Visual classification of running economy by distance running coaches.

AbstractObjectives: This study assessed the ability of coaches to rank-order distance runners on running economy (RE) through visual assessment. Methods: Running economy was measured in five trained recreational distance runners at a speed of 3.57 m·s-1, with a minimum VO2 difference of 2-mL.kg-1.min-1 between adjacent runners. Following measurement of RE, participants were filmed from the front, side, and rear while running on a treadmill. Endurance coaches from high school to international level (N = 121) viewed each video and ranked the runners on a scale from 1 (most economical) to 5 (least economical). Coaches also completed a demographic questionnaire and listed running style biomechanical observations they used in determining each ranking. A cumulative odds ordinal logistic regression with proportional odds was run to determine the effect of coaching level, years of coaching experience, years of training experience, competition level, certification status, and education level, on the ability of distance running coaching to accurately rank RE among a group of runners. Results: No coaching characteristic was a significant predictor of ranking accuracy, χ2 = 3.566, p = .735. Conclusions: Visual assessment of effort, based on RE, is a difficult task, even for the trained eye and could be related to difficulty in understanding the interaction effect of various RE factors or the translation of scientific-based knowledge to the field of play. Practically, coaches should be cautious when recommending biomechanical adjustments without considering the interconnected factors related to such changes.

Cluster analysis of impairment measures to inform an evidence-based classification structure in RaceRunning, a new World Para Athletics event for athletes with hypertonia, ataxia or athetosis.

RaceRunning enables athletes with limited or no walking ability to propel themselves independently using a three-wheeled frame that has a saddle, handle bars and a chest plate. For RaceRunning to be included as a para athletics event, an evidence-based classification system is required. This study assessed the impact of trunk control and lower limb impairment measures on RaceRunning performance and evaluated whether cluster analysis of these impairment measures produces a valid classification structure for RaceRunning. The Trunk Control Measurement Scale (TCMS), Selective Control Assessment of the Lower Extremity (SCALE), the Australian Spasticity Assessment Scale (ASAS), and knee extension were recorded for 26 RaceRunning athletes. Thirteen male and 13 female athletes aged 24 (SD = 7) years participated. All impairment measures were significantly correlated with performance (rho = 0.55-0.74). Using ASAS, SCALE, TCMS and knee extension as cluster variables in a two-step cluster analysis resulted in two clusters of athletes. Race speed and the impairment measures were significantly different between the clusters (p < 0.001). The findings of this study provide evidence for the utility of the selected impairment measures in an evidence-based classification system for RaceRunning athletes.

Using the interaction of speed and acceleration to detect repeated-sprint activity in team sports.

PURPOSE: To compare the occurrence and characteristics of repeated-sprint (RS) activity in elite team sport competition when classified according to speed and/or acceleration, and their interaction via metabolic power (Pmet). Methods: Elite male hockey players (N = 16) wore player-tracking devices in six international matches. Sprint efforts were defined using four separate classifications: speed >5.5 m∙s-1, acceleration >1.5 m∙s-2, speed-or-acceleration, and Pmet >25.5 W∙kg-1. RS bouts were defined as ≥3 efforts with mean recovery ≤21 s. For Pmet, RS bouts were also classified using a maximal recovery period ≤21 s. The number of sprint efforts and RS bouts, and the number of efforts, effort durations and recovery periods within RS bouts, were compared across each classification method, and between mean and maximal recovery criteria. Results: More RS bouts were identified via Pmet (8.5 ± 2.8) than either speed and/or acceleration, and comprised more efforts (4.0 ± 0.4) with shorter recovery periods (11.5 ± 1.8 s). Fewer RS bouts (7.3 ± 2.8 vs. 8.5 ± 2.8) were identified with a maximum rather than mean recovery criterion. Conclusions: Definitions of sprint efforts and recovery periods which reflect ATP depletion and replenishment via Pmet suggest that RS activity occurs frequently in team sport competition, and is more demanding than when speed and/or acceleration are used to define RS activity in variable-speed locomotion.

Defining Off-road Running: A Position Statement from the Ultra Sports Science Foundation.

