Normalization of overweight and obesity in family relations: a personal network analysis study

Normalization of overweight and obesity in family relations: a personal network analysis study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Normalization of overweight and obesity in family relations: a personal network analysis study Iulian Oană, Marian-Gabriel Hâncean, Marius Geantă, Cosmina Cioroboiu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8059527/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Most research on weight status perception focuses on self-evaluation, with studies on perceptions of others largely limited to parent–child assessments. Moreover, studies incorporating a network analysis design into how social relations influence weight perception are even fewer and focused rather on friendship networks from school data. The aim of this study is to investigate the accuracy of evaluations made by respondents regarding the BMI category of persons from their social circle. Methods We analysed 444 evaluator–evaluated dyads from a Personal Network Analysis study including respondents (egos) and their close contacts (alters). Egos self-reported height and weight were used to compute BMI (kg/m²) and BMI categories. Alters’ weight status was assessed by egos using BMI-based pictograms. Only pairs where both egos and alters were respondents were retained, enabling comparison between actual BMI category and perceived category. Cross-classified logistic regression models examined accuracy, underestimation, and overestimation as binary outcomes in separate regression models. Results When alter BMI category and family member status interacted, respondents were more likely to underestimate (OR 4.74, 95% CI 1.74–12.92, p = 0.002) family members or be accurate (OR 0.37, 95% CI 0.18–0.74, p = 0.005) in evaluating non-family members, with no significant effect for overestimation (OR 1.18, 95% CI 0.50–2.78, p = 0.713). Underestimation was also associated with broader network perceptions: respondents reporting few alters matched to overweight-or-higher body figures were more likely to underestimate others’ BMI category (OR 0.29, 95% CI 0.19–0.45, p < 0.001). Conclusions Findings suggest weight control programs and health interventions, in general, should address not only self-perception but also network influences. Underestimation biases within family relationships may persist into adulthood, potentially limiting social support for weight management and other health-related behaviours. personal network analysis overweight normalization weight status evaluation family relations obesity Figures Figure 1 Figure 2 Figure 3 Background Overweight and obesity have been found to be linked with various forms of cancers [ 1 , 2 ] and cardiovascular diseases [ 3 , 4 ]. For this reason, the extent to which a person can have an accurate assessment of their weight and the weight of others has important implications for intervention programs addressing weight loss and personal health status [ 5 , 6 ], and, also, for how they engage in (un)healthy weight control behaviours [ 7 ]. In the literature on weight perception two main areas of research can be found. First, and the most widespread, are studies on how people evaluate their own weight status or BMI category [ 6 , 8 , 9 , 10 , 11 , 12 , 13 ], finding that the most common type of misperception in weight self-evaluation is that of underestimation. Studies looking into how individuals self-evaluate their weight status found that various factors are at play, but with mixed results, indicating the influence of other contextual factors. In terms of sociodemographic indicators, research on self-evaluation of weight status has shown that sex or gender were both covariates for the underestimation [ 9 ] and the overestimation [ 10 ] of self-reported weight in females. Others found no association between sex and accuracy of weight perception [ 8 ]. Education was shown to be a more reliable indicator across cultural contexts, like Turkey [ 8 ], Mexico [ 11 ], Malaysia [ 12 ], or United States [ 13 ], studies showing that individuals with lower levels of education are less likely to make an accurate evaluation of their weight. In terms of BMI indicators, studies have shown that the likelihood for underestimation is higher for persons who are overweight or living with obesity [ 14 , 15 ]. Second, there is the area of research focused on how someone perceives the weight or BMI of others. As others have observed [ 5 ], this thematic area is narrower and often focused on specific groups, for example women evaluating pictograms with female bodies [ 16 ], or having respondents evaluating only images that depict Caucasian bodies [ 17 ]. A more developed interest in this area is on how parents (or caregivers) evaluate the weight status of their children [ 18 , 19 , 20 ], results showing that parents have a greater tendency to underestimate the weight status of their children than overestimate it when they misperceive the BMI category. A plausible explanation in explaining inaccuracies in perceptions of weight status (self or others) is the visual normalization theory , developed by E. Robinson [ 21 ]. According to the visual normalization theory, which was developed primarily to explain weight status underestimation , the increase in persons who are overweight or living with obesity has shifted the threshold for what is considered “normal weight” by generating, through frequent interaction, an overexposure to body types that are at least overweight. According to data from Eurostat [ 22 ], in 2022, for overall education and sex, the percentage of overweight (BMI: 25– = 30) was 10.1% (compared to the EU mean of 14.6%). The percentage of persons with normal BMI (BMI: 18.5 –< 25) was 37.1% (compared to the EU mean of 45.5%). The overall percentage of persons classified as pre-obese (overweight) or with obesity was 58.9% (compared to the EU mean of 50.6%). Following Robinson’s visual normalisation theory [ 21 ], the life context of Romanians, in general, is one where overweight and obesity are common, possibly shifting the perception of what a normal weight is. While individual factors, like sex, age, education, income or socioeconomic status, are important in assessing the accuracy that persons have when evaluating their weight status or the weight status of others, the inclusion of extra-individual characteristics – i.e., the social context of people –, brings additional insights. Social and personal network analysis techniques were employed to study various health-related outcomes, such as consumption of processed foods high in salt [ 23 ], smoking [ 24 ], or COVID-19 vaccination [ 25 , 26 ]. Among such outcomes, weight-related indicators are also subject to network influences [ 27 , 28 ]. Authors using network analysis techniques to study overweight or obesity [ 29 ], diets [ 30 ], or physical activity [ 30 ] found that weight-related opinions or behaviours can be affected by processes of social selection , influence , or context . Social selection (or homophily [ 31 ]) processes indicate that persons create ties with those who display similar opinions or behaviours. Applied to weight-related behaviours, studies found that friendship ties are more likely shared by students engaged in unhealthy weight control behaviours (e.g., using laxatives or inducing vomiting after eating) [ 7 ], or by adolescents engaged in physical activities [ 32 ]. Social influence (or contagion) is expressed through the idea of imitation. Specifically, studies found that change in one’s weight-related behaviours, like losing weight [ 33 ] was present when the same change occurred in their peer group. Weight-related behaviours and opinions are also shaped by the socio-cultural context or environment. People are more likely to gain weight when living in areas with higher rates of obesity [ 34 ]. Such processes are not mutually exclusive, but can rather act as reinforcements for each other. For example, D. Centola [ 35 ] found that persons were more likely to adopt healthier diets and various fitness exercises if they saw these uptakes in individuals of similar BMI category, age, and gender. Regarding the influence of network characteristics on weight-perception outcomes, the literature is scant, based mainly on studies made on adolescents and looking at how persons self-evaluate their weight status based on network composition [ 36 , 37 ]. Such studies found that American adolescent females were more likely to underestimate their overweight status if they perceived their close friends as heavy [ 36 ], or that Canadian children and adolescents were more likely to underestimate their weight status if their parents and schoolmates have a higher BMI [ 37 ]. Social environments shape not only behaviours, but also the perceptual frameworks that sustain them. The objective of our study is to fill gaps in the literature dedicated to: a) looking into how accurate persons evaluate the weight status of others, as this thematic area is narrower than that based on studies aiming to understand how people self-evaluate weight or BMI; and b) studies employing network analysis techniques to investigate the relation between one’s BMI and the BMI of their close contacts. Not least, we believe that results stemming from analyses performed on data from personal networks of adults living in a rural area of Eastern Europe will contribute to the diversity of evidence on this subject. In this geographical area, it has been found that life in rural environments carries an added risk factor for obesity [ 38 , 39 , 40 ]. Methods Data collection The data presented in this study were collected during the first two waves of the 4P-CAN project (Personalized CANcer Primary Prevention research through Citizen Participation and digitally-enabled social innovation; HORIZON-MISS-2022-CANCER-01, project ID 101104432, program HORIZON) [41]. We collected data from one of 4P-CAN’s living labs in Lerești, a rural locality in Argeș county, Romania (N=4557). For the first wave, the data collection took place between September 13 and 30, 2023, where we interviewed 83 persons. For the second wave, the data collection took place between March 21 and 29, 2024, where we interviewed 94 persons. The overlap between the two waves is of 68 respondents (a retention rate of approximately 82%). The interviews used for data collection followed a Personal Network Analysis (PNA) design [42]. In contrast to the standard Social Network Analysis designs, where researchers map various relations between all nodes inside a bounded population [43] (e.g., pupils inside a school or classroom, employees inside a company or department, etc.), the PNA design is focused on nodes of interest, dubbed egos , and various persons from their social circle, dubbed alters . The standard PNA interview can be viewed as a four-part interview containing: 1) data about the respondent (ego); 2) various name generators containing prompts through which alters are elicited; 3) name interpreters, through which data about alters and ego-alter relations are gathered; 4) alter-alter ties, where egos are asked to map the ties between the elicited alters. At the time of the interview, all respondents were at least 18 years old. The study participants were recruited using a respondent-driven link-tracing sampling methodology [44, 45]. We preferred this network-oriented sampling method to a probabilistic non-network strategy in order to avoid low response rates given: a) the length of the interview (Mean: 88.47 and SD : 20.57 in wave 1; Mean: 46.13 and SD : 20.37 in wave 2) and b) our purpose to create a panel of respondents throughout the duration of our study. We started from six persons known as seeds , and sought to have variation regarding their sex (four males and two females), age (Mean:53.17; SD : 11.11), education (five had a university degree and one lower than university degree), personal income (three were above the average net salary in Romania), and employment sector (three unemployed, two employed in the private sector or self-employed, and one retired). After responding to our questionnaire, we asked these persons to recommend other individuals to participate in our study. After these referrals were interviewed, they were also asked to nominate other persons to participate, and so on, creating referee-referral chains in the link-tracing network. We also report that of the initial six seeds, only four responded to our questionnaire. The other two only made recommendations. We interviewed only persons who are at least 18 years old and collected data about alters who were also at least 18 years old. In the second wave, we contacted all previous participants. For those who agreed to participate, we repeated the process of asking them for other nominations and contact those persons to participate in our study. Prior to initiating data collection (in the first wave), information regarding the 4P-CAN project was disseminated to the community via Facebook (Meta Platforms, Inc) and through press briefings organized by local news outlets. In addition, engagement activities included meetings with local officials, community members, and relevant key actors such as educators, community representatives, and local healthcare professionals. To ensure full transparency regarding the research process and to promote active involvement from community members, each interview was preceded by a discussion with the participant about the 4P-CAN project. Participants were given a printed dossier containing detailed information about the project, which included a consent form and a document outlining compliance with the General Data Protection Regulation within the European Union. These materials were read and voluntarily signed by the participants. They were also informed of their right to withdraw at any time, assured that all information collected would be anonymised, and directed to the project’s website, which features a Romanian-language section outlining the methodology in detail [46]. Additionally, participants were provided with the contact information (telephone numbers and email addresses) of the project coordinator and the research team for any current or future inquiries. In both waves, egos participated in an interview designed to gather data on their attitudes and behaviours, and perceptions of individuals within their personal networks. During the initial wave of data collection, each respondent was asked to nominate up to 25 individuals aged 18 or older with whom they maintained regular contact, either through in-person encounters or other forms of communication. The nomination prompt provided was: “Please nominate 25 people (18 years old plus) you interact with (or meet). You can start with the people you interact with most often. These may be family members, friends, acquaintances, neighbours, work colleagues, etc.”. In the second wave, the name generators were changed to limit the alters to individuals with whom they have daily or weekly face-to-face interactions, using as prompts: household members, family members (other than household members) , friends and acquaintances , and work colleagues (for respondents who were employed). To have consistency across waves, from the first wave, we kept only data referring to alters with whom they had daily or weekly interactions at the time of the interview. On average, respondents nominated approximately 24 alters in wave 1 ( SD : 2.62) and 17 in wave 2 ( SD : 6.38). Given the purpose of our analysis–to test the accuracy of respondents in matching the alter with a body picture that depicts the alter’s BMI category–, our analysis took into account only ego-alter relations where both the ego and the alter were respondents, creating “evaluator-evaluated” dyads. However, we also computed a variable pertaining to the overall number of alters who are at least overweight . This was computed at the level of the personal networks as a whole, with the purpose to account for a general perception that egos have about their social circle. Ego and alter variables Body Mass Index (BMI) and BMI categories (weight status) For egos’ (respondents’) BMI we used self-declared weight and height. For alters’ BMI, we used an ordinal visual scale developed by Harris et al. [17], similar to the figure-rating scale developed by Stunkard et al. [47], but containing pictures of actual bodies corresponding to a weight category. We used two showcards, one for male and one for female bodies. After eliciting their alters, the respondents were asked to match them with a body type on the visual scale. The visual scales (for males and females) had ten body types, containing the following categories: underweight (1); normal weight (2 and 3); overweight (4); class 1 obesity (5 and 6); class 2 obesity (7 and 8); class 3 obesity (9 and 10). The respondents were shown only images of body types and a letter attached to each figure (from A to J). No weight-related descriptors were used (e.g., “underweight”, “healthy”, “normal”, “with obesity”, “ideal”, “about right”, etc.). The “actual” BMI of respondents was also recoded into the ten categories of the visual scale using the following intervals: a) underweight : < 18.5; b) normal weight category 1 : 18.5-21.6; c) normal weight category 2 : 21.7-24.9; d) overweight : 25-29.9; e) class 1 obesity category 1 : 30-32.4; f) class 1 obesity category 2 : 32.5-34.9; g) class 2 obesity category 1 : 35-37.4; h) class 2 obesity category 2 : 37.5-39.9; i) class 3 obesity category 1 : 40-44.9; j) class 3 obesity category 2 : >= 45. We also used the WHO classification [48] to recode the “actual” and perceived BMI category of alters: 1) underweight : < 18.5 (category 1 on the visual scale); 2) normal weight : 18.5-24.9 (categories 2 and 3 on the visual scale); 3) overweight (pre-obesity): 25-29.9 (category 4 on the visual scale); 4) class 1 obesity : 30-34.9 (categories 5 and 6 on the visual scale); 5) class 2 obesity : 35-39.9 (categories 7 and 8 on the visual scale); 6) class 3 obesity : >= 40 (categories 9 and 10 on the visual scale). Attributes and ego-alter relation As respondents’ attributes introduced in the analysis, we measured: biological sex (0 = Male; 1 = Female); age in years (at the moment of the interview); household income as an ordinal variable with 15 categories. The relationship between egos and alters was measured as a binary variable distinguishing between family members (1) and others (0). Since both egos and their alters were respondents, the aforementioned attributes and relations were collected and analysed for both actors in the “evaluator-evaluated” dyad. Ego-Alter BMI evaluation Given the objective of our study, in the analysis, we kept as alters only those individuals who were also respondents - i.e., persons for whom we also had the declared height and weight. In this manner, we could compute the difference between the “actual” BMI category of a person (as alter) and the way they were perceived by others (egos), resulting in overestimation (negative difference), underestimation (positive difference), and accuracy (no difference). To correct for possible imperfections in the images or memory recall biases, the difference between “actual” BMI category of alter and the evaluation of ego was computed using the WHO classification of weight status [48]. Thus, for example, it was not of interest if an alter who is in the lower end of class 1 obesity (category 5 on the Harris et al. visual scale [17], BMI interval 30.0-32.4) was accurately matched with this specific category, but with the larger category of class 1 obesity (BMI interval 30.0-34.9). These differences were further recoded into binary variables that were used as outcomes in the statistical models: accurate evaluation (1 = yes; 0 = other); tendency to overestimate (1 = yes; 0 = other); and tendency to underestimate (1 = yes; 0 = other). Personal network composition An additional variable introduced in the analysis was measured at the level of each personal network, taking into account the proportion of alters matched with bodies categorised as at least overweight (categories 4 through 10 on the Harris et al. [17] visual scale), regardless of the fact that the alters were respondents or not. Even though these categorisations are prone to errors, the purpose of this variable was to measure the extent to which the respondents believe that they are surrounded by persons who are at least overweight. One important mention is that the respondents did not classify the alters as “with obesity”, “underweight”, “overweight”, etc. These are post-factum classifications made by us. Therefore, this variable can be described as to what extent do respondents believe that they are surrounded by persons that can be classified as overweight or with obesity . An example of our workflow is presented in Fig. 1, illustrating the whole personal network of a respondent– Ego Z . This personal network contains a total of 20 alters for whom the ego made evaluations regarding their weight status. Using this data, we computed the proportion of alters matched with bodies that can be categorised as at least overweight . Out of the 20 alters, five were also respondents in our panel ( Ego-Alter1 through 5 ). Thus, the dataset will contain five dyads where Ego Z is the evaluator and alters Ego-Alter1, 2, 3, 4, and 5 are the evaluated individuals. Out of the five evaluations, two represent accurate evaluations made by Z (black arrows), two indicate underestimations (orange arrows), and one overestimation (olive arrow). Out of the five alters who are also, in their turn, egos, only three nominated Ego Z in their personal networks ( Ego-Alter1 , 4 , and 5 ). Additionally, Ego Z was nominated and had its BMI category evaluated inside the personal network of Ego W who was not nominated by Ego Z amongst their alters. Consequently, in the dataset, four more dyads are added, having Ego Z as the evaluated person and the four other nodes as evaluators, where in two instances Ego Z has its BMI category underestimated and in other two instances their BMI category is accurately categorized. Models The final data frame is structured in the form of dyadic data, containing the evaluators and their attributes (egos), the evaluated persons and their attributes (alters who were also respondents), ego-alter family relation, ego-alter evaluations, and the way the ego categorised the alter(s) used as dependent variables. We constructed three dependent variables in a binary format: 1) accurate evaluation–the egos matched the body type of the alter with the alter’s BMI category, 2) tendency to overestimate –the egos matched the body type of the alter with a BMI category higher than that of alter’s, and 3) tendency to underestimate –the egos matched the body type of the alter with a BMI category lower than that of alter’s. Given that some respondents participated in both waves (n = 68), we have evaluation dyads specific to wave 1 (n = 168), specific to wave 2 (n = 302), and dyads that repeat across waves (n = 96). Considering the presence of dyadic interdependencies, we modelled the data using cross-classified multilevel logistic regression models [49, 50] with random intercepts for egos (evaluators) and alters (evaluated). Similar to multilevel models, they control for random intercepts, but for data that don’t have a standard nesting structure (e.g., unique pupils nested in unique classrooms nested in unique schools), given that in a network context a node can be both sender (i.e., BMI evaluator) and receiver (i.e., BMI evaluated), and a node can be evaluated by multiple other nodes or evaluate multiple other nodes. We fitted different models for outcomes regarding accuracy , overestimation , and underestimation . The first set of models take into account as predictors only the BMI category of egos and alters, and their interaction, controlling, also, for wave. As additional independent variables, our full models included: ego’s and alter’s sex ; ego’s and alter’s age ; ego’s and alter’s household income ; ego-alter family relation ; the ego’s overall proportion of alters perceived as being at least overweight in the personal network; alter’s wave level indegree (how many times a respondent had their BMI category classified by others);and wave . In the full models, we introduced an interaction term between ego’s BMI category, alter’s BMI category, and ego-alter family relation . The models were fitted using the lme4 package [51] in R . Data exclusion Given the measurement of “actual” BMI from self-reported weight and height, recall biases, or images being unclear to the respondent, alter-ego evaluation differences higher than 2 (≥ 3) and lower than -2 (≤ -3) were excluded. In other words, we excluded cases of extreme under- or overevaluation. Exclusions were made on differences computed according to the extended scale proposed by Harris et al. [17] with 10 BMI categories, not the WHO class. First of all, such differences bring the risk of respondents under- or over-evaluating their characteristics–i.e., respondents misreporting their weight and height, while the evaluations made by others about them are fairly accurate. Second, high inaccuracies of under- or over-estimation when assessing the weight of alters might be an indicator of respondents not being able to differentiate between body shapes, or distorted memories about alters, as they had to picture the alters when making the categorisations. We also note that respondents who did not nominate as alters others who were also members in our panel or who were not nominated as alters by other egos were also excluded, as they represent situations where the difference between “actual” BMI category – perceived BMI category cannot be computed. Furthermore, we mention that in the regression models we kept only the cases that had valid scores on all variables. For this reason, the regression models were run on 376 dyads, meaning 87 egos and 88 alters. Results Table 1 contains the characteristics of egos (i.e., the persons evaluating the BMI of alters) and alters (i.e., the persons having their weight category evaluated), for each wave, taking into account unique egos and alters. Wave 1 contains 60 egos and 61 alters. For wave 2, we report a number of 82 egos and 81 alters. Across the two waves, we report 93 unique egos and 93 unique alters. The indegree score indicates that each alter (who was also a study participant) had, on average, their weight category evaluated by three other respondents. In terms of biological sex, the sample is fairly balanced between males and females. In wave 2, the percentage of females is slightly higher, for both egos (54%) and alters (56%). The age distributions are similar across waves, for both egos and alters, gravitating around an approximate mean of 55 years old and a standard deviation of 18. The median household income ranges between 11 (6001–7000 RON) and 12 (7001–8000 RON), translating, roughly, to a range between 1.200 and 1.600 EUR, using an approximate conversion rate of 1 EUR = 5 RON. At the national level, these incomes are close to the mean household income in Romania for the rural area [ 52 ]. Distributions for the BMI of egos and alters, regardless of variable type–numeric or categorical BMI–indicate that, in our sample, most individuals fall into the overweight category. Approximately 45% of egos and alters, in both wave 1 and wave 2, were recorded as overweight using their declared weight and height. Persons with obesity represent the second largest category. Regarding the proportions of alters that egos matched with body types that can be classified as healthy , at least overweight , or with obesity in their entire personal network , results indicate that their social circles also skew towards overweight – the average proportion in wave 1 was 0.55 ( SD : 18) and 0.58 ( SD : 0.20). This means that, on average, for at least half of the nominated alters, respondents matched them with body types that can be categorized as at least overweight. These results should be interpreted at the perceptual level, as we did not measure the BMI of all alters nominated in all personal networks. Table 1 Sample characteristics for egos and alters, by waves Ego Alter wave 1 wave 2 wave 1 wave 2 Sex, n (%) male 30 (50.0%) 38 (46.3%) 30 (49.2%) 36 (44.4%) female 30 (50.0%) 44 (53.7%) 31 (50.8%) 45 (55.6%) n (%), NA (%) 60 (100%), 0 (0%) 82 (100%), 0 (0%) 61 (100%), 0 (0%) 81 (100%), 0 (0%) Age Mean (SD) 54.13 (15.67) 56.55 (13.90) 54.70 (14.69) 56.14 (13.92) Median (IQR) 54.00 (18.75) 57.00 (17.75) 55.00 (18.00) 56.00 (18.00) n (%), NA (%) 60 (100%), 0 (0%) 82 (100%), 0 (0%) 61 (100%), 0 (0%) 81 (100%), 0 (0%) Household income Mean (SD) 10.80 (3.29) 11.51 (3.25) 11.02 (3.34) 11.43 (3.29) Median (IQR) 11 (5.00) 12 (4.00) 11.00 (5.00) 12.00 (5.00) n (%), NA (%) 60 (100%), 0 (0%) 82 (100%), 0 (0%) 60 (98.3%), 1 (1.7%) 81 (100%), 0 (0%) BMI Mean (SD) 27.88 (4.57) 27.61 (4.42) 27.90 (4.52) 27.59 (4.43) Median (IQR) 27.80 (5.53) 27.50 (5.57) 27.80 (5.30) 27.50 (5.60) n (%), NA (%) 60 (100%), 0 (0%) 82 (100%), 0 (0%) 61 (100%), 0 (0%) 81 (100%), 0 (0%) BMI category, n (%) A (underweight) 2 (3.3%) 2 (2.4%) 2 (3.3%) 2 (2.5%) B (normal weight) 2 (3.3%) 3 (3.7%) 2 (3.3%) 3 (3.7%) C (normal weight) 9 (15.0%) 17 (20.7%) 8 (13.1%) 17 (21.0%) D (overweight) 27 (45.0%) 36 (43.9%) 28 (45.9%) 36 (44.4%) E (class 1 obesity) 10 (16.7%) 14 (17.1%) 11 (18.0%) 13 (16%) F (class 1 obesity) 6 (10.0%) 5 (6.1%) 6 (9.8%) 5 (6.2%) G (class 2 obesity) 3 (5.0%) 4 (4.9%) 3 (4.9%) 4 (4.9%) H (class 2 obesity) 1 (1.7%) 1 (1.2%) 1 (1.6%) 1 (1.2%) I (class 3 obesity) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) J (class 3 obesity) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) n (%), NA (%) 60 (100%), 0 (0%) 82 (100%), 0 (0%) 61 (100%), 0 (0%) 81 (100%), 0 (0%) Proportion healthy Mean (SD) 0.36 (0.16) 0.34 (0.17) - - Median (IQR) 0.32 (0.21) 0.31 (0.26) - - n (%), NA (%) 59 (98.3%), 1 (1.7%) 68 (82.9%), 14 (17.1%) - - Proportion with obesity Mean (SD) 0.38 (0.18) 0.40 (0.20) - - Median (IQR) 0.40 (0.24) 0.40 (0.31) - n (%), NA (%) 59 (98.3%), 1 (1.7%) 68 (82.9%), 14 (17.1%) - - Proportion at least overweight Mean (SD) 0.55 (0.18) 0.58 (0.20) - - Median (IQR) 0.56 (0.26) 0.63 (0.23) - - n (%), NA (%) 59 (98.3%), 1 (1.7%) 68 (82.9%), 14 (17.1%) - - Indegree Mean (SD), NA - - 2.75 (2.39) 3.72 (3.28) Median (IQR), NA - - 2.00 (3.00) 3.00 (4.00) n (%), NA (%) - - 61 (100%), 0 (0%) 81 (100%), 0 (0%) Note: n = absolute frequency. NA = missing data. SD = standard deviation from the mean. IQR = interquartile range. Table 2 presents the ego-alter relations and evaluations at the dyadic level, for wave 1 (162 dyads), wave 2 (282 dyads), and the combined dataset across waves (444 dyads). Regarding egos and alters sharing any family relation, we report that, in our sample, most evaluations were made between persons who are not related (67.8% in the combined data set). For weight perception, the most frequent case is that of accuracy–approximately 45% of evaluations, for both waves, were classified as accurate. The second most frequent case is that of underestimation–in approximately 36% of instances, for both waves, the egos have underestimated the weight category of their alters. Another information presented in Table 2 relates to the consistency of egos in underestimating, overestimating, or being accurate about the weight category of their alters from the first wave to the second. For 85 dyads, that repeated across waves, we report that in most cases (65.5%) respondents were consistent with their answers, regardless of whether they were accurate or not. Table 2 Sample characteristics for dyadic relations and evaluations Dyads by wave wave 1 wave 2 waves 1 and 2 Ego-Alter family, n (%) no 103 (63.6%) 198 (70.2%) 301 (67.8%) yes 59 (36.4%) 84 (29.8%) 143 (32.2%) n (%), NA (%) 162 (100%), 0 (0%) 282 (100%), 0 (0%) 444 (100%), 0 (0%) Alter BMI perception (binary) underestimation 56 (34.6%) 103 (36.5%) 159 (35.8%) accuracy 69 (42.6%) 130 (46.1%) 199 (44.8%) overestimation 34 (21.0%) 49 (17.4%) 83 (18.7%) n (%), NA (%) 159 (98.2%), 3 (1.8%) 282 (100%), 0 (0%) 441 (99.3%), 3 (0.7%) BMI perception consistency no - - 28 (32.2%) yes - - 57 (65.5%) n (%), NA (%) - - 85 (97.7%), 2 (2.3%) Note: n = absolute frequency. NA = missing data. In Table 3 we present how many times egos from a specific BMI category evaluated the weight status of alters by alters’ BMI category. Results from Table 3 align with previous results which indicate that the general tendency is skewed toward persons who are overweight or with obesity – i.e., egos, regardless of their BMI category, nominated in their personal networks other study participants whose BMI category can be, on average, classified as at least overweight. The result of the chi-squared test ( \(\:{\chi\:}_{16}^{2}=25.135\) , p = 0.112) indicates that the distributions are independent, suggesting an overall diversity in terms of interactions between egos and alters from various weight categories, especially for egos who are under- and normal weight. However, for egos who are at least overweight the general trend is to be rather surrounded by persons who are also at least overweight. Table 3 Number of evaluations between egos’ and alters’ BMI categories Ego BMI category Alter BMI category Underweight Normal weight Overweight Class 1 obesity Class 2 obesity Total \(\:{\varvec{\chi\:}}_{\varvec{d}\varvec{f}}^{2}\) p-value Underweight 0 (0%) 4 (36.4%) 4 (36.4%) 3 (27.3%) 0 (0%) 11 (100%) \(\:{\chi\:}_{16}^{2}=25.135\) 0.112 Normal weight 4 (5.1%) 13 (16.5%) 29 (36.7%) 25 (31.6%) 8 (10.1%) 79 (100%) Overweight 3 (1.6%) 35 (18.1%) 87 (45.1%) 42 (21.8%) 26 (13.5%) 193 (100%) Class 1 obesity 1 (0.8%) 31 (25.6%) 53 (43.8%) 26 (21.5%) 10 (8.5%) 121 (100%) Class 2 obesity 0 (0%) 4 (10%) 26 (65%) 8 (20%) 2 (5%) 40 (100%) Total 8 (1.8%) 87 (19.6%) 199 (44.8%) 104 (23.4%) 46 (10.4%) 444 (100%) Note: the table presents how many times and ego evaluated the weight category of alters by alters’ BMI category, using the WHO classification of BMI. The reported p-values are for Fisher’s p , as the table contains cells with counts smaller than 5. \(\:{\chi\:}_{}^{2}\) = chi-squared. df = degrees of freedom. In Table 4 we report the results of three cross-classified multilevel logistic regression models using as dependent variables: a) being accurate (1 = yes; 0 = other); b) underestimation (1 = yes; 0 = other); and c) overestimation (1 = yes; 0 = other). In all models, we controlled for the cross-classified structure of the data, given by dyadic interdependence, using random intercepts for both egos and alters. The models included only the BMI category of nodes (using the WHO classification), varying from 1 (underweight) to 5 (class 2 obesity), the interaction between egos’ and alters’ BMI category, and the wave. The first model, tested for accuracy in the categorization of alters’ weight category on the visual scale. We report that only the alters’ BMI category was the significant predictor (OR 0.57, 95% CI 0.40–0.82, p = 0.003), indicating that alters who are on the lower end of the BMI scale are more likely to be accurately evaluated by egos. For the underestimation model, both the egos’ (OR 2.11, 95% CI 1.29–3.44, p = 0.003) and alters’ (OR 1.70, 95% CI 1.01–2.86, p = 0.047) BMI category showed a positive effect. However, their interaction does not have an effect. These results indicate that, first, respondents who are on the higher end of the BMI scale might normalize their own weight status, leading also to the underestimation of others. Second, alters who are on the higher end of the BMI scale might also have their weight normalized and, as a consequence, underestimated. Third, the non-significant interaction between the two, shows that possible hypotheses such as “an overweight evaluator underestimating the weight status of an overweight alter” are partially refuted by our data. In the last model, of overestimation , none of the variable had a significant effect. However, results that are marginally significant, mirror the results from the underestimation models for egos’ BMI category. The negative effects for egos’ BMI category show that in our data egos who are on the lower end of the BMI scale are more likely to overestimate the weight category of alters (OR 0.58, 95% CI 0.33–1.02, p = 0.058). The obtained results are also explained by standard deviations of grouping variables, which indicate that for the accuracy model there is more variability in alters ( SD : 0.66), for the underestimation model there is a higher variability on the ego side ( SD : 1.77), and, also, a higher variation in egos for the overestimation model ( SD : 2.28). In none of the models the interaction between the BMI category of egos and alters was statistically significant. Table 4 Predicting accuracy, underestimation, and overestimation of alters’ BMI category – BMI only models Accuracy (1 = yes) Underestimation (1 = yes) Overestimation (1 = yes) Predictors OR (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-value (Intercept) 0.76 (0.46–1.23) 0.259 0.34 (0.17–0.68) 0.002 0.10 (0.04–0.26) < 0.001 Ego BMI ordinal category (scaled) 0.75 (0.54–1.04) 0.084 2.11 (1.29–3.44) 0.003 0.58 (0.33–1.02) 0.058 Alter BMI ordinal category (scaled) 0.57 (0.40–0.82) 0.003 1.70 (1.01–2.86) 0.047 1.57 (0.99–2.48) 0.056 wave (ref = 1) 1.29 (0.76–2.20) 0.346 0.86 (0.43–1.71) 0.670 0.83 (0.40–1.72) 0.613 Ego x Alter BMI ordinal category (scaled) 1.24 (0.93–1.65) 0.139 0.85 (0.58–1.25) 0.412 0.85 (0.54–1.31) 0.455 Random Effects Variance 3.29 3.29 3.29 SD alter 0.66 1.44 0.86 SD ego 0.58 1.77 2.28 ICC 0.27 0.49 0.49 N ego 87 87 87 N alter 88 88 88 Observations 376 376 376 Marginal R 2 / Conditional R 2 0.097 / 0.344 0.121 / 0.555 0.077 / 0.528 Deviance 479.789 402.590 318.440 AIC 493.789 416.590 332.440 AICc 494.093 416.895 332.744 log-Likelihood -239.895 -201.295 -159.220 The combined results of models reported in Table 4 indicate that the BMI category alone, of either egos or alters, cannot fully account for the variation in whether the visual evaluation of alters by egos resulted in accurate classification, under- or overevaluations. Results for the random part of the models show that interdependencies between egos and alters (who evaluates whom) can bring important insights. In all three models from Table 4 , the intraclass correlation coefficients (ICC) are high. The ICC for the accuracy model indicates that 27% of variation is explained by random effects, while for the underestimation and overestimation models the random effects account for 49% of variation in the dependent variable. In Table 5 , we present the results of three cross-classified multilevel logistic regression models, using the same dependent binary variables ( accuracy , underestimation , and overestimation ), with the inclusion of additional fixed effects; ego and alter attributes regarding their sex , age , and household income , as well as interactions between variables for the same attribute (e.g., interaction between ego’s and alter’s sex). For egos, the full models also included the overall proportion of alters that they matched on the visual scale with categories that can be classified as at least overweight . For alters we included how many times they were evaluated by different egos in each wave ( indegree ). As ego-alter relation, in the full models we capture the family relations as well as the interaction between these relations and the BMI category of egos and alters. Table 5 Predicting accuracy, underestimation, and overestimation of alters’ BMI category – full models Accuracy (1 = yes) Underestimation (1 = yes) Overestimation (1 = yes) Predictors OR (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-value (Intercept) 0.49 (0.23–1.03) 0.060 0.49 (0.19–1.27) 0.141 0.19 (0.07–0.56) 0.002 Ego BMI ordinal category (scaled) 0.90 (0.62–1.30) 0.572 1.07 (0.69–1.68) 0.753 0.82 (0.41–1.64) 0.547 Alter BMI ordinal category (scaled) 0.77 (0.50–1.18) 0.236 1.15 (0.65–2.04) 0.623 1.52 (0.89–2.59) 0.122 Ego-Alter family relation (ref = no) 1.22 (0.61–2.42) 0.573 0.41 (0.16–1.06) 0.066 1.64 (0.69–3.95) 0.266 Ego sex (ref = M) 1.92 (0.83–4.40) 0.125 0.63 (0.21–1.89) 0.411 0.49 (0.16–1.50) 0.212 Alter sex (ref = M) 1.54 (0.61–3.85) 0.358 2.41 (0.74–7.84) 0.143 0.10 (0.02–0.40) 0.001 Ego age (scaled) 1.21 (0.88–1.66) 0.247 1.43 (0.91–2.24) 0.121 0.53 (0.33–0.87) 0.011 Alter age (scaled) 0.90 (0.62–1.29) 0.559 0.88 (0.55–1.42) 0.597 1.49 (0.90– 2.48) 0.121 Ego hh. income (scaled) 1.14 (0.85–1.53) 0.385 0.86 (0.57–1.28) 0.446 0.84 (0.57–1.26) 0.405 Alter hh. income (scaled) 0.91 (0.64–1.29) 0.592 1.90 (1.15–3.12) 0.012 0.64 (0.42–0.97) 0.037 Ego prop. alters in PN at least overweight (scaled) 1.62 (1.20–2.20) 0.002 0.29 (0.19–0.45) < 0.001 2.92 (1.66–5.15) < 0.001 Alter indegree in wave (scaled) 1.48 (0.98–2.23) 0.063 0.82 (0.48–1.39) 0.464 0.64 (0.41–1.00) 0.050 wave (ref = 1) 1.00 (0.56–1.79) 0.996 0.80 (0.38–1.69) 0.559 1.11 (0.52–2.41) 0.784 Ego x Alter BMI ordinal category (scaled) 1.32 (0.95–1.84) 0.101 0.98 (0.64–1.49) 0.919 0.73 (0.39–1.37) 0.323 Ego BMI ordinal category (scaled) x Ego-Alter family relation (ref = no) 0.94 (0.50–1.74) 0.833 2.34 (0.91–6.02) 0.077 1.04 (0.43–2.55) 0.927 Alter BMI ordinal category (scaled) x Ego-Alter family relation (ref = no) 0.36 (0.18–0.71) 0.003 4.31 (1.77–10.51) 0.001 1.06 (0.47–2.41) 0.890 Ego x Alter sex (ref = M) 0.77 (0.25–2.36) 0.642 0.62 (0.15–2.61) 0.510 5.40 (0.93–31.33) 0.060 Ego x Alter age (scaled) 1.09 (0.80–1.51) 0.580 0.83 (0.52–1.32) 0.432 1.08 (0.73–1.59) 0.703 Ego x Alter hh. income (scaled) 1.10 (0.83–1.45) 0.514 1.04 (0.71–1.51) 0.842 0.79 (0.56–1.13) 0.198 Ego BMI x Alter BMI x Family relations 0.65 (0.31–1.34) 0.239 0.43 (0.11–1.59) 0.205 1.61 (0.62–4.14) 0.327 Random Effects Variance 3.29 3.29 3.29 SD alter 0.51 1.08 0.03 SD ego 0.14 0.25 1.02 ICC 0.17 0.29 0.24 N ego 87 87 87 N alter 88 88 88 Observations 376 376 376 Marginal R 2 / Conditional R 2 0.214 / 0.345 0.520 / 0.658 0.441 / 0.577 Deviance 452.106 339.247 271.565 AIC 496.106 383.247 315.565 AICc 498.973 386.114 318.432 log-Likelihood -226.053 -169.624 -135.783 As a first result, we observed that in the full models the BMI category of either ego (as evaluator) or alter (as evaluated) are not significant as main effects, compared to the BMI only models (see Table 4 ). In terms of sociodemographic characteristics, we report significant results for egos’ age, alters’ sex, and