Treadmill walking economy is not affected by body fat and body mass index in adults.

To determine whether body fat and body mass index (BMI) affect the energy cost of walking (Cw; J/kg/m), ventilation, and gas exchange data from 205 adults (115 females; percent body fat range = 3.0%-52.8%; BMI range = 17.5-43.2 kg/m2) were obtained at rest and during treadmill walking at 1.34 m/s to calculate gross and net Cw. Linear regression was used to assess relationships between body composition indices, Cw, and standing metabolic rate (SMR). Unpaired t-tests were used to assess differences between sex, and one-way ANOVA was used to assess differences by BMI categories: normal weight, <25.0 kg/m2; overweight, 25.0-29.9 km/m2; and obese, ≥30 kg/m2. Net Cw was not related to body fat percent, fat mass, or BMI (all R2 ≤ 0.011). Furthermore, mean net Cw was similar by sex (male: 2.19 ± 0.30 J/kg/m; female: 2.24 ± 0.37 J/kg/m, p = 0.35) and across BMI categories (normal weight: 2.23 ± 0.36 J/kg/m; overweight: 2.18 ± 0.33 J/kg/m; obese: 2.26 ± 0.31, p = 0.54). Gross Cw and SMR were inversely associated with percent body fat, fat mass, and BMI (all R2 between 0.033 and 0.270; all p ≤ 0.008). In conclusion, Net Cw is not influenced by body fat percentage, total body fat, and BMI and does not differ by sex.

Determining the Optimal Workrate for Cycle Ergometer Verification Phase Testing in Males with Obesity.

The aim of the present study was to assess the validity of verification phase (VP) testing and a 3 min all-out test to determine critical power (CP) in males with obesity. Nine young adult males with a body mass index (BMI) ≥ 30 kg·m-2 completed a cycle ergometer ramp-style VO2max test, four randomized VP tests at 80, 90, 100, and 105% of maximum wattage attained during the ramp test, and a 3 min all-out test. There was a significant main effect for VO2max across all five tests (p = 0.049). Individually, 8 of 9 participants attained a higher VO2max (L/min) during a VP test compared to the ramp test. A trend (p = 0.06) was observed for VO2max during the 90% VP test (3.61 ± 0.54 L/min) when compared to the ramp test (3.37 ± 0.39 L/min). A significantly higher VO2max (p = 0.016) was found in the VP tests that occurred below 130% of CP wattage (N = 15, VO2max = 3.76 ± 0.52 L/min) compared to those that were above (N = 21, VO2max = 3.36 ± 0.41 L/min). Our findings suggest submaximal VP tests at 90% may elicit the highest VO2max in males with obesity and there may be merit in using % of CP wattage to determine optimal VP intensity.

Supra-Versus Submaximal Cycle Ergometer Verification of VO2max in Males and Females.

This study was designed to determine the optimal intensity for verification phase testing (VP) in healthy, young adults. Thirty one young, active participants (16 females) completed a cycle ergometer graded exercise test (GXT) VO2max test and 4 VP tests at 80, 90, 100, and 105% of the maximum wattage achieved during the GXT. GXT and VP VO2max values showed a significant test x sex interaction (p = 0.02). The males elicited significantly higher VO2max values during the GXT, 80%, and 90% when compared to the 105%, (105 vs. GXT: p = 0.05; 105% vs. 80%: p < 0.01; 105% vs. 90%: p = 0.02). There were no significant differences in VO2max across the tests in the females (p > 0.05); 80% of the males achieved their highest VP VO2max during a submaximal VP test compared to only 37.5% of the females. A secondary study conducted showed excellent reliability (ICCs > 0.90) and low variation (CVs < 3%) for the 90% VP. Our findings show that a submaximal verification phase intensity is ideal for young healthy males to elicit the highest VO2max during cycle ergometer testing. For females, a range of intensities (80-105%) produce similar VO2max values. However, the 80% VP yields an unnecessarily high time to exhaustion.

Predicting Maximal Oxygen Uptake Using the 3-Minute All-Out Test in High-Intensity Functional Training Athletes.

