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      Identification and comparison of heart-rate dynamics during cycle ergometer and treadmill exercise

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          Abstract

          Aim and methods

          The aim of this study was to compare the dynamics of heart rate (HR) response to exercise using a cycle ergometer (CE) and a treadmill (TM). Using a sample of 25 healthy male participants, the time constant of HR dynamics was estimated for both modalities in response to square-wave excitation.

          Results

          The principal finding was that the time constant of heart-rate dynamics around somewhat-hard exercise intensity (Borg rating of perceived exertion = 13) does not differ significantly between the CE and TM (68.7 s ± 21.5 s vs. 62.5 s ± 18.5 s [mean ± standard deviation]; CE vs. TM; p = 0.20). An observed moderate level of evidence that root-mean-square model error was higher for the CE than for the TM (2.5 bpm ± 0.5 bpm vs. 2.2 bpm ± 0.5 bpm, p = 0.059) may reflect a decrease in heart rate variability with increasing HR intensity because, in order to achieve similar levels of perceived intensity, mean heart rate for the CE was ∼25 bpm lower than for the TM.

          Conclusion and significance

          These results have important implications for model-based design of automatic HR controllers, because, in principle, the same dynamic controller, merely scaled according to the differing steady-state gains, should be able to be applied to the CE and TM exercise modalities.

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          Most cited references12

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          Effects of Low-Volume High-Intensity Interval Training (HIT) on Fitness in Adults: A Meta-Analysis of Controlled and Non-Controlled Trials

          Background Low-volume high-intensity interval training (HIT) appears to be an efficient and practical way to develop physical fitness. Objective Our objective was to estimate meta-analysed mean effects of HIT on aerobic power (maximum oxygen consumption [VO2max] in an incremental test) and sprint fitness (peak and mean power in a 30-s Wingate test). Data Sources Five databases (PubMed, MEDLINE, Scopus, BIOSIS and Web of Science) were searched for original research articles published up to January 2014. Search terms included ‘high intensity’, ‘HIT’, ‘sprint’, ‘fitness’ and ‘VO2max’. Study Selection Inclusion criteria were fitness assessed pre- and post-training; training period ≥2 weeks; repetition duration 30–60 s; work/rest ratio 18 years. Data Extraction The final data set consisted of 55 estimates from 32 trials for VO2max, 23 estimates from 16 trials for peak sprint power, and 19 estimates from 12 trials for mean sprint power. Effects on fitness were analysed as percentages via log transformation. Standard errors calculated from exact p values (where reported) or imputed from errors of measurement provided appropriate weightings. Fixed effects in the meta-regression model included type of study (controlled, uncontrolled), subject characteristics (sex, training status, baseline fitness) and training parameters (number of training sessions, repetition duration, work/rest ratio). Probabilistic magnitude-based inferences for meta-analysed effects were based on standardized thresholds for small, moderate and large changes (0.2, 0.6 and 1.2, respectively) derived from between-subject standard deviations (SDs) for baseline fitness. Results A mean low-volume HIT protocol (13 training sessions, 0.16 work/rest ratio) in a controlled trial produced a likely moderate improvement in the VO2max of active non-athletic males (6.2 %; 90 % confidence limits ±3.1 %), when compared with control. There were possibly moderate improvements in the VO2max of sedentary males (10.0 %; ±5.1 %) and active non-athletic females (3.6 %; ±4.3 %) and a likely small increase for sedentary females (7.3 %; ±4.8 %). The effect on the VO2max of athletic males was unclear (2.7 %; ±4.6 %). A possibly moderate additional increase was likely for subjects with a 10 mL·kg−1·min−1 lower baseline VO2max (3.8 %; ±2.5 %), whereas the modifying effects of sex and difference in exercise dose were unclear. The comparison of HIT with traditional endurance training was unclear (−1.6 %; ±4.3 %). Unexplained variation between studies was 2.0 % (SD). Meta-analysed effects of HIT on Wingate peak and mean power were unclear. Conclusions Low-volume HIT produces moderate improvements in the aerobic power of active non-athletic and sedentary subjects. More studies are needed to resolve the unclear modifying effects of sex and HIT dose on aerobic power and the unclear effects on sprint fitness.
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            Physiological differences between cycling and running: lessons from triathletes.

