A wealth of scientific evidence has shown that regular physical activity (PA) improves metabolic health and reduces the risk of cardiovascular disease (11,32). However, less than 50% of U.S. adults obtain the recommended minimum of 150 min·wk−1 of at least moderate-intensity activity (equivalent to brisk walking) or 75 min·wk−1 of vigorous-intensity activity (11,30) according to self-report (5), and fewer than 5% do so according to recent objective estimates (29). Furthermore, data from nationally representative samples suggest that there are considerable ethnic differences in the achieved level of PA. For example, racial and ethnic minorities report lower levels of leisure-time PA but higher levels of work-related PA compared with white men and women (4,5,8,19). Whether these differences contribute to the persistent gap in metabolic risk among ethnic minorities is not known (16,26). Few studies have systematically investigated the relationship between the amount and the intensity of PA and metabolic health across ethnicity (26).
The lack of certainty in the relationship between PA dose (duration and intensity) and health outcomes is in part due to the difficulty of accurately measuring PA. Historically, PA has been assessed by self-report questionnaires. These subjective instruments suffer from significant measurement error because of recall bias, lack of precision in quantifying intensity, and cultural biases in the perceived desirability of PA (25). More recently, wearable electronic devices, particularly accelerometers, have gained widespread use in the assessment of free-living PA (6,12,29). Accelerometers are small motion sensors that measure the intensity of movement minute-by-minute over multiple days, providing an objective quantification of the frequency, duration, and intensity of activity. As such, accelerometers are an ideal tool to study the relationship between PA dose and health outcomes.
To elucidate the relationship between PA dose and metabolic risk factors in different ethnic groups, we measured PA using the Actical accelerometer in 2566 non-Hispanic white, non-Hispanic black, and Hispanic individuals from the Dallas Heart Study (DHS), a population-based cohort from Dallas, TX. Although several previous studies have investigated the association between PA and metabolic risk factors using objective measures of PA, the majority of these studies were in small, homogeneous samples (3,13) or did not explicitly study the dose–response relationship by ethnicity (1,2). To our knowledge, this is the largest multiethnic population-based study (including more than 50% of non-White participants) to examine the relationship between PA and metabolic risk factors.
The DHS is a multiethnic population-based probability sample of Dallas County adults, weighted to include approximately 50% of black and nonblack participants (31). The study was initiated in 2000, and all original participants were invited to the clinic for a repeat evaluation in 2008–2009 (DHS-2). Each participant completed a detailed staff-administered survey, which included questions about demographic variables, socioeconomic status, and medical history, and underwent a health examination. Race/ethnicity was self-reported. The study was approved by the institutional review board of the University of Texas Southwestern Medical Center. All participants provided written informed consent. A total of 3401 individuals (51% non-Hispanic black, 32% non-Hispanic white, 14% Hispanic, and 3% other ethnicities) were examined in DHS-2, and 2902 of these agreed to participate in PA assessment.
Clinical and anthropometric variables
Height and weight were measured using a standard physician’s scale without shoes and in light clothing. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Waist circumference was measured to the nearest centimeter at the level of the umbilicus. Five blood pressure measurements were obtained using an automated blood pressure machine (Model CE0050; Welch Allyn, Inc., Arden, NC). The average of the third through fifth measurements was used in the analysis. Fasting concentrations of glucose and insulin were determined from venous blood samples, and the homeostatic model assessment of insulin resistance (HOMA-IR) was calculated. Plasma levels of total cholesterol, high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) were determined by standard beta-quantification. Low-density lipoprotein cholesterol (LDL-C) concentrations were estimated using the Friedewald equation (10). Diabetes was defined as a self-reported physician diagnosis of diabetes, use of prescription medication, fasting glucose ≥126 mg·dL−1 or nonfasting glucose ≥200 mg·dL−1, or HbA1c ≥6.5%.
After the examination, all DHS-2 participants were asked to wear an Actical (Philips Respironics, Bend, OR) PA monitor on their nondominant wrist, attached by a vinyl bracelet, for seven consecutive days. Actical is a small (29 × 37 × 11 mm), lightweight (17 g), waterproof, watchlike device that contains an omnidirectional motion sensor and measures body acceleration. Raw acceleration signal is filtered (pass band = 0.5–3.2 Hz), digitized (sampling frequency = 32 Hz), summed over user-specified time intervals (epochs), and stored in memory in the form of activity counts, a measure of relative intensity of activity. In the present study, data were collected in 1-min epochs.
