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From a health and performance-related perspective, it is crucial to evaluate subjective symptoms and objective signs of acute training-induced immunological responses in young athletes. The limited number of available studies focused on immunological adaptations following aerobic training. Hardly any studies have been conducted on resistance-training induced stress responses. Therefore, the aim of this observational study was to investigate subjective symptoms and objective signs of immunological stress responses following resistance training in young athletes. Fourteen (7 females and 7 males) track and field athletes with a mean age of 16.4 years and without any symptoms of upper or lower respiratory tract infections participated in this study. Over a period of 7 days, subjective symptoms using the Acute Recovery and Stress Scale (ARSS) and objective signs of immunological responses using capillary blood markers were taken each morning and after the last training session. Differences between morning and evening sessions and associations between subjective and objective parameters were analyzed using generalized estimating equations (GEE). In post hoc analyses, daily change-scores of the ARSS dimensions were compared between participants and revealed specific changes in objective capillary blood samples. In the GEE models, recovery (ARSS) was characterized by a significant decrease while stress (ARSS) showed a significant increase between morning and evening-training sessions. A concomitant increase in white blood cell count (WBC), granulocytes (GRAN) and percentage shares of granulocytes (GRAN%) was found between morning and evening sessions. Of note, percentage shares of lymphocytes (LYM%) showed a significant decrease. Furthermore, using multivariate regression analyses, we identified that recovery was significantly associated with LYM%, while stress was significantly associated with WBC and GRAN%. Post hoc analyses revealed significantly larger increases in participants' stress dimensions who showed increases in GRAN%. For recovery, significantly larger decreases were found in participants with decreases in LYM% during recovery. More specifically, daily change-scores of the recovery and stress dimensions of the ARSS were associated with specific changes in objective immunological markers (GRAN%, LYM%) between morning and evening-training sessions. Our results indicate that changes of subjective symptoms of recovery and stress dimensions using the ARSS were associated with specific changes in objectively measured immunological markers.
Effects of Drop Height on Jump Performance in Male and Female Elite Adolescent Handball Players
(2019)
Purpose: To examine the effects of drop height on drop-jump (DJ) performance and on associations between DJ and horizontal-jump/sprint performances in adolescent athletes. Methods: Male (n = 119, 2.5 [0.6] y post-peak-height velocity) and female (n = 120, 2.5 [0.5] y post-peak-height velocity) adolescent handball players (national level) performed DJs in randomized order using 3 drop heights (20, 35, and 50 cm). DJ performance (jump height, reactive strength index [RSI]) was analyzed using the Optojump Next system. In addition, correlations were computed between DJ height and RSI with standing-long-jump and 20-m linear-sprint performances. Results: Statistical analyses revealed medium-size main effects of drop height for DJ height and RSI (P <.001, 0.63 <= d <= 0.71). Post hoc tests indicated larger DJ heights from 20 to 35 and 35 to 50 cm (P <=.031, 0.33 <= d <= 0.71) and better RSI from 20- to 35-cm drop height (P <.001, d = 0.77). No significant difference was found for RSI between 35- and 50-cm drop height. Irrespective of drop height, associations of DJ height and RSI were small with 5-m-split time (-.27 <= r <=.05), medium with 10-m-split time (-.44 <= r <=.14), and medium to large with 20-m sprint time and standing-long-jump distance (-.57 <= r <=.22). Conclusions: The present findings indicate that, irrespective of sex, 35-cm drop heights are best suited to induce rapid and powerful DJ performance (ie, RSI) during reactive strength training in elite adolescent handball players. Moreover, training-related gains in DJ performance may at least partly translate to gains in horizontal jump and longer sprint distances (ie, >= 20-m) and/or vice versa in male and female elite adolescent athletes, irrespective of drop height.
Gait analysis is an important tool for the early detection of neurological diseases and for the assessment of risk of falling in elderly people. The availability of low-cost camera hardware on the market today and recent advances in Machine Learning enable a wide range of clinical and health-related applications, such as patient monitoring or exercise recognition at home. In this study, we evaluated the motion tracking performance of the latest generation of the Microsoft Kinect camera, Azure Kinect, compared to its predecessor Kinect v2 in terms of treadmill walking using a gold standard Vicon multi-camera motion capturing system and the 39 marker Plug-in Gait model. Five young and healthy subjects walked on a treadmill at three different velocities while data were recorded simultaneously with all three camera systems. An easy-to-administer camera calibration method developed here was used to spatially align the 3D skeleton data from both Kinect cameras and the Vicon system. With this calibration, the spatial agreement of joint positions between the two Kinect cameras and the reference system was evaluated. In addition, we compared the accuracy of certain spatio-temporal gait parameters, i.e., step length, step time, step width, and stride time calculated from the Kinect data, with the gold standard system. Our results showed that the improved hardware and the motion tracking algorithm of the Azure Kinect camera led to a significantly higher accuracy of the spatial gait parameters than the predecessor Kinect v2, while no significant differences were found between the temporal parameters. Furthermore, we explain in detail how this experimental setup could be used to continuously monitor the progress during gait rehabilitation in older people.
TRIPOD
(2021)
Inertial measurement units (IMUs) enable easy to operate and low-cost data recording for gait analysis. When combined with treadmill walking, a large number of steps can be collected in a controlled environment without the need of a dedicated gait analysis laboratory. In order to evaluate existing and novel IMU-based gait analysis algorithms for treadmill walking, a reference dataset that includes IMU data as well as reliable ground truth measurements for multiple participants and walking speeds is needed. This article provides a reference dataset consisting of 15 healthy young adults who walked on a treadmill at three different speeds. Data were acquired using seven IMUs placed on the lower body, two different reference systems (Zebris FDMT-HQ and OptoGait), and two RGB cameras. Additionally, in order to validate an existing IMU-based gait analysis algorithm using the dataset, an adaptable modular data analysis pipeline was built. Our results show agreement between the pressure-sensitive Zebris and the photoelectric OptoGait system (r = 0.99), demonstrating the quality of our reference data. As a use case, the performance of an algorithm originally designed for overground walking was tested on treadmill data using the data pipeline. The accuracy of stride length and stride time estimations was comparable to that reported in other studies with overground data, indicating that the algorithm is equally applicable to treadmill data. The Python source code of the data pipeline is publicly available, and the dataset will be provided by the authors upon request, enabling future evaluations of IMU gait analysis algorithms without the need of recording new data.