Off-road running continues to grow in popularity, with differing event categories existing, and terminologies are often used interchangeably and without precision. Trail running, mountain running, skyrunning, fell running, orienteering, obstacle course racing and cross-country running all take place predominantly in off-road terrain. Ultramarathon running refers to any running event over marathon distance conducted in any terrain and surface. Although some overlap may exist between these running events, mainly through the common denominator of off-road terrain, distinct features need to be recognised. As scientific interest in these activities grows, it becomes important to clarify these terms and develop a universal language for discussing these events. Similarly, off-road running athletes are generally not properly defined within the scientific literature, which makes intra- and inter-study comparisons difficult. The current position statement of the Ultra Sports Science Foundation highlights the different off-road running events and recommends clear reference to distance, surface, elevation change and altitude, type of event (continuous vs. staged), type of support, name and year of the event, governing body, and guidance on terminology. We further recommend to describe off-road running athletes by basic data, physiological determinants, training and competition characteristics in the scientific literature in order to facilitate and guide further research and practice.

Differences Between Long Distance Road Runners and Trail Runners in Achilles Tendon Structure and Jumping and Balance Performance.

BACKGROUND: Load and joint kinematics change with differences in running surface. Running regularly on trails compared to roads might influence the load on the Achilles tendon and its adaptations, along with other factors such as balance, strength, and proprioception. OBJECTIVE: To investigate Achilles tendon structure and functional tests in road and trail runners. DESIGN: Cross-sectional study. SETTING: Laboratory, sport sciences college. PARTICIPANTS: The study included 26 road and 17 trail runners who run at least three times per week with a minimum of 20 km per week and who participated in running competitions over the preceding 2 years. METHODS: Each participant was examined for Achilles tendon structure (via ultrasound tissue characterization [UTC] imaging) and underwent functional tests in addition to completing a demographic questionnaire. MAIN OUTCOME MEASUREMENTS: The percentages of echo types I, II, III, and IV (degree of structural homogeneity) within the tendon, tendon length and width, tendon cross-sectional area (via UTC imaging); Ankle inversion movement discrimination ability (via Active Movement Extent Discrimination Apparatus device); dynamic postural balance (via Y balance test); jumping performance (by Triple hop distance test); and Hip muscle abduction muscle strength (by hand-held dynamometry). RESULTS: Percentage of echo type I was significantly lower while echo type II was higher in the road group compared with the trail group (67.3% type I and 28.9% type II in the road group compared with 74.1% type I and 22.1% type II in the trail group, P < .001). No differences between genders were found and no significant differences between groups were found for the other tests. CONCLUSION: Tendon integrity as examined with UTC is different between road and trail runners. This suggests an influence of running surface on Achilles tendon structure. This difference was not reflected in other tests, thus the influence of tendon structure on function needs further examination.

Free-living Evaluation of Laboratory-based Activity Classifiers in Preschoolers.

Machine learning classification models for accelerometer data are potentially more accurate methods to measure physical activity in young children than traditional cut point methods. However, existing algorithms have been trained on laboratory-based activity trials, and their performance has not been investigated under free-living conditions. PURPOSE: This study aimed to evaluate the accuracy of laboratory-trained hip and wrist random forest and support vector machine classifiers for the automatic recognition of five activity classes: sedentary (SED), light-intensity activities and games (LIGHT_AG), walking (WALK), running (RUN), and moderate to vigorous activities and games (MV_AG) in preschool-age children under free-living conditions. METHODS: Thirty-one children (4.0 ± 0.9 yr) were video recorded during a 20-min free-living play session while wearing an ActiGraph GT3X+ on their right hip and nondominant wrist. Direct observation was used to continuously code ground truth activity class and specific activity types occurring within each class using a bespoke two-stage coding scheme. Performance was assessed by calculating overall classification accuracy and extended confusion matrices summarizing class-level accuracy and the frequency of specific activities observed within each class. RESULTS: Accuracy values for the hip and wrist random forest algorithms were 69.4% and 59.1%, respectively. Accuracy values for hip and wrist support vector machine algorithms were 66.4% and 59.3%, respectively. Compared with the laboratory cross validation, accuracy decreased by 11%-15% for the hip classifiers and 19%-21% for the wrist classifiers. Classification accuracy values were 72%-78% for SED, 58%-79% for LIGHT_AG, 71%-84% for MV_AG, 9%-15% for WALK, and 66%-75% for RUN. CONCLUSION: The accuracy of laboratory-based activity classifiers for preschool-age children was attenuated when tested on new data collected under free-living conditions. Future studies should train and test machine learning activity recognition algorithms using accelerometer data collected under free-living conditions.

