The purpose of this study was to validate the ability of the 1-mile jog test to predict VO2max in fit teenagers. Forty-one males and 42 females performed the steady-state, submaximal jogging test on an indoor track, along with a maximal graded exercise test (GXT) on a treadmill. Open circuit calorimetry was used during the GXT to measure maximal oxygen consumption (VO2max). We generated the following age-specific prediction equation applicable to boys and girls 13–17 years old (n = 83, Radj = .88, SEE = 3.26 ml · kg−1 · min−1): VO2max = 92.91 + 6.50 × gender (0 = female, 1 = male) − 0.141 × body mass (kg) − 1.562 × jog time (min) − 0.125 × heart rate (bpm). Cross-validation results were acceptable (SEEpress = 3.44 ml · kg−1 · min−1). As a field test, the submaximal 1-mile jogging test may alleviate problems associated with pacing, motivation, discouragement, injury, and fatigue that are sometimes associated with maximal effort timed or distance run tests.
Brian R. Hunt, James D. George, Pat R. Vehrs, A. Garth Fisher and Gilbert W. Fellingham
Hermann-J. Engels, John C. Wirth, Sueda Celik and Jodee L. Dorsey
This study assessed the influence of caffeine on metabolic and cardiovascular functions during sustained, light intensity cycling and at rest. Eight healthy, recreationally active adults participated in four randomly assigned, double-blind experimental trials of 60 min upright seated cycle exercise (30% VO2max) or equivalent rest with caffeine (5 mg ⋅ kg−1) or placebo consumed 60 min prior to data collection. Gas exchange was measured by open-circuit spirom-etry indirect calorimetry. Global blood flow was evaluated by thoracic impedance cardiography and arterial blood pressure by auscultation. A repeated measures ANOVA indicated that pretrial caffeine increased oxygen uptake and energy expenditure rate (p < 0.05) but did not change respiratory exchange ratio. Systolic, diastolic, and mean arterial blood pressure were elevated following caffeine intake (p < 0.05). Cardiac output, heart rate, stroke volume, and systemic vascular resistance were not significantly different between caffeine and placebo sessions. For each of the metabolic and hemodynamic variables examined, the effects of caffeine were similar during constant-load, light intensity cycling and at rest. These data illustrate that caffeine's mild thermogenic influence can be mediated without a major shift in substrate oxidation mixture. Caffeine at this dosage level alters cardiovascular dynamics by augmenting arterial blood pressure.
Richard R. Suminski, Larry T. Wier, Walker Poston, Brian Arenare, Anthony Randles and Andrew S. Jackson
Nonexercise models were developed to predict maximal oxygen consumption (VO2max). While these models are accurate, they don’t consider smoking, which negatively impacts measured VO2max. The purpose of this study was to examine the effects of smoking on both measured and predicted VO2max.
Indirect calorimetry was used to measure VO2max in 2,749 men and women. Physical activity using the NASA Physical Activity Status Scale (PASS), body mass index (BMI), and smoking (pack-y = packs·day * y of smoking) also were assessed. Pack-y groupings were Never (0 pack-y), Light (1–10), Moderate (11–20), and Heavy (>20). Multiple regression analysis was used to examine the effect of smoking on VO2max predicted by PASS, age, BMI, and gender.
Measured VO2max was significantly lower in the heavy smoking group compared with the other pack-y groups. The combined effects of PASS, age, BMI, and gender on measured VO2max were significant. With smoking in the model, the estimated effects on measured VO2max from Light, Moderate, and Heavy smoking were –0.83, –0.85, and –2.56 ml·kg−1·min−1, respectively (P < .05).
Given that 21% of American adults smoke and 12% of them are heavy smokers, it is recommended that smoking be considered when using nonexercise models to predict VO2max.
Barbara E. Ainsworth, Robert G. McMurray and Susan K. Veazey
The purpose of this study was to determine the accuracy of two submaximal exercise tests, the Sitting-Chair Step Test (Smith & Gilligan. 1983) and the Modified Step Test (Amundsen, DeVahl, & Ellingham, 1989) to predict peak oxygen uptake (VO2 peak) in 28 adults ages 60 to 85 years. VO2 peak was measured by indirect calorimetry during a treadmill maximal graded exercise test (VO2 peak, range 11.6–31.1 ml · kg −l · min−1). In each of the submaximal tests, VO2 was predicted by plotting stage-by-stage submaximal heart rate (HR) and perceived exertion (RPE) data against VO2 for each stage and extrapolating the data to respective age-predicted maximal HR or RPE values. In the Sitting-Chair Step Test (n = 23), no significant differences were observed between measured and predicted VO2 peak values (p > .05). However, predicted VO2 peak values from the HR were 4.3 ml · kg−1 · min−1 higher than VO2 peak values predicted from the RPE data (p < .05). In the Modified Step Test (n = 22), no significant differences were observed between measured and predicted VO2 peak values (p > .05). Predictive accuracy was modest, explaining 49–78% of the variance in VO2 peak. These data suggest that the Sitting-Chair Step Test and the Modified Step Test have moderate validity in predicting VO2 peak in older men and women.
