Adolescents' Nutrition and Physical Activity Knowledge and Practices.

University of Porto Faculty of Sport Research Center in Physical Activity, Health and Leisure Adolescents’ Nutrition and Physical Activity Knowledge and Practices Vera Ferro Lebres Julho de 2014 Supervisors Professor José Carlos Ribeiro Professor Pedro Moreira Academic dissertation submitted to the Faculty of Sports, University of Porto for the degree of Doctor of Philosophy (Ph.D.), under the doctoral course in Physical Activity and Health, according to the Law 74/2006 of March 24th. Ferro-Lebres, V. (2014). Adolescents’ Nutrition and Physical Activity Knowledge and Practices. Porto: V. Ferro-Lebres. Dissertação de Doutoramento em Atividade Física e Saúde apresentada à Faculdade de Desporto da Universidade do Porto. KEYWORDS: ADOLESCENTS, NUTRITION KNOWLEDGE, PHYSICAL ACTIVITY, DIETARY INTAKE Founding sources Support received from the Portuguese Foundation for Science and Technology (FCT), with an individual grant conceded to the candidate, reference SFRH/PROTEC/49529/2009; and a project grant, reference FCOMP-01-0124FEDER-028619 (Ref. FCT: PTDC/DTP-DES/1328/2012) ), and Research Center supported by PEst-OE/SAU/UI0617/2011. “Knowing is not enough; we must apply. Willing is not enough; we must do.” Johann Wolfgang von Goethe To my parents and sisters, To my grandparents, To Ovídio IX Acknowledgements When I started this journey someone told me no one could do a PhD by himself, I sure didn’t! I would like to acknowledge those who were somehow involved and helped this project and myself in the past five years. To Professor José Carlos Ribeiro, my supervisor, that guided me throughout this PhD and had such a huge contribution to this dissertation, but also to my personal and professional development. You knew to be firm and demanding, but also facilitator and motivator. Thank you for all our productive discussions, for the help organizing my ideas, and for enhancing them... Thank you also for facilitating the technicians, schools and teachers access. But most of all, thank you for believing in my work and for submitting this project idea for a FCT grant. It was an honor for me to work with you. To Professor Pedro Moreira, my co-supervisor, that was so often the voice of reason. I will always remember your calm and kind voice and attitude, especially in those days where the end seemed too far away… You always found a plan B, so that we could carry on! You were always helpful, critical, and assertive. You always had some idea to add and improve the project. Thank you for all your dedication to this project and for embracing the task of co-supervising my work. It was a great privilege. With both my supervisors I learned the sense of being a “Professor”, I wish to be one, someday! I should express all my respect and gratitude to you both. To Gustavo Silva, thank you for your statistical expertise and for the always helpful suggestions. You always found the time and will to help. I shall also thank his wife Luísa Aires, who was involved in AFINA-te from the beginning, and allowed me to use her classes for test retest of GNKQA. To Parmenter K, Wardle J., Almeida-de-Souza J. and Hagstromer M, authors of the original versions of the questionnaires I used, I must acknowledge your generosity for allowing me to use your work, and your availability to help me with data and score analysis. To the Research Center in Physical Activity, Health and Leisure, all its researchers, professors, and staff for the contribution to the website design, with food diaries analysis and all the other unmentioned support. I should thank in particular to Raquel Esteves, Nuno Guimarães and Tânia Silva. XVII List of Tables Paper I. Table 1. Demographic and academic characteristics of adolescents and dietetics students used for concurrent validity ................………………………………….. 60 Table 2. Internal reliability and test retest reliability ………….………………….. 65 Table 3. Score differences between sub samples ……………………………….. 66 Paper II. Table I. Descriptive data and Mann-Whitney U comparison of anthropometric characteristics and physical activity obtained by accelerometer and IPAQA, for adolescents aged 14 years or under …………………………………..………….. 78 Table II. Descriptive data and Mann-Whitney U comparison of anthropometric characteristics and physical activity obtained by accelerometer and IPAQA, for adolescents aged 15 years or over ……………………..…………………………. 79 Table III. Spearman’s Rank correlation coefficient of physical activity measured by the accelerometer and reported with IPAQA, for the total sample …………. 84 Table IV. Spearman’s Rank correlation coefficient of physical activity measured by the accelerometer and reported with IPAQA, by age group and gender …….85 Table V. Tertiles classification percentage of agreement and Cohen’s Kappa statistics, between IPAQA and Accelerometer. …………………………..…….... 86 XVIII Paper III. Table 1. Sample characteristics and GNKQA and PAKQ scores differences between groups …………………..…………………………………………….…. 100 Table 2. GNKQA and PAKQ scores differences between physical activity and body fat percentage groups ………………………………………….…………… 104 Paper IV. Table 1. Sample and subsamples characteristics ……………………….……... 121 Table 2. Nutrition and physical activity knowledge percentage of correct answers, nutritional adequacy percentage and MVPA descriptive data ….…..…..…….. 127 Table 3. Nutritional mean intake and nutritional adequacy frequency, per nutrient. ………………………………………………….……………………….……………128 Table 4. Differences between groups in GKNQA and PAKQ score percentages, MVPA and nutritional adequacy…………...…………………..…..…..…..…….. 130 Table 5. Kruskal–Wallis comparison of dietary intake and physical activity variables between GNKQA and PAKQ adjusted quartiles. ………………….… 131 Table 6. Partial correlation (adjusted for age, sex and school year) between nutrition and physical activity knowledge scores, moderate to vigorous physical activity and nutritional adequacy score ……………….……………………….… 132 XIX List of Appendices Appendix I. General Nutrition Knowledge Questionnaire for Adolescents ………………………………………………….……………………….……….… XXXI Appendix II. International Physical Activity Questionnaire for Adolescents ...……………………………………….……………………….………………... XXXIX XXI Abstract In the past decade the scientific community and health professionals showed a growing concern with the epidemiological data that revealed increasing prevalence of obesity in young ages. Resulting from unhealthy lifestyles, this reality concerns also the policymakers, who have been searching for ways of promoting healthy lifestyles, namely through intervention programs, that aim to reduce the risk behaviors, namely improving dietary and physical activity practices. Several intervention programs have been implemented, mainly in adults and small children, however they are scarce, particularly in adolescents, especially using attractive methodologies for this age range, as the new technologies, although these technologies proved to be effective in other health behaviors. This work aimed to validate a questionnaire to assess adolescents’ nutrition knowledge (Paper I) and a questionnaire to measure adolescents’ physical activity level (Paper II), to analyze the nutrition and physical activity knowledge relation with adiposity (Paper III), and to study the relationship between knowledge and practices in nutrition and physical activity (Paper IV). In this context, the Paper I was performed with 1315 adolescents. After content validity and questionnaire refinement, item difficulty (percentage), internal reliability (Cronbach’s Alpha), test-retest reliability (Spearman correlation) and concurrent reliability (Spearman correlation) were analyzed. Paper II included a sub-sample of 222 adolescents, and after translation and cross cultural adaptation, Spearman correlation coefficient and Cohen’s Kappa agreement were calculated between the questionnaire and accelerometer. A cross sectional study was performed and reported in Papers III and IV, 734 adolescents were assessed for anthropometric measurements, and for nutrition and physical activity knowledge and practices. In paper III Kruskal-Wallis and Mann Whitney U tests were performed to compare knowledge scores between groups, created according the levels of body fat percentage and moderate to vigorous physical activity engagement. In paper IV Kruskal-Wallis and Mann Whitney U tests were XXII performed to compare knowledge scores, physical activity and nutritional adequacy between groups; adjusted Spearman correlation tested the association between knowledge and practices, for nutrition and physical activity. Results of Paper I revealed that the Portuguese version of the General Nutrition Knowledge Questionnaire for Adolescents is a valid (U=22766.0; p<.01) and reliable (Cronbach’s alpha=.92) instrument to assess nutrition knowledge. Paper II reported that the International Physical Activity Questionnaire for Adolescents didn’t correlate significantly with accelerometer for younger adolescents and girls. Paper III and IV results reflect the poor knowledge levels of the Portuguese adolescents, either for nutrition, in average with 46.5% (SD=11.82) of correct answers and for physical activity with a mean of 66.2% (SD=15.50) of correct answers. It seems important to emphasize also the reduced mean nutritional adequacy percentage (57.4; SD=10.24) and the low mean values of moderate to vigorous physical activity (47.9 min.day-1; SD=27.49). In addition, in Paper III, considering groups created based on physical activity engagement and body fat percentage, overfat/ low physical activity adolescents seem to be significantly (p=.003) less aware of the experts’ nutritional recommendations, and the high physical activity adolescents are significantly (p=.044) more knowledgeable on physical activity. However, paper IV present results considering the continuous variables and no association seems to exist between knowledge and practices, regarding nutrition (ρ=-.03; p=.636) and physical activity (ρ=.12; p=.083). The main conclusions of this thesis are the following: the Portuguese version of the general nutrition knowledge questionnaire for adolescents is valid and reliable (Paper I); Portuguese adolescents’ physical activity should be measured with objective methods, like accelerometers, and not by questionnaires (Paper II); overfat and low physical activity adolescents have the worse knowledge about experts’ nutritional recommendations (Paper III); there is no association between knowledge and practices, in nutrition and physical activity (Paper IV). It is therefore suggested that although necessary, knowledge seems to be insufficient to produce adequate nutrition and physical activity practices in Portuguese adolescents. XXIII Resumo Na última década, tem sido manifestada pela comunidade científica e pelos profissionais de saúde uma preocupação crescente com os dados epidemiológicos que revelam um aumento acentuado de patologias crónicas em idades jovens. Resultante da adoção de estilos de vida pouco saudáveis, esta realidade preocupa também os decisores políticos, que têm procurado desenhar políticas promotoras de saúde, nomeadamente através de programas de intervenção, que visam reduzir os comportamentos de risco, nomeadamente melhorando as práticas alimentares e aumentando a prática de atividade física. Vários projetos de intervenção têm sido implementados, principalmente em crianças e adultos, no entanto escasseiam os direcionados para adolescentes, nomeadamente com metodologias atrativas para esta faixa etária, como as novas tecnologias, apesar de estas terem mostrado recentemente ser ferramentas importantes em contextos de promoção da saúde. Este trabalho objetivou validar um questionário para avaliar os conhecimentos nutricionais dos adolescentes (Artigo I) e um questionário para medir o nível de atividade física dos adolescentes (Artigo II), pretendeu também analisar a relação entre os conhecimento nutricionais e de atividade física e a adiposidade (Artigo III), e estudar a relação entre conhecimento e práticas, quer de nutrição quer de atividade física (Artigo IV). Neste contexto, o Artigo I incluiu uma amostra de 1315 adolescentes. Após validação de conteúdo e aperfeiçoamento do questionário, avaliação do índice de dificuldade dos itens (percentagem), consistência interna (alfa de Cronbach), fiabilidade teste-reteste (correlação de Spearman) e validade concorrente (correlação de Spearman) foram testadas. O Artigo II incluiu uma subamostra de 222 adolescentes e após tradução e adaptação cultural, testes de correlação de Spearman e o teste de concordância Kappa de Cohen foram calculados entre os dados do acelerómetro e do questionário. Um estudo transversal foi desenvolvido e reportado nos artigos III e IV, onde 734 adolescentes foram avaliados relativamente aos seus dados antropométricos, e conhecimentos e XXIV práticas nutricionais e de atividade física. No Artigo III os testes de Kruskal Wallis, Mann Whitney U foram utilizados para comparar os scores de conhecimentos entre grupos, criados de acordo com os níveis de percentagem de gordura corporal e tempo de atividade física moderada a vigorosa. No Artigo IV os testes de Kruskal Wallis, Mann Whitney U foram utilizados para comparar os scores de conhecimentos, níveis de atividade física e adequação nutricional entre grupos; a correlação de Spearman ajustada serviu para testar a associação entre conhecimentos e práticas, de nutrição e atividade física. Os resultados do Artigo I revelam que a versão portuguesa do General Nutrition Knowledge Questionnaire for Adolescents é um instrumento válido (U = 22766.0; p<.01) e fiável (Cronbach’s alpha=.92) para avaliar o conhecimento nutricional. O Artigo II revelou que o International Physical Activity Questionnaire for Adolescents não tem uma correlação significativa com a atividade física objetivamente medida com o acelerómetro. Os resultados dos Artigos III e IV refletem que os adolescentes portugueses têm baixos conhecimentos, quer em termos de nutrição, com uma média de respostas corretas de 46.5% (SD=11.82), quer de atividade física com uma média de 66.2% (SD=15.50) de respostas corretas. Neste contexto parece importante salientar também os baixos níveis médios de adequação nutricional (57.4%; SD=10.24) e a média reduzida de atividade física moderada a vigorosa (47.9 min.dia-1; SD=27.49). No Artigo III os dados sugerem também que, considerando grupos criados com base na prática de atividade física e na percentagem de gordura corporal, os adolescentes com excesso de gordura e menor atividade física têm significativamente (p=.003) menos conhecimentos sobre as recomendações nutricionais; e os adolescentes com mais tempo de atividade física moderada a vigorosa têm significativamente (p=.044) mais conhecimentos de atividade física. Contudo, O artigo IV, apresenta resultados considerando as variáveis contínuas e parece não existir uma relação entre os conhecimentos e as práticas, quer em termos de nutrição (ρ=-.03; p=.636) quer de atividade física (ρ=.12; p=.083). As principais conclusões desta tese são as seguintes: a versão portuguesa do General Nutrition Knowledge Questionnaire for Adolescents é um instrumento XXV válido e fiável (Artigo I); a atividade física dos adolescentes portugueses deve ser avaliada preferencialmente como métodos objetivos, como os acelerómetros (Artigo II); os adolescentes com excesso de gordura e baixos níveis de atividade física têm piores conhecimentos sobre as recomendações nutricionais dos especialistas (Artigo III); não existe associação entre conhecimentos e práticas, em termos de atividade física e nutrição (Artigo IV). É assim sugerido que, apesar de necessário, o conhecimento parece ser insuficiente para produzir comportamentos saudáveis, quer de atividade física, quer de nutrição e alimentação, nos adolescentes portugueses. 5 Adolescence Definition Adolescence is considered to be the time period that starts after childhood and ends before adulthood, however, the scientific community does not have a consensus about the exact age that corresponds to this definition. It is clinically accepted that an individual variation exists (Lee, 1980), chronological age is not the best indicator of the true maturational age (Baxter-Jones et al., 2005; Lloyd et al., 2014), making it difficult to create age cut off points. For research purposes it is broadly accepted the World Health Organization definition that considers an adolescent any young person that is between the ages of 10 and 19 years (WHO, 2013), and is frequently divided in two stages, the early adolescence, from 10 to 14 years, and the late adolescence from 15 to 19 years (Sawyer et al., 2012). Physiological characteristics During this life period many physiological changes happen, puberty occurs in the beginning of adolescence and, by its end, full sexual maturity is reached (Steinberg, 2005). Several studies suggest that the puberty timing is related to adolescents’ previous and present health issues, namely nutrition and dietary intake and obesity. A recent review suggested that associations between dietary intakes and pubertal timing, such as girls with the highest intakes of vegetable protein experienced pubertal onset up to 7 months later, on the contrary the highest intakes of animal protein could lead up to a 7 months earlier pubertal onset (Cheng et al., 2012). In addition, children with high isoflavone intakes may have the onset of breast development and peak height velocity about 7–8 months later (Cheng et al., 2012). Secular trends in the timing of puberty have been studied, and it seems that in most developed countries the age at menarche seems to be reducing (Cheng et al., 2012). Similarly, overweight seems to be increasing in the same countries (Cheng et al., 2012). There is some evidence suggesting that the early menarche 6 onset occurs more frequently in obese girls (Alberga et al., 2012; Davison et al., 2003; He & Karlberg, 2001; Lee et al., 2007; Tsang et al., 2012; Wattigney et al., 1999). During puberty there is also a growth rate increase, with visible height and weight gains, at the same time as a change in body composition (Freedman et al., 2002). The first years of adolescence are associated with a substantial increase in adipocyte size and number (Alberga et al., 2012), while once pubertal maturity is attained gender differences are apparent, hormonal maturation contributes to a decrease in males body fat and an increase for females (Alberga et al., 2012; Brambilla et al., 2006; Loomba-Albrecht & Styne, 2009). Also the body distribution of fat differs, male adolescents face a bigger deposit of fat in the abdominal region, both subcutaneous and visceral fat; whereas females tend to have peripheral fat, mainly in the hips, a pattern also identified in adults (Pietrobelli et al., 2005), by the end of puberty males assume a more android body shape and females assume a more gynoid