# **Monitoring and Promoting Physical Activity, Physical Fitness and Motor Competence in Children**

Edited by Georgian Badicu, Ana Filipa Silva and Hugo Miguel Borges Sarmento Printed Edition of the Special Issue Published in *Children*

www.mdpi.com/journal/children

## **Monitoring and Promoting Physical Activity, Physical Fitness and Motor Competence in Children**

## **Monitoring and Promoting Physical Activity, Physical Fitness and Motor Competence in Children**

Editors

**Georgian Badicu Ana Filipa Silva Hugo Miguel Borges Sarmento**

MDPI ' Basel ' Beijing ' Wuhan ' Barcelona ' Belgrade ' Manchester ' Tokyo ' Cluj ' Tianjin

*Editors* Georgian Badicu Physical Education and Special Motricity Transilvania University of Brasov Brasov Romania

Ana Filipa Silva Instituto Politecnico de Viana ´ do Castelo Escola Superior Desporto e Lazer Viana do Castelo Portugal

Hugo Miguel Borges Sarmento Faculty of Sport Sciences and Physical Education University of Coimbra Coimbra Portugal

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Children* (ISSN 2227-9067) (available at: www.mdpi.com/journal/children/special issues/Physical Motor Competence Children).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-6651-1 (Hbk) ISBN 978-3-0365-6650-4 (PDF)**

Cover image courtesy of Georgian Badicu

© 2023 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

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 He has published about 69 papers in JCR journals and is the author or co-author of 51 papers published by MDPI. His main interests are physical activity, fitness, education, obesity, well-being, recreation, football, public health, quality of life, sports activities, physical education, didactics of physical education, and sports.

#### **Ana Filipa Silva**

Ana Filipa Silva currently works as Assistant Professor in the Polytechnic Institute of Viana do Castelo (IPVC) in Melgaco, Portugal and as a researcher in Research Centre in Sports Sciences, Health Sciences and Human Development (CIDESD, Portugal). She has a European Ph.D. in Sport Sciences, Sports Training in Faculty of Sport, University of Porto, Porto, Portugal (2017, Portugal). Among others, the main publications are within the following topics: (i) motor control, (ii) youth sports performance, (iii) decision making in sports, and (iv) cognitive performance in sports. She is currently guest associate editor for *Frontiers* in Physiology, Frontiers in Sport, and Active Living and Human Movement. She has been guest editor of many Special Issues: (i) Training and Performance in Youth Sports at International Journal Environmental Research and Public Health; (ii) In Search of Individually Optimal Movement Solutions in Sport: Learning between Stability and Flexibility at Frontiers in Physiology and Frontiers in Sport and Active Living and Human Movement; (iii) Decision Making in Youth Sport Flexibility at Frontiers in Physiology and Frontiers in Sport and Active Living and Human Movement; and (iv) Children's Exercise Physiology, Volume II at Frontiers in Physiology and Frontiers in Sport and Active Living and Human Movement.

#### **Hugo Miguel Borges Sarmento**

Hugo Sarmento is professor at Faculty of Sport Sciences and Physical Education, University of Coimbra, Portugal. His main interests are: performance analysis and talent indentification and development; sport psychology; and physical activity and health.

## *Article* **Body Image Perception in High School Students: The Relationship with Gender, Weight Status, and Physical Activity**

**Stefania Toselli 1 , Luciana Zaccagni 2 , Natascia Rinaldo 2 , Mario Mauro 1,\* , Alessia Grigoletto 3,\* , Pasqualino Maietta Latessa 1 and Sofia Marini 1**

**Abstract:** Body image perception includes body size assessment, body desirability estimation, and perceptions concerning one's own body shape and size. Adolescence is a period of intense and prompt physical transformation, which changes the perception of one's body. This represents a critical period for the development of body image. Therefore, the present cross-sectional study aimed to evaluate body image perception and investigate the relationships between it, weight status, sex, and physical activity in a sample of high school students living in Italy. General demographic information and details about physical activity were collected. Body image perception was measured with a body silhouette and two indexes were calculated: the FID (Feel minus Ideal Discrepancy) to evaluate the discrepancy between the perceived current figure and the ideal figure; and the FAI (Feel weight status minus Actual weight status Inconsistency) to observe improper perception of weight status. In addition, body shape concerns were evaluated with the Body Shape Questionnaire (BSQ), in which participants reported the frequency of experiencing negative thoughts about their body shape in the last four weeks. Two hundred and four students were included in the study (155 = female, mean age = 17.13 ± 1.70; 49 = male, mean age = 17.25 ± 1.69). Females felt more concerned about body shape than males (χ 2 = 11.347, *p* = 0.001). Distinctions emerged in terms of body mass index, the scores of Feel minus Ideal Discrepancy (FID), Feel weight status minus Actual weight status Inconsistency (FAI), the Body Shape Questionnaire (BSQ), and of the silhouette mean comparisons due to sex, weight status, and PA interaction effects (*p* < 0.001). Additionally, 94% of the BSQ variability could be explained by sex, weight status, and PA. Although no direct effects were observed on body image perception, healthy habit promotion, such as physical activity, could positively affect adolescent lives.

**Keywords:** body image; self-perception; physical activity; body shape questionnaire

#### **1. Introduction**

Body image is a multidimensional construct, which therefore encompasses perceptual, cognitive, and affective elements concerning one's own body and the bodies of others [1–3]. Body image perception includes body size assessment (how a person perceives his or her body), body attractiveness estimation (what is the type of body that a person considers most attractive), and perceptions related to one's own body shape and size [4]. Thus, interactions among physiological, cognitive, and sociocultural factors contribute to body image development [5]. During life, people are subjected to physical and psychological changes that affect the perception of their image. Thus, body image is not a static concept but rather a dynamic characteristic influenced by the perceptions of the individual about him- or herself [6]. Adolescence represents a period of intense and prompt physical changes and it therefore also changes the perception of one's body characteristics, and it can be a

**Citation:** Toselli, S.; Zaccagni, L.; Rinaldo, N.; Mauro, M.; Grigoletto, A.; Maietta Latessa, P.; Marini, S. Body Image Perception in High School Students: The Relationship with Gender, Weight Status, and Physical Activity. *Children* **2023**, *10*, 137. https://doi.org/10.3390/ children10010137

Academic Editors: Ferdinand Salonna and Tonia Vassilakou

Received: 24 October 2022 Revised: 26 December 2022 Accepted: 6 January 2023 Published: 10 January 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

critical time for the development of body image [2,6–8]. Acceptance of and adaptation to these changes are essential for adolescents' feelings about their body image [9]. However, these transformations can represent a risk factor in adolescents' body image perception [6], thereby causing a distorted image of one's body. Moreover, distorted perception of one's body image can result from inaccurate evaluations of one's body, undesirable effects regarding one's body, miscognition of body-related stimuli, and specific body-related behaviours such as checking of body weight or avoidance of the consideration of body [10]. Worse still, it can result in unhealthy behaviours and adverse psychological outcomes such as an unhealthy diet, low self-esteem, and psychological disturbances [1,11,12].

There are several factors that influence the risk of body image disturbance during adolescence. The body image perception of adolescents has been reported to be associated with bio-social factors such as sex, age, socioeconomic status, and opportunities for health education [13,14]; environmental factors such as media exposure, peers, and school [1,14–17]; and behavioural factors such as weight control behaviour, physical activity, sexual behaviour, and eating patterns [11,18,19].

Regarding biological factors, the influence of sex on body image is well-known; body image dissatisfaction is more frequent among girls, but it is also present in thin boys [20,21].

Behavioural factors play a fundamental role, as they can be modified. Adolescents are responsive to the acquisition of habits and routines that are often influenced by their environment, which can be significant for their future life and of great importance in promoting healthy lifestyles during this time [22]. Among these lifestyle factors, proper nutrition and regular physical activity are of particular importance. Unfortunately, the data in the literature suggest a decline of optimal eating patterns and a decrease in physical activity in adolescents, who frequently assume sedentary behaviour [23].

Furthermore, adolescents are influenced by markets and advertising, which contribute to making their diet unbalanced and excessively caloric; rich in refined fats and sugars; poor in fruit, vegetables, legumes, and fish; and in many cases omitting breakfast [23]. Thus, in 2018, an international study conducted by the WHO found that one in five adolescent (21%) respondents was overweight or obese [24]. In Italy, in 11–15-year-old adolescents, the prevalence of overweight was 16.6% and obesity was 3.2%; excess weight decreased slightly with age and was greater in males (HBSC 2018: https://www.epicentro.iss.it/ hbsc/indagine-2018, accessed on 22 February 2022). Distorted perception of weight status is quite prevalent in adolescents [25,26]. High-school-aged students tend to overrate height and underreport weight, thereby decreasing the prevalence of overweight and obesity estimates with lower average self-reported BMI [27]. Proper perception of weight is the key to determining the nutritional habits and weight management of adolescents. Indeed, it has been demonstrated that many overweight students are unlikely to participate in weight control practices [26,28–31].

As reported above, sports participation and physical activity (PA) can affect body image in adolescents [32–35]. In general, female and male adolescents who play sports present a lower body dissatisfaction; this can be connected to the role of sports participation in contributing to lower adiposity levels and, consequently, to lower body dissatisfaction in the adolescent population [36–38]. However, the associations between body image and sports participation in adolescents are not consistent, especially according to sex. Tebar et al. [39] reported that sports participation was associated with low body dissatisfaction in boys but not in girls. In sports contexts, females and males are not encouraged in the same way due to sex-stereotyped beliefs [40]. This appears in sex disparities in sports participation: in Italy, scant physical activity was higher among females and older adolescents [41]. Morano et al. [40] reported that 41% of girls and 59% of boys 11–19 years old practice sports. Despite the large importance of sports participation, the sport experience of adolescents has not been widely investigated, and the studies on body image concerns in adolescents involved in sports are rare and inconclusive.

Given this information, the main goal of the present study was to evaluate body image perception and to explore the relationships among it, weight status, sex, and physical

activity in a sample of high school students living in Italy. Our hypothesis was that there would be a significant interaction effect between weight status, sex, and physical activity on body image perception in the sample. Specifically, we assumed that overweight/obese adolescents with a lower level of reported physical activity would have the lowest body perception and that these effects would be especially evident in females.

#### **2. Materials and Methods**

A cross-sectional study design was chosen, and data were collected and analysed in a sample of 204 adolescents attending secondary school in Piemonte (northern Italy). All adolescents attending the school could answer the questionnaires. The inclusion criteria were attending the school, completing all the parts of the questionnaires, having parental written consent, and agreeing to participate. The exclusion criteria were not having parental written consent and not completing all the parts of the questionnaires. So, we analysed only the fully completed questionnaires. The study was approved by the Bioethics Committee of the University of Bologna (approval no. 25027).

#### *2.1. Outcome Assessment*

General information about demographic variables (e.g., sex and age) and physical activity participation, both structured sport (e.g., volleyball with a team and coach) and independent physical activity (e.g., walking, yoga, etc.), was collected by an ad hoc questionnaire that was created for this purpose. The sports participation frequency of each subject was determined by the number of days per week of physical activity during a typical week as declared by the subject. Concerning anthropometric evaluation, participants' self-reported height and weight were converted to meters and kilograms to calculate body mass index (BMI).

#### *2.2. Body Image Perception*

The perception of body image was evaluated using the validated Body Silhouette Chart [42,43]. Nine male or female silhouettes, ordered in morphology from emaciation to obesity, were shown to the students, who were asked to select the one which they most desired (ideal figure) as well as the silhouette which they believed was most similar to their own (perceived current figure) [42]. The silhouette series was divided into underweight, normal weight, overweight, and obese [42]. The discrepancy between the perceived current figure and the ideal figure represented the degree of body image dissatisfaction (FID or Feel minus Ideal Discrepancy) [44]. By subtracting the number of the figure selected by the students as the ideal figure from the one selected as their perceived current figure, it was possible to calculate the FID index. A negative FID score indicates that the perceived current figure was thinner than the ideal figure, and a positive score indicates that the perceived current figure was bigger than the ideal figure. An FID score of 0 indicates that the student has chosen the same figure as perceived current and perceived ideal, showing no discrepancy.

The FAI (Feel weight status minus Actual weight status Inconsistency) index was used to evaluated the improper perception of weight status [45]. FAI inconsistency was calculated by subtracting the conventional code assigned to the real weight status of the participant from the code of her or his perceived weight status, which assigns a specific weight status category to each silhouette. The conventional code of real weight status of the participant and the perceived weight were classified according to the BMI assessed by Cole cut-off values by sex and age [46,47] (1 = underweight; 2 = normal weight; 3 = overweight; and 4 = obese). Instead, the classification recommended by Sànchez-Villegas was used for the silhouettes obtained [42]. In this classification method, silhouettes one, two, and three correspond to underweight (=1), silhouettes four and five correspond to normal weight (=2), silhouettes six and seven correspond to overweight (=3), and silhouettes eight and nine correspond to obesity (=4). A positive FAI score indicates that weight status is

overestimated, a negative score indicates that weight status is underestimated, and a score of zero indicates no inconsistency in weight status perception.