Maximal oxygen uptake (VO2max) and critical speed (CS) are key fatigue-related measurements that demonstrate a relationship to one another and are indicative of athletic endurance performance. This is especially true for those that participate in competitive fitness events. However, the accessibility to a metabolic analyzer to accurately measure VO2max is expensive and time intensive, whereas CS may be measured in the field using a 3 min all-out test (3MT). Therefore, the purpose of this study was to examine the relationship between VO2max and CS in high-intensity functional training (HIFT) athletes. Twenty-five male and female (age: 27.6 ± 4.5 years; height: 174.5 ± 18.3 cm; weight: 77.4 ± 14.8 kg; body fat: 15.7 ± 6.5%) HIFT athletes performed a 3MT as well as a graded exercise test with 48 h between measurements. True VO2max was determined using a square-wave supramaximal verification phase and CS was measured as the average speed of the last 30 s of the 3MT. A statistically significant and positive correlation was observed between relative VO2max and CS values (r = 0.819, p < 0.001). Based on the significant correlation, a linear regression analysis was completed, including sex, in order to develop a VO2max prediction equation (VO2max (mL/kg/min) = 8.449(CS) + 4.387(F = 0, M = 1) + 14.683; standard error of the estimate = 3.34 mL/kg/min). Observed (47.71 ± 6.54 mL/kg/min) and predicted (47.71 ± 5.7 mL/kg/min) VO2max values were compared using a dependent t-test and no significant difference was displayed between the observed and predicted values (p = 1.000). The typical error, coefficient of variation, and intraclass correlation coefficient were 2.26 mL/kg/min, 4.90%, and 0.864, respectively. The positive and significant relationship between VO2max and CS suggests that the 3MT may be a practical alternative to predicting maximal oxygen uptake when time and access to a metabolic analyzer is limited.

Assessing the ability of the Fitbit Charge 2 to accurately predict VO2max.

BACKGROUND: The aim of this study was to assess the ability of the Fitbit Charge 2 (FBC2) to accurately estimate VO2max in comparison to both the gold standard VO2max test and a non-exercise VO2max prediction equation. METHODS: Thirty healthy subjects (17 men, 13 women) between the ages of 18 and 35 (age =21.7±3.1 years) were given a FBC2 to wear for seven days and followed instructions on how to obtain a cardio fitness score (CFS). VO2max was measured with an incremental test on the treadmill followed by a verification phase. VO2max was predicted via a non-exercise prediction model (N-Ex) using self-reported physical activity level. RESULTS: Measured VO2max was significantly lower than FBC2 predicted CFS (VO2max =49.91±6.83; CFS =52.53±8.43, P=0.03). N-Ex prediction was significantly lower than CFS but not significantly lower than measured VO2max (N-Ex =48.79±6.32; CFS vs. N-Ex: P=0.01; VO2max vs. N-Ex: P=0.54). Relationships between both VO2max vs. CFS and VO2max vs. N-Ex were good (ICC: VO2max vs. CFS=0.87, VO2max vs. N-Ex =0.87); Bland-Altman analysis indicated consistency of CFS measurement and lack of bias. The coefficient of variation (CV) and mean absolute percent error (MAPE) were greater with CFS than N-Ex (CV: CFS =6.5%±4.1%, N-Ex =5.6%±3.6%; MAPE: CFS =10.2%±6.7%, N-Ex =7.8%±5.0%). Heart rate (HR) estimated by the FBC2 was lower than estimated (Est) HR for pace based on HR extrapolation (FBC2 =155±18 bpm, Est =183±15 bpm, P<0.001). The difference in CFS and VO2max was inversely correlated with the difference in FBC2 HR and Estimated HR (r =-0.45, P<0.001). CONCLUSIONS: The FBC2 shows consistent, unbiased measurement of CFS while overestimating VO2max in healthy men and women. The non-exercise VO2max prediction equation provides a similar, slightly more accurate, VO2max prediction than the CFS without the need for an exercise test or purchase of a Fitbit.

Physiological Performance Measures as Indicators of CrossFit® Performance.

