Participants
Children (8–13 years) and adolescents (14–18 years) were recruited from 11 elementary and secondary schools. Schools with a specific focus on sport and schools for pupils with special educational needs were not included. Participants were recruited to participate on a voluntary basis via information flyers that were distributed through the school staff after the school management approved the research. The main inclusion criteria were participant age and good health condition. The participants whose parents reported medical complications that could affect PA and sleep were excluded from study. A total of 907 children and adolescents were enrolled in this study. Of all initial participants, 228 were excluded because they voluntarily withdrew from the study or became ill (n = 45), provided incomplete data (n = 129), their data could not be assessed due technical failures (n = 17), or did not meet accelerometer wear time criteria (n = 37). Hence, the final sample consisted of 355 children (44% boys) and 324 adolescents (43% boys). The detailed characteristics of the participants are shown in Table 1.
Table 1. Descriptive characteristics of children and adolescents
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Children
n = 355
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Adolescents
n = 324
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p-valueb
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Mean
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SD
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Min
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Max
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Mean
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SD
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Min
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Max
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Personal data
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Age (years)
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11.7
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1.6
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8.1
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13.9
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16.3
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1.3
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14.0
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18.0
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<0.001
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Height (cm)
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151.6
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12.0
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117.7
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185.6
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170.2
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8.8
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147.0
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194.9
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<0.001
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Weight (kg)
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43.6
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11.3
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17.8
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79.4
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63.0
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11.6
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41.5
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120.8
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<0.001
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BMI z-score
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0.24
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1.13
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–3.35
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3.32
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0.20
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0.99
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–2.83
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3.49
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0.587
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Movement behaviors
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MVPA (min/day)a
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58.1
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24.3
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7.2
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151.7
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39.3
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19.1
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2.9
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100.7
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<0.001
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ST (h/day)
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3.0
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1.8
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0.1
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11.9
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2.8
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2.1
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0.1
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12.9
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0.206
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Sleep duration (h/day)a
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8.6
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0.7
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6.4
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10.6
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7.5
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0.8
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5.4
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10.2
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<0.001
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BMI: Body mass index; Max: maximum; Min: minimum; MVPA: Moderate-to-vigorous physical activity; SD: Standard deviation; ST: Screen time
a Accelerometer-based 24-hour assessment; adjusted to 24 hours before analysis
b The differences between age categories were analyzed using the t-test for independent samples
Physical activity and sleep
PA and sleep were monitored using the wGT3X-BT and GT9X Link ActiGraph accelerometers (ActiGraph, Pensacola, FL, USA) worn by children and adolescents, respectively. The devices were initialized using the ActiLife software version 6.13.3 (ActiGraph, Pensacola, FL, USA), all three axes were used, and sampling interval was set to 100 Hz. To limit reactivity, the displays of GT9X Link accelerometers were set to show only date and time and the official start of monitoring was the next full day following the day on which the devices were distributed. Participants wore the activity monitor on their non-dominant wrist for 24 hours over 7 consecutive days. They were instructed to remove the device only for swimming and bathing. Raw accelerometer data were analyzed using the R-package GGIR version 1.10-7. Time spent in MVPA was estimated using the Hildebrand’s cut points for the Euclidian Norm Minus One metric [37]. Sleep duration (difference between sleep onset and waking up time) was calculated using the heuristic van Hees algorithm guided by participants’ sleep logs [38]. Sleep efficiency was calculated as the ratio of time spent in sustained inactivity periods divided by sleep duration. Only data from participants who had worn the accelerometer for at least 16 hours per day for at least 4 days (including 1 weekend day) were included in the analyses. A more detailed description of PA and sleep assessment has been published elsewhere [39,40].
Screen time
Recreational ST was self-reported. A parent proxy report was required in children aged 12 years and younger (i.e., those in the first stage of elementary school). Participants, their parents, or guardians answered the questions taken from the questionnaire of the international Health Behaviour in School-aged Children study [41] as follows: “About how many hours a day do you usually spend watching television, DVDs, videos (including YouTube or similar online service) in your free time on weekdays/weekend days?” and “About how many hours a day do you usually spend playing games on a computer, games console (PlayStation, Xbox, etc.), smartphone, tablet or similar electronic device in your free time on weekdays/weekend days?”. Questions were separated for weekdays and weekend days. Nine different answers were available for each question (none, half an hour, 1, 2, 3, 4, 5, 6, and 7 or more hours a day). The validity and reliability of 7-day recall questions have been demonstrated in comparison with 7-day 24-hour diaries both on weekdays and weekends [42]. Total amount of ST was calculated as the sum of weighted averages of ST during weekdays and weekend days.
Adherence to the combined movement guidelines
Participants adhere to the combined movement guidelines if they accumulate at least 60 minutes of MVPA per day for PA recommendation, 2 hours or less of recreational ST per day for SB recommendation, and 9–11 hours per day for children and 8–10 hours per day for adolescents for sleep recommendation.
Correlates
Seventeen potential correlates were selected based on systematic reviews [2,32–36] showing plausible associations with at least single recommendation included in the combined movement guidelines. Correlates were grouped into three categories: (1) biological and cognitive correlates, (2) behavioral correlates, and (3) family correlates. They were obtained through multiple research sources. Biological correlates except sex were measured directly using standard anthropometric measurements and the multi-frequency bioimpedance analyzer InBody 720 (InBody, Seoul, Korea). Cognitive and behavioral correlates were self-reported except for sleep efficiency, which was measured by accelerometry. Parent proxy report was required for participants aged 12 years and younger. Family correlates were reported by parents. The full list of correlates with information about their use in the analysis is displayed in Table 2.
