Next Article in Journal
Specific Relapse Predictors: Could Cognitive-Behavioral Treatment for Smoking Cessation Be Improved?
Next Article in Special Issue
Association of Physical Fitness with Intelligence and Academic Achievement in Adolescents
Previous Article in Journal
The Multilevel Mechanism of Multifoci Service Orientation on Emotional Labor: Based on the Chinese Hospitality Industry
Previous Article in Special Issue
Association between Weight Status and Physical Fitness in Chinese Mainland Children and Adolescents: A Cross-Sectional Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 17
Issue 12
10.3390/ijerph17124316
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Association between Health-Related Physical Fitness and Self-Rated Risk of Depression in Adolescents: Dados Study

by
Carlos Bou-Sospedra
,
Mireia Adelantado-Renau
,
Maria Reyes Beltran-Valls
and
Diego Moliner-Urdiales
*
LIFE Research Group, Universtiy Jaume I, PC 12071 Castellón de la Plana, Spain
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(12), 4316; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17124316
Submission received: 26 May 2020 / Revised: 11 June 2020 / Accepted: 15 June 2020 / Published: 17 June 2020
(This article belongs to the Special Issue Fitness, Physical Activity, and Health in Youth)
Review Reports Versions Notes

Abstract

:
Depression is the most common mental disorder, affecting around 5% of adolescents. Physical fitness is considered a powerful marker of physical and mental health. The scientific results on the relationship between physical fitness and depression in the adolescent population are mixed. Therefore, the aim of the current study was to analyse the association between objectively assessed physical fitness and self-rated risk of depression in a group of adolescents. A total of 225 participants (44% girls), aged 13.9 ± 0.3 years, from the Deporte, ADOlescencia y Salud (DADOS) study were included in the analyses. Field-based Assessing Levels of Physical fitness and Health in Adolescents (ALPHA) health-related fitness test battery was used to objectively assess physical fitness components. The Behavior Assessment System for Children (BASC) level 3 was used to evaluate self-rated risk of depression. Our results showed that self-rated risk of depression was inversely associated with cardiorespiratory fitness (β = −0.172), as well as positively associated with body mass index (β = 0.146) and waist circumference (β = 0.137) (all p < 0.05). Adolescents with low levels of cardiorespiratory fitness had significantly higher odds of self-rated risk of depression (OR = 7.17; 95% CI, 1.51–33.95). These findings suggest that health-related physical fitness, particularly cardiorespiratory fitness and body composition, is associated with depression in adolescents.

1. Introduction

Depression is the most common mental disorder, and it could be defined as low mood, pessimism, and apathy experienced over an extended length of time [1]. Adolescent depression has around 5% of prevalence and a tendency to increased occurrence over recent years has been observed [2]. Adolescence and early adulthood are critical time frames in terms of depression onset, probably due to a confluence of physical, hormonal, social, emotional, neuronal, behavioural psychological and sleep changes [3,4,5]. Depression during adolescence appears to be associated with some unhealthy behaviours, such as low levels of physical activity [6,7], and with some unhealthy attributes, such as obesity [8]. Hence, adolescence represents a critical period of depression risk that underscores the need to identify factors that can prevent or minimize the deleterious effects of this mental disease [9].
Health-related physical fitness is as a set of attributes related to a person’s ability to perform physical activity, which includes cardiorespiratory fitness, musculoskeletal and motor capacities, as well as body composition [10]. It is determined by a combination of genetic inheritance and health-related behaviours [11,12], and it is considered a powerful marker of physical and mental health [13]. Indeed, findings from cross-sectional [14,15] and longitudinal studies [16] have shown an inverse association between cardiorespiratory fitness and depression in adolescents. With regard to body composition, a meta-analysis reported that obese adolescents had a 40% greater risk of being depressed [8]. Although muscular fitness and speed-agility have shown to be associated with depression in children [17,18] and young adults [19], to our knowledge, no prior studies have investigated these associations in adolescent population.
Expanding the previous scientific literature with an overall approach to the relationship between all health-related physical fitness components and risk of depression is needed. Increasing knowledge about the influence of low health-related physical fitness on risk of depression in adolescents would help to stablish future promotion strategies aimed to improve adolescents’ mental health. Based on the previous literature about health-related physical fitness and depression in adolescents [14,15,16] and other populations [17,18,19], we hypothesized that adolescents with lower levels of physical fitness are at higher risk of depression. Therefore, the aim of the current study was to explore the associations between all health-related physical fitness components and self-rated risk of depression in a sample of adolescents.

