Development and validation of a weight-loss predictor to assist weight loss management.

This study aims to develop and validate a modeling framework to predict long-term weight change on the basis of self-reported weight data. The aim is to enable focusing resources of health systems on individuals that are at risk of not achieving their goals in weight loss interventions, which would help both health professionals and the individuals in weight loss management. The weight loss prediction models were built on 327 participants, aged 21-78, from a Finnish weight coaching cohort, with at least 9 months of self-reported follow-up weight data during weight loss intervention. With these data, we used six machine learning methods to predict weight loss after 9 months and selected the best performing models for implementation as modeling framework. We trained the models to predict either three classes of weight change (weight loss, insufficient weight loss, weight gain) or five classes (high/moderate/insufficient weight loss, high/low weight gain). Finally, the prediction accuracy was validated with an independent cohort of overweight UK adults (n = 184). Of the six tested modeling approaches, logistic regression performed the best. Most three-class prediction models achieved prediction accuracy of > 50% already with half a month of data and up to 97% with 8 months. The five-class prediction models achieved accuracies from 39% (0.5 months) to 89% (8 months). Our approach provides an accurate prediction method for long-term weight loss, with potential for easier and more efficient management of weight loss interventions in the future. A web application is available: https://elolab.shinyapps.io/WeightChangePredictor/ .The trial is registered at clinicaltrials.gov/ct2/show/NCT04019249 (Clinical Trials Identifier NCT04019249), first posted on 15/07/2019.

The Effectiveness of eHealth Interventions for Weight Loss and Weight Loss Maintenance in Adults with Overweight or Obesity: A Systematic Review of Systematic Reviews.

PURPOSE OF REVIEW: The purpose of this study is to evaluate the effectiveness of eHealth interventions for weight loss and weight loss maintenance among adults with overweight or obesity through a systematic review of systematic reviews. RECENT FINDINGS: This study included 26 systematic reviews, covering a total of 338 original studies, published between 2018 and 2023. The review indicates that eHealth interventions are more effective than control interventions or no care and comparable to face-to-face interventions. The effect sizes remain relatively small when comparing eHealth interventions to any control conditions, with mean differences of weight loss results from - 0.12 kg (95% CI - 0.64 to 0.41 kg) in a review comparing eHealth interventions to face-to-face care to - 4.32 kg (- 5.08 kg to - 3.57 kg) in a review comparing eHealth interventions to no care. The methodological quality of the included studies varies considerably. However, it can be concluded that interventions with human contact work better than those that are fully automated. In conclusion, this systematic review of systematic reviews provides an updated understanding of the development of digital interventions in recent years and their effectiveness for weight loss and weight loss maintenance among adults with overweight or obesity. The findings suggest that eHealth interventions can be a valuable tool for delivering obesity care to more patients economically. Further research is needed to determine which specific types of eHealth interventions are most effective and how to best integrate them into clinical practice.

Weight Loss Trajectories in Healthy Weight Coaching: Cohort Study.

BACKGROUND: As global obesity prevalence continues to increase, there is a need for accessible and affordable weight management interventions, such as web-based programs. OBJECTIVE: This paper aims to assess the outcomes of healthy weight coaching (HWC), a web-based obesity management program integrated into standard Finnish clinical care. METHODS: HWC is an ongoing, structured digital 12-month program based on acceptance and commitment therapy. It includes weekly training sessions focused on lifestyle, general health, and psychological factors. Participants received remote one-on-one support from a personal coach. In this real-life, single-arm, prospective cohort study, we examined the total weight loss, weight loss profiles, and variables associated with weight loss success and program retention in 1189 adults (963 women) with a BMI >25 kg/m² among participants of the program between October 2016 and March 2019. Absolute (kg) and relative (%) weight loss from the baseline were the primary outcomes. We also examined the weight loss profiles, clustered based on the dynamic time-warping distance, and the possible variables associated with greater weight loss success and program retention. We compared different groups using the Mann-Whitney test or Kruskal-Wallis test for continuous variables and the chi-squared test for categorical variables. We analyzed changes in medication using the McNemar test. RESULTS: Among those having reached the 12-month time point (n=173), the mean weight loss was 4.6% (SE 0.5%), with 43% (n=75) achieving clinically relevant weight loss (≥5%). Baseline BMI ≥40 kg/m² was associated with a greater weight loss than a lower BMI (mean 6.6%, SE 0.9%, vs mean 3.2%, SE 0.6%; P=.02). In addition, more frequent weight reporting was associated with greater weight loss. No significant differences in weight loss were observed according to sex, age, baseline disease, or medication use. The total dropout rate was 29.1%. Dropouts were slightly younger than continuers (47.2, SE 0.6 years vs 49.2, SE 0.4 years; P=.01) and reported their weight less frequently (3.0, SE 0.1 entries per month vs 3.3, SE 0.1 entries per month; P<.001). CONCLUSIONS: A comprehensive web-based program such as HWC is a potential addition to the repertoire of obesity management in a clinical setting. Heavier patients lost more weight, but weight loss success was otherwise independent of baseline characteristics.

