Physical activity and health assessment in children and adolescents: application and usefulness of Physical Activity Questionnaire (PAQ)

PhD THESIS INTERNATIONAL MENTION Physical activity and health assessment in children and adolescents: application and usefulness of Physical Activity Questionnaire (PAQ) PhD Programme EDUCATIONAL RESEARCH AND INNOVATION Faculty of Education Sciences University of Málaga AUTHOR: D. JAVIER BENÍTEZ PORRES DIRECTORS: Prof. Dr. JOSÉ RAMÓN ALVERO CRUZ Prof. Dr. ELVIS ÁLVAREZ CARNERO May 2016 AUTOR: Javier Benítez Porres http://orcid.org/0000-0001-7546-7965 EDITA: Publicaciones y Divulgación Científica. Universidad de Málaga Esta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercialSinObraDerivada 4.0 Internacional: http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode Cualquier parte de esta obra se puede reproducir sin autorización pero con el reconocimiento y atribución de los autores. No se puede hacer uso comercial de la obra y no se puede alterar, transformar o hacer obras derivadas. Esta Tesis Doctoral está depositada en el Repositorio Institucional de la Universidad de Málaga (RIUMA): riuma.uma.es A mi familia y especialmente a mis padres Marisa y Antonio, por educarme, ayudarme y empujarme a ser quien soy apoyándome incondicionalmente. A Luz, por su paciencia y comprensión, por su amor, por ser tal y como es y por complementarme para encontrar el equilibrio en cada momento. ACKNOWLEDGMENTS Gracias a mis padres por haberme forjado como persona, inculcándome los valores necesarios para vivir y pensar libremente, de forma honesta y honrada. Habéis sido un claro ejemplo de lucha constante y sacrificio a lo largo de la vida. Gracias por anteponerme a vuestras necesidades y dedicar parte de vuestras vidas a darlo todo para que siempre fuese feliz. De igual mofo, gracias también a mis hermanas por contribuir en este proceso. Gracias a Luz María Santos Moreno , por ser testigo y compartir conmigo este camino. Tu forma de ser y esa ilusión y bondad tan natural que te caracterizan han sido determinantes para llevar este barco a buen puerto, solventando los momentos más arduos y sosegándome cuando ha sido necesario. Gracias a mis directores de tesis, José Ramón Alvero Cruz y Elvis Álvarez Carnero , por hacer posible este proyecto y apostar por mí y sobre todo por vuestros consejos, estímulos, asesoramiento y apoyo. En el mismo sentido, gracias a Iván López Fernández , que aunque no sea director de esta tesis, se ha comportado como tal, aportando su inigualable calidad humana. Gracias a la Facultad de Motricidad Humana de la Universidad de Lisboa, a los compañeros del Laboratorio de Ejercicio Físico y Salud, en especial a Vera y a Ana , y a mi tutor Luis B. Sardinha , por permitir realizar mi estancia y guiarme en ella despertando esas inquietudes tan necesarias para pulir la presente tesis doctoral. Fue un lugar ideal para aprender, recapacitar y estimular el conocimiento. Muito obrigado. Gracias a los investigadores del proyecto GEOS , amigos todos, por compartir esta experiencia aportando vuestro tiempo y esfuerzo en las numerosas evaluaciones y tareas para que esto saliese adelante. Ninguno de nosotros es tan bueno como todos nosotros juntos. Ha sido un placer trabajar codo con codo con todos ellos. Gracias a las diferentes instituciones que han contribuido con su financiación al desarrollo y transmisión de resultados de la presente tesis: al Ministerio de Educación, Cultura y Deporte (AP2010-0583), a la Universidad de Málaga y al Campus de Excelencia Internacional Andalucía TECH . Por último, gracias a todos aquellos profesionales de la enseñanza , maestros y profesores con vocación, que en algún momento despertaron en mi la ilusión por aprender y el afán de superación. A todos ellos, mi más sincero agradecimiento por hacerme progresar… este trabajo es tan vuestro como mío. Benítez-Porres J, 2016 International PhD Thesis INDEX OF CONTENTS ABBREVIATIONS ................................................................................... 8 LIST OF TABLES AND FIGURES ........................................................ 10 ABSTRACT .............................................................................................. 12 CHAPTER I: INTRODUCTION ............................................................ 15 Physical activity and health in youth ...................................................... . 15 Physical activity measurements for youth .............................................. . 17 Physical activity questionnaire (PAQ) ................................................... . 28 References .............................................................................................. . 30 CHAPTER II: AIMS AND HYPOTHESIS ............................................ 41 CHAPTER III: MATERIAL AND METHODS .................................... 43 Sample and recruitment .......................................................................... . 43 Body composition specifications............................................................ . 44 CHAPTER IV: RESULTS AND DISCUSSION .................................... 46 Study I. Reliability and validity of the PAQ-C questionnaire to assess physical activity in children ................................................................... . 46 Study II. The physical activity questionnaire score cut offs to classify physical activity level in children and adolescents ................................ . 69 Benítez-Porres J, 2016 International PhD Thesis Study III. The influence of 2-year changes in physical activity, maturation, and nutrition on adiposity in adolescent youth ................... . 95 CHAPTER V: LIMITATIONS ............................................................... 116 CHAPTER VI: GENERAL CONCLUSIONS ...................................... 118 APPENDIX ............................................................................................... 121 I. Physical activity questionnaire for children (Spanish) ....................... . 122 II. Physical activity questionnaire for adolescents (Spanish) ................. . 125 III. GEOS project documents (Spanish) ................................................. . 128 IV. Curriculum vitae (Spanish) .............................................................. . 135 V. Global summary (Spanish) ................................................................ . 146 Benítez-Porres J, 2016 International PhD Thesis c) In the longitudinal study, significant differences for FMP were found among S1, S2 and S3 (23.41±8.24 vs. 21.89±7.82 vs. 22.05±8.06, P<0.05; respectively); a significant interaction with sex was observed (P<0.05), but not for maturation. Regarding PA, S2 was significantly higher than S3 (2.58±0.72 vs. 2.29±0.73, P<0.001). An interaction between PA and maturation was statically significant (P<0.05). Our results suggest that body composition changes observed during adolescence are not driven by changes in PA. Moreover, the interaction analysis suggests that sex affects PA behavior, but not maturation or nutritional variables. These overall results suggest that the PAQ appears to be a more appropriate tool to measure PA in adolescents than in children, and it may discriminate active and inactive students in adolescence according to international guidelines. In addition, assessments conducted longitudinally in this dissertation show the problems of progressive decline of PA among adolescents, which seems to be influenced by the gender, regardless of maturation. 14 Benítez-Porres J, 2016 International PhD Thesis CHAPTER I: INTRODUCTION PHYSICAL ACTIVITY AND HEALTH IN YOUTH Physical activity (PA) is recognized to have important benefits for all segments of the population 1-5. The World Health Organization (WHO) has published official PA guidelines 6, which provide specific recommendations for the type and amount of PA needed for different segments of the population including children and adolescents, adults, the elderly, and those with special needs. A review by Janssen & Leblanc 3 summarized the various health benefits of regular PA. According to the review, PA has been shown to improve body composition, promote glucose homeostasis, enhance insulin sensitivity, reduce blood pressure, improve lipid lipoprotein profiles (e.g., through reduced triglyceride levels, increased high density lipoprotein HDL cholesterol levels and decreased low-density lipoprotein LDL), reduce systemic inflammation and blood coagulation, promote autonomic tone and enhance cardiac and endothelial function. In short, regular PA is absolutely critical for good health. PA is important for all segments of the population but there is considerable interest in promoting PA in youth. This is due in large part to concerns over the increasing prevalence of obesity, but also to the growing consensus about the importance for good health. Childhood and adolescence are important periods of life because of dramatic changes in various physiological and psychological aspects, such as hormonal regulation, body composition, transient changes in insulin sensitivity 7. More significantly, many lifestyle habits established during childhood and adolescence periods tend to track into adulthood 8. Preventing obesity in early life is critical since evidence suggests that overweight youth have a five times greater risk of being overweight than normal weight 15 Benítez-Porres J, 2016 International PhD Thesis children of the same age 9. In this sense, childhood and adolescent overweight and obesity show a high prevalence in Spain 10,11. Longitudinal studies, both follow-up and intervention, help move researchers closer to understanding determinants and mediators of PA and adiposity 12. An advantage of these designs is that they can address reverse causality. The needs for children and adolescents warrant unique activity guidelines. The PA guidelines suggest that children and adolescents should accumulate 60 minutes or more of PA daily. The amount of PA recommended to youth is twice that of adults, not only because youth have more freedom and greater needs for PA, but also because forming a healthy lifestyle at an early age has an influence on lifestyle later on. The United States is not the only country that has adopted national PA guidelines for youth. A number of other countries including Australia, the United Kingdom, and Canada have published their own guidelines. Though there are minor discrepancies between them, all of the guidelines suggest that youth should engage in at least 60 minutes of MVPA on a daily basis. The message of formalized youth PA guidelines has generated considerable interest in understanding and promoting levels of PA in children and adolescents. Schools have been targeted as one of the most promising settings for reaching and impacting youth. A variety of assessment tools are available but they differ in validity and feasibility. There are various options or methods for assessing PA. However, emphasis will be based on more practical methods that can be used in schools since there is specific interest in documenting levels of activity during the school day. 16 Benítez-Porres J, 2016 International PhD Thesis PHYSICAL ACTIVITY MEASUREMENTS FOR YOUTH Following the postulates of Corder 13 and Sirard 14, this section will provide a brief overview of the most common methods to measure or estimate PA (not criterion standards such as DLW or indirect calorimetric) in youth, and additional strengths and weaknesses (table 1). We can classify these methods in two groups: subjective and objective methods. The first category include questionnaires, interviews, activity diaries, and direct observation. These methods vary in the measured variables and therefore in their primary outcomes. The second category involve the measurement of physiological or biomechanical parameters and use this information to estimate PA outcomes. Table 1. Advantages and disadvantages of methods used to assess physical activity Advantages Disadvantages Heart rate monitoring Suitable for all populations. Low respondent burden for short period. Physiological parameter. Provides information about intensity. Good association with energy expenditure. Easy and quick data collection. Relatively cheap. Only useful for aerobic activities Conditions unrelated to PA can cause an increase in heart rate w ithout a corresponding increase in VO2 Multi-sensor systems Suitable for all populations Low respondent burden Relative ease of data collection Data analysis relatively complex. Monitors relatively expensive. Pedometers Suitable for all populations Low respondent burden Objective measure of common activity behaviour Easy data collection and analysis Cheap Children may tamper or alter behaviour. Are specifically designed to assess walking only. Inability to record non-locomotor movements. Inability to examine the rate or intensity of movement. Direct observation Mostly used in paediatric studies No respondent burden provides excellent quantitative and qualitative information Expensive as labour intensive. Observer presence may artificially alter normal PA patterns. 17 Benítez-Porres J, 2016 International PhD Thesis Accelerometers Suitable for all populations. Low respondent burden. Objective indicator of body movement (acceleration). Provides information about intensity, frequency and duration. Relatively easy data collection. Inaccurate assessment of a large range of activities. Financial cost may prohibit assessment of large numbers of participants. Self-report Suitable for all populations Low respondent burden captures quantitative and qualitative information Ease of data collection and analysis Cheap Proxy reporters required for children and possibly elderly. Reliability and validity problems associated with recall of activity. PA, physical activity; VO2, oxygen uptake. From Warren et al. 2010, partially modified.  