An updated study protocol for a real-life digital 12-month weight management program, the Healthy Weight Coaching.

Obesity is an important health concern that poses many public health challenges. Evidence-based treatment modalities, capable of cost-effectively reaching large patient groups are needed. In this paper, we present the design and methods of the updated national, 12-month, digital weight management program, the Healthy Weight Coaching (HWC). The major updates, as compared to the previous version, are related to the theoretical background of the obesity management and updated BMI cut-offs. The HWC is available, based on physicians' referrals, to adult Finnish citizens with BMI ≥30 kg/m2 or ≥27 kg/m2 with a comorbidity, who have a health-based need to lose weight. Rooted in the principles of behavioural therapy, the HWC focuses on teaching coping skills, guides to healthy self-reflection, and supports concrete lifestyle changes as part of healthy weight loss. The automated weekly training sessions, supplemented by 3-8 exercises, form the basis of the program. These sessions address topics such as diet, physical activity, stress management, and rest and recovery. Additionally, a personal coach is allocated to each patient to provide tailored support. At baseline, patients record their weight, height, and waist circumference, online, and complete questionnaires on lifestyle, diet, physical activity, sleep, psychological factors, and health. Thereafter weight recording is conducted at least every 4 weeks, while the questionnaires and measuring the weight circumference are repeated at 3, 6, 9, and 12 months. In addition, patients can make use of diaries and peer group chats for additional support. Data collected from the consenting patients will be used for research purposes with the weight change from baseline to 12 months as the main outcome in the real-life observational study. The study will provide invaluable insights into the application of digital modalities in the treatment of obesity.

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.