2. Materials and Methods

2.1. Study Design and Sample Selection

This study is part of the Deporte, ADOlescencia y Salud (DADOS) research project, a 3-year longitudinal study aimed to analyse the influence of physical activity on health, academic performance, and psychological wellness through adolescence. All participants were recruited from secondary schools located in Castellon (Spain), and met the general inclusion criteria: enrolled in second grade of secondary school, and without diagnosed physical or neurological chronic diseases (as reported by participants’ parents). The results presented in this study belong to baseline data obtained between February and May of 2015. A total of 225 adolescents aged 13.9 ± 0.3 years (44% girls) completed the baseline assessment with valid data for health-related physical fitness components and self-rated risk of depression.
Adolescents and their parents or guardians were informed about the nature and characteristics of the study, and all provided a written informed consent. The DADOS study protocol was designed in accordance with the ethical guidelines of the Declaration of Helsinki 1964 (last revision of Fortaleza, Brazil, 2013), and was approved by the Research Ethics Committee of the University Jaume I of Castellon (Spain).

2.2. Health-Related Physical Fitness

The Assessing Levels of Physical fitness and Health in Adolescents (ALPHA) health-related fitness field-based test battery was used, in order to objectively assess physical fitness components [10].
Cardiorespiratory fitness was assessed by the 20 m shuttle run test [20]. Briefly, each participant runs straight between 2 lines 20 m apart, while keeping the pace with audio signals. The test was completed when participants could not reach the end lines at the pace of the audio signals 2 consecutive times, or when they stopped because of fatigue. The number of laps (20 m each) was registered and used in the analyses. Cardiorespiratory fitness level was dichotomized into low and high according to the 60th sex-specific percentile based on normative values [21].
Upper limb muscular strength was measured with a hand dynamometer with adjustable grip (TKK 5101 Grip D; Takey, Tokyo, Japan). Briefly, the participant squeezes gradually and continuously for at least 2 s, performing the test with the right and left hands in turn, and using the optimal grip span. The maximum score in kilograms for each hand was recorded, and the average of the scores achieved in both handgrip tests was used in the analyses. The upper limb muscular strength level was dichotomized into low and high, according to the 60th sex-specific percentile, based on normative values [21].
Lower limb muscular strength was assessed with the standing broad jump test. Briefly, the participant jumps as far as possible from a starting position immediately behind a line, standing with feet approximately shoulder’s width apart. The maximum score in centimetres was used in the analyses. Lower limb muscular strength level was dichotomized into low and high, according to the 60th sex-specific percentile based on normative values [21].
Speed–agility was assessed with the 4 × 10 m shuttle run test. Briefly, the participant runs as fast as possible 4 times between two parallel lines 10 m apart. The minimum time taken to complete the test was used in the analyses. For analytic purposes, this variable was multiplied by −1, so a higher score indicates better speed-agility. Speed-agility level was dichotomized into high and low, according to the 66th sex-specific percentile.
Weight was measured with an electronic scale (model 861; Seca, Hamburg, Germany) to the nearest 0.1 kg. Height was measured in the Frankfort plane with a wall-mounted stadiometer (model 213; Seca, Hamburg, Germany) to the nearest 0.1 cm. Weight and height were measured in duplicate, and the average was used for data analyses. Body mass index (BMI) was calculated as weight in kilograms divided by the square of the height in meters (kg/m2). Participants were classified into normal weight or overweight/obese, according to the international age and sex-specific BMI cut-offs proposed by Cole et al. [22].
Waist circumference was measured to the nearest 1 mm with a non-elastic tape (Harpenden anthropometric tape; Holtain Ltd., Crymych, UK) applied horizontally midway between the lowest rib margin and the iliac crest, at the end of gentle expiration with the participant in a standing position. Speed-agility level was dichotomized into high and low according to the 33rd sex-specific percentile.
Tricipital and subscapular skinfold thicknesses were measured by duplicate to the nearest 0.2 mm with a Holtain Caliper (Crymmych, UK). Body fat percentage was calculated by the equation described by Slaughter, Lohman, and Boileau [23].