shape (Loomba-Albrecht & Styne, 2009). In what concerns fat free mass, although males gain greater amounts of fat free mass and skeletal mass during puberty (Loomba-Albrecht & Styne, 2009), it seems that both genders face an increase of fat free mass during adolescence (Alberga et al., 2012), particularly during the growth spurt phase. The adolescent growth spurt is associated with a rapid growth and some hormonal changes. A longitudinal study suggests that experiencing an early pubertal growth spurt increase progressively more fat-free mass during the first years of puberty when compared to late-maturing peers of the same age (Buyken et al., 2011). As a result of intensive growth and muscular development, an increase in blood volume is observed, and therefore the adolescent faces elevated iron needs (Mesias et al., 2013). During this period of great growth rates, it seems crucial to the overall health, and in particular to bone health to engage in a healthy diet. Nutrition factors such as calcium (Mesias et al., 2011; Zofkova et al., 2013), vitamin D , vitamin K, zinc, copper, fluorine, manganese, magnesium, iron and boron proved to be important for the integrity of the skeleton (Zofkova et al., 2013). Deficiency of these elements slows down the increase of bone mass during adolescence and 7 accelerates bone loss later in life (Zofkova et al., 2013). Also engaging in physical activity during adolescence have benefits on bone health (Duckham et al., 2014). In short, differences between boys and girls are accentuated during puberty years, particularly differences in adiposity, fat free mass and bone mass, reflecting differences in the endocrine status (estrogens, androgens, growth hormone and IGF-1), genetic factors, ethnicity and the influence of the environment (Loomba-Albrecht & Styne, 2009). Conceptually, pubertal maturation can be described in terms of sequence and timing. The order of the secondary sexual development has been categorized by several groups, by different authors, but the staging system applied most often is that published by Marshall and Tanner (Marshall & Tanner, 1969, 1970). This system categorizes individuals on an ordinal puberty scale from 1 to 5, development of pubic hair and breast development is used for females (Marshall & Tanner, 1969), and pubic hair and genital development for males (Marshall & Tanner, 1970). Some limitations have been pointed out to this method, namely, the scale was developed with a single ethnic group and in a relatively small sample and overweight girls will tend to be erroneously over-estimated (Blakemore et al., 2010). Psychosocial characteristics The psychosocial characteristics in puberty and adolescence more frequently mentioned are the abstract thinking, the increasing ability of captivating others perspectives, a greater ability of introspection, the development of personal and sexual identity (Remschmidt, 1994; Tsang et al., 2012), the development of a personal system of values, rising autonomy and independence from family, bigger significance of social recognition and peer relationships (Remschmidt, 1994; Steinberg, 2005), that frequently assume a role model position (Steinberg, 2005). Social groups and peer relationships can influence adolescents' lifestyle options and health behaviors where friendships are usually formed around shared 8 behaviors (Valente et al., 2009), but also shared body images, as it seems that people look for a mate with similar body composition (Speakman et al., 2007). In fact, body mass index and body composition are of major importance in adolescence, as in this period major changes in these measures and shapes occur, which often precipitates self-consciousness and insecurity (Alberga et al., 2012). Adolescents who reach to develop a clear and positive identity seem to advance more smoothly into adulthood (Tsang et al., 2012). However in this phase there is not a complete psychological development and maturation, the impulse control, the anticipation of consequences, and the ability to pre-planning are not completely acquired (Steinberg, 2007). Adolescence is also where one’s interaction with the socioeconomic environment is defined (Viner et al., 2012), during this period educational opportunities are either taken up or not, depending on the individual choices, but also on the social and political context (Raphael, 2011, 2013). Hence, besides health issues that result from childhood circumstances, there should be given attention to social determinants of health such as educational and training opportunities (Viner et al., 2012). All these bio-psycho-social characteristics make adolescence a unique phase of the life cycle with many health risks and opportunities. Health risks Usually people think of adolescents as healthy individuals. Despite, many premature deaths do occur due to accidents, suicide, violence, pregnancy related complications and other illnesses (WHO, 2013). A great number of adolescents suffer from chronic pathologies and disability (Demmer et al., 2013; May et al., 2012; WHO, 2013). Additionally, many adulthood health problems have their origin in an unhealthy lifestyle in adolescence, including substance use, unprotected sex, sedentary behavior, poor diet practices (Astrup, 2001; WHO, 2013), smoking and alcohol drinking (Lucas et al., 2012). These health risks may be a consequence of the previously mentioned psychological and social characteristics. The decision making process is more influenced by social than by health consequences (Steinberg, 2005), not having 9 in consideration long term results. It has been mentioned that some health problems, like adolescent obesity tracks into adulthood, therefore it seems important to consider prevention efforts at this point of life (Lee et al., 2013). The increase of psycho physiological arousal leads to an increase in risk taking behaviors, with unknown or ignored costs (Craeynest et al., 2008; Schneider & Graham, 2009; Wood et al., 2013). In fact, it has been mentioned that adolescence is the life period were people engage in more risk behaviors (Steinberg, 2007), which may negatively affect the ongoing ages (Guo & Chumlea, 1999; Singh et al., 2008; WHO, 2013). Health opportunities Despite all the health threats mentioned before, adolescence has also some health opportunities. The more complex reasoning and perspective, together with the identity questioning and developing may be used with positive health outcomes (Tsang et al., 2012). The adolescent is able to think about the importance that health and specific health outcomes have to him (Tsang et al., 2012). Social recognition and peer relationships are fundamental at this point of life (Steinberg, 2005), if an adolescent is in a group of friends with a healthy lifestyle, he is more likely to engage with similar behaviors (Viner et al., 2012). In parallel with the group identity, it occurs as well an individual identity development (Tsang et al., 2012). The adolescent is now able to choose what he wants to do, based on his own opinion, as for physical activity and food preferences (Craeynest et al., 2008; Taylor et al., 2005), what may also be influenced from the surrounding overall environment (Anderson-Bill et al., 2011). The health opportunities are even more evident for older adolescents, since they are more likely to possess the needed competencies for health-related instruction and behavior change (Hintze et al., 2012). 10 Adolescents’ lifestyles The lifestyle concept is used in health and social sciences, usually referring to the activities, interests and opinions that characterize an individual (Vyncke, 2002). It is broadly recognized that not all members of one class share the same everyday activities. Lifestyles are, apart from age, determined by the important socio-structural dimensions, education, political, economic and cultural resources (Howell & Ingham, 2001; von Normann, 2009). It has been suggested that lifestyles are reproduced inter-generationally, teenagers follow its parents, but they often mix and rearrange the styles (von Normann, 2009), influenced by their independent environments (Anderson-Bill et al., 2011; Stahl et al., 2001). Many studies have proved that health preventive and risk behaviors tend to cluster (Fisberg et al., 2006; Iannotti & Wang, 2013; Marques et al., 2013; Nutbeam et al., 1991; von Normann, 2009). An international 11-country study focusing on young people's health behavior, clustered health lifestyles into two groups: a health enhancing behaviors lifestyle; and a health-compromising behaviors lifestyle. This seemed to be a consistent pattern, indicating that health-related lifestyles of adolescents may not differ greatly between countries (Nutbeam et al., 1991). A more recent research conducted in Germany with young adolescents, described five lifestyles: the outdoor sociability group, the multimedia orientation group, the high culture group, the sports groups and the indoor secluded group. The outdoor sociable lifestyle included adolescents that preferred activities such as: stroll around, be in contact by phone or SMS, listen to music, meet someone in fast food restaurants, go to pop concerts, dance, hang around, go to the cinema, use drugs, go to youth clubs, watch videos and read the newspapers. The multimedia oriented lifestyle was based on computer activities, either on week and weekend days, such as games, internet and videos, but also watching television. The high culture lifestyle was based on theatre, to play and listen to music (either classic or pop), and visits to museums. The sports oriented lifestyle was related to practicing sports, going to sport events or being member of a club. 11 The indoor secluded lifestyle integrated the teenagers that preferred reading all sort of books, comics or newspapers and visiting museums (von Normann, 2009). The same research concluded that the family-oriented lifestyle influences the children’s food patterns in a positive way; while the non-family-oriented lifestyles, including outdoor lifestyle, lead to less preferable food patterns (von Normann, 2009). On the contrary, a Portuguese study, revealed that everyday outdoor play and structured exercise/ sport were positively associated with healthier lifestyle patterns (Marques et al., 2013). Another research with a nationally representative sample of 9174 American adolescents aged 11 to 16 years, concluded that behaviors clustered into three classes: Class 1 - high physical activity and high fruit and vegetable intake and low sedentary behavior and intake of sweets, soft drinks, chips, and fries; Class 2 - high sedentary behavior and high intake of sweets, soft drinks, chips, and fries; and Class 3 - low physical activity, low fruit and vegetable intake, and low intake of sweets, chips, and fries (Iannotti & Wang, 2013), the first two classes suggest that both diet and physical activity behaviors cluster, either healthy or non-healthy. Data from the Portuguese survey of Health Behaviour School-Aged Children lead to a similar conclusion (Veloso et al., 2012). Diet and physical activity in adolescence Adolescents’ diet In the early 2000 a review about eating practices revealed that in the past decades it were observed major changes, with an increase in energy, fat and soft drinks consumptions, and a decrease in fruit, vegetables and milk ingestion (French et al., 2001), a study revealing Portuguese trends stated that the consumption of meat, milk and vegetables increased and the consumption of soup, fish and fruit decreased in 1998–1999 relative to 1995–1996 (MarquesVidal et al., 2006). The reported increase of eating out in restaurants, the use of food prepared away from home and the bigger food portions have an undeniable 12 influence on the mentioned changes (French et al., 2001; Musaiger & Al-Hazzaa, 2012). Cheese consumption also increased in the same period, as a consequence of the consumption of pizzas and other cheesy fast foods (French et al., 2001). The same authors also mentioned the possible effect on eating behavior of the increased exposure to food messages with an incentive to consumption, in all the mass media (French et al., 2001). These diet changes seem even more concerning in what concerns children and adolescents, which is why adolescents’ diet and eating behavior has been the focus of innumerous studies. It seems that the transition from childhood to adolescence results in changes in diet quality. A longitudinal study concluded that the consumption of fruit, vegetables and milk decreased while the consumption of carbonated drinks increased from childhood to adolescence (Lytle et al., 2000). Other cohort studies corroborate these results, the Norwegian Longitudinal Study showed decreased fruit consumption and increased consumption of sweetened carbonated beverages in young adulthood (Lien et al., 2001); the Bogalusa Heart Study showed that the overall diet quality decreases from childhood to young adulthood (Demory-Luce et al., 2004). Among all the concerns with adolescents, alcohol consumption is a worrying aspect on adolescents’ nutritional intake, as longitudinal findings indicate that the alcohol intake increased (Nelson et al., 2009). In Portugal, although less than 15 % of Portuguese adolescents drink alcohol on a regular basis, about a quarter of the older male adolescents assume to drink beer or spirits regularly, it seems that age and gender have a significant impact on adolescents’ alcohol use (Simoes et al., 2008). Ten to 30 % of American adolescents skip breakfast, which is associated with higher body mass index (BMI) and unhealthier diets (Rampersaud et al., 2005), the same conclusion was verified in Portugal, where 5 to 13 % adolescents skip breakfast (Mota, Fidalgo, et al., 2008). In fact, there is a consistent association of skipping meals with an increased obesity risk in children (Koletzko & Toschke, 2010), also for Portuguese adolescents (Mota, Fidalgo, et al., 2008). The study of the association of meal 13 frequency and “skipping” meals on obesity in urban adolescents from Porto, Portugal showed that the proportion of overweight/obese subjects that consumed fewer than three meals was significantly higher than those reported from normal weight peers (Mota, Fidalgo, et al., 2008). Each additional meal reduced the risk of being overweight/obese (Mota, Fidalgo, et al., 2008), as German and American studies showed as well (Koletzko & Toschke, 2010). The trends of food intake for Portuguese adolescents were studied using national data from 1987 compared with data from 1995 and 1999. It was revealed a reduction of the number of meals and the decrease in the prevalence of fish and soup intake; and an increase in the intake prevalence of meat, milk, starchy foods, vegetables and fruit (Marques-Vidal et al., 2006). A tracking of healthy food choices seems to exist, adolescents alter their behaviors over time, but that change is relative to the actions of other students, results of a follow up study revealed that quintile groups maintain relative rankings in nearly all follow up moments, the students identified in the first assessment as making more healthy food choices continued that way, and those reporting less remained low (Kelder et al., 1994). Overall, recent population studies show that adolescents do not meet the daily recommendations for fruit, vegetable, and whole grain consumption, and overconsume energy dense, sugary and salty foods (Holman & White, 2011). A research with 13 year old Portuguese teenagers can lead us to a similar conclusion, as it was referred that the main sources of energy were starchy foods (fries and chips included), dairy products, meat and sweets/ pastry (Araujo et al., 2011). This same research had other concerning results, when we analyze protein sources, seafood contributes with only 13.6 %, after meat, dairy and starchy foods (Araujo et al., 2011). In what concerns total fat sources, fats and oils represent only 11.6 %, with meat, starchy food, sweets and pastry and dairy with higher proportions (Araujo et al., 2011). Looking for adolescents’ nutritional intake, in Spain, Italy and Greece, it is rich in total fat, in monounsaturated fatty acids, due to a high consumption of olive oil and in saturated fatty acids (Cruz, 2000), probably due to a high meat consumption. This means that two important characteristics of the Mediterranean 20 Overall the dietary and nutritional intake correlates coincide with the correlates for physical activity, and include all the factors that may be associated with the dietary, physical activity and sedentary behaviors. Usually they are categorized into two groups: external and internal (Hoelscher et al., 2002). The external determinants comprise social/ cultural, organizational, physical environment and policies/ Incentives. Figure 1. Ecological Model of Adolescents' Diet and Physical Activity Adapted from the Ecological Model of Diet, Physical Activity and Obesity developed for the NHLBI Workshop on Predictors of Obesity, Weight Gain, Diet, and Physical Activity; August 4-5, 2004, Bethesda MD Body Weight, Fat and Distribution Risk Factors, CVD, Diabetes, Cancer Energy Balance Dietary and Nutritional Intake Biological & Demographic (Age, Sex, Race, Ethnicity, Social Economic Status, Genes, Sensory experiences, Hunger...) Psychological (Beliefs, Preferences, Emotions, Self-efficacy, Intentions, Behavior change skills, Body image, Motivation, Knowledge,...) Physical activity Social/ Cultural (Social support, Modeling, Family, Social norms, Cultural beliefs, Acculturation, ...) Organizational (Practices, Programs, Norms & Policies in schools and worksites, Health care settings, Businesses, Community organizations) Physical Environment (Access to & quality of foods, Recreational facilities, Sedentary entertainment, Urban design, Transportation infrastructure, Information) Policies/ Incentives (Cost of foods, Incentives for behaviors, Regulation of environments, Taxes) 21 The internal determinants include biological and demographic and psychological determinants. Figure 1 presents an ecological model of diet and physical activity and resumes the different dimensions of correlates of diet and physical activity. In order to guide the food choice of an individual or community researchers and public health professionals should have a great understanding on each of these aspects and on the interaction between them. It seems that intervening on one single determinant although important may be insufficient to produce a dietary or physical activity behavior change (Raine, 2005). Dietary and nutritional intake correlates Choices about food and eating have to me made several times a day, and this decision making process is a complex multifactorial procedure. As said above, the factors contributing to the diet behavior seem to coincide with the ones influencing physical activity, however some particularities have to be explored. Figure 2 resumes the different determinants of food choices. Figure 2. Biological, personal, social and environmental determinants of food choices In: Contento, I. R. (2011). Nutrition education linking research, theory, and practice (2nd ed.). Sudbury, Mass.: Jones and Bartlett 22 External correlates refer to the social and environmental aspects that although being outside the person, influence the individual behavior. In this group are included features of the physical environment, the social norms and support, organizational resolutions, economic and political issues. It seems that public policies decisions, including food taxes and subsidies contribute to healthy consumption patterns at the population level (Thow et al., 2010), larger taxes tend to be related with more significant alterations in consumption, body weight and disease incidence (Thow et al., 2010), particularly for children and adolescents, low-socio economic status populations, and those most at risk for overweight (Powell & Chaloupka, 2009). So it has been suggested that taxes on carbonated drinks and saturated fat and subsidies on fruits and vegetables would be associated with beneficial dietary changes, with the potential for health gains (Eyles et al., 2012). In fact, some economic theories assume that the price of food play a role in food choices. When considering the price per calorie, processed foods, high in fat and sugar are the cheapest, while the perishable products and animal protein are the more expensive ones (Drewnowski & Barratt-Fornell, 2004; Drewnowski et al., 2004). Although some cheap healthy options are available, like whole grain bread and beans, studies reveal that people with higher incomes spent the higher percentage of income in food (Putman & Allhouse, 1999), and have higher quality diets (Mancino, 2004). The household with low incomes seem to be saving money also by buying products on discount and from generic brands (Putman & Allhouse, 1999). Also for children and adolescents price is a diets’ correlate (Engler-Stringer et al., 2014), as at these ages financial autonomy is greater than before (Verstraeten et al., 2014), and therefore an economic consciousness exists. Cheapness of fast food is acting as a barrier to the adoption of a healthy diet (Shepherd et al., 2006), in fact 15% of Portuguese students refer price as a barrier for an adequate dietary intake (Kearney & McElhone, 1999). The physical environment includes the aspect of food availability and accessibility. Availability may be defined as the assortment of food items included in the food system on a consistent basis and affordable (WHO, 2014). 