#### *2.3. Body Shape Concerns*

Body shape concerns were assessed with a 14-item version [48] of the Body Shape Questionnaire (BSQ) [49], which was validated in Italian by [50]. The BSQ utilizes a six-point Likert scale ranging from one to six (one = never, six = always) to assess how frequently in the past four weeks a participant reported experiencing negative thoughts or feelings about their body shape (e.g., "Have you felt ashamed of your body?"). In addition, the questionnaire investigated if they had tried to control or lose weight with the same six-point Likert scale. Scores from all items are added to get a total score. Higher scores reflected greater body shape concerns. The total score was calculated on the sum of all the values was multiplied by 34/14 and subsequently related to the specific thresholds that refer to the complete form of the questionnaire: a score below 80 indicated "no concern", a score between 80 and 110 indicated a "slight concern", a score between 111 and 140 indicated a "moderate concern", and a score above 140 indicated a "marked concern".

#### *2.4. Statistical Analysis*

The statistical analysis was performed with STATA® software, version 17 (Publisher: StataCorp. 2021. Stata Statistical Software: Release 17. College Station, TX, USA, StataCorp LP). Descriptive statistics were calculated and reported such as mean ± standard deviation (SD) for quantitative variables (age, height, weight, BMI, silhouette think to look, silhouette want to be, FID, FAI, and BSQ) and frequency (number of observations) and percentage (%) for qualitative variables (weight status, weight control, body shape concern, and trying to lose weight). The chi-squared test (χ 2 ) was used to assess discrepancies in frequencies among categorial and binomial variables. The Shapiro–Wilk test was performed to check the variables' normal distribution. Due to the presence of predictors with different data levels, an analysis of covariances and the interaction effect between sex, weight status, and physical activity frequency were tested. A stepwise procedure with backward selection was carried out to perform the multiple regression analysis and to assess possible predictors of FID, FAI, and BSQ. Stepwise estimation included only predictors with a significance level for removal from the model of at most *p* < 0.05.

#### **3. Results**

A total of 204 students, consisting of 155 females (mean age = 17.13 ± 1.70) and 49 males (mean age = 17.25 ± 1.69), were included in the final analysis (Figure 1). Considering weight status, normal weight was the most represented category in both females and males.

Table 1 shows no statistically significant differences in frequencies between sexes except for body shape concern, which displayed more trouble in females. Additionally, male adolescents were less worried about weight loss.

Table 2 shows the mean comparisons and effects of the interaction between sex, weight status, and frequencies of PA. Sex differences were significant only for the desired figure and body shape questionnaire, while all variables related to body perception were influenced by weight status. In the underweight category, FID and FAI scores showed negative discrepancies, indicating a misperception tending to underestimation. Regarding effects of PA frequency, it did not show significant outcomes. However, interaction effects of sex, weight status, and PA frequency were relevant for every variable.

To assess possible predictors of body image dissatisfaction (assessed by FID score), inconsistency of weight status (assessed by FAI score), and body shape concern (assessed by BSQ score), three backward multiple regressions were performed. Table 3 shows the predictive model results. Regarding FID, weight and all the categories of PA frequency were positive predictors, whereas the conditions of being younger, male, and having tried to lose weight sometimes, were found to be associated with better body image satisfaction. Although the whole model explained 60% of the FID variance, adolescent weight seemed to be the most influential variable (*η* 2 = 0.417).

**Figure 1.** Sample flowchart.

**Table 1.** Frequencies of weight status, body shape concern, weight control and trying to lose weight by sex.

Note: F, female; M, male; χ2, chi-squared test; *p*, *p*-value; %, percentage; N, number of observations.

(11.24)

(0.70)

(11.24)

(0.86)

(0.75)

(0.73)

(0.51)

(11.24)

(29.52)

89.13 (29.52)

3.50 (1.08)

2.89 (1.01)

0.62 (1.28)

0.12 (0.42)

89.13 (29.52)

BSQ 82.17 (27.78)

SWB 3.51

BSQ 82.17 (27.78)

SWB 3.51

BSQ 82.17 (27.78)

SWB 3.51

BSQ 82.17 (27.78)

BSQ 82.17 (27.78)

BSQ 82.17 (27.78)

BSQ 82.17 (27.78)

FAI 0.14 (0.50)

FAI 0.14 (0.50)

FID 0.76 (1.56)

FAI 0.14 (0.50)

FAI 0.14 (0.50)

FID 0.76 (1.56)

FAI 0.14 (0.50)

STL 4.27 (1.66)

FID 0.76 (1.56)

FAI 0.14 (0.50)

STL 4.27 (1.66)

FID 0.76 (1.56)

20.99 (1.79)

(35.20)

112.2 (35.20)

5.78 (1.01)

3.64 (0.96)

1.93 (0.91)

0.38 (0.49)

112.2 (35.20)

(1.02)2.97

(4.82)22.52

(1.02)2.97

(1.02)2.97

132.76

101.03

1.08

101.03

1.08

101.03

3.61

1.08

101.03

2.56

1.08

101.03

101.03

132.76

101.03

0.28

0.14

0.14

0.14

0.14

0.14

4.06

6.17

0.14

293.0 7

31.71 (2.58)

to be the most influential variable (*η*2 = 0.417).

26.58 (1.85)

BSQ 82.17 (27.78)

BMI 23.75 (4.82)

BSQ 82.17 (27.78)

STL 4.27 (1.66)

SWB 3.51 (1.02)

FID 0.76 (1.56)

FAI 0.14 (0.50)

BSQ 82.17 (27.78) 101.03

4.06

2.97

1.08

0.14

101.03

**FID (N = 202)**

**FID (N = 202)**

**FID (N = 202)**

(34.55) 12.10 ƚ 71.37

(3.95) 3.23 0.07 17.46

(34.55) 12.10 ƚ 71.37

(1.55) 0.64 0.42 2.50

(1.01) 10.38 § 2.72

(1.41) 1.92 0.17 −0.22

(0.51) 0.01 0.93 −0.50

(34.55) 12.10 ƚ 71.37

*Children* **2023**, *10*, x FOR PEER REVIEW 6 of 14

*Children* **2023**, *10*, x FOR PEER REVIEW 6 of 14

figure and body shape questionnaire, while all variables related to body perception were influenced by weight status. In the underweight category, FID and FAI scores showed negative discrepancies, indicating a misperception tending to underestimation. Regarding effects of PA frequency, it did not show significant outcomes. However, interaction

Body Shape Questionnaire; SD, standard deviation; WS, weight status; PA, physical activity; F, sta-

(0.73)

Weight 0.795 0.007 11.75 <0.001 0.417 Age −0.12 0.047 −2.58 0.01 0.033 PA frequency 0.092

≤1 d·w−1 0.878 0.208 4.22 <0.001

minus Ideal Discrepancy; FAI, Feel weight status minus Actual weight status Inconsistency; BSQ,

89.13

2.89

112.2

3.11

(0.91)

132.76

3.61

2.73

101.03

22.52

*Children* **2023**, *10*, x FOR PEER REVIEW 6 of 14

*Children* **2023**, *10*, x FOR PEER REVIEW 6 of 14

**Table 2.** Mean comparisons and interaction effects between sex, weight status, and physical activity frequency.  **Variable Sex Weight Status Physical Activity Frequency Interaction Effect** frequency. **Variable Sex Weight Status Physical Activity Frequency Interaction Effect Table 2.** Mean comparisons and interaction effects between sex, weight status, and physical activity frequency. **Table 2.** Mean comparisons and interaction effects between sex, weight status, and physical activity frequency. effects of sex, weight status, and PA frequency were relevant for every variable. **Table 2.** Mean comparisons and interaction effects between sex, weight status, and physical activity effects of sex, weight status, and PA frequency were relevant for every variable. **Table 2.** Mean comparisons and interaction effects between sex, weight status, and physical activity ing effects of PA frequency, it did not show significant outcomes. However, interaction effects of sex, weight status, and PA frequency were relevant for every variable. ing effects of PA frequency, it did not show significant outcomes. However, interaction effects of sex, weight status, and PA frequency were relevant for every variable. negative discrepancies, indicating a misperception tending to underestimation. Regarding effects of PA frequency, it did not show significant outcomes. However, interaction negative discrepancies, indicating a misperception tending to underestimation. Regarding effects of PA frequency, it did not show significant outcomes. However, interaction figure and body shape questionnaire, while all variables related to body perception were influenced by weight status. In the underweight category, FID and FAI scores showed negative discrepancies, indicating a misperception tending to underestimation. Regardfigure and body shape questionnaire, while all variables related to body perception were influenced by weight status. In the underweight category, FID and FAI scores showed negative discrepancies, indicating a misperception tending to underestimation. Regardfigure and body shape questionnaire, while all variables related to body perception were influenced by weight status. In the underweight category, FID and FAI scores showed figure and body shape questionnaire, while all variables related to body perception were influenced by weight status. In the underweight category, FID and FAI scores showed figure and body shape questionnaire, while all variables related to body perception were influenced by weight status. In the underweight category, FID and FAI scores showed *Children* **2023**, *10*, x FOR PEER REVIEW 6 of 14

