Group-based Behavioral Weight Loss Research Articles

Yoga Participation And Weight Loss Within A Behavioral Intervention

Yoga may elicit numerous benefits including weight loss. However, it is unclear if adults with obesity will lose more weight as frequency and amount of yoga increases within a behavioral weight loss intervention. PURPOSE: To compare the association between yoga participation and weight loss across two styles of yoga. METHODS: Fifty adults with obesity (BMI: 31.3±3.8 kg/m2; 45.8±9.5 years) participated in a 6-month group-based behavioral weight loss intervention. All participants were prescribed a calorie and fat-reduced diet (1200-1800 kcal/day, 20-30% fat intake). Randomization was to either a Restorative (RES) or Vinyasa (VIN) style of yoga, with one supervised session per week and 4 home-based sessions using videos developed and provided by the investigators on an electronic tablet. Yoga sessions increased from 20 to 40 to 60 minutes per session across the intervention. Weight was assessed at baseline and 6 months. Analysis of variance with repeated measures was used to assess weight loss. Linear regression analyzed the association between yoga participation with weight loss. RESULTS: Weight significantly decreased from 87.3±2.6 kg to 81.8±2.6 kg in RES and from 88.4±2.6 kg to 82.5±2.6 kg in VIN (p<0.0001), with no difference between yoga conditions (p=0.882). Total participation days in yoga was significantly and linearly related to weight loss in both RES and VIN styles (β=0.088, p=0.018; β=0.089, p≤0.001, respectively). Total participation minutes in yoga was significantly and linearly related to weight loss in both RES and VIN styles (β=0.002, p=0.034; β=0.003, p≤0.001, respectively). CONCLUSIONS: Findings indicate that the amount and frequency of participation in yoga is associated with weight loss within the context of a comprehensive behavioral intervention. Future studies need to examine strategies to enhance yoga participation in adults with overweight or obesity, and to understand the pathways by which yoga may influence body weight regulation.

Changes in the Gut Microbiota During a Weight Loss Intervention of Daily Caloric Restriction Versus Intermittent Fasting: The DRIFT2 Randomized Clinical Trial

Abstract Objectives Intermittent fasting (IMF) is an alternative to the standard weight loss approach of daily caloric restriction (DCR). Although altered gut microbiota has been linked to obesity and may influence weight loss, it is unknown how the gut microbiota is impacted by these weight loss strategies or its association with responsiveness. In this study, we examine the gut microbial diversity and composition during an intervention of DCR versus IMF. Methods Fecal microbiota communities were profiled by 16S rRNA gene sequencing in 59 individuals with overweight and obesity (mean BMI: 33.1 (SD: 4.4) kg/m2, age: 40.7 (SD: 9.8) years; 76.3% female) undergoing a comprehensive, group-based behavioral weight loss intervention of DCR (n = 25) versus IMF (n = 34) at baseline and 3-months into the intervention. Mixed effects linear regressions, permutational ANOVA, and ANCOM were used to examine differences in gut microbiota over time and by intervention group, and regression-based methods were used to examine the association between baseline gut microbiota and percentage change in weight and waist circumference. Results Overall, participants lost 5.9 (SD: 3.7) kilograms at 3 months. Weight loss within groups is not reported, as the trial is ongoing. Alpha diversity increased in both intervention groups (P &amp;lt; 0.034), with no significant differences between groups (P &amp;gt; 0.847). Microbiota composition (beta diversity) changed significantly (R2 = 7.1%; P = 0.001) over the course of the intervention, with no significant differences between groups (P &amp;gt; 0.325). Numerous taxa showed changes over time with differences between groups (FDR &amp;lt; 0.05). Baseline gut microbiota composition significantly predicted change in waist circumference (P = 0.009) but not in weight (P = 0.172). Conclusions Our results support that a behavioral weight loss intervention of reduced energy intake impacts gut microbiota with some differences between DCR and IMF, which may provide insight into the physiological effects of these weight loss approaches. The baseline gut microbiota was significantly associated with decrease in waist circumference. These results may inform ways to target the gut microbiota before initiating weight loss in order to improve responsiveness. Funding Sources NIH NIDDK R01; AHA Innovative Project Award.

Participant and interventionist perceptions of challenges during behavioral weight loss treatment.

