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Symptoms of anxiety and depression in young athletes using the hospital anxiety and depression scale
(2018)
Elite young athletes have to cope with multiple psychological demands such as training volume, mental and physical fatigue, spatial separation of family and friends or time management problems may lead to reduced mental and physical recovery. While normative data regarding symptoms of anxiety and depression for the general population is available (Hinz and Brahler, 2011), hardly any information exists for adolescents in general and young athletes in particular. Therefore, the aim of this study was to assess overall symptoms of anxiety and depression in young athletes as well as possible sex differences. The survey was carried out within the scope of the study "Resistance Training in Young Athletes" (KINGS-Study). Between August 2015 and September 2016, 326 young athletes aged (mean +/- SD) 14.3 +/- 1.6 years completed the Hospital Anxiety and Depression Scale (HAD Scale). Regarding the analysis of age on the anxiety and depression subscales, age groups were classified as follows: late childhood (12-14 years) and late adolescence (15-18 years). The participating young athletes were recruited from Olympic weight lifting, handball, judo, track and field athletics, boxing, soccer, gymnastics, ice speed skating, volleyball, and rowing. Anxiety and depression scores were (mean +/- SD) 4.3 +/- 3.0 and 2.8 +/- 2.9, respectively. In the subscale anxiety, 22 cases (6.7%) showed subclinical scores and 11 cases (3.4%) showed clinical relevant score values. When analyzing the depression subscale, 31 cases (9.5%) showed subclinical score values and 12 cases (3.7%) showed clinically important values. No significant differences were found between male and female athletes (p >= 0.05). No statistically significant differences in the HADS scores were found between male athletes of late childhood and late adolescents (p >= 0.05). To the best of our knowledge, this is the first report describing questionnaire based indicators of symptoms of anxiety and depression in young athletes. Our data implies the need for sports medical as well as sports psychiatric support for young athletes. In addition, our results demonstrated that the chronological classification concerning age did not influence HAD Scale outcomes. Future research should focus on sports medical and sports psychiatric interventional approaches with the goal to prevent anxiety and depression as well as teaching coping strategies to young athletes.
Background: Dynamic balance keeps the vertical projection of the center of mass within the base of support while walking. Dynamic balance tests are used to predict the risks of falls and eventual falls. The psychometric properties of most dynamic balance tests are unsatisfactory and do not comprise an actual loss of balance while walking. Objectives: Using beam walking distance as a measure of dynamic balance, the BEAM consortium will determine the psychometric properties, lifespan and patient reference values, the relationship with selected “dynamic balance tests,” and the accuracy of beam walking distance to predict falls. Methods: This cross-sectional observational study will examine healthy adults in 7 decades (n = 432) at 4 centers. Center 5 will examine patients (n = 100) diagnosed with Parkinson’s disease, multiple sclerosis, stroke, and balance disorders. In test 1, all participants will be measured for demographics, medical history, muscle strength, gait, static balance, dynamic balance using beam walking under single (beam walking only) and dual task conditions (beam walking while concurrently performing an arithmetic task), and several cognitive functions. Patients and healthy participants age 50 years or older will be additionally measured for fear of falling, history of falls, miniBESTest, functional reach on a force platform, timed up and go, and reactive balance. All participants age 50 years or older will be recalled to report fear of falling and fall history 6 and 12 months after test 1. In test 2, seven to ten days after test 1, healthy young adults and age 50 years or older (n = 40) will be retested for reliability of beam walking performance. Conclusion: We expect to find that beam walking performance vis-à-vis the traditionally used balance outcomes predicts more accurately fall risks and falls. Clinical Trial Registration Number: NCT03532984.
