Overcoming Challenges to Inclusive User-based Testing of Health Information Technology with Vulnerable Older Adults: Recommendations from a Human Factors Engineering Expert Inquiry
Bibliographic record
Abstract
OBJECTIVES: Involving representative users in usability testing of health information technology (HIT) is central to user-centered design. However, (vulnerable) older adults as representative users have unique requirements. Aging processes may affect physical capabilities and cognitive skills, which can hamper testing with this demographic and may require special attention and revised protocols. This study was performed to provide expert-based recommendations for HIT user-testing with (vulnerable) older adults to support inclusive HIT design and evaluation. METHODS: First, we conducted a structured workshop with ten experts in HIT implementation and research, recruited through purposeful sampling, to generate insights into how characteristics of older adults may influence user-testing. Next, five Human Factor researchers experienced in HIT user-testing with (vulnerable) older adults validated the results and provided additional textual insights to gain consensus on the most important recommendations. A thematic analysis was performed on the resulting inquiries. Applied codes were based on the User-Centered Design framework. RESULTS: The analysis resulted in nine recommendations for user-testing of HIT with older adults, divided into three main themes: (1) empathetic approach and trust-building, (2) new requirements for testing and study design, and (3) adjustments to usability evaluation methods. For each theme a checklist of relevant items to follow-up on the recommendation is provided. CONCLUSIONS: The recommendations generated through expert inquiry contribute to more effective usability testing of HIT with older adults. This provides an important step towards improved accessibility of HIT amongst older adults through inclusive user-centered design.
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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 | 0.314 | 0.380 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".