23 Accessibility refers to having sufficient resources to obtain food (WHO, 2014). It seems that sugary and fatty snacks accessibility is associated negatively with school children fruit consumption, as well as, salad bar availability and accessibility were positively associated with green vegetable consumption; and fruit and vegetables accessibility was associated positively with consumption (Terry-McElrath et al., 2014), it seems that a wider availability of healthy foods in general acts as a facilitator to a healthy diet for children and adolescents (Shepherd et al., 2006). Some studies have been performed on the neighborhood influences in diet choices, and it seems that the availability of fruits and vegetables in the local grocery stores is correlated with a higher quality of the diet (Morland et al., 2002). A cross-sectional study from Canada revealed that the proximity to convenience stores in adolescents' home or school environments is associated with a poorer diet (He et al., 2012), similar conclusions are pointed in a recent review emphasizing a moderate association between the community and dietary intake of children and adolescents (Engler-Stringer et al., 2014). The food items that are available in the several organizations where people spent most of the day, like schools in the case of children and adolescents, seem to determine dietary choices during the working/ school day, also because usually they are convenient and affordable (Finkelstein et al., 2008). Several descriptive and intervention studies proved that the school canteen, cafeterias and vending machines contribute greatly for the nutritional intake of children and adolescents, and are privileged places to design interventions to reduce the burden of obesity and the intake of sugar and fat, as well as increase the fruit and vegetables intake (Briefel et al., 2009). School environment and policy changes can increase healthy eating (Belansky et al., 2013), as well as a poor school meal provision is assumed as a barrier to healthy eating (Shepherd et al., 2006). All the potential of inducing a healthy diet on adolescents in a school environment, resulted in schools being recommended as a primary setting for obesity prevention efforts in adolescence (Lee et al., 2013; WHO, 2009). 24 Other organizational contexts, like after-school activities (Hyland et al., 2006), sports facilities (Chaumette et al., 2009) and summer camps (Tilley et al., 2014) have also been suggested as having potential to promote adolescents healthy eating, or to act as a barrier. Most eating events occur in the presence of other people, what enhances the effect social environments and cultural contexts have on dietary behavior. The social and cultural correlates are varied and include social support, social modelling, family context, social norms, cultural beliefs and acculturation. Social modelling refers to the fact that people may absorb eating behaviors by observation of others (Grusec, 1992), including family, peers and social relevant people. But others may also be perceived as evaluators of the quality and quantity of food chosen and eaten (Herman et al., 2003). Particularly in adolescent girls the food choice may change to adapt to others choice (Bevelander et al., 2011), more evidently if the body size is considered, young women imitate the food intake of slim peers (Hermans et al., 2008). Also the quantity of food is biased, as evidenced by a study where the energy consumed on a breakfast by young females suffer a significant influence by the control females group (Hermans et al., 2010). Another aspect to consider about the social modelling relies on the body image, appearance and attractiveness, in particular for adolescents the desire to take care of the appearance is referred as a facilitator to engage in a healthy diet (Shepherd et al., 2006). Body image is also a significant correlate of food choices (Bargiota et al., 2013), even after weight loss (Vieira et al., 2013). Conclusions from studies on the social modelling evolved to researches testing positive social support and negative social pressure, contributing either to poor or nutritious eating habits. Social support can refer either to the strategies adopted to manage the reinforcement of healthy consumptions and the discouragement of the unhealthy food choices, or from the emotional support from family and friends to engage in a certain behavior, both at home or on other social circles (Verheijden et al., 2005). A positive social support is related to positive changes in the dietary intake, it was associated with a positive progress in fruit and vegetables consumption 25 and with an evolution in the preparative stage for improving eating habits (Engbers et al., 2006). For children and adolescents family (Johnson et al., 2000; Shepherd et al., 2006; Stanton et al., 2007) and peers (Stanton et al., 2007; Verstraeten et al., 2014) support seems to be a strong facilitator of a healthy diet, mainly when eating with family and peers (Bargiota et al., 2013), in fact family meal frequency during adolescence has been positively associated with better meal quality and healthy meal patterns in young adulthood (Gillman et al., 2000; Larson et al., 2007) and children who eat fewer family meals are more likely to become overweight (Gable et al., 2007). Adolescents who are more involved in meal preparation (Gable et al., 2007) or who have better cooking habits and skills have better dietary habits (da Rocha Leal et al., 2011). It seems like some of the parental influence in food preferences passed on childhood is maintained through adolescence, (Pakpreo et al., 2004; Savage et al., 2007) in fact, a higher parental education seems to be associated with a higher contribution from healthier food groups to nutritional intake among Portuguese adolescents (Araujo et al., 2011). It seems that parents eating pattern have the potential of influencing their children (Contento et al., 2005; Fisher et al., 2002), furthermore children and adolescents who eat with their families have diets with higher quality (Gillman et al., 2000). The social pressure over dietary choices during childhood and adolescence seems to be very important also later in life, as several participants of a previous study stated that the motives behind their food choices were consequence of the way they were educated early in life (Furst et al., 1996). Most participants in the mentioned research specified the influence from the social pressure applied by their families and the cultural and social norms (Furst et al., 1996), in fact, more recently, it has been mentioned that norms have a strong consistent correlation with children and adolescents eating behavior, being positively associated with fruit and vegetables consumption and associated with sweetened beverages. Peer norms and parent norms were positively associated with sweetened beverage consumption and milk norms were negatively associated with sweetened beverage consumption (McClain et al., 2009). Time is also a correlate to consider in the food choice process. Nowadays people refer the time constraint as a limitation for several activities, including food 26 preparation and cooking (Mancino, 2004). The time issue can be perceived as a barrier to a healthy eating also by adolescents (Verstraeten et al., 2014) that spend most of the day away from home, therefore eating out and replacing meals for convenient snacks. It has been reported that 34 % of the European students perceive time as one of the major barriers to eat healthier (Kearney & McElhone, 1999), and among Portuguese adolescents available time was the main reason stated for the eating out frequency (Almeida et al., 2011). Diet social correlates include out of home meals, in its several forms: traditional restaurants, fast food restaurants, cafeterias and canteens. For Greek adolescents, eating out with peers and eating from the school canteen was related with higher consumption of 'junk type of food' (Bargiota et al., 2013). Portuguese adolescents eat out often, mainly at lunch and snacks, but no information on the nutritional balance of these meals are available (Almeida et al., 2011). The social economic status and the education level are diet related. It seems like more educated people and the ones with a higher income eat more healthfully (Mancino, 2004), reflecting the surrounding health promoting environments, but also the acculturation of the social economic class. The middle and high classes are more willing to make money, time and energy investments in their health (Mancino, 2004). A study with adolescent pointed out that disadvantaged school was significantly associated with obesity in adolescence for males and females and a disadvantaged family was significantly associated with obesity in young adulthood for females (Lee et al., 2013), in accordance mothers with a higher educational level facilitate healthier eating options (Bargiota et al., 2013). Nowadays the social environment has a strong presence of the several sorts of media, television, radio, press and the internet. Therefore, communication is faster and ubiquitous, even nutrition communication. The media have been mentioned as the main source of information about food, diet and nutrition (Lanigan, 2011), however quantity does not equal quality. A great amount of information available on the media is not evidence based and may result in a negative change in dietary behavior, what is of concern particularly in the case of children and adolescents. It has been suggested that efforts need 27 to counter inaccurate information and address the rationale for health practices (Lanigan, 2011). Even so, when the information available is correct, it seems that media have the potential to influence positively the dietary intake, as adolescents consumption of fruit and vegetables have a positive association with the use of newspaper articles, the Internet and booklets as a source of nutrition information (Freisling et al., 2010), and exposure to television advertisements for fruit and vegetables appear to be associated with fruit and vegetables consumption among European 11-year-old adolescents, but this relationship seems to be mediated through cognitive factors such as attitudes and preferences (Klepp et al., 2007). It is globally recognized that the marketing activities influence food choices and dietary intake (Buijzen et al., 2008; Coon & Tucker, 2002; Story & French, 2004). On one hand adolescents tend to buy and eat more products that were advertised, which generally include high sugar, fat and/or salt products, on the other hand, while exposed to television adolescents are more compelled to eat (Story & French, 2004). A study with Portuguese adolescents stated that television viewing is associated with higher consumption of fatty and sugary foods and a lower consumption of fruits and vegetables, translating into a higher intake of fat and a lower intake of minerals and vitamins (Ramos et al., 2013). Internal determinants are inherent to the individual and are subject to its control. In this group of determinants, biological characteristics, cognitive factors and capabilities are included, like genetics, age, gender, knowledge, attitudes, beliefs, values, self-efficacy and expectations. Although the above-mentioned influence from the environment, ultimately is the individual that makes the choice of eating or not, and of what to eat. Biological factors seem to be behind food choices, when asked most people refer the sensory perception of food (taste, flavor, smell, sight and texture) as a major correlate of their preferences and choices (Small & Prescott, 2005). The preferred flavors seem to have a biological predisposition, in particular the preference for sweet (Beauchamp & Mennella, 2011) that seems to be culturally universal and to remain during the course of life (Pepino & Mennella, 2005). The preference for 28 other flavors and nutrients seems to appear later in infancy (Mattes, 2009; Stein et al., 2012), and the early experiences may change the innately organization of taste preferences and aversions (Beauchamp & Mennella, 2011). The individual sensory response differences seem to be related to differences in the fungiform taste buds (Tepper & Nurse, 1998; Tepper et al., 2009), but also in food intake patters and body weight variations (Keller & Tepper, 2004). Nutrition educators should always consider the potential underneath the fact that most food preferences can be learned or conditioned (Beauchamp & Mennella, 2009; Contento, 2011), and there is strong evidence of preferences correlating to eating behavior of children and adolescents (McClain et al., 2009), either the overall taste preference (Verstraeten et al., 2014) and the preference for fast food, that was mentioned as a barrier to healthy eating (Shepherd et al., 2006). In fact for 29 % of the European students taste is mentioned as one of the major barriers to eat healthier (Kearney & McElhone, 1999), it seems like adolescents believe that healthy food is not tasty (O'Dea, 2003). Hunger is originally the biological mechanism signalizing the need to have some energy input in the body. The physiological mechanism of hunger and satiety is complex, and it relates to the energy deposits of the body. These processes are not adapted to today’s food energy density and overall environment, it seems that the weight control in the developed countries is no longer an unconscious, instinctual behavior, but instead, requires a great cognitive effort, and those who are not dedicating themselves to that effort will probably develop overweight (Peters et al., 2002). Hunger, appetite and satiety can however be influenced by several eating behaviors of the individual, as results tend to support a role of slow eating on decreased hunger and higher inter-meal satiety (Andrade et al., 2012). Besides biological internal determinants, the perceptions, expectations, beliefs, attitudes, motivations and emotions also have a powerful interaction with dietary intake. It is also important to understand why people don’t engage with a healthy behavior, and the major reason seems to be the fact that 71 % of Europeans and 73% of Portuguese consumers believe they don’t need to make any changes to what they eat, as they perceive it as already healthy enough (Kearney & McElhone, 1999). For children and adolescents there is a strong evidence of 29 intentions and the perceived modelling correlating to eating behavior (McClain et al., 2009). In addition, food choices may be influenced by personal meanings some food items have for each individual (Contento, 2011). The social and environmental correlates mentioned before are experienced and interiorized differently by individuals in the same culture, based on their previous and forthcoming involvements. The environmental and social stimuli are processed cognitively and emotionally. For example although most people are exposed to information on the benefits of consuming more fruit and vegetables, the fact of knowing someone that suffered from obesity or someone that has kidney failure may act as a filter to this information (Contento, 2011), therefore an individual’s perceptions concerning healthy eating and the perceived needs to alter eating behavior, can totally differ from those of the public and health professionals. Food and nutrition-related behaviors are influenced by attitudes and motivations towards healthy eating. Some unfavorable attitudes can act as barriers towards the success of nutrition-information interventions. For example, an intervention study showed that consumers who were overweight, tended to have attitudes that negatively influenced the impact of a nutrition-information intervention. It showed that the attitudes of overweight consumers towards eating less fat in the intervention group was negative at the baseline and decreased even more towards the end of the intervention (12 months), leading to an ineffective intervention (Engbers et al., 2006). Will power can be a possible explanation to the failure of some behavioral change interventions, as it seems that will power is a strong facilitator to adopt a healthy diet (Shepherd et al., 2006), as well as self-efficacy (Johnson et al., 2000). One of the most mentioned barriers to a healthy diet for adolescents is the belief that healthy food is not tasty nor convenient (O'Dea, 2003). From all the individual correlates, nutrition knowledge is frequently mentioned in cross-sectional studies and targeted in most informational interventions and prospective studies. In this thesis the nutrition knowledge will be addressed in particular in a following section. 