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Article

Association of Physical Fitness, Screen Time, and Sleep Hygiene According to the Waist-to-Height Ratio in Children and Adolescents from the Extreme South of Chile

by
Javier Albornoz-Guerrero
1,
Fernanda Carrasco-Marín
2,
Rafael Zapata-Lamana
3,
Igor Cigarroa
4,
Daniel Reyes-Molina
5,
Olga Barceló
6,
Guillermo García-Pérez-de-Sevilla
7 and
Sonia García-Merino
8,*
1
Departamento de Educación y Humanidades, Universidad de Magallanes, Punta Arenas 62000000, Chile
2
Centro de Vida Saludable, Universidad de Concepción, Concepción 4030000, Chile
3
Escuela de Educación, Universidad de Concepción, Los Ángeles 4440000, Chile
4
Escuela de Kinesiología, Facultad de Salud, Universidad Santo Tomás, Los Ángeles 4440000, Chile
5
Facultad de Ciencias Sociales, Universidad de Concepción, Concepción 4030000, Chile
6
Department of Sports Sciences, Faculty of Sports Sciences, Universidad Europea de Madrid, 28670 Madrid, Spain
7
Department of Physiotherapy, Faculty of Sports Sciences, Universidad Europea de Madrid, 28670 Madrid, Spain
8
Facultad de Ciencias de la Salud, Universidad Francisco de Vitoria, 28223 Madrid, Spain
*
Author to whom correspondence should be addressed.
Healthcare 2022, 10(4), 627; https://doi.org/10.3390/healthcare10040627
Submission received: 1 March 2022 / Revised: 23 March 2022 / Accepted: 23 March 2022 / Published: 27 March 2022
Review Reports Versions Notes

Abstract

:
Objective: To analyze the perception of physical fitness, screen time, and self-reported sleep hygiene in children and adolescents (CA) from the extreme south of Chile and its associations with waist-to-height ratio (WtHr). Material and methods: An observational cross-sectional study was conducted in a sample of 594 schoolchildren from 5th to 8th grade of primary education, belonging to municipal educational establishments in the Magallanes region, Chile. Cardiorespiratory fitness was assessed through the 20-m shuttle run test, muscle strength through handgrip and the standing broad jump test, physical fitness perception through the International Fitness Scale, and central obesity through the waist-to-height index. In addition, sleep hygiene and screen time were measured. Results: More than 92% of CA spent more than two hours a day watching or using screens. In addition, CA with excess central adiposity had a lower perception of physical fitness, and lower muscle strength and cardiorespiratory fitness compared to CA with normal values of adiposity. Conclusions: CA of the present study spent a high number of hours watching or using screens and had poor sleep quality. In addition, excessive central adiposity was associated with lower physical fitness.

1. Introduction

Reducing obesity in children and adolescents (CA) is a major challenge for public health worldwide. It is estimated that more than 330 million children aged 5–19 years suffer from excess malnutrition [1]. In Chile, 25.4% of CA are obese/overweight. Specifically, the Magallanes region, where this study was carried out, belongs to an area in the extreme south of Chile characterized by extremely cold weather, which has 53.8% of overweight/obese CA in Chile [2]. Many authors have reported that presenting this condition at early ages has important short-, medium-, and long-term health consequences, such as high blood pressure, type 2 diabetes, dyslipidemia, sleep apnea, asthma, and atherosclerosis [3,4].
Within this context, the most commonly used indicator for the diagnostic of overweight and obesity in CA is the body mass index (BMI). However, BMI has been questioned due to its limitations in detecting adiposity in CA [5]. In contrast, waist-to-height ratio (WtHr) has shown to be a highly applicable [6], accurate anthropometric index to measure central adiposity and a strong predictor of arterial hypertension and type 2 diabetes [7,8,9].
Although several studies associating lifestyle with obesity use BMI, other authors have incorporated the WtHr as a marker of central adiposity [9], showing greater sensitivity to the association with physical fitness, muscle strength, and cognitive functioning [10]. Likewise, low physical activity levels, high amounts of screen time, and low sleeping hours have been associated with obesity assessed by the WtHr and above all during the SARS-CoV-2 lockdown [11,12,13].
Overweight/obesity is a multifactorial condition where lifestyle plays a key role in its development in CA [14]. In this regard, sleeping fewer hours than recommended has been associated with higher adiposity while adequate physical fitness seems to help reduce adiposity and has also been reported as a mediator of cognitive performance in CA [10,11,15,16].
Few studies have investigated the influence of extremely cold weather in controlling overweight/obesity in CA [17]. In this context, it has been reported that being overweight is more prevalent in territories with extremely low temperatures [18]. This type of climate favors physical inactivity and modifies hormonal production, such as increased ghrelin and cortisol, which increase appetite and lipid storage mechanisms [19]. Additionally, in countries with cold climates, daylight varies markedly along the seasons, with only 35–60 days a year of sunlight and the rest of the year with darkness for much of the day, with a negative impact on mental health and sleeping hours, reducing the hours of sleep in CA to seven hours per day or less [17,18,19,20,21].
To date, few studies have analyzed physical fitness and nutritional status in CA from territories with extremely cold climates. Furthermore, few have used WtHr as a marker of nutritional health and analyzed its association with lifestyle and physical fitness in CA. No research has been found studying the association between WtHr and physical fitness, screen time, and sleep habits in CA from territories with extremely cold climates. The present study is relevant because the Magallanes region has the highest prevalence of childhood overweight and obesity in Chile [22].
Additionally, it is a region that, due to geographic, climatic, and light-dark cycle conditions, does not favor systematic programs for the control of overweight, obesity, and the practice of physical activity. This study could make it possible to generate a profile of lifestyles (screen time and sleep hygiene) and perception of physical fitness of CA in a region with an extremely cold climate, being the first study of this kind conducted in South America.
This study aimed to analyze the perception of physical fitness, screen time, and self-reported sleep hygiene in CA from the extreme south of Chile and its associations with the WtHr.

2. Material and Methods

2.1. Study Design

An observational, cross-sectional study was conducted using data from the health survey of CA from the extreme south of Chile (2019). The variables analyzed were screen time, physical fitness, sleep hygiene, and WtHr.

2.2. Participants

CA from the second cycle of primary education (5th to 8th year) from three public educational establishments in Punta Arenas city (region of Magallanes and Chilean Antarctica) were invited to participate. A representative sample of 615 CA completed all the evaluations after acquiring the signed consent of the parents/legal guardians. A total of 14 CA were excluded for not having the signed informed consent, 4 CA did not participate due to inability to perform the physical tests, and 3 CA did not complete all the evaluations, so the final sample was 594 CA. The sample calculation was obtained considering 50% of heterogeneity, a margin of error of 5%, and a confidence level of 95%.