2.3. Risk of Depression

The Behavior Assessment System for Children (BASC), level 3 for adolescents, which has shown extensive psychometric properties in both non-referred and clinical populations with reliabilities for the subscales ranging from 0.80 to 0.87, Spanish version [24] was completed by participants to assess self-rated risk of depression [25]. For risk of depression, standard T-score with an average of 50 and a standard deviation of 10 points were used in the analyses. Risk of depression was dichotomized into “non-risk” (<60) and “at risk” (≥60), according to the established cut off point [25].

2.4. Covariates

According to the previous scientific literature, all the analyses were adjusted by pubertal stage and socioeconomic status, due its strong association with physical fitness components [26,27,28,29] and depression [30,31,32,33] in the adolescent population.
Pubertal stage was self-reported according to the five stages described by Tanner and Whitehouse [34]. It is based on external primary and secondary sexual characteristics, which are described by the participants using standardized pictures.
Socioeconomic status was reported by The Family Affluence Scale (FAS) questionnaire, developed by Currie et al. [35]. It was used as a proxy of socioeconomic status, which is based on material conditions in the family, such as car ownership, bedroom occupancy, computer ownership, and home internet access. Results ranging from 0 to 8 points were categorized as follows: low 1–2, medium 3–5, high 6–8.

2.5. Statistical Analyses

Study sample characteristics are presented as mean ± standard deviation (SD) and percentages for continuous and categorical variables, respectively. As preliminary analyses did not show a significant interaction of sex with the study variables in relation to risk of depression (p > 0.01), all analyses were performed for the whole sample.
Linear regression analyses were conducted to analyses the association between self-rated risk of depression with each health-related physical fitness components. Each physical fitness components at baseline was entered as the independent variable and self-rated depression was entered as depended variable in separate models. To avoid the influence of sex, pubertal stage, and socioeconomic status, all models included these factors as covariates. The Durbin-Watson coefficient, ranging from 0 to 4, was reported to assess the autocorrelation of the residuals. Binary logistic regression models were used to assess the likelihood of being at risk of depression depending on the level of each physical fitness component (low vs. high), after adjusting for sex, pubertal stage, and socioeconomic status. In these analyses the R2 of Cox/Snell is reported. All the analyses were performed using the IBM SPSS Statistics for Windows version 22.0 (Armonk, NY, USA: IBM Corp), and the level of significance was set to p < 0.05.

3. Results

Descriptive characteristics of the study population by sex are presented at Table 1.
Linear regression analyses were performed to analyse the association between each health-related physical fitness component and self-rated risk of depression adjusting for sex, pubertal stage and socioeconomic status (Table 2). Cardiorespiratory fitness, but not muscular strength and speed agility, was inversely associated with self-rated risk of depression (β = −0.172, p < 0.05). BMI and waist circumference were positively associated with self-rated risk of depression (β = 0.146, p < 0.05; β = 0.137, p < 0.05). Similar results were found in partial correlations analyses (Table S1).
Binary logistic regression analyses examining the associations between categories for each health-related physical fitness component and categories of self-rated risk of depression are shown in Table 3. According to health-related physical fitness components, adolescents with low levels of cardiorespiratory fitness showed higher likelihood to be at self-rated risk of depression (OR = 7.17; 95% CI, 1.51–33.95). No statistically significant associations between muscular strength or speed-agility categories and self-rated risk of depression were identified.