Agreement of bioelectrical impedance with dual-energy X-ray absorptiometry and MRI to estimate changes in body fat, skeletal muscle and visceral fat during a 12-month weight loss intervention.

The aim of the present study was to analyse the agreement of bioelectrical impedance analysis (BIA) compared with dual-energy X-ray absorptiometry (DXA) and MRI in estimating body fat, skeletal muscle and visceral fat during a 12-month weight loss intervention. A total of nineteen obese adults (twelve females, seven males) aged 20·2-48·6 years, mean BMI 34·6 (SE 0·6) kg/m², participated in the study. Body fat, skeletal muscle and visceral fat index were measured by BIA (Omron BF-500; Omron Medizintechnik) and compared with DXA (body fat and skeletal muscle) at baseline, 5 and 12 months, and with MRI (visceral fat) at baseline and 5 months. The subjects lost 8·9 (SE 1·8) kg (9·0 (SE 1·7) %) of body weight during the 12-month intervention. BIA, as compared to DXA, accurately assessed loss of fat (7·0 (SE 1·5) v. 7·0 (SE 1·4) kg, P= 0·94) and muscle (1·0 (SE 0·2) v. 1·4 (SE 0·3) kg, P= 0·18). While body fat was similar by the two methods, skeletal muscle was underestimated by 1-2 kg using BIA at each time point. Compared to MRI, BIA overestimated visceral fat, especially in males. BIA and DXA showed high correlations for kg fat, both cross-sectionally and longitudinally (r 0·91-0·99). BIA, compared with DXA and MRI, detected kg muscle and visceral fat more accurately cross-sectionally (r 0·77-0·87 and r 0·40-0·78, respectively) than their changes longitudinally (r 0·24-0·61 and r 0·46, respectively). BIA is at its best when assessing the amount or changes in fat mass. It is a useful method for measuring skeletal muscle, but limited in its ability to measure visceral fat.

Genetic variation in PNPLA3 (adiponutrin) confers sensitivity to weight loss-induced decrease in liver fat in humans.

BACKGROUND: The rs738409 C→G single nucleotide polymorphism in the patatin-like phospholipase domain-containing 3 (PNPLA3; adiponutrin) leads to a missense mutation (I148M), which is associated with increased liver fat but not insulin resistance. The I148M mutation impedes triglyceride hydrolysis in vitro, and its carriers have an increased risk of developing severe liver disease. OBJECTIVE: We explored whether the rs738409 PNPLA3 G allele influences the ability of weight loss to decrease liver fat or change insulin sensitivity. DESIGN: We recruited 8 subjects who were homozygous for the rs738409 PNPLA3 G allele (PNPLA3-148MM) and 10 who were homozygous for the rs738409 PNPLA3 C allele (PNPLA3-148II). To allow comparison of changes in liver fat, the groups were matched with respect to baseline age, sex, body mass index, and liver fat. The subjects were placed on a hypocaloric low-carbohydrate diet for 6 d. Liver fat content (proton magnetic resonance spectroscopy), whole-body insulin sensitivity of glucose metabolism (euglycemic clamp technique), and lipolysis ([(2)H(5)]glycerol infusion) were measured before and after the diet. RESULTS: At baseline, fasting serum insulin and C-peptide concentrations were significantly lower in the PNPLA3-148MM group than in the PNPLA3-148II group, as predicted by study design. Weight loss was not significantly different between groups (PNPLA3-148MM: -3.1 ± 0.5 kg; PNPLA3-148II: -3.1 ± 0.4 kg). Liver fat decreased by 45% in the PNPLA3-148MM group (P < 0.001) and by 18% in the PNPLA3-148II group (P < 0.01). CONCLUSION: Weight loss is effective in decreasing liver fat in subjects who are homozygous for the rs738409 PNPLA3 G or C allele. This trial was registered at www.hus.fi as 233775.