Heavy Lifting Strength Training In Head And Neck Cancer Survivors (The Lifting Trial)
Bibliographic record
Abstract
PURPOSE: Despite improvements in surgical neck dissection (ND) procedures, head and neck cancer survivors (HNCS) still experience acute and chronic side effects such as loss of muscular strength, limitations in physical functioning, and fatigue, that impact quality of life (QoL) and return to work. Light-to-moderate intensity strength training (LMST) improves muscular strength, physical functioning, and some side effects in HNCS. Heavy lifting strength training (HLST) may further improve these outcomes, however, it has not been studied in HNCS. The primary aim of the LIFTING trial was to examine the feasibility and safety of a HLST program in HNCS ≥1-year post-surgical neck dissection. METHODS: In this single arm feasibility and safety study, HNCS were asked to complete a twice weekly, 12-week, supervised HLST program, gradually progressing to lifting heavy loads of 80%-90% of 1 repetition maximum (1RM) for barbell squat, bench press, and deadlift. Feasibility outcomes included recruitment rate, 1RM completion rate, program adherence, and follow-up assessment rate. The primary efficacy outcomes were changes in upper and lower body strength from baseline to postintervention assessed via reliable 1RM tests. Wilcoxon signed rank tests were used to compare the pre-post changes in efficacy outcomes. RESULTS: Sixteen HNCS were assessed for eligibility. Nine were recruited over an 8-month period during the COVID-19 pandemic. All 9 (100%) successfully completed the baseline1RM tests and progressed to heavy loads at approximately 5-weeks. Seven participants (77.7%) completed all follow-up 1RM tests. Median attendance was 95.8% (range: 71%-100%). Weight lifted increased for squat/leg press (median change: +34 kg; 95% CI: +25 to +47; p = 0.008), bench press (median change: +6 kg; 95% CI: +2 to +10; p = 0.012), and deadlift (median change: +12 kg; 95% CI: +7 to +24; p = 0.012). No adverse events were reported. CONCLUSIONS: HLST may be feasible and safe for HNCS at least 1-year post-ND, and result in significant improvements in muscular strength. Future research should consider additional recruitment strategies and compare HLST to LMST to determine the optimal strength training regimen for this understudied population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".