Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:20 DOI 10.1186/s12966-015-0183-7
RESEARCH
Open Access
Physical activity and sedentary behavior patterns using accelerometry from a national sample of United States adults Kelly R Evenson1*, Fang Wen1, Jesse S Metzger2 and Amy H Herring3
Abstract Background: This study described the patterns of accelerometer-determined physical activity and sedentary behavior among adults using a nationally representative sample from the United States. Methods: Using 2003-2006 National Health and Nutrition Examination Survey (NHANES) data, 7931 adults at least 18 years old wore an ActiGraph accelerometer for one week, providing at least 3 days of wear for >=8 hours/day. Cutpoints defined moderate to vigorous physical activity (MVPA; >= 2020 and >=760 counts/minute), vigorous physical activity (> = 5999 counts/minute), and sedentary behavior ( =5999 counts/minute and moderate intensity as 2020-5998 counts/minute. This higher cutpoint approximates moderate activity based primarily on treadmill walking or running. A lower moderate intensity threshold was calculated based on studies that incorporated more lifestyle activities, defined as > =760 counts/minute [9]. We refer to these two MPVA cutpoints based on the first author’s last names (Troiano and Matthews, respectively). Another type of MVPA was categorized based on time spent in MVPA bouts, separately for the Troiano and Matthews cutpoints, with a bout defined as at least 10 minutes of consecutive MVPA with allowance for interruptions of up to 20% below the threshold and with = 65 Non-Hispanic Non-Hispanic Hispanic Other (n = 4118) (n = 3813) (n = 2530) (n = 1790) (n = 1627) (n = 1984) White Black (N = 1927) (N = 324) (n = 3953) (n = 1727)
Average counts/minute Class 1 - Least active
2088 61.0
39.0
10.9
15.5
25.2
48.4
76.9
11.0
6.1
6.0
Class 2
3372 58.9
41.1
27.7
31.6
28.3
12.4
72.2
11.8
10.2
5.7
Class 3
1596 46.5
53.5
35.1
37.9
21.7
5.2
71.4
10.4
14.0
4.2
Class 4
181
31.5
68.5
40.2
42.6
15.6
1.6
79.3
6.0
6.9
7.8
Class 5
571
25.0
75.0
42.0
39.9
15.1
3.0
67.2
7.2
22.4
3.3
Class 6 - Most active
123
18.6
81.4
49.3
40.7
9.1
0.9
48.2
13.2
34.5
4.1
Percent of MVPA (Troiano) out of total wearing time per day Class 1 - Least active
5410 61.6
38.4
21.4
26.7
27.9
23.9
73.3
11.4
9.5
5.7
Class 2
1768 40.1
59.9
37.2
37.4
19.4
6.1
72.6
9.9
13.4
4.2
Class 3
207
26.6
73.4
37.3
46.1
14.3
2.3
78.4
5.3
9.1
7.3
Class 4
473
25.4
74.6
44.8
35.8
15.8
3.5
63.8
10.9
20.5
4.8
Class 5 - Most active
73
16.6
83.4
55.1
32.7
12.2
0.0
44.0
13.5
40.1
2.4
Percent of MVPA bouts (Troiano) out of total wearing time per day Class 1 - Least active
4765 58.0
42.0
22.7
27.7
26.1
23.6
73.2
11.6
9.7
5.6
Class 2
569
43.7
56.3
31.6
36.7
23.1
8.5
74.8
7.8
13.3
4.1
Class 3
814
45.1
54.9
34.2
32.4
23.6
9.8
71.0
10.7
13.8
4.5
Class 4
1573 47.8
52.2
34.2
35.1
21.5
9.3
71.5
9.9
13.1
5.5
Class 5 - Most active
210
67.4
39.1
30.9
23.4
6.7
64.9
12.7
17.8
4.7
32.6
Percent of MVPA (Matthews) out of total wearing time per day Class 1 - Least active
2330 62.7
37.3
12.4
15.7
25.0
46.9
76.0
11.2
6.4
6.4
Class 2
3277 57.6
42.4
29.6
32.2
27.8
10.4
72.7
11.6
10.3
5.4
Class 3
1500 44.4
55.6
34.3
38.2
21.6
5.9
72.9
10.3
13.1
3.7
Class 4
245
32.6
67.4
34.8
45.5
17.1
2.7
71.2
7.2
11.6
10.0
Class 5
470
26.1
73.9
39.5
41.1
16.9
2.5
63.2
8.6
25.5
2.8
Class 6 - Most active
109
17.4
82.6
48.6
37.3
13.3
0.8
44.1
7.1
46.2
2.7
