Cross-associations between physical activity and sedentary time on metabolic health: a comparative assessment using self-reported and objectively measured activity

Cross-associations between physical activity and sedentary time on metabolic health: a... Abstract Purpose Physical activity and sedentary time have distinct physiologic and metabolic effects, but little is known about their joint associations. Methods The Canadian Health Measures Survey (n = 5950) was used to (i) examine the joint relationship between active/non-sedentary (referent group), active/sedentary, inactive/non-sedentary and inactive/sedentary phenotypes on obesity and metabolic health; and (ii) compare these relationships when using objective (accelerometer) total activity or subjective (self-report) leisure-time measures. Weighted associations for the metabolic syndrome (MetS), individual MetS components, 1+ disease (1 or more of diabetes, myocardial infarction, stroke, cardiovascular disease) and obesity were estimated using logistic regression. Results After adjustments, the odds (OR, 95% CI) of 1+ disease (OR = 3.05, 1.47–6.34) and abdominal obesity (OR = 2.75, 1.16–6.55) were higher in the inactive/sedentary group versus the referent group (OR = 1.00) when measured objectively. Within self-report leisure-time groups, elevated odds were observed for the inactive/sedentary group for MetS, obesity, abdominal obesity and elevated triglycerides. Inactive/non-sedentary and active/sedentary groups were similarly protective when measured by accelerometer. Conclusion Using accelerometer data, the inactive/sedentary group was at higher risk for 1+ disease and abdominal obesity only, whereas the active/sedentary and inactive/non-sedentary groups were not at higher risk for any health outcome. epidemiology, measurement, physical activity, sedentary time Introduction Self-reported Canadian physical activity surveillance data suggests that levels of moderate-to-vigorous physical activity (MVPA) have increased since the 1980s,1–3 while evidence regarding temporal changes in the diets of Canadians is inconclusive.4,5 Paradoxically, the prevalence of obesity and type 2 diabetes has greatly increased during the same time frame.6,7 Two contributing factors to this disconnect may be: (i) changes in sedentary time and (ii) physical activity measurement. There is no long-term systematic surveillance data on temporal changes in sedentary time among Canadians; however, evidence suggests that occupational sitting time3 and overall screen time have increased in recent decades,8 while self-reported physical activity data is subject to over-reporting.9 Although physical inactivity and sedentary time are associated with adverse effects on similar metabolic risk factors,10,11 the mechanisms of action may not be the same.12 Current universally adopted physical activity guidelines recommend ≥150 min/wk of MVPA in bouts of 10 min or more13 in order to reduce risk of premature mortality and various chronic diseases.14 However, even amongst those who meet these recommendations, the majority of people spend only 2–4% of their waking hours in MVPA.15,16 Because current guidelines offer no framework for the other ~96% of time, conventional physical activity surveillance has primarily focused on MVPA and leisure-time physical activity and largely overlooked a significant portion of daily activity energy expenditure (EE). Duvivier et al.17 recently observed that the acute effect of 13 h of sitting activity on insulin and other metabolic markers was not offset by 1 h of vigorous exercise, highlighting the need for a more thorough exploration of the inter-relationship between sedentary time and MVPA. Complicating the relationships between sedentary time, physical activity and metabolic health is the widespread use of subjective leisure-time physical activity data in Canadian health surveillance. In 2007, an estimated 65% of Canadian adults met guidelines (30–60 min of MVPA 4 days/wk) by self-reported leisure-time physical activity.18 In 2009, the inaugural Canadian Health Measures Survey (CHMS)19—the first nationally representative study to calculate MVPA from total activity time using accelerometers—revealed that only 15% of Canadians were sufficiently active. Given that self-reported leisure-time information is subject to both healthy responder bias, recall bias20 and only captures a portion of total activity, objective total activity measures are vital to improve our understanding of the relationships between sedentary time, physical activity and metabolic health.20 Nevertheless, the overwhelming evidence of an epidemiological association between physical activity and health is based on traditional self-reported leisure-time activity, and likely to persist in national surveillance due to its relative ease of collection and cost-effectiveness.21 The objective of this study was 2-fold: First, to examine the joint effects of physical activity and sedentary time on obesity and metabolic health, and second; to compare these relationships when using subjective (self-report) leisure-time or objective (accelerometer) total activity data. Methods Participants Initiated in 2007, the CHMS is a cross-sectional study conducted biannually, designed to collect key surveillance information concerning the health of a nationally representative sample of Canadians aged 3–79.22 The survey collects information through household interviews, direct physical measures, physical activity monitors, blood and urine samples, and environmental measures. Approximately 96% of Canadians are represented. Excluded are full-time members of the Canadian Forces; residents of aboriginal settlements or reserves; select remote regions, and; institutionalized residents.19 Two cycles of the CHMS were used in the present study; Cycle 1 (2007–9) and Cycle 2 (2009–11) were combined with an initial sample size of n = 11 387. The final analytical sample was n = 5950 after only those ≥18 years (range: 18–79 years) with valid accelerometer data (≥4 valid days) were included. Objectively measured physical/sedentary activity Data from Actical accelerometers were used to provide an objective assessment of total physical activity and sedentary time. In accordance with Colley et al.,23 minimum adherence for inclusion in the study was 4 valid days of wear time, wherein 10 h of wear time was required for a valid day. One valid weekend day was not required.23 Wear time was calculated by subtracting non-wear time from 24 h. Non-wear time was characterized as at least 60 consecutive minutes of zero accelerometer counts with allowance for up to 2 consecutive minutes of counts between 0 and 100.23 In order to capture intensity of activities, Actical monitors measure acceleration in all directions in 1 min epochs by summing total counts per minute (CPM). Each intensity level has a corresponding CPM cut-point, and the time spent in each intensity was summed and converted into total minutes per day. The cut-points applied in this study were previously published guidelines specific to the Actical monitors:24 <100 CPM (sedentary intensity); 100–1534 CPM (light intensity), and; >1534 CPM (MVPA). Physical activity guideline adherence was defined as accumulating 150 min or more of MVPA in bouts of 10 min or more in 7 days,13 denoted as ‘active’. Not meeting physical activity guidelines was denoted as ‘inactive’ (see Supplementary material online, Table S1). An allowance of 2 min of not meeting the cut-point throughout the 10 consecutive minutes of MVPA was permitted.23 For participants with only 4–6 valid days of accelerometer wear, their average daily time in MVPA was calculated and multiplied by 7. Sedentary time was dichotomized into ≥480 min/day (‘sedentary’) and <480 min/day (‘non-sedentary’). Accelerometer measured groups were created by cross classifying by physical activity and sedentary time. The four groups were subsequently denoted (i) active/non-sedentary; (ii) active/sedentary; (iii) inactive/non-sedentary; and (iv) inactive/sedentary, with the active/non-sedentary group serving as the referent group. Self-reported physical/sedentary activity Self-reported leisure-time physical activity and sedentary time data were collected during the household interview. Information was collected on the type of activity (walking for exercise, gardening or yard work, swimming, bicycling, popular or social dance, home exercises, ice hockey, ice skating, in-line skating or rollerblading, jogging or running, golfing, exercise classes or aerobics, downhill skiing or snowboarding, bowling, baseball or softball, tennis, weight-training, fishing, volleyball, basketball, soccer or any other) duration and frequency. Pre-determined (average) MET (metabolic equivalent) levels were assigned to each activity, expressed in kcal/kg/h. EE was converted from yearly EE to daily EE and all activities were summed, producing daily leisure-time EE in kcal/kg/day.19,25 This index has good reliability (r = 0.90) and criterion validity (r = 0.36) when compared to other questionnaire-based methods of physical activity (r = 0.77).26 Self-reported leisure-time physical activity was dichotomized into ‘active’ (≥3 kcal/kg/day) and ‘inactive’ (<3 kcal/kg/day) groups. Self-reported leisure-time sedentary time was calculated by summing time spent (hours) in a typical week in the past 3 months engaged in computer, computer games and Internet, video games, television or videos, and reading.27,28 This composite measure of activities outside of work included response categories ranging from <5 h to 45 or more hours per week. Sedentary time was subsequently dichotomized as ‘sedentary’ (≥20 h/wk) or ‘non-sedentary’ (<20 h/wk) (Supplementary material online, Table S1). Analogous to accelerometer measured groups; four self-reported leisure-time groups were created: (i) active/non-sedentary; (ii) active/sedentary; (iii) inactive/non-sedentary; and (iv) inactive/sedentary, with the active/non-sedentary group serving as the referent group. Outcome variables Participants were classified as having diabetes if they self-reported a diagnosis of diabetes or had elevated blood glucose (≥7.1 mmol/L) or HbA1c levels (≥6.5%).29 Cardiovascular disease (CVD), heart attack and stroke were self-reported. In order to have sufficient power for physical activity-by-sedentary time comparisons, diabetes, CVD, heart attack and stroke were collapsed into a single variable (‘1+ disease’). Obesity was defined by measured height and weight as a body mass index (BMI) ≥30 kg/m2. Metabolic syndrome (MetS) was classified according to the harmonized definition30 as having three or more of: elevated blood pressure (≥130/85 mmHg) or hypertensive medication use; abdominal obesity (waist circumference (WC) ≥ 102 cm (men) or 88 cm (women)); elevated triglycerides (TG) (≥1.69 mmol/L); low HDL (<1.04 mmol/L (men) or 1.29 mmol/L (women)) or cholesterol medication, or; elevated blood glucose (5.6 mmol/L) or diabetes medications. Physical fitness Aerobic fitness was determined using the Modified Canadian Aerobic Fitness Test31 step test, an indirect submaximal fitness test used to determine aerobic capacity.27,28 A composite musculoskeletal fitness score was derived from tests of grip strength, sit and reach, and partial curl ups.27,28 Both aerobic fitness and musculoskeletal fitness were scored on a 5-point scale (needs improvement—excellent) and were dichotomized as ‘high’ (good, very good, excellent) and ‘low’ (needs improvement, fair). Statistical analysis To compare baseline demographics within the sample, x2 and analysis of variance were used across to assess differences in frequency counts and mean values, respectively. Logistic regression was then applied to estimate the odds (OR, 95% confidence interval (CI)) of having 1+ disease, obesity, MetS and each individual MetS component, for each group (active/sedentary; inactive/non-sedentary; inactive/sedentary) compared to the active/non-sedentary referent group (OR = 1.00). This analysis was done twice, first with the self-reported leisure-time physical activity groups, and second with the objectively measured total activity groups. Models were adjusted for age, sex, education, ethnicity, income adequacy (total household income divided by number of residents), accelerometer wear time and BMI. Smoking status and alcohol consumption were initially included in the model but were not statistically significant and subsequently removed. All analyses were weighted to be representative of the Canadian population using survey procedures in SAS Version 9.4 (SAS Institute Inc., Cary, NC, USA). The bootstrap technique32 was used to calculate 95% CIs and standard errors. Analyses with cell counts under 10 were suppressed and statistical significance was set at α <0.05 for all analyses. Results Characteristics of the sample are described in Table 1. Comparing across accelerometer measured total activity groups, the active/non-sedentary group was the youngest (40.3 years) and primarily male (59.6%) while the inactive/sedentary group was the oldest (46.5 years) and primarily female (54.4%). The mean WC and BMI were lower in the active groups (non-sedentary, WC: 86.1 cm; BMI: 25.4 kg/m2 || sedentary, WC: 86.3 cm; BMI: 25.5 kg/m2) compared to the inactive groups (non-sedentary, WC: 91.8 cm; BMI: 27.4 kg/m2 || sedentary, WC: 91.6 cm; BMI: 27.2 kg/m2). There were overall significant differences between groups for income, SBP, DBP, Glucose, HDL, TG and Hba1c. Table 1 Weighted characteristics by accelerometer measured total activity groups Active Inactive Non-sedentary Sedentary Non-sedentary Sedentary P-value Age (years) N = 102 N = 623 N = 450 N = 4775 <0.0001 40.3 (33.7–47.1) 43.0 (40.9–45.0) 43.4 (40.8–46.0) 46.5 (45.9–47.0)bcd Sex N = 102 N = 623 N = 450 N = 4775 <0.05  Male 59.6% (37.7–81.6) 51.4% (46.6–56.2) 58.5% (50.3–66.7) 45.6% (44.1–47.2)d  Female 40.4% (18.4–62.3) 48.6% (43.8–53.4) 41.5% (33.3–49.7) 54.3% (52.8–55.9)d Ethnicity N = 102 N = 618 N = 449 N = 4693 NS  White 82.0% (65.7–98.3) 81.6% (74.7–88.4) 85.7% (79.4–91.9) 86.0% (80.4–91.6)  Other 18.0% (1.7–34.3) 18.4% (11.6–25.3) 14.3% (8.1–20.6) 14.0% (8.5–19.6) Education N = 102 N = 623 N = 450 N = 4733 NS  <HS 13.8% (5.9–25.5) 9.1% (5.8–12.3) 12.5% (9.0–16.0) 11.8% (10.0–13.6)  HS grad 36.9% (14.9–59.0) 27.4% (20.8–34.0) 32.4% (24.9–39.9) 24.9% (21.9–27.8)  University 49.2% (27.0–71.5) 63.6% (55.7–71.4) 55.2% (45.7–64.6) 63.3% (59.2–67.4) Income N = 97 N = 606 N = 437 N = 4648 <0.05  Low 23.7% (8.8–38.7) 18.0% (13.8–22.1) 18.9% (12.9–24.9) 18.1% (15.5–21.0)  Middle 49.9% (37.4–62.5) 26.0% (20.5–31.4)b 31.6% (24.5–38.6) 30.5% (27.4–33.6)  High 26.3% (12.0–40.7) 56.0% (49.4–62.8) 49.5% (40.5–58.5) 51.4% (47.6–55.2) Smoking N = 102 N = 621 N = 450 N = 4775 NS  Yes 21.2% (1.9–40.4) 13.6% (8.5–18.7) 26.0% (18.4–33.7) 18.1% (16.1–20.2)  Former 22.1% (5.9–38.3) 29.1% (24.0–34.3) 32.6% (22.2–42.9) 30.4% (27.4–33.3)  Never 56.7% (30.9–82.5) 57.3% (50.5–64.1) 41.4% (33.6–49.2)d 51.5% (48.3–54.7) Alcohol N = 84 N = 542 N = 379 N = 4020 NS  <1/wk 64.8% (45.0–84.6) 58.9% (53.4–64.3) 49.9% (41.7–58.1) 57.4% (54.2–61.0)  >1/wk 35.2% (15.4–55.0) 41.1% (35.7–46.6) 50.1% (41.9–58.3) 42.4% (39.0–45.8) WCa (cm) N = 102 N = 621 N = 447 N = 4711 <0.0001 86.1 (82.9–89.3) 86.3 (84.5–88.2) 91.8 (90.0–93.6)bd 91.6 (90.3–92.9)bd BMIa (kg/m2) N = 102 N = 621 N = 446 N = 4731 <0.0001 25.4 (24.1–26.6) 25.5 (25.0–26.1) 27.4 (26.7–28.2) 27.2 (26.8–27.7)d SBP (mmHg) N = 102 N = 623 N = 450 N = 4773 <0.05 112.4 (109.7–115.1) 111.0 (108.8–113.3) 113.7 (112.0–115.3) 112.8 (111.6–114.0) DBP (mmHg) N = 102 N = 623 N = 450 N = 4773 <0.0001 72.5 (70.8–74.1) 70.7 (69.2–72.3) 73.8 (72.4–75.2) 71.8 (71.0–72.5) Glucose (mM) N = 100 N = 619 N = 443 N = 4728 <0.05 4.9 (4.7–5.1) 4.9 (4.9–5.0) 4.9 (4.8–5.1) 5.1 (5.0–5.1)d HDL (mM) N = 98 N = 613 N = 439 N = 4715 <0.05 1.4 (1.2–1.5) 1.4 (1.4–1.5) 1.4 (1.3–1.4) 1.4 (1.4-1.4) TG (mM) N = 53 N = 326 N = 216 N = 2315 <0.0001 1.1 (0.9–1.2) 1.1 (1.0–1.2) 1.2 (1.1–1.4) 1.3 (1.3–1.4)bd Hba1c (%) N = 97 N = 604 N = 431 N = 4655 <0.0001 5.6 (5.4–5.7) 5.6 (5.5–5.7) 5.6 (5.5–5.7) 5.7 (5.6–5.8)d Active Inactive Non-sedentary Sedentary Non-sedentary Sedentary P-value Age (years) N = 102 N = 623 N = 450 N = 4775 <0.0001 40.3 (33.7–47.1) 43.0 (40.9–45.0) 43.4 (40.8–46.0) 46.5 (45.9–47.0)bcd Sex N = 102 N = 623 N = 450 N = 4775 <0.05  Male 59.6% (37.7–81.6) 51.4% (46.6–56.2) 58.5% (50.3–66.7) 45.6% (44.1–47.2)d  Female 40.4% (18.4–62.3) 48.6% (43.8–53.4) 41.5% (33.3–49.7) 54.3% (52.8–55.9)d Ethnicity N = 102 N = 618 N = 449 N = 4693 NS  White 82.0% (65.7–98.3) 81.6% (74.7–88.4) 85.7% (79.4–91.9) 86.0% (80.4–91.6)  Other 18.0% (1.7–34.3) 18.4% (11.6–25.3) 14.3% (8.1–20.6) 14.0% (8.5–19.6) Education N = 102 N = 623 N = 450 N = 4733 NS  <HS 13.8% (5.9–25.5) 9.1% (5.8–12.3) 12.5% (9.0–16.0) 11.8% (10.0–13.6)  HS grad 36.9% (14.9–59.0) 27.4% (20.8–34.0) 32.4% (24.9–39.9) 24.9% (21.9–27.8)  University 49.2% (27.0–71.5) 63.6% (55.7–71.4) 55.2% (45.7–64.6) 63.3% (59.2–67.4) Income N = 97 N = 606 N = 437 N = 4648 <0.05  Low 23.7% (8.8–38.7) 18.0% (13.8–22.1) 18.9% (12.9–24.9) 18.1% (15.5–21.0)  Middle 49.9% (37.4–62.5) 26.0% (20.5–31.4)b 31.6% (24.5–38.6) 30.5% (27.4–33.6)  High 26.3% (12.0–40.7) 56.0% (49.4–62.8) 49.5% (40.5–58.5) 51.4% (47.6–55.2) Smoking N = 102 N = 621 N = 450 N = 4775 NS  Yes 21.2% (1.9–40.4) 13.6% (8.5–18.7) 26.0% (18.4–33.7) 18.1% (16.1–20.2)  Former 22.1% (5.9–38.3) 29.1% (24.0–34.3) 32.6% (22.2–42.9) 30.4% (27.4–33.3)  Never 56.7% (30.9–82.5) 57.3% (50.5–64.1) 41.4% (33.6–49.2)d 51.5% (48.3–54.7) Alcohol N = 84 N = 542 N = 379 N = 4020 NS  <1/wk 64.8% (45.0–84.6) 58.9% (53.4–64.3) 49.9% (41.7–58.1) 57.4% (54.2–61.0)  >1/wk 35.2% (15.4–55.0) 41.1% (35.7–46.6) 50.1% (41.9–58.3) 42.4% (39.0–45.8) WCa (cm) N = 102 N = 621 N = 447 N = 4711 <0.0001 86.1 (82.9–89.3) 86.3 (84.5–88.2) 91.8 (90.0–93.6)bd 91.6 (90.3–92.9)bd BMIa (kg/m2) N = 102 N = 621 N = 446 N = 4731 <0.0001 25.4 (24.1–26.6) 25.5 (25.0–26.1) 27.4 (26.7–28.2) 27.2 (26.8–27.7)d SBP (mmHg) N = 102 N = 623 N = 450 N = 4773 <0.05 112.4 (109.7–115.1) 111.0 (108.8–113.3) 113.7 (112.0–115.3) 112.8 (111.6–114.0) DBP (mmHg) N = 102 N = 623 N = 450 N = 4773 <0.0001 72.5 (70.8–74.1) 70.7 (69.2–72.3) 73.8 (72.4–75.2) 71.8 (71.0–72.5) Glucose (mM) N = 100 N = 619 N = 443 N = 4728 <0.05 4.9 (4.7–5.1) 4.9 (4.9–5.0) 4.9 (4.8–5.1) 5.1 (5.0–5.1)d HDL (mM) N = 98 N = 613 N = 439 N = 4715 <0.05 1.4 (1.2–1.5) 1.4 (1.4–1.5) 1.4 (1.3–1.4) 1.4 (1.4-1.4) TG (mM) N = 53 N = 326 N = 216 N = 2315 <0.0001 1.1 (0.9–1.2) 1.1 (1.0–1.2) 1.2 (1.1–1.4) 1.3 (1.3–1.4)bd Hba1c (%) N = 97 N = 604 N = 431 N = 4655 <0.0001 5.6 (5.4–5.7) 5.6 (5.5–5.7) 5.6 (5.5–5.7) 5.7 (5.6–5.8)d Mean or prevalence (%) and 95% confidence interval. HS, high school; NS, not significant. aPregnant women excluded. bSignificantly different from active/non-sedentary. cSignificantly different from active/sedentary group. dSignificantly different from inactive/non-sedentary group. Table 1 Weighted characteristics by accelerometer measured total activity groups Active Inactive Non-sedentary Sedentary Non-sedentary Sedentary P-value Age (years) N = 102 N = 623 N = 450 N = 4775 <0.0001 40.3 (33.7–47.1) 43.0 (40.9–45.0) 43.4 (40.8–46.0) 46.5 (45.9–47.0)bcd Sex N = 102 N = 623 N = 450 N = 4775 <0.05  Male 59.6% (37.7–81.6) 51.4% (46.6–56.2) 58.5% (50.3–66.7) 45.6% (44.1–47.2)d  Female 40.4% (18.4–62.3) 48.6% (43.8–53.4) 41.5% (33.3–49.7) 54.3% (52.8–55.9)d Ethnicity N = 102 N = 618 N = 449 N = 4693 NS  White 82.0% (65.7–98.3) 81.6% (74.7–88.4) 85.7% (79.4–91.9) 86.0% (80.4–91.6)  Other 18.0% (1.7–34.3) 18.4% (11.6–25.3) 14.3% (8.1–20.6) 14.0% (8.5–19.6) Education N = 102 N = 623 N = 450 N = 4733 NS  <HS 13.8% (5.9–25.5) 9.1% (5.8–12.3) 12.5% (9.0–16.0) 11.8% (10.0–13.6)  HS grad 36.9% (14.9–59.0) 27.4% (20.8–34.0) 32.4% (24.9–39.9) 24.9% (21.9–27.8)  University 49.2% (27.0–71.5) 63.6% (55.7–71.4) 55.2% (45.7–64.6) 63.3% (59.2–67.4) Income N = 97 N = 606 N = 437 N = 4648 <0.05  Low 23.7% (8.8–38.7) 18.0% (13.8–22.1) 18.9% (12.9–24.9) 18.1% (15.5–21.0)  Middle 49.9% (37.4–62.5) 26.0% (20.5–31.4)b 31.6% (24.5–38.6) 30.5% (27.4–33.6)  High 26.3% (12.0–40.7) 56.0% (49.4–62.8) 49.5% (40.5–58.5) 51.4% (47.6–55.2) Smoking N = 102 N = 621 N = 450 N = 4775 NS  Yes 21.2% (1.9–40.4) 13.6% (8.5–18.7) 26.0% (18.4–33.7) 18.1% (16.1–20.2)  Former 22.1% (5.9–38.3) 29.1% (24.0–34.3) 32.6% (22.2–42.9) 30.4% (27.4–33.3)  Never 56.7% (30.9–82.5) 57.3% (50.5–64.1) 41.4% (33.6–49.2)d 51.5% (48.3–54.7) Alcohol N = 84 N = 542 N = 379 N = 4020 NS  <1/wk 64.8% (45.0–84.6) 58.9% (53.4–64.3) 49.9% (41.7–58.1) 57.4% (54.2–61.0)  >1/wk 35.2% (15.4–55.0) 41.1% (35.7–46.6) 50.1% (41.9–58.3) 42.4% (39.0–45.8) WCa (cm) N = 102 N = 621 N = 447 N = 4711 <0.0001 86.1 (82.9–89.3) 86.3 (84.5–88.2) 91.8 (90.0–93.6)bd 91.6 (90.3–92.9)bd BMIa (kg/m2) N = 102 N = 621 N = 446 N = 4731 <0.0001 25.4 (24.1–26.6) 25.5 (25.0–26.1) 27.4 (26.7–28.2) 27.2 (26.8–27.7)d SBP (mmHg) N = 102 N = 623 N = 450 N = 4773 <0.05 112.4 (109.7–115.1) 111.0 (108.8–113.3) 113.7 (112.0–115.3) 112.8 (111.6–114.0) DBP (mmHg) N = 102 N = 623 N = 450 N = 4773 <0.0001 72.5 (70.8–74.1) 70.7 (69.2–72.3) 73.8 (72.4–75.2) 71.8 (71.0–72.5) Glucose (mM) N = 100 N = 619 N = 443 N = 4728 <0.05 4.9 (4.7–5.1) 4.9 (4.9–5.0) 4.9 (4.8–5.1) 5.1 (5.0–5.1)d HDL (mM) N = 98 N = 613 N = 439 N = 4715 <0.05 1.4 (1.2–1.5) 1.4 (1.4–1.5) 1.4 (1.3–1.4) 1.4 (1.4-1.4) TG (mM) N = 53 N = 326 N = 216 N = 2315 <0.0001 1.1 (0.9–1.2) 1.1 (1.0–1.2) 1.2 (1.1–1.4) 1.3 (1.3–1.4)bd Hba1c (%) N = 97 N = 604 N = 431 N = 4655 <0.0001 5.6 (5.4–5.7) 5.6 (5.5–5.7) 5.6 (5.5–5.7) 5.7 (5.6–5.8)d Active Inactive Non-sedentary Sedentary Non-sedentary Sedentary P-value Age (years) N = 102 N = 623 N = 450 N = 4775 <0.0001 40.3 (33.7–47.1) 43.0 (40.9–45.0) 43.4 (40.8–46.0) 46.5 (45.9–47.0)bcd Sex N = 102 N = 623 N = 450 N = 4775 <0.05  Male 59.6% (37.7–81.6) 51.4% (46.6–56.2) 58.5% (50.3–66.7) 45.6% (44.1–47.2)d  Female 40.4% (18.4–62.3) 48.6% (43.8–53.4) 41.5% (33.3–49.7) 54.3% (52.8–55.9)d Ethnicity N = 102 N = 618 N = 449 N = 4693 NS  White 82.0% (65.7–98.3) 81.6% (74.7–88.4) 85.7% (79.4–91.9) 86.0% (80.4–91.6)  Other 18.0% (1.7–34.3) 18.4% (11.6–25.3) 14.3% (8.1–20.6) 14.0% (8.5–19.6) Education N = 102 N = 623 N = 450 N = 4733 NS  <HS 13.8% (5.9–25.5) 9.1% (5.8–12.3) 12.5% (9.0–16.0) 11.8% (10.0–13.6)  HS grad 36.9% (14.9–59.0) 27.4% (20.8–34.0) 32.4% (24.9–39.9) 24.9% (21.9–27.8)  University 49.2% (27.0–71.5) 63.6% (55.7–71.4) 55.2% (45.7–64.6) 63.3% (59.2–67.4) Income N = 97 N = 606 N = 437 N = 4648 <0.05  Low 23.7% (8.8–38.7) 18.0% (13.8–22.1) 18.9% (12.9–24.9) 18.1% (15.5–21.0)  Middle 49.9% (37.4–62.5) 26.0% (20.5–31.4)b 31.6% (24.5–38.6) 30.5% (27.4–33.6)  High 26.3% (12.0–40.7) 56.0% (49.4–62.8) 49.5% (40.5–58.5) 51.4% (47.6–55.2) Smoking N = 102 N = 621 N = 450 N = 4775 NS  Yes 21.2% (1.9–40.4) 13.6% (8.5–18.7) 26.0% (18.4–33.7) 18.1% (16.1–20.2)  Former 22.1% (5.9–38.3) 29.1% (24.0–34.3) 32.6% (22.2–42.9) 30.4% (27.4–33.3)  Never 56.7% (30.9–82.5) 57.3% (50.5–64.1) 41.4% (33.6–49.2)d 51.5% (48.3–54.7) Alcohol N = 84 N = 542 N = 379 N = 4020 NS  <1/wk 64.8% (45.0–84.6) 58.9% (53.4–64.3) 49.9% (41.7–58.1) 57.4% (54.2–61.0)  >1/wk 35.2% (15.4–55.0) 41.1% (35.7–46.6) 50.1% (41.9–58.3) 42.4% (39.0–45.8) WCa (cm) N = 102 N = 621 N = 447 N = 4711 <0.0001 86.1 (82.9–89.3) 86.3 (84.5–88.2) 91.8 (90.0–93.6)bd 91.6 (90.3–92.9)bd BMIa (kg/m2) N = 102 N = 621 N = 446 N = 4731 <0.0001 25.4 (24.1–26.6) 25.5 (25.0–26.1) 27.4 (26.7–28.2) 27.2 (26.8–27.7)d SBP (mmHg) N = 102 N = 623 N = 450 N = 4773 <0.05 112.4 (109.7–115.1) 111.0 (108.8–113.3) 113.7 (112.0–115.3) 112.8 (111.6–114.0) DBP (mmHg) N = 102 N = 623 N = 450 N = 4773 <0.0001 72.5 (70.8–74.1) 70.7 (69.2–72.3) 73.8 (72.4–75.2) 71.8 (71.0–72.5) Glucose (mM) N = 100 N = 619 N = 443 N = 4728 <0.05 4.9 (4.7–5.1) 4.9 (4.9–5.0) 4.9 (4.8–5.1) 5.1 (5.0–5.1)d HDL (mM) N = 98 N = 613 N = 439 N = 4715 <0.05 1.4 (1.2–1.5) 1.4 (1.4–1.5) 1.4 (1.3–1.4) 1.4 (1.4-1.4) TG (mM) N = 53 N = 326 N = 216 N = 2315 <0.0001 1.1 (0.9–1.2) 1.1 (1.0–1.2) 1.2 (1.1–1.4) 1.3 (1.3–1.4)bd Hba1c (%) N = 97 N = 604 N = 431 N = 4655 <0.0001 5.6 (5.4–5.7) 5.6 (5.5–5.7) 5.6 (5.5–5.7) 5.7 (5.6–5.8)d Mean or prevalence (%) and 95% confidence interval. HS, high school; NS, not significant. aPregnant women excluded. bSignificantly different from active/non-sedentary. cSignificantly different from active/sedentary group. dSignificantly different from inactive/non-sedentary group. The mean time spent in MVPA (see Supplementary material online, Table S2) decreased systematically across accelerometer measured total activity groups. Active groups accumulated 77.0 min/day (non-sedentary) and 53.2 min/day (sedentary) while inactive groups accumulated 26.3 min/day (non-sedentary) and 16.4 min/day (sedentary). Across self-report leisure-time groups, MVPA ranged from 18.3 to 33.0 min/day. Daily sedentary time ranged from 425.2 to 601.9 min/day and from 570.2 to 591.7 min/day in accelerometer measured groups and self-report groups, respectively. Prevalence of chronic disease and MetS components are shown in Fig. 1. Overall, only the self-reported leisure-time inactive/sedentary groups had a higher prevalence of every chronic disease. Within the accelerometer measured groups, obesity, abdominal obesity, elevated blood glucose, TG and HDL were significantly different across all groups (P < 0.05). When compared to the referent group, the prevalence of abdominal obesity was significantly greater in the inactive/sedentary group (36.6 versus 15.2%) while the prevalence of elevated blood pressure was significantly greater in both sedentary groups (active: 23.1 versus 14.6%; inactive: 27.8 versus 14.6%). Within self-report leisure-time groups, all components of MetS varied across groups, and both sedentary groups had a significantly higher prevalence of abdominal obesity (active: 30.0 versus 20.6%; inactive: 43.2 versus 20.6%) and elevated blood pressure (active: 28.4 versus 16.9%; inactive: 32.5 versus 16.9%). Fig. 1 View largeDownload slide Prevalence of chronic disease and metabolic syndrome components by accelerometer measured groups (Accel) and self-report groups (S-R). Prevalence (%) and 95% confidence intervals || N-Estimate suppressed. **Significant for overall x2 for both Accel and S-R. *Significantly different from referent group (active/non-sedentary). (A) 1+ disease, (B) metabolic syndrome, (C) obesity, (D) abdominal obesity, (E) elevated blood pressure, (F) elevated blood glucose, (G) elevated triglycerides and (H) low HDL. Fig. 1 View largeDownload slide Prevalence of chronic disease and metabolic syndrome components by accelerometer measured groups (Accel) and self-report groups (S-R). Prevalence (%) and 95% confidence intervals || N-Estimate suppressed. **Significant for overall x2 for both Accel and S-R. *Significantly different from referent group (active/non-sedentary). (A) 1+ disease, (B) metabolic syndrome, (C) obesity, (D) abdominal obesity, (E) elevated blood pressure, (F) elevated blood glucose, (G) elevated triglycerides and (H) low HDL. Aerobic fitness levels (Fig. 2) were similar between accelerometer measured total activity and self-reported leisure-time groups. When measured by accelerometer, 75.4% of the referent group had high aerobic fitness while 70.2% of the referent group did by self-report. Conversely, the prevalence of high musculoskeletal fitness was significantly lower in the inactive/sedentary group (51.2%) relative to the referent group (70.8%) in the self-report leisure-time groups. Fig. 2 View largeDownload slide Prevalence of ‘high’ fitness levels by accelerometer measured groups (Accel) and self-report groups (S-R). Prevalence (%) and 95% confidence interval. High musculoskeletal fitness—‘good’ rating or higher. **Significant for overall x2 for both Accel and S-R. *Significantly different from referent group (active/non-sedentary). (A) Aerobic fitness and (B) musculo skeletal fitness. Fig. 2 View largeDownload slide Prevalence of ‘high’ fitness levels by accelerometer measured groups (Accel) and self-report groups (S-R). Prevalence (%) and 95% confidence interval. High musculoskeletal fitness—‘good’ rating or higher. **Significant for overall x2 for both Accel and S-R. *Significantly different from referent group (active/non-sedentary). (A) Aerobic fitness and (B) musculo skeletal fitness. The age and sex adjusted odds ratio (95% CI) for chronic disease and MetS revealed various significant relationships within accelerometer measured total activity and self-reported leisure-time groups. Upon including ethnicity, education, income, accelerometer wear time and BMI into the models, only two relationships retained significance within accelerometer measured groups (Table 2). Table 2 Multivariable adjusted odds ratios of chronic disease and individual metabolic syndrome components by accelerometer measured total activity groups (Accel) and self-reported leisure-time groups (S-R) Active Inactive Chronic disease Non-sedentary Sedentary Non-sedentary Sedentary 1+ Disease  Accel 1.00 1.57 (0.71, 3.48) 1.37 (0.64, 2.95) 3.05 (1.47, 6.34)  S-R 1.00 0.72 (0.42, 1.23) 1.08 (0.64, 1.82) 1.26 (0.79, 2.01) Obesityab  Accel 1.00 0.79 (0.20, 3.15) 1.25 (0.32, 4.86) 1.53 (0.38, 6.08)  S-R 1.00 1.52 (0.86, 2.67) 1.40 (0.87, 2.24) 2.77 (1.63, 4.70) MetSa  Accel 1.00 1.65 (0.36, 7.47) 1.19 (0.29, 4.88) 1.94 (0.52, 7.29)  S-R 1.00 1.77 (0.88, 3.55) 2.20 (1.13, 4.29) 2.87 (1.39, 5.94) MetS components Abdominal obesityab  Accel 1.00 1.62 (0.69, 3.81) 2.38 (0.91, 6.23) 2.75 (1.16, 6.55)  S-R 1.00 1.59 (1.09, 2.31) 1.55 (0.92, 2.60) 2.88 (1.86, 4.46) Blood pressure  Accel 1.00 1.38 (0.73, 2.62) 1.65 (0.68, 4.04) 1.36 (0.71, 2.61)  S-R 1.00 1.28 (0.79, 2.08) 1.41 (0.87, 2.29) 1.52 (0.99, 2.35) Glucose  Accel 1.00 1.10 (0.52, 2.34) 1.58 (0.55, 4.57) 1.70 (0.82, 3.55)  S-R 1.00 0.92 (0.57, 1.48) 1.28 (0.79, 2.06) 1.13 (0.69, 1.85) TG  Accel 1.00 1.65 (0.28, 9.73) 2.03 (0.35, 11.78) 2.44 (0.43, 13.95)  S-R 1.00 0.93 (0.44, 1.93) 1.40 (0.76, 2.57) 2.09 (1.25, 3.50) HDL  Accel 1.00 2.44 (0.67, 8.88) 2.08 (0.59–7.32) 2.90 (0.85, 9.91)  S-R 1.00 1.08 (0.76, 1.53) 1.23 (0.80, 1.91) 1.32 (0.94, 1.85) Active Inactive Chronic disease Non-sedentary Sedentary Non-sedentary Sedentary 1+ Disease  Accel 1.00 1.57 (0.71, 3.48) 1.37 (0.64, 2.95) 3.05 (1.47, 6.34)  S-R 1.00 0.72 (0.42, 1.23) 1.08 (0.64, 1.82) 1.26 (0.79, 2.01) Obesityab  Accel 1.00 0.79 (0.20, 3.15) 1.25 (0.32, 4.86) 1.53 (0.38, 6.08)  S-R 1.00 1.52 (0.86, 2.67) 1.40 (0.87, 2.24) 2.77 (1.63, 4.70) MetSa  Accel 1.00 1.65 (0.36, 7.47) 1.19 (0.29, 4.88) 1.94 (0.52, 7.29)  S-R 1.00 1.77 (0.88, 3.55) 2.20 (1.13, 4.29) 2.87 (1.39, 5.94) MetS components Abdominal obesityab  Accel 1.00 1.62 (0.69, 3.81) 2.38 (0.91, 6.23) 2.75 (1.16, 6.55)  S-R 1.00 1.59 (1.09, 2.31) 1.55 (0.92, 2.60) 2.88 (1.86, 4.46) Blood pressure  Accel 1.00 1.38 (0.73, 2.62) 1.65 (0.68, 4.04) 1.36 (0.71, 2.61)  S-R 1.00 1.28 (0.79, 2.08) 1.41 (0.87, 2.29) 1.52 (0.99, 2.35) Glucose  Accel 1.00 1.10 (0.52, 2.34) 1.58 (0.55, 4.57) 1.70 (0.82, 3.55)  S-R 1.00 0.92 (0.57, 1.48) 1.28 (0.79, 2.06) 1.13 (0.69, 1.85) TG  Accel 1.00 1.65 (0.28, 9.73) 2.03 (0.35, 11.78) 2.44 (0.43, 13.95)  S-R 1.00 0.93 (0.44, 1.93) 1.40 (0.76, 2.57) 2.09 (1.25, 3.50) HDL  Accel 1.00 2.44 (0.67, 8.88) 2.08 (0.59–7.32) 2.90 (0.85, 9.91)  S-R 1.00 1.08 (0.76, 1.53) 1.23 (0.80, 1.91) 1.32 (0.94, 1.85) Odds ratios and 95% confidence intervals. Adjusted for age, sex, ethnicity, education, income, wear time and BMI. Chronic disease—1+ disease: 1 or more of diabetes, myocardial infarction, stroke or cardiovascular disease; Obesity: BMI ≥ 30 kg/m2; MetS: ≥3 components. MetS components—abdominal obesity: ≥102 cm (men) and ≥88 cm (women); blood pressure: ≥130 mmHg (systolic) or ≥85 mmHg (diastolic); Glucose: ≥5.6 mM; triglycerides: ≥1.69 mM; HDL < 1.04 (men) and <1.29 (women). Self-report groups based on leisure-time activity/sedentary time cut-point. Bold indicates p < 0.05. Accel, accelerometer measured group; S-R, self-reported group; TG, triglycerides; Abd. obesity, abdominal obesity. aPregnant women excluded. bNot adjusted for BMI. Table 2 Multivariable adjusted odds ratios of chronic disease and individual metabolic syndrome components by accelerometer measured total activity groups (Accel) and self-reported leisure-time groups (S-R) Active Inactive Chronic disease Non-sedentary Sedentary Non-sedentary Sedentary 1+ Disease  Accel 1.00 1.57 (0.71, 3.48) 1.37 (0.64, 2.95) 3.05 (1.47, 6.34)  S-R 1.00 0.72 (0.42, 1.23) 1.08 (0.64, 1.82) 1.26 (0.79, 2.01) Obesityab  Accel 1.00 0.79 (0.20, 3.15) 1.25 (0.32, 4.86) 1.53 (0.38, 6.08)  S-R 1.00 1.52 (0.86, 2.67) 1.40 (0.87, 2.24) 2.77 (1.63, 4.70) MetSa  Accel 1.00 1.65 (0.36, 7.47) 1.19 (0.29, 4.88) 1.94 (0.52, 7.29)  S-R 1.00 1.77 (0.88, 3.55) 2.20 (1.13, 4.29) 2.87 (1.39, 5.94) MetS components Abdominal obesityab  Accel 1.00 1.62 (0.69, 3.81) 2.38 (0.91, 6.23) 2.75 (1.16, 6.55)  S-R 1.00 1.59 (1.09, 2.31) 1.55 (0.92, 2.60) 2.88 (1.86, 4.46) Blood pressure  Accel 1.00 1.38 (0.73, 2.62) 1.65 (0.68, 4.04) 1.36 (0.71, 2.61)  S-R 1.00 1.28 (0.79, 2.08) 1.41 (0.87, 2.29) 1.52 (0.99, 2.35) Glucose  Accel 1.00 1.10 (0.52, 2.34) 1.58 (0.55, 4.57) 1.70 (0.82, 3.55)  S-R 1.00 0.92 (0.57, 1.48) 1.28 (0.79, 2.06) 1.13 (0.69, 1.85) TG  Accel 1.00 1.65 (0.28, 9.73) 2.03 (0.35, 11.78) 2.44 (0.43, 13.95)  S-R 1.00 0.93 (0.44, 1.93) 1.40 (0.76, 2.57) 2.09 (1.25, 3.50) HDL  Accel 1.00 2.44 (0.67, 8.88) 2.08 (0.59–7.32) 2.90 (0.85, 9.91)  S-R 1.00 1.08 (0.76, 1.53) 1.23 (0.80, 1.91) 1.32 (0.94, 1.85) Active Inactive Chronic disease Non-sedentary Sedentary Non-sedentary Sedentary 1+ Disease  Accel 1.00 1.57 (0.71, 3.48) 1.37 (0.64, 2.95) 3.05 (1.47, 6.34)  S-R 1.00 0.72 (0.42, 1.23) 1.08 (0.64, 1.82) 1.26 (0.79, 2.01) Obesityab  Accel 1.00 0.79 (0.20, 3.15) 1.25 (0.32, 4.86) 1.53 (0.38, 6.08)  S-R 1.00 1.52 (0.86, 2.67) 1.40 (0.87, 2.24) 2.77 (1.63, 4.70) MetSa  Accel 1.00 1.65 (0.36, 7.47) 1.19 (0.29, 4.88) 1.94 (0.52, 7.29)  S-R 1.00 1.77 (0.88, 3.55) 2.20 (1.13, 4.29) 2.87 (1.39, 5.94) MetS components Abdominal obesityab  Accel 1.00 1.62 (0.69, 3.81) 2.38 (0.91, 6.23) 2.75 (1.16, 6.55)  S-R 1.00 1.59 (1.09, 2.31) 1.55 (0.92, 2.60) 2.88 (1.86, 4.46) Blood pressure  Accel 1.00 1.38 (0.73, 2.62) 1.65 (0.68, 4.04) 1.36 (0.71, 2.61)  S-R 1.00 1.28 (0.79, 2.08) 1.41 (0.87, 2.29) 1.52 (0.99, 2.35) Glucose  Accel 1.00 1.10 (0.52, 2.34) 1.58 (0.55, 4.57) 1.70 (0.82, 3.55)  S-R 1.00 0.92 (0.57, 1.48) 1.28 (0.79, 2.06) 1.13 (0.69, 1.85) TG  Accel 1.00 1.65 (0.28, 9.73) 2.03 (0.35, 11.78) 2.44 (0.43, 13.95)  S-R 1.00 0.93 (0.44, 1.93) 1.40 (0.76, 2.57) 2.09 (1.25, 3.50) HDL  Accel 1.00 2.44 (0.67, 8.88) 2.08 (0.59–7.32) 2.90 (0.85, 9.91)  S-R 1.00 1.08 (0.76, 1.53) 1.23 (0.80, 1.91) 1.32 (0.94, 1.85) Odds ratios and 95% confidence intervals. Adjusted for age, sex, ethnicity, education, income, wear time and BMI. Chronic disease—1+ disease: 1 or more of diabetes, myocardial infarction, stroke or cardiovascular disease; Obesity: BMI ≥ 30 kg/m2; MetS: ≥3 components. MetS components—abdominal obesity: ≥102 cm (men) and ≥88 cm (women); blood pressure: ≥130 mmHg (systolic) or ≥85 mmHg (diastolic); Glucose: ≥5.6 mM; triglycerides: ≥1.69 mM; HDL < 1.04 (men) and <1.29 (women). Self-report groups based on leisure-time activity/sedentary time cut-point. Bold indicates p < 0.05. Accel, accelerometer measured group; S-R, self-reported group; TG, triglycerides; Abd. obesity, abdominal obesity. aPregnant women excluded. bNot adjusted for BMI. Discussion Main finding of this study The results of the present study demonstrate that, when measured objectively, not meeting physical activity guidelines in combination with being sedentary (≥480 min/day) is associated with greater odds of abdominal obesity and having a chronic disease. However, the associations differed when measured by self-reported leisure-time activity. What is already known on this topic Objectively measured physical activity/sedentary time and metabolic health Numerous studies have noted the