Comparing the reliability of muscle oxygen saturation with common performance and physiological markers across cycling exercise intensity.

Introduction: Wearable near-infrared spectroscopy (NIRS) measurements of muscle oxygen saturation (SmO2) demonstrated good test-retest reliability at rest. We hypothesized SmO2 measured with the Moxy monitor at the vastus lateralis (VL) would demonstrate good reliability across intensities. For relative reliability, SmO2 will be lower than volume of oxygen consumption (V̇O2) and heart rate (HR), higher than concentration of blood lactate accumulation ([BLa]) and rating of perceived exertion (RPE). We aimed to estimate the reliability of SmO2 and common physiological measures across exercise intensities, as well as to quantify within-participant agreement between sessions. Methods: Twenty-one trained cyclists completed two trials of an incremental multi-stage cycling test with 5 min constant workload steps starting at 1.0 watt per kg bodyweight (W·kg-1) and increasing by 0.5 W kg-1 per step, separated by 1 min passive recovery intervals until maximal task tolerance. SmO2, HR, V̇O2, [BLa], and RPE were recorded for each stage. Continuous measures were averaged over the final 60 s of each stage. Relative reliability at the lowest, median, and highest work stages was quantified as intraclass correlation coefficient (ICC). Absolute reliability and within-subject agreement were quantified as standard error of the measurement (SEM) and minimum detectable change (MDC). Results: Comparisons between trials showed no significant differences within each exercise intensity for all outcome variables. ICC for SmO2 was 0.81-0.90 across exercise intensity. ICC for HR, V̇O2, [BLa], and RPE were 0.87-0.92, 0.73-0.97, 0.44-0.74, 0.29-0.70, respectively. SEM (95% CI) for SmO2 was 5 (3-7), 6 (4-9), and 7 (5-10)%, and MDC was 12%, 16%, and 18%. Discussion: Our results demonstrate good-to-excellent test-retest reliability for SmO2 across intensity during an incremental multi-stage cycling test. V̇O2 and HR had excellent reliability, higher than SmO2. [BLa] and RPE had lower reliability than SmO2. Muscle oxygen saturation measured by wearable NIRS was found to have similar reliability to V̇O2 and HR, and higher than [BLa] and RPE across exercise intensity, suggesting that it is appropriate for everyday use as a non-invasive method of monitoring internal load alongside other metrics.

The effect of severe intensity bouts on muscle oxygen saturation responses in trained cyclists.

Near-infrared spectroscopy (NIRS) quantifies muscle oxygenation (SmO2) during exercise. Muscle oxygenation response to self-paced, severe-intensity cycling remains unclear. Observing SmO2 can provide cycling professionals with the ability to assess muscular response, helping optimize decision-making. We aimed to describe the effect of self-paced severe intensity bouts on SmO2, measured noninvasively by a wearable NIRS sensor on the vastus lateralis (VL) muscle, and examine its reliability. We hypothesized a greater desaturation response with each bout, whereas, between trials, good reliability would be observed. Fourteen recreationally trained, and trained cyclists completed a ramp test to determine the power output (PO) at the respiratory compensation point (RCP). Athletes completed two subsequent visits of 50-minute sessions that included four severe-intensity bouts done at 5% above RCP PO. Muscle oxygenation in the VL was monitored using a wearable NIRS device. Measures included mean PO, heart-rate (HR), cadence, and SmO2 at bout onset, during work (work SmO2), and ΔSmO2. The bouts were compared using a one-way repeated measures ANOVA. For significant differences, a Fisher's least square difference post-hoc analysis was used. A two-way repeated measures ANOVA was used using trial and bout as main factors. Intraclass correlations (ICC) were used to quantify relative reliability for mean work, and standard error of the measurement (SEM) was used to quantify absolute agreement of mean work SmO2. Both PO and cadence showed no effect of bout or trial. Heart-rate at bout 2 (168 ± 8 bpm) and 4 (170 ± 7 bpm) were higher than bout 1 (160 ± 6 bpm). Onset SmO2 (%) response significantly increased in the final two bouts of the session. Mean work SmO2 increased across bouts, with the highest value displayed in bout 4 (36 ± 22%). ΔSmO2 showed a smaller desaturation response during bout 4 (27 ± 10%) compared to bout 3 (31 ± 10%). Mean work SmO2 ICC showed good reliability (ICC = 0.87), and SEM was 12% (CI 9-15%). We concluded that a non-invasive, affordable, wearable NIRS sensor demonstrated the heterogeneous muscle oxygenation response during severe intensity cycling bouts with good reliability in trained cyclists.