CrossFit® began as another exercise program to improve physical fitness and has rapidly grown into the "sport of fitness". However, little is understood as to the physiological indicators that determine CrossFit® sport performance. The purpose of this study was to determine which physiological performance measure was the greatest indicator of CrossFit® workout performance. Male (n = 12) and female (n = 5) participants successfully completed a treadmill graded exercise test to measure maximal oxygen uptake (VO2max), a 3-minute all-out running test (3MT) to determine critical speed (CS) and the finite capacity for running speeds above CS (D'), a Wingate anaerobic test (WAnT) to assess anaerobic peak and mean power, the CrossFit® total to measure total body strength, as well as the CrossFit® benchmark workouts: Fran, Grace, and Nancy. It was hypothesized that CS and total body strength would be the greatest indicators of CrossFit® performance. Pearson's r correlations were used to determine the relationship of benchmark performance data and the physiological performance measures. For each benchmark-dependent variable, a stepwise linear regression was created using significant correlative data. For the workout Fran, back squat strength explained 42% of the variance. VO2max explained 68% of the variance for the workout Nancy. Lastly, anaerobic peak power explained 57% of the variance for performance on the CrossFit® total. In conclusion, results demonstrated select physiological performance variables may be used to predict CrossFit® workout performance.

High-intensity interval exercise attenuates but does not eliminate endothelial dysfunction after a fast food meal.

We investigated whether two different bouts of high-intensity interval exercise (HIIE) could attenuate postprandial endothelial dysfunction. Thirteen young (27 ± 1 yr), nonexercise-trained men underwent three randomized conditions: 1) four 4-min intervals at 85-95% of maximum heart rate separated by 3 min of active recovery (HIIE 4 × 4), 2) 16 1-min intervals at 85-95% of maximum heart rate separated by 1 min of active recovery (HIIE 16 × 1), and 3) sedentary control. HIIE was performed in the afternoon, ~18 h before the morning fast food meal (1,250 kcal, 63g of fat). Brachial artery flow-mediated dilation (FMD) was performed before HIIE ( baseline 1), during fasting before meal ingestion ( baseline 2), and 30 min, 2 h, and 4 h postprandial. Capillary glucose and triglycerides were assessed at fasting, 30 min, 1 h, 2 h, and 4 h (triglycerides only). Both HIIE protocols increased fasting FMD compared with control (HIIE 4 × 4: 6.1 ± 0.4%, HIIE 16 × 1: 6.3 ± 0.5%, and control: 5.1 ± 0.4%, P < 0.001). For both HIIE protocols, FMD was reduced only at 30 min postprandial but never fell below baseline 1 or FMD during control at any time point. In contrast, control FMD decreased at 2 h (3.8 ± 0.4%, P < 0.001) and remained significantly lower than HIIE 4 × 4 and 16 × 1 at 2 and 4 h. Postprandial glucose and triglycerides were unaffected by HIIE. In conclusion, HIIE performed ~18 h before a high-energy fast food meal can attenuate but not entirely eliminate postprandial decreases in FMD. This effect is not dependent on reductions in postprandial lipemia or glycemia. NEW & NOTEWORTHY Two similar high-intensity interval exercise (HIIE) protocols performed ∼18 h before ingestion of a high-energy fast food meal attenuated but did not entirely eliminate postprandial endothelial dysfunction in young men largely by improving fasting endothelial function. Both HIIE protocols produced essentially identical results, suggesting high reproducibility of HIIE effects.

Cycling efficiency and energy cost of walking in young and older adults.

To determine whether age affects cycling efficiency and the energy cost of walking (Cw), 190 healthy adults, ages 18-81 yr, cycled on an ergometer at 50 W and walked on a treadmill at 1.34 m/s. Ventilation and gas exchange at rest and during exercise were used to calculate net Cw and net efficiency of cycling. Compared with the 18-40 yr age group (2.17 ± 0.33 J·kg-1·m-1), net Cw was not different in the 60-64 yr (2.20 ± 0.40 J·kg-1·m-1) and 65-69 yr (2.20 ± 0.28 J·kg-1·m-1) age groups, but was significantly ( P < 0.03) higher in the ≥70 yr (2.37 ± 0.33 J·kg-1·m-1) age group. For subjects >60 yr, net Cw was significantly correlated with age ( R2 = 0.123; P = 0.002). Cycling net efficiency was not different between 18-40 yr (23.5 ± 2.9%), 60-64 yr (24.5 ± 3.6%), 65-69 yr (23.3 ± 3.6%) and ≥70 yr (24.7 ± 2.7%) age groups. Repeat tests on a subset of subjects (walking, n = 43; cycling, n = 37) demonstrated high test-retest reliability [intraclass correlation coefficients (ICC), 0.74-0.86] for all energy outcome measures except cycling net energy expenditure (ICC = 0.54) and net efficiency (ICC = 0.50). Coefficients of variation for all variables ranged from 3.1 to 7.7%. Considerable individual variation in Cw and efficiency was evident, with a ~2-fold difference between the least and most economical/efficient subjects. We conclude that, between 18 and 81 yr, net Cw was only higher for ages ≥70 yr, and that cycling net efficiency was not different across age groups. NEW & NOTEWORTHY This study illustrates that the higher energy cost of walking in older adults is only evident for ages ≥70 yr. For older adults ages 60-69 yr, the energy cost of walking is similar to that of young adults. Cycling efficiency, by contrast, is not different across age groups. Considerable individual variation (∼2-fold) in cycling efficiency and energy cost of walking is observed in young and older adults.

Breaks in Sitting Time: Effects on Continuously Monitored Glucose and Blood Pressure.

PURPOSE: We examined the effects of interrupting prolonged sitting with multiple 2-min walking breaks or one 30-min continuous walking session on glucose control and ambulatory blood pressure (ABP). METHODS: Ten overweight/obese, physically inactive participants (five men; 32 ± 5 yr; BMI, 30.3 ± 4.6 kg·m) participated in this randomized four-trial crossover study, with each trial performed on a separate, simulated workday lasting 9 h: 1) 30 min of continuous moderate-intensity (30-min MOD) walking at 71% ± 4% HRmax; 2) 21 × 2 min bouts of moderate-intensity (2-min MOD) walking at 53% ± 5% HRmax, each performed every 20 min (42 min total); 3) 8 × 2 min bouts of vigorous-intensity (2-min VIG) walking at 79% ± 4% HRmax, each performed every hour (16 min total); 4) 9 h of prolonged sitting (SIT). Participants underwent continuous interstitial glucose monitoring and ABP monitoring during and after the simulated workday spent in the laboratory, with primary data analysis from 12:30 h to 07:00 h the next morning. RESULTS: Compared with SIT (5.6 ± 1.1 mmol·L), mean 18.7-h glucose was lower during the 2-min MOD (5.2 ± 1.1 mmol·L) and 2-min VIG (5.4 ± 0.9 mmol·L) trials and mean 18.7-h glucose during the 30-min MOD trial (5.1 ± 0.8 mmol·L) was lower than all other trials (P < 0.001). Postprandial glucose was approximately 7% to 13% lower during all trials compared with SIT (P < 0.001), with 30-min MOD having the greatest effect. Only the 30-min MOD trial was effective in reducing systolic ABP from 12:30 to 07:00 h (119 ± 15 mm Hg) when compared with SIT (122 ± 16 mm Hg; P < 0.05). CONCLUSIONS: Replacing sitting with 2-min MOD walking every 20 min or 2 min of vigorous-intensity walking every hour during a simulated workday reduced 18.7 h and postprandial glucose, but only 30-min MOD walking was effective for reducing both glucose and systolic ABP.

Effects of high-intensity interval training and moderate-intensity continuous training on endothelial function and cardiometabolic risk markers in obese adults.

We hypothesized that high-intensity interval training (HIIT) would be more effective than moderate-intensity continuous training (MICT) at improving endothelial function and maximum oxygen uptake (V̇o2 max) in obese adults. Eighteen participants [35.1 ± 8.1 (SD) yr; body mass index = 36.0 ± 5.0 kg/m(2)] were randomized to 8 wk (3 sessions/wk) of either HIIT [10 × 1 min, 90-95% maximum heart rate (HRmax), 1-min active recovery] or MICT (30 min, 70-75% HRmax). Brachial artery flow-mediated dilation (FMD) increased after HIIT (5.13 ± 2.80% vs. 8.98 ± 2.86%, P = 0.02) but not after MICT (5.23 ± 2.82% vs. 3.05 ± 2.76%, P = 0.16). Resting artery diameter increased after MICT (3.68 ± 0.58 mm vs. 3.86 ± 0.58 mm, P = 0.02) but not after HIIT (4.04 ± 0.70 mm vs. 4.09 ± 0.70 mm; P = 0.63). There was a significant (P = 0.02) group × time interaction in low flow-mediated constriction (L-FMC) between MICT (0.63 ± 2.00% vs. -2.79 ± 3.20%; P = 0.03) and HIIT (-1.04 ± 4.09% vs. 1.74 ± 3.46%; P = 0.29). V̇o2 max increased (P < 0.01) similarly after HIIT (2.19 ± 0.65 l/min vs. 2.64 ± 0.88 l/min) and MICT (2.24 ± 0.48 l/min vs. 2.55 ± 0.61 l/min). Biomarkers of cardiovascular risk and endothelial function were unchanged. HIIT and MICT produced different vascular adaptations in obese adults, with HIIT improving FMD and MICT increasing resting artery diameter and enhancing L-FMC. HIIT required 27.5% less total exercise time and ∼25% less energy expenditure than MICT.

Validity of SenseWear® Armband v5.2 and v2.2 for estimating energy expenditure.

We compared SenseWear Armband versions (v) 2.2 and 5.2 for estimating energy expenditure in healthy adults. Thirty-four adults (26 women), 30.1 ± 8.7 years old, performed two trials that included light-, moderate- and vigorous-intensity activities: (1) structured routine: seven activities performed for 8-min each, with 4-min of rest between activities; (2) semi-structured routine: 12 activities performed for 5-min each, with no rest between activities. Energy expenditure was measured by indirect calorimetry and predicted using SenseWear v2.2 and v5.2. Compared to indirect calorimetry (297.8 ± 54.2 kcal), the total energy expenditure was overestimated (P < 0.05) by both SenseWear v2.2 (355.6 ± 64.3 kcal) and v5.2 (342.6 ± 63.8 kcal) during the structured routine. During the semi-structured routine, the total energy expenditure for SenseWear v5.2 (275.2 ± 63.0 kcal) was not different than indirect calorimetry (262.8 ± 52.9 kcal), and both were lower (P < 0.05) than v2.2 (312.2 ± 74.5 kcal). The average mean absolute per cent error was lower for the SenseWear v5.2 than for v2.2 (P < 0.001). SenseWear v5.2 improved energy expenditure estimation for some activities (sweeping, loading/unloading boxes, walking), but produced larger errors for others (cycling, rowing). Although both algorithms overestimated energy expenditure as well as time spent in moderate-intensity physical activity (P < 0.05), v5.2 offered better estimates than v2.2.

Using a Verification Test for Determination of V[Combining Dot Above]O2max in Sedentary Adults With Obesity.

A constant-load exercise bout to exhaustion after a graded exercise test to verify maximal oxygen uptake (V[Combining Dot Above]O2max) during cycle ergometry has not been evaluated in sedentary adults with obesity. Nineteen sedentary men (n = 10) and women (n = 9) with obesity (age = 35.8 ± 8.6 years; body mass index [BMI] = 35.9 ± 5.1 kg·m; body fat percentage = 44.9 ± 7.2) performed a ramp-style maximal exercise test (ramp), followed by 5-10 minutes of active recovery, and then performed a constant-load exercise bout to exhaustion (verification test) on a cycle ergometer for determination of V[Combining Dot Above]O2max and maximal heart rate (HRmax). V[Combining Dot Above]O2max did not differ between tests (ramp: 2.29 ± 0.71 L·min, verification: 2.34 ± 0.67 L·min; p = 0.38). Maximal heart rate was higher on the verification test (177 ± 13 b·min vs. 174 ± 16 b·min; p = 0.03). Thirteen subjects achieved a V[Combining Dot Above]O2max during the verification test that was ≥2% (range: 2.0-21.0%; 0.04-0.47 L·min) higher than during the ramp test, and 8 subjects achieved a HRmax during the verification test that was 4-14 b·min higher than during the ramp test. Duration of verification or ramp tests did not affect V[Combining Dot Above]O2max results, but the difference in HRmax between the tests was inversely correlated with ramp test duration (r = -0.57, p = 0.01). For both V[Combining Dot Above]O2max and HRmax, differences between ramp and verification tests were not correlated with BMI or body fat percentage. A verification test may be useful for identifying the highest V[Combining Dot Above]O2max and HRmax during cycle ergometry in sedentary adults with obesity.

Physiological Responses to High-Intensity Interval Exercise Differing in Interval Duration.

We determined the oxygen uptake (V[Combining Dot Above]O2), heart rate (HR), and blood lactate responses to 2 high-intensity interval exercise protocols differing in interval length. On separate days, 14 recreationally active males performed a 4 × 4 (four 4-minute intervals at 90-95% HRpeak, separated by 3-minute recovery at 50 W) and 16 × 1 (sixteen 1-minute intervals at 90-95% HRpeak, separated by 1-minute recovery at 50 W) protocol on a cycle ergometer. The 4 × 4 elicited a higher mean V[Combining Dot Above]O2 (2.44 ± 0.4 vs. 2.36 ± 0.4 L·min) and "peak" V[Combining Dot Above]O2 (90-99% vs. 76-85% V[Combining Dot Above]O2peak) and HR (95-98% HRpeak vs. 81-95% HRpeak) during the high-intensity intervals. Average power maintained was higher for the 16 × 1 (241 ± 45 vs. 204 ± 37 W), and recovery interval V[Combining Dot Above]O2 and HR were higher during the 16 × 1. No differences were observed for blood lactate concentrations at the midpoint (12.1 ± 2.2 vs. 10.8 ± 3.1 mmol·L) and end (10.6 ± 1.5 vs. 10.6 ± 2.4 mmol·L) of the protocols or ratings of perceived exertion (7.0 ± 1.6 vs. 7.0 ± 1.4) and Physical Activity Enjoyment Scale scores (91 ± 15 vs. 93 ± 12). Despite a 4-fold difference in interval duration that produced greater between-interval transitions in V[Combining Dot Above]O2 and HR and slightly higher mean V[Combining Dot Above]O2 during the 4 × 4, mean HR during each protocol was the same, and both protocols were rated similarly for perceived exertion and enjoyment. The major difference was that power output had to be reduced during the 4 × 4 protocol to maintain the desired HR.

Predictors of fat mass changes in response to aerobic exercise training in women.

Aerobic exercise training in women typically results in minimal fat loss, with considerable individual variability. We hypothesized that women with higher baseline body fat would lose more body fat in response to exercise training and that early fat loss would predict final fat loss. Eighty-one sedentary premenopausal women (age: 30.7 ± 7.8 years; height: 164.5 ± 7.4 cm; weight: 68.2 ± 16.4 kg; fat percent: 38.1 ± 8.8) underwent dual-energy x-ray absorptiometry before and after 12 weeks of supervised treadmill walking 3 days per week for 30 minutes at 70% of (Equation is included in full-text article.). Overall, women did not lose body weight or fat mass. However, considerable individual variability was observed for changes in body weight (-11.7 to +4.8 kg) and fat mass (-11.8 to +3.7 kg). Fifty-five women were classified as compensators and, as a group, gained fat mass (25.6 ± 11.1 kg to 26.1 ± 11.3 kg; p < 0.001). The strongest correlates of change in body fat at 12 weeks were change in body weight (r = 0.52) and fat mass (r = 0.48) at 4 weeks. Stepwise regression analysis that included change in body weight and body fat at 4 weeks and submaximal exercise energy expenditure yielded a prediction model that explained 37% of the variance in fat mass change (R = 0.37, p < 0.001). Change in body weight and fat mass at 4 weeks were moderate predictors of fat loss and may potentially be useful for identification of individuals who achieve less than expected weight loss or experience unintended fat gain in response to exercise training.

Validity and reliability of Nike + Fuelband for estimating physical activity energy expenditure.

BACKGROUND: The Nike + Fuelband is a commercially available, wrist-worn accelerometer used to track physical activity energy expenditure (PAEE) during exercise. However, validation studies assessing the accuracy of this device for estimating PAEE are lacking. Therefore, this study examined the validity and reliability of the Nike + Fuelband for estimating PAEE during physical activity in young adults. Secondarily, we compared PAEE estimation of the Nike + Fuelband with the previously validated SenseWear Armband (SWA). METHODS: Twenty-four participants (n = 24) completed two, 60-min semi-structured routines consisting of sedentary/light-intensity, moderate-intensity, and vigorous-intensity physical activity. Participants wore a Nike + Fuelband and SWA, while oxygen uptake was measured continuously with an Oxycon Mobile (OM) metabolic measurement system (criterion). RESULTS: The Nike + Fuelband (ICC = 0.77) and SWA (ICC = 0.61) both demonstrated moderate to good validity. PAEE estimates provided by the Nike + Fuelband (246 ± 67 kcal) and SWA (238 ± 57 kcal) were not statistically different than OM (243 ± 67 kcal). Both devices also displayed similar mean absolute percent errors for PAEE estimates (Nike + Fuelband = 16 ± 13 %; SWA = 18 ± 18 %). Test-retest reliability for PAEE indicated good stability for Nike + Fuelband (ICC = 0.96) and SWA (ICC = 0.90). CONCLUSION: The Nike + Fuelband provided valid and reliable estimates of PAEE, that are similar to the previously validated SWA, during a routine that included approximately equal amounts of sedentary/light-, moderate- and vigorous-intensity physical activity.

