Feasibility Of A Hybrid Delivery Of Home-based Resistance Training In Lung Cancer
Bibliographic record
Abstract
PURPOSE: Individuals with non-small cell lung cancer (NSCLC) are burdened by long-lasting symptoms post-treatment. These symptoms often reduce physical activity levels and increase the risk of functional decline and development of comorbidities such as cardiovascular disease. Symptom-generated reductions in exercise capacity can lead to exercise avoidance, ultimately resulting in accelerated deconditioning and a poor overall prognosis following treatment. Exercise interventions tailored towards lung cancer survivors should therefore aim to mitigate symptoms impacting exercise capacity (e.g., dyspnea and fatigue). The purpose of this study was to investigate the feasibility and acceptability of a hybrid-delivery home-based cluster-set resistance training program in individuals with NSCLC. METHODS: This study aimed to recruit individuals with NSCLC stage I-III following primary treatments to participate in 8-weeks of home-based resistance training, 3 days per week. The program included supervised sessions in the participants' home and virtual supervision via video conferencing. The primary outcome measure of feasibility was evaluated through retention and intervention fidelity (proportion of exercise completed, relative to what was prescribed). Intervention acceptability was assessed using a 4-point Likert-type scale from “Strongly disagree” to “Strongly agree” to rate the acceptability of intervention components. RESULTS: Fourteen participants were recruited over a 6-month period, with 11 completing the intervention (2 withdrew due to unrelated illness, 1 withdrew due to being on active treatment), yielding a retention rate of 79%. Characteristics of the participants who completed the intervention (n = 11) were: 71 ± 10 years; mean BMI 29.1 + 6.5. Average time (months) since diagnosis was 62 + 51. Mean session attendance was 86.4 ± 9.5%. Mean intervention fidelity was 83.1 ± 13.1%. With regards to acceptability, >90% of participants highly rated all aspects of the intervention delivery (i.e., ease and quality of virtual delivery, level of difficulty, and home-based approach). No adverse events related to exercise were recorded. CONCLUSIONS: The hybrid delivery of a home-based resistance exercise program for individuals previously treated for NSCLC was found to be safe and feasible.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| 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".