*Children* **2023**, *10*, x FOR PEER REVIEW 6 of 14

*Children* **2023**, *10*, x FOR PEER REVIEW 6 of 14

*Children* **2023**, *10*, x FOR PEER REVIEW 6 of 14

figure and body shape questionnaire, while all variables related to body perception were influenced by weight status. In the underweight category, FID and FAI scores showed negative discrepancies, indicating a misperception tending to underestimation. Regarding effects of PA frequency, it did not show significant outcomes.  To assess possible predictors of body image dissatisfaction (assessed by FID score), inconsistency of weight status (assessed by FAI score), and body shape concern (assessed by BSQ score), three backward multiple regressions were performed. Table 3 shows the predictive model results. Regarding FID, weight and all the categories of PA frequency tistic test of Snedecor–Fisher; d, days; w, week; *p*, *p*-value; adj-R2, adjusted R-squared; η2, etasquared effect size; #, interaction; §, *p* < 0.01; ƚ, *p* < 0.001. To assess possible predictors of body image dissatisfaction (assessed by FID score), inconsistency of weight status (assessed by FAI score), and body shape concern (assessed by BSQ score), three backward multiple regressions were performed. Table 3 shows the predictive model results. Regarding FID, weight and all the categories of PA frequency Body Shape Questionnaire; SD, standard deviation; WS, weight status; PA, physical activity; F, statistic test of Snedecor–Fisher; d, days; w, week; *p*, *p*-value; adj-R2, adjusted R-squared; η2, etasquared effect size; #, interaction; §, *p* < 0.01; ƚ, *p* < 0.001. To assess possible predictors of body image dissatisfaction (assessed by FID score), inconsistency of weight status (assessed by FAI score), and body shape concern (assessed by BSQ score), three backward multiple regressions were performed. Table 3 shows the Body Shape Questionnaire; SD, standard deviation; WS, weight status; PA, physical activity; F, statistic test of Snedecor–Fisher; d, days; w, week; *p*, *p*-value; adj-R2, adjusted R-squared; η2, etasquared effect size; #, interaction; §, *p* < 0.01; ƚ, *p* < 0.001. To assess possible predictors of body image dissatisfaction (assessed by FID score), inconsistency of weight status (assessed by FAI score), and body shape concern (assessed by BSQ score), three backward multiple regressions were performed. Table 3 shows the Body Shape Questionnaire; SD, standard deviation; WS, weight status; PA, physical activity; F, statistic test of Snedecor–Fisher; d, days; w, week; *p*, *p*-value; adj-R2, adjusted R-squared; η2, etasquared effect size; #, interaction; §, *p* < 0.01; ƚ, *p* < 0.001. To assess possible predictors of body image dissatisfaction (assessed by FID score), inconsistency of weight status (assessed by FAI score), and body shape concern (assessed by BSQ score), three backward multiple regressions were performed. Table 3 shows the Note: BMI, body mass index; STL, silhouette think to look; SWB, silhouette want to be; FID, Feel minus Ideal Discrepancy; FAI, Feel weight status minus Actual weight status Inconsistency; BSQ, Body Shape Questionnaire; SD, standard deviation; WS, weight status; PA, physical activity; F, statistic test of Snedecor–Fisher; d, days; w, week; *p*, *p*-value; adj-R2, adjusted R-squared; η2, etasquared effect size; #, interaction; §, *p* < 0.01; ƚ, *p* < 0.001. To assess possible predictors of body image dissatisfaction (assessed by FID score), inconsistency of weight status (assessed by FAI score), and body shape concern (assessed Note: BMI, body mass index; STL, silhouette think to look; SWB, silhouette want to be; FID, Feel minus Ideal Discrepancy; FAI, Feel weight status minus Actual weight status Inconsistency; BSQ, Body Shape Questionnaire; SD, standard deviation; WS, weight status; PA, physical activity; F, statistic test of Snedecor–Fisher; d, days; w, week; *p*, *p*-value; adj-R2, adjusted R-squared; η2, etasquared effect size; #, interaction; §, *p* < 0.01; ƚ, *p* < 0.001. To assess possible predictors of body image dissatisfaction (assessed by FID score), inconsistency of weight status (assessed by FAI score), and body shape concern (assessed (34.55) 12.10 ƚ 71.37 (11.24) (29.52) (35.20) (32.86) 19.42 ƚ 99.05 (35.84) (31.17) (29.53) (33.49) 0.71 0.55 3.54 ƚ 0.24 0.33 Note: BMI, body mass index; STL, silhouette think to look; SWB, silhouette want to be; FID, Feel minus Ideal Discrepancy; FAI, Feel weight status minus Actual weight status Inconsistency; BSQ, Body Shape Questionnaire; SD, standard deviation; WS, weight status; PA, physical activity; F, statistic test of Snedecor–Fisher; d, days; w, week; *p*, *p*-value; adj-R2, adjusted R-squared; η2, etasquared effect size; #, interaction; §, *p* < 0.01; ƚ, *p* < 0.001. To assess possible predictors of body image dissatisfaction (assessed by FID score), BSQ 82.17 (27.78) (34.55) 12.10 ƚ 71.37 (11.24) 89.13 (29.52)112.2 (35.20) (32.86) 19.42 ƚ 99.05 (35.84) (31.17) Note: BMI, body mass index; STL, silhouette think to look; SWB, silhouette want to be; FID, Feel minus Ideal Discrepancy; FAI, Feel weight status minus Actual weight status Inconsistency; BSQ, Body Shape Questionnaire; SD, standard deviation; WS, weight status; PA, physical activity; F, statistic test of Snedecor–Fisher; d, days; w, week; *p*, *p*-value; adj-R2, adjusted R-squared; η2, etasquared effect size; #, interaction; §, *p* < 0.01; ƚ, *p* < 0.001. To assess possible predictors of body image dissatisfaction (assessed by FID score), (0.51) (0.42) (0.49) (0.42) (0.33) (0.54) 0.04 0.99 2.49 ƚ 0.16 0.26 (34.55) 12.10 ƚ 71.37 (11.24) 89.13 (29.52) 112.2 (35.20) 132.76 (32.86) 19.42 ƚ 99.05 (35.84) 95.17 (31.17) 84.73 (29.53) 92.66 (33.49) 0.71 0.55 3.54 ƚ 0.24 0.33 Note: BMI, body mass index; STL, silhouette think to look; SWB, silhouette want to be; FID, Feel minus Ideal Discrepancy; FAI, Feel weight status minus Actual weight status Inconsistency; BSQ, Body Shape Questionnaire; SD, standard deviation; WS, weight status; PA, physical activity; F, statistic test of Snedecor–Fisher; d, days; w, week; *p*, *p*-value; adj-R2, adjusted R-squared; η2, etasquared effect size; #, interaction; §, *p* < 0.01; ƚ, *p* < 0.001. (0.50) (0.51) (0.49) (0.42) BSQ 82.17 (27.78) 101.03 (34.55) 12.10 ƚ 71.37 (11.24) 89.13 (29.52)112.2 (35.20) 132.76 (32.86) 19.42 ƚ 99.05 (35.84) 95.17 (31.17) Note: BMI, body mass index; STL, silhouette think to look; SWB, silhouette want to be; FID, Feel minus Ideal Discrepancy; FAI, Feel weight status minus Actual weight status Inconsistency; BSQ, Body Shape Questionnaire; SD, standard deviation; WS, weight status; PA, physical activity; F, statistic test of Snedecor–Fisher; d, days; w, week; *p*, *p*-value; adj-R2, adjusted R-squared; η2, etasquared effect size; #, interaction; §, *p* < 0.01; ƚ, *p* < 0.001. (0.51) 0.01 0.93 −0.50 (0.51) 0.12 (0.42) 0.38 (0.49) 0.28 (0.57) 16.40 ƚ 0.14 (0.55) 0.12 (0.42) 0.11 (0.33) 0.15 (0.54) 0.04 0.99 2.49 ƚ 0.16 0.26 (34.55) 12.10 ƚ 71.37 (11.24) 89.13 (29.52) 112.2 (35.20) 132.76 (32.86) 19.42 ƚ 99.05 (35.84) 95.17 (31.17) 84.73 (29.53) 92.66 (33.49) 0.71 0.55 3.54 ƚ 0.24 0.33 Note: BMI, body mass index; STL, silhouette think to look; SWB, silhouette want to be; FID, Feel minus Ideal Discrepancy; FAI, Feel weight status minus Actual weight status Inconsistency; BSQ, Body Shape Questionnaire; SD, standard deviation; WS, weight status; PA, physical activity; F, statistic test of Snedecor–Fisher; d, days; w, week; *p*, *p*-value; adj-R2, adjusted R-squared; η2, eta-FAI 0.14 (0.50) 0.14 (0.51) 0.01 0.93 −0.50 (0.51) 0.12 (0.42)0.38 (0.49) 0.28 (0.57) 16.40 ƚ 0.14 (0.55) 0.12 (0.42) BSQ 82.17 (27.78) 101.03 (34.55) 12.10 ƚ 71.37 (11.24) 89.13 (29.52)112.2 (35.20) 132.76 (32.86) 19.42 ƚ 99.05 (35.84) 95.17 (31.17) Note: BMI, body mass index; STL, silhouette think to look; SWB, silhouette want to be; FID, Feel minus Ideal Discrepancy; FAI, Feel weight status minus Actual weight status Inconsistency; BSQ, Body Shape Questionnaire; SD, standard deviation; WS, weight status; PA, physical activity; F, statistic test of Snedecor–Fisher; d, days; w, week; *p*, *p*-value; adj-R2, adjusted R-squared; η2, eta-(0.96) 1.300.282.30ƚ 0.140.24FID 0.76 (1.56) (1.41) 1.92 0.17 −0.22 (0.73) 0.62 (1.28) 1.93 (0.91) 2.56 (1.76) 28.41 ƚ 1.00 (1.41) 0.97 (1.61) 1.11 (1.54) 1.08 (1.29) 0.05 0.99 4.02 ƚ 0.27 0.36 (0.51) 0.01 0.93 −0.50 (0.51) 0.12 (0.42) 0.38 (0.49) 0.28 (0.57) 16.40 ƚ 0.14 (0.55) 0.12 (0.42) 0.11 (0.33) 0.15 (0.54) 0.04 0.99 2.49 ƚ 0.16 0.26 (34.55) 12.10 ƚ 71.37 (11.24) 89.13 (29.52) 112.2 (35.20) 132.76 (32.86) 19.42 ƚ 99.05 (35.84) 95.17 (31.17) 84.73 (29.53) 92.66 (33.49) 0.71 0.55 3.54ƚ 0.24 0.33 Note: BMI, body mass index; STL, silhouette think to look; SWB, silhouette want to be; FID, Feel minus Ideal Discrepancy; FAI, Feel weight status minus Actual weight status Inconsistency; BSQ, Body Shape Questionnaire; SD, standard deviation; WS, weight status; PA, physical activity; F, sta-(0.96) 1.300.282.30ƚ 0.140.24FID 0.76 (1.56) 1.08 (1.41) 1.92 0.17 −0.22 (0.73) 0.62 (1.28)1.93 (0.91) 2.56 (1.76) 28.41 ƚ 1.00 (1.41) 0.97 (1.61) FAI 0.14 (0.50) 0.14 (0.51) 0.01 0.93 −0.50 (0.51) 0.12 (0.42)0.38 (0.49) 0.28 (0.57) 16.40 ƚ 0.14 (0.55) 0.12 (0.42) BSQ 82.17 (27.78) 101.03 (34.55) 12.10 ƚ 71.37 (11.24) 89.13 (29.52)112.2 (35.20) 132.76 (32.86) 19.42 ƚ 99.05 (35.84) 95.17 (31.17) Note: BMI, body mass index; STL, silhouette think to look; SWB, silhouette want to be; FID, Feel minus Ideal Discrepancy; FAI, Feel weight status minus Actual weight status Inconsistency; BSQ, Body Shape Questionnaire; SD, standard deviation; WS, weight status; PA, physical activity; F, sta-(0.98) 9.08 ƚ 3.17 (1.03) 3.14 (1.10) (0.78) (0.96) 1.30 0.28 2.30 ƚ 0.14 0.24 (1.76) 28.41 ƚ 1.00 (1.41) 0.97 (1.61) 1.11 (1.54) 1.08 (1.29) 0.05 0.99 4.02 ƚ 0.27 0.36 (0.57) 16.40 ƚ 0.14 (0.55) 0.12 (0.42) 0.11 (0.33) 0.15 (0.54) 0.04 0.99 2.49 ƚ 0.16 0.26 (32.86) 19.42 ƚ 99.05 (35.84) 95.17 (31.17) 84.73 (29.53) 92.66 (33.49) 0.71 0.55 3.54 ƚ 0.24 0.33 Note: BMI, body mass index; STL, silhouette think to look; SWB, silhouette want to be; FID, Feel (1.01) 10.38 §2.72 (0.75) (1.01)3.64 (0.96) (0.98) 9.08 ƚ 3.17 (1.03) 3.14 (1.10)3.11 (0.78)2.73 (0.96) 1.30 0.28 2.30 ƚ 0.14 0.24 (1.41) 1.92 0.17 −0.22 (0.73) 0.62 (1.28) 1.93 (0.91) 2.56 (1.76) 28.41 ƚ 1.00 (1.41) 0.97 (1.61) 1.11 (1.54) 1.08 (1.29) 0.05 0.99 4.02 ƚ 0.27 0.36 (0.51) 0.01 0.93 −0.50 (0.51) 0.12 (0.42) 0.38 (0.49) 0.28 (0.57) 16.40 ƚ 0.14 (0.55) 0.12 (0.42) 0.11 (0.33) 0.15 (0.54) 0.04 0.99 2.49 ƚ 0.16 0.26 (34.55) 12.10 ƚ 71.37 (11.24) 89.13 (29.52) 112.2 (35.20) 132.76 (32.86) 19.42 ƚ 99.05 (35.84) 95.17 (31.17) 84.73 (29.53) 92.66 (33.49) 0.71 0.55 3.54ƚ 0.24 0.33 Note: BMI, body mass index; STL, silhouette think to look; SWB, silhouette want to be; FID, Feel SWB 3.51 (1.02)2.97 (1.01) 10.38 § 2.72 (0.75) (1.01)3.64 (0.96)3.61 (0.98) 9.08 ƚ 3.17 (1.03) 3.14 (1.10)3.11 FID 0.76 (1.56) 1.08 (1.41) 1.92 0.17 −0.22 (0.73) 0.62 (1.28)1.93 (0.91) 2.56 (1.76) 28.41 ƚ 1.00 (1.41) 0.97 (1.61) FAI 0.14 (0.50) 0.14 (0.51) 0.01 0.93 −0.50 (0.51)0.12 (0.42)0.38 (0.49) 0.28 (0.57) 16.40 ƚ 0.14 (0.55) 0.12 (0.42) BSQ 82.17 (27.78) 101.03 (34.55) 12.10 ƚ 71.37 (11.24) 89.13 (29.52)112.2 (35.20) 132.76 (32.86) 19.42 ƚ 99.05 (35.84) 95.17 (31.17) Note: BMI, body mass index; STL, silhouette think to look; SWB, silhouette want to be; FID, Feel BMI 23.75 (4.82) (3.95) 3.23 0.07 17.46 (0.70) (1.79) (1.85) (2.58) 7 ƚ 22.96 (4.29) STL 4.27 (1.66) 4.06 (1.55) 0.64 0.42 2.50 (0.86) 3.50 (1.08) 5.78 (1.01) 6.17 (1.50) 73.98 ƚ 4.17 (1.63) 4.10 SWB 3.51 (1.02) 2.97 (1.01) 10.38 § 2.72 (0.75) 2.89 (1.01) 3.64 (0.96) 3.61 (0.98) 9.08 ƚ 3.17 (1.03) 3.14 FID 0.76 (1.56) 1.08 (1.41) 1.92 0.17 −0.22 (0.73) 0.62 (1.28) 1.93 (0.91) 2.56 (1.76) 28.41 ƚ 1.00 (1.41) 0.97 FAI 0.14 0.14 (0.51) 0.01 0.93 −0.50 0.12 0.38 0.28 (0.57) 16.40 ƚ 0.14 (0.55) 0.12 Concerning FAI, being underweight, normal weight, and never trying to lose weight appeared as negative predictors of weight status estimation. Contrarily, body weight overestimation was associated with all the categories of PA frequency. The model explained 24% of the variance and had a highly significant R2. Being male and not obese was significantly associated with a lower score of body shape concern. The BSQ score increased as body image dissatisfaction and PA frequency increased. The model explained 94% of the variance.

