Predicting Walking METs and Energy Expenditure from Speed or Accelerometry

1720 PURPOSE: The ability to accurately predict energy expenditure is essential for exercise prescription; however, little research is available when comparing the measured energy expenditure with prediction equations. Furthermore, little research is available comparing walking and running energy expenditure on a track to treadmill. The primary purpose of this study was to establish how well the published prediction equations for the energy expenditure of walking and running compare to the actual measured values. To accomplish this we reexamined whether differences exist in energy expenditure of walking vs. running 1600 meters, and if energy expenditure differences occur due to being on the track or treadmill which could confound the estimation of energy expenditure. METHODS: Indirect calorimetry was measured on 12 male and 12 female subjects while walking (4.8 km/hr) and running (9.6 km/hr) 1600 meters on the treadmill. A subgroup of 7 males and 10 females also performed the 1600 meter run/walk on the track. The measured energy expenditures were compared to prediction equations available in the literature. RESULTS: Running utilized significantly more energy for 1600 meters than walking in both genders (treadmill: running 481 ± 20, walking 340 ± 19 kJ; track: running 480 ± 23, walking 430 ± 14 kJ, P<0.01), and greater energy expenditures were found in the males than females (P<0.05). When adjusted for lean body mass, the greater energy expenditures for 1600 meters in males disappeared. There were no differences between the track and treadmill energy expenditures regardless of speed. The predictions using the ACSM or Léger equations for running did not differ significantly from the actual energy expenditures (total error-ACSM: 20 and −14.4 kJ for running and walking respectively; Légers walking: 10.1 kJ). Both the McArdle's table value for walking and running and van der Walt's prediction for walking significantly overestimated (P<0.01) energy expenditure for 1600 meters. CONCLUSIONS: ACSM's prediction model for walking and running as well as the Léger equation for walking appear to be the most suitable for the prediction of energy expenditure for both running and walking, while McArdle's table and van der Walt equation were not as strong predictors of energy expenditure.

A Validation of Equations for Predicting the Energy Expenditures of Hospitalized Patients

The purpose of this study was to investigate the accuracy of the published prediction equations for determining level overground walking energy cost in young adults. In total, 148 healthy young adults volunteered to participate in this study. Resting metabolic rate and energy expenditure variables at speeds of 4, 5, and 6km/h were measured by indirect calorimetry, walking energy expenditure was estimated by 3 published equations. The gross and net metabolic rate per mile of level overground walking increased with increased speed (all P < .01). Females were less economical than males. The present findings revealed that the American College of Sports Medicine and Pandolf etal equations significantly underestimated the energy cost of overground walking at all speeds (all P < .01) in young adults. The percentage mean bias for American College of Sports Medicine, Pandolf etal, and Weyand etal was 12.4%, 16.8%, 1.4% (4km/h); 21.6%, 15.8%, 7.1% (5km/h); and 27.6%, 12%, 6.6% (6km/h). Bland-Altman plots and prediction error analysis showed that the Weyand etal was the most accurate in 3 existing equations. The Weyand etal equation appears to be the most suitable for the prediction of overground walking energy expenditure in young adults.

The purpose of this study was to determine the influence of weight classification on predicting energy expenditure (EE) in women. Twelve overweight (body mass index [BMI] = 25-29.99 kg/m2) and 12 normal-weight (BMI = 18.5-24.99 kg/m2) women walked and jogged 1,609 m at 1.34 m.s-1 and 2.23 m.s-1, respectively, while EE was assessed using indirect calorimetry and compared to several other prediction methods (American College of Sports Medicine [ACSM], 2010; Heyward, 2006; Léger & Mercier, 1984; McArdle, Katch, & Katch, 2006; Pandolf, Givoni, & Goldman, 1978; van der Walt & Wyndham, 1973). More error occurred with overweight EE prediction compared to normal-weight EE prediction. The ACSM and Heyward methods for walking and the McArdle et al. method for jogging most accurately predicted EE for both groups. Weight classification influences EE prediction accuracy and, thus, is important to consider when using these prediction methods.

