The Effect of Exercise Intervention Based Upon the Selective Functional Movement Assessment in an Athlete With Non-specific Low Back Pain: A Case Report and Pilot Study
Bibliographic record
Abstract
OBJECTIVES: To illustrate the effectiveness of the Selective Functional Movement Assessment (SFMA) as a guide to exercise intervention on chronic non-specific low back pain (CNLBP). METHODS: A 23-year-old male volleyball athlete with CNLBP was evaluated using the SFMA to assess the degree of physical dysfunctions. And then two-stage exercise protocol was designed based on the results of SFMA. The athlete conducted the exercise intervention for 8 weeks, 1 h each time, three times a week. Transverse abdominal muscles and multifidus muscle thickness, the degree of low back pain, and the degree of physical dysfunctions were measured at pre-intervention, midintervention, and post-intervention. RESULTS: Based on the results of SFMA, the exercise protocol in the first 4-week session was designed mainly to develop the mobility of ankle, hip, and chest and the stability of lumbar, hip, and knee, in order to improve core strength and gluteal muscle strength. The second 4-week session was an advanced stage with the increase of exercise load on the basis of flexibility and stability; its main purpose was to loosen the hamstring muscles and continue strengthening the core stability and finally help the participant to establish the correct movement pattern and solve the problems of dysfunctions. After 8-week exercise intervention, all movement patterns became functional/non-painful except the deep squat pattern; the Quebec Back Pain Disability Scale score decreased from 11 to 2; visual analog scale score decreased from 4 to 2; the thicknesses of the transverse abdominis muscles (right side: 0.2 vs. 0.31 cm, left side: 0.22 vs. 0.33 cm) and multifidus muscles (right side: 2.09 vs. 2.26 cm, left side: 2.15 vs. 2.29 cm) were both increased. CONCLUSION: In this case, the SFMA helped to recognize problems related to mobility and stability on the hip joint, thoracic spine, and even areas far away from the lumbar spine in an athlete with CNLBP that were not seen with more conventional examination procedures. The improvements of physical function, the increase in deep core muscles thickness, and the released pain after exercise intervention all verified the effectiveness of SFMA to qualitatively analyze movement patterns at examination and to direct subsequent exercise intervention.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".