No prior studies have examined how interventionists' perceptions of participants' weight control challenges or the agreement between participants and interventionists on these perceptions relate to outcomes during group-based behavioral weight loss treatment. This study characterized participants' and interventionists' perceptions of, and agreement about, weight control challenges and assessed how these factors relate to weight loss. Three months into treatment, participants and interventionists independently selected three weight control challenges believed to be most relevant for each participant. Weight was measured at baseline, 3months, and 12months. Interventionists and participants had "no" (κ < 0) or "slight" (0 < κ< .20) agreement on most challenges. Although endorsement of certain challenges by participants and/or interventionists was related to 3- and 12-month weight losses, agreement between participants and interventionists was unrelated to weight loss at either time point. Additional research is needed to better understand the role of perceived challenges and participant/interventionist agreement about challenges on treatment outcomes.

The role of group cohesion in a group-based behavioral weight loss intervention.

Behavioral weight loss interventions are often delivered in groups. Group cohesion may enhance program attendance and, thereby, weight loss. In this secondary analysis, our goals were to: (1a) assess whether group cohesion measured early in a behavioral weight loss intervention predicts program attendance and weight loss outcomes and, if so, (1b) explore whether attendance mediates the link between group cohesion and weight loss; (2) characterize the association between change in group cohesion and weight loss throughout the intervention. Veterans (n=324) initiated a 16-week, group-based behavioral weight loss program involving biweekly in-person group visits. In linear regression models, early group cohesion was unrelated to group attendance or weight loss. Although group cohesion significantly increased during the intervention, this change was not associated with weight loss. These findings are consistent with the limited literature; however, they are inconsistent with theoretical assertions and clinical observations of the influence of group factors on outcomes.

Randomized trial comparing group size of periodic in-person sessions in a remotely delivered weight loss intervention

BackgroundFew randomized studies have examined differential effects of group size in behavioral weight control, especially in hybrid programs that include Internet treatment approaches.MethodsRandomized controlled trial (n = 195) comparing a 4 month hybrid internet weight loss program coupled with monthly face to face groups of 100 persons (Large Group, LG; 1 group) or to the same approach with monthly groups of 20 persons (Small Group, SG; 4 groups). Repeated-measures mixed-model analysis with age and race as covariates were used to estimate primary (weight) and secondary outcomes, and to test group differences in change over time.ResultsThe sample was 46.3 years old ±10.4, 90.3% female, and 51.9% non-white, with BMI 37.9 ± 8.4 kg/m2. Participants in the LG were more likely to return for the 4-month assessment visit than those in the SG (p = 0.04). Participants randomized to both the LG and SG conditions experienced significant WL over time (no between group difference: −4.1 kg and −3.7 kg, respectively) and weight loss was positively associated with attendance at monthly meetings and logins to the website. Satisfaction with the program was high and similar in both groups (94.4% reported that they were “satisfied” or “very satisfied”).ConclusionsUsing a hybrid approach of in-person and online weight loss interventions may be an effective way to reach larger and more diverse populations. Delivering the face to face component of the intervention in groups larger than those traditionally delivered (20–25 people) could increase the cost-effectiveness of group-based behavioral weight loss interventions.Clinical trials registration numberNCT01615471. Registered June 6, 2012. Registered retrospectively.

Applying a technology-based system for weight loss in adults with obesity.

SummaryObjectiveThe aim of this study was to compare an in‐person, group‐based behavioral weight loss intervention to technology‐based interventions in adults with obesity.MethodsAdults (N = 39; body mass index: 39.5 ± 2.8 kg m−2; age: 39.9 ± 11.5 years) participated in a 6‐month program with randomization to one of three intervention groups: standard behavioral weight loss, a technology‐based system combined with a monthly intervention telephone call (TECH) or an enhanced technology‐based system combined with a monthly intervention telephone call (EN‐TECH). All groups were prescribed an energy‐restricted diet and physical activity. Assessments occurred at 0, 3 and 6 months. Separate mixed‐effects models using unstructured dependence structure were fit to the outcomes.ResultsWeight loss (least square means ± standard error) at 6 months was −6.57 ± 1.65 kg in standard behavioral weight loss, −5.18 ± 1.72 kg in TECH and −6.25 ± 1.95 kg in EN‐TECH (p‐value for time effect ≤ 0.0001). A similar pattern was observed for change in body mass index, waist circumference and percent body fat. There was a decrease in total energy intake (p = 0.0005) and percent dietary fat intake (p = 0.0172), and physical activity increased (p = 0.0003).ConclusionsFindings provide initial information on the use of technology‐based interventions that include wearable devices combined with brief monthly telephone calls for weight loss in adults with obesity.

