A non-linear analysis of running in the heavy and severe intensity domains

Remote Determination of Critical Speed and Critical Power in Recreational Runners

PURPOSE

This study aimed to compare estimations of critical speed (CS) and work completed above CS (D'), and their analogies for running power (critical power [CP] and W'), derived from raw data obtained from habitual training (HAB) and intentional maximal efforts in the form of time trials (TTs) and 3-minute all-out tests (3MTs) in recreational runners. The test-retest reliability of the 3MT was further analyzed.

METHODS

Twenty-three recreational runners (4 female) used a foot pod to record speed, altitude, and power output for 8 consecutive weeks. CS and D', and CP and W', were calculated from the best 3-, 7-, and 12-minute segments recorded in the first 6 weeks of their HAB and in random order in weeks 7 and 8 from 3 TTs (3, 7, and 12 min) and three 3MTs (to assess test-retest reliability).

RESULTS

There was no difference between estimations of CS or CP derived from HAB, TT, and 3MT (3.44 [0.63], 3.42 [0.53], and 3.76 [0.57] m · s-1 and 281 [41], 290 [45], and 305 [54] W, respectively), and strong agreement between HAB and TT for CS (r = .669) and CP (r = .916). Limited agreement existed between estimates of D'/W'. Moderate reliability of D'/W' was demonstrated between the first and second 3MTs, whereas excellent reliability was demonstrated for CS/CP.

CONCLUSION

These data suggest that estimations of CS/CP can be derived remotely, from either HAB, TT, or 3MT, although the lower agreement between D'/W' warrants caution when using these measures interchangeably.

Running gait produces long range correlations: A systematic review

BACKGROUND

Walking and running are common forms of locomotion, both of which exhibit variability over many gait cycles. Many studies have investigated the patterns generated from that ebb and flow, and a large proportion suggests human gait exhibits Long Range Correlations (LRCs). LRCs refer to the observation that healthy gait characteristic, like stride times, are positively correlated to themselves over time. Literature on LRCs in walking gait is well known but less attention has been given to LRCs in running gait.

RESEARCH QUESTION

What is the state of the art concerning LRCs in running gait?

METHODS

We conducted a systematic review to identify the typical LRC patterns present in human running gait, in addition to disease, injury, and running surface effects on LRCs. Inclusion criteria were human subjects, running related experiments, computed LRCs, and experimental design. Exclusion criteria were studies on animals, non-humans, walking only, non-running, non-LRC analysis, and non-experiments.

RESULTS

The initial search returned 536 articles. After review and deliberation, our review included 26 articles. Almost every article produced strong evidence for LRCs apparent in running gait and in all running surfaces. Additionally, LRCs tended to decrease due to fatigue, past injury, increased load carriage and seem to be lowest at preferred running speed on a treadmill. No studies investigated disease effects on LRCs in running gait.

SIGNIFICANCE

LRCs seem to increase with deviations away from preferred running speed. Previously injured runners produced decreased LRCs compared to non-injured runners. LRCs also tended to decrease due to an increase in fatigue rate, which has been associated with increased injury rate. Lastly, there is a need for research on the typical LRCs in an overground environment, for which the typical LRCs found in a treadmill environment may or may not transfer.

Variability in exercise tolerance and physiological responses to exercise prescribed relative to physiological thresholds and to maximum oxygen uptake

NEW FINDINGS

What is the central question of this study? Does prescribing exercise intensity using physiological thresholds create a more homogeneous exercise stimulus than using traditional intensity anchors? What is the main finding and its importance? Prescribing exercise using physiological thresholds, notably critical power, reduced the variability in exercise tolerance and acute metabolic responses. At higher intensities, approaching or exceeding the transition from heavy to severe intensity exercise, the imprecision of using fixed %V ̇ O 2 max ${\dot V_{{{\rm{O}}_{\rm{2}}}{\rm{max}}}}$  as an intensity anchor becomes amplified.

ABSTRACT

The objective of this study was to determine whether the variability in exercise tolerance and physiological responses is lower when exercise is prescribed relative to physiological thresholds (THR) compared to traditional intensity anchors (TRAD). Ten individuals completed a series of maximal exercise tests and a series of moderate (MOD), heavy (HVY) and severe intensity (HIIT) exercise bouts prescribed using THR intensity anchors (critical power and gas exchange threshold) and TRAD intensity anchors (maximum oxygen uptake;V ̇ O 2 max ${\dot V_{{{\rm{O}}_{\rm{2}}}{\rm{max}}}}$ ). There were no differences in exercise tolerance or acute response variability between MODTHR and MODTRAD . All individuals completed HVYTHR but only 30% completed HVYTRAD . Compared to HVYTHR , where work rates were all below critical power, work rates in HVYTRAD exceeded critical power in 70% of individuals. There was, however, no difference in acute response variability between HVYTHR and HVYTRAD . All individuals completed HIITTHR but only 20% completed HIITTRAD . The variability in peak (F = 0.274) and average (F = 0.318) blood lactate responses was lower in HIITTHR compared to HIITTRAD . The variability in W' depletion (the finite work capacity above critical power) after the final interval bout was lower in HIITTHR compared to HIITTRAD (F = 0.305). Using physiological thresholds to prescribe exercise intensity reduced the heterogeneity in exercise tolerance and physiological responses to exercise spanning the boundary between the heavy and severe intensity domains. To increase the precision of exercise intensity prescription, it is recommended that, where possible, physiological thresholds are used in place ofV ̇ O 2 max ${\dot V_{{{\rm{O}}_{\rm{2}}}{\rm{max}}}}$ .

A critical review of critical power

The elegant concept of a hyperbolic relationship between power, velocity, or torque and time to exhaustion has rightfully captivated the imagination and inspired extensive research for over half a century. Theoretically, the relationship's asymptote along the time axis (critical power, velocity, or torque) indicates the exercise intensity that could be maintained for extended durations, or the "heavy-severe exercise boundary". Much more than a critical mass of the extensive accumulated evidence, however, has persistently shown the determined intensity of critical power and its variants as being too high to maintain for extended periods. The extensive scientific research devoted to the topic has almost exclusively centered around its relationships with various endurance parameters and performances, as well as the identification of procedural problems and how to mitigate them. The prevalent underlying premise has been that the observed discrepancies are mainly due to experimental 'noise' and procedural inconsistencies. Consequently, little or no effort has been directed at other perspectives such as trying to elucidate physiological reasons that possibly underly and account for those discrepancies. This review, therefore, will attempt to offer a new such perspective and point out the discrepancies' likely root causes.

Analysis of the Model for Sports Enhancing Human Health Using Data Mining

The problems of low reliability and the high fitting degree of mutual information feature extraction of traditional sports to human health enhancement model are analyzed. We analyze and study the sports to human health enhancement model using data mining. The model consists of a data layer, a logic layer, and a presentation layer. Sports project data, real-time sports data, and health monitoring data are collected in the data layer, and the collected data are transmitted to the logic layer. The logical layer uses the dynamic difference feature classification algorithm of data mining to fuse human health data, extract the mutual information features of human health, and input the features into the long short-term memory (LSTM) neural network, which outputs the pattern recognition results of sports health after forward and reverse operations. The results of sports health pattern recognition are input into the display layer, and the enhancing effect of sports on human health is presented for users by constructing a model of sports on human health. The results show that the effect of sports on human health enhancement analyzed by the model in this paper is extremely accurate, which can significantly improve the health level of community residents and college students. When the number of data is about 600, it remains at about 0.05, indicating that this model has high reliability, and the fitting degree of mutual information feature extraction is up to 99.82%. It has certain practical application value.