Abstract Obesity during childhood and adolescence is a growing problem in the United States, Canada, and around the world that leads to significant physical, psychological, and social consequences. ...Peer experiences have been theoretically and empirically related to the “Big Two” contributors to the obesity epidemic, unhealthy eating and physical inactivity 1. In this article, we synthesize the empirical literature on the influence of peers and friends on youth's eating and physical activity. Limitations and issues in the theoretical and empirical literatures are also discussed, along with future research directions. In conclusion, we argue that the involvement of children's and adolescents' peer networks in prevention and intervention efforts may be critical for promoting and maintaining positive behavioral health trajectories. However, further theoretical and empirical work is needed to better understand the specific mechanisms underlying the effects of peers on youth's eating and physical activity.
•Social modeling is a primary determinant of food intake and food choice.•Sixty-nine experimental studies of modeling were reviewed.•Modeling is not moderated by hunger, restraint, age, or ...weight.•Modeling is strongest for intake of snack foods and for in-group models.•Modeling has relevance for public health interventions to encourage healthy eating.
A major determinant of human eating behavior is social modeling, whereby people use others' eating as a guide for what and how much to eat. We review the experimental studies that have independently manipulated the eating behavior of a social referent (either through a live confederate or remotely) and measured either food choice or intake. Sixty-nine eligible experiments (with over 5800 participants) were identified that were published between 1974 and 2014. Speaking to the robustness of the modeling phenomenon, 64 of these studies have found a statistically significant modeling effect, despite substantial diversity in methodology, food type, social context and participant demographics. In reviewing the key findings from these studies, we conclude that there is limited evidence for a moderating effect of hunger, personality, age, weight or the presence of others (i.e., where the confederate is live vs. remote). There is inconclusive evidence for whether sex, attention, impulsivity and eating goals moderate modeling, and for whether modeling of food choice is as strong as modeling of food intake. Effects with substantial evidence were: modeling is increased when individuals desire to affiliate with the model, or perceive themselves to be similar to the model; modeling is attenuated (but still significant) for healthy-snack foods and meals such as breakfast and lunch, and modeling is at least partially mediated through behavioral mimicry, which occurs without conscious awareness. We discuss evidence suggesting that modeling is motivated by goals of both affiliation and uncertainty-reduction, and outline how these might be theoretically integrated. Finally, we argue for the importance of taking modeling beyond the laboratory and bringing it to bear on the important societal challenges of obesity and disordered eating.
Numerous studies have shown that people adjust their intake directly to that of their eating companions; they eat more when others eat more, and less when others inhibit intake. A potential ...explanation for this modeling effect is that both eating companions' food intake becomes synchronized through processes of behavioral mimicry. No study, however, has tested whether behavioral mimicry can partially account for this modeling effect. To capture behavioral mimicry, real-time observations of dyads of young females having an evening meal were conducted. It was assessed whether mimicry depended on the time of the interaction and on the person who took the bite. A total of 70 young female dyads took part in the study, from which the total number of bites (N = 3,888) was used as unit of analyses. For each dyad, the total number of bites and the exact time at which each person took a bite were coded. Behavioral mimicry was operationalized as a bite taken within a fixed 5-second interval after the other person had taken a bite, whereas non-mimicked bites were defined as bites taken outside the 5-second interval. It was found that both women mimicked each other's eating behavior. They were more likely to take a bite of their meal in congruence with their eating companion rather than eating at their own pace. This behavioral mimicry was found to be more prominent at the beginning than at the end of the interaction. This study suggests that behavioral mimicry may partially account for social modeling of food intake.
•Narrative review on how parents influence children's dietary behavior.•We propose a new model of the relation between parenting and child dietary behavior.•Parental dietary behavior and food ...parenting practices are interactive sources.•Parental effects are most importantly mediated by the home food environment.•Studies testing our model may inform effective parent–child overweight interventions.
Until now, the literatures on the effects of food parenting practices and parents' own dietary behavior on children's dietary behavior have largely been independent from one another. Integrating findings across these areas could provide insight on simultaneous and interacting influences on children's food intake. In this narrative review, we provide a conceptual model that bridges the gap between both literatures and consists of three main hypotheses. First, parental dietary behavior and food parenting practices are important interactive sources of influence on children's dietary behavior and Body Mass Index (BMI). Second, parental influences are importantly mediated by changes in the child's home food environment. Third, parenting context (i.e., parenting styles and differential parental treatment) moderates effects of food parenting practices, whereas child characteristics (i.e., temperament and appetitive traits) mainly moderate effects of the home food environment. Future studies testing (parts of) this conceptual model are needed to inform effective parent–child overweight preventive interventions.
