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Purpose: To identify differences in heart rate (HR) and concentration of blood lactate ([La]) relationships between laboratory- and field-based skate-roller-skiing tests. Methods: Fourteen world-class biathletes (8 women, 6 men) completed a laboratory- and field-based roller-skiing test using the skate technique. The laboratory-based test comprised 5 to 7 submaximal steps at a fixed incline and speed on a roller-skiing treadmill. The field-based test comprised 5 steps on a course where the final hill was designed to mimic the conditions of the laboratory test. HR and [La] were measured for each step. The HR associated with 2 mmol·L−1 (HR@2 mmol) and 4 mmol·L−1 (HR@4 mmol) of [La] was calculated using an interpolation method. A 1-way analysis of variance and Bland–Altman analyses with 95% limits of agreement (LoA) were used to determine if test type influenced HR@2 mmol or HR@4 mmol. A second-order polynomial was fitted to group-level data to highlight the HR–[La] relationships for laboratory- and field-based tests. Results: HR@2 mmol was lower for field tests than for laboratory tests (mean bias: 1.9%HRmax; 95% LoA: −4.5 to +8.3%HRmax; P @4 mmol was also lower for field tests compared to laboratory tests (mean bias: 2.4%HRmax; 95% LoA: −1.2 to +6.0%HRmax; P Conclusions: The findings from this study confirm that for a given HR, [La] was greater in field- compared with laboratory-based conditions. These results might have implications for how coaches define training-intensity “zones” during skate roller skiing based on laboratory tests.

Purpose: To critically appraise the utility of heart rate (HR) and power output (PO) to predict metabolic rate (MR) and oxygen consumption () during variable-intensity roller skiing and cycling. Methods: National-level cyclists (n = 8) and cross-country skiers (n = 9) completed a preliminary session to determine , and a variable-intensity protocol with 3 high-intensity stages at 90% for 3 minutes interspersed with 3 moderate-intensity stages at 70% for 6 minutes. Cardiorespiratory measures were recorded throughout. Linear HR–MR, , PO–MR, and regressions were computed from the preliminary session, individually, for all athletes and used to predict MR and from both HR and PO, separately, during the variable-intensity protocol. Mean differences with 95% limits of agreement (LOA) between measured and predicted MR and were calculated. Results: MR and estimated from HR displayed a mean bias close to zero but wide LOA. HR overestimated MR and during moderate intensity but underestimated MR and during high intensity, for both roller skiing and cycling. MR and estimated from PO were more consistent across the experimental trial, displaying a mean bias farther from zero but with tighter LOA. Conclusions: This study has demonstrated that HR has limited utility to predict metabolic intensity during variable-intensity roller skiing and cycling because of wide LOA. On the other hand, metabolic intensity predicted from PO had tighter LOA, suggesting better consistency. PO might provide a better prediction of metabolic intensity compared with HR, particularly when longer-duration steps are performed during preliminary testing.

Purpose To compare the anaerobic work capacity (AnWC, i.e., attributable anaerobic mechanical work) assessed using four different approaches/models applied to time-trial (TT) cycle-ergometry exercise. Methods Fifteen male cyclists completed a 7 × 4-min submaximal protocol and a 3-min all-out TT (TTAO). Linear relationships between power output (PO) and submaximal metabolic rate were constructed to estimate TT-specific gross efficiency (GE) and AnWC, using either a measured resting metabolic rate as a Y-intercept (7 + YLIN) or no measured Y-intercept (7-YLIN). In addition, GE of the last submaximal bout (GELAST) was used to estimate AnWC, and critical power (CP) from TTAO (CP3´AO) was used to estimate mechanical work above CP (W’, i.e., “AnWC”). Results Average PO during TTAO was 5.43 ± 0.30 and CP was 4.48 ± 0.23 W∙kg−1. The TT-associated GE values were ~ 22.0% for both 7 + YLIN and 7-YLIN and ~ 21.1% for GELAST (both P −1 for the 7 + YLIN, 7-YLIN, GELAST, and CP3´AO models, respectively (7 + YLIN and 7-YLIN versus GELAST, both P LIN, 7-YLIN, and GELAST versus CP3´AO, all P LIN, 7-YLIN, and GELAST, typical errors in AnWC values ranged from 7 to 11 J∙kg−1, whereas 7 + YLIN, 7-YLIN, and GELAST versus CP3´AO revealed typical errors of 55–59 J∙kg−1. Conclusion These findings demonstrate a substantial disagreement in AnWC between CP3´AO and the other models. The 7 + YLIN and 7-YLIN generated 10% lower AnWC values than the GELAST model, whereas 7 + YLIN and 7-YLIN generated similar values of AnWC.

Purpose: To examine the relationship between cardiorespiratory and accelerometer-derived measures of exercise during trail running and determine the influence of accelerometer location. Methods: Eight trail runners (7 males and 1 female; age 26 [5] y; maximal oxygen consumption [] 70 [6] mL·kg−1·min−1) completed a 7-km trail run (elevation gain: 486 m), with concurrent measurements of , heart rate, and accelerations recorded from 3 triaxial accelerometers attached at the upper spine, lower spine, and pelvis. External exercise intensity was quantified from the accelerometers using PlayerLoad™ per minute and accelerometry-derived average net force. External exercise volume was calculated using accumulated PlayerLoad and the product of average net force and duration (impulse). Internal intensity was calculated using heart rate and -metrics; internal volume was calculated from total energy expenditure (work). All metrics were analyzed during both uphill (UH) and downhill (DH) sections of the trail run. Results: PlayerLoad and average net force were greater during DH compared with UH for all sensor locations (P ≤ .004). For all accelerometer metrics, there was a sensor position × gradient interaction (F2,1429.003; P P ≤ .003) and lower spine (P ≤ .002) for all accelerometer metrics during both UH and DH running. Relationships between accelerometer and cardiorespiratory measures during UH running ranged from moderate negative to moderate positive (r = −.31 to .41). Relationships were stronger during DH running where there was a nearly perfect correlation between work and impulse (r = .91; P < .001). Conclusions: Simultaneous monitoring of cardiorespiratory and accelerometer-derived measures during trail running is suggested because of the disparity between internal and external intensities during changes in gradient. Sensor positioning close to the center of mass is recommended.