Development of RisObIn.Com, a screening tool for risk of childhood obesity in the community

nutrients Article Development of RisObIn.Com, a Screening Tool for Risk of Childhood Obesity in the Community Ana Catarina Moreira 1,2,* , Patrícia Almeida Oliveira 1,3 , Rute Borrego 1, Telma Nogueira 4,5 , Raquel Ferreira 1,6 and Daniel Virella 7 1 Escola Superior de Tecnologia da Sa ú de de Lisboa, Instituto Polit é cnico de Lisboa, 1990-096 Lisboa, Portugal; [email protected] (P.A.O.); rute.borr[email protected] (R.B.); [email protected] (R.F.) 2H &TRC—Health & Technology Research Center, 1990-096 Lisboa, Portugal 3Faculdade de Medicina, Universidade de Lisboa, 1649-004 Lisboa, Portugal 4Laboratório de Nutrição, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisboa, Portugal; [email protected] 5Instituto de Saúde Ambiental, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisboa, Portugal 6Núcleo de Educação e Qualidade Alimentar, Câmara Municipal de Sintra, 2710-307 Sintra, Portugal 7Research Unit, Centro Hospitalar Universitário Lisboa Central, 1150-199 Lisboa, Portugal; [email protected] *Correspondence: ana.mor[email protected] Received: 28 September 2020; Accepted: 23 October 2020; Published: 27 October 2020   Abstract: The prevalence of childhood overweight has increased considerably in the past three decades and there is evidence that childhood obesity can persist into adulthood. A simple tool to identify relevant risk factors may alert families and prevent overweight and obesity. This study aims to develop a pre-school screening tool to assess the risk of childhood obesity. Child anthropometric measurements and several risk factors for childhood obesity factors were obtained. The effect of the variables on the outcome of obesity (defined as increased anthropometry-estimated adiposity) was assessed by binary logistic regression analyses. The identified variables were submitted for expert panel validation and combined for the tool development. A total of 304 children were included. Eight items were included in the tool. A higher score of the tool indicates a greater risk for obesity in childhood with the cutoffpoint set at 0. The tool sensitivity for obesity was 95%, specificity was 74.4%, the positive predictive value was 37.3%, and negative predictive value was 98.9%. The Risk of childhood Obesity In the Community (RisObIn.Com) tool is proposed to be a comprehensive tool to identify children at high risk for late childhood obesity at admission to primary school. Further studies are needed to assess the performance of the tool. Keywords: childhood overweight; children; risk; community; screening; tool 1. Introduction The prevalence of childhood overweight and obesity has increased considerably in the past decades, mostly in high-income countries but recently also rising in lowand middle-income countries. Although in high-income countries, a recent decrease has been observed [ 1 ], prevalence remains very high [ 2 , 3 ]. Obesity is defined as an abnormal fat accumulation that impairs health [ 4 ] but it has been shown that obesity defined by Body Mass Index (BMI) alone is a remarkably heterogeneous condition with varying cardiovascular and metabolic manifestations across individuals, which may differ according to age and gender [ 5 ]. This is a chronic disease that increases heavily the burden on citizens, health care systems, productivity, cities, and society and should be considered a top priority and main target to combat the increasing non-communicable diseases epidemic [ 6 ]. There is evidence that childhood obesity can persist into adulthood [ 7 ], lead to physical obesity-related complications, Nutrients 2020,12, 3288; doi:10.3390/nu12113288 www.mdpi.com/journal/nutrients Nutrients 2020,12, 3288 2 of 18 and affect psychological health and social and emotional well-being [ 8 ]. This emphasizes the importance of early intervention to prevent the onset of obesity in childhood. A comprehensive and proactive strategy to deal with the challenges imposed by the obesity epidemic is needed and requires the development and implementation of programs for prevention, early diagnosis, and treatment, especially in children [ 6 ]. However, interventions to reduce childhood obesity show limited effectiveness, particularly for weight-related outcomes [9]. Therefore, sustainable and effective interventions to prevent childhood obesity should target higher-risk children [ 10 ]. Obesity development involves a complex interplay between physiological environmental, psychological, social, and behavioral exposures [ 11 , 12 ]. There is evidence of epigenetic processes in utero that contribute to infant obesity, including DNA methylation, and gut microbiome alterations [ 13 ]. Breastfeeding is also associated with obesity protection [ 14 , 15 ]. Additional life course exposures include socio-economic status, food production and marketing, food insecurity, and obesogenic environments, that can promote unhealthy lifestyles. In this environment, some individuals are genetically more susceptible to develop obesity [16]. A simple tool to identify relevant obesity risk factors early in life may alert families and caretakers into positive changes, improving a child’s weight trajectory and preventing overweight and obesity. Tools to identify children at risk for obesity have been published [ 17 – 19 ]. To our best knowledge, none of these include a large diversity of parameters known to affect weight gain trajectory; the broad variables related to obesity require a more comprehensive tool. This study aims to develop a pre-school screening tool to assess the risk for childhood obesity based on a broad spectrum of risk factors considering peri-natal, anthropometric, sociodemographic, past eating habits, current eating habits, subjective anthropometry perception, subjective eating habits perception and physical activity, and sleeping habits, at a multivariable level. 2. Materials and Methods Data from the community-based participatory research Sintra Grows Healthy (SGH) [ 20 ] were used for independent analysis, as a cross-sectional with nested case-control study. The study sample comprises schoolchildren aged 6–12 years attending the first to fourth grades of six public primary schools in Sintra municipality, Portugal. Anthropometric measurements of children were assessed and a wide set of data was obtained by the application of a questionnaire to the children’s legal guardian, mainly their parents. For the purpose of the present study, an additional set of questions relevant for the study of obesity risk factors was collected by applying a second questionnaire to the children’s legal guardian. Only children for whom both these questionnaires were filled, were selected for this study. Data were collected between 2017 and 2018. Written informed consent was obtained and the safety and confidentiality of all the collected and archived data were ensured. Approval was obtained from the National Commission of Data Protection and the Ethics Commission of Lisbon Academic Medical Center. Anthropometric measurements were obtained directly by trained members of the SGH research team, using standardized anthropometric procedures [ 21 ]. Children were barefoot and wearing minimal clothing to assess height and weight. Height was assessed using a portable stadiometer to the nearest 0.1 cm (SECA 213 ® ) in the vertical position, with feet together and the head in the Frankfort plane. Weight was assessed through a portable calibrated scale (SECA Robusta 813 ® , SECA Deutschland, Hamburg, Germany), expressed up to 0.1 kg. Body Mass Index (BMI) was calculated as weight (kg) divided by height squared (cm 2 ). BMI was classified according to age and gender z-scores of the World Health Organization for children aged 5 to 19 years old [ 22 ]. Waist circumference was directly measured on the skin to the nearest 0.1 cm according to the World Health Organization method with a non-extensible and flexible tape (SECA 201 ® , SECA Deutschland, Hamburg, Germany) [ 23 ]. Waist-to-Height Ratio (WtHR) was calculated as waist circumference (cm) divided by height (cm) and classified as an indicator for early health risk as ≤0.5 or >0.5 [24]. Nutrients 2020,12, 3288 3 of 18 The set of questions specifically included for the present study was gathered and developed through a literature review regarding childhood obesity [ 11 , 12 , 16 ]. Children were asked to fill the questionnaire at home with their parents. To assure data confidentiality, each child was assigned a subject identification code. The questionnaires were collected by teachers and sent back to the SGH team. Data entry and revision was conducted through a standardized procedure. The information collected for the present analysis included (a) parental nationality; (b) parental level of education; (c) family type (nuclear/extended two-/one-parented); (d) number and age of siblings; (e) mean monthly income; (f) parental current employment status; (g) present parental weight and height (from which BMI was calculated and categorized into underweight, normal weight, overweight, or obese) and (h) father’s and mother’s body image perception. The caretakers were asked to recall peri-natal information: (a) maternal weight before and after pregnancy (from which BMI pre-pregnancy and adequacy of weight gain during pregnancy were calculated according to the Institute of Medicine recommendations [ 25 ]); (b) maternal tobacco consumption during pregnancy; (c) diagnosis of gestational diabetes and/or pre-eclampsia; (d) information