ACTIVOT: PRELIMINARY RESULTS OF AN INTERVENTION TO IMPROVE HEALTH-PROMOTING DAILY ACTIVITY
Bibliographic record
Abstract
Abstract Nearly half of older adults in the U.S. have multiple chronic conditions in addition to functional limitations that restrict health-promoting daily activity (e.g., sleep, medication-taking, physical activity). We tested an innovative combination of behavioral activation and occupational therapy techniques to improve performance of meaningful, healthy activity. The ActivOT intervention was delivered by occupational therapists in 10 weekly sessions in participant homes. The primary outcome is performance of health-promoting daily activity, measured by the Canadian Occupational Performance Measure (COPM) at baseline, 10-weeks, and 22-weeks. We report outcome data from 17 participants who were randomized to ActivOT or brief education (n=9 Tx, n=8 Control). On average, participants were 73.0 years old (SD=6.9), majority female (n=13), and White (n=1 Black, n=1 Hispanic/Latino). They had an average of 3.9 chronic conditions (SD=1.4), and 1.9 functional limitations (SD=1.7). The average COPM performance score (out of 10) at baseline was similar for the ActivOT group (M=3.6, SD=1.6) and control group (M=3.3, SD=1.1). At 10-week follow-up, the ActivOT group average increased to 7.0 (SD=1.7) and further increased to 7.2 (SD=1.0) at 22-weeks. In comparison, the control group average increased to 5.9 (SD=2.0) at 10-weeks, then decreased to 5.0 (SD=2.2) at 22-weeks. Further, 89% percent of the ActivOT group reported clinically significant improvements on the COPM (MCID=3). Results show an initial increase in both groups, but the increases are only maintained in the ActivOT group. This study is in progress (aiming for n=40) but shows promise for improving performance of health-promoting daily activity in a difficult-to-treat population.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".