Eating rate is a basic determinant of appetite regulation: people who eat more slowly feel sated earlier and eat less. A high eating rate contributes to overeating and potentially to weight gain. ...Previous studies showed that an augmented fork that delivers real-time feedback on eating rate is a potentially effective intervention to decrease eating rate in naturalistic settings. This study assessed the impact of using the augmented fork during a 15-week period on eating rate and body weight.
In a parallel randomized controlled trial, 141 participants with overweight (age: 49.2 ± 12.3 y; BMI: 31.5 ± 4.48 kg/m2) were randomized to intervention groups (VFC, n = 51 or VFC+, n = 44) or control group (NFC, n = 46). First, we measured bite rate and success ratio on five consecutive days with the augmented fork without feedback (T1). The intervention groups (VFC, VFC+) then used the same fork, but now received vibrotactile feedback when they ate more than one bite per 10 s. Participants in VFC+ had additional access to a web portal with visual feedback. In the control group (NFC), participants ate with the fork without either feedback. The intervention period lasted four weeks, followed by a week of measurements only (T2) and another measurement week after eight weeks (T3). Body weight was assessed at T1, T2, and T3.
Participants in VFC and VFC+ had a lower bite rate (p < .01) and higher success ratio (p < .0001) than those in NFC at T2. This effect persisted at T3. In both intervention groups participants lost more weight than those in the control group at T2 (p < .02), with no rebound at T3.
The findings of this study indicate that an augmented fork with vibrotactile feedback is a viable tool to reduce eating rate in naturalistic settings. Further investigation may confirm that the augmented fork could support long-term weight loss strategies.
The research reported in this manuscript was registered on 4 November 2015 in the Netherlands Trial Register with number NL5432 ( https://www.trialregister.nl/trial/5432 ).
The rapidly increasing prevalence of overweight and obesity has heightened the need for a better understanding of obesity-related eating patterns and dietary behaviours. Recent work suggests that ...distracted eating is causally related to increased immediate and later food, pushing the need for a better understanding of the prevalence of distracted consumption and how this relates to body weight. To extract insights in the relationship between demographics, daily consumption settings, and BMI, we performed secondary data analyses on data from 1011 individuals representative of the Dutch population (adults, 507F, BMI 17–50 kg/m2). The most commonly reported distractions were talking to others (32.7%) and watching television (21.7%). Only 18.4% of respondents reported no distractions during meals. To examine how different distractions related to BMI, we performed OLS regression which showed, among other things, that watching tv while eating lunch (η2 = 0.37) and working during dinner were associated with a higher BMI (η2 = 1.63). To examine the robustness of these findings, machine learning techniques were used. A random forest analysis (RMSE = 4.09) showed that next to age and education level, distraction during lunch and snack was amongst the largest predictors of BMI. Multiple linear regression with lasso penalty (RMSE = 4.13) showed that specifically watching tv while eating lunch or snacks was associated with a higher BMI. In conclusion, our analyses confirmed the assumption that people are regularly distracted during their daily meals, with distinct distractors relating to BMI. These findings provide a starting point for evidence-based recommendations on which consumption settings are associated with healthier eating patterns and body weight.
There is evidence that perceived peer eating norms can influence dietary behaviour. This cross-sectional study examined whether certain personality traits increase the likelihood that personal eating ...habits are similar to perceived peer eating habits. We assessed frequency of consumption of sugar-sweetened soda (SSS) and sweet pastries (SP), as well as perceived peer descriptive eating norms for SSS and SP in a group of 1056 young adults. We examined whether individual differences in the need for social acceptance and self-control moderated whether participants were likely to display similar dietary habits to their peers. Perceived peer eating norms for SSS and SP predicted frequency of consumption; believing that one's peers frequently consumed SSS and SP was associated with increased personal consumption for both. Individuals with low self-control, as opposed to high self-control, were more likely to adhere to peer norms for SP, but not for SSS. Trait social acceptance needs did not significantly moderate similarity between peer norms and personal consumption for either SSS or SP. The extent to which young adults adhere to descriptive peer dietary norms may depend upon self-control, whereby individuals with low self-control are less able to inhibit social influence of descriptive peer norms on dietary behaviour.