International Society of Sports Nutrition Position Stand: nutritional considerations for single-stage ultra-marathon training and racing.

Background In this Position Statement, the International Society of Sports Nutrition (ISSN) provides an objective and critical review of the literature pertinent to nutritional considerations for training and racing in single-stage ultra-marathon. Recommendations for Training. i) Ultra-marathon runners should aim to meet the caloric demands of training by following an individualized and periodized strategy, comprising a varied, food-first approach; ii) Athletes should plan and implement their nutrition strategy with sufficient time to permit adaptations that enhance fat oxidative capacity; iii) The evidence overwhelmingly supports the inclusion of a moderate-to-high carbohydrate diet (i.e., ~ 60% of energy intake, 5-8 g·kg- 1·d- 1) to mitigate the negative effects of chronic, training-induced glycogen depletion; iv) Limiting carbohydrate intake before selected low-intensity sessions, and/or moderating daily carbohydrate intake, may enhance mitochondrial function and fat oxidative capacity. Nevertheless, this approach may compromise performance during high-intensity efforts; v) Protein intakes of ~ 1.6 g·kg- 1·d- 1 are necessary to maintain lean mass and support recovery from training, but amounts up to 2.5 g.kg- 1·d- 1 may be warranted during demanding training when calorie requirements are greater; Recommendations for Racing. vi) To attenuate caloric deficits, runners should aim to consume 150-400 Kcal·h- 1 (carbohydrate, 30-50 g·h- 1; protein, 5-10 g·h- 1) from a variety of calorie-dense foods. Consideration must be given to food palatability, individual tolerance, and the increased preference for savory foods in longer races; vii) Fluid volumes of 450-750 mL·h- 1 (~ 150-250 mL every 20 min) are recommended during racing. To minimize the likelihood of hyponatraemia, electrolytes (mainly sodium) may be needed in concentrations greater than that provided by most commercial products (i.e., > 575 mg·L- 1 sodium). Fluid and electrolyte requirements will be elevated when running in hot and/or humid conditions; viii) Evidence supports progressive gut-training and/or low-FODMAP diets (fermentable oligosaccharide, disaccharide, monosaccharide and polyol) to alleviate symptoms of gastrointestinal distress during racing; ix) The evidence in support of ketogenic diets and/or ketone esters to improve ultra-marathon performance is lacking, with further research warranted; x) Evidence supports the strategic use of caffeine to sustain performance in the latter stages of racing, particularly when sleep deprivation may compromise athlete safety.

Automatic Identification of Physical Activity Type and Duration by Wearable Activity Trackers: A Validation Study.

BACKGROUND: Activity trackers are now ubiquitous in certain populations, with potential applications for health promotion and monitoring and chronic disease management. Understanding the accuracy of this technology is critical to the appropriate and productive use of wearables in health research. Although other peer-reviewed validations have examined other features (eg, steps and heart rate), no published studies to date have addressed the accuracy of automatic activity type detection and duration accuracy in wearable trackers. OBJECTIVE: The aim of this study was to examine the ability of 4 commercially available wearable activity trackers (Fitbits Flex 2, Fitbit Alta HR, Fitbit Charge 2, and Garmin Vívosmart HR), in a controlled setting, to correctly and automatically identify the type and duration of the physical activity being performed. METHODS: A total of 8 activity types, including walking and running (on both a treadmill and outdoors), a run embedded in walking bouts, elliptical use, outdoor biking, and pool lap swimming, were tested by 28 to 34 healthy adult participants (69 total participants who participated in some to all activity types). Actual activity type and duration were recorded by study personnel and compared with tracker data using descriptive statistics and mean absolute percent error (MAPE). RESULTS: The proportion of trials in which the activity type was correctly identified was 93% to 97% (depending on the tracker) for treadmill walking, 93% to 100% for treadmill running, 36% to 62% for treadmill running when preceded and followed by a walk, 97% to 100% for outdoor walking, 100% for outdoor running, 3% to 97% for using an elliptical, 44% to 97% for biking, and 87.5% for swimming. When activities were correctly identified, the MAPE of the detected duration versus the actual activity duration was between 7% and 7.9% for treadmill walking, 8.7% and 144.8% for treadmill running, 23.6% and 28.9% for treadmill running when preceded and followed by a walk, 4.9% and 11.8% for outdoor walking, 5.6% and 9.6% for outdoor running, 9.7% and 13% for using an elliptical, 9.5% and 17.7% for biking, and was 26.9% for swimming. CONCLUSIONS: In a controlled setting, wearable activity trackers provide accurate recognition of the type of some common physical activities, especially outdoor walking and running and walking on a treadmill. The accuracy of measurement of activity duration varied considerably by activity type and tracker model and was poor for complex sets of activity, such as a run embedded within 2 walking segments.