were positive predictors, whereas the conditions of being younger, male, and having tried to lose weight sometimes, were found to be associated with better body image satisfaction. Although the whole model explained 60% of the FID variance, adolescent weight seemed to be the most influential variable (*η*2 = 0.417). were positive predictors, whereas the conditions of being younger, male, and having tried to lose weight sometimes, were found to be associated with better body image satisfaction. Although the whole model explained 60% of the FID variance, adolescent weight seemed to be the most influential variable (*η*2 = 0.417). predictive model results. Regarding FID, weight and all the categories of PA frequency were positive predictors, whereas the conditions of being younger, male, and having tried to lose weight sometimes, were found to be associated with better body image satisfaction. Although the whole model explained 60% of the FID variance, adolescent weight seemed predictive model results. Regarding FID, weight and all the categories of PA frequency were positive predictors, whereas the conditions of being younger, male, and having tried to lose weight sometimes, were found to be associated with better body image satisfaction. Although the whole model explained 60% of the FID variance, adolescent weight seemed predictive model results. Regarding FID, weight and all the categories of PA frequency were positive predictors, whereas the conditions of being younger, male, and having tried to lose weight sometimes, were found to be associated with better body image satisfaction. Although the whole model explained 60% of the FID variance, adolescent weight seemed by BSQ score), three backward multiple regressions were performed. Table 3 shows the predictive model results. Regarding FID, weight and all the categories of PA frequency were positive predictors, whereas the conditions of being younger, male, and having tried to lose weight sometimes, were found to be associated with better body image satisfaction. by BSQ score), three backward multiple regressions were performed. Table 3 shows the predictive model results. Regarding FID, weight and all the categories of PA frequency were positive predictors, whereas the conditions of being younger, male, and having tried to lose weight sometimes, were found to be associated with better body image satisfaction. inconsistency of weight status (assessed by FAI score), and body shape concern (assessed by BSQ score), three backward multiple regressions were performed. Table 3 shows the predictive model results. Regarding FID, weight and all the categories of PA frequency were positive predictors, whereas the conditions of being younger, male, and having tried inconsistency of weight status (assessed by FAI score), and body shape concern (assessed by BSQ score), three backward multiple regressions were performed. Table 3 shows the predictive model results. Regarding FID, weight and all the categories of PA frequency were positive predictors, whereas the conditions of being younger, male, and having tried To assess possible predictors of body image dissatisfaction (assessed by FID score), inconsistency of weight status (assessed by FAI score), and body shape concern (assessed by BSQ score), three backward multiple regressions were performed. Table 3 shows the predictive model results. Regarding FID, weight and all the categories of PA frequency To assess possible predictors of body image dissatisfaction (assessed by FID score), inconsistency of weight status (assessed by FAI score), and body shape concern (assessed by BSQ score), three backward multiple regressions were performed. Table 3 shows the predictive model results. Regarding FID, weight and all the categories of PA frequency squared effect size; #, interaction; §, *p* < 0.01; ƚ, *p* < 0.001. To assess possible predictors of body image dissatisfaction (assessed by FID score), inconsistency of weight status (assessed by FAI score), and body shape concern (assessed by BSQ score), three backward multiple regressions were performed. Table 3 shows the squared effect size; #, interaction; §, *p* < 0.01; ƚ, *p* < 0.001. To assess possible predictors of body image dissatisfaction (assessed by FID score), inconsistency of weight status (assessed by FAI score), and body shape concern (assessed by BSQ score), three backward multiple regressions were performed. Table 3 shows the tistic test of Snedecor–Fisher; d, days; w, week; *p*, *p*-value; adj-R2, adjusted R-squared; η2, etasquared effect size; #, interaction; §, *p* < 0.01; ƚ, *p* < 0.001. To assess possible predictors of body image dissatisfaction (assessed by FID score), tistic test of Snedecor–Fisher; d, days; w, week; *p*, *p*-value; adj-R2, adjusted R-squared; η2, etasquared effect size; #, interaction; §, *p* < 0.01; ƚ, *p* < 0.001. To assess possible predictors of body image dissatisfaction (assessed by FID score), minus Ideal Discrepancy; FAI, Feel weight status minus Actual weight status Inconsistency; BSQ, Body Shape Questionnaire; SD, standard deviation; WS, weight status; PA, physical activity; F, statistic test of Snedecor–Fisher; d, days; w, week; *p*, *p*-value; adj-R2, adjusted R-squared; η2, etasquared effect size; #, interaction; §, *p* < 0.01; ƚ, *p* < 0.001. minus Ideal Discrepancy; FAI, Feel weight status minus Actual weight status Inconsistency; BSQ, Body Shape Questionnaire; SD, standard deviation; WS, weight status; PA, physical activity; F, statistic test of Snedecor–Fisher; d, days; w, week; *p*, *p*-value; adj-R2, adjusted R-squared; η2, etasquared effect size; #, interaction; §, *p* < 0.01; ƚ, *p* < 0.001. minus Ideal Discrepancy; FAI, Feel weight status minus Actual weight status Inconsistency; BSQ, Body Shape Questionnaire; SD, standard deviation; WS, weight status; PA, physical activity; F, statistic test of Snedecor–Fisher; d, days; w, week; *p*, *p*-value; adj-R2, adjusted R-squared; η2, etasquared effect size; #, interaction; §, *p* < 0.01; ƚ, *p* < 0.001. (0.50) (0.51) (0.42) (0.49) BSQ 82.17 (27.78) 101.03 (34.55) 12.10 ƚ 71.37 (11.24) 89.13 (29.52) 112.2 (35.20) 132.76 (32.86) 19.42 ƚ 99.05 (35.84) (31.17) Note: BMI, body mass index; STL, silhouette think to look; SWB, silhouette want to be; FID, Feel minus Ideal Discrepancy; FAI, Feel weight status minus Actual weight status Inconsistency; BSQ, Finally, Figures 2–4 shows the linear prediction of the BSQ influenced by PA frequency categories and the weight status (A), PA frequency categories, FID values (B), and PA frequency categories and sex (C). Lower BSQ values corresponded to 4–5 days per week of PA in each plot except for overweight subjects (Figures 2–4, green line).

to be the most influential variable (*η*2 = 0.417).

≤1 d·w−1 0.878 0.208 4.22 <0.001

≤1 d·w−1 0.878 0.208 4.22 <0.001

≤1 d·w−1 0.878 0.208 4.22 <0.001

≤1 d·w−1 0.878 0.208 4.22 <0.001

PA frequency 0.092

Weight 0.795 0.007 11.75 <0.001 0.417 Age −0.12 0.047 −2.58 0.01 0.033 PA frequency 0.092

Variable β SE *t p η2* Height −0.633 0.013 −4.89 <0.001 0.11 Weight 0.795 0.007 11.75 <0.001 0.417 Age −0.12 0.047 −2.58 0.01 0.033

to be the most influential variable (*η*2 = 0.417).

≤1 d·w−1 0.878 0.208 4.22 <0.001 ≤1 d·w−1 0.878 0.208 4.22 <0.001 Age −0.12 0.047 −2.58 0.01 0.033 PA frequency 0.092 Age −0.12 0.047 −2.58 0.01 0.033 PA frequency 0.092 Weight 0.795 0.007 11.75 <0.001 0.417 Age −0.12 0.047 −2.58 0.01 0.033 Weight 0.795 0.007 11.75 <0.001 0.417 Age −0.12 0.047 −2.58 0.01 0.033 Height −0.633 0.013 −4.89 <0.001 0.11 Height −0.633 0.013−4.89 0.11 Height −0.633 0.013−4.89 0.11 Model 388.62 9 43.18 35.26 <0.001 0.604 Residual 236.39 193 1.22 **Figure 2.** Linear prediction of BSQ by Weight Status for four Physical Activities frequencies.

Total 625 202 3.09

≤1 d·w−1 0.878 0.208 4.22 <0.001

≤1 d·w−1 0.878 0.208 4.22 <0.001

≤1 d·w−1 0.878 0.208 4.22 <0.001

PA frequency 0.092

Weight 0.795 0.007 11.75 <0.001 0.417 Age −0.12 0.047 −2.58 0.01 0.033 PA frequency 0.092

**Table 3.** Predictors of FID, FAI, and BSQ: multiple regression analysis results.

Note: SS, sum of squares; MS, mean of squares; df, degree of freedom; *F*, Snedecor–Fisher's statistical test; β, regression coefficient; SE, standard error; *t*, student's statistic test *p*, *p*-value; adj-R2 , adjusted R2 ; η 2 , eta-squared.

**Figure 3.** Linear prediction of BSQ by FID for four Physical Activities frequencies.

**Figure 4.** Linear prediction of BSQ by sex for four Physical Activities frequencies.

#### **4. Discussion**

The present study aimed to evaluate body image perception in a sample of high school students living in Italy and to explore the relationships between it, sex, weight status, and physical activity.

In the present study, no gender differences were observed in terms of weight status and weight control. Nonetheless, girls showed higher values of weight concerns.  Admittedly, the absence of differences in weight status was unexpected since in Italy, a significantly higher proportion of males were overweight/obese compared with females [51,52]. These data confirmed that girls in this age range present significant weight concerns, as seen in similar studies [53–55]. It is noteworthy that even though the present study showed a

greater prevalence of weight concern among girls, males also reported it. This result is of particular interest, and it cannot be neglected given the established sex-specific patterns, such as muscularity-oriented disordered eating [54,56]. Relatedly, a longitudinal study by Bucchianeri et al. [57] showed that in adolescence, body dissatisfaction increased with time in both sexes, but the levels of body dissatisfaction were remarkably higher in girls. Generally, body image dissatisfaction and weight control behaviour are greater issues for girls compared with males [55]. In our study, FID was higher in females, although it was not a significant difference between the sexes. Girls were found to be more inclined than boys to emphasize the aesthetic values of their bodies rather than functional ones. Moreover, females reported more dissatisfaction with both values than boys [58]. Consistent with this, even in childhood, girls seemed to be more conscious about how their body weight affects their appearance compared with boys [59]. The difference between real and ideal silhouettes with a higher incidence in girls may result in a differential diagnosis of body dysmorphic disorder, since it is more prevalent in women, beginning at the age of between 15 and 30 years [55]. The beauty standard currently imposed by media that values thinness reinforces this condition, which may lead to the adoption of restrictive diets regardless of the real need and may contribute to the occurrence and maintenance of low weight as well as the compromise of eating habits [60] and the presence of mental disorders [61]. For this purpose, it is noteworthy that even though the weight control outcome did not show significant differences between sexes, the percentage of individuals who reported putting attention on weight control is high in both sexes (76.77% in females and 69.39% in males), although a higher percentage of females declared that they tried to lose weight often or always compared to males (38.06% vs. 16.66%). Undoubtedly, the role of gender in body image has been changing; a study by Dzielska et al. [62] targeted adolescents from 26 countries from 2001/2002 to 2017/2018 and reported that the overall age-adjusted prevalence rates of weight-reduction behaviours (WRB) were 10.2% among boys and 18.0% among girls. The prevalence was higher for girls, but in more recent surveys, sex differences in WRB decreased, and a significant increase in the percentage of WRB among boys was observed in most countries. Therefore, from the present results of our study, being a male seems to be still a protective factor. Additionally, being male and having sometimes tried to lose weight was found to be associated with better body image satisfaction. Likewise, being male and not obese was significantly associated with a lower score of body shape concern.

Regarding the second investigated factor, weight status, overweight and obese students showed higher concerns, while no concern and slight concern were observed in underweight and normal weight subjects. FID was found to be related to a growing dissatisfaction with body image or to an increase in weight status. Thus, being underweight is the only category whose members would desire to be fatter, whereas in the other categories, the desire to be thinner prevailed. A relationship with weight also emerged from the regression analysis since weight was significantly associated with body image dissatisfaction. Relatedly, de Pinho et al. [55] reported a significant association between nutritional status and body image in adolescents; in particular, underweight and overweight adolescents were dissatisfied with their body image. Admittedly, weight status is associated with body dissatisfaction, weight concerns, and dietary restraints [63], potentially resulting in both restrictive and binge-purge disordered eating [64,65]. For this reason, studies regarding body image perception in this age group may be valuable for the early detection of disorders. Considering FAI, overweight subjects overestimate their weight status more than normal weight and obese subjects, while a misperception tending to underestimation is observed in underweight students. In addition, the regression analysis revealed that being underweight, normal weight, and having never trying to lose weight are negative predictors, and are thus protective factors, of weight status estimation. The number of teenagers who perceive themselves as overweight and adopt unhealthy strategies to lose weight is constantly increasing, as was previously reported by epidemiological surveys carried out in Italy [66].

The frequency of PA did not show significant associations with body image perception. The influence of sport participation and physical activity (PA) on body image in adolescents is not consistent. In general, adolescents of both sexes who play sports present a lower body dissatisfaction; however, associations between body image and sports practice in adolescents are not consistent, especially according to sex [32–39]. Our study showed that only intense physical activity effected perception [21]. From the results of the regression analysis, PA was associated with FID, FAI, and BSQ. In particular, all the categories of PA frequency were significantly associated with body image dissatisfaction and with body weight overestimation. Similarly, being physically active seemed to be associated with body shape concerns. Arguably, this is related to wider body awareness, which might contribute to generating more concern about body shape.

From the results of the present study, sex and weight status were found to be the prevailing factors influencing body image perception, while physical activity seemed to have a less decisive influence. Therefore, interventions should be directed at factors that influence the acceptance of their own body and the promotion of a healthy lifestyle. The sex-specific patterns in body image investigated in the present study underlined the need to focus on the risk factors for body image misperception and concerns by sex and weight status categories. Moreover, specific health promotion initiatives taking these aspects into account should be implemented.

The primary limitation of the study was the cross-sectional nature of the data, which limited causal inferences. A longitudinal study design during the whole adolescent period would be valuable to determine the direction of the relationship among the variables considered. In addition, height and weight were self-reported due to the COVID-19 public health emergency period, and this could have influenced the accuracy of the results. Information about sociodemographic variables was not collected, so it was not possible to evaluate the influence of these factors in explaining some of the possible links between physical activity participation and body perception. The sample size was limited, and further research with a larger sample is needed to verify the validity of the present results.

#### **5. Conclusions**

Although the prevalence of high school students who are of normal weight is high, many adolescents exhibit an altered body image and body shape perception, especially in the female sex. The daily practice of physical activity should reduce concerns related to body shape and facilitate better self-awareness. Despite more evidence being needed, the promotion of healthy habits such as physical activity could be an optimal strategy to improve mental wellness in adolescents.