36 It has been suggested that individual characteristics, such as self-efficacy, and enjoyment related to sports, can significantly predict moderate to vigorous physical activity (Silva et al., 2012). Enjoyment with physical activity has also been studied and correlational studies revealed that high scores on enjoyment of sedentary behaviors was associated with increased likelihood of being in the high-sedentary group for girls; but not for boys (Norman et al., 2005), the same was concluded using cluster analysis, as the high sedentary group reported higher levels of enjoyment for sedentary behaviors and this result differed significantly from the low and medium sedentary groups (Zabinski et al., 2007). Self-efficacy is the evaluation a person does of his ability to overcome relevant obstacles for a certain behavior, like physical activity. Self-efficacy has been positively associated with physical activity (Craggs et al., 2011; Horst et al., 2007; Sallis et al., 2000). A study showed that self-efficacy directly influenced physical activity in 6th grade girls, but not in 8th grade. For the younger girls, self-management some strategies, like thoughts, goals, and acts partially mediated this relationship. For the older girls, self-efficacy had indirect effects on physical activity that seemed to be interceded by self-management strategies and perceived barriers. This study suggested that the development of self-management strategies like positive thoughts, thinking about the perceived benefits, and making physical activity more enjoyable may positively impact physical activity. With older girls, strategies to overcome barriers as anxiety of humiliation and poor knowledge may be important aspects of the association between self-efficacy and physical activity (Dishman et al., 2005). Previous authors planned an intervention with assessments of self-efficacy, outcome-expectancy, goal setting, and physical activity satisfaction. Results suggested that self-efficacy had a significant, direct effect on physical activity (Dishman et al., 2004), in accordance with other studies conclusions (Trost et al., 2003). Self-efficacy also seems to mediate the relationship between peer social support and physical activity (Beets, Pitetti, et al., 2007), and the effect of interventions on physical activity (Dishman et al., 2004). For Portuguese 37 adolescents self-efficacy correlates with parental social support, moderate to vigorous physical activity (P. Silva et al., 2014), and active commuting to school (K. S. Silva et al., 2014). Motivation has consistently proved to be positively related to physical activity in adults, in particular intrinsic motivation being predictive of long-term exercise adherence (Teixeira et al., 2012). But in adolescents, although some support for self-determination theory do exist, a recent review revealed that there is a substantial heterogeneity in most the associations reported in the studies and many methodological shortcomings (Owen et al., 2014). The physical activity correlate more deeply assessed in this thesis is physical activity knowledge, and will be addressed in the next pages. Adolescents’ nutrition and physical activity knowledge Adolescents are confronted at every moment with the need to make decisions that will somehow influence their health: to cook or to order dinner, to ask for vegetables or chips, to walk or drive somewhere, to watch television or go for a bike ride, to go to the hospital or search the web for a medicine… Health professionals, parents, teachers or other adults are not always around when these decisions need to be made, so adolescents have to be informed, empowered and engaged to make the best possible choice (USDHHS, 2010). Little is known about adolescents’ health knowledge, but some studies on children revealed that they acquire their health knowledge through direct instruction, modeling and experiences with surrounding environments (Lanigan, 2011). The contexts in which children grow interfere with both their understanding and decisions regarding diet and physical activity, these contexts are mainly family and educational institutions (Lanigan, 2011). For adolescents also friends and peers contribute to health knowledge (Baheiraei et al., 2014; de Looze et al., 2012). A better knowledge may not be a guarantee of a better health option, however not knowing leaves the health decisions as a random event. 38 Health knowledge has innumerous arms but in this thesis only nutrition and physical activity are approached, focusing on adolescents. In fact, education and information about physical activity and nutrition seems to be a way of promoting the development of positive health attitudes (WHO, 2004). Nutrition knowledge Nutrition knowledge can be defined as the individual cognitive process regarding diet, food and nutrition information (Axelson & Brinberg, 1992). Considering the broad range of the eating behavior, dietetics and nutritional sciences it seems insufficient to have one broad knowledge definition to include all these issues (Gleason & Rangarajan, 2000). Several different authors attempted to find nutrition knowledge definitions to cover all the mentioned concepts. The social psychology divides the nutrition knowledge into two arms: the motivational knowledge and the instrumental knowledge (Contento et al., 1995). The motivational knowledge enhances consciousness and stimulates motivation (Contento et al., 1995), by the anticipation of the consequences or expected results (Contento et al., 2002). Instrumental knowledge is the practical knowledge of what and how to do (Contento et al., 1995). Another concept that is explained by psychologist divides knowledge into declarative knowledge, as the knowledge of things and processes, for example knowing that a specific food item is rich/ poor in a specific nutrient or that one nutrient can prevent a disease; and procedural knowledge, as the knowledge of how to do something, for example how to choose a low salt packet of soup (Worsley, 2002). For other authors the nutrition knowledge is divided in the knowledge about the food item and the knowledge about the effects of consuming one particular food item, therefore nutrition knowledge is not one single concept, and may interfere differently the dietary and nutritional intake (Wansink et al., 2005). But all these definitions can be combined in a simple, but not simplistic idea, nutrition knowledge is the knowledge about nutrients and nutrition in all its 39 domains, hence it will accurately distinguish between experts from less informed people (Worsley, 2002). Confusion between knowledge and beliefs is common, even among health professionals. Nutritional beliefs refers to the personal perceptions of food, diet, nutrition and health; while nutritional knowledge is a concept based in factual, evidence based information (Worsley, 2002). Differences in nutrition knowledge across different samples have been studied. To the best of our knowledge, the first published studies on nutrition knowledge were performed on adolescents (Goshtigian et al., 1976; Podell et al., 1975; Wagstaff, 1976), and in the eighties a study about nutrition knowledge on adolescents was performed with high school athletes and the results indicated that the female athletes had better knowledge and some significant relationships between sport forms, seasons, and nutrition knowledge and food practices were found (Douglas & Douglas, 1984). Navy recruits nutrition knowledge was also studied, which at the time had score comparable with that of American adolescent students. Less than a half of the recruits were aware of: “how one assesses nutrient needs and whether those needs are being met”; “the four major food groups and recommended servings”; and “effects of alcohol and drugs on nutritional status” (Conway et al., 1989). In the nineties a study with Nigerian individuals revealed a fair nutrition knowledge (Eneobong & Akosa, 1993). In the same decade, a longitudinal study with runners revealed that thy had fair nutrition knowledge scores, but over a 3-year time period, nutrition knowledge did not improve (Wiita & Stombaugh, 1996). Some years later a study with hockey players suggested little sport nutrition knowledge (Reading et al., 1999). More recently, other authors suggest that American (DeVault et al., 2009) and British (Lakshman et al., 2010) children have good nutrition knowledge scores, prior to any intervention, but there is no consensus as other studies emphasize that the poor nutrition knowledge may be setting the stage for the obesity epidemic to continue (Zapata et al., 2008). In particular it has been emphasized that in multicultural contexts, adolescents demonstrate knowledge about the cultural and psychological aspects of nutrition, but are not able to identify the food 40 sources of nutrients or nutrient functions, and don’t follow daily food guidelines to choose foods, although being aware of the importance of milk and vegetable consumption (Pirouznia, 2000). Younger Taiwanese adolescents revealed a fair knowledge in nutrition basics, but poor in the “physiological function of nutrients”, “relationships between diet/nutrients and disease”, and the “daily serving requirement for different food groups”, suggesting that they general valued the importance of nutrition, but did not concern the health benefit of foods (Lin et al., 2007). A study from Italy with athlete and non-athlete adolescents revealed some nutritional misconceptions, but even so athletes scored significantly better, suggesting a favorable role of sport practice on nutrition knowledge (Cupisti et al., 2002), maybe a result from coaches nutrition knowledge (Juzwiak & AnconaLopez, 2004). Some studies revealed that large proportions of populations have misconceptions about personal dietary intake level and may misunderstand general dietary information (Zapata et al., 2008). Results from the HELENA study, with adolescents from nine European countries, but not Portugal, state that adolescents have modest nutrition knowledge (Sichert-Hellert et al., 2011). It is interesting to observe the evolution on the nutrition knowledge studies, however it should be emphasized the differences in the methodology, different questionnaires were used, and not all were validated instruments, so broader conclusions should be made with caution. In Portugal no data on adolescents’ nutrition knowledge was available, and a valid and reliable questionnaire to assess was inexistent prior to this thesis. Some authors studied different correlates of the nutrition knowledge. Life experiences, social groups, role models, beliefs, physical and biological environments have been mentioned (Worsley, 2002). Higher educational level (Boulanger et al., 2002; De Vriendt et al., 2009; Parmenter et al., 2000), female gender (Heaney et al., 2011; Parmenter et al., 2000; Pirouznia, 2001; SichertHellert et al., 2011), married status (Parmenter et al., 2000), also seem to positively correlate with nutrition knowledge. In addition nutrition knowledge increases with age (Sichert-Hellert et al., 2011); and being a no-smoker (De 41 Vriendt et al., 2009) and physically active (De Vriendt et al., 2009) also seem to be positive correlates of nutrition knowledge. Nutrition knowledge also seems to be related to socio-economic status (Boulanger et al., 2002), as individuals with higher income have better scores. However, parental health behavior , particularly regarding nutrition and physical activity seems not to be associated with adolescent knowledge (Pakpreo et al., 2004), on the contrary parental educational level is correlated, either for adolescent boys and girls (SichertHellert et al., 2011). Another study suggested positive relationships among nutrition knowledge and nutrition attitude (Lin et al., 2007). Nutrition education interventions ultimately aim to improve dietary and nutritional intake and health outcomes, like BMI. Many authors have attempted to test the association between intake and nutrition knowledge, but despite the intuitive appeal of education as a means of improving diet, many studies in this area have failed to find significant associations between nutritional knowledge and nutritional intake (Eneobong & Akosa, 1993). Since the way in which nutrition knowledge translates into dietary behavior and nutrient intake may vary among populations, it appears important to assess whether nutrition knowledge is associated with particular food choices and nutrient intakes before any nutrition intervention is initiated in a given population. Contradictory results about nutrition knowledge and intake have been presented. Some authors refer a weak (Axelson & Brinberg, 1992; Heaney et al., 2011; Spronk et al., 2014) or no association (Bravo et al., 2006; de Jersey et al., 2013; Peltzer, 2002; Saarela et al., 2013), while others refer an association between nutrition knowledge and: the consumption of fruit and vegetable (Beydoun & Wang, 2008; De Vriendt et al., 2009; Escalon et al., 2013; Wardle et al., 2000), dairy products (Escalon et al., 2013), starchy food (Escalon et al., 2013), and fish (Escalon et al., 2013); total fat (Berg et al., 2002; Wardle et al., 2000) and fiber (Arnold & Sobal, 2000; Berg et al., 2002) intake; and the overall adherence to dietary recommendations (Sharma et al., 2008), or to the Mediterranean Diet Quality Index (Sahingoz & Sanlier, 2011) Studies in adolescents (Bargiota et al., 2013; Lin et al., 2007), and adolescent athletes (Heaney et al., 2011) suggest that nutrition knowledge is related to 42 dietary behavior. Also American older adolescents presented a correlation between nutrition knowledge and food choices (Pirouznia, 2001). Results from Italian adolescents suggest a positive relation between nutrition knowledge and several dietary and nutritional intake issues. After controlling for covariates, nutrition knowledge was positively associated with pasta/rice, fish, vegetable and fruit intakes, and negatively with sweets, snacks, fried foods and sugary drinks consumption; even more the adolescents with higher nutrition knowledge scores were less likely to have two or more snacks daily and to spend more than three hours in sedentary activities daily (Grosso et al., 2013) Despite no consensus on the direct causal effect of nutrition knowledge on behaviors, recommendations have been made in order to enhance health curricula to devote adequate attention to promote nutrition and energy balance awareness (Budd & Volpe, 2006). However, knowledge was not perceived as a major obstacle to trying to eat healthily, only 7 % of Europeans selected it as a barrier (Kearney & McElhone, 1999). No consistent results exist regarding BMI relation with nutrition knowledge, some authors suggest a negative association (De Vriendt et al., 2009), while others refer that no association exist (Sichert-Hellert et al., 2011; Thakur & D'Amico, 1999). Physical activity knowledge Considering that health literacy has been defined as “the degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions” (USDHHS, 2010), physical activity knowledge can be defined as the ability to obtain, process and understand the benefits of physical activity on overall health. Public health experts have studied physical activity knowledge. A cross sectional survey was carried out in 23 countries, Portugal included, and the results emphasize that knowledge was very low, with only 40 to 60 % of university students being aware that physical activity was relevant to heart disease risk (Haase et al., 2004). Even more concerning are Portugal specific results, showing 43 that only 35 % recognized the association between physical activity and heart disease (Haase et al., 2004), these results are even more important when mentioning the significant reduction of students with this knowledge between 1990 (40,5 %) and 2000 (35 %) (Steptoe et al., 2002). There was a strong correlation between men and women knowledge, but significant differences were found between countries (Haase et al., 2004). Thus, it was suggested that a strong association between physical activity knowledge and economic development of a country probably exists (Haase et al., 2004). American children aged three to five years old scored badly in a physical activity knowledge assessment, with 50 % of them being unable to score any point (Lanigan, 2011). As well a study with physical education students, from Brazil, reported most students do not have an adequate knowledge, in most items (Ribeiro et al., 2001). Also in Brazil elementary teachers obtained a medium physical activity knowledge score (Sousa et al., 2012). However a study with urban Indigenous Australians, aged over 18, showed that they had excellent knowledge of the current physical activity guidelines, with between 66 and 92 % correct answers.(Marshall et al., 2008) Few studies report demographic and socioeconomic correlates of physical activity guidelines knowledge. One study with kindergartens reported that high socioeconomic class and male gender had significantly higher scores, no differences were found regarding BMI (Nemet et al., 2012) In Israel, researchers reported a physical activity knowledge score around 50 % in low socioeconomic kindergartens (Nemet et al., 2012; Nemet et al., 2013), considering it poor and significantly reduced compared to moderate and high socioeconomic children (Nemet et al., 2012). The association of physical activity knowledge and practice has no consensus. Some authors proved significant associations between knowledge and practices, in women (Laosupap et al., 2008) and older adults (Salehi et al., 2010); knowledge was mentioned as being used to make and maintain changes in physical activity in type 2 diabetics (Rise et al., 2013). While other studies failed to prove this association, either in adults (Morrow et al., 2004), in indigenous Australians (Marshall et al., 2008) and in university students (Haase et al., 2004). 