Percent of MVPA bouts (Matthews) out of total wearing time per day Class 1 - Least active
1233 66.2
33.8
12.2
15.4
22.3
50.1
77.7
11.0
6.8
4.5
Class 2
1410 66.1
33.9
23.6
26.2
29.1
21.1
71.2
12.7
8.2
7.8
Class 3
2827 54.5
45.5
29.5
32.1
25.4
13.0
73.0
10.9
10.6
5.6
Class 4
1997 41.5
58.5
31.5
36.5
23.1
8.9
73.8
9.8
12.6
3.8
Class 5 - Most active
464
76.5
41.0
38.9
16.7
3.4
55.6
9.1
30.6
4.8
23.5
Percent of sedentary behavior out of total wearing time per day Class 1 - Most sedentary
662
49.7
50.3
11.4
11.1
20.2
57.4
79.8
10.7
4.0
5.5
Class 2
2090 51.2
48.8
23.8
25.0
25.4
25.7
75.5
11.4
7.2
6.0
Class 3
2848 57.5
42.5
27.1
31.3
27.2
14.4
73.8
11.4
8.8
5.9
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:20
Page 11 of 13
Table 2 Sociodemographic characteristics by accelerometry derived classes among adults (n = 7931); NHANES 2003-2006 (Continued) Class 4
1749 53.0
47.0
32.9
37.3
22.5
7.3
70.0
9.4
16.5
4.1
Class 5 - Least sedentary
582
66.8
38.5
41.1
18.0
2.3
57.1
10.6
28.7
3.6
33.2
Percent of sedentary bouts out of total wearing time per day Class 1 - Most sedentary
587
48.5
51.5
9.6
8.4
18.3
63.8
81.0
9.9
3.0
6.1
Class 2
1469 47.7
52.3
20.9
24.2
25.3
29.5
76.8
10.9
6.1
6.2
Class 3
656
57.7
42.3
30.8
33.6
25.4
10.2
73.9
11.3
9.1
5.6
Class 4
1449 54.4
45.6
25.8
25.9
26.9
21.4
73.4
12.3
9.2
5.1
Class 5
1951 54.6
45.4
29.0
33.5
26.8
10.7
73.0
10.6
11.1
5.3
Class 6
288
48.0
52.0
28.4
38.3
27.1
6.2
69.4
10.4
13.8
6.4
Class 7 - Least sedentary
1531 52.9
47.1
35.8
39.6
19.8
4.8
65.2
9.9
20.8
4.1
MVPA = moderate to vigorous physical activity. Note: row percents are presented by category.
the data, with a high proportion of adults not engaging in any MVPA bouts. To handle zero inflation and over dispersion, a LCA with zero-inflated negative binomial model was used. Future studies applying LCA to accelerometry should carefully assess the skewness of the data and when normality is violated, consider other types of modeling approaches. Based on self-reported national data from 1999-2004, approximately 1% to 3% of adults belonged to the weekend warrior group [3]. This distinct pattern was subsequently confirmed using accelerometry from 2003-2004 NHANES data [4]. Using four years of NHANES data representing the US population, we also confirmed the weekend warrior pattern, identified among 3.2% of the sample (MVPA using the Troiano definition). Interestingly, the pattern of lower weekday and higher weekend for the total volume of physical activity was also identified for 9.1% of adults when viewing total counts/minute (class 5 and 6). Previously, Lee et al [2] found that men classified as weekend warriors from self-reported data had a lower risk of all-cause mortality when compared to sedentary men, particularly among those without major risk factors. Metzger et al. [5] found among adults that membership to the weekend warrior class was associated with a lower odds of the metabolic syndrome when compared to the least active class. The classes we derived can be used to explore these associations using NHANES data. Although Hispanics have often self-reported low levels of MVPA relative to Non-Hispanic Whites when asked about leisure-time physical activity [13] or walking [14], our analyses indicated that Hispanics comprised a relatively larger proportion of the more active classes. Thus, Hispanics may accumulate more of their MVPA in activities other than during leisure, such as through active transportation and work activities.
