Dataset Information

Association between Indicators of Inequality and Weight Change following a Behavioural Weight Loss Intervention.

ABSTRACT:

Introduction

Weight loss through behavioural weight management interventions can have important health benefits for people with obesity. However, to maximise the health benefits, weight loss must be maintained. Evidence suggests that behavioural weight loss interventions do not exacerbate inequalities in the short term. However, no study has yet considered whether inequalities exist in long-term weight change following intervention. We aimed to investigate if there are inequalities in weight change following weight loss intervention.

Methods

We conducted a cohort analysis of data from the Weight Loss Referrals for Adults in Primary Care (WRAP) trial (N = 1,267). WRAP randomised participants to receive a brief intervention information booklet or vouchers for 12-weeks or 52-weeks of WW (formerly WeightWatchers) and followed them for 5 years. Multiple linear regression estimated the association between exposures (indicators of inequality) and outcomes (change in weight between 1- and 5-years). Each model was adjusted for the intervention group, baseline weight, weight change between baseline and 1-year, research centre, and source of the 5-year weight data.

Results

Of the 1,267 participants in WRAP, 708 had weight change data available. Mean weight change between 1- and 5-years was +3.30 kg (SD 9.10 kg). A 1 year difference in age at baseline was associated with weight change of 0.11 kg ((95% CI 0.06, 0.16), p < 0.001). We did not find evidence of associations between ethnicity, gender, education, indices of multiple deprivation, household income, or other family members participating in a weight loss programme and weight change.

Conclusion

Except for age, we did not find evidence of inequalities in weight change following a behavioural intervention. Findings further support the use of behavioural weight management interventions as part of a systems-wide approach to improving population health.

SUBMITTER: Birch JM

PROVIDER: S-EPMC10028366 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Association between Indicators of Inequality and Weight Change following a Behavioural Weight Loss Intervention.

Birch Jack M JM Mueller Julia J Sharp Stephen J SJ Griffin Simon J SJ Kelly Michael P MP Halford Jason C G JCG Ahern Amy L AL

Obesity facts 20221215 2

<h4>Introduction</h4>Weight loss through behavioural weight management interventions can have important health benefits for people with obesity. However, to maximise the health benefits, weight loss must be maintained. Evidence suggests that behavioural weight loss interventions do not exacerbate inequalities in the short term. However, no study has yet considered whether inequalities exist in long-term weight change following intervention. We aimed to investigate if there are inequalities in we ...[more]

PMID: 36521444

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Project description:BackgroundPrior research on the relationship between sleep and attempted weight loss failed to recognize the multidimensional nature of sleep. We examined the relationship between a composite measure of sleep health and change in weight and body composition among adults in a weight loss intervention.MethodsAdults (N = 125) with overweight or obesity (50.3 ± 10.6 years, 91% female, 81% white) participated in a 12-month behavioral weight loss intervention, with assessments of sleep, weight, fat mass, and fat-free mass at baseline, 6 months, and 12 months. Six sleep dimensions (regularity, satisfaction, alertness, timing, efficiency, and duration) were categorized as "good" or "poor" using questionnaires and actigraphy. A composite score was calculated by summing the number of "good" dimensions. Obstructive sleep apnea (OSA) was assessed in a subsample (n = 117), using the apnea-hypopnea index (AHI) to determine OSA severity. Linear mixed modeling was used to examine the relationships between sleep health and outcomes of percent weight, fat mass, or fat-free mass change during the subsequent 6-month interval, adjusting for age, sex, bed partner, and race; an additional model adjusted for AHI.ResultsMean baseline and 6-month sleep health was 4.5 ± 1.1 and 4.5 ± 1.2, respectively. Mean weight, fat mass, and fat-free mass changes from 0 to 6 months were -9.3 ± 6.1%, -16.9 ± 13.5%, and -3.4 ± 3.4%, respectively, and 0.4 ± 4.8%, -0.3 ± 10.3%, and 0.7 ± 4.1% from 6 to 12 months. Better sleep health was associated with greater subsequent weight loss (P = 0.016) and fat loss (P = 0.006), but not fat-free mass loss (P = 0.232). Following AHI adjustment, the association between sleep health and weight loss was attenuated (P = 0.102) but remained significant with fat loss (P = 0.040). Regularity, satisfaction, timing, and efficiency were each associated with weight and/or fat loss (P ≤ 0.041).ConclusionsBetter sleep health was associated with greater weight and fat loss, with associations attenuated after accounting for OSA severity. Future studies should explore whether improving sleep health, OSA, or the combination improves weight loss.

