficulty with meal planning.

Conclusion

A VLC-DPP is feasible and acceptable and shows preliminary efficacy among Veterans with prediabetes. The program's weight loss effectiveness compared to standard MOVE! should be evaluated in a larger-scale trial. Such a program may be offered in addition to the standard MOVE! program to expand the menu of evidence-based lifestyle counseling options for Veterans.

Clinical Trial Registration

https://clinicaltrials.gov/ct2/show/NCT04881890, identifier NCT04881890.

What Intervention Elements Drive Weight Loss in Blended-Care Behavior Change Interventions? A Real-World Data Analysis with 25,706 Patients

BACKGROUND

Blended-care behavior change interventions (BBCI) are a combination of digital care and coaching by health care professionals (HCP), which are proven effective for weight loss. However, it remains unclear what specific elements of BBCI drive weight loss.

OBJECTIVES

This study aims to identify the distinct impact of HCP-elements (coaching) and digital elements (self-monitoring, self-management, and education) for weight loss in BBCI.

METHODS

Long-term data from 25,706 patients treated at a digital behavior change provider were analyzed retrospectively using a ridge regression model to predict weight loss at 3, 6, and 12 months.

RESULTS

Overall relative weight loss was -1.63 kg at 1 month, -3.61 kg at 3 months, -5.28 kg at 6 months, and -6.55 kg at 12 months. The four factors of BBCI analyzed here (coaching, self-monitoring, self-management, and education) predict weight loss with varying accuracy and degree. Coaching, self-monitoring, and self-management are positively correlated with weight losses at 3 and 6 months. Learn time (i.e., self-guided education) is clearly associated with a higher degree of weight loss. Number of appointments outside of app coaching with a dietitian (coach) was negatively associated with weight loss.

CONCLUSIONS

The results testify to the efficacy of BBCI for weight loss-with particular positive associations per time point-and add to a growing body of research that characterizes the distinct impact of intervention elements in real-world settings, aiming to inform the design of future interventions for weight management.

Weight Loss Trajectories and Related Factors in a 16-Week Mobile Obesity Intervention Program: Retrospective Observational Study

Background

In obesity management, whether patients lose ≥5% of their initial weight is a critical factor in clinical outcomes. However, evaluations that take only this approach are unable to identify and distinguish between individuals whose weight changes vary and those who steadily lose weight. Evaluation of weight loss considering the volatility of weight changes through a mobile-based intervention for obesity can facilitate understanding of an individual’s behavior and weight changes from a longitudinal perspective.

Objective

The aim of this study is to use a machine learning approach to examine weight loss trajectories and explore factors related to behavioral and app use characteristics that induce weight loss.

Methods

We used the lifelog data of 13,140 individuals enrolled in a 16-week obesity management program on the health care app Noom in the United States from August 8, 2013, to August 8, 2019. We performed k-means clustering with dynamic time warping to cluster the weight loss time series and inspected the quality of clusters with the total sum of distance within the clusters. To identify use factors determining clustering assignment, we longitudinally compared weekly use statistics with effect size on a weekly basis.

Results

The initial average BMI value for the participants was 33.6 (SD 5.9) kg/m², and it ultimately reached 31.6 (SD 5.7) kg/m². Using the weight log data, we identified five clusters: cluster 1 (sharp decrease) showed the highest proportion of participants who reduced their weight by >5% (7296/11,295, 64.59%), followed by cluster 2 (moderate decrease). In each comparison between clusters 1 and 3 (yo-yo) and clusters 2 and 3, although the effect size of the difference in average meal record adherence and average weight record adherence was not significant in the first week, it peaked within the initial 8 weeks (Cohen d>0.35) and decreased after that.

Conclusions

Using a machine learning approach and clustering shape-based time series similarities, we identified 5 weight loss trajectories in a mobile weight management app. Overall adherence and early adherence related to self-monitoring emerged as potential predictors of these trajectories.

Increases in physical activity are associated with a faster rate of weight loss during dietary energy restriction in women with overweight and obesity