Sedentary behavior, such as sitting, constitutes time spent in periods of little or no movement while awake, and at an energy expenditure ranging from 1.0-1.5 metabolic equivalents [15]. To our knowledge, this is the first paper to explore sedentary patterns among adults using LCA techniques. Of concern, the two most sedentary classes represented 31.4% of the population, with a weighted mean of 9.3 (class 2) to 12.4 (class 1) hours/day of sedentary behavior over the week. The least sedentary class that emerged had a relatively low percent of time spent in sedentary behavior on the weekdays but higher on the weekends (class 5). Even so, their percent of sedentary behavior was still lower on Saturdays and Sundays than the other four classes. When exploring bout minutes of sedentary behavior, several classes generally showed stable amounts throughout the week, though at different absolute percents. However, patterns also emerged with a lower percent of sedentary bouts out of total wearing time per day on the weekdays and more on the weekends (weekend couch potato), as well as higher percent of sedentary bouts out of total wearing time per day on the weekdays and fewer on the weekends (indicative of a weekend warrior pattern for sedentary behavior). In our analyses, we also explored other formulations of sedentary behavior and sedentary bouts, including minutes/day and minutes/day controlling for sedentary wearing time. We found that wearing time greatly affected the classification of sedentary behavior and that representing the time as a percent of wearing time was the best representation of this variable to both account for wearing time and to maintain consistency throughout our analysis. Future use of this variable as an independent variable should also consider including accelerometer wear time as a potential confounder when appropriate.
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:20
Limitations
These analyses are subject to several limitations. First, the uniaxial accelerometer used by NHANES under counts some activities, such as bicycling and weight lifting, and misses other activities, such as swimming, because the monitor was not waterproof and participants were told to remove it for any water-based activity. Second, the LCA models with sampling weights applied to these data assume data are missing at random. This assumption may not always be true, particularly when the accelerometer is removed for water activities. However, national data indicate that the proportion of adults who report swimming regularly is relatively low [16]. Third, the bootstrap likelihood ratio test we applied was based on unweighted data, such that it does not account for the sampling design in the test. However, we also used other criteria to make the final determination for the number of classes to use, including class sample size, substantive knowledge, and visual inspection. Fourth, it is possible that our latent class assignments still missed underlying patterns [4]. For example, there may be some workers whose weekend does not fall on Saturday or Sunday. The ordering of days could be explored differently, such as from least to most physical active, rather than from Monday to Sunday. Fifth, a strength is that our analyses resulted in latent class assignments that are available and can be used by others to address research questions (Additional file 3). The limitation is that this approach of deriving assignments separately from the modeling has lower statistical efficiency. However, we felt this trade-off was justified because assignments will remain stable to enhance comparability across future analyses.