Heart rate monitors and multi-sensor systems (objective method) Heart rate monitors are the most common direct physiological measure used in freeliving settings 15. There is a strong linear relationship between heart rate and energy expenditure across the moderate and more vigorous intensity PA, although this relationship is not as strong in the light intensity range 16,17. Overall error rates relative to the criterion measure are typically <3%, although this accuracy may be higher for some individuals 18. Agreement across heart rate monitor units is also very strong. Heart rate monitor are excellent options for activities that may not be measured well with an accelerometer including cycling, swimming, and other non-ambulatory activities 19. Some of the existing limitations of heart rate monitors are the necessity to account for blood pressure attenuating medications, focus of relative over absolute intensity, and potential discomfort of wearing the unit for long periods of time. Multi-sensor systems combine multiple physiological and mechanical sensors to provide more precise measures of PA and energy expenditure. Parameters may include accelerometry (at multiple placements), heart rate, galvanic skin response, respiration, skin and core temperature, bioimpedance, global positioning, among others. The 18 Benítez-Porres J, 2016 International PhD Thesis advantages of multi-sensor systems are the additional precision, especially among nonambulatory activities, that may come from triangulating energy expenditure estimates from multiple sensors. However, the cost and potential inconvenience of more complex systems may make these advantages less important depending on the application 15. There is no gold standard objective wearable monitor. The choice of objective wearable monitors is complex due to many factors including the specific PA component of interest, the rapid and evolving evolution of technology and algorithm development, and practical considerations include ease of use, cost, and logistics.  Pedometers (objective method) In recent years, with the advancement of computing technologies, and the desire to track and evaluate PA, pedometers have become increasingly complex. Early forms have used a hip-worn mechanical sensor to identify steps based on the force on the unit generated from a typical heel-strike during ambulation. In the past 10 years the technology underlying pedometers has transitioned primarily to microelectromechanical systems and algorithm-based processing of the microelectromechanical systems signal to identify steps. Accuracy of these pedometers has improved with the transition toward microelectromechanical systems 20 and are excellent in measuring steps at walking speeds > 2 mph 21. Crouter et al. 22 evaluated the accuracy of 10 different hip worn pedometers and found 8 of the 10 devices had excellent test–retest reliability and accuracy was > 95% and increased as walking speed increased. Karabulut et al. 23 examined the accuracy of two models of Omron pedometers on a treadmill at varying walking speeds (2-4 mph) and found both monitors were within 1.5% of actual steps taken. While most pedometers 19 Benítez-Porres J, 2016 International PhD Thesis are designed to be hip-worn, the most accurate placement for detecting steps appears to be the ankle 23. The accuracy of pedometers is more compromised at slower walking speeds (≤ 2 mph), at other monitor placements (e.g., wrist, pocket), and among older adults and those with gait impairments 15. The appeal of the pedometer to objectively monitor PA is their ability to quantify ambulatory activity during walking, jogging, and running through a common and easily understood metric (i.e., steps). Pedometers are of relative low cost and can be an important means for providing behavioural feedback and motivation. Pedometers may also be capable of providing a valid estimate of PA intensity 24. The primary disadvantages of the pedometer is their inability to measure non-ambulatory activities, posture, and energy expenditure, and their reliance on proprietary algorithms to determine steps 15.  Accelerometers (objective method) Accelerometers are small wearable monitors that record accelerations in gravitational units on one or more planes at sampling rates >1 time/second (typically 40–100 hz). Captured accelerations are then processed to a lower resolution (i.e., epoch) and then calibrated to a known criterion measure (e.g., oxygen consumption or DLW). Most of the existing calibration studies rely on a unitless intensity metric or “counts” and then apply thresholds to summarized data to output the duration and frequency of PA into sedentary, light, moderate, and vigorous intensities. There is controversy over the appropriate preand post-processing methods (many of which are proprietary in nature) and thresholds for various populations and desired PA component 25. More recently, pattern recognition 20 Benítez-Porres J, 2016 International PhD Thesis techniques have been explored that develop algorithms to detect PA types (e.g., running vs. walking) and EE that do not rely on proprietary processing and threshold methods 26,27. These efforts have been modestly fruitful (although have limited validity in freeliving settings) and are computationally resource-intensive and therefore not practical for most end users. Most single-sensor accelerometer systems perform poorly compared to the gold standard of DLW 28. Calibration studies have shown a wide range of correlations (r = 0.45 to 0.93) with measures of oxygen consumption and METs 29. This wide range is due to a number of protocol-related variations including the monitor under study (i.e., some monitors and their associated algorithms are more accurate than others), monitor placement (i.e., hip, wrist, ankle, trunk), activities under investigation (e.g., ambulatory PAs are more accurate than non-ambulatory PAs such as cycling and household chores), and context (laboratory-based studies have greater accuracy than free-living studies). Accelerometers are typically worn on the hip, although increasingly are being fixed to the wrist or ankle. Hip-worn accelerometers are assumed to provide the most accurate assessments of normal ambulation, although recent comparative studies have shown only minimal differences in accuracy between the hip and wrist 30. There is an increased interest in moving monitor placement from the hip to the wrist for practical reasons including increased wear time (i.e., fewer need to remove the device) and ability to accurately monitor sleep. The appeal of the accelerometer for measuring PA (and sedentary behaviours) is the detailed and relatively precise manner, with minimal invasiveness, in which the frequency, duration, pattern, and intensity of activity can be monitored over days, weeks, 21 Benítez-Porres J, 2016 International PhD Thesis and even longer. However, accelerometers are not without considerable limitations. Some of these limitations include the proprietary nature of many algorithms to quantify PA, lack of sensitivity on sedentary and light-intensity range of the activity spectrum, and inability to detect non-ambulatory activities such as cycling and weight-lifting 16.  Self-reports (subjective method) Self-report instruments are the most widely used tools to assess PA and include self or interviewer-administered questionnaires, recalls, logs and activity diaries 15. Selfreport methods are the cheapest and easiest way to collect PA data from a large number of people in a short time. There are numerous limitations to self-reported methods, which include: difficulties in ascertaining the frequency, duration and intensity of PA, capturing all domains of PA, social desirability bias and the cognitive demands of recall 31. The sequential cognitive processes underlying the storage of memories have been described along with models explaining their retrieval, illustrating the complexity of the task especially to report durations 14. These issues along with problems with reliability, validity and sensitivity have been comprehensively summarized 32. However, structured questionnaires provide an assessment of PA by domains, which is not obtained when using objective measurement of PA and may have the potential to provide valid estimates of PA and time spent at different intensity levels on group level. A list of PA questionnaires designed to measure PA in young shown in table 2 (children), table 3 (adolescents) and table 4 (both): 22 Benítez-Porres J, 2016 International PhD Thesis Table 2. Self-report methods validated for assessing physical activity in children. Self-report Sample Evaluation Aim Admin. Criterion Validity 2013. Youth Activity Profile (YAP) 33 343 children Past week Habitual PA at school and out of school and sedentary behaviour Selfadministered ACL SWA Armband PA: r = 0.58 (P <0.001) Sedentary behaviour: r = 0.75 (P <0.001) 2011. Pre-PAQ 34 67 children Past 3 days Habitual and sedentary activities in home environment Parent reported ACL Actigraph r = -0.07 - 0.19 (P >0.05) 2010. BONES physical activity survey 35 40 children Past 2 days Common activities and Total METs Interview ACL Actigraph rho = 0.470.48 (P <0.01) 2010. MRPARQ 36 86 children Past week All organised and non-organised PA Interview ACL Actigraph r = 0.31 (P <0.05) 2006. The Multimedia Activity Recall for Children and Adolescents (MARCA) 37 66 children Past day PA intensities and EE Selfadministered (computerized) ACL Actigraph rho = 0.45 (P <0.01) 2006. School Health Action, Planning and Evaluation System (SHAPES) 38 67 children Past 7 days MVPA and sedentary behaviour Selfadministered (computerized) ACL MTI rho = 0.44 (P <0.01) 2004. Checklist to record outdoor playtime 39 250 children 3 days Outdoor playtime Parent reported ACL RT3 Triaxial r = 0.33 (P <0.001) 2004. Children's Leisure Activities Study Survey (CLASS) 40 280 children Past week (weekdays and weekend) Habitual PA Parent Reported and selfadministered ACL Actigraph rho = -0.04 (P >0.05). 2004. Recall of outdoor playtime 39 250 children Past month Outdoor playtime Parent reported HR r = 0.20 (P <0.01) 2003. GEMS Activity Questionnaire (GAQ) 41 68 girls Past day Habitual PA Selfadministered ACL CSA Previous day: r = 0.27 (P <0.03) Habitual: r = 0.28 (P <0.02) 2001. 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Med Sci Sport Exer. 1997;29(1):138-43. 36 Benítez-Porres J, 2016 International PhD Thesis 64. Warren JM, Ekelund U, Besson H, Mezzani A, Geladas N, Vanhees L, et al. Assessment of physical activity - a review of methodologies with reference to epidemiological research: a report of the exercise physiology section of the European Association of Cardiovascular Prevention and Rehabilitation. European journal of cardiovascular prevention and rehabilitation : official journal of the European Society of Cardiology, Working Groups on Epidemiology & Prevention and Cardiac Rehabilitation and Exercise Physiology. 2010;17(2):127-39. 65. Zaki R, Bulgiba A, Ismail R, Ismail NA. Statistical methods used to test for agreement of medical instruments measuring continuous variables in method comparison studies: a systematic review. Plos One. 2012;7(5):e37908. 66. Bland JM, Altman DG. Measuring agreement in method comparison studies. Statistical methods in medical research. 1999;8(2):135-60. 67. Schmidt ME, Steindorf K. Statistical methods for the validation of questionnaires- -discrepancy between theory and practice. Methods of information in medicine. 2006;45(4):409-13. 68. Kowalski K, Crocker PRE, Donen RM. The Physical Activity Questionnaire for Older Children (PAQ-C) and Adolescents (PAQ-A) Manual. 2004. 69. Crocker PR, Bailey DA, Faulkner RA, Kowalski KC, McGrath R. Measuring general levels of physical activity: preliminary evidence for the Physical Activity Questionnaire for Older Children. Med Sci Sports Exerc. 1997;29(10):1344-9. 70. Crocker PR, Eklund RC, Kowalski KC. Children's physical activity and physical self-perceptions. J Sports Sci. 2000;18(6):383-94. 71. Janz KF, Lutuchy EM, Wenthe P, Levy SM. Measuring activity in children and adolescents using self-report: PAQ-C and PAQ-A. Med Sci Sport Exer. 2008;40(4):76772. 37 Benítez-Porres J, 2016 International PhD Thesis 72. Moore JB, Hanes JC, Barbeau P, Gutin B, Trevino RP, Yin ZN. Validation of the Physical Activity Questionnaire for Older Children in children of different races. Pediatr Exerc Sci. 2007;19(1):6-19. 73. Martinez-Gomez D, Martinez-de-Haro V, Pozo T, Welk GJ, Villagra A, Calle ME, et al. [Reliability and validity of the PAQ-A questionnaire to assess physical activity in Spanish adolescents]. Revista espanola de salud publica. 2009;83(3):427-39. 74. Chinapaw MJM, Mokkink LB, van Poppel MNM, van Mechelen W, Terwee CB. Physical Activity Questionnaires for Youth A Systematic Review of Measurement Properties. Sports Med. 2010;40(7):539-63. 75. Tessier S, Vuillemin A, Briancon S. Review of physical activity questionnaires validated for children and adolescents. Sci Sport. 2008;23(3-4):118-25. 76. Biddle SJH, Gorely T, Pearson N, Bull FC. An assessment of self-reported physical activity instruments in young people for population surveillance: Project ALPHA. Int J Behav Nutr Phy. 2011;8. 77. Lamonte MJ, Ainsworth BE. Quantifying energy expenditure and physical activity in the context of dose response. Med Sci Sport Exer. 2001;33(6):S370-S8. 78. Terwee CB, Mokkink LB, van Poppel MNM, Chinapaw MJM, van Mechelen W, de Vet HCW. Qualitative Attributes and Measurement Properties of Physical Activity Questionnaires A Checklist. Sports Med. 2010;40(7):525-37. 79. Laara E. A Comparison of Regression-Models for Ordinal Categorical Variables with Medical Applications. Biometrics. 1985;41(4):1097-. 80. Wareham NJ, Rennie KL. The assessment of physical activity in individuals and populations: Why try to be more precise about how physical activity is assessed? Int J Obesity. 1998;22:S30-S8. 38 Benítez-Porres J, 2016 International PhD Thesis 81. Andersson B, von Davier AA. Test Equating, Scaling, and Linking: Methods and Practices. Psychometrika. 2015;80(3):856-8. 