This secondary analysis examined the influence of changes in physical activity (PA), sedentary time and energy expenditure (EE) during dietary energy restriction on the rate of weight loss (WL) and 1-year follow-up weight change in women with overweight/obesity.Measurements of body weight and composition (air-displacement plethysmography), resting metabolic rate (indirect calorimetry), total daily (TDEE) and activity EE (AEE), minutes of PA and sedentary time (PA monitor) were taken at baseline, after 2 weeks, after ≥5% WL or 12 weeks of continuous (25% daily energy deficit) or intermittent (75% daily energy deficit alternated with ad libitum day) energy restriction, and at 1-year post-WL. The rate of WL was calculated as total %WL/number of dieting weeks. Data from both groups were combined for analyses.Thirty-seven participants (age=35±10y; BMI=29.1±2.3kg/m2) completed the intervention (WL=-5.9±1.6%) and 18 returned at 1-year post-WL (weight change=+4.5±5.2%). Changes in sedentary time at 2 weeks were associated with the rate of WL during energy restriction (r=-0.38; p=0.03). Changes in total (r=0.54; p<0.01), light (r=0.43; p=0.01) and moderate-to-vigorous PA (r=0.55; p<0.01), sedentary time (r=-0.52; p<0.01), steps per day (r=0.39; p=0.02), TDEE (r=0.46; p<0.01) and AEE (r=0.51; p<0.01) during energy restriction were associated with the rate of WL. Changes in total (r=-0.50; p=0.04) and moderate-to-vigorous PA (r=-0.61; p=0.01) between post-WL and follow-up were associated with 1-year weight change (r=-0.51; p=0.04).These findings highlight that PA and sedentary time could act as modifiable behavioural targets to promote better weight outcomes during dietary energy restriction and/or weight maintenance.

Developing weight navigation program to support personalized and effective obesity management in primary care settings: protocol for a quality improvement program with an embedded single-arm pilot study

BACKGROUND

Primary care providers (PCPs) are expected to help patients with obesity to lose weight through behavior change counseling and patient-centered use of available weight management resources. Yet, many PCPs face knowledge gaps and clinical time constraints that hinder their ability to successfully support patients' weight loss. Fortunately, a small and growing number of physicians are now certified in obesity medicine through the American Board of Obesity Medicine (ABOM) and can provide personalized and effective obesity treatment to individual patients. Little is known, however, about how to extend the expertise of ABOM-certified physicians to support PCPs and their many patients with obesity.

AIM

To develop and pilot test an innovative care model - the Weight Navigation Program (WNP) - to integrate ABOM-certified physicians into primary care settings and to enhance the delivery of personalized, effective obesity care.

METHODS

Quality improvement program with an embedded, 12-month, single-arm pilot study. Patients with obesity and ≥1 weight-related co-morbidity may be referred to the WNP by PCPs. All patients seen within the WNP during the first 12 months of clinical operations will be compared to a matched cohort of patients from another primary care site. We will recruit a subset of WNP patients (n = 30) to participate in a remote weight monitoring pilot program, which will include surveys at 0, 6, and 12 months, qualitative interviews at 0 and 6 months, and use of an electronic health record (EHR)-based text messaging program for remote weight monitoring.

DISCUSSION

Obesity is a complex chronic condition that requires evidence-based, personalized, and longitudinal care. To deliver such care in general practice, the WNP leverages the expertise of ABOM-certified physicians, health system and community weight management resources, and EHR-based population health management tools. The WNP is an innovative model with the potential to be implemented, scaled, and sustained in diverse primary care settings.

Impact of the COVID-19 pandemic on initial weight loss in a digital weight management program: A natural experiment

OBJECTIVE

The aim of this study was to assess the impact of the coronavirus disease 2019 (COVID-19) pandemic on initial weight loss during a digital weight management program.

METHODS

Participants (n = 866,192; BMI 33.6 [SD 7.4] kg/m² ) who joined a digital weight management program (WW) in the first 30 weeks of 2020 (COVID-19 cohort) were compared with participants (n = 624,043; BMI 33.1 [SD 7.2] kg/m² ) who joined the same program during the same time period in 2019 (control cohort). Weight change (percentage) and self-monitoring over the first 4 weeks of enrollment were compared between the cohorts. Significance was defined as meeting the criteria for a small effect (d ≥ 0.2).

RESULTS

Over the 30-week enrollment period, the COVID-19 cohort experienced significantly less weight loss than the control cohort but only for 7 weeks of enrollments. The COVID-19 cohort also had fewer days of food tracking but only for 3 weeks of enrollments. There were no differences in the self-monitoring of weight and activity at any time between the two cohorts.

CONCLUSIONS

Over a 30-week enrollment period, COVID-19 had negative effects on both weight loss and food self-monitoring, but the effects were short-lived. Those participating in evidence-based weight management programs can expect similar levels of initial weight loss as those experienced before the pandemic.

