Capturing Usability Problems for People Living With Dementia by Applying the DEMIGNED Principles in Usability Evaluation Methods: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Dementia-related impairments can cause