Running patterns for male and female competitive and recreational runners based on accelerometer data.

The purpose of this study was to classify runners in sex-specific groups as either competitive or recreational based on center of mass (CoM) accelerations. Forty-one runners participated in the study (25 male and 16 female), and were labeled as competitive or recreational based on age, sex, and race performance. Three-dimensional acceleration data were collected during a 5-minute treadmill run, and 24 features were extracted. Support vector machine classification models were used to examine the utility of the features in discriminating between competitive and recreational runners within each sex-specific subgroup. Competitive and recreational runners could be classified with 82.63 % and 80.4 % in the male and female models, respectively. Dominant features in both models were related to regularity and variability, with competitive runners exhibiting more consistent running gait patterns, but the specific features were slightly different in each sex-specific model. Therefore, it is important to separate runners into sex-specific competitive and recreational subgroups for future running biomechanical studies. In conclusion, we have demonstrated the ability to analyze running biomechanics in competitive and recreational runners using only CoM acceleration patterns. A runner, clinician, or coach may use this information to monitor how running patterns change as a result of training.

Using wearable sensors to classify subject-specific running biomechanical gait patterns based on changes in environmental weather conditions.

Running-related overuse injuries can result from a combination of various intrinsic (e.g., gait biomechanics) and extrinsic (e.g., running surface) risk factors. However, it is unknown how changes in environmental weather conditions affect running gait biomechanical patterns since these data cannot be collected in a laboratory setting. Therefore, the purpose of this study was to develop a classification model based on subject-specific changes in biomechanical running patterns across two different environmental weather conditions using data obtained from wearable sensors in real-world environments. Running gait data were recorded during winter and spring sessions, with recorded average air temperatures of -10° C and +6° C, respectively. Classification was performed based on measurements of pelvic drop, ground contact time, braking, vertical oscillation of pelvis, pelvic rotation, and cadence obtained from 66,370 strides (~11,000/runner) from a group of recreational runners. A non-linear and ensemble machine learning algorithm, random forest (RF), was used to classify and compute a heuristic for determining the importance of each variable in the prediction model. To validate the developed subject-specific model, two cross-validation methods (one-against-another and partitioning datasets) were used to obtain experimental mean classification accuracies of 87.18% and 95.42%, respectively, indicating an excellent discriminatory ability of the RF-based model. Additionally, the ranked order of variable importance differed across the individual runners. The results from the RF-based machine-learning algorithm demonstrates that processing gait biomechanical signals from a single wearable sensor can successfully detect changes to an individual's running patterns based on data obtained in real-world environments.

Establishing cut-points for physical activity classification using triaxial accelerometer in middle-aged recreational marathoners.

The purpose of this study was to establish GENEA (Gravity Estimator of Normal Everyday Activity) cut-points for discriminating between six relative-intensity activity levels in middle-aged recreational marathoners. Nighty-eight (83 males and 15 females) recreational marathoners, aged 30-45 years, completed a cardiopulmonary exercise test running on a treadmill while wearing a GENEA accelerometer on their non-dominant wrist. The breath-by-breath V̇O2 data was also collected for criterion measure of physical activity categories (sedentary, light, moderate, vigorous, very vigorous and extremely vigorous). GENEA cut-points for physical activity classification was performed via Receiver Operating Characteristic (ROC) analysis. Spearman's correlation test was applied to determine the relationship between estimated and measured intensity classifications. Statistical analysis were done for all individuals, and separating samples by sex. The GENEA cut-points established were able to distinguish between all six-relative intensity levels with an excellent classification accuracy (area under the ROC curve (AUC) values between 0.886 and 0.973) for all samples. When samples were separated by sex, AUC values were 0.881-0.973 and 0.924-0.968 for males and females, respectively. The total variance in energy expenditure explained by GENEA accelerometer data was 78.50% for all samples, 78.14% for males, and 83.17% for females. In conclusion, the wrist-worn GENEA accelerometer presents a high capacity of classifying the intensity of physical activity in middle-aged recreational marathoners when examining all samples together, as well as when sample set was separated by sex. This study suggests that the triaxial GENEA accelerometers (worn on the non-dominant wrist) can be used to predict energy expenditure for running activities.