independent effects of sedentary time and MVPA on metabolic health and CVD.9,11,33–35 Similar to our study, Healy et al.11 noted significant associations between time spent in sedentary activities and MVPA with abdominal obesity, while Chomistek et al.33 noted the joint effect of low physical activity with prolonged sitting increased the risk of CVD relative to highly active and non-sedentary women. Comparable to a previous self-report study examining steps/day and BMI by cross classifying sufficient/insufficiently active and low/high occupational sitting time into four groups,36 the active/sedentary and inactive/non-sedentary phenotypes displayed similar BMIs and steps/day. Likewise, in the present study the active/sedentary and inactive/non-sedentary groups displayed similar metabolic risk profiles and neither group had significantly greater odds of any of the observed outcomes relative to the referent group. The finding that the effect of prolonged sitting (≥480 min/day) on metabolic risk is attenuated by meeting the physical activity guidelines is consistent with previous research34,35; however, the finding that the excess risk incurred by being inactive is offset by low sitting time for all outcomes is, to the authors’ knowledge, novel. Although only two groups (active/non-sedentary; active/sedentary) in our study actually achieved the recommended level of physical activity, it is notable that three groups (active/non-sedentary; active/sedentary; and inactive/non-sedentary) all averaged ≥10 000 steps/day, a threshold proposed as a reasonable target to be categorized as ‘active’.37 In line with this, the inactive/sedentary group in our study had a significantly lower prevalence of ‘high’ aerobic fitness, while the active/sedentary group and the inactive/non-sedentary groups did not differ from the referent. What this study adds Accelerometers versus self-report Accelerometer measured total physical activity and sedentary time was associated with abdominal obesity and 1+ disease, with only the inactive/sedentary group demonstrating elevated risk. However, associations were observed for several distinct outcomes in addition to abdominal obesity, namely, MetS, obesity and elevated TG, when measured by self-reported leisure-time activity. Similar to the accelerometer measured groups, self-reported leisure-time groups yielded higher odds of obesity and metabolic risk predominantly in the inactive/sedentary group. In addition, MetS and abdominal obesity displayed elevated odds in the active/sedentary (abdominal obesity) or inactive/non-sedentary (MetS) groups in the self-report groups. These findings are in contrast to two previous studies which found stronger associations between objectively assessed physical activity and metabolic health as compared to self-report.9,38 Similar to our study, Atienza et al.’s38 self-reported physical activity did not capture occupational physical activity; however, they did not account for sedentary time. Celis-Morales et al.9 measured self-reported activity using the International physical activity questionnaire (IPAQ)39 which accounts for both sitting time and occupational activity. The extent to which differences in the measurement tools could have contributed to this divergent finding is unclear. Atienza et al.38 proposed that muscular strength could account for the differences in metabolic risk between objective and self-reported physical activity due to its inverse association with metabolic risk.40 This may partially explain the differences in our sample as musculoskeletal fitness varied across self-report leisure-time groups, but not accelerometer measured groups. Here, the inactive/sedentary group had a significantly lower prevalence of ‘high’ musculoskeletal fitness relative to the referent group. There are also several alternative explanations for the weaker observed relationship between objectively measured activity and metabolic health. First, the sedentary cut-point of 100 CPM does not distinguish between different sedentary activities such as standing and sitting, meaning that important differences in total EE and blood glucose levels could be masked within our objectively measured sedentary groups.41 Other intensities are susceptible to misclassification due to cut-point ambiguity. CHMS cut-points were 100–1534 and ≥1535 CPM for light intensity activity and MVPA, respectively; however, previous studies have used different cut-points42,43 when using the same monitors. Therefore, it is possible the cut-points used in the CHMS do not capture intensity appropriately in all individuals, and may misclassify some participants. Indeed, the prevalence of MVPA was 21.8% by self-report and 12.2% by accelerometry, whereas, non-sedentary time was also much higher by self-report (41.5%) than objective measure (9.3%). The level of agreement in accelerometer versus self-report physical activity was κ = 0.22 and 0.038 in accelerometer versus self-report sedentary time, highlighting the difficulties in accurately capturing sedentary activities. Second, accelerometers are prone to the Hawthorne effect (reactivity), wherein participants who are aware of being observed (via accelerometry) may increase their physical activity level during the course of the study.44 Lastly, our self-reported activity measure only accounted for leisure-time physical activity and sedentary time and did not capture occupational sitting. Limitations of this study There are several limitations that warrant discussion. First, because the study is cross-sectional, causality cannot be inferred. Second, although a missing sample analysis revealed minimal differences between the full sample and those with valid accelerometer data, we cannot exclude the possibility of a healthy responder effect.44 Third, self-reported physical activity is also subject to recall bias and influence from social desirability,20 which may bias towards the null. Because aerobic fitness was measured using a submaximal step test, it may also underestimate actual VO2 for some participants, whereas BMI may not reflect the same body composition in younger and older adults.45 Lastly, dietary intake was not accounted for, and may differ between activity groups. Implications The main finding of this study was that self-report leisure-time physical activity and sedentary time demonstrate different associations with metabolic health compared to accelerometer measured activity. Using accelerometer data, the inactive/sedentary group was at higher risk for 1+ disease and abdominal obesity only, whereas the active/sedentary and inactive/non-sedentary groups were not at higher risk for any health outcome. Given that traditional self-reported and accelerometer-derived activity data may identify different aspects of health,39 complementary use of these methods may still provide value. Supplementary data Supplementary material is available at Journal of Public Health online. Acknowledgements This research was conducted at the Canadian Research Data Centre Network (CRDCN). Although the research and analysis are based on data from Statistics Canada, the opinions expressed are those of the authors alone. References 1 Bruce MJ , Katzmarzyk PT . Canadian population trends in leisure-time physical activity levels, 1981–1998 . Can J Appl Physiol 2002 ; 6 : 681 – 90 . Google Scholar CrossRef Search ADS 2 Craig CL , Russell SJ , Cameron C et al. . Twenty-year trends in physical activity among Canadian adults . Can J Public Health 2004 ; 95 ( 1 ): 59 – 63 . Google Scholar PubMed 3 Juneau CE , Potvin L . Trends in leisure-, transport-, and work-related physical activity in Canada 1994–2005 . Prev Med 2010 ; 51 : 384 – 6 . Google Scholar CrossRef Search ADS PubMed 4 Garriguet D . Canadians’ eating habits . Health Rep 2007 ; 18 ( 2 ): 17 – 32 . Google Scholar PubMed 5 Slater J , Green CG , Sevenhuysen G et al. . The growing Canadian energy gap: more the can than the couch? Public Health Nutr 2009 ; 12 ( 11 ): 2216 – 24 . Google Scholar CrossRef Search ADS PubMed 6 Lipscombe LL , Hux JE . Trends in diabetes prevalence, incidence, and mortality in Ontario, Canada 1995–2005: a population based study . Lancet 2007 ; 369 ( 9563 ): 750 – 6 . Google Scholar CrossRef Search ADS PubMed 7 Shields M , Carrol MD , Ogde CL . Adult obesity prevalence in Canada and the United States. NCHS data brief no. 56, Hyattsville, MD: National Center for Health Statistics, 2011 . Adv Nutr 2011 ; 2 : 368 – 9A . Google Scholar CrossRef Search ADS PubMed 8 Shields M , Tremblay MS . Screen time among Canadian adults: a profile . Health Rep 2008 ; 19 ( 2 ): 31 – 43 . Google Scholar PubMed 9 Celis-Morales CA , Perez-Bravo F , Ibanez L et al. . Objective vs. self-reported physical activity and sedentary time: effects of measurement method on relationships with risk biomarkers . PLoS One 2012 ; 7 ( 5 ): e36345 . doi:10.1371/journal.pone.0036345 . Google Scholar CrossRef Search ADS PubMed 10 Dunstan DW , Salmon J , Owen N et al. . Associations of TV viewing and physical activity with the metabolic syndrome in Australian Adults . Diabetologia 2005 ; 48 : 2254 – 61 . Google Scholar CrossRef Search ADS PubMed 11 Healy GN , Wijndaele K , Dunstan DW et al. . Objectively measured sedentary time, physical activity, and metabolic risk . Diabetes Care 2008 ; 31 : 369 – 71 . Google Scholar CrossRef Search ADS PubMed 12 Hamilton MT , Hamilton DG , Zderic T . Role of low energy expenditure and sitting in obesity, metabolic syndrome, type 2 diabetes, and cardiovascular disease . Diabetes 2007 ; 56 : 2655 – 67 . Google Scholar CrossRef Search ADS PubMed 13 World Health Organization . Global Recommendations on Physical Activity for Health . Geneva, Switzerland : WHO , 2010 . 14 Warburton D , Chalresworth S , Ivey A et al. . A systematic review of the evidence for Canada’s Physical Activity Guidelines for Adults . Int J Behav Nutr Phys Act 2010 ; 7 : 39 . Google Scholar CrossRef Search ADS PubMed 15 Craft LL , Zderic TW , Gapstur SM et al. . Evidence that women meeting physical activity guidelines do not sit less: an observational inclinometry study . Int J Behav Nutr Phys Act 2012 ; 9 : 122 . doi:10.1186/1479-5868-9-122 . Google Scholar CrossRef Search ADS PubMed 16 Healy GN , Dunstan DW , Salmon J et al. . Objectively measured light-intensity physical activity is independently associated with 2-h plasma glucose . Diabetes Care 2007 ; 30 ( 6 ): 1384 – 9 . Google Scholar CrossRef Search ADS PubMed 17 Duvivier BMFM , Schaper NC , Bremers MA et al. . Minimal intensity physical activity (standing and walking) of longer duration improves insulin action and plasma lipids more than shorter periods of moderate to vigorous exercise (cycling) in sedentary subjects when energy expenditure is comparable . PLoS One 2013 ; 8 ( 2 ): e55542 . doi:10.1371/journal.pone.0055542 . Google Scholar CrossRef Search ADS PubMed 18 Bryan NS , Katzmarzyk PT . Are Canadians meeting the guidelines for moderate and vigorous leisure-time physical activity? Appl Physiol Nutr Metab 2009 ; 34 : 707 – 15 . Google Scholar CrossRef Search ADS PubMed 19 Statistics Canada . 2010 a. Canadian Health Measures Survey (CHMS) Data User Guide: Cycle 1. Ottawa, Ontario, Canada. 20 Tremblay MS . The need for directly measured health data in Canada . Can J Public Health 2004 ; 95 : 165 – 8 . Google Scholar PubMed 21 Katzmarkzy PT , Tremblay MS . Limitations of Canada’s physical activity data: implications for monitoring trends . Appl Physiol Nutr Metab 2007 ; 32 : s185 – 94 . Google Scholar CrossRef Search ADS 22 Tremblay M , Wolfson M , Gorber SC . Canadian Health Measures Survey: rationale, background and overview . Health Rep 2007 ; 18s : 7 – 20 . 23 Colley RC , Garriguet D , Janssen I et al. . Physical activity of Canadian adults: accelerometer results from the 2007 to 2009 Canadian Health Measures Survey . Health Rep 2011 ; 22 ( 1 ): 7 – 14 . Google Scholar PubMed 24 Colley RC , Tremblay MS . Moderate and vigorous physical activity intensity cut-points for the Actical accelerometer . J Sports Sci 2011 ; 29 ( 8 ): 783 – 9 . Google Scholar CrossRef Search ADS PubMed 25 Statistics Canada . 