The determination of the maximal lactate steady state (MLSS) requires at least two constant load tests. Therefore, different testing procedures to indirectly determine MLSS based on one single test have been developed. One such method is the application of the lactate minimum tests (LMT), where workload and heart rate-based protocols exist. The latter showed significant correlations between parameters at lactate minimum (LM) and MLSS for running and cycling. However, LM clearly underestimated MLSS. Therefore, the aim of this study was to optimize the already existing test protocol in terms of an improved agreement between LM and MLSS. Fourteen healthy endurance-trained male athletes (age: 39.7±8.2 y; height: 180.9±6.2 cm; body mass: 78.6±7.1 kg) performed four different heart rate-based LMT protocols, the original and three new protocols. Additionally, they performed several constant heart rate endurance tests for assessing MLSS exercise intensity. Heart rate, blood lactate concentration, oxygen uptake and power at LM of two of our new test protocols with an increased start intensity were closer to and no longer different from MLSS data. We conclude that these two new test protocols can be used in practice to estimate heart rate-based MLSS by means of one single exercise test.
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http://dx.doi.org/10.1055/a-1618-5588 | DOI Listing |
Bioengineering (Basel)
December 2024
Department of Physical Therapy, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA 02215, USA.
Background: Indirect calorimetry is the gold standard field-testing technique for measuring energy expenditure and exercise intensity based on the volume of oxygen consumed (VO, mL O/min). Although heart rate is often used as a proxy for VO, heart rate-based estimates of VO may be inaccurate after stroke due to changes in the heart rate-VO relationship. Our objective was to evaluate in people post stroke the accuracy of using heart rate to estimate relative walking VO (wVO) and classify exercise intensity.
View Article and Find Full Text PDFJ Med Internet Res
December 2024
Division of Cancer Prevention and Control, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, United States.
Background: To date, no studies have examined adherence to the 2018 Physical Activity Guidelines for Americans (PAGA) in real-world longitudinal settings using objectively measured activity monitoring data. This study addresses this gap by using commercial activity monitoring (Fitbit) data from the All of Us dataset.
Objective: The primary objectives were to describe the prevalence of adherence to the 2018 PAGA and identify associated sociodemographic determinants.
Eur Arch Paediatr Dent
November 2024
Institute of Odontology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Aim: A reliable tool to visualise children's early stress signs to prevent dental fear development is needed. The aim was to evaluate the commercially available, CE marked, Shimmer3 GSR + unit's ability to indicate for stress as a reaction of fear or pain for a non-invasive dental treatment (NI) and an invasive dental treatment (I).
Methods: Patients 14-16 years old were invited to undergo an oral check-up (NI) or an orthodontic premolar extraction (I), respectively.
Sensors (Basel)
October 2024
School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
Remote photoplethysmography (rPPG) refers to a non-contact technique that measures heart rate through analyzing the subtle signal changes of facial blood flow captured by video sensors. It is widely used in contactless medical monitoring, remote health management, and activity monitoring, providing a more convenient and non-invasive way to monitor heart health. However, factors such as ambient light variations, facial movements, and differences in light absorption and reflection pose challenges to deep learning-based methods.
View Article and Find Full Text PDFSports Med Open
September 2024
Department of Sport and Exercise Science, University of Salzburg, Schlossallee 49, 5400, Hallein/Rif, Salzburg, Austria.
Background: Various studies have shown that the type of intensity measure affects training intensity distribution (TID) computation. These conclusions arise from studies presenting data from meso- and macrocycles, while microcycles, e.g.
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