Overweight is associated with a range of negative health consequences, such as type 2 diabetes, cardiovascular disease, gastrointestinal disorders, and premature mortality.1 One means to combat ...overweight is through encouraging people to eat more slowly.2 People who eat quickly tend to consume more3, 4 and 5 and have a higher body mass index,6, 7, 8 and 9 whereas people who eat more slowly feel sated earlier and eat less.10, 11, 12 and 13. Unfortunately, eating rate is difficult to modify, because of its highly automatic nature.14 In clinical settings, researchers have had some success changing behavior by using devices that deliver feedback in real time.15, 16 and 17 However, existing technologies are either too cumbersome18 or not engaging enough19 for use in daily life contexts. Training people to eat more slowly in everyday eating contexts, therefore, requires creative and engaging solutions. This article presents a qualitative evaluation of the feasibility of a smart fork to decelerate eating rate in daily life contexts. Furthermore, we outline the planned research to test the efficacy of this device in both laboratory and community settings
Purpose of Review
There is abundant evidence that food marketing influences children’s and adults’ food preferences and consumption. As such, exposure to unhealthy food marketing is a widely ...acknowledged risk factor contributing to the development of overweight and obesity. Less is known about the effects of healthy food promotion on people’s dietary behavior. This narrative review describes research from the past 5 years focused on the effects of healthy food marketing on children’s and adults’ food preferences and dietary intake. Our aim is to gain insight into the potential effects and mechanistic underpinnings of healthy food promotion, thereby building on existing knowledge on underlying mechanisms of the effectiveness of unhealthy food marketing.
Recent Findings
Only a small number of studies directly examined the effects of healthy food promotion on children’s and adults’ dietary behavior. Most studies targeted children’s fruit and/or vegetable intake and used a variety of marketing techniques, ranging from television adverts to social media influencer marketing. Six out of ten studies found a positive effect of healthy food promotion, indicating that healthy food marketing has the potential to influence dietary behavior.
Summary
Food marketing is highly effective in stimulating and reinforcing food consumption, in particular for energy-dense foods. Further investigation and experimentation into the efficiency and effectiveness of healthy food promotion are needed to determine how marketing techniques could be used to improve dietary behavior. The healthy food promotion model provides a framework for future research in this area.
On March 15, 2020, the Dutch Government implemented COVID-19 lockdown measures. Although self-quarantine and social-distancing measures were implemented, restrictions were less severe compared to ...several other countries. The aim of this study was to assess changes in eating behavior and food purchases among a representative adult sample in the Netherlands (n = 1030), five weeks into lockdown. The results show that most participants did not change their eating behaviors (83.0%) or food purchases (73.3%). However, socio-demographic differences were observed among those that reported changes during lockdown. For example, participants with overweight (OR = 2.26, 95%CI = 1.24–4.11) and obesity (OR = 4.21, 95%CI = 2.13–8.32) were more likely to indicate to eat unhealthier during lockdown compared to participants with a healthy weight. Those with a high educational level (OR = 2.25, 95%-CI = 1.03–4.93) were also more likely to indicate to eat unhealthier during lockdown compared to those with a low educational level. Older participants were more likely to indicate to experience no differences in their eating behaviors compared to those of younger age, who were more likely to indicate that they ate healthier (OR = 1.03, 95%CI = 1.01–1.04) as well as unhealthier (OR = 1.04, 95%CI = 1.02–1.06) during lockdown. Participants with obesity were more likely to indicate to purchase more chips/snacks (OR = 2.79, 95%CI = 1.43–5.45) and more nonalcoholic beverages (OR = 2.74, 95%CI = 1.36–5.50) during lockdown in comparison with those with a healthy weight. Of those that used meal delivery services before, 174 (29.5%) indicated to use meal delivery services more frequently during lockdown. Although the results confirm the persistence of dietary routines, profound socio-demographic differences were observed for those that did report changes. Especially for individuals with overweight and obesity, the lockdown has taken its toll on healthy dietary choices. Further research should unravel underlying mechanisms for these observations.
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