Classifying running speed conditions using a single wearable sensor: Optimal segmentation and feature extraction methods.

Accelerometers have been used to classify running patterns, but classification accuracy and computational load depends on signal segmentation and feature extraction. Stride-based segmentation relies on identifying gait events, a step avoided by using window-based segmentation. For each segment, discrete points can be extracted from the accelerometer signal, or advanced features can be computed. Therefore, the purpose of this study was to examine how different segmentation and feature extraction methods influence the accuracy and computational load of classifying running conditions. Forty-four runners ran at their preferred speed and 25% faster than preferred while an accelerometer at the lower back recorded 3D accelerations. Computational load was determined as the accelerometer signal was segmented into single and five strides, and corresponding small and large windows, with discrete points extracted from the single stride segments and advanced features computed from all four segment types. Each feature set was used to classify speed conditions and classification accuracy was recorded. Computational load and classification accuracy were compared across all feature sets using a repeated-measures MANOVA, with follow-up t-tests to compare feature type (discrete vs. advanced), segmentation method (stride- vs. window-based), and segment size (small vs. large), using a Bonferroni-adjusted α = 0.003. The five-stride (97.49 (±4.57)%) and large-window advanced (97.23 (±5.51)%) feature sets produced the greatest classification accuracy, but the large-window advanced feature set had a lower computational load (0.0041 (±0.0002)s) than the stride-based feature sets. Therefore, using a few advanced features and large overlapping window sizes yields the best performance of both classification accuracy and computational load.

Evaluating the predictability of distance race performance in NCAA cross country and track and field from high school race times in the United States.

Successful recruiting for collegiate track & field athletes has become a more competitive and essential component of coaching. This study aims to determine the relationship between race performances of distance runners at the United States high school and National Collegiate Athletic Association (NCAA) levels. Conditional inference classification tree models were built and analysed to predict the probability that runners would qualify for the NCAA Division I National Cross Country Meet and/or the East or West NCAA Division I Outdoor Track & Field Preliminary Round based on their high school race times in the 800 m, 1600 m, and 3200 m. Prediction accuracies of the classification trees ranged from 60.0 to 76.6 percent. The models produced the most reliable estimates for predicting qualifiers in cross country, the 1500 m, and the 800 m for females and cross country, the 5000 m, and the 800 m for males. NCAA track & field coaches can use the results from this study as a guideline for recruiting decisions. Additionally, future studies can apply the methodological foundations of this research to predicting race performances set at different metrics, such as national meets in other countries or Olympic qualifications, from previous race data.

Aerial and Terrestrial Patterns: A Novel Approach to Analyzing Human Running.

Biomechanical parameters are often analyzed independently, although running gait is a dynamic system wherein changes in one parameter are likely to affect another. Accordingly, the Volodalen® method provides a model for classifying running patterns into 2 categories, aerial and terrestrial, using a global subjective rating scoring system. We aimed to validate the Volodalen® method by verifying whether the aerial and terrestrial patterns, defined subjectively by a running coach, were associated with distinct objectively-measured biomechanical parameters. The running patterns of 91 individuals were assessed subjectively using the Volodalen® method by an expert running coach during a 10-min running warm-up. Biomechanical parameters were measured objectively using the OptojumpNext® during a 50-m run performed at 3.3, 4.2, and 5 m·s(-1) and were compared between aerial- and terrestrial-classified subjects. Longer contact times and greater leg compression were observed in the terrestrial compared to the aerial runners. The aerial runners exhibited longer flight time, greater center of mass displacement, maximum vertical force and leg stiffness than the terrestrial ones. The subjective categorization of running patterns was associated with distinct objectively-quantified biomechanical parameters. Our results suggest that a subjective holistic assessment of running patterns provides insight into the biomechanics of running gaits of individuals.