82. Welk GJ. Principles of design and analyses for the calibration of accelerometrybased activity monitors. Med Sci Sport Exer. 2005;37(11):S501-S11. 83. Ferrari P, Friedenreich C, Matthews CE. The role of measurement error in estimating levels of physical activity. Am J Epidemiol. 2007;166(7):832-40. 84. Carter LM, Whiting SJ, Drinkwater DT, Zello GA, Faulkner RA, Bailey DA. Selfreported calcium intake and bone mineral content in children and adolescents. Journal of the American College of Nutrition. 2001;20(5):502-9. 85. Chen SR, Lee YJ, Chiu HW, Jeng C. Impact of physical activity on heart rate variability in children with type 1 diabetes. Child Nerv Syst. 2008;24(6):741-7. 86. Heinonen A, McKay HA, Whittall KP, Forster BB, Khan KM. Muscle crosssectional area is associated with specific site of bone in prepubertal girls: A quantitative magnetic resonance imaging study. Bone. 2001;29(4):388-92. 87. Muratova VN, Islam SS, Demerath EW, Minor VE, Neal WA. Cholesterol screening among children and their parents. Prev Med. 2001;33(1):1-6. 88. Rourke KM, Brehm BJ, Cassell C, Sethuraman G. Effect of weight change on bone mass in female adolescents. J Am Diet Assoc. 2003;103(3):369-72. 89. Woodruff SJ, Hanning RM. Associations between diet quality and physical activity measures among a southern Ontario regional sample of grade 6 students. Appl Physiol Nutr Me. 2010;35(6):826-33. 90. Mutlu EK, Mutlu C, Taskiran H, Ozgen IT. Association of physical activity level with depression, anxiety, and quality of life in children with type 1 diabetes mellitus. Journal of pediatric endocrinology & metabolism : JPEM. 2015. 39 Benítez-Porres J, 2016 International PhD Thesis Table 5. Summary of the methodology used in the current dissertation Study design Participants Main variables studied Material Statistical analysis Study I. Reliability and validity of the PAQ-C questionnaire to assess physical activity in children. Cross-sectional 83 children (46 boys, 37 girls) Age, height, weight, BMI, FMP, subcutaneous skinfold thickness and PA. Stadiometer SECA, Tanita UM-050 digital, anthropometric equipment, PAQ-C, Actigraph GT3X and SPSS. Kolmogorov-Smirnov test. Spearman rank correlation coefficient. Intraclass Correlation Coefficient. Cronbach’s α Coefficient. Bland and Altman method. Study II. The physical activity questionnaire score cut offs to classify physical activity level in children and adolescents. Cross-sectional 146 children (83 boys, 63 girls) and 234 adolescents (115 boys, 119 girls) Age, height, weight, BMI, FMP, subcutaneous skinfold thickness and PA. Stadiometer SECA, Tanita UM-050 digital, anthropometric equipment, PAQ-C, PAQ-A, Actigraph GT3X, SPSS and MedCalc. Kolmogorov-Smirnov test. Spearman rank correlation coefficient. ROC curves. Study III. The influence of 2-year changes in physical activity, maturation, and nutrition on adiposity in adolescent youth. Longitudinal 80 adolescents (38 boys, 42 girls) Age, height, weight, BMI, FMP, subcutaneous skinfold thickness, maturation level, nutrition and PA. Stadiometer SECA, Tanita UM-050 digital, anthropometric equipment, PAQ-A, FFQ and SPSS. Kolmogorov-Smirnov test. Spearman rank correlation coefficient. Khamis-Roche method. Repeated measures. General linear model. BMI: Body Mass Index, FFQ: Food-frequency questionnaire, FMP: Fat mass percent, PA: Physical activity, PAQ-A: Physical activity questionnaire for adolescents, PAQC: Physical activity questionnaire for children, ROC: Receiver operating characteristic, SPSS: Statistical package for the social sciences. 46 Benítez-Porres J, 2016 International PhD Thesis CHAPTER IV: RESULTS AND DISCUSSION I RELIABILITY AND VALIDITY OF THE PAQ-C QUESTIONNAIRE TO ASSESS PHYSICAL ACTIVITY IN CHILDREN Benítez-Porres J. 1, López-Fernández, I. 1, Raya J.F. 1, Carnero, S.A. 1, Alvero-Cruz J.R. 2, Carnero E.A. 1 1 Biodynamic and Body Composition Laboratory. University of Málaga, Spain. 2 Exercise Physiology Laboratory. University of Málaga, Spain. 46 Benítez-Porres J, 2016 International PhD Thesis INTRODUCTION Physical activity (PA) has been identified as an important agent in the prevention of chronic diseases such as obesity, cardiovascular diseases, and metabolic syndrome 1-3. Therefore, in order to know more precisely the levels of PA during childhood and to identify the impact on health, it is necessary to develop and validate instruments able to adequately and widely assess PA in school populations. Survey instruments continue to provide useful information in population-based studies of children's PA. The most accurate method for measuring energy expenditure during PA, such as doubly labelled water or indirect calorimetry are complex, timeconsuming, and are expensive and impractical procedures when evaluating large populations 4. Over the last decade a rise in the technology of accelerometers has allowed us to obtain reliable measurements of the duration of PA, and has provided an indirect, yet reasonably accurate measure of PA in this area. However, this continues to be an expensive method for application in certain settings such as primary schools. Self-report instruments provide a convenient way to assess activity patterns in large populations, however, there are few questionnaires designed to estimate PA in children. One of the most widely used questionnaires for this age group is the Physical Activity Questionnaire for Children (PAQ-C) 5, though it has never been validated with Spanish children. The PAQ-C is a simple questionnaire that assesses the PA a child has performed over the last 7 days. The overall result of the test is a score of 1-5 points that allows for a graded level of PA performed by each subject. PA measured by PAQ-C has been associated with indicators of adiposity, bone mineral content, heart rate variability 47 Benítez-Porres J, 2016 International PhD Thesis and certain psychological indicators (sports competition, body satisfaction, anxiety) 6-8. The PAQ-C have acceptable reliability and convergent validity 9. The mean of all items is used to indicate level of PA. A high score indicates higher levels of PA. Moreover, attempts to obtain cut-off points from PAQ-C final score have been reported for English children 10. In addition, this questionnaire has adequate validity and reliability in other countries 9, although the validity and reliability assessed varies by ethnicity and requires additional development to be a useful measure of physical activity in American and European children from diverse ethnic populations 11. However, not enough is known about the capacity of PAQ-C to estimate PA measured objectively. Moreover, discrepancies and high variability in children’s PA measured by accelerometers around the world have been reported, which may introduce a bias in validation studies 12,13. So transcultural validation of PA questionnaires will allow us to compare results among countries and a more reliable and valid assessment of PA in each country. Considering the need for adequate and viable methods of measuring PA in school settings, the aim of this study was to evaluate the reliability and validity of the PAQ-C when applied to Spanish children using as a reference criterion the objective measurement of PA by triaxial accelerometry. METHODS Participants An invitation to participate in the study was sent to all parents who had their children in fourth, fifth or sixth grade in two different primary schools (Málaga and 48 Benítez-Porres J, 2016 International PhD Thesis Orense, Spain). Eighty-three potentially eligible subjects responded, and gave their written informed consent after receiving detailed information about the aims and procedures of the study. Subjects with incomplete PA (n=0) data or technical errors in the instrument (n=5) were excluded. A final sample of 83 children (46 boys, 37 girls) participated in the reliability study and 78 children (42 boys, 36 girls) cover the criteria for the validity study. There were no differences on age and body mass index (BMI) between the excluded participants and the final sample. This study is part of two larger studies where the sample size was randomized from all eligible students of each primary school who met the inclusion criteria. Briefly, in the Orense school we had only thirty possible candidates (budget limitation) to participate in a doubly labeled water follow-up study during four weeks within one year. One hundredfifty informed consents were distributed among children who were interested in the study. Of those, twenty-five met the inclusion criteria and only nineteen completed the final protocol. Regarding the Malaga school, all students were called to participate in a randomized control trial study, and one hundred informed consents were distributed to the fourth, fifth and sixth grades of the school. Among those children/parents who signed the consent, a randomization was performed to select sixty who had availability to perform two separate physical and body composition assessments in our laboratory. Sixty-four students participated in the study, although only forty-one finished the intervention. In summary, data for this analysis comes from a randomized sample from two primary schools in Spain, where physical activity assessment by questionnaires was one of the variables in those longitudinal studies. 49 Benítez-Porres J, 2016 International PhD Thesis The research protocol was reviewed and approved by the Ethics Committee of the Sports Medicine School, at Faculty of Medicine (Málaga, Spain). The study was developed following the ethical guidelines of the Declaration of Helsinki-Seoul, last modified in 2008. Instrumentation Initial measurements Anthropometric measurements (waist and hip circumferences, sagittal abdominal diameter and skinfolds), including height and body mass, were performed according to the International Society for the Advancement of Kinanthropometry (ISAK) standards for anthropometric assessment. Height was assessed with socks while shoes were taken off, using a stadiometer (SECA Leicester, Birmingham, UK). A Tanita UM-050 digital weighing scale (Tanita UK Ltd,Yiewsley, Middlesex, UK) was used to assess body mass. Fat mass percent (FMP) was calculated using Slaughter´s equation 14 from anthropometric measures. BMI were calculated using the classical equation and were categorized into 3 levels: normal-weight, overweight and obesity, according Cole´s cut-off points 15. Physical Activity Questionnaire for Children (PAQ-C) PA was assessed using the PAQ-C 5. The PAQ-C is a nine item, 7-day PA recall designed for use with elementary and middle school children in a field-based setting. A 50 Benítez-Porres J, 2016 International PhD Thesis tenth item not used in the calculation of the activity score asks children if they were sick or otherwise prevented from engaging in regular PA. The PAQ-C was administered twice and children were asked to recall their participation in activities over the last 7 days to compute an activity score. Once a value from 1 to 5 for each of the 9 items (items 1 to 9) used in the PA composite score is obtained, the mean of these 9 items is taken, which results in the final PAQ-C activity summary score. Cultural adaptation of the Spanish PAQ-C was performed following the basic steps of standardized questionnaires cultural adaptation process 16. The research team members made the original Spanish translation. Subsequently, two bilingual researchers outside the group performed the reverse translation. The differences between the original version and the translations were reviewed and discussed by the research group and external researchers. Typically, the questionnaire was completed at school in a quiet room, and researchers were available to help children and confirm that all items were answered. The entire process lasted approximately 10-15 minutes. Accelerometry The Actigraph GT3X monitor device (Actigraph, Pensacola, FL, USA), was used to assess PA. The accelerometer is lightweight (27 g), compact (3.8×3.7×1.8 cm) and has a rechargeable lithium polymer battery. It uses a solid-state tri-axial accelerometer to collect motion data on 3 axes: vertical (Y), horizontal right-left (X) and horizontal frontback axis (Z). The GT3X measures accelerations in the range of 0.05g to 2g, which is digitized by a 12-bit analog-to-digital converter at a rate of 30 Hz. Once digitized, the data are filtered using a band-limited frequency of 0.25 to 2.5 Hz. The Actigraph 51 Benítez-Porres J, 2016 International PhD Thesis accelerometer has been shown to be a reliable and valid tool for the assessment of different types of physical activities 17,18. Researchers distributed pre-initialized accelerometers face-to-face at schools. Participants wore the accelerometers on the right side of the hip, secured with an adjustable elastic belt, underneath clothing, near to the center of gravity. Participants received a demonstration from a trained researcher on how to wear the accelerometer. They were asked to only remove the device when sleeping and engaging in water-based activities. Additionally, children received a brochure about accelerometer use including the instructions. Accelerometers were set to register 1-second epoch cycles, and were programmed to start the record at midnight of the following day they received the monitor and to record activity for the following 7 days. The version 6.11.1 of Actilife Software (Actigraph, Pensacola, FL, USA) was used to process the accelerometer data. Periods of ≥60 minutes of zero values, allowing for 2 minutes of non-zero interruptions, were defined as accelerometer “non-wear” time and were removed from the analyses. The first day of recording was not included in the analysis. Only participants with ≥4 complete days, including one weekend day, were included 19. A day was considered valid if it contained ≥10 hours of wear time for weekdays and ≥8 hours for weekend days considering different sleep patterns over weekends 20. We selected the cut points from Evenson et al. 21 to determine the time spent on different intensity levels of PA: ≤100 cpm for sedentary behavior, <2296 cpm for light, <4012 cpm for moderate, and ≥4012 cpm for vigorous physical activity. 