The Influence of Speed on the Metabolic and Mechanical Task Costs of Treadmill Running

Harris, Chas. K.; Smith, Gerald A.; Wilcox, A.; Fujimoto, K.

doi:10.1249/00005768-199505001-00515

Cited by 5 publications

(7 citation statements)

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“…Mechanical cost values found for the six participants (reported in the first part of the results) were in agreement with expected values when the total work is calculated by assuming no energy transfer between potential and kinetic energies [41][42][43][44]. High values of Cm at low speeds reveal inefficient running patterns, as described previously [41,44,45].…”

Section: Reference Measuressupporting

confidence: 87%

Validity of the Stryd Power Meter in Measuring Running Parameters at Submaximal Speeds

Imbach

Candau

Chailan³

et al. 2020

Sports

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This study assessed the Stryd running power meter validity at sub-maximal speeds (8 to 19 km/h). Six recreational runners performed an incremental indoor running test. Power output (PO), ground contact time (GCT) and leg spring stiffness (LSS) were compared to reference measures recorded by portable metabolic analyser, force platforms and motion capture system. A Bayesian framework was conducted for systems validity and comparisons. We observed strong and positive linear relationships between Stryd PO and oxygen consumption ( R 2 = 0 . 82 , B F 10 > 100 ), and between Stryd PO and external mechanical power ( R 2 = 0 . 88 , B F 10 > 100 ). Stryd power meter underestimated PO ( B F 10 > 100 ) whereas GCT and LSS values did not show any significant differences with the reference measures ( B F 10 = 0 . 008 , B F 10 = 0 . 007 , respectively). We conclude that the Stryd power meter provides valid measures of GCT and LSS but underestimates the absolute values of PO.

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Section: Reference Measuressupporting

confidence: 87%

Validity of the Stryd Power Meter in Measuring Running Parameters at Submaximal Speeds

Imbach

Candau

Chailan³

et al. 2020

Sports

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“…In this situation, if water is endogenously liberated via carbohydrate oxidation, this may explain faster water turnover rates in the active group if this water production does, in fact, contribute to hydration status [ 19 ]. As an example, the energy cost of running a marathon for an average 70 kg male is roughly 12,000 kJ (4.18 kJ·kg −1 ·min −1 ) [ 43 ]. Estimates of carbohydrate oxidation during this event would indicate that an elite male runner would utilise 400 g of glycogen [ 44 ]; given the accepted value of 3 g of water per gram of oxidised glycogen [ 18 ], this would result in a 1200 mL endogenous water release.…”

Section: Discussionmentioning

confidence: 99%

Contribution of Dietary Composition on Water Turnover Rates in Active and Sedentary Men

et al. 2021

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Body water turnover is a marker of hydration status for measuring total fluid gains and losses over a 24-h period. It can be particularly useful in predicting (and hence, managing) fluid loss in individuals to prevent potential physical, physiological and cognitive declines associated with hypohydration. There is currently limited research investigating the interrelationship of fluid balance, dietary intake and activity level when considering body water turnover. Therefore, this study investigates whether dietary composition and energy expenditure influences body water turnover. In our methodology, thirty-eight males (19 sedentary and 19 physically active) had their total body water and water turnover measured via the isotopic tracer deuterium oxide. Simultaneous tracking of dietary intake (food and fluid) is carried out via dietary recall, and energy expenditure is estimated via accelerometery. Our results show that active participants display a higher energy expenditure, water intake, carbohydrate intake and fibre intake; however, there is no difference in sodium or alcohol intake between the two groups. Relative water turnover in the active group is significantly greater than the sedentary group (Mean Difference (MD) [95% CI] = 17.55 g·kg−1·day−1 [10.90, 24.19]; p = < 0.001; g[95% CI] = 1.70 [0.98, 2.48]). A penalised linear regression provides evidence that the fibre intake (p = 0.033), water intake (p = 0.008), and activity level (p = 0.063) predict participants’ relative body water turnover (R2= 0.585). In conclusion, water turnover is faster in individuals undertaking regular exercise than in their sedentary counterparts, and is, in part, explained by the intake of water from fluid and high-moisture content foods. The nutrient analysis of the participant diets indicates that increased dietary fibre intake is also positively associated with water turnover rates. The water loss between groups also contributes to the differences observed in water turnover; this is partly related to differences in sweat output during increased energy expenditure from physical activity.

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“…However, it is yet to be determined how accurate these algorithms will be at estimating these parameters during everyday life activities and other exercising regimens, such as weight-lifting, swimming, contact sports, daily household activities, and the like. There are several studies emphasizing the importance of caution when applying VO 2 estimation models to universal physical activity [9, 12, 22, 25, 28, 30, 32, 33]. Moreover, it is not clear how well these algorithms will predict resting energy expenditure (REE) or energy expended during sedentary activity, where caloric expenditure is dominated by the metabolic rate of the individual as opposed to physical activity.…”

Section: Discussionmentioning

confidence: 99%

Earbud-Based Sensor for the Assessment of Energy Expenditure, HR, and V˙O2max

LeBoeuf

Aumer

Kraus

et al. 2014

Medicine &Amp; Science in Sports &Amp; Exercise

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Introduction/Purpose The goal of this program was to determine the feasibility of a novel noninvasive, highly miniaturized optomechanical earbud sensor for accurately estimating total energy expenditure (TEE) and maximum oxygen consumption (VO2max). The optomechanical sensor module, small enough to fit inside commercial audio earbuds, was previously developed to provide a seamless way to measure blood flow information during daily life activities. The sensor module was configured to continuously measure physiological information via photoplethysmography (PPG) and physical activity information via accelerometry. This information was digitized and sent to a microprocessor where digital signal processing (DSP) algorithms extract physiological metrics in real-time. These metrics were streamed wirelessly from the earbud to a computer. Methods In this study, 23 subjects of multiple physical habitus were divided into a training group of 14 subjects and a validation group of 9 subjects. Each subject underwent the same exercise measurement protocol consisting of treadmill-based cardiopulmonary exercise (CPX) testing to reach VO2max. Benchmark sensors included a 12-lead electrocardiography (ECG) sensor for measuring heart rate, a calibrated treadmill for measuring distance and speed, and a gas-exchange analysis instrument for measuring TEE and VO2max. The earbud sensor was the device under test (DUT). Benchmark and DUT data collected from the 14-person training dataset study were integrated into a preconceived statistical model for correlating benchmark data with earbud sensor data. Coefficients were optimized, and the optimized model was validated in the 9-person validation dataset. Results It was observed that the earbud sensor estimated TEE and VO2max with mean ± SD percent estimation errors of −0.7 ± 7.4% and −3.2 ± 7.3% respectively. Conclusion The earbud sensor can accurately estimate TEE and VO2max during CPX testing.

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The Influence of Speed on the Metabolic and Mechanical Task Costs of Treadmill Running

Cited by 5 publications

References 0 publications

Validity of the Stryd Power Meter in Measuring Running Parameters at Submaximal Speeds

Validity of the Stryd Power Meter in Measuring Running Parameters at Submaximal Speeds

Contribution of Dietary Composition on Water Turnover Rates in Active and Sedentary Men

Earbud-Based Sensor for the Assessment of Energy Expenditure, HR, and V˙O2max

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