Feasibility of a Remotely Delivered Strength and Balance Training Program for Older Adults with Cancer
Bibliographic record
Abstract
Falls are a major issue among older adults with cancer and lead to interruptions in cancer treatment. Resistance and balance training can prevent falls in older adults, but minimal evidence is available regarding the older cancer population, who often have unique risk factors. We used a pre–post design to assess the feasibility of a remotely delivered exercise program that progressed in difficulty and its efficacy on lower body strength, balance, and falls in older adults with cancer who had prior in-person exercise experience. Twenty-six older adults with cancer completed the intervention. Attendance rate for the virtual component was 97.6% and for the independent component was 84.7%. Participants perceived the program as rewarding and enjoyable (100%), felt this program prepared them to exercise on their own (92%), were confident to continue exercising on their own (81%), and would recommend the program to other patients (100%). The median balance score at baseline and end-of-study was 4 (IQR = 0). The median chair-stand time decreased from 9.2 s (IQR = 3.13) to 7.7 s (IQR = 4.6). A statistically significant difference in lower body strength (r = 0.68, p = 0.001) was detected post-intervention. The findings from this study can inform the design of a larger randomized trial.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".