Postural control is important to cope with demands of everyday life. It has been shown that both attentional demand (i.e., cognitive processing) and fatigue affect postural control in young adults. However, their combined effect is still unresolved. Therefore, we investigated the effects of fatigue on single-(ST) and dual-task (DT) postural control. Twenty young subjects (age: 23.7 +/- 2.7) performed an all-out incremental treadmill protocol. After each completed stage, one-legged-stance performance on a force platform under ST (i.e., one-legged-stance only) and DT conditions (i.e., one-legged-stance while subtracting serial 3s) was registered. On a second test day, subjects conducted the same balance tasks for the control condition (i.e., non-fatigued). Results showed that heart rate, lactate, and ventilation increased following fatigue (all p < 0.001; d = 4.2-21). Postural sway and sway velocity increased during DT compared to ST (all p < 0.001; d = 1.9-2.0) and fatigued compared to non-fatigued condition (all p < 0.001; d = 3.3-4.2). In addition, postural control deteriorated with each completed stage during the treadmill protocol (all p < 0.01; d = 1.9-3.3). The addition of an attention-demanding interference task did not further impede one-legged-stance performance. Although both additional attentional demand and physical fatigue affected postural control in healthy young adults, there was no evidence for an overadditive effect (i.e., fatigue-related performance decrements in postural control were similar under ST and DT conditions). Thus, attentional resources were sufficient to cope with the DT situations in the fatigue condition of this experiment.
Effects of Backpack Carriage on Dual-Task Performance in Children During Standing and Walking
(2016)
During the stages of long-term athlete development (LTAD), resistance training (RT) is an important means for (i) stimulating athletic development, (ii) tolerating the demands of long-term training and competition, and (iii) inducing long-term health promoting effects that are robust over time and track into adulthood. However, there is a gap in the literature with regards to optimal RT methods during LTAD and how RT is linked to biological age. Thus, the aims of this scoping review were (i) to describe and discuss the effects of RT on muscular fitness and athletic performance in youth athletes, (ii) to introduce a conceptual model on how to appropriately implement different types of RT within LTAD stages, and (iii) to identify research gaps from the existing literature by deducing implications for future research. In general, RT produced small -to -moderate effects on muscular fitness and athletic performance in youth athletes with muscular strength showing the largest improvement. Free weight, complex, and plyometric training appear to be well -suited to improve muscular fitness and athletic performance. In addition, balance training appears to be an important preparatory (facilitating) training program during all stages of LTAD but particularly during the early stages. As youth athletes become more mature, specificity, and intensity of RT methods increase. This scoping review identified research gaps that are summarized in the following and that should be addressed in future studies: (i) to elucidate the influence of gender and biological age on the adaptive potential following RT in youth athletes (especially in females), (ii) to describe RT protocols in more detail (i.e., always report stress and strain based parameters), and (iii) to examine neuromuscular and tendomuscular adaptations following RT in youth athletes.
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.
Objectives
The aims of this study were to investigate the effects of a six-week in-season period of soccer training and games (congested period) on plasma volume variations (PV), hematological parameters, and physical fitness in elite players. In addition, we analyzed relationships between training load, hematological parameters and players’ physical fitness.
Methods
Eighteen elite players were evaluated before (T1) and after (T2) a six-week in-season period interspersed with 10 soccer matches. At T1 and T2, players performed the Yo-Yo intermittent recovery test level 1 (YYIR1), the repeated shuttle sprint ability test (RSSA), the countermovement jump test (CMJ), and the squat jump test (SJ). In addition, PV and hematological parameters (erythrocytes [M/mm3], hematocrit [%], hemoglobin [g/dl], mean corpuscular volume [fl], mean corpuscular hemoglobin content [pg], and mean hemoglobin concentration [%]) were assessed. Daily ratings of perceived exertion (RPE) were monitored in order to quantify the internal training load.
Results
From T1 to T2, significant performance declines were found for the YYIR1 (p<0.001, effect size [ES] = 0.5), RSSA (p<0.01, ES = 0.6) and SJ tests (p< 0.046, ES = 0.7). However, no significant changes were found for the CMJ (p = 0.86, ES = 0.1). Post-exercise, RSSA blood lactate (p<0.012, ES = 0.2) and PV (p<0.01, ES = 0.7) increased significantly from T1 to T2. A significant decrease was found from T1 to T2 for the erythrocyte value (p<0.002, ES = 0.5) and the hemoglobin concentration (p<0.018, ES = 0.8). The hematocrit percentage rate was also significantly lower (p<0.001, ES = 0.6) at T2. The mean corpuscular volume, mean corpuscular hemoglobin content and the mean hemoglobin content values were not statistically different from T1 to T2. No significant relationships were detected between training load parameters and percentage changes of hematological parameters. However, a significant relationship was observed between training load and changes in RSSA performance (r = -0.60; p<0.003).
