Validity of the Online Athlete Management System to Assess Training Load

Menaspà, Miranda J; Menaspà, Paolo; Clark, Sally A.; Fanchini, Maurizio

doi:10.1123/ijspp.2017-0379

Cited by 19 publications

(12 citation statements)

References 24 publications

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“…Training load variables (Table 1) were coded as continuous data for daily s‐RPE [calculated using Borg's CR10 scale for rating of perceived exertion (RPE) multiplied by session duration (minutes)], 28 monotony [weekly mean training load/standard deviation (SD)], strain [weekly training load x monotony], acute training load (ATL) [preceding 7‐day mean training load], chronic training load (CTL) [preceding 28‐day mean training load], and ACWR [ratio of ATL:CTL] 12,27 . These methods have been previously reported as valid indicators of internal training load in sport 13 ; however, practitioners should also understand limitations associated with these methods, particularly the ACWR 5‐7 …”

Section: Methodsmentioning

confidence: 99%

“…Monitoring of subjective data is commonplace in high performance environments, 10 where athletes’ self‐report questionnaires, daily monitoring, and surveillance tools have consistently been found to be valid 11 and reliable for monitoring training preparedness and training load 12,13 . The majority of research on this topic examines the responses of males, 1,14 while only 35%‐37% of study participants are female athletes 15 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Impaired recovery is associated with increased injury and illness: A retrospective study of 536 female netball athletes

Horgan

Drew

Halson

et al. 2020

Scandinavian Med Sci Sports

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Sport science and medicine practitioners are interested in the relationships between training load, injury, and illness. The extent to which training preparedness is associated with workload‐related injury and illness risk is debated. Therefore, this study applied multi‐level mixed effect logistic regression to investigate time‐dependent (±7‐ and ±28‐day) relationships between training preparedness (fatigue, mood, motivation, soreness, stress, sleep duration, and quality), training load, injury, and illness in 536 elite and pre‐elite female netball athletes. Absolute risk (AR ± 95% CI) of sustaining an injury (0.98 ± 0.06%, n = 1122 injuries, N = 254 athletes) or illness (1.09 ± 0.10%, n = 2881, N = 432 athletes) was calculated. All training preparedness variables combined resulted in an absolute risk of 0.88%‐5.88% and 0.87%‐20% for injury and illness, respectively. Injury and illness had significant (P < .05) bidirectional (ie, both increased and decreased) associations with physical (soreness) and physiological (sleep duration and quality), while illness also had negative (mood, motivation) and positive (stress) associations with psychological training preparedness variables. Low sleep duration in the 48‐h period prior was associated (P = .005) with increased injury risk (OR = 0.91 ± 0.03; AR = 4.00%), while “very poor” sleep quality (OR = 0.59 ± 0.02; AR = 7.83%) or extremes of too little (<5 hours, OR = 1.01 ± 0.03; AR = 3.13%‐14.29%) and too much (>10 hours, OR = 1.01 ± 0.03; AR = 2.61%‐10.98%) sleep had bidirectional associations (P < .001) with an increased illness risk. Changes in training preparedness variables demonstrated bidirectional associations with injury and illness. These outcomes suggest that sport science and medicine practitioners should monitor sleep, physical, and psychological recovery status, to aid early detection and intervention regarding injury and illness symptomology.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impaired recovery is associated with increased injury and illness: A retrospective study of 536 female netball athletes

Horgan

Drew

Halson

et al. 2020

Scandinavian Med Sci Sports

View full text Add to dashboard Cite

show abstract

“…Basketball is a collective sport that puts the ball into the opponent's basket to score points and prevents the opponent from getting the ball and scoring points under specific rules. Compared with other ball games, basketball has many skills, various tactical forms, and strong skills of players, which also reflects the characteristics of individual combat and cooperation [ 1 ]. With the national requirements for quality education, more and more colleges and universities begin to use information technology to realize the auxiliary teaching of physical education.…”

Section: Introductionmentioning

confidence: 99%

The Construction of Basketball Training System Based on Motion Capture Technology

Nie

et al. 2021

Journal of Healthcare Engineering

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Motion capture is a cross-cutting application field developed in recent years, which comprises electronics, communications, control, computer graphics, ergonomics, navigation, and other disciplines. The accurate application of basketball technical movements in the basketball game is very important. Therefore, it is of great significance to capture and standardize athletes’ movements and improve their training. Unfortunately, there are numerous issues in traditional classroom teaching that largely helps to train the athletes. To solve the issues of traditional basketball classroom teaching, a virtual simulation system for students’ sports training is designed in this paper. Firstly, the information of basketball dribbling movement is captured and simulated in three dimensions. Secondly, we compare it with the standard database to judge the irregularities of athletes’ movements, and carry out digital processing on athletes’ movements and skill improvements statistics in combination with system functions. Thirdly, we set up a gradual training cycle. Finally, the Kinect-based capture technology is adopted to obtain the activity information of different joints of the human body. Through processing the motion data, relevant motion analysis data are fed to the established motion model, to realize the comparative analysis of motion pictures. In our experiments, we observed better training of the physical education.

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“…In larger professional sports organisations, more advanced athlete management systems (AMS) [268,541] are used instead these consumer-level training diaries.…”

Section: Precision Strength Trainingmentioning

confidence: 99%

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

Teikari¹,

Pietrusz²

2021

Preprint

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In strength training, personalised strength training (autoregulation) approaches have been used to individualise exercise programs with monitoring an for dynamic adjustment based on their responses to training. While this transition from tradition-based training to evidence-based training framework has been an improvement in training practices, we argue that the future of strength training will also incorporate deep learning models powered by data. We refer to this data-driven framework as precision strength training inspired by the similar modeling frameworks used in precision medicine. In contrast to current personalised training in which the acquired athlete data is often subject to human expert decision-making, we are anticipating the rise of human-in-the-loop systems with an augmented coach who will be doing decisions collaboratively with the machine. Similar to other precision frameworks, such as precision health, we envision such a future to take decades to be realised and we focus here on practical short-term targets on a way to long-term realisation. In this chapter, we will review the measurement technology needed for continuous data acquisition from an individual during training/physical activity, how to acquire these datasets for the development of such systems and, how a proof-of-concept system could be developed for powerlifting training with applicability to general strength and conditioning (S&C) and physical rehabilitation purposes. Additionally, we will evaluate how the user experience (UX) of the system feedback and visualisation could be designed.

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Validity of the Online Athlete Management System to Assess Training Load

Abstract: The online athlete management system for assessing s-RPE was shown to be a valid indicator of internal training load and can be used in elite sport.

Cited by 19 publications

References 24 publications

Impaired recovery is associated with increased injury and illness: A retrospective study of 536 female netball athletes

Impaired recovery is associated with increased injury and illness: A retrospective study of 536 female netball athletes

The Construction of Basketball Training System Based on Motion Capture Technology

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

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