Richard James Stubbs

Background: Digital approaches to weight management have the potential to produce cost-effective and scalable weight management solutions. Effective behavior change interventions typically promote self-regulation of energy balance behaviors, which may be enhanced by incorporating emotion regulation strategies. Objective: This study aimed to evaluate the effectiveness of a digital behavior change toolkit for weight loss maintenance in European adults who had achieved ≥5% weight loss in the previous 12 months. We hypothesized that a combined intervention targeting self-regulation or motivation and emotion regulation would be more effective than either component alone, and that each would outperform an active control. Methods: The Navigating to a Healthier Weight (NoHoW) trial was a 2×2 factorial randomized, single-blind, controlled trial involving 1627 adults who had achieved ≥5% weight loss in the previous 12 months (initial BMI ≥25 kg/m ² ) across 3 European centers (the United Kingdom, Denmark, and Portugal). The trial evaluated a digital toolkit for weight management subsequent to an initial ≥5% weight loss in the prior 12 months. Participants were assigned using adaptive stratified sampling to one of four groups: (1) self-regulation or motivation (n=403), (2) emotion regulation (n=416), (3) combined motivation and emotion (n=408), or (4) active control (generic content, regular self-weighing, and Fitbit use, n=400). The primary outcome was weight change from baseline to 12 months. Prespecified secondary outcomes included cardiometabolic markers. Linear models adjusted for recruitment center, sex, age group, BMI group, and pretrial weight loss. Subgroup analyses were conducted by sex. Results: At 12 months, 76% (364/1627) of participants remained in the study. In the primary ITT analysis in all participants, none of the intervention arms (motivation, emotion, or combined) differed significantly from the active control for weight change at 12 months. Completer and per-protocol analyses produced similar patterns and did not change the overall interpretation. In the per-protocol sample, men regained 0.14 kg, and women regained 0.54 kg of their pretrial weight loss. Subgroup analyses indicated a small effect of the motivation intervention in men, but this was not clinically meaningful and did not alter the primary null findings. Nearly half of ITT participants regained weight, and no significant intervention effects were observed for cardiometabolic secondary outcomes. Conclusions: The NoHoW trial was the first large-scale, multicountry 2×2 factorial randomized controlled trial to evaluate a digital-only toolkit based on self-regulation or motivation and emotion regulation techniques for weight loss maintenance. NoHoW found no evidence in the primary ITT analysis that digital interventions targeting self-regulation or emotion regulation improved weight loss maintenance compared with the active control. A small subgroup effect in men should be interpreted cautiously and does not change this conclusion. The trial provides evidence on both the limitations and potential of digital behavior change interventions for long-term weight outcomes. Future digital interventions may benefit from enhanced engagement and tailored content to improve long-term weight outcomes.

Background/Objectives: Long-term weight loss maintenance is a major challenge in the treatment of overweight and obesity. Metabolic and hormonal signalling systems are believed to influence weight trajectories during weight management attempts. However, very limited research has previously been conducted in this area. The aim of the present analysis was to investigate the relationship between metabolic health and weight changes at 18-months in adults who had achieved clinically significant (≥ 5%) initial weight loss. Subjects/Methods: This post-hoc analysis used 18-month follow-up data from 879 participants from the Navigating to a Healthy Weight (NoHoW) trial. Participants were categorised at baseline into metabolic health subgroups according to BMI (kg/m2) and the presence of cardiometabolic risk factors for metabolic syndrome, defined according to the NCEP-ATP III criteria. These subgroups were: 1) metabolically healthy overweight or obesity (n = 568), 2) metabolically unhealthy overweight or obesity (n = 151), 3) normal weight and zero risk factors for metabolic syndrome (n = 104), and 4) normal weight and ≥1 risk factors for metabolic syndrome (n = 56). The primary outcome was 18-month changes in body weight (kg and %). Secondary outcomes were 18-month changes in waist circumference (cm), fat mass (kg and %) and fat-free mass (kg and %). Multiple linear regression models were used to examine the relationship between outcomes and metabolic health subgroup in those with overweight and obesity and those with normal weight. Results: No differences in 18-month changes in body weight or body composition were found between individuals with metabolically healthy vs unhealthy overweight or obesity or between individuals with normal weight with and without risk factors for metabolic syndrome. Conclusions: Baseline metabolic health did not predict long-term weight loss maintenance. All individuals should have access to the highest quality weight loss maintenance support possible to ensure equitable and effective health outcomes.