Pilot Feasibility Study of a Campaign Intervention for Weight Loss among Overweight and Obese Adults

Background: Behavioral interventions produce significant short-term weight loss. However, these interventions typically require regular in-person sessions, which may not be feasible for all individuals. Purpose: The purpose of this pilot study was to evaluate the feasibility of a 12-week campaign intervention (CI) compared to a standard on-site, group-based behavioral weight loss intervention (SBWL) among overweight/obese adults. Methods: SBWL participants (n=13; age: 42.5 ± 9.1 years; BMI: 33.4 ± 3.8 kg/m²) attended weekly group meetings, were prescribed a daily reduced caloric goal and 200 minutes of moderate-intensity physical activity per week. CI participants (n=13; age: 43.8 ± 9.0 years; BMI: 33.2 ± 3.8 kg/m²) received the same recommendations as the SBWL, attended in-person group meetings at weeks 0 and 12, and received e-mail messages weeks 2-11. Additional CI features included a thematic framework and an incentive-based point system targeting behavioral goals. Results: Significant weight loss was demonstrated for intention-to-treat (SBWL: -5.6 ± 2.9 kg; CI: -3.1 ± 3.4 kg) (p

Television use and binge eating in adults seeking weight loss treatment

ObjectiveBinge eating has a complex etiology and is likely influenced by a wide range of biological, psychological, social, and environmental factors. Among the environmental and behavioral contributors, television use has been strongly linked to obesity and unhealthy eating behaviors. The current study tested whether television use predicts binge eating symptomatology in adults seeking behavioral weight loss treatment. MethodParticipants (N=116) were adults seeking weight loss treatment in group-based behavioral weight loss programs. Average body mass index was 38.5; average age was 45.3. They completed measures of binge eating symptomatology, television use, internalized weight stigma, depression, body satisfaction, and habitual physical activity. ResultsThe amount of television participants watched per week was associated with binge eating symptomatology even after controlling for relevant covariates. Binge eating symptomatology was positively associated with television use, internalized weight stigma, depression, and decreased body satisfaction. DiscussionThe findings of the current study support the hypothesis that television use is a significant predictor of binge eating symptomatology for adults attempting weight loss. Determining the causal nature of the relationship and whether binge eating is occurring during television viewing will be important areas of future inquiry.

Health-e-call, a smartphone-assisted behavioral obesity treatment: pilot study.

BackgroundIndividual and group-based behavioral weight loss treatment (BWL) produces average weight loss of 5-10% of initial body weight, which improves health and wellbeing. However, BWL is an intensive treatment that is costly and not widely available. Smartphones may be a useful tool for promoting adherence to key aspects of BWL, such as self-monitoring, thereby facilitating weight loss while reducing the need for intensive in-person contact.ObjectiveThe objective of this study was to evaluate smartphones as a method of delivering key components of established and empirically validated behavioral weight loss treatment, with an emphasis on adherence to self-monitoring.MethodsTwenty overweight/obese participants (95% women; 85% non-Hispanic White; mean age 53.0, SE 1.9) received 12-24 weeks of behavioral weight loss treatment consisting of smartphone-based self-monitoring, feedback, and behavioral skills training. Participants also received brief weekly weigh-ins and paper weight loss lessons.ResultsAverage weight loss was 8.4kg (SE 0.8kg; 9%, SE 1% of initial body weight) at 12 weeks and 10.9kg (SE 1.1kg; 11%, SE 1% of initial body weight) at 24 weeks. Adherence to the self-monitoring protocol was 91% (SE 3%) during the first 12 weeks and 85% (SE 4%) during the second 12 weeks.ConclusionsSmartphones show promise as a tool for delivering key components of BWL and may be particularly advantageous for optimizing adherence to self-monitoring, a cornerstone of BWL.