44 Some authors refer that there is a missing link between knowledge and practices, suggesting that the health outcome expectancy of needing more physical activity than recommended by experts is correlated with achieving more physical activity (Heinrich et al., 2011). The stage of change can be the gap between knowledge and behavior, previous results showed that knowledge and perceived benefits could predict physical activity stage of change (Salehi et al., 2010) In what concerns physical activity preference, it seems that it is significantly correlated to physical activity knowledge, however preferences are significantly higher than knowledge (Nemet et al., 2012). Parents’ knowledge has also been referred as having a direct relationship with children’s BMI z score, the “Obesity Resistance Model” showed that lower parental knowledge was associated with a higher BMI z score in children (Hendrie, Coveney, et al., 2008). However, parental behavior is not associated with adolescent knowledge (Pakpreo et al., 2004). Despite the relation between physical activity knowledge and practices remaining a research question, recommendations have been made in order to improve health school curricula to devote adequate attention to reducing sedentary behaviors and increasing physical activity awareness (Budd & Volpe, 2006). However, if someone is planning an intervention or a research, it is important to remember that for physical activity knowledge a measurement effect has been reported (van Sluijs et al., 2006). It has been previously mentioned that physical activity and nutrition knowledge scores are not significantly different in Israelis kindergartens (Nemet et al., 2012), but young American children demonstrated to have significantly better healthy eating knowledge compared to physical activity knowledge (Lanigan, 2011), the same result was reported from Brazilian elementary teachers (Sousa et al., 2012). Nutrition and physical activity health interventions Several studies and organizations (WHO, 2009) highlighted the importance of designing interventions in order to improve health behaviors, particularly diet (Barker et al., 1995) and physical activity (Pratt et al., 2008). 45 Early intervention and prevention are more effective and less costly (DeMattia & Lee Denney, 2008), so obesity prevention interventions are the most common, and frequently they integrate both nutrition and physical activity contents (DeVault et al., 2009). Nutrition and physical activity school-based effective interventions include curriculum on diet and/or physical activity taught by trained teachers, supportive school environment/ policies, a physical activity program, a parental/ family component and healthy food options available through school food services: cafeteria, vending machines, etc. (WHO, 2009). School-based interventions show consistent improvements in knowledge and attitudes, behavior and/or biological/ clinical outcomes (WHO, 2009), as in schools there is a triple opportunity in the classroom, gymnasium and cafeteria (DeMattia & Lee Denney, 2008). Parents and teachers should be actively engaged in the process of affecting and supervising policies and practices that foster a healthy school food environment (Kubik et al., 2005). Several studies proved the value of integrating teachers. In Portugal, an intervention with children and younger adolescents, using teachers previously trained reduced significantly the consumption of low nutrient energy dense foods in the intervention group (Rosario et al., 2013) and the increase in the BMI z score was lower in the same group (Rosario et al., 2012). A nutrition knowledge intervention results suggest that there is the possibility of increasing knowledge and improving nutritional intake at the same time (Raiha et al., 2012), but few interventions examine long-term results. School-based interactive approaches like Top Grub, a card game, proved to be capable of improving modestly nutrition knowledge among primary school children (Lakshman et al., 2010), but also more traditional approaches implemented by trained school teachers achieved a reduction of solid low-nutrient energy-dense foods (Rosario et al., 2013). Message tailoring according to the extent of nutrition knowledge can represent more positive outcomes (Aldridge, 2006) 53 Chapter 3. Original Research 55 Paper I. ACCEPTED Adaptation, Update and Validation of the General Nutrition Questionnaire in a Portuguese Adolescent Sample Vera Ferro-Lebres; Pedro Moreira; José Carlos Ribeiro Ecology of Food and Nutrition 2014;53:528-542. 56 Ecology of Food and Nutrition, 53:528–542, 2014 Copyright © Taylor & Francis Group, LLC ISSN: 0367-0244 print/1543-5237 online DOI: 10.1080/03670244.2013.873424 Adaptation, Update and Validation of the General Nutrition Questionnaire in a Portuguese Adolescent Sample VERA FERRO-LEBRES CIAFEL – Research Center in Physical Activity, Health and Leisure, Faculty of Sports, University of Porto, and Diagnostic and Therapeutic Technologies Department, School of Health Sciences, Polytechnic Institute of Braganza, Portugal PEDRO MOREIRA CIAFEL – Research Center in Physical Activity, Health and Leisure, Department of Sports, and Department of Nutrition and Food Sciences, University of Porto, Porto, Portugal JOSÉ CARLOS RIBEIRO CIAFEL – Research Center in Physical Activity, Health and Leisure, Faculty of Sports, University of Porto, Porto, Portugal This article describes the adaptation of the adult Portuguese version of the General Nutrition Knowledge Questionnaire (GNKQ) for adolescents, and its validation. Respondents were 1,315 adolescents, who completed the questionnaire in two phases. A subsample of 73 adolescents was used to measure test–retest reliability. Concurrent validity was tested using a sample of 32 dietetic students. The adapted version showed high internal consistency (Cronbach’s alpha =0.92), test–retest reliability (R =0.71) and concurrent validity (U =22766.0; p <.01). Adolescents’ nutrition knowledge can now be assessed with a valid and reliable instrument. Future validation works of this or others questionnaires for children and elderly are warranted. KEYWORDS adolescents, nutrition knowledge, questionnaire, validation Address correspondence to Vera Ferro-Lebres, RD, CIAFEL – Research Center in Physical Activity, Health and Leisure, Rua Dr. Plácido Costa, 91, 4200-450 Porto, Portugal. E-mail: [email protected] 528 Downloaded by [b-on: Biblioteca do conhecimento online UP] at 06:42 11 August 2014 57 Nutrition Knowledge Questionnaire for Adolescents 529 Several eating-behavior determinants have been studied, including nutrition knowledge (Raine 2005; Taylor, Evers, and McKenna 2005; Viswanath and Bond 2007). The influence of nutrition knowledge on food behavior has no consensus (Taylor et al. 2005): Some studies found a weak association or no correlation at all (Mullaney, Corish, and Loxley 2009; Pirouznia 2001; SerraMajem et al. 2007); others found a significant positive association (De Vriendt et al. 2009;Shahetal.2010; Sharma, Gernand, and Day 2008), or a positive association only with fruits and vegetables (De Vriendt et al. 2009; Parmer et al. 2009; Wardle, Parmenter, and Waller 2000) or fat consumption (Wardle et al. 2000). Reasons have been pointed out for the weak associations: (1) poor nutrition-knowledge conceptualization; (2) lack of nutrition-knowledge relevance for the studied population; (3) poor correspondence between knowledge dimensions and food-consumption domains; (4) a small sample size; (5) data analysis inaccuracies; and (6) questionnaire inadequacy (using non-validated questionnaires) (Wardle et al. 2000; Worsley 2002). Thus, it seems important to validate questionnaires, adapted to sample characteristics such as age and cultural context. Considering that several recommendations have been made regarding the importance of planning interventions on nutrition education for children and adolescents, namely focusing nutrition knowledge (Pratt, Stevens, and Daniels 2008;WHO2009), it seems important to validate questionnaires that allow researchers to evaluate the impact of such interventions, allowing researchers to score nutrition knowledge also in younger samples. The General Nutritional Knowledge Questionnaire (GNKQ; Parmenter and Wardle 1999) is one of the few that tests general knowledge and not a nutrition–knowledge specific area. It includes different sections: (1) dietary recommendations, (2) nutrient content of different food items, (3) dietary best choices, and (4) health/disease issues regarding diet. GNKQ has been proven to be valid and reliable in a UK adult sample (Parmenter and Wardle 1999), in an Australian community sample (Hendrie, Cox, and Coveney 2008), in a Turkish adult Sample (Alsaffar 2012), and in a Portuguese adult sample (Almeida-de-Souza 2009). However, in Portuguese adolescents, to the best of our knowledge, no validation has been published so far for this or other general nutrition-knowledge questionnaire, and few exist for other countries. Since GNKQ development and validation, scientific knowledge and food practices have been in constant evolution, making necessary the instrument’s update according to the latest scientific evidences. The present work aims to update the Portuguese version of the GNKQ and to determine its validity and reliability in a Portuguese adolescent sample. Downloaded by [b-on: Biblioteca do conhecimento online UP] at 06:42 11 August 2014 58 530 V. Ferro-Lebres et al. METHODOLOGY The process of adaptation, updating and validation was done in two phases. Phase 1 consisted of making minor adjustments, and adapting the language of the original version of the GNKQ questionnaire. This adapted version was pilot-tested with 603 individuals to ensure age-appropriateness. In Phase 2, some food items were included and the score of each item was revised according to the latest scientific evidence (Anderson et al. 2009;Brownetal. 2009;deSaandLock2008; He and MacGregor 2009; Hoffmann et al. 2003; Kipping, Jago, and Lawlor 2008; Kushi et al. 2006; Mirmiran et al. 2009; Ruxton, Gardner, and McNulty 2010). Results of the 603 Phase 1 questionnaires were analyzed regarding item difficulty, and the questionnaire was changed in accordance. The Phase 2 questionnaire was then tested with a sample of 712 individuals. Sample characterization data for age, sex, and grade was collected in both moments. Participants The sample size was clearly over the minimum 400 individuals recommended for Internal Reliability studies (Charter 2003). The minimum subsample size of 30 for test–retest reliability was also assumed (Charter 2008). For Phase 1,603 high school students aged 11 to 19 years (mean =16.4 SD =1.71), who attended three different schools in the north of Portugal, comprised the study sample. For Phase 2,the study sample consisted of 712 high school students aged 10 to 19 years (mean =15.0, SD =2.00), from 12 different schools distributed geographically in the north and center of Portugal. In Phase 2 we aimed to have a broader age-range representation, including more students from the younger-age group, which was considered insufficient in Phase 1. Table 1 summarizes the Phase 1 and Phase 2 sample characteristics. Ethical Approval This study was conducted according to the guidelines from the Declaration of Helsinki and all procedures were approved by the Research Center in Physical Activity, Health and Leisure Scientific Committee. Written informed consent was obtained from all parents. Directors of the involved schools gave their ethical approval. Adolescents were given an opportunity to refuse participation. There was a guarantee of anonymous and confidential data analysis, for both the paper and online versions of the questionnaire. Downloaded by [b-on: Biblioteca do conhecimento online UP] at 06:42 11 August 2014 59 TABLE 1 Demographic and Academic Characteristics of Adolescents and Dietetics Students Used for Concurrent Validity Phase 1 Phase 2 Dietetics students Total Girls Boys Total Girls Boys Total Age n(%) 601 (87.2) 315 (45.7) 230 (33.4) 712 (100) 294 (41.3) 289 (40.6) 18 (56.3) Mean (SD) 16.37 (1.72) 16.44 (1.68) 16.06 (1.72) 15.01 (2.00) 15.28 (1.94) 14.83 (1.94) 22.3 (0.77) Grade n(%) Total 653 (94.8) 324 (42.0) 239 (34.7) 708 (99.4) 294 (41.3) 287 (40.3) <9th grade 222 (32.2) 108 (15.7) 100 (14.5) 206 (28.9) 75 (10.5) 101 (14.2) 9th to 10th grade 280 (40.6) 137 (19.9) 89 (12.9) 296 (41.6) 122 (17.1) 117 (16.4) >10th grade 151 (21.9) 79 (11.5) 50 (7.3) 206 (28.9) 97 (13.6) 69 (9.7) Mean (SD) 9.36 (1.55) 9.42 (1.56) 9.06 (1.59) 6.35 (2.58) 6.45 (2.37) 5.79 (2.27) Note. SD =standard deviation. 531 Downloaded by [b-on: Biblioteca do conhecimento online UP] at 06:42 11 August 2014 60 532 V. Ferro-Lebres et al. Data Collection and Analysis Data was collected between 2009 and 2011. This time frame did not affect the results of this study. Two versions of the questionnaire were created: a paper version (machine-readable) and an online version. The latter was developed in order to reduce the item-non-response rate, as previously suggested (Denscombe 2009). The first author and/or the responsible teacher from each class supervised the self-administration of the questionnaire to small groups, in a classroom environment. In Phase 1 of the study, each answer was coded numerically and converted into two scores: the original score (according to the original version; Parmenter and Wardle 1999) and the Portuguese adapted score (considering the Portuguese Healthy Eating Recommendations; Rodrigues et al. 2006). In Phase 2, each answer was coded numerically and converted into one score (the Portuguese adapted and updated score), considering the Healthy Eating Portuguese Recommendations (Rodrigues et al. 2006) and the most recent scientific evidence (Anderson et al. 2009;Brownetal.2009;deSa and Lock 2008; He and MacGregor 2009; Hoffmann et al. 2003; Kipping et al. 2008; Kushi et al. 2006;Mirmiranetal.2009; Ruxton, Gardner, and McNulty 2010). In both phases, each correct item was scored 1 point. Incorrect or missing answers were scored 0 points. Data analysis was performed using IBM Statistical Package for Social Sciences, version 19. Statistical significance was set at p<.05. Content Validity and Questionnaire Refinement The content validity is defined as the extent to which the questionnaire covers all dimensions present in the concept it is intended to reflect (Raykov and Marcoulides 2011;Terweeetal.2007; Thorndike 1995). The GNKQ intends to measure nutritional knowledge in a broad range of the concept. In this adaptation and validation process, for the Portuguese adolescent population, it was decided to keep the original authors’ four areas: (1) expert dietary recommendations; (2) nutrient content of food; (3) healthier food choices; and (4) diet-disease relation. The same was done in the Portuguese adult version (Almeida-de-Souza 2009). PHASE 1 For the adaptation and validation for the Portuguese adolescent population, the adult Portuguese version was used and minor adjustments were made. The translation, cross-cultural adaptation, and validation to the adult population processes are described elsewhere (Almeida-de-Souza 2009). Downloaded by [b-on: Biblioteca do conhecimento online UP] at 06:42 11 August 2014 61 Nutrition Knowledge Questionnaire for Adolescents 539 values of Cronbach’s alpha as moderate reliability indicators (Hendrie et al. 2008). Test–retest reliability was acceptable, showing the ability of GNKQA to consistently assess knowledge over time, enhancing its aptitude to adequately evaluate changes after nutrition-education interventions. The correlation coefficient of the Portuguese version was slightly inferior to the ones mentioned in the previous validation studies (Alsaffar 2012; Hendrie et al 2008; Parmenter and Wardle 1999), which may be explained by the age differences of the studied samples. Concurrent validity proved that this questionnaire was able to effectively distinguish between groups with different nutrition-knowledge levels, as its previous versions did (Hendrie et al 2008; Parmenter and Wardle 1999). Regarding the two data-collection methods, the online version had a significantly higher score than that of the paper version. Previous studies found the same results, and suggested that this finding could be related to a smaller item-non-response rate (Denscombe 2009; Kongsved et al. 2007). As previously suggested for adults (Parmenter, Waller, and Wardle 2000), as in our adolescent sample, girls had a significantly higher total score. These results suggest that the girls’ greater interest in nutrition starts early in adolescence. We can state as a limitation to the present validation process the fact that the sample of Dietetics students included only girls, but the highereducation institution that collaborated with this research had only two boys in the selected academic years, and they refused to participate. The length of the questionnaire was also mentioned by the expert panel and by adolescents as a negative factor. On average, it took 20 minutes for an adolescent to answer the whole questionnaire. We should highlight that comments reporting test duration as a constraint were written by the subsample that answered the paper version, and not by those who responded online. The possibility of administering sections 2 and 4 independently can be part of a solution, whenever it would be sufficient to assess only some aspects of nutrition knowledge. CONCLUSION In conclusion, Portuguese adolescents’ nutrition knowledge can now be assessed with a valid and reliable instrument. The GNKQA may be of general use for researchers or dietetics and nutrition professionals working within nutrition-education interventions for adolescents; offering the possibility to evaluate the results of interventions in a reliable and consistent way. We consider that the GNKQA can also be used in clinical contexts, and by non-experts in the nutritional sciences, as the instrument is easy to use and score—the online version, in particular. It has been suggested that Downloaded by [b-on: Biblioteca do conhecimento online UP] at 06:42 11 August 2014 68 540 V. Ferro-Lebres et al. online assessment is a good methodology, as it saves resources, provides more complete answers, and facilitates data collection in follow-up studies (Kongsved et al. 2007). The process of translating and validating this adapted version for other languages/countries would be of great benefit to the research of adolescents’ diet correlates, particularly in regards to the relationship between nutrition knowledge and diet. FUNDING This work was supported by FCTPortuguese Foundation for Science and Technology for the PROTEC project grant SFRH/PROTEC/49529/2009, conceded to the first author and project grant FCOMP-01-0124-FEDER-028619 (Ref.FCT:PTDC/DTP-DES/1328/2012). REFERENCES Almeida-de-Souza, J. 2009. Nutritional knowledge: Reproduction and validation questionnaire. [In Portuguese.] Porto, Portugal: Faculty of Medicine, University of Porto. Alsaffar, A. A. 2012. Validation of a general nutrition knowledge questionnaire in a Turkish student sample. 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Journal of Clinical Epidemiology 60 (1): 34–42. Thorndike, R. M. 1995. Book review: Psychometric theory. 3rd ed. by J. Nunnally and I. Bernstein. Applied Psychological Measurement 19 (3): 303–305. Viswanath, K., and K. Bond. 2007. Social determinants and nutrition: Reflections on the role of communication. Journal of Nutrition Education & Behavior 39 (2 Suppl): S20–24. Wardle, J., K. Parmenter, and J. Waller. 