            The purpose of this review was to provide a synopsis of the literature concerning the physiological differences between cycling and running. By comparing physiological variables such as maximal oxygen consumption (V O(2max)), anaerobic threshold (AT), heart rate, economy or delta efficiency measured in cycling and running in triathletes, runners or cyclists, this review aims to identify the effects of exercise modality on the underlying mechanisms (ventilatory responses, blood flow, muscle oxidative capacity, peripheral innervation and neuromuscular fatigue) of adaptation. The majority of studies indicate that runners achieve a higher V O(2max) on treadmill whereas cyclists can achieve a V O(2max) value in cycle ergometry similar to that in treadmill running. Hence, V O(2max) is specific to the exercise modality. In addition, the muscles adapt specifically to a given exercise task over a period of time, resulting in an improvement in submaximal physiological variables such as the ventilatory threshold, in some cases without a change in V O(2max). However, this effect is probably larger in cycling than in running. At the same time, skill influencing motor unit recruitment patterns is an important influence on the anaerobic threshold in cycling. Furthermore, it is likely that there is more physiological training transfer from running to cycling than vice versa. In triathletes, there is generally no difference in V O(2max) measured in cycle ergometry and treadmill running. The data concerning the anaerobic threshold in cycling and running in triathletes are conflicting. This is likely to be due to a combination of actual training load and prior training history in each discipline. The mechanisms surrounding the differences in the AT together with V O(2max) in cycling and running are not largely understood but are probably due to the relative adaptation of cardiac output influencing V O(2max) and also the recruitment of muscle mass in combination with the oxidative capacity of this mass influencing the AT. Several other physiological differences between cycling and running are addressed: heart rate is different between the two activities both for maximal and submaximal intensities. The delta efficiency is higher in running. Ventilation is more impaired in cycling than in running. It has also been shown that pedalling cadence affects the metabolic responses during cycling but also during a subsequent running bout. However, the optimal cadence is still debated. Central fatigue and decrease in maximal strength are more important after prolonged exercise in running than in cycling.
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              Modelling Heart Rate Kinetics

              The objective of the present study was to formulate a simple and at the same time effective mathematical model of heart rate kinetics in response to movement (exercise). Based on an existing model, a system of two coupled differential equations which give the rate of change of heart rate and the rate of change of exercise intensity is used. The modifications introduced to the existing model are justified and discussed in detail, while models of blood lactate accumulation in respect to time and exercise intensity are also presented. The main modification is that the proposed model has now only one parameter which reflects the overall cardiovascular condition of the individual. The time elapsed after the beginning of the exercise, the intensity of the exercise, as well as blood lactate are also taken into account. Application of the model provides information regarding the individual’s cardiovascular condition and is able to detect possible changes in it, across the data recording periods. To demonstrate examples of successful numerical fit of the model, constant intensity experimental heart rate data sets of two individuals have been selected and numerical optimization was implemented. In addition, numerical simulations provided predictions for various exercise intensities and various cardiovascular condition levels. The proposed model can serve as a powerful tool for a complete means of heart rate analysis, not only in exercise physiology (for efficiently designing training sessions for healthy subjects) but also in the areas of cardiovascular health and rehabilitation (including application in population groups for which direct heart rate recordings at intense exercises are not possible or not allowed, such as elderly or pregnant women).
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2019
                22 August 2019
                : 14
                : 8
                : e0220826
                Affiliations
                [001] Institute for Rehabilitation and Performance Technology, Division of Mechanical Engineering, Department of Engineering and Information Technology, Bern University of Applied Sciences, Burgdorf, Switzerland
                University of Bourgogne France Comté, FRANCE
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-6521-9455
                Article
                PONE-D-19-13112
                10.1371/journal.pone.0220826
                6705825
                31437177
                81eecdbc-96d0-4f9b-bc64-dd8f543f8acc
                © 2019 Hunt et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 9 May 2019
                : 23 July 2019
                Page count
                Figures: 4, Tables: 1, Pages: 9
                Funding
                The authors received no specific funding for this work.
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