1. Comparison of Four "Time in Intensity“ Physical Activity Indices as
Predictors of Cardiometabolic Health
David A. Rowe1 Minsoo Kang2 Youngdeok Kim2
1University of Strathclyde, Glasgow, Scotland
2Middle Tennessee State University, Murfreesboro, TN
Background
Recent physical activity (PA) guidelines recognize the greater health benefits of higher intensity PA, but still dichotomize intensity into moderate or vigorous categories[1,2].It is possible using modern technology to measure PA intensity along the continuum from moderate intensity upwards so the need to dichotomize PA intensity is not evident. For PA promotion and research, more sensitive measures of “time in intensity” may provide greater motivation to improve, and increased accuracy in determining the PA-health relationship.
Results
On average, participants engaged in 28.9 min of PA that was of at least moderate intensity, and 1.4 min of PA that was of vigorous intensity. All PA indices were highly interrelated (r = ).
The baseline model explained between 1% (BMI) and 13% (HDL-C) of the six health outcomes. As expected in this large sample, all PA variables added significantly (p < .05) to the baseline model in explaining each of the six health outcomes. However, variance explained additional to the baseline model was generally low, ranging from R2 = .004 for mean arterial pressure to R2 = .041 for waist circumference.
Adjusted correlations with several of the cardiometabolic health measures were higher for the MET-min points systems, especially MET-MinsNet, in comparison to MinsMVPA. This pattern was evident for R2 and F-change, as well as other indices of fit ( RMSE,  AIC, and  BIC).
Similar patterns were evident for the logistic regression, with several indices of fit ( AIC,  BIC,  AUC and Wald 2) showing improved sensitivity for a protective effect from metabolic syndrome for MET-MinsGross and MET-MinsNet compared to MinsMVPA. However, diagnostic accuracy was unimpressive for all four PA indices, with the area under the curve being only 0.67.
Table 1 Multiple regression results
Health measure
 R2adj
F-value*
BMI:
Baseline model
0.0084
MinsMVPA
0.0310
76.37
W-MinsMVPA
0.0335
81.77
MET-MinsGross
0.0356
85.82
MET-MinsNet
0.0364
87.22
HDL-C:
0.1273
0.0164
27.58
0.0175
28.26
0.0182
29.27
0.0185
29.54
Fasting glucose:
0.0776
0.0065
22.33
0.0069
26.79
0.0068
29.00
31.02
Waist circumference
0.0961
0.0363
56.16
0.0388
60.04
0.0403
61.65
0.0408
62.66
Purpose
To compare two versions of a MET-min points system to two interpretations of recent guidelines, in relation to cardiometabolic health.
Methods
NHANES and accelerometry data were screened according to previous NHANES protocols[3], and converted into four PA indices: a) minutes in moderate intensity or above (3+ METs; MinsMVPA); b) weighted minutes, where vigorous (6+ METs) minutes were scored double (W-MinsMVPA); c) a MET-min points system (3+ METs) based on gross MET-min (MET-MinsGross); and d) a similar points system based on net MET-min (MET-MinsNet). Participants with at least 1 day, of at least 13 hours of data were included[4,5]. These data processing criteria produced a final sample of N = 2,943. The PA indices were adjusted for age, gender, income and race/ethnicity using multiple regression (Baseline model). They were then regressed in six separate analyses, on six measures of cardiometabolic health: BMI; HDL-C; triglycerides; fasting glucose; mean arterial pressure; and waist circumference. Logistic regression was also used to compare the associations between the four PA indices and the metabolic syndrome[6].
* All p < .001 [triglycerides and mean arterial pressure are not presented]
Conclusions
It appears that a W-MinsMVPA index incorporating the new PA guidelines (giving double-credit for time in VPA) is more strongly related to cardiometabolic health than measures based on the previous MinsMVPA guideline. Two novel MET-min outcomes also were more strongly related to cardiometabolic health.
Although all four measures were weakly related to cardiometabolic health, the MET-min indices could be converted to an activity points system and may hold promise for converting PA guidelines into a simple metric for popular use, incorporating ubiquitous technology such as mobile phones.
Table 2 Results of logistic regression models for predicting metabolic syndrome
PA index
Comparison
Odds Ratio
(95% CI)
MinsMVPA
Q1 vs. Q2
0.78 ( )
Q1 vs. Q3
0.52 ( )*
Q1 vs. Q4
0.25 ( )*
W-MinsMVPA
0.73 ( )
0.50 ( )*
0.23 ( )*
MET-MinsGross
0.71 ( )*
0.51 ( )*
0.23 ( )*
MET-MinsNet
0.72 ( )*
0.51 ( )*
0.22 ( )*
Fig 1 Illustration of the four “time in intensity” indices
Bout 1
Bout 2
Bout 3
Intensity (METs)
10-minute PA bout
Bout
MinsMVPA
W-MinsMVPA
MET-MinsGross
MET-MinsNet 1 10 30 20 2 50 40 3 70 60
* p < .05
References
Physical Activity Guidelines Advisory Committee. (2008). Physical Activity Guidelines Report, Washington, DC: U.S. Department of Health and Human Services.
O’Donovan G, et al. (2010). The ABC of physical activity for health: A consensus statement from the British Association of Sport and Exercise Sciences. Journal of Sports Science, 28,
Troiano, R.P., et al. (2008). Physical activity in the United States measured by accelerometer. Medicine & Science in Sports & Exercise, 40,
Herrmann, S.D., et al. (2012). Impact of accelerometer wear time on physical activity data: a NHANES semisimulation data approach. British Journal of Sports Medicine, Epub ahead of print.
Tudor-Locke, C., et al. (2010). Accelerometer profiles of physical activity and inactivity in normal weight, overweight, and obese U.S. men and women. International Journal of Behavioral Nutrition and Physical Activity, 7, 60.
Grundy, S. M., et al. (2004). Definition of metabolic syndrome: Report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Circulation, 109,
Contact details: