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Adherence to weight management strategies may be undermined where lengthy strategy explanations limit engagement and understanding, weakening intervention efficacy. By contrast, implementation intentions have been shown to promote adherence across various health behaviors. This study aimed to investigate the impact of explanation length and implementation intentions on adherence to brief weight management strategies. Participants (N=200) with a BMI above 25 and an interest in losing weight were recruited from a commercial digital weight management service provider. Participants received information about 1 of 4 weight management strategies on a smartphone app in either a brief or detailed format and were asked to plan their use of the strategy with implementation intentions or were given tips on strategy use. Participants received daily prompts over a 2-week period to report whether they used their assigned strategy. Proposed moderators (need for cognition and planning skills) were measured at baseline. Strategy adherence was greater with brief information (mean 74%, SD 23%) compared with detailed information (mean 69%, SD 23%); however, this small effect size (Cohen d=0.24) was not statistically significant (P=.13). There was no moderation by need for cognition (P=.25). Adherence did not differ significantly between implementation intentions (mean 71%, SD 27%) and tips (mean 72%, SD 21%; P=.73); however, there was moderation by planning skills (P=.04). As predicted, adherence was greater with implementation intentions compared with tips among those with poorer planning skills. Shorter explanation length and implementation intentions (in poorer planners) may enhance adherence to brief weight management strategies, and further investigation is required to confirm these effects.

Obesity affects more than one-quarter of adults in the United Kingdom and is a leading cause of preventable disease and health care costs. Digital behavior change programs can provide scalable weight management support, but maintaining engagement is challenging, and engagement is strongly associated with weight loss success. Tailoring interventions to cognitive-behavioral phenotypes, distinct patterns of thinking and behavior, offers one strategy to improve adherence. Although such approaches show promise in controlled settings, evidence from real-world digital programs is limited. This study evaluated whether phenotype-tailored weekly advice improved engagement and weight loss in a national digital weight management program. Secondary aims were to assess correlations between advice use and outcomes, explore moderation by socioeconomic status, and capture participants' perceptions of the advice. We conducted a quasi-experimental study among UK adults enrolled in a free 12-week program commissioned by the National Health Service. Eligible participants were aged 18-80 years with a BMI greater than 25 kg/m². The phenotype group (n=148; mean age 48 years; 127/148, 86% female; mean BMI 39 kg/m²) completed a 17-item questionnaire, were classified into one of 4 phenotypes, and received weekly tailored advice for 7 weeks. Comparators included a historical cohort enrolled 1 year earlier without phenotype advice (n=241; mean age 44 years; 171/241, 71% female) and nonresponders who did not complete the questionnaire (n=394; mean age 44 years; 299/394, 76% female). Primary outcomes were program engagement (any in-app activity such as meal logging, activity tracking, content reading, or coach messaging) and self-reported weight. The phenotype group recorded a mean of 257 (SD 232) engagements over 7 weeks, significantly higher than both the historical cohort (mean 159, SD 187; P<.001) and nonresponders (mean 135, SD 198; P<.001), representing 62%-90% greater activity. All engagement types were significantly elevated (P<.001 for all). Mean weight loss was -2.23 kg (SD 7.97) in the phenotype group, compared with -1.60 kg (SD 5.39; P=.29) in the historical cohort and -0.69 kg (SD 13.23; P=.23) in nonresponders. The number of phenotype-specific advice documents opened correlated with engagement (r=0.48; P<.001) but not with weight loss (P=.42). Socioeconomic status did not moderate outcomes. Posttrial interviews (n=16) provided mixed feedback: many participants described the advice as clear, relevant, and motivating, whereas others considered it too general or poorly matched. Phenotype-tailored weekly advice was associated with substantially higher engagement in a real-world digital program, although short-term weight differences were not statistically significant. While limited by a nonrandomized design, short follow-up, and reliance on self-reported weight, this study suggests phenotype-based tailoring may be a scalable strategy to strengthen adherence in digital weight loss interventions. Larger randomized trials with longer follow-up are warranted to determine whether increased engagement translates into clinically meaningful weight loss.