Can weight loss improve migraine headaches in obese women? Rationale and design of the Women's Health and Migraine (WHAM) randomized controlled trial

BackgroundResearch demonstrates a link between migraine and obesity. Obesity increases the risk of frequent migraines and is associated with migraine prevalence among reproductive-aged women. These findings are substantiated by several plausible mechanisms and emerging evidence of migraine improvements after surgical and non-surgical weight loss. However, no previous study has examined the effect of weight loss on migraine within a treatment-controlled framework. The WHAM trial is an RCT to test the efficacy of behavioral weight loss as a treatment for migraine. Study designOverweight/obese women (n=140; BMI=25.0–49.9kg/m2) who meet international diagnostic criteria for migraine and record ≥3 migraines and 4–20 migraine days using a smartphone-based headache diary during a 4-week baseline period, will be randomly assigned to 4months of either group-based behavioral weight loss (intervention) or migraine education (control). Intervention participants will be taught strategies to increase physical activity and consume fewer calories in order to lose weight. Control participants will receive general education on migraine symptoms/triggers and various treatment approaches. Both groups will use smartphones to record their headaches for 4weeks at baseline, after the 16-week treatment period, and at the end of a 16-week follow-up period. Changes in weight and other potential physiological (inflammation), psychological (depression), and behavioral (diet and physical activity) mediators of the intervention effect will also be assessed. ConclusionThe WHAM trial will evaluate the efficacy of a standardized behavioral weight loss intervention for reducing migraine frequency, and the extent to which weight loss and other potential mediators account for intervention effects.

Electronic feedback in a diet- and physical activity-based lifestyle intervention for weight loss: a randomized controlled trial

BackgroundThe SenseWear™ Armband (SWA) (BodyMedia, Inc. Pittsburgh, PA) is a physical activity and lifestyle monitor that objectively and accurately measures free-living energy balance and sleep and includes software for self-monitoring of daily energy expenditure and energy intake. The real-time feedback of the SWA can improve individual self-monitoring and, therefore, enhance weight loss outcomes.MethodsWe recruited 197 sedentary overweight or obese adults (age, 46.8 ± 10.8 y; body mass index (BMI), 33.3 ± 5.2 kg/m2; 81% women, 32% African-American) from the greater Columbia, South Carolina area. Participants were randomized into 1 of 4 groups, a self-directed weight loss program via an evidence-based weight loss manual (Standard Care, n = 50), a group-based behavioral weight loss program (GWL, n = 49), the armband alone (SWA-alone, n = 49), or the GWL plus the armband (GWL+SWA, n = 49), during the 9-month intervention. The primary outcome was change in body weight and waist circumference. A mixed-model repeated-measures analysis compared change in the intervention groups to the standard care group on weight and waist circumference status after adjusting for age, sex, race, education, energy expenditure, and recruitment wave.ResultsBody weight was available for 62% of participants at 9 months (52% standard care, 70% intervention). There was significant weight loss in all 3 intervention groups (GWL, 1.86 kg, P = 0.05; SWA-alone, 3.55 kg, P = 0.0002; GWL+SWA, 6.59 kg, P < 0.0001) but not in the Standard Care group (0.89 kg, P = 0.39) at month 9. Only the GWL+SWA group achieved significant weight loss at month 9 compared to the Standard Care group (P = 0.04). Significant waist circumference reductions were achieved in all 4 groups at month 9 (Standard Care, 3.49 cm, P = 0.0004; GWL, 2.42 cm, P = 0.008; SWA-alone, 3.59 cm, P < 0.0001; GWL+SWA, 6.77 cm, P < 0.0001), but no intervention group had significantly reduced waist circumference compared to the Standard Care group.ConclusionsContinuous self-monitoring from wearable technology with real-time feedback may be particularly useful to enhance lifestyle changes that promote weight loss in sedentary overweight or obese adults. This strategy, combined with a group-based behavioral intervention, may yield optimal weight loss.Trial RegistrationClinicalTrials.gov: NCT00957008