After the data were downloaded to a computer, visual displays of activity were inspected by a trained reader to determine valid monitor wearing time and check for device malfunction. Actical devices used in the current study were of two different versions as identified by a serial number beginning with B or C. It was found that series C monitors recorded consistently higher activity counts than those of the B series. Therefore, we developed a normalization algorithm to remove systematic differences between monitor batches. A detailed description of the algorithm and other quality control procedures is provided in supplementary methods (see Supplemental Digital Content 1, http://links.lww.com/MSS/A312, Accelerometer data processing and quality control procedures). In short, accelerometer files were obtained from a total of 2902 participants. Individuals who provided less than 1 d of monitor data (n = 84) or whose monitors were not in calibration upon return (n = 63) were excluded from analysis. Of the remaining 2755 participants, 2628 (95%) had at least 4 d of monitor wear. To ensure reliable estimates of PA, we included individuals with ≥4 d of monitor wear in the present analysis. However, similar results were obtained when using participants with ≥1 d of monitor wear or restricting the sample to those with ≥7 d of data (n = 1870). Individuals with <4 d of monitor wear were slightly younger (48 ± 11 vs 51 ± 11 yr, P = 0.002) than the analytical sample but were not significantly different in sex-ethnic composition.
PA levels were reported as mean activity counts per minute (cpm) and estimates of time spent in moderate and vigorous activity. Mean activity counts were calculated as total counts accumulated during each day of monitor wear divided by 1440 min; daily mean counts were averaged across valid days. Mean counts per minute are proportional to total activity volume (duration × intensity) and provide a summary of raw accelerometer output, without imposing any other analytical criteria.
Time spent in moderate and vigorous activity was determined by converting activity counts into intensity levels through an application of count thresholds. There is a limited number of calibration studies establishing count thresholds for a wrist-worn Actical (14). Therefore, we chose thresholds based on data from several recent studies that used a waist-worn Actical (7,15) and our own tests with a wrist-worn Actical (unpublished observations). The resulting thresholds were 1500 cpm for moderate (3–5.99 METs) and 4000 cpm for vigorous-intensity (≥6 METs) activity.
Time in minutes per day spent in moderate and vigorous PA (separately and combined) was estimated by counting minutes with counts exceeding corresponding intensity thresholds across all valid days and by dividing by the number of days. Similar to previous studies (27,29) and in reference to current PA guidelines (11,30), we calculated duration variables for every minute above the threshold and for activity occurring in ≥10-min and <10-min bouts. A ≥10-min bout was defined as at least 10 consecutive minutes above threshold with an allowance of 1–2 min below threshold. This criterion follows the recommendation of Masse et al. (20), who suggested that allowance for a 1- to 2-min interruption when classifying bouts leads to a more inclusive measure. Small breaks during exercise bouts are quite plausible (e.g., one may stop at a traffic light while walking or jogging or take a break to drink water). We used this modified definition of a ≥10-min bout to make the results more comparable with other large studies that used accelerometers to measure PA (6,29). Weekly duration of moderate and vigorous activity for individuals with less than 7 d of monitor wear was estimated by multiplying mean daily duration by 7.
Characteristics of ethnic and sex groups were compared using linear regression models for continuous variables (age, BMI, waist circumference, and monitor wear days) and logistic regression for categorical outcomes (education level, income category, employment status, obesity, and diabetes prevalence). To determine the factors associated with daily activity levels, we examined daily mean counts per minute using linear mixed models including random effects for participant and monitor unit. This approach accounts for correlation between repeated observations on the same individual and makes it possible to compare activity levels between days of the week. Square root transformation was applied to mean counts per minute to achieve approximate normality of the residuals. Differential effects of age and day of the week by ethnicity and sex were tested by including corresponding interaction terms. Estimates of adherence to PA guidelines were examined using logistic regression including age, BMI, and socioeconomic factors as covariates.
