Feasibility of eccentric overloading and neuromuscular electrical stimulation to improve muscle strength and muscle mass after treatment for head and neck cancer
Bibliographic record
Abstract
Purpose: Treatment of head and neck cancer (HNC) results in severe weight loss, mainly due to loss of lean body mass. Consequently, decreases in muscular strength and health-related quality of life (HRQL) occur. This study investigated the feasibility of a 12-week experimental (EXP) and conventional (CON) strength training intervention delivered after HNC treatment.Methods: Participants were randomized to an EXP group (n=11) involving eccentric strength training and neuromuscular electrical stimulation (NMES), or a CON group (n=11) involving dynamic strength training matched for training volume. Feasibility outcomes included recruitment, completion, adherence and evidence of progression. A neuromuscular assessment involving maximal isometric voluntary contractions (MIVCs) in the knee extensors was evaluated prior to and during incremental cycling to volitional exhaustion at baseline and after the interventions. Anthropometrics and patient-reported outcomes (PROs) were also assessed.Results: Although recruitment was challenging, completion was 82% in CON and 100% in EXP. Adherence was 81% in CON and 92% in EXP. Overall, MIVC increased by 19 ± 23%, muscle mass improved 18 ± 22%, cycling exercise time improved by 18 ± 13%, and improvements in HRQL and fatigue were clinically relevant.Conclusions: Both interventions were found to be feasible for HNC patients after treatment. Strength training significantly improved maximal muscle strength, muscle mass, and PROs after HNC treatment. Future research should include fully powered trials and consider the use of eccentric overloading and NMES during HNC treatment.Implications for Cancer Survivors: Eccentric overloading and NMES may be useful alternatives to conventional strength training after HNC treatment.
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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 | 0.001 |
| 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".