Stand When You Can: Feasibility of a Multilevel, Multisite Sedentary Behavior Intervention in Assisted-Living Residences
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Prolonged sedentary time poses health risks for older adults, and residents in assisted living spend more time sedentary than peers who live independently. The purpose of this study was to examine the feasibility of Stand When You Can, a sedentary behavior intervention designed to encourage residents of assisted living to stand and move throughout the day without requiring structured program delivery or additional staff time. METHODS: Five assisted-living residences in three provinces volunteered to use the Stand When You Can toolkit for 12 weeks. Feasibility outcomes included recruitment capability for ambassadors and participants, participant characteristics, and pre-post assessment of target outcome measures. Intervention fidelity was assessed at 6 weeks using on-site observations and informal interviews with staff and participants. Target outcomes included movement behaviors, physical function, and health status. RESULTS: Sixty-eight older adults volunteered to participate, and a staff ambassador volunteered from each site. Only one site demonstrated good intervention fidelity by implementing a variety of different strategies to reduce sedentary time. Assessment of target outcomes was feasible, although there was significant attrition and >50% of individual participants did not complete follow-up testing at 12 weeks. CONCLUSION: While Stand When You Can was designed to be embedded in normal daily activities and require minimal staff time, it may not be feasible for most assisted-living residences to implement without additional staff. SIGNIFICANCE: To help residents maintain function and quality of life, assisted-living residences must invest in adequate staff with the time and tools to promote a culture of movement.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".