2012 a. Canadian Health Measures Survey (CHMS) Data User Guide: Cycle 2. Ottawa, Ontario, Canada. 26 Craig CL , Russell SJ , Cameron C . Reliability and validity of Canada’s Physical Activity Monitor for assessing trends . Med Sci Sports Exerc 2002 ; 34 : 1462 – 7 . Google Scholar CrossRef Search ADS PubMed 27 Statistics Canada . 2010 b. Canadian Health Measures Survey (CHMS) Questionnaire. Ottawa, Ontario, Canada. 28 Statistics Canada . 2012 b. Canadian Health Measures Survey (CHMS) Questionnaire (Cycle 2). Ottawa, Ontario, Canada. 29 Stamatakis E , Davis M , Stathi A et al. . Associations between multiple indicators of objectively-measured and self-reported sedentary behavior and cardiometabolic risk in older adults . Prev Med 2012 ; 54 : 82 – 7 . Google Scholar CrossRef Search ADS PubMed 30 Alberti KGMM , Eckel RE , Grundy SM et al. . Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association, World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity . Circulation 2009 ; 120 : 1640 – 5 . Google Scholar CrossRef Search ADS PubMed 31 Weller IM , Thomas SG , Corey PN et al. . Prediction of maximal oxygen uptake from a modified Canadian aerobic fitness test . Can J Appl Physiol 1993 ; 18 ( 2 ): 175 – 88 . Google Scholar CrossRef Search ADS PubMed 32 Statistics Canada . 2013 . Canadian Health Measures Survey (CHMS): Instructions for Combining Cycle 1 and Cycle 2 Data. Ottawa, Ontario, Canada. 33 Chomistek AK , Manson JE , Stefanick ML et al. . Relationship of sedentary behaviour and physical activity on incident cardiovascular disease . J Am Coll Cardiol 2013 ; 61 ( 23 ): 2346 – 54 . Google Scholar CrossRef Search ADS PubMed 34 Sisson SB , Camhi SM , Chruch TS et al. . Leisure time sedentary behaviour, occupational/domestic physical activity, and metabolic syndrome in U.S. men and women . Metab Syndr Relat Disord 2009 ; 7 ( 6 ): 529 – 36 . Google Scholar CrossRef Search ADS PubMed 35 Maher CA , Mire E , Harrington DM et al. . The independent and combined associations of physical activity and sedentary behaviour with obesity in adults: NHANES 2003–06 . Obesity 2013 ; 21 : E730 – 7 . doi:10.1002/oby.20430 . Google Scholar CrossRef Search ADS PubMed 36 Tudor-Locke C , Burton NW , Brown WJ . Leisure-time physical activity and occupational sitting time: associations with steps/day and BMI in 54–59 year old Australian women . Prev Med 2009 ; 48 : 64 – 8 . Google Scholar CrossRef Search ADS PubMed 37 Tudor-Locke C , Craig CL , Brown WJ et al. . How many steps/day are enough? For adults . Int J Behav Nutr Phys Act 2011 ; 8 : 79 . Google Scholar CrossRef Search ADS PubMed 38 Atenzia AA , Moser RP , Perna F et al. . Self-reported and objectively measured activity related to biomarkers using NHANES . Med Sci Sports Exerc 2011 ; 43 ( 5 ): 815 – 21 . Google Scholar CrossRef Search ADS PubMed 39 Craig CL , Marshall AL , Sjostrom M et al. . International physical activity questionnaire: 12-country reliability and validity . Med Sci Sports Exerc 2003 ; 35 ( 8 ): 1381 – 95 . Google Scholar CrossRef Search ADS PubMed 40 Jurca R , Lamonthe MJ , Barlow CE et al. . Association of muscular strength with incidence of metabolic syndrome in men . Med Sci Sports Exerc 2005 ; 37 ( 11 ): 1849 – 55 . Google Scholar CrossRef Search ADS PubMed 41 Buckley JP , Mellor DD , Morris M et al. . Standing-based office work shows encouraging signs of attenuating post-prandial glycemic excursion . Occup Eviron Med 2013 ; 0 : 1 – 3 . doi:10.1136/oemed-2013-101823 . 42 Heil DP . Predicting activity energy expenditure using the Actical activity monitor . Res Q Exerc Sport 2006 ; 77 : 64 – 80 . Google Scholar CrossRef Search ADS PubMed 43 Welk GJ , Schaben JA , Morrow JR Jr . Reliability of accelerometer-based activity monitors. A generalizability study . Med Sci Sports Exerc 2004 ; 36 : 1637 – 45 . Google Scholar PubMed 44 Delgado-Rodriguez M , Llorca J . Bias . J Epidemiol Community Health 2004 ; 58 : 635 – 41 . Google Scholar CrossRef Search ADS PubMed 45 Gallagher D , Visse M , Sepúlved D et al. . How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? Am J Epidemiol 1996 ; 3 ( 3 ): 228 – 39 . Google Scholar CrossRef Search ADS © The Author(s) 2018. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. 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Cross-associations between physical activity and sedentary time on metabolic health: a comparative assessment using self-reported and objectively measured activity

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Abstract

Abstract Purpose Physical activity and sedentary time have distinct physiologic and metabolic effects, but little is known about their joint associations. Methods The Canadian Health Measures Survey (n = 5950) was used to (i) examine the joint relationship between active/non-sedentary (referent group), active/sedentary, inactive/non-sedentary and inactive/sedentary phenotypes on obesity and metabolic health; and (ii) compare these relationships when using objective (accelerometer) total activity or subjective (self-report) leisure-time measures. Weighted associations for the metabolic syndrome (MetS), individual MetS components, 1+ disease (1 or more of diabetes, myocardial infarction, stroke, cardiovascular disease) and obesity were estimated using logistic regression. Results After adjustments, the odds (OR, 95% CI) of 1+ disease (OR = 3.05, 1.47–6.34) and abdominal obesity (OR = 2.75, 1.16–6.55) were higher in the inactive/sedentary group versus the referent group (OR = 1.00) when measured objectively. Within self-report leisure-time groups, elevated odds were observed for the inactive/sedentary group for MetS, obesity, abdominal obesity and elevated triglycerides. Inactive/non-sedentary and active/sedentary groups were similarly protective when measured by accelerometer. Conclusion Using accelerometer data, the inactive/sedentary group was at higher risk for 1+ disease and abdominal obesity only, whereas the active/sedentary and inactive/non-sedentary groups were not at higher risk for any health outcome. epidemiology, measurement, physical activity, sedentary time Introduction Self-reported Canadian physical activity surveillance data suggests that levels of moderate-to-vigorous physical activity (MVPA) have increased since the 1980s,1–3 while evidence regarding temporal changes in the diets of Canadians is inconclusive.4,5 Paradoxically, the prevalence of obesity and type 2 diabetes has greatly increased during the same time frame.6,7 Two contributing factors to this disconnect may be: (i) changes in sedentary time and (ii) physical activity measurement. There is no long-term systematic surveillance data on temporal changes in sedentary time among Canadians; however, evidence suggests that occupational sitting time3 and overall screen time have increased in recent decades,8 while self-reported physical activity data is subject to over-reporting.9 Although physical inactivity and sedentary time are associated with adverse effects on similar metabolic risk factors,10,11 the mechanisms of action may not be the same.12 Current universally adopted physical activity guidelines recommend ≥150 min/wk of MVPA in bouts of 10 min or more13 in order to reduce risk of premature mortality and various chronic diseases.14 However, even amongst those who meet these recommendations, the majority of people spend only 2–4% of their waking hours in MVPA.15,16 Because current guidelines offer no framework for the other ~96% of time, conventional physical activity surveillance has primarily focused on MVPA and leisure-time physical activity and largely overlooked a significant portion of daily activity energy expenditure (EE). Duvivier et al.17 recently observed that the acute effect of 13 h of sitting activity on insulin and other metabolic markers was not offset by 1 h of vigorous exercise, highlighting the need for a more thorough exploration of the inter-relationship between sedentary time and MVPA. Complicating the relationships between sedentary time, physical activity and metabolic health is the widespread use of subjective leisure-time physical activity data in Canadian health surveillance. In 2007, an estimated 65% of Canadian adults met guidelines (30–60 min of MVPA 4 days/wk) by self-reported leisure-time physical activity.18 In 2009, the inaugural Canadian Health Measures Survey (CHMS)19—the first nationally representative study to calculate MVPA from total activity time using accelerometers—revealed that only 15% of Canadians were sufficiently active. Given that self-reported leisure-time information is subject to both healthy responder bias, recall bias20 and only captures a portion of total activity, objective total activity measures are vital to improve our understanding of the relationships between sedentary time, physical activity and metabolic health.20 Nevertheless, the overwhelming evidence of an epidemiological association between physical activity and health is based on traditional self-reported leisure-time activity, and likely to persist in national surveillance due to its relative ease of collection and cost-effectiveness.21 The objective of this study was 2-fold: First, to examine the joint effects of physical activity and sedentary time on obesity and metabolic health, and second; to compare these relationships when using subjective (self-report) leisure-time or objective (accelerometer) total activity data. Methods Participants Initiated in 2007, the CHMS is a cross-sectional study conducted biannually, designed to collect key surveillance information concerning the health of a nationally representative sample of Canadians aged 3–79.22 The survey collects information through household interviews, direct physical measures, physical activity monitors, blood and urine samples, and environmental measures. Approximately 96% of Canadians are represented. Excluded are full-time members of the Canadian Forces; residents of aboriginal settlements or reserves; select remote regions, and; institutionalized residents.19 Two cycles of the CHMS were used in the present study; Cycle 1 (2007–9) and Cycle 2 (2009–11) were combined with an initial sample size of n = 11 387. The final analytical sample was n = 5950 after only those ≥18 years (range: 18–79 years) with valid accelerometer data (≥4 valid days) were included. Objectively measured physical/sedentary activity Data from Actical accelerometers were used to provide an objective assessment of total physical activity and sedentary time. In accordance with Colley et al.,23 minimum adherence for inclusion in the study was 4 valid days of wear time, wherein 10 h of wear time was required for a valid day. One valid weekend day was not required.23 Wear time was calculated by subtracting non-wear time from 24 h. Non-wear time was characterized as at least 60 consecutive minutes of zero accelerometer counts with allowance for up to 2 consecutive minutes of counts between 0 and 100.23 In order to capture intensity of activities, Actical monitors measure acceleration in all directions in 1 min epochs by summing total counts per minute (CPM). Each intensity level has a corresponding CPM cut-point, and the time spent in each intensity was summed and converted into total minutes per day. The cut-points applied in this study were previously published guidelines specific to the Actical monitors:24 <100 CPM (sedentary intensity); 100–1534 CPM (light intensity), and; >1534 CPM (MVPA). Physical activity guideline adherence was defined as accumulating 150 min or more of MVPA in bouts of 10 min or more in 7 days,13 denoted as ‘active’. Not meeting physical activity guidelines was denoted as ‘inactive’ (see Supplementary material online, Table S1). An allowance of 2 min of not meeting the cut-point throughout the 10 consecutive minutes of MVPA was permitted.23 For participants with only 4–6 