Improvements in Hip Flexibility Do Not Transfer to Mobility in Functional Movement Patterns
Bibliographic record
Abstract
The purpose of this study was to analyze the transference of increased passive hip range of motion (ROM) and core endurance to functional movement. Twenty-four healthy young men with limited hip mobility were randomly assigned to 4 intervention groups: group 1, stretching; group 2, stretching plus hip/spine disassociation exercises; group 3, core endurance; and group 4, control. Previous work has documented the large increase in passive ROM and core endurance that was attained over the 6-week interventions, but whether these changes transferred to functional activities was unclear. Four dynamic activities were analyzed before and after the 6-week interventions: active standing hip extension, lunge, a standing twist/reach maneuver, and exercising on an elliptical trainer. A Vicon motion capture system collected body segment kinematics, with hip and lumbar spine angles subsequently calculated in Visual 3D. Repeated measures analyses of variance determined group effects on various hip and spine angles, with paired t-tests on specific pre/post pairs. Despite the large increases in passive hip ROM, there was no evidence of increased hip ROM used during functional movement testing. Similarly, the only significant change in lumbar motion was a reduction in lumbar rotation during the active hip extension maneuver (p < 0.05). These results indicate that changes in passive ROM or core endurance do not automatically transfer to changes in functional movement patterns. This implies that training and rehabilitation programs may benefit from an additional focus on 'grooving' new motor patterns if newfound movement range is to be used.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".