Conclusion Using accelerometry data, this study identified patterns of overall physical activity, MVPA, and sedentary behavior from a national sample of adults. These findings can assist with intervention development to better understand how accelerometry-assessed physical activity and sedentary behavior are frequently patterned overall and by sociodemographic characteristics. Future NHANES analyses with these data can assess correlates of these patterns and associations with health outcomes. Moreover, exploration into whether the latent classes contribute over and above the absolute number of minutes for the same variable (counts/minute, MVPA, sedentary behavior) would help determine the further contribution of the patterning of the behavior. There are also other possible uses of the LCA methods that could be applied to these data. For example, the methods can be used to develop clusters of health behaviors, including lack of physical activity as others have done using self-reported data [17]. These methods have also been applied to explore longitudinal patterns of self-
Page 12 of 13
reported leisure-time physical activity [18,19], walking [19], and bicycling [19] using an extension of LCA called latent class growth analysis. Another unique application combined self-report and accelerometry data to derive latent classes among a sample of youth [20]. These examples, along with our findings, offer exciting possibilities into studying physical activity patterns using detailed physical activity data.
Additional files Additional file 1: Weighted mean percents by day of week for latent classes derived from accelerometry among adults (n=7931); NHANES 2003-2006. Additional file 2: Wear time by the latent classes derived from accelerometry among adults (n=7931); NHANES 2003-2006. Additional file 3: Data dictionary for latent classes variables based on accelerometry measures among adults (NHANES 2003-2006). Abbreviations LCA: Latent class analysis; MVPA: Moderate to vigorous physical activity; NHANES: National Health and Nutrition Examination Survey. Competing interests The authors declare that they have no competing interests. Authors’ contributions KRE developed the aims of the study and drafted the paper, while all the remaining authors provided critical feedback on several earlier drafts of the paper. AHH, JM, and FW provided input on the statistical analysis of the study. FW wrote all analytic programs with help from JM and AHH. All authors read and approved the final manuscript. Acknowledgment This work was supported by the National Institutes of Health (NIH), National Heart, Lung, and Blood Institute #R21 HL115385. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Author details 1 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina Chapel Hill, 137 East Franklin Street, Suite 306, Chapel Hill, NC, USA. 2Center for Behavioral Health Research and Services, University of Alaska – Anchorage, Anchorage, AK, USA. 3Department of Biostatistics, Gillings School of Global Public Health, Carolina Population Center, University of North Carolina – Chapel Hill, Chapel Hill, NC, USA. Received: 5 September 2014 Accepted: 3 February 2015
References 1. U.S. Department of Health and Human Services: 2008 Physical Activity Guidelines for Americans. ODPHP Publication No. U0036. Washington, D.C.; 2008: 1-61. Accessed September 4, 2014 at http://www.health.gov/ paguidelines/. 2. Lee I-M, Sesso H, Oguma Y, Paffenbarger Jr R. The “weekend warrior” and risk of mortality. Am J Epidemiol. 2004;160(7):636–41. 3. Kruger J, Ham SA, Kohl HW. Characteristics of a “weekend warrior”: results from two national surveys. Med Sci Sports Exerc. 2007;39(5):796–800. 4. Metzger JS, Catellier DJ, Evenson KR, Treuth MS, Rosamond WD, Siega-Riz AM. Patterns of objectively measured physical activity in the United States. Med Sci Sports Exerc. 2008;40(4):630–8. 5. Metzger J, Catellier D, Evenson K, Treuth M, Rosamond W, Siega-Riz A. Associations between patterns of objectively measured physical activity and risk factors for the metabolic syndrome. Am J Health Promot. 2010;24(3):161–9.