**Author Contributions:** Conceptualization, S.T., N.R. and L.Z.; methodology, M.M. and S.M.; software, A.G. and P.M.L.; validation, S.T., S.M. and L.Z.; formal analysis, M.M., S.M. and N.R.; investigation, A.G. and S.M.; resources, P.M.L.; data curation, M.M. and S.T.; writing—original draft preparation, S.T., S.M., A.G. and M.M.; writing—review and editing, L.Z. and N.R.; visualization, A.G.; supervision, P.M.L.; project administration, S.T., L.Z. and N.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Bioethics Committee of the University of Bologna (approval no. 25027 and date of approval 10 July 2020).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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## *Systematic Review* **Recreational Soccer Training Effects on Pediatric Populations Physical Fitness and Health: A Systematic Review**

**Filipe Manuel Clemente 1,2,\* , Jason Moran 3 , Rodrigo Ramirez-Campillo 4 , Rafael Oliveira 5,6,7 , João Brito 5,6,7 , Ana Filipa Silva 1,6,8 , Georgian Badicu 9 , Gibson Praça 10 and Hugo Sarmento 11**

	- 4 Exercise and Rehabilitation Sciences Institute, School of Physical Therapy, Faculty of Rehabilitation Sciences, Universidad Andres Bello, Santiago 7591538, Chile

**Abstract:** This systematic review analyzed the effects of recreational soccer programs on physical fitness and health-related outcomes in youth populations. Studies were sought in the following databases: (i) PubMed, (ii) Scopus, (iii) SPORTDiscus, and (iv) Web of Science. The eligibility criteria included (1) population: youth (<18 years old) populations with no restrictions on sex or health condition; (2) intervention: exposure to a recreational soccer training program of at least four weeks duration; (3) comparator: a passive or active control group not exposed to a recreational soccer training program; (4) outcomes: physical fitness (e.g., aerobic, strength, speed, and change-ofdirection) or health-related measures (e.g., body composition, blood pressure, heart rate variability, and biomarkers); (5) study design: a randomized parallel group design. The search was conducted on 6 September 2022 with no restrictions as to date or language. The risk of bias was assessed using the PEDro scale for randomized controlled studies. From a pool of 37,235 potentially relevant articles, 17 were eligible for inclusion in this review. Most of the experimental studies revealed the beneficial effects of recreational soccer for improving aerobic fitness and its benefits in terms of blood pressure and heart-rate markers. However, body composition was not significantly improved by recreational soccer. The main results revealed that recreational soccer training programs that are implemented twice a week could improve the generality of physical fitness parameters and beneficially impact cardiovascular health and biomarkers. Thus, recreational soccer meets the conditions for being included in the physical education curriculum as a good strategy for the benefit of the general health of children and young people.

**Keywords:** football; sports medicine; physical exercise; physical fitness; physical conditioning; child; adolescent

**Citation:** Clemente, F.M.; Moran, J.; Ramirez-Campillo, R.; Oliveira, R.; Brito, J.; Silva, A.F.; Badicu, G.; Praça, G.; Sarmento, H. Recreational Soccer Training Effects on Pediatric Populations Physical Fitness and Health: A Systematic Review. *Children* **2022**, *9*, 1776. https:// doi.org/10.3390/children9111776

Academic Editor: Niels Wedderkopp

Received: 26 October 2022 Accepted: 16 November 2022 Published: 18 November 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

Youth populations are experiencing rising levels of obesity and declining levels of physical activity in recent times [1,2]. Moreover, epidemiological studies indicate the rise of health issues related to inactivity and sedentarism [3]. Consequently, there is growing concern over the effects of sedentary lifestyles on the health-based parameters of young people [4] as this sedentary behavior might be related to reduced quality of life and life expectancy. This issue might be explained by the different cultural and social changes experienced in recent decades, such as the increase in time spent looking at screens (TVs and video games, for example) and the development of motorized transport systems, which reduce the demand for actively physical behaviors in the youth population [5]. Hence, the World Health Organization strongly recommended in their 2020 physical activity guidelines for children and adolescents that these populations should be exposed to a minimum of 60 min/day of moderate-to-vigorous intensity physical activity. In the particular case of vigorous activities, these should be performed at least three times per week, focusing on vigorous aerobic exercise and strength-related activities that strengthen muscles and bones [6]. Based on that recommendation, programs focusing on providing youths with physical activity have recently been proposed and tested. These programs can include physical education activities, which are usually offered to children during schooltime, and recreational practices, such as soccer, which are not necessarily related to scholarly activities. In the current study, we will review the effects of recreational soccer physical activity programs on the respective pediatric populations' physical fitness and health.

Studies have shown that the time spent in sedentary activities, defined as low-energy expenditure during waking hours, accounts for a large proportion of the day, between roughly 50 and 60% [2]. Physical activity has also been reported to reduce morbidity and mortality in adolescence [7]. Therefore, exercise—a non-sedentary behavior—might be seen as a non-pharmacological approach to decreasing the exposure to health risks for sedentary populations [5]. For example, aerobic training programs improved health parameters, such as insulin action and plasma lipids, in sedentary people [8]. Therefore, it seems that these other activities might also benefit other sedentary populations.

Different training programs could be offered to the general population when considering physical activities on a wider scale. For example, more prolonged-duration low-intensity aerobic exercises showed positive effects in sedentary subjects [3]. On the other hand, recreational sports participation—such as soccer practices—might offer higher motivation and enjoyment [9]. Indeed, a previous systematic review showed that activities with highlighted social, motivational, and competitive components were as effective and efficient as continuous running-based programs [10]. Another previous study showed that running and football-based training programs effectively improved the health-based parameters of sedentary women. However, training-induced cardiac adaptations appeared to be more consistent after football training than after running [11]. Furthermore, another study showed that recreational soccer training programs could also offer an opportunity to positively impact body composition, in comparison to people performing continuous running [12]. Hence, sports-based activities should increase adherence and improve upon the positive effects of physical activity on health parameters.

Previous studies have tested if recreational soccer practice [13,14] can positively impact physical fitness (e.g., aerobic capacity, strength, power, speed, and change of direction) health-based parameters, such as body composition (e.g., fat mass) and biomarkers (e.g., insulin) or other markers (e.g., blood pressure and heart rate variability). For example, a systematic review showed that recreational soccer increased cardiovascular and bone health and improved body composition [15]. Another study demonstrated the positive effects of recreational soccer practice on untrained women's risk factors for bone fractures [16]. With regard to pediatric populations, it has been shown that recreational soccer can reduce risk factors associated with obesity [17] and improve different health markers [18]. Indeed, in relation to pediatric populations, adherence to physical activity programs through recreational sports practice seems even more relevant. Soccer is a popular sport worldwide and many children support clubs and players regularly. Therefore, including recreational soccer instead of generic physical activity practices could facilitate long-term adherence to physical activity and enhance the positive effects of this practice on health-related parameters in youth. Indeed, a previous study showed that greater motivation was associated with increased recreational sports participation [19]. However, to our knowledge, no previous systematic review has examined the effects of recreational soccer on health-based parameters in pediatric populations, a gap in knowledge that is addressed by the current study.

The diversity of systematic reviews in recreational soccer is clear from the extant literature [20–27]. As can be seen from the available systematic reviews, some have focused on specific outcomes, such as bone health [26,27], fat mass [22], or maximal oxygen uptake [20], while others summarized the effects on different outcomes [21,23–25]. Additionally, half of these systematic reviews were exclusively dedicated to adult populations [21,22,26,27], while the remaining reviews focused on mixed populations, such as children, adolescents, adults, and older individuals (both healthy and unhealthy), thus making it difficult to determine a clear overview of the effects on children and youths [20,23–25]. Considering the relevance of physical exercise for promoting positive physical fitness adaptations in children and young people, and the opportunities offered by recreational soccer, there is scope for a systematic review of this population. A systematic review of recreational soccer's effects on health-related outcomes and the physical fitness of children and untrained youth populations can facilitate a precise understanding of the methodologies used in interventions and identify the adaptations induced by such interventions, in comparison to control groups who are not exposed to the same stimulus. Therefore, this systematic review aimed to analyze the effects of recreational soccer programs on physical fitness (e.g., aerobic capacity, strength, power, speed, and change of direction) and health-related markers (e.g., body composition, blood pressure, heart rate variability, and biomarkers) in untrained child and youth populations. This systematic review mainly focuses on comparisons with the control groups, aiming to understand the real effects of increased activity on health-related outcomes and physical fitness.

#### **2. Materials and Methods**

This systematic review followed the PRISMA 2020 guidelines [28]. The systematic review protocol was first submitted and then published on the Open Science Framework, with the registration number 10.17605/OSF.IO/FY4PX on 6 September 2022. The protocol can be accessed via the web address https://osf.io/nuebg/?view\_only=11a89f39e2b34516 a482953db17d2acf (accessed on 5 September 2022).

#### *2.1. Eligibility Criteria*

Original articles published in peer-reviewed journals or "ahead of print" were eligible for consideration. We imposed no restrictions on the language in which the gathered articles were written [29]. Table 1 presents the eligibility criteria, based on the PICOS criteria.

#### *2.2. Information Sources*

The following databases were searched: (i) PubMed; (ii) Scopus, (iii) SPORTDiscus, and (iv) Web of Science (core collection). The searches were performed on 6 September 2022. Additionally, manual searches were performed on the included studies' reference lists to identify potentially relevant titles. The abstracts of these articles were checked for the relevant inclusion criteria, and, if necessary, the full text was investigated. Moreover, a consultation of two external experts (as recognized by Expertscape at the Worldwide level: https://expertscape.com/ex/soccer) (accessed on 5 September 2022) was performed, aiming to strengthen the search. Errata and article retractions were searched for each of the included articles, to identify possible sources of bias [31].

**Table 1.** Eligibility criteria for the current study.

#### *2.3. Search Strategy*

The search was conducted using the Boolean operators AND/OR. No filters or limitations were applied to the publication date or language, to increase the chances of identifying appropriate studies [32]. The search strategy presented the following codes:

[Title/Abstract] "Soccer\*" OR "Football\*"

AND

[All fields/Full text] "recreation\*" OR "untrain\*" OR "health" The entire search strategy can be found in Table 2.

#### *2.4. Selection Process*

The retrieved records (title, abstracts, and full texts) were independently screened by two of the authors (F.M.C. and H.S.). Disagreements between the two authors were discussed in a joint reanalysis. In cases where no consensus could be reached, a third author (A.F.S.) participated in a collaborative meeting to come to a final decision. The EndNoteTM

20.3 software (ClarivateTM) was used to manage the records, including the removal of duplicates, either automatically or manually.

\*: is the code for extension of the word.

#### *2.5. Data Extraction Process*

Two authors (F.M.C. and A.F.S.) independently extracted the data from the included studies. Information about study methods, results, and principal conclusions was extracted. A third author (H.S.) verified the collected information and helped in the case of any disagreements. A Microsoft® Excel worksheet was designed to collect the data. If relevant information was omitted from an article, the corresponding author was contacted to help to obtain the required information. If no reply was received after establishing the first contact, we sent a second message after three days. Following that, the author was contacted twice in two days, using the same message to achieve a response or attain one two weeks after the first contact.

#### *2.6. Data Items*

Participant-related and context information was obtained for the following items: the date of publication, the main goal of the research; sample size; country of origin; age; sex and clinical information.

The intervention-related information included: the timing of the academic season; program duration; training frequency; level of adherence to training; dose (e.g., duration, repetitions, rest, intensity, frequency, and density); rules of play; format of play and pitch size.

The physical fitness outcomes included (but were not restricted to) cardiorespiratoryrelated measures (e.g., maximal oxygen uptake, maximal aerobic speed, maximal heart rate), neuromuscular-related measures (e.g., muscular power and strength), speed and changeof-direction-related measures (e.g., sprint performance, change-of-direction performance), and balance and mobility-related measures (e.g., dynamic and static balance). Healthrelated outcomes focused on body characteristics and body composition (e.g., body mass index, lean mass, and fat mass), blood pressure (e.g., systolic and diastolic pressure), echocardiographic measures (e.g., cardiac output), bone health (e.g., bone mineral content), biochemical parameters (e.g., total cholesterol and glucose tolerance), and inflammatory parameters (e.g., leptin).

The comparators included information about passive control groups or active control groups (namely, type of exercise, intensity, and volume).

#### *2.7. Study Risk of Bias Assessment*

The physiotherapy evidence database (PEDro) scale was utilized to assess the risk of bias in the included studies. This scale has previously been tested for validity and

reliability [33]. The scale facilitates the rating of eleven specific study criteria, ten of which are used to classify the overall score of the article, which ranges from 0 (lowest quality) to 10 (highest quality). Usually, score thresholds provide a qualitative classification of "poor" (<4 points), "fair" (4–5 points), "good" (6–8 points), and "excellent" (9–10 points). The scale assesses the following items: C1 means that the eligibility criteria were specified; C2 means that subjects were randomly allocated to groups; C3 means that allocation was concealed; C4 means that the groups were similar at baseline regarding the most important prognostic indicators; C5 means that blinding was applied to all subjects; C6 means that there was blinding of all therapists who administered the therapy; C7 means that there was blinding of all assessors who measured at least one key outcome; C8 means that the measures of at least one key outcome were obtained from more than 85% of the subjects who were initially allocated to groups; C9 means that all subjects for whom outcome measures were available received the treatment or control condition as allocated or, where this was not the case, the data for at least one key outcome were analyzed by "intention to treat"; C10 means that the results of between-group statistical comparisons are reported for at least one key outcome; C11 means that the study provides both point measures and measures of variability for at least one key outcome. Two authors (F.M.C. and R.O.) independently reviewed and rated the included articles, based on the PEDro scale. After that, two authors (F.M.C. and R.O.) shared the scores and discussed them on a point-by-point basis. In cases where a consensus could not be reached, a third author (A.F.S.) was invited to provide their own score and make a final decision.

