Behaviour Change Techniques in Weight Gain Prevention Interventions in Adults of Reproductive Age: Meta-Analysis and Meta-Regression

Analysis of behavioral change techniques used in exercise and nutritional interventions targeting adults around retirement age with sarcopenic obesity in a systematic review

BACKGROUND

Sarcopenic obesity significantly burdens health and autonomy. Strategies to intervene in or prevent sarcopenic obesity generally focus on losing body fat and building or maintaining muscle mass and function. For a lifestyle intervention, it is important to consider psychological aspects such as behavioral change techniques (BCTs) to elicit a long-lasting behavioral change.

PURPOSE

The study was carried out to analyze BCTs used in exercise and nutritional interventions targeting community-dwelling adults around retirement age with sarcopenic obesity.

METHODS

We conducted an analysis of articles cited in an existing systematic review on the effectiveness of exercise and nutritional interventions on physiological outcomes in community-dwelling adults around retirement age with sarcopenic obesity. We identified BCTs used in these studies by applying a standardized taxonomy.

RESULTS

Only nine BCTs were identified. Most BCTs were not used intentionally (82 %), and those used derived from the implementation of lifestyle components, such as exercise classes ("instructions on how to perform a behavior," "demonstration of the behavior," "behavioral practice/rehearsal," and "body changes"). Only two studies used BCTs intentionally to reinforce adherence in their interventions.

CONCLUSIONS

Few studies integrated BCTs in lifestyle interventions for community-dwelling persons around retirement age with sarcopenic obesity. Future studies on interventions to counteract sarcopenic obesity should include well-established BCTs to foster adherence and, therefore, their effectiveness.

Regulation of craving training to support healthy food choices under stress: A randomized control trial employing the hierarchical drift-diffusion model

Stress increases the likelihood of consuming unhealthy food in some individuals. Previous research has demonstrated that the Regulation of Craving - Training (ROC-T) intervention can reduce unhealthy food intake. However, its effectiveness under stress and the underlying mechanism remained uncertain. This study aimed to assess the efficacy of the ROC-T intervention in improving healthy food choices and to explore the intervention mechanism through computational modeling employing the hierarchical drift-diffusion model (HDDM). This study adopted a 2 (ROC-T intervention vs. control) * 2 (stress vs. no-stress) between-subject experimental design. A total of 118 employees (72 women, Mage  = 28.74) participated in the online experiment. Results show that the ROC-T intervention increases healthy food choices under stress and no-stress conditions. The HDDM results reveal a significant two-way interaction for non-decision time (Bayes factor, BF = 32.722) and initial bias (BF = 27.350). Specifically, in the no-stress condition, the ROC-T intervention resulted in lower non-decision time and higher initial bias compared with the control group. The findings validated the negative impact of stress on healthy food choices, and that the ROC-T intervention promotes healthy food choices both under stress and no-stress conditions.

Systematic review and meta-analysis of the effectiveness of chatbots on lifestyle behaviours