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John D, Freedson P. ActiGraph and Actical physical activity monitors: a peek under the hood. Med Sci Sports Exerc. 2012;44(1 Suppl 1):S86–9. Choi L, Liu Z, Matthews CE, Buchowski MS. Validation of accelerometer wear and nonwear time classification algorithm. Med Sci Sports Exerc. 2011;43(2):357–64. Troiano R, Berrigan D, Dodd K, Masse L, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181–8. Matthews C. Calibration of accelerometer output for adults. Med Sci Sports Exerc. 2005;37(11 Suppl):S512–22. Matthews C, Chen K, Freedson P, Buchowski M, Beech B, Pate R, et al. Amount of time spent in sedentary behaviors in the United States, 2003-2004. Am J Epidemiol. 2008;167(7):875–81. Carson V, Janssen I. Volume, patterns, and types of sedentary behavior and cardio-metabolic health in children and adolescents: a cross-sectional study. BMC Public Health. 2011;11:274. Muthén LK, Muthén BO. Mplus user’s guide. 5th Edition (1998-2007). Los Angeles, CA: Muthén and Muthén; 2007. Centers for Disease Control and Prevention. Adult participation in aerobic and muscle-strengthening physical activities - United States, 2011. Morb Mort Week Rep. 2013;62(17):326–30. Centers for Disease Control and Prevention. Vital signs: walking among adults - United States, 2005 and 2010. Morb Mort Week Rep. 2012;61 (31):595–601. Owen N, Healy G, Matthews C, Dunstan D. Too much sitting: the population health science of sedentary behavior. Exerc Sport Sci Rev. 2010;38(3):105–13. Evenson K, Huston S, Wood J, Bors P. Does the number of leisure activities recalled change the estimated prevalence of activity? Med Sci Sports Exerc. 2003;35(11):1882–6. Leventhal AM, Huh J, Dunton GF. Clustering of modifiable biobehavioral risk factors for chronic disease in US adults: a latent class analysis. Perspect Public Health. 2014;134(6):331–8. Barnett TA, Gauvin L, Craig CL, Katzmarzyk PT. Distinct trajectories of leisure time physical activity and predictors of trajectory class membership: a 22 year cohort study. Intl J Behavioral Nutrition Phys Act. 2008;5:57. Silverwood RJ, Nitsch D, Pierce M, Kuh D, Mishra GD. Characterizing longitudinal patterns of physical activity in mid-adulthood using latent class analysis: results from a prospective cohort study. Am J Epidemiol. 2011;174(12):1406–15. Patnode CD, Lytle LA, Erickson DJ, Sirard JR, Barr-Anderson DJ, Story M. Physical activity and sedentary activity patterns among children and adolescents: a latent class analysis approach. J Phys Act Health. 2011;8(4):457–67.
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RESEARCH
Open Access
Physical activity and sedentary behavior patterns using accelerometry from a national sample of United States adults Kelly R Evenson1*, Fang Wen1, Jesse S Metzger2 and Amy H Herring3
Abstract Background: This study described the patterns of accelerometer-determined physical activity and sedentary behavior among adults using a nationally representative sample from the United States. Methods: Using 2003-2006 National Health and Nutrition Examination Survey (NHANES) data, 7931 adults at least 18 years old wore an ActiGraph accelerometer for one week, providing at least 3 days of wear for >=8 hours/day. Cutpoints defined moderate to vigorous physical activity (MVPA; >= 2020 and >=760 counts/minute), vigorous physical activity (> = 5999 counts/minute), and sedentary behavior ( =5999 counts/minute and moderate intensity as 2020-5998 counts/minute. This higher cutpoint approximates moderate activity based primarily on treadmill walking or running. A lower moderate intensity threshold was calculated based on studies that incorporated more lifestyle activities, defined as > =760 counts/minute [9]. We refer to these two MPVA cutpoints based on the first author’s last names (Troiano and Matthews, respectively). Another type of MVPA was categorized based on time spent in MVPA bouts, separately for the Troiano and Matthews cutpoints, with a bout defined as at least 10 minutes of consecutive MVPA with allowance for interruptions of up to 20% below the threshold and with = 65 Non-Hispanic Non-Hispanic Hispanic Other (n = 4118) (n = 3813) (n = 2530) (n = 1790) (n = 1627) (n = 1984) White Black (N = 1927) (N = 324) (n = 3953) (n = 1727)