#### **3. Results**

#### *3.1. Study Identification and Selection*

The initial search resulted in the identification of 37,235 titles (Figure 1). Duplicates (10,444 titles) were subsequently removed, either automatically or manually. The remaining 26,791 titles were screened for relevance, based on their titles and abstracts. Of those papers, 26,727 titles were removed. The full texts of the remaining 64 titles were then inspected, and from there, 48 more were removed, based on the eligibility criteria. Four of the potentially included articles [34–37] reported the same clinical trial registration number (NCT02000492). Based on that finding and considering that multiple reports of the same study should be collated so that each study, rather than each report, is the unit of interest in the review, as suggested by Cochrane, we chose only article [38] to remain. From the manual searches, five articles were retrieved. Of those, one was considered eligible for inclusion in the systematic review. Following the full search, 17 articles were included in the final analysis.

#### *3.2. Study Characteristics*

The current study's characteristics can be found in Table 3. Among the included studies, nine were conducted in Denmark, with two performed in Brazil. Germany, China, the Faroe Islands, and Saudi Arabia yielded one study each. The remaining articles did not present any details about each study's country of origin.

The characteristics of the recreational soccer training programs can be found in Table 4. The range of training program durations provided was between a minimum of eight weeks and a maximum of ten months. Weekly training frequency varied from two to five days. Training duration varied between 12 and 90 min. Small-sided games were used as training drills in most of the studies.

The characteristics of the control groups included in the studies can be found in Table 5. Most control groups comprised youths who were only enrolled in regular physical education classes.

**Figure 1.** PRISMA 2020 flow diagram. \* Reason for exclusion in table \*\* records excluded **Figure 1.** PRISMA 2020 flow diagram. \* Reason for exclusion in table \*\* records excluded.

variability; biochemical markers; endothelial function

reactive protein; fasting glucose; glucose tolerance; insulin; HOMA-

**Table 3.** *Cont.*

Abbreviations: CMJ: counter-movement jump; BMI: body mass index; POmax: maximal power output; VO2max: maximal oxygen uptake; HR: heart rate; OLS: one-leg-standing; BP: blood pressure; CO, cardiac output; DT, transmitral deceleration time; IVRTglobal, global isovolumetric relaxation time; IVT, interventricular septum thickness; LA, left atrial; LVDD, left ventricular diastolic diameter; LVEF, left ventricular ejection fraction; LVPWD, left ventricular posterior wall diameter; LVSD, left ventricular systolic diameter; RVDD, right ventricular diastolic diameter; TAPSE, tricuspid annular plane systolic excursion; YYIRT: yo-yo intermittent recovery test level 1; IL-6, interleukin-6; TNF-α, tumoral necrosis factor-α; ET-1, endothelin-1; NEFA, non-esterified fatty acids; HDL, high-density lipoprotein; LDL, low-density lipoprotein; Ln, normal logarithm; SDNN, standard deviation of the normalized R–R intervals; RMSSD, root mean square of the standard deviation; HF, high frequency; LF, low frequency, SD1 geometric parameter of the Poincaré plot; LNSDNN: log-natural standard deviation of the NN (R-R) intervals; NA: not available.

**Table 4.** Characteristics of the recreational soccer training programs.

Abbreviations: SSGs: small-sided games; NA: not available; HRmax: maximal heart rate.

#### **Table 5.** Characteristics of control groups.

#### **Table 5.** *Cont.*

Abbreviations: NA: not available; HRmax: maximal heart rate.

#### *3.3. Risk of Bias in Studies*

Table 6 presents the assessment of the risk of bias. The criteria with the lowest scores were the ones associated with eligibility, the allocation being concealed, the blinding of participants and the person who administrated the protocol, and the blinding of the person who made the assessments.

**Table 6.** Assessment of the risk of bias.

C1: eligibility criteria were specified; C2: subjects were randomly allocated to groups; C3: allocation was concealed; C4: the groups were similar at baseline regarding the most important prognostic indicators; C5: there was blinding of all subjects; C6: there was blinding of all therapists who administered the therapy; C7: there was blinding of all assessors who measured at least one key outcome; C8: measures of at least one key outcome were obtained from more than 85% of the subjects initially allocated to groups; C9: all subjects for whom outcome measures were available received the treatment or control condition as allocated, or, where this was not the case, data for at least one key outcome were analyzed according to "intention to treat"; C10: the results of between-group statistical comparisons are reported for at least one key outcome; C11: the study provides both point measures and measures of variability for at least one key outcome.

#### *3.4. Results of Individual Studies*

Table 7 shows the main results for the physical fitness variables. The main findings showed improvements in cardiorespiratory fitness.

**Table 7.** Main findings of the physical fitness outcomes.

#### **Table 7.** *Cont.*

Abbreviations:VO2max: maximal oxygen uptake; VO2peak: the peak of oxygen uptake.

Table 8 shows the main results for the health variables. The main findings showed improvements in blood pressure or heart rate variables.

#### **Table 8.** The main findings regarding health-related outcomes.

#### **Table 8.** *Cont.*

Table 9 shows the main results for body composition changes. Most of the studies did not find a significant impact of recreational soccer on body composition.

**Table 9.** Main findings of body composition.

#### **4. Discussion**

The purpose of this systematic review was to analyze physical fitness and healthrelated markers in untrained children and youths exposed to recreational soccer (RS). The study mainly focused on comparisons with the control groups, aiming to understand the isolated effects on body composition, health-related outcomes, and physical fitness.

The main findings from this review are: (a) RS programs are appropriate for young people when an adequate and properly supervised program is followed; (b) youth RS programs spanning a period of eight to eleven weeks significantly improved cardiorespiratory fitness, blood pressure, and heart rate-related variables; (c) RS programs seem to be beneficial in improving body composition, although the results do not present a clearly discernible pattern.

#### *4.1. Main Findings Regarding Physical Fitness*

From the analyzed studies, the main findings showed improvements in cardiorespiratory fitness (CRF) in twelve studies. Although occurrences of cardiovascular disease (CVD) are rarely seen during childhood, the associated pathophysiological processes often begin as early as adolescence [52]. Since there is a correlation between childhood obesity and the eventual emergence of CVD risk factors in adulthood, recent studies [53–55] considered the importance of CVD risk factors, such as systolic blood pressure, body fat percentage, and aerobic fitness as the main outcomes related to the level of physical activity in children from 9 years of age and older. Moreover, CRF seems to prevent cardiovascular disease, regardless of body mass or composition [56,57], which highlights engagement in regular physical activity as an important behavior in controlling the aforementioned risk factors.

There is evidence from the original research in the present review that chronic exposure to RS has a high potential to improve CRF in children and adolescents with and without excess weight or obesity. RS seems to elicit high cardiovascular demands (endurance performance or VO2max) in healthy children and clinical populations. Moreover, this type of exercise is considered safe, with positive long-term effects on physical fitness and health indices [58].

The improvements reported by studies in terms of cardiorespiratory fitness may be as a consequence of exposure to RS, with the most effective interventions occurring two to five times per week, with intensities above ~75% HRmax, for eight weeks to ten months. Indeed, improvements in cardiorespiratory fitness for young schoolchildren have been shown to be closely related to time spent in the highest aerobic intensity zone [59].

The greater levels of daily physical activity observed in children who regularly play RS [57] may also partly explain these increases in physical fitness. In overweight and obese children, together with large improvements in terms of motor skill performance, increased exercise capacity may also facilitate greater participation in everyday activities.

Along with improvements in cardiorespiratory fitness, the studies in this review report similar improvements in 10-m [43], 20-m [40], 30-m, and 50-m [37] sprint times, increased explosive power [39], jump ability [60], agility [39], balance [36], and flexibility [36].

In contrast to the above results, Ørntoft et al. [40] report that during an 11-week period, balance and jump performance remained unchanged in both groups (IG, intervention group and CG, control group) in their study. Similarly, Larsen et al. [60] reported that ten months of soccer training did not cause significant changes in physical fitness in children. Differences in the magnitude of adaptive responses across the various studies are possibly related to the different methodologies used and the baseline values of the groups of participants in those investigations.

#### *4.2. Main Findings in Relation to Body Composition*

The early prevention of overweight and related diseases in children and adolescents is crucial, meaning that regular physical activity has been accepted as a means to reduce the incidence of obesity and related comorbidities [61].

Although several school-based physical activity interventions, lasting between ten and fifty-two weeks, have been reported to have had positive effects on aerobic fitness and other fitness components, few have had positive effects on body composition and body fat percentage. Some studies [39] have reported that body composition (as expressed by BMI) remained largely similar over a six-month period. In two soccer-based interventions reported by Krustrup et al. [34], in normal weight and overweight children (aged 8–12 years) who were playing small-sided soccer matches for 3 × 40 and 3 × 60 min/week for 10 and 12 weeks, respectively, there were improvements in physical fitness, but there were no changes in BMI or fat percentage. Several other studies on RS also reported that body composition variables did not change in untrained, normal-weight, overweight and obese children, and adolescents. Based on these consistent results, it is perhaps the case that short-duration intervention programs may not promote changes in body composition and that longer studies are required to observe such changes. Conversely, another study [62]

observed significant decreases in BMI z-scores over similar time periods to those described herein. However, these studies also included educational and nutritional advice programs, which may have contributed to the observed decreases in BMI. It should, additionally, be noted that the use of BMI z-scores, adjusted for age and sex and using a WHO reference population, increases the sensitivity of the index [63].

Vasconcellos et al. [47] reported between- and within-group differences for body weight, BMI, and waist circumference (WC) in obese adolescents (12–17 years) who played RS only during a 12-week physical activity intervention (3 times/week; 60-min/sessions). Additionally, the intervention group showed significant decreases in body-fat percentage, while no changes were observed in the control group. Interestingly, shorter soccer-based programs (for an 11-week intervention period) such as the "FIFA 11 for Health" program, applied in several studies [40,44], and the "11 for Health in Denmark" used by Ryom et al. [50] also had positive effects on body composition, BMI (∆−0.15kg/m2 ) and fat percentage (∆−0.8%) when compared with a CG. There were within-group decreases of 23.1 ± 8.4 to 22.5 ± 8.3% in terms of body fat percentage in the intervention group, as well as an increase in lean body mass (1.0 ± 1.7 vs 0.7 ± 1.6 kg) and a lower BMI when compared to the control group.

Since changes in body fat percentage and blood pressure can be achieved through dietary manipulation, physical exercise, and other daily behaviors, an additive effect can be obtained when such methods are combined [64]. It could be speculated that there are additional effects of the "FIFA 11 for Health" program, other than the football activity itself. For example, Ørntoft et al. [40] report that education nutritional habits, when combined with high-intensity physical training, may positively influence children's behavior. However, further studies are required to elucidate whether the increases in awareness of a healthier lifestyle that were observed following the "FIFA 11 for Health" program can result in the desired behavioral changes [65].

Another approach when analyzing the study results shows that the effects of PA on body composition parameters, such as body weight or body mass index, are inconsistent in young age groups and can distort any changes in body composition. The effects of PA on body weight are controversial, since such assessments may not consider the body composition (e.g., fat mass) [66]. The calculation of BMI uses two measures (height and weight) with a high level of variability in terms of pediatric age, which can decrease the accuracy in assessing obesity in the early stages of life.

The peri-pubertal period, in which the majority of participants in the studies herein are found, may also be a confounding factor.

Another plausible consideration is that the increase in average energy expenditure during intervention training programs can also contribute to an increase in appetite and, thus, body composition may remain constant as a result [39]. However, in most studies, nutrition was not controlled for during the intervention period and, therefore, a definitive conclusion cannot be made.

The meta-analysis conducted by Atlantis et al. [67] recommended 155–180 min of aerobic exercise per week to positively impact the fat-mass levels of overweight children. Thus, the nearly unchanged body composition reported in some studies [39,60] might be at least partly explained by an insufficient training volume (~120 min/wk). Possibly, two hours of moderate to vigorous exercise, or an energy expenditure of about 700 kcal per week, can be recommended based on the American College of Sports Medicine and the American Heart Association, aiming to maintain cardiovascular health in adults [68].

As already mentioned above regarding the study by Ørntoft et al. [40], BMI and the body-fat percentage were reduced during the 11-week period of the "FIFA 11 for Health" program. It is noteworthy that this reduction was achieved with 90 min of activity per week over an 11-week intervention period, which is much less than in previous investigations observing similar reductions in BMI and body fat percentage, as reported in interventions with higher training volume and longer duration. For example, Faude et al. [60] prescribed

180 min over 6 months, while Cvetkovi´c et al. [36] prescribed 180 min over 12 weeks, and Hadjicharalambous et al. [49] recommended 180 min/week over 8 weeks.

To allow more definite conclusions with regard to the long/short term and higher/low intensity and volume effectiveness of exercise programs in improving the body composition of normal and overweight children, further studies appear to be necessary.

However, it is important to consider that several studies [69–72] suggest that exercise improves health, even if no weight is lost, and that improved health and fitness may increase daily physical activity levels and compliance with exercise programs.

#### *4.3. Main Findings Regarding Health-Related Outcomes*

Of the studies in this review that analyzed the health-related outcomes of RS, two did not find any significant improvement [49,60]. The main findings showed improvements in blood pressure [34,51,73], cardiac function and HR variables [34,40], and mental and cognitive health [43,60].

Physical inactivity and lifestyle-related diseases during childhood and adolescence are associated with an increase in the risk of cardiovascular disease and largely contribute to disease and disability during adulthood. Early intervention with excessively overweight children seems mandatory for a healthier adult life [39]. This is of enormous importance to the current and future health of children and adolescents since it has been observed that obesity tends to track from childhood into adulthood [74]. Likewise, mental health is one of the current major concerns in children and adolescents [75], which may also have consequences in the future; however, in the present review, only three studies addressed this crucial issue [39].

The relationship between higher fitness levels and physical activity and the achievement of better cognitive health and performance is well established [76–78]. Faude et al. [60] report that the self-esteem of overweight children was considerably improved through training, with a larger effect found in the soccer-playing group. This may be explained not only by the social interactions experienced but also because of the competitive nature of the game, leading to feelings of success and coherence among the team [75]. In particular, emotional support may be enhanced when sports are conducted together with peers, an effect that is likely to be more pronounced in team sports such as soccer [79].

The study by Lind et al. [44] included an RS program ("FIFA 11 for Health for Europe") on cognitive performance in pre-adolescent children. The authors reported a reduction in reaction time in terms of psychomotor function, attention, and working memory. Several studies have highlighted the relationship between cognitive functioning and soccer, suggesting the importance of cognitive functions for performance in this sport.

#### *4.4. Behavioral Area*

In the behavioral area, only the authors of [46] report that the implementation of RS in regular physical education classes seems to be a potentially appropriate stimulus for reducing aggression (physical aggression, verbal aggression, hostility, and anger) in adolescents. A recent systematic review states that the prosocial behavior of RS plays a key role in interpersonal relationships concerning the growth of children and adolescents [80].

#### *4.5. Cardiovascular Adaptations*

Other health-related outcomes of RS programs, such as cardiovascular adaptations, have been reported in studies showing significant structural and functional effects on the cardiovascular system. Thus, studies of normal and overweight children have reported considerable cardiovascular adaptations to medium- and short-term soccer interventions [34,38,40]. Only Hadjicharalambous et al. [49] reported that an 8-week soccer training intervention did not cause any significant changes in cardiometabolic health in adolescents.

The analyzed studies report structural and functional cardiovascular changes, such as: (a) increased left ventricular posterior wall diameter; (b) improvements in right ventricular systolic function and increased global isovolumetric relaxation time; (c) improved

interventricular septum thickness, cuspid annular plane systolic excursion, and left atrial volume index and peak transmitral flow velocity in early diastole; (d) beneficial changes in endothelial function and vascular conductance; (e) decreased submaximal HR and resting and maximal HR. Such cardiac adaptations to physical exercise, known as the "athlete's heart", can be elicited in obese and non-obese children, who are heterogeneous in terms of fitness levels and sports participation [34]. These observations demonstrate that in childhood, the heart adapts quickly to the physiological changes induced by physical training [81]. Despite the relatively long-term duration (10 months) in the study by Larsen, Nielsen, et al. [51], short-term interventions report similar adaptations (over 10 weeks) from the application of an RS program.

It has been shown that regular and extensive training in adults is associated with changes in cardiac morphology, namely, increased left and right ventricular cavity dimension, wall thickness, and mass [82–84]. Cardiac adaptations to exercise also occur in children and adolescents, independent of the influence of growth and maturation [85]; a recent systematic review of echocardiographic studies concludes that these adaptations are more pronounced in structural left ventricular parameters, with the functional parameters being preserved or slightly improved by exercise [86].

However, there are still very few studies on the effects of RS training on cardiac morphology and function that use more accurate measurement methods, such as cardiac magnetic resonance or 3-D echocardiography [81].

As a consequence of the above-described cardiac adaptations to RS, in the current review, although Ørntoft et al. [40] state that there seem to be no changes in terms of resting HR, studies revealed that RS was effective in decreasing resting, submaximal, and maximal HR in normal weight, overweight, and obese children [35]. Wang et al. [37] also reported improvements in the HR index, which is determined via resting HR, exercise HR, and recovery HR (1 min post-exercise), after 30 squats in 30 s.

Autonomic heart rate regulation was evaluated via heart rate variability in two included studies [39,51]. Studies show that autonomic modulation, both at rest and during exercise, increases when a positive adaptation to training occurs, leading to an improvement in physical performance [87].

Changes that were reported in cardiac autonomic activity, namely, higher parasympathetic outflow and lower sympathetic outflow after RS programs, are concomitant with a lowered systolic blood pressure. Altogether, an improvement in vagal modulation and a reduction in sympathetic activity reflect the enhancement of hemodynamic and cardiac autonomic function in short-term programs, which can be considered a cardio-protective effect of an RS program [39,51].

#### *4.6. Blood Pressure*

Blood pressure (BP) is reported in some studies as showing significant reductions after the application of intervention programs, namely, the "FIFA 11 for Health" program and other RS programs [51,73]. The positive effects of physical activity programs on BP in children aged 6–12 years have been reported in a meta-analysis of physical activity intervention studies. Despite the positive effects of the interventions, there was no consensus on a reduction in all blood pressure parameters in the studies analyzed. Some studies reported a decrease only in systolic blood pressure (SBP), while others reported it only in diastolic pressure or mean blood pressure. Interestingly, some studies reported that programs of shorter duration (≤8 weeks) appear to have no effect on BP [49].

Despite the relative ambiguity of the pooled results, the beneficial effects of high levels of physical activity on blood pressure in children are clear [88,89]; even 2 mmHg reductions in systolic and diastolic blood pressure are associated with a reduction in coronary heart disease in adults [90–92]. It is well established that a reduction in BP, if sustained, is associated with lowered arterial stiffness and a decreased atherosclerosis progression rate in adulthood [93,94].

Thus, the present findings suggest that RS programs could be an efficient strategy for BP regulation, even in overweight and obese children, with positive health implications as one progresses through the various developmental stages to adulthood.

#### *4.7. Biochemical Parameters*

Besides the various tests for assessment of the cardiorespiratory system and body composition, blood biochemical parameters are a significant indicator of health status. In the present review, the studies by Vasconcellos et al. [41] and Cvetkovi´c et al. [36] report that RS promoted a statistically significant increase in the number of erythrocytes at the end of the intervention period. Both studies also verified a decrease in blood glucose. Glucose was assessed using insulin concentration, following the homeostasis model assessment of insulin resistance (HOMA-IR). The index of HOMA-IR takes under consideration both the fasting insulin values and fasting glucose values. Overweight and obese adolescents often present higher negative health outcomes than those of normal weight. This can be explained by the higher values of insulin concentration and HOMA-IR [95,96].

Concerning triglyceride blood levels, RS was shown to have favorable effects (small effect size) according to Vasconcellos et al. [41]. These results are not consistent across the different studies, perhaps due to the baseline fitness levels, the duration of interventions, and the cardiometabolic profiles of the children and adolescents [49].

It is also important to understand individual responses, due to the complexity of the participants' levels of overweight and obesity in some studies. Studies reporting the effects of individual and group interventions on cardiometabolic risk factors in overweight or obese pubescent girls and boys are still lacking.

The completion of considerably larger and longer-term studies, including groups undergoing high and low volume and intensity soccer, will shed more light on the possible temporal developments and dose-response relationships, as well as the associated blood biochemical parameter adaptations.

#### *4.8. Limitations*

One of this study's limitations is associated with the sample size, related to the included studies. Although there is no explicit information in the articles, some similar aspects were reported in papers from the same research groups that could be associated with using partial data from the same dataset (i.e., data slicing). This can artificially inflate the sample size of the current systematic review. However, since there is no reported information about that fact (in the original articles), all the included studies were considered for selection, based on the assumption of trust, and based on the fact that the articles researched different outcomes. Another limitation of this review is the limited number of subjects investigated in the studies, as well as the relatively short intervention periods, and, in this regard, the likely suboptimal statistical power. Furthermore, the studies were analyzed on a "per-protocol" basis; therefore, only the efficacy under ideal conditions can be assessed. Another limitation is that activity and caloric intake in daily living were not controlled, while seasonal influences on the investigated parameters may have occurred.

In addition, hormonal changes resulting from the onset of puberty cannot be ruled out as an influential confounding factor. Specifically, some children in the various studies were at the beginning of puberty and rapid hormonal changes may have occurred.

#### *4.9. Practical Implications*

Based on the current findings, it is verified that recreational soccer can effectively improve the physical fitness and health of children and young people. A two-week course of training sessions of about 60 min each, using small-sided games, can be a recommended strategy for achieving a beneficial impact on participants. However, regarding fat mass, it is recommended that this should be complemented by dietary and nutritional advice, which allows for increasing the effect since recreational soccer seems insufficient for achieving a good impact.

#### **5. Conclusions**

The 11-week intervention of a football-based health education program, as used in some studies, presents itself as an effective program to be used within a school's curriculum to induce improvements in psychosocial and physiological health profiles, along with an increase in health knowledge. We recommend the tentative use of such programs alongside a call for greater efforts to engage in more studies on this particular issue. However, we would like to highlight the point that a major limitation of this review is the limited number of subjects investigated in the studies and the relatively short intervention periods, and, in this regard, the possibly suboptimal statistical power. Thus, generalizations from the findings should be made with caution.

**Author Contributions:** F.M.C. conceived the idea, drafted the project, wrote and revised the article, and approved the article; J.M. wrote and revised the article and approved the article; R.R.-C. wrote and revised the article and approved the article; R.O. wrote and revised the article and approved the article; J.B. wrote and revised the article and approved the article; A.F.S., wrote and revised the article and approved the article; G.B., wrote and revised the article and approved the article; G.P., wrote and revised the article and approved the article; H.S. wrote and revised the article and approved the article. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is funded by the Fundação para a Ciência e Tecnologia/Ministério da Ciência, Tecnologia e Ensino Superior, through national funds and, when applicable, co-funded EU funds, under the project UIDB/50008/2020.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

### *Article* **Motor Competence Assessment (MCA) Scoring Method**

**Luis Paulo Rodrigues 1,2,3,\* , Carlos Luz 3,4,5 , Rita Cordovil 6,7 , André Pombo 3,4 and Vitor P. Lopes 2,8**

**Abstract:** The Motor Competence Assessment (MCA) is a quantitative test battery that assesses motor competence across the whole lifespan. It is composed of three sub-scales: locomotor, stability, and manipulative, each of them assessed by two different objectively measured tests. The MCA construct validity for children and adolescents, having normative values from 3 to 23 years of age, and the configural invariance between age groups, were recently established. The aim of this study is to expand the MCA's development and validation by defining the best and leanest method to score and classify MCA sub-scales and total score. One thousand participants from 3 to 22 years of age, randomly selected from the Portuguese database on MC, participated in the study. Three different procedures to calculate the sub-scales and total MCA values were tested according to alternative models. Results were compared to the reference method, and Intraclass Correlation Coefficient, Cronbach's Alpha, and Bland–Altman statistics were used to describe agreement between the three methods. The analysis showed no substantial differences between the three methods. Reliability values were perfect (0.999 to 1.000) for all models, implying that all the methods were able to classify everyone in the same way. We recommend implementing the most economic and efficient algorithm, i.e., the configural model algorithm, averaging the percentile scores of the two tests to assess each MCA sub-scale and total scores.

**Keywords:** human development; motor development; motor test; motor performance; lifespan

#### **1. Introduction**

Motor competence (MC) relates to human movement, its development, and performance. It has been described as the ability to be skilful in a wide variety of gross motor skills (stability, locomotor, and manipulative) that are associated with multiple developmental outcomes including physical health [1–3], psychological, social-emotional, and cognition/achievement [4–7]. Importantly, it is expected that adequate levels of MC will facilitate the proficiency of novel motor tasks throughout the lifespan and the learning of new skills [8].

The Motor Competence Assessment (MCA) is an instrument developed to assess motor competence across a lifespan. For the first time, it is possible to assess MC from childhood to old age using the same instrument, without a developmental (age) ceiling effect and using a feasible and objective test battery [8–10]. Although there are other instruments specifically developed or adapted to evaluate motor competence throughout a lifespan, such as the Test of Motor Competence (TMC) [11] and the last form of the Körperkoordinationstest für Kinder (KTK3+) [12], the first includes a fine motor component that is outside our

**Citation:** Rodrigues, L.P.; Luz, C.; Cordovil, R.; Pombo, A.; Lopes, V.P. Motor Competence Assessment (MCA) Scoring Method. *Children* **2022**, *9*, 1769. https://doi.org/ 10.3390/children9111769

Academic Editors: Hugo Miguel Borges Sarmento, Georgian Badicu and Ana Filipa Silva

Received: 10 September 2022 Accepted: 14 November 2022 Published: 17 November 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

definition of MC, and the second shows a clear ceiling effect on one of the tests used (balancing backwards) and no locomotor component is included.

The MCA is a quantitative test battery composed of six tests divided into three subscales: stability, locomotor, and manipulative. Each sub-scale is calculated from two tests, objectively measured. All motor tests in the MCA are quantitative (product-oriented), with no developmental (age) ceiling effect, and are easy to execute even with little practice. After determining the construct validity of the MCA for children and adolescents [9], the normative values from 3 to 23 years of age were established [10]. Very recently, the MCA configural invariance between age groups was tested, proving the usefulness of the MCA model throughout growth and development periods from early childhood to young adulthood [12].

Typically, the scales or batteries used to assess motor development or motor competence of children use a classification system that involves transforming the tests' raw data to standard scores relative to age and sex, but with a limited range (from 1 to 10, or more usually 1 to 20). Examples are the KTK, the Peabody Developmental Motor Scales 2 (PDMS-2) [13], the Test of Gross Motor Development 3 (TGMD-3) [14], and the Movement Assessment Battery for Children (MABC-2) [15], where a singular value of a standard score can accommodate a range of results from the test. For example, at the age of six, all values between 15 and 23 s on the one-leg balance test of the MABC are transformed into a standard score of eight. The same reduction in information happens when assessing the sub-scales or components, and the total value, or motor quotient, of the child. In such a procedure, a chunk of information is lost each time a transformation to standard scores is performed, and consequently we lose possible discrimination between children's real values. Since the MCA always uses quantitative ratio scales to assess the six tests, we argue all this information relative to age and sex (normative percentile values) should be used to better discriminate between subjects.

Although the MCA has been used with different populations and cultures [16–18], the method for scoring each of the three sub-scales (locomotor, stability, and manipulative) and for total MCA score, still needs to be defined. The initial theoretical framework on the development of motor skills [19,20] proposed an equal participation of the locomotor, stability, and manipulative components in overall motor competence. However, this structural relationship needs to be tested relative to the circumstances of the tests used to mark each component, and to the developmental ages where the evaluation takes place. Furthermore, since each MCA test is not a perfect marker of the component but only a proxy of it, there is a need to interpret possible different weights of the tests in the classification method of the components (sub-scales) and total MCA scores. The previous work undertaken on the validation of the MCA established the metrics for these questions, i.e., the relative weight of each test to the sub-scale according to different age periods [8–10]. Hence, the aim of this study will be to expand the MCA's development and validation by using the previous findings on the developmental characteristics of the MCA tests to define the best and leanest method to score and classify MCA sub-scales and total score.

#### **2. Materials and Methods**

#### *2.1. Participants*

An initial pool of 2150 participants (1003 females; 1147 males) from the Portuguese database on MC, a convenience sample consisting of students from preschool to university, was utilized to select the sample for this study. A computer program was used to randomly select 1000 participants (250 per age group from 3 to 6, 7 to 10, 11 to 16, and 17 to 23 years of age, gender-balanced within age groups). No differences were found on any of the MCA tests (all p's > 0.50), between the selected and the non-selected participants. All participants showed no motor or cognitive impairment.

The study was approved by the Ethics Committee of the Faculty of Human Kinetics at the University of Lisbon and the Scientific Council of the School of Sports and Leisure at the Polytechnic Institute of Viana do Castelo. All school directors authorized the study, and the informed consent of adult participants or parents/tutors of underage children was obtained. All children gave their verbal assent prior to data collection. All procedures were in accordance with the 1964 Declaration of Helsinki and its later amendments [21].

#### *2.2. Instruments and Procedures*

The MCA is composed of three sub-scales, stability, locomotor, and manipulative. Each sub-scale has two tests, specifically: lateral jumps (LJs) and shifting platforms (SPs) for stability; standing long jump (SLJ) and 4 × 10 m shuttle run (SHR) for locomotor; and ball kicking velocity (BKV) and ball throwing velocity (BTV) for the manipulative sub-scale. All tests were assessed in a quantitative scale (i.e., distance, time, number of executions, or velocity), and do not have a ceiling effect related to age or sex (for full description see Rodrigues et al., 2019). The literature describes the tests' reliability as ranging from good to excellent [22–25], and the values of the Intraclass Correlation Coefficient (ICC)were 0.95, 0.99, 0.97, 0.99, 0.98, and 0.98 respectively for SP, JS, SHR, SLJ, BKV, and BTV.

Participants completed a 10 min general and standardized warm-up before the testing. The test setting was organized in small groups (5 participants per task). Trained examiners administered the tests. A proficient demonstration and a verbal explanation of each test were provided before each test. A test trial was always provided to the participant before the actual test administration. Instructions were given for participants to perform each task at their maximum. Only motivational feedback was given during the test. All data collection was supervised by one of the authors of this study. Testing always took place in a gymnasium.

For the calculation of the sub-scales and total MCA, participants' results in each of the MCA tests were transformed into age- and sex-normative values (percentiles) according to the process previously described [8]. Three different procedures to calculate the sub-scales values were tested according to three alternative models [25]: (1) the Weighted Age Group Model (WAGM) accounted for different weights (percentage) of each test's representation in the final sub-scale score, according to the age groups' previous results [8]; (2) the Overall Model (OM)used the same weight of each test, independent of the age, for calculating the sub-scale score, according to the loading coefficient of the model found for the overall sample [8]; and (3) the Configural Model (CM), in which all tests represented an equal value for the sub-scale's calculation (see Table 1).

**Table 1.** Loading coefficients for each sub-scale of the three tested models.

LJs—lateral jumps; SPs—shifting platforms; SLJ—standing long jump; SHR—shuttle run; BKV. ball kicking velocity; BTV—ball throwing velocity.

According to the different methods used, the formula to calculate each sub-scale score was:

where LC = Loading Coefficient and P = percentile value.

After defining the best method for calculating the sub-scales scores, identical methodological procedures were followed to calculate the total MCA score. The relative representation of each sub-scale was used for calculating the final MCA score according to the same three models previously used (WAGM, OM, and CM):

#### **TOTAL MCA =** (((LC1+LC2)/sum LC) \* STAB) + (((LC3+LC4)/sum LC) \* LOC) + (((LC5+LC6)/sum LC) \* MAN) (2)

where LC1 = Loading Coefficient test 1; STAB = stability sub-scale score; LOC = locomotor sub-scale score; MAN = manipulative sub-scale score.

#### *2.3. Statistical Analysis*

The results for the sub-scales and total MCA according to the three tested models were compared with the values produced by the Weighted Age Group Model (WAGM), the one selected as our reference method. This choice relates to the fact that the WAGM model provides more information on the weight of each test to the sub-scale score and the total score (configural, loading coefficients, and age-related information). The Intraclass Correlation Coefficient tested for the relationship between the results accounting for the absolute value; the Cronbach's Alpha was used as a measure of reliability. The Bland– Altman plot with limits of agreement was used to describe agreement between the three methods taking the WAGM as the reference method [26,27]. *Children* **2022**, *9*, x FOR PEER REVIEW 4 of 9

> Subsequently, the chosen method for calculating the MCA sub-scales scores was then used with our data to find each sub-scale score, and the MCA total score was then assessed according to the three different models previously used (WAGM, OM, and CM). **Table 1.** Loading coefficients for each sub-scale of the three tested models.

> Initial analyses were made using IBM SPSS Statistics for Windows, Version 26.0. (Armonk, NY, USA: IBM Corp). The Bland–Altman plot and subsequent analyses were calculated in MedCalc® Statistical Software version 20.106 (MedCalc Software Ltd., Ostend, Belgium; https://www.medcalc.org; accessed on 1 February 2022).  **Weighted Age Group Model Overall Model Configural 3 to 6 7 to 10 11 to 16 17 to 22 Model**  Stability LJ 0.86 0.85 0.77 0.73 0.94 1 SP 0.90 0.59 0.82 0.66 0.93 1

#### **3. Results** Locomotor SLJ 0.89 0.63 0.94 0.88 0.94 1 SHR −0.82 -0.86 −0.86 −0.79 0.89 1

As depicted in Table 2, an almost perfect reliability level was found between the tested methods when compared to the reference method, with values ranging from 0.999 to 1.000 in the ICC and the Cronbach's Alpha. Additionally, the mean difference (bias) resulting from the Bland–Altman technique always returned a very low value, very close to zero (not significantly different from zero in all cases except for WAGM \* CM), with also a very narrow interval between 95% limits of agreement (Figure 1). Manipulative BKV 0.82 0.85 0.89 0.93 0.97 1 BTV 0.78 0.76 0.91 0.91 0.95 1 LJs—lateral jumps; SPs—shifting platforms; SLJ—standing long jump; SHR—shuttle run; BKV. ball kicking velocity; BTV—ball throwing velocity. *2.3. Statistical Analysis*

As depicted in Table 2, an almost perfect reliability level was found between the tested methods when compared to the reference method, with values ranging from 0.999 to 1.000 in the ICC and the Cronbach's Alpha. Additionally, the mean difference (bias) resulting from the Bland–Altman technique always returned a very low value, very close to zero (not significantly different from zero in all cases except for WAGM \* CM), with

> WAGM \* OM Stability

**3. Results Figure 1.** *Cont*.

WAGM \* CM Stability

also a very narrow interval between 95% limits of agreement (Figure 1).

**Figure 1.** Bland–Altman graphs representing differences between the predicted models and the Weighted Age Group Model (WAGM) reference values for the MCA sub-scales, according to the percentile positions of the reference values of the WAGM vs. the mean of the two measurements, **Figure 1.** Bland–Altman graphs representing differences between the predicted models and the Weighted Age Group Model (WAGM) reference values for the MCA sub-scales, according to the percentile positions of the reference values of the WAGM vs. the mean of the two measurements, with limits of agreement and respective 95% confidence intervals.

**Table 2.** Reliability and Bland–Altman agreement analysis (mean bias and 95% limits of agreement) between the MCA sub-scale scores. Comparisons of the Configural Model and the Overall Model procedures with the Weighted Age Group Model. **Table 2.** Reliability and Bland–Altman agreement analysis (mean bias and 95% limits of agreement) between the MCA sub-scale scores. Comparisons of the Configural Model and the Overall Model procedures with the Weighted Age Group Model.

(−0.0004 to 0.0013) −0.0263 0.0272

with limits of agreement and respective 95% confidence intervals.

Manipulative

#### **Table 2.** *Cont*.

WAGM—Weighted Age Group Model; CM—Configural Model; OM—Overall Model.

The sub-scales scores according to the CM method were then used (Table 3) with our data to find the MCA total score according to the three different models previously used (WAGM, OM, and CM).

**Table 3.** Reliability and Bland–Altman agreement analysis (mean bias and 95% limits of agreement) between the MCA total scores). Comparisons of the Configural Model and the Overall Model procedures with the Weighted Age Group Model.

WAGM—Weighted Age Group Model; CM—Configural Model; OM—Overall Model

When looking at Figure 1, which shows the Bland–Altman plot with the 95% limits of agreement, all differences between the tested (predicted) models and the reference values found when the WAGM model was used, were very small. Even the biggest differences were within a 0.1 percentile margin of error, meaning that any difference between the three tested models was negligible. Very few cases were beyond the 95% limits of agreement.

#### **4. Discussion**

The aim of this study was to determine the best algorithm for assessing the MCA sub-scales and total scores, and one representative of the developmental MC relationships. All test batteries for motor competence or motor development use sub-scales and total scores. In fact, the MCA has been used in recent years with each author using a composite of the tests to represent sub-scales and total MCA scores e.g., [16–18]. This practice has reflected the theoretical model assumed for MCA, long used in the field, that all tests and sub-scales have equal participation in the determination of the motor competence, but this had not been tested until now. To test this, we used three different models previously fitted to a sample of 1000 participants within 3 to 23 years of age, and their loading weights were used for calculation of the sub-scales and total MCA [26]. Since we hypothesized that the better-adjusted model should consider age-related constraints, the Weighted Age Group Model values were used as a reference model to compare to the other two: an Overall Model (using all the sample), and the Configural Model where all weights are assumed to be equal.

The analysis showed that no substantial differences could be found between the three methods used for assessing the sub-scales. The reliability values were perfect or in the vicinity of perfection (0.999 to 1.000), which implies that all the methods were able to classify everyone in the same way. The Bland–Altman plots represented in Figure 1 show

the dimension of the differences between the reference model and the other two. The 95% limits of agreement found for both the CM and the OM resulted in a very narrow range, between −0.01 and 0.01 percentiles. That means that using either of the three methods would produce almost the same results for all individuals tested. Furthermore, the Bland– Altman bias analysis showed negligible values, indicating that all the methods can be used with similar results independent of the test scores' magnitude.

Given these results for the sub-scales, we suggest implementing the most economic and efficient algorithm, i.e., the configural model algorithm, averaging the percentile scores of the two tests to assess each sub-scale. This method was used with our data to find each sub-scale score, and the MCA total score was then assessed according to the three different models previously used (WAGM, OM, and CM). Again, the results proved very similar in outcome, independent of the method used, with reliability values ranging from 0.999 to 1.000, and the 95% confidence intervals for the differences found for both the CM (−0.0010 to 0.000) and the OM (−0.0011 to −0.0001) were very small.

Other scoring methods used in comparable test batteries usually include the transformation of individual test's raw values into z-scores related to age and sex (test z-score), and the transformation of the sum of all the z-scores into a new value that is then transformed into a new z-score (total or motor quotient z-score). That is the case for KTK3+ [12], or the TMC [11] (although the latter uses a general z-score value for each test, and not an age- and sex-related z-score). These methods are similar to the technique we used, where the mean and standard deviation used to find the z-scores results from a previous populational study. Other tests, such as the PDMS-2 [13], the TGMD-3 [14], or the MABC-2 [15], include a first transformation of the raw values into age- and sex-related standard scores (with a range of 1 to 10, or 1 to 20), and then its sum into a new standard score or percentile interval to represent the total motor quotient or percentile classification. In this case, each raw value is transformed two times, losing some information in each transformation, and resulting in less discriminating scores than the MCA.

A limitation of our results is the fact that no single gold standard is established for these specific measures of the MCA (sub-scales and total score). As explained previously, we choose the WAGM model as the most appropriate and complete model to determine the real value of the MCA scores. We also intend, in the future, to compare the MCA scores with other batteries used for assessing MC such as the KTK 3+, TMC, MABC-2, TGMD-3, or the Bruininks–Oseretsky.

#### **5. Practical Implications**

This study represents the final step for validating the MCA testing battery as a tool for assessing motor competence across all ages. MCA stability, locomotor, and manipulative sub-scales and MCA total score values can now be used when testing motor competence with any age and sex. The average of the normative age- and sex-related values for each test can be used to calculate the MCA sub-scales and total MCA scores. MCA is now ready to be used as a classification tool for MC throughout the human lifespan in different contexts (research, clinical, sports, education). Classification of the MCA cut-off values for the motor-impaired, underperformers, and high performers can now be established.

#### **6. Conclusions**