Mindfulness, mindful eating and intuitive eating practices are associated with healthier eating and lower body weight. However, experimental research in this area has shown mixed effects on food intake and theoretical accounts are underdeveloped. This systematic review and meta-analysis aimed to examine the effect of mindfulness, mindful eating and intuitive eating interventions on food intake and appetite (hunger and fullness) in adults and children and compare effects across different subgroups to investigate potential mechanisms of action. Five electronic databases (PsycINFO, MEDLINE, EMBASE, Web of Science and Scopus) were searched for studies that experimentally manipulated mindfulness and/or mindful eating and/or intuitive eating, included a non-mindfulness control group and measured food intake (kcal or grams or percentage consumed or number of pieces consumed) and/or appetite (using visual analogue scales). Forty-one articles assessing mindfulness and mindful eating interventions were included (no relevant intuitive eating interventions were identified). Random-effects meta-analyses showed that mindfulness/mindful eating reduced food intake (n = 46 studies, SMD = -0.24, 95% CI [-0.35, -0.12], p < 0.001) but had no statistically significant effect on appetite (n = 11 studies). There were no significant subgroup differences observed between studies with different settings, interventions or food intake measures. However, effect sizes were substantially larger in laboratory-based studies. Overall, findings indicate that mindfulness and mindful eating reliably reduce food intake in controlled settings, but currently there is no evidence they influence appetite. The review underscores the need for higher quality and more ecologically valid studies using sensitive, real-world measures of appetite and food intake, and further work to clarify the mechanisms of action underpinning the effects of mindfulness and mindful eating.

Calorie labelling has been implemented as a public health strategy to address obesity, but its mechanisms of action are not well understood. Drawing on expectancy-value models, this study explored whether calorie labelling influences the calorie content of items selected from a hypothetical coffee shop menu via changes in outcome expectancies and whether effects are moderated by food choice motives. Adults (n = 577) were randomly assigned to view a menu with (n = 290) or without (n = 287) calorie information and select their preferred items(s). The primary outcome was total calories selected. Secondary outcomes were change in weight control, health, taste, value for money and fullness expectations for each menu item and participants' motivation for weight control, health, price and sensory appeal. Exploratory outcomes included participants' calorie estimates for each menu item. Labelling did not significantly reduce calories selected (p = 0.18), though means were in the predicted direction (labelling M = 371, SD = 261; no labelling M = 392, SD = 249, 5% decrease). Labelling significantly increased health, weight control and value for money expectations of menu items but these changes did not influence calories selected. A sensitivity analysis suggested moderation by weight control motivation whereby labelling reduced calories selected among highly motivated participants, although this effect was not observed across other models. Labelling was associated with better calorie estimation accuracy. Findings suggest that while calorie labelling may influence beliefs and knowledge, its acute impact on population level behaviour may be minimal.

BACKGROUND:Eating while distracted [e.g., television (TV) watching, phone use] is believed to increase food intake. A previous small meta-analysis of experimental studies (published in American Journal of Clinical Nutrition) supported this. Many studies have since been published, but there has been no updated analysis. OBJECTIVES:This study aimed to conduct an updated systematic review and meta-analysis to examine the effect of distraction on concurrent and later energy intake. METHODS:Eligible articles (searching up to December 2024) were identified from: a previously conducted meta-analysis which included studies up until 2012; database searches from 2012 to 2024 (PsycINFO, Medline, and PubMed); and both forward and backward citation searching. We followed PRISMA guidelines and conducted generic variance inverse meta-analyses with intake as the outcome variable for both concurrent and later energy intake. RESULTS:A total of 50 eligible studies were included (40 measuring concurrent intake, 10 measuring later intake). Random effects meta-analyses revealed that the overall effect of distraction on concurrent energy intake was nonsignificant [standardized mean difference (SMD) = 0.123, 95% confidence interval (CI): <-0.01, 0.25; P = 0.051]. Moderator analyses revealed that type of distractor moderated the effect of distraction on eating, with passive distractor tasks (e.g., TV watching) resulting in greater energy intake when distracted [SMD = 0.272 (95% CI: 0.128, 0.417)], whereas physically demanding distractors [SMD = -0.139 (95% CI: -0.334, 0.057)] and cognitively demanding distractors [SMD = 0.202 (95% CI: -0.028, 0.432)] did not. The effect of distraction on later energy intake was statistically significant, such that eating while distracted led to greater intake at a subsequent eating episode [SMD = 0.419 (95% CI: 0.195, 0.642)]. CONCLUSIONS:Distracted eating increases later energy intake; however, the effect of distracted eating on concurrent energy intake is less consistent, and only relatively passive distractors may increase energy intake. Collectively, these findings suggest that distraction is a potential contributor to overeating. This systematic review and meta-analysis was registered at PROSPERO as CRD42024518245.

