Days at Home for Older Adults Receiving a Remote Monitoring Intervention Compared with Usual Home Care Recipients
Bibliographic record
Abstract
OBJECTIVES: Many older adults will experience physical and or cognitive decline, limiting their ability to live independently. To better support these individuals, remote monitoring programs use technology to track patient falls, location, and medication adherence. Our main objective was to compare outcomes of patients enrolled in a remote monitoring program with matched individuals who received home care. DESIGN: Propensity score-matched cohort study. SETTING AND PARTICIPANTS: Toronto, Ontario, Canada. Older adults (aged 65 or older) in a remote monitoring program (N = 1587) compared with a control group of older adults initiating a new episode of home care (N = 33,207). METHODS: The primary outcome was days spent at home in the following 100 days. Additional outcomes included days in different health care settings, days alive, and health care costs. RESULTS: Before matching, patients enrolled in the remote monitoring program (946 female) were younger [mean (SD) = 80.1 years (8.2) vs 82.4 (8.5), standardized mean difference 0.21] than those in the home care group (20,877 female). After matching, there were 1494 individuals in each group. In the 100 days following the index date, those in the remote monitoring group spent 3.4 more days at home than the home care group (92.3 vs 88.9 days, 95% CI, 1.9-5.0 days; rate ratio, 1.04; 95% CI, 1.02-1.06). This was attributable to more days alive (98.6 vs 96.4 days; 95% CI, 1.3-3.1 days; rate ratio, 1.03; 95% CI, 1.01-1.03). In addition, the remote monitoring group had lower health care costs overall (difference of $1635.54 CAD, 95% CI, $228.83-$3087.11). CONCLUSIONS AND IMPLICATIONS: Individuals enrolled in a remote monitoring program spent more days at home compared with those who received home care services, mainly attributed to more days alive. Further study is needed to confirm our findings; however, remote monitoring is a promising solution to support older adults with high care needs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".