given by the assistant physician during pregnancy on adequateness of the fetus for gestational age; and (e) the gestational age in complete weeks (to determine if the birth was pre-term or term). Information regarding anthropometric data throughout childhood was retrieved from the records in the child health bulletin: (a) birth weight and length [from which BMI was calculated through World Health Organization Anthro software for Windows, version 3.2.2. (World Health Organization—Department of Nutrition, Geneva, Switzerland), and classification into small for gestational age (SGA, <10th percentile), appropriate for gestational age (10th–89th percentile), and large for gestational age (LGA, >90th percentile) were obtained]; (b) weight and length at the ages of 12, 18, and 24 months and 3 and 5 years old [from which BMI z-score was calculated through the World Health Organization Anthro software for Windows, version 3.2.2. (World Health Organization—Department of Nutrition, Geneva, Switzerland)]. Overweight (including obesity) was defined according to age and sex z-scores (above 2) of the World Health Organization for children up to 5 years old [ 26 ]; since the medical visits from birth to 5-years-old may not have occurred at the exact dates selected to recall anthropometric data, the health record information closer to those ages and respective dates were collected to correctly assess z-scores. To classify anthropometric measurements at each visit, the exact age was calculated by the difference between birth date and the records visit date. We collected data on child and family feeding patterns at two moments. We asked about breastfeeding (total and exclusive duration) and the introduction of solid foods (age, appetite, and type of meal first introduced). For present feeding pattern, we asked about: child’s appetite, the Mediterranean diet pattern index of the child (KIDMED [ 27 ]) through adapted questions, and its family (PREDIMED [ 28 ]), and one question to both child and caretakers regarding child’s intake when worried, irritated, or anxious, extracted from the from Child Eating Behaviour Questionnaire [ 29 ]. To assess the child’s and caretaker’s perception of the quantity of child usual intake, we used images of four meal plates with different portions of food (A to D, ascendingly). According to children’s height-for-age, we determined the two images closer to their recommended portion (z-score ≤ 1 corresponded to images A and B; z-score >1 corresponded to images B and C). Image D represented an excessive food portion for any children of our sample. We compared the adequacy of the caretaker’s answers and classified them as adequate, excessive, or lower. Caretakers were asked to select an option regarding the child’s nutritional status for age between “low weight,” “adequate weight,” or “excessive weight” and compared to the child’s BMI. This perception was categorized as correct, underestimated, or overestimated. Children and caretakers identified the child’s body figure through body image scales [ 30 ]. We compared that perception with the corresponding child’s BMI and categorized it as incorrect, relatively correct, or correct to child’s BMI, as previously done [ 31 ]. The perceptions were additionally categorized as correct, underestimated, or overestimated. Caretakers also identified their own body figures through body image scales [ 32 ]. We compared that perception with the corresponding parental Nutrients 2020,12, 3288 4 of 18 BMI and categorized it as incorrect, relatively correct, or correct to child’s BMI, as previously done [ 33 ]. The perceptions were additionally categorized as correct, underestimated, or overestimated. We questioned about the physical activity and sedentary behaviors of the child, the frequency of consumption of meals in front of a screen, and the number of hours of sleep. Variables were grouped into eight dimensions: peri-natal, anthropometric, sociodemographic, previous eating habits, current eating habits, subjective anthropometry (perception), subjective eating habits (perception), physical activity, and hours of sleep. 2.1. Development of the Risk Index Tool and Scoring As the BMI, as a single measurement of obesity, does not reflect the whole complexity of the condition [ 6 ], the European Association for the Study of Obesity proposed to improve the diagnostic criteria for obesity with the inclusion of other dimensions, including the degree of adiposity [ 34 ]. Therefore, to increase the accuracy of the outcome measure, to reflect an adiposity-based condition, a composite variable was created using BMI and WtHr. Thus, the primary outcome measure in this study is increased adiposity, defined as overweight (including obesity) with WtHr >0.5, while in primary school. To test the effect of the factors under investigation on the primary outcome, binary logistic regression analyses were performed. Exposures were tested within the aforementioned dimensions (dependent variable: overweight (including obesity) with WtHr >0.5; factors: all risk factors by dimension). The regression analysis was used to identify factors associated with the primary outcome on each of the eight dimensions. The studied risk factors that showed an association with our primary outcome were presented to an expert pediatric panel (nutrition, education, nursing, pediatrician, and exercise physiology experts) for external construct validation with the purpose of developing the proposed tool: RisObIn.Com (Risk of childhood Obesity In the Community). The most agreed risk factor variables were then combined to develop the RisObIn.Com tool. At least one item from every considered dimension was included in the score if any of the items revealed significant statistical relevance. The Beta ( β ) values to a decimal case were used to generate the scoring system as an indicator of the association between each variable, and 0 and 1 scores were assigned to the response option regarding their association with the outcome variable (overweight including obesity with WtHr >0.5). As an example, on the physical activity item, a score of 0 was assigned to the response option “plays regular and programmed physical activity,” and a score of 1 to the response option “doesn’t play regular and programmed physical activity.” This score was then multiplied by 1.6 to obtain the item’s score, as β was 1.642. The final score of the RisObIn.Com tool was obtained by the sum of all item scores and a correction factor was added to obtain zero as the cut-offvalue. 2.2. Statistical Analysis All data were checked for entry errors. Statistical analysis was done using IBM SPSS Statistics for Windows, version 26 (IBM Corp, Armonk, NY, USA). OpenEpi Version 3.01 was used to calculate confidence intervals (CI) [ 35 ]. Continuous data were checked for normal distribution using the Kolmogorov-Smirnov test and graphically by evaluating histograms and expressed as mean and standard deviation. Non-normally distributed data were expressed as median (Min-Max). Comparisons between the participants studied and those not included were made by using t-tests for normally distributed continuousvariables, Mann-Whitneyrank-sum testsfornon-normallydistributedcontinuous variables, and χ2 tests for categorical variables. Cutoffpoint analysis was performed to identify the optimal value that differentiates the risk of obesity from non-risk of obesity in children. The threshold was defined by the largest distance from the diagonal line of the receiver operating characteristic (ROC) curve (sensitivity × (1 − specificity)). Using the cutoffpoint obtained, both sensitivity and specificity and positive and negative predictive Nutrients 2020,12, 3288 5 of 18 values were calculated, with their 95% CI. All Pvalues reported were based on two-sided hypotheses and compared to a significance level of 5%. 3. Results 3.1. Study Sample Characteristics Data were collected from 593 subjects. From those, 289 had incomplete data on crucial information to proceed with the analysis (for example, sex information) and therefore were excluded. The remain 304 gathered anthropometric measurements and data regarding our set of questions and therefore were included, despite some were not complete. In the portion of not included subjects, the children’s median age was 8.0 (5.8–10.8) years old (missing 162), 7.8 (5.9–10.8) for girls, and 8.1 (5.8–10.4) for boys. The mother’s median age was 39.0 (25.0–54.0) years old (missing 62), and father’s was 41.0 (25.0–66.0) years old (missing 62). There were no significant differences between the child’s age (U =2371.0, p=0.166), mother’s age (U =41,256.5, p=0.153), child’s BMI z-score (U =23,156.0, p=0.204), mother’s (U =38,080.0, p=0.240) or father’s nationality (U =33,481.5, p=0.207), parental current employment status (U =30,097.5, p=0.308), mean monthly income (U =33,703.5, p=0.510), and father’s level of education (U =36,689.0, p=0.939) between the children included in the sample and those not included. The father’s age (U =33,362.5, p=0.021) and the mother’s level of education (U =39,273.0, p=0.035) was significantly different between the children included in the sample and those not included. The sociodemographic characteristics of the sample are presented in Table 1. Table 1. Sociodemographic characteristics of the study sample. Sample Characteristics n%Girls Boys n n Child Age n=300 Median 8.0 (5.9–10.2) years old Sex n=304 Girls 148 48.7 n/a n/a Boys 156 51.3 n/a n/a Body Mass Index n=304 Underweight 4 1.3 1 3 Normal weight 208 68.4 95 113 