Background: Video analysis (VA) is commonly used in the assessment of sports injuries and has received considerable research interest. Until now, no tool has been available for the assessment of study quality. Therefore, the objective of this study was to develop and evaluate a valid instrument that reliably assesses the methodological quality of VA studies. Methods: The Quality Appraisal for Sports Injury Video Analysis Studies (QA-SIVAS) scale was developed using a modified Delphi approach including expert consensus and pilot testing. Reliability was examined through intraclass correlation coefficient (ICC_3,1) and free-marginal kappa statistics by three independent raters. Construct validity was investigated by comparing QA-SIVAS with expert ratings by using Kendall’s tau analysis. Rating time was studied by applying the scale to 21 studies and computing the mean time for rating per study article. Results: The QA-SIVAS scale consists of an 18-item checklist addressing the study design, data source, conduct, report, and discussion of VA studies in sports injury research. Inter- and intra-rater reliability were excellent with ICCs > 0.97. Expert ratings revealed a high construct validity (0.71; p < 0.001). Mean rating time was 10 ± 2 min per article. Conclusion: QA-SIVAS is a reliable and valid instrument that can be easily applied to sports injury research. Future studies in the field of VA should adhere to standardized methodological criteria and strict quality guidelines.

Background: Running is a very popular sport among both recreational and competitive athletes. However, participating in running is associated with a comparably high risk of sustaining an exercise-related injury. Due to the often multifactorial and individual reasons for running injuries, a shift in thinking is required to account for the dynamic process of the various risk factors. Therefore, a machine learning approach will be used to comprehensively analyze biomechanical, biological, and loading parameters in order to identify risk factors and to detect risk patterns in runners. Methods: The prospective longitudinal cohort study will include competitive adult athletes, running at least 20 km per week and being free of injuries three months before the start of the study. At baseline and the end of the study period, subjective questionnaires (demographics, injury history, sports participation, menstruation, medication, psychology), biomechanical measures (e.g., stride length, cadence, kinematics, kinetics, tibial shock, and tibial acceleration) and a medical examination (BMI, laboratory: blood count, creatinine, calcium, phosphate, parathyroid hormone, vitamin D, osteocalcin, bone-specific alkaline phosphatase, DPD cross-links) will be performed. During the study period (one season), continuous data collection will be performed for biomechanical parameters, injuries, internal and external load. Statistical analysis of the data is performed using machine learning (ML) methods. For this purpose, the correlation of the collected data to possible injuries is automatically learned by an ML model and from this, a ranking of the risk factors can be determined with the help of sensitivity analysis methods. Discussion: To achieve a comprehensive risk reduction of injuries in runners, a multifactorial and individual approach and analysis is necessary. Recently, the use of ML processes for the analysis of risk factors in sports was discussed and positive results have been published. This study will be the first prospective longitudinal cohort study in runners to investigate the association of biomechanical, bone health, and loading parameters as well as injuries via ML models. The results may help to predict the risk of sustaining an injury and give way for new analysis methods that may also be transferred to other sports. Trial registration: DRKS00026904 (German Clinical Trial Register DKRS), date of registration 18.10.2021.

Running stability is the ability to withstand naturally occurring minor perturbations during running. It is susceptible to external and internal running conditions such as footwear or fatigue. However, both its reliable measurability and the extent to which laboratory measurements reflect outdoor running remain unclear. This study aimed to evaluate the intra- and inter-day reliability of the running stability as well as the comparability of different laboratory and outdoor conditions. Competitive runners completed runs on a motorized treadmill in a research laboratory and overground both indoors and outdoors. Running stability was determined as the maximum short-term divergence exponent from the raw gyroscope signals of wearable sensors mounted to four different body locations (sternum, sacrum, tibia, and foot). Sacrum sensor measurements demonstrated the highest reliabilities (good to excellent; ICC = 0.85 to 0.91), while those of the tibia measurements showed the lowest (moderate to good; ICC = 0.55 to 0.89). Treadmill measurements depicted systematically lower values than both overground conditions for all sensor locations (relative bias = −9.8% to −2.9%). The two overground conditions, however, showed high agreement (relative bias = −0.3% to 0.5%; relative limits of agreement = 9.2% to 15.4%). Our results imply moderate to excellent reliability for both overground and treadmill running, which is the foundation of further research on running stability.

