Demarcating Exercise Intensity Domains in Freestyle Swimming: Is There an Alternative to Incremental Step Test and Beats Below HRmax Method?

Utilizing IoMT-Based Smart Gloves for Continuous Vital Sign Monitoring to Safeguard Athlete Health and Optimize Training Protocols

This paper presents the development of a vital sign monitoring system designed specifically for professional athletes, with a focus on runners. The system aims to enhance athletic performance and mitigate health risks associated with intense training regimens. It comprises a wearable glove that monitors key physiological parameters such as heart rate, blood oxygen saturation (SpO2), body temperature, and gyroscope data used to calculate linear speed, among other relevant metrics. Additionally, environmental variables, including ambient temperature, are tracked. To ensure accuracy, the system incorporates an onboard filtering algorithm to minimize false positives, allowing for timely intervention during instances of physiological abnormalities. The study demonstrates the system's potential to optimize performance and protect athlete well-being by facilitating real-time adjustments to training intensity and duration. The experimental results show that the system adheres to the classical "220-age" formula for calculating maximum heart rate, responds promptly to predefined thresholds, and outperforms a moving average filter in noise reduction, with the Gaussian filter delivering superior performance.

Muscle oxygen saturation rates coincide with lactate-based exercise thresholds

INTRODUCTION

Monitoring muscle metabolic activity via blood lactate is a useful tool for understanding the physiological response to a given exercise intensity. Recent indications suggest that skeletal muscle oxygen saturation (SmO2), an index of the balance between local O2 supply and demand, may describe and predict endurance performance outcomes.

PURPOSE

We tested the hypothesis that SmO2 rate is tightly related to blood lactate concentration across exercise intensities, and that deflections in SmO2 rate would coincide with established blood lactate thresholds (i.e., lactate thresholds 1 and 2).

METHODS

Ten elite male soccer players completed an incremental running protocol to exhaustion using 3-min work to 30 s rest intervals. Blood lactate samples were collected during rest and SmO2 was collected continuously via near-infrared spectroscopy from the right and left vastus lateralis, left biceps femoris and the left gastrocnemius.

RESULTS

Muscle O2 saturation rate (%/min) was quantified after the initial 60 s of each 3-min segment. The SmO2 rate was significantly correlated with blood lactate concentrations for all muscle sites; RVL, r = - 0.974; LVL, r = - 0.969; LG, r = - 0.942; LHAM, r = - 0.907. Breakpoints in SmO2 rate were not significantly different from LT1 or LT2 at any muscle sites (P > 0.05). Bland-Altman analysis showed speed threshold estimates via SmO2 rate and lactate are similar at LT2, but slightly greater for SmO2 rate at LT1.

CONCLUSIONS

Muscle O2 saturation rate appears to provide actionable information about maximal metabolic steady state and is consistent with bioenergetic reliance on oxygen and its involvement in the attainment of metabolic steady state.

Does Smartphone Use Affect a Subsequent Swimming Training Session? Preliminary Results in Amateur Triathletes

To date, the literature has failed to individuate a clear motivation for the performance decrement after a mental fatigue-inducing task. This study aimed to evaluate biomechanical and perceptual variables during a swimming training session in different mental fatigue states. Seven amateur triathletes watched a documentary, utilized a smartphone, or performed an AX-CPT for 45 min randomly on three different days. After, they performed a 15-min warm-up followed by 6 × 200 m at constant pre-set speed plus one 200 m at maximal effort. The mental fatigue status was assessed by the visual analog scale (VAS) and short-Stroop task results before, post-mental task, and post-swimming session. The biomechanical and motor coordination variables during swimming were assessed using five IMU sensors and video analysis. The heart rate and rate of perceived exertion were monitored during the task. No differences in biomechanical and perceptual variables were found between and within conditions. Higher mental fatigue was found only in the AX-CPT condition at post task by VAS. In this preliminary study, no changes in swimming biomechanics were highlighted by mental fatigue, but the warm-up performed may have counteracted its negative effects. Further studies are recommended.

Young Swimmers' Middle-Distance Performance Variation within a Training Season

The current study aimed to longitudinally evaluate anthropometric, physiological, and biomechanical variables related to middle-distance performance during a 45-week swimming training season. Thirty-four swimmers (age: 12.07 ± 1.14 years) performed a maximum of 400 m front crawl at the beginning (T1) and finish of the first macrocycle (T2, 15 weeks) and the finish of the second (T3, 18 weeks) and third macrocycles (T4, 12 weeks). Time-related variables, stroke rate (SR), stroke length (SL), and stroke index (SI) were recorded during the test, and blood lactate ([La]) and glucose ([Glu]) concentrations were measured post-exercise. The time of the 400 m effort decreased after each macrocycle (T2 vs. T1, 7.8 ± 5.6%; T3 vs. T2, 3.7 ± 3.1%; T4 vs. T3, 3.8 ± 3.4%; p < 0.01). Four hundred meter speed changes between T1 and T2 were positively related to variations in [La], [Glu], SL, and SI (r = 0.36–0.60, p < 0.05). Changes between T2 and T3 were related to SI only (r = 0.5, p < 0.05), and modifications between T3 and T4 were associated with SL and SI variations (r = 0.34 and 0.65, p < 0.05). These results indicate that a well-structured year plan including three macrocycles leads to a significant age-group swimming performance improvement, mostly connected with an increase in technical proficiency.

Variability in exercise physiology: Can capturing intra-individual variation help better understand true inter-individual responses?

Exploring individual responses to exercise training is a growing area of interest. Understanding reasons behind true observed inter-individual responses may help personalise exercise training to maximise the benefits received. While numerous factors have been explored, an often underappreciated consideration in the sport and exercise science field is the influence intra-individual variation, both in a single measurement and in response to an intervention, may have on training outcomes. Several study designs and statistical approaches are available to incorporate intra-individual variation into interventions and accordingly provide information on whether 'true' inter-individual responses are present or if they are an artefact of intra-individual variation. However, such approaches are sparingly applied. Moreover, intra-individual variation may also be important when true inter-individual response differences are present. In this perspective piece, the concept of intra-individual variation is described before briefly summarising study designs and statistical practices to account for intra-individual variation. We then outline two examples of physiological practices (stratified randomisation and prescribing exercise programmes upon training parameters) to demonstrate why sport and exercise scientists should acknowledge intra-individual variation prior to the implementation of an intervention, which potentially offers an additional explanation behind observed true inter-individual responses to training. Repeated testing pre-implementation of exercise training would conceptually provide more confident estimates of training parameters, which if utilised in a study design will help attenuate biases that may dictate inter-individual differences. Moreover, the incorporation of intra-individual differences will facilitate insights into alternative factors that may predict and/or explain true observed individual responses to an exercise training programme.