scholarly journals The Current State of Subjective Training Load Monitoring—a Practical Perspective and Call to Action

2018 ◽  
Vol 4 (1) ◽  
Author(s):  
Joseph O. C. Coyne ◽  
G. Gregory Haff ◽  
Aaron J. Coutts ◽  
Robert U. Newton ◽  
Sophia Nimphius
Keyword(s):  
Training Load ◽  
Call To Action ◽  
Current State ◽  
Load Monitoring

scholarly journals Training Load and Its Role in Injury Prevention, Part I: Back to the Future

2020 ◽  
Vol 55 (9) ◽  
pp. 885-892
Author(s):  
Franco M. Impellizzeri ◽  
Paolo Menaspà ◽  
Aaron J. Coutts ◽  
Judd Kalkhoven ◽  
Miranda J Menaspà
Keyword(s):  
Injury Prevention ◽  
Injury Risk ◽  
Training Load ◽  
Training Process ◽  
Current State ◽  
Training Principles ◽  
Load Monitoring ◽  
Quantitative Indication ◽  
Practical Recommendations

The purpose of this 2-part commentary series is† to explain why we believe our ability to control injury risk by manipulating training load (TL) in its current state is an illusion and why the foundations of this illusion are weak and unreliable. In part 1, we introduce the training process framework and contextualize the role of TL monitoring in the injury-prevention paradigm. In part 2, we describe the conceptual and methodologic pitfalls of previous authors who associated TL and injury in ways that limited their suitability for the derivation of practical recommendations. The first important step in the training process is developing the training program: the practitioner develops a strategy based on available evidence, professional knowledge, and experience. For decades, exercise strategies have been based on the fundamental training principles of overload and progression. Training-load monitoring allows the practitioner to determine whether athletes have completed training as planned and how they have coped with the physical stress. Training load and its associated metrics cannot provide a quantitative indication of whether particular load progressions will increase or decrease the injury risk, given the nature of previous studies (descriptive and at best predictive) and their methodologic weaknesses. The overreliance on TL has moved the attention away from the multifactorial nature of injury and the roles of other important contextual factors. We argue that no evidence supports the quantitative use of TL data to manipulate future training with the purpose of preventing injury. Therefore, determining “how much is too much” and how to properly manipulate and progress TL are currently subjective decisions based on generic training principles and our experience of adjusting training according to an individual athlete's response. Our message to practitioners is to stop seeking overly simplistic solutions to complex problems and instead embrace the risks and uncertainty inherent in the training process and injury prevention.

scholarly journals Quantifying Accelerations and Decelerations in Elite Women Soccer Players during Regular In-Season Training as an Index of Training Load

Sports ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 109
Author(s):  
Tom Douchet ◽  
Allex Humbertclaude ◽  
Carole Cometti ◽  
Christos Paizis ◽  
Nicolas Babault
Keyword(s):  
Heart Rate ◽  
Perceived Exertion ◽  
Training Load ◽  
Soccer Players ◽  
Unit Recording ◽  
Fitness Level ◽  
Total Distance ◽  
Load Monitoring ◽  
Soccer Training ◽  
Stress Fatigue

Accelerations (ACC) and decelerations (DEC) are important and frequent actions in soccer. We aimed to investigate whether ACC and DEC were good indicators of the variation of training loads in elite women soccer players. Changes in the training load were monitored during two different selected weeks (considered a “low week” and a “heavy week”) during the in-season. Twelve elite soccer women playing in the French first division wore a 10-Hz Global Positioning System unit recording total distance, distance within speed ranges, sprint number, ACC, DEC, and a heart rate monitor during six soccer training sessions and rated their perceived exertion (RPE). They answered the Hooper questionnaire (sleep, stress, fatigue, DOMS) to get an insight of their subjective fitness level at the start (Hooper S) and at the end of each week (Hooper E). A countermovement jump (CMJ) was also performed once a week. During the heavy week, the training load was significantly greater than the low week when considering number of ACC >2 m·s−2 (28.2 ± 11.9 vs. 56.1 ± 10.1, p < 0.001) and number of DEC < −2 m·s−2 (31.5 ± 13.4 vs. 60.9 ± 14.4, p < 0.001). The mean heart rate percentage (HR%) (p < 0.05), RPE (p < 0.001), and Hooper E (p < 0.001) were significantly greater during the heavy week. ACC and DEC showed significant correlations with most outcomes: HR%, total distance, distance per min, sprint number, Hooper index of Hooper E, DOMS E, Fatigue E, RPE, and session RPE. We concluded that, for elite women soccer players, quantifying ACC and DEC alongside other indicators seemed to be essential for a more complete training load monitoring. Indeed, it could lead to a better understanding of the reasons why athletes get fatigued and give insight into neuromuscular, rather than only energetic, fatigue.

