Gamification and neurotraining to engage men in behavioral weight loss: Protocol for a factorial randomized controlled trial.

Over 70% of men are overweight, and most desire weight loss; however, men are profoundly underrepresented in weight loss programs. Gamification represents a novel approach to engaging men and may enhance efficacy through two means: (1) game-based elements (e.g., streaks, badges, team-based competition) to motivate weight control behaviors and (2) arcade-style "neurotraining" to enhance neurocognitive capacities to resist the temptation of unhealthy foods and more automatically select healthy foods. This study will use a 2 × 2 factorial design to examine the independent and combinatory efficacy of gamification and inhibitory control training (ICT). Men with overweight/obesity (N = 228) will receive a 12-month mobile weight loss program that incorporates behavioral weight loss strategies (e.g., self-monitoring, goal setting, stimulus control). Men will be randomly assigned to a non-gamified or gamified version, and an active or sham ICT. A game design company will create the program, with input from a male advisory panel. Aims of the project are to test whether a gamified (versus non-gamified) weight loss program and/or ICT (versus sham) promotes greater improvements in weight, diet, and physical activity; whether these treatment factors have combinatory or synergistic effects; to test whether postulated mechanisms of action (increased engagement, for gamification, and inhibitory control, for ICT) mediate treatment effects; and whether baseline gameplay frequency and implicit preferences for ICT-targeted foods moderate effects. It is hoped this study will contribute to improved mHealth programs for men and enhance our understanding of the impact of gamified elements and neurocognitive training on weight control.

Associations between behaviour change technique clusters and weight loss outcomes of automated digital interventions: a systematic review and meta-regression.

Automated digital interventions for weight loss represent a highly scalable and potentially cost-effective approach to treat obesity. However, current understanding of the active components of automated digital interventions is limited, hindering efforts to improve efficacy. Thus, the current systematic review and meta-analysis (preregistration: PROSPERO 2021-CRD42021238878) examined relationships between utilisation of behaviour change techniques (BCTs) and the efficacy of automated digital interventions for producing weight loss. Electronic database searches (December 2020 to March 2021) were used to identify trials of automated digital interventions reporting weight loss as an outcome. BCT clusters were coded using Michie's 93-item BCT taxonomy. Mixed-effects meta-regression was used to examine moderating effects of BCT clusters and techniques on both within-group and between-group measures of weight change. One hundred and eight conditions across sixty-six trials met inclusion criteria (13,672 participants). Random-effects meta-analysis revealed a small mean post-intervention weight loss of -1.37 kg (95% CI, -1.75 to -1.00) relative to control groups. Interventions utilised a median of five BCT clusters, with goal-setting, feedback and providing instruction on behaviour being most common. Use of Reward and Threat techniques, and specifically social incentive/reward BCTs, was associated with a higher between-group difference in efficacy, although results were not robust to sensitivity analyses.