Purpose: This study aimed to compare running economy across habituated and nonhabituated advanced footwear technology (AFT) in trained long-distance runners. Methods: A total of 16 participants completed up to six 5-minute trials in 1 to 3 pairs of their own habituated shoes and 3 different and standardized AFTs at individual marathon pace. We measured oxygen uptake and carbon dioxide production and expressed running economy as oxygen uptake (in milliliters oxygen per kilogram per minute), oxygen cost of transport (oxygen per kilogram per minute), energetic cost (in watts per kilogram), and energetic cost of transport (in joules per kilogram per kilometer). We used linear mixed-effect models to evaluate differences. Relative shoe weight and shoe mileage (distance worn during running) were covariates. Results: Forty-eight standardized and 29 individual AFT conditions were measured (mileage 117.0 [128.8] km, range 0–522 km; 25 habituated 135.7 [129.2] km, range 20–522 km; 4 nonhabituated 0 [0] km, range 0–0 km). Rating of perceived exertion, blood [La], and respiratory exchange ratio ranged from 9 to 15, 1.11 to 4.54 mmol/L, and 0.76 to 1.01. There was no effect for habituation on energetic cost of transport (t_habituation = −.232, P = .409, b = −0.006; 95% CI, −0.058 to 0.046) or other running economy metrics. Neither shoe weight nor shoe mileage had an effect. Conclusions: Our results suggest that habituation to AFTs does not result in greater benefits in the use of AFTs. This means that implementation in training may not be needed, even if we cannot rule out any other possible benefits of habituation at this stage, such as adaptation of the musculoskeletal system.

Aim was to evaluate alterations of the non‐linear short‐term scaling exponent alpha1 of detrended fluctuation analysis (DFAa1) of heart rate (HR) variability (HRV) as a sensitive marker for assessing global physiological demands during multiple running intervals. As a secondary analysis, agreement of ECG‐derived respiratory frequency (EDR) compared to respiratory frequency (RF) derived from the metabolic cart was evaluated with the same chest belt device. Fifteen trained female and male long‐distance runners completed four running bouts over 5 min on a treadmill at marathon pace. During the last 3 min of each bout gas exchange data and a single‐channel ECG for the determination of HR, DFAa1 of HRV, EDR and RF were analyzed. Additionally, blood lactate concentration (BLC) was determined and rating of perceived exertion (RPE) was requested. DFAa1, oxygen consumption, BLC, and RPE showed stable behaviors comparing the running intervals. Only HR (p < 0.001, d = 0.17) and RF (p = 0.012, d = 0.20) indicated slight increases with small effect sizes. In addition, results point towards inter‐individual differences in all internal load metrics. The comparison of EDR with RF during running revealed high correlations (r = 0.80, p < 0.001, ICC_3,1 = 0.87) and low mean differences (1.8 ± 4.4 breaths/min), but rather large limits of agreement with 10.4 to −6.8 breaths/min. Results show the necessity of EDR methodology improvement before being used in a wide range of individuals and sports applications. Relationship of DFAa1 to other internal load metrics, including RF, in quasi‐steady‐state conditions bears the potential for further evaluation of exercise prescription and may enlighten decoupling mechanisms during prolonged exercise bouts.

Lower body positive pressure treadmills (LBPPTs) offer precise body weight unloading for injury rehabilitation and performance training in runners. This study investigated biomechanical changes during running at varying body weight support (BWS) levels (0%–80%) in competitive runners, including sex‐specific responses. Twenty‐six runners (age: 33.6 ± 9.8 years; 15 female, 11 male) completed randomized 3‐min running bouts at 12 km/h across nine BWS levels. Spatiotemporal parameters, plantar force, peak tibial acceleration, and running stability were measured using pressure insoles and inertial sensors placed at the tibia and foot. Our results revealed significant reductions in step rate (b = −0.24 steps•min⁻¹/%BWS, p < 0.001), normalized ground contact time (b = −0.001 1/%BWS, p < 0.001), maximum plantar force (b = −0.010 BW/%BWS, p < 0.001), and peak tibial acceleration (b = −0.03 g/%BWS, p < 0.001) with increased BWS. Swing time increased (b = 1.50 ms/%BWS, p < 0.001), while stance time decreased (b = −0.41 ms/%BWS, p < 0.001). Running stability showed marginal changes (foot: b = −0.001 1/%BWS, p = 0.017; tibia: b = 0.001 1/%BWS, p = 0.009). Sex differences were observed in step rate (b = −6.79 steps•min⁻¹, p = 0.045) and maximum plantar force (b = −0.128 BW, p = 0.034), but there were no significant sex × BWS interaction effects for any of the investigated parameters. Findings from this study highlight the effectiveness of LBPPTs for reducing musculoskeletal loading while revealing associated gait changes. Athletes, therapists, and coaches should consider individual biomechanical responses to optimize rehabilitation and performance strategies. Future research should explore long‐term adaptations and injury prevention outcomes.