Capturing the Complex Relationship Between Internal and External Training Load: A Data-Driven Approach.

BACKGROUND: Training load is typically described in terms of internal and external load. Investigating the coupling of internal and external training load is relevant to many sports. Here, continuous kernel-density estimation (KDE) may be a valuable tool to capture and visualize this coupling. AIM: Using training load data in speed skating, we evaluated how well bivariate KDE plots describe the coupling of internal and external load and differentiate between specific training sessions, compared to training impulse scores or intensity distribution into training zones. METHODS: On-ice training sessions of 18 young (sub)elite speed skaters were monitored for velocity and heart rate during 2 consecutive seasons. Training session types were obtained from the coach's training scheme, including endurance, interval, tempo, and sprint sessions. Differences in training load between session types were assessed using Kruskal-Wallis or Kolmogorov-Smirnov tests for training impulse and KDE scores, respectively. RESULTS: Training impulse scores were not different between training session types, except for extensive endurance sessions. However, all training session types differed when comparing KDEs for heart rate and velocity (both P < .001). In addition, 2D KDE plots of heart rate and velocity provide detailed insights into the (subtle differences in) coupling of internal and external training load that could not be obtained by 2D plots using training zones. CONCLUSION: 2D KDE plots provide a valuable tool to visualize and inform coaches on the (subtle differences in) coupling of internal and external training load for training sessions. This will help coaches design better training schemes aiming at desired training adaptations.

Modeling Match Performance in Elite Volleyball Players: Importance of Jump Load and Strength Training Characteristics.

In this study, we investigated the relationships between training load, perceived wellness and match performance in professional volleyball by applying the machine learning techniques XGBoost, random forest regression and subgroup discovery. Physical load data were obtained by manually logging all physical activities and using wearable sensors. Daily wellness of players was monitored using questionnaires. Match performance was derived from annotated actions by a video scout during matches. We identified conditions of predictor variables that related to attack and pass performance (p < 0.05). Better attack performance is related to heavy weights of lower-body strength training exercises in the preceding four weeks. However, worse attack performance is linked to large variations in weights of full-body strength training exercises, excessively heavy upper-body strength training, low jump heights and small variations in the number of high jumps in the four weeks prior to competition. Lower passing performance was associated with small variations in the number of high jumps in the preceding week and an excessive amount of high jumps performed, on average, in the two weeks prior to competition. Differences in findings with respect to passing and attack performance suggest that elite volleyball players can improve their performance if training schedules are adapted to the position of a player.

Personalized machine learning approach to injury monitoring in elite volleyball players.

ABSTRACTWe implemented a machine learning approach to investigate individual indicators of training load and wellness that may predict the emergence or development of overuse injuries in professional volleyball. In this retrospective study, we collected data of 14 elite volleyball players (mean ± SD age: 27 ± 3 years, weight: 90.5 ± 6.3 kg, height: 1.97 ± 0.07 m) during 24 weeks of the 2018 international season. Physical load was tracked by manually logging the performed physical activities and by capturing the jump load using wearable devices. On a daily basis, the athletes answered questions about their wellness, and overuse complaints were monitored via the Oslo Sports Trauma Research Center (OSTRC) questionnaire. Based on training load and wellness indicators, we identified subgroups of days with increased injury risk for each volleyball player using the machine learning technique Subgroup Discovery. For most players and facets of overuse injuries (such as reduced sports participation), we have identified personalized training load and wellness variables that are significantly related to overuse issues. We demonstrate that the emergence and development of overuse injuries can be better understood using daily monitoring, taking into account interactions between training load and wellness indicators, and by applying a personalized approach.Highlights With detailed, athlete-specific monitoring of overuse complaints and training load, practical insights in the development of overuse injuries can be obtained in a player-specific fashion contributing to injury prevention in sports.A multi-dimensional and personalized approach that includes interactions between training load variables significantly increases the understanding of overuse issues on a personal basis.Jump load is an important predictor for overuse injuries in volleyball.

Training Distribution in 1500-m Speed Skating: A Case Study of an Olympic Gold Medalist.