Cardiometabolic results from an armband-based weight loss trial

Purpose:This report examines the blood chemistry and blood pressure (BP) results from the Lifestyle Education for Activity and Nutrition (LEAN) study, a randomized weight loss trial. A primary purpose of the study was to evaluate the effects of real-time self-monitoring of energy balance (using the SenseWear™ Armband, BodyMedia, Inc Pittsburgh, PA) on these health factors.Methods:164 sedentary overweight or obese adults (46.8 ± 10.8 years; BMI 33.3 ± 5.2 kg/m2; 80% women) took part in the 9-month study. Participants were randomized into 4 conditions: a standard care condition with an evidence-based weight loss manual (n = 40), a group-based behavioral weight loss program (n = 44), an armband alone condition (n = 41), and a group plus armband (n = 39) condition. BP, fasting blood lipids and glucose were measured at baseline and 9 months.Results:99 participants (60%) completed both baseline and follow-up measurements for BP and blood chemistry analysis. Missing data were handled by baseline carried forward. None of the intervention groups had significant changes in blood lipids or BP when compared to standard care after adjustment for covariates, though within-group lowering was found for systolic BP in group and group + armband conditions, a rise in total cholesterol and LDL were found in standard care and group conditions, and a lowering of triglycerides was found in the two armband conditions. Compared with the standard care condition, fasting glucose decreased significantly for participants in the group, armband, and group + armband conditions (all P < 0.05), respectively.Conclusion:Our results suggest that using an armband program is an effective strategy to decrease fasting blood glucose. This indicates that devices, such as the armband, can be a successful way to disseminate programs that can improve health risk factors. This can be accomplished without group-based behavioral programs, thereby potentially reducing costs.

The Influence of Behavior Self-Monitoring Using Technology on Weight Loss in Overweight Adults

The BodyMedia® FIT System (BodyMedia Inc.) is a commercially available tool that includes a multi-sensor armband, a display that provides real time feedback of energy expenditure and physical activity, and is integrated with a website that allows for monitoring of energy intake and body weight. It is unclear if use of these self-monitoring components are predictive of weight loss. PURPOSE: To examine which of the monitored energy balance variables using the BodyMedia® FIT System (wearing the multi-sensor armband, self-monitoring of energy intake, self-monitoring of weight) was predictive of weight loss in overweight and obese adults. METHODS: Thirty overweight and obese adults (BMI: 33.9±3.5 kg/m2; Age: 44.2±8.7 yrs) were randomized to one of two groups: standard in-person group-based behavioral weight loss with the addition of the BodyMedia® FIT System (SBWL+TECH), or the BodyMedia® FIT System plus one brief telephone contact per month (TECH). Energy intake was prescribed at 1200-1800 kcal/d and physical activity progressed to 300 min/wk. Weight was assessed at 0 and 6 months. Use of the multi-sensor armband, self-monitoring of energy intake, and self-monitoring of weight were computed for the 6 month intervention period. RESULTS: For TECH, there were significant correlations between weight loss and both self-monitoring of weight (r=0.77, p=0.01) and self-monitoring of energy intake (r=0.63, p=0.01), with no association between weight loss and time wearing the armband. Regression analysis revealed that only frequency of self-monitoring of body weight was predictive of weight loss, which explained 56% of the variability in weight loss (p=0.002). None of the self-monitoring variables using technology were significantly associated with weight change in SBWL+TECH. CONCLUSION: These findings provide insight into the importance of regular self-monitoring of body weight within interventions delivered remotely using technology that do not include an in-person intervention component. These results require replication using a larger sample followed over a longer period of time, and do not downplay the importance of self-monitoring of physical activity and energy intake within weight loss interventions.

Using a technology-based intervention to promote weight loss in sedentary overweight or obese adults: a randomized controlled trial study design

Purpose:The SenseWear™ Armband is an activity monitor developed to improve lifestyle self-monitoring. Currently, few studies assess electronic self-monitoring and weight loss with a lifestyle intervention program. To our knowledge, only one study has used the SenseWear Armband in combination with a lifestyle intervention to improve weight loss, and no studies have evaluated whether a self-monitoring intervention based solely on the armband can promote weight loss. Consequently, the aims of the study were to assess weight loss from electronic self-monitoring, to compare these values to the lifestyle intervention and standard care groups, and to compare weight loss with lifestyle intervention with and without the armband.Patients and methods:We recruited 197 sedentary overweight or obese adults (age, 46.8 ± 10.8 years; BMI, 33.3 ± 5.2 kg/m2) to participate in the 9-month study. Participants were randomized into one of four weight loss groups: 1) the standard care group received a self-directed weight loss program, complete with an evidence-based weight loss manual (standard care, n = 50); 2) a 14-week group-based behavioral weight loss program followed by weekly, biweekly, and monthly telephone counseling calls (GWL, n = 49); 3) the use of the armband to help improve lifestyle self-monitoring (SWA alone, n = 49); or (4) the group-based behavioral weight loss program and follow-up telephone counseling calls plus the armband (GWL + SWA, n = 49). All participants received the evidence-based weight loss manual at baseline. All measures were performed at baseline and months 4 and 9. The primary outcomes were weight loss and waist circumference reduction.Results:This study is a well-designed randomized controlled study powered to detect a 0.5-kg weight loss and 0.6-cm waist circumference reduction in overweight and obese sedentary adults.Conclusion:Innovative technologies are providing lifestyle self-monitoring and weight loss tools. Utilizing these technologies may be an important step in improving the current obesity epidemic.