alters’ household income. Thus, younger persons were more likely to overestimate the BMI category of alters (OR 0.53, 95% CI 0.33–0.87, p = 0.011, overestimation model), men had a higher probability in being overestimated (OR 0.10, 95% CI 0.02–0.40, p = 0.001, overestimation model), and alters with a higher income had higher odds in being underestimated (OR 1.90, 95% 1.15–3.12, p = 0.012, underestimation model), while those with a lower income had higher odds in having their weight category overestimated (OR 0.64, 95% CI 0.42–0.97, p = 0.037, overestimation model). Regarding the presence of family relations between egos and alters, results indicate that they play an important role in how a person will classify the weight category of others. Individuals tend to be more accurate when categorizing those who are on the lower end of the BMI scale and who are not their relatives (OR 0.36, 95% CI 0.18–0.71, p = 0.003, accuracy model). Conversely, they will underestimate the weight category of family members who are on the higher end of the BMI scale (OR 4.31, 95% CI 1.77–10.51, p = 0.001, underestimation model). The interaction between alters’ BMI category and ego-alter family relation has no effect in the overestimation model (OR 1.06, 95% CI 0.47–2.41, p = 0.890), indicating that when it comes to family members, respondents tend to either be accurate or underestimate their weight status. The overall perception of egos regarding the composition of their personal networks is also important in our models. Respondents with a low overall proportion of alters whom they matched with body figures that are at least overweight present a tendency to underestimate the BMI category of others (OR 0.29, 95% CI 0.19–0.45, p < 0.001, underestimation model), while those who indicate a high proportion tend to either be accurate (OR 1.62, 95% CI 1.20–2.20, p = 0.002, accuracy model) or overestimate (OR 2.92, 95% CI 1.66–5.15, p < 0.001, overestimation model). Such results indicate differences in profiles of perception accuracy. While some respondents might have an accurate representation of the weight status of their alters, others might tend to underestimate their BMI category, while others will overestimate it. Similar to previous regression models (Table 4 ), the random part of the models reported in Table 5 also indicate the importance of dyadic interdependence between evaluator and the evaluated person. The ICC for the accuracy model indicates that 17% of variation is explained by random effects, while for the underestimation model the random effects account for 29% of variation in the dependent variable specific to this model, and for the overestimation model the random effects account for 24% of variation in the dependent variable. Moreso, for the underestimation model we observe that the highest variation is given not by variation in evaluators (egos; SD : 0.25), but by variation in the evaluated alters ( SD : 1.08), indicating that, when controlling for all other factors, the identity of the evaluator matters less than their relationship with the alter. [Table 5 Predicting accuracy, underestimation, and overestimation of alters’ BMI category – full models] For the underestimation of weight status, we also show a visual representation, in Fig. 2 , taking into account the interaction between alters’ BMI category and the existence of a family relation between egos and evaluated alters. Results are based on coefficients from the model presented in Table 5 . Predicted probabilities of underestimating alters’ weight status are relatively constant across all alters’ BMI category when the evaluated persons are not family members. In turn, for those who are family members, we observe that the probability of underestimation starts to increase with overweight alters. Family members whose actual BMI category is class 2 obesity obtained the highest probability in having their weight status underestimated by egos with whom they share kinship ties. For brevity, we have not included the interaction plot for predicted probabilities in being accurate about alters’ weight status. This plot can be found in the Supplementary material, Fig. S1 , showing a similar trend–there is a steep decrease, below the 0.50 threshold in the predicted probability of accuracy, starting from the overweight category of alters who are family members. In turn, the predicted probabilities of accuracy are fairly constant across the BMI categories of alters. The results regarding the underestimation of alters who are family members were explored through supplementary analyses, in order to see if there is any bias or not for consistently underestimating the weight category of family members (see also Supplementary data). Our results indicate such a potential bias is missing. Figure 3 presents several distributions through density plots. In panel A, we report the distributions for the mean underestimation scores obtained by each ego split by family and non-family evaluations. Further, we have tested if there is any difference between the averages of mean underestimation ego scores, grouped by family versus non-family evaluations. The independent samples t-test (t 129 = 1.13; p = 0.261; Table S1 ), indicates that there is no significant difference between the average for non-family evaluations (Mean: 0.358) compared to the average for family evaluations (Mean: 0.283). In panel B, we report the distributions for the mean underestimation scores obtained by each alter split family and non-family evaluations. Similar to results obtained for egos, the independent samples t-test (t 124 = 0.98; p = 0.331; Table S2), indicates that there is no significant difference between the average for non-family evaluations received by alters (Mean: 0.390), compared to the average for family evaluations (Mean: 0.320). While it does not completely eliminate suspicions of an underestimation bias that some respondents might have when evaluating family members, or when some alters are being evaluated by family members, such results indicate that a possible masking effect is not consistent enough to interfere with the results of our analyses. Discussion While in most instances the egos have accurately categorised their alters, the most common mis-categorisation situation is that of underestimation. This brings support to other studies indicating that, whether the outcome is the self-evaluation of weight [ 6 , 8 , 9 , 10 ] or the evaluation of others [ 17 , 20 ], weight underestimation is the most prevalent case of inaccuracy. Adding that in Romania the general context is to be rather surrounded by persons who are at least pre-obese (58.9% in 2022, according to data from Eurostat [ 22 ]), and even more so in our sample (see Table 1 ), all information is furthering support to explanations regarding the normalization of overweight [ 21 ]. The argument that persons tend to normalize overweight and obesity based on their social context [ 53 ], is strengthened by results obtained for the predictor related to respondents’ overall perception about the composition of their personal networks. Perceiving that you are less surrounded by alters who are at least overweight is correlated with higher probabilities in making underestimations, indicating the probability to perceive some bodies as less heavy than they really are. Furthermore, the inverse result for accuracy and overestimation are logical and separate between: a) those who actually are surrounded by such persons and can actually make accurate categorizations about those surrounding them, and b) individuals who think that they are surrounded by people who are overweight or with obesity more than they really are. Regarding sex, our results indicate that males tend to have their weight status overestimated compared to females. As stated in the Introduction section, research results linking sex to self-evaluation are mixed. Various authors found either no link between sex and self-evaluation accuracy [ 8 ], either that females have a higher probability of self-overestimation [ 10 ]. When evaluating others, a controlled experiment on American adults (20–44 years old) found that overweight male bodies tended to be categorized as normal weight, while underweight female bodies followed the same pattern [ 5 ]. Our results indicate that more research is needed to assess how the sex (or gender) of a person might influence how their weight status is perceived by others, taking into account that such perceptions are dependent on cultural ideals of body types [ 54 ]. Related to age, our results found that it represents a factor only for overestimation, and only taking into account the age of the evaluator, not of the evaluated person. These results are in line with other studies who looked at the relationship between age and self-evaluation, having as subjects Korean women [ 55 ], who had a higher probability of overestimating their weight as age decreased, or Galician general population [ 6 ], where younger age groups had a higher rate of self-overestimation than those aged 65 or older. While our results refer to the evaluation of others, not self-evaluation, they might indirectly offer evidence for the link between self-evaluation and how we see others. There are no studies about the relationship between weight status (mis)classification and the income (or socioeconomic status (SES)) of the evaluated person. Our results, that indicate an underestimation of those with higher household incomes and overestimation of those with lower incomes open further hypotheses that combine weight-stigma and income or SES-based stereotypes – i.e., people of lower SES are being perceived as more overweight than they actually are. Low income or SES can be seen as a deterrent for both the accuracy in the self-perception of weight and the adoption necessary practices for weight control [ 56 ]. However, results from our study bring into attention extra-individual factors. Whether underestimation of weight category by family members and others in one’s social circle shapes weight control behaviours differently across SES groups remains to be determined. Studies involving parent-child evaluation of weight status report that often parents, or caregivers, tend to underestimate the weight category of their children [ 18 , 19 ]. Such results are supported by cross-national comparisons, where, in an analysis of 22 countries, researchers found that in all countries those parents who don’t have a correct evaluation of their children have a tendency to rather underestimate than overestimate their child’s weight status [ 20 ]. To this, we can also bring into discussion the fact that results of a meta-analysis found that in 24 countries the parents’ BMI is positively corelated with the BMI of their children [ 57 ]. Our study completes this literature by providing a picture of how such biases are carried through adult life, persons having the tendency to underestimate and normalize overweight and obesity in family relations. Such insights indicate that primary prevention interventions should target persons throughout their life-course, as the underestimation bias is present also for adult weight status assessments. Additionally, our results can also be related to the literature looking at the positive effect that social support, stemming from family or peers, has on weight control practices [ 58 ]. For example, studies have shown that social support is positively associated with physical activity, healthy eating, body appreciation, and body satisfaction in women [ 59 ], lower BMI during adolescence [ 60 ], or successful engagement in the follow-up of weight control programs [ 61 ]. For this reason, weight control interventions that include a social support component should also make an assessment regarding the BMI perception between patients and members of their support group. However, it must also be taken into account that the relationship between perception accuracy of weight statuses and healthy behaviours is complex. As reviews [ 62 ], and studies on adults [ 63 ] and adolescents [ 64 ] have shown, accurate or underestimations about own overweight status can lead to unhealthy weight-related behaviours. Persons who accurately perceive themselves as overweight or living with obesity can be subject to internalized weight stigma and depression, leading to actions that on medium or long term can have negative effects on their health. For this reason, we also point that further research is necessary on how to approach weight status awareness, especially in social contexts of close relations, in order to. The findings of this study suggest that overweight and obesity prevention strategies must adapt to the social mechanisms that support their normalization. In contexts where obesity is highly prevalent, social norms can make the problem invisible, making it crucial to intervene at the community level to modify shared perceptions and increase awareness of health risks [ 28 ]. Furthermore, given that people tend to associate with others who share similar characteristics–a selection process–promoting diverse social environments that facilitate exposure to healthier lifestyles may help disrupt reinforcing cycles of unhealthy behaviour [ 65 ]. Finally, considering that individual behaviour can be shaped by peer influence, interventions that leverage social networks–for example, by engaging change agents or healthy behaviour role models within the group–can be particularly effective in fostering positive habits [ 66 ]. Together, these approaches allow us to address obesity not only as an individual phenomenon, but as a dynamic social process that requires interventions at multiple ecological levels. In addition, our findings highlight the practical importance of a consistent underestimation of overweight and obesity in others, particularly within the family. This perceptual bias may reduce the perceived urgency of adopting preventive health behaviours. When excess weight is not recognized in significant others–especially children or partners–it can delay early interventions. Public health strategies should therefore address not only the behaviours themselves but also the social perception and recognition of overweight and obesity. Educational campaigns might be more effective if they include normative feedback mechanisms that help individuals recalibrate their perceptions [ 21 ], particularly in familial contexts where emotional bonds and protective biases may hinder a more objective assessment. Tackling misperceptions within the family unit could serve as a critical leverage point for early detection and prevention efforts [ 67 ]. The limits of our study can be addressed regarding, first of all, the measurement of “actual” BMI using self-declared weight and height of study participants. Besides being an imperfect measure of health status [ 68 ], relying on declared indicators of weight and height adds to the probability of study participants under- or overestimating, first of all, themselves [ 69 ]. We tried to reduce the impact of these self-declared indicators by using in our models not the actual BMI of a person, but the general BMI categories that use broader intervals. Second of all, the way that respondents classified their alters, using a visual scale is also prone to errors, as respondents were asked to classify their alters using pictures where it might have been hard to distinguish close categories (e.g., the upper end of overweight versus the lower end of obesity class 1). Third, our study does not include qualitative descriptors regarding how respondents reflect on their own weight category and the weight category of their alters, as other studies have used [ 6 , 20 ]. Knowing if they consider themselves, and others, being of “normal weight”, “overweight”, “a little overweight”, “about right”, “healthy”, “unhealthy”, or other types of such descriptors, can add further insights into explaining why a person will tend to underestimate (or be accurate about) the weight category of someone from their personal network. Fourth of all, is hard to distinguish between misclassification due to visual normalization and the false consensus effect, the latter referring to the egocentric bias that persons have in viewing their opinions and behaviour replicated by others [ 70 ]. Thus, and overweight person might underestimate the weight category of another overweight person not because of overexposure and normalization of overweight, but because they think about themselves as healthy. Conclusions Our study brings into attention the importance of social context and family relations when persons evaluate the weight status of others from their personal network. The obtained results indicate that close relationships are prone to lead to the underestimation of someone’s weight status. As social support is important for the success of weight control actions, the underestimation bias in weight statuses of family members can hinder social support for all weight-loss related practices, delay interventions, and lead to complications (e.g., diabetes or cardiovascular diseases) because of late check-ups. Abbreviations AIC : Akaike Information Criterion AICc : Corrected Akaike Information Criterion BMI : Body Mass Index CI : Confidence Interval ICC : Intra Class Correlation IQR : Interquartile Range OR : Odds Ratio WHO : World Health Organization PNA : Personal Network Analysis SD : Standard Deviation SNA : Social Network Analysis Declarations Ethics approval and consent to participate All research procedures complied with the Declaration of Helsinki and the European General Data Protection Regulation (GDPR). The study protocol was approved by the Ethics Committee of the Center for Innovation in Medicine (EC-INOMED Decision No. D001/09- 06-2023 and No. D001/19-01-2024). Written informed consent was obtained from all participants. We anonymized personal identifying information after each interview and securely stored data on encrypted drives accessible only to authorized personnel. Consent for publication Not Applicable. Availability of data and materials The data were deposited into the Zenodo repository and are available at the following URL: https://doi.org/10.5281/zenodo.17209784. Please cite as: Oană, I., Hâncean, M.-G., Geantă, M., Cioroboiu, C., Maya-Jariego, I., Lerner, J., Bianca-Elena, M., & Vidrașcu, B.-A. (2025). Replication data for: Normalization of overweight and obesity in family relations: a personal network analysis study [Data set]. Zenodo. https://doi.org/10.5281/zenodo.17209784. In the Supplementary Material we offer only the supplementary analyses mentioned in the article. For all analyses and code, consult the aforementioned repository. Competing interests The authors declare no competing interests. Funding I.O., M.-G.H., M.G., C.C., B.-E.M., and B.-A.V. were supported by the European Commission, 4P-CAN project, HORIZON-MISS-2022-CANCER-01, project ID 101104432, programme HORIZON. Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the granting authority. Neither the European Union nor the granting authority can be held responsible for them. J.L.was supported by Deutsche Forschungsgemeinschaft (DFG), Grant number 555455503. Contributions I.O.: Conceptualization, Methodology, Formal analysis, Investigation, Data Curation, Visualization, Writing – original draft. M.-G.H.: Conceptualization, Methodology, Formal analysis, Investigation, Data Curation, Validation, Writing – review & editing. M.G.: Conceptualization, Methodology, Writing – review & editing, Project administration, Funding acquisition. C.C.: Conceptualization, Writing – review & editing. I.M.-J.: Conceptualization, Methodology, Validation, Writing – review & editing. J.L.: Conceptualization, Methodology, Validation, Writing – review & editing. B.-E.M.: Conceptualization, Writing – review & editing, Data curation. 