The purpose of this study was to determine the influence of weight classification on predicting energy expenditure (EE) in women. Twelve overweight (body mass index [BMI] = 25-29.99 kg/m2) and 12 normal-weight (BMI = 18.5-24.99 kg/m2) women walked and jogged 1,609 m at 1.34 m.s-1 and 2.23 m.s-1, respectively, while EE was assessed using indirect calorimetry and compared to several other prediction methods (American College of Sports Medicine [ACSM], 2010; Heyward, 2006; Léger & Mercier, 1984; McArdle, Katch, & Katch, 2006; Pandolf, Givoni, & Goldman, 1978; van der Walt & Wyndham, 1973). More error occurred with overweight EE prediction compared to normal-weight EE prediction. The ACSM and Heyward methods for walking and the McArdle et al. method for jogging most accurately predicted EE for both groups. Weight classification influences EE prediction accuracy and, thus, is important to consider when using these prediction methods.

Physical Activity, Energy Expenditure and Work Intensity of Care‐Workers on Shift Work in a Special Nursing Home for the Elderly: Tadaaki Wakui, et al. Department of Wellness and Welfare, Ube College—To evaluate the workload of care‐workers on shift work in special nursing homes for the elderly (SNHs), physical activity, energy expenditure and work intensity were measured. Nine healthy female care‐workers volunteered to participate in our study; 3 subjects worked the day shift, 3 subjects worked the night shift, and 3 subjects worked both shifts. Time spent in actual care, heart rate, number of walking steps, energy expenditure, work intensity and time spent in different postures was determined for the day shift and night shifts. The time on duty, work and break (recess) times were significantly greater for the night shift than for the day shift. The percent care time was significantly greater for the day shift than the night shift, and the percent break time was significantly greater for the night shift than the day shift. Individual care of residents accounted for the greatest percent of work time. Minimum and mean heart rates were significantly greater for the day shift than for the night shift, and minimum heart rate in relation to estimated maximum heart rate was significantly greater for the day shift than for the night shift. Although total walking steps for the night shift was significantly greater than that for the day shift, the number of steps per h did not differ between shifts. Energy expenditure (kcal) for the night shift was significantly greater than that for the day shift, but the difference between the two shifts in work intensity (kcal/kg/min) was not significant. Standing (posture) accounted for the largest percentage of work time in relation to other work postures regardless of shift. Physical activity, energy expenditure and work intensity of the SNH care‐workers for both shifts appear to be higher than those reported for nursery school teachers and hospital nurses. We agree with other investigators that appropriate countermeasures are needed to reduce the workload of such care‐workers.

Because of the well-documented linear relationship between heart rate and oxygen consumption (VO2), heart rate is commonly used to estimate energy expenditure during exercise. However, previous research suggests that heart rate increases without a concomitant rise in VO2 when arm movements are added to exercise. If so, this could impact the accuracy of heart rate monitors in estimating energy expenditure during combined arm and leg exercise. This study compared the cardiorespiratory responses to a bench step aerobics routine performed with and without arm movements and evaluated whether the accuracy of the Polar F6 heart rate monitor in predicting energy expenditure was impacted by the inclusion of arm movements. Thirty-two women performed the same routine with and without arm movements while stepping up and down off of a 15.24-cm bench at a cadence of 128 b·min-1. Heart rate and VO2 increased, whereas oxygen pulse (VO2·heart rate-1) decreased when arm movements were added (p < 0.001). However, the differences between the energy expenditure estimated by the Polar F6 heart rate monitor and the energy expenditure measured by indirect calorimetry were similar during the same aerobic bench stepping routine performed with and without arms (Δ∼2 kCal·min-1, p ≥ 0.05). Results confirm that arm movements during aerobic bench stepping elicit a disproportionate rise in heart rate relative to V[Combining Dot Above]O2. However, results do not support that these movements increase the prediction error in energy expenditure, as the Polar F6 heart rate monitor over predicted energy expenditure when arm movements were involved and when they were not involved.