While "micro" goals (small, frequent actions aimed at building lasting habits) have gained widespread popularity, their effectiveness in increasing physical activity remains underexplored. This study tested whether a daily, short-duration exercise goal increased physical activity more effectively than a standard goal of less frequent, longer workouts. Participants (n = 142) were randomly assigned to a micro goal group (3 min daily plus two 20-minute sessions weekly) or a standard goal group (two 30-minute sessions weekly). Participants tracked their daily exercise duration, enjoyment, and ease of adherence over 3 wk. The micro goal group exercised significantly more time overall than the standard goal group, with ease of adherence mediating this effect. The evidence supports using micro goals to increase physical activity and highlights their potential for habit formation and public health interventions.

BackgroundConsuming 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. ObjectiveOur 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). MethodsA 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. ResultsThe 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. ConclusionsThe 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.

BackgroundObesity 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. ObjectiveThis 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. MethodsWe 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. ResultsThe 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. ConclusionsPhenotype-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: 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. Objective: This study aimed to investigate the impact of explanation length and implementation intentions on adherence to brief weight management strategies. Methods: 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. Results: 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. Conclusions: 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 remains a critical public health challenge, requiring innovative strategies to improve weight loss. Although preliminary evidence indicates that tailoring interventions to both cognitive and behavioral factors may enhance engagement and effectiveness, most research has focused on these elements in isolation. With the increasing scalability and convenience of digital weight loss programs, developing reliable methods for automated personalization has become essential. To address this, we constructed a questionnaire that assigns patients to cognitive-behavioral phenotypes, to enable tailored advice. This study evaluates the effectiveness of this approach in improving patient engagement and weight loss within a digital intervention. To assess whether sending personalized weight loss advice can increase program engagement and lead to greater weight loss. A quasi-experimental design was employed. Patients on a weight loss program were sent a 17-item questionnaire which matched them to one of four cognitive-behavioral profiles (phenotypes). Those who completed the questionnaire were sent phenotype-tailored weight loss advice once per week. As part of their weight loss program, patients used an app to track their meals and activities. Number of in-app events (i.e. engagements) was used as a proxy measure of program engagement. Self-reported weight was submitted as part of the structured weight loss program. Outcomes were compared to a historical cohort of patients who participated in the same weight loss program one year earlier, and those who were sent an invitation to complete the questionnaire but did not (i.e. non-responders). Those who received phenotype-tailored advice generated significantly more in-app engagements (M=257, SD=232), than those in the historical cohort (M=159, SD=187; P<.001), and compared to non-responders (M=135, SD=198; P<.001). There was a trend towards greater weight loss for those who received the tailored advice (M=-2.23kg, SD=7.97), compared to the historical cohort (M=-1.6kg, SD=5.39), and the non-responders (M=-0.69kg, SD=13.23) but these differences were not significant, (P=.29 and P=.23, respectively). This study provides preliminary evidence that receiving weight loss advice tailored to an individual’s cognitive-behavioral phenotype can increase engagement on a structured weight loss program, which could in turn support greater body mass reduction.

A key component of mindful eating is paying attention to the sensory properties of one's food as one eats ("sensory eating"). Some studies have found this reduces subsequent food intake while others have failed to replicate these effects. We report four laboratory studies that (a) examine effects of sensory eating on subsequent intake and (b) explore potential mechanisms of action. In each study, participants ate a small high-calorie snack with or without sensory eating and, 5-15 min later, were given larger snack portions from which they could eat freely. Sensory eating reduced intake of the second snack and could not be explained by increased sensory-specific satiety or priming of health-related goals. However, this effect disappeared when we controlled eating rate for the first snack. Given evidence that slower eating increases satiation and reduces intake, we conclude that sensory eating reduces intake by slowing eating rate. Exploratory analyses also revealed that (among nondieters) effects of sensory eating were pronounced when participants reported higher hunger. Thus, for weight management, sensory eating may be most beneficial for those who are naturally fast eaters and/or in situations where people are inclined to eat more quickly, for example, when hungry or in a hurry. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

We need to reduce household food waste, but the complexity of its determinants makes this difficult. Here we put forward a model for understanding some of the key behavioural and psychological drivers thought to promote or undermine food waste reduction. The model draws on multiple theories and integrates reflective processes (e.g., the influence of goals and outcome expectancies on planning behaviours) with automatic processes (e.g., habitual behaviours), psychological traits (e.g., self-efficacy and disgust sensitivity) and environmental influences (e.g., stress and time pressure). It leads us to identify two important potential levers for change: promoting shorter term, flexible meal planning and changing the way we feel about food waste.