Food and eating are fundamental for survival but also have significant impacts on health, psychology, sociology, and economics. Understanding what motivates people to eat can provide insights into "adaptive" eating behavior, which is especially important due to the increasing prevalence of health-related conditions such as obesity. There has been considerable interest in developing theoretical models and associated constructs that explain individual differences in eating behavior. However, many of these models contain overlapping theories and shared theoretical mechanisms of action. Currently, there is no recognized standard framework that integrates psychological, physiological, and neurobiological theory to help explain human eating behavior. The aim of the current article was to review key psychological theories in relation to energy balance, homeostasis, energy intake, and motivation to eat and begin to develop a comprehensive framework of relevant factors that drive eating behavior. The key findings from this review suggest that eating behavior is conceptualized by elements of dual process models, which include conscious processing (reflective factors) and automatic responses to desires, environmental cues, habits, and associative learning. These processes are mediated by neurobiology and physiological signaling (homeostatic feedback) of energy balance, which is more tolerant of positive than negative energy balances. From a synthesis of available evidence, it is suggested that eating behavior constructs (traits) can be explained by three latent constructs: reflective, reactive, and homeostatic eating. By understanding the interplay between reflective, reactive, and homeostatic processes, interventions can be developed that tailor treatments to target key aspects of eating behavior. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Objective: Physical activity and nutrition-related behaviors are crucial factors in maintaining weight loss. The interplay between the motivations to consistently engage in both behaviors is a fundamental yet understudied topic. The current study examines the reciprocal effects between exercise and eating motivation and their effect on steps and energy intake over a 12-month period. Methods: Data were gathered from 678 NoHoW participants (a weight loss management intervention) in 3 European countries (the United Kingdom, Denmark, and Portugal), with baseline, 6-, and 12-month measures (46 ± 11 years, 70% women, 83 ± 15 kg), assessing exercise motivation, eating motivation, steps taken, and energy intake. The analytical plan included latent growth models and cross-lagged panels. Results: Both autonomous and controlled motivation models indicated a positive spillover effect from exercise to eating motivation and vice versa for 12 months. Increased exercise autonomous motivation was linked to heightened eating autonomous motivation, and the same relation was observed with controlled motivation. However, when it came to the relationship between motivation and steps taken or food intake, the effects were either smaller or nonexistent, except for higher initial levels of exercise-controlled motivation being associated with higher food intake after 6 months. Conclusions: These findings suggest that during the yearlong study, there was a motivational spillover from exercise motivation to eating motivation and vice versa. This suggests that interventions focusing on increasing motivation toward behavior change in one domain also benefit other domains and thus might help increase the effectiveness of interventions.

Eating Behaviour Traits (EBTs) are psychological constructs developed to explain patterns of eating behaviour, including factors that motivate people to (over or under) eat. There is a need to align and clarify their unique contributions and harmonise the understanding they offer for human eating behaviour. Therefore, the current study examined whether 18 commonly cited EBTs could be explained by underlying, latent factors (domains of eating behaviour). An exploratory factor analysis (EFA) was used to identify latent factors, and these factors were validated using a confirmatory factor analysis (CFA). A total of 1279 participants including the general public and members of a weight management programme were included in the analysis (957 females, 317 males, 3 others, 2 prefer not to say), with a mean age of 54 years (median = 57 years, SD = 12.03) and a mean BMI of 31.93 kg/m² (median = 30.86, SD = 6.00). The participants completed 8 questionnaires which included 18 commonly cited EBTs and the dataset was split at random with a 70/30 ratio to conduct the EFA (n = 893) and CFA (n = 383). The results supported a four-factor model which indicated that EBTs can be organised into four domains: reactive, restricted, emotional, and homeostatic eating. The four-factor model also significantly predicted self-reported BMI, weight change and perceived stress. Future research should test whether this factor structure is replicated in more diverse populations, and including other EBTs, to advance these domains of eating as a unifying framework for studying individual differences in human eating behaviour.

Purpose: Preventing weight regain can only be achieved by sustained changes in energy balance-related behaviors that are associated with weight, such as diet and physical activity. Changes in motivation and self-regulatory skills can support long-term behavioral changes in the context of weight loss maintenance. We propose that experiencing a supportive climate care is associated with enhanced satisfaction of basic psychological needs, intrinsic goals, and autonomous motivation. These factors are expected to be associate with the utilization of self-regulation skills, leading to more sustained behavior changes and ultimately preventing weight regain. This hypothesis was tested in this ancillary analysis of the NoHoW trial, where the study arms were pooled and followed for 12 months. Methods: The NoHoW was a three-center, large-scale weight regain prevention full factorial trial. In this longitudinal study, data were collected in adults who lost > 5% weight in the past year (N = 870, complete data only, 68.7% female, 44.10 ± 11.86 years, 84.47 ± 17.03 kg) during their participation in a 12-month digital behavior change intervention. Weight and validated measures of motivational- and self-regulatory skills-related variables were collected at baseline, six- and 12 months. Change variables were used in Mplus’ path analytical models informed by NoHoW’s logic model. Results: The bivariate correlations confirmed key mediators’ potential effect on weight outcomes in the expected causal direction. The primary analysis showed that a quarter of the variance (r2 = 23.5%) of weight regain prevention was achieved via the mechanisms of action predicted in the logic model. Specifically, our results show that supportive climate care is associated with needs satisfaction and intrinsic goal content leading to better weight regain prevention via improvements in self-regulatory skills and exercise-controlled motivation. The secondary analysis showed that more mechanisms of action are significant in participants who regained or maintained their weight. Conclusions: These results contribute to a better understanding of the mechanisms of action leading to behavior change in weight regain prevention. The most successful participants used only a few intrinsic motivation-related mechanisms of action, suggesting that habits may have been learned. While developing a digital behavior change intervention, researchers and practitioners should consider creating supportive climate care to improve needs satisfaction and intrinsic goal contents. Trial registration: ISRCTN, ISRCTN88405328, registered 12/22/2016.

Absolute energy from fats and carbohydrates and the proportion of carbohydrates in the food supply have increased over 50 years. Dietary energy density (ED) is primarily decreased by the water and increased by the fat content of foods. Protein, carbohydrates and fat exert different effects on satiety or energy intake (EI) in the order protein > carbohydrates > fat. When the ED of different foods is equalized the differences between fat and carbohydrates are modest. Covertly increasing dietary ED with fat, carbohydrate or mixed macronutrients elevates EI, producing weight gain and vice versa. In more naturalistic situations where learning cues are intact, there appears to be greater compensation for the different ED of foods. There is considerable individual variability in response. Macronutrient-specific negative feedback models of EI regulation have limited capacity to explain how availability of cheap, highly palatable, readily assimilated, energy-dense foods lead to obesity in modern environments. Neuropsychological constructs including food reward (liking, wanting and learning), reactive and reflective decision making, in the context of asymmetric energy balance regulation, give more comprehensive explanations of how environmental superabundance of foods containing mixtures of readily assimilated fats and carbohydrates and caloric beverages elevate EI through combined hedonic, affective, cognitive and physiological mechanisms. This article is part of a discussion meeting issue ‘Causes of obesity: theories, conjectures and evidence (Part II)’.