Exploring Older Adults' Perceptions of Stair Hazards and an m-health Fall Prevention App: A Focus Group Study
Bibliographic record
Abstract
Older adults are disproportionately susceptible to hospitalizations and fatalities due to stair-related falls. While many intrinsic risk factors, such as mobility and vision, may increase the likelihood of falls on stairs, features of the stairs that increase the risk of falls are understudied. This study aimed to capture older adults&rsquo; perspectives of stair falls, as well as introduce the Safer Steps app and explore its feasibility in this population. This m-health technology was designed to gather data on stair-related falls and identify hazardous stair features (e.g., the absence of handrails, uneven steps). Capturing older adults&rsquo; perspectives of the role of the built environment in stair-related falls is crucial for fostering the adoption and use of the Safer Steps app in this demographic. Fifteen older adults (<em>M</em> = 73 years, SD = 5.29) participated in focus groups discussing falls, stair-related falls, technology use and the design concept of the Safer Steps app. Reflexive thematic analysis revealed that participants expressed fear of falling and cited intrinsic risk factors, such as age, medications, and footwear, alongside extrinsic risk factors related to the built environment, to be major causes of stair-related falls. They highlighted the significance of the built environment in fall prevention, particularly surface conditions, step dimensions, and handrails. Most participants were familiar with apps and endorsed the Safer Steps app design to mitigate stair-related fall risk. Findings indicate older adults&#39; willingness to engage in strategies which reduce stair fall risk, such as modifying their behaviour by using handrails and changing footwear, viewing the Safer Steps app as a practical tool for identifying built environment hazards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".