Smart Grab Bars: A Potential Initiative to Encourage Bath Grab Bar Use in Community Dwelling Older Adults
Bibliographic record
Abstract
Grab bars are often prescribed to ensure safe and independent bathing and toileting. Studies have shown that seniors do not always use grab bars when they are present or are reluctant to install them due to the associated stigma. This study sought to determine if artificial intelligence could increase grab bar use by seniors and to determine the efficacy of different cues (auditory, visual, and audiovisual combination) on the frequency of use of a grab bar. Sixty-nine healthy participants aged 60 to 86 years (average 68.7 years) were randomly assigned to three subgroups. Each subgroup tested two different cueing conditions: the no cue and one of three cued conditions (visual, auditory, or combined audio-visual). Results suggest that the smart grab increased seniors' grab bars use by 39% and that the effect was maintained after removal of the cues. Participants preferred the visual cue but the auditory cue was the most powerful. Results suggest that artificial intelligence may be an interesting avenue to increase grab bar use in community-dwelling older adults and in people requiring supervision to use grab bars on a regular basis to decrease the risk of falls during bathing or bathtub transfers.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".