2000. Nutrition knowledge and food intake. Appetite 34 (3): 269–275. WHO. 2009. Interventions on diet and physical activity: What works. Summary report. Rome: World Health Organization. Worsley, A. 2002. Nutrition knowledge and food consumption: Can nutrition knowledge change food behavior? Asia Pacific Journal of Clinical Nutrition 11 (Suppl 3): S579–585. Downloaded by [b-on: Biblioteca do conhecimento online UP] at 06:42 11 August 2014 71 73 Paper II. SUBMITTED Validation of the Portuguese version of the International Physical Activity Questionnaire for Adolescents (IPAQA) Vera Ferro-Lebres; Gustavo Silva; Pedro Moreira; José Carlos Ribeiro 75 Validation of the Portuguese version of the International Physical Activity Questionnaire for Adolescents (IPAQA) Vera Ferro-Lebres; Gustavo Silva, Pedro Moreira; José Carlos Ribeiro Abstract Questionnaires have been broadly used to assess physical activity in adolescents, however validation studies, although essential, are not always performed. The present work aims to determine the validity of the Portuguese version of the International Physical Activity Questionnaire for Adolescents against 3 axis Actigraph accelerometers. A cross-sectional study was conducted, with a sample of 222 adolescents, with a mean age of 15.6 years (SD=2.05). After translation and cross cultural adaptation, data obtained from the questionnaire was correlated to accelerometers data, using Spearman correlation coefficient. Percentages of agreement of physical activity tertiles obtained by each method were tested using Cohen’s Kappa. Statistical analysis was performed for the total sample, per sex and per age group. A significant correlation between the questionnaire and accelerometer was found for older adolescent boys, for total physical activity (ρ=.372; P<.01), and for moderate to vigorous physical activity (ρ=.428; P<.01) No correlations were found for the younger adolescents and girls. A 42.3 % agreement was found for the questionnaire and accelerometer tertiles of total physical activity. The concurrent validity proved that the questionnaire might be valid only for older adolescent boys. The authors consider that whenever available physical activity objective measurements should be used instead of questionnaires. KEYWORDS: Motor activity, Questionnaires, Adolescent 76 Introduction Regular physical activity has been widely mentioned as contributing to several health benefits in all age ranges, namely for mental health (Biddle & Asare, 2011), bone health (Boreham & McKay, 2011), diabetes (Chimen et al., 2012), cardiovascular disease (Shiroma & Lee, 2010) and obesity (Janssen & LeBlanc, 2010). Physical activity (PA) assessment is therefore essential in surveillance, screening, programme evaluation and intervention studies. In order to obtain valid and reliable measures of PA, objective and improved methods of evaluation are recommended (Rowlands & Eston, 2007; Vanhelst et al., 2012), such as those from accelerometers, although they have a high cost and frequently are unavailable. In children and adolescents the difficulties of use have been referred (Audrey et al., 2012; Ottevaere, Huybrechts, De Meester, et al., 2011; Van Coevering et al., 2005). And considering the recommended protocols, particularly in larger sample studies, questionnaires have been used as an alternative (Araújo-Soares et al., 2009; Lacy et al., 2012; Lopes et al., 2013). The International Physical Activity Questionnaire (IPAQ) is the more widely used and accepted questionnaire; it has proven to be valid and reliable for adults, in several countries and in different formats: long version, short version, selfreported and telephone interview (Craig et al., 2003). Researchers detected the need to validate a PA questionnaire for adolescents, because the type and duration of activities are unique for this age group, although adolescents’ PA is more similar to that of adults than of children (Janssen, 2007). Hence, adaptations and validations of IPAQ for Adolescents (IPAQA) have been published in some countries (Guedes et al., 2005; Hagstromer et al., 2008; Lachat et al., 2008; Rangul et al., 2008). Although a validation of IPAQ for Portuguese adults has been done (Marshall & Bauman, 2001), the Adolescents version, IPAQA, is not yet validated for Portuguese adolescents. The present work aims to determine the validity of the Portuguese version of the IPAQA using GT3X+ Actigraph accelerometers. 77 Methods Study Sample A convenience sample of 222 high school students (123 girls), from two different schools in the north of Portugal, completed the questionnaire. Schools were included based on their willingness to participate, and on socioeconomic similarity; the 222 students included were the ones present at classrooms on the days of data collection and when the accelerometers were distributed. Participants had a mean age of 15.6 years (SD=2.05) and body mass index (BMI) age centile classification revealed 31.2 % of adolescents with overweight/ obesity. Tables 1 and 2 summarize sample characteristics, per age group. Ethical Approval This study was conducted according to the guidelines defined in the Declaration of Helsinki and all procedures involving human participants were approved by the Research Centre in Physical Activity, Health and Leisure Scientific Committee. Written informed consent was obtained from all the parents or legal guardians. The involved schools’ directors gave their ethical approval. Adolescents were given the opportunity to refuse participation. Data collection Data were collected between 2011 and 2013. In the first visit to each classroom, anthropometric assessment was performed and accelerometers were distributed. Height was measured using a SECA 217 portable stadiometer, with a 0.1 cm precision. Weight was assessed with 100g precision, using TANITA BC-545 body composition analyser. This equipment was also used to determine body fat percentage. Body mass index (BMI) was calculated and categorized according to Centers for Disease Control and Prevention 2000 (Ogden et al., 2002). The minimum perimeter between the iliac crest and the rib cage corresponded to the waist circumference and the maximum protuberance of the buttocks corresponded to the hip circumference. A nonelastic tape was used to measure circumferences. For all anthropometric assessments adolescents wore light clothes. 84 Table 3. Spearman’s Rank correlation coefficient of physical activity measured by the accelerometer and reported with IPAQA, for the total sample. Accelerometer Total Measured Time (min.day-1) Sedentary PA (min.day-1) Light PA (min.day-1) Moderate PA (min.day-1) Vigorous PA (min.day-1) MVPA (min.day-1) Total PA (counts.min-1) Total PA (steps.day-1) IPAQA Total Reported Time (min.day-1) .131 -.067 .174** .184** .143* .185** .205** .226** Motor Transportation (min.day-1) -.047 .098 -.108 -.121 .042 -.069 -.085 -.121 Walking (min.day-1) .063 -.05 .099 .067 .084 .085 .116 .072 Moderate PA (min.day-1) .103 -.078 .192** .157* .072 .126 .148* .223** Vigorous PA (min.day-1) .222** -.042 .184** .338** .214** .319** .300** .381** Total Reported Time (min.day-1) .131 -.067 .174** .184** .143* .185** .205** .226** MVPA (min.day-1) .184** -.064 .218** .268** .169* .250** .252** .328** Total PA (MET.min.day-1) .163* -.063 .195** .233** .162* .226** .237** .280** Abbreviations: PA: Physical Activity; IPAQA: International Physical Activity Questionnaire for Adolescents; MVPA: Moderate to Vigorous Physical Activity; MET: Metabolic Equivalents. * Correlation is significant at the 0.05 level (2-tailed); ** Correlation is significant at the 0.01 level (2-tailed). In the older adolescent boys group, the minutes per day reported in vigorous PA according to IPAQA were significantly correlated to the time measured with accelerometer on the same intensity level (ρ=0.428; P<0.01), and in the moderate intensity (ρ=0.363; P<0.01); when considering MVPA, the time per day according to IPAQA was significantly correlated (ρ=0.428; P<0.01) to the one measured with accelerometer; the total PA (MET.min.day-1) according to IPAQA was significantly correlated (ρ=0.372; P<0.01) to the total PA (counts/min) according to accelerometer, though this was a poor correlation; there was no significant correlation between moderate PA level obtained by the two methods, however reported moderate PA was correlated with measured vigorous PA (ρ=0.321; P<0.05) (Table 4). The tertiles agreement (Table 5) between the accelerometer and the questionnaire for total PA was 42.3% (K=0.135; P<0.01); and for MVPA tertiles there was a 39.6% agreement (K=0.094; P<0.05). 85 Table 4. Spearman’s Rank correlation coefficient of physical activity measured by the accelerometer and reported with IPAQA, by age group and gender. Accelerometer ≤ 14 years ≥ 15 years Total Measured Time (min.day-1) Sedentary PA (min.day-1) Light PA (min.day-1) Moderate PA (min.day-1) Vigorous PA (min.day-1) MVPA (min.day-1) Total PA (counts.min-1) Total PA (Steps.day-1) Total Measured Time (min.day-1) Sedentary PA (min.day-1) Light PA (min.day1) Moderate PA (min.day-1) Vigorous PA (min.day-1) MVPA (min.day-1) Total PA (counts.min-1) Total PA (steps.day-1) IPAQA Girls Total PA (min.day-1) .025 -.380* .434* .313 .146 .273 .363* .199 .077 -.053 .139 .113 .122 .109 .153 .291** Motor Transportation (min.day-1) .146 .297 -.227 -.243 .190 -.092 -.177 .004 -.005 0 -.010 .058 .065 .088 .026 -.057 Walking (min.day-1) .150 -.187 .332 .421* .170 .407* .412* .142 .072 -.100 .188 .131 .142 .147 .217* .323** Moderate PA (min.day-1) -.043 -.413* .415* .182 .055 .094 .186 .152 .109 -.011 .154 .050 .087 .050 .076 .182 Vigorous PA (min.day-1) .038 -.245 .279 .037 .035 .038 .158 .002 .146 .060 .028 .190 .115 .134 .095 .286** MVPA (min.day1) -.024 -.376* .366* .109 .048 .072 .200 .135 .137 .002 .145 .114 .108 .091 .110 .251* Total PA (MET.min.day-1) .027 -.367* .387* .250 .108 .212 .317 .169 .097 -.029 .129 .129 .114 .107 .139 .286** Boys Total PA (min.day-1) -.054 -.100 .028 -.077 -.054 -.082 -.011 -.175 .24 .086 .050 .311* .478** .396** .346** .376** Motor Transportation (min.day-1) -.121 -.089 -.106 .146 .183 .16 .296 .152 .095 .071 .034 -.169 -.037 -.141 -.111 -.038 Walking (min.day-1) .041 -.051 .140 .099 -.008 .062 .105 -.164 .102 .022 -.020 .220 .345** .281* .256 .244 Moderate PA (min.day-1) -.109 -.181 .013 .060 -.079 .028 .109 .075 .237 .026 .132 .209 .321* .224 .182 .315* Vigorous PA (min.day-1) .037 .049 -.005 -.328* -.089 -.277 -.253 -.239 .257 .086 .178 .363** .428** .425** .397** .434** MVPA (min.day1) -.097 -.117 -.038 -.156 -.062 -.138 -.071 -.12 .313* .077 .179 .352** .489** .428** .375** .463** Total PA (MET.min.day-1) -.042 -.086 .026 -.146 -.05 -.131 -.065 -.185 .281* .104 .110 .334* .489** .420** .372** .418** Abbreviations: PA: Physical Activity; IPAQA: International Physical Activity Questionnaire for Adolescents; MVPA: Moderate to Vigorous Physical Activity; MET: Metabolic Equivalents.; * Correlation is significant at the 0.05 level (2-tailed); ** Correlation is significant at the 0.01 level (2-tailed) 86 Table 5. Tertiles classification percentage of agreement and Cohen’s Kappa statistics, between IPAQA and Accelerometer. % Agreement K P-value Total PA (MET.min.day-1 – Steps.day-1) 41.9 .128 .007 Total PA (MET.min.day-1 – Counts.min-1) 42.3 .135 .004 MVPA (min.day-1) 39.6 .094 .047 Abbreviations: IPAQA: International Physical Activity Questionnaire for Adolescents; PA: Physical Activity; MET: Metabolic Equivalents MVPA: Moderate to Vigorous Physical Activity. Discussion The present paper describes the translation and validation for the IPAQA in Portuguese adolescents. The original IPAQA was developed in 2008, in an international study including several countries, but not Portugal (Hagstromer et al., 2008). Although some Portuguese investigation has been carried out with adolescents using this questionnaire (Araújo-Soares et al., 2009; Lopes et al., 2013), its validation has not been performed in Portuguese adolescents yet. The authors and the translation experts felt no need to change the content and structure of the questionnaire, finding it suitable for the age range and culture. As with previous research on PA questionnaire validation, the accelerometers were used for concurrent validity (Boon et al., 2010; Guedes et al., 2005; Hagstromer et al., 2008; Lachat et al., 2008; Wong et al., 2006). This study has the advantage of using a GT3X+ model, which considers acceleration in 3 axes. By doing so it guarantees the measurement of PA in the three dimensions of space, making it more accurate for PA measurement in free living conditions (Plasqui et al., 2005). Data from the present study corroborate previous epidemiological studies (Baptista et al., 2012; Mota et al., 2007), where boys were significantly more active than girls and younger adolescents engaged significantly more time in MVPA than older adolescents, although IPAQA data failed to show these differences between sexes. In this study no results are presented regarding the comparison between total time reported by IPAQA and measured by the accelerometer, as authors believe 87 that the measures are not directly equivalent. One minute of IPAQA reported PA is not directly equivalent to one minute of measured PA. In fact this same conclusion was pointed out in studies with the adults version of IPAQ (CelisMorales et al., 2012). Additionally, the IQR also reveals that while IPAQA and accelerometer give information on physical activity, expressed in the same units, the two methods do have different scales. The present data are also in accordance with previous research that indicated that questionnaires overestimate time (Celis-Morales et al., 2012; Wong et al., 2006), particularly in the higher intensity levels (Guedes et al., 2005). Indeed, in this sample a systematic error of over-reporting seems to exist. However, a previous attempt to validate a Swedish version of IPAQA pointed out the amount of unreported time as the explanation for the non-validation of the questionnaire (Arvidsson et al., 2005). Thus, considering the systematic error on the activities duration, IPAQA should not be used to evaluate compliance with the guidelines, expressed as minutes per day in a specific intensity level. Similarly to this study, previous attempts to validate PA reporting methods for children and adolescents have consistently mentioned weak associations with accelerometer findings, especially if considering recall methods, and for light and moderate PA levels the associations tend to be particularly weak or inexistent (Guedes et al., 2005; Hagstromer et al., 2008; Lachat et al., 2008; Wong et al., 2006). Sports participation, consistent with MVPA intensity, seems to be consistent over time (every week on the same day, with the same duration), making it easier to report and estimate duration, which may explain the significant correlation coefficient for physical activity in this intensity level, reinforcing the results from previous studies (Guedes et al., 2005; Hagstromer et al., 2008; Lachat et al., 2008). Considering that boys and older adolescents engage more in sports activities, while younger adolescents have more moments of spontaneous movement and PA, this has been mentioned (Hagstromer et al., 2008) as a possible explanation for IPAQA having moderate correlation coefficients in the older adolescent boys group, but not in the other groups. 88 Difficulties in the full understanding of the concepts, as previously mentioned (Hagstromer et al., 2008), is another possible reason for the low or non-existent correlations. Studies about the cognitive development of children and adolescents referred to the difficulty of reporting time duration of a certain activity, although there is an improvement in time sensitivity throughout childhood (DroitVolet, 2013). Considering that IPAQA asked specifically about the duration of specific physical activities, this might be one of the reasons why poor correlations were found, specifically for the younger adolescents. This notion is reinforced by research that concluded positively about the effect of motion on time perception (Kroger-Costa et al., 2013). Also related to the IPAQA concepts misunderstanding, the authors believe that adolescents cannot successfully distinguish between moderate and vigorous PA when filling in a questionnaire, which may explain why older boys IPAQA moderate time had a significant correlation coefficient (ρ=0.321; P<0.05) with accelerometer vigorous PA, but no correlation with accelerometer moderate PA. Using a single category of moderate to vigorous physical activity (MVPA) may be useful to overcome this issue, and is in accordance with international guidelines for children and adolescents PA (USDHHS, 2008). The concurrent validity proved that the IPAQA questionnaire might be valid when used to determine PA for older adolescent boys (≥15 years), but not with younger adolescents. The same conclusion was reached by the original version (Hagstromer et al., 2008) and by other countries validation (Guedes et al., 2005). It is important to emphasize that although there was a weak correlation coefficient, the tertiles percentage of agreement showed that IPAQA could divide a sample in groups of PA, which is of particular interest to epidemiologists that frequently use categories of PA, instead of a quantitative approach. In addition, questionnaires are often a more practical data collection method than the high non-usage rates of instruments that require several day evaluations, such as accelerometers and pedometers (Audrey et al., 2012; Ottevaere, Huybrechts, De Meester, et al., 2011; Van Coevering et al., 2005). 89 Conclusions IPAQA may be used in adolescent boys 15 years of age and older, to assess PA in a daily life context. For studies with large samples and budget constraints with no accelerometers availability, IPAQA can be a valid method of data collection. However, the authors would suggest to researchers that choose to use IPAQA that instead of working with time per day in each PA level, creating tertiles of PA may be a better option. This method would enable them to distinguish between more and less active adolescents; or alternatively, it would also be valid to combine moderate and vigorous physical activities into one single category (MVPA). Nevertheless, at this point the authors strongly discourage the use of IPAQA to assess PA in girls or in adolescents under 15 years old. In fact, the authors suggest that, whenever possible and available, objective methods should be used to assess PA in all adolescents. These findings call into question the conclusions of previous studies that used this questionnaire with Portuguese adolescents of all ages and sexes, enhancing the importance of validated tools as the only way of obtaining correct conclusions. This study confirmed the validation of the first version of IPAQA for Portuguese older adolescent boys, nevertheless, further studies and additional efforts are needed to improve IPAQA and to make it valid for girls, younger adolescents and children. Sources of Funding: This work was supported by FCTPortuguese Foundation for Science and Technology for the PROTEC project grant SFRH/PROTEC/49529/2009, conceded to the first author and project grant FCOMP-01-0124-FEDER-028619 (Ref. 