Pain management in advanced cancer: physical activity as an outcome – accelerometer feasibility study
Bibliographic record
Abstract
OBJECTIVE: Cancer pain is a common distressing symptom. Numerical Pain Scales (NPS) assess pain but lack information about function and quality of life. This feasibility study assesses the use of triaxial accelerometers to measure function as an outcome measure in pain studies in advanced cancer. METHODS: Advanced cancer participants were recruited from two palliative care services, with an average pain score of ≥3 on NPS. ActiGraph wGT3X-BT Accelerometers were worn for 1 week on the wrist. Patients recorded daily pain scores, Edmonton Symptom Assessment Scale (ESAS) scores, and their daily opioid use. RESULTS: 24 participants were recruited. A total of 142 days of accelerometer data was collected (5.9 days/participant). The average daily step count was 5723.7. The average acceleration was 14.4 milligravity units/day. An average of 93 min/day total activity across all intensities was recorded. No correlation was seen between acceleration or average daily minutes in activity and total daily oral morphine equivalent, ESAS, 'average pain' score or 'worst pain' scores using spearman's correlation coefficients. Overall, participants were satisfied with the study. CONCLUSIONS: Accelerometers are a feasible method to measure activity as an outcome measure in advanced cancer. Further study is required to assess the impact of pain management strategies on function.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".