Using the Functional Movement Screen™ to Evaluate the Effectiveness of Training
Bibliographic record
Abstract
The Functional Movement Screen™ (FMS) has demonstrated some efficacy in the prediction of injuries and is thus used by many practitioners to make recommendations for exercise. However, questions remain regarding its utility as a means to evaluate the effectiveness of training. Sixty firefighters volunteered to participate, and their FMS scores were examined before and after 12 weeks of training. Individuals were graded on how they chose to perform rather than how they could perform. The participants were assigned to 1 of 3 groups: intervention 1, intervention 2, or control. The 2 intervention groups received three 1.5-hour training sessions each week and differed in the emphasis that was placed on movement quality. Sagittal and frontal plane videos were used to grade the FMS with 3 methods: the standard 0-3 scale, a 100-point scale that weighted specific compensations (research standard), and a modified 100-point scale whereby grades were assigned based on the total number of compensations present. There were no significant differences in the total FMS scores for any group posttraining. However, the scores of 85% of the firefighters who did not receive training did change. The 100-point scale methods resulted in more FMS score changes posttraining, but the between-group interactions were identical to those found with the standard scoring method. The control group's scores were not consistent pretraining and posttraining; thus, the influence of each intervention could not be evaluated. Currently, the FMS might provide a momentary impression of general movement quality, although further efforts would likely assist in the development of better ways to implement the test, interpret the results, and generate reliable scores.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".