52 Benítez-Porres J, 2016 International PhD Thesis A recording of more than 15,000 counts per minute was considered as a potential malfunction of the accelerometer and the value was excluded from the analyses, based on the recommendations from Esliger et.al. 22. Procedure Following agreement to participate in the study, participants were assessed (initial measurements) and received an accelerometer and later (8 days) completed PAQ-C twice, with a gap of 6 hours between the two questionnaires. After the trial period, the material and questionnaires were collected by the researcher, and the data was stored in a spreadsheet using Microsoft Excel for further analysis. Data Analysis The characteristics of subjects were described with frequency distribution and mean, standard deviation (SD). The reliability (within-subject variability) was calculated by applying the PAQ-C during the same day twice and Intraclass Correlation Coefficient (ICC) was used to confirm the reproducibility. Additionally, agreement analysis was performed between first and second measurements of total score, also systematic and proportional bias were calculated by independent sample T-test and Kendall´s tau rank correlation. Individual item reliability was also carried out by the same procedure. The internal-consistency of the questionnaire was analyzed using Cronbach's α coefficient. Removing every item in order to confirm or exclude redundancy of the individual items was performed also by Cronbach's α. Additionally, we carried out an inter-item raw 53 Benítez-Porres J, 2016 International PhD Thesis correlation coefficient to complete reliability analysis as suggested by Clark and Watson 23. The relationship between the PAQ-C and the accelerometer scores was performed using Spearman’s rank correlation coefficients (Rs). Agreement between the PAQ-C (Total score values) and the accelerometer (MVPA minutes per day) was assessed using the Bland and Altman method 24; after Z Score transformation for PAQ-C and accelerometer values. The Bland and Altman plots give an indication of random error and bias (see previous paragraph). The first PAQ-C was selected always as reference, and the second one was used to carry out the intra-day and/or intra-subject reliability. In respect to statistical power analysis, if we assume an 80% of power and alpha value of 0.001, it will permit us to detect coefficients of correlation as low as 0.3 with a sample size of 66 students, which is below of our sample size, so we could confirm our correlation analysis for validity will not be biased by the sample size (type II error). The analyses were done using SPSS 21.0 (SPSS Inc. Chicago, Illinois) and the level of significance was set at P<0.05. RESULTS Characteristics of the participants are reported as mean and standard deviations in table 6. No significant gender differences were found in the variables shown. 54 Benítez-Porres J, 2016 International PhD Thesis compare both instruments with Z value transformation, nevertheless, the results were similar, and significant differences and low correlations were confirmed; therefore, the observed agreement between the PAQ-C (a measure of self-report) and accelerometer (an objective measure) should be interpreted in this light. The output from accelerometers is a dimensionless unit commonly referred to as accelerometer counts. Researchers have attempted to calibrate these counts with energy expenditure in order to get a biological meaning to the output 32. This has resulted in the publication of count thresholds relating to various categories of energy expenditure that allow researchers to summarize time spent in a given intensity of activity 33. The availability of multiple cut points or equations has led to much confusion in the accelerometer literature 34. We used the Evenson et al. 21 cut points, recommended in Trost et al. 35 comparative study to estimate time spent in sedentary, light-, moderate-, and vigorous-intensity activity in children and adolescents. Other cut points would have yielded different results. Nonetheless, the associations and differences with total PA will continue to be the same since this variable must not be highly dependent of cut-off values. The Bland-Altman plots showed that PAQ-C gave higher values of total PA than the accelerometer. Subjects are likely to overestimate the frequency of activities and this is not reflected in real movement data. In other words, higher the time spent in a certain level of PA assessed by the accelerometer, higher the difference between both methods. Finally, another source of error must be related with the adiposity level. In adolescents, an overestimation of PA has been reported when assessed by self-reported tools 36. In our sample an interaction between reliability and BMI groups was not plausible 61 Benítez-Porres J, 2016 International PhD Thesis since similar coefficients of correlations were found among normal, overweight and obese children. Also, non-significant correlations were observed between BMI or FMP and differences in total PA for the PAQ-C and accelerometry. However, it could be speculated that such results could be influenced by a low rate of overweight and obese children, but in this study the prevalence of overweight and obesity (40.9%, table 6) was similar to the Spanish prevalence (34.9%) 37. In summary, we can conclude that the overestimation of PA founded in overweight and obese adolescents was not confirmed in children. Bearing in mind the use of the cut-off points for PA intensities Yngve et al. 38 reported, when establishing cut-off values the results are affected by the types of activities performed and the setting. Age-specific equations must be used to ensure the correct use of accelerometer in children. However, the PAQ-C questionnaire only assessed the frequency of PA and not intensity, so the total PA must be more valuable to compare both instruments. Meanwhile, a recent study have reported that the PAQ-C can be calibrated to provide accurate group-level estimates of MVPA, which can be used to improve the usability of the questionnaire 39. Limitations We could speculate the low correlations observed in this study might be due to sample size as well as the level of maturity associated with the age of the children. However, the sample size was similar to the original validation studies 9,40 and the statistical power analysis informs our simple size is enough to detect even stronger correlations. Other limitations could explain these low associations, so there must be inherent subjectivity when individuals are asked to respond to questions about their 62 Benítez-Porres J, 2016 International PhD Thesis behavior. Some issues as recalling errors, deliberate misrepresentations, social desirability and other biases have been pointed out to be particularly important when dealing with children 41. All these previous limitations may be hard to overcome with the current protocol of the questionnaire, which assume a self-report procedure. So, only an enhanced protocol, which includes additional control items or provides more help to children to fill the PAQ-C (in example by showing actual references such as PA-related videos of children), would improve the validity of the questionnaire. However, we could not implement any of these strategies since we followed the published procedure thoroughly. Conclusions In summary, this was the first study that analyzes validity and reliability of PAQ-C in Spanish children, which may help to understand the meaning and applicability of the questionnaire. The results suggest that PAQ-C had a high reliability but questionable validity for assessing PA in our sample of Spanish children. These findings would suggest that the PAQ-C requires additional development to be a useful measure of PA in Spanish children. Therefore, PA measurement in children should not be limited to self-report measures solely and whenever possible an ACL or other capture-movement device should be used. This approach will allow us to obtain a better interpretation of the actual results of PA in which children are involved. In view of the known benefits of PA in this population 42-44, there is a need to develop new self-report measures or validate other existing PA questionnaires. 63 Benítez-Porres J, 2016 International PhD Thesis REFERENCES 1. 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Study protocol of physical activity and sedentary behaviour measurement among schoolchildren by accelerometry--cross-sectional survey as part of the ENERGY-project. BMC public health. 2011;11:182. 21. Evenson KR, Catellier DJ, Gill K, Ondrak KS, McMurray RG. Calibration of two objective measures of physical activity for children. J Sport Sci. 2008;26(14):1557-65. 22. Esliger DW, Copeland JL, Barnes JD, Tremblay MS. Standardizing and Optimizing the Use of Accelerometer Data for Free-Living Physical Activity Monitoring. J Phys Act Health. 2005;2(3):366-83. 23. Clark LA, Watson D. Constructing validity: Basic issues in objective scale development. Psychological Assessment. 1995;7:309-19. 24. Bland JM, Altman DG. Statistical-Methods for Assessing Agreement between 2 Methods of Clinical Measurement. Lancet. 1986;1(8476):307-10. 25. Janz KF, Lutuchy EM, Wenthe P, Levy SM. Measuring activity in children and adolescents using self-report: PAQ-C and PAQ-A. Med Sci Sport Exer. 2008;40(4):76772. 26. Streiner DL. Starting at the beginning: an introduction to coefficient alpha and internal consistency. Journal of personality assessment. 2003;80(1):99-103. 66 Benítez-Porres J, 2016 International PhD Thesis 27. Martinez-Gomez D, Martinez-de-Haro V, Pozo T, Welk GJ, Villagra A, Calle ME, et al. Reliability and Validity of the PAQ-A Questionnaire to Assess Physical Activity in Spanish Adolescents. Revista espanola de salud publica. 2009;83(3):427-39. 28. Ward DS, Evenson KR, Vaughn A, Rodgers AB, Troiano RP. Accelerometer use in physical activity: Best practices and research recommendations. Med Sci Sport Exer. 2005;37(11):S582-S8. 29. Vanhelst J, Hardy L, Gottrand F, Beghin L. [Technical aspects and relevance of physical activity assessment in children and adolescents in free-living conditions]. Archives de pediatrie : organe officiel de la Societe francaise de pediatrie. 2012;19(11):1219-25. 30. Ham SA, Reis JP, Strath SJ, Dubose KD, Ainsworth BE. Discrepancies between methods of identifying objectively determined physical activity. Medicine and science in sports and exercise. 2007;39(1):52-8. 31. Kavanaugh KM, J. B.; Hibbett, L. J.; Kaczynski, A. T. Correlates of subjectively and objectively measured physical activity in young adolescents. J Sport Health Sci. 2014. 32. Freedson P, Pober D, Janz KF. Calibration of accelerometer output for children. Med Sci Sport Exer. 2005;37(11):S523-S30. 33. Rowlands AV. Accelerometer assessment of physical activity in children: An update. Pediatr Exerc Sci. 2007;19(3):252-66. 34. Welk GJ, McClain J, Ainsworth BE. Protocols for Evaluating Equivalency of Accelerometry-Based Activity Monitors. Med Sci Sport Exer. 2012;44:S39-S49. 35. Trost SG, Loprinzi PD, Moore R, Pfeiffer KA. Comparison of Accelerometer Cut Points for Predicting Activity Intensity in Youth. Med Sci Sport Exer. 2011;43(7):13608. 67 Benítez-Porres J, 2016 International PhD Thesis 36. Elliott SA, Baxter KA, Davies PS, Truby H. Accuracy of self-reported physical activity levels in obese adolescents. Journal of nutrition and metabolism. 2014;2014:808659. 37. Sanchez-Cruz JJ, Jimenez-Moleon JJ, Fernandez-Quesada F, Sanchez MJ. Prevalence of child and youth obesity in Spain in 2012. Rev Esp Cardiol (Engl Ed). 2013;66(5):371-6. 38. Yngve A, Nilsson A, Sjostrom M, Ekelund U. Effect of monitor placement and of activity setting on the MTI accelerometer output. Med Sci Sport Exer. 2003;35(2):320-6. 39. Saint-Maurice PF, Welk GJ, Beyler NK, Bartee RT, Heelan KA. Calibration of selfreport tools for physical activity research: the Physical Activity Questionnaire (PAQ). BMC public health. 2014;14. 40. Crocker PR, Bailey DA, Faulkner RA, Kowalski KC, McGrath R. Measuring general levels of physical activity: preliminary evidence for the Physical Activity Questionnaire for Older Children. Med Sci Sports Exerc. 1997;29(10):1344-9. 41. Sirard JR, Pate RR. Physical activity assessment in children and adolescents. Sports medicine. 2001;31(6):439-54. 42. Ekelund U, Luan JA, Sherar LB, Esliger DW, Griew P, Cooper A, et al. Moderate to Vigorous Physical Activity and Sedentary Time and Cardiometabolic Risk Factors in Children and Adolescents. Jama-J Am Med Assoc. 2012;307(7):704-12. 43. Janssen I, LeBlanc AG. Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. Int J Behav Nutr Phy. 2010;7. 44. Sothern MS, Loftin M, Suskind RM, Udall JN, Blecker U. The health benefits of physical activity in children and adolescents: implications for chronic disease prevention. Eur J Pediatr. 1999;158(4):271-4. 68 Benítez-Porres J, 2016 International PhD Thesis II THE PHYSICAL ACTIVITY QUESTIONNAIRE SCORE CUT OFFS TO CLASSIFY PHYSICAL ACTIVITY LEVEL IN CHILDREN AND ADOLESCENTS Benítez-Porres J. 1, Alvero-Cruz J.R. 2, Sardinha, L.B. 3, López-Fernández, I. 1, Carnero E.A. 1 1 Biodynamic and Body Composition Laboratory. University of Málaga, Spain. 2 Exercise Physiology Laboratory. University of Málaga, Spain. 3 Exercise and Health Laboratory, University of Lisbon, Lisbon, Portugal. 