Average counts/minute Class 1 - Least active
2088 61.0
39.0
10.9
15.5
25.2
48.4
76.9
11.0
6.1
6.0
Class 2
3372 58.9
41.1
27.7
31.6
28.3
12.4
72.2
11.8
10.2
5.7
Class 3
1596 46.5
53.5
35.1
37.9
21.7
5.2
71.4
10.4
14.0
4.2
Class 4
181
31.5
68.5
40.2
42.6
15.6
1.6
79.3
6.0
6.9
7.8
Class 5
571
25.0
75.0
42.0
39.9
15.1
3.0
67.2
7.2
22.4
3.3
Class 6 - Most active
123
18.6
81.4
49.3
40.7
9.1
0.9
48.2
13.2
34.5
4.1
Percent of MVPA (Troiano) out of total wearing time per day Class 1 - Least active
5410 61.6
38.4
21.4
26.7
27.9
23.9
73.3
11.4
9.5
5.7
Class 2
1768 40.1
59.9
37.2
37.4
19.4
6.1
72.6
9.9
13.4
4.2
Class 3
207
26.6
73.4
37.3
46.1
14.3
2.3
78.4
5.3
9.1
7.3
Class 4
473
25.4
74.6
44.8
35.8
15.8
3.5
63.8
10.9
20.5
4.8
Class 5 - Most active
73
16.6
83.4
55.1
32.7
12.2
0.0
44.0
13.5
40.1
2.4
Percent of MVPA bouts (Troiano) out of total wearing time per day Class 1 - Least active
4765 58.0
42.0
22.7
27.7
26.1
23.6
73.2
11.6
9.7
5.6
Class 2
569
43.7
56.3
31.6
36.7
23.1
8.5
74.8
7.8
13.3
4.1
Class 3
814
45.1
54.9
34.2
32.4
23.6
9.8
71.0
10.7
13.8
4.5
Class 4
1573 47.8
52.2
34.2
35.1
21.5
9.3
71.5
9.9
13.1
5.5
Class 5 - Most active
210
67.4
39.1
30.9
23.4
6.7
64.9
12.7
17.8
4.7
32.6
Percent of MVPA (Matthews) out of total wearing time per day Class 1 - Least active
2330 62.7
37.3
12.4
15.7
25.0
46.9
76.0
11.2
6.4
6.4
Class 2
3277 57.6
42.4
29.6
32.2
27.8
10.4
72.7
11.6
10.3
5.4
Class 3
1500 44.4
55.6
34.3
38.2
21.6
5.9
72.9
10.3
13.1
3.7
Class 4
245
32.6
67.4
34.8
45.5
17.1
2.7
71.2
7.2
11.6
10.0
Class 5
470
26.1
73.9
39.5
41.1
16.9
2.5
63.2
8.6
25.5
2.8
Class 6 - Most active
109
17.4
82.6
48.6
37.3
13.3
0.8
44.1
7.1
46.2
2.7
Percent of MVPA bouts (Matthews) out of total wearing time per day Class 1 - Least active
1233 66.2
33.8
12.2
15.4
22.3
50.1
77.7
11.0
6.8
4.5
Class 2
1410 66.1
33.9
23.6
26.2
29.1
21.1
71.2
12.7
8.2
7.8
Class 3
2827 54.5
45.5
29.5
32.1
25.4
13.0
73.0
10.9
10.6
5.6
Class 4
1997 41.5
58.5
31.5
36.5
23.1
8.9
73.8
9.8
12.6
3.8
Class 5 - Most active
464
76.5
41.0
38.9
16.7
3.4
55.6
9.1
30.6
4.8
23.5
Percent of sedentary behavior out of total wearing time per day Class 1 - Most sedentary
662
49.7
50.3
11.4
11.1
20.2
57.4
79.8
10.7
4.0
5.5
Class 2
2090 51.2
48.8
23.8
25.0
25.4
25.7
75.5
11.4
7.2
6.0
Class 3
2848 57.5
42.5
27.1
31.3
27.2
14.4
73.8
11.4
8.8
5.9
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:20
Page 11 of 13
Table 2 Sociodemographic characteristics by accelerometry derived classes among adults (n = 7931); NHANES 2003-2006 (Continued) Class 4
1749 53.0
47.0
32.9
37.3
22.5
7.3
70.0
9.4
16.5
4.1
Class 5 - Least sedentary
582
66.8
38.5
41.1
18.0
2.3
57.1
10.6
28.7
3.6
33.2
Percent of sedentary bouts out of total wearing time per day Class 1 - Most sedentary
587
48.5
51.5
9.6
8.4
18.3
63.8
81.0
9.9
3.0
6.1
Class 2
1469 47.7
52.3
20.9
24.2
25.3
29.5
76.8
10.9
6.1
6.2
Class 3
656
57.7
42.3
30.8
33.6
25.4
10.2
73.9
11.3
9.1
5.6
Class 4
1449 54.4
45.6
25.8
25.9
26.9
21.4
73.4
12.3
9.2
5.1
Class 5
1951 54.6
45.4
29.0
33.5
26.8
10.7
73.0
10.6
11.1
5.3
Class 6
288
48.0
52.0
28.4
38.3
27.1
6.2
69.4
10.4
13.8
6.4
Class 7 - Least sedentary
1531 52.9
47.1
35.8
39.6
19.8
4.8
65.2
9.9
20.8
4.1
MVPA = moderate to vigorous physical activity. Note: row percents are presented by category.
