The COMPASS study is an ongoing prospective cohort study (2012-2021) that collects annual hierarchical longitudinal data from a convenience sample of secondary schools in Ontario and Alberta, Canada (grades 9-12). A total of 89 schools were sampled, 79 in Ontario and 10 in Alberta. The study examines the impact of school policies and programs on a variety of student health behaviours, including physical activity, health eating, substance use and mental health, as well as academic achievement. Details of the COMPASS study, including sampling and data collection are available online (www.compass.uwaterloo.ca). The COMPASS study was approved by the Human Research Ethics Board at the University of Waterloo. This study uses data from years 2 (2013-14) and 4 (2015-16) of the COMPASS study.
In year 2 (2013-2014) and year 4 (2015-2016) of the COMPASS study, participants were recruited using active-information passive-consent permission protocols. The parents or guardians were mailed an information letter and then were asked to either call or email the COMPASS recruitment leader in the event that they did not want their child to participate, and students could refuse to participate at any time. In year 2, 45,298 students (participation rate 80.2%), while in year 4, 40,436 students (participation rate 79.9%) participated. The majority of missing respondents were from absenteeism or scheduled spares at the time of the data collection.
Students were linked anonymously over time using a self-generated identification code. Details of the data linkage process are described elsewhere . This study examined a longitudinal sample of 11,220 students who were successfully linked across years 2 and 4 of the study. Among this group, students with missing data on any of the measures included in the analysis were excluded, resulting in a final longitudinal sample of 9,898 students.
Data Collection Tool:
The COMPASS student questionnaire (Cq), is an anonymous, self-administered questionnaire and was used to collect student level data on a number of health-related outcomes such as obesity, sedentary behaviour and substance use, as well as correlates on behaviours and demographic attributes . Cq data was collected as large whole-school samples during class time and was therefore designed to be completed in one 30 to 40-minute class period . Survey items were chosen in order to meet both science- based and practice-based concerns.
Physical Activity Measures
Students completed two questions on the Cq regarding their daily number of minutes of both vigorous and moderate physical activity. Vigorous physical activity was defined as activities such as “jogging, team sports, fast dancing, and any other physical activities that increase your heart rate and make you breathe hard and sweat.” Moderate physical activity was defined as “lower intensity activities such as walking, biking to school, and recreational swimming.” Based on responses to these questions, a continuous measure of average daily moderate to vigorous physical activity (MVPA) was calculated. Additionally, a binary measure of whether students met the minimum Canadian physical activity guidelines of at least 60 minutes of MVPA per day was utilized. The measures were found to have high test-retest reliability and were significantly correlated with accelerometer-measured behaviours . While the correlation between self-report and accelerometer measures were low to modest, the results are comparable to most other studies using accelerometers to validate self-report physical activity .
Students were also asked about their participation in intramural, varsity and league sports. To determine intramural sports participation students were asked “Do you participate in before-school, noon hour, or after-school physical activities organized by your school?” To determine varsity sports participation students were asked “Do you participate in competitive school sports teams that compete against other schools?” To determine league sports participation, students were asked “Do you participate in league or team sports outside of school?”
Academic Achievement Measures
To measure academic achievement, students were asked questions about their most recent math and English grades with the questions “In your current or most recent Math course, what is your approximate overall mark?” and “In your current or most recent English course, what is your approximate overall mark?” Response options ranged from “Less than 50%” to “90% - 100%.” Grade categories were treated as continuous variables ranging from 1 to 6, with 1 indicating “Less than 50%” to 6 indicating “90% - 100%.”
Students were also asked demographic information on grade, sex, ethnicity and weekly spending money (as a proxy for socioeconomic status). To assess ethnicity, students were asked “How would you describe yourself,” and could select any number of response options of “White”, “Black”, “Asian”, “Aboriginal (First Nations, Metis, Inuit)”, “Latin American/Hispanic” and “Other”. Due to sample sizes, response options were collapsed to “White”, “Asian”, “Other” and “Mixed”. To assess socioeconomic status, students were asked “About how much money do you usually get each week to spend on yourself or save?” Response options ranged from “Zero” to “More than $100”, as well as “I don’t know”.
Additional control variables included students’ perceived importance of grades, time spent sleeping, and time spent doing homework. To measure perceived importance of grades, students were asked their agreement with the statement “Getting good grades is important to me”. Response options were collapsed into “Strongly agree”, “Agree” and “Disagree/Strongly Disagree”. Time spent sleeping and doing homework were measured in minutes per day based on answers to the question “How much time per day do you usually spend doing the following activities?”
Descriptive statistics were calculated for all variables in the analysis at baseline (year 2), as well as academic achievement variables at follow-up (year 4). Effect sizes were calculated for the relationship between baseline physical activity and grade at follow-up using correlations and Cohen’s f as effect size estimates (for raw and covariate-adjusted effects, respectively).
Sequential linear regression mixed models with random intercept, and multinomial ordinal generalized estimating equations (GEE) models were used to examine the association between baseline physical activity and sports participation on both academic outcomes at follow-up. All models controlled for baseline school grade, sex, ethnicity, weekly spending money, perceived importance of grades, time spent sleeping and time spent doing homework, as well as corresponding baseline Math/English grade. Models also accounted for school-level clustering, with the assumption that students from the same school will be more alike than students from different schools. However, equivalent non-clustered models were used to calculate Cohen’s f effect sizes. All analyses were conducted using the statistical software SAS 9.4. The procedure PROC MIXED was used for linear models and PROC GENMOD was used for the logistic GEE model. Statistical significance was set a p < 0.05 for all analyses.