Table 2. Potential correlates of meeting the combined movement guidelines
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Method of measurement
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Measurement/Question
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Use in analysis
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Biological and cognitive
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Sex
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Self-reported
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Sex.
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Binary variable:
girl (1) or boy (0*)
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School achievement
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Self-reported
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Did you pass with distinction on your final school report in the previous school year?
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Binary variable: yes (1) or no (0*)
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Adiposity
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Device-measured
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Assessed by multi-frequency bioimpedance analysis.
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Re-coded as dichotomous: <85th percentile (1*) or ≥85th percentile (0)
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BMI z-score
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Anthropometric measurement
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World Health Organization BMI z-score based on direct measurement of body height and weight.
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Re-coded as dichotomous: <1 SD (1*) or ≥1 SD (0)
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Behavioral
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Organized PA
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Self-reported
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About how many hours a week do you usually spend in organized sport activities in your free time on weekdays/weekend days?
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Re-coded as dichotomous: ≥1 hour a week (1) or <1 hour a week (0*)
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Active play
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Self-reported
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About how many hours a day do you usually spend in unorganized PA in your free time on weekdays/weekend days?
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Re-coded as dichotomous: ≥2 hours a day (1) or <2 hours a day (0*)
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AT to school
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Self-reported
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Check the prevailing mode (walk, bicycle, in-line, skateboard, car, bus, train) of transportation on the way to school.
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Re-coded as dichotomous: ≥3 days a week of AT (1) or <3 days a week of AT (0*)
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AT from school
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Self-reported
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Check the prevailing mode (walk, bicycle, in-line, skateboard, car, bus, train) of transportation on the way from school.
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Re-coded as dichotomous: ≥3 days a week of AT (1) or <3 days a week of AT (0*)
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Sleep efficiency
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Device-measured
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Measured by accelerometry and defined as the ratio of time spent in sustained inactivity periods divided by sleep duration.
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Re-coded as dichotomous: ≥0.85 (1) or <0.85 (0*)
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Fruit and vegetable intake
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Self-reported
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About how many times a week do you usually eat or drink a) fruits and b) vegetables?
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Re-coded as dichotomous: ≥1 day a week (1) or <1 day a week (0*) for "a" and "b"
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Unhealthy snacking
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Self-reported
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About how many times a week do you usually eat or drink c) sweets (candy or chocolate), d) coke or other soft drinks that contain sugar, and e) crisps, chips, salt sticks, etc.?
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Re-coded as dichotomous: ≥1 day a week (1) or <1 day a week (0*) for "c", "d" or "e"
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Skipping breakfast
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Self-reported
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How often do you usually have breakfast (more than a glass of milk or fruit juice) on weekdays/weekend days?
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Re-coded as dichotomous: <4 days a week (1) or ≥4 days a week (0*)
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Family
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Maternal BMI
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Parent-reported
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Calculated from the self-reported body height and weight.
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BMI computed and re-coded as dichotomous: <25 kg/m² (1*) or ≥25 kg/m² (0)
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Maternal education
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Parent-reported
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Highest educational level.
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Re-coded as dichotomous: university and higher education (1) or lower than university education (0*)
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Paternal BMI
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Parent-reported
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Calculated from the self-reported body height and weight.
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BMI computed re-coded as dichotomous: <25 kg/m² (1*) or ≥25 kg/m² (0)
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Paternal education
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Parent-reported
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Highest educational level.
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Re-coded as dichotomous: university and higher education (1) or lower than university education (0*)
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Family income
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Parent-reported
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Is the total gross income of your household greater than 48,000 CZK per month (twice the median in Czechia)?
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Binary variable: yes (1) or no (0*)
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AT: Active travel; BMI: Body Mass Index; PA: Physical activity; SD: Standard deviation
Parent proxy report was required for participants aged 12 years and younger
* indicates reference category
Procedure
Data were collected from 2018 to 2019 during regular school weeks. Participants were given accelerometers in the classrooms and were instructed on how to wear them properly and how to complete relevant sleep logs. Participants and/or their parents or guardians were asked to fill in the questionnaires. Parents or guardians responded to family characteristic questions. Participants responded to the remaining questions (except children aged 12 years and younger where answering by parents was required). After 8 days, accelerometers, sleep logs, and questionnaires were collected.
Statistical analyses
Statistical analyses were conducted using the IBM SPSS Statistics version 23 (IBM, Armonk, NY, USA) and R version 3.4.2 (R Foundation for Statistical Computing, Vienna, Austria). All analyses were performed separately for children and adolescents. The differences between children and adolescents were analyzed using the t-test for continuous variables and the chi-squared test for categorical variables.
Univariable analysis was conducted to examine associations between potential correlates and adherence to the combined movement guidelines and the specific combinations of any two recommendations. Binary logistic regression models were used because of the inherent nature of dependent variables ("0" for not meeting and "1" for meeting the combined movement guidelines or combinations of any two recommendations). If an explanatory variable reached a less-strict criterion level of p < 0.1, it was retained for further analysis to prevent the exclusion of potentially important correlates.
Multi-level multivariable logistic regression analysis was performed to identify correlates of adhering to the combined movement guidelines and of meeting combinations of any two recommendations. The potential correlates were included in the final models as fixed effects (Level 1), while the school location was considered a random effect (Level 2) in all mixed effects models. The necessity to include the factor of school location in the model was tested (by the likelihood-ratio test) and the factor was omitted whenever possible. Odds ratios (OR) and the 95% confidence intervals (CI) corresponding to the individual correlates as well as their significance were calculated. The forward selection method was used to set up the final model. The final models include all correlates whose omission would lead to a significant decrease in the Akaike information criterion. All statistical analyses were conducted at a significance level of p < 0.05.