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Changes in lifestyle and self-rated health among high school students: A prospective analysis of the Saúde na Boa project

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Changes in lifestyle and self-rated health among high school students: A prospective analysis of the Saúde na Boa project
  DOI: http://dx.doi.org/10.5007/1980󰀭0037.2014v16s1p55 srcinal article Licence Creative Commom   CC BY RBCDH 1 Federal University of Santa Cata-rina, Graduate Program of Physical Education, Florianopolis, Santa Catarina, Brazil. 2 Pernambuco University, Gradua-te Program of Physical Education, Recife, Pernambuco, Brazil Received: 11 January 2014Accepted: 22 March 2014 Changes in lifestyle and self-rated health among high school students: A prospective analysis of the “Saúde na Boa” project  Mudanças no estilo de vida e na percepção da saúde em estudantes do ensino médio: análise prospectiva do  projeto “Saúde na Boa”  Valter Cordeiro Barbosa Filho 1 Kelly Samara Silva 1 Cassiano Ricardo Rech 1 Anísio Luiz Silva Brito 2 Elusa Santina Antunes de Oliveira 2 Markus Vinicius Nahas 1 Abstract  – Lifestyle characteristics can modify the self-rated health of young people, but additional prospective evidence is needed. 󰀀is study examined the association be-tween changes in lifestyle and self-rated health among students. A secondary analysis of the “Saúde na Boa” project was performed, considering data from 984 students (14-24 years old, 56.9% girls) who were randomly selected from 20 public schools in Recife and Florianopolis, Brazil. Two sets of data 9-months apart were collected, and self-reported data about lifestyle characteristics (physical activity practices, TV watching time, dietary habits, alcohol and tobacco consumption, and sleep time) and self-rated health (poor, fair, good, very good and excellent) were obtained. Differences in self-rated health between collections were categorized as negative changes, stable (no changes) or positive changes. Adjusted multinomial logistic regression analysis was used (p < 0.05). Aer adjustment for confounding variables, increasing the weekly frequency of active commuting to school (adjusted odds ratio [aOR] = 2.06) and intake of fruits/fruit juice (aOR = 1.81), as well as reducing the monthly frequency of alcohol consumption (aOR = 2.17), was significantly associated with positive changes in self-rated health. Consumption of sweets was also associated with stable self-rated health. In conclusion, our prospective evidence demon-strated that changes in lifestyle characteristics appear to be essential to ensure or generate positive self-rated health in youth. Key words : Eating habits; Health status; Motor activity; Prospective studies; Young. Resumo   – O estilo de vida pode modificar a percepção da saúde em jovens, porém evidências  prospectivas são necessárias. Este estudo analisou a associação entre mudanças no estilo de vida e na percepção da saúde em estudantes. Análise secundária do projeto “Saúde na Boa”, com uma amostra de 984 estudantes (14 a 24 anos, 56,9% de meninas), selecionados aleatoriamente, em 20 escolas públicas de Recife e Florianópolis, Brasil. Duas coletas foram realizadas com nove meses de diferença para obter “self-reported” dados do estilo de vida (prática de atividade física, tempo de TV, hábitos alimentares, consumo de álcool e tabaco, e duração do sono) e da percepção da saúde (ruim, regular, boa, muito boa e excelente).  A percepção da saúde foi categorizada em três níveis, considerando as alterações entre os intervalos das coletas: mudou negativamente, manteve ou mudou positivamente. A regressão logística multinominal ajustada foi utilizada, com p<0.05. Após ajuste para variáveis de confusão, aumentar a frequência semanal de deslocamento ativo para escola (odds ratio ajustado [ORa] = 2.06) e de consumo de frutas/suco de frutas (ORa = 1.81), bem como reduzir a frequência mensal de consumo de álcool (ORa = 2.17) estiveram significativamente associados à alterações positivas na percepção da saúde após os nove meses de acompanha-mento. O consumo de doces também mostrou associação com a manutenção na percepção da saúde. Em conclusão, evidências prospectivas demonstraram que mudanças do estilo de vida em diferentes componentes parecem ser fundamentais para garantir ou gerar uma  percepção positiva da saúde na juventude. Palavras-chave :  Atividade motora; Estado de saúde; Estudos prospectivos; Jovem; Hábitos alimentares.  56Lifestyle and self-rated health among students Barbosa Filho et al. INTRODUCTION  A relatively old concept from the World Health Organization (1946) emphasizes that health is “not merely the absence of disease but a state of complete physical, mental and social well-being” 1 . Based on this defini-tion, psychological measures such as self-rated health (i.e., self-assessment about a person’s own conditions and his/her current health status) have become objects of epidemiological studies as important as the identifica-tion of physiological diseases 2,3 . 󰀀ere is evidence indicating that self-rated health in youth 4  and adulthood 5,6  is inversely associated with morbidity and mortality. 󰀀us, building positive self-ratings of health in earlier years can be an important step for assuring the present and future health status of a population.By contrast, approximately 15% of the youth population of European countries report negative self-rated health 2 . In the United States, the esti-mate is approximately 10% 7 , which is similar to the rate observed in Brazil-ian adolescents 8 . 󰀀ere was a trend towards stabilization of self-rated health in the European youth population from 2006 to 2010 2 , but an increase occurred among the North American young 7 . Brazil, a middle-income country, experienced a slight increase in the proportion of young people with negative self-rated health from 2003 to 2008 (from 9.3% to 10.0%) 8 . 󰀀erefore, studying health perceptions and their potential determinants is crucial to help plan and target interventions for the modifiable factors that are associated with self-rated health in youth 3,9 . Lifestyle factors, such as physical activity  9-16 , watching TV 11 , eating habits 10,16 , substance abuse (e.g., alcohol and tobacco) 10,13,17  and sleep time 11 , have been associated with self-rated health among young people. However, most of this evidence has been obtained in cross-sectional studies, and they are limited in their ability to explore causal relationships between two  variables. Limited longitudinal evidence has focused on the relationship be-tween lifestyle and other psychological components (e.g., mental well-being and depression) 18-21  or has assessed only self-rated health in adulthood 12,15 . 󰀀erefore, a study evaluating prospective changes in lifestyle factors and their role in self-rated health among young people is needed for targeting interventions to promote health and well-being in young populations.󰀀us, the present study prospectively examined the association between changes in lifestyle factors (physical activity, watching TV, eating habits, alcohol and tobacco consumption and sleep time) and self-rated health in a sample of Brazilian adolescents. METHODOLOGICAL PROCEDURES 󰀀is study was a secondary and prospective analysis of data from a rand-omized-controlled intervention entitled the “Saúde na Boa” project 22 . 󰀀e purpose of this intervention was to promote healthy behaviors (primarily physical activity and healthy eating) among high school students who stud-  Rev Bras Cineantropom Desempenho Hum 2014, 16(Suppl. 1):55-6757 ied at night in public schools from two Brazilian cities: Florianopolis (Santa Catarina), southern Brazil, and Recife (Pernambuco), northeastern Brazil.Approximately two thousand youths were evaluated in March 2006 (aged 14-24 years) from 20 randomly selected schools (10 in each city, with 5 schools for the experimental group and 5 for the control group). 󰀀e selection was stratified by school size (small 200 students or less, medium 200-499 students, and large 500 students or more) and geographical loca-tion. Nine months aer baseline (December 2006), a new data collection wave was performed, with response rates of 45.9% (989 students assessed at follow-up). Detailed information about the characteristics of the cities involved, the target population and the sample selection procedure were described in a previous publication 22 . Additionally, the dropout sample’s demographic, socioeconomic and behavioral characteristics have been analyzed in another manuscript in this supplement 23 . For the present study, we considered students that had valid data for self-rated health, totaling a sample of 984 high school students. It was possible to detect a statistically significant odds ratio > 1.34 for watching TV (factor with higher exposure to positive change) and an odds ratio > 1.63 for tobacco consumption (fac-tor with lower exposure to positive change). For other lifestyle factors, the sample size could detect statistical significance for an odds ratio in this range. 󰀀e prevalence of outcomes (positive change in self-rated health) in unexposed groups was 25.2% and 24.4% for watching TV and tobacco consumption, respectively. A confidence interval (CI) of 95% and a power of 80% was fixed in this estimate.In March and December 2006, students answered the questionnaire the “Saúde na Boa” project, which was previously validated 24 . 󰀀e ques-tionnaire included close answer items on physical activity practices, eating habits and other lifestyle factors (e.g., alcohol and tobacco consumption and sleep duration) based on the PACE+ questionnaire (Patient-Centered Assessment and Counseling for Exercise Plus Nutrition) 24 . 󰀀e question-naire also included sections for personal and sociodemographic informa-tion, sedentary behaviors, body weight control and preventive behaviors.󰀀e instrument was applied in the classroom following previous in-structions. 󰀀e application of standardized collection protocols in both cities was conducted by a properly trained team consisting of students and teachers of Physical Education and Nutrition.Students answered the following question, “Overall, how would you rate your health?” Each student reported their health on a Likert scale with five points (“poor,” “fair,” “good,” “very good” or “excellent”). 