To examine the associations between PA and metabolic risk factors, we used time in moderate and vigorous activity (separately and combined) as predictors in linear regression. All analyses were adjusted for age, sex, ethnicity, and BMI (where appropriate). Natural logarithm transformation was applied to all risk factors except for glucose and LDL-C levels. Activity duration was expressed in units of 10 min and mean counts per minute in units of 100 cpm. To facilitate the comparison across factors, all response variables were standardized to have mean zero and variance of one, so that reported beta coefficients correspond to a difference in SD units in the response associated with a 10-min difference in activity duration or a 100-point difference in mean counts per minute, respectively. To evaluate independent effects of moderate and vigorous activity, we adjusted the time in vigorous activity for time in moderate activity, and vice versa. Similarly, to assess independent effects of activity accumulated in ≥10-min and <10-min bouts, both variables were included in multivariate models and adjusted for each other.
All analyses were performed in R statistical computing language and environment (version 2.12.2, www.r-project.org).
DHS participants who had at least 4 d of accelerometer data (820 non-Hispanic white, 1368 non-Hispanic black, and 378 Hispanic) were included in this study (Table 1). Non-Hispanic whites were significantly older (mean ± SD = 53 ± 10 yr), and Hispanics were younger (46 ± 10 yr) than non-Hispanic blacks (50 ± 11 yr, P < 0.0001). Whites were more likely to have a college degree than were blacks or Hispanics (48%, 20%, and 16%, respectively, P < 0.0001) and to report family income above $40,000 (68%, 32%, and 36%, respectively, P < 0.0001). Compared with whites, blacks and Hispanics had a higher prevalence of obesity (defined as BMI > 30 kg·m−2) (58% and 51% vs 39%, P < 0.0001) and diabetes (20% and 17% vs 10%, P < 0.0001).
Correlates of PA levels
Mean activity counts were inversely related to age within each ethnic group (Fig. 1A). Hispanics had higher mean counts per minute than other ethnicities across all age groups, and the difference increased with age (Pinteraction = 0.009). No significant differences in activity counts were found between men and women overall (P = 0.58; Fig. 1B). Both sexes had lower activity levels on Sundays than on other days of the week (P <0.0001) (no difference was found between Saturday and other weekdays), but men were significantly less active on Sundays than women (Fig. 1C; Pinteraction<0.0001). Individuals with higher BMI and diabetes had lower mean counts per minute (P < 0.0001). In a multivariable adjusted model, college enrollment, retirement, and disability were independently associated with lower activity counts (P < 0.0001). We found no relationship between mean counts per minute and income level after controlling for the above covariates.
Adherence to PA guidelines
Sixty percent of participants obtained the recommended ≥150 min·wk−1 of moderate PA, when counting every minute above the threshold, and yet only 14% accumulated this amount in ≥10-min bouts. A significantly greater proportion of Hispanics than either whites or blacks met the 150-min guideline (24%, 14%, and 10%, respectively, P < 0.0001). Fewer than 4% of participants engaged in ≥75 min·wk−1 of vigorous PA, and <2% did so in ≥10-min bouts. Whites had a slightly higher proportion adhering to the 75-min guideline than blacks (3.2% vs 0.7%, P = 0.016). A similar proportion of males and females met PA guidelines among blacks and Hispanics (P > 0.05). Among whites, men were significantly more likely to meet PA guidelines than women (17.5% vs 12%, P = 0.022 for moderate PA, and 5.0% vs 1.7%, P = 0.008 for vigorous PA, Table 2).
Association with metabolic risk factors
To examine the relationship between PA levels and metabolic risk factors, we used time in moderate and vigorous activity as predictors in multiple linear regression analyses (Table 3). In three ethnic groups combined, total time in moderate-to-vigorous PA was inversely associated with BMI, waist circumference, HOMA-IR, heart rate, and positively associated with HDL-C levels (P < 0.0001). The relationship was similar in all three ethnic groups (P interaction > 0.05), although some trends did not reach statistical significance in Hispanics. We found a significant inverse association between PA and TG levels in whites (P = 0.0007), but not in blacks or Hispanics (P interaction = 0.05). No consistent association between PA and fasting glucose, LDL-C levels, or blood pressure was found. Both moderate and vigorous PA were independently associated with risk factors listed above, although vigorous activity was associated with larger differences in risk factors than moderate PA (e.g., β = −0.30 vs β = −0.02 for BMI). Furthermore, the associations with some risk factors differed by intensity. For example, moderate activity was associated with higher diastolic blood pressure (P = 0.01 in the combined cohort), whereas vigorous activity was associated with lower diastolic blood pressure (P = 0.0007). Vigorous but not moderate PA was associated with TG (P = 5.4 × 10−5 in the combined population) in a model that controlled for both intensities. Lastly, associations between vigorous (but not moderate) PA and metabolic risk factors remained significant even after controlling for mean activity counts (see Supplementary Digital Content 2, http://links.lww.com/MSS/A313, Association between metabolic risk factors, mean activity counts, and duration of vigorous activity).
