Exercise Therapy for Improved Neck Muscle Function in Helicopter Aircrew
Bibliographic record
Abstract
INTRODUCTION: To address the high prevalence of neck dysfunction in helicopter aircrew, a 12-wk training program was designed to examine the effects on neck muscular strength and endurance. METHODS: Subjects were recruited from Canadian Forces (CF) helicopter aircrew and randomized into either a neck coordination training program (CTP; N = 10), an endurance training program (ETP; N = 11), or a nontreatment control (CON; N = 8). Baseline assessments determined maximal voluntary contraction (MVC) strength and endurance capacity using a submaximal contraction to fatigue at 70% of their MVC for extension, flexion, and left (Ltflx) and right (Rtflx) lateral flexion. The ETP subjects performed dynamic contractions at 30% of their MVC in the four testing directions using a head harness and Thera-band tubing. The CTP consisted of exercises that focused on strengthening the deep cervical musculature using the mass of the head as resistance and progressing to exercises that incorporated the superficial cervical muscles. RESULTS: Post-intervention, the ETP achieved the only statistically significant increase in maximal force when compared to the CON (14.4%). Improved times to fatigue were achieved by the CTP for flexion (26.34 +/- 20.72 s), Ltflx (23.54 +/- 13.94 s), and Rtflx (28.72 +/- 4.88 s). CONCLUSION: The provision of an ETP and CTP resulted in a positive trend toward improved maximal force and muscular endurance. The greatest improvements in endurance and strength were found for those subjects assigned to the CTP treatment. Our research demonstrates the importance of including a designed and supervised training program into the daily routine of helicopter aviators.
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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 | 0.000 |
| 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) | 0.003 | 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".