Leslie Peacock, Allan Hewitt, David A. Rowe and Rona Sutherland
The study investigated (a) walking intensity (stride rate and energy expenditure) under three speed instructions; (b) associations between stride rate, age, height, and walking intensity; and (c) synchronization between stride rate and music tempo during overground walking in a population of healthy older adults.
Twenty-nine participants completed 3 treadmill-walking trials and 3 overground-walking trials at 3 self-selected speeds. Treadmill VO2 was measured using indirect calorimetry. Stride rate and music tempo were recorded during overground-walking trials.
Mean stride rate exceeded minimum thresholds for moderate to vigorous physical activity (MVPA) under slow (111.41 ± 11.93), medium (118.17 ± 11.43), and fast (123.79 ± 11.61) instructions. A multilevel model showed that stride rate, age, and height have a significant effect (p < .01) on walking intensity.
Healthy older adults achieve MVPA with stride rates that fall below published minima for MVPA. Stride rate, age, and height are significant predictors of energy expenditure in this population. Music can be a useful way to guide walking cadence.
Sarah Kozey, Kate Lyden, John Staudenmayer and Patty Freedson
To compare intensity misclassification and activity MET values using measured RMR (measMET) compared with 3.5 ml·kg−1·min−1 (standMET) and corrected METs [corrMET = mean standMET × (3.5 ÷ Harris-Benedict RMR)] in subgroups.
RMR was measured for 252 subjects following a 4-hr fast and before completion of 11 activities. VO2 was measured during activity using indirect calorimetry (n = 2555 activities). Subjects were classified by BMI category (normal-weight or overweight/obese), sex, age (decade 20, 30, 40, or 50 y), and fitness quintiles (low to high). Activities were classified into low, moderate, and vigorous intensity categories.
The (mean ± SD) measMET was 6.1 ± 2.64 METs. StandMET [mean (95% CI)] was (0.51(0.42, 0.59) METs) less than measMET. CorrMET was not statistically different from measMET (−0.02 (−0.11, 0.06) METs). 12.2% of the activities were misclassified using standMETs compared with an 8.6% misclassification rate for METs based on predicted RMR (P < .0001). StandMET differences and misclassification rates were highest for low fit, overweight, and older individuals while there were no differences when corrMETs were used.
Using 3.5 ml·kg−1·min−1 to calculate activity METs causes higher misclassification of activities and inaccurate point estimates of METs than a corrected baseline which considers individual height, weight, and age. These errors disproportionally impact subgroups of the population with the lowest activity levels.
Jeanne F. Nichols, Hilary Aralis, Sonia Garcia Merino, Michelle T. Barrack, Lindsay Stalker-Fader and Mitchell J. Rauh
There is a growing need to accurately assess exercise energy expenditure (EEE) in athletic populations that may be at risk for health disorders because of an imbalance between energy intake and energy expenditure. The Actiheart combines heart rate and uniaxial accelerometry to estimate energy expenditure above rest. The authors’ purpose was to determine the utility of the Actiheart for predicting EEE in female adolescent runners (N = 39, age 15.7 ± 1.1 yr). EEE was measured by indirect calorimetry and predicted by the Actiheart during three 8-min stages of treadmill running at individualized velocities corresponding to each runner’s training, including recovery, tempo, and 5-km-race pace. Repeated-measures ANOVA with Bonferroni post hoc comparisons across the 3 running stages indicated that the Actiheart was sensitive to changes in intensity (p < .01), but accelerometer output tended to plateau at race pace. Pairwise comparisons of the mean difference between Actiheart- and criterion-measured EEE yielded values of 0.0436, 0.0539, and 0.0753 kcal · kg−1 · min−1 during recovery, tempo, and race pace, respectively (p < .0001). Bland–Altman plots indicated that the Actiheart consistently underestimated EEE except in 1 runner’s recovery bout. A linear mixed-model regression analysis with height as a covariate provided an improved EEE prediction model, with the overall standard error of the estimate for the 3 speeds reduced to 0.0101 kcal · kg−1 · min−1. Using the manufacturer’s equation that combines heart rate and uniaxial motion, the Actiheart may have limited use in accurately assessing EEE, and therefore energy availability, in young, female competitive runners.