Getting a head start: identifying pretreatment correlates associated with early weight loss for individuals participating in weight loss treatment

Early weight loss is associated with greater weight loss following treatment cessation and years later. The present study aimed to identify pretreatment correlates associated with early weight loss in adults participating in weight-loss treatment in primary care. Participants (N = 89) were in the overweight/obesity range seeking weight-loss treatment in primary-care settings and randomized to one of three treatments: Motivational Interviewing and Internet Condition (MIC), Nutrition Psychoeducation and Internet Condition (NPC), or Usual Care (UC). At baseline, participants were assessed with the Eating Disorder Examination (EDE) interview and completed self-report measures of emotional overeating, exercise, exercise self-efficacy, and depression. Percent weight loss at week six was used as the Early Weight Loss variable. MIC/NPC groups had significantly greater Early Weight Loss than UC. Among MIC/NPC participants only, greater Early Weight Loss was associated with significantly lower pretreatment disordered eating and depressive symptoms. Participants in MIC/NPC who achieved clinically meaningful weight loss (>2.5%) by week six compared with those who did not (<2.5%) reported lower pretreatment disordered eating. Demographic factors and binge-eating disorder diagnosis were unrelated to Early Weight Loss. Our findings suggest that greater early weight loss may be associated with less pretreatment disordered eating and depressive symptoms.

CLINICAL TRIALS

NCT01558297.

A Pragmatic Approach to Translating Low- and Very Low-Carbohydrate Diets Into Clinical Practice for Patients With Obesity and Type 2 Diabetes

Across all eating patterns, individuals demonstrate marked differences in treatment response; some individuals gain weight and others lose weight with the same approach. Policy makers and research institutions now call for the development and use of personalized nutrition counseling strategies rather than one-size-fits-all dietary recommendations. However, challenges persist in translating some evidence-based eating patterns into the clinical practice due to the persistent notion that certain dietary approaches-regardless of individuals' preferences and health outcomes-are less healthy than others. For example, low- and very low-carbohydrate ketogenic diets (VLCKDs)-commonly defined as 10-26% and <10% total daily energy from carbohydrate, respectively-are recognized as viable lifestyle change options to support weight loss, glycemic control, and reduced medication use. Yet, critics contend that such eating patterns are less healthy and encourage general avoidance rather than patient-centered use. As with all medical treatments, the potential benefits and risks must be considered in the context of patient-centered, outcome-driven care; this is the cornerstone of evidence-based medicine. Thus, the critical challenge is to identify and safely support patients who may prefer and benefit from dietary carbohydrate restriction. In this Perspective, we propose a pragmatic, 4-stepped, outcome-driven approach to help health professionals use carbohydrate-restricted diets as one potential tool for supporting individual patients' weight loss and metabolic health.

Describing the Weight-Reduced State: Physiology, Behavior, and Interventions

Although many persons with obesity can lose weight by lifestyle (diet and physical activity) therapy, successful long-term weight loss is difficult to achieve, and most people who lose weight regain their lost weight over time. The neurohormonal, physiological, and behavioral factors that promote weight recidivism are unclear and complex. The National Institute of Diabetes and Digestive and Kidney Diseases convened a workshop in June 2019, titled "The Physiology of the Weight-Reduced State," to explore the mechanisms and integrative physiology of adaptations in appetite, energy expenditure, and thermogenesis that occur in the weight-reduced state and that may oppose weight-loss maintenance. The proceedings from the first session of this workshop are presented here. Drs. Michael Rosenbaum, Kevin Hall, and Rudolph Leibel discussed the physiological factors that contribute to weight regain; Dr. Michael Lowe discussed the biobehavioral issues involved in weight-loss maintenance; Dr. John Jakicic discussed the influence of physical activity on long-term weight-loss maintenance; and Dr. Louis Aronne discussed the ability of drug therapy to maintain weight loss.

Determining Predictors of Weight Loss in a Behavioral Intervention: A Case Study in the Use of Lasso Regression

OBJECTIVE

This study investigates predictors of weight loss among individuals with serious mental illness participating in an 18-month behavioral weight loss intervention, using Lasso regression to select the most powerful predictors.

METHODS

Data were analyzed from the intervention group of the ACHIEVE trial, an 18-month behavioral weight loss intervention in adults with serious mental illness. Lasso regression was employed to identify predictors of at least five-pound weight loss across the intervention time span. Once predictors were identified, classification trees were created to show examples of how to classify participants into having likely outcomes based on characteristics at baseline and during the intervention.

RESULTS

The analyzed sample contained 137 participants. Seventy-one (51.8%) individuals had a net weight loss of at least five pounds from baseline to 18 months. The Lasso regression selected weight loss from baseline to 6 months as a primary predictor of at least five pound 18-month weight loss, with a standardized coefficient of 0.51 (95% CI: -0.37, 1.40). Three other variables were also selected in the regression but added minimal predictive ability.

CONCLUSIONS

The analyses in this paper demonstrate the importance of tracking weight loss incrementally during an intervention as an indicator for overall weight loss, as well as the challenges in predicting long-term weight loss with other variables commonly available in clinical trials. The methods used in this paper also exemplify how to effectively analyze a clinical trial dataset containing many variables and identify factors related to desired outcomes.

The Role of Early and Later Response on Overall Outcomes in School-Based Obesity Intervention

OBJECTIVE

Early response to obesity intervention consistently predicts long-term BMI reductions. However, little is known about how changes in weight at other times in an intervention may impact long-term outcomes. This study examined the relationship between weight-related changes that occurred early and later during an intervention and the association between these changes with overall outcomes.

METHODS

A secondary analysis of a school-based obesity intervention with replicated efficacy among Hispanic middle school students was conducted (n = 174). Linear regression models were developed in which first and second semester changes in BMI represented as a percentage of the 95th BMI percentile (%BMIp95) were separately used to predict overall %BMIp95 outcomes. First semester changes in %BMIp95 were used to predict subsequent %BMIp95 change (i.e., second semester).

RESULTS

Changes in %BMIp95 during both the first and second semesters were independently associated with overall changes from baseline (e.g., at 24 months: first semester, β = 0.59, P < 0.01; second semester, β = 1.02, P < 0.001). First semester %BMIp95 change was not associated with second semester change (β = -0.07, P = 0.32).

CONCLUSIONS

Change at any point during the intervention was predictive of overall weight outcomes. Additional research is needed to understand patterns of weight changes throughout interventions to better understand long-term outcomes.

Effects on body weight, eating behavior, and quality of life of a low-energy diet combined with behavioral group treatment of persons with class II or III obesity: A 2-year pilot study

Objective

Obesity is associated with reduced health‐related quality of life (HRQoL). Outcomes of nonsurgical weight loss treatment on HRQoL are inconsistent and it is unclear how much weight reduction, or what type of treatment, is required for significant improvements. This study aimed to evaluate the effects of a lifestyle intervention program on weight, eating behaviors, and HRQoL, and to describe participants' experiences of treatment.

Methods

This 2‐year intervention trial in persons with class II or III obesity comprised a 3‐month liquid low‐energy diet (880 kcal/d) followed by a 3‐month reintroduction to regular foods, combined with behavioral group treatment.

Results

Fifty‐five participants (73% women) were included, mean (SD) age 43.2 (12.4) years, and mean body mass index 42.0 (6.0) kg/m². Mean weight loss at 6, 12, and 24 months was 18.9%, 13.7%, and 7.2%, respectively. Short‐ and long‐term effects on eating behavior were favorable. Twelve of 14 HRQoL domains were improved at 6 months, compared to eight domains at 12 months. After 24 months, 2 of 14 domains, physical and psychosocial functioning, were improved. The treatment program was well accepted by the participants.

Conclusions

Substantial weight loss after 6 months was associated with extensive improvements in HRQoL, comprising the physical, psychosocial, and mental domains. Significant weight regain was observed between 6 and 24 months follow‐up. Modest weight loss after 24 months was associated with moderate improvement in physical functioning and large improvement in psychosocial functioning. The effect on psychosocial functioning is most likely related to both weight loss and behavioral treatment.

Identifying and Addressing Individuals Resistant to Behavioral Lifestyle Treatment

The prevention and treatment of lifestyle disease involves overlapping factors. Those at the highest risk for the development of lifestyle disease are also at the highest risk for poor treatment outcomes. Although behavioral treatment of lifestyle disease has demonstrated efficacy on average, considerable individual variation exists in treatment response. In clinical settings, individuals unresponsive to treatment are typically provided escalated care. Community-based care is designed to reach high-risk populations unlikely to seek medical care. However, in community settings escalated treatment options are not usually available for individuals unresponsive to treatment. Addressing this gap is imperative to improve health outcomes of high-risk populations and to identify individuals who may be resistant to behavioral lifestyle treatment.

Efficacy of weight loss intervention can be predicted based on early alterations of fMRI food cue reactivity in the striatum

Increased fMRI food cue reactivity in obesity, i.e. higher responses to high- vs. low-calorie food images, is a promising marker of the dysregulated brain reward system underlying enhanced susceptibility to obesogenic environmental cues. Recently, it has also been shown that weight loss interventions might affect fMRI food cue reactivity and that there is a close association between the alteration of cue reactivity and the outcome of the intervention. Here we tested whether fMRI food cue reactivity could be used as a marker of diet-induced early changes of neural processing in the striatum that are predictive of the outcome of the weight loss intervention. To this end we investigated the relationship between food cue reactivity in the striatum measured one month after the onset of the weight loss program and weight changes obtained at the end of the six-month intervention. We observed a significant correlation between BMI change measured after six months and early alterations of fMRI food cue reactivity in the striatum, including the bilateral putamen, right pallidum, and left caudate. Our findings provide evidence for diet-induced early alterations of fMRI food cue reactivity in the striatum that can predict the outcome of the weight loss intervention.