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Predicting Sports Injuries with Wearable Technology and Data Analysis

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Abstract

Injuries resulting from sports and physical activities can be persistent and pose a substantial problem for player’s economic wellbeing and quality of life. Wearable technologies in conjunction with analytics can help mitigate the risk to players by identifying injury risk factors and focusing on risk reduction. Prior to engaging in strenuous sport activities, wearables can be employed to facilitate the quantification of relevant functional capabilities, ultimately advancing the field of sports injury management. In this paper, we discuss how wearable technologies can improve the health and athletic performance of athletes by monitoring participants across many variables. A cohort of 54 army ROTC cadets participated in this study. Using Zephyr BioHarness Wearable technology, we gathered quantifiable data to generate insights that allow us to predict and prevent injuries related the wearer’s physical exertion during sporting activities. This study finds that a combination of high BMI and high mechanical loads could result in injury. Therefore, in creating an exercise program, it is imperative to ensure that mechanical load is incrementally increased through the practice season as athletes become conditioned. While, a high level repetitious mechanical load with unconditioned athletes could cause injuries in short time, it is important to impose enough mechanical loads in the training program to ensure good musculoskeletal development. While our analyses identified several factors associated with injury data during ROTC activities, other wearable variables might become significant in other situations. In summary, results from this study demonstrate that wearable technology allows players with an increased risk of injury to be identified and targeted for intervention.

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Notes

  1. Youden’s index = sum of [sensitivity (Sn) + specificity (Sp) – 1]

  2. Under the assumption that that the prediction model is reasonably accurate and the false positive rate and false negative rates are zero. Otherwise, athletics identified as “high risk” may not be truly high-risk athletics.

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Acknowledgements

This research was supported by the Office of Research and Sponsored Programs at Wright State University.

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Authors and Affiliations

  1. Department of Information Systems and Supply Chain Management, Wright State University, Dayton, OH, USA

    Amir Zadeh

  2. Department of kinesiology and Health, Wright State University, Dayton, OH, USA

    David Taylor, Margaret Bertsos & Timothy Tillman

  3. Department of Biomedical Engineering, Marshall University, Huntington, WV, USA

    Nasim Nosoudi

  4. Department of kinesiology and Health, Arkansas State University, Jonesboro, AR, USA

    Scott Bruce

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  1. Amir Zadeh
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Correspondence to Amir Zadeh.

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Appendix

Appendix

Below are the details of the Regression analysis performed in this study

Table 10 Variables in the Equation
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Zadeh, A., Taylor, D., Bertsos, M. et al. Predicting Sports Injuries with Wearable Technology and Data Analysis. Inf Syst Front 23, 1023–1037 (2021). https://doi.org/10.1007/s10796-020-10018-3

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