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07:01:32","extension":"json","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10467,"visible":true,"origin":"","legend":"","description":"","filename":"9830fcf520fa47c2994237d2b8de1db1.json","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/385d8c54e34650b9b4435a05.json"},{"id":95825823,"identity":"d26c4973-687d-4b02-bb9d-f7d2b8ae43f8","added_by":"auto","created_at":"2025-11-13 11:09:50","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":307859,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/a11742bc5e02c51e80dbd33e.pdf"},{"id":95825835,"identity":"6c3084af-0a1c-4ddb-8c5f-1c80e613244e","added_by":"auto","created_at":"2025-11-13 11:09:51","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":310004,"visible":true,"origin":"","legend":"","description":"","filename":"9830fcf520fa47c2994237d2b8de1db11enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/0adec43aafbaa7f17b1c87d4.xml"},{"id":96239333,"identity":"73b1f312-6460-4ba7-b4fb-c1b0112598f1","added_by":"auto","created_at":"2025-11-19 07:06:08","extension":"jpeg","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":97681,"visible":true,"origin":"","legend":"","description":"","filename":"Fig1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/fd6982579bb40ae6aef3bcce.jpeg"},{"id":96239001,"identity":"ae839c28-df37-40e5-be2e-ce0dc010b7d5","added_by":"auto","created_at":"2025-11-19 07:00:26","extension":"jpeg","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":197513,"visible":true,"origin":"","legend":"","description":"","filename":"Fig2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/f68c47e3caeef3b5ecda691d.jpeg"},{"id":95825831,"identity":"e13bdf39-50b5-49e1-b352-7d73ec54e6f6","added_by":"auto","created_at":"2025-11-13 11:09:50","extension":"jpeg","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":202115,"visible":true,"origin":"","legend":"","description":"","filename":"Fig3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/eee410313de697e806b75692.jpeg"},{"id":96239319,"identity":"268c06af-3e55-44f5-86b4-ed33e7a747c0","added_by":"auto","created_at":"2025-11-19 07:06:07","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":97681,"visible":true,"origin":"","legend":"","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/d9ff3cca4facfc296705557a.png"},{"id":95825838,"identity":"664791a1-d306-4567-a79c-3bead6ee5cd9","added_by":"auto","created_at":"2025-11-13 11:09:51","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":142870,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/c8478acf5b5da5569e3e0221.png"},{"id":95825821,"identity":"44428777-dbad-407b-b15d-b79d4e033071","added_by":"auto","created_at":"2025-11-13 11:09:50","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":193287,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/fd0189f1efc311d64d5bcfdc.png"},{"id":95825829,"identity":"9b855979-d7c3-46dc-8225-46375dd8746c","added_by":"auto","created_at":"2025-11-13 11:09:50","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37484,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/473f3757024e678e14a13014.png"},{"id":96240134,"identity":"0de3d561-4e94-4ed7-b517-06ee929223a8","added_by":"auto","created_at":"2025-11-19 07:08:26","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":61909,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/39c92fe15e2d5d838d1740f9.png"},{"id":96239040,"identity":"dc376ca8-e45f-45aa-91eb-b421b884eace","added_by":"auto","created_at":"2025-11-19 07:01:49","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":47621,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/64b2512880e1f7172610210f.png"},{"id":96239147,"identity":"1855d11c-e19e-4d6a-bba9-dee414eabce8","added_by":"auto","created_at":"2025-11-19 07:03:18","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37484,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/e03ecf314df103dc4d93c661.png"},{"id":95825833,"identity":"8c152c42-b645-4048-8e50-a8270121d420","added_by":"auto","created_at":"2025-11-13 11:09:50","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":31266,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/c5262df98f58bf1a82821b58.png"},{"id":95825836,"identity":"95dc4bd9-2c23-4d8e-85ad-5be3c34478af","added_by":"auto","created_at":"2025-11-13 11:09:51","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28132,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/ac0d8fadbc5899cbcbff24ee.png"},{"id":95825837,"identity":"a71fa48d-6213-4542-9df5-e991228109f6","added_by":"auto","created_at":"2025-11-13 11:09:51","extension":"xml","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":313741,"visible":true,"origin":"","legend":"","description":"","filename":"9830fcf520fa47c2994237d2b8de1db11structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/1cf7d59b9a55c9aabf1a995d.xml"},{"id":95825834,"identity":"e820e351-537e-41c7-9e88-c7513ae01eb2","added_by":"auto","created_at":"2025-11-13 11:09:50","extension":"html","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":334840,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/2ad22cdad102aa1ea46c311d.html"},{"id":95825809,"identity":"a6087754-5633-4014-bcd7-4261ff10876e","added_by":"auto","created_at":"2025-11-13 11:09:50","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97681,"visible":true,"origin":"","legend":"\u003cp\u003eExample of personal network composition, BMI evaluation, and ego-alter relations between respondents\u003c/p\u003e\n\u003cp\u003eNote:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNode visual variables. colour\u003c/strong\u003e: \u003cem\u003eblue\u003c/em\u003e–ego of interest; \u003cem\u003ewhite\u003c/em\u003e–respondent who evaluated ego, but not part of the personal network; \u003cem\u003egrey\u003c/em\u003e–non-family alters; \u003cem\u003eviolet\u003c/em\u003e–family alters. \u003cstrong\u003eshape\u003c/strong\u003e: \u003cem\u003esquare\u003c/em\u003e–ego; \u003cem\u003ecircle\u003c/em\u003e: alters. \u003cstrong\u003enames\u003c/strong\u003e: \u003cem\u003eA1 through A15\u003c/em\u003e–simple alters (who are not also respondents); \u003cem\u003eEgo-Alter1 through 5–\u003c/em\u003ealters who were also respondents. \u003cstrong\u003esize\u003c/strong\u003e: \u003cem\u003ebigger\u003c/em\u003e nodes–egos and alters who were also respondents; \u003cem\u003esmaller\u003c/em\u003e nodes–simple alters (who are not also respondents).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEdge visual variables. colour\u003c/strong\u003e: \u003cem\u003egrey\u003c/em\u003e–evaluations made by Ego about simple alters’ weight category; \u003cem\u003eblack\u003c/em\u003e–accurate weight category evaluations; \u003cem\u003eorange\u003c/em\u003e–underestimation of weight category; \u003cem\u003eolive\u003c/em\u003e – overestimation of weight category. \u003cstrong\u003eshape\u003c/strong\u003e: \u003cem\u003econtinuous\u003c/em\u003e–evaluations made by ego about alters; \u003cem\u003ediscontinuous\u003c/em\u003e–evaluations made about the ego. \u003cstrong\u003earrows\u003c/strong\u003e: \u003cem\u003earrow presence\u003c/em\u003e–indicates the direction from evaluator to evaluated; \u003cem\u003earrow absence\u003c/em\u003e–simple alter-alter ties.\u003c/p\u003e","description":"","filename":"Fig1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/450bf91fb06a1a21e9e8570c.jpeg"},{"id":96239314,"identity":"fe44af4f-3bf0-45bf-8e22-9a83d6ba2209","added_by":"auto","created_at":"2025-11-19 07:06:05","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":197513,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction plot for predicted probabilities of underestimation based on alter BMI category and ego-alter family relation\u003c/p\u003e","description":"","filename":"Fig2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/9607d6bb529ed85b6050cecd.jpeg"},{"id":95825814,"identity":"481986f4-13ff-4ada-a321-41d1b5e11f2b","added_by":"auto","created_at":"2025-11-13 11:09:50","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":202115,"visible":true,"origin":"","legend":"\u003cp\u003eDensity plots - mean ego and alter underestimation scores grouped by family/non-family relation\u003c/p\u003e\n\u003cp\u003eNote:\u003c/p\u003e\n\u003cp\u003ePanel\u003cstrong\u003e A\u003c/strong\u003e: \u003cem\u003ethe distribution of the mean underestimation score obtained by each ego (evaluator) split by the existence or non-existence of a family relation between ego and alter.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePanel\u003cstrong\u003e B\u003c/strong\u003e: \u003cem\u003ethe distribution of the mean underestimation score obtained by each alter (evaluated) split by the existence or non-existence of a family relation between alter and ego.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/2771619f67a65bdcdda93cd2.jpeg"},{"id":96452853,"identity":"121f46d7-7ddc-4869-a4dd-194d3867cffa","added_by":"auto","created_at":"2025-11-21 09:48:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1947026,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/3a0bcc8c-01a4-4d8d-9d85-330949324ae1.pdf"},{"id":95825820,"identity":"9ac1bc89-0b1b-4fd1-a403-767ebd0b529c","added_by":"auto","created_at":"2025-11-13 11:09:50","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":307859,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8059527/v1/4431ed4354d17f281dd7cf2f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Normalization of overweight and obesity in family relations: a personal network analysis study","fulltext":[{"header":"Background","content":"\u003cp\u003eOverweight and obesity have been found to be linked with various forms of cancers [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and cardiovascular diseases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. For this reason, the extent to which a person can have an accurate assessment of their weight and the weight of others has important implications for intervention programs addressing weight loss and personal health status [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and, also, for how they engage in (un)healthy weight control behaviours [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the literature on weight perception two main areas of research can be found. First, and the most widespread, are studies on how people evaluate their own weight status or BMI category [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], finding that the most common type of misperception in weight self-evaluation is that of underestimation. Studies looking into how individuals self-evaluate their weight status found that various factors are at play, but with mixed results, indicating the influence of other contextual factors. In terms of sociodemographic indicators, research on self-evaluation of weight status has shown that sex or gender were both covariates for the underestimation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and the overestimation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] of self-reported weight in females. Others found no association between sex and accuracy of weight perception [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Education was shown to be a more reliable indicator across cultural contexts, like Turkey [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], Mexico [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], Malaysia [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], or United States [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], studies showing that individuals with lower levels of education are less likely to make an accurate evaluation of their weight. In terms of BMI indicators, studies have shown that the likelihood for underestimation is higher for persons who are overweight or living with obesity [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSecond, there is the area of research focused on how someone perceives the weight or BMI of others. As others have observed [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], this thematic area is narrower and often focused on specific groups, for example women evaluating pictograms with female bodies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], or having respondents evaluating only images that depict Caucasian bodies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A more developed interest in this area is on how parents (or caregivers) evaluate the weight status of their children [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], results showing that parents have a greater tendency to underestimate the weight status of their children than overestimate it when they misperceive the BMI category.\u003c/p\u003e\u003cp\u003eA plausible explanation in explaining inaccuracies in perceptions of weight status (self or others) is the \u003cem\u003evisual normalization theory\u003c/em\u003e, developed by E. Robinson [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. According to the visual normalization theory, which was developed primarily to explain weight status \u003cem\u003eunderestimation\u003c/em\u003e, the increase in persons who are overweight or living with obesity has shifted the threshold for what is considered \u0026ldquo;normal weight\u0026rdquo; by generating, through frequent interaction, an overexposure to body types that are at least overweight. According to data from Eurostat [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], in 2022, for overall education and sex, the percentage of overweight (BMI: 25\u0026ndash;\u0026lt; 30) persons in Romania was 48.4% (compared to EU mean of 36.4%) and the percentage of persons living with obesity (BMI\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;30) was 10.1% (compared to the EU mean of 14.6%). The percentage of persons with normal BMI (BMI: 18.5 \u0026ndash;\u0026lt; 25) was 37.1% (compared to the EU mean of 45.5%). The overall percentage of persons classified as pre-obese (overweight) or with obesity was 58.9% (compared to the EU mean of 50.6%). Following Robinson\u0026rsquo;s visual normalisation theory [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], the life context of Romanians, in general, is one where overweight and obesity are common, possibly shifting the perception of what a normal weight is.\u003c/p\u003e\u003cp\u003eWhile individual factors, like sex, age, education, income or socioeconomic status, are important in assessing the accuracy that persons have when evaluating their weight status or the weight status of others, the inclusion of extra-individual characteristics \u0026ndash; i.e., the social context of people \u0026ndash;, brings additional insights. Social and personal network analysis techniques were employed to study various health-related outcomes, such as consumption of processed foods high in salt [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], smoking [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], or COVID-19 vaccination [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Among such outcomes, weight-related indicators are also subject to network influences [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAuthors using network analysis techniques to study overweight or obesity [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], diets [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], or physical activity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] found that weight-related opinions or behaviours can be affected by processes of social \u003cem\u003eselection\u003c/em\u003e, \u003cem\u003einfluence\u003c/em\u003e, or \u003cem\u003econtext\u003c/em\u003e. Social selection (or homophily [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]) processes indicate that persons create ties with those who display similar opinions or behaviours. Applied to weight-related behaviours, studies found that friendship ties are more likely shared by students engaged in unhealthy weight control behaviours (e.g., using laxatives or inducing vomiting after eating) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], or by adolescents engaged in physical activities [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Social influence (or contagion) is expressed through the idea of imitation. Specifically, studies found that change in one\u0026rsquo;s weight-related behaviours, like losing weight [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] was present when the same change occurred in their peer group. Weight-related behaviours and opinions are also shaped by the socio-cultural context or environment. People are more likely to gain weight when living in areas with higher rates of obesity [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Such processes are not mutually exclusive, but can rather act as reinforcements for each other. For example, D. Centola [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] found that persons were more likely to adopt healthier diets and various fitness exercises if they saw these uptakes in individuals of similar BMI category, age, and gender. Regarding the influence of network characteristics on weight-perception outcomes, the literature is scant, based mainly on studies made on adolescents and looking at how persons self-evaluate their weight status based on network composition [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Such studies found that American adolescent females were more likely to underestimate their overweight status if they perceived their close friends as heavy [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], or that Canadian children and adolescents were more likely to underestimate their weight status if their parents and schoolmates have a higher BMI [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSocial environments shape not only behaviours, but also the perceptual frameworks that sustain them. The objective of our study is to fill gaps in the literature dedicated to: a) looking into how accurate persons evaluate the weight status of others, as this thematic area is narrower than that based on studies aiming to understand how people self-evaluate weight or BMI; and b) studies employing network analysis techniques to investigate the relation between one\u0026rsquo;s BMI and the BMI of their close contacts. Not least, we believe that results stemming from analyses performed on data from personal networks of adults living in a rural area of Eastern Europe will contribute to the diversity of evidence on this subject. In this geographical area, it has been found that life in rural environments carries an added risk factor for obesity [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data presented in this study were collected during the first two waves of the 4P-CAN project (Personalized CANcer Primary Prevention research through Citizen Participation and digitally-enabled social innovation; HORIZON-MISS-2022-CANCER-01, project ID 101104432, program HORIZON)\u0026nbsp;[41]. We collected data from one of 4P-CAN’s living labs in Lerești, a rural locality in Argeș county, Romania (N=4557). For the first wave, the data collection took place between September 13 and 30, 2023, where we interviewed 83 persons. For the second wave, the data collection took place between March 21 and 29, 2024, where we interviewed 94 persons. The overlap between the two waves is of 68 respondents (a retention rate of approximately 82%).\u003c/p\u003e\n\u003cp\u003eThe interviews used for data collection followed a Personal Network Analysis (PNA) design\u0026nbsp;[42]. In contrast to the standard Social Network Analysis designs, where researchers map various relations between all nodes inside a bounded population\u0026nbsp;[43]\u0026nbsp;(e.g., pupils inside a school or classroom, employees inside a company or department, etc.), the PNA design is focused on nodes of interest, dubbed \u003cem\u003eegos\u003c/em\u003e, and various persons from their social circle, dubbed \u003cem\u003ealters\u003c/em\u003e. The standard PNA interview can be viewed as a four-part interview containing: 1) data about the respondent (ego); 2) various \u003cem\u003ename generators\u003c/em\u003e containing prompts through which alters are elicited; 3) name interpreters, through which data about alters and ego-alter relations are gathered; 4) alter-alter ties, where egos are asked to map the ties between the elicited alters.\u003c/p\u003e\n\u003cp\u003eAt the time of the interview, all respondents were at least 18 years old. The study participants were recruited using a respondent-driven link-tracing sampling methodology\u0026nbsp;[44, 45]. We preferred this network-oriented sampling method to a probabilistic non-network strategy in order to avoid low response rates given: a) the length of the interview (Mean: 88.47 and \u003cem\u003eSD\u003c/em\u003e: 20.57 in wave 1; Mean: 46.13 and \u003cem\u003eSD\u003c/em\u003e: 20.37 in wave 2) and b) our purpose to create a panel of respondents throughout the duration of our study. We started from six persons known as \u003cem\u003eseeds\u003c/em\u003e, and sought to have variation regarding their \u003cem\u003esex\u003c/em\u003e (four males and two females), \u003cem\u003eage\u003c/em\u003e (Mean:53.17; \u003cem\u003eSD\u003c/em\u003e: 11.11), \u003cem\u003eeducation\u003c/em\u003e (five had a university degree and one lower than university degree), \u003cem\u003epersonal income\u0026nbsp;\u003c/em\u003e(three were above the average net salary in Romania), and \u003cem\u003eemployment sector\u003c/em\u003e (three unemployed, two employed in the private sector or self-employed, and one retired). After responding to our questionnaire, we asked these persons to recommend other individuals to participate in our study. After these referrals were interviewed, they were also asked to nominate other persons to participate, and so on, creating referee-referral chains in the link-tracing network. We also report that of the initial six seeds, only four responded to our questionnaire. The other two only made recommendations. We interviewed only persons who are at least 18 years old and collected data about alters who were also at least 18 years old. In the second wave, we contacted all previous participants. For those who agreed to participate, we repeated the process of asking them for other nominations and contact those persons to participate in our study.\u003c/p\u003e\n\u003cp\u003ePrior to initiating data collection (in the first wave), information regarding the 4P-CAN project was disseminated to the community via Facebook (Meta Platforms, Inc) and through press briefings organized by local news outlets. In addition, engagement activities included meetings with local officials, community members, and relevant key actors such as educators, community representatives, and local healthcare professionals.