Feasibility of using quadriceps-strengthening exercise to improve pain and sleep in a severely demented elder with osteoarthritis – a case report
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis (OA) of the knee, which is prevalent among older adults in nursing homes, causes significant pain and suffering, including disturbance of nocturnal sleep. One nonpharmacologic treatment option is quadriceps-strengthening exercise, however, the feasibility of such a treatment for reducing pain from OA in severely demented elders has not been studied. This report describes our test of the feasibility of such an exercise program, together with its effects on pain and sleep, in a severely demented nursing home resident. CASE PRESENTATION: The subject was an elderly man with severe cognitive impairment (Mini-Mental Status Exam score 4) and knee OA (Kellgren-Lawrence radiographic grade 4). He was enrolled in a 5-week, 10-session standardized progressive-resistance training program to strengthen the quadriceps, and completed all sessions. Pain was assessed with the Western Ontario and MacMaster OA Index (WOMAC) pain subscale, and sleep was assessed by actigraphy.The patient was able to perform the exercises, with a revision to the protocol. However, the WOMAC OA pain subscale proved inadequate for measuring pain in a patient with low cognitive functioning, and therefore the effects on pain were inconclusive. Although his sleep improved after the intervention, the influence of his medications and the amount of daytime sleep on his nighttime sleep need to be considered. CONCLUSIONS: A quadriceps-strengthening exercise program for treating OA of the knee is feasible in severely demented elders, although a better outcome measure is needed for pain.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".