This cross-sectional study is based on a comprehensive population-based study (MOPO), which aims to promote PA and prevent social marginalization among young conscription-aged men [15]. The civic duty or military service is compulsory for male citizens in Finland and conscription is organized every year concerning boys the year they turn 18. Conscription-aged men provide a large, population-based representative sample of Finnish young men.
Study population
All conscription-aged men who attended the conscription for military service in the Oulu area in 2010 (n = 997) were invited to the present study. The final number of those who agreed to participate was 616 (61.8 % of the population). The study protocol included a medical examination before the conscription, a questionnaire, and physiological measurements at the conscription.
The study was conducted according to the Declaration of Helsinki of 1964, as revised in 2000, and was approved by the Ethical Committee of Northern Ostrobothnia Hospital District (ETTM123/2009). The subjects had the right to refuse to participate or withdraw from the study without any effects on their future health care or military service. Written informed consent was obtained from all participants.
Questionnaire
The questionnaire included items related to socioeconomic situation, physical health and fitness, mental health, PA and time spent sitting, other health behaviors, and time spent on the Internet.
Socioeconomic situation was classified as full-time employee/student, part-time employee/student, or unemployed. Participants were asked to rate their fitness compared to coeval as significantly lower, somewhat lower, similar, somewhat higher, or significantly higher. They also rated their health as good, pretty good, moderate, pretty poor, or poor. In addition, ICD-10 diagnoses from the medical examination were recorded.
Depressive symptoms were evaluated with Raitasalo’s modification of the widely used Beck Depression Inventory (BDI), which has shown to have high validity compared to the unmodified BDI [16]. The four-item life satisfaction (LS) scale measured overall well-being (happiness, interest in life, feelings of loneliness and ease of living), and has shown to be closely related to many psychometric scales [17] and to be able to predict several health outcomes among adults [18, 19]. The participants were also asked whether they were able to discuss their problems with friends and family (1-5: never – mostly or always) and how often they spent time with friends (1 -5: almost never - almost every day).
PA was assessed by the following question: “Approximately, how much are you on the move per day (i.e., biking or walking to school or work, on breaks in school, household chores, or in hobbies and leisure time, etc.)?” The response alternatives were <1 h, 1–2 h, and >2 h. Daily leisure sitting time was also assessed with a question: “How much do you approximately sit per day outside school or work (for example, watching TV, reading, spending time on a computer, playing video games, and using Internet use)?” The respondents were categorized according to daily sitting time as sedentary (≥5 h/day), moderate (2.1–4.9 h/day) and non-sedentary group (≤2 h/day). Time spent on the Internet and frequency of playing Internet games were also separately asked.
Restrictions for PA were asked on a 5-point scale (1-5: not at all - very much) modified from Nigg et al. [20]. The restrictive factors were grouped by principal component analysis (PCA) as follows: lack of resources (i.e., sports equipment, sports facilities, appropriate group, exercise guidance, money, or poor public transport); lack of personal factors (i.e., interest, sports skills, appropriate sports type, knowledge of how to exercise, or laziness); lack of time or tiredness; and lack of health (i.e., illness or injury).
The motivational factors for PA were also rated on a 5-point scale (1-5: not at all - very important) [20] and classified into four categories: health promotion (i.e., enhancing health, mood or energy; enjoying the good feeling coming from exercising; relieving stress); fitness improvement (i.e., competing; enhancing muscle mass or physical fitness); social reasons (i.e., creating or maintaining social affairs; exercising because of the request of a family member or a friend; increasing the appreciation among friends); and appearance (i.e., enhancing exterior or sexual attraction; losing weight). Respondents were also asked whether physical education at school ignited a spark to exercise with a 5-point scale (1-5: strongly disagree - strongly agree).
Participants were asked about their current behavior pertaining to smoking and snuffing, and binge alcohol drinking was assessed by the following question: “How often do you drink alcohol six servings or more at once?” The men were asked whether they usually ate breakfast and the frequency of weekly intake of vegetables, fruits, and berries, as well as weekly intake of fast food and sweets. The study participants rated their dietary habits with the Finnish school grade scale from 4 (poor) to 10 (excellent).