69 Benítez-Porres J, 2016 International PhD Thesis INTRODUCTION Physical activity (PA) is a powerful predictor of cardiovascular 1, skeletal 2-4, and mental health 5, in children and adolescents. Moreover, PA has been identified as an important agent in the prevention of chronic diseases such as obesity, cardiovascular diseases, and metabolic syndrome 1,6,7. However, current youth, and especially girls, are often not enough active 8-12. PA assessment by questionnaires is a cornerstone in the field of sport epidemiology studies. Survey instruments continue to provide useful information in population-based studies of young's PA and they enable a convenient way to assess activity patterns on large populations 13. The Physical Activity Questionnaire for children and adolescents (PAQ-C & PAQ-A) are a cost-effective tools to assess PA patterns during childhood and adolescence 14 and it has been widely used in research and field settings. However, a limitation is that the outcome score is not readily interpretable 15. The PAQ asks for frequency spent in physical activities, which is a subjective rating of intensity, moreover their items are scored using ordinal scales (1-5 scale) and the outcome measure is computed as a simple mean of the individual items. This makes it difficult to relate the PAQ score with the established international PA recommendations 16. Meanwhile, objective measures are often used to validate less accurate measures, such as subjective instruments, but this does not directly improve the accuracy or precision of the self-report instrument. Equivalent estimates of PA could be generate in a more efficient and costeffective way if we use handle self-report instruments. In this line, the utility in youth can be greatly enhanced by calibrating self-report output against 70 Benítez-Porres J, 2016 International PhD Thesis light PA >116 min/day; respectively). Receiver operating characteristic (ROC) curves 45 were carried out to identify PAQ-C and PAQ-A score cut-off point for each factor. Classification accuracy for each set of cut-points was evaluated by calculating weighted statistics, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). An area of 1 represents perfect classification, whereas an area of 0.5 represents an absence of classification accuracy. ROC–AUC values of >0.90 are considered excellent, 0.80–0.89 good, 0.70–0.79 fair, and <0.70 poor 46. The analyses were performed using SPSS 22.0 (Chicago, Illinois) and MedCalc 14.12.0 (Mariakerke, Belgium) for ROC curves. The level of significance was set at P<0.05. RESULTS Characteristics of the participants (children and adolescents) for both sex combined and separately are presented in table 9. All values are reported as mean and standard deviations (SD). Significant differences between boys and girls were found in adolescents for weight, height, FMP, PAQ-A score, all PA intensities and number of steps, with higher values for boys, except for FMP. No differences between sexes were found in children. 53.4% of children met the 60 minutes of MVPA recommended; while 41.9% of adolescents met this recommendation. PAQ score was positively associated with vigorous PA, MVPA and number of steps (rho=0.19, rho=0.17, rho=0.16, respectively; all P<0.05) for children. In adolescents, PAQ score was positively associated with all intensities (light, moderate, vigorous and MVPA) and number of steps supplied by the accelerometer (rho=0.33, rho=0.21, rho=0.39, rho=0.36 rho=0.41, respectively; P<0.001). 77 Benítez-Porres J, 2016 International PhD Thesis Table 9. Characteristics of study participants by age and sex (n=480). Children Adolescents All (n=146) Girls (n=63) Boys (n=83) All (n=234) Girls (n=119) Boys (n=115) Age (years) 10.8±1.3 10.7±1.3 10.9±1.2 15.3±1.4 15.2±1.4 15.4±1.3 Weight (Kg) 41.5±11.7 40.0±12.7 42.7±10.8 59.5±13.3 57.5±1.7 61.5±13.7* Height (cm) 144.6±10.5 143.2±10.9 145.6±10.1 163.9±8.2 160.2±6.0 167.6±8.4*** BMI (Kg/m2) 19.5±3.7 19.1±4.0 19.9±3.4 22.1±4.4 22.4±4.6 21.8±4.2 FMP (%) 22.9±9.2 22.9±6.6 22.9±10.9 18.9±8.0 21.2±7.7 16.6±7.6*** PA Score (PAQ-C) 3.09±0.64 3.11±0.60 3.07±0.66 - - - PA Score (PAQ-A) - - - 2.51±0.72 2.29±0.68 2.73±0.70*** Sedentary time (min/day) 603.5±60.9 609.0±61.2 599.3±60.7 642.9±75.7 642.7±83.4 643.1±67.2 Light PA (min/day) 120.3±33.8 121.7±46.2 119.3±20.3 92.7±28.4 88.0±27.4 97.6±28.7* Moderate PA (min/day) 33.7±6.8 33.6±6.2 33.8±7.3 33.8±12.8 31.5±11.9 36.2±13.4** Vigorous PA (min/day) 29.1±8.9 27.8±8.3 30.1±9.2 23.0±14.9 16.6±10.4 29.7±15.8*** MVPA (min/day) 62.8±13.9 61.3±12.7 63.9±14.7 56.9±22.9 48.1±18.9 65.9±23.3*** Steps/day 10668±1938 10556±1594 10752±2170 9320±3561 8434±3682 10264±3180*** BMI: body mass index; FMP: fat mass percent; PA: physical activity; * P<0.05; ** P<0.01. *** P<0.001; independent sample t test between boys and girls. Physical activity questionnaire for children (PAQ-C) Details for AUC, as well as PAQ-C scores and number of steps equivalent to the coordinates with the greatest sum of sensitivity and specificity are shown in Table 10 and 11. Table 10. Area under the ROC curve of PAQ-C score and steps/day, based on PA recommendations. PA Recommendati ons 60 MVPA 30 Vigorous PA 116 Light PA Score Steps Score Steps Score Steps AUC 0.551 0.896 0.545 0.879 0.527 0.756 EE 0.0483 0.0259 0.0488 0.0278 0.0482 0.0408 95% CI 0.467 to 0.634 0.835 to 0.940 0.460 to 0.627 0.815 to 0.927 0.443 to 0.610 0.678 to 0.823 P 0.2896 <0.0001 0.3579 <0.0001 0.5728 <0.0001 Youden index 0.1572 0.6497 0.1659 0.6575 0.1269 0.439 PA: physical activity; MVPA: moderate to vigorous physical activity; AUC: area under the curve; EE: standard error; CI: confidence interval; P: significance level. 78 Benítez-Porres J, 2016 International PhD Thesis Table 11. PAQ-C score and steps/day cut-off points and sensitivity, specificity, likelihood ratios and predictive values, based on PA recommendations. PA Recommendations Cut Point Sens 95% CI Spec 95% CI +LR 95% CI -LR 95% CI +PV 95% CI -PV 95% CI 60 MVPA PAQ score >2.75 73.08 61.8 - 82.5 42.65 30.7 - 55.2 1.27 1.0 - 1.6 0.63 0.4 - 1.0 59.4 48.9 - 69.3 58 43.2 - 71.8 Steps/day >10664 78.21 67.4 - 86.8 86.76 76.4 - 93.8 5.91 3.2 - 11.0 0.25 0.2 - 0.4 87.1 77.0 - 93.9 77.6 66.6 - 86.4 30 Vigorous PA PAQ score >2.75 75.41 62.7 - 85.5 41.18 30.6 - 52.4 1.28 1.0 - 1.6 0.6 0.4 - 1.0 47.9 37.6 - 58.4 70 55.4 - 82.1 Steps/day >11038 78.69 66.3 - 88.1 87.06 78.0 - 93.4 6.08 3.5 - 10.7 0.24 0.2 - 0.4 81.4 69.1 - 90.3 85.1 75.8 - 91.8 116 Light PA PAQ score >2.78 68.75 57.4 - 78.7 43.94 31.7 - 56.7 1.23 0.9 - 1.6 0.71 0.5 - 1.1 59.8 49.0 - 69.9 53.7 39.6 - 67.4 Steps/day >10190 78.75 68.2 - 87.1 65.15 52.4 - 76.5 2.26 1.6 - 3.2 0.33 0.2 - 0.5 73.3 62.6 - 82.2 71.7 58.6 - 82.5 PA: physical activity; MVPA: moderate to vigorous physical activity; Sens: sensitivity; CI: confidence interval; Spec: specificity; LR: likelihood ratios positives (+) and negatives (-); PV: predictive values positives (+) and negatives (-). Table 13. PAQ-A score and steps/day cut-off points and sensitivity, specificity, likelihood ratios and predictive values, based on PA recommendations. PA Recommendations Cut Point Sens 95% CI Spec 95% CI +LR 95% CI -LR 95% CI +PV 95% CI -PV 95% CI 60 MVPA PAQ score >2.75 51.02 40.7 - 61.3 77.94 70.0 - 84.6 2.31 1.6 - 3.4 0.63 0.5 - 0.8 62.5 51.0 - 73.1 68.8 60.9 - 76.0 Steps/day >9701 86.6 78.2 - 92.7 93.08 87.3 - 96.8 12.51 6.6 - 23.6 0.14 0.09 - 0.2 90.3 82.4 - 95.5 90.3 84.0 - 94.7 30 Vigorous PA PAQ score >2.77 57.38 44.1 - 70.0 75.14 68.0 - 81.4 2.31 1.6 - 3.2 0.57 0.4 - 0.8 44.9 33.6 - 56.6 83.3 76.5 - 88.8 Steps/day >9806 86.89 75.8 - 94.2 77.11 70.0 - 83.3 3.8 2.8 - 5.1 0.17 0.09 - 0.3 58.2 47.4 - 68.5 94.1 88.7 - 97.4 116 Light PA PAQ score >2.73 53.33 37.9 - 68.3 68.78 61.7 - 75.3 1.71 1.2 - 2.4 0.68 0.5 - 0.9 28.9 19.5 - 39.9 86.1 79.5 - 91.2 Steps/day >12511 46.51 31.2 - 62.3 91.85 86.9 - 95.4 5.71 3.2 - 10.2 0.58 0.4 - 0.8 57.1 39.4 - 73.7 88 82.6 - 92.3 PA: physical activity; MVPA: moderate to vigorous physical activity; Sens: sensitivity; CI: confidence interval; Spec: specificity; LR: likelihood ratios positives (+) and negatives (-); PV: predictive values positives (+) and negatives (-). 79 Benítez-Porres J, 2016 International PhD Thesis AUC of PAQ-C score for MVPA >60 min/day, vigorous PA >30min/day, and light PA >116 min/day were no significant (P>0.05) and only weak (AUC<0.7) discriminators between “active” and “non-active” individuals. However, AUC of number of steps for all intensities were significant (P<0.001) and good (AUC>0.8 for 60 minutes of MVPA) discriminators. ROC analysis showed PAQ-C score cut-off points >2.75 to discriminate active children. 60 minutes of MVPA in children appears to be achieved, on average, within a total volume of 10664 steps/day; 30 minutes of vigorous PA within a total volume of 11038 steps/day; and 116 minutes of light PA within a total volume of 10190 steps/day. The sensitivity associated with the different factors were moderate for PAQ-C score and steps/day. However, the specificity associated were low for PAQ-C score (42.7%, 41.2% and 43.9%, respectively) and high for steps/day (86.8%, 87.1% and 65.2%, respectively). This shows the low capacity of the PAQ-C to identify inactive children. A sample ROC curves is illustrated in Figure 3. Average_Steps/day 020 40 60 80 100 0 20 40 60 80 100 100-Specificity Sensitivity 80 Benítez-Porres J, 2016 International PhD Thesis Figure 3. Examples receiver-operator curves for number of steps and the Physical Activity Questionnaire for Children (PAQC)’s ability to identify 60 minutes of moderate to vigorous physical activity (MVPA) (n=146). Physical activity questionnaire for adolescents (PAQ-A) Details coordinates with the greatest sum of sensitivity and specificity are shown for AUC, as well as PAQ-A scores and number of steps equivalent to the in Table 12 and 13. Table 12. Area under the ROC curve of PAQ-A score and steps/day, based on PA recommendations. PA Recommendati ons 60 MVPA 30 Vigorous PA 116 Light PA Score Steps Score Steps Score Steps AUC 0.677 0.957 0.658 0.879 0.631 0.724 EE 0.0356 0.0124 0.0408 0.0223 0.0476 0.0464 95% CI 0.613 to 0.736 0.921 to 0.979 0.594 to 0.719 0.829 to 0.918 0.565 to 0.693 0.661 to 0.781 P <0.0001 <0.0001 0.0001 <0.0001 0.0061 <0.0001 Youden index 0.2896 0.7967 0.3252 0.6399 0.2212 0.3836 PA: physical activity; MVPA: moderate to vigorous physical activity; AUC: area under the curve; EE: standard error; CI: confidence interval; P: significance level. AUC of PAQ-A score for all factors were significant (P<0.01) but only weak (AUC<0.7) discriminators between “active” and “non-active” youth. AUC of number of PAQ-C Score 020 40 60 80 100 0 20 40 60 80 100 100-Specificity Sensitivity 81 Benítez-Porres J, 2016 International PhD Thesis steps for all intensities were significant too (P<0.001) and excellent (AUC>0.9 for 60 minutes of MVPA) discriminators. ROC analysis showed PAQ-A score cut-off points >2.73 to discriminate active adolescents. 60 minutes of MVPA in children appears to be achieved, on average, within a total volume of 9701 steps/day; 30 minutes of vigorous PA within a total volume of 9806 steps/day; and 116 minutes of light PA within a total volume of 12511 steps/day. The sensitivity associated with the different factors were low for PAQ-A score and high for steps/day (except for 116 minutes of light PA). The capacity of the PAQ-A to identify inactive adolescent (specificity) was moderate (77.9%, 75.1% and 68.8%, respectively) and moderate-high for steps/day (93.1%, 77.1% and 68.8%, respectively). A sample ROC curves is illustrated in Figure 4. Average_Steps/day 020 40 60 80 100 0 20 40 60 80 100 100-Specificity Sensitivity 82 Benítez-Porres J, 2016 International PhD Thesis Figure 4. Examples receiver-operator curve for number of steps and the Physical Activity Questionnaire for Adolescents (PAQA)’s ability to identify 60 minutes of moderate to vigorous physical activity (MVPA) (n=234). The capacity of number of steps to determine “active” or “inactive” youth is greater than the capacity of the questionnaire score, giving higher likelihood ratios positives values and lower likelihood ratios negatives values in all factors. The same applies to positives and negatives predictive values, as well as with sensitivity and specificity values (except for sensitivity of PAQ-A for 116 minutes of light PA). DISCUSSION The present study evaluated the capacity of PAQ-C and PAQ-A to differentiate active and non-active youth based on international PA guidelines. The main finding of this cross-sectional study was to determine a PAQ-C and PAQ-A score cut-off point of 2.75 to discriminate 60 minutes of MVPA, which is associated within a total volume of 10664 steps/day for children and 9701 steps/day for adolescents. PAQ-A Score 020 40 60 80 100 0 20 40 60 80 100 100-Specificity Sensitivity 83 Benítez-Porres J, 2016 International PhD Thesis To our knowledge, this is the first study to define PAQ-C and PAQ-A cut-points values by accelerometry based on PA recommendations. Details to assess the PAQ-A score as cardiorespiratory fitness parameter have been published for English children. Our results are similar to obtained in the study of Voss et al. 47, in which a cut-off points of 2.9 for boys and 2.7 for girls were established, using cardiorespiratory fitness as the criterion-referenced standard. However, the ROC analysis reported differences between the two questionnaires and these results should be interpreted cautiously. The sensitivity and the specificity analysis revealed that the PAQ-C cut-points were no able to distinguish the true negatives, but not the true positives. Furthermore, the AUC value indicates that the PAQ-C is unable to discriminate inactive children. In case of PAQ-A cut-off points, which proved sufficiently specificity to discriminate the true negatives but moderately the true positives, manifest an AUC value near to 0.7 (P<0.001). A diagnostic test that yields an AUC of <0.7, as observed here, may be deemed unacceptable for clinical use, given the potentially severe repercussions of misclassifying presence or absence of disease. However, the PAQ is not a clinical diagnostic test and comparatively low AUC are often published in a public health context. This could be related to the PAQ validity in Spanish youth. While the PAQ-A shown reasonable validity for this age range (rho=0.39; P<0.001) 48; the PAQ-C shown a questionable validity (rho=0.28, P<0.05) for assessing total PA and MVPA in Spanish children. Our correlation results between both instruments also concur with the line of evidence that suggests PA questionnaires for adolescents correlated better with accelerometer scores than PA questionnaires for children 13. 