PA levels did not account for all ethnic differences in metabolic risk factors. For example, both blacks and Hispanics had significantly higher BMI, larger waist circumference, and higher HOMA-IR compared with whites, regardless of their PA levels (P < 0.0001). Hispanics had the lowest HDL-C and the highest TG levels, despite being more active than other ethnic groups (P < 0.0001). These differences remained highly significant even after controlling for smoking, educational attainment, employment status, and income category in addition to PA and demographic variables (P < 0.001).
Finally, to assess the effect of bout length on the association between PA and risk factors, we included time in ≥10-min and <10-min bouts as predictors in multiple regression models (Table 4). In these analyses, ≥10-min bouts remained independently associated with BMI, waist circumference, heart rate, and HOMA-IR (P < 0.0001–0.03 in the combined population), whereas the associations for <10-min bouts were marginal (P = 0.01–0.1). Only ≥10-min (but not <10-min) bouts were independently associated with HDL-C (P = 0.002 in the combined cohort) and TG levels (P = 5.2 × 10−4 in whites). Estimated beta coefficients were larger in absolute value for 10 min of activity accumulated as a single bout rather than several <10-min bouts (for example, β = −0.05 and β = −0.02 for BMI). Participants spent a median of 159 min·wk−1 in <10-min bouts compared with only 22 min·wk−1 in ≥10-min bouts. The activity accumulated in ≥10-min bouts was performed at a significantly higher average intensity than activity during <10-min bouts (2676 and 1992 cpm, respectively, P < 0.0001).
In this large population-based cohort, we examined ethnic differences in objectively measured PA and the relationship between PA dose and markers of metabolic health. Our data show that Hispanics had higher levels of moderate activity than non-Hispanic whites or non-Hispanic blacks, whereas whites, in particular males, were more likely to engage in vigorous activity than other sex and ethnic groups. Despite these differences, we saw a similar dose–response relationship between PA and several metabolic risk factors in all three ethnic groups. Vigorous activity was associated with greater health benefits than moderate activity. Yet even moderate activity was associated with improved metabolic risk profiles. Activity accumulated in ≥10-min bouts was more robustly associated with metabolic risk factors than intermittent activity. Of note, differences in PA levels only partially accounted for ethnic disparities in metabolic risk factors.
Prior estimates from nationally representative samples showed similar ethnic differences in PA. Data based on self-reported measures of PA indicate that compared with whites, ethnic and racial minorities in the United States have lower levels of leisure-time PA (5) but higher levels of work-related PA (4,5,8,19). More recent estimates based on accelerometer data from the U.S. National Health and Nutrition Examination Survey (NHANES) showed that Hispanics were more active than other groups (29). Our findings are consistent with the NHANES data, likely reflecting higher levels of occupational activity among Hispanics. On the other hand, we found that white men were more likely to engage in vigorous activity, consistent with higher levels of self-reported leisure-time PA in this group. Because leisure-time PA accounts for less than 10% of overall PA (23), and given the percentage of individuals engaging in vigorous PA was extremely low in this population, it is not surprising that overall levels of PA were lower in whites than Hispanics.
It has been hypothesized that differences in PA may contribute to ethnic disparities in health outcomes that persist even after accounting for socioeconomic factors and access to medical care (23). In the current study, differences in PA levels did not explain all ethnic disparities in metabolic risk factors. For example, Hispanics had a higher prevalence of diabetes and obesity and higher TG levels than whites regardless of their activity level. These differences were not eliminated even after accounting for socioeconomic variables measured in our study. Genetic, dietary, and other unmeasured factors are potentially responsible for part of the remaining variation. Yet a similar relationship was seen between PA dose and several metabolic risk factors across ethnicity, despite differences in activity accumulation patterns. Our data therefore suggest that PA may not offset all ethnicity-attributable risk but can help reduce the metabolic risk factor burden among racial and ethnic minorities.