Strength training increases endurance time to exhaustion during high-intensity exercise despite no change in critical power.

The purpose of this study was to determine whether improvements in endurance exercise performance elicited by strength training were accurately reflected by changes in parameters of the power-duration hyperbola for high-intensity exercise. Before and after 8 weeks of strength training (N = 14) or no exercise, control (N = 5), 19 males (age: 20.6 ± 2.0 years; weight: 78.2 ± 15.9 kg) performed a maximal incremental exercise test on a cycle ergometer and also cycled to exhaustion during 4 constant-power exercise bouts. Critical power (CP) and anaerobic work capacity (W') were estimated using nonlinear and linear models. Subjects in the strength training group improved significantly more than controls (p < 0.05) for strength (~30%), power at V[Combining Dot Above]O2peak (7.9%), and time to exhaustion (TTE) for all 4 constant-power tests (~39%). Contrary to our hypothesis, CP did not change significantly after strength training (p > 0.05 for all models). Strength training improved W' (mean range of improvement = +5.8 to +10.0 kJ; p < 0.05) for both linear models. Increases in W' were consistently positively correlated with improvements in TTE, whereas changes in CP were not. Our findings indicate that strength training alters the power-duration hyperbola such that W' is enhanced without any improvement in CP. Consequently, CP may not be robust enough to track changes in endurance capacity elicited by strength training, and we do not recommend it to be used for this purpose. Conversely, W' may be the better indicator of improvement in endurance performance elicited by strength training.

Oxygen Cost of Performing Selected Adult and Child Care Activities.

Little is known about the oxygen cost of caring for infants and older adults. Many people perform these activities so it is useful to know the energy cost and if the activities are of sufficient intensity to contribute to meeting physical activity recommendations. The purpose of this study was to assess the oxygen cost of four care-related activities in the Compendium of Physical Activities. Nineteen participants (n = 10 women, n = 9 men; Age = 36.4 ± 13.6 y; % Fat = 34.1 ± 10.5; BMI = 28.1 ± 4.5 kg/m2) performed four activities: 1) pushing an infant in a stroller, 2) pushing an adult in a wheelchair, 3) carrying an infant, and 4) bathing and dressing an infant. The oxygen cost was assessed using a portable metabolic unit. Activities were performed in random order for 8 minutes. The oxygen cost and heart rates, respectively, for healthy adults during care related activities were 3.09 METs and 90 ± 8 beats per minute (bpm) for pushing an infant in a stroller, 3.69 METs and 97 ± 9 bpm for pushing an adult in a wheelchair, 2.37 METs and 85 ± 9 bpm for carrying an infant, and 2.00 METs and 87 ± 9 bpm for bathing and dressing an infant. Carrying an infant and bathing an infant are light-intensity physical activities and pushing a wheelchair or a stroller are moderate intensity activities. The latter activities are of sufficient intensity to meet health-related physical activity recommendations.

V˙O2max may not be reached during exercise to exhaustion above critical power.