4. Discussion

To our knowledge, this is the first study analysing the association between a complete range of health-related physical fitness components and self-rated risk of depression in adolescents. The main findings of the present study revealed that cardiorespiratory fitness was inversely, but weakly, associated with self-rated risk of depression. Moreover, BMI and waist circumference were positively, but weakly, associated with self-rated risk of depression in adolescents. Additionally, adolescents with low levels of cardiorespiratory fitness showed higher likelihood to be at risk of depression. Our results complement the prior literature, indicating a unique contribution of each health-related fitness component on the risk of depression in adolescents.
Part of our data are in accordance with the results reported by Rieck et al. [14], suggesting that adolescents with low levels of cardiorespiratory fitness have an increased likelihood of depressive symptoms. In addition, our results partially concur with previous cross-sectional [15] and longitudinal [16] studies, showing an inverse association between cardiorespiratory fitness and levels of depression in girls but not in boys. We speculate that the association found in this study is related to the physiological effects of regular physical activity [36,37], since cardiorespiratory fitness has been shown to improve through sufficient physical activity practice in adolescents [38]. Although the mechanisms explaining the association between mental health and physical activity remain to be elucidated [36,37,38,39], it is likely that improvements in cardiorespiratory fitness through physical activity may lead to lower risk of depression in adolescents.
With regard to muscular strength and speed-agility, no association was found with self-rated risk of depression in our sample. This is the first study focused on adolescents, directly hampering comparisons among studies. Our results concur with the previous data reported in children showing null associations [17,18]. However, the results in young adults [19] have shown an inverse association between muscular fitness and depressive symptoms. The lack of agreement among studies might be due to the fact that adolescence is a critical period of life, in which the confluence of physical and hormonal changes could lead to different growth curves [40].
According to body composition, our results revealed positive, but weak, associations of BMI and waist circumference with self-rated risk of depression. These findings concur with previous studies, revealing a positive association between BMI and risk of depression in adolescents [41]. However, the association between body composition and risk of depression is controversial, since Rieck et al. [14] reported a null association. The findings of the present study could be partially explained by the fact that a healthy body composition has been related to improved body satisfaction, self-esteem, and social functioning, which in turn, may lead to lower depressive symptoms [42].
Our data may have significant implications from a public health point of view for promoting psychological well-being in adolescents. Indeed, high levels of cardiorespiratory fitness and a healthy composition seem to be protective factors for risk of depression. Active behaviours should be promoted among adolescents by educators, families, and policy makers, in order to improve cardiorespiratory fitness and body composition levels. This is especially important in this age group, since adolescence is a crucial period of life, during which health-related behaviours are established [43].

Limitations and Strengths of the Study

The results of this study should be interpreted with caution, because the cross-sectional design does not allow us to infer causality between health-related fitness components and self-rated risk of depression. The Spanish version of the FAS questionnaire was used as proxy measure of socioeconomic status, but no specific validation study is available for Spanish adolescents. The strengths of this study comprised the relatively large and homogeneous sample in terms of age and sex, the use of validated tests to assess health-related physical fitness components and self-rated risk of depression, and the inclusion of several confounders in the statistical analyses with great relevance for physical fitness and depression (i.e., pubertal development and socioeconomic status).

5. Conclusions

In conclusion, our findings add new information about the relationship between objectively health-related physical fitness components and self-rated risk of depression in adolescents. Specifically, our results indicated an association of cardiorespiratory fitness and body composition with self-rated risk of depression. Nevertheless, muscular strength and speed agility do not seem to be associated with a self-rated risk of depression. These attributes should be considered in order to reduce the risk of depression in adolescents. Future longitudinal and interventional studies are needed, in order to identify the mechanisms involved in the associations between health-related physical fitness components and psychological wellbeing in adolescents.

Supplementary Materials

The following are available online at https://0-www-mdpi-com.brum.beds.ac.uk/1660-4601/17/12/4316/s1, Table S1: Partial correlations analyses of health-related physical fitness components and self-reported risk of depression score.

Author Contributions

Conceptualization, C.B.-S.; methodology, C.B.-S. and M.A.-R.; formal analysis, C.B.-S., M.A.-R., M.R.B.-V., and D.M.-U.; data curation, C.B.-S.; analysis interpretation, C.B.-S., M.A.-R., M.R.B.-V., and D.M.-U.; writing—original draft preparation, C.B.-S.; writing—review and editing, M.A.-R., M.R.B.-V., and D.M.-U.; project administration, D.M.-U.; funding acquisition, D.M.-U. All authors have read and agreed to the published version of the manuscript.