valid days of accelerometer wear, their average daily time in MVPA was calculated and multiplied by 7. Sedentary time was dichotomized into ≥480 min/day (‘sedentary’) and <480 min/day (‘non-sedentary’). Accelerometer measured groups were created by cross classifying by physical activity and sedentary time. The four groups were subsequently denoted (i) active/non-sedentary; (ii) active/sedentary; (iii) inactive/non-sedentary; and (iv) inactive/sedentary, with the active/non-sedentary group serving as the referent group. Self-reported physical/sedentary activity Self-reported leisure-time physical activity and sedentary time data were collected during the household interview. Information was collected on the type of activity (walking for exercise, gardening or yard work, swimming, bicycling, popular or social dance, home exercises, ice hockey, ice skating, in-line skating or rollerblading, jogging or running, golfing, exercise classes or aerobics, downhill skiing or snowboarding, bowling, baseball or softball, tennis, weight-training, fishing, volleyball, basketball, soccer or any other) duration and frequency. Pre-determined (average) MET (metabolic equivalent) levels were assigned to each activity, expressed in kcal/kg/h. EE was converted from yearly EE to daily EE and all activities were summed, producing daily leisure-time EE in kcal/kg/day.19,25 This index has good reliability (r = 0.90) and criterion validity (r = 0.36) when compared to other questionnaire-based methods of physical activity (r = 0.77).26 Self-reported leisure-time physical activity was dichotomized into ‘active’ (≥3 kcal/kg/day) and ‘inactive’ (<3 kcal/kg/day) groups. Self-reported leisure-time sedentary time was calculated by summing time spent (hours) in a typical week in the past 3 months engaged in computer, computer games and Internet, video games, television or videos, and reading.27,28 This composite measure of activities outside of work included response categories ranging from <5 h to 45 or more hours per week. Sedentary time was subsequently dichotomized as ‘sedentary’ (≥20 h/wk) or ‘non-sedentary’ (<20 h/wk) (Supplementary material online, Table S1). Analogous to accelerometer measured groups; four self-reported leisure-time groups were created: (i) active/non-sedentary; (ii) active/sedentary; (iii) inactive/non-sedentary; and (iv) inactive/sedentary, with the active/non-sedentary group serving as the referent group. Outcome variables Participants were classified as having diabetes if they self-reported a diagnosis of diabetes or had elevated blood glucose (≥7.1 mmol/L) or HbA1c levels (≥6.5%).29 Cardiovascular disease (CVD), heart attack and stroke were self-reported. In order to have sufficient power for physical activity-by-sedentary time comparisons, diabetes, CVD, heart attack and stroke were collapsed into a single variable (‘1+ disease’). Obesity was defined by measured height and weight as a body mass index (BMI) ≥30 kg/m2. Metabolic syndrome (MetS) was classified according to the harmonized definition30 as having three or more of: elevated blood pressure (≥130/85 mmHg) or hypertensive medication use; abdominal obesity (waist circumference (WC) ≥ 102 cm (men) or 88 cm (women)); elevated triglycerides (TG) (≥1.69 mmol/L); low HDL (<1.04 mmol/L (men) or 1.29 mmol/L (women)) or cholesterol medication, or; elevated blood glucose (5.6 mmol/L) or diabetes medications. Physical fitness Aerobic fitness was determined using the Modified Canadian Aerobic Fitness Test31 step test, an indirect submaximal fitness test used to determine aerobic capacity.27,28 A composite musculoskeletal fitness score was derived from tests of grip strength, sit and reach, and partial curl ups.27,28 Both aerobic fitness and musculoskeletal fitness were scored on a 5-point scale (needs improvement—excellent) and were dichotomized as ‘high’ (good, very good, excellent) and ‘low’ (needs improvement, fair). Statistical analysis To compare baseline demographics within the sample, x2 and analysis of variance were used across to assess differences in frequency counts and mean values, respectively. Logistic regression was then applied to estimate the odds (OR, 95% confidence interval (CI)) of having 1+ disease, obesity, MetS and each individual MetS component, for each group (active/sedentary; inactive/non-sedentary; inactive/sedentary) compared to the active/non-sedentary referent group (OR = 1.00). This analysis was done twice, first with the self-reported leisure-time physical activity groups, and second with the objectively measured total activity groups. Models were adjusted for age, sex, education, ethnicity, income adequacy (total household income divided by number of residents), accelerometer wear time and BMI. Smoking status and alcohol consumption were initially included in the model but were not statistically significant and subsequently removed. All analyses were weighted to be representative of the Canadian population using survey procedures in SAS Version 9.4 (SAS Institute Inc., Cary, NC, USA). The bootstrap technique32 was used to calculate 95% CIs and standard errors. Analyses with cell counts under 10 were suppressed and statistical significance was set at α <0.05 for all analyses. Results Characteristics of the sample are described in Table 1. Comparing across accelerometer measured total activity groups, the active/non-sedentary group was the youngest (40.3 years) and primarily male (59.6%) while the inactive/sedentary group was the oldest (46.5 years) and primarily female (54.4%). The mean WC and BMI were lower in the active groups (non-sedentary, WC: 86.1 cm; BMI: 25.4 kg/m2 || sedentary, WC: 86.3 cm; BMI: 25.5 kg/m2) compared to the inactive groups (non-sedentary, WC: 91.8 cm; BMI: 27.4 kg/m2 || sedentary, WC: 91.6 cm; BMI: 27.2 kg/m2). There were overall significant differences between groups for income, SBP, DBP, Glucose, HDL, TG and Hba1c. Table 1 Weighted characteristics by accelerometer measured total activity groups Active Inactive Non-sedentary Sedentary Non-sedentary Sedentary P-value Age (years) N = 102 N = 623 N = 450 N = 4775 <0.0001 40.3 (33.7–47.1) 43.0 (40.9–45.0) 43.4 (40.8–46.0) 46.5 (45.9–47.0)bcd Sex N = 102 N = 623 N = 450 N = 4775 <0.05  Male 59.6% (37.7–81.6) 51.4% (46.6–56.2) 58.5% (50.3–66.7) 45.6% (44.1–47.2)d  Female 40.4% (18.4–62.3) 48.6% (43.8–53.4) 41.5% (33.3–49.7) 54.3% (52.8–55.9)d Ethnicity N = 102 N = 618 N = 449 N = 4693 NS  White 82.0% (65.7–98.3) 81.6% (74.7–88.4) 85.7% (79.4–91.9) 86.0% (80.4–91.6)  Other 18.0% (1.7–34.3) 18.4% (11.6–25.3) 14.3% (8.1–20.6) 14.0% (8.5–19.6) Education N = 102 N = 623 N = 450 N = 4733 NS  <HS 13.8% (5.9–25.5) 9.1% (5.8–12.3) 12.5% (9.0–16.0) 11.8% (10.0–13.6)  HS grad 36.9% (14.9–59.0) 27.4% (20.8–34.0) 32.4% (24.9–39.9) 24.9% (21.9–27.8)  University 49.2% (27.0–71.5) 63.6% (55.7–71.4) 55.2% (45.7–64.6) 63.3% (59.2–67.4) Income N = 97 N = 606 N = 437 N = 4648 <0.05  Low 23.7% (8.8–38.7) 18.0% (13.8–22.1) 18.9% (12.9–24.9) 18.1% (15.5–21.0)  Middle 49.9% (37.4–62.5) 26.0% (20.5–31.4)b 31.6% (24.5–38.6) 30.5% (27.4–33.6)  High 26.3% (12.0–40.7) 56.0% (49.4–62.8) 49.5% (40.5–58.5) 51.4% (47.6–55.2) Smoking N = 102 N = 621 N = 450 N = 4775 NS  Yes 21.2% (1.9–40.4) 13.6% (8.5–18.7) 26.0% (18.4–33.7) 18.1% (16.1–20.2)  Former 22.1% (5.9–38.3) 29.1% (24.0–34.3) 32.6% (22.2–42.9) 30.4% (27.4–33.3)  Never 56.7% (30.9–82.5) 57.3% (50.5–64.1) 41.4% (33.6–49.2)d 51.5% (48.3–54.7) Alcohol N = 84 N = 542 N = 379 N = 4020 NS  <1/wk 64.8% (45.0–84.6) 58.9% (53.4–64.3) 49.9% (41.7–58.1) 57.4% (54.2–61.0)  >1/wk 35.2% (15.4–55.0) 41.1% (35.7–46.6) 50.1% (41.9–58.3) 42.4% (39.0–45.8) WCa (cm) N = 102 N = 621 N = 447 N = 4711 <0.0001 86.1 (82.9–89.3) 86.3 (84.5–88.2) 91.8 (90.0–93.6)bd 91.6 (90.3–92.9)bd BMIa (kg/m2) N = 102 N = 621 N = 446 N = 4731 <0.0001 25.4 (24.1–26.6) 25.5 (25.0–26.1) 27.4 (26.7–28.2) 27.2 (26.8–27.7)d SBP (mmHg) N = 102 N = 623 N = 450 N = 4773 <0.05 112.4 (109.7–115.1) 111.0 (108.8–113.3) 113.7 (112.0–115.3) 112.8 (111.6–114.0) DBP (mmHg) N = 102 N = 623 N = 450 N = 4773 <0.0001 72.5 (70.8–74.1) 70.7 (69.2–72.3) 73.8 (72.4–75.2) 71.8 (71.0–72.5) Glucose (mM) N = 100 N = 619 N = 443 N = 4728 <0.05 4.9 (4.7–5.1) 4.9 (4.9–5.0) 4.9 (4.8–5.1) 5.1 (5.0–5.1)d HDL (mM) N = 98 N = 613 N = 439 N = 4715 <0.05 1.4 (1.2–1.5) 1.4 (1.4–1.5) 1.4 (1.3–1.4) 1.4 (1.4-1.4) TG (mM) N = 53 N = 326 N = 216 N = 2315 <0.0001 1.1 (0.9–1.2) 1.1 (1.0–1.2) 1.2 (1.1–1.4) 1.3 (1.3–1.4)bd Hba1c (%) N = 97 N = 604 N = 431 N = 4655 <0.0001 5.6 (5.4–5.7) 5.6 (5.5–5.7) 5.6 (5.5–5.7) 5.7 (5.6–5.8)d Active Inactive Non-sedentary Sedentary Non-sedentary Sedentary P-value Age (years) N = 102 N = 623 N = 450 N = 4775 <0.0001 40.3 (33.7–47.1) 43.0 (40.9–45.0) 43.4 (40.8–46.0) 46.5 (45.9–47.0)bcd Sex N = 102 N = 623 N = 450 N = 4775 <0.05  Male 59.6% (37.7–81.6) 51.4% (46.6–56.2) 58.5% (50.3–66.7) 45.6% (44.1–47.2)d  Female 40.4% (18.4–62.3) 48.6% (43.8–53.4) 41.5% (33.3–49.7) 54.3% (52.8–55.9)d Ethnicity N = 102 N = 618 N = 449 N = 4693 NS  White 82.0% (65.7–98.3) 81.6% (74.7–88.4) 85.7% (79.4–91.9) 86.0% (80.4–91.6)  Other 18.0% (1.7–34.3) 18.4% (11.6–25.3) 14.3% (8.1–20.6) 14.0% (8.5–19.6) Education N = 102 N = 623 N = 450 N = 4733 NS  <HS 13.8% (5.9–25.5) 9.1% (5.8–12.3) 12.5% (9.0–16.0) 11.8% (10.0–13.6)  HS grad 36.9% (14.9–59.0) 27.4% (20.8–34.0) 32.4% (24.9–39.9) 24.9% (21.9–27.8)  University 49.2% (27.0–71.5) 63.6% (55.7–71.4) 55.2% (45.7–64.6) 63.3% (59.2–67.4) Income N = 97 N = 606 N = 437 N = 4648 <0.05  Low 23.7% (8.8–38.7) 18.0% (13.8–22.1) 18.9% (12.9–24.9) 18.1% (15.5–21.0)  Middle 49.9% (37.4–62.5) 26.0% (20.5–31.4)b 31.6% (24.5–38.6) 30.5% (27.4–33.6)  High 26.3% (12.0–40.7) 56.0% (49.4–62.8) 49.5% (40.5–58.5) 51.4% (47.6–55.2) Smoking N = 102 N = 621 N = 450 N = 4775 NS  Yes 21.2% (1.9–40.4) 13.6% (8.5–18.7) 26.0% (18.4–33.7) 18.1% (16.1–20.2)  Former 22.1% (5.9–38.3) 29.1% (24.0–34.3) 32.6% (22.2–42.9) 30.4% (27.4–33.3)  Never 56.7% (30.9–82.5) 57.3% (50.5–64.1) 41.4% (33.6–49.2)d 51.5% (48.3–54.7) Alcohol N = 84 N = 542 N = 379 N = 4020 NS  <1/wk 64.8% (45.0–84.6) 58.9% (53.4–64.3) 49.9% (41.7–58.1) 57.4% (54.2–61.0)  >1/wk 35.2% (15.4–55.0) 41.1% (35.7–46.6) 50.1% (41.9–58.3) 42.4% (39.0–45.8) WCa (cm) N = 102 N = 621 N = 447 N = 4711 <0.0001 86.1 (82.9–89.3) 86.3 (84.5–88.2) 91.8 (90.0–93.6)bd 91.6 (90.3–92.9)bd BMIa (kg/m2) N = 102 N = 621 N = 446 N = 4731 <0.0001 25.4 (24.1–26.6) 25.5 (25.0–26.1) 27.4 (26.7–28.2) 27.2 (26.8–27.7)d SBP (mmHg) N = 102 N = 623 N = 450 N = 4773 <0.05 112.4 (109.7–115.1) 111.0 (108.8–113.3) 113.7 (112.0–115.3) 112.8 (111.6–114.0) DBP (mmHg) N = 102 N = 623 N = 450 N = 4773 <0.0001 72.5 (70.8–74.1) 70.7 (69.2–72.3) 73.8 (72.4–75.2) 71.8 (71.0–72.5) Glucose (mM) N = 100 N = 619 N = 443 N = 4728 <0.05 4.9 (4.7–5.1) 4.9 (4.9–5.0) 4.9 (4.8–5.1) 5.1 (5.0–5.1)d HDL (mM) N = 98 N = 613 N = 439 N = 4715 <0.05 1.4 (1.2–1.5) 1.4 (1.4–1.5) 1.4 (1.3–1.4) 1.4 (1.4-1.4) TG (mM) N = 53 N = 326 N = 216 N = 2315 <0.0001 1.1 (0.9–1.2) 1.1 (1.0–1.2) 1.2 (1.1–1.4) 1.3 (1.3–1.4)bd Hba1c (%) N = 97 N = 604 N = 431 N = 4655 <0.0001 5.6 (5.4–5.7) 5.6 (5.5–5.7) 5.6 (5.5–5.7) 5.7 (5.6–5.8)d Mean or prevalence (%) and 95% confidence interval. HS, high school; NS, not significant. aPregnant women excluded. bSignificantly different from active/non-sedentary. cSignificantly different from active/sedentary group. dSignificantly different from inactive/non-sedentary group. Table 1 Weighted characteristics by accelerometer measured total activity groups Active Inactive Non-sedentary Sedentary Non-sedentary Sedentary P-value Age (years) N = 102 N = 623 N = 450 N = 4775 <0.0001 40.3 (33.7–47.1) 43.0 (40.9–45.0) 43.4 (40.8–46.0) 46.5 (45.9–47.0)bcd Sex N = 102 N = 623 N = 450 N = 4775 <0.05  Male 59.6% (37.7–81.6) 51.4% (46.6–56.2) 58.5% (50.3–66.7) 45.6% (44.1–47.2)d  Female 40.4% (18.4–62.3) 48.6% (43.8–53.4) 41.5% (33.3–49.7) 54.3% (52.8–55.9)d Ethnicity N = 102 N = 618 N = 449 N = 4693 NS  White 82.0% (65.7–98.3) 81.6% (74.7–88.4) 85.7% (79.4–91.9) 86.0% (80.4–91.6)  Other 