Submitted to the 2008 Annual Meeting of the American College of Sports Medicine for presentation

　

Activity Energy Expenditure is Under-Predicted for Walking in Adults in Peripheral Neuropathy

Corey L. Black, Megan Duet, Daniel P. Heil, FACSM, Li Li, FACSM

Louisiana State University, Baton Rouge, LA

Montana State University, Bozeman, MT

Accurate energy expenditure calculations are important for assessing free-living physical activity (PA), especially for sedentary populations. One such group, those afflicted with peripheral neuropathy (PN), posses locomotion and balance problems due to pain and lack of peripheral sensations. The impairments of PN lead to extremely low PA and challenge the existing energy expenditure measurement methods. Purpose: This study compared measured activity energy expenditure (AEE, kcals/kg/min) during overground walking with predicted AEE derived from a commonly-used prediction equation to determine accuracy of the prediction for this population. Methods: Five men (Mean±SD: 71±17 yrs age, 85.6±10.3 kg body mass, 28.6±3.2 kg/m² BMI) and 12 women (72±6 yrs, 71.1±12.0 kg, 27.5±4.3 kg/m² BMI), all diagnosed with peripheral neuropathy by physicians, volunteered to perform several structured activities: Sitting quietly (to estimate resting metabolic rate, RMR), and self-selected “slow” (52.6±15.1 m/min) and “brisk” (68.6±14.8 m/min) overground walking. Oxygen uptake (VO₂) was measured using a portable metabolic system worn using a modified backpack. Steady-state VO₂ for each activity was transformed into AEE by subtracting subjects’ estimated RMR. Predicted AEE was derived from the ACSM walking equation (VO₂ = 0.1xS + 3.5; S= walking speed, m/min) where gross VO₂ was adjusted by subtracting either 3.5 (AEE1) or measured RMR (AEE2). Measured AEE was compared with AEE1 and AEE2 using repeated measures ANOVA. Results: Predicted AEE1 for “slow” walking (Mean±SE: 0.0263±0.0018) and “brisk” walking (0.0343±0.0018) were significantly (P<0.05) lower than measured AEE “slow” (0.0356±0.0025) and “brisk” (0.0457±0.0038), respectively. Predicted AEE2 “slow” (0.0307±0.0022) and “brisk” (0.0387±0.0021) did not differ from either measured AEE or predicted AEE1. Conclusions: AEE prediction in this population is more accurate when subtracting measured RMR from V0₂ as opposed to subtracting an assumed RMR constant. Future studies with this population should focus on predicting AEE for locomotion-based activities to avoid the confounding influence of an assumed RMR.