Disordered eating behavior was assessed by the two subscales of the Eating Disorder Inventory (EDI) [21]. Both the Drive for thinness subscale as well as the Bulimia subscale consists of seven questions with a 6-point Likert scale. The questions concerned binge eating, compensation behavior, emotional eating, and losing/gaining weight. The point distribution – with the exception of the first question (inverse scoring) – was as follows: never, rarely or sometimes = 0 points; often = 1 point; usually = 2 points; always = 3 points.
Measurements
All participants went through a medical and physiological examination as part of the conscription process. Information on diseases, injuries, and use of medication were collected. Height (cm) was measured with 0.5 cm accuracy using a wall-mounted measuring tape. Waist circumference was measured to the nearest 1 cm with a plastic tape measure midway between the lowest rib and the iliac crest at the end of a gentle expiration while the study participant was standing legs apart. Body composition (weight with 0.1 kg accuracy, body mass index, percentage body fat) was measured by direct segmental multi-frequency bioelectrical impedance analysis (DSM-BIA) (InBody720, Biospace Co., Ltd., Seoul, Korea). During the measurement the subject was standing without shoes and socks and wearing light indoor clothing. The method has been validated against whole-body dual X-ray absorptiometry (DXA) [22]. BMI was calculated by dividing the weight (in kilograms) by the height squared (in meters).
The physical performance tests included a bilateral hand grip strength and aerobic fitness test. Grip strength was measured with a hand dynamometer (Saehan, SAEHAN Corporation, Korea). During the test, the subject was instructed to stand legs apart, with elbow at 90° angle, and to grip the instrument with maximum strength. The best result of the two attempts for each hand was recorded. The mean grip strength for the right and left hands was used in the analysis. Aerobic fitness was evaluated using a fitness test (Polar Fitness Test™, Polar Electro, Finland), which was conducted while the subject was resting comfortably for 5 min. The Polar Fitness Test™ predicts maximal oxygen uptake (mL∙min-1∙kg-1) from the resting heart rate, heart rate variability, gender, age, height, body weight, and self-assessed PA [23]. The Polar Fitness Test has been compared with an ergo-spirometry for measuring aerobic fitness with high correlation (0.96) and with high accuracy (mean error 6.5 %) [24]. The physical measures were piloted among preceding conscripts (a year earlier) and the results were congruent.
Statistical analysis
In the analyses, the sedentary group (leisure time sitting ≥5 h/day) was compared with the non-sedentary group (sitting ≤2 h/day). The statistical significance of the differences between these two groups was determined using cross-tabulation and chi-squared test for the categorical variables and the Student’s t-test for the continuous variables. The strength of the association between continuous variables was analyzed using the Pearson’s correlation coefficient. Restrictions and motivations for PA were grouped by PCA with Varimax rotation [25]. Using the Varimax rotation method minimizes the number of variables that have high loadings on each component and, as such, simplifies the interpretation of the components. Components (profiles) for sedentary and non-sedentary groups were also formed by PCA with the same method. The profiles were named by the nature of the variables loaded into each component. Furthermore, two criteria were tested: the Kaiser-Meyer-Olkin Measure of Adequacy (KMO), a measure of sampling adequacy (threshold: KMO >0.60) and Bartlett’s test of sphericity, which is used to test the null hypothesis that the variables in the population correlation matrix are uncorrelated (threshold: p < 0.05). Variables (n = 30) included in the PCA were chosen from 112 variables correlated with sedentary behavior. Selection was based on the maximal amount of variables that can be included in the analysis taking into account the sample size, and only one variable per phenomenon was included. Variables with factor loadings ≥ 0.4 were used to calculate factor scores for each of the factors. The number of components for both sedentary and non-sedentary groups was determined by eigenvalue > 1.5 and visual examination of the scree plots. Missing data in PCA were replaced with the means of the groups. Statistical significance was set at p < 0.05. The data were analyzed with PASW Statistics software (SPSS version 18, SPSS Inc.).
