Evidence-Based Digital Tools for Weight Loss Maintenance: The NoHoW Project

Abstract

Background: Effective interventions and commercial programmes for weight loss (WL) are widely available, but most people regain weight. Few effective WL maintenance (WLM) solutions exist. The most promising evidence-based behaviour change techniques for WLM are self-monitoring, goal setting, action planning and control, building self-efficacy, and techniques that promote autonomous motivation (e.g., provide choice). Stress management and emotion regulation techniques show potential for prevention of relapse and weight regain. Digital technologies (including networked-wireless tracking technologies, online tools and smartphone apps, multimedia resources, and internet-based support) offer attractive tools for teaching and supporting long-term behaviour change techniques. However, many digital offerings for weight management tend not to include evidence-based content and the evidence base is still limited. The Project: First, the project examined why, when, and how many European citizens make WL and WLM attempts and how successful they are. Second, the project employed the most up-to-date behavioural science research to develop a digital toolkit for WLM based on 2 key conditions, i.e., self-management (self-regulation and motivation) of behaviour and self-management of emotional responses for WLM. Then, the NoHoW trial tested the efficacy of this digital toolkit in adults who achieved clinically significant (≥5%) WL in the previous 12 months (initial BMI ≥25). The primary outcome was change in weight (kg) at 12 months from baseline. Secondary outcomes included biological, psychological, and behavioural moderators and mediators of long-term energy balance (EB) behaviours, and user experience, acceptability, and cost-effectiveness.

Impact: The project will directly feed results from studies on European consumer behaviour, design and evaluation of digital toolkits self-management of EB behaviours into development of new products and services for WLM and digital health. The project has developed a framework and digital architecture for interventions in the context of EB tracking and will generate results that will help inform the next generation of personalised interventions for effective self-management of weight and health.

Keywords: Emotion regulation; Information and communication technologies; Motivation; Obesity; Self-regulation; Weight loss maintenance.

Fig. 1

The majority of studies examining psychosocial predictors of longer-term weight outcomes, as mechanisms of action of behaviour change interventions, have used either pre-treatment predictors [98, 99] or correlates of WL and maintenance rather than sequential measures of outcomes throughout intervention and follow-up periods. Recent evidence suggests that factors influencing changes in behaviours during the course of weight management interventions are important determinants of success of failure at longer-term weight management [44]. It therefore appears (albeit from limited evidence) that it is important to understand processes of change in the self-management of EB behaviours during the course of weight management interventions.

Fig. 2

The primary objectives of the NoHoW project were to understand how weight is lost and managed in Europe, develop a digital TK to support WL maintenance, evaluate the impact of the TK on WL maintenance through a 2 × 2 factorial randomised controlled trial, identify predictive signatures of weight relapse and maintenance, and inform future digital interventions for longer-term weight management.

Fig. 3

Conceptual basis for the development of the NoHoW TK and its intended impact on the cycle of WL and weight regain.

Fig. 4

Design of the NoHoW trial, a 2 × 2 factorial randomised controlled intervention (arm 1: self-regulation, and arm 2: emotion regulation and stress management, self-regulation plus emotion regulation/stress management, and active control) in 1,627 obese adults to identify moderators and mediators of behavioural self-regulation.

Fig. 5

Schematic diagram of the overall digital architecture of the NoHoW project.

Fig. 6

Gannt chart showing the WP of the NoHoW project.