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Record W4411594043 · doi:10.2196/66854

Understanding Barriers to Home Safety Assessment Adoption in Older Adults: Qualitative Human-Centered Design Study

2025· article· en· W4411594043 on OpenAlexvenueno aff
Jonathan J. Lee, Devika Patel, Meghana Gadgil, Simone Langness, Christiana von Hippel, Amanda Sammann

Bibliographic record

VenueJMIR Human Factors · 2025
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsGerontologyQualitative researchMedicineFall preventionRisk assessmentHuman factors and ergonomicsPsychologyPoison controlNursingEnvironmental health

Abstract

fetched live from OpenAlex

Background: Falls are the leading cause of injury-related death among adults aged 65 and older. The fear of falling can further limit older adults' independence by contributing to activity restriction, social isolation, and physical decline-ironically increasing the risk of mechanical falls. Although home safety assessments have been shown to reduce fall risk by up to 36% and decrease serious injuries such as hip fractures, their adoption remains low. Understanding the barriers to implementing these assessments is critical to improving their uptake and effectiveness. Objective: This study aimed to (1) identify specific barriers perceived by older adults in implementing home safety assessments and modifications to reduce the risk of mechanical falls, (2) explore the attitudes of health care professionals and other stakeholders toward these assessments, and (3) identify novel design opportunities to guide the development and implementation of more effective home safety assessment techniques and practices to reduce mechanical fall risk. Methods: This explanatory qualitative study drew on the "inspiration" phase of the human-centered design (HCD) research process. We conducted 35 interviews (28 initial and 7 follow-up) with 28 purposefully sampled participants in the San Francisco Bay Area between February and June 2021. Participants included community-dwelling older adults (n=3), geriatricians (n=4), therapists (n=6), product developers (n=2), older health researchers (n=8), and community program leaders (n=5). Interview notes were analyzed inductively by the research team to extract themes and generate insight statements and design opportunities. Results: Analysis yielded three key insights: (1) older adults often experience a conflict between maintaining independence and implementing safety modifications. One participant described living with a "repeating mantra in my head throughout the day saying 'above all, don't fall.'" (2) aesthetic and privacy concerns frequently override safety benefits. Participants rejected modifications that made their homes feel "institutional." (3) access to occupational therapy services-already limited in rural areas-was further constrained by the COVID-19 pandemic, with some providers reporting that travel time "took up the majority of their day just assessing one home." These barriers help explain the low adoption of home safety assessments despite strong supporting evidence. The study identified design opportunities to address these challenges, including customizable, user-friendly safety solutions, dignity-preserving approaches to assessment, and technology-enabled remote alternatives. Conclusions: This study identified specific emotional, aesthetic, logistical, and access-related barriers to the adoption of home safety assessments among older adults. The proposed design solutions offer promising directions to increase uptake, improve user experience, and enhance safety. However, further validation through co-design with a larger and more diverse group of older adults is needed. Future research should pilot test these ideas across varied contexts and evaluate their implementation and impact.

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 imitation

Not 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.

metaresearch head score (Codex)0.035
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.183

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0060.006
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.162
GPT teacher head0.484
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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