the data, with a high proportion of adults not engaging in any MVPA bouts. To handle zero inflation and over dispersion, a LCA with zero-inflated negative binomial model was used. Future studies applying LCA to accelerometry should carefully assess the skewness of the data and when normality is violated, consider other types of modeling approaches. Based on self-reported national data from 1999-2004, approximately 1% to 3% of adults belonged to the weekend warrior group [3]. This distinct pattern was subsequently confirmed using accelerometry from 2003-2004 NHANES data [4]. Using four years of NHANES data representing the US population, we also confirmed the weekend warrior pattern, identified among 3.2% of the sample (MVPA using the Troiano definition). Interestingly, the pattern of lower weekday and higher weekend for the total volume of physical activity was also identified for 9.1% of adults when viewing total counts/minute (class 5 and 6). Previously, Lee et al [2] found that men classified as weekend warriors from self-reported data had a lower risk of all-cause mortality when compared to sedentary men, particularly among those without major risk factors. Metzger et al. [5] found among adults that membership to the weekend warrior class was associated with a lower odds of the metabolic syndrome when compared to the least active class. The classes we derived can be used to explore these associations using NHANES data. Although Hispanics have often self-reported low levels of MVPA relative to Non-Hispanic Whites when asked about leisure-time physical activity [13] or walking [14], our analyses indicated that Hispanics comprised a relatively larger proportion of the more active classes. Thus, Hispanics may accumulate more of their MVPA in activities other than during leisure, such as through active transportation and work activities.
Sedentary behavior, such as sitting, constitutes time spent in periods of little or no movement while awake, and at an energy expenditure ranging from 1.0-1.5 metabolic equivalents [15]. To our knowledge, this is the first paper to explore sedentary patterns among adults using LCA techniques. Of concern, the two most sedentary classes represented 31.4% of the population, with a weighted mean of 9.3 (class 2) to 12.4 (class 1) hours/day of sedentary behavior over the week. The least sedentary class that emerged had a relatively low percent of time spent in sedentary behavior on the weekdays but higher on the weekends (class 5). Even so, their percent of sedentary behavior was still lower on Saturdays and Sundays than the other four classes. When exploring bout minutes of sedentary behavior, several classes generally showed stable amounts throughout the week, though at different absolute percents. However, patterns also emerged with a lower percent of sedentary bouts out of total wearing time per day on the weekdays and more on the weekends (weekend couch potato), as well as higher percent of sedentary bouts out of total wearing time per day on the weekdays and fewer on the weekends (indicative of a weekend warrior pattern for sedentary behavior). In our analyses, we also explored other formulations of sedentary behavior and sedentary bouts, including minutes/day and minutes/day controlling for sedentary wearing time. We found that wearing time greatly affected the classification of sedentary behavior and that representing the time as a percent of wearing time was the best representation of this variable to both account for wearing time and to maintain consistency throughout our analysis. Future use of this variable as an independent variable should also consider including accelerometer wear time as a potential confounder when appropriate.