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[Technical aspects and relevance of physical activity assessment in children and adolescents in freeliving conditions]. Arch Pediatr, 19(11), 1219-1225. doi: 10.1016/j.arcped.2012.08.020 Wong, S. L., Leatherdale, S. T., & Manske, S. R. (2006). Reliability and validity of a school-based physical activity questionnaire. Med Sci Sports Exerc, 38(9), 1593-1600. doi: 10.1249/01.mss.0000227539.58916.35 100 Table 1. Sample Characteristics and GNKQA and PAKQ Scores Differences Between Groups GNKQA: General Nutrition Knowledge for Adolescents; PAKQ: Physical Activity Knowledge Questionnaire n (%) GNKQA PAKQ Total Score Percentage Score Percentage Mean (SD) Mean Rank p value Mean (SD) Mean Rank p value Sex Male 388 (52.9) 44.8 (11.39) 333.52 0.000 65.2 (14.53) 352.49 0.067 Female 346 (47.1) 48.1 (11.99) 397.80 67.1 (14.43) 380.89 School Grade 7th to 9th grade 285 (38.8) 44.6 (11.28) 328.11 0.000 63.7 (14.95) 329.53 0.000 10th to 12th grade 449 (61.2) 47.8 (12.00) 392.50 67.7 (14.00) 391.60 Family Income /month (euros) Less than 1001 112 (25.0) 45.5 (11.11) 203.32 0.117 64.8 (15.30) 205.07 0.222 From 1001 to 2000 213 (47.5) 48.0 (11.18) 231.95 66.4 (15.46) 225.87 From 2001 to 3000 83 (18.5) 48.6 (11.80) 234.13 68.9 (13.71) 247.17 From 3001 to 4000 28 (6.3) 49.9 (13.09) 248.16 66.4 (15.45) 232.84 More than 4001 12 (2.7) 42.3 (11.50) 168.08 66.7 (14.35) 205.33 BMI Age Centile Underweight 15 (2.0) 45.2 (13.50) 362.77 0,504 60.0 (17.72) 291.1 0.157 Normalweight 584 (79.6) 46.7 (11.87) 372.16 66.6 (14,47) 375.16 Overweight 98 (13.4) 46.4 (11.78) 358.45 65.5 (15.16) 351.2 Obese 37 (5.0) 44.2 (10.48) 319.77 64.4 (11.06) 320.72 Body Fat Percentage Underfat 7 (1.0) 42.2 (13.95) 314.50 0.910 75.4 (8.13) 526.14 0.158 Normal 501 (68.3) 46.6 (11.42) 368.05 66.1 (14.31) 370.14 Overfat 109 (14.9) 45.7 (13.60) 363.37 66.7 (16.60) 366.96 Obese 117 (15.9) 47.2 (11.66) 372.18 65.2 (13.37) 347.21 101 General Nutrition Knowledge Nutrition knowledge was assessed with a previously validated questionnaire for Portuguese adolescents 29. This questionnaire consists of four sections: Section 1 - Dietary recommendations (13 items); Section 2 - Sources of nutrients (73 items); Section 3 - Choosing everyday foods (9 items); and Section 4 - Diet-disease relationship (42 items). A total score of 137 points and 4 partial scores, one for each section, were calculated considering one point for each correct answer. For statistical analysis a percentage of correct answers was considered, for the total questionnaire and for each section. Physical Activity Knowledge A questionnaire specially created for this study was used to assess physical activity knowledge. For the development of the questionnaire age specific physical activity guidelines 30 were used. The questionnaire consisted of 10 questions: 7 True or False and 3 multiple choice. For each of the 10 questions a “Don’t Know” option was included, in order to reduce the non-response rate. Each corrected answer scored one point, for a total score of 10 points. For statistical analysis a percentage of correct answers was considered. Physical Activity Adolescents physical activity was assessed with Actigraph GT3X+ accelerometers, that were used during 7 consecutive days, according to previously suggested protocols 31. Adolescents and their parents received written information on how to use the accelerometer, before giving written consent. Additional oral information was given individually to each adolescent with the accelerometer delivering. Instructions considered the use for 7 sequential days, starting straightaway after awakening and until going to bed, with the exception of water activities (bath and swimming). The device was used with an elastic belt in the waist line, positioned in the anterior axillary line of the non-dominant side. The accelerometer was initialized, selecting the 3 second epoch. In the 8th day researchers collected the equipment. After usage, data were processed using Actilife (version 6.9, Actigraph, Florida), and reduced to one minute periods (epochs). Wear and non-wear time was determined in accordance to previous recommendations 32. Time periods with at 102 least 10 consecutive minutes of zero counts recorded were excluded from analysis assuming that the monitor was not used. A minimum record of 8hours/day (480-minutes/day) was set so that the data could be considered as valid. Participants were required to have used in accordance the accelerometer for a minimum of 3 days, in agreement with previous researches 33, 34. The outcome variables reflected time measured in minutes per day (min/day) spent in each of the following intensities: sedentary activity (0-100 Counts/min); light activity (101-2295 Counts/min); moderate activity (2296-4011 Counts/min) or vigorous activity (≥ 4012 Counts/min), according to the Evenson cut-points35, as previously suggested for studies with adolescents36. Moderate-to-vigorous physical activity (MVPA) was defined as the sum of moderate and vigorous activity. MVPA tertiles, adjusted for age and sex, were calculated. Adolescents were divided in 2 groups, one group classified as “high physical activity” (HPA), including adolescents in the 3rd tertile, and one group classified as “low physical activity” (LPA), including the 1st and 2nd tertiles. Four groups were created based on BFP and MVPA engagement: “high physical activity/ non-overfat”, “high physical activity/ overfat”, “low physical activity/ nonoverfat” and “low physical activity/ overfat”. Procedure In a classroom environment, with teachers’ collaboration, anthropometric measurements were performed, along with the filling of the knowledge two questionnaires, one to assess nutrition knowledge and another to assess physical activity knowledge. A set of sociodemographic questions were added, to assess age, sex, school grade and monthly family income. Data Analysis Descriptive analysis (frequencies, mean and standard deviation) were used for sample characterization and variables description. 103 Tertiles for MVPA were adjusted for age and sex, and used to define high and low physical activity groups. After testing for normal distribution, Kruskal-Wallis and Mann Whitney U tests were performed to compare knowledge scores between groups, according the levels of BFP and MVPA engagement, as previously defined. Data analysis was performed using IBM Statistical Package for Social Sciences, version 22 (SPSS Inc; Chicago, IL, USA). Statistical significance was set at P<.05. Results Most adolescents included in this study enrolled from the 10th to the 12th grade (61.2%), had a monthly family income under 2000 euros (72.5%), and were classified as normal weight, according to BMI (79.6%) and BFP (68.3%) (Table1). GNKQA percentage of correct answers was in average 46.5 (SD=11.82) %, and mean scores were significantly higher in females than males (48.1 versus 44.8, p<.001), and in adolescents attending the 10th through 12th grades (7th to 9th grade=44.6; 10th to 12th grade=47.8; p=.000). No significant differences were found between different categories of family income, BMI, and BFP (Table 1). No significant differences in the PAKQ percentage of correct answers were found between genders. Students following from 7th to the 9th grade scored significantly less than students from other grades (7th to 9th grade=63.7; 10th to 12th grade=67.7; p=.000). Family income, BMI, and BFP groups had no significant differences in the mean PAKQ score percentage. For the overall sample a mean score of 66.2 (SD=14.50) % was found. Adolescents engaged in MVPA in average for 47.9 (SD=27.49) minutes per day. When analysing the knowledge differences between MVPA groups, the HPA group scored significantly higher in the PAKQ (HPA=69.2; LPA=64.9; p=.044), but no significant differences were found for the total GNKQA, nor for its sections (Table 2). 104 Table 2. GNKQA and PAKQ Scores Differences Between Physical Activity and Body Fat Percentage Groups MVPA BFP MVPA and BFP Groups High physical activity Low physical activity Pvalue Non-overfat Overfat Pvalue High physical activity/ Non-overfat Low physical activity/ Non-overfat High physical activity/ Overfat Low physical activity/ Overfat Pvalue Mean SD Mean Rank Mean SD Mean Rank Mean SD Mean Rank Mean SD Mean Rank Mean SD Mean Rank Mean SD Mean Rank Mean SD Mean Rank Mean SD Mean Rank GNKQA Total (%) 47.5 12.94 113.0 46.8 11.55 110.8 .806 46.6 11.46 367.3 46.4 12.63 367.9 .971 45.1 13.40 102.2 47.6 10.29 113.5 51.9 10.99 133.0 44.9 13.90 104.7 .209 GNKQA S1 (%) 61.1 17.59 116.4 59.2 17.07 109.1 .418 59.9 16.56 376.0 57.6 17.34 348.5 .101 58.0 18.16 106.5 62.5 14.42 120.2 66.9 15.19 134.7 51.7 20.04 84.42 .003 GNKQA S2 (%) 47.3 14.30 115.9 46.0 12.24 109.3 .471 46.3 12.10 367.4 46.1 13.23 367.8 .979 44.7 15.24 104.5 46.7 11.40 111.9 52.2 11.07 137.0 44.5 13.93 103.6 .146 GNKQA S3 (%) 49.0 17.52 119.1 46.2 16.67 107.7 .206 46.3 16.69 369.2 46.3 17.59 363.7 .740 48.5 18.10 119.3 46.98 15.68 110.2 50.0 16.70 118.6 44.5 18.75 102.3 .552 GNKQA S4 (%) 43.2 18.03 109.5 44.4 16.43 112.5 .748 42.97 18.06 365.2 43.55 19.12 372.8 .652 41.1 19.07 103.9 44.7 15.58 112.8 47.1 15.57 120.0 43.5 18.32 111.8 .760 PAKQ (%) 69.2 13.97 123.6 64.9 15.22 105.5 .044 66.3 14.28 372.3 65.9 15.00 356.7 .353 67.7 14.09 118.9 64.7 14.63 105.7 71.9 13.57 132.4 65.3 16.63 104.9 .185 MVPA: Moderate to Vigorous Physical Activity; BFP: Body Fat Percentage; GNKQA: General Nutrition Knowledge for Adolescents; S1: Section 1; S2: Section2; S3: Section 3; S4: Section 4; PAKQ: Physical Activity Knowledge Questionnaire 105 Considering the knowledge differences between two groups created from BFP classification, no significant differences were found for PAKQ score, for the total and sections GNKQA scores (Table 2). Comparing the knowledge scores between the BFP/ MVPA four groups, significant differences were found only for the GNKQA 1st section, were the low physical activity/ overfat group scored significantly less than the other groups (p=.003). DISCUSSION According to our best information, this study described for the first time that poor knowledge was significantly associated with the simultaneous occurrence of overfat and low physical activity levels in adolescents, after a comparison of nutrition and physical activity knowledge between the four groups created based on BFP and MVPA engagement. Considering that significant differences were found only for the GNKQA section 1, about dietary recommendations, it seems that low physical activity/ overfat adolescents are significantly less aware of these recommendations, even after considering the adjustment effect of age and gender, and may be a target group for knowledge enhancement in obesity prevention programmes 20, 37. These results revealed that no differences in physical activity and nutrition knowledge were found between BFP overfat and non-overfat groups. Previous studies with adults 9, 12 and younger children 14 reported similar conclusions in what regards nutrition knowledge. Even though some contrary statements were made in studies with Belgian and Italian adults, that suggest a negative association between nutrition knowledge and BMI 13, 38, and with American children, being suggested that the obesity prevention efforts need to fight inaccurate information, and in particular fill the gap on the understanding of the benefits of engaging in physical activity and a healthy diet 39. The few studies that were previously performed with adolescents included nutrition knowledge alone, and suggest that it does not differ between obese and non-obese adolescents 11, 40. 106 This study showed that physical activity knowledge was significantly higher among adolescents in the “high physical activity” group. Although studies with physical activity knowledge are scarce, it was previously stated in studies with adults that a higher physical activity knowledge could be related to high levels of physical activity 41, 42. A conclusion that is aligned with the recommendations to increase physical activity in communities, that highlight the importance of informational approaches to increase physical activity, by incrementing knowledge about exercise and physical activity benefits 20. Additionally, data from the present study evidenced the low MVPA levels of Portuguese adolescents, far below the recommended levels 30, which may contribute to the obesity and overfat rates. Although the obesity prevalence found in the present study are under previous data with Portuguese adolescents 43, 44 when considering BMI, is still of concern to realize that more than 30% of the adolescents are classified as overfat/ obese by BFP criteria. These results reveal that adolescents have poor nutrition knowledge with an average of less than 50% of correct answers. Previous data from young adults45, adults 9, 38 and elderly 46 presented similar conclusions. Studies with adolescents have been performed in the USA 40, 47 and on Germany 11 and although comparisons with the present data are difficult, considering different questionnaires were used, the results revealed lack of knowledge in several aspects, as for example the recommendation for five fruits and vegetables per day 40, 47. Physical activity knowledge had a positive, but still low, mean score, and although physical activity knowledge has not been broadly studied some comparisons can be made. Results from Australian adults revealed good knowledge of physical activity recommendations 48, 49, but among Portuguese young adults less than 40% were aware about the physical activity role in the heart disease prevention50. As far as we acknowledge, adolescents’ physical activity knowledge was studied only in the USA, and results were disturbing, as only 27% identified experts’ recommendations 47. 107 It seems that, besides numerous nutrition and physical activity interventions have been made, efficacy of knowledge is far from the desirable, and other motives beside lack of knowledge may account for the adolescence obesity rates. Females scored significantly best for nutrition knowledge, as previously reported14, 51, 52, but not for physical activity knowledge, also a result in accordance with previously published data14. What can be reflecting the typical gender interests differences, as usually females are more interested in the dietary subject, and health in general 53, mainly due to weight management issues54. Demographic data also revealed that nutrition and physical activity knowledge improve with school grade. What can be related to the age increase, as previous studies also stated a positive significant correlation between age and health related knowledge 51, 52, 55. This trend can be related to health and diseases experiences, the fact that younger adolescents did not live a negative consequence of unhealthy behaviors can cause a sense of immunity, and therefore a lack of interest on health related subjects 56. In addition adolescence presents as a period of life where the high risk behaviors become more frequent3. Limitations Physical activity knowledge questionnaires are scarce, and often incomplete, no validated questionnaire for Portuguese adolescents was found, and one was specially designed for this study. The absence of validity and reliability information of this questionnaire has to be assumed as a limitation for this work. It seems important to perform a validation study in the future for this instrument. The reduced number of accelerometers available did not allowed to have accelerometer data for all 734 adolescents, which was a limitation, however valid accelerometer information was collected for 222 adolescents. We also considered as a limitation not considering nutritional intake in this study. Conclusions In conclusion, knowledge seems to play a role in body fat and physical activity in adolescence. HPA adolescents have higher PAKQ scores, and “low physical 108 activity/ overfat” adolescents score the worst in the experts’ recommendations section. IMPLICATIONS FOR SCHOOL HEALTH Considering that low physical activity/ overfat adolescents scored significantly less on the knowledge on experts’ nutritional recommendations, and the knowledge differences between MVPA groups revealed that the most active have greater PAKQ scores, knowledge may be a target variable to prevent excessive body fat in low physical active adolescents, and to improve MVPA engagement. Other health determinants, like preferences and availability, may play a stronger role for dietary and physical activity behaviors 57, 58, but as previously suggested by other authors 20, 37, 59, it is our believe that enhancing health curricula to devote adequate attention to physical activity enhancement and promoting nutrition and energy balance awareness and knowledge, although not sufficient as a single measure, is essential to reduce overfat and obesity particularly among the less active adolescents. Furthermore, Portuguese schools should include in physical education classes also a health component, addressing the physical activity recommendations and health advantages 30. The implementation of the previous recommended 30 minutes of moderate to vigorous physical activity during the school day 59, should be considered as a national health and education policy. These outcomes should be used by education and public health professionals, along with planning nutrition and physical activity informative interventions, aiming knowledge enhancement, activities should be included to make every day choices coherent with experts’ recommendations, as well as presenting healthy food and physical activity feasible and attractive for adolescents. Human Subjects Approval Statement This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were approved by the Research Centre in Physical Activity, Health and Leisure Scientific Committee. 109 Acknowledgements Sources of Funding: This work was supported by FCTPortuguese Foundation for Science and Technology for the PROTEC project grant SFRH/PROTEC/49529/2009, conceded to the first author and project grant FCOMP-01-0124-FEDER-028619 (Ref. FCT: PTDC/DTP-DES/1328/2012), and Research Center supported by PEst-OE/SAU/UI0617/2011. References [1] Astrup A. Healthy lifestyles in Europe: prevention of obesity and type II diabetes by diet and physical activity. Public Health Nutr. 2001; 4(2b): 499-515. [2] Wyatt SB, Winters KP, Dubbert PM. Overweight and obesity: prevalence, consequences, and causes of a growing public health problem. The American journal of the medical sciences. 2006; 331(4): 166-174. [3] Alberga AS, Sigal RJ, Goldfield G, Homme DP, Kenny GP. Overweight and obese teenagers: why is adolescence a critical period? Pediatric Obesity. 