Overweight 63 20.7 38 25 Obesity 29 9.5 14 15 Caretakers Mother’s age n=300 Median 38.0 (26.0–52.0) Father’s age n=280 Median 40.0 (26.0–63.0) Mother’s Body Mass Index n=288 Underweight 9 3.1 3 6 Normal weight 165 57.3 82 83 Overweight 84 29.2 43 41 Obesity 30 10.4 14 16 Father’s Body Mass Index n=275 Underweight 0 0 0 0 Normal weight 104 37.8 57 47 Overweight 132 48.0 55 77 Obesity 39 14.2 22 17 Mother’s nationality n=290 Portuguese 270 93.1 137 133 Non-Portuguese 20 6.9 9 11 Father’s nationality n=277 Portuguese 265 95.7 131 134 Non-Portuguese 12 4.3 6 6 Nutrients 2020,12, 3288 6 of 18 Table 1. Cont. Sample Characteristics n%Girls Boys n n Caretakers Mother’s education n=297 Basic education or lower 62 20.9 32 30 Higher secondary education or a professional course 119 40.1 68 51 Graduation or bachelor’s degree 99 33.3 44 55 Master or doctoral degree 17 5.7 3 14 Father’s education n=280 Basic education or lower 91 32.5 48 43 Higher secondary education or a professional course 129 46.1 59 70 Graduation or bachelor’s degree 46 16.4 22 24 Master or doctoral degree 14 5.0 6 8 Professional situation n=263 Both parents are employed 209 79.5 108 101 Only one parent is employed 44 16.7 21 23 Both parents are jobless 10 3.8 3 7 Mean monthly household income n=271 Less than €500 9 3.32 4 5 €500–€1000 80 29.52 40 40 €1000–€1500 80 29.52 36 44 €1500–€2000 52 19.18 28 24 €2000–€3000 41 15.12 17 24 Above €3000 9 3.32 3 6 n/a—not applicable. The overall prevalence estimates of underweight, normal weight, overweight, and obesity, and central adiposity are shown in Table 2. Overall, the prevalence rate was 20.7% for overweight and 9.5% for obesity. Most children (80.8%, n=244) had WtHr ≤ 0.5. Combining BMI and WtHr, 16.8% [95% CI 13.0–21.4] (n=51) children were overweight (including obesity) with WtHr >0.5. Only seven children had a WtHr >0.5 with a normal weight and four children had a WtHr ≤0.5 with obesity. 3.2. Risk Factors for Overweight (Including Obesity) with WtHr >0.5 Risk estimation models for overweight (including obesity) with WtHr >0.5 were explored within each dimension of variables. The significant risk factors on each of the eight dimensions are presented in Table 3and Supplementary Table S1. 3.2.1. Peri-Natal Dimension The only variable retained in the final peri-natal dimension estimation model was maternal pre-pregnancy BMI; being classified as overweight increases, in mean, 2.6-fold the risk of overweight (including obesity) with WtHr >0.5, and being classified as obese increases, in mean, 4.1-fold the risk of overweight (including obesity) with WtHr >0.5. 3.2.2. Anthropometric Dimension For the anthropometric dimension, the retained variable was the BMI at 5 years old; being classified as overweight increases, in mean, 4.2-fold the risk of overweight (including obesity) with WtHr >0.5. Nutrients 2020,12, 3288 7 of 18 Table 2. Characterization of anthropometric measurements and calculated indexes. Total Girls Boys n%95% Confidence Interval n%95% Confidence Interval n%95% Confidence Interval Lower Level Upper Level Lower Level Upper Level Lower Level Upper Level Body Mass Index z-Score Median 0.36 (−2.77–3.89) Body Mass Index Class Underweight 4 1.3 0.5 3.3 1 0.68 0.1 3.7 3 1.92 0.7 5.5 Normal Weight 208 68.4 62.9 73.4 95 64.19 56.2 71.5 113 72.44 64.9 78.8 Overweight 63 20.7 16.6 25.6 38 25.68 19.3 33.3 25 16.02 11.1 22.6 Obesity 29 9.5 6.7 13.4 14 9.45 5.7 15.3 15 9.62 5.9 15.3 nTotal 304 100.0 - - 148 100.0 - - 156 100.0 - - Waist Circumference Median 58.5 (37.5–91.5) Waist-to-Height ratio Median 0.46 (0.34–0.66) ≤0.5 244 80.8 75.9 84.8 110 74.8 67.2 81.2 134 86.5 80.2 90.9 >0.5 58 19.2 15.5 24.4 37 25.2 18.9 32.8 21 13.5 9.0 19.8 nTotal 302 100.0 - - 147 100.0 - - 155 100.0 - - Overweight (Including Obesity) with Waist-to-Height ratio >0.5 51 16.8 13.0 21.4 31 20.9 15.3 28.4 20 12.8 8.5 18.9 nTotal 304 - - - 148 - - - 156 - - - Nutrients 2020,12, 3288 8 of 18 Table 3. Variables retained in the statistical models. Dimension Variables Exp(β) (95% Confidence Interval) p-Value Peri-natal Pre-pregnancy Body Mass Index Pre-pregnancy Body Mass Index of Overweight 2.591 (1.164–5.766) 0.020 Pre-pregnancy Body Mass Index of Obesity 4.145 (0.925–8.570) 0.063 Body Mass Index z-score 5-years-old 4.159 (2.404–8.497) 0.000 Paternal Body Mass Index Paternal Body Mass Index of Overweight 0.772 (0.252–2.364) 0.650 Paternal Body Mass Index of Obesity 4.041 (1.271–12.844) 0.018 Type of meal introduced in solid food introduction Soup 0.401 (0.176–0.914) 0.030 Current eating habits Child’s appetite Would eat only with insistence or frequently would not eat in totality; Would eat all and be satisfied 0.174 (0.050–0.603) 0.006 Caretaker’s perception of child’s intake through image 1.489 (0.985–2.249) 0.059 Family Mediterranean