Sprint mechanics in world-class athletes: a new insight into the limits of human locomotion.

The objective of this study was to characterize the mechanics of maximal running sprint acceleration in high-level athletes. Four elite (100-m best time 9.95-10.29 s) and five sub-elite (10.40-10.60 s) sprinters performed seven sprints in overground conditions. A single virtual 40-m sprint was reconstructed and kinetics parameters were calculated for each step using a force platform system and video analyses. Anteroposterior force (FY), power (PY), and the ratio of the horizontal force component to the resultant (total) force (RF, which reflects the orientation of the resultant ground reaction force for each support phase) were computed as a function of velocity (V). FY-V, RF-V, and PY-V relationships were well described by significant linear (mean R(2) of 0.892 ± 0.049 and 0.950 ± 0.023) and quadratic (mean R(2) = 0.732 ± 0.114) models, respectively. The current study allows a better understanding of the mechanics of the sprint acceleration notably by modeling the relationships between the forward velocity and the main mechanical key variables of the sprint. As these findings partly concern world-class sprinters tested in overground conditions, they give new insights into some aspects of the biomechanical limits of human locomotion.

Joints and their relations as critical features in action discrimination: evidence from a classification image method.

Classifying an action as a runner or a walker is a seemingly effortless process. However, it is difficult to determine which features are used with hypothesis-driven research, because biological motion stimuli generally consist of about a dozen joints, yielding an enormous number of potential relationships among them. Here, we develop a hypothesis-free approach based on a classification image method, using experimental data from relatively few trials (∼1,000 trials per subject). Employing ambiguous actions morphed between a walker and a runner, we identified three types of features that play important roles in discriminating bipedal locomotion presented in a side view: (a) critical joint feature, supported by the finding that the similarity of the movements of feet and wrists to prototypical movements of these joints were most reliably used across all participants; (b) structural features, indicated by contributions from almost all other joints, potentially through a form-based analysis; and (c) relational features, revealed by statistical correlations between joint contributions, specifically relations between the two feet, and relations between the wrists/elbow and the hips. When the actions were inverted, only critical joint features remained to significantly influence discrimination responses. When actions were presented with continuous depth rotation, critical joint features and relational features associated strongly with responses. Using a double-pass paradigm, we estimated that the internal noise is about twice as large as the external noise, consistent with previous findings. Overall, our novel design revealed a rich set of critical features that are used in action discrimination. The visual system flexibly selects a subset of features depending on viewing conditions.

The effect of prior exercise intensity on oxygen uptake kinetics during high-intensity running exercise in trained subjects.

PURPOSE: The aim of this study was to compare the effects of two different kinds of prior exercise protocols [continuous exercise (CE) versus intermittent repeated sprint (IRS)] on oxygen uptake (VO2) kinetics parameters during high-intensity running. METHODS: Thirteen male amateur futsal players (age 22.8 ± 6.1 years; mass 76.0 ± 10.2 kg; height 178.7 ± 6.6 cm; VO2max 58.1 ± 4.5 mL kg(-1) min(-1)) performed a maximal incremental running test for the determination of the gas exchange threshold (GET) and maximal VO2 (VO2max). On two different days, the subjects completed a 6-min bout of high-intensity running (50 % ∆) on a treadmill that was 6-min after (1) an identical bout of high-intensity exercise (from control to CE), and (2) a protocol of IRS (6 × 40 m). RESULT: We found significant differences between CE and IRS for the blood lactate concentration ([La]; 6.1 versus 10.7 mmol L(-1), respectively), VO2 baseline (0.74 versus 0.93 L min(-1), respectively) and the heart rate (HR; 102 versus 124 bpm, respectively) before the onset of high-intensity exercise. However, both prior CE and prior IRS significantly increased the absolute primary VO2 amplitude (3.77 and 3.79 L min(-1), respectively, versus control 3.54 L min(-1)), reduced the amplitude of the VO2 slow component (0.26 and 0.21 L min(-1), respectively, versus control 0.50 L min(-1)), and decreased the mean response time (MRT; 28.9 and 28.0 s, respectively, versus control 36.9 s) during subsequent bouts. CONCLUSION: This study showed that different protocols and intensities of prior exercise trigger similar effects on VO2 kinetics during high-intensity running.

