Usability-In-Place—Remote Usability Testing Methods for Homebound Older Adults: Rapid Literature Review
Bibliographic record
Abstract
BACKGROUND: Technology can benefit older adults in many ways, including by facilitating remote access to services, communication, and socialization for convenience or out of necessity when individuals are homebound. As people, especially older adults, self-quarantined and sheltered in place during the COVID-19 pandemic, the importance of usability-in-place became clear. To understand the remote use of technology in an ecologically valid manner, researchers and others must be able to test usability remotely. OBJECTIVE: Our objective was to review practical approaches for and findings about remote usability testing, particularly remote usability testing with older adults. METHODS: We performed a rapid review of the literature and reported on available methods, their advantages and disadvantages, and practical recommendations. This review also reported recommendations for usability testing with older adults from the literature. RESULTS: Critically, we identified a gap in the literature-a lack of remote usability testing methods, tools, and strategies for older adults, despite this population's increased remote technology use and needs (eg, due to disability or technology experience). We summarized existing remote usability methods that were found in the literature as well as guidelines that are available for conducting in-person usability testing with older adults. CONCLUSIONS: We call on the human factors research and practice community to address this gap to better support older adults and other homebound or mobility-restricted individuals.
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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 | 0.019 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".