84 Benítez-Porres J, 2016 International PhD Thesis Moreover, discrepancies and high variability in children’s PA measured by accelerometers around the world have been reported, which may introduce a bias in this study 49,50. The output from accelerometers is a dimensionless unit commonly referred to as accelerometer counts. Researchers have attempted to calibrate these counts with energy expenditure in order to get a biological meaning to the output 51. This has resulted in the publication of count thresholds relating to various categories of energy expenditure, that allow researchers to summarize time spent in a given intensity of activity 52. The availability of multiple cut points or equations has led to much confusion in the accelerometer literature 53. We used the Evenson et al. 42 cut points, recommended in Trost et al. 43 comparative study to estimate time spent in sedentary, light-, moderate-, and vigorous-intensity activity in children and adolescents. Other cut points would have yielded different results. Nonetheless, the associations and differences with total PA will continue to be same since this variable must not be highly dependent of cut-off values. The discriminative power of steps/day was, however, excellent, as evidenced by the high AUC values (near of 0.9 for PAQ-C and >0.9 for PAQ-A). The AUC provides an estimate of the “goodness” of a diagnostic test, whereby a theoretical perfect test with 100% specificity and 100% sensitivity yields an AUC of 1, and a non-discriminating test an AUC of 0.5. Sensitivity and specificity obtained were 78.2% (95% CI 67.4 to 86.8), 86.8% (95% CI 76.4 to 93.8) for 60 minutes of MVPA in children; and 86.6% (95% CI 78.2 to 92.7), 93.1% (95% CI 87.3 – 96.8) for 60 minutes of MVPA in adolescents. The cut-off points associated were 10664 steps/day for children and 9701 steps/day for adolescents. These values are similar to those reviewed by Tudor-Locke 23. These aspects are of interest for public health since they corroborate the insights into PA needs and recommendations for children and adolescents that may use to evaluate scholar 85 Benítez-Porres J, 2016 International PhD Thesis population and implement intervention strategies by healthcare workers and physical education teachers. In addition, quantifying PA, with a low-cost way, will be helpful in order to focus school and community interventions on youth with unhealthy lifestyles. The specific criteria used to categorize individuals as meeting or not meeting PA recommendations were selected based on WHO guidelines 16 and other studies that propose new data. Recent studies indicate the need to increase the recommendation of MVPA. Thus, Jimenez-Pavón et al. 27 recommend around 60 and 85 min/day of MVPA, including 20 min/day of vigorous PA. On the other hand, data from the European Youth Heart Study with objectively measured PA suggest 90 minutes of MVPA based on metabolic health and the metabolic syndrome 26. Similarly, the criteria applied in this study (MVPA >60 min/day, vigorous PA >30min/day, and light PA >116 min/day) are in agreement with the proposed guidelines, but our approach support the hypothesis that 60 min or more of MVPA could be enough, if enough vigorous PA is accumulated during such period (at least 30 minutes). Limitations This study has several limitations that should be considered. First, subjectivity and limited recall ability are known limitations of self-reported PA, particularly in young people 54. Limitations of self-reports items include the tendency for people to report socially desirable responses. Moreover, although objective measures of PA, such as triaxial accelerometry or heart rate monitors are ideal, even these methods have their limitations, and this practice has been criticized due to the fact that accelerometers and self-report instruments measure different things 55. Other limitations could explain the 86 Benítez-Porres J, 2016 International PhD Thesis 42. Evenson KR, Catellier DJ, Gill K, Ondrak KS, McMurray RG. Calibration of two objective measures of physical activity for children. J Sport Sci. 2008;26(14):1557-65. 43. Trost SG, Loprinzi PD, Moore R, Pfeiffer KA. Comparison of accelerometer cut points for predicting activity intensity in youth. Med Sci Sports Exerc. 2011;43(7):13608. 44. Esliger DW, Copeland JL, Barnes JD, Tremblay MS. Standardizing and Optimizing the Use of Accelerometer Data for Free-Living Physical Activity Monitoring. J Phys Act Health. 2005;2(3):366-83. 45. Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical chemistry. 1993;39(4):561-77. 46. Metz CE. Basic principles of ROC analysis. Seminars in nuclear medicine. 1978;8(4):283-98. 47. Voss C, Ogunleye AA, Sandercock GRH. Physical Activity Questionnaire for children and adolescents: English norms and cut-off points. Pediatr Int. 2013;55(4):498507. 48. Martinez-Gomez D, Martinez-de-Haro V, Pozo T, Welk GJ, Villagra A, Calle ME, et al. [Reliability and validity of the PAQ-A questionnaire to assess physical activity in Spanish adolescents]. Revista espanola de salud publica. 2009;83(3):427-39. 49. Guinhouya BC, Samouda H, de Beaufort C. Level of physical activity among children and adolescents in Europe: a review of physical activity assessed objectively by accelerometry. Public health. 2013;127(4):301-11. 50. Loprinzi PD, Smit E, Cardinal BJ, Crespo C, Brodowicz G, Andersen R. Valid and invalid accelerometry data among children and adolescents: comparison across demographic, behavioral, and biological variables. American journal of health promotion : AJHP. 2014;28(3):155-8. 93 Benítez-Porres J, 2016 International PhD Thesis 51. Freedson P, Pober D, Janz KF. Calibration of accelerometer output for children. Med Sci Sport Exer. 2005;37(11):S523-S30. 52. Rowlands AV. Accelerometer assessment of physical activity in children: An update. Pediatr Exerc Sci. 2007;19(3):252-66. 53. Welk GJ, McClain J, Ainsworth BE. Protocols for Evaluating Equivalency of Accelerometry-Based Activity Monitors. Med Sci Sport Exer. 2012;44:S39-S49. 54. Warren JM, Ekelund U, Besson H, Mezzani A, Geladas N, Vanhees L, et al. Assessment of physical activity - a review of methodologies with reference to epidemiological research: a report of the exercise physiology section of the European Association of Cardiovascular Prevention and Rehabilitation. European journal of cardiovascular prevention and rehabilitation : official journal of the European Society of Cardiology, Working Groups on Epidemiology & Prevention and Cardiac Rehabilitation and Exercise Physiology. 2010;17(2):127-39. 55. Ham SA, Reis JP, Strath SJ, Dubose KD, Ainsworth BE. Discrepancies between methods of identifying objectively determined physical activity. Med Sci Sport Exer. 2007;39(1):52-8. 56. Steene-Johannessen J, Anderssen SA, van der Ploeg HP, Hendriksen IJ, Donnelly AE, Brage S, et al. Are Self-Report Measures Able to Define Individuals as Physically Active or Inactive? Med Sci Sports Exerc. 2015. 94 Benítez-Porres J, 2016 International PhD Thesis III THE INFLUENCE OF 2-YEAR CHANGES IN PHYSICAL ACTIVITY, MATURATION, AND NUTRITION ON ADIPOSITY IN ADOLESCENT YOUTH Benítez-Porres J. 1, Alvero-Cruz J.R. 2, Moore J.B. 3, Carrillo de Albornoz M. 2, CorreasGómez L. 1, Barrera-Expósito J. 1, Dorado-Guzmán M. 1, Carnero E.A. 1 1 Biodynamic and Body Composition Laboratory. University of Málaga, Spain. 2 Exercise Physiology Laboratory. University of Málaga, Spain. 3 Department of Family & Community Medicine, Wake Forest School of Medicine, USA. 95 Benítez-Porres J, 2016 International PhD Thesis INTRODUCTION Adolescence obesity has increased dramatically in several countries in recent decades 1,2; however, the contribution of physical activity (PA) to adiposity levels during adolescence is unknown, as adiposity is influenced by several factors (e.g., age, maturation, sex, diet) in a complex manner, which requires clarification. PA during adolescence can exert both direct and indirect positive effects on adult health 3, and track from adolescence to adulthood 4, which suggests that PA promotion must start early in life 5. However, little is known about how maturational differences between boys and girls of similar chronological age predict PA changes over youth. Longitudinal studies, both follow-up and intervention, help move researchers closer to understanding determinants of PA and mediators of adiposity 6. An advantage of prospective longitudinal designs is that they can address reverse causality. The interaction between PA and maturation has been studied in many context, namely: childhoodadolescence 7, adolescence 8-11 and childhood-adulthood 4, most of them including body composition variables 12,13. However, the tracking of PA and body composition, while considering the influence of nutrition and maturational status during adolescence has been rarely reported utilizing a longitudinal approach. A longitudinal approach that considers a broader set of biological and behavioral variables will be useful in guiding future public health PA policies and interventions for youth. It is presumed that PA level declines during the lifespan, particularly in adolescence 14-20. The literature supports the contention that boys are more active than girls at all ages during the circumpubertal years when PA is measured using a variety of 96 Benítez-Porres J, 2016 International PhD Thesis self-report 21 and objective measures 22. Several factors can influence the habits of PA in youth: social, family, biological, and environmental factors behavioral exert an important role in the PA change among youngsters and they move into adolescence 23. Preliminary research suggests that the adolescent decline in PA may be more closely associated with biological age than chronological age 15,24,25. However, the results are inconsistent across studies 26. While Cumming et al. 25 concluded that sex-related differences in biological maturity contribute to sex-related differences in PA behavior during adolescence; Fawkner et al., in a longitudinal study, reported that neither maturation nor absolute changes in physical size appear to directly influence changes in PA in adolescent girls 9. Therefore, the influence of sex, age, and maturation on changes in PA remains to be determined during adolescence. In light of the inconclusive evidence, it is important to further explore the relationship between PA, adiposity, nutrition, and maturation during adolescence period, which has not been extensively studied. The aim of this study was to longitudinally explore PA and adiposity changes in Spanish students during adolescence to evaluate the effects of sex, maturation, and nutrition on changes in PA and body composition. METHODS Sample An invitation to participate in the study was sent to all parents who had adolescent youth enrolled in schools of secondary education in Málaga and Ronda (Spain) during 97 Benítez-Porres J, 2016 International PhD Thesis the beginning of the academic year in 2011. The subjects each received an information sheet and written informed consent form for parents, and were asked to return the forms to their school. Parents of one hundred and twenty-three potentially eligible participants who received detailed information about the aims and procedures of the study provided written informed consent. A final analytical sample of 80 healthy adolescents provided longitudinal data (42 girls and 38 boys) after excluding those youth (n=43) with incomplete data at one of the three observational periods. There were no differences in age or body mass index (BMI) between the excluded participants and those in the final analytical sample. The research protocol was reviewed and approved by the Ethics Committee of the Sports Medicine School, at the Faculty of Medicine (Málaga, Spain). The study was developed following the ethical guidelines of the Declaration of Helsinki-Seoul, last modified in 2008. Measures Body Composition. Participant´s heights were assessed with socks and shoes removed using a stadiometer (SECA Leicester, Birmingham, UK). A Tanita UM-050 digital weighing scale (Tanita UK Ltd, Yiewsley, Middle-sex, UK) was used to assess body mass. Body mass index (BMI; weight/height; kg/m2) was then calculated. 98 Benítez-Porres J, 2016 International PhD Thesis Anthropometric measurements, including skinfolds (triceps, subscapular, abdominal, thigh and calf), height and body mass, were performed from the certified personnel by International Society for the Advancement of Kinanthropometry (ISAK), according to the ISAK standards for anthropometric assessment 27. Fat mass percent (FMP) was calculated using Slaughter´s equation 28. PA Assessment (PAQ-A). PA was assessed using the PAQ-A 29. The PAQ-A is a nine-item, 7-day PA recall designed for use with older adolescents in a field-based setting. A ninth item not used in calculation of the activity score, asks adolescents if they were sick or otherwise prevented from engaging in regular PA. The PAQ-A is designed to be administered once and asks adolescents to recall their participation in activities over the last 7 days to compute an activity score, but it is not intended to estimate metabolic-equivalent expenditure. The PAQ-A has previously acceptable reliability and convergent validity 30, and is an appropriate instrument for measuring PA in Spanish adolescents 31. The mean of all items is used to indicate the level of PA. A high score indicates higher levels of PA. Sexual maturity status. Sexual maturity was assessed using the criterion of predicted percentage of maturity (adult stature). Briefly, the percentage of predicted mature (adult) height attained during measurement was used as an objective indicator of biological maturation. The method 99 Benítez-Porres J, 2016 International PhD Thesis assumes that among adolescents of the same chronological age, the child that is closer to his or her predicted mature height is more advanced in biological maturity 32. The Khamis-Roche method 33 was used to predict the mature height from current age, height, and weight of the participant and mid-parent height (average height of biological parents). The median error bound (median absolute deviation) between actual and predicted mature height at 18 years of age is 2.2 cm in males and 1.7 cm in females. Biological parents of the students reported their heights. Percentages of predicted mature height were expressed as z-scores relative to age-specific means and standard deviations for percentage of mature height attained. Z-scores were used to estimate maturity status: on time, z-score between -1.0 and +1.0; late, z-score below -1.0; early, z-score greater than +1.0. Relative skeletal age, the difference between skeletal age and chronological age, was used as the criterion. On time was defined as a skeletal age within 1.0 year of chronological age. Late maturing was defined as a skeletal age behind chronological age by more than 1.0 year. Early maturing was defined as a skeletal age in advance of chronological age by more than 1.0 year 32. Food-frequency questionnaire (FFQ). Dietary intake was assessed by a self-administered, semi-quantitative foodfrequency questionnaire (FFQ). The FFQ was an electronic version based on a questionnaire designed to be used in Spain 34. The questionnaire consisted of 290 specific foods or food groups (including 15 fruit items and 28 vegetable and legume items) with nine response options ranging from “never” to “6 or more times per day” for the frequency of consumption of specified serving sizes. Questions on cooking methods, specific types 100 Benítez-Porres J, 2016 International PhD Thesis of fats, oils, margarines, breakfast cereals, takeaway foods, and self-prescribed nutritional supplements were also included on the questionnaire. Subjects were asked to recall their frequency of consumption for common serving sizes of food per month, week or day over the preceding six months. Afterwards, a specific Excel-based macro was used to calculate Calories and macronutrients composition from each food multiplies by number of serving sizes and extrapolated for day. Dietary intake assessment was only performed at S1 and S3. Procedure All procedures were performed during one day for each adolescent and identically along the three assessment time points. An assessment day was as follow: Adolescents, whom completed consent forms, attend to sport facility of the school at usual opening times (8:30 a.m.) in fasting conditions. Firstly, a general overview about the organization was explained by the leader of research team, in summary the evaluations follow the next order: Weight and height were measured, subsequently they started to complete the PAQA and FFQ questionnaires, and considerable time was taken to fully explain the questionnaires and examples were provided. Students who finished PAQ-A were asked to go to body composition assessment, which was carried out by research assistants using standard protocols and taken in same-sex pairs into a private room to complete anthropometric measurements. Two researchers were always with them in the room. Three assessments were performed: September 2011, 2012 and 2013 (S1, S2, and S3, respectively). Approximately 12 months and 24 months later, data were collected using the same procedures described above. 101 Benítez-Porres J, 2016 International PhD Thesis Statistical Analysis The characteristics of participants were described as mean and standard deviation (SD). Spearman rank correlation coefficient was used to explore associations between variables. A repeated measures ANOVA (two-factor mixed model 2x3x3) were carried out among three time points for PA, BMI and FMP, and compared by maturation level and sex. Differences between baseline and second year were calculated for FMP, PA, and nutrition and maturation level. A general linear model was used to estimate predictors of FMP changes, where PA, nutrition, sex and change in maturation level (as change of early (C0) or late state (C2) to on time and no change (C1)) were selected as independent variables. Interactions among sex, PA, nutrition and maturation level were explored. Regarding statistical power analysis, if we assume an alpha value of 0.05 for a multiple linear regression with 4 predictors (PA, nutrition, maturation and sex), our final sample size of 80 adolescents will permit us confirm our statistical analysis with 78.5% of power. The analyses were performed using SPSS 22.0 (SPSS Inc. Chicago, Illinois) and the level of significance was set at P<0.05. 102 Benítez-Porres J, 2016 International PhD Thesis (2000), our results provide evidence that male subjects decline more in PA than female subjects, specifically between S2 and S3. Davison et al. (2007) found similar decreases in PA in adolescent girls of similar chronological age. Results of analyses of interactions among maturity indicators in longitudinal data sets highlight that maturation had marginal influence on the PA behaviors at this age. Late maturing adolescents reported lower levels of PA, although not statistically significant. These findings are in line with relatively more mature adolescents. They may, in fact, be more active than their less mature peers 9, but we could not confirmed, according to other studies 7,26,35, that sexual maturity status is an important determinant in PA pattern. However, nutrition was not a determinant in the changes in PA or FMP analyzed. In this sense, it is important to appreciate that self-report dietary assessment can introduce bias since participants providing data are aware that their dietary habits are is under investigation, which may affect their reported dietary intake 36. This is often subconscious and has been shown for adolescents, especially girls 37. Therefore, the obtained results must be analyzed cautiously. Regarding adiposity levels, in the present study, late and on time maturing girls had higher levels of FMP than boys. A probable explanation for the gender influence on the change in adiposity levels could be due to anthropometric differences in muscle mass between boys and girls at puberty. FMP tends to decline during male adolescence because of the rapid growth of lean mass, specifically muscle mass 32. However, in this study the influence of maturation was similar in boys and girls since no interactions between sex, maturation and dependent variables (PA and FMP) were found. Moreover, we observed that change in level of maturation seemed to be a determinant for change in FMP, but 108 Benítez-Porres J, 2016 International PhD Thesis with a similar trend for both genders. So, those students, who change from a less mature state to another more mature (C2), had a trend to a greater FMP reduction (figure 3), although similar in boys and girls. The strength of this study was to explore the relationship among PA, adiposity, nutrition, and maturation, which have not been previously analyzed all together in a longitudinal perspective. However, it is important to recognize a number of limitations associated with the current investigation. Firstly, the height of the parents was asked in a general health questionnaire instead of measured with stadiometer. Another potential limitation is that the method used to estimate biological maturity status was devised from data collected in the United States and further research must be required to validate the equation in Spanish people. An additional limitation is the use of the PAQ-A, a self-report measure, to assess PA levels. Limitations of self-reports items include the tendency for people to report socially desirable responses. Despite these limitations, the PAQ-A has demonstrated to be a low cost, easy to use, and reasonably valid measure of PA behaviors that is well suited for use with Spanish adolescents 31. Although objective measures of PA, such as triaxial accelerometry or heart rate monitors are ideal, even these methods have their limitations such as cost, and this practice has been criticized due to the fact that accelerometers and self-report instruments measure different things 38. Accelerometry measures body movement, while questionnaires often ask respondents to rate the activities related to the effort or frequency. Moreover, the activities reported in the first item of the PAQ-A, as the skateboarding and cycling, are difficult to be captured with accelerometers because these devices only acceleration from activities where center of 109 Benítez-Porres J, 2016 International PhD Thesis gravity has oscillation. Additionally, questionnaires are valuable instruments to track PA in school settings, where the use of expensive and complex instruments is unviable. In summary, our findings provide long-term longitudinal evidence that body composition and PA changes observed were not parallel. Also, these data seem to suggest that PA and FM alteration patterns were more influenced by sex than by maturation. Finally, the drop in PA levels describes the issue of physical inactivity among adolescents and the need to develop appropriate interventions to prevent this decrease. 110 Benítez-Porres J, 2016 International PhD Thesis REFERENCES 1. Reilly JJ, Kelly J. Long-term impact of overweight and obesity in childhood and adolescence on morbidity and premature mortality in adulthood: systematic review. Int J Obesity. 2011;35(7):891-8. 2. Spruijt-Metz D. Etiology, Treatment, and Prevention of Obesity in Childhood and Adolescence: A Decade in Review. J Res Adolescence. 2011;21(1):129-52. 3. 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Cumming SP, Sherar LB, Esliger DW, Riddoch CJ, Malina RM. Concurrent and prospective associations among biological maturation, and physical activity at 11 and 13 years of age. Scandinavian journal of medicine & science in sports. 2014;24(1):e20-8. 36. Cade J, Thompson R, Burley V, Warm D. Development, validation and utilisation of food-frequency questionnaires - a review. Public Health Nutr. 2002;5(4):567-87. 37. Livingstone MB, Robson PJ, Wallace JM. Issues in dietary intake assessment of children and adolescents. The British journal of nutrition. 2004;92 Suppl 2:S213-22. 38. Ham SA, Reis JP, Strath SJ, Dubose KD, Ainsworth BE. Discrepancies between methods of identifying objectively determined physical activity. Med Sci Sport Exer. 2007;39(1):52-8. 115 Benítez-Porres J, 2016 International PhD Thesis CHAPTER V: LIMITATIONS In addition to the limitations contained in each study, several limitations of the current dissertation should be mentioned:  The selection of the sample studied was not randomized (volunteer participants sample). Our sample size was relatively small but significant for the purpose of studies. Moreover, our participants had similar BMI and PAQ-A scores when compared with other Spanish children and adolescents from previous studies. Nevertheless, an intention to treat analysis must be necessary in order to confirm our sample was representative of Spanish population in this range of age.  In general, the questionnaire (PAQ) used in the studies does not capture some important windows of the day where activity is likely to occur (e.g., before school, commuting to school). Therefore, predicted minutes of activity obtained from the PAQ would most likely underestimate daily activity. The PAQ also captures total weekend activity with a single item but it is likely that activity patterns vary considerably between Saturday and Sunday for most youth.  Other limitation of the PAQ, and perhaps most significant, is that it does not include any measure of sedentary behaviors, which may influence the determination of the cut-off points. 116 Benítez-Porres J, 2016 International PhD Thesis  Subjectivity and limited recall ability are known limitations of self-reported PA, particularly in young people 1.  Finally, in the third study, there was a lack of assessment of PA level with objective methods, as well as a lack of dietetic supervision on sample studied. 1 Sirard JR, Pate RR. Physical activity assessment in children and adolescents. Sports Med. 2001;31(6):43954 117 Benítez-Porres J, 2016 International PhD Thesis 124 Benítez-Porres J, 2016 International PhD Thesis APPENDIX II Physical Activity Questionnaire for Adolescents 125 Benítez-Porres J, 2016 International PhD Thesis 126 Benítez-Porres J, 2016 International PhD Thesis 127 Benítez-Porres J, 2016 International PhD Thesis APPENDIX III GEOS Project Documents 128 Benítez-Porres J, 2016 International PhD Thesis 129 Benítez-Porres J, 2016 International PhD Thesis 130 Benítez-Porres J, 2016 International PhD Thesis 131 Benítez-Porres J, 2016 International PhD Thesis 132 Benítez-Porres J, 2016 International PhD Thesis 133 Benítez-Porres J, 2016 International PhD Thesis Autores: Benítez-Porres J., López-Fernández I. Raya J.F., Carnero S.A., AlveroCruz J.R., Carnero E.A. Título: Validity and Reliability of the PAQ-C Questionnaire in Spanish Children. Lugar y año: California, 2015. Tipo de participación: Póster debatible.  Congreso: 62nd Annual Meeting, 6th World Congress on Exercise is Medicine and World Congress on The Basic Science of Exercise Fatigue. Autores: García Romero J, Fernández Millán J, Alvero-Cruz J.R., Peñaloza P., Jimenez M., Benitez-Porres J., Lopez I., Carrillo de Albornoz M., Carnero E.A. Título: Effects of High Intensity Interval or Continuous Moderate Training on Metabolic Thresholds: the PTRAINIM Randomized Control Trial. Lugar y año: California, 2015. Tipo de participación: Comunicación oral.  Congreso: 6º Congreso Internacional de Actividad Físico-Deportivas para Mayores. Autores: Benítez-Porres J., García Vega M.M., Rodríguez Linares M.V., Martínez Blanco J. Título: Relationship between body composition and cognitive function in elderly. Lugar y año: Málaga, 2015. Tipo de participación: Comunicación oral.  