Movement Screening for Performance: What Information Do We Need to Guide Exercise Progression?
Bibliographic record
Abstract
Coaches often use a “movement screen” to identify compensations prior to the prescription of exercise. The Functional Movement Screen (FMS) is a tool that has received recognition as it has demonstrated efficacy in the prediction of injuries. Guidelines have been published to administer the test and grade quality of movement; however, it is not clear how the findings should be interpreted. Individuals are given task objectives and scored from 0 to 3, whereby a 2 implies any compensation. Therefore, in its current form, the FMS may not be sensitive to changes in movement, and thus, be appropriate to guide the progression of exercise. PURPOSE: To examine the sensitivity of the incumbent grading scheme and that of a novel method to observable differences in movement. METHODS: Sixty-five men were screened with the FMS and assigned to one of two groups (intervention and control). The intervention group (n = 41) participated in a supervised exercise program for 12 weeks, but like the controls, were blinded to the results and objectives of the FMS. After the intervention all participants were screened a second time. Video was collected from the sagittal and frontal planes and 4 reps of each task were performed. Only the ‘best’ rep was scored. Aside from verbal instructions no specific cues were given. Participants were graded on how they chose to perform rather than how they could perform the tasks given feedback. Video was used to objectively assign scores using two methods: 1) Current - a 3 (perfect), 2 (compensation), 1 (can't perform) or 0 (pain); 2) Novel - Each task was assigned a primary objective and secondary compensations. If the objective was met, one point was given for each compensation (0 was perfect). If it was not met, the base score was made to be one point higher than the total number of compensations possible. Scores for each task were given the same weight in the total score (100 was worst). The number of subjects demonstrating screen score differences was used to represent the sensitivity of each grading scheme. RESULTS: The correlation between the grading schemes was 0.86 and 0.85 for the pre and post test respectively (p<0.01). The number of participants demonstrating a change in score was not only different between the two methods, but also task dependent (Table). There was also no difference in the number of changes between the intervention and control groups, using either method. CONCLUSIONS: The novel approach to grading was more sensitive to changes in movement, but it is not clear whether the detectable differences were meaningful. Because the changes among the control group were similar to those of the men receiving supervised exercise it is difficult to speculate as to how the screening information should be used to guide the progression of training. PRACTICAL APPLICATIONS: The FMS is not purported to be a diagnostic tool and perhaps the current findings are evidence that, although valuable for identifying pain and overt asymmetries, additional tests must be used to guide exercise progression. If the goal is to assess physical competence or capacity and to evaluate how an individual chooses to move, it is arguably more appropriate to examine performance over multiple repetitions and to modify the screening tasks to parallel the progression of loads/speeds of the physical skills being taught.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".