Effect of Uphill Running on VO2, Heart Rate and Lactate Accumulation on Lower Body Positive Pressure Treadmills

Lower body positive pressure treadmills (LBPPTs) as a strategy to reduce musculoskeletal load are becoming more common as part of sports conditioning, although the requisite physiological parameters are unclear. To elucidate their role, ten well-trained runners (30.2 ± 3.4 years; VO2max: 60.3 ± 4.2 mL kg−1 min−1) ran at 70% of their individual velocity at VO2max (vVO2max) on a LBPPT at 80% body weight support (80% BWSet) and 90% body weight support (90% BWSet), at 0%, 2% and 7% incline. Oxygen consumption (VO2), heart rate (HR) and blood lactate accumulation (LA) were monitored. It was found that an increase in incline led to increased VO2 values of 6.8 ± 0.8 mL kg−1 min−1 (0% vs. 7%, p < 0.001) and 5.4 ± 0.8 mL kg−1 min−1 (2% vs. 7%, p < 0.001). Between 80% BWSet and 90% BWSet, there were VO2 differences of 3.3 ± 0.2 mL kg−1 min−1 (p < 0.001). HR increased with incline by 12 ± 2 bpm (0% vs. 7%, p < 0.05) and 10 ± 2 bpm (2% vs. 7%, p < 0.05). From 80% BWSet to 90% BWSet, HR increases of 6 ± 1 bpm (p < 0.001) were observed. Additionally, LA values showed differences of 0.10 ± 0.02 mmol l−1 between 80% BWSet and 90% BWSet. Those results suggest that on a LBPPT, a 2% incline (at 70% vVO2max) is not yet sufficient to produce significant physiological changes in VO2, HR and LA—as opposed to running on conventional treadmills, where significant changes are measured. However, a 7% incline increases VO2 and HR significantly. Bringing together physiological and biomechanical factors from previous studies into this practical context, it appears that a 7% incline (at 80% BWSet) may be used to keep VO2 and HR load unchanged as compared to unsupported running, while biomechanical stress is substantially reducedPeer Reviewe

Validity of a Local Positioning System during Outdoor and Indoor Conditions for Team Sports

This study aimed to compare the validity of a local positioning system (LPS) during outdoor and indoor conditions for team sports. The impact of different filtering techniques was also investigated. Five male team sport athletes (age: 27 ± 2 years; maximum oxygen uptake: 48.4 ± 5.1 mL/min/kg) performed 10 trials on a team sport-specific circuit on an artificial turf and in a sports hall. During the circuit, athletes wore two devices of a recent 20-Hz LPS. From the reported raw and differently filtered velocity data, distances covered during different walking, jogging, and sprinting sections within the circuit were computed for which the circuit was equipped with double-light timing gates as criterion measures. The validity was determined by comparing the known and measured distances via the relative typical error of estimate (TEE). The LPS validity for measuring distances covered was good to moderate during both environments (TEE: 0.9–7.1%), whereby the outdoor validity (TEE: 0.9–6.4%) was superior than indoor validity (TEE: 1.2–7.1%). During both environments, validity outcomes of an unknown manufacturer filter were superior (TEE: 0.9–6.2%) compared to those of a standard Butterworth filter (TEE: 0.9–6.4%) and to unprocessed raw data (TEE: 1.0–7.1%). Our findings show that the evaluated LPS can be considered as a good to moderately valid tracking technology to assess running-based movement patterns in team sports during outdoor and indoor conditions. However, outdoor was superior to indoor validity, and also impacted by the applied filtering technique. Our outcomes should be considered for practical purposes like match and training analyses in team sport environments

High-performance giant magnetoresistive sensorics on flexible Si membranes

We fabricate high-performance giant magnetoresistive (GMR) sensorics on Si wafers, which are subsequently thinned down to 100 μm or 50 μm to realize mechanically flexible sensing elements. The performance of the GMR sensors upon bending is determined by the thickness of the Si membrane. Thus, bending radii down to 15.5 mm and 6.8 mm are achieved for the devices on 100 μm and 50 μm Si supports, respectively. The GMR magnitude remains unchanged at the level of (15.3 ± 0.4)% independent of the support thickness and bending radius. However, a progressive broadening of the GMR curve is observed associated with the magnetostriction of the containing Ni81Fe19 alloy, which is induced by the tensile bending strain generated on the surface of the Si membrane. An effective magnetostriction value of λs = 1.7 × 10−6 is estimated for the GMR stack. Cyclic bending experiments showed excellent reproducibility of the GMR curves during 100 bending cycles

The Concept of Optimal Dynamic Pedalling Rate and Its Application to Power Output and Fatigue in Track Cycling Sprinters—A Case Study