Background There are well documented socioeconomic disparities in diet quality and obesity. Menu energy labelling is a public health policy designed to improve diet and reduce obesity. However, it is unclear whether the impact energy labelling has on consumer behaviour is socially equitable or differs based on socioeconomic position (SEP). Methods Systematic review and meta-analysis of experimental (between-subjects) and pre-post implementation field studies examining the impact of menu energy labelling on energy content of food and/or drink selections in higher vs. lower SEP groups. Results Seventeen studies were eligible for inclusion. Meta-analyses of 13 experimental studies that predominantly examined hypothetical food and drink choices showed that energy labelling tended to be associated with a small reduction in energy content of selections that did not differ based on participant SEP (X 2 (1) = 0.26, p  = .610). Effect estimates for higher SEP SMD = 0.067 [95% CI: -0.092 to 0.226] and lower SEP SMD = 0.115 [95% CI: -0.006 to 0.237] were similar. A meta-analysis of 3 pre-post implementation studies of energy labelling in the real world showed that the effect energy labelling had on consumer behaviour did not significantly differ based on SEP (X 2 (1) = 0.22, p  = .636). In higher SEP the effect was SMD = 0.032 [95% CI: -0.053 to 0.117] and in lower SEP the effect was SMD = -0.005 [95% CI: -0.051 to 0.041]. Conclusions Overall there was no convincing evidence that the effect energy labelling has on consumer behaviour significantly differs based on SEP. Further research examining multiple indicators of SEP and quantifying the long-term effects of energy labelling on consumer behaviour in real-world settings is now required. Review registration Registered on PROSPERO (CRD42022312532) and OSF ( https://doi.org/10.17605/OSF.IO/W7RDB ).

Consuming high amounts of foods or beverages with high levels of saturated fats, salt, or sugar (HFSS) can be harmful for health. Many snacks fall into this category (HFSS snacks). However, the palatability of these snacks means that people can sometimes struggle to reduce their intake. Machine learning algorithms could help in predicting the likely occurrence of HFSS snacking so that just-in-time adaptive interventions can be deployed. However, HFSS snacking data have certain characteristics, such as sparseness and incompleteness, which make snacking prediction a challenge for machine learning approaches. Previous attempts have employed several potential predictor variables and have achieved considerable success. Nevertheless, collecting information from several dimensions requires several potentially burdensome user questionnaires, and thus, this approach may be less acceptable for the general public. Our aim was to consider the capacity of standard (unmodified in any way; to tailor to the specific learning problem) machine learning algorithms to predict HFSS snacking based on the following minimal data that can be collected in a mostly automated way: day of the week, time of the day (divided into time bins), and location (divided into work, home, and other). A total of 111 participants in the United Kingdom were asked to record HFSS snacking occurrences and the location category over a period of 28 days, and this was considered the UK dataset. Data collection was facilitated by a purpose-specific app (Snack Tracker). Additionally, a similar dataset from the Netherlands was used (Dutch dataset). Both datasets were analyzed using machine learning methods, including random forest regressor, Extreme Gradient Boosting regressor, feed forward neural network, and long short-term memory. We additionally employed 2 baseline statistical models for prediction. In all cases, the prediction problem was the time to the next HFSS snack from the current one, and the evaluation metric was the mean absolute error. The ability of machine learning methods to predict the time of the next HFSS snack was assessed. The quality of the prediction depended on the dataset, temporal resolution, and machine learning algorithm employed. In some cases, predictions were accurate to as low as 17 minutes on average. In general, machine learning methods outperformed the baseline models, but no machine learning method was clearly better than the others. Feed forward neural network showed a very marginal advantage. The prediction of HFSS snacking using sparse data is possible with reasonable accuracy. Our findings offer a foundation for further exploring how machine learning methods can be used in health psychology and provide directions for further research.

Consistent with the insurance hypothesis, research shows that when children experience restricted access to food, they display increased intake when restrictions are lifted. This effect appears more robust for girls compared to boys, and for children with lower levels of inhibitory control. The insurance hypothesis has potentially important implications for parental feeding practices.