\u003c/p\u003e\n\u003cp\u003eTo ensure full transparency regarding the research process and to promote active involvement from community members, each interview was preceded by a discussion with the participant about the 4P-CAN project. Participants were given a printed dossier containing detailed information about the project, which included a consent form and a document outlining compliance with the General Data Protection Regulation within the European Union. These materials were read and voluntarily signed by the participants. They were also informed of their right to withdraw at any time, assured that all information collected would be anonymised, and directed to the project’s website, which features a Romanian-language section outlining the methodology in detail\u0026nbsp;[46]. Additionally, participants were provided with the contact information (telephone numbers and email addresses) of the project coordinator and the research team for any current or future inquiries.\u003c/p\u003e\n\u003cp\u003eIn both waves, egos participated in an interview designed to gather data on their attitudes and behaviours, and perceptions of individuals within their personal networks. During the initial wave of data collection, each respondent was asked to nominate up to 25 individuals aged 18 or older with whom they maintained regular contact, either through in-person encounters or other forms of communication. The nomination prompt provided was: “Please nominate 25 people (18 years old plus) you interact with (or meet). You can start with the people you interact with most often. These may be family members, friends, acquaintances, neighbours, work colleagues, etc.”. In the second wave, the name generators were changed to limit the alters to individuals with whom they have daily or weekly face-to-face interactions, using as prompts: \u003cem\u003ehousehold members, family members\u0026nbsp;\u003c/em\u003e(other than household members)\u003cem\u003e, friends and acquaintances\u003c/em\u003e, and \u003cem\u003ework colleagues\u003c/em\u003e (for respondents who were employed). To have consistency across waves, from the first wave, we kept only data referring to alters with whom they had daily or weekly interactions at the time of the interview.\u0026nbsp;On average, respondents nominated approximately 24 alters in wave 1 (\u003cem\u003eSD\u003c/em\u003e:\u0026nbsp;2.62)\u0026nbsp;and\u0026nbsp;17 in wave 2 (\u003cem\u003eSD\u003c/em\u003e: 6.38).\u003c/p\u003e\n\u003cp\u003eGiven the purpose of our analysis–to test the accuracy of respondents in matching the alter with a body picture that depicts the alter’s BMI category–, our analysis took into account only ego-alter relations where both the ego and the alter were respondents, creating “evaluator-evaluated” dyads. However, we also computed a variable pertaining to the \u003cem\u003eoverall number of alters who are at least overweight\u003c/em\u003e. This was computed at the level of the personal networks as a whole, with the purpose to account for a general perception that egos have about their social circle.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEgo and alter variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBody Mass Index (BMI) and BMI categories (weight status)\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor egos’ (respondents’) BMI we used self-declared weight and height. For alters’ BMI, we used an ordinal visual scale developed by Harris et al.\u0026nbsp;[17], similar to the figure-rating scale developed by Stunkard et al.\u0026nbsp;[47], but containing pictures of actual bodies corresponding to a weight category. We used two showcards, one for male and one for female bodies. After eliciting their alters, the respondents were asked to match them with a body type on the visual scale. The visual scales (for males and females) had ten body types, containing the following categories: underweight (1); normal weight (2 and 3); overweight (4); class 1 obesity (5 and 6); class 2 obesity (7 and 8); class 3 obesity (9 and 10). The respondents were shown only images of body types and a letter attached to each figure (from A to J). No weight-related descriptors were used (e.g., “underweight”, “healthy”, “normal”, “with obesity”, “ideal”, “about right”, etc.).\u003c/p\u003e\n\u003cp\u003eThe “actual” BMI of respondents was also recoded into the ten categories of the visual scale using the following intervals: a) \u003cem\u003eunderweight\u003c/em\u003e: \u0026lt; 18.5; b) \u003cem\u003enormal weight category 1\u003c/em\u003e: 18.5-21.6; c) \u003cem\u003enormal weight category\u003c/em\u003e \u003cem\u003e2\u003c/em\u003e: 21.7-24.9; d) \u003cem\u003eoverweight\u003c/em\u003e: 25-29.9; e) \u003cem\u003eclass 1 obesity category 1\u003c/em\u003e: 30-32.4; f) \u003cem\u003eclass 1 obesity category 2\u003c/em\u003e: 32.5-34.9; g) \u003cem\u003eclass 2 obesity category 1\u003c/em\u003e: 35-37.4; h) \u003cem\u003eclass 2 obesity category 2\u003c/em\u003e: 37.5-39.9; i) \u003cem\u003eclass 3 obesity category 1\u003c/em\u003e: 40-44.9; j) \u003cem\u003eclass 3 obesity category 2\u003c/em\u003e: \u0026gt;= 45. We also used the WHO classification [48] to recode the “actual” and perceived BMI category of alters: 1) \u003cem\u003eunderweight\u003c/em\u003e: \u0026lt; 18.5 (category 1 on the visual scale); 2) \u003cem\u003enormal weight\u003c/em\u003e: 18.5-24.9 (categories 2 and 3 on the visual scale); 3) \u003cem\u003eoverweight\u003c/em\u003e (pre-obesity): 25-29.9 (category 4 on the visual scale); 4) \u003cem\u003eclass 1 obesity\u003c/em\u003e: 30-34.9 (categories 5 and 6 on the visual scale); 5) \u003cem\u003eclass 2 obesity\u003c/em\u003e: 35-39.9 (categories 7 and 8 on the visual scale); 6) \u003cem\u003eclass 3 obesity\u003c/em\u003e: \u0026gt;= 40 (categories 9 and 10 on the visual scale).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAttributes and ego-alter relation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs respondents’ attributes introduced in the analysis, we measured: biological \u003cem\u003esex\u0026nbsp;\u003c/em\u003e(0 = Male; 1 = Female); \u003cem\u003eage in years\u003c/em\u003e (at the moment of the interview); \u003cem\u003ehousehold income\u003c/em\u003e as an ordinal variable with 15 categories. The relationship between egos and alters was measured as a binary variable distinguishing between \u003cem\u003efamily members\u003c/em\u003e (1) and others (0). Since both egos and their alters were respondents, the aforementioned attributes and relations were collected and analysed for both actors in the “evaluator-evaluated” dyad.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEgo-Alter BMI evaluation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the objective of our study, in the analysis, we kept as alters only those individuals who were also respondents - i.e., persons for whom we also had the declared height and weight. In this manner, we could compute the difference between the “actual” BMI category of a person (as alter) and the way they were perceived by others (egos), resulting in \u003cem\u003eoverestimation\u003c/em\u003e (negative difference), \u003cem\u003eunderestimation\u003c/em\u003e (positive difference), and \u003cem\u003eaccuracy\u003c/em\u003e (no difference).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo correct for possible imperfections in the images or memory recall biases, the difference between “actual” BMI category of alter and the evaluation of ego was computed using the WHO classification of weight status [48]. Thus, for example, it was not of interest if an alter who is in the lower end of class 1 obesity (category 5 on the Harris et al. visual scale\u0026nbsp;[17], BMI interval 30.0-32.4) was accurately matched with this specific category, but with the larger category of class 1 obesity (BMI interval 30.0-34.9). These differences were further recoded into binary variables that were used as outcomes in the statistical models: \u003cem\u003eaccurate evaluation\u003c/em\u003e (1 = yes; 0 = other); \u003cem\u003etendency to overestimate\u003c/em\u003e (1 = yes; 0 = other); and \u003cem\u003etendency to underestimate\u003c/em\u003e (1 = yes; 0 = other).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePersonal network composition\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn additional variable introduced in the analysis was measured at the level of each personal network, taking into account the proportion of alters matched with bodies categorised as \u003cem\u003eat least overweight\u003c/em\u003e (categories 4 through 10 on the Harris et al.\u0026nbsp;[17]\u0026nbsp;visual scale), regardless of the fact that the alters were respondents or not. Even though these categorisations are prone to errors, the purpose of this variable was to measure the extent to which the respondents believe that they are surrounded by persons who are at least overweight. One important mention is that the respondents did not classify the alters as “with obesity”, “underweight”, “overweight”, etc. These are post-factum classifications made by us. Therefore, this variable can be described as \u003cem\u003eto what extent do respondents believe that they are surrounded by persons\u0026nbsp;that can be classified as overweight or with obesity\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eAn example of our workflow is presented in Fig. 1, illustrating the whole personal network of a respondent–\u003cem\u003eEgo\u003c/em\u003e \u003cem\u003eZ\u003c/em\u003e. This personal network contains a total of 20 alters for whom the ego made evaluations regarding their weight status. Using this data, we computed the proportion of alters matched with bodies that can be categorised as \u003cem\u003eat least overweight\u003c/em\u003e. Out of the 20 alters, five were also respondents in our panel (\u003cem\u003eEgo-Alter1\u003c/em\u003e through \u003cem\u003e5\u003c/em\u003e). Thus, the dataset will contain five dyads where \u003cem\u003eEgo\u003c/em\u003e \u003cem\u003eZ\u003c/em\u003e is the evaluator and alters \u003cem\u003eEgo-Alter1, 2, 3, 4,\u0026nbsp;\u003c/em\u003eand \u003cem\u003e5\u0026nbsp;\u003c/em\u003eare the evaluated individuals. Out of the five evaluations, two represent accurate evaluations made by \u003cem\u003eZ\u0026nbsp;\u003c/em\u003e(black arrows), two indicate underestimations (orange arrows), and one overestimation (olive arrow). Out of the five alters who are also, in their turn, egos, only three nominated \u003cem\u003eEgo\u003c/em\u003e \u003cem\u003eZ\u003c/em\u003e in their personal networks (\u003cem\u003eEgo-Alter1\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, and \u003cem\u003e5\u003c/em\u003e). Additionally, \u003cem\u003eEgo\u003c/em\u003e \u003cem\u003eZ\u0026nbsp;\u003c/em\u003ewas nominated and had its BMI category evaluated inside the personal network of Ego \u003cem\u003eW\u003c/em\u003e who was not nominated by Ego \u003cem\u003eZ\u003c/em\u003e amongst their alters. Consequently, in the dataset, four more dyads are added, having Ego \u003cem\u003eZ\u003c/em\u003e as the evaluated person and the four other nodes as evaluators, where in two instances Ego Z has its BMI category underestimated and in other two instances their BMI category is accurately categorized.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final data frame is structured in the form of dyadic data, containing the evaluators and their attributes (egos), the evaluated persons and their attributes (alters who were also respondents), ego-alter family relation, ego-alter evaluations, and the way the ego categorised the alter(s) used as dependent variables. We constructed three dependent variables in a binary format: 1) \u003cem\u003eaccurate\u003c/em\u003e evaluation–the egos matched the body type of the alter with the alter’s BMI category, 2) tendency to \u003cem\u003eoverestimate\u003c/em\u003e–the egos matched the body type of the alter with a BMI category higher than that of alter’s, and 3) tendency to \u003cem\u003eunderestimate\u003c/em\u003e–the egos matched the body type of the alter with a BMI category lower than that of alter’s. Given that some respondents participated in both waves (n = 68), we have evaluation dyads specific to wave 1 (n = 168), specific to wave 2 (n = 302), and dyads that repeat across waves (n = 96). Considering the presence of dyadic interdependencies, we modelled the data using \u003cem\u003ecross-classified multilevel logistic regression models\u003c/em\u003e [49, 50] with random intercepts for egos (evaluators) and alters (evaluated). Similar to multilevel models, they control for random intercepts, but for data that don’t have a standard nesting structure (e.g., unique pupils nested in unique classrooms nested in unique schools), given that in a network context a node can be both sender (i.e., BMI evaluator) and receiver (i.e., BMI evaluated), and a node can be evaluated by multiple other nodes or evaluate multiple other nodes.\u003c/p\u003e\n\u003cp\u003eWe fitted different models for outcomes regarding \u003cem\u003eaccuracy\u003c/em\u003e, \u003cem\u003eoverestimation\u003c/em\u003e, and \u003cem\u003eunderestimation\u003c/em\u003e. The first set of models take into account as predictors only the BMI category of egos and alters, and their interaction, controlling, also, for wave. As additional independent variables, our full models included: ego’s and alter’s \u003cem\u003esex\u003c/em\u003e; ego’s and alter’s \u003cem\u003eage\u003c/em\u003e; ego’s and alter’s \u003cem\u003ehousehold income\u003c/em\u003e; ego-alter \u003cem\u003efamily relation\u003c/em\u003e; the ego’s overall proportion of \u003cem\u003ealters perceived as being at least overweight\u0026nbsp;\u003c/em\u003ein the personal network; alter’s wave level \u003cem\u003eindegree\u003c/em\u003e (how many times a respondent had their BMI category classified by others);and \u003cem\u003ewave\u003c/em\u003e. In the full models, we introduced an interaction term between ego’s BMI category, alter’s BMI category, and ego-alter \u003cem\u003efamily relation\u003c/em\u003e. The models were fitted using the \u003cem\u003elme4\u003c/em\u003e package [51] in \u003cem\u003eR\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData exclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the measurement of “actual” BMI from self-reported weight and height, recall biases, or images being unclear to the respondent, alter-ego evaluation differences higher than 2 (≥ 3) and lower than -2 (≤ -3) were excluded. In other words, we excluded cases of extreme under- or overevaluation. Exclusions were made on differences computed according to the extended scale proposed by Harris et al. [17] with 10 BMI categories, not the WHO class. First of all, such differences bring the risk of respondents under- or over-evaluating their characteristics–i.e., respondents misreporting their weight and height, while the evaluations made by others about them are fairly accurate. Second, high inaccuracies of under- or over-estimation when assessing the weight of alters might be an indicator of respondents not being able to differentiate between body shapes, or distorted memories about alters, as they had to picture the alters when making the categorisations. We also note that respondents who did not nominate as alters others who were also members in our panel or who were not nominated as alters by other egos were also excluded, as they represent situations where the difference between “actual” BMI category – perceived BMI category cannot be computed. Furthermore, we mention that in the regression models we kept only the cases that had valid scores on all variables. For this reason, the regression models were run on 376 dyads, meaning 87 egos and 88 alters.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e contains the characteristics of egos (i.e., the persons evaluating the BMI of alters) and alters (i.e., the persons having their weight category evaluated), for each wave, taking into account unique egos and alters. Wave 1 contains 60 egos and 61 alters. For wave 2, we report a number of 82 egos and 81 alters. Across the two waves, we report 93 unique egos and 93 unique alters. The indegree score indicates that each alter (who was also a study participant) had, on average, their weight category evaluated by three other respondents.\u003c/p\u003e\u003cp\u003eIn terms of biological sex, the sample is fairly balanced between males and females. In wave 2, the percentage of females is slightly higher, for both egos (54%) and alters (56%). The age distributions are similar across waves, for both egos and alters, gravitating around an approximate mean of 55 years old and a standard deviation of 18. The median household income ranges between 11 (6001\u0026ndash;7000 RON) and 12 (7001\u0026ndash;8000 RON), translating, roughly, to a range between 1.200 and 1.600 EUR, using an approximate conversion rate of 1 EUR\u0026thinsp;=\u0026thinsp;5 RON. At the national level, these incomes are close to the mean household income in Romania for the rural area [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDistributions for the BMI of egos and alters, regardless of variable type\u0026ndash;numeric or categorical BMI\u0026ndash;indicate that, in our sample, most individuals fall into the overweight category. Approximately 45% of egos and alters, in both wave 1 and wave 2, were recorded as overweight using their declared weight and height. Persons with obesity represent the second largest category.\u003c/p\u003e\u003cp\u003eRegarding the proportions of alters that egos matched with body types that can be classified as \u003cem\u003ehealthy\u003c/em\u003e, \u003cem\u003eat least overweight\u003c/em\u003e, or \u003cem\u003ewith obesity\u003c/em\u003e in their \u003cem\u003eentire personal network\u003c/em\u003e, results indicate that their social circles also skew towards overweight \u0026ndash; the average proportion in wave 1 was 0.55 (\u003cem\u003eSD\u003c/em\u003e: 18) and 0.58 (\u003cem\u003eSD\u003c/em\u003e: 0.20). This means that, on average, for at least half of the nominated alters, respondents matched them with body types that can be categorized as at least overweight. These results should be interpreted at the perceptual level, as we did not measure the BMI of all alters nominated in all personal networks.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSample characteristics for egos and alters, by waves\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eEgo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eAlter\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ewave 1\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ewave 2\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ewave 1\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ewave 2\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38 (46.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30 (49.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36 (44.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44 (53.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31 (50.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e45 (55.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%), NA (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e81 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.13 (15.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56.55 (13.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54.70 (14.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e56.14 (13.92)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.00 (18.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57.00 (17.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55.00 (18.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e56.00 (18.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%), NA (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e81 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold income\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.80 (3.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.51 (3.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.02 (3.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.43 (3.29)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (5.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.00 (5.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.00 (5.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%), NA (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60 (98.3%), 1 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e81 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.88 (4.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.61 (4.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.90 (4.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.59 (4.43)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.80 (5.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.50 (5.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.80 (5.