2.3. Implementation

The municipal corporation of Punta Arenas (CORMUPA) and the research team signed a collaboration agreement. Then, three educational establishments were randomly selected. The management team, the teachers, and the research team planned the study design and its execution together.
The research team was in charge of hiring a kinesiologist, a nutritionist, a psychologist, and a physical education teacher. Then, they conducted a short training session on assessment instruments to reduce the risk of inter-rater bias. The data collection was carried out in the first semester of the year 2020 within the educational establishments in spaces adapted for each test and test. All the tests were carried out within the class schedule and on the same day. Families, directors, teachers, and students were informed about the purpose of the study and agreed to collaborate in it. All schoolchildren who participated in the study gave their consent and their parents and legal guardians signed their informed consent. The project was approved by the Ethics Committee of the south-central macro zone of the Universidad Santo Tomás de Chile, code number 96–20, and all procedures were carried out following the Declaration of Helsinki and Singapore.

2.4. Variables

2.4.1. Grouping Variable

Waist-to-height ratio (WtHr): WtHr is a specific index highly correlated with visceral adiposity. Waist circumference was measured taking as reference the equidistant point between the last non-floating rib and the iliac crest. Height was measured in centimeters. Then, WtHr was calculated by dividing waist (in cm) by height (in cm). Pediatric reference values are ≤0.47 normal adiposity; 0.47–0.50 moderate adiposity; and >0.50 excess adiposity [23,24].

2.4.2. Physical Fitness

Cardio-respiratory fitness: The 20-m shuttle run test was used, measuring speed (m/s), and number of paliers, following the protocol and reference values given by Alpha Fitness for schoolchildren [25].
Muscle strength: handgrip strength dominant hand (digital dinamometer Smedley Baseline) and the standing broad jump test were performed, using the reference values given by Alpha Fitness for schoolchildren [26].
Physical fitness perception (IFIS): Evaluation of self-perception of the physical condition. This survey has five questions focused on knowing the self-perception of general physical fitness, cardio-respiratory fitness, muscle strength, flexibility, speed, and agility [27,28].
Nutritional status: The bodyweight of the schoolchildren was measured with a SECA® brand digital scale (model 804, USA), the waist circumference was measured while standing with a 1.5 m tape, and the height was measured with a SECA® brand portable stadiometer (model 213, USA). These measures were performed according to the standardized procedures described by the International Society of Film anthropometry (ISAK) [29] and according to the Habicht method [30]. Subsequently, the BMI was calculated to obtain the BMI/age indicator, and to classify the nutritional status of each child according to sex, considering as malnutrition a standard deviation (SD) ≥ −2, risk of malnutrition SD ≥ −1, and normality between 0.99 and −0.99 SD. Overweight, obesity, and severe obesity were determined by values of ≥1 SD, ≥2 SD, and ≥3 SD, respectively [31].

2.4.3. Lifestyle

Screen time: A self-reported survey was applied by adding the responses (hours) of three questions: “How many hours a day do you usually watch television?”; “How many hours a day do you usually play video games on a tablet, computer, or cell phone?”; and “How many hours a day do you usually use a tablet, computer, or cell phone for purposes other than gaming, for example, email, chats, social networks, internet, or doing homework?” [32,33].
Sleep hygiene: The questionnaire used to assess sleep habits was the Sleep Self-Report (SSR) in its Spanish version. Each item has a 3-point scale to indicate the frequency of each habit: usually (2 = 5 to 7 times a week), sometimes (1 = 2 to 4 times a week), and rarely (0 = never or once a week). The questionnaire consists of 19 questions (three of them provide additional information but are not included in any subscale), grouped into 7 for sleep quality, 6 for sleep-related anxiety, 4 for rejection of sleep, 3 for routines to go to sleep, and 16 for the score total [33,34].
In addition, sociodemographic values such as sex (male/female), age (9–12 years/13–15 years), school grade (fifth grade/six grade/seventh grade/eight grade), place of residence (urban/rural), and if they belong to the school integration program (yes/no) were measured.

2.5. Statistical Analysis

Data were analyzed with the statistical software SPSS 25.0 (Windows, SPSS Inc., Chicago, IL, USA). Continuous variables were presented as mean and standard deviation, and categorical as percentages. After performing the Kolmogorov–Smirnov test to assess normality and the Levene’s test to assess homogeneity, all the variables showed a normal distribution and homogeneity of variances, so parametric statistics were used. Then, to establish an association between categorical variables, the Chi-square test was used. To establish differences between the three groups (normal adiposity, moderate adiposity, and excess adiposity) a one-factor ANOVA test was performed (level of adiposity). To determine differences between groups, a post hoc analysis (Bonferroni) was used. The association of adiposity and physical fitness were investigated using linear regression analyses. Data were presented as coefficient and its 95% CI. Multivariable models were adjusted for relevant confounding variables, selected from descriptive Table 1: Model 0—unadjusted; and Model 1—adjusted socio-educational and lifestyle variables (age, sex, school grade, special needs integration program, nutrition status, bedtime routine). Significance was set at the level of p < 0.05.

3. Results

Table 1 presents the physical fitness of CA according to socio-educational characteristics and lifestyle. In this sample of 594 CA, 58.8% of the participants were male, 61.3% were aged between 9 and 12 years, 83.4% did not belong to the school integration program, and 97.8% lived in urban places. As might be expected, boys performed better on tests of muscle strength (p < 0.001) and aerobic capacity (p < 0.001) than girls. In addition, children aged 13–15 years had better performance on tests of muscle strength (p < 0.001) and aerobic capacity (p < 0.01) than children aged 9–12 years. Furthermore, CA that belong to school integration programs had a lower standing broad jump test (p < 0.05) performance, compared with other CA. Moreover, CA with healthy adiposity had better performance in the standing broad jump test (p < 0.001) and the 20 m shuttle run test (p < 0.001) than children with overweight/obesity. Finally, children without sleeping problems had higher handgrip strength (p < 0.05) than children with sleeping problems.
Table 2 shows screen time, sleep hygiene, and perception of physical fitness according to central adiposity. A high percentage (>92%) of CA spent more than 2 h a day watching or using screens. In addition, 11–18% of the CA presented bedtime routine problems, anxiety related to sleep, or refusal to sleep, and 18–24% of the CA had poor sleep quality. All of these sleeping problems were independent of central adiposity. In contrast, adiposity was associated with the perception of physical fitness, as children with excessive adiposity had a lower perception of their general physical fitness (p < 0.001), cardiorespiratory fitness (p < 0.001), speed/agility (p < 0.001), and flexibility (p < 0.001).
Table 3 shows the cardiorespiratory fitness and muscle strength according to adiposity. There were significant differences in the standing broad jump test (p < 0.001) and in the 20 m shuttle run test (speed and shuttle) (p < 0.001) between CA with normal adiposity and CCA with moderate or excess adiposity. CA with normal adiposity had a better physical performance in all the tests performed.
Table 4 shows the association between cardio-respiratory fitness and muscle strength with the adiposity category. In the non-adjusted model, the CA with normal adiposity presented a better performance in the standing broad jump test (p < 0.001) compared to those who had excessive adiposity. Along the same lines, CA with normal (p < 0.001) and moderate (p < 0.001) adiposity presented a better performance in the 20 m shuttle run test than children with excessive adiposity. The same association was found (Table 4) once the model was adjusted for socio-educational and lifestyle variables such as age, sex, grade, participation in a school integration program, BMI, and bedtime routine (Table 1).