Conclusions
An intensive period of “congested match play” over 6 weeks significantly compromised players’ physical fitness. These changes were not related to hematological parameters, even though significant alterations were detected for selected measures.
Objectives
The aims of this study were to investigate the effects of a six-week in-season period of soccer training and games (congested period) on plasma volume variations (PV), hematological parameters, and physical fitness in elite players. In addition, we analyzed relationships between training load, hematological parameters and players’ physical fitness.
Methods
Eighteen elite players were evaluated before (T1) and after (T2) a six-week in-season period interspersed with 10 soccer matches. At T1 and T2, players performed the Yo-Yo intermittent recovery test level 1 (YYIR1), the repeated shuttle sprint ability test (RSSA), the countermovement jump test (CMJ), and the squat jump test (SJ). In addition, PV and hematological parameters (erythrocytes [M/mm3], hematocrit [%], hemoglobin [g/dl], mean corpuscular volume [fl], mean corpuscular hemoglobin content [pg], and mean hemoglobin concentration [%]) were assessed. Daily ratings of perceived exertion (RPE) were monitored in order to quantify the internal training load.
Results
From T1 to T2, significant performance declines were found for the YYIR1 (p<0.001, effect size [ES] = 0.5), RSSA (p<0.01, ES = 0.6) and SJ tests (p< 0.046, ES = 0.7). However, no significant changes were found for the CMJ (p = 0.86, ES = 0.1). Post-exercise, RSSA blood lactate (p<0.012, ES = 0.2) and PV (p<0.01, ES = 0.7) increased significantly from T1 to T2. A significant decrease was found from T1 to T2 for the erythrocyte value (p<0.002, ES = 0.5) and the hemoglobin concentration (p<0.018, ES = 0.8). The hematocrit percentage rate was also significantly lower (p<0.001, ES = 0.6) at T2. The mean corpuscular volume, mean corpuscular hemoglobin content and the mean hemoglobin content values were not statistically different from T1 to T2. No significant relationships were detected between training load parameters and percentage changes of hematological parameters. However, a significant relationship was observed between training load and changes in RSSA performance (r = -0.60; p<0.003).
Conclusions
An intensive period of “congested match play” over 6 weeks significantly compromised players’ physical fitness. These changes were not related to hematological parameters, even though significant alterations were detected for selected measures.
How We Found Our IMU
(2020)
Inertial measurement units (IMUs) are commonly used for localization or movement tracking in pervasive healthcare-related studies, and gait analysis is one of the most often studied topics using IMUs. The increasing variety of commercially available IMU devices offers convenience by combining the sensor modalities and simplifies the data collection procedures. However, selecting the most suitable IMU device for a certain use case is increasingly challenging. In this study, guidelines for IMU selection are proposed. In particular, seven IMUs were compared in terms of their specifications, data collection procedures, and raw data quality. Data collected from the IMUs were then analyzed by a gait analysis algorithm. The difference in accuracy of the calculated gait parameters between the IMUs could be used to retrace the issues in raw data, such as acceleration range or sensor calibration. Based on our algorithm, we were able to identify the best-suited IMUs for our needs. This study provides an overview of how to select the IMUs based on the area of study with concrete examples, and gives insights into the features of seven commercial IMUs using real data.
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.
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.
How We Found Our IMU
(2020)
Inertial measurement units (IMUs) are commonly used for localization or movement tracking in pervasive healthcare-related studies, and gait analysis is one of the most often studied topics using IMUs. The increasing variety of commercially available IMU devices offers convenience by combining the sensor modalities and simplifies the data collection procedures. However, selecting the most suitable IMU device for a certain use case is increasingly challenging. In this study, guidelines for IMU selection are proposed. In particular, seven IMUs were compared in terms of their specifications, data collection procedures, and raw data quality. Data collected from the IMUs were then analyzed by a gait analysis algorithm. The difference in accuracy of the calculated gait parameters between the IMUs could be used to retrace the issues in raw data, such as acceleration range or sensor calibration. Based on our algorithm, we were able to identify the best-suited IMUs for our needs. This study provides an overview of how to select the IMUs based on the area of study with concrete examples, and gives insights into the features of seven commercial IMUs using real data.