18.0% (1.7–34.3) 18.4% (11.6–25.3) 14.3% (8.1–20.6) 14.0% (8.5–19.6) Education N = 102 N = 623 N = 450 N = 4733 NS  <HS 13.8% (5.9–25.5) 9.1% (5.8–12.3) 12.5% (9.0–16.0) 11.8% (10.0–13.6)  HS grad 36.9% (14.9–59.0) 27.4% (20.8–34.0) 32.4% (24.9–39.9) 24.9% (21.9–27.8)  University 49.2% (27.0–71.5) 63.6% (55.7–71.4) 55.2% (45.7–64.6) 63.3% (59.2–67.4) Income N = 97 N = 606 N = 437 N = 4648 <0.05  Low 23.7% (8.8–38.7) 18.0% (13.8–22.1) 18.9% (12.9–24.9) 18.1% (15.5–21.0)  Middle 49.9% (37.4–62.5) 26.0% (20.5–31.4)b 31.6% (24.5–38.6) 30.5% (27.4–33.6)  High 26.3% (12.0–40.7) 56.0% (49.4–62.8) 49.5% (40.5–58.5) 51.4% (47.6–55.2) Smoking N = 102 N = 621 N = 450 N = 4775 NS  Yes 21.2% (1.9–40.4) 13.6% (8.5–18.7) 26.0% (18.4–33.7) 18.1% (16.1–20.2)  Former 22.1% (5.9–38.3) 29.1% (24.0–34.3) 32.6% (22.2–42.9) 30.4% (27.4–33.3)  Never 56.7% (30.9–82.5) 57.3% (50.5–64.1) 41.4% (33.6–49.2)d 51.5% (48.3–54.7) Alcohol N = 84 N = 542 N = 379 N = 4020 NS  <1/wk 64.8% (45.0–84.6) 58.9% (53.4–64.3) 49.9% (41.7–58.1) 57.4% (54.2–61.0)  >1/wk 35.2% (15.4–55.0) 41.1% (35.7–46.6) 50.1% (41.9–58.3) 42.4% (39.0–45.8) WCa (cm) N = 102 N = 621 N = 447 N = 4711 <0.0001 86.1 (82.9–89.3) 86.3 (84.5–88.2) 91.8 (90.0–93.6)bd 91.6 (90.3–92.9)bd BMIa (kg/m2) N = 102 N = 621 N = 446 N = 4731 <0.0001 25.4 (24.1–26.6) 25.5 (25.0–26.1) 27.4 (26.7–28.2) 27.2 (26.8–27.7)d SBP (mmHg) N = 102 N = 623 N = 450 N = 4773 <0.05 112.4 (109.7–115.1) 111.0 (108.8–113.3) 113.7 (112.0–115.3) 112.8 (111.6–114.0) DBP (mmHg) N = 102 N = 623 N = 450 N = 4773 <0.0001 72.5 (70.8–74.1) 70.7 (69.2–72.3) 73.8 (72.4–75.2) 71.8 (71.0–72.5) Glucose (mM) N = 100 N = 619 N = 443 N = 4728 <0.05 4.9 (4.7–5.1) 4.9 (4.9–5.0) 4.9 (4.8–5.1) 5.1 (5.0–5.1)d HDL (mM) N = 98 N = 613 N = 439 N = 4715 <0.05 1.4 (1.2–1.5) 1.4 (1.4–1.5) 1.4 (1.3–1.4) 1.4 (1.4-1.4) TG (mM) N = 53 N = 326 N = 216 N = 2315 <0.0001 1.1 (0.9–1.2) 1.1 (1.0–1.2) 1.2 (1.1–1.4) 1.3 (1.3–1.4)bd Hba1c (%) N = 97 N = 604 N = 431 N = 4655 <0.0001 5.6 (5.4–5.7) 5.6 (5.5–5.7) 5.6 (5.5–5.7) 5.7 (5.6–5.8)d Active Inactive Non-sedentary Sedentary Non-sedentary Sedentary P-value Age (years) N = 102 N = 623 N = 450 N = 4775 <0.0001 40.3 (33.7–47.1) 43.0 (40.9–45.0) 43.4 (40.8–46.0) 46.5 (45.9–47.0)bcd Sex N = 102 N = 623 N = 450 N = 4775 <0.05  Male 59.6% (37.7–81.6) 51.4% (46.6–56.2) 58.5% (50.3–66.7) 45.6% (44.1–47.2)d  Female 40.4% (18.4–62.3) 48.6% (43.8–53.4) 41.5% (33.3–49.7) 54.3% (52.8–55.9)d Ethnicity N = 102 N = 618 N = 449 N = 4693 NS  White 82.0% (65.7–98.3) 81.6% (74.7–88.4) 85.7% (79.4–91.9) 86.0% (80.4–91.6)  Other 18.0% (1.7–34.3) 18.4% (11.6–25.3) 14.3% (8.1–20.6) 14.0% (8.5–19.6) Education N = 102 N = 623 N = 450 N = 4733 NS  <HS 13.8% (5.9–25.5) 9.1% (5.8–12.3) 12.5% (9.0–16.0) 11.8% (10.0–13.6)  HS grad 36.9% (14.9–59.0) 27.4% (20.8–34.0) 32.4% (24.9–39.9) 24.9% (21.9–27.8)  University 49.2% (27.0–71.5) 63.6% (55.7–71.4) 55.2% (45.7–64.6) 63.3% (59.2–67.4) Income N = 97 N = 606 N = 437 N = 4648 <0.05  Low 23.7% (8.8–38.7) 18.0% (13.8–22.1) 18.9% (12.9–24.9) 18.1% (15.5–21.0)  Middle 49.9% (37.4–62.5) 26.0% (20.5–31.4)b 31.6% (24.5–38.6) 30.5% (27.4–33.6)  High 26.3% (12.0–40.7) 56.0% (49.4–62.8) 49.5% (40.5–58.5) 51.4% (47.6–55.2) Smoking N = 102 N = 621 N = 450 N = 4775 NS  Yes 21.2% (1.9–40.4) 13.6% (8.5–18.7) 26.0% (18.4–33.7) 18.1% (16.1–20.2)  Former 22.1% (5.9–38.3) 29.1% (24.0–34.3) 32.6% (22.2–42.9) 30.4% (27.4–33.3)  Never 56.7% (30.9–82.5) 57.3% (50.5–64.1) 41.4% (33.6–49.2)d 51.5% (48.3–54.7) Alcohol N = 84 N = 542 N = 379 N = 4020 NS  <1/wk 64.8% (45.0–84.6) 58.9% (53.4–64.3) 49.9% (41.7–58.1) 57.4% (54.2–61.0)  >1/wk 35.2% (15.4–55.0) 41.1% (35.7–46.6) 50.1% (41.9–58.3) 42.4% (39.0–45.8) WCa (cm) N = 102 N = 621 N = 447 N = 4711 <0.0001 86.1 (82.9–89.3) 86.3 (84.5–88.2) 91.8 (90.0–93.6)bd 91.6 (90.3–92.9)bd BMIa (kg/m2) N = 102 N = 621 N = 446 N = 4731 <0.0001 25.4 (24.1–26.6) 25.5 (25.0–26.1) 27.4 (26.7–28.2) 27.2 (26.8–27.7)d SBP (mmHg) N = 102 N = 623 N = 450 N = 4773 <0.05 112.4 (109.7–115.1) 111.0 (108.8–113.3) 113.7 (112.0–115.3) 112.8 (111.6–114.0) DBP (mmHg) N = 102 N = 623 N = 450 N = 4773 <0.0001 72.5 (70.8–74.1) 70.7 (69.2–72.3) 73.8 (72.4–75.2) 71.8 (71.0–72.5) Glucose (mM) N = 100 N = 619 N = 443 N = 4728 <0.05 4.9 (4.7–5.1) 4.9 (4.9–5.0) 4.9 (4.8–5.1) 5.1 (5.0–5.1)d HDL (mM) N = 98 N = 613 N = 439 N = 4715 <0.05 1.4 (1.2–1.5) 1.4 (1.4–1.5) 1.4 (1.3–1.4) 1.4 (1.4-1.4) TG (mM) N = 53 N = 326 N = 216 N = 2315 <0.0001 1.1 (0.9–1.2) 1.1 (1.0–1.2) 1.2 (1.1–1.4) 1.3 (1.3–1.4)bd Hba1c (%) N = 97 N = 604 N = 431 N = 4655 <0.0001 5.6 (5.4–5.7) 5.6 (5.5–5.7) 5.6 (5.5–5.7) 5.7 (5.6–5.8)d Mean or prevalence (%) and 95% confidence interval. HS, high school; NS, not significant. aPregnant women excluded. bSignificantly different from active/non-sedentary. cSignificantly different from active/sedentary group. dSignificantly different from inactive/non-sedentary group. The mean time spent in MVPA (see Supplementary material online, Table S2) decreased systematically across accelerometer measured total activity groups. Active groups accumulated 77.0 min/day (non-sedentary) and 53.2 min/day (sedentary) while inactive groups accumulated 26.3 min/day (non-sedentary) and 16.4 min/day (sedentary). Across self-report leisure-time groups, MVPA ranged from 18.3 to 33.0 min/day. Daily sedentary time ranged from 425.2 to 601.9 min/day and from 570.2 to 591.7 min/day in accelerometer measured groups and self-report groups, respectively. Prevalence of chronic disease and MetS components are shown in Fig. 1. Overall, only the self-reported leisure-time inactive/sedentary groups had a higher prevalence of every chronic disease. Within the accelerometer measured groups, obesity, abdominal obesity, elevated blood glucose, TG and HDL were significantly different across all groups (P < 0.05). When compared to the referent group, the prevalence of abdominal obesity was significantly greater in the inactive/sedentary group (36.6 versus 15.2%) while the prevalence of elevated blood pressure was significantly greater in both sedentary groups (active: 23.1 versus 14.6%; inactive: 27.8 versus 14.6%). Within self-report leisure-time groups, all components of MetS varied across groups, and both sedentary groups had a significantly higher prevalence of abdominal obesity (active: 30.0 versus 20.6%; inactive: 43.2 versus 20.6%) and elevated blood pressure (active: 28.4 versus 16.9%; inactive: 32.5 versus 16.9%). Fig. 1 View largeDownload slide Prevalence of chronic disease and metabolic syndrome components by accelerometer measured groups (Accel) and self-report groups (S-R). Prevalence (%) and 95% confidence intervals || N-Estimate suppressed. **Significant for overall x2 for both Accel and S-R. *Significantly different from referent group (active/non-sedentary). (A) 1+ disease, (B) metabolic syndrome, (C) obesity, (D) abdominal obesity, (E) elevated blood pressure, (F) elevated blood glucose, (G) elevated triglycerides and (H) low HDL. Fig. 1 View largeDownload slide Prevalence of chronic disease and metabolic syndrome components by accelerometer measured groups (Accel) and self-report groups (S-R). Prevalence (%) and 95% confidence intervals || N-Estimate suppressed. **Significant for overall x2 for both Accel and S-R. *Significantly different from referent group (active/non-sedentary). (A) 1+ disease, (B) metabolic syndrome, (C) obesity, (D) abdominal obesity, (E) elevated blood pressure, (F) elevated blood glucose, (G) elevated triglycerides and (H) low HDL. Aerobic fitness levels (Fig. 2) were similar between accelerometer measured total activity and self-reported leisure-time groups. When measured by accelerometer, 75.4% of the referent group had high aerobic fitness while 70.2% of the referent group did by self-report. Conversely, the prevalence of high musculoskeletal fitness was significantly lower in the inactive/sedentary group (51.2%) relative to the referent group (70.8%) in the self-report leisure-time groups. Fig. 2 View largeDownload slide Prevalence of ‘high’ fitness levels by accelerometer measured groups (Accel) and self-report groups (S-R). Prevalence (%) and 95% confidence interval. High musculoskeletal fitness—‘good’ rating or higher. **Significant for overall x2 for both Accel and S-R. *Significantly different from referent group (active/non-sedentary). (A) Aerobic fitness and (B) musculo skeletal fitness. Fig. 2 View largeDownload slide Prevalence of ‘high’ fitness levels by accelerometer measured groups (Accel) and self-report groups (S-R). Prevalence (%) and 95% confidence interval. High musculoskeletal fitness—‘good’ rating or higher. **Significant for overall x2 for both Accel and S-R. *Significantly different from referent group (active/non-sedentary). (A) Aerobic fitness and (B) musculo skeletal fitness. The age and sex adjusted odds ratio (95% CI) for chronic disease and MetS revealed various significant relationships within accelerometer measured total activity and self-reported leisure-time groups. Upon including ethnicity, education, income, accelerometer wear time and BMI into the models, only two relationships retained significance within accelerometer measured groups (Table 2). Table 2 Multivariable adjusted odds ratios of chronic disease and individual metabolic syndrome components by accelerometer measured total activity groups (Accel) and self-reported leisure-time groups (S-R) Active Inactive Chronic disease Non-sedentary Sedentary Non-sedentary Sedentary 1+ Disease  Accel 1.00 1.57 (0.71, 3.48) 1.37 (0.64, 2.95) 3.05 (1.47, 6.34)  S-R 1.00 0.72 (0.42, 1.23) 1.08 (0.64, 1.82) 1.26 (0.79, 2.01) Obesityab  Accel 1.00 0.79 (0.20, 3.15) 1.25 (0.32, 4.86) 1.53 (0.38, 6.08)  S-R 1.00 1.52 (0.86, 2.67) 1.40 (0.87, 2.24) 2.77 (1.63, 4.70) MetSa  Accel 1.00 1.65 (0.36, 7.47) 1.19 (0.29, 4.88) 1.94 (0.52, 7.29)  S-R 1.00 1.77 (0.88, 3.55) 2.20 (1.13, 4.29) 2.87 (1.39, 5.94) MetS components Abdominal obesityab  Accel 1.00 1.62 (0.69, 3.81) 2.38 (0.91, 6.23) 2.75 (1.16, 6.55)  S-R 1.00 1.59 (1.09, 2.31) 1.55 (0.92, 2.60) 2.88 (1.86, 4.46) Blood pressure  Accel 1.00 1.38 (0.73, 2.62) 1.65 (0.68, 4.04) 1.36 (0.71, 2.61)  S-R 1.00 1.28 (0.79, 2.08) 1.41 (0.87, 2.29) 1.52 (0.99, 2.35) Glucose  Accel 1.00 1.10 (0.52, 2.34) 1.58 (0.55, 4.57) 1.70 (0.82, 3.55)  S-R 1.00 0.92 (0.57, 1.48) 1.28 (0.79, 2.06) 1.13 (0.69, 1.85) TG  Accel 1.00 1.65 (0.28, 9.73) 2.03 (0.35, 11.78) 2.44 (0.43, 13.95)  S-R 1.00 0.93 (0.44, 1.93) 1.40 (0.76, 2.57) 2.09 (1.25, 3.50) HDL  Accel 1.00 2.44 (0.67, 8.88) 2.08 (0.59–7.32) 2.90 (0.85, 9.91)  S-R 1.00 1.08 (0.76, 1.53) 1.23 (0.80, 1.91) 1.32 (0.94, 1.85) Active Inactive Chronic disease Non-sedentary Sedentary Non-sedentary Sedentary 1+ Disease  Accel 1.00 1.57 (0.71, 3.48) 1.37 (0.64, 2.95) 3.05 (1.47, 6.34)  S-R 1.00 0.72 (0.42, 1.23) 1.08 (0.64, 1.82) 1.26 (0.79, 2.01) Obesityab  Accel 1.00 0.79 (0.20, 3.15) 1.25 (0.32, 4.86) 1.53 (0.38, 6.08)  S-R 1.00 1.52 (0.86, 2.67) 1.40 (0.87, 2.24) 2.77 (1.63, 4.70) MetSa  Accel 1.00 1.65 (0.36, 7.47) 1.19 (0.29, 4.88) 1.94 (0.52, 7.29)  S-R 1.00 1.77 (0.88, 3.55) 2.20 (1.13, 4.29) 2.87 (1.39, 5.94) MetS components Abdominal obesityab  Accel 1.00 1.62 (0.69, 3.81) 2.38 (0.91, 6.23) 2.75 (1.16, 6.55)  S-R 1.00 1.59 (1.09, 2.31) 1.55 (0.92, 2.60) 2.88 (1.86, 4.46) Blood pressure  Accel 1.00 1.38 (0.73, 2.62) 1.65 (0.68, 4.04) 1.36 (0.71, 2.61)  S-R 1.00 1.28 (0.79, 2.08) 1.41 (0.87, 2.29) 1.52 (0.99, 2.35) Glucose  Accel 1.00 1.10 (0.52, 2.34) 1.58 (0.55, 4.57) 1.70 (0.82, 3.55)  S-R 1.00 0.92 (0.57, 1.48) 1.28 (0.79, 2.06) 1.13 (0.69, 1.85) TG  Accel 1.00 1.65 (0.28, 9.73) 2.03 (0.35, 11.78) 2.44 (0.43, 13.95)  S-R 1.00 0.93 (0.44, 1.93) 1.40 (0.76, 2.57) 2.09 (1.25, 3.50) HDL  Accel 1.00 2.44 (0.67, 8.88) 2.08 (0.59–7.32) 2.90 (0.85, 9.91)  S-R 1.00 1.08 (0.76, 1.53) 1.23 (0.80, 1.91) 1.32 (0.94, 1.85) Odds ratios and 95% confidence intervals. Adjusted for age, sex, ethnicity, education, income, wear time and BMI. Chronic disease—1+ disease: 1 or more of diabetes, myocardial infarction, stroke or cardiovascular disease; Obesity: BMI ≥ 30 kg/m2; MetS: ≥3 components. MetS components—abdominal obesity: ≥102 cm (men) and ≥88 cm (women); blood pressure: ≥130 mmHg (systolic) or ≥85 mmHg (diastolic); Glucose: ≥5.6 mM; triglycerides: ≥1.69 mM; HDL < 1.04 (men) and <1.29 (women). Self-report groups based on leisure-time activity/sedentary time cut-point. Bold indicates p < 0.05. Accel, accelerometer measured group; S-R, self-reported group; TG, triglycerides; Abd. obesity, abdominal obesity. aPregnant women excluded. bNot adjusted for BMI. Table 2 Multivariable adjusted odds ratios of chronic disease and individual metabolic syndrome components by accelerometer measured total activity groups (Accel) and self-reported leisure-time groups (S-R) Active Inactive Chronic disease Non-sedentary Sedentary Non-sedentary Sedentary 1+ Disease  Accel 1.00 1.57 (0.71, 3.48) 1.37 (0.64, 2.95) 3.05 (1.47, 6.34)  S-R 1.00 0.72 (0.42, 1.23) 1.08 (0.64, 1.82) 1.26 (0.79, 2.01) Obesityab  Accel 1.00 0.79 (0.20, 3.15) 1.25 (0.32, 4.86) 1.53 (0.38, 6.08)  S-R 1.00 1.52 (0.86, 2.67) 1.40 (0.87, 2.24) 2.77 (1.63, 4.70) MetSa  Accel 1.00 1.65 (0.36, 7.47) 1.19 (0.29, 4.88) 1.94 (0.52, 7.29)  S-R 1.00 1.77 (0.88, 3.55) 2.20 (1.13, 4.29) 2.87 (1.39, 5.94) MetS components Abdominal obesityab  Accel 1.00 1.62 (0.69, 3.81) 2.38 (0.91, 6.23) 2.75 (1.16, 6.55)  S-R 1.00 1.59 (1.09, 2.31) 1.55 (0.92, 2.60) 2.88 (1.86, 4.46) Blood pressure  Accel 1.00 1.38 (0.73, 2.62) 1.65 (0.68, 4.04) 1.36 (0.71, 2.61)  S-R 1.00 1.28 (0.79, 2.08) 1.41 (0.87, 2.29) 1.52 (0.99, 2.35) Glucose  Accel 1.00 1.10 (0.52, 2.34) 1.58 (0.55, 4.57) 1.70 (0.82, 3.55)  S-R 1.00 0.92 (0.57, 1.48) 1.28 (0.79, 2.06) 1.13 (0.69, 1.85) TG  Accel 1.00 1.65 (0.28, 9.73) 2.03 (0.35, 11.78) 2.44 (0.43, 13.95)  S-R 1.00 0.93 (0.44, 1.93) 1.40 (0.76, 2.57) 2.09 (1.25, 3.50) HDL  Accel 1.00 2.44 (0.67, 8.88) 2.08 (0.59–7.32) 2.90 (0.85, 9.91)  S-R 1.00 1.08 (0.76, 1.53) 1.23 (0.80, 1.91) 1.32 (0.94, 1.85) Active Inactive Chronic disease Non-sedentary Sedentary Non-sedentary Sedentary 1+ Disease  Accel 1.00 1.57 (0.71, 3.48) 1.37 (0.64, 2.95) 3.05 (1.47, 6.34)  S-R 1.00 0.72 (0.42, 1.23) 1.08 (0.64, 1.82) 1.26 (0.79, 2.01) Obesityab  Accel 1.00 0.79 (0.20, 3.15) 1.25 (0.32, 4.86) 1.53 (0.38, 6.08)  S-R 1.00 1.52 (0.86, 2.67) 1.40 (0.87, 2.24) 2.77 (1.63, 4.70) MetSa  Accel 1.00 1.65 (0.36, 7.47) 1.19 (0.29, 4.88) 1.94 (0.52, 7.29)  S-R 1.00 1.77 (0.88, 3.55) 2.20 (1.13, 4.29) 2.87 (1.39, 5.94) MetS components Abdominal obesityab  Accel 1.00 1.62 (0.69, 3.81) 2.38 (0.91, 6.23) 2.75 (1.16, 6.55)  S-R 1.00 1.59 (1.09, 2.31) 1.55 (0.92, 2.60) 2.88 (1.86, 4.46) Blood pressure  Accel 1.00 1.38 (0.73, 2.62) 1.65 (0.68, 4.04) 1.36 (0.71, 2.61)  S-R 1.00 1.28 (0.79, 2.08) 1.41 (0.87, 2.29) 1.52 (0.99, 2.35) Glucose  Accel 1.00 1.10 (0.52, 2.34) 1.58 (0.55, 4.57) 1.70 (0.82, 3.55)  S-R 1.00 0.92 (0.57, 1.48) 1.28 (0.79, 2.06) 1.13 (0.69, 1.85) TG  Accel 1.00 1.65 (0.28, 9.73) 2.03 (0.35, 11.78) 2.44 (0.43, 13.95)  S-R 1.00 0.93 (0.44, 1.93) 1.40 (0.76, 2.57) 2.09 (1.25, 3.50) HDL  Accel 1.00 2.44 (0.67, 8.88) 2.08 (0.59–7.32) 2.90 (0.85, 9.91)  S-R 1.00 1.08 (0.76, 1.53) 1.23 (0.80, 1.91) 1.32 (0.94, 1.85) Odds ratios and 95% confidence intervals. Adjusted for age, sex, ethnicity, education, income, wear time and BMI. Chronic disease—1+ disease: 1 or more of diabetes, myocardial infarction, stroke or cardiovascular disease; Obesity: BMI ≥ 30 kg/m2; MetS: ≥3 components. MetS components—abdominal obesity: ≥102 cm (men) and ≥88 cm (women); blood pressure: ≥130 mmHg (systolic) or ≥85 mmHg (diastolic); Glucose: ≥5.6 mM; triglycerides: ≥1.69 mM; HDL < 1.04 (men) and <1.29 (women). Self-report groups based on leisure-time activity/sedentary time cut-point. Bold indicates p < 0.05. Accel, accelerometer measured group; S-R, self-reported group; TG, triglycerides; Abd. obesity, abdominal obesity. aPregnant women excluded. bNot adjusted for BMI. Discussion Main finding of this study The results of the present study demonstrate that, when measured objectively, not meeting physical activity guidelines in combination with being sedentary (≥480 min/day) is associated with greater odds of abdominal obesity and having a chronic disease. However, the associations differed when measured by self-reported leisure-time activity. What is already known on this topic Objectively measured physical activity/sedentary time and metabolic health Numerous studies have noted the independent effects of sedentary time and MVPA on metabolic health and CVD.9,11,33–35 Similar to our study, Healy et al.11 noted significant associations between time spent in sedentary activities and MVPA with abdominal obesity, while Chomistek et al.33 noted the joint effect of low physical activity with prolonged sitting increased the risk of CVD relative to highly active and non-sedentary women. Comparable to a previous self-report study examining steps/day and BMI by cross classifying sufficient/insufficiently active and low/high occupational sitting time into four groups,36 the active/sedentary and inactive/non-sedentary phenotypes displayed similar BMIs and steps/day. Likewise, in the present study the active/sedentary and inactive/non-sedentary groups displayed similar metabolic risk profiles and neither group had significantly greater odds of any of the observed outcomes relative to the referent group. The finding that the effect of prolonged sitting (≥480 min/day) on metabolic risk is attenuated by meeting the physical activity guidelines is consistent with previous research34,35; however, the finding that the excess risk incurred by being inactive is offset by low sitting time for all outcomes is, to the authors’ knowledge, novel. Although only two groups (active/non-sedentary; active/sedentary) in our study actually achieved the recommended level of physical activity, it is notable that three groups (active/non-sedentary; active/sedentary; and inactive/non-sedentary) all averaged ≥10 000 steps/day, a threshold proposed as a reasonable target to be categorized as ‘active’.37 In line with this, the inactive/sedentary group in our study had a significantly lower prevalence of ‘high’ aerobic fitness, while the active/sedentary group and the inactive/non-sedentary groups did not differ from the referent. What this study adds Accelerometers versus self-report Accelerometer measured total physical activity and sedentary time was associated with abdominal obesity and 1+ disease, with only the inactive/sedentary group demonstrating elevated risk. However, associations were observed for several distinct outcomes in addition to abdominal obesity, namely, MetS, obesity and elevated TG, when measured by self-reported leisure-time activity. Similar to the accelerometer measured groups, self-reported leisure-time groups yielded higher odds of obesity and metabolic risk predominantly in the inactive/sedentary group. In addition, MetS and abdominal obesity displayed elevated odds in the active/sedentary (abdominal obesity) or inactive/non-sedentary (MetS) groups in the self-report groups. These findings are in contrast to two previous studies which found stronger associations between objectively assessed physical activity and metabolic health as compared to self-report.9,38 Similar to our study, Atienza et al.’s38 self-reported physical activity did not capture occupational physical activity; however, they did not account for sedentary time. Celis-Morales et al.9 measured self-reported activity using the International physical activity questionnaire (IPAQ)39 which accounts for both sitting time and occupational activity. The extent to which differences in the measurement tools could have contributed to this divergent finding is unclear. Atienza et al.38 proposed that muscular strength could account for the differences in metabolic risk between objective and self-reported physical activity due to its inverse association with metabolic risk.40 This may partially explain the differences in our sample as musculoskeletal fitness varied across self-report leisure-time groups, but not accelerometer measured groups. Here, the inactive/sedentary group had a significantly lower prevalence of ‘high’ musculoskeletal fitness relative to the referent group. There are also several alternative explanations for the weaker observed relationship between objectively measured activity and metabolic health. First, the sedentary cut-point of 100 CPM does not distinguish between different sedentary activities such as standing and sitting, meaning that important differences in total EE and blood glucose levels could be masked within our objectively measured sedentary groups.41 Other intensities are susceptible to misclassification due to cut-point ambiguity. CHMS cut-points were 100–1534 and ≥1535 CPM for light intensity activity and MVPA, respectively; however, previous studies have used different cut-points42,43 when using the same monitors. Therefore, it is possible the cut-points used in the CHMS do not capture intensity appropriately in all individuals, and may misclassify some participants. Indeed, the prevalence of MVPA was 21.8% by self-report and 12.2% by accelerometry, whereas, non-sedentary time was also much higher by self-report (41.5%) than objective measure (9.3%). The level of agreement in accelerometer versus self-report physical activity was κ = 0.22 and 0.038 in accelerometer versus self-report sedentary time, highlighting the difficulties in accurately capturing sedentary activities. Second, accelerometers are prone to the Hawthorne effect (reactivity), wherein participants who are aware of being observed (via accelerometry) may increase their physical activity level during the course of the study.44 Lastly, our self-reported activity measure only accounted for leisure-time physical activity and sedentary time and did not capture occupational sitting. Limitations of this study There are several limitations that warrant discussion. First, because the study is cross-sectional, causality cannot be inferred. Second, although a missing sample analysis revealed minimal differences between the full sample and those with valid accelerometer data, we cannot exclude the possibility of a healthy responder effect.44 Third, self-reported physical activity is also subject to recall bias and influence from social desirability,20 which may bias towards the null. Because aerobic fitness was measured using a submaximal step test, it may also underestimate actual VO2 for some participants, whereas BMI may not reflect the same body composition in younger and older adults.45 Lastly, dietary intake was not accounted for, and may differ between activity groups. Implications The main finding of this study was that self-report leisure-time physical activity and sedentary time demonstrate different associations with metabolic health compared to accelerometer measured activity. Using accelerometer data, the inactive/sedentary group was at higher risk for 1+ disease and abdominal obesity only, whereas the active/sedentary and inactive/non-sedentary groups were not at higher risk for any health outcome. Given that traditional self-reported and accelerometer-derived activity data may identify different aspects of health,39 complementary use of these methods may still provide value. Supplementary data Supplementary material is available at Journal of Public Health online. Acknowledgements This research was conducted at the Canadian Research Data Centre Network (CRDCN). Although the research and analysis are based on data from Statistics Canada, the opinions expressed are those of the authors alone. References 1 Bruce MJ , Katzmarzyk PT . Canadian population trends in leisure-time physical activity levels, 1981–1998 . Can J Appl Physiol 2002 ; 6 : 681 – 90 . Google Scholar CrossRef Search ADS 2 Craig CL , Russell SJ , Cameron C et al. . 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Journal of Public HealthOxford University Press

Published: Apr 5, 2018

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