At the Olympic level, optimally distributing training intensity is crucial for maximizing performance. PURPOSE: The authors evaluated the effect of training-intensity distribution on anaerobic power as a substitute for 1500-m speed-skating performance in the 4 y leading up to an Olympic gold medal. METHODS: During the preparation phase of the speed-skating season, anaerobic power was recorded periodically (n = 15) using the mean power (in watts) with a 30-s Wingate test. For each training session in the 4 wk prior to each Wingate test, the volume (in hours), training type (specific, simulation, nonspecific, and strength training), and the rating of perceived exertion (RPE; CR-10) were recorded. RESULTS: Compared with the 8 lowest, the 7 highest-scoring tests were preceded by a significantly (P < .01) higher volume of strength training. Furthermore, the RPE distribution of the number of nonspecific training sessions was significantly different (P < .01). Significant (P < .05) correlations highlighted that a larger nonspecific training volume in the lower intensities RPE 2 (r = .735) and 3 (r = .592) was associated positively and the medium intensities RPE 4 (r = -.750) and 5 (r = -.579) negatively with Wingate performance. CONCLUSION: For the subject, the best results were attained with a high volume of strength training and the bulk of nonspecific training at RPE 2 and 3, and specifically not at the adjoining RPE 4 and 5. These findings are surprising given the aerobic nature of training at RPE 2 and 3 and the importance of anaerobic capacity in this middle-distance event.

Articles with impact: insights into 10 years of research with machine learning.

Worldwide scientific output is growing faster and faster. Academics should not only publish much and fast, but also publish research with impact. The aim of this study is to use machine learning to investigate characteristics of articles that were published in the Journal of Applied Physiology between 2009 and 2018, and characterize high-impact articles. Article impact was assessed for 4,531 publications by three common impact metrics: the Altmetric Attention Scores, downloads, and citations. Additionally, a broad collection of (more than 200) characteristics was collected from the article's title, abstract, authors, keywords, publication, and article engagement. We constructed random forest (RF) regression models to predict article impact and articles with the highest impact (top-25% and top-10% for each impact metric), which were compared with a naive baseline method. RF models outperformed the baseline models when predicting the impact of unseen articles (P < 0.001 for each impact metric). Also, RF models predicted top-25% and top-10% high-impact articles with a high accuracy. Moreover, RF models revealed important article characteristics. Higher impact was observed for articles about exercise, training, performance and V̇o2max, reviews, human studies, articles from large collaborations, longer articles with many references and high engagement by scientists, practitioners and public or via news outlets and videos. Lower impact was shown for articles about respiratory physiology or sleep apnea, editorials, animal studies, and titles with a question mark or a reference to places or individuals. In summary, research impact can be predicted and better understood using a combination of article characteristics and machine learning.NEW & NOTEWORTHY Common measures of article impact are the Altmetric Attention Scores, number of downloads, and number of citations. To our knowledge, this is the first study that applies machine learning on a comprehensive collection of article characteristics to predict article attention scores, downloads, and citations. Using 10 years of research articles, we obtained accurate predictions of high-impact articles and discovered important article characteristics related to article impact.

Building robust models for Human Activity Recognition from raw accelerometers data using Gated Recurrent Units and Long Short Term Memory Neural Networks.

Human Activity Recognition (HAR) is a growing field of research in biomedical engineering and it has many potential applications in the treatment and prevention of several diseases. Due to the recent advancement in technology, devices that collect position and orientation measurements (e.g. accelerometers and gyroscopes) are becoming ubiquitous. These measurements can then be used to train machine learning models for HAR. In this research, we propose one recurrent neural network architecture and a data augmentation approach for building robust and accurate models for HAR. We compared models with Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers. The proposed data augmentation approach was used to make the models robust to the cases where one or more sensors are missing. In this empirical study, we could also understand some relations between the ideal locations of the sensors in the participants and the types of activities performed. The proposed approaches were tested in the GOTOv dataset from a study which involved 35 participants performing 16 sedentary, ambulatory and lifestyle activities in a semi-structured environment. The results presented, clearly show that the models are able to detect these activities in a robust way.

Effects of Pacing Properties on Performance in Long-Distance Running.