Funding

The DADOS Study is funded by the Spanish Ministry of Economy and Competitiveness, MINECO (DEP2013–45515-R) and by the Jaume I University of Castellon, UJI (P1·1A2015-05 and UJI-A2019-12). This work is partly supported by a Sunny Sport research grant from the Schweppes Suntory Spain Company. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding institutions.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. American Psychiatric Association. Diagnostric and Statistical Manual of Mental Disorders, 5th ed.; American Psychiatric Association: Washington, DC, USA, 2013. [Google Scholar]
  2. Thapar, A.; Collishaw, S.; Pine, D.S.; Thapar, A.K. Depression in adolescence. Lancet 2012, 379, 1056–1067. [Google Scholar] [CrossRef] [Green Version]
  3. Kessler, R.C.; Berglund, P.; Demler, O.; Jin, R.; Merikangas, K.R.; Walters, E.E. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch. Gen. Psychiatry 2005, 62, 593–602. [Google Scholar] [CrossRef] [Green Version]
  4. Gogtay, N.; Giedd, J.N.; Lusk, L.; Hayashi, K.M.; Greenstein, D.; Catherine Vaituzis, A.; Nugent, T.F., III; Herman, D.H.; Clasen, L.S.; Toga, A.W.; et al. Dynamic mapping of human cortical development during childhood through early adulthood. Proc. Natl. Acad. Sci. USA 2004, 101, 8174–8179. [Google Scholar] [CrossRef] [Green Version]
  5. Roberts, R.E.; Duong, H.T. The Prospective Association between Sleep Deprivation and Depression among Adolescents. Sleep 2014, 37, 239–244. [Google Scholar] [CrossRef] [Green Version]
  6. Farren, G.L.; Zhang, T.; Gu, X.; Thomas, K.T. Sedentary behavior and physical activity predicting depressive symptoms in adolescents beyond attributes of health-related physical fitness. J. Sport Health Sci. 2018, 7, 489–496. [Google Scholar] [CrossRef] [PubMed]
  7. Korczak, D.J.; Madigan, S.; Colasanto, M. Children’s Physical Activity and Depression: A Meta-analysis. Pediatrics 2017, 139, e20162266. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Mannan, M.; Mamun, A.; Doi, S.; Clavarino, A. Prospective Associations between Depression and Obesity for Adolescent Males and Females- A Systematic Review and Meta-Analysis of Longitudinal Studies. PLoS ONE 2016, 11, e0157240. [Google Scholar] [CrossRef] [PubMed]
  9. Cuijpers, P.; Van Straten, A.; Smit, F.; Mihalopoulos, C.; Beekman, A. Preventing the onset of depressive disorders: A meta-analytic review of psychological interventions. Am. J. Psychiatry 2008, 165, 1272–1280. [Google Scholar] [CrossRef] [PubMed]
  10. Ruiz, J.R.; Castro-Piñero, J.; Artero, E.G.; Ortega, F.B.; Sjöström, M.; Suni, J.; Castillo, M.J. Predictive validity of health-related fitness in youth: A systematic review. Br. J. Sport Med. 2009, 43, 909–923. [Google Scholar] [CrossRef]
  11. Schutte, N.M.; Nederend, I.; Hudziak, J.J.; Bartels, M.; de Geus, E.J.C. Twin-sibling study and meta-analysis on the heritability of maximal oxygen consumption. Physiol. Genom. 2016, 48, 210–219. [Google Scholar] [CrossRef] [Green Version]
  12. Tabacchi, G.; Faigenbaum, A.; Jemni, M.; Thomas, E.; Capranica, L.; Palma, A.; Breda, J.; Bianco, A. Profiles of physical fitness risk behaviours in school adolescents from the ASSO project: A latent class analysis. Int. J. Environ. Res. Public Health 2018, 15, 1933. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. 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] [Green Version]
  14. Rieck, T.; Jackson, A.; Martin, S.B.; Petrie, T.; Greenleaf, C. Health-related fitness, body mass index, and risk of depression among adolescents. Med. Sci. Sports Exerc. 2013, 45, 1083–1088. [Google Scholar] [CrossRef] [PubMed]
  15. Greenleaf, C.A.; Petrie, T.A.; Martin, S.B. Psychosocial variables associated with body composition and cardiorespiratory fitness in middle school students. Res. Q. Exerc. Sport 2010, 81 (Suppl. 3), 65–74. [Google Scholar] [CrossRef] [PubMed]
