The relationship of alcohol use to weight loss in the context of behavioral weight loss treatment

Combination of Machine Learning Techniques to Predict Overweight/Obesity in Adults

(1) Background: Artificial intelligence using machine learning techniques may help us to predict and prevent obesity. The aim was to design an interpretable prediction algorithm for overweight/obesity risk based on a combination of different machine learning techniques. (2) Methods: 38 variables related to sociodemographic, lifestyle, and health aspects from 1179 residents in Madrid were collected and used to train predictive models. Accuracy, precision, and recall metrics were tested and compared between nine classical machine learning techniques and the predictive model based on a combination of those classical machine learning techniques. Statistical validation was performed. The shapely additive explanation technique was used to identify the variables with the greatest impact on weight gain. (3) Results: Cascade classifier model combining gradient boosting, random forest, and logistic regression models showed the best predictive results for overweight/obesity compared to all machine learning techniques tested, reaching an accuracy of 79%, precision of 84%, and recall of 89% for predictions for weight gain. Age, sex, academic level, profession, smoking habits, wine consumption, and Mediterranean diet adherence had the highest impact on predicting obesity. (4) Conclusions: A combination of machine learning techniques showed a significant improvement in accuracy to predict risk of overweight/obesity than machine learning techniques separately.

What could be the reasons for not losing weight even after following a weight loss program?

INTRODUCTION

Approximately four million people worldwide die annually because of obesity. Weight loss is commonly recommended as a first-line therapy in overweight and obese patients. Although many individuals attempt to lose weight, not everyone achieves optimal success. Few studies point out that weight loss eventually slows down, stagnates or reverses in 85% of the cases.

RESEARCH QUESTION

What could be the reasons for not losing weight even after following a weight loss program?

METHODS

A scoping review of the literature was performed using weight loss-related search terms such as 'Obesity,' 'Overweight,' 'Lifestyle,' 'weight loss,' 'Basal Metabolism,' 'physical activity,' 'adherence,' 'energy balance,' 'Sleep' and 'adaptations. The search involved reference tracking and database and web searches (PUBMED, Science Direct, Elsevier, Web of Science and Google Scholar). Original articles and review papers on weight loss involving human participants and adults aged > 18 years were selected. Approximately 231 articles were reviewed, and 185 were included based on the inclusion criteria.

DESIGN

Scoping review.

RESULTS

In this review, the factors associated with not losing weight have broadly been divided into five categories. Studies highlighting each subfactor were critically reviewed and discussed. A wide degree of interindividual variability in weight loss is common in studies even after controlling for variables such as adherence, sex, physical activity and baseline weight. In addition to these variables, variations in factors such as previous weight loss attempts, sleep habits, meal timings and medications can play a crucial role in upregulating or downregulating the association between energy deficit and weight loss results.

CONCLUSION

This review identifies and clarifies the role of several factors that may hinder weight loss after the exploration of existing evidence. Judging the effectiveness of respective lifestyle interventions by simply observing the 'general behavior of the groups' is not always applicable in clinical practice. Each individual must be monitored and advised as per their requirements and challenges.

The Fully Understanding Eating and Lifestyle Behaviors (FUEL) trial: Protocol for a cohort study harnessing digital health tools to phenotype dietary non-adherence behaviors during lifestyle intervention

Objective

Lifestyle intervention can produce clinically significant weight loss and reduced disease risk/severity for many individuals with overweight/obesity. Dietary lapses, instances of non-adherence to the recommended dietary goal(s) in lifestyle intervention, are associated with less weight loss and higher energy intake. There are distinct "types" of dietary lapse (e.g., eating an off-plan food, eating a larger portion), and behavioral, psychosocial, and contextual mechanisms may differ across dietary lapse types. Some lapse types also appear to impact weight more than others. Elucidating clear lapse types thus has potential for understanding and improving adherence to lifestyle intervention.

Methods

This 18-month observational cohort study will use real-time digital assessment tools within a multi-level factor analysis framework to uncover "lapse phenotypes" and understand their impact on clinical outcomes. Adults with overweight/obesity (n = 150) will participate in a 12-month online lifestyle intervention and 6-month weight loss maintenance period. Participants will complete 14-day lapse phenotyping assessment periods at baseline, 3, 6, 12, and 18 months in which smartphone surveys, wearable devices, and geolocation will assess dietary lapses and relevant phenotyping characteristics. Energy intake (via 24-h dietary recall) and weight will be collected at each assessment period.