󰀀e difference in responses between baseline and 9-month follow-up collection allowed for the generation of a score categorized as one of three levels: 1) negative change (e.g., from good to fair), 2) stable (not modified), or 3) positive change (e.g., from very good to excellent). Ten lifestyle factors were considered in this study. In the physical activity section, the weekly frequency (days/week) of days in which the students performed at least 60 minutes of physical activity was obtained.  58Lifestyle and self-rated health among students Barbosa Filho et al. 󰀀e weekly frequency (days/week) of active commuting (walking/biking) to school and exercise for muscular strength/endurance was also evaluated. We evaluated the daily duration of TV viewing (hours/day) as a sedentary behavior factor. In the eating habits section, we considered the weekly frequency (days/week) of the consumption of fruits/fruit juice and sweet/so drinks. We also investigated the monthly frequency (days/month) of tobacco and alcohol consumption and the daily sleep duration (hours/day). For each lifestyle component, we calculated the difference in responses be-tween baseline and 9-month follow-up. 󰀀e scores were grouped into three categories: decreased, stable or increased. We ordered the categories from the worst to the most favorable scenario for a healthy lifestyle outcome.Demographic and socioeconomic variables were included as potential confounds: gender (boys and girls), age group (14-16 years, 17-19 years and 20-24 years), skin color (white and non-white), marital status (single or otherwise), occupation (work, volunteer or not working), residence with family (yes or no) and type of property (house, apartment/other). Nutritional status at follow-up was determined by calculating body mass index (body weight [kg]/height² [m²]) and its classification according to gender and age 25 .Absolute and relative frequencies (with 95% CI) were used to describe the control variables, the lifestyle components, and self-rated health. We also showed the proportion of students in each lifestyle category and self-rated health change group. Multinomial logistic regression was used to calculate odds ratios for prospective changes in lifestyle and changes in self-rated health among students. A negative change in self-rated health was considered to be the outcome reference. For the exposure variables, the categories that represented the worst lifestyle changes (e.g., decrease in the frequency of weekly physical activity) were considered to be the exposure reference. Gender was not associated with changes in self-rated health and did not moderate the results when we performed these analyses using the entire sample. All analyses were adjusted for potential confounds (gender, age group, skin color, marital status, occupation, type of property, residence with family, nutritional status, situation in the intervention, city and self-rated health and lifestyle factor at baseline). All analyses were per-formed with Stata v. 11 (Stata Corp., College Station, TX, USA) considering a significance level of p < .05.All procedures were approved by the Ethics Committee of the Federal University of Santa Catarina (031/2005) and the Instituto Materno Infantil de Pernambuco  (587/2005). 󰀀e negative consent term (“passive parental consent form”) of the parents or guardians of students under 18 years was obtained, as well as from students with 18 or more years. RESULTS 󰀀e study sample was composed of students from Florianopolis (53.3%) and Recife (46.6%). For the “Saúde na Boa” project, 52.0% of students were from  Rev Bras Cineantropom Desempenho Hum 2014, 16(Suppl. 1):55-6759 intervention schools and 48.0% were from control schools. 󰀀e majority of the sample was female, aged 17-19 years, non-white, single, unemployed, living with his/her family and living in houses. Two out of ten students were overweight (Table 1). Table 1.  Baseline characteristics from the sample with valid data. (n=984). Variables † n% (95% CI)City (n=984)Florianopolis, SC52553.3 (50.2; 56.5)Recife, PE45946.6 (43.5; 49.8)Project condition (n=984)Intervention 51252.0 (48.9; 55.2)Control47248.0 (44.8; 51.1)Gender (n=984)Boys 39740.4 (37.3; 43.5)Girls58559.6 (56.5; 62.7)Age groups (n=984)14-16 years31331.8 (28.9; 34.7)17-19 years41141.8 (38.7; 44.8)20-24 years26026.4; 23.7; 29.2)Skin color (n=981)White43444.2 (41.1; 47.3)Non-white54755.8 (52.6; 58.9)Marital status (n=983)Singe81482.8 (80.4; 85.2)Married/others16917.2 (14.8; 19.5)Ocupation status (n=977)Did not word42943.9 (40.8; 47.0)Volunteer37438.3 (35.2; 41.3)Paid work17417.8 (15.4; 20.2)Lived with the family (n=977)Yes85287.2 (85.1; 89.3)No12512.8 (10.7; 14.9)Property of the family (n=979)House86087.8 (85.8; 89.9)Apartament/others11912.2 (10.1; 14.2)Nutritional status (n=959)Overweight 77080.3 (77.8; 82.8)Normal weight18919.7 (17.2; 22.2) †  Sample values in parentheses indicate the valid data for the respective variable.95% CI = 95% confidence interval 󰀀ere was overlap in the 95% CI between baseline and 9-month fol-low-up in the prevalence of students in each category of self-rated health (poor, fair, good, very good, excellent). However, approximately half of the students had changes in self-rated health aer 9 months. One in four students showed a positive change (e.g., from good to very good), while 23.3% of students showed a negative change in self-rated health (Figure 1).
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