Weight loss outcomes achieved by adults accessing an online programme offered as part of Public Health England's 'Better Health' campaign

Digital Intervention for behaviouR changE and Chronic disease prevenTION (DIRECTION): Study protocol for a randomized controlled trial of a web-based platform integrating nutrition, physical activity, and mindfulness for individuals with obesity

Excess body weight, suboptimal diet, physical inactivity, alcohol consumption, sleep disruption, and elevated stress are modifiable risk factors associated with the development of chronic diseases. Digital behavioural interventions targeting these factors have shown promise in improving health and reducing chronic disease risk. The Digital Intervention for behaviouR changE and Chronic disease prevenTION (DIRECTION) study is a parallel group, two-arm, randomized controlled trial evaluating the effects of adding healthcare professional guidance and peer support via group-based sessions to a web-based wellness platform (experimental group, n = 90) compared to a self-guided use of the platform (active control group, n = 90) among individuals with a body mass index (BMI) of 30 to <35 kg/m2 and aged 40-65 years. Obesity is defined by a high BMI. The web-based wellness platform employed in this study is My Viva Plan (MVP)®, which holistically integrates nutrition, physical activity, and mindfulness programs. Over 16 weeks, the experimental group uses the web-based wellness platform daily and engages in weekly online support group sessions. The active control group exclusively uses the web-based wellness platform daily. Assessments are conducted at baseline and weeks 8 and 16. The primary outcome is between-group difference in weight loss (kg) at week 16, and secondary outcomes are BMI, percent weight change, proportion of participants achieving 5% or more weight loss, dietary intake, physical activity, alcohol consumption, sleep, and stress across the study. A web-based wellness platform may be a scalable approach to promote behavioural changes that positively impact health. This study will inform the development and implementation of interventions using web-based wellness platforms and personalized digital interventions to improve health outcomes and reduce chronic disease risk among individuals with obesity.

Why more successful? An analysis of participants' self-monitoring data in an online weight loss intervention

BACKGROUND

Self-monitoring is crucial for behavioral weight loss. However, few studies have examined the role of self-monitoring using mixed methods, which may hinder our understanding of its impact.

METHODS

This study examined self-monitoring data from 61 Chinese adults who participated in a 5-week online group intervention for weight loss. Participants reported their baseline Body Mass Index (BMI), weight loss motivation, and engaged in both daily quantitative self-monitoring (e.g., caloric intake, mood, sedentary behavior, etc.) and qualitative self-monitoring (e.g., daily log that summarizes the progress of weight loss). The timeliness of participants' daily self-monitoring data filling was assessed using a scoring rule. One-way repeated measurement ANOVA was employed to analyze the dynamics of each self-monitoring indicator. Correlation and regression analyses were used to reveal the relationship between baseline data, self-monitoring indicators, and weight change. Content analysis was utilized to analyze participants' qualitative self-monitoring data. Participants were categorized into three groups based on their weight loss outcomes, and a chi-square test was used to compare the frequency distribution between these groups.

RESULTS

After the intervention, participants achieved an average weight loss of 2.52 kg (SD = 1.36) and 3.99% (SD = 1.96%) of their initial weight. Daily caloric intake, weight loss satisfaction, frequency of daily log, and the speed of weight loss showed a downward trend, but daily sedentary time gradually increased. Moreover, regression analysis showed that baseline BMI, weight loss motivation, and timeliness of daily filling predicted final weight loss. Qualitative self-monitoring data analysis revealed four categories and nineteen subcategories. A significant difference in the frequency of qualitative data was observed, with the excellent group reporting a greater number of daily logs than expected in all categories and most subcategories, and the moderate and poor groups reporting less than expected in all categories and most subcategories.

CONCLUSION

The self-monitoring data in short-term online group intervention exhibited fluctuations. Participants with higher baseline BMI, higher levels of weight loss motivation, and timely self-monitoring achieved more weight loss. Participants who achieved greater weight loss reported a higher quantity of qualitative self-monitoring data. Practitioners should focus on enhancing dieters' weight loss motivation and promote adherence to self-monitoring practices.