Results

This trial is ongoing; data collection began on 31 October 2022 and is scheduled to complete by February 2027.

Conclusion

Results will inform novel precision tools to improve dietary adherence in lifestyle intervention, and support updated theoretical models of adherence behavior. Additionally, these phenotyping methods can likely be leveraged to better understand non-adherence to other health behavior interventions.

Trial Registration

This study was prospectively registered https://clinicaltrials.gov/study/NCT05562427.

Changes in health behaviours during the COVID-19 pandemic and effect on weight and obesity among older people in England

During COVID-19 lockdown, negative changes in health behaviours have been reported in European older adults. However, less is known about the consequences of these changes on weight gain and obesity, especially in older adults living in England. This study explored the association of health behaviour changes with weight and obesity in English older adults aged 50 years and older, during lockdowns in 2020. We included 4182 participants of the English Longitudinal Study of Ageing COVID-19 sub-study in June/July and Nov/Dec 2020 who also had pre-pandemic data. Perceived changes in health behaviours were regressed on weight and obesity, adjusted for pre-pandemic weight or obesity, and several covariates. Results suggested that less exercise, more sedentariness, eating more and alcohol drinking were associated with a significant increase in weight at both timepoints. Meanwhile, less sedentariness and eating less significantly reduced weight in Nov/Dec 2020. A higher risk of obesity at both timepoints was found in adults sitting, eating, or sleeping more than usual. To conclude, during UK lockdown, older people who engaged in risky health behaviours were at higher risks of weight gain and obesity both in the short run and long term. Considering potential health risks associated with obesity and disruptions in routine lifestyle in the older population even after the pandemic, improved weight management interventions are necessary nationwide.

Bad situation, treat yourself: a qualitative exploration of the factors influencing healthy eating habits during the COVID-19 pandemic

Purpose

To explore barriers and facilitators to healthy eating during the COVID-19 pandemic among adults enrolled in an internet-based weight loss program.

Methods

Adults in an internet-delivered weight loss program were recruited to participate. Participants completed online study surveys and a semi-structured interview via telephone between June 1, 2020 and June 22, 2020. The interview included questions to explore how the COVID-19 pandemic has influenced dietary behaviors. Constant comparative analysis was used to identify key themes.

Results

Participants (n = 30) were primarily female (83%) and white (87%), 54.6 ± 10.0 years old, and had a mean body mass index of 31.1 ± 4.5 kg/m². Barriers included snacking/ease of access to food, eating as a coping mechanism, and lack of routine/planning. Facilitators included calorie control, regular routine/scheduling, and self-monitoring. General themes with eating were a change in eating out frequency or modality, cooking more, and changes in alcohol consumption.

Conclusion

Eating habits among adults enrolled in a weight loss program changed during the COVID-19 pandemic. Future weight loss programs and public health recommendations should consider modifying recommendations to place increased emphasis on strategies to overcome barriers to healthy eating and promote facilitators that may help with healthy eating, particularly during unexpected circumstances or events.

'Joining the Dots': Individual, Sociocultural and Environmental Links between Alcohol Consumption, Dietary Intake and Body Weight-A Narrative Review

Alcohol is energy-dense, elicits weak satiety responses relative to solid food, inhibits dietary fat oxidation, and may stimulate food intake. It has, therefore, been proposed as a contributor to weight gain and obesity. The aim of this narrative review was to consolidate and critically appraise the evidence on the relationship of alcohol consumption with dietary intake and body weight, within mainstream (non-treatment) populations. Publications were identified from a PubMed keyword search using the terms 'alcohol', 'food', 'eating', 'weight', 'body mass index', 'obesity', 'food reward', 'inhibition', 'attentional bias', 'appetite', 'culture', 'social'. A snowball method and citation searches were used to identify additional relevant publications. Reference lists of relevant publications were also consulted. While limited by statistical heterogeneity, pooled results of experimental studies showed a relatively robust association between acute alcohol intake and greater food and total energy intake. This appears to occur via metabolic and psychological mechanisms that have not yet been fully elucidated. Evidence on the relationship between alcohol intake and weight is equivocal. Most evidence was derived from cross-sectional survey data which does not allow for a cause-effect relationship to be established. Observational research evidence was limited by heterogeneity and methodological issues, reducing the certainty of the evidence. We found very little qualitative work regarding the social, cultural, and environmental links between concurrent alcohol intake and eating behaviours. That the evidence of alcohol intake and body weight remains uncertain despite no shortage of research over the years, indicates that more innovative research methodologies and nuanced analyses are needed to capture what is clearly a complex and dynamic relationship. Also, given synergies between 'Big Food' and 'Big Alcohol' industries, effective policy solutions are likely to overlap and a unified approach to policy change may be more effective than isolated efforts. However, joint action may not occur until stronger evidence on the relationship between alcohol intake, food intake and weight is established.