Sprint races in track cycling are characterised by maximal power requirements and high-power output over 15 to 75 s. As competition rules limit the athlete to a single gear, the choice of gear ratio has considerable impact on performance. Traditionally, a gear favouring short start times and rapid acceleration, i.e., lower transmission ratios, was chosen. In recent years, track cyclists tended to choose higher gear ratios instead. Based on a review of the relevant literature, we aimed to provide an explanation for that increase in the gear ratio chosen and apply this to a 1000 m time trial. Race data with continuous measurements of crank force and velocity of an elite track cyclist were analysed retrospectively regarding the influence of the selected gear on power, cadence and resulting speed. For this purpose, time-dependent maximal force-velocity (F/v) profiles were used to describe changes in performance with increasing fatigue. By applying these profiles to a physical model of track cycling, theoretical power output, cadence and resulting speed were calculated for different scenarios. Based on previous research results, we assume a systematic and predictable decline in optimal cadence with increasing fatigue. The choice of higher gear ratios seems to be explained physiologically by the successive reduction in optimal cadence as fatigue sets in. Our approach indicates that average power output can be significantly increased by selecting a gear ratio that minimises the difference between the realised cadence and the time-dependent dynamic optimum. In view of the additional effects of the gear selection on acceleration and speed, gear selection should optimally meet the various requirements of the respective sprint event

Fatigue-Free Force-Velocity and Power-Velocity Profiles for Elite Track Sprint Cyclists: The Influence of Duration, Gear Ratio and Pedalling Rates

Background: Maximal force-velocity (F/v) profiles for track cyclists are commonly derived from ergometer sprints using an isovelocity or isoinertial approach. Previously, an attempt was made to derive maximal F/v profiles from a single maximal 65-m sprint on the cycling track. Hypothesising that this approach may not accurately reflect the fatigue-free F/v profile, we propose an alternative procedure and compare it to the previous method. Moreover, we test for the impact of gear ratio on diagnostic results. Methods: Twelve elite track cyclists completed a high-cadence low-resistance pedalling test on a freestanding roller (motoric test) and two series of three maximal 65-m sprints on a cycling track with different gear ratios. F/v profiles were calculated based on the measured crank force and cadence either during the first 6&ndash;7 revolutions (&le;6 s) on the track (model I) or were derived from the first 3&ndash;4 revolutions (&le;3 s) on the track combined with 1 or 2 fatigue-free cycles at cadences above 160 rpm from the motoric test (model II). Results: Although both models exhibit high-to-excellent linearity between force and velocity, the extrapolated isometric force was higher (1507.51 &plusmn; 257.60 N and 1384.35 &plusmn; 276.84 N; p &lt; 0.002; d = 2.555) and the slope steeper (&minus;6.78 &plusmn; 1.17 and &minus;5.24 &plusmn; 1.11; p &lt; 0.003, d = &minus;2.401) with model I. An ICC of 1.00 indicates excellent model consistency when comparing the F/v profiles (model II) derived from the different geared sprints. Conclusions: Assuring fatigue-free measurements and including high-cadence data points in the calculations provide valid maximal F/v and P/v profiles from a single acceleration-sprint independent of gear ratio

A Novel Approach to the Determination of Time- and Fatigue-Dependent Efficiency during Maximal Cycling Sprints

Background: During maximal cycling sprints, efficiency (η) is determined by the fiber composition of the muscles activated and cadence-dependent power output. To date, due to methodological limitations, it has only been possible to calculate gross efficiency (i.e., the ratio of total mechanical to total metabolic work) in vivo without assessing the impact of cadence and changes during exercise. Eliminating the impact of cadence provides optimal efficiency (ηopt), which can be modeled as a function of time. Here, we explain this concept, demonstrate its calculation, and compare the values obtained to actual data. Furthermore, we hypothesize that the time course of maximal power output (Pmax) reflects time-dependent changes in ηopt. Methods: Twelve elite track cyclists performed four maximal sprints (3, 8, 12, 60 s) and a maximal-pedaling test on a cycle ergometer. Crank force and cadence were monitored continuously to determine fatigue-free force-velocity profiles (F/v) and fatigue-induced changes in Pmax. Respiratory gases were measured during and for 30 min post-exercise. Prior to and following each sprint, lactate in capillary blood was determined to calculate net blood lactate accumulation (ΔBLC). Lactic and alactic energy production were estimated from ΔBLC and the fast component of excess post-exercise oxygen consumption. Aerobic energy production was determined from oxygen uptake during exercise. Metabolic power (MP) was derived from total metabolic energy (WTOT). ηopt was calculated as Pmax divided by MP. Temporal changes in Pmax, WTOT, and ηopt were analyzed by non-linear regression. Results: All models showed excellent quality (R2 &gt; 0.982) and allowed accurate recalculation of time-specific power output and gross efficiency (R2 &gt; 0.986). The time-constant for Pmax(t) (τP) was closely correlated with that of ηopt (τη; r = 0.998, p &lt; 0.001). Estimating efficiency using τP for τη led to a 0.88 ± 0.35% error. Conclusions: Although efficiency depends on pedal force and cadence, the latter influence can be eliminated by ηopt(t) using a mono-exponential equation whose time constant can be estimated from Pmax(t).Validerad;2023;Nivå 2;2023-03-07 (joosat);Funder: Federal Ministry of the Interior and Community, Germany (AD-5-17)Licens fulltext: CC BY License</p