Purpose: The purpose of this study was to develop and cross-validate an equation based on ActiGraph accelerometer GT3X output to predict children and youth's energy expenditure (EE) of physical activity (PA). Method: Participants were 367 Chinese children and youth (179 boys and 188 girls, aged 9 to 17 years old) who wore 1 ActiGraph GT3X accelerometer on their right hip during the following tests/activities: resting metabolic rate (RMR), six 5-min treadmill walk/runs (tested at different speeds: 3 km·h− 1, 4 km·h− 1, 5 km·h− 1, 6 km·h− 1, 7 km·h− 1, and 8 km·h− 1), 1 broadcast gymnastics, and 2 table-tennis exercises. Participants' oxygen consumption was measured using Cosmed K4b2. The participants were randomly divided into a calibration group (n = 331, 90%) and a cross-validation group (n = 36, 10%). The calibration group's data were used to determine the relationship between EE and triaxial vector magnitude counts (VM) using the Pearson correlation and to derive the equation using a stepwise multiple regression. In the cross-validation group, differences between measured and predicted EE were evaluated using pairwise t tests. Results: VM activity counts had a moderately high correlation with EE (r = .758, p < .01). An EE prediction equation was developed: EE (kcal·min− 1) = 0.00083 × VM+0.073 × weight − 2.01 (R 2 = .72, SEE = 1.45 kcal·min− 1). According to the cross-validation study results, this equation could predict the EE within the range of known accuracy (i.e., about 20% error). Conclusions: An equation based on ActiGraph accelerometer VM activity counts was derived to predict EE of PA in Chinese children and youth within the range of known accuracy.

Energy expenditure of walking at different intensities in Brazilian college women

This study aimed to determine, if there is a difference in energy expenditure during walking among athletes and non-athletes at two different speeds of walking. Ninety five healthy female students (47 athletes and 48 non-athletes) with a mean age of 22.4 (±1.6) years purposively participated in this study. Medical and sport participation history of the subjects was acquired through a questionnaire. Two experimental tests including anthropometric measurements, VO2max, and walking tests on treadmill at speeds of 3.00 and 3.5 mph were conducted. Results showed no difference in weight, height, body mass index, and leg length between both groups. The non-athletes expended a greater amount of energy than athletes (3.78±.1 and 2.95±.6 kcal.min-1, respectively) at both speeds of 3.00mph and 3.5mph (4.89±1 and 3.94±.7 kcal.min-1). Based on energy requirements for walking at similar weights and speeds by ACSM’s guideline, the female athletes walked at a slow, moderate and brisk pace. Most of the female non-athletes walked at a moderate, brisk and very brisk pace. This study revealed that regular exercise could improve walking efficiency, and the energy expenditure of walking would play an important role in the information processing for total energy requirement that progressively affects weight management and health.

Deficits in energy production and utilization have been linked to higher fatigue and functional decline with aging. Lesser known is whether individuals with a combination of low peak energy capacity and high energy costs for mobility (eg, impaired energy regulation) are more likely to experience the onset and progression of high fatigability with aging. Participants in the Baltimore Longitudinal Study of Aging (n = 651, 49.0% male, mean age 71.9, range 50-94) with ≥2 visits who completed fatigability (Borg rating of perceived exertion [RPE] after a 5-minute 1.5 mph treadmill walk), slow walking energy expenditure (VO2 mL/kg/min), and peak walking energy expenditure (VO2 mL/kg/min), testing between 2007 and 2018. The longitudinal association between each measure of energy expenditure, a ratio of energy cost-to-capacity, and perceived fatigability was modeled using mixed effects models adjusted for age, body composition, and comorbidities. Time to higher perceived fatigability (RPE ≥ 10) was modeled using Cox proportional hazards models. In continuous analyses, higher slow walking energy expenditure (p < .05) and a higher cost ratio (p ≤ .001) were associated with greater perceived fatigability over time. Cox proportional hazards models using tertiles of the cost ratio suggest that, compared to those in the lowest tertile, those in the middle and highest tertiles had 1.89 (95% confidence interval [CI]: 1.57-5.16) and 2.85 (95% CI: 1.05-3.40) times greater risk of developing higher fatigability, respectively. Findings suggest that strategies to prevent fatigability should consider methods to improve energy regulation by targeting both the independent and combined effects of declining peak capacity and rising energy costs for mobility with aging.