2012; 7(4): 261-273. [4] de Vet E, de Ridder DTD, de Wit JBF. Environmental correlates of physical activity and dietary behaviours among young people: a systematic review of reviews. Obesity Reviews. 2011; 12(501): e130-e142. [5] Peterson KE, Fox MK. Addressing the epidemic of childhood obesity through school-based interventions: What has been done and where do we go from here? J Law Med Ethics. 2007; 35(1): 113-+. [6] Larson N, Story M. The adolescent obesity epidemic: why, how long, and what to do about it. Adolescent medicine: state of the art reviews. 2008; 19(3): 357-379, vii. [7] Bandura A. Health promotion by social cognitive means. Health education & behavior : the official publication of the Society for Public Health Education. 2004; 31(2): 143-164. [8] Morrow JR, Jr., Krzewinski-Malone JA, Jackson AW, Bungum TJ, FitzGerald SJ. American adults' knowledge of exercise recommendations. Res Q Exerc Sport. 2004; 75(3): 231-237. 117 Nutritional and physical activity knowledge is not associated with practices in adolescents Vera Ferro-Lebres; Gustavo Silva, Pedro Moreira; José Carlos Ribeiro Abstract Objective: To study the correlation between nutrition and physical activity, knowledge and practices, among adolescents. Design: It was designed a cross-sectional study with adolescents. Sociodemographic characteristics, and nutrition and physical activity knowledge were assessed using questionnaires. Anthropometry was objectively measured. Nutritional and dietary intake was assessed with a three-day food diary. Physical activity was evaluated with Actigraph GT3X+ accelerometers. Setting: North of Portugal. Subjects: A sample of 734 adolescents, aged 11 to 19, was involved in the study. Food diaries were valid for 291 adolescents and it was only possible to collect accelerometer information for 222 subjects. Results: Overall adolescents revealed poor nutritional knowledge (Mean=46.5%; SD=11.82%), low engagement in moderate to vigorous physical activity (Mean=47.9; SD=27.49 min/day) and a diet with low nutritional adequacy (Mean=57.4%; SD=10.24%). Although nutrition knowledge seems to be greater in female adolescents (p<0.001) and in students above the 10th grade (p<0.05), male adolescents reveal better values regarding nutritional adequacy of diets (p<0.001) and physical activity engagement (p<0.001), as do students below the 10th grade (p<0.001). No significant correlation was found between knowledge and practices regarding nutrition (ρ=-0.03; p=0.636)and physical activity (ρ=0.12; p=0.083). Conclusions: Concerning values of nutrition knowledge, diets nutritional adequacy and physical activity engagement highlight the urgent need of efficient health behaviour interventions. Public health professionals should be made aware of these results that reinforce the concept that, although necessary, 118 knowledge seems to be insufficient to produce adequate nutrition and physical activity behaviours in adolescents. KEYWORDS Nutritional Knowledge, Physical activity knowledge, Adolescents Introduction Obesity(1), type 2 diabetes(2) and cardiovascular diseases(3) are major health problems affecting adolescents and are well recognized as key public health issues. Diet and physical activity behaviours are strategic modifiable factors that can contribute to reduce the burden of these diseases(4). Several epidemiological studies proved adolescents’ diet is nutritionally poor(57). Studies with Portuguese adolescents revealed inadequate intakes of calcium, vitamin E, folate, molybdenum and fibre, while the intakes of total fat, saturated fat and sugars were far above the recommended values(8), suggesting that diet is as a major risk behaviour. In accordance, adolescent physical activity practices are far from achieving the recommended duration and intensity in developed countries(9, 10), particularly in Portugal, where only 36% of the subjects aged 10 to 11 accomplished 60 minutes of moderate to vigorous physical activity, in the 16-17 age group the percentage is even worse, with only 4% engaging in such levels of physical activity(11). Health literacy, namely physical activity and nutrition knowledge, has been associated with healthier behaviours. However one or more aspects of health literacy seem not to be sufficient for several population groups(12-14), suggesting the need for multidisciplinary approaches in health interventions. Reinforcing this idea an association between nutrition and physical activity knowledge was found(15). There is no consensus regarding the relation between nutritional knowledge and practices, and few studies have been performed in adolescents. Although some authors reported no association between these variables(12, 16-18), a recent 119 review, including only studies with adults, referred a positive but weak association between nutrition knowledge and dietary intake(19). A greater nutrition knowledge seems to be associated with better food preferences(15), and with consumptions of fruit and vegetables(20, 21), dairy products(20), starchy food(20), and fish(20). However poor nutrition knowledge of American adolescents from Florida has been reported, with only 17.8% identifying the correct recommendation for daily fruit and vegetable consumption(14). To the best of the current knowledge, no studies with Portuguese adolescents were ever conducted assessing nutrition knowledge. Physical activity knowledge seems to be related to physical activity preferences(15), but no consensus exists in its association with practice(16, 22, 23). It is a matter of concern to realize that only 27% of American adolescents identified the experts’ recommendation for physical activity(14). No studies about Portuguese adolescent physical activity knowledge were ever performed, but a study with Portuguese university students assessed the awareness of the role of physical activity in heart disease and revealed that less than 40% knew about this association(13). According to social cognitive theory, knowledge seems to be an essential precursor in the process of behaviour change (24), although some authors suggest it’s not the only correlate, and therefore not efficient as a single factor(12, 22, 25). In addition, several health interventions that improved knowledge, successfully improved health behaviours in parallel(26-31). Understanding diet and physical activity, including its individual variability, as well as recognizing the knowledge adolescents have about experts’ recommendations and advices, seem to be a crucial step while planning and developing health promotion interventions. To date, little is known about the correlation between knowledge and practice in adolescents’ nutrition and physical activity. As a matter of fact, no studies have ever been performed with Portuguese adolescents analysing the overall relation between nutrition and physical activity, concerning knowledge and behaviours. 120 This study aims at describing Portuguese adolescents’ nutrition and physical activity knowledge, as well as their diet and physical activity behaviours, and analysing the association between knowledge and behaviours. Experimental Methods Design and Participants A cross sectional study was designed, involving a sample of 734 adolescents. A subsample of 346 individuals filled a three-day food diary, and a subsample of 222 individuals wore an accelerometer for physical activity assessment. The overall sample had a mean age of 15.8 (SD=1.87), and included 52.9% girls. Table 1 resumes the sample characteristics. Socio-demographic characteristics Adolescents completed a questionnaire asking age, sex, school grade, parents’ educational level and monthly family income. They were instructed not to answer when they were not absolutely certain. Anthropometric measurements Height was measured using a SECA 217 portable stadiometer, with a 0.1 cm precision. Weight was assessed with 100g precision, using TANITA BC-545 body composition analyser. This equipment was also used to determine body fat percentage. Body mass index (BMI) was calculated and categorized according to Centers for Disease Control and Prevention (2000)(32). McCarthy (2006) criteria were used to classify groups of normal to obese subjects, considering body fat percentage(33). The minimum perimeter between the iliac crest and the rib cage corresponded to the waist circumference, which was categorized according to Taylor (2000)(34). A non-elastic tape was used for waist circumference measurements. For all anthropometric assessments adolescents wore light cloths. General Nutrition Knowledge Nutrition Knowledge was assessed with a previously validated questionnaire for Portuguese adolescents, the General Nutrition Knowledge Questionnaire for Adolescents (GNKQA)(35). This questionnaire has 137 items, distributed through 121 Table 1. Sample and subsamples Characteristics MVPA, Moderate to vigorous physical activity † 276 missing values; ‡ 272 missing values; § 286 missing values Total Sample Food Diary Subsample Accelerometer Subsample n=734 n=291 n=222 n % n % n % Sex Female 388 52.9 171 58.8 123 55.4 Male 346 47.1 120 41.2 99 44.6 School Grade 7th to 9th grade 285 38.8 157 45.4 89 40.1 10th to 12th grade 449 61.2 189 54.6 133 59.9 Mothers’ Education Level† Less than 5th grade 23 5.0 10 4.0 5 4.1 5th to 9th grade 118 25.8 64 25.5 38 31.1 10th to 12th grade 123 26.9 63 25.1 25 20.5 More than 12th grade 194 42.4 114 45.4 54 44.3 Fathers’ Education Level‡ Less than 5th grade 56 12.1 32 12.6 19 15.2 5th to 9th grade 139 30.1 75 29.6 42 33.6 10th to 12th grade 112 24.2 64 25.3 26 20.8 More than 12th grade 155 33.5 82 32.4 38 30.4 Family Income /month (euros)§ Less than 1001 112 25.0 71 28.3 41 34.5 From 1001 to 2000 213 47.5 111 44.2 53 44.5 From 2001 to 3000 83 18.5 48 19.1 17 14.3 From 3001 to 4000 28 6.3 15 6.0 6 5.0 More than 4000 12 2.7 6 2.4 2 1.7 BMI Age Centile Underweight 15 2.0 5 1.7 4 1.8 Normalweight 584 79.6 226 77.7 171 77.0 Overweight 98 13.4 50 17.2 36 16.2 Obese 37 5.0 10 3.4 11 5.0 Waist Circumference Normal 615 83.8 238 81.8 184 82.9 Risk 119 16.2 53 18.2 38 17.1 Body Fat Percentage Under 7 1.0 1 0.3 2 0.9 Normal 501 68.3 187 64.3 148 66.7 Overfat 109 14.9 57 19.6 34 15.3 Obese 117 15.9 46 15.8 38 17.1 Nutritional Adequacy < 50% 40 13.7 ≥ 50% 251 86.3 MVPA < 60 min/day 156 70.3 ≥ 60 min/day 66 29.7 122 four sections: Section 1 - Dietary recommendations (13 items); Section 2 - Sources of nutrients (73 items); Section 3 - Choosing everyday foods (9 items); and Section 4 - Diet-disease relationship (42 items). Each correct answer scores one point, and 5 different scores are considered, a total 137 points score, and a score for each section(35). For statistical analysis a percentage of correct answers was calculated, per section and for the total GNKQA. Physical Activity Knowledge The Physical Activity Knowledge Questionnaire (PAKQ) was specially created for this study. For the development of the questionnaire the Global Recommendations on Physical Activity for Health(36) were used and 10 questions created, 7 True or False and 3 multiple choice. For each of the 10 questions a “Don’t Know” option was included, in order to reduce the nonresponse rate. Each correct answer was scored one point, for a total score of 10 points. For statistical analysis a percentage of correct answers was calculated. Nutrition and Dietary Intake The adolescents’ diet was evaluated with a three-day food diary, including two week days and one weekend day. Verbal and written instructions were given to each participant, together with an age appropriate example. Adolescents were asked to report every food or beverage, as detailed as possible, including cooking methods, type, brand, and portion sizes. Food diaries of 346 adolescents were correctly filled-in and were analyzed, by a team of dietitians/ nutritionists. An interpretation and quantification manual was specially developed for this study, for bias reduction. This manual included all the food items and household measures mentioned by adolescents in the food diaries, and its objective correspondence for nutritional calculations. The analysis of the food diaries was done in order to obtain information regarding energy and nutrients intake, number of eating episodes and food portions consumed. The Portuguese food composition table(37) was used for the calculation of the week mean energy and nutrients intake. Usual intake was estimated using the National Research Council/ Institute of Medicine method, as described by Dodd, reducing the within and betweenperson variations(38). 123 Misreporters were identified using the Goldberg cut-off method(39), as adapted by Black(40). In short, basal metabolic rate (BMR) was estimated by sex and age specific Schofield equations(41). A ratio between the energy intake (EI) and the BMR was compared to the 95% confidence cut-offs. The cut-offs were calculated using sample specific values for: physical activity level (PAL), within-subject coefficient of variation in energy intake, betweensubject variation in physical activity and number of days of diet assessment. A figure value of 8.5% was used for the coefficient of variation of repeated BMR measurements, as previously suggested(40). Adolescents with EI: BMR below 0.844 and over 2.610 were excluded from the statistical analysis, as a result a final sample of 291 adolescents was used. Nutritional adequacy was assessed using the Estimated Average Requirement (EAR) cut-off method(42, 43), for vitamin A, vitamin C, vitamin E, thiamin, riboflavin, niacin, vitamin B6, folate, vitamin B12, iron, magnesium, phosphorus, zinc, vitamin D and calcium(43, 44). The tolerable upper intake level was used to set adequate intake for sodium(43). For protein, carbohydrates, simple sugars, fat and linoleic acid the acceptable macronutrient distribution ranges (AMDR) were assumed(43). The maximum energy percentage from saturated fatty acids recommended by the American Heart Association was considered for this nutrient(45). A nutritional adequacy score was calculated including all 22 nutrients, each nutrient with adequate intake scored one point, and inadequate intakes scored 0 points. For statistical analysis a nutritional adequacy percentage was used. The number of eating episodes was measured, considering as an eating episode any eating occasion when food or drink was consumed, also including drinks (i.e. soft drinks or coffee) consumed in the absence of food. It was also considered a minimum time of 15 minutes between eating episodes, hence two eating episodes occurring within 15 minute or less period counted as a single episode(46). Portuguese healthy eating recommendations(47) and the Portuguese standard serving portions, were considered for food portions counting. Physical Activity 124 Adolescent physical activity was assessed with Actigraph GT3X+ accelerometers, that were used during 7 consecutive days, according to previously suggested protocols(48). Students and parents received written information on how to use the accelerometer, before giving written consent. Additional oral information was given individually to each adolescent. On the 8th day the accelerometers were collected. The accelerometer was used with an elastic belt in the waistline, positioned in the anterior axillary line of the non-dominant side. The device was initialized, selecting a 3-second epoch. Adolescents received both verbal and written information on how to use the accelerometer, based on previous researches(48); instructions were given considering the use during 7 consecutive days, starting immediately after waking up and until going to bed, except for water activities (bath and swimming). After usage, accelerometer data was processed using Actilife (version 6.9, Actigraph, Florida), data was reduced to one-minute periods (epochs), and wear and non-wear time was determined in accordance to previous recommendations(49). Time periods with at least 90 consecutive minutes of zero counts recorded were excluded from analysis assuming that the monitor was not worn. A minimum recording of 8-hours/day (480-minutes/day) was the criteria to accept daily physical activity data as valid. Participants were required to have a minimum recorded data for 3 days, a criterion in accordance with previous researches(50, 51). The outcome variables were time (min/day) spent in each of the following categories: sedentary activity (0-100 Counts/min); light activity (101-2295 Counts/min); moderate activity (2296-4011 Counts/min) or vigorous activity (≥ 4012 Counts/min), according to the Evenson cut-points(52), as previously suggested for studies with adolescents(53). Moderate-to-vigorous physical activity (MVPA) was defined as the sum of moderate and vigorous activity. The Trost (1998) formula was used to calculate total energy expenditure from the accelerometer data(54). PAL used for the Goldberg equation was obtained from the ratio between total energy expenditure and resting energy expenditure. Ethical Considerations 125 This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human participants were approved by the Research Centre in Physical Activity, Health and Leisure Scientific Committee. Written informed consent was obtained from all the parents or legal tutors. The schools directors involved gave their ethical approval. Adolescents were given the opportunity to refuse participation. Statistical Analysis Descriptive analysis (frequencies, mean, standard deviation and percentiles) was used for sample characterization and for description of the knowledge scores, physical activity engagement and nutritional and dietary intake. Adjusted quartiles were calculated for GNKQA and PAKQ scores. After testing for normal distribution, Kruskal-Wallis and Mann Whitney U tests were performed to compare knowledge scores, physical activity and nutritional adequacy between groups. Non-parametric adjusted partial correlation was calculated to test the association between nutritional and physical activity, knowledge and practices. Data analysis was performed using IBM Statistical Package for Social Sciences, version 22 (SPSS Inc; Chicago, IL, USA). Statistical significance was set at P<0.05. Results The overall sample consisted of 734 adolescents, with a mean age of 15.8 (SD=1.87), and included 388 (52.9%) female and 449 (61.2%) attending grade levels 10th through 12th. The parents’ educational level was predominantly above the 10th grade (43.2% of the mothers and 36.4% of the fathers) and 325 (44.3%) adolescents had a family income under 2000 euros per month. BMI Age Centile revealed that 584 (79.6%) had normal weight for height, age and gender; waist circumference results showed 615 (83.8%) with normal values and body fat percentage was normal in 501 (68.3%) of adolescents (Table 1). The column proportions z-test revealed that the individuals returning a complete food diary (n=346) differed significantly (p<0.05) from the ones who did not: a