  16. Ruggero, C.J.; Petrie, T.; Sheinbein, S.; Greenleaf, C.; Martin, S. Cardiorespiratory fitness may help in protecting against depression among middle school adolescents. J. Adolesc. Health 2015, 57, 60–65. [Google Scholar] [CrossRef]
  17. Esmaeilzadeh, S. Relationship between depressive symptoms with physical activity and physical fitness among children. Ment. Health Prev. 2014, 2, 11–17. [Google Scholar] [CrossRef]
  18. Rodriguez-Ayllon, M.; Cadenas-Sanchez, C.; Esteban-Cornejo, I.; Migueles, J.H.; Mora-Gonzalez, J.; Henriksson, P.; Martín-Matillas, M.; Mena-Molina, A.; Molina-García, P.; Estévez-López, F.; et al. Physical fitness and psychological health in overweight/obese children: A cross-sectional study from the ActiveBrains project. J. Sci. Med. Sport 2018, 21, 179–184. [Google Scholar] [CrossRef]
  19. Suija, K.; Timonen, M.; Suviola, M.; Jokelainen, J.; Järvelin, M.R.; Tammelin, T. The association between physical fitness and depressive symptoms among young adults: Results of the Northern Finland 1966 birth cohort study. BMC Public Health 2013, 13, 535. [Google Scholar] [CrossRef] [Green Version]
  20. Leger, L.A.; Mercier, D.; Gadoury, C.; Lambert, J. The multistage 20 metre shuttle run test for aerobic fitness. J. Sports Sci. 1988, 6, 93–101. [Google Scholar] [CrossRef]
  21. Tomkinson, G.R.; Carver, K.D.; Atkinson, F.; Daniell, N.; Lewis, L.; Fitzgerald, J.S.; Lang, J.; Ortega, F.B. European normative values for physical fitness in children and adolescents aged 9–17 years: Results from 2 779 165 Eurofit performances representing 30 countries. Br. J. Sports Med. 2018, 52, 1445–1456. [Google Scholar] [CrossRef] [Green Version]
  22. Cole, T.J.; Billizi, M.C.; Flegal, K.M.; Dietz, W.H. Establishing a standard definition for child overweight and obesity worldwide: International survey. BMJ Clin. Res. Ed. 2000, 320, 1240–1243. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Slaughter, M.; Lohman, T.; Boileau, R. Skinfold equations for estimation of body fatness in children and youth. Hum. Biol. 1988, 60, 709–723. [Google Scholar] [PubMed]
  24. González, J.; Fernández, S.; Pérez, E.; Santamaría, P. Adaptación Española del Sistema de Evaluación de la Conducta en Niños y Adolescentes: BASC; TEA ediciones: Madrid, Spain, 2004. [Google Scholar]
  25. Reynolds, C.R.; Kamphaus, R.W. Behavior Assessment System for Children, 2nd ed.; AGS publishing: Circle Pines, MN, USA, 2004. [Google Scholar]
  26. Pavón, D.J.; Ortega, F.B.; Ruiz, J.R.; España Romero, V.; García Artero, E.; Moliner Urdiales, D.; Gómez Martínez, S.; Vicente Rodríguez, G.; Manios, Y.; Béghin, L.; et al. Socioeconomic status influences physical fitness in European adolescents independently of body fat and physical activity: The HELENA Study. Nutr. Hosp. 2010, 25, 311–316. [Google Scholar] [CrossRef]
  27. Wolfe, A.M.; Lee, J.A.; Laurson, K.R. Socioeconomic status and physical fitness in youth: Findings from the NHANES National Youth Fitness Survey. J. Sports Sci. 2020, 38, 534–541. [Google Scholar] [CrossRef]
  28. Gammon, C.; Pfeiffer, K.A.; Kazanis, A.; Ling, J.; Robbins, L.B. Cardiorespiratory fitness in urban adolescent girls: Associations with race and pubertal status. J. Sports Sci. 2017, 35, 29–34. [Google Scholar] [CrossRef] [Green Version]
  29. Towlson, C.; Cobley, S.; Parkin, G.; Lovell, R. When does the influence of maturation on anthropometric and physical fitness characteristics increase and subside? Scand. J. Med. Sci. Sport 2018, 28, 1946–1955. [Google Scholar] [CrossRef]
  30. Pino, E.C.; Damus, K.; Jack, B.; Henderson, D.; Milanovic, S.; Kalesan, B. Adolescent socioeconomic status and depressive symptoms in later life: Evidence from structural equation models. J. Affect. Disord. 2018, 225, 702–708. [Google Scholar] [CrossRef]