The objectives of this study were to investigate the role of coenzyme Q10 and/or circuit resistance training on energy expenditure at rest and during submaximal treadmill walking and to determine whether CoQ10 could alter energy expenditure in 40-60 years old overweight women. The sample used in this study were employees of the Bank of Thailand for 48 people, divided into four groups by the selection of volunteers who met the criteria for selection into the sample by electronic mail (Email) and were obtained by purposive sampling for more specific predefined groups based on the particular purpose of the experiment. Participants were divided into 4 groups of 12 people, including group 1 (CoQ10) with supplemented CoQ10 only, group 2 (Ex) with circuit resistance training only, group 3 (Ex&amp;CoQ10) with both supplemented coenzyme Q10 and circuit resistance training and Group 4 control group. Instruments used in this research for pretest and posttest after twelve weeks of program were The Physical Activity Readiness Questionnaire (PAR-Q) for assessing participation before exercise, elastic exercise band (Sanctband&amp;reg;) for fitness training and coenzyme Q10 (100 mg soft-gels by TrueNature&amp;reg;, USP Verified, USA). CoQ10 was taken as 100 mg capsules two times daily after a meal for 12 weeks. Statistical analyses were performed with SPSS software version 17. Descriptive statistic including frequency distribution, percentage, mean and standard deviation were used to analyze the demographic data. The Kolmogorov - Smirnov test was used to assess normal distribution. One-way Analysis of Covariance (ANCOVA) was used to determine the difference between groups of subjects by using pretreatment evaluation as covariate and the differences in pairs of means among groups were made by Fisher&amp;#39;s Least Significant Difference (LSD) test. Paired t-test was used to determine the difference between results of before and after treatment. Pearson&amp;rsquo;s Product Moment Correlation was used to present the relationships. P values of less than 0.05 were considered to indicate statistical significance.After treatment, the results showed that VO2Max was found to be significantly increased in group 1 (CoQ10), group 2 (Ex) and group3 (Ex&amp;CoQ10) and there were significant differences between all experiment groups and the control group. Moreover, plasma coenzyme Q10 levels was found to be significantly increased within all groups. However, there were significant differences in plasma coenzyme Q10 levels between group 1 (CoQ10) and control group, group 1 (CoQ10) and group 2 (Ex), group 3 (Ex&amp;CoQ10) and control group as well as group 3 (Ex&amp;CoQ10) and group 2 (Ex). In addition, it was also found that weight and BMI were significantly decreased in group 2 (Ex) and group 3 (Ex&amp;CoQ10). Fat free mass (FFM) was found to be significantly decreased in group 3 (Ex&amp;CoQ10) but fat mass (FM) was found to be significantly decreased in group 2 (Ex), group 3 (Ex&amp;CoQ10) and group 4 (control group). Resting energy expenditure (REE) was found to be significantly decreased in group 1 (CoQ10), group 2 (Ex) and group 4 (control group).Triglyceride (mg/dl) was found to be significantly increased in control group. Walking energy expenditure, glucose levels, cholesterol, LDL and HDL were found to be no significant differences among all groups in both before and after treatment.It was concluded in this study that after treatment no significant differences were found in energy expenditure at rest and during submaximal treadmill walking among all groups. However, maximum oxygen consumption (Vo2Max) and plasma coenzyme Q10 levels were likely found to have positive effects in overweight subjects with supplemented coenzyme Q10 and/or circuit resistance training. Moreover, the researcher found low positive correlation between maximum oxygen consumption (VO2Max) and plasma coenzyme Q10 levels.