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.50 (5.60)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%), NA (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e81 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI category, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA (underweight)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2 (2.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB (normal weight)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (3.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (3.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC (normal weight)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (15.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (20.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (13.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17 (21.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eD (overweight)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (45.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36 (43.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28 (45.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36 (44.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE (class 1 obesity)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (16.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (17.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (18.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13 (16%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF (class 1 obesity)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (10.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (6.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (9.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG (class 2 obesity)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (5.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (4.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (4.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4 (4.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH (class 2 obesity)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (1.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (1.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI (class 3 obesity)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJ (class 3 obesity)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%), NA (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e81 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eProportion healthy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.36 (0.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.34 (0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.32 (0.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.31 (0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%), NA (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59 (98.3%), 1 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68 (82.9%), 14 (17.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eProportion with obesity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.38 (0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.40 (0.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.40 (0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.40 (0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%), NA (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59 (98.3%), 1 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68 (82.9%), 14 (17.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eProportion at least overweight\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.55 (0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.58 (0.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.56 (0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.63 (0.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%), NA (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59 (98.3%), 1 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68 (82.9%), 14 (17.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIndegree\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD), NA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.75 (2.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.72 (3.28)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian (IQR), NA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.00 (3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.00 (4.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%), NA (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e81 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;absolute frequency. \u003cem\u003eNA\u003c/em\u003e\u0026thinsp;=\u0026thinsp;missing data. \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;standard deviation from the mean. \u003cem\u003eIQR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;interquartile range.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the ego-alter relations and evaluations at the dyadic level, for wave 1 (162 dyads), wave 2 (282 dyads), and the combined dataset across waves (444 dyads). Regarding egos and alters sharing any family relation, we report that, in our sample, most evaluations were made between persons who are not related (67.8% in the combined data set). For weight perception, the most frequent case is that of accuracy\u0026ndash;approximately 45% of evaluations, for both waves, were classified as accurate. The second most frequent case is that of underestimation\u0026ndash;in approximately 36% of instances, for both waves, the egos have underestimated the weight category of their alters. Another information presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e relates to the consistency of egos in underestimating, overestimating, or being accurate about the weight category of their alters from the first wave to the second. For 85 dyads, that repeated across waves, we report that in most cases (65.5%) respondents were consistent with their answers, regardless of whether they were accurate or not.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSample characteristics for dyadic relations and evaluations\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eDyads by wave\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ewave 1\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ewave 2\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ewaves 1 and 2\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEgo-Alter family, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eno\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e103 (63.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e198 (70.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e301 (67.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59 (36.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84 (29.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e143 (32.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%), NA (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e162 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e282 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e444 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlter BMI perception (binary)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eunderestimation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56 (34.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103 (36.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e159 (35.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eaccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (42.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e130 (46.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e199 (44.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eoverestimation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (21.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49 (17.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83 (18.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%), NA (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e159 (98.2%), 3 (1.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e282 (100%), 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e441 (99.3%), 3 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI perception consistency\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eno\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28 (32.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57 (65.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%), NA (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e85 (97.7%), 2 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;absolute frequency. \u003cem\u003eNA\u003c/em\u003e\u0026thinsp;=\u0026thinsp;missing data.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e we present how many times egos from a specific BMI category evaluated the weight status of alters by alters\u0026rsquo; BMI category. Results from Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e align with previous results which indicate that the general tendency is skewed toward persons who are overweight or with obesity \u0026ndash; i.e., egos, regardless of their BMI category, nominated in their personal networks other study participants whose BMI category can be, on average, classified as at least overweight. The result of the chi-squared test (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{16}^{2}=25.135\\)\u003c/span\u003e\u003c/span\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.112) indicates that the distributions are independent, suggesting an overall diversity in terms of interactions between egos and alters from various weight categories, especially for egos who are under- and normal weight. However, for egos who are at least overweight the general trend is to be rather surrounded by persons who are also at least overweight.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNumber of evaluations between egos\u0026rsquo; and alters\u0026rsquo; BMI categories\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEgo BMI category\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\u003cp\u003eAlter BMI category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eUnderweight\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eNormal weight\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eOverweight\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eClass 1 obesity\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eClass 2 obesity\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{\\chi\\:}}_{\\varvec{d}\\varvec{f}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUnderweight\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003cp\u003e(0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e(36.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e(36.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e(27.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003cp\u003e(0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11\u003c/p\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{16}^{2}=25.135\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNormal weight\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e(5.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003cp\u003e(16.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29\u003c/p\u003e\u003cp\u003e(36.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003cp\u003e(31.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003cp\u003e(10.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e79\u003c/p\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOverweight\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e(1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35\u003c/p\u003e\u003cp\u003e(18.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87\u003c/p\u003e\u003cp\u003e(45.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42\u003c/p\u003e\u003cp\u003e(21.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26\u003c/p\u003e\u003cp\u003e(13.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e193\u003c/p\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eClass 1 obesity\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e(0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003cp\u003e(25.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53\u003c/p\u003e\u003cp\u003e(43.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26\u003c/p\u003e\u003cp\u003e(21.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003cp\u003e(8.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e121\u003c/p\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eClass 2 obesity\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003cp\u003e(0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e(10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26\u003c/p\u003e\u003cp\u003e(65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003cp\u003e(20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e(5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e40\u003c/p\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003cp\u003e(1.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87\u003c/p\u003e\u003cp\u003e(19.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e199\u003c/p\u003e\u003cp\u003e(44.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e104\u003c/p\u003e\u003cp\u003e(23.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e46\u003c/p\u003e\u003cp\u003e(10.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e444\u003c/p\u003e\u003cp\u003e(100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: the table presents how many times and ego evaluated the weight category of alters by alters\u0026rsquo; BMI category, using the WHO classification of BMI. The reported p-values are for \u003cem\u003eFisher\u0026rsquo;s p\u003c/em\u003e, as the table contains cells with counts smaller than 5. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{}^{2}\\)\u003c/span\u003e\u003c/span\u003e = chi-squared. \u003cem\u003edf\u003c/em\u003e = degrees of freedom.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e we report the results of three cross-classified multilevel logistic regression models using as dependent variables: a) \u003cem\u003ebeing accurate\u003c/em\u003e (1\u0026thinsp;=\u0026thinsp;yes; 0\u0026thinsp;=\u0026thinsp;other); b) \u003cem\u003eunderestimation\u003c/em\u003e (1\u0026thinsp;=\u0026thinsp;yes; 0\u0026thinsp;=\u0026thinsp;other); and c) \u003cem\u003eoverestimation\u003c/em\u003e (1\u0026thinsp;=\u0026thinsp;yes; 0\u0026thinsp;=\u0026thinsp;other). In all models, we controlled for the cross-classified structure of the data, given by dyadic interdependence, using random intercepts for both egos and alters. The models included only the BMI category of nodes (using the WHO classification), varying from 1 (underweight) to 5 (class 2 obesity), the interaction between egos\u0026rsquo; and alters\u0026rsquo; BMI category, and the wave. The first model, tested for \u003cem\u003eaccuracy\u003c/em\u003e in the categorization of alters\u0026rsquo; weight category on the visual scale. We report that only the alters\u0026rsquo; BMI category was the significant predictor (OR 0.57, 95% CI 0.40\u0026ndash;0.82, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), indicating that alters who are on the lower end of the BMI scale are more likely to be accurately evaluated by egos.\u003c/p\u003e\u003cp\u003eFor the \u003cem\u003eunderestimation\u003c/em\u003e model, both the egos\u0026rsquo; (OR 2.11, 95% CI 1.29\u0026ndash;3.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) and alters\u0026rsquo; (OR 1.70, 95% CI 1.01\u0026ndash;2.86, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047) BMI category showed a positive effect. However, their interaction does not have an effect. These results indicate that, first, respondents who are on the higher end of the BMI scale might normalize their own weight status, leading also to the underestimation of others. Second, alters who are on the higher end of the BMI scale might also have their weight normalized and, as a consequence, underestimated. Third, the non-significant interaction between the two, shows that possible hypotheses such as \u0026ldquo;an overweight evaluator underestimating the weight status of an overweight alter\u0026rdquo; are partially refuted by our data.\u003c/p\u003e\u003cp\u003eIn the last model, of \u003cem\u003eoverestimation\u003c/em\u003e, none of the variable had a significant effect. However, results that are marginally significant, mirror the results from the \u003cem\u003eunderestimation\u003c/em\u003e models for egos\u0026rsquo; BMI category. The negative effects for egos\u0026rsquo; BMI category show that in our data egos who are on the lower end of the BMI scale are more likely to overestimate the weight category of alters (OR 0.58, 95% CI 0.33\u0026ndash;1.02, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.058).\u003c/p\u003e\u003cp\u003eThe obtained results are also explained by standard deviations of grouping variables, which indicate that for the \u003cem\u003eaccuracy\u003c/em\u003e model there is more variability in alters (\u003cem\u003eSD\u003c/em\u003e: 0.66), for the \u003cem\u003eunderestimation\u003c/em\u003e model there is a higher variability on the ego side (\u003cem\u003eSD\u003c/em\u003e: 1.77), and, also, a higher variation in egos for the \u003cem\u003eoverestimation\u003c/em\u003e model (\u003cem\u003eSD\u003c/em\u003e: 2.28). In none of the models the interaction between the BMI category of egos and alters was statistically significant.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePredicting accuracy, underestimation, and overestimation of alters\u0026rsquo; BMI category \u0026ndash; BMI only models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003cp\u003e(1\u0026thinsp;=\u0026thinsp;yes)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eUnderestimation\u003c/p\u003e\u003cp\u003e(1\u0026thinsp;=\u0026thinsp;yes)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eOverestimation\u003c/p\u003e\u003cp\u003e(1\u0026thinsp;=\u0026thinsp;yes)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eOR (95% CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eOR (95% CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eOR (95% CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003cp\u003e(0.46\u0026ndash;1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003cp\u003e(0.17\u0026ndash;0.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003cp\u003e(0.04\u0026ndash;0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEgo BMI ordinal category (scaled)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003cp\u003e(0.54\u0026ndash;1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.11\u003c/p\u003e\u003cp\u003e(1.29\u0026ndash;3.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003cp\u003e(0.33\u0026ndash;1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlter BMI ordinal category (scaled)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003cp\u003e(0.40\u0026ndash;0.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003cp\u003e(1.01\u0026ndash;2.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.57\u003c/p\u003e\u003cp\u003e(0.99\u0026ndash;2.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewave (ref\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003cp\u003e(0.76\u0026ndash;2.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003cp\u003e(0.43\u0026ndash;1.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.670\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003cp\u003e(0.40\u0026ndash;1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.613\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEgo x Alter BMI ordinal category (scaled)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003cp\u003e(0.93\u0026ndash;1.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003cp\u003e(0.58\u0026ndash;1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003cp\u003e(0.54\u0026ndash;1.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.455\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRandom Effects\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e3.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u0026nbsp;\u003csub\u003ealter\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u0026nbsp;\u003csub\u003eego\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e2.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u0026nbsp;\u003csub\u003eego\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u0026nbsp;\u003csub\u003ealter\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e376\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarginal R\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;/ Conditional R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.097 / 0.344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.121 / 0.555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.077 / 0.528\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeviance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e479.789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e402.590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e318.440\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e493.789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e416.590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e332.440\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAICc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e494.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e416.895\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e332.744\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elog-Likelihood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e-239.895\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-201.295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e-159.220\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe combined results of models reported in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e indicate that the BMI category alone, of either egos or alters, cannot fully account for the variation in whether the visual evaluation of alters by egos resulted in accurate classification, under- or overevaluations. Results for the random part of the models show that interdependencies between egos and alters (who evaluates whom) can bring important insights. In all three models from Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the intraclass correlation coefficients (ICC) are high. The ICC for the \u003cem\u003eaccuracy\u003c/em\u003e model indicates that 27% of variation is explained by random effects, while for the \u003cem\u003eunderestimation\u003c/em\u003e and \u003cem\u003eoverestimation\u003c/em\u003e models the random effects account for 49% of variation in the dependent variable.\u003c/p\u003e\u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, we present the results of three cross-classified multilevel logistic regression models, using the same dependent binary variables (\u003cem\u003eaccuracy\u003c/em\u003e, \u003cem\u003eunderestimation\u003c/em\u003e, and \u003cem\u003eoverestimation\u003c/em\u003e), with the inclusion of additional fixed effects; ego and alter attributes regarding their \u003cem\u003esex\u003c/em\u003e, \u003cem\u003eage\u003c/em\u003e, and \u003cem\u003ehousehold income\u003c/em\u003e, as well as interactions between variables for the same attribute (e.g., interaction between ego\u0026rsquo;s and alter\u0026rsquo;s sex). For egos, the full models also included the \u003cem\u003eoverall proportion of alters that they matched on the visual scale with categories that can be classified as at least overweight\u003c/em\u003e. For alters we included how many times they were evaluated by different egos in each wave (\u003cem\u003eindegree\u003c/em\u003e). As ego-alter relation, in the full models we capture the \u003cem\u003efamily relations\u003c/em\u003e as well as the interaction between these relations and the BMI category of egos and alters.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePredicting accuracy, underestimation, and overestimation of alters\u0026rsquo; BMI category \u0026ndash; full models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003cp\u003e(1\u0026thinsp;=\u0026thinsp;yes)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eUnderestimation\u003c/p\u003e\u003cp\u003e(1\u0026thinsp;=\u0026thinsp;yes)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eOverestimation\u003c/p\u003e\u003cp\u003e(1\u0026thinsp;=\u0026thinsp;yes)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eOR (95% CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eOR (95% CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eOR (95% CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003cp\u003e(0.23\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003cp\u003e(0.19\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003cp\u003e(0.07\u0026ndash;0.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEgo BMI ordinal category (scaled)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003cp\u003e(0.62\u0026ndash;1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003cp\u003e(0.69\u0026ndash;1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.753\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003cp\u003e(0.41\u0026ndash;1.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.547\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlter BMI ordinal category (scaled)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003cp\u003e(0.50\u0026ndash;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003cp\u003e(0.65\u0026ndash;2.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003cp\u003e(0.89\u0026ndash;2.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEgo-Alter family relation (ref\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003cp\u003e(0.61\u0026ndash;2.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003cp\u003e(0.16\u0026ndash;1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003cp\u003e(0.69\u0026ndash;3.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEgo sex (ref\u0026thinsp;=\u0026thinsp;M)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.92\u003c/p\u003e\u003cp\u003e(0.83\u0026ndash;4.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003cp\u003e(0.21\u0026ndash;1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003cp\u003e(0.16\u0026ndash;1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlter sex (ref\u0026thinsp;=\u0026thinsp;M)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003cp\u003e(0.61\u0026ndash;3.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.41\u003c/p\u003e\u003cp\u003e(0.74\u0026ndash;7.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003cp\u003e(0.02\u0026ndash;0.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEgo age (scaled)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003cp\u003e(0.88\u0026ndash;1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003cp\u003e(0.91\u0026ndash;2.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003cp\u003e(0.33\u0026ndash;0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlter age (scaled)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003cp\u003e(0.62\u0026ndash;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003cp\u003e(0.55\u0026ndash;1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.49 (0.90\u0026ndash;\u0026nbsp;2.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEgo hh. income (scaled)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003cp\u003e(0.85\u0026ndash;1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003cp\u003e(0.57\u0026ndash;1.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.446\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003cp\u003e(0.57\u0026ndash;1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.405\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlter hh. income (scaled)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003cp\u003e(0.64\u0026ndash;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.90\u003c/p\u003e\u003cp\u003e(1.15\u0026ndash;3.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003cp\u003e(0.42\u0026ndash;0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEgo prop. alters in PN at least overweight (scaled)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003cp\u003e(1.20\u0026ndash;2.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003cp\u003e(0.19\u0026ndash;0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.92\u003c/p\u003e\u003cp\u003e(1.66\u0026ndash;5.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlter indegree in wave (scaled)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003cp\u003e(0.98\u0026ndash;2.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003cp\u003e(0.48\u0026ndash;1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003cp\u003e(0.41\u0026ndash;1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.050\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewave (ref\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003cp\u003e(0.56\u0026ndash;1.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003cp\u003e(0.38\u0026ndash;1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003cp\u003e(0.52\u0026ndash;2.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.784\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEgo x Alter BMI ordinal category (scaled)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003cp\u003e(0.95\u0026ndash;1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003cp\u003e(0.64\u0026ndash;1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003cp\u003e(0.39\u0026ndash;1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.323\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEgo BMI ordinal category (scaled) x Ego-Alter family relation (ref\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003cp\u003e(0.50\u0026ndash;1.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.34\u003c/p\u003e\u003cp\u003e(0.91\u0026ndash;6.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003cp\u003e(0.43\u0026ndash;2.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.927\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlter BMI ordinal category (scaled) x Ego-Alter family relation (ref\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003cp\u003e(0.18\u0026ndash;0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.31\u003c/p\u003e\u003cp\u003e(1.77\u0026ndash;10.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003cp\u003e(0.47\u0026ndash;2.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.890\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEgo x Alter sex (ref\u0026thinsp;=\u0026thinsp;M)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003cp\u003e(0.25\u0026ndash;2.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003cp\u003e(0.15\u0026ndash;2.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.40\u003c/p\u003e\u003cp\u003e(0.93\u0026ndash;31.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEgo x Alter age (scaled)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003cp\u003e(0.80\u0026ndash;1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003cp\u003e(0.52\u0026ndash;1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003cp\u003e(0.73\u0026ndash;1.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.703\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEgo x Alter hh. income (scaled)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003cp\u003e(0.83\u0026ndash;1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003cp\u003e(0.71\u0026ndash;1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003cp\u003e(0.56\u0026ndash;1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.198\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEgo BMI x Alter BMI x Family relations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003cp\u003e(0.31\u0026ndash;1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.43 (0.11\u0026ndash;1.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.61 (0.62\u0026ndash;4.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.327\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRandom Effects\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e3.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u0026nbsp;\u003csub\u003ealter\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u0026nbsp;\u003csub\u003eego\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u0026nbsp;\u003csub\u003eego\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u0026nbsp;\u003csub\u003ealter\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e376\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarginal R\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;/ Conditional R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.214 / 0.345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.520 / 0.658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.441 / 0.577\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeviance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e452.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e339.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e271.565\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e496.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e383.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e315.565\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAICc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e498.973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e386.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e318.432\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elog-Likelihood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e-226.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-169.624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e-135.783\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs a first result, we observed that in the full models the BMI category of either ego (as evaluator) or alter (as evaluated) are not significant as main effects, compared to the BMI only models (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In terms of sociodemographic characteristics, we report significant results for egos\u0026rsquo; age, alters\u0026rsquo; sex, and alters\u0026rsquo; household income. Thus, younger persons were more likely to overestimate the BMI category of alters (OR 0.53, 95% CI 0.33\u0026ndash;0.87, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011, \u003cem\u003eoverestimation\u003c/em\u003e model), men had a higher probability in being overestimated (OR 0.10, 95% CI 0.02\u0026ndash;0.40, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, \u003cem\u003eoverestimation\u003c/em\u003e model), and alters with a higher income had higher odds in being underestimated (OR 1.90, 95% 1.15\u0026ndash;3.12, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012, \u003cem\u003eunderestimation\u003c/em\u003e model), while those with a lower income had higher odds in having their weight category overestimated (OR 0.64, 95% CI 0.42\u0026ndash;0.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037, \u003cem\u003eoverestimation\u003c/em\u003e model).\u003c/p\u003e\u003cp\u003eRegarding the presence of family relations between egos and alters, results indicate that they play an important role in how a person will classify the weight category of others. Individuals tend to be more accurate when categorizing those who are on the lower end of the BMI scale and who are not their relatives (OR 0.36, 95% CI 0.18\u0026ndash;0.71, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, \u003cem\u003eaccuracy\u003c/em\u003e model). Conversely, they will underestimate the weight category of family members who are on the higher end of the BMI scale (OR 4.31, 95% CI 1.77\u0026ndash;10.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, \u003cem\u003eunderestimation\u003c/em\u003e model). The interaction between alters\u0026rsquo; BMI category and ego-alter family relation has no effect in the \u003cem\u003eoverestimation\u003c/em\u003e model (OR 1.06, 95% CI 0.47\u0026ndash;2.41, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.890), indicating that when it comes to family members, respondents tend to either be accurate or underestimate their weight status.\u003c/p\u003e\u003cp\u003eThe overall perception of egos regarding the composition of their personal networks is also important in our models. Respondents with a low \u003cem\u003eoverall\u003c/em\u003e proportion of alters whom they matched with body figures that are at least overweight present a tendency to underestimate the BMI category of others (OR 0.29, 95% CI 0.19\u0026ndash;0.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eunderestimation\u003c/em\u003e model), while those who indicate a high proportion tend to either be accurate (OR 1.62, 95% CI 1.20\u0026ndash;2.20, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, \u003cem\u003eaccuracy\u003c/em\u003e model) or overestimate (OR 2.92, 95% CI 1.66\u0026ndash;5.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eoverestimation\u003c/em\u003e model). Such results indicate differences in profiles of perception accuracy. While some respondents might have an accurate representation of the weight status of their alters, others might tend to underestimate their BMI category, while others will overestimate it.\u003c/p\u003e\u003cp\u003eSimilar to previous regression models (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the random part of the models reported in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e also indicate the importance of dyadic interdependence between evaluator and the evaluated person. The ICC for the \u003cem\u003eaccuracy\u003c/em\u003e model indicates that 17% of variation is explained by random effects, while for the \u003cem\u003eunderestimation\u003c/em\u003e model the random effects account for 29% of variation in the dependent variable specific to this model, and for the \u003cem\u003eoverestimation\u003c/em\u003e model the random effects account for 24% of variation in the dependent variable. Moreso, for the \u003cem\u003eunderestimation\u003c/em\u003e model we observe that the highest variation is given not by variation in evaluators (egos; \u003cem\u003eSD\u003c/em\u003e: 0.25), but by variation in the evaluated alters (\u003cem\u003eSD\u003c/em\u003e: 1.08), indicating that, when controlling for all other factors, the identity of the evaluator matters less than their relationship with the alter.\u003c/p\u003e\u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e Predicting accuracy, underestimation, and overestimation of alters\u0026rsquo; BMI category \u0026ndash; full models]\u003c/p\u003e\u003cp\u003eFor the \u003cem\u003eunderestimation\u003c/em\u003e of weight status, we also show a visual representation, in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, taking into account the interaction between alters\u0026rsquo; BMI category and the existence of a family relation between egos and evaluated alters. Results are based on coefficients from the model presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Predicted probabilities of underestimating alters\u0026rsquo; weight status are relatively constant across all alters\u0026rsquo; BMI category when the evaluated persons are not family members. In turn, for those who are family members, we observe that the probability of underestimation starts to increase with overweight alters. Family members whose actual BMI category is class 2 obesity obtained the highest probability in having their weight status underestimated by egos with whom they share kinship ties. For brevity, we have not included the interaction plot for predicted probabilities in being accurate about alters\u0026rsquo; weight status. This plot can be found in the Supplementary material, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, showing a similar trend\u0026ndash;there is a steep decrease, below the 0.50 threshold in the predicted probability of accuracy, starting from the overweight category of alters who are family members. In turn, the predicted probabilities of accuracy are fairly constant across the BMI categories of alters.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe results regarding the underestimation of alters who are family members were explored through supplementary analyses, in order to see if there is any bias or not for consistently underestimating the weight category of family members (see also Supplementary data). Our results indicate such a potential bias is missing. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents several distributions through density plots. In panel A, we report the distributions for the mean underestimation scores obtained by each ego split by family and non-family evaluations. Further, we have tested if there is any difference between the averages of mean underestimation ego scores, grouped by family versus non-family evaluations. The independent samples t-test (t\u003csub\u003e129\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.13; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.261; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), indicates that there is no significant difference between the average for non-family evaluations (Mean: 0.358) compared to the average for family evaluations (Mean: 0.283). In panel B, we report the distributions for the mean underestimation scores obtained by each alter split family and non-family evaluations. Similar to results obtained for egos, the independent samples t-test (t\u003csub\u003e124\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.98; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.331; Table S2), indicates that there is no significant difference between the average for non-family evaluations received by alters (Mean: 0.390), compared to the average for family evaluations (Mean: 0.320). While it does not completely eliminate suspicions of an underestimation bias that some respondents might have when evaluating family members, or when some alters are being evaluated by family members, such results indicate that a possible masking effect is not consistent enough to interfere with the results of our analyses.