4. Discussion

This novel research suggests that a high percentage of CA of the south of Chile spend more than 2 h a day using or watching screens and have a low sleep quality. Children with excessive adiposity have a poor perception of physical fitness and poor cardiorespiratory fitness and muscle strength compared with children with normal adiposity.
The participants of our study spent too much screen time and not enough hours sleeping, regardless of their adiposity. This differs from what was expected since the evidence shows that high amounts of screen time and insufficient sleep are associated with obesity [11]. A study carried out on children in India found that poor sleep quality and low physical activity levels were associated with an increased risk of obesity, while high exposure to screens and low physical activity were associated with a higher BMI and WtHr [11]. The present study took place in a cold geographical area, which could have a fundamental role in metabolic processes such as the sleep cycle, hunger, and body temperature [35]. The evidence suggests that external conditions such as cold weather could be associated with higher screen time, less physical activity, and less exposure to sunlight, which may explain the decrease in sleeping hours, affecting the sleep cycle in all children [36,37].
On the other hand, it was observed that children with excessive adiposity reported a poor perception of physical condition more frequently than children with normal fat mass. In this regard, the perception of physical fitness is part of the multidimensional construct of physical self-concept [38]. In this sense, the role that fat has in the physical self-concept of children is not entirely clear. However, the direct association between physical self-concept and physical activity practice in CA is well documented [39,40]. In this line, Fernández-Bustos et al. conducted a study with CA aged 12–17 years [41], where they proposed a mediation model based on well-known theoretical models of physical self-concept [42,43]. They reported a direct effect of the fat mass on the body image and a mediating effect of fat mass and body image on physical self-concept [41].
The mediation model described above provides a plausible explanation for the findings of this study regarding the relationship between excessive fat mass and poor perception of physical fitness in children from the extreme south of Chile. These results evidence how environments with extreme cold weather favor excessive adiposity in CA [17,44,45]. Therefore, children from the extreme south of Chile could be in an environment that makes it hard to practice physical activity and do not favor physical self-concept.
CA with normal adiposity showed a higher aerobic capacity and muscle strength compared to those with excessive adiposity. These results are in line with the extensive existing evidence, where an inverse relationship between BMI and physical fitness is described [46,47,48]. Specifically, studies carried out in Chile indicate that overweight/obese children are the ones who present the worst aerobic capacity and muscle strength capacity [49,50,51]. However, BMI and physical performance do not show a linear relationship. Children with low weight also have lower physical fitness than those with adequate weight. In this sense, in a study carried out in 73,561 adolescents aged 13–15 years from Chile and Colombia, those with low or high BMI presented worse aerobic capacity than their peers with an adequate BMI [52].
In this context, we have previously reported on differences in anthropometric indicators according to the physical fitness of CA who live in the extreme south of Chile [23]. Overweight/obese students with higher muscle strength had a healthier waist circumference and WtHr [23].

4.1. Strengths and Limitations

This study has several strengths: a representative sample of CA belonging to educational centers in a city in the extreme south of Chile; the WtHr used as a grouping variable of the fat mass, which provides originality, relevance, and pertinence; and the context of childhood obesity within a territorial context of extreme cold weather. This study provides useful information about lifestyles and physical fitness of the CA for teachers and health professionals, particularly those who work in extremely cold territories.
The present study is a pioneer in Chile and South America in characterizing lifestyles, physical fitness, and body composition in children from territories with extremely cold climates. In addition, according to the literature studied, it is the first study that analyzes WtHr and its association with physical fitness, screen time, and sleep hygiene in children from territories with extremely cold climates.
However, this study has some limitations. For example, the variables of screen time and perception of physical fitness used self-reporting instruments so that the responses of the CA were subject to their mood, understanding, and even the maturity to understand the relevance of their responses. Nevertheless, the teachers who used the surveys answered all the doubts that the children had. This method of collecting information is widely used in the literature [32] and allows collecting data from a large sample in a short time. In addition, the type of study design only establishes relationships between adiposity and physical fitness and lifestyles but does not imply causality. In addition, although a multivariate analysis was used to evaluate the influence of covariates on the association between physical fitness and adiposity, there may be other variables that were not measured and that could interfere (example: type of diet, physical activity levels).

4.2. Practical Implications and Future Lines of Investigation

To date, there is little evidence about childhood overweight/obesity in territories with extremely cold climates. In the present study, children who had excessive adiposity had a low physical fitness perception and poor physical fitness [18]. In addition, climatic conditions such as extremely cold weather modify the lifestyles of CA, forcing them to spend more hours in closed spaces [44], leading to increased screen time, sleeping problems, anxiety, appetite, and fat percentage [11]. On the other hand, sleep and overweight/obesity are highly linked, especially in adverse weather conditions in countries with characteristics such as longer days in summer, many hours of darkness in winter, and low temperatures in winter [14].
In addition, this study shows the relevance of lifestyle and environmental factors such as sleep, screen time, physical activity, and extremely cold weather concerning overweight and obesity in children. These results can be used in educational centers and health centers to analyze how healthy lifestyles are being promoted and how weight control is being addressed in CA.
Future studies should further investigate the consequences of childhood obesity on the physical, mental, and emotional health of children living in extremely cold regions. Moreover, future research should focus on the risk factors leading to childhood obesity and use the WtHr as an adiposity marker.
As future lines of research, many studies use BMI, waist circumference, and WtHr as predictors of cardiovascular risk and physical fitness in children [29,30,31]. However, BMI has been questioned due to the limitations to detect adiposity in CA [5], so WtHr should be used instead. WtHr has been shown to be accurate in measuring abdominal fat predicting chronic diseases such as type 2 diabetes mellitus [7].

5. Conclusions

In the extreme south of Chile, children and adolescents spend a high number of hours watching or using screens and indicate having a low sleep quality. In addition, children with excessive central adiposity have a poor perception of their physical fitness and concordantly have worse cardio-respiratory fitness and muscle strength compared to those with normal central obesity. These results are independent of socio-educational and lifestyle variables such as age, sex, school grade, participation in a school integration program, BMI, and bedtime routine.

Author Contributions

Conceptualization. J.A.-G.; data curation, J.A.-G., R.Z.-L., I.C., F.C.-M. and D.R.-M.; formal analysis, J.A.-G., O.B., S.G.-M., R.Z.-L. and I.C.; funding acquisition, J.A.-G.; investigation, J.A.-G., S.G.-M., O.B. and R.Z.-L.; methodology, J.A.-G., I.C., D.R.-M. and F.C.-M.; project administration, J.A.-G., O.B., S.G.-M. and R.Z.-L.; resources, J.A.-G.; software, J.A.-G. and I.C.; supervision, J.A.-G., O.B., S.G.-M., R.Z.-L., G.G.-P.-d.-S. and I.C.; validation, O.B., S.G.-M., R.Z.-L., F.C.-M. and G.G.-P.-d.-S.; visualization, J.A.-G., O.B., S.G.-M., D.R.-M., I.C., G.G.-P.-d.-S., F.C.-M. and R.Z.-L.; writing—original draft, J.A.-G., O.B., S.G.-M., D.R.-M., I.C., G.G.-P.-d.-S., F.C.-M. and R.Z.-L.; writing—review and editing, J.A.-G., O.B., S.G.-M., D.R.-M., I.C., G.G.-P.-d.-S., F.C.-M. and R.Z.-L. All authors have read and agreed to the published version of the manuscript.

Funding

The authors received no financial support for the research

Institutional Review Board Statement

The current study was approved by the Research Ethics Committee of the Institutional Review Board of the Santo Tomás University, Los Angeles, Chile, and also respected the Helsinki guidelines throughout the study.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are available upon request due to ethical and privacy restrictions.

Acknowledgments

The authors wish to thank the team of the Sports and Physical Activity Unit of the University of Magallanes for their help in the field stage and the municipal schools of Punta Arenas for granting the spaces and support in each activity carried out.

Conflicts of Interest

The authors declare no potential conflict of interest concerning the research, authorship, and/or publication of this article.

References

  1. 2021 Global Nutrition Report. Available online: https://globalnutritionreport.org/ (accessed on 26 November 2021).
  2. JUNAEB, Mapa Nutricional. Available online: https://www.junaeb.cl/mapa-nutricional (accessed on 26 November 2021).
  3. Güngör, N.K. Overweight and Obesity in Children and Adolescents. J. Clin. Res. Pediatr. Endocrinol. 2014, 6, 129–143. [Google Scholar] [CrossRef] [PubMed]
  4. Fang, X.; Zuo, J.; Zhou, J.; Cai, J.; Chen, C.; Xiang, E.; Li, H.; Cheng, X.; Chen, P. Childhood obesity leads to adult type 2 diabetes and coronary artery diseases: A 2-sample mendelian randomization study. Medicine 2019, 98, e16825. [Google Scholar] [CrossRef]
  5. Javed, A.; Jumean, M.; Murad, M.H.; Okorodudu, D.; Kumar, S.; Somers, V.K.; Erwin, P.J.; Lopez-Jimenez, F. Diagnostic performance of body mass index to identify obesity as defined by body adiposity in children and adolescents: A systematic review and meta-analysis. Pediatr. Obes. 2015, 10, 234–244. [Google Scholar] [CrossRef] [PubMed]
  6. Rodríguez, J.H.; Jimbo, P.N.D. Índice cintura/talla y su utilidad para detectar riesgo cardiovascular y metabólico. Rev. Cuba Endocrinol. 2015, 26, 66–76. Available online: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S1561-29532015000100006 (accessed on 13 June 2021).
  7. Moosaie, F.; Abhari, S.M.F.; Deravi, N.; Behnagh, A.K.; Esteghamati, S.; Firouzabadi, F.D.; Rabizadeh, S.; Nakhjavani, M.; Esteghamati, A. Waist-To-Height Ratio Is a More Accurate Tool for Predicting Hypertension Than Waist-To-Hip Circumference and BMI in Patients with Type 2 Diabetes: A Prospective Study. Front. Public Health 2021, 9, 726288. [Google Scholar] [CrossRef] [PubMed]
  8. Ashwell, M.; Gunn, P.; Gibson, S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: Systematic review and meta-analysis. Obes. Rev. 2012, 13, 275–286. [Google Scholar] [CrossRef] [PubMed]
  9. Hernández-Jaña, S.; Sanchez-Martinez, J.; Solis-Urra, P.; Esteban-Cornejo, I.; Castro-Piñero, J.; Sadarangani, K.P.; Aguilar-Farias, N.; Ferrari, G.; Cristi-Montero, C. Mediation Role of Physical Fitness and Its Components on the Association Between Distribution-Related Fat Indicators and Adolescents’ Cognitive Performance: Exploring the Influence of School Vulnerability. The Cogni-Action Project. Front. Behav. Neurosci. 2021, 15. [Google Scholar] [CrossRef] [PubMed]
  10. Yang, X.; Telama, R.; Leskinen, E.; Mansikkaniemi, K.; Viikari, J.; Raitakari, O.T. Testing a model of physical activity and obesity tracking from youth to adulthood: The cardiovascular risk in young Finns study. Int. J. Obes. 2007, 31, 521–527. [Google Scholar] [CrossRef] [Green Version]
  11. Moitra, P.; Madan, J.; Verma, P. Independent and combined influences of physical activity, screen time, and sleep quality on adiposity indicators in Indian adolescents. BMC Public Health 2021, 21, 2093. [Google Scholar] [CrossRef] [PubMed]
  12. Álvarez, O.R.; Giráldez, V.A.; Prieto, D.C.; García, A.I. Changes in Physical Fitness, Dietary Habits and Family Habits for Spanish Children during SARS-CoV-2 Lockdown. Int. J. Environ. Res. Public Health 2021, 18, 13293. [Google Scholar] [CrossRef]
  13. Ramos-Álvarez, O.; Arufe-Giráldez, V.; Cantarero-Prieto, D.; Ibáñez-García, A. Impact of SARS-CoV-2 Lockdown on Anthropometric Parameters in Children 11/12 Years Old. Nutrients 2021, 13, 4174. [Google Scholar] [CrossRef]
  14. Miller, M.A.; Kruisbrink, M.; Wallace, J.; Ji, C.; Cappuccio, F.P. Sleep duration and incidence of obesity in infants, children, and adolescents: A systematic review and meta-analysis of prospective studies. Sleep 2018, 41, zsy018. [Google Scholar] [CrossRef] [PubMed]
  15. Åberg, M.A.I.; Pedersen, N.L.; Torén, K.; Svartengren, M.; Bäckstrand, B.; Johnsson, T.; Cooper-Kuhn, C.M.; Aberg, N.D.; Nilsson, M.; Kuhn, H.G. Cardiovascular fitness is associated with cognition in young adulthood. Proc. Natl. Acad. Sci. USA 2009, 106, 20906–20911. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Donnelly, J.E.; Hillman, C.H.; Castelli, D.; Etnier, J.L.; Lee, S.; Tomporowski, P.; Lambourne, K.; Szabo-Reed, A.N. Physical Activity, Fitness, Cognitive Function, and Academic Achievement in Children: A Systematic Review. Med. Sci. Sports Exerc. 2016, 48, 1197–1222. [Google Scholar] [CrossRef] [Green Version]
  17. Habibzadeh, N. Why Physiologically Cold weather can Increase Obesity Rates? Int. Physiol. J. 2018, 2, 11–13. [Google Scholar] [CrossRef]
  18. Albornoz-Guerrero, J.; García, S.; de Sevilla, G.G.P.; Cigarroa, I.; Zapata-Lamana, R. Characteristics of multicomponent interventions to treat childhood overweight and obesity in extremely cold climates: A systematic review of a randomized controlled trial. Int. J. Environ. Res. Public Health 2021, 18, 3098. [Google Scholar] [CrossRef] [PubMed]
  19. Ghobadi, S.; Hassanzadeh-Rostami, Z.; Salehi-Marzijarani, M.; Bellissimo, N.; Brett, N.R.; de Zepetnek, J.O.T.; Faghih, S. Association of eating while television viewing and overweight/obesity among children and adolescents: A systematic review and meta-analysis of observational studies. Obes. Rev. 2018, 19, 313–320. [Google Scholar] [CrossRef] [PubMed]
  20. Instituto Europeo del Sueño. Available online: https://institutoeuropeodelsueno.cl/las-temperaturas-extremas-no-te-dejan-dormir-bien-esto-es-lo-que-debes-hacer/ (accessed on 26 November 2021).
  21. Ciudades del Mundo con Más y Menos Luz Solar—Greening. Available online: https://greening-e.com/ciudades-del-mundo-con-mas-y-menos-luz-solar/ (accessed on 26 November 2021).
  22. Mapa de la Obesidad en Chile: 1 de Cada 3 Personas Vive con Obesidad en Chile—El Mostrador, Abril. 2021. Available online: https://www.elmostrador.cl/agenda-pais/2021/04/09/mapa-de-la-obesidad-en-chile-1-de-cada-3-personas-vive-con-obesidad-en-chile/ (accessed on 9 April 2021).
  23. Albornoz-Guerrero, J.; Zapata-Lamana, R.; Reyes-Molina, D.; Cigarroa, I.; de Sevilla, G.G.P.; García-Merino, S. Overweight/Obese Schoolchildren with Low Muscle Strength Have a Lower Cardiorespiratory Capacity and Greater Cardiovascular Risk: Results of the School Health Survey of the Extreme South of Chile 2019. Children 2021, 8, 734. [Google Scholar] [CrossRef]
  24. García, J.A.; Cárdenas, A.; Burgos, S.; Santiago, C.; Hernández, F.; Sanz, V.; del Valle, M.F.; Alonso, M.R.; Perez, M. Estilo de vida y distribución de grasa en adolescentes asmáticos y sanos. Rev. Int. Med. Cienc. Act. Física Deport. 2019, 19, 107–118. [Google Scholar] [CrossRef]
  25. Ruiz, J.R.; Castro-Piñero, J.; España-Romero, V.; Artero, E.G.; Ortega, F.B.; Cuenca, M.A.M.; Jimenez-Pavon, D.; Chillon, P.; Girela-Rejon, M.J.; Mora, J. Field-based fitness assessment in young people: The ALPHA health-related fitness test battery for children and adolescents. Br. J. Sports Med. 2011, 45, 518–524. [Google Scholar] [CrossRef]
  26. Batería ALPHA-Fitness: Test de Campo para la Evaluación de la Condición Física Relacionada con la Salud en Niños y Adolescentes. Available online: https://scielo.isciii.es/scielo.php?script=sci_arttext&pid=S0212-16112011000600003 (accessed on 1 December 2021).
  27. Grupo de Investigación PROmoting FITness and Health > IFIS/Universidad de Granada. Available online: http://profith.ugr.es/ (accessed on 1 December 2021).
  28. Sánchez-López, M.; Martínez-Vizcaíno, V.; García-Hermoso, A.; Jiménez-Pavón, D.; Ortega, F.B. Construct validity and test–retest reliability of the International Fitness Scale (IFIS) in Spanish children aged 9–12 years. Scand. J. Med. Sci. Sports 2015, 25, 543–551. [Google Scholar] [CrossRef] [PubMed]
  29. Da Silva, V.S.; Vieira, M.F.S. International society for the advancement of kinanthropometry (Isak) global: International accreditation scheme of the competent anthropometrist. Rev. Bras. Cineantropometria Desempenho Hum. 2020, 22, 1–6. [Google Scholar] [CrossRef]
  30. Habicht, J.-P. Estandarizacion de metodos epidemiologicos cuantitativos sobre el terreno. Boletín Oficina Sanit. Panam. (OSP) 1974, 76, 375–384. Available online: https://iris.paho.org/handle/10665.2/10766 (accessed on 1 February 2022).
  31. Ministerio de Salud de Chile, Patrones de Crecimiento para la Evaluación Nutricional de Niños, Niñas y Adolescentes desde el Nacimiento hasta los 19 Años de Edad. 2018. Available online: https://diprece.minsal.cl/wp-content/uploads/2018/07/Patrones-de-Crecimiento-para-la-Evaluación-Nutrición-de-niños-niñas-y-adolescentes-desde-el-nacimiento-a-19-años.pdf (accessed on 24 March 2022).
  32. Zapata-Lamana, R.; Ibarra-Mora, J.; Henriquez-Beltrán, M.; Sepúlveda-Martin, S.; Martínez-González, L.; Cigarroa, I. Aumento de horas de pantalla se asocia con un bajo rendimiento escolar. Andes Pediatr. 2021, 92, 565–575. [Google Scholar] [CrossRef]
  33. Aguilar, M.M.; Vergara, F.A.; Velásquez, E.J.A.; García-Hermoso, A. Physical activity, screen time and sleep patterns in Chilean girls. An. Pediatr. 2015, 83, 304–310. [Google Scholar] [CrossRef] [PubMed]
  34. Orgilés, M.; Owens, J.; Espada, J.P.; Piqueras, J.A.; Carballo, J.L. Spanish version of the Sleep Self-Report (SSR): Factorial structure and psychometric properties. Child Care Health Dev. 2013, 39, 288–295. [Google Scholar] [CrossRef] [PubMed]
  35. Basolo, A.; Genzano, S.B.; Piaggi, P.; Krakoff, J.; Santini, F. Energy Balance and Control of Body Weight: Possible Effects of Meal Timing and Circadian Rhythm Dysregulation. Nutrients 2021, 13, 3276. [Google Scholar] [CrossRef] [PubMed]
  36. Eisinga, R.; Franses, P.H.; Vergeer, M. Weather conditions and daily television use in the Netherlands, 1996–2005. Int. J. Biometeorol. 2011, 55, 555. [Google Scholar] [CrossRef] [Green Version]
  37. Rich, C.; Griffiths, L.J.; Dezateux, C. Seasonal variation in accelerometer-determined sedentary behaviour and physical activity in children: A review. Int. J. Behav. Nutr. Phys. Act. 2012, 9, 49. [Google Scholar] [CrossRef] [Green Version]
  38. Fox, K.R. Self-esteem, self-perceptions and exercise. Int. J. Sport Psychol. 2000, 31, 228–240, ISSN 0047-0767. [Google Scholar]
  39. Babic, M.J.; Morgan, P.J.; Plotnikoff, R.C.; Lonsdale, C.; White, R.L.; Lubans, D.R. Physical Activity and Physical Self-Concept in Youth: Systematic Review and Meta-Analysis. Sports Med. 2014, 44, 1589–1601. [Google Scholar] [CrossRef]
  40. Liu, M.; Wu, L.; Ming, Q. How Does Physical Activity Intervention Improve Self-Esteem and Self-Concept in Children and Adolescents? Evidence from a Meta-Analysis. PLoS ONE 2015, 10, e0134804. [Google Scholar] [CrossRef] [PubMed]
  41. Fernández-Bustos, J.G.; Infantes-Paniagua, Á.; Cuevas, R.; Contreras, O.R. Effect of Physical Activity on Self-Concept: Theoretical Model on the Mediation of Body Image and Physical Self-Concept in Adolescents. Front. Psychol. 2019, 10, 1537. [Google Scholar] [CrossRef]
  42. Sonstroem, R.J.; Morgan, W.P. Exercise and self-esteem: Rationale and model. Med. Sci. Sports Exerc. 1989, 21, 329–337. [Google Scholar] [CrossRef]
  43. Fox, K.R.; Corbin, C.B. The Physical Self-Perception Profile: Development and preliminary validation. J. Sport Exerc. Psychol. 1989, 11, 408–430, ISSN 1543-2904. [Google Scholar] [CrossRef]
  44. Young, T.K. Are the circumpolar inuit becoming obese? Am. J. Hum. Biol. 2007, 19, 181–189. [Google Scholar] [CrossRef] [PubMed]
  45. Braghetto, I.; Taladriz, C.; Lanzarini, E.; Romero, C. Plasma ghrelin levels in the late postoperative period of vertical sleeve gastrectomy. Rev. Med. Chil. 2015, 143, 864–869. [Google Scholar] [CrossRef] [Green Version]
  46. Ortega, F.B.; Ruiz, J.R.; Castillo, M.J.; Sjöström, M. Physical fitness in childhood and adolescence: A powerful marker of health. Int. J. Obes. 2008, 32, 1–11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Kelley, G.A.; Kelley, K.S.; Pate, R.R. Exercise and adiposity in overweight and obese children and adolescents: A systematic review with network meta-analysis of randomised trials. BMJ Open 2019, 9, e031220. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. García-Hermoso, A.; Esteban-Cornejo, I.; Olloquequi, J.; Ramírez-Vélez, R. Cardiorespiratory Fitness and Muscular Strength as Mediators of the Influence of Fatness on Academic Achievement. J. Pediatr. 2017, 187, 127–133.e3. [Google Scholar] [CrossRef] [PubMed]
  49. Ibarra-Mora, J.; Hernández-Mosqueira, C.; Palma, F.H.; Adasme, G.P.; Salazar, C.M. Estado Nutricional y Desempeño Físico de una muestra de Escolares de 14 y 15 años de la ciudad de Chillán, Chile. Rev. Española Nutr. Hum. Dietética 2017, 21, 248–255. [Google Scholar] [CrossRef] [Green Version]
  50. Floody, P.D.; Navarrete, F.C.; Guzmán, I.P.G.; Mayorga, D.J.; Ramírez-Campillo, R.; Jara, C.C.; Lagos, G.R.; Inostroza, H.D. Levels of obesity, fasting glycemia and physical condition in chilean students. Nutr. Hosp. 2015, 31, 2445–2450. [Google Scholar] [CrossRef]
  51. Hernández-Mosqueira, C.M.; Quezada, H.E.C.; da Silva, S.F.; Troncoso, S.P.; Barría, M.A.C.; Cárcamo-Oyarzun, J.; Martinez-Salazar, C.; Caniuqueo-Vargas, A.; Filho, J.F. Assessment of the nutritional status and physical condition of basic education students in Chile. Nutr. Hosp. 2020, 37, 1166–1172. [Google Scholar] [CrossRef] [PubMed]
  52. García-Hermoso, A.; Correa-Bautista, J.E.; Olloquequi, J.; Ramírez-Vélez, R. Health-related physical fitness and weight status in 13- to 15-year-old Latino adolescents. A pooled analysis. J. Pediatr. 2019, 95, 435–442. [Google Scholar] [CrossRef] [PubMed]
Table
Table 1. Physical fitness according to socio-educational characteristics and lifestyle.
Table 1. Physical fitness according to socio-educational characteristics and lifestyle.
Fitness
Muscle StrengthCardiorespiratory Fitness
Handgrip Strength Dominant Hand (kg)Standing Broad Jump Test (cm)20 m Shuttle Run Test (Speed)20 m Shuttle Run Test (Palier)
Variables N (%)Mean [95% CI]Mean [95% CI]Mean [95% CI]Mean [95% CI]
Socio-educational variables
Sex
Male302 (50.8%)24.6 [23.7; 25.4] ***128.5 [125.2; 131.9] ***2.57 [2.56; 2.59] ***31.2 [29.3; 33.0] ***
Female292 (49.2%)21.6 [21.0; 22.1]110.8 [108.0; 113.6]2.52 [2.50; 2.53]24.7 [23.3; 26.2]
Age (years)
9–12 years364 (61.3%)21.2 [20.6; 21.7] ***114.4 [111.9; 116.9] ***2.54 [2.52; 2.55]26.9 [25.5; 28.4] **
13–15 years230 (38.7%)26.1 [25.2; 27.0]128.4 [124.2; 132.5]2.56 [2.54; 2.58]29.7 [27.6; 31.7]
Grade
Fifth grade166 (27.9%)19.3 [18.6; 20.0] ***111.0 [107.8; 114.1] ***2.53 [2.51; 2.55]26.1 [24.0; 28.1]
Sixth grade153 (25.8%)22.1 [21.3; 22.8]116.8 [112.8; 120.8]2.55 [2.53; 2.57]28.1 [25.8; 30.5]
Seventh grade123 (20.7%)24.1 [23.1; 25.1]119.5 [114.0; 125.0]2.53 [2.51; 2.56]27.2 [24.6; 29.8]
Eightth grade152 (25.6%)27.3 [26.2; 28.5]132.7 [127.5; 137.9]2.57 [2.54; 2.59]30.5 [27.9; 33.2]
School Integration
Program
Yes99 (16.8%)23.7 [22.4; 25.0]125.1 [119.5; 130.7] *2.57 [2.54; 2.59]30.0 [27.0; 33.0]
No495 (83.4%)23.0 [22.4; 23.5]118.7 [116.2; 121.2]2.54 [2.53; 2.55]27.6 [26.3; 28.9]
Place of residence
Urban581 (97.8%)23.1 [22.6; 23.6]120.0 [117.7; 122.3]2.55 [2.53; 2.56]28.0 [26.8; 29.2]
Rural13 (2.2%)21.9 [18.4; 25.5]112.3 [97.8; 126.8]2.53 [2.46; 2.60]26.2 [18.7; 33.8]
Lifestyle variables
Nutritional status
Normal weight162 (27.3%)22.5 [21.5; 23.4]130.4 [126.0; 134.7] ***2.59 [2.57; 2.61] ***33.8 [31.4; 36.2] ***
Overweight421 (72.7%)23.4 [22.8; 24.1]115.6 [113.0; 118.2]2.52 [2.51; 2.54]25.5 [24.2; 26.8]
Screen time
<2 h/day33 (7.7%)22.4 [20.3; 24.5]117.9 [107.2; 128.5]2.52 [2.48; 2.57]24.5 [20.2; 28.7]
≥2 h/day548 (92.3%)23.1 [22.5; 23.6]120.1 [117.8; 122.5]2.55 [2.53; 2.56]28.2 [26.9; 29.4]
Bedtime routine
Adequate514 (86.5%)23.3 [22.7; 23.9] *120.0 [117.5; 122.5]2.55 [2.53; 2.56]28.2 [26.9; 29.5]
With problems80 (13.5%)21.7 [20.5; 22.9]118.4 [112.4; 124.5]2.54 [2.51; 2.57]26.7 [23.6; 29.8]
Sleep anxiety
Without anxiety512 (86.2%)23.2 [22.7; 23.7]119.9 [117.4; 122.4]2.54 [2.53; 2.55]27.7 [26.4; 29.0]
With anxiety 82 (13.8%)22.4 [20.9; 23.8]119.1 [113.1; 125.1]2.57 [2.54; 2.59]29.5 [26.4; 32.6]
Sleep quality
Adequate456 (76.8%)23.3 [22.7; 23.8]120.4 [117.8; 123.1]2.54 [2.53; 2.55]27.5 [26.2; 38.9]
Poor138 (23.2%)22.5 [21.4; 23.6]117.8 [113.3; 122.4]2.56 [2.54; 2.58]29.5 [26.9; 32.0]
Refusal to sleep
No503 (84.8%)23.3 [22.7; 23.8]120.0 [117.5; 122.5]2.54 [2.53; 2.56]27.9 [26.6; 29.1]
Yes91 (15.2%)22.0 [20.8; 23.3]118.9 [113.2; 124.6]2.55 [2.52; 2.58]28.7 [25.5; 31.9]
The qualitative variables are presented in absolute and percentage frequency and the quantitative ones are presented in mean and their respective CI 95%. *** = the differences are significant at p < 0.001, ** = the differences are significant at p < 0.01. * = the differences are significant at p < 0.05. n = 594.
Table
Table 2. Screen time, bedtime routine, and physical fitness perception.
Table 2. Screen time, bedtime routine, and physical fitness perception.
VariablesNormal Adiposity
N (%)		Moderate Adiposity
N (%)		Excess Adiposity
N (%)		p-Value
Screen Time and Sleep Hygiene
Screen time
<2 h/day20 (7.5%)6 (6.5%)7 (3.1%)0.106
≥2 h/day246 (92.5%)86 (93.5%)216 (96.9%)
Bedtime routine
Adequate239 (87.9%)80 (84.2%)195 (85.9%)0.627
With problems33 (12.1%)15 (15.8%)32 (14.1%)
Sleep anxiety
Without anxiety240 (88.2%)78 (82.1%)194 (85.5%)0.303
With anxiety32 (11.8%)17 (17.9%)33 (14.5%)
Sleep quality
Adequate205 (75.4%)77 (81.1%)174 (76.7%)0.528
Poor67 (24.6%)18 (18.9%)53 (23.3%)
Refusal to sleep
No233 (85.7%)82 (86.3%)188 (82.8%)0.605
Yes39 (14.3%)13 (13.7%)39 (17.2%)
Total sleep score (SSR)
Healthy sleep232 (85.3%)78 (82.1%)186 (81.9%)0.557
With sleep problems40 (14.7%)17 (17.9%)41 (18.1%)
Physical Fitness Perception (IFIS)
General physical fitness
Very good72 (26.5%)10 (10.5%)30 (13.2%)<0.001
Good81 (29.8%)28 (29.5%)48 (21.1%)
Fair90 (33.1%)36 (37.9%)81 (35.7%)
Poor23 (8.5%)18 (18.9%)55 (24.2%)
Very poor6 (2.2%)3 (3.2%)13 (5.7%)
Cardiorespiratory fitness
Very good72 (26.5%)9 (9.5%)25 (11.0%)<0.001
Good80 (29.4%)22 (23.2%)41 (18.1%)
Fair83 (30.5%)30 (31.6%)68 (30.0%)
Poor34 (12.5%)30 (31.6%)72 (31.7%)
Very poor3 (1.1%)4 (4.2%)21 (9.3%)
Muscle strength
Very good58 (21.3%)12 (12.6%)31 (13.7%)0.124
Good79 (29.0%)25 826.3%)62 (27.3%)
Fair79 (29.0%)38 (40.0%)70 (30.8%)
Poor46 (16.9%)18 (18.9%)52 (22.9%)
Very poor10 (3.7%)2 (2.1%)12 (5.3%)
Velocity/agility
Very good55 (20.2%)11 (11.6%)27 (11.9%)<0.001
Good88 (32.4%)31 (32.6%)43 (18.9%)
Fair86 (31.6%)34 (35.8%)87 (38.3%)
Poor34 (12.5%)13 (13.7%)58 (25.6%)
Very poor9 (3.3%)6 (6.3%)12 (5.3%)
Flexibility
Very good55 (20.2%)9 (9.5%)19 (8.4%)<0.001
Good 63 (23.2%)33 (34.7%)37 (16.3%)
Fair76 (27.9%)22 (23.2%)66 (29.1%)
Poor65 (23.9%)22 (23.2%)70 (30.8%)
Very poor13 (4.8%)9 (9.5%)35 (15.4%)
Qualitative variables are presented in absolute and percentage frequency. To establish an association between categorical variables, the Chi-square test was used. Significance was set at the level of p < 0.05. n = 594.
Table
Table 3. Cardiorespiratory fitness and muscle strength according to adiposity.
Table 3. Cardiorespiratory fitness and muscle strength according to adiposity.
VariablesNormal AdiposityModerate AdiposityExcess AdiposityOne Way ANOVA
Mean [95% CI]Mean [95% CI]Mean [95% CI]Fp-Value
Muscle strength
Handgrip strength dominant hand (kg)23.1 [22.4; 23.8]22.65 [21.28; 24.02]23.24 [22.37; 24.12]0.2920.747
Standing broad jump test (cm)128.6 [125.2; 132.0] a116.9 [110.9; 122.9] b110.5 [107.3; 113.6] b28.410<0.001
Cardiorespiratory fitness
20 m shuttle run test (speed) (m/s)2.60 [2.58; 2.61] a2.55 [2.53; 2.57] b2.48 [2.47; 2.50] c51.692<0.001
20 m shuttle run test (n° palier)33.86 [32.0; 35.72] a26.49 [24.01; 28.98] b21.57 [20.02; 23.12] c50.120<0.001
Each group are presented in mean and their respective CI 95%. The statistical analysis was carried out through a one-way ANOVA. In the same row, abc different lowercase letters indicate statistically significant differences between groups. (One-way ANOVA and Bonferroni post hoc). Significance was set at the level of p < 0.05. n = 594.
Table
Table 4. Association between physical fitness and the adiposity category.
Table 4. Association between physical fitness and the adiposity category.
Model 0 Model 1
Unadjusted Model Adjusted Model
Variables βi [95% CI]p-Valueβi [95% CI]p-Value
Muscle strength
Handgrip strength dominant hand
Normal adiposity −0.15 [−1.27; 0.98]0.799−0.11 [−1.26; 1.04]0.849
Moderate adiposity −0.59 [−2.12; 094]0.446−0.83 [−2.14; 0.48]0.213
Exceed adiposityReference Reference
Standing broad jump test
Normal adiposity 18.18 [13.39; 22.97]<0.00114.17 [8.98; 19.36]<0.001
Moderate adiposity6.42 [−0.10; 12.93]0.0545.75 [−0.16; 11.66]0.057
Exceed adiposityReference Reference
Cardiorespiratory fitness
20 m shuttle run test (speed)
Normal adiposity 0.11 [0.09; 0.13]<0.0010.11 [0.08; 0.13]<0.001
Moderate adiposity 0.06 [0.03; 0.09]<0.0010.07 [0.04; 0.10]<0.001
Exceed adiposity Reference Reference
20 m shuttle run test (n° palier)
Normal adiposity12.29 [9.86; 14.72]<0.00111.49 [8.69; 14.30]<0.001
Moderate adiposity 4.93 [1.63; 8.22]0.0035.24 [2.04; 8.43]0.001
Exceed adiposity Reference Reference
The data are presented as β levels with their corresponding CI 95%, according to the physical fitness. The statistical analysis was conducted through a multiple linear regression analysis. Exceed fat was considered the reference values (ref.). The statistical analyses were progressively adjusted. Model 0: non adjusted, Model 1: adjusted for socio-educational and lifestyle variables (age, sex, school grade, special needs integration program, nutrition status, bedtime routine). Significance was set at the level of p < 0.05. n = 594.
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Albornoz-Guerrero, J.; Carrasco-Marín, F.; Zapata-Lamana, R.; Cigarroa, I.; Reyes-Molina, D.; Barceló, O.; García-Pérez-de-Sevilla, G.; García-Merino, S. Association of Physical Fitness, Screen Time, and Sleep Hygiene According to the Waist-to-Height Ratio in Children and Adolescents from the Extreme South of Chile. Healthcare 2022, 10, 627. https://doi.org/10.3390/healthcare10040627

AMA Style

Albornoz-Guerrero J, Carrasco-Marín F, Zapata-Lamana R, Cigarroa I, Reyes-Molina D, Barceló O, García-Pérez-de-Sevilla G, García-Merino S. Association of Physical Fitness, Screen Time, and Sleep Hygiene According to the Waist-to-Height Ratio in Children and Adolescents from the Extreme South of Chile. Healthcare. 2022; 10(4):627. https://doi.org/10.3390/healthcare10040627

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Albornoz-Guerrero, Javier, Fernanda Carrasco-Marín, Rafael Zapata-Lamana, Igor Cigarroa, Daniel Reyes-Molina, Olga Barceló, Guillermo García-Pérez-de-Sevilla, and Sonia García-Merino. 2022. "Association of Physical Fitness, Screen Time, and Sleep Hygiene According to the Waist-to-Height Ratio in Children and Adolescents from the Extreme South of Chile" Healthcare 10, no. 4: 627. https://doi.org/10.3390/healthcare10040627

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