Data are from the Canadian Health Measures Survey (CHMS), a nationally representative sample of Canadians aged 6 to 79 years living in private households [12–15]. Residents of Indian Reserves or Crown lands, institutions, certain remote regions, and full-time members of the Canadian Forces are excluded. Approximately 96% of Canadians are represented. The CHMS involved two components: 1) an in-home interview (completed by proxy for those aged <12 years) to gather information on socio-demographic characteristics, health behaviors, and environmental factors, and 2) a mobile examination clinic visit for direct physical measures and biospecimen collection. To be included in this study, participants had to be 6–19 years old, have acceptable accelerometry measures of MVPA, and have measures available for all of the cardiometabolic risk factors.
Ethics approval was obtained from Health Canada’s Research Ethics Board . Informed written consent was obtained from respondents aged 14 years or older. For a child, a parent/guardian provided written consent, in addition to written assent from the child. Data were collected at 15 sites from March 2007 through February 2009.
Of the households selected to participate in the CHMS, the response rate was 69.6%. Within each household, one (if there were no children aged 6–11 years) or two (if there was ≥ one child aged 6–11 years) members were randomly chosen to participate. Half of the CHMS participants made up the fasted subsample (these participants completed their clinic visit in the morning) that was examined in this paper. Of the selected 6–19 year olds, 86.8% completed the household questionnaire, 87.5% of those participated in the mobile clinic visit, and 85.4% of those were fasted for a total of 933. The overall response rate for the fasted 6–19 year olds is therefore 45.2%. An additional 188 were excluded because of missing or incomplete data, leaving a final sample size of 745.
Measurement of physical activity
After the clinic visit, participants were asked to wear an Actical accelerometer (Phillips – Respironics, Oregon, USA) during their waking hours over their right hip on an elasticized belt for 7 consecutive days. The monitors started collecting data at midnight following the clinic visit. The Actical measures time-stamped accelerations in all directions and provides an indication of physical activity intensity. The digitized values were summed over 1 minute intervals, resulting in 10,080 count per minute (cpm) values over the 7 day measurement period. The Actical has been validated to measure physical activity in children and youth [16, 17].
Cleaning and reduction of accelerometer data
Accelerometer data quality were assessed to determine whether files should be included in analyses . Published guidelines were followed to identify and remove days with incomplete (invalid) accelerometer wear time [18, 19]. A valid day was defined as ≥10 hours of wear time, which was determined by subtracting nonwear time from 24 hours. Nonwear time was defined as a period of ≥60 consecutive minutes of zero counts, with allowance for 1 to 2 minutes of counts between 0 and 100.
The next data cleaning step involved removing participants with an insufficient number of days of valid wear time. Only participants with ≥4 valid days were included . A 4–5 day accelerometer monitoring period for MVPA has a test-retest reliability of 0.7 to 0.8 among children and youth . Of those child and youth CHMS participants who wore the accelerometer, 84.8% had at least 4 valid days (8.2% with 4 days, 12.7% with 5 days, 24.0% with 6 days, 39.8% with 7 days) .
Derivation of MVPA variables
Time spent in MVPA was determined for each day with valid wear time by summing minutes with values ≥1500 cpm . Average daily minutes of MVPA was calculated for each participant. The number of days over the 7 day measurement period in which the participants accumulated ≥60 minutes of MVPA was also determined.
Physical activity recommendations are that school-aged children and youth accumulate ≥60 minutes of MVPA daily. Participants in this study were divided into the following 3 groups based on whether they achieved the 60 minute/day target on average over the 7 day measurement period, and if so, the number of days that this target was achieved: 1) Insufficiently Active = <60 minutes/day of MVPA on average over the 7 day measurement period (N = 455); 2) Infrequently Active = ≥60 minutes/day of MVPA on average over the 7 day measurement period, and active for ≥60 minutes on 1 to 4 days over the 7 days (N = 106); and 3) Frequently Active = ≥60 minutes/day MVPA on average over the 7 day measurement period, and active for ≥60 minutes on 5 to 7 days over the 7 days (N = 184). The Infrequently Active and Frequently Active groups were differentiated using the 5 day cut-point because this cut-point resulted in the most comparably sized groups.
To maximize the sample size, participants with less than 7 valid days of wear time were classified into the 3 MVPA groups based on an equivalent percentage of active days for the number of days they had valid wear time. For participants with 7 out of 7 days of valid wear time, 4 out of 7 active days equates to having 57% of all valid days as active days, for participants with 6 days of valid wear time this is roughly equivalent to 3 active days (50%), for participants with 5 days of valid wear time this is roughly equivalent to 3 active days (60%), and for participants with 4 days of valid wear time, this is roughly equivalent to 2 active days (50%).