Steven Gastinger, Guillaume Nicolas, Anthony Sorel, Hamid Sefati and Jacques Prioux
The aim of this article was to compare 2 portable devices (a heart-rate monitor and an electromagnetic-coil system) that evaluate 2 different physiological parameters—heart rate (HR) and ventilation (VE)—with the objective of estimating energy expenditure (EE). The authors set out to prove that VE is a more pertinent setting than HR to estimate EE during light to moderate activities (sitting and standing at rest and walking at 4, 5, and 6 km/hr). Eleven healthy men were recruited to take part in this study (27.6 ± 5.4 yr, 73.7 ± 9.7 kg). The authors determined the relationships between HR and EE and between VE and EE during light to moderate activities. They compared EE measured by indirect calorimetry (EEREF) with EE estimated by HR monitor (EEHR) and EE estimated by electromagnetic coils (EEMAG) in upright sitting and standing positions and during walking exercises. They compared EEREF with EEHR and EEMAG. The results showed no significant difference between the values of EEREF and EEMAG. However, they showed several significant differences between the values of EEREF and EEHR (for standing at rest and walking at 5 and 6 km/hr). These results showed that the electromagnetic-coil system seems to be more accurate than the HR monitor to estimate EE at rest and during exercise. Taking into consideration these results, it would be interesting to associate the parameters VE and HR to estimate EE. Furthermore, a new version of the electromagnetic-coil device was recently developed and provides the possibility to perform measurement under daily life conditions.
Shelby L. Francis, Ajay Singhvi, Eva Tsalikian, Michael J. Tansey and Kathleen F. Janz
Determining fitness is important when assessing adolescents with type 1 diabetes mellitus (T1DM). Submaximal tests estimate fitness, but none have been validated in this population. This study cross-validates the Ebbeling and Nemeth equations to predict fitness (VO2max (ml/kg/min)) in adolescents with T1DM.
Adolescents with T1DM (n = 20) completed a maximal treadmill test using indirect calorimetry. Participants completed one 4-min stage between 2.0 and 4.5 mph and 5% grade (Ebbeling/Nemeth protocol). Speed and grade were then increased until exhaustion. Predicted VO2max was calculated using the Ebbeling and Nemeth equations and compared with observed VO2max using paired t tests. Pearson correlation coefficients, 95% confidence intervals, coefficients of determination (R2), and total error (TE) were calculated.
The mean observed VO2max was 47.0 ml/kg/min (SD = 6.9); the Ebbeling and Nemeth mean predictions were 42.4 (SD = 9.4) and 43.5 ml/kg/min (SD = 6.9), respectively. Paired t tests resulted in statistically significant (p < .01) mean differences between observed and predicted VO2max for both predictions. The association between the Ebbeling prediction and observed VO2max was r = .90 (95% CI = 0.76, 0.96), R 2 = .81, and TE = 6.5 ml/kg/min. The association between the Nemeth prediction and observed VO2max was r = .81 (95% CI = 0.57, 0.92), R 2 = .66, and TE = 5.6 ml/kg/min.
The Nemeth submaximal treadmill protocol provides a better estimate of fitness than the Ebbeling in adolescents with T1DM.
Alexander H.K. Montoye, Jordana Dahmen, Nigel Campbell and Christopher P. Connolly
Purpose: This purpose of this study was to validate consumer-based and research-grade PA monitors for step counting and Calorie expenditure during treadmill walking. Methods: Participants (n = 40, 24 in second trimester and 16 in third trimester) completed five 2-minute walking activities (1.5–3.5 miles/hour in 0.5 mile/hour increments) while wearing five PA monitors (right hip: ActiGraph Link [AG]; left hip: Omron HJ-720 [OM]; left front pants pocket: New Lifestyles NL 2000 [NL]; non-dominant wrist: Fitbit Flex [FF]; right ankle: StepWatch [SW]). Mean absolute percent error (MAPE) was used to determine device accuracy for step counting (all monitors) and Calorie expenditure (AG with Freedson equations and FF) compared to criterion measures (hand tally for steps, indirect Calorimetry for Calories). Results: For step counting, the SW had MAPE ≤ 10% at all walking speeds, and the OM and NL had MAPE ≤ 10% for all speeds but 1.5 miles/hour. The AG had MAPE ≤ 10% for only 3.0–3.5 miles/hour speeds, and the FF had high MAPE for all speeds. For Calories, the FF and AG had MAPE > 10% for all speeds, with the FF overestimating Calories expended. Trimester did not affect PA monitor accuracy for step counting but did affect accuracy for Calorie expenditure. Conclusion: The ankle-worn SW and hip-worn OM had high accuracy for measuring step counts at all treadmill walking speeds, whereas the NL had high accuracy for speeds ≥2.0 miles/hour. Conversely, the monitors tested for Calorie expenditure have poor accuracy and should be interpreted cautiously for walking behavior.