Congreso: II Congreso de Nutrición Deportiva, Cineantropometría y Salud. Autores: Benítez-Porres J., López-Fernández I., Raya J.F., Correas-Gómez L., Carrillo de Albornoz M., Carnero E.A., Alvero-Cruz J.R. Título: Actividad física y la adiposidad visceral abdominal en la niñez. Lugar y año: Cocentaina (Alicante), 2014. Tipo de participación: Comunicación oral.  Congreso: II Congreso de Nutrición Deportiva, Cineantropometría y Salud. Autores: Benítez-Porres J., Barrera-Expósito J., Sainz-Martín N., DoradoGuzmán M., Carrillo de Albornoz M., Correas-Gómez L., Carnero E.A., AlveroCruz J.R. 140 Benítez-Porres J, 2016 International PhD Thesis Título: Influencia del tipo de actividad física sobre la composición corporal y presión arterial en adolescentes. Lugar y año: Cocentaina (Alicante), 2014. Tipo de participación: Póster debatible.  Congreso: 13th International Sport Sciences Congress. Autores: Benítez-Porres J., Raya J.F., Carrillo M., Alvero-Cruz J.R., Carnero E.A. Título: Relationship between frequency of physical activity and abdominal visceral fat in children. Lugar y año: Konya (Turquía), 2014. Tipo de participación: Póster debatible.  Congreso: World Conference on Kinanthropometry. Autores: Benítez-Porres J., Raya J.F., Correas-Gómez L., Carrillo M., AlveroCruz J.R., Carnero E.A. Título: Estimation of Internal Abdominal Fat from Anthropometry Measurements in Children. Lugar y año: Murcia, 2014. Tipo de participación: Póster.  Congreso: World Conference on Kinanthropometry. Autores: Raya J.F., Benítez-Porres J., Correas-Gómez L., Carrillo M., AlveroCruz J.R., Carnero E.A. Título: An anthropometric model to estimate appendicular lean muscle in children. Lugar y año: Murcia, 2014. Tipo de participación: Póster.  Congreso: 19th Annual Congress of the European College of Sport Science: Sport Science around the Canals. Autores: Benítez-Porres J., Correas-Gómez L., Carrillo M., Martín-Sanz N., Alvero-Cruz J.R., Carnero E.A. 141 Benítez-Porres J, 2016 International PhD Thesis Título: Associations of Physical Activity with Blood Pressure, Body Composition and Maturation Level in Adolescents: the GEOS Study. Lugar y año: Ámsterdam, 2014. Tipo de participación: Póster debatible.  Congreso: 61st Annual Meeting of the American College of Sports Medicine, World Congress on Exercise is Medicine and World Congress on The Role of Inflammation in Exercise, Health and Disease. Autores: Benítez-Porres J., Alvero-Cruz J.R., Barrera-Expósito, J., DoradoGuzmán M., Carnero E.A. Título: Longitudinal differences of physical activity and adiposity in adolescents: a 2-year follow-up. Lugar y año: Orlando, 2014. Tipo de participación: Póster debatible.  Congreso: IUNS 20th International Congress of Nutrition. Autores: Ruíz-Cabello P., Benítez-Porres J., Moratalla N., Fernández M., Aparicio V.A., Errami M., Senhaji M., Fernández M., Aranda P. Título: Diet quality of Moroccan perimenopausal women integrated multidisciplinary intervention study. Lugar y año: Granada, 2013. Tipo de participación: Póster debatible.  Congreso: 20th International Congress of Nutrition. Workshop. Physical Activity in the Prevention and Treatment of Chronic Diseases: From Science to Practice. Autores: Benítez-Porres J. et al. Título: Comparison IPAQ and an objective measure of physical activity in fibromyalgia patients: the Al-Andalus study. Lugar y año: Granada, 2011. Tipo de participación: Póster debatible. 142 Benítez-Porres J, 2016 International PhD Thesis Publicaciones científicas en revistas JCR  Benítez-Porres J., Alvero-Cruz J.R., Moore J.B., Barrera-Expósito J., Dorado-Guzmán M., Carnero E.A. (2016). The influence of 2-year changes in physical activity, maturation, and nutrition on adiposity in adolescent youth. Sometido en Medicine & Science in Sports & Exercise.  Benítez-Porres J., Alvero-Cruz J.R. Sardinha L.B., López-Fernández I., Carnero E.A (2016). The physical activity questionnaire score cut offs to classify physical activity level in children and adolescents. Sometido en Plos ONE.  Benítez-Porres J., López-Fernández I. Raya J.F., Carnero S.A., Alvero-Cruz J.R., Carnero E.A (2015). Reliability and Validity of the PAQ-C Questionnaire to Assess Physical Activity in Children. Aceptado en Journal of School Health.  Benítez-Porres J., Delgado M. y Ruiz J.R. (2013). Comparison of physical activity estimates using IPAQ and accelerometry in fibromyalgia patients: the alandalus study. Journal of Sport Science, 31 (16), 1741-1752. Publicaciones científicas en otras revistas  Benítez-Porres, J. (2011). Planificación en educación secundaria obligatoria: unidad didáctica “Salvamento y Socorrismo”. Lecturas educación física y deportes. Revista digital, Año 15, 153.  Benítez-Porres, J. (2011). Síndrome del piriforme: protocolo de readaptación física. Lecturas educación física y deportes. Revista digital, Año 15, 152. Libros y capítulos de libro  Autores: Javier Benítez Porres, María del Mar Fernández Martínez, José Ramón Alvero Cruz y Pilar Aranda Título del Libro: Nutrición y cineantropometría en el deporte. 143 Benítez-Porres J, 2016 International PhD Thesis Título Capítulo: Calidad de la dieta de mujeres marroquíes en edad perimenopáusica y menopáusica Edita: Universidad de Alicante Páginas (inicial y final): Pendiente País de Publicación: España Año de publicación: 2016 ISBN/Deposito legal: Pendiente  Autores: José Ramón Alvero Cruz, Rosalía Fernández Vázquez, Javier Benítez Porres y José Enrique Sirvent Belando Título del Libro: Nutrición y cineantropometría en el deporte Título Capítulo: Evaluación del estado nutricional y valoración de la composición corporal. Modelos de investigación en composición corporal Edita: Universidad de Alicante Páginas (inicial y final): Pendiente País de Publicación: España Año de publicación: 2016 ISBN/Deposito legal: Pendiente  Autores: Javier Benítez-Porres, Iván López-Fernández, Juan Francisco Raya, Margarita Carrillo de Albornoz, Elvis Álvarez Carnero y José Ramón Alvero Cruz Título del Libro: Nutrición y cineantropometría en el deporte Título Capítulo: Actividad física y adiposidad visceral abdominal en niños Edita: Universidad de Alicante Páginas (inicial y final): Pendiente País de Publicación: España Año de publicación: 2016 ISBN/Deposito legal: Pendiente  Autores: Javier Benítez Porres Título del Libro: Innovaciones con tecnologías emergentes Título Capítulo: Aprender jugando: una experiencia con Kahoot en docencia universitaria Edita: Universidad de Málaga 144 Benítez-Porres J, 2016 International PhD Thesis Páginas (inicial y final): 1-11 País de Publicación: España Año de publicación: 2015 ISBN/Deposito legal: 978-84-606-5930-3  Autores: Javier Benítez Porres Título del Libro: Educar para transformar: Aprendizaje experiencial Título Capítulo: Socrative como herramienta para la integración de contenidos Edita: Universidad Europea de Madrid Páginas (inicial y final): 824-831 País de Publicación: España Año de publicación: 2015 ISBN/Deposito legal: 78-84-95433-70-1  Autores: Javier Benítez Porres, Javier Martínez Blanco, Rosalía Fernández Vázquez, José Ramón Alvero Cruz Título del Libro: Longevidad y salud. Innovación en la actividad física. Título Capítulo: Sarcopenia y ejercicio físico Edita: Área de Cultura y Deportes de la Diputación de Málaga Páginas (inicial y final): 700-708 País de Publicación: España Año de publicación: 2015 ISBN/Deposito legal: 978-84-7785-955-0 145 Benítez-Porres J, 2016 International PhD Thesis APPENDIX V Global Summary INTRODUCCIÓN La actividad física (AF) ha sido identificada como un agente importante en la prevención de enfermedades crónicas como la obesidad, las cardiopatías, y el síndrome metabólico 1,2. Con objeto de estudiar e interpretar con mayor precisión los patrones de AF durante la infancia y la adolescencia, se hace necesario desarrollar y validar instrumentos capaces de evaluar adecuada y ampliamente la AF realizada, para identificar su impacto en la salud en la población escolar de educación primaria y secundaria. De esta forma, cuantificar correctamente la AF será de gran ayuda a la hora de diseñar las intervenciones en el ámbito escolar y comunitario, en aquellos grupos de población con un estilo de vida poco saludable. Actividad Física y Salud en Niños y Adolescentes La AF es reconocida por aportar importantes beneficios en todos los segmentos de población 1-5. La Organización Mundial de la Salud (OMS) viene publicando y actualizando directrices oficiales 6 que proporcionan recomendaciones específicas sobre el tipo y la cantidad de AF necesaria en las diferentes etapas de la vida, diferenciando entre niños y adolescentes, adultos, ancianos y personas con necesidades especiales. En la revisión publicada en 2010 por Janssen y Leblanc 1 se resumen los diversos beneficios de la AF realizada de forma regular para con la salud. De acuerdo con esta revisión, se ha demostrado que la AF mejora los niveles de composición corporal, el proceso de homeostasis de la glucosa, la sensibilidad a la insulina, el perfil de lipoproteínas (por ejemplo, a través de la 146 Benítez-Porres J, 2016 International PhD Thesis reducción de los niveles de triglicéridos, el aumento de los niveles de la lipoproteína HDL y la disminución de la lipoproteína LDL); y reduce la presión arterial, la inflamación sistémica y la coagulación en sangre, fomentando la tonificación muscular y mejorando la función cardiaca y endotelial. En resumen, la práctica de AF regular es fundamental para obtener y conservar un buen estado de salud física. Como se ha comentado, realizar AF de forma regular es importante en todas las etapas de la vida, pero actualmente hay un considerable interés en la promoción de la misma en nuestros jóvenes. Esto se debe en gran parte a la creciente preocupación por los índices de obesidad. La infancia y la adolescencia son períodos importantes debido a los notables cambios fisiológicos y psicológicos que se producen: como la regulación hormonal, el desarrollo de la composición corporal o los cambios transitorios en la sensibilidad a la insulina 7. Concretamente, muchos de los hábitos establecidos en los períodos de la infancia y la adolescencia tienden a perpetuarse en la adultez. La prevención de la obesidad en los primeros años de vida resulta fundamental, ya que la evidencia científica sugiere que los jóvenes con sobrepeso tienen un riesgo cinco veces mayor de padecer sobrepeso en su vida adulta que aquellos niños con normo-peso en la misma etapa 8. En España, la prevalencia de padecer obesidad tanto en la niñez como en la adolescencia es alta 9,10. En este sentido, los estudios longitudinales, tanto de seguimiento como de intervención, pueden ayudar a los investigadores a estar más cerca de comprender los determinantes y mediadores entre la AF y la adiposidad 11. Además, una de las principales ventajas de este tipo de diseño es que se puede abordar la causalidad inversa. Las necesidades de niños y adolescentes, por su naturaleza, requieren unas pautas de actuación concretas y específicas. Las directrices sobre AF sugieren que ambos deben 147 Benítez-Porres J, 2016 International PhD Thesis acumular, al menos, 60 minutos de AF moderada-vigorosa (AFMV). El volumen recomendado en jóvenes es el doble del recomendado para las personas adultas, principalmente porque los primeros tienen necesidades mayores de gasto energético y por la necesidad e importancia de formarlos en un estilo de vida saludable desde una edad temprana. Los Estados Unidos no es el único país que ha adoptado directrices de AF para sus jóvenes. Otras naciones, entre las que destacan Australia, Reino Unido y Canadá, han publicado sus propias directrices. Y aunque existen discrepancias menores entre ellas, todas ellas sugieren que los jóvenes deben realizar 60 minutos o más de AFMV diaria. El mensaje de estas recomendaciones internacionales ha generado un gran interés en la comprensión y la promoción de AF en niños y adolescentes. Y es en este punto donde los centros educativos se perfilan como un escenario prometedor para alcanzar estas metas e impactar en el estilo de vida de nuestros jóvenes. En este contexto, contamos con una variedad de herramientas y métodos destinadas a la evaluación de la AF, que difieren en cuanto a validez y viabilidad dentro del aula. Sin embargo, debemos diseñar nuestras acciones con métodos más prácticos que puedan ser utilizados en el contexto escolar, ya que existe un creciente interés por registrar los niveles de AF que acontecen durante la jornada escolar. Medidas de la Actividad Física en Jóvenes Siguiendo los postulados de Corder 12 y Sirard 13, esta sección ofrecerá una breve clasificación de los métodos existentes para medir o estimar AF en niños y adolescentes (obviando los métodos destinados a evaluar puramente el gasto energético como el agua 148 Benítez-Porres J, 2016 International PhD Thesis doblemente marcada o la calorimetría indirecta). Las fortalezas y debilidades pueden consultarse en la tabla 1. Dichos métodos pueden ser clasificados en dos grupos según su naturaleza: métodos objetivos y subjetivos. La primera categoría implica la medición de parámetros fisiológicos o biomecánicos y utilizan esta información para estimar distintos parámetros en la AF. Esta categoría englobaría a los monitores de frecuencia cardiaca, la acelerometría, los podómetros y los sistemas combinados. La segunda categoría incluye cuestionarios, entrevistas, diarios de actividad, y la técnica de observación directa. Estos métodos varían en cuanto a las variables que miden o estiman y por lo tanto en sus objetivos y resultados finales. En las tablas 2, 3 y 4 se especifican aquellos cuestionarios que han sido diseñados en los últimos 20 años para estimar la AF en niños, adolescentes o ambos. Independientemente del método utilizado, tres conceptos clave deben entenderse al considerar la exactitud y precisión de cualquier técnica de medición: la fiabilidad, la validez y la capacidad de respuesta 14. Un aspecto relativo a la fiabilidad es la reproducibilidad de un método, es decir, la obtención de los mismos resultados cuando el método es utilizado por diferentes evaluadores independientes. La fiabilidad es un requisito previo para la validez. 149