  31. Zhou, Q.; Fan, L.; Yin, Z. Association between family socioeconomic status and depressive symptoms among Chinese adolescents: Evidence from a national household survey. Psychiatry Res. 2018, 259, 81–88. [Google Scholar] [CrossRef]
  32. Keenan, K.; Culbert, K.M.; Grimm, K.J.; Hipwell, A.E.; Stepp, S.D. Timing and Tempo: Exploring the complex association between pubertal development and depression in African American and European American girls. J. Abnorm. Psychol. 2014, 123, 725–736. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Hamlat, E.J.; Stange, J.P.; Abramson, L.Y.; Alloy, L.B. Early pubertal timing as a vulnerability to depression symptoms: Differential effects of race and sex. J. Abnorm. Child Psychol. 2014, 42, 527–538. [Google Scholar] [CrossRef] [PubMed]
  34. Tanner, J.M.; Whitehouse, R.H.; Takaishi, M. Clinical longitudinal standards for height, weight, height velocity, weight velocity, and stages of puberty. Arch. Dis. Child 1976, 51, 170–179. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Currie, C.; Molcho, M.; Boyce, W.; Holstein, B.; Torsheim, T.; Richter, M. Researching health inequalities in adolescents: The development of the Health Behaviour in School-Aged Children (HBSC) Family Affluence Scale. Soc. Sci. Med. 2008, 66, 1429–1436. [Google Scholar] [CrossRef] [PubMed]
  36. Lubans, D.; Richards, J.; Hillman, C.; Faulkner, G.; Beauchamp, M.; Nilsson, M.; Kelly, P.; Smith, J.; Raine, L.; Biddle, S. Physical Activity for Cognitive and Mental Health in Youth: A Systematic Review of Mechanisms. Pediatrics 2016, 138, e20161642. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Bailey, P.A.; Hetrick, S.E.; Rosenbaum, S.; Purcell, R.; Parker, A.G. Treating depression with physical activity in adolescents and young adults: A systematic review and meta-analysis of randomised controlled trials. Psychol. Med. 2017, 48, 1068–1083. [Google Scholar] [CrossRef]
  38. Beltran-valls, M.R.; Adelantado-renau, M.; Moliner-urdiales, D. Reallocating time spent in physical activity intensities: Longitudinal associations with physical fitness (DADOS Study). J. Sci. Med. Sport 2020. [Google Scholar] [CrossRef]
  39. Opdal, I.M.; Morseth, B.; Handegård, B.H.; Lillevoll, K.; Ask, H.; Nielsen, C.S.; Horsch, A.; Furberg, A.S.; Rosenbaum, S.; Rognmo, K. Change in physical activity is not associated with change in mental distress among adolescents: The Tromsø study: Fit Futures. BMC Public Health 2019, 19, 1–11. [Google Scholar] [CrossRef] [Green Version]
  40. Beunen, G.; Thomis, M. Muscular strength development in children and adolescents. Pediatr. Exerc. Sci. 2000, 12, 174–197. [Google Scholar] [CrossRef] [Green Version]
  41. Oddy, W.H.; Allen, K.L.; Trapp, G.S.A.; Ambrosini, G.L.; Black, L.J.; Huang, R.C.; Rzehak, P.; Runions, K.C.; Pan, F.; Beilin, L.J.; et al. Dietary patterns, body mass index and inflammation: Pathways to depression and mental health problems in adolescents. Brain Behav. Immun. 2018, 69, 428–439. [Google Scholar] [CrossRef] [Green Version]
  42. Russell-Mayhew, S.; McVey, G.; Bardick, A.; Ireland, A. Mental health, wellness, and childhood overweight/obesity. J. Obes. 2012, 2012, 281801. [Google Scholar] [CrossRef] [Green Version]
  43. Hayes, G.; Dowd, K.P.; MacDonncha, C.; Donnelly, A.E. Tracking of Physical Activity and Sedentary Behavior From Adolescence to Young Adulthood: A Systematic Literature Review. J. Adolesc. Health 2019, 65, 446–454. [Google Scholar] [CrossRef]
Table
Table 1. Descriptive characteristics of the study sample by sex.
Table 1. Descriptive characteristics of the study sample by sex.
VariablesAll
(n = 225)		Boys
(n = 127)		Girls
(n = 98)
Age (y)13.89 ± 0.2913.89 ± 0.2913.89 ± 0.29
Pubertal stage II-V (%)8.0/34.7/46.7/10.710.2/33.1/42.5/14.25.1/36.7/52.0/6.1
Socioeconomic status (0–8)4.12 ± 1.424.29 ± 1.513.98 ± 1.33
Health-related physical Fitness
Cardiorespiratory fitness (laps)68.43 ± 23.3778.85 ± 20.3454.93 ± 19.95
Upper limb muscular strength (kg)29.31 ± 6.0630.93 ± 6.8127.20 ± 4.09
Lower limb muscular strength (cm)174.52 ± 24.66180.94 ± 23.55166.19 ± 23.65
Speed-Agility (s)12.37 ± 0.8812.02 ± 0.6912.88 ± 0.85
Body composition
Height (cm) 163.14 ± 8.00164.69 ± 8.65161.13 ± 6.60
Weight (kg) 53.93 ± 8.8954.07 ± 8.9253.74 ± 8.91
Body mass index (kg/m2) 20.19 ± 2.5619.83 ± 2.2320.65 ± 2.89
Waist circumference (cm)67.19 ± 5.5267.81 ± 5.0366.38 ± 6.03
Total body fat (%)9.69 ± 3.3218.30 ± 6.8325.20 ± 5.53
Self-rated risk of depression
Score45.78 ± 7.7544.84 ± 5.7147.01 ± 9.69
At risk (%)8 (3.6)2 (1.6)6 (6.1)
Data are presented as mean ± standard deviation or percentages.
Table
Table 2. Linear regression analyses of health-related physical fitness components with self-rated risk of depression.
Table 2. Linear regression analyses of health-related physical fitness components with self-rated risk of depression.
VariablesR2B95% CIβPDW
LowerUpper
Cardiorespiratory fitness0.049−0.057−0.107−0.007−0.1720.0251.99
Upper limb muscular strength0.0270.006−0.1870.1990.0050.9531.98
Lower limb muscular strength0.027−0.004−0.0480.040−0.0130.8561.98
Speed-Agility *0.034−0.869−2.2130.474−0.0990.2042.00
Body mass index0.0460.4420.0280.8570.1460.0372.02
Waist circumference0.0440.1920.0010.3840.1370.0492.03
Total body fat0.0380.133−0.0280.2940.1230.1042.00
Analyses adjusted for sex, pubertal stage, and socioeconomic status. Statistically significant values are highlighted in bold. B, unstandardized coefficient; β, standardized coefficient; CI, confidence interval; DW, Durbin-Watson coefficient. * For analytic purposes, speed-agility variable was multiplied by −1, so a higher score indicates better speed-agility.
Table
Table 3. Logistic regression analyses of health-related physical fitness components categories with self-rated risk of depression.
Table 3. Logistic regression analyses of health-related physical fitness components categories with self-rated risk of depression.
Variablesn (%)R2OR95% CI
Cardiorespiratory fitnessHigh188 (83.56) 1Reference
Low37 (16.44)0.0637.171.51–33.95
Upper limb muscular strengthHigh125 (55.6) 1Reference
Low100 (44.4)0.0371.140.25–5.19
Lower limb muscular strengthHigh141 (62.7) 1Reference
Low84 (37.3)0.0380.750.14–3.96
Speed-AgilityHigh75 (33.3) 1Reference
Low150 (66.7)0.0380.750.14–4.06
Body mass indexNon-overweight200 (88.9) 1Reference
Overweight25 (11.1)0.0381.620.28–9.31
Waist circumferenceLow152 (67.6) 1Reference
High73 (32.4)0.0402.370.27–20.79
Total body FatLow145 (64.4) 1Reference
High80 (35.6)0.0433.190.37–27.45
Analyses were adjusted for sex, pubertal stage, and socioeconomic status. Statistically significant values are in bold. OR, Odds Ratio; CI, confidence interval.

Share and Cite

MDPI and ACS Style

Bou-Sospedra, C.; Adelantado-Renau, M.; Beltran-Valls, M.R.; Moliner-Urdiales, D. Association between Health-Related Physical Fitness and Self-Rated Risk of Depression in Adolescents: Dados Study. Int. J. Environ. Res. Public Health 2020, 17, 4316. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17124316

AMA Style

Bou-Sospedra C, Adelantado-Renau M, Beltran-Valls MR, Moliner-Urdiales D. Association between Health-Related Physical Fitness and Self-Rated Risk of Depression in Adolescents: Dados Study. International Journal of Environmental Research and Public Health. 2020; 17(12):4316. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17124316

Chicago/Turabian Style

Bou-Sospedra, Carlos, Mireia Adelantado-Renau, Maria Reyes Beltran-Valls, and Diego Moliner-Urdiales. 2020. "Association between Health-Related Physical Fitness and Self-Rated Risk of Depression in Adolescents: Dados Study" International Journal of Environmental Research and Public Health 17, no. 12: 4316. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17124316

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop