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Research Review: Evaluating the FMS as an Injury Prediction Tool

Written by FMS FMS

Can the Functional Movement Screen (FMS) actually predict injuries—or is it just one piece of a much bigger risk puzzle?

Sports physical therapists Stephanie Newbold and Kyle Matsel review 25 years of evidence on the FMS, the Selective Functional Movement Assessment (SFMA), Y-Balance Test, injury-risk algorithms, and the emerging role of AI in movement health.

The evidence shows that the FMS is reliable and clinically useful, but not a standalone injury-prediction tool. Its value increases when paired with Y-Balance, prior injury history, current pain, and lifestyle factors. Pain with movement and major limitations (1s or asymmetries) are consistently linked to higher risk, with studies reporting up to a 2.7× greater likelihood of injury in certain groups.

Today, self-screening tools like Symmio expand access and support early intervention, while AI and machine learning models use FMS as one input among many to improve risk stratification.

Takeaway: Coaches, clinicians, and performance professionals should start with movement quality, then add context—injury history, pain, and lifestyle data—to make more informed, evidence-based decisions.

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Interested in one part of this video? Here is a breakdown of what's covered:

  • 00:07 Intro & clinical context
  • 01:45 Why FMS was created (beyond basic sports physicals)
  • 04:01 The “injury prediction” debate—what FMS can/can’t do
  • 06:15 Variability analogy & why single metrics fall short
  • 08:41 Meta-analyses: AUC, reliability, and 2.7× risk findings
  • 10:58 Pain & major movement limitations vs composite score
  • 13:24 1s/asymmetries and injury odds (beyond the 14/21 cut)
  • 15:46 Multifactorial risk: add Y-Balance + history + pain
  • 18:02 Risk categories (substantial → optimal) & lost-time injury rates
  • 20:19 AI/ML models with FMS: performance + risk insights
  • 21:30 “Piece of the puzzle” mindset
  • 23:47 Broader risk factors (BMI, dorsiflexion, readiness, etc.)
  • 26:07 Cardiometaphor: stacking risk factors changes decisions
  • 28:23 Compounding risks → sharply higher injury likelihood
  • 30:10 Return-to-duty studies: residual pain/readiness gaps
  • 32:21 Efficiency: fast pain screens (SFMA) at scale
  • 32:58 Self-screening & patient empowerment (Symmio)
  • 39:52 Lifestyle domains drive MSK health
  • 42:12 Re-screening & discharge education
  • 43:43 Key takeaways & wrap

References:

  1. Bonazza NA, Smuin D, Onks CA, Silvis ML, Dhawan A. Reliability, validity, and injury predictive value of the functional movement screen: A systematic review and meta-analysis. Am J Sports Med. 2017;45(3):725-732.
  2. Busch AM, Clifton DR, Onate JA, Ramsey VK, Cromartie F. Relationship of preseason movement screens with overuse symptoms in collegiate baseball players. Int J Sports Phys Ther. 2017;12(6):960-966.
  3. Kiesel K, Plisky PJ, Voight ML. Can serious injury in professional football be predicted by a preseason functional movement screen? N Am J Sports Phys Ther. 2007;2(3):147-158
  4. Lehr ME, Plisky PJ, Butler RJ, et al. Field-expedient screening and injury risk algorithm categories as predictors of noncontact lower extremity injury. Scand J Med Sci Sports. 2013;23(4):e225-232.
  5. Mokha M, Sprague PA, Gatens DR. Predicting musculoskeletal injury in national collegiate athletic association division ii athletes from asymmetries and individual-test versus composite functional movement screen scores. J Athl Train. 2016;51(4):276-282.
  6. Moran RW, Schneiders AG, Major KM, Sullivan SJ. How reliable are functional movement screening scores? A systematic review of rater reliability. Br J Sports Med. 2016;50(9):527-536.
  7. Moran RW, Schneiders AG, Mason J, Sullivan SJ. Do functional movement screen (fms) composite scores predict subsequent injury? A systematic review with meta-analysis. Br J Sports Med. 2017.
  8. Teyhen DS, Rhon DI, Butler RJ, et al. Association of physical inactivity, weight, smoking, and prior injury on physical performance in a military setting. J Athl Train. 2016;51(11):866-875.
  9. Whittaker JL, Booysen N, de la Motte S, et al. Predicting sport and occupational lower extremity injury risk through movement quality screening: A systematic review. Br J Sports Med. 2017;51(7):580-585.
  10. Matsel K, Kirsch J, Netelbeek T, et al. Self-movement screening using the symmio application is reliable and valid for identifying musculoskeletal risk factors. Int J Sports Phys Ther. 2023;18(2):439-449.
  11. Teyhen DS, Shaffer SW, Goffar SL, et al. Identification of risk factors prospectively associated with musculoskeletal injury in a warrior athlete population. Sports Health. 20201941738120902991.
  12. Rhon DI, Teyhen DS, Kiesel K, et al. Recovery, rehabilitation, and return to full duty in a military population after a recent injury: Differences between lower-extremity and spine injuries. Arthrosc Sports Med Rehabil. 2022;4(1):e17-e27.
  13. Rhon DI, Plisky PJ, Kiesel K, et al. Predicting subsequent injury after being cleared to return to work from initial lumbar or lower extremity injury. Med Sci Sports Exerc. 2023;55(12):2115-2122.
  14. Cook G. Data-driven prognosis and improved outcomes part 1: The demands of practice-based evidence. Int J Sports Phys Ther. 2024;19(4):507-509.
  15. Cook G. Data-driven prognosis and improved outcomes part 3: Clinical intelligence vs. clinical wisdom. Int J Sports Phys Ther. 2024;19(6):773-775.
  16. Cook G. Data-driven prognosis and improved outcomes part 2: The opportunity of grading risk. Int J Sports Phys Ther. 2024;19(5):646-648.
  17. Jianjun Q, Isleem HF, Almoghayer WJK, Khishe M. Predictive athlete performance modeling with machine learning and biometric data integration. Scientific Reports. 2025;15(1):16365.

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