scholarly journals Women in oncology pharmacy leadership: A white paper

2019 ◽  
Vol 26 (1) ◽  
pp. 175-186
Author(s):  
Alexandra Shillingburg ◽  
Laura B Michaud ◽  
Rowena Schwartz ◽  
Jaime Anderson ◽  
David W Henry ◽  
...  
Keyword(s):  
Leadership Development ◽  
White Paper ◽  
Gender Disparity ◽  
Professional Organizations ◽  
Leadership Roles ◽  
Leadership Positions ◽  
Call To Action ◽  
Current State ◽  
Key Issues ◽  
Development Committee

Gender disparity exists in leadership roles within healthcare. While the majority of the healthcare workforce is comprised of women, significantly fewer women occupy leadership positions, particularly at executive and board levels. As the field of oncology pharmacy continues to rapidly expand and evolve, an assessment of the current state of women in oncology pharmacy leadership roles is vital to the growth and development of the profession. In the fall of 2017, the Hematology/Oncology Pharmacy Association (HOPA) hosted a summit to explore leadership issues facing women in oncology pharmacy which have the potential to affect our membership and our profession. This meeting included invited participants from across the fields of oncology and pharmacy and was part of HOPA’s strategic leadership initiative developed through the work of the HOPA Leadership Development Committee in 2016. This promotes a primary goal of HOPA, which is to support oncology pharmacists as they assume leadership roles within their practices and within healthcare to assure oncology pharmacy is integrated into cancer care. The purpose of this white paper is to (1) summarize key issues that were identified through a membership survey; (2) review ongoing efforts to address the needs of female oncology pharmacists in leadership development; (3) serve as a call to action for individuals and professional organizations to assist with and disseminate these efforts and highlight available resources, and (4) to provide practical steps to meet the needs of individuals, training programs, and institutions/employers.

scholarly journals OR-027 Research about Training Monitoring during Different training periods in Chinese Elite Swimmers

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Junqiang Qiu ◽  
Mingxing Li ◽  
Longyan Yi ◽  
Zhaoran Hou ◽  
Fan Yang ◽  
...  
Keyword(s):  
Physical Function ◽  
Blood Lactate ◽  
Vital Capacity ◽  
Female Athletes ◽  
Training Load ◽  
Z Score ◽  
Male Athletes ◽  
Elite Swimmers ◽  
Load Monitoring ◽  
Preparation Period

Objective Training monitoring has become an integral component of total athlete training. Systematically monitoring the physiological and biochemical variables related to performance helps coaches and athletes to measure the effectiveness of their training programs and decide how to revise or update those programs, especially in swimming training. The key purpose of this study is to evaluate the physical function characteristics during preparation season and stress response during competition training sessions in 2017, and provides the helpful data for scientific training for the implementation of the preparation process. Methods During the preparation period, the National Swimming Team athletes were planed to screen and test the physical function characteristics. There are 39 male athletes and 37 female athletes to participate in the study. Body composition was assessed with dual energy X-ray (DXA). Anthropometric characteristics were assessed using Anthroscan 3D VITUS body scanner, and pulmonary function test using CHEST portable lung function meter(HI-101). During the competition period, the training load monitoring targets were 2 elite players who participated in XVII World Aquatics Championship in Budapest-2017 and the National Games 2017. The monitoring methods mainly included: blood tests (including Hb, CK, BU, testosterone, cortisol and ferritin etc.) were used to monitor the athlete's fitness functional status, and the Z-score method was used to express the index changes of two athletes; blood lactate was used to monitor the training load of athletes, and urine indexes were used to monitor body fluid balance and fatigue. Results 1. During the preparation period, the weight of male athletes is 78.4±8.2kg, the percentage of body fat is 15.9±2.8%, the weight of female athletes is 64.8±6.6kg, and the percentage of body fat is 24.2±3.5%. The vital capacity(VC) was 6.65±0.87 L for males and 4.86±0.69 L for females, the value of forced vital capacity(FVC) was 4.29±1.33 L for males and 3.43±0.96 L for females, and the mean value of ventilation per minute was 148.1±23.12 L for males and 110.4 ± 19.67 L for females. 2. During the competition preparation period, Z score was used to express the blood indicators of two athletes, before the XVII World Aquatics Championship in Budapest-2017, the Z score of Hb, T, T/C ratio and ferritin were (-0.5, 0, -0.4, 1.1) and (-0.8, -0.1, -1.0, 0), respectively. Before the competition of the National Games, the Z scores were (1.0, 0.3, 0.7, 0.6) and (1.4, 1.0, 0.1, -0.6) respectively. 3. Training load monitoring was carried out using the blood lactate control test, as the training load increased, the athletes' performance improved and the lactate level increased slightly. 4. The urine indicator test is used to observe the athlete's dehydration and recovery. On the second morning after the intensive training day, both athletes were negative for urine protein and with normal urine specific gravity. Conclusions 1. The screen and tests about the physical function characteristics of swimming athletes during preparation period is useful to develop a personalized training plan; 2. Z-score is easy and feasible for the elite swimmers to monitoring physical fitness capabilities, and higher Z-score is related with better athletic performance; 3. Blood lactate control test can be used for the training intensity monitoring of swimmers, athletes show higher levels of lactic acid metabolism and higher athletic performance before the competition.