Measurement of cardiometabolic risk factors
Cardiometabolic risk factor values vary by sex and change with growth and maturation [23–25]. Therefore, percentile scores by sex and age were computed for each cardiometabolic risk factor, and these percentile scores were used in all regression analyses. Thus, participants with percentile scores of 2%, 16%, 50%, 84%, and 98% would reflect individuals with risk factor values for their sex and age that were 2 SD below the mean, 1 SD below the mean, at the mean, 1 SD above the mean, and 2SD above the mean, respectively.
Height was measured to the nearest 0.1 cm using a ProScale M150 digital stadiometer (Accurate Technology Inc., Fletcher, USA) and weight was measured to the nearest 0.1 kg with a Mettler Toledo VLC scale (Mettler Toledo Canada, Mississauga, Canada). The body mass index (BMI) was calculated as weight divided by height squared (kg/m2). Waist circumference was measured with an anthropometric tape at the end of a normal expiration to the nearest 0.1 cm at the mid-point between the last rib and iliac crest . Overweight and obesity were determined using the International Obesity Task Force BMI thresholds .
Blood pressure was measured with the BpTRU™ BP-300 (BpTRU Medical Devices Ltd., Coquitlam, British Columbia) . The device automatically inflates and deflates the arm cuff and uses an oscillometric technique to calculate systolic and diastolic blood pressure. It has passed international validation protocols [12, 28]. Once appropriate seat height, arm positioning, and cuff size were determined, a five minute quiet rest was followed by a minimum of six measurements taken one minute apart. Systolic and diastolic blood pressure values were calculated by averaging the last 5 of 6 measures.
Fasting blood samples were taken and analyzed at the Health Canada Laboratory (Bureau of Nutritional Sciences, Nutrition Research Division). The lipid/lipoprotein measures considered here were high density lipoprotein (HDL)-cholesterol and triglycerides. We did not examine low density lipoprotein-cholesterol because it is poorly correlated with MVPA in children and youth . HDL-cholesterol and triglycerides were measured using a non-HDL precipitation method and enzymatic method, respectively, and were performed on the Vitros 5,1FS (Ortho Clinical Diagnostics). Insulin resistance was estimated according to the homoeostasis model assessment (HOMA) as the product of fasting glucose (mmol/L) and insulin (μU/mL) divided by 22.5 . Insulin was measured using a solid-phase, two-site chemiluminescent immunometric assay (Immulite 2000 by DPC). Glucose was measured on the Vitros 5,1FS (Ortho Clinical Diagnostics).
Percentile scores of the seven individual risk factors were averaged for each participant to create a clustered metabolic syndrome risk score . HDL-cholesterol scores were multiplied by −1 prior to incorporating them into the metabolic syndrome score.
Covariates included visible minority status (white or other) and total household income adjusted for the number of people in the household (lowest quartile vs. other). Age and sex were not included as covariates because the cardiometabolic risk factor percentile scores were age- and sex-specific.
Statistical analyses were performed using SAS v9.1 and SUDAAN v10. Some of the cardiometabolic risk factor values were positively skewed (triglycerides and HOMA) and were log transformed prior to analyses. All analyses were weighted to obtain estimates that are representative of the Canadian population . To account for survey design effects, 95% confidence intervals (CI) were estimated using the bootstrap technique [33, 34], with the degrees of freedom specified as 11 .
Mean and prevalence estimates were produced. Linear regression tests were used to assess the univariate relations between MVPA and the cardiometabolic risk factors. Differences between cardiometabolic risk factors across the three physical activity groups were assessed using general linear models. The 95% CI of the parameter estimates (b) from these regression models were used to identify group differences. Visible minority status and household income were included as covariates in the models. Two models were run for each cardiometabolic risk factor. The first model included the 3 physical activity groups and the covariates. The second model included the 3 physical activity groups, the covariates, and the volume of MVPA (average min/day). There was a 21 minute/day difference in the average daily MVPA in the Infrequently Active and Frequently Active groups, and the second model controlled for the volume of MVPA to estimate the independent effects of the weekly frequency of being active for ≥60 minutes.
Statistical results were interpreted as recommended by the British Medical Journal Group . These recommendations indicate that: 1) CI for effect sizes are preferable to p-values, 2) inferences drawn by comparing p-values are dubious, and 3) interpretation of a lack of significance as evidence of no effect is usually incorrect.
Based on a sample size of 106 or more per group, a p value of 0.05, and assuming a standard deviation for the cardiometabolic risk factor percentile scores of 15 within each group, the study had an 80% power to detect a 5 unit difference between the mean percentile scores of two groups.