pattern (PREDIMED) How many vegetable servings do you consume per day? 0.624 (0.389–0.999) 0.050 How many servings of butter, margarine, or cream do you consume per day? 1.535 (0.976–2.413) 0.063 Subjective anthropometry perception Adequacy of father’s body image perception vs. actual Body Mass Index Relatively correct 4.902 (1.116–21.536) 0.035 Correct 2.597 (0.634–10.643) 0.185 Adequacy of the caretaker’s opinion on the child’s nutritional status vs. child’s Body Mass Index Relatively correct 3.483 (0.882–13.753) 0.075 Correct 31.605 (6.055–164.951) 0.000 Subjective eating habits perception Caretaker’s perception regarding child’s intake when anxious No 0.260 (0.056–1.204) 0.085 Caretaker’s perception of the adequacy of the child’s food intake for age Inferior or adequate 0.083 (0.024–0.286) 0.000 Physical activity and hours of sleep Child’s participation in programmed sport activity Yes 0.194 (0.052–0.724) 0.015 Nutrients 2020,12, 3288 9 of 18 3.2.3. Sociodemographic Dimension The sociodemographic variable retained in the final estimation model was paternal BMI; paternal BMI reflecting overweight decreases, in mean, 33% the risk of overweight (including obesity) with WtHr >0.5, and paternal BMI reflecting obesity increases, in mean, 4-fold the risk of overweight (including obesity) with WtHr >0.5. 3.2.4. Past Eating Habits Dimension For the past eating habits dimension, the only variable included in the final model was the type of meal used for solid foods introduction; if soup (rather than cereals) was the first solid food introduced, it decreases, in mean, 60% (the risk of overweight (including obesity) with WtHr >0.5. 3.2.5. Current Eating Habits Dimension For current eating habits, the variables child’s appetite, the caretaker’s perception of the child’s intake through image, and the PREDIMED questions regarding vegetable daily intake and butter, margarine, and cream daily intake were included in the final model. The child’s appetite decreases, in mean, 83% the risk of overweight (including obesity) with WtHr >0.5. The caretaker’s perception of the child’s intake through image increases, in mean, 1.5-fold the risk of overweight (including obesity) with WtHr >0.5. The PREDIMED question regarding vegetable daily intake decreases, in mean, 38% (the risk of overweight (including obesity) with WtHr >0.5, and the PREDIMED question regarding butter, margarine, and cream daily intake increases, in mean, 1.5-fold the risk of overweight (including obesity) with WtHr >0.5. 3.2.6. Subjective Anthropometry Perception Dimension For subjective anthropometry perception, the two variables retained in the final model were (1) the adequacy of father’s own body image perception in comparison to his real BMI; and (2) the adequacy of the caretaker’s opinion regarding the child’s nutritional status in comparison to the child’s real BMI. The relatively correct adequacy of father’s own body image perception compared to real BMI increases, in mean, 4.9-fold the risk of overweight (including obesity) with WtHr >0.5, and the correct adequacy of father’s own body image perception compared to real BMI increases, in mean, 2.6-fold the risk of overweight (including obesity) with WtHr >0.5. Regarding the adequacy of the caretaker’s opinion regarding the child’s nutritional status compared to the child’s real BMI, correct adequacy decreases, in mean, 31.6-fold the risk of overweight (including obesity) with WtHr >0.5. 3.2.7. Subjective Eating Habits Perception Dimension In the eating habits subjective data, the variables retained in the final estimation model were the caretaker’s perception of child’s food intake when worried, irritated, or anxious and the caretaker’s perception of the adequacy of the child’s food intake for age. The caretaker’s perception that the child’s food intake when worried, irritated, or anxious is not affected decreases, in mean, 74% the risk of overweight (including obesity) with WtHr >0.5. The caretaker’s perception that the child’s food intake is inferior or adequate for age decreases, in mean, 92% the risk of overweight (including obesity) with WtHr >0.5. 3.2.8. Physical Activity and Sleeping Habits Dimension For physical activity and hours of sleep, the variables included in the final model were the child’s participation in programmed sports activity, the number of sedentary hours in a weekday, the number of sedentary hours on a weekend day, and the total number of sedentary hours in a week. The child’s participation in programmed sports activity decreases, in mean, 81% the risk of overweight (including obesity) with WtHr >0.5. Nutrients 2020,12, 3288 16 of 18 18. Manios, Y.; Birbilis, M.; Moschonis, G.; Birbilis, G.; Mougios, V.; Lionis, C.; Chrousos, G.P. 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