Validity of accelerometry for predicting physical activity and sedentary time in ambulatory children and young adults with cerebral palsy

In studies that involve the use of a room calorimeter, 24 h energy intake is often larger than 24 h energy expenditure (24 h EE) because of a decrease in activity energy expenditure due to the confined space. This positive energy balance can have large consequences for the interpretation of substrate balances. The objective of this study was to develop a method for predicting an individual's 24 h EE in a room calorimeter at both low (1.4xRMR) and high (1.8xRMR) levels of physical activity. Two methods are presented that predict an individual's 24 h EE in a metabolic chamber. The first method was based on three components: (1) a 30 min measurement of resting metabolic rate (RMR) using a ventilated hood system; (2) measurement of exercise energy expenditure during 10 min of treadmill walking; and (3) estimation of free-living energy expenditure using a tri-axial motion sensor. Using these measurements we calculated the amount of treadmill time needed for each individual in order to obtain a total 24 h EE at either a low (1.4xRMR) or a high (1.8xRMR) level of physical activity. We also developed a method to predict total 24 h EE during the chamber stay by using the energy expenditure values for the different levels of activity as measured during the hours already spent in the chamber. This would provide us with a tool to adjust the exercise time and/or energy intake during the chamber stay. Method 1: there was no significant difference in expected and measured 24 h EE under either low (9.35+/-0.56 vs 9.51+/-0.47 MJ/day; measured vs predicted) or high activity conditions (13.41+/-0.74 vs 13.97+/-0.78 MJ/day; measured vs predicted). Method 2: the developed algorithm predicted 24 h EE for 97.6+/-4.0% of the final value at 3 h into the test day, and for 98.6+/-3.7% at 7 h into the test day. Both methods provide accurate prediction of energy expenditure in a room calorimeter at both high and low levels of physical activity. It equally shows that it is possible to accurately predict total 24 h EE from energy expenditure values obtained at 3 and 7 h into the study.

BackgroundPhysical activity tracking wearable devices have emerged as an increasingly popular method for consumers to assess their daily activity and calories expended. However, whether these wearable devices are valid at different levels of exercise intensity is unknown.ObjectiveThe objective of this study was to examine heart rate (HR) and energy expenditure (EE) validity of 3 popular wrist-worn activity monitors at different exercise intensities.MethodsA total of 62 participants (females: 58%, 36/62; nonwhite: 47% [13/62 Hispanic, 8/62 Asian, 7/62 black/ African American, 1/62 other]) wore the Apple Watch, Fitbit Charge HR, and Garmin Forerunner 225. Validity was assessed using 2 criterion devices: HR chest strap and a metabolic cart. Participants completed a 10-minute seated baseline assessment; separate 4-minute stages of light-, moderate-, and vigorous-intensity treadmill exercises; and a 10-minute seated recovery period. Data from devices were compared with each criterion via two-way repeated-measures analysis of variance and Bland-Altman analysis. Differences are expressed in mean absolute percentage error (MAPE).ResultsFor the Apple Watch, HR MAPE was between 1.14% and 6.70%. HR was not significantly different at the start (P=.78), during baseline (P=.76), or vigorous intensity (P=.84); lower HR readings were measured during light intensity (P=.03), moderate intensity (P=.001), and recovery (P=.004). EE MAPE was between 14.07% and 210.84%. The device measured higher EE at all stages (P<.01). For the Fitbit device, the HR MAPE was between 2.38% and 16.99%. HR was not significantly different at the start (P=.67) or during moderate intensity (P=.34); lower HR readings were measured during baseline, vigorous intensity, and recovery (P<.001) and higher HR during light intensity (P<.001). EE MAPE was between 16.85% and 84.98%. The device measured higher EE at baseline (P=.003), light intensity (P<.001), and moderate intensity (P=.001). EE was not significantly different at vigorous (P=.70) or recovery (P=.10). For Garmin Forerunner 225, HR MAPE was between 7.87% and 24.38%. HR was not significantly different at vigorous intensity (P=.35). The device measured higher HR readings at start, baseline, light intensity, moderate intensity (P<.001), and recovery (P=.04). EE MAPE was between 30.77% and 155.05%. The device measured higher EE at all stages (P<.001).ConclusionsThis study provides one of the first validation assessments for the Fitbit Charge HR, Apple Watch, and Garmin Forerunner 225. An advantage and novel approach of the study is the examination of HR and EE at specific physical activity intensities. Establishing validity of wearable devices is of particular interest as these devices are being used in weight loss interventions and could impact findings. Future research should investigate why differences between exercise intensities and the devices exist.