Project description:ObjectiveTo determine if the characteristics of behavioural weight loss programmes influence the rate of change in weight after the end of the programme.DesignSystematic review and meta-analysis.Data sourcesTrial registries, 11 electronic databases, and forward citation searching (from database inception; latest search December 2019). Randomised trials of behavioural weight loss programmes in adults with overweight or obesity, reporting outcomes at ≥12 months, including at the end of the programme and after the end of the programme.Review methodsStudies were screened by two independent reviewers with discrepancies resolved by discussion. 5% of the studies identified in the searches met the inclusion criteria. One reviewer extracted the data and a second reviewer checked the data. Risk of bias was assessed with Cochrane's risk of bias tool (version 1). The rate of change in weight was calculated (kg/month; converted to kg/year for interpretability) after the end of the programme in the intervention versus control groups by a mixed model with a random intercept. Associations between the rate of change in weight and prespecified variables were tested.ResultsData were analysed from 249 trials (n=59 081) with a mean length of follow-up of two years (longest 30 years). 56% of studies (n=140) had an unclear risk of bias, 21% (n=52) a low risk, and 23% (n=57) a high risk of bias. Regain in weight was faster in the intervention versus the no intervention control groups (0.12-0.32 kg/year) but the difference between groups was maintained for at least five years. Each kilogram of weight lost at the end of the programme was associated with faster regain in weight at a rate of 0.13-0.19 kg/year. Financial incentives for weight loss were associated with faster regain in weight at a rate of 1-1.5 kg/year. Compared with programmes with no meal replacements, interventions involving partial meal replacements were associated with faster regain in weight but not after adjustment for weight loss during the programme. Access to the programme outside of the study was associated with slower regain in weight. Programmes where the intensity of the interaction reduced gradually were also associated with slower regain in weight in the multivariable analysis, although the point estimate suggested that the association was small. Other characteristics did not explain the heterogeneity in regain in weight.ConclusionFaster regain in weight after weight loss was associated with greater initial weight loss, but greater initial weight loss was still associated with reduced weight for at least five years after the end of the programme, after which data were limited. Continued availability of the programme to participants outside of the study predicted a slower regain in weight, and provision of financial incentives predicted faster regain in weight; no other clear associations were found.Study registrationPROSPERO CRD42018105744.

Project description:BackgroundThere is large variance in weight loss outcomes of digital behavior change interventions (DBCIs). It has been suggested that different patterns of engagement in the program could be responsible for this variance in outcomes. Previous studies have found that the amount of engagement on DBCIs, such as the number of meals logged or articles read, is positively associated with weight loss.ObjectiveThis retrospective study extends previous research by observing how important weight loss outcomes (high weight loss: 10% or greater body weight loss; moderate weight loss: between 5% to 10%; stable weight: 0 plus or minus 1%) are associated with engagement on a publicly available mobile DBCI (Noom) from 9 to 52 weeks.MethodsEngagement and weight data for eligible participants (N=11,252) were extracted from the Noom database. Engagement measures included the number of articles read, meals logged, steps recorded, messages to coach, exercise logged, weigh-ins, and days with 1 meal logged per week. Weight was self-reported on the program. Multiple linear regressions examined how weight loss outcome (moderate and high vs stable) was associated with each engagement measure across 3 study time periods: 9-16 weeks, 17-32 weeks, and 33-52 weeks.ResultsAt 9-16 weeks, among the 11,252 participants, 2594 (23.05%) had stable weight, 6440 (57.23%) had moderate weight loss, and 2218 (19.71%) had high weight loss. By 33-52 weeks, 525 (18.21%) had stable weight, 1214 (42.11%) had moderate weight loss, and 1144 (39.68%) had high weight loss. Regression results showed that moderate weight loss and high weight loss outcomes were associated with all engagement measures to a significantly greater degree than was stable weight (all P values <.001). These differences held across all time periods with the exception of exercise for the moderate weight loss category at 1 time period of 33-52 weeks. Exercise logging increased from 9 to 52 weeks regardless of the weight loss group.ConclusionsOur results suggest that these clinically important weight loss outcomes are related to the number of articles read, meals logged, steps recorded, messages to coach, exercise logged, weigh-ins, and days with 1 meal logged per week both in the short-term and long-term (ie, 1 year) on Noom. This provides valuable data on engagement patterns over time on a self-directed mobile DBCI, can help inform how interventions tailor recommendations for engagement depending on how much weight individuals have lost, and raises important questions for future research on engagement in DBCIs.

Project description:Aims/hypothesisWeight loss is often recommended in the treatment of type 2 diabetes. While evidence has shown that large weight loss may lead to diabetes remission and improvement in cardiovascular risk factors, long-term impacts are unclear. We performed a systematic review of studies of weight loss and other weight changes and incidence of CVD among people with type 2 diabetes.MethodsObservational studies of behavioural (non-surgical and non-pharmaceutical) weight changes and CVD events among adults with type 2 diabetes, and trials of behavioural interventions targeting weight loss, were identified through searches of MEDLINE, EMBASE, Web of Science, CINAHL, and The Cochrane Library (CENTRAL) until 9 July 2019. Included studies reported change in weight and CVD and/or mortality outcomes among adults with type 2 diabetes. We performed a narrative synthesis of observational studies and meta-analysis of trial data.ResultsOf 13,227 identified articles, 17 (14 observational studies, three trials) met inclusion criteria. Weight gain (vs no change) was associated with higher hazard of CVD events (HRs [95% CIs] ranged from 1.13 [1.00, 1.29] to 1.63 [1.11, 2.39]) and all-cause mortality (HRs [95% CIs] ranged from 1.26 [1.12, 1.41] to 1.57 [1.33, 1.85]). Unintentional weight loss (vs no change) was associated with higher risks of all-cause mortality, but associations with intentional weight loss were unclear. Behavioural interventions targeting weight loss showed no effect on CVD events (pooled HR [95% CI] 0.95 [0.71, 1.27]; I2 = 50.1%). Risk of bias was moderate in most studies and was high in three studies, due to potential uncontrolled confounding and method of weight assessment.Conclusions/interpretationWeight gain is associated with increased risks of CVD and mortality, although there is a lack of data supporting behavioural weight-loss interventions for CVD prevention among adults with type 2 diabetes. Long-term follow-up of behavioural intervention studies is needed to understand effects on CVD and mortality and to inform policy concerning weight management advice and support for people with diabetes. PROSPERO registration CRD42019127304.

Project description:ObjectiveThe effect of body weight variability (BWV) and body weight change (BWC) in high-risk individuals with hypertension, but without diabetes mellitus (DM) remains unclear. We examined the effect of BWV and BWC on the primary outcome [the composite of myocardial infarction (MI), other acute coronary syndromes, stroke, acute decompensated heart failure (HF), or cardiovascular (CV) death] and all-cause mortality in the Systolic Blood Pressure Intervention Trial (SPRINT).MethodsIn this post-hoc analysis, we used multivariate Cox regression models to examine the risk associated with BWV and BWC for the primary outcome in SPRINT. BWV was defined as the intra-individual average successive variability (ASV). BWC was defined as baseline weight minus final weight.ResultsA total of 8714 SPRINT participants (mean age 67.8 ± 9.4 years, 35.1 % women, 58.9 % Whites) with available data on body weight were included. The median follow-up was about 3.9 years (IQR, 3.3-4.4). In multivariable-adjusted Cox models, each 1 unit standard deviation (SD) of BWV was significantly associated with a higher risk for the primary outcome, all-cause mortality, HF, MI, and stroke [HR(95 % CI)]: 1.13 (1.07-1.19; p < 0.0001), 1.22 (1.14-1.30; p < 0.0001), 1.16 (1.07-1.26; p < 0.001), 1.10 (1.00-1.20; p = 0.047), and 1.15 (1.05-1.27; p = 0.005), respectively. Similarly, each 1 unit SD of BWC was significantly associated with a higher risk of the primary outcome, all-cause mortality, MI, and HF: 1.11(1.02-1.21; p = 0.017), 1.44 (1.26-1.65; p < 0.0001), 1.16 (1.01-1.32; p = 0.041) and 1.19 (1.02-1.40; p = 0.031) respectively. However, there was no significant association with CV death (for both BWV and BWC) or stroke (BWC).ConclusionIn high-risk hypertension, BWV and BWC were both associated with higher risk of the primary outcome and all-cause mortality. These results further stress the clinical importance of sustained weight loss and minimizing fluctuations in weight in hypertension.

Dataset Information

Association between Indicators of Inequality and Weight Change following a Behavioural Weight Loss Intervention.

Introduction

Methods

Results

Conclusion

Publications

Association between Indicators of Inequality and Weight Change following a Behavioural Weight Loss Intervention.

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