The Failure of Therapist Assistance and Stepped‐care to Improve Weight Loss Outcomes

This investigation was designed to examine whether: (i) individuals could successfully lose 5% of their body weight with minimal assistance, (ii) weight loss would be improved by the addition of therapist assistance, and (iii) individuals unsuccessful at losing 5% total body weight during the minimal assistance phase (with or without therapist assistance) would benefit from a weekly weight loss group. Fifty-four overweight or obese adults (BMI > 27 kg/m(2)) initially participated in a 14-week self-help (SH) or therapist-assisted SH (TASH) weight loss program. Participants who were unsuccessful at losing 5% total body weight were stepped-up to a 3-month, group-based behavioral weight loss program (BWLP) with weekly weigh-ins. Although approximately 60% of the participants were successful at losing 5% of their total body weight (lb) during the minimal assistance phase (M = 10.6; s.d. = 11.5; P < 0.01), treatment outcome was not improved by the addition of therapist assistance. For individuals who were unsuccessful at losing 5% of total body weight during a minimal assistance phase, the addition of a group-based BWLP did not improve their weight loss. While many individuals were quite successful at losing weight with minimal assistance, other individuals evidenced difficulties losing weight, even when participating in a greater intensity intervention (i.e., BWLP group).

Changes in Markers of Insulin Resistance Following a 6-Month Weight Loss Program in Obese Women

PURPOSE: To examine the relationship between markers of insulin resistance prior to and following a 6-month weight loss program in obese women. METHODS: One hundred eighty five (N=1 85) overweight and obese (BMI = 32.7+4.2 kg/m2; age = 37.7+5.6 years) women participated in a 6-month weight loss intervention to examine the impact of different doses (moderate: 1000 vs. high: 2000 kcal/wk) and intensities (moderate: 55–69% HRmax vs. vigorous: 70–85% HRmax) of exercise on weight loss, fitness, and biomarkers of insulin resistance. Subjects were randomly assigned to one of the following exercise conditions: 1) moderate dose, moderate intensity; 2) moderate dose, vigorous intensity; 3) high dose, moderate intensity; high dose, vigorous intensity). All groups reduced energy intake to 1200 to 1500 kcal/d, and received a group-based behavioral weight loss intervention. Assessments at baseline and 6 months included body weight, fitness using a submaximal graded exercise test to 85% of HRmax, and fasting blood samples for glucose and insulin, which were used to compute HOMA-IR. RESULTS: Analysis revealed no significant difference between the exercise groups for change in body weight, fitness, or markers of insulin resistance. Overall, there was a significant decrease in body weight (8.4+4.8 kg; 9.7+5.4%), increase in fitness as measured by time to achieve 85% HRmax (3.8+3.1 min), decrease in fasting insulin (3.8+8.2 μU/ml), and decrease in HOMA-IR (0.7+2.4) (p <0.001). Change in fitness was not significantly correlated with change in fasting insulin or HOMA-IR. However change in body weight was significantly correlated with reductions in fasting insulin (r=0.14; p <0.05) and HOMA-IR (r=0.17; p <0.05). CONCLUSION: In this sample of overweight and obese women, weight loss rather than improvements in cardiorespiratory fitness were associated with improvements in markers of insulin resistance. Thus, while exercise is important to improve fitness in overweight adults, it may be important for exercise to elicit significant reductions in body weight to have the greatest impact on insulin resistance. However, because of the modest correlations between change in body weight and markers of insulin resistance, this suggests that additional factors contribute to insulin resistance in overweight adult women.