Consuming too much food or drink with high levels of saturated fats, salt or sugar can be harmful for health. Many snack foods 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 by predicting the likely occurrence of HFSS snacking, so that just-in-time adaptive (JITAI) interventions can be deployed. However, HFSS snacking data has characteristics (such as sparseness and incompleteness), which make snacking prediction a challenging machine learning problem. Previous attempts have employed several potential predictor variables, achieving considerable success. Nevertheless, collecting information along several dimensions requires several potentially burdensome user questionnaires per day, so that this approach may be less acceptable among the general public. Our aim is to consider the capacity of machine learning algorithms to predict HFSS snacking based on minimal data that can be collected in a mostly automated way: day of week, time of day, and location (coarsened as work, home, other). A sample of 111 participants in the UK were asked to record HFSS snack occurrences and location category, over a period of 28 days, leading to a new dataset on HFSS snacks. Data collection was facilitated by a purpose-specific app. Additionally, we use a similar dataset from the Netherlands. For both datasets, we employ machine learning methods (random forests and neural networks). We report results concerning the ability of machine learning methods to predict the time of the next HFSS snack. The quality of the prediction depended on both the dataset and temporal resolution employed. In some cases, predictions were accurate to as few as 17 minutes on average. We demonstrated that prediction of HFSS snacks on sparse data is possible to reasonable accuracy. We consider the type of prediction problem which may be most suitable for putative interventions in relation to HFSS snacking. While we think it was important to employ standard machine learning algorithms in this work, we also discuss ways to tailor both the machine learning algorithms and the prediction problem to better align with the unique characteristics of the problem.

Objective: 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 behaviours. This study investigated the impact of explanation length and implementation intentions on adherence to brief weight management strategies. Methods: Participants (n=200) with a BMI above 25 and an interest in losing weight were recruited from a commercial weight management service provider. Participants received information about one of four weight management strategies on a smartphone application 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. Results: Strategy adherence was greater with brief information (M=74%, SD=23) compared to detailed information (M=69%, SD=23), however this small effect size (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 (M=71%, SD=27) and tips (M=72%, SD=21; p=.73), however there was moderation by planning skills (p=.04); as predicted, adherence was greater with implementation intentions compared to tips among those with poorer planning skills. Conclusions: Shorter explanation length and implementation intentions (in poorer planners) may enhance adherence to brief weight management strategies; further investigation is required to confirm these effects.

In some studies mindfulness is associated with reduced food consumption, but the underlying mechanisms are less well researched. One potential mechanism is that mindfulness increases attention toward feelings of fullness. Additionally, experimental research on mindfulness and food intake has primarily been conducted in constrained laboratory settings, where it may be easier for participants to notice their internal bodily signals, as opposed to the real world where individuals are often engaged in other activities while eating. The effect of mindfulness on food intake while participants are distracted remains unexplored. This study therefore aimed to examine whether a mindfulness-based body scan exercise reduced food consumption within a distracted environment by increasing attention toward feelings of fullness. Participants (n = 137) listened to a 10-minute body scan meditation, or a 10-minute visualisation (control) meditation. They were then given a bowl of crisps to consume while watching a 10-minute TV show segment. Participants also completed measures assessing proposed mediators, including state mindfulness, attention to bodily sensations and eating automaticity. The body scan manipulation increased state mindfulness but had no direct effect on the other mediators or on food intake (intervention M = 34.79g, SD = 24.06; control M = 33.16g, SD = 23.88). State mindfulness was positively correlated with attention to bodily sensations while eating. Lower eating automaticity and greater reliance on decreased food appeal and physical satisfaction to stop eating were found to be associated with lower food intake. Contrary to previous studies, we found no evidence that a mindfulness body scan reduces food consumption when participants are distracted. Future research should examine the specific conditions under and mechanisms by which mindfulness may influence food consumption.

Menu energy labelling has been implemented as a public health policy to promote healthier dietary choices and reduce obesity. However, it is unclear whether the influence energy labelling has on consumer behaviour differs based on individuals' demographics or characteristics and may therefore produce inequalities in diet. Data were analysed from 12 randomized control trials (N = 8508) evaluating the effect of food and drink energy labelling (vs. labelling absent) on total energy content of food and drink selections (predominantly hypothetical) in European and US adults. Analyses examined the moderating effects of participant age, sex, ethnicity/race, education, household income, body mass index, dieting status, food choice motives and current hunger on total energy content of selections. Energy labelling was associated with a small reduction (f2 = 0.004, -50 kcal, p < 0.001) in total energy selected compared to the absence of energy labelling. Participants who were female, younger, white, university educated, of a higher income status, dieting, motivated by health and weight control when making food choices, and less hungry, tended to select menu items of lower energy content. However, there was no evidence that the effect of energy labelling on the amount of energy selected was moderated by any of the participants' demographics or characteristics. Energy labelling was associated with a small reduction in energy content of food selections and this effect was similar across a range of participants' demographics and characteristics. These preliminary findings suggest that energy labelling policies may not widen existing inequalities in diet.