At the same time, we observed some notable differences in the relationship between PA and metabolic risk factors. For example, the association with plasma TG levels was only significant in whites. The absence of the association in other groups may be due to differences in the achieved intensity of PA. For example, only vigorous activity was associated with TG levels in whites. It is possible that blacks and Hispanics did not accumulate sufficient amounts of vigorous activity for the association to reach statistical significance. The fact that the association for vigorous PA approached significance in blacks (P = 0.098) is consistent with this view.
Although the benefits of PA are clear, there remains some debate about the dose–response relationship between PA and health outcomes. One important question is whether vigorous activity confers greater benefits than moderate activity and whether there is a threshold intensity required to elicit health benefits (28). Several prior epidemiologic studies and randomized clinical trials found that vigorous-intensity exercise was associated with greater reductions in the risk of cardiovascular disease and insulin resistance than moderate-intensity exercise of the same energy expenditure (9,17), whereas others reported no intensity effect on weight loss or blood lipid levels (18). In the present study, vigorous activity was associated with greater differences in risk factors than moderate activity. Moreover, beneficial effects of vigorous activity went beyond its contribution to total activity volume. In particular, vigorous activity was associated with metabolic risk factors even after controlling for mean counts per minute (Table, Supplementary Digital Content 2, http://links.lww.com/MSS/A313, which presents the association between metabolic risk factors, mean activity counts, and duration of vigorous activity). Our data therefore provide objective evidence regarding the added benefits of vigorous activity.
Furthermore, moderate and vigorous activity appeared to have opposite effects on several risk factors. For example, moderate activity was associated with higher diastolic blood pressure, whereas vigorous activity was associated with lower diastolic blood pressure. The reasons for this divergent association are not clear. It is possible that moderate-intensity activity captured by accelerometer represents primarily occupational activity, which could be accompanied by higher stress levels and lead in turn to higher blood pressure. Vigorous activity more likely represents leisure-time PA, which is known to have positive effects on blood pressure (22,33).
Another important question addressed in the current study was whether PA had to be accumulated in sustained bouts to produce health benefits. A review of prior studies comparing one continuous session of exercise to several shorter sessions of the same total duration and intensity concluded that there was little evidence for the effect of session duration (21). Strath et al. (27) further showed that <10-min and ≥10-min bouts of activity, assessed by accelerometer, were independently associated with reductions in markers of obesity. Here, we found that ≥10-min bouts of activity were more strongly associated with metabolic parameters than <10-min bouts, although it is possible that these differences were due to increased intensity of activity performed in sustained bouts rather than the length of bouts per se.
Our study has several limitations. First, because the data are cross-sectional, we cannot establish cause and effect. For example, it is likely that obesity and diabetes can both result from and contribute to low PA levels. Second, it is possible that other potentially important unmeasured confounders affect the relationship between PA levels and metabolic risk factors. For example, individuals who engage in vigorous exercise are more likely to be health conscious, consume better diets, and lead otherwise healthier lives. No information on nutrition was available in the current study; therefore, we could not control for energy intake. Finally, our analytical sample was slightly older than the entire DHS cohort; therefore, our estimates of PA levels in the three ethnic groups may not be fully representative of the larger Dallas population. Nevertheless, the fact that our estimates broadly agree with the NHANES data suggests that the amount of bias is likely small.