Peak blood lactate parameters in athletes of different running events during low-intensity recovery after ramp-type protocol.

The aims of this study were to explore the differences in the peak blood lactate concentration (La(peak)) and time to reach Lapeak during low-intensity recovery after an all-out treadmill ramp test in runners of 4 diverse running disciplines and different training regimes and to identify the most opportune sampling time to determine Lapeak in these athlete groups. The participants were 48 Croatian national-level male track runners (10 sprinters [S], fifteen 400-m runners [S4], 10 middle-distance [MD] runners and 13 long-distance [LD] runners). The runners performed an incremental treadmill protocol until volitional exhaustion, with the inclination of 1.5%, and speed increments of 1 km·h(-1) every 60 seconds. During recovery, they walked at 5 km·h(-1) for 5 minutes. Fingertip capillary blood samples were collected at the end of the first, third, and fifth minute of recovery. Peak blood lactate concentration in LD was significantly lower than in runners of other disciplines (10.9 ± 2.4 mmol·L(-1) for LD; 13.7 ± 2.9 for MD; 14.0 ± 1.4 for S4; 15.1 ± 2.7 for S) (p < 0.01). No significant difference was observed among other groups. There was no significant difference in tLa(peak) among the 4 participant groups. In conclusion, LD had a significantly lower La(peak) in comparison to other runners; runners of all disciplines reached La(peak) within the first 2 minutes of recovery.

Acute hamstring strain injury in track-and-field athletes: A 3-year observational study at the Penn Relay Carnival.

This study aimed to observe the incidence rates of hamstring strain injuries (HSIs) across different competition levels and ages during the Penn Relays Carnival. Over a 3-year period, all injuries treated by the medical staff were recorded. The type of injury, anatomic location, event in which the injury occurred, competition level, and demographic data were documented. Absolute and relative HSI (per 1000 participants) were determined, and odds ratios (ORs) were calculated between sexes, competition levels, and events. Throughout the study period 48,473 athletes registered to participate in the Penn Relays Carnival, with 118 HSIs treated by the medical team. High school girls displayed lesser risk of HSI than high school boys (OR = 0.55, P = 0.021), and masters athletes were more likely than high school- (OR = 4.26, P < 0.001) and college-level (OR = 3.55, P = 0.001) athletes to suffer HSI. The 4 × 400-m relay displayed a greater likelihood of HSI compared with the 4 × 100-m relay (OR = 1.77, P = 0.008). High school boys and masters-level athletes are most likely to suffer HSI, and there is higher risk in 400-m events compared with 100-m events.

Gait-specific metabolic costs and preferred speeds in ring-tailed lemurs (Lemur catta), with implications for the scaling of locomotor costs.

Metabolic costs of resting and locomotion have been used to gain novel insights into the behavioral ecology and evolution of a wide range of primates; however, most previous studies have not considered gait-specific effects. Here, metabolic costs of ring-tailed lemurs (Lemur catta) walking, cantering and galloping are used to test for gait-specific effects and a potential correspondence between costs and preferred speeds. Metabolic costs, including the net cost of locomotion (COL) and net cost of transport (COT), change as a curvilinear function of walking speed and (at least provisionally) as a linear function of cantering and galloping speeds. The baseline quantity used to calculate net costs had a significant effect on the magnitude of speed-specific estimates of COL and COT, especially for walking. This is because non-locomotor metabolism constitutes a substantial fraction (41-61%, on average) of gross metabolic rate at slow speeds. The slope-based estimate of the COT was 5.26 J kg(-1) m(-1) for all gaits and speeds, while the gait-specific estimates differed between walking (0.5 m s(-1) : 6.69 J kg(-1) m(-1) ) and cantering/galloping (2.0 m s(-1) : 5.61 J kg(-1) m(-1) ). During laboratory-based overground locomotion, ring-tailed lemurs preferred to walk at ~0.5 m s(-1) and canter/gallop at ~2.0 m s(-1) , with the preferred walking speed corresponding well to the COT minima. Compared with birds and other mammals, ring-tailed lemurs are relatively economical in walking, cantering, and galloping. These results support the view that energetic optima are an important movement criterion for locomotion in ring-tailed lemurs, and other terrestrial animals.