A post-market, cluster randomised controlled trial of the effect of the TENA SmartCare Change Indicator on continence care efficiency and skin health in nursing homes
Bibliographic record
Abstract
BACKGROUND: Urinary Incontinence (UI) is highly prevalent in older residents of nursing homes. For many, management consists of using appropriate products (pads) maintained by a check and change regimen; this is often poorly managed. Digital health technology devices, like urine saturation sensors, may aid ensure timely change of pads. This study examined the efficacy and safety of the use of the TENA SmartCare Change Indicator (Change Indicator) device for nursing home residents with UI. METHODS: Cluster randomised controlled trial of device use in older nursing home residents with UI, compared to usual care. Co-primary outcomes were a compound care efficiency score and change in skin health. Secondary resident and caregiver outcomes were also compared. RESULTS: Fourteen sites were recruited, nine of which operated routine check and change regimens. Included units comprised 108 residents, 53 in the intervention group and 49 in the usual care group and 83 caregivers (31 usual care, 52 intervention). The median age of residents was 87 (range 59-101) years; 21.5% were male. Care efficiency was improved by 30 min/day in the intervention group and 16 min/day in the usual care group (P > .05). There was no change in skin health. There were statistically significant improvements in sleep quality and a total of 24 hour absorbency of pads used, favouring device use. One device related harm was reported. DISCUSSION: This comparative trial of a device designed to improve the delivery of continence care to older residents of nursing homes resulted in reductions in time spent in continence care by 30 min/day, or a 31% reduction. Given that the study failed to demonstrate its primary outcome, it is difficult to assess the clinical relevance of device use. The reduction in pad use and in number of sleep interruptions are valuable outcomes. A longer-term implementation and efficacy study is warranted.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".