Chatbots (also known as conversational agents and virtual assistants) offer the potential to deliver healthcare in an efficient, appealing and personalised manner. The purpose of this systematic review and meta-analysis was to evaluate the efficacy of chatbot interventions designed to improve physical activity, diet and sleep. Electronic databases were searched for randomised and non-randomised controlled trials, and pre-post trials that evaluated chatbot interventions targeting physical activity, diet and/or sleep, published before 1 September 2022. Outcomes were total physical activity, steps, moderate-to-vigorous physical activity (MVPA), fruit and vegetable consumption, sleep quality and sleep duration. Standardised mean differences (SMD) were calculated to compare intervention effects. Subgroup analyses were conducted to assess chatbot type, intervention type, duration, output and use of artificial intelligence. Risk of bias was assessed using the Effective Public Health Practice Project Quality Assessment tool. Nineteen trials were included. Sample sizes ranged between 25-958, and mean participant age ranged between 9-71 years. Most interventions (n = 15, 79%) targeted physical activity, and most trials had a low-quality rating (n = 14, 74%). Meta-analysis results showed significant effects (all p < 0.05) of chatbots for increasing total physical activity (SMD = 0.28 [95% CI = 0.16, 0.40]), daily steps (SMD = 0.28 [95% CI = 0.17, 0.39]), MVPA (SMD = 0.53 [95% CI = 0.24, 0.83]), fruit and vegetable consumption (SMD = 0.59 [95% CI = 0.25, 0.93]), sleep duration (SMD = 0.44 [95% CI = 0.32, 0.55]) and sleep quality (SMD = 0.50 [95% CI = 0.09, 0.90]). Subgroup analyses showed that text-based, and artificial intelligence chatbots were more efficacious than speech/voice chatbots for fruit and vegetable consumption, and multicomponent interventions were more efficacious than chatbot-only interventions for sleep duration and sleep quality (all p < 0.05). Findings from this systematic review and meta-analysis indicate that chatbot interventions are efficacious for increasing physical activity, fruit and vegetable consumption, sleep duration and sleep quality. Chatbot interventions were efficacious across a range of populations and age groups, with both short- and longer-term interventions, and chatbot only and multicomponent interventions being efficacious.

Combining Persuasive System Design Principles and Behavior Change Techniques in Digital Interventions Supporting Long-term Weight Loss Maintenance: Design and Development of eCHANGE (Preprint)

Background

Long-term weight maintenance after weight loss is challenging, and innovative solutions are required. Digital technologies can support behavior change and, therefore, have the potential to be an effective tool for weight loss maintenance. However, to create meaningful and effective digital behavior change interventions that support end user values and needs, a combination of persuasive system design (PSD) principles and behavior change techniques (BCTs) might be needed.

Objective

This study aimed to investigate how an evidence-informed digital behavior change intervention can be designed and developed by combining PSD principles and BCTs into design features to support end user values and needs for long-term weight loss maintenance.

Methods

This study presents a concept for how PSD principles and BCTs can be translated into design features by combining design thinking and Agile methods to develop and deliver an evidence-informed digital behavior change intervention aimed at supporting weight maintenance. Overall, 45 stakeholders participated in the systematic and iterative development process comprising co-design workshops, prototyping, Agile development, and usability testing. This included prospective end users (n=17, 38%; ie, people with obesity who had lost ≥8% of their weight), health care providers (n=9, 20%), healthy volunteers (n=4, 9%), a service designer (n=1, 2%), and stakeholders from the multidisciplinary research and development team (n=14, 31%; ie, software developers; digital designers; and eHealth, behavior change, and obesity experts). Stakeholder input on how to operationalize the design features and optimize the technology was examined through formative evaluation and qualitative analyses using rapid and in-depth analysis approaches.

Results

A total of 17 design features combining PSD principles and BCTs were identified as important to support end user values and needs based on stakeholder input during the design and development of eCHANGE, a digital intervention to support long-term weight loss maintenance. The design features were combined into 4 main intervention components: Week Plan, My Overview, Knowledge and Skills, and Virtual Coach and Smart Feedback System. To support a healthy lifestyle and continued behavior change to maintain weight, PSD principles such as tailoring, personalization, self-monitoring, reminders, rewards, rehearsal, praise, and suggestions were combined and implemented into the design features together with BCTs from the clusters of goals and planning, feedback and monitoring, social support, repetition and substitution, shaping knowledge, natural consequences, associations, antecedents, identity, and self-belief.

Conclusions

Combining and implementing PSD principles and BCTs in digital interventions aimed at supporting sustainable behavior change may contribute to the design of engaging and motivating interventions in line with end user values and needs. As such, the design and development of the eCHANGE intervention can provide valuable input for future design and tailoring of evidence-informed digital interventions, even beyond digital interventions in support of health behavior change and long-term weight loss maintenance.

Trial Registration

ClinicalTrials.gov NCT04537988; https://clinicaltrials.gov/ct2/show/NCT04537988