Influence of Neck Pain, Cervical Extensor Muscle Fatigue, and Manual Therapy on Wrist Proprioception
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to examine the effects of submaximal isometric neck muscle fatigue and manual therapy on wrist joint position sense (JPS) within healthy individuals and individuals with subclinical neck pain (SCNP). METHODS: Twelve healthy participants and 12 participants with SCNP were recruited. Each group completed 2 sessions, with 48 hours between sessions. On day 1, both groups performed 2 wrist JPS tests using a robotic device. The tests were separated by a submaximal isometric fatigue protocol for the cervical extensor muscles (CEM). On day 2, both groups performed a wrist JPS test, followed by a cervical treatment consisting of manual therapy (SCNP) or neck rest (20 minutes, control group) and another wrist JPS test. Joint position sense was measured as the participant's ability to recreate a previously presented wrist angle. Each wrist JPS test included 12 targets, 6 into wrist flexion and 6 into wrist extension. Kinematic data from the robot established absolute, variability, and constant error. RESULTS: Absolute error significantly decreased (P = .01) from baseline to post-fatigue in the SCNP group (baseline = 4.48 ± 1.58°; post-fatigue = 3.90 ± 1.45°) and increased in the control group (baseline = 3.12 ± 0.98°; post-fatigue = 3.81 ± 0.90°). The single session of manual cervical treatment significantly decreased absolute error in participants with SCNP (P = .004). CONCLUSION: This study demonstrated that neck pain or fatigue can lead to altered afferent input to the central nervous system and can affect wrist JPS. Our findings demonstrate that acute wrist proprioception may be improved in individuals with SCNP by a single cervical manual therapy session.
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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 | 0.000 | 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".