David A. Rowe1, Catrine Tudor-Locke2 Walking and running studies Walking the (curved) line: Investigation of the cadence-energy expenditure relationship during ambulatory activity David A. Rowe1, Catrine Tudor-Locke2 1Physical Activity for Health Research Group, University of Strathclyde, Glasgow, Scotland 2Walking Behavior Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA Figs 1-3. Three ways to analyze walking and running cadence vs. energy expenditure Background National guidelines on physical activity for health incorporate intensity or energy expenditure (EE) recommendations, usually categorized as moderate and vigorous intensity equivalent to 3 and 6 METs(3,5). Walking is a popular, convenient mode of physical activity for gaining health benefits, but conveying accurate messages about the intensity of walking is a difficult task in real-world situations. Six recent studies investigated the utility of cadence (steps/min) to estimate EE (in METs) during ambulatory physical activity in adults(1,2,4,6-8). The findings of these studies have generally been applied to recommendations for walking. There was a general convergence on a 100 steps/min recommendation equivalent to 3 METs/moderate intensity(9). However, there was limited consideration in these studies of the cadence-EE relationship at other intensities, the appropriateness of using linear regression analysis, or the influence of design factors such as the range of speeds investigated and combining walking and running data. Results Mean speeds in the six studies ranged from 1.12-4.10 mph (across 20 walking bouts) and from 5.00-7.50 mph (across 5 running bouts). Mean cadences ranged from 64-134 steps/min (walking) and from 157-166 steps/min (running). Mean EE ranged from 2.00-6.85 METs (walking) and from 8.90-13.00 METs (running) (see Figs 1-3). In WALK-RUN studies, the weighted mean intercept was -8.71 and weighted mean slope was 0.116. In WALK studies, the weighted mean intercept was -2.95, and weighted mean slope was 0.059. Slopes developed on WALK-RUN data were therefore steeper than slopes developed on WALK data (see Fig 4), and consequently this results in different cadence guidelines, although the slopes and guidelines tend to converge around a 100 steps/min = 3 METs interpretation (see Fig 4 and Table 1). From secondary regression analysis of bout means for cadence and EE, the data appeared to follow an exponential growth curve. This was corrected by log-transformation of the EE data (raw data r2 = .856; log-transformed data r2 = .953); however, graphing also indicates that walking and running cadence have a differential relationship with EE (see Figs 1-3). Walking data Running data r (combined data) = .93 Running data r = .91 Walking data r = .87 r (log-transformed data) = .98 Fig 4. Regression slopes from studies of walking only (solid lines) and studies of walking and running combined (broken lines) Conclusions Across a wide range of walking and running cadences, the cadence-EE relationship appears to be non-linear. This is primarily due to the combination of walking and running data and the inclusion of slow- and fast-speed walking. In future studies of health-enhancing ambulatory activity, either an appropriate curvilinear analysis should be used to capture a full range of cadences, or data should be restricted to normal walking speeds (approximately 1.7-4.0 mph, or 80-130 steps/min). Cadence guidelines for specific intensities should probably be made separately for walking and for running. i Purpose To explore recent evidence on the cadence-energy expenditure relationship in walking and running Table 1. Example cadence/energy expenditure guidelines based on weighted average regression coefficients from walking only data (4 studies) and on walking and running data (2 studies) Energy expenditure Cadence guidelines Walking and running studies Walking only studies 3 METs 102 steps/min 101 steps/min 4 METs 110 steps/min 118 steps/min 5 METs 119 steps/min 135 steps/min Methods Secondary analysis of data from six studies of the cadence-EE relationship during walking and running bouts. Two studies(1,8) combined walking and running (WALK-RUN), and four studies(2,4,6,7) investigated walking only (WALK). Slopes and intercepts were compared, and mean bout values for cadence and EE were analyzed using linear regression. References Abel M, et al. Determination of step rate thresholds corresponding to physical activity classifications in adults. J Phys Act Health 2011; 8: 45-51 Beets MW, et al. Adjusting step count recommendations for anthropometric variations in leg length. J Sci Med Sport 2010 Jan 21; 13 (5): 509-12 O’Donovan G, et al. The ABC of physical activity for health: A consensus statement from the British Association of Sport and Exercise Sciences. J Sports Sci 2010 Apr; 28 (6): 573-91 Marshall SJ, et al. Translating physical activity recommendations into a pedometer-based step goal: 3000 steps in 30 minutes. Am J Prev Med 2009 May; 36 (5): 410-5 Physical Activity Guidelines Advisory Committee. Physical Activity Guidelines Report, 2008. Washington, DC: U.S. Department of Health and Human Services, 2008 Rowe DA, et al. Stride rate recommendations for moderate intensity walking. Med Sci Sports Exerc 2011 Jun 11; 43 (2): 312-8 Rowe, DA, et al. (in review). Cadence of older women walking at self-selected and music-guided pace. Submitted for presentation at the World Conference on Active Ageing, Glasgow, Scotland Tudor-Locke C, et al. Pedometer-determined step count guidelines for classifying walking intensity in a young ostensibly healthy population. Can J Appl Physiol 2005 Dec; 30 (6): 666-76 Tudor-Locke C & Rowe, DA. Using cadence to study free-living ambulatory behaviour. Sports Med, In Press. Contact details: david.rowe@strath.ac.uk