Comparing the Respiratory Compensation Point With Muscle Oxygen Saturation in Locomotor and Non-locomotor Muscles Using Wearable NIRS Spectroscopy During Whole-Body Exercise.

The relationship between the muscle deoxygenation breakpoint (Deoxy-BP) measured with near-infrared spectroscopy (NIRS), and the respiratory compensation point (RCP) has been well established. This relationship has also been reported using wearable NIRS, however not in locomotor and non-locomotor muscles simultaneously during whole-body cycling exercise. Our aim was to measure muscle oxygen saturation (SmO2) using wearable NIRS sensors, and to compare the Deoxy-BPs at each muscle with RCP during a ramp cycling exercise test. Twenty-two trained female and male cyclists completed a ramp exercise test to task intolerance on a cycling ergometer, at a ramp rate of 1 W every 2 s (30 W/min). SmO2 was recorded at the subjects' right vastus lateralis (VL) and right lateral deltoid. SmO2 and the Deoxy-BPs were assessed using a piecewise double-linear regression model. Ventilation (V̇E) and gas exchange were recorded, and RCP was determined from V̇E and gas exchange using a V-slope method and confirmed by two physiologists. The SmO2 profiles of both muscles and gas exchange responses are reported as V̇O2, power output (W), and time of occurrence (TO). SmO2 profiles at both muscles displayed a near-plateau or breakpoint response near the RCP. No differences were detected between the mean RCP and mean Deoxy-BP from either the locomotor or non-locomotor muscles; however, a high degree of individual variability was observed in the timing and order of occurrence of the specific breakpoints. These findings add insight into the relationships between ventilatory, locomotor, and non-locomotor muscle physiological breakpoints. While identifying a similar relationship between these breakpoints, individual variability was high; hence, caution is advised when using wearable NIRS to estimate RCP in an incremental ramp test.

Evaluating Arterial Blood Flow Limitation Using Muscle Oxygenation and Cycling Power.

OBJECTIVE: To explore the combination of measuring muscle oxygenation with near-infrared spectroscopy (NIRS) and cycling power during provocative incremental exercise for the detection of iliac arterial blood flow limitation (IAFL) in an otherwise healthy, well-trained cyclist. DESIGN: Case report and methodological pilot study. SETTING: University research setting. PATIENT: A well-trained amateur competitive male cyclist, aged 31 years, presenting with symptoms consistent with IAFL, but in whom diagnostic imaging was equivocal. INTERVENTIONS: Four ramp incremental cycling tests performed on separate days to exercise intolerance, in a randomized order, in either typical race position (RP) or modified upright position (UP). MAIN OUTCOME: A novel ratio of unilateral cycling power to NIRS-derived muscle oxygenation termed "power-deoxygenation factor" was measured during provocative incremental exercise and compared with other NIRS-derived measures of vascular responsiveness and performance outcomes across the 2 body position conditions. RESULTS: The power-deoxygenation factor was able to show clinically important, progressive differences between the affected and unaffected limbs, coinciding with worsening performance impairments related to the body position that were not detected with traditional measures of vascular responsiveness taken after exercise. CONCLUSIONS: This method was used to detect bilateral differences consistent with IAFL in a cyclist where traditional diagnostic criteria were equivocal, but subsequent intraoperative findings confirmed the diagnosis. A similar screening test could be performed noninvasively and without requiring specialized medical care. Future work should investigate the validity and sensitivity of this methodology to improve the ability to identify and monitor athletes with IAFL.

The Critical Power Model as a Potential Tool for Anti-doping.

Existing doping detection strategies rely on direct and indirect biochemical measurement methods focused on detecting banned substances, their metabolites, or biomarkers related to their use. However, the goal of doping is to improve performance, and yet evidence from performance data is not considered by these strategies. The emergence of portable sensors for measuring exercise intensities and of player tracking technologies may enable the widespread collection of performance data. How these data should be used for doping detection is an open question. Herein, we review the basis by which performance models could be used for doping detection, followed by critically reviewing the potential of the critical power (CP) model as a prototypical performance model that could be used in this regard. Performance models are mathematical representations of performance data specific to the athlete. Some models feature parameters with physiological interpretations, changes to which may provide clues regarding the specific doping method. The CP model is a simple model of the power-duration curve and features two physiologically interpretable parameters, CP and W'. We argue that the CP model could be useful for doping detection mainly based on the predictable sensitivities of its parameters to ergogenic aids and other performance-enhancing interventions. However, our argument is counterbalanced by the existence of important limitations and unresolved questions that need to be addressed before the model is used for doping detection. We conclude by providing a simple worked example showing how it could be used and propose recommendations for its implementation.