The Association Between Poor Sleep and the Incidence of Sport and Physical Training-Related Injuries in Adult Athletic Populations: A Systematic Review

Dobrosielski, Devon A.; Sweeney, Lisa; Lisman, Peter

doi:10.1007/s40279-020-01416-3

Cited by 24 publications

(15 citation statements)

References 72 publications

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“…Most movements of athletes are expressed by limbs. Limb joints contain a lot of characteristic information related to athletes' posture, which is mainly composed of the left and right elbows, wrists, and knees and ankle joints [20,21].…”

Section: Acquisition and Fusion Processing Of Monitoringmentioning

confidence: 99%

[Retracted] Research on Sports Class Load Monitoring System Based on Threshold Classification Algorithm

Zhao

2021

Wireless Communications and Mobile Computing

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In order to reduce the sports injury caused by high intensity sports classes, it is necessary to monitor the state of the sports load. Therefore, the sport’s load monitoring system based on a threshold classification algorithm is proposed. In this paper, we design the hardware and software structures of the sports load monitoring systems in a physical education class. In this system, the state parameters of the sports load are collected by wireless sensor network nodes, and the feature parameters are fused and clustered by the integrated information fusion method. After that, we establish the movement target image acquisition model, which unifies the ZigBee networking realization to the high intensity sports classroom movement load monitoring. Simulation results show that the designed PE classroom sports load monitoring system based on the threshold classification algorithm has high performance for sports parameter monitoring and can effectively avoid sports injury caused by overload.

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Section: Acquisition and Fusion Processing Of Monitoringmentioning

confidence: 99%

[Retracted] Research on Sports Class Load Monitoring System Based on Threshold Classification Algorithm

Zhao

2021

Wireless Communications and Mobile Computing

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“…Due to the limited number of professional coaches, not all sports enthusiasts can get training guidance from excellent coaches. Secondly, only through visual observation, coaches cannot obtain quantitative data information like acceleration and angular velocity; it is difficult to accurately grasp the deep-level information that is crucial for athletes [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence and Big Data Technology in Basketball Sports Training

Zhong

2022

Wireless Communications and Mobile Computing

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Basketball is a sport with a wide range of complicated human movements, and the ability to accurately identify these movements is critical in both competition and training. Training athletes is largely based on the subjective observations and experiences of the coaches. Artificial intelligence and big data technologies can be used to monitor athlete training. It can also assist coaches in making decisions which significantly increase the athletic ability by detecting their movements. This research work proposed a mechanism called BSTARNet which is an action recognition method for basketball sports training. The method is based on artificial neural network (ANN), and network is trained through basketball sports big data. First, this work uses the Convolution Long Short-Term Memory (ConvLSTM) unit to extract the spatiotemporal information features of basketball sports training from videos. Second, this work establishes the Attention Long Short-Term Memory (AttLSTM) unit that combines the attention mechanism with the LSTM. The unit selectively scans each location, giving more attention to the area where the action takes place. Finally, the network framework is built by improving the ordinary encoder-decoder model. After that, the spatiotemporal information contained in the video is encoded based on the Darknet network model. In the decoding stage, the AttLSTM structure is used to replace the ordinary LSTM. These units are combined to form the BSTARNet architecture. Experiments are conducted to verify the effectiveness of the proposed method applied on action recognition in basketball sports training and achieved 89.5% mAP and 95.4% accuracy.

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“…However, Clemente et al seem to suggest that insufficient sleep affects soccer player performance and increases the risk of injury and need to this complex relationship further. A systematic review from Dobrosielski et al [23], also said that there is insufficient evidence supporting the association between subjective or objective measures of sleep and injury in collegiate and elite athletes. When applied to this current finding, the researcher may consider examining the role of sleep as a moderating variable for incidence of injury, and must be able to exclude other confounding factors: sleep quality, sport, and training load.…”

Section: The Relationship Of Adequate Sleep Time With Injury and Illnessmentioning

confidence: 99%

Athletes sleep duration during COVID-19 pandemic and its relationship with health condition

Kurniarobbi

Chikih

Andeansah

et al. 2022

IJPHS

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During the coronavirus desease 2019 (COVID-19) pandemic, athletes must be able to adapt to new habits, such as: social restriction, change of competition schedule, and sending back home. This situation has led to an 18.2% increase in sleep disturbances. This study aimed to look at the sleep duration of adolescent athletes during the COVID-19 pandemic and its impact on athlete’s health conditions. A total of 126 adolescent athletes from 15 sports were recruited in this cross-sectional study. Adequate sleep duration was assessed according to the American Academy of Sleep Medicine criteria. Data on health conditions, medical history, injury history, were taken using a questionnaire. Behavioral assessment was assessed through the strengths and difficulties questionnaire. About 80.2% of adolescent athletes had insufficient sleep, and there was a significant relationship between sleep duration and disease incidence with a relative risk (RR) of 3.31 (1.32-8.28) 95% convidence interval (CI). There was no significant relationship between adequate sleep duration and health conditions (p>0.05). Things that may be the cause of sleep disturbances; use of electronic devices (37.6%), environmental conditions (53.4%), and worry (9%). In the end, this indicates adolescent athletes face difficulties in achieving adequate sleep duration during this pandemic and a continuous surveillance system is needed to monitor athletes outside the dormitory.

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The Association Between Poor Sleep and the Incidence of Sport and Physical Training-Related Injuries in Adult Athletic Populations: A Systematic Review

Cited by 24 publications

References 72 publications

[Retracted] Research on Sports Class Load Monitoring System Based on Threshold Classification Algorithm

[Retracted] Research on Sports Class Load Monitoring System Based on Threshold Classification Algorithm

Application of Artificial Intelligence and Big Data Technology in Basketball Sports Training

Athletes sleep duration during COVID-19 pandemic and its relationship with health condition

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