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:20
Limitations
These analyses are subject to several limitations. First, the uniaxial accelerometer used by NHANES under counts some activities, such as bicycling and weight lifting, and misses other activities, such as swimming, because the monitor was not waterproof and participants were told to remove it for any water-based activity. Second, the LCA models with sampling weights applied to these data assume data are missing at random. This assumption may not always be true, particularly when the accelerometer is removed for water activities. However, national data indicate that the proportion of adults who report swimming regularly is relatively low [16]. Third, the bootstrap likelihood ratio test we applied was based on unweighted data, such that it does not account for the sampling design in the test. However, we also used other criteria to make the final determination for the number of classes to use, including class sample size, substantive knowledge, and visual inspection. Fourth, it is possible that our latent class assignments still missed underlying patterns [4]. For example, there may be some workers whose weekend does not fall on Saturday or Sunday. The ordering of days could be explored differently, such as from least to most physical active, rather than from Monday to Sunday. Fifth, a strength is that our analyses resulted in latent class assignments that are available and can be used by others to address research questions (Additional file 3). The limitation is that this approach of deriving assignments separately from the modeling has lower statistical efficiency. However, we felt this trade-off was justified because assignments will remain stable to enhance comparability across future analyses.
Conclusion Using accelerometry data, this study identified patterns of overall physical activity, MVPA, and sedentary behavior from a national sample of adults. These findings can assist with intervention development to better understand how accelerometry-assessed physical activity and sedentary behavior are frequently patterned overall and by sociodemographic characteristics. Future NHANES analyses with these data can assess correlates of these patterns and associations with health outcomes. Moreover, exploration into whether the latent classes contribute over and above the absolute number of minutes for the same variable (counts/minute, MVPA, sedentary behavior) would help determine the further contribution of the patterning of the behavior. There are also other possible uses of the LCA methods that could be applied to these data. For example, the methods can be used to develop clusters of health behaviors, including lack of physical activity as others have done using self-reported data [17]. These methods have also been applied to explore longitudinal patterns of self-
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reported leisure-time physical activity [18,19], walking [19], and bicycling [19] using an extension of LCA called latent class growth analysis. Another unique application combined self-report and accelerometry data to derive latent classes among a sample of youth [20]. These examples, along with our findings, offer exciting possibilities into studying physical activity patterns using detailed physical activity data.
Additional files Additional file 1: Weighted mean percents by day of week for latent classes derived from accelerometry among adults (n=7931); NHANES 2003-2006. Additional file 2: Wear time by the latent classes derived from accelerometry among adults (n=7931); NHANES 2003-2006. Additional file 3: Data dictionary for latent classes variables based on accelerometry measures among adults (NHANES 2003-2006). Abbreviations LCA: Latent class analysis; MVPA: Moderate to vigorous physical activity; NHANES: National Health and Nutrition Examination Survey. Competing interests The authors declare that they have no competing interests. Authors’ contributions KRE developed the aims of the study and drafted the paper, while all the remaining authors provided critical feedback on several earlier drafts of the paper. AHH, JM, and FW provided input on the statistical analysis of the study. FW wrote all analytic programs with help from JM and AHH. All authors read and approved the final manuscript. Acknowledgment This work was supported by the National Institutes of Health (NIH), National Heart, Lung, and Blood Institute #R21 HL115385. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Author details 1 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina Chapel Hill, 137 East Franklin Street, Suite 306, Chapel Hill, NC, USA. 2Center for Behavioral Health Research and Services, University of Alaska – Anchorage, Anchorage, AK, USA. 3Department of Biostatistics, Gillings School of Global Public Health, Carolina Population Center, University of North Carolina – Chapel Hill, Chapel Hill, NC, USA. Received: 5 September 2014 Accepted: 3 February 2015
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