Although accelerometers provide an objective estimate of PA levels, they measure activity intensity in relative units; absolute estimates of activity duration can be affected by the choice of intensity cut points (20). It is possible that our choice of cut points was less restrictive than in other studies, which may explain why our estimates of adherence to PA guidelines were higher than those in the NHANES (29). Our decision in choosing the cut points was motivated by the desire to capture all instances of structured PA with intensity levels exceeding those of normal activities of daily living. At the same time, we note that although a particular choice of cut points can affect absolute estimates of PA levels, it is unlikely to change relative comparisons between population subgroups. Sensitivity analyses using alternate cut points (1200 and 1600 cpm for moderate, and 5000 cpm for vigorous PA) led to similar conclusions (see Supplementary Digital Content 3, http://links.lww.com/MSS/A314, Estimates of adherence to PA guidelines by count cut point). Furthermore, the results based on the application of cut points were consistent with those based on mean counts per minute, suggesting that our conclusions should be robust with respect to the choice of cut points. Because accelerometers measure the acceleration of the body part to which they are attached, the estimates can also be affected by monitor placement. Wrist placement may be one reason why we did not see a difference in mean activity counts between men and women, in contrast to other studies that found women to be less active than men, using waist-mounted monitors (29) or self-report (5,19). Women may perform more activities that require fast hand movement (e.g., household chores) compared with men, generating higher activity counts. Finally, accelerometers cannot detect the context of activity (e.g., occupational vs leisure-time PA). Therefore, we were unable to explore the dose–response relationships for intentional exercise and activities of daily living explicitly.
PA is now recognized as an independent modifiable risk factor for cardiovascular disease and several chronic conditions including obesity and diabetes. The present study demonstrates a significant dose–response relationship between objectively measured PA and multiple metabolic risk factors in white, black, and Hispanic men and women. Although differences in PA levels did not account for all ethnic disparities in health outcomes, we saw similar associations with several risk factors across all three ethnic groups. Vigorous activity was associated with greater benefits than moderate activity, yet even moderate activity was associated with improved metabolic health through its contribution to total energy expenditure.
The authors are grateful to the DHS participants for their contributions. They thank the DHS clinic staff for their assistance with data collection and processing. They are thankful to Drs. Helen H. Hobbs and Jonathan C. Cohen for critical reviews of the manuscript. This work was supported by a grant from the Donald W. Reynolds Foundation. The authors declare no conflict of interest. The results of the present study do not constitute endorsement by the American College of Sports Medicine.
1. Atienza 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.
2. Camhi SM, Sisson SB, Johnson WD, Katzmarzyk PT, Tudor-Locke C. Accelerometer-determined moderate intensity lifestyle activity and cardiometabolic health. Prev Med
. 2011; 52 (5): 358–60.
3. Celis-Morales CA, Perez-Bravo F, Ibanez L, Salas C, Bailey ME, Gill JM. Objective vs. self-reported physical activity and sedentary time: effects of measurement method on relationships with risk biomarkers. PLoS One
. 2012; 7 (5): e36345.
4. Centers for Disease Control and Prevention. Prevalence of leisure-time and occupational physical activity among employed adults—United States, 1990. MMWR Morb Mortal Wkly Rep
. 2000; 49 (19): 420–4.
5. Centers for Disease Control and Prevention. Prevalence of regular physical activity among adults–United States, 2001 and 2005. MMWR Morb Mortal Wkly Rep
. 2007; 56 (46): 1209–12.
6. Colley RC, Garriguet D, Janssen I, Craig CL, Clarke J, Tremblay MS. Physical activity of Canadian adults: accelerometer results from the 2007 to 2009 Canadian Health Measures Survey. Health Rep
. 2011; 22 (1): 7–14.
7. Colley RC, Tremblay MS. Moderate and vigorous physical activity intensity cut-points for the Actical accelerometer. J Sports Sci
. 2011; 29 (8): 783–9.
8. Crespo CJ, Smit E, Andersen RE, Carter-Pokras O, Ainsworth BE. Race/ethnicity, social class and their relation to physical inactivity during leisure time: results from the Third National Health and Nutrition Examination Survey, 1988–1994. Am J Prev Med
. 2000; 18 (1): 46–53.
9. DiPietro L, Dziura J, Yeckel CW, Neufer PD. Exercise and improved insulin sensitivity in older women: evidence of the enduring benefits of higher intensity training. J Appl Physiol
. 2006; 100 (1): 142–9.
10. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem
. 1972; 18 (6): 499–502.
11. Garber CE, Blissmer B, Deschenes MR, et al. American College of Sports Medicine Position Stand. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med Sci Sports Exerc
. 2011; 43 (7): 1334–59.
12. Hagstromer M, Oja P, Sjostrom M. Physical activity and inactivity in an adult population assessed by accelerometry. Med Sci Sports Exerc
. 2007; 39 (9): 1502–8.
