Weight Loss and Usage of an Online Commercial Weight Loss Program (the CSIRO Total Wellbeing Diet Online) Delivered in an Everyday Context: Five-Year Evaluation in a Community Cohort

Australians' willingness to change their discretionary food intake: findings from the CSIRO junk food analyser

Introduction

Overconsumption of energy dense, nutrient poor foods and beverages is a major problem globally. This study describes what and how Australian adults consume and are willing to change their intake in terms of discretionary food and beverage categories.

Methods

Cross-sectional data were collected via the CSIRO Junk Food Analyser. This online tool contains short questions on discretionary food and beverage consumption, and items relating to stage of and willingness and strategies to reduce discretionary food and beverage intake. Analyses focussed on describing discretionary intake, adherence to guidelines and the prediction of willingness to change discretionary food intake amongst those exceeding guidelines.

Results

In 2021, 41,109 Australian adults completed the CSIRO Junk Food Analyser. Participants were mostly female (73.1%) and aged 31-70 years (78.9%). Most participants exceeded dietary guidelines for discretionary food and beverage intake (67.4%, 27,694/41,109) with 40% reporting actively trying to reduce intake. Most people exceeding guidelines did so in categories of alcohol (39.3%) and cakes and biscuits (21.0%). Yet, willingness to change intake was lowest for alcohol (median, IQR of 3, 2:4 out of 5). Almost half of the participants were willing to try 'having a few days off per week' (46.0%), while only 13.4% were willing to try to 'eliminate' their highest ranked category.

Discussion

Australian adults are willing to reduce their discretionary food and beverage intake, but simply targeting the foods and beverages consumed most may not be the best place to start. Messages encouraging days off frequently consumed discretionary foods and beverages may be well received.

Weight Loss Using an mHealth App Among Individuals With Obesity in Different Economic Regions of China: Cohort Study

Background

With the increasing prevalence of obesity, weight loss has become a critical issue in China. Self-managed weight loss through a mobile health (mHealth) app may be a prospective method. However, its practicability in different economic regions of China is unknown.

Objective

This study aims to evaluate the effectiveness of self-managed weight loss through an mHealth app among individuals with obesity in different economic regions of China and to demonstrate the feasibility of online self-management for weight loss.

Methods

A total of 165,635 Chinese adults who signed up for the mHealth app were included to analyze the body composition characteristics of individuals from different economic regions by χ2 analyses. Furthermore, 2 types of participants with obesity using mHealth monitoring, including 74,611 participants with a BMI ≥24.0 kg/m2 and 22,903 participants with a normal BMI but an excessive percentage of body fat (PBF), were followed for 6 months to explore the weight loss and fat loss effects in different economic regions of China and to find independent predictors associated with weight loss success by 2-tailed Student t test and multivariable logistic regression analysis.

Results

There were 32,129 users from low-income regions and 133,506 users from high-income regions. The proportion of users with obesity in low-income regions was higher than in high-income regions, both based on BMI (15,378/32,129, 47.9% vs 59,233/133,506, 44.4%; P<.001) and PBF classification (19,146/32,129, 59.6% vs 72,033/133,506, 54%; P<.001). Follow-up analyses showed that the weight loss effect among participants with overweight or obesity in low-income regions was greater than in high-income regions (mean -4.93, SD 6.41 vs mean -4.71, SD 6.14 kg; P<.001), while there was no significant difference in fat loss (mean -2.06%, SD 3.14% vs mean -2.04%, SD 3.19%; P=.54). In the population with normal-weight obesity, the weight loss (mean -2.42, SD 4.07 vs mean -2.23, SD 4.21 kg; P=.004) and fat loss effects (mean -1.43%, SD 2.73% vs mean -1.27%, SD 2.63%; P<.001) were stronger in high-income regions than in low-income regions. In addition, multivariable logistic regression analyses showed that age, baseline PBF, skeletal muscle rate, and measurement frequency were related to weight loss, whereas gender and baseline body metabolic rate only showed a correlation with weight loss in the population in high-income regions.

Conclusions

This study found a high proportion of mHealth app users with obesity in low-income regions. Individuals with overweight and obesity in different economic regions of China experienced significant weight loss and fat loss using an mHealth app. Moreover, individuals in high-income regions paid more attention to body fat and had better fat reduction effects. Therefore, promoting self-monitoring of weight and PBF through an mHealth app could be an important intervention that could be implemented across all regions of China.

Weight Management Interventions Provided by a Dietitian for Adults with Overweight or Obesity: An Evidence Analysis Center Systematic Review and Meta-Analysis

BACKGROUND

Obesity is associated with a multitude of comorbidities and considerable health care costs.