PURPOSE: This study was designed to determine whether V˙O(2) reaches a maximum, equivalent to that attained in an incremental exercise test to exhaustion, during "submaximal" fatigue-inducing constant-power exercise bouts above critical power (CP). METHODS: Nine males (age = 24.6 ± 3.6 yr, height = 182.8 ± 6.9 cm, weight = 77.8 ± 12.1 kg) and four females (age = 29.0 ± 7.3 yr, height = 170.8 ± 3.2 cm, weight = 61.8 ± 8.2 kg) underwent an incremental V˙O(2max) test (IET) on a cycle ergometer, followed by four or five randomly assigned constant-power exercise bouts to exhaustion, on separate days. The CP for each subject was estimated using linear and nonlinear regression. RESULTS: IET V˙O(2max) averaged 3.55 ± 0.92 L·min (RER = 1.21 ± 0.05, HR = 186 ± 10 bpm, 96.1% ± 6.3% of age-predicted maximum). Mean peak V˙O(2) (range = 3.32 ± 0.88 to 3.54 ± 0.91 L·min) during the three highest constant-power bouts (two of which were 53 to 82 W less than peak power output attained during IET) was not significantly different from IET V˙O(2max). Eleven of 13 subjects exceeded their IET V˙O(2max) during at least one of the constant-power exercise bouts. However, peak V˙O(2) (3.11 ± 0.79 L·min) during the lowest constant-power exercise bout, which ranged from 10 to 36 W above CP estimated with a two-parameter nonlinear model, was significantly lower than IET V˙O(2max) (88.2% ± 9.4% of IET V˙O(2max)). CONCLUSIONS: At power outputs above CP, V˙O(2) does not necessarily increase to maximum during constant-power exercise to exhaustion. In addition, the highest V˙O(2) values measured during a traditional V˙O(2) "max" test (i.e., IET) may not reflect the highest attainable V˙O(2) despite V˙O(2max) criteria being met.

Exercise and diet, independent of weight loss, improve cardiometabolic risk profile in overweight and obese individuals.

Diet and/or exercise are routinely advised as methods for weight loss in overweight/obese individuals, particularly those who are at high risk for cardiovascular disease and type 2 diabetes mellitus. However, physical activity and structured exercise programs rarely result in significant loss of body weight or body fat, and weight-loss diets have extraordinarily high recidivism rates. Despite only modest effects on body weight, exercise and ad libitum nutrient-dense diets for overweight/obese individuals have many health benefits, including skeletal muscle adaptations that improve fat and glucose metabolism, and insulin action; enhance endothelial function; have favorable changes in blood lipids, lipoproteins, and hemostatic factors; and reduce blood pressure, postprandial lipemia and glycemia, and proinflammatory markers. These lifestyle-induced adaptations occur independently of changes in body weight or body fat. Thus, overweight/obese men and women who are at increased risk for cardiovascular disease and type 2 diabetes as a result of sedentary lifestyle, poor diet, and excess body weight should be encouraged to engage in regular physical activity and improve their diet, regardless of whether the healthier lifestyle leads to weight loss.

Walking and running economy: inverse association with peak oxygen uptake.

PURPOSE: The purpose of this study was to test the hypothesis that V˙O2peak is positively correlated with the regression coefficients of the curve-linear relationship between V˙O2 and speed during a protocol consisting of submaximal walking and running. METHODS: Nineteen healthy men (mean ± SD: age = 26.4 ± 6.4 yr, height = 179.9 ± 7.2 cm, weight = 77.7 ± 8.7 kg, % fat = 16.3 ± 7.3) and 21 healthy women (age = 25.6 ± 4.9 yr, height = 167.2 ± 5.4 cm, weight = 61.6 ± 7.7 kg, % fat = 24.0 ± 6.8) underwent an incremental treadmill test to determine VO2peak and on two separate days performed an exercise protocol consisting of treadmill walking on a level grade at 2.0 mph (54 m·min−¹), 3.0 mph (80 m·min−¹), and 4.0 mph (107 m·min−¹) and running at 6.0 mph (161 m·min−¹). Subjects exercised for 5 min at each velocity, with 3 min of rest in between each exercise bout. Pulmonary ventilation (VE) and gas exchange were measured breath-by-breath each minute. The average of VO2 values obtained during the last 2 min of exercise for both exercise sessions was used in polynomial random coefficient regression analysis. RESULTS: In the polynomial random coefficient regression analysis for walking speeds only, both linear (r = 0.31, P = 0.053) and quadratic (r = 0.35, P = 0.029) coefficients were modestly correlated with VO2peak. Steady-state VO2 during walking at 3.0 and 4.0 mph and running at 6.0 mph was also modestly correlated with VO2peak (r = 0.30-0.48). CONCLUSIONS: The results confirm our hypothesis and suggest that, as walking speed increases, the increase in VO2 is positively correlated with the VO2peak. Our findings are consistent with the notion that cardiorespiratory fitness and exercise economy are inversely related.