Early Engagement is Associated with Better Weight Loss in Emerging Adults

Objectives: Predictors of success among emerging adults (EAs; ages 18-25) within behavioral weight loss (BWL) trials are largely unknown. We examined whether early program engagement predicted overall engagement and weight loss in EAs. Methods: Data were pooled from 2 randomized controlled pilot trials in EAs. Participants (N = 99, 80% female, BMI = 33.7±5.1 kg/m²) received a 3-month BWL intervention. Weight was objectively assessed at 0 and 3 months; engagement was tracked weekly; retention was assessed at 3 months. Results: Greater engagement during the initial 4 weeks of treatment predicted greater weight loss (p = .001). Compared to those who did not engage in all 4 initial weeks, participants meeting this threshold experienced greater overall engagement (9.6 vs 4.2 weeks, p < .001), weight losses (intent-to-treat = -3.8% vs -1.3%, p = .004), and retention (78% vs 53%, p = .012). Conclusions: Early engagement in BWL is associated with better outcomes among EAs. Monitoring engagement in real-time during the initial 4 weeks of treatment may be necessary to intervene effectively. Early engagement did not vary by sex or race; future work should identify characteristics associated with poor early engagement.

Weight-Loss Resilience Among Low-Income Postpartum Women: Association With Health Habits

Using a weight resilience framework, health habits of diet and physical activity, social support, and perceived stress were compared in women who lost weight (resilient) and those who did not lose or gained weight (nonresilient) during a weight-loss intervention. Participants were low-income postpartum women participating in a 13-week randomized treatment-control group intervention, with 20 of 50 classified as resilient in losing weight. Measures included the Postpartum Support Scale, the Perceived Stress Scale, and health habit items from the Self Care Inventory. Weight-loss resilient women showed significantly more frequent healthful dietary habits, such as eating a nutritious breakfast, and less frequent unhealthy habits, such as substituting junk food for meals, and less perceived stress than their nonresilient counterparts at both the midpoint and end of the study. Weight-loss resilient women also showed significantly more frequent physical activity habits at the end of the study. No social support differences were found.

Alcohol Intake and Weight Loss During Intensive Lifestyle Intervention for Adults with Overweight or Obesity and Diabetes

OBJECTIVE

This study aimed to assess whether alcohol consumption decreases during an intensive lifestyle intervention (ILI) and whether alcohol consumption is associated with weight loss among participants with overweight or obesity and type 2 diabetes.

METHODS

Participants (n = 4,901) were from the Action for Health in Diabetes (Look AHEAD) study, a randomized controlled trial that compared an ILI with a diabetes support and education (DSE) control. Mixed-effects models were used to estimate the effect of the ILI on alcohol consumption and the influence of alcohol consumption on weight loss at year 4.

RESULTS

ILI and DSE participants did not differ in changes in alcohol consumption. Alcohol intake was not associated with weight loss at year 1 of the ILI. ILI participants who abstained from alcohol lost 5.1% ± 0.3% of initial weight at year 4 compared with a significantly (P = 0.04) smaller 2.4% ± 1.3% for consistent heavy drinkers. ILI participants who abstained from alcohol consumption over the 4 years lost 1.6% ± 0.5% more weight relative to individuals who drank alcohol at any time during the intervention (P = 0.003). DSE participants did not differ in weight loss by alcohol consumption.

CONCLUSIONS

Heavy alcohol drinkers are at risk for suboptimal long-term weight loss. Decreasing alcohol consumption may improve weight management among individuals with diabetes.