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  • The Ethical Committee of the Institute of Cardiology in Warsaw approved the study protocol. Informed written consent was obtained from each participant. Data were made anonymous before analysis. Study protocol and sample selection: For the purpose of this report we analyzed datasets taken from two nationwide representative cross-sectional surveys: WOBASZ carried out in the years 2003–2005 and WOBASZ II carried out in 2013–2014. The methodology of the surveys has been kept as similar as possible in order to enable a relevant monitoring of trends in collected data. A detailed description of the WOBASZ survey has already been published in previous papers [12–14]. For both surveys, an independent random sample was drawn from the national population register (adults aged 20–74 years in WOBASZ and adults aged ≥ 20 years in WOBASZ II). However, due to organizational and financial constraints, the random sample size of the WOBASZ II survey was smaller as compared with the previous survey (15 120 vs 19 200 individuals). The two stages sampling scheme covered the whole territory of Poland and was stratified according to province and commune type. In each province of Poland 6 areas were selected (two rural, two small urban and two large urban areas) and next, in each area, samples of 70 men and 70 women aged 20 years and above years were selected using personal identification number. Personal invitations to participation in the study were sent to all chosen individuals. A total of 13.563 personal invitations were sent by post in the WOBASZ II survey. Both WOBASZ surveys enrolled a total of 20 939 adults (14 769 participants in the WOBASZ and 6 170 participants in the WOBASZ II). The response rates were about 76% (WOBASZ) and 45.5% (WOBASZ II). Due to a relatively low response rate in the latest survey, we assessed the similarity index according to the distribution of age and educational level using current nationwide sociodemographic data for Polish adults [15] and the data gathered within the WOBASZ II survey. According to the Central Statistical Office, the proportion of young adults (18–34 years) and persons with elementary education decreased while the proportions of seniors (above 64 years) and persons with university education increased in Poland in the years 2002–2013 [16]. The same trend was found in the distribution of these variables among the participants of our surveys. The similarity index was 91.2% for age and 88.6% for educational level between the Central Statistical Office data and the WOBASZ II sample. As the age range of the participants in the first edition of WOBASZ Study was 20–74 years, the subjects aged above 74 years old were excluded from WOBASZ II database for the purpose of this analysis. After excluding subjects with incomplete data on the required questions, the final sample comprised 8545 men (5943 in WOBASZ and 2602 in WOBASZ II) and 9755 women (6609 in WOBASZ and 3146 in WOBASZ II) aged 20–74 years and above. All procedures were carried out by nurses and trained interviewers in the participants’ houses or in selected out-patient clinics and were comparable in both WOBASZ surveys. The methodology has closely followed the WHO MONICA protocol [17], and consisted of the following parts: a questionnaire interview, blood pressure, heart rate and anthropometric measurements, and a blood sample collection. The WOBASZ questionnaire included detailed questions on medical history, sociodemographic and economic factors, health knowledge, attitudes, lifestyle, nutrition, social support and depression. All employed interviewers were trained in the application, completion and codification of the questionnaire. Fieldwork supervisors conducted controls in the selected samples of interviewers. In the present analysis the following sociodemographic measures were taken into account: age, residential status, educational level, marital status, smoking. The participants were divided into the three categories of place of residence according to the number of inhabitants in their living area (“rural” area < 8 000 inhabitants; small urban area 8 000–40 000 inhabitants; large urban area >40 000 inhabitants). Educational level was categorized as elementary (no education/primary school), secondary (high school vocational/ incomplete high school/high school/vocational higher than high school) and university attainment (incomplete university/complete university education). All the analysed socio-demographic factors were thought as factors related to PA level of and therefore called “contributing factors”. Assessment of leisure time, occupational and commuting physical activity: Physical activity assessment was based on the WHO MONICA protocol and CINDI Health Monitor Questionnaire [17]. Similar set of questions was used in previous studies carried out in Polish population [11,13,18]. Self-reported data on PA were assessed in three domains: leisure-time, commuting (transportation) and occupational (work-related) physical activity. Physical activity in leisure time was defined as regularly doing physical exercises accumulating at least 30 minutes per day. The participants were asked: “Do you regularly do physical exercises (for ex. running, walking, swimming, cycling, gymnastics, gardening etc.) for at least 30 minutes per day?” The possible answers were: “yes” or”no”. Those who answered “yes” were asked: “How often are you physically active?” There were six possible answers: “everyday”, “4–6 days per week”, “Every second or every third day per week”, “once a week”, “two or three times per month”, “once a month or less frequent”. Individuals who did not declare doing any physical exercises in their leisure time were defined as “physically inactive” and asked about the reasons of sedentary lifestyle. Occupationally active respondents were asked: “What is the kind of your work?”. There were three possible answers: “mainly sitting or standing work (more than a half of time spent on sitting/standing), “mainly heavy physical work (more than a half of time spent on heavy physical work)” “other, not defined by the above answers”. Therefore occupational PA was assessed according to the following three categories: “low” (mostly sitting or standing office work), “moderate” (light/moderate physical work) and “high” (heavy manual work). Commuting PA was measured by asking the participants whether they walked, rode a bicycle, or used motorized transportation to/from work. The daily commuting return journey was categorized into four possibilities: using motorised transportation; walking/bicycling 1–14 min; 15–29 min or ≥ 30 min. The following questions were used in the questionnaire: “How do you usually travel to and from your work/school? There were three possible answers: “By means of public transport”, “by car”, “walking or bicycling”. Those who declare active commuting were asked: “How much tim How much time do you spent walking or cycling to and from your work/school per day?” There were four answers: “less than 15 minutes “, 15–30 minutes”, “31–60 minutes”, “more than 60 minutes”. To compare the frequency and assess statistical significance of the categories of qualitative characteristics in the analysed groups the chi-square test was implemented. Additionally to p-values, omega-squared formula (ω2) was implemented as an effect size. Given that the potential correlates might differ between genders, all the analyses were performed separately for men and women. In order to eliminate the potential influence of age in the calculations, a direct standardization was implemented, following the Polish population structure as of 31.12.2013 [14]. In order to identify socio-demographic factors (age, place of residence, educational level, marital level) that can contribute to physical inactivity, logistic regression analysis was performed. The results were shown as odds ratios (OR) with 95% confidence intervals (CI) for being inactive in leisure time. Individuals aged <35 years, residents of rural settings, with university education, single and not smoking were used as a reference, being assigned an OR value of 1.00. The multivariate logistic regression analyses were adjusted for age, education, place of residence, smoking and other domains of PA. All p values were two-sided and p<0.05 was set as statistically significant. Statistical analyses were performed using STATISTICA Windows XP version 12. Data used in this analysis are available at S1 File.
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