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.
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.
Cognitive resources contribute to balance control. There is evidence that mental fatigue reduces cognitive resources and impairs balance performance, particularly in older adults and when balance tasks are complex, for example when trying to walk or stand while concurrently performing a secondary cognitive task.
We conducted a systematic literature search in PubMed (MEDLINE), Web of Science and Google Scholar to identify eligible studies and performed a random effects meta-analysis to quantify the effects of experimentally induced mental fatigue on balance performance in healthy adults. Subgroup analyses were computed for age (healthy young vs. healthy older adults) and balance task complexity (balance tasks with high complexity vs. balance tasks with low complexity) to examine the moderating effects of these factors on fatigue-mediated balance performance.
We identified 7 eligible studies with 9 study groups and 206 participants. Analysis revealed that performing a prolonged cognitive task had a small but significant effect (SMDwm = −0.38) on subsequent balance performance in healthy young and older adults. However, age- and task-related differences in balance responses to fatigue could not be confirmed statistically.
Overall, aggregation of the available literature indicates that mental fatigue generally reduces balance in healthy adults. However, interactions between cognitive resource reduction, aging and balance task complexity remain elusive.
The aim of this case series approach was to analyze weekly changes in force-velocity relationship (FvR) parameters ((v) over bar, (F) over bar (0), (P) over bar (max)) and theoretical snatch performance (snatchth) assessed through a specific snatch pull test in preparation of the European and World Championships in 2 male elite weightlifters.
A second aim was to examine associations of training load (volume, volume load, average load), barbell -, and snatchth over a period of 2 macrocycles in preparation of the same competitions. FvR-parameters, snatchth, training load data, and body mass were assessed weekly over 40 weeks.
Using the smallest real difference approach, significant (p <= 0.05) decreases in (v) over bar (0) and increases in (v) over bar, (F) over bar (0), (P) over bar (max), and snatchth were found within macrocycles.
However, the large significant loss in body mass (approximate to 11%) in athlete 1 during macrocycle 2 represents most likely a main factor for diminished (P) over bar (max), and snatchth in macrocycle 2.
Based on cross-correlation analyses, barbell FvR-parameters and snatchth were significantly (p <= 0.05) associated with maximal strength, muscle power, and speed training load variables.
Moderate correlations (0.31-0.47) were found between training load and (P) over bar (max) and snatchth in athlete 2. It can be concluded that the applied training loads elicits improvements in <(P)(max) and snatchth because the athlete approached the main competitions.
However, because of the large loss in body mass, the relations between training load and barbell FvR-parameters and snatchth were less clear in athlete 1. It seems that a loss in body mass as a result of a change in bodyweight category mitigates <(P)over bar>(max) development during the macrocycle and hindered to reach peak snatchth at the main competitions.
DUO-GAIT
(2023)
In recent years, there has been a growing interest in developing and evaluating gait analysis algorithms based on inertial measurement unit (IMU) data, which has important implications, including sports, assessment of diseases, and rehabilitation. Multi-tasking and physical fatigue are two relevant aspects of daily life gait monitoring, but there is a lack of publicly available datasets to support the development and testing of methods using a mobile IMU setup. We present a dataset consisting of 6-minute walks under single- (only walking) and dual-task (walking while performing a cognitive task) conditions in unfatigued and fatigued states from sixteen healthy adults. Especially, nine IMUs were placed on the head, chest, lower back, wrists, legs, and feet to record under each of the above-mentioned conditions. The dataset also includes a rich set of spatio-temporal gait parameters that capture the aspects of pace, symmetry, and variability, as well as additional study-related information to support further analysis. This dataset can serve as a foundation for future research on gait monitoring in free-living environments.