OBJECTIVE

The objective of this review was to examine the efficacy of weight management interventions provided by a registered dietitian or international equivalent (referred to as "dietitian").

METHODS

This systematic review and meta-analysis of randomized controlled trials (RCTs) examined the effect of weight management interventions provided by a dietitian, compared with usual care or no intervention, on several cardiometabolic outcomes and quality of life in adults with overweight or obesity. MEDLINE, Embase, PsycINFO, Cochrane CENTRAL, Cochrane Database of Systematic Reviews, and CINAHL databases were searched for eligible RCTs published between January 2008 and January 2021 in the English language. Meta-analyses were conducted using a random-effects model, publication bias was assessed using funnel plots and Egger's statistics, and heterogeneity was assessed by interpreting I2 values. Efficacy of intervention components, such as telehealth or group contacts, were explored in sub-group analyses. Version 2 of the risk-of-bias tool for RCTs was used to assess risk of bias. The Grading of Recommendations Assessment, Development and Evaluation method was used to determine certainty of evidence.

RESULTS

This systematic review included 62 RCTs. Compared with control conditions, weight management interventions provided by a dietitian resulted in improved body mass index (mean difference [MD] -1.5; 95% CI -1.74 to -1.26; moderate evidence certainty); percent weight loss (MD -4.01%; 95% CI -5.26% to -2.75%; high evidence certainty); waist circumference (MD -3.45 cm; 95% CI -4.39 to -2.51 cm; high evidence certainty); blood pressure (MD -3.04 mm Hg; 95% CI -5.10 to -0.98 mm Hg and MD -1.99 mm Hg; 95% CI -3.02 to -0.96 mm Hg for systolic blood pressure and diastolic blood pressure, respectively; moderate and low evidence certainty); and quality of life using the 36-Item Short Form Survey (MD 5.84; 95% CI 2.27 to 9.41 and 2.39; 95% CI 1.55 to 3.23 for physical and mental quality of life, respectively; low and moderate evidence certainty).

CONCLUSIONS

For adults with overweight or obesity, weight management interventions provided by a dietitian are efficacious for improving several examined cardiometabolic outcomes and quality of life.

Predicting Disengagement to Better Support Outcomes in a Web-Based Weight Loss Program Using Machine Learning Models: Cross-Sectional Study

BACKGROUND

Engagement is key to interventions that achieve successful behavior change and improvements in health. There is limited literature on the application of predictive machine learning (ML) models to data from commercially available weight loss programs to predict disengagement. Such data could help participants achieve their goals.

OBJECTIVE

This study aimed to use explainable ML to predict the risk of member disengagement week by week over 12 weeks on a commercially available web-based weight loss program.

METHODS

Data were available from 59,686 adults who participated in the weight loss program between October 2014 and September 2019. Data included year of birth, sex, height, weight, motivation to join the program, use statistics (eg, weight entries, entries into the food diary, views of the menu, and program content), program type, and weight loss. Random forest, extreme gradient boosting, and logistic regression with L1 regularization models were developed and validated using a 10-fold cross-validation approach. In addition, temporal validation was performed on a test cohort of 16,947 members who participated in the program between April 2018 and September 2019, and the remaining data were used for model development. Shapley values were used to identify globally relevant features and explain individual predictions.

RESULTS

The average age of the participants was 49.60 (SD 12.54) years, the average starting BMI was 32.43 (SD 6.19), and 81.46% (39,594/48,604) of the participants were female. The class distributions (active and inactive members) changed from 39,369 and 9235 in week 2 to 31,602 and 17,002 in week 12, respectively. With 10-fold-cross-validation, extreme gradient boosting models had the best predictive performance, which ranged from 0.85 (95% CI 0.84-0.85) to 0.93 (95% CI 0.93-0.93) for area under the receiver operating characteristic curve and from 0.57 (95% CI 0.56-0.58) to 0.95 (95% CI 0.95-0.96) for area under the precision-recall curve (across 12 weeks of the program). They also presented a good calibration. Results obtained with temporal validation ranged from 0.51 to 0.95 for area under a precision-recall curve and 0.84 to 0.93 for area under the receiver operating characteristic curve across the 12 weeks. There was a considerable improvement in area under a precision-recall curve of 20% in week 3 of the program. On the basis of the computed Shapley values, the most important features for predicting disengagement in the following week were those related to the total activity on the platform and entering a weight in the previous weeks.

CONCLUSIONS

This study showed the potential of applying ML predictive algorithms to help predict and understand participants' disengagement with a web-based weight loss program. Given the association between engagement and health outcomes, these findings can prove valuable in providing better support to individuals to enhance their engagement and potentially achieve greater weight loss.