This article focuses on the performance of runners in official races. Based on extensive public data from participants of races organized by the Boston Athletic Association, we demonstrate how different pacing profiles can affect the performance in a race. An athlete's pacing profile refers to the running speed at various stages of the race. We aim to provide practical, data-driven advice for professional as well as recreational runners. Our data collection covers 3 years of data made public by the race organizers, and primarily concerns the times at various intermediate points, giving an indication of the speed profile of the individual runner. We consider the 10 km, half marathon, and full marathon, leading to a data set of 120,472 race results. Although these data were not primarily recorded for scientific analysis, we demonstrate that valuable information can be gleaned from these substantial data about the right way to approach a running challenge. In this article, we focus on the role of race distance, gender, age, and the pacing profile. Since age is a crucial but complex determinant of performance, we first model the age effect in a gender- and distance-specific manner. We consider polynomials of high degree and use cross-validation to select models that are both accurate and of sufficient generalizability. After that, we perform clustering of the race profiles to identify the dominant pacing profiles that runners select. Finally, after having compensated for age influences, we apply a descriptive pattern mining approach to select reliable and informative aspects of pacing that most determine an optimal performance. The mining paradigm produces relatively simple and readable patterns, such that both professionals and amateurs can use the results to their benefit.

Benefits of a short, practical questionnaire to measure subjective perception of nasal appearance after aesthetic rhinoplasty.

BACKGROUND: The authors tested a short, practically designed questionnaire to assess changes in subjective perception of nasal appearance in patients before and after aesthetic rhinoplasty. METHODS: A prospective cohort study was conducted in a group of 121 patients who desired aesthetic rhinoplasty and were operated on by one surgeon. The questionnaire contained five questions (E1-E5) based on a five-point Likert scale and a visual analogue scale (range, 0 to 10). Two questions were designed as trick questions to help the surgeon screen for signs of body dysmorphic disorder. RESULTS: All patients rated the appearance of their nose as improved after surgery. The visual analogue scale revealed a Gaussian curve of normal distribution (range, 0.5 to 10) around a significant improvement (mean, 4.36 points, p = 0.018). Also, question E1, question E2, and the sum of questions E1 through E5 showed a statistically significant improvement after surgery (p = 1.74 × 10, p = 4.29 × 10, and p = 9.23 × 10, respectively). The authors found a linear relationship between preoperative score on the trick questions and postoperative increase in visual analogue scale score. Test-retest reliability could be investigated in 74 of 121 patients (61 percent) and showed a positive correlation between postoperative (1 year after surgery) and repostoperative response (2 to 4 years after surgery). CONCLUSIONS: The authors concluded that a surgeon performing aesthetic rhinoplasty can benefit from using this questionnaire. It is simple, takes no more than 2 minutes to complete, and provides helpful subjective information regarding patients' preoperative nasal appearance and postoperative surgical outcome. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.

Split hump technique for reduction of the overprojected nasal dorsum: a statistical analysis on subjective body image in relation to nasal appearance and nasal patency in 97 patients undergoing aesthetic rhinoplasty.

OBJECTIVES: To describe the split hump technique (SHT) and to examine its effectiveness for correction of an overprojected nasal dorsum in patients undergoing aesthetic rhinoplasty. METHODS: This prospective study included 97 patients. Objective assessment was performed using a short, practical questionnaire. Investigation focused on nasal patency and the patient perception of body image in relation to nasal appearance using 5-point Likert scale questions and visual analog scales. RESULTS: Use of the SHT resulted in a significant improvement in nasal patency and aesthetic nasal perception. Sum functional question scores decreased from 9.154 to 6.351 and aesthetic question scores from 13.897 to 6.825 (P < .001 for both). Mean aesthetic visual analog scale scores improved in all patients, from 3.346 to 7.782 (P < .001). Graphic illustration of this improvement revealed a gaussian curve of normal distribution around a mean (SD) improvement of 4.48 (1.93). CONCLUSIONS: Traditional en bloc humpectomy maneuvers are frequently combined with spreader graft use to avoid postoperative inferomedial repositioning of the upper lateral cartilages and inverted-V deformity. The SHT for correction of the overprojected dorsum creates a paradigm change in this patient group. The transverse segments of the upper lateral cartilages are saved and repositioned instead of being resected as a part of an en bloc osseocartilaginous composite hump resection in a transverse plane. Several modifications of the SHT enable the surgeon to deproject the nose while keeping sufficient strength in the keystone area and augmenting dorsal width. Using statistical analysis of subjective patient data, we could prove a broad acceptance and appreciation for the SHT.