Positional Differences in Elite Basketball: Selecting Appropriate Training-Load Measures

2018 ◽  
Vol 13 (7) ◽  
pp. 947-952 ◽  
Author(s):  
Luka Svilar ◽  
Julen Castellano ◽  
Igor Jukic ◽  
David Casamichana
Keyword(s):  
Principal Components ◽  
Perceived Exertion ◽  
Principal Component ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Change Of Direction ◽  
External Variables ◽  
Load Monitoring ◽  
External Training ◽  
Almost All

Purpose: To study the structure of interrelationships among external-training-load measures and how these vary among different positions in elite basketball. Methods: Eight external variables of jumping (JUMP), acceleration (ACC), deceleration (DEC), and change of direction (COD) and 2 internal-load variables (rating of perceived exertion [RPE] and session RPE) were collected from 13 professional players with 300 session records. Three playing positions were considered: guards (n = 4), forwards (n = 4), and centers (n = 5). High and total external variables (hJUMP and tJUMP, hACC and tACC, hDEC and tDEC, and hCOD and tCOD) were used for the principal-component analysis. Extraction criteria were set at an eigenvalue of greater than 1. Varimax rotation mode was used to extract multiple principal components. Results: The analysis showed that all positions had 2 or 3 principal components (explaining almost all of the variance), but the configuration of each factor was different: tACC, tDEC, tCOD, and hJUMP for centers; hACC, tACC, tCOD, and hJUMP for guards; and tACC, hDEC, tDEC, hCOD, and tCOD for forwards are specifically demanded in training sessions, and therefore these variables must be prioritized in load monitoring. Furthermore, for all playing positions, RPE and session RPE have high correlation with the total amount of ACC, DEC, and COD. This would suggest that although players perform the same training tasks, the demands of each position can vary. Conclusion: A particular combination of external-load measures is required to describe the training load of each playing position, especially to better understand internal responses among players.

External Training Load Monitoring and the Impact on Training Load Management in Collegiate Male Soccer Players

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Jacob R. Gdovin ◽  
Riley Galloway ◽  
Lorenzo S. Tomasiello ◽  
Michael Seabolt ◽  
Robert Booker
Keyword(s):  
Training Load ◽  
Soccer Players ◽  
Load Management ◽  
Load Monitoring ◽  
External Training ◽  
The Impact

scholarly journals Training load monitoring algorithms on highly sub-metered home electricity consumption data

2008 ◽  
Vol 13 (S1) ◽  
pp. 406-411 ◽  
Author(s):  
Mario Berges ◽  
Ethan Goldman ◽  
H. Scott Matthews ◽  
Lucio Soibelman
Keyword(s):  
Electricity Consumption ◽  
Training Load ◽  
Consumption Data ◽  
Load Monitoring

scholarly journals On the Use of Concentrated Time–Frequency Representations as Input to a Deep Convolutional Neural Network: Application to Non Intrusive Load Monitoring

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 911 ◽  
Author(s):  
Sarra Houidi ◽  
Dominique Fourer ◽  
François Auger
Keyword(s):  
Pattern Recognition ◽  
Recognition Task ◽  
Time Frequency ◽  
Neural Architecture ◽  
Signal Representations ◽  
Current State ◽  
Signal Features ◽  
Load Monitoring ◽  
Pattern Recognition Task

Since decades past, time–frequency (TF) analysis has demonstrated its capability to efficiently handle non-stationary multi-component signals which are ubiquitous in a large number of applications. TF analysis us allows to estimate physics-related meaningful parameters (e.g., F0, group delay, etc.) and can provide sparse signal representations when a suitable tuning of the method parameters is used. On another hand, deep learning with Convolutional Neural Networks (CNN) is the current state-of-the-art approach for pattern recognition and allows us to automatically extract relevant signal features despite the fact that the trained models can suffer from a lack of interpretability. Hence, this paper proposes to combine together these two approaches to take benefit of their respective advantages and addresses non-intrusive load monitoring (NILM) which consists of identifying a home electrical appliance (HEA) from its measured energy consumption signal as a “toy” problem. This study investigates the role of the TF representation when synchrosqueezed or not, used as the input of a 2D CNN applied to a pattern recognition task. We also propose a solution for interpreting the information conveyed by the trained CNN through different neural architecture by establishing a link with our previously proposed “handcrafted” interpretable features thanks to the layer-wise relevant propagation (LRP) method. Our experiments on the publicly available PLAID dataset show excellent appliance recognition results (accuracy above 97%) using the suitable TF representation and allow an interpretation of the trained model.

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