13. Healy GN, Wijndaele K, Dunstan DW, et al. Objectively measured sedentary time, physical activity, and metabolic risk: the Australian Diabetes, Obesity and Lifestyle Study (AusDiab). Diabetes Care
. 2008; 31 (2): 369–71.
14. Heil DP. Predicting activity energy expenditure using the Actical activity monitor. Res Q Exerc Sport
. 2006; 77 (1): 64–80.
15. Hooker SP, Feeney A, Hutto B, et al. Validation of the actical activity monitor in middle-aged and older adults. J Phys Act Health
. 2011; 8 (3): 372–81.
16. Kesaniemi YK, Danforth E Jr, Jensen MD, Kopelman PG, Lefebvre P, Reeder BA. Dose–response issues concerning physical activity and health: an evidence-based symposium. Med Sci Sports Exerc
. 2001; 33 (6 Suppl): S351–8.
17. Lee IM, Paffenbarger RS Jr. Associations of light, moderate, and vigorous intensity physical activity with longevity. The Harvard Alumni Health Study. Am J Epidemiol
. 2000; 151 (3): 293–9.
18. Manson JE, Hu FB, Rich-Edwards JW, et al. A prospective study of walking as compared with vigorous exercise in the prevention of coronary heart disease in women. N Engl J Med
. 1999; 341 (9): 650–8.
19. Marshall SJ, Jones DA, Ainsworth BE, Reis JP, Levy SS, Macera CA. Race/ethnicity, social class, and leisure-time physical inactivity. Med Sci Sports Exerc
. 2007; 39 (1): 44–51.
20. Masse LC, Fuemmeler BF, Anderson CB, et al. Accelerometer data reduction: a comparison of four reduction algorithms on select outcome variables. Med Sci Sports Exerc
. 2005; 37 (11 Suppl): S544–54.
21. Murphy MH, Blair SN, Murtagh EM. Accumulated versus continuous exercise for health benefit: a review of empirical studies. Sports Med
. 2009; 39 (1): 29–43.
22. Pescatello LS, Franklin BA, Fagard R, et al. American College of Sports Medicine Position Stand. Exercise and hypertension. Med Sci Sports Exerc
. 2004; 36 (3): 533–53.
23. Saffer H, Dave DM, Grossman M. Racial, Ethnic and Gender Differences in Physical Activity
. Cambridge (MA): National Bureau of Economic Research; 2011. p. 41.
24. Sattelmair J, Pertman J, Ding EL, Kohl HW 3rd, Haskell W, Lee IM. Dose response between physical activity and risk of coronary heart disease: a meta-analysis. Circulation. 2011; 124 (7): 789–95.
25. Shephard RJ. Limits to the measurement of habitual physical activity by questionnaires. Br J Sports Med
. 2003; 37 (3): 197–206; discussion.
26. Shiroma EJ, Lee IM. Physical activity and cardiovascular health: lessons learned from epidemiological studies across age, gender, and race/ethnicity. Circulation
. 2010; 122 (7): 743–52.
27. Strath SJ, Holleman RG, Ronis DL, Swartz AM, Richardson CR. Objective physical activity accumulation in bouts and nonbouts and relation to markers of obesity in US adults. Prev Chronic Dis
. 2008; 5 (4): A131.
28. Swain DP, Franklin BA. Comparison of cardioprotective benefits of vigorous versus moderate intensity aerobic exercise. Am J Cardiol
. 2006; 97 (1): 141–7.
29. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc
. 2008; 40 (1): 181–8.
30. US Department of Health and Human Services. 2008 Physical Activity Guidelines for Americans
. Atlanta: Public Health Service, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion; 2011. p. vii.
31. Victor RG, Haley RW, Willett DL, et al. The Dallas Heart Study: a population-based probability sample for the multidisciplinary study of ethnic differences in cardiovascular health. Am J Cardiol
. 2004; 93 (12): 1473–80.
32. Warburton DE, Nicol CW, Bredin SS. Health benefits of physical activity: the evidence. CMAJ
. 2006; 174 (6): 801–9.
33. Whelton SP, Chin A, Xin X, He J. Effect of aerobic exercise on blood pressure: a meta-analysis of randomized, controlled trials. Ann Intern Med
. 2002; 136 (7): 493–503.