Skip to main content
SpringerLink
Log in

Predicting Sports Injuries with Wearable Technology and Data Analysis

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

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.

References

  • Arnold, J. F., & Sade, R. M. (2017). Wearable Technologies in Collegiate Sports: The ethics of collecting biometric data from student-athletes. American Journal of Bioethics, 17(1), 67–70.

    Article  Google Scholar 

  • Bahr, R. 2014, Demise of the fittest: are we destroying our biggest talents?, BMJ Publishing Group Ltd and British Association of Sport and Exercise Medicine.

  • Beneciuk, J. M., Bishop, M. D., & George, S. Z. (2009). Clinical prediction rules for physical therapy interventions: A systematic review. Physical Therapy, 89(2), 114–124.

    Article  Google Scholar 

  • Billings, C. E. (2004). Epidemiology of injuries and illnesses during the United States air Force academy 2002 basic cadet training program: Documenting the need for prevention. Military Medicine, 169(8), 664–670.

    Article  Google Scholar 

  • Borotikar, B. S., Newcomer, R., Koppes, R., & McLean, S. G. (2008). Combined effects of fatigue and decision making on female lower limb landing postures: Central and peripheral contributions to ACL injury risk. Clinical biomechanics, 23(1), 81–92.

    Article  Google Scholar 

  • Bourdon, P. C., Cardinale, M., Murray, A., Gastin, P., Kellmann, M., Varley, M. C., Gabbett, T. J., Coutts, A. J., Burgess, D. J., & Gregson, W. (2017). Monitoring athlete training loads: Consensus statement. International Journal of Sports Physiology and Performance, 12(s2), S2-161-S2-170.

    Google Scholar 

  • Brenner, A. K. (2008). Clinical prediction rule for those soldiers most likely to develop lower extremity stress fractures during initial entry training. In: Program and abstracts of the 2008 American Physical Therapy Association combined sections meeting. Nashville, Tennessee, February 6–9. Journal of Orthopaedic & Sports Physical Therapy, 38(1), A76.

    Google Scholar 

  • Bruce, S. L., & Wilkerson, G. B. (2010a). Clinical prediction rules, part 1: Conceptual overview. Athletic Therapy Today, 15(2), 4–9.

    Article  Google Scholar 

  • Bruce, S. L., & Wilkerson, G. B. (2010b). Clinical prediction rules, part 2: Data analysis procedures and clinical application of results. Athletic Therapy Today, 15(2), 10–13.

    Article  Google Scholar 

  • Bruce, S. L., Rush, J. R., Torres, M. M., and Lipscomb, K. J. 2016a, Test-retest and inter-rater reliability of core muscular endurance tests used for injury screening. International Journal of Athletic Therapy & Training, Accepted for publication: p. 1–23.

  • Bruce, S. L., Crawford, E., Wilkerson, G. B., Rausch., D., Dale, R. B., & Harris, M. (2016b). Prediction modeling for academic success in professional master's athletic training programs. Athletic Training Education Journal, 11(4), 194–207.

    Article  Google Scholar 

  • Caparrós, T., Casals, M., Solana, Á., & Peña, J. (2018). Low external workloads are related to higher injury risk in professional male basketball games. Journal of sports science & medicine, 17(2), 289–297.

    Google Scholar 

  • Chambers, R., Gabbett, T. J., Cole, M. H., & Beard, A. (2015). The use of wearable microsensors to quantify sport-specific movements. Sports Medicine, 45(7), 1065–1081.

    Article  Google Scholar 

  • Childs, J. D., & Cleland, J. A. (2006). Development and application of clinical prediction rules to improve decision making in physical therapist practice. Physical Therapy, 86(1), 122–131.

    Article  Google Scholar 

  • Childs, J. D., Fritz, J. M., Flynn, T. W., Irrgang, J. J., Johnson, K. K., Majkowski, G. R., & Delitto, A. (2004). A clinical prediction rule to identify patients with low back pain most likely to benefit from spinal manipulation: A validation study. Annals of Internal Medicine, 141(12), 920–928.

    Article  Google Scholar 

  • Chimera, N. J., Knoeller, S., Cooper, R., Kothe, N., Smith, C., & Warren, M. (2017). Prediction of functional movement screen™ performance from lower extremity range of motion and core tests. International Journal of Sports Physical Therapy, 12(2), 173–181.

    Google Scholar 

  • Clark, J. F., Ellis, J. K., Bench, J., Khoury, J., & Graman, P. (2012). High-performance vision training improves batting statistics for University of Cincinnati baseball players. PLoS One, 7(1), 1–6.

    Google Scholar 

  • Clark, J. F., Graman, P. A. P., Ellis, M. A., O. D. J. K., Mangine, M. P. T. A. T. C. R. E., Rauch, D. S. A. J. T., Bixenmann, B., Hasselfeld, K. A., Divine, J. G., Colosimo, A. J., & Myer, P. F. G. D. (2015). An Exploratory Study of the Potential Effects of Vision Training on Concussion Incidence in Football. Optometry & Visual Performance, 3(2), 116–125–116–116.

  • Cleland, J. A., Childs, J. D., Fritz, J. M., Whitman, J. M., & Eberhart, S. L. (2007). Development of a clinical prediction rule for guiding treatment of a subgroup of patients with neck pain: Use of thoracic spine manipulation, exercise, and patient education. Physical Therapy, 87(1), 9–23.

    Article  Google Scholar 

  • Clermont, C. A., Duffett-Leger, L., Hettinga, B. A., & Ferber, R. (2020). Runners’ perspectives on ‘Smart’wearable technology and its use for preventing injury. International Journal of Human–Computer Interaction, 36(1), 31–40.

    Article  Google Scholar 

  • Colby, M. J., Dawson, B., Heasman, J., Rogalski, B., & Gabbett, T. J. (2014). Accelerometer and GPS-derived running loads and injury risk in elite Australian footballers. Journal of Strength and Conditioning Research, 28(8), 2244–2252.

    Article  Google Scholar 

  • Comstock, R. D., Currie, D., and Pierpoint, L. A. 2016, National High School Sports-Related Injury Surveillance Study: 2014-2015 school year. Center for Injury Research & policy: Aurora, CO.

  • Creighton, D. W., Shrier, I., Shultz, R., Meeuwisse, W. H., & Matheson, G. O. (2010). Return-to-play in sport: A decision-based model. Clinical Journal of Sport Medicine, 20(5), 379–385.

    Article  Google Scholar 

  • Davenport, T. E., Cleland, J., & Kulig, K. (2009). Patient classification based on psychosocial variables predicts treatment outcomes in patients with lower back pain who meet a clinical prediction rule. Journal of Orthopaedic & Sports Physical Therapy, 39(1), A19–A20.

    Google Scholar 

  • Dennis, J., Dawson, B., Heasman, J., Rogalski, B., & Robey, E. (2016). Sleep patterns and injury occurrence in elite Australian footballers. Journal of Science and Medicine in Sport, 19(2), 113–116.

    Article  Google Scholar 

  • Düking, P., Hotho, A., Holmberg, H. C., Fuss, F. K., & Sperlich, B. (2016). Comparison of non-invasive individual monitoring of the training and health of athletes with commercially available wearable technologies. Frontiers in Physiology, 7, 71.

    Article  Google Scholar 

  • Emparanza, J. I., & Aginaga, J. R. (2001). Validation of the Ottawa knee rules. Annals of Emergency Medicine, 38(4), 364–368.

    Article  Google Scholar 

  • Eusea, J., Nasypany, A., Seegmiller, J., & Baker, R. (2015). Utilizing mulligan sustained natural apophyseal glides within a clinical prediction rule for treatment of low back pain in a secondary school football player. International Journal of Athletic Therapy and Training, 20(1), 18–24.

    Article  Google Scholar 

  • Flynn, T., Fritz, J., Whitman, J., Wainner, R., Magel, J., Rendeiro, D., Butler, B., Garber, M., & Allison, S. (2002). A clinical prediction rule for classifying patients with low back pain who demonstrate short-term improvement with spinal manipulation. Spine, 27(24), 2835–2843.

    Article  Google Scholar 

  • Gabbett, T. J., & Ullah, S. (2012). Relationship between running loads and soft-tissue injury in elite team sport athletes. Journal of Strength and Conditioning Research, 26(4), 953–960.

    Article  Google Scholar 

  • Gentles, J., Coniglio, C., Besemer, M., Morgan, J., & Mahnken, M. (2018a). The demands of a Women’s college soccer season. Sports, 6(1), 16.

    Article  Google Scholar 

  • Gentles, J. A., Coniglio, C. L., Besemer, M. M., Morgan, J. M., & Mahnken, M. T. (2018b). The demands of a Women’s college soccer season. Sports, 6(1), 16.

    Article  Google Scholar 

  • Grooms, D., Appelbaum, G., & Onate, J. (2015). Neuroplasticity following anterior cruciate ligament injury: a framework for visual-motor training approaches in rehabilitation. journal of orthopaedic & sports physical therapy, 45(5), 381–393.

    Article  Google Scholar 

  • Gupta, A., Wilkerson, G. B., Sharda, R., & Colston, M. A. (2019). Who is more injury-prone? Prediction and assessment of injury risk. Decision Sciences, 50(2), 374–409.

    Article  Google Scholar 

  • Hägglund, M., Waldén, M., & Ekstrand, J. (2006). Previous injury as a risk factor for injury in elite football: A prospective study over two consecutive seasons. British Journal of Sports Medicine, 40(9), 767–772.

    Article  Google Scholar 

  • Hamilton, G. M., Meeuwisse, W. H., Emery, C. A., Steele, R. J., & Shrier, I. (2011). Past injury as a risk factor: An illustrative example where appearances are deceiving. American Journal of Epidemiology, 173(8), 941–948.

    Article  Google Scholar 

  • Heyworth, J. (2003). Ottawa ankle rules for the injured ankle: Useful clinical rules save on radiographs and need to be used widely. British Medical Journal, 326(7386), 405–406.

    Article  Google Scholar 

  • Hicks, G. E., Fritz, J. M., Delitto, A., & McGill, S. M. (2005). Preliminary development of a clinical prediction rule for determining which patients with low back pain will respond to a stabilization exercise program. Archives of Physical Medicine and Rehabilitation, 86(9), 1753–1762.

    Article  Google Scholar 

  • Hides, J., Stanton, W., McMahon, S., Sims, K., & Richardson, C. (2008). Effect of stabilization training on multifidus muscle cross-sectional area among young elite cricketers with low back pain. Journal of Orthopaedic & Sports Physical Therapy, 38(3), 101–108.

    Article  Google Scholar 

  • Hootman, J. M., Dick, R., & Agel, J. (2007). Epidemiology of collegiate injuries for 15 sports: Summary and recommendations for injury prevention initiatives. Journal of Athletic Training, 42(2), 311–319.

    Google Scholar 

  • Iverson, C. A., Sutlive, T. G., Crowell, M. S., Morrell, R. L., Perkins, M. W., Garber, M. B., Moore, J. H., & Wainner, R. S. (2008). Lumbopelvic manipulation for the treatment of patients with patellofemoral pain syndrome: Development of a clinical prediction rule. Journal of Orthopaedic & Sports Physical Therapy, 38(6), 297–312.

    Article  Google Scholar 

  • Johnston, W., O’Reilly, M., Argent., R., & Caulfield, B. (2019). Reliability, validity and utility of inertial sensor systems for postural control assessment in sport science and medicine applications: A systematic review. Sports Medicine, 49(5), 783–818.

    Article  Google Scholar 

  • Johnstone, J. A., Ford, P. A., Hughes, G., Watson, T., Mitchell, A. C., & Garrett, A. T. (2012). Field based reliability and validity of the bioharness™ multivariable monitoring device. Journal of sports science & medicine, 11(4), 643.

    Google Scholar 

  • Joseph F. Clark, P. A. T. C., Graman, M. A. Patricia, and Ellis, O. D. James K. (2015), Depth Perception Improvement in Collegiate Baseball Players with Vision Training. Optometry & Visual Performance, Vol 3, Iss 2, Pp 106–115, 2015(2): p. 106.

  • Kellis, E., & Katis, A. (2007). Quantification of functional knee flexor to extensor moment ratio using Isokinetics and electromyography. Journal of Athletic Training, 42(4), 477–486.

    Google Scholar 

  • Kerr, Z. Y., Marshall, S. W., Dompier, T. P., Corlette, J., Klossner, D. A., & Gilchrist, J. (2015). College sports-related Injuries - United States, 2009-10 through 2013-14 academic years. Morbidity and Mortality Weekly Report, 64(48), 1330–1336.

    Article  Google Scholar 

  • Kiernan, D., Hawkins, D. A., Manoukian, M. A., McKallip, M., Oelsner, L., Caskey, C. F., & Coolbaugh, C. L. (2018). Accelerometer-based prediction of running injury in National Collegiate Athletic Association track athletes. Journal of Biomechanics, 73, 201–209.

    Article  Google Scholar 

  • Kiesel, K. B., Butler, R. J., & Plisky, P. J. (2014). Prediction of injury by limited and asymmetrical fundamental movement patterns in American football players. Journal of Sport Rehabilitation, 23(2), 88–95.

    Article  Google Scholar 

  • Kuijpers, T., van der Windt, D. A. W. M., Boeke, A. J. P., Twisk, J. W. R., Vergouwe, Y., Bouter, L. M., & van der Heijden, G. J. M. G. (2006). Clinical prediction rules for the prognosis of shoulder pain in general practice. Pain, 120(3), 276–285.

    Article  Google Scholar 

  • Kuijpers, T., van der Heijden, G. J. M. G., Vergouwe, Y., Twisk, J. W. R., Boeke, A. J. P., Bouter, L. M., and Van Der Windt, D. A., A. W. M. , Good generalizability of a prediction rule for prediction of persistent shoulder pain in the short term. Journal of Clinical Epidemiology, 2007. 60(9): p. 947–953.

  • Lasko, T. A., Bhagwat, J. G., Zou, K. H., & Ohno-Machado, L. (2005). The use of receiver operating characteristic curves in biomedical informatics. Journal of Biomedical Informatics, 38(5), 404–415.

    Article  Google Scholar 

  • Leisey, J. (2004). Prospective validation of the Ottawa ankle rules in a deployed military population. Military Medicine, 169(10), 804–806.

    Article  Google Scholar 

  • Lesher, J. D., Sutlive, T. G., Miller, G. A., Chine, N. J., Garber, M. B., & Wainner, R. S. (2006). Development of a clinical prediction rule for classifying patients with patellofemoral pain syndrome who respond to patellar taping. Journal of Orthopaedic & Sports Physical Therapy, 36(11), 854–866.

    Article  Google Scholar 

  • Li, R. T., Kling, S. R., Salata, M. J., Cupp, S. A., Sheehan, J., & Voos, J. E. (2016). Wearable performance devices in sports medicine. Sports Health: A Multidisciplinary Approach, 8(1), 74–78.

    Article  Google Scholar 

  • Linnell, E. 2015, Effects of training load indicators of recovery and injury occurrence in collegiate women volleyball players.

    Google Scholar 

  • Mahieu, N. N., Witvrouw, E., Stevens, V., Van Tiggelen, D., & Roget, P. (2006). Intrinsic risk factors for the development of Achilles tendon overuse injury: A prospective study. American Journal of Sports Medicine, 34(2), 226–235.

    Article  Google Scholar 

  • Mann, J. B., Bryant, K. R., Johnstone, B., Ivey, P. A., & Sayers, S. P. (2016). Effect of physical and academic stress on illness and injury in division 1 college football players. The Journal of Strength & Conditioning Research, 30(1), 20–25.

    Article  Google Scholar 

  • Mokha, M., Sprague, P. A., & Gatens, D. R. (2016). Predicting musculoskeletal injury in national collegiate athletic association division II athletes from asymmetries and individual-test versus composite functional movement screen scores. Journal of Athletic Training, 51(4), 276–282.

    Article  Google Scholar 

  • Murphy, D., Connolly, D., & Beynnon, B. (2003). Risk factors for lower extremity injury: A review of the literature. British Journal of Sports Medicine, 37(1), 13–29.

    Article  Google Scholar 

  • Nanni, G., Villa, F. D., Ricci, M., Rizzo, D., Villa, S. D., & Injuries, H. (2016). In P. Volpi (Ed.), Arthroscopy Sport Inj (p. 97-102). London: Springer international publishing.

    Google Scholar 

  • Nazari, G., & MacDermid, J. C. (2020). Reliability of zephyr bioHarness respiratory rate at rest, during the modified Canadian aerobic fitness test and recovery. The Journal of Strength & Conditioning Research, 34(1), 264–269.

    Article  Google Scholar 

  • Nazari, G., Bobos, P., MacDermid, J. C., Sinden, K. E., Richardson, J., & Tang, A. (2018). Psychometric properties of the Zephyr bioharness device: a systematic review. BMC Sports Science, Medicine and Rehabilitation, 10(1), 6.

    Article  Google Scholar 

  • Needle, A., Baumeister, J., Kaminski, T., Higginson, J., Farquhar, W., & Swanik, C. (2014). Neuromechanical coupling in the regulation of muscle tone and joint stiffness. Scandinavian Journal of Medicine & Science in Sports, 24(5), 737–748.

    Article  Google Scholar 

  • O’Reilly, M., Caulfield, B., Ward, T., Johnston, W., & Doherty, C. (2018). Wearable inertial sensor systems for lower limb exercise detection and evaluation: A systematic review. Sports Medicine, 48(5), 1221–1246.

    Article  Google Scholar 

  • Opar, D. A., & Serpell, B. G. (2014). Is there a potential relationship between prior hamstring strain injury and increased risk for future anterior cruciate ligament injury? Archives of Physical Medicine and Rehabilitation, 95(2), 401–405.

    Article  Google Scholar 

  • Phillips, C., Stover, P., Bower, R., and Bruce, S. L. 2016, Sleep quality & stress relationship to injury, recovery & performance, in Celebration of Research, Scholarship, and Creative Activities. Wright State University: Dayton, OH.

  • Richardson, C. A., Snijders, C. J., Hides, J. A., Damen, L., Pas, M. S., & Storm, J. (2002). The relation between the transversus abdominis muscles, sacroiliac joint mechanics, and low back pain. Spine, 27(4), 399–405.

    Article  Google Scholar 

  • Robson, K., Pitt, L. F., & Kietzmann, J. (2016). APC Forum 1: Extending Business Values through Wearables. MIS Quarterly Executive, 15(2).

  • Rosin, A., & Sinopoli, M. (1999). Impact of the Ottawa ankle rules in a U.S. Army troop medical clinic in South Korea. Military Medicine, 164(11), 793.

    Article  Google Scholar 

  • Scott, S. A., Simon, J. E., Van Der Pol, B., & Docherty, C. L. (2015). Risk factors for sustaining a lower extremity injury in an army reserve officer training corps cadet population. Military Medicine, 180(8), 910–916.

    Article  Google Scholar 

  • Seshadri, D. R., Li, R. T., Voos, J. E., Rowbottom, J. R., Alfes, C. M., Zorman, C. A., & Drummond, C. K. (2019). Wearable sensors for monitoring the internal and external workload of the athlete. NPJ digital medicine, 2(1), 1–18.

    Article  Google Scholar 

  • Springer, B. A., Arciero, R. A., Tenuta, J. J., & Taylor, D. C. (2000). A prospective study of modified Ottawa ankle rules in a military population: Interobserver agreement between physical therapists and orthopaedic surgeons. American Journal of Sports Medicine, 28(6), 864–868.

    Article  Google Scholar 

  • Stevan Jr., S. L., Bonfati, L. V., Santos, C. P., Smaniotto, L. E., Mendes Jr., J. J. A., & Vargas, L. M. (2018). Sensing Devices to Aid Coaches and Sports Training of People with Motor and Intellectual Limitations. Archives of Sports Medicine, 2(2).

  • Stiell, I. (1996). Ottawa ankle rules. Canadian Family Physician, 42, 478–480.

    Google Scholar 

  • Stiell, I., Greenberg, G., McKnight, R., Nair, R., McDowell, I., & Worthington, J. (1992). A study to develop clinical decision rules for the use of radiography in acute ankle injuries. Annals of Emergency Medicine, 21(4), 384–390.

    Article  Google Scholar 

  • Sutlive, T. G., Lopez, H. P., Schnitker, D. E., Yawn, S. E., Halle, R. J., Mansfield, L. T., Boyles, R. E., & Childs, J. D. (2008). Development of a clinical prediction rule for diagnosing hip osteoarthritis in individuals with unilateral hip pain. Journal of Orthopaedic & Sports Physical Therapy, 38(9), 542–550.

    Article  Google Scholar 

  • Teyhen, D. S., Flynn, T. W., Childs, J. D., & Abraham, L. D. (2007). Arthrokinematics in a subgroup of patients likely to benefit from a lumbar stabilization exercise program. Physical Therapy, 87(3), 313–325.

    Article  Google Scholar 

  • Vallverdú, J. (2008). The false dilemma: Bayesian vs. frequentist. E-logos - electronic journal for. Philosophy.

  • Van Hooren, B., Goudsmit, J., Restrepo, J., & Vos, S. (2020). Real-time feedback by wearables in running: Current approaches, challenges and suggestions for improvements. Journal of Sports Sciences, 38(2), 214–230.

    Article  Google Scholar 

  • Wainner, R. S., Fritz, J. M., Irrgang, J. J., Delitto, A., Allison, S., & Boninger, M. L. (2005). Development of a clinical prediction rule for the diagnosis of carpal tunnel syndrome. Archives of Physical Medicine and Rehabilitation, 86, 609–618.

    Article  Google Scholar 

  • Warren, M., Lininger, M. R., Chimera, N. J., & Smith, C. A. (2018). Utility of FMS to understand injury incidence in sports: Current perspectives. Open access Journal of Sports Medicine, 9, 171–182.

    Article  Google Scholar 

  • Wilkerson, G. B., & Colston, M. A. (2015). A refined prediction model for core and lower extremity sprains and strains among collegiate football players. Journal of Athletic Training, 50(6), 643–650.

    Article  Google Scholar 

  • Wilkerson, G. B., & Denegar, C. R. (2014). Cohort study design: An underutilized approach for advancement of evidence-based and patient-centered practice in athletic training. Journal of Athletic Training, 49(4), 561–567.

    Article  Google Scholar 

  • Wilkerson, G. B., Bullard, J. T., & Bartal, D. W. (2010). Identification of cardiometabolic risk among collegiate football players. Journal of Athletic Training, 45(1), 67–74.

    Article  Google Scholar 

  • Wilkerson, G. B., Giles, J. L., & Seibel, D. K. (2012). Prediction of core and lower extremity strains and sprains in collegiate football players: A preliminary study. Journal of Athletic Training, 47(3), 264–272.

    Article  Google Scholar 

  • Wilkerson, G. B., Gupta, A., Allen, J. R., Keith, C. M., & Colston, M. A. (2016a). Utilization of practice session average inertial load to quantify college football injury risk. Journal of Strength and Conditioning Research, 30(9), 2369–2375.

    Article  Google Scholar 

  • Wilkerson, G. B., Colston, M. A., & Baker, C. S. (2016b). A sport fitness index for assessment of sport-related injury risk. Clinical Journal of Sport Medicine, 26(5), 423–428.

    Article  Google Scholar 

  • Wilkerson, G. B., Gupta, A., & Colston, M. A. (2018). Mitigating sports injury risks using internet of things and analytics approaches. Risk Analysis, 38(7), 1348–1360.

    Article  Google Scholar 

  • Yuen, M. (2001). The Ottawa ankle rules in children. Emergency Medicine Journal, 18(6), 466–467.

    Article  Google Scholar 

Download references

Acknowledgements

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

Author information

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

Authors
  1. Amir Zadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Taylor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Margaret Bertsos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Timothy Tillman
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nasim Nosoudi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Scott Bruce
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amir Zadeh.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

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

Table 10 Variables in the Equation
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-020-10018-3

Keywords

Search

Navigation