A Novel Approach to the Determination of Time- and Fatigue-Dependent Efficiency during Maximal Cycling Sprints

Background: During maximal cycling sprints, efficiency (η) is determined by the fiber composition of the muscles activated and cadence-dependent power output. To date, due to methodological limitations, it has only been possible to calculate gross efficiency (i.e., the ratio of total mechanical to total metabolic work) in vivo without assessing the impact of cadence and changes during exercise. Eliminating the impact of cadence provides optimal efficiency (ηopt), which can be modeled as a function of time. Here, we explain this concept, demonstrate its calculation, and compare the values obtained to actual data. Furthermore, we hypothesize that the time course of maximal power output (Pmax) reflects time-dependent changes in ηopt. Methods: Twelve elite track cyclists performed four maximal sprints (3, 8, 12, 60 s) and a maximal-pedaling test on a cycle ergometer. Crank force and cadence were monitored continuously to determine fatigue-free force-velocity profiles (F/v) and fatigue-induced changes in Pmax. Respiratory gases were measured during and for 30 min post-exercise. Prior to and following each sprint, lactate in capillary blood was determined to calculate net blood lactate accumulation (ΔBLC). Lactic and alactic energy production were estimated from ΔBLC and the fast component of excess post-exercise oxygen consumption. Aerobic energy production was determined from oxygen uptake during exercise. Metabolic power (MP) was derived from total metabolic energy (WTOT). ηopt was calculated as Pmax divided by MP. Temporal changes in Pmax, WTOT, and ηopt were analyzed by non-linear regression. Results: All models showed excellent quality (R2 > 0.982) and allowed accurate recalculation of time-specific power output and gross efficiency (R2 > 0.986). The time-constant for Pmax(t) (τP) was closely correlated with that of ηopt (τη; r = 0.998, p P for τη led to a 0.88 ± 0.35% error. Conclusions: Although efficiency depends on pedal force and cadence, the latter influence can be eliminated by ηopt(t) using a mono-exponential equation whose time constant can be estimated from Pmax(t)

Validity of a Local Positioning System during Outdoor and Indoor Conditions for Team Sports

This study aimed to compare the validity of a local positioning system (LPS) during outdoor and indoor conditions for team sports. The impact of different filtering techniques was also investigated. Five male team sport athletes (age: 27 ± 2 years; maximum oxygen uptake: 48.4 ± 5.1 mL/min/kg) performed 10 trials on a team sport-specific circuit on an artificial turf and in a sports hall. During the circuit, athletes wore two devices of a recent 20-Hz LPS. From the reported raw and differently filtered velocity data, distances covered during different walking, jogging, and sprinting sections within the circuit were computed for which the circuit was equipped with double-light timing gates as criterion measures. The validity was determined by comparing the known and measured distances via the relative typical error of estimate (TEE). The LPS validity for measuring distances covered was good to moderate during both environments (TEE: 0.9–7.1%), whereby the outdoor validity (TEE: 0.9–6.4%) was superior than indoor validity (TEE: 1.2–7.1%). During both environments, validity outcomes of an unknown manufacturer filter were superior (TEE: 0.9–6.2%) compared to those of a standard Butterworth filter (TEE: 0.9–6.4%) and to unprocessed raw data (TEE: 1.0–7.1%). Our findings show that the evaluated LPS can be considered as a good to moderately valid tracking technology to assess running-based movement patterns in team sports during outdoor and indoor conditions. However, outdoor was superior to indoor validity, and also impacted by the applied filtering technique. Our outcomes should be considered for practical purposes like match and training analyses in team sport environments

Validity of a Local Positioning System during Outdoor and Indoor Conditions for Team Sports

This study aimed to compare the validity of a local positioning system (LPS) during outdoor and indoor conditions for team sports. The impact of different filtering techniques was also investigated. Five male team sport athletes (age: 27 ± 2 years; maximum oxygen uptake: 48.4 ± 5.1 mL/min/kg) performed 10 trials on a team sport-specific circuit on an artificial turf and in a sports hall. During the circuit, athletes wore two devices of a recent 20-Hz LPS. From the reported raw and differently filtered velocity data, distances covered during different walking, jogging, and sprinting sections within the circuit were computed for which the circuit was equipped with double-light timing gates as criterion measures. The validity was determined by comparing the known and measured distances via the relative typical error of estimate (TEE). The LPS validity for measuring distances covered was good to moderate during both environments (TEE: 0.9–7.1%), whereby the outdoor validity (TEE: 0.9–6.4%) was superior than indoor validity (TEE: 1.2–7.1%). During both environments, validity outcomes of an unknown manufacturer filter were superior (TEE: 0.9–6.2%) compared to those of a standard Butterworth filter (TEE: 0.9–6.4%) and to unprocessed raw data (TEE: 1.0–7.1%). Our findings show that the evaluated LPS can be considered as a good to moderately valid tracking technology to assess running-based movement patterns in team sports during outdoor and indoor conditions. However, outdoor was superior to indoor validity, and also impacted by the applied filtering technique. Our outcomes should be considered for practical purposes like match and training analyses in team sport environments