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWhile in most instances the egos have accurately categorised their alters, the most common mis-categorisation situation is that of underestimation. This brings support to other studies indicating that, whether the outcome is the self-evaluation of weight [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] or the evaluation of others [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], weight underestimation is the most prevalent case of inaccuracy. Adding that in Romania the general context is to be rather surrounded by persons who are at least pre-obese (58.9% in 2022, according to data from Eurostat [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]), and even more so in our sample (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), all information is furthering support to explanations regarding the normalization of overweight [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe argument that persons tend to normalize overweight and obesity based on their social context [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], is strengthened by results obtained for the predictor related to respondents\u0026rsquo; overall perception about the composition of their personal networks. Perceiving that you are less surrounded by alters who are at least overweight is correlated with higher probabilities in making underestimations, indicating the probability to perceive some bodies as less heavy than they really are. Furthermore, the inverse result for accuracy and overestimation are logical and separate between: a) those who actually are surrounded by such persons and can actually make accurate categorizations about those surrounding them, and b) individuals who think that they are surrounded by people who are overweight or with obesity more than they really are.\u003c/p\u003e\u003cp\u003eRegarding sex, our results indicate that males tend to have their weight status overestimated compared to females. As stated in the Introduction section, research results linking sex to self-evaluation are mixed. Various authors found either no link between sex and self-evaluation accuracy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], either that females have a higher probability of self-overestimation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. When evaluating others, a controlled experiment on American adults (20\u0026ndash;44 years old) found that overweight male bodies tended to be categorized as normal weight, while underweight female bodies followed the same pattern [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Our results indicate that more research is needed to assess how the sex (or gender) of a person might influence how their weight status is perceived by others, taking into account that such perceptions are dependent on cultural ideals of body types [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRelated to age, our results found that it represents a factor only for overestimation, and only taking into account the age of the evaluator, not of the evaluated person. These results are in line with other studies who looked at the relationship between age and self-evaluation, having as subjects Korean women [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], who had a higher probability of overestimating their weight as age decreased, or Galician general population [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], where younger age groups had a higher rate of self-overestimation than those aged 65 or older. While our results refer to the evaluation of others, not self-evaluation, they might indirectly offer evidence for the link between self-evaluation and how we see others.\u003c/p\u003e\u003cp\u003eThere are no studies about the relationship between weight status (mis)classification and the income (or socioeconomic status (SES)) of the evaluated person. Our results, that indicate an underestimation of those with higher household incomes and overestimation of those with lower incomes open further hypotheses that combine weight-stigma and income or SES-based stereotypes \u0026ndash; i.e., people of lower SES are being perceived as more overweight than they actually are. Low income or SES can be seen as a deterrent for both the accuracy in the self-perception of weight and the adoption necessary practices for weight control [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. However, results from our study bring into attention extra-individual factors. Whether underestimation of weight category by family members and others in one\u0026rsquo;s social circle shapes weight control behaviours differently across SES groups remains to be determined.\u003c/p\u003e\u003cp\u003eStudies involving parent-child evaluation of weight status report that often parents, or caregivers, tend to underestimate the weight category of their children [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Such results are supported by cross-national comparisons, where, in an analysis of 22 countries, researchers found that in all countries those parents who don\u0026rsquo;t have a correct evaluation of their children have a tendency to rather underestimate than overestimate their child\u0026rsquo;s weight status [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. To this, we can also bring into discussion the fact that results of a meta-analysis found that in 24 countries the parents\u0026rsquo; BMI is positively corelated with the BMI of their children [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Our study completes this literature by providing a picture of how such biases are carried through adult life, persons having the tendency to underestimate and normalize overweight and obesity in family relations. Such insights indicate that primary prevention interventions should target persons throughout their life-course, as the underestimation bias is present also for adult weight status assessments. Additionally, our results can also be related to the literature looking at the positive effect that social support, stemming from family or peers, has on weight control practices [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. For example, studies have shown that social support is positively associated with physical activity, healthy eating, body appreciation, and body satisfaction in women [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], lower BMI during adolescence [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], or successful engagement in the follow-up of weight control programs [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. For this reason, weight control interventions that include a social support component should also make an assessment regarding the BMI perception between patients and members of their support group.\u003c/p\u003e\u003cp\u003eHowever, it must also be taken into account that the relationship between perception accuracy of weight statuses and healthy behaviours is complex. As reviews [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], and studies on adults [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] and adolescents [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] have shown, accurate or underestimations about own overweight status can lead to unhealthy weight-related behaviours. Persons who accurately perceive themselves as overweight or living with obesity can be subject to internalized weight stigma and depression, leading to actions that on medium or long term can have negative effects on their health. For this reason, we also point that further research is necessary on how to approach weight status awareness, especially in social contexts of close relations, in order to.\u003c/p\u003e\u003cp\u003eThe findings of this study suggest that overweight and obesity prevention strategies must adapt to the social mechanisms that support their normalization. In contexts where obesity is highly prevalent, social norms can make the problem invisible, making it crucial to intervene at the community level to modify shared perceptions and increase awareness of health risks [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, given that people tend to associate with others who share similar characteristics\u0026ndash;a selection process\u0026ndash;promoting diverse social environments that facilitate exposure to healthier lifestyles may help disrupt reinforcing cycles of unhealthy behaviour [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Finally, considering that individual behaviour can be shaped by peer influence, interventions that leverage social networks\u0026ndash;for example, by engaging change agents or healthy behaviour role models within the group\u0026ndash;can be particularly effective in fostering positive habits [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Together, these approaches allow us to address obesity not only as an individual phenomenon, but as a dynamic social process that requires interventions at multiple ecological levels.\u003c/p\u003e\u003cp\u003eIn addition, our findings highlight the practical importance of a consistent underestimation of overweight and obesity in others, particularly within the family. This perceptual bias may reduce the perceived urgency of adopting preventive health behaviours. When excess weight is not recognized in significant others\u0026ndash;especially children or partners\u0026ndash;it can delay early interventions. Public health strategies should therefore address not only the behaviours themselves but also the social perception and recognition of overweight and obesity. Educational campaigns might be more effective if they include normative feedback mechanisms that help individuals recalibrate their perceptions [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], particularly in familial contexts where emotional bonds and protective biases may hinder a more objective assessment. Tackling misperceptions within the family unit could serve as a critical leverage point for early detection and prevention efforts [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe limits of our study can be addressed regarding, first of all, the measurement of \u0026ldquo;actual\u0026rdquo; BMI using self-declared weight and height of study participants. Besides being an imperfect measure of health status [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], relying on declared indicators of weight and height adds to the probability of study participants under- or overestimating, first of all, themselves [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. We tried to reduce the impact of these self-declared indicators by using in our models not the actual BMI of a person, but the general BMI categories that use broader intervals. Second of all, the way that respondents classified their alters, using a visual scale is also prone to errors, as respondents were asked to classify their alters using pictures where it might have been hard to distinguish close categories (e.g., the upper end of overweight versus the lower end of obesity class 1). Third, our study does not include qualitative descriptors regarding how respondents reflect on their own weight category and the weight category of their alters, as other studies have used [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Knowing if they consider themselves, and others, being of \u0026ldquo;normal weight\u0026rdquo;, \u0026ldquo;overweight\u0026rdquo;, \u0026ldquo;a little overweight\u0026rdquo;, \u0026ldquo;about right\u0026rdquo;, \u0026ldquo;healthy\u0026rdquo;, \u0026ldquo;unhealthy\u0026rdquo;, or other types of such descriptors, can add further insights into explaining why a person will tend to underestimate (or be accurate about) the weight category of someone from their personal network. Fourth of all, is hard to distinguish between misclassification due to visual normalization and the false consensus effect, the latter referring to the egocentric bias that persons have in viewing their opinions and behaviour replicated by others [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Thus, and overweight person might underestimate the weight category of another overweight person not because of overexposure and normalization of overweight, but because they think about themselves as healthy.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study brings into attention the importance of social context and family relations when persons evaluate the weight status of others from their personal network. The obtained results indicate that close relationships are prone to lead to the underestimation of someone\u0026rsquo;s weight status. As social support is important for the success of weight control actions, the underestimation bias in weight statuses of family members can hinder social support for all weight-loss related practices, delay interventions, and lead to complications (e.g., diabetes or cardiovascular diseases) because of late check-ups.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAIC\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eAkaike Information Criterion\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAICc\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eCorrected Akaike Information Criterion\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBMI\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eBody Mass Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCI\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eConfidence Interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eICC\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eIntra Class Correlation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIQR\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eInterquartile Range\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eOdds Ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWHO\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eWorld Health Organization\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePNA\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003ePersonal Network Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eStandard Deviation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSNA\u003c/em\u003e:\u0026nbsp;\u003c/strong\u003eSocial Network Analysis\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll research procedures complied with the Declaration of Helsinki and the European General Data Protection Regulation (GDPR). The study protocol was approved by the Ethics Committee of the Center for Innovation in Medicine (EC-INOMED Decision No. D001/09- 06-2023 and No. D001/19-01-2024). Written informed consent was obtained from all participants. We anonymized personal identifying information after each interview and securely stored data on encrypted drives accessible only to authorized personnel.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data were deposited into the Zenodo repository and are available at the following URL: https://doi.org/10.5281/zenodo.17209784. Please cite as: Oană, I., H\u0026acirc;ncean, M.-G., Geantă, M., Cioroboiu, C., Maya-Jariego, I., Lerner, J., Bianca-Elena, M., \u0026amp; Vidrașcu, B.-A. (2025). Replication data for: Normalization of overweight and obesity in family relations: a personal network analysis study [Data set]. Zenodo. https://doi.org/10.5281/zenodo.17209784. In the Supplementary Material we offer only the supplementary analyses mentioned in the article. For all analyses and code, consult the aforementioned repository.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI.O., M.-G.H., M.G., C.C., B.-E.M., and B.-A.V. were supported by the European Commission, 4P-CAN project, HORIZON-MISS-2022-CANCER-01, project ID 101104432, programme HORIZON. Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the granting authority. Neither the European Union nor the granting authority can be held responsible for them. J.L.was supported by Deutsche Forschungsgemeinschaft (DFG), Grant number 555455503.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI.O.: Conceptualization, Methodology, Formal analysis, Investigation, Data Curation, Visualization, Writing \u0026ndash; original draft. M.-G.H.: Conceptualization, Methodology, Formal analysis, Investigation, Data Curation, Validation, Writing \u0026ndash; review \u0026amp; editing. M.G.: Conceptualization, Methodology, Writing \u0026ndash; review \u0026amp; editing, Project administration, Funding acquisition. C.C.: Conceptualization, Writing \u0026ndash; review \u0026amp; editing. I.M.-J.: Conceptualization, Methodology, Validation, Writing \u0026ndash; review \u0026amp; editing. J.L.: Conceptualization, Methodology, Validation, Writing \u0026ndash; review \u0026amp; editing. B.-E.M.: Conceptualization, Writing \u0026ndash; review \u0026amp; editing, Data curation. B.-A.V.: Conceptualization, Writing \u0026ndash; review \u0026amp; editing, Data curation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShi X, Deng G, Wen H, Lin A, Wang H, Zhu L, et al. Role of body mass index and weight change in the risk of cancer: A systematic review and meta-analysis of 66 cohort studies. J Glob Health. 2024;14:04067. https://doi.org/10.7189/jogh.14.04067\u003c/li\u003e\n\u003cli\u003eMandic M, Li H, Safizadeh F, Niedermaier T, Hoffmeister M, Brenner H. Is the association of overweight and obesity with colorectal cancer underestimated? An umbrella review of systematic reviews and meta-analyses. Eur J Epidemiol. 2023;38:135\u0026ndash;44. https://doi.org/10.1007/s10654-022-00954-6.\u003c/li\u003e\n\u003cli\u003ePowell-Wiley TM, Poirier P, Burke LE, Despr\u0026eacute;s J-P, Gordon-Larsen P, Lavie CJ, et al. 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The \u0026ldquo;false consensus effect\u0026rdquo;: An egocentric bias in social perception and attribution processes. J Exp Soc Psychol. 1977;13(3):279\u0026ndash;301. https://doi.org/10.1016/0022-1031(77)90049-X.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"personal network analysis, overweight normalization, weight status evaluation, family relations, obesity","lastPublishedDoi":"10.21203/rs.3.rs-8059527/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8059527/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eMost research on weight status perception focuses on self-evaluation, with studies on perceptions of others largely limited to parent\u0026ndash;child assessments. Moreover, studies incorporating a network analysis design into how social relations influence weight perception are even fewer and focused rather on friendship networks from school data. The aim of this study is to investigate the accuracy of evaluations made by respondents regarding the BMI category of persons from their social circle.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe analysed 444 evaluator\u0026ndash;evaluated dyads from a Personal Network Analysis study including respondents (egos) and their close contacts (alters). Egos self-reported height and weight were used to compute BMI (kg/m\u0026sup2;) and BMI categories. Alters\u0026rsquo; weight status was assessed by egos using BMI-based pictograms. Only pairs where both egos and alters were respondents were retained, enabling comparison between actual BMI category and perceived category. Cross-classified logistic regression models examined accuracy, underestimation, and overestimation as binary outcomes in separate regression models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWhen alter BMI category and family member status interacted, respondents were more likely to underestimate (OR 4.74, 95% CI 1.74\u0026ndash;12.92, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) family members or be accurate (OR 0.37, 95% CI 0.18\u0026ndash;0.74, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) in evaluating non-family members, with no significant effect for overestimation (OR 1.18, 95% CI 0.50\u0026ndash;2.78, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.713). Underestimation was also associated with broader network perceptions: respondents reporting few alters matched to overweight-or-higher body figures were more likely to underestimate others\u0026rsquo; BMI category (OR 0.29, 95% CI 0.19\u0026ndash;0.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eFindings suggest weight control programs and health interventions, in general, should address not only self-perception but also network influences. Underestimation biases within family relationships may persist into adulthood, potentially limiting social support for weight management and other health-related behaviours.\u003c/p\u003e","manuscriptTitle":"Normalization of overweight and obesity in family relations: a personal network analysis study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 11:09:45","doi":"10.21203/rs.3.rs-8059527/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-12-30T12:31:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223469038247740834988601321425172572289","date":"2025-12-09T22:21:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-21T16:47:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-10T14:35:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-10T11:28:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-10T11:27:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-11-07T18:14:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a8eab553-5171-4eb1-a812-85fd4915a5c2","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-21T16:53:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 11:09:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8059527","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8059527","identity":"rs-8059527","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}