This study examined the effect of implementation intentions on use of dental chewing gum. A total of 80 participants reported intentions to chew gum, read information about the benefits of dental gum, reported intentions again, and formed implementation intentions relating to gum use (experimental group) or solved word puzzles (control group). Seven days later, they reported the amount chewed. Results showed that among those motivated to chew gum, implementation intentions significantly increased the total amount chewed. Time 1 intentions were more highly correlated with behaviour than time 2 intentions. Further research is needed to establish the effectiveness of implementation intentions in dental settings.

The HealthValues Healthy Eating Programme was developed on the basis of psychological principles that influence behaviour—established techniques targeting motivation, volition, and maintenance as well as a novel strategy in which individuals are asked to spend time thinking about reasons for health values. In this exploratory study, we examined the effects of the programme on intake of (1) fruit and vegetables, (2) saturated fat, and (3) added sugar during 6 months. Participants were 82 women and 18 men (mean body-mass index [BMI] 27·68 [SD 5·73], mean age 39 years [SD 14], 23 participants dieting to lose weight) who expressed an interest in eating a healthier diet in response to advertisements in Cardiff. Participants were allocated to an intervention or control group with a stratified block randomisation protocol on the basis of dieting status (dieting vs non-dieting) and fruit and vegetable consumption (five or more a day vs fewer than five a day; appendix). Participants were not masked to group allocation but were informed that both groups would monitor eating behaviours and that this procedure had been shown to be useful for reaching health goals. All participants logged onto a website every week for 24 weeks and completed health-related measures and measures assessing potential moderators and mediators. Those in the intervention group also completed the intervention tasks at these sessions. Additionally, all participants attended laboratory sessions at baseline, 3 months, and 6 months, at which they completed a food frequency questionnaire (the block fat/sugar/fruit/vegetable screener, adapted for the UK), and measures of BMI and waist-to-hip ratio (WHR) were taken by researchers who were masked to group allocation. Outliers greater than 3·5 SDs from the mean were excluded to ensure that the assumptions of the ANOVA tests were met. Participants received £150 for completing all online sessions and laboratory assessments. 91 participants completed 12 or more online sessions. Data for these participants were analysed with a series of ANOVA models. These analyses showed a significant interaction for fruit and vegetables (F[1, 89] 4·47; p=0·037) with intervention participants increasing their mean daily intake between baseline and 6 months (3·72 cups [95% CI 3·25–4·19] to 4·17 cups [3·71–4·63], respectively) and controls decreasing their intake (3·59 cups [3·14–4·04] to 3·36 cups [2·92–3·81], respectively). Results also showed overall reductions in saturated fat (two outliers excluded; F[1, 87] 28·09; p<0·001) and added sugar (six outliers excluded; F[1, 83] 15·81; p<0·001) between baseline and 6 months (saturated fat 19·61 g [95% CI 17·89–21·16] to 14·96 g [13·55–16·30]; sugar 39·66 g [33·02–45·01] to 28·29 g [23·04–32·64]) but no interaction with group. Similarly, there were overall reductions in BMI (one outlier excluded; F[1, 88] 10·86; p<0·001; BMI change 27·48 [95% CI 26·32–28·62] to 27·05 [25·90–28.18]) and WHR (F[1, 89] 7·17; p=0·009; WHR change 0·82 [0·80–0·84] to 0·81 [0·79–0·82]), but no interactions with group. Including the outliers in the analyses had no effect on whether or not the results were significant. The results show that the programme helped individuals increase their fruit and vegetable intake and sustain this increase over 6 months. Levels of attrition, and replication of present results, in the absence of remuneration, need to be established. Nevertheless, the online nature of the programme makes it a potentially cost-effective way of promoting healthy eating. Economic and Social Research Council.

This study compared the effects of mindful eating and eating without distractions on energy intake and diet over a 3-day period among healthy-weight females. Mindful eating was defined as attending to the sensory properties of one’s food as one eats. Participants (n = 99) were asked to either focus on the sensory properties of their food (MIND), eat without distractions (CON-D) or they were not provided with any instructions (CON-I). All participants completed an online food recall measure at the end of each day. Those in the MIND and CON-D groups also rated strategy adherence at the end of each day. Results showed no significant effects of condition on energy intake (ηp2 = 0.00), saturated fat, added sugar and fiber (ηp2 = 0.03), or fruit and vegetables (ηp2 = 0.04). There was also no significant relationship between energy intake and strategy adherence in the MIND group (r = −0.02). For those in the CON-D group, there was a trend toward a negative relationship between energy intake and strategy adherence (r = −0.31, p = 0.085). Among this population, there was no evidence that asking people to attend to the sensory properties of their food improved their diet. Further research is needed to identify mechanisms underpinning significant effects observed in laboratory studies, to help understand when this strategy is, and is not, likely to be helpful.

Research shows that social values often function as truisms—ie, these values are convictions that are widely held and strongly endorsed, but rarely questioned. Thus individuals tend to behave in accordance with the value only when they can do so fairly easily. However, value-consistent behaviour can be increased by building cognitive support for the value, which can be achieved by thinking about reasons supporting or opposing the value. We examined whether health values function as truisms and explored the effect of building cognitive support for health values on fruit and vegetable consumption and exercise. In experiment 1, 150 participants (aged ≥18 years) were recruited via local advertisements, posters, and flyers, and tested in community and workplace settings. This sample was comparable to the general population in terms of age, sex, ethnicity, education level, smoking status, and BMI, and there were no significant differences between experimental and control groups in terms of these characteristics. Participants rated the importance of a range of values (including four health values), on a scale from −1 (opposed to my values) to 7 (extremely important). Participants then wrote down reasons why health values were important or unimportant to them (experimental group, n=75), or wrote reasons why they liked or disliked particular television shows (control group, n=75). Finally, all participants completed a second questionnaire assessing value importance, including the four health values. In experiment 2, 43 students (aged ≥18 years) either analysed reasons for health values (experimental group, n=22) or completed anagram and word search tasks that made the value of health salient (control group, n=21). 1 week later, they reported their diet and exercise behaviours during the previous week (number of days that they had consumed fruit and vegetables; average number of servings eaten; number of times they had participated in vigorous physical activity; and number of times they had exercised for ≥20 min). We analysed data with ANOVA and post-hoc t tests. Experiment 1 showed that health values ratings changed more after the analysis of reasons than in the control group (p<0·02; mean [SD] in experimental group=0·62 [0·41], mean [SD] in control group=0·44 [0·45]), indicating that health values were functioning as truisms. This effect occurred across a range of different social groupings, and irrespective of whether individuals lead healthy or unhealthy lifestyles. In experiment 2, responses to the health and diet items were standardised and combined to form diet and exercise indices. Participants who completed the reasons analysis subsequently reported more exercise (p=0·08, mean [SD]=0·56 [2·08]) and less food consumption (p=0·06, mean [SD]=–0·44 [1·33]) than did those in the control group (mean [SD] exercise=–0·78 [2·51]; mean [SD] food=0·48 [1·68]). This research shows that health values function as truisms. This research shows that health values function as truisms. As with other values, people do not think to question the importance of health. That this effect was true for a range of different social groupings suggests that health reasons analysis could be a helpful strategy for most people. Consistent with this hypothesis, experiment 2 showed that thinking about reasons for health values resulted in participants reporting higher amounts of exercise 1 week later. That they also reported lower amounts of food consumption could be an indication of increased dieting. Further research with physiological and observational measures, and more detailed dietary questionnaires, should help to confirm both this interpretation and the usefulness of this technique in motivation of health behaviour change. The research was funded by an UK Economic and Social Research Council (ESRC) grant “Lifestyle change: values and volition” awarded to KT, GRM, GH, and ML, and by an ESRC grant “Explicit and implicit bases of social values: implications for behaviour change” awarded to GRM and GH.

Bayesian inference offers an optimal means of processing environmental information and so an advantage in natural selection. We consider the apparent, recent trend in increasing dysfunctional disagreement in, for example, political debate. This is puzzling because Bayesian inference benefits from powerful convergence theorems, precluding dysfunctional disagreement. Information overload is a plausible factor limiting the applicability of full Bayesian inference, but what is the link with dysfunctional disagreement? Individuals striving to be Bayesian-rational, but challenged by information overload, might simplify by using Bayesian networks or the separation of questions into knowledge partitions, the latter formalized with quantum probability theory. We demonstrate the massive simplification afforded by either approach, but also show how they contribute to dysfunctional disagreement.