PERSON-CENTERED WALKING TO MAINTAIN THE MOBILITY, ADL FUNCTION, AND QUALITY OF LIFE OF LTC RESIDENTS
Bibliographic record
Abstract
Aim: To determine the efficacy of a person-centered multifaceted walking intervention (MWI) to maintain the mobility, ADL function, and QOL of LTC residents with dementia. Method: An interrupted time-series design was used. Measures of mobility (2-minute walk test[2MWT], Timed-Up-and-Go[TUG], gait speed), ADL function (Functional Independence Measure[FIM]), and QOL (Alzheimer’s Related Quality of Life Scale[ARQOL]) were collected before and after the 2-month pre-MWI phase, and at the middle and end of the 4-month MWI phase. The MWI consisted of a one-to-one walking regime and an individualized communication care plan tailored to the resident provided up to 4x/week for 4 months. RM-ANOVA was used to evaluate MWI efficacy. Results: All eligible residents (n=26), PSW staff (n=21) and power of attorneys (n=25) enrolled in the study. During the pre-MWI phase, residents experienced a significant decline in mobility: TUG increased by 4.4% (mean difference= 4.15 sec., P=0.01), 2MWT decreased by 9.7% (mean difference= -5.78 m., P=0.03), gait speed decreased by 11.3% (mean difference= -0.05 m/sec, P=0.022), decline in ADL function (mean difference= -17.88, P=0.03), and a loss of QOL (mean difference= -1.84, P=0.030). During the MWI phase, the TUG improved by 32.1% (mean difference= -8.58, P=0.000), 2MWT improved by 51.2% (mean difference= 27.47, P= 0.000), gait speed improved by 55.1% (mean difference= 0.23, P=0.000), ADL function increased by 25% (mean difference= 15.60, P=0.000), and QOL increased by 7.8% (mean difference= 2.44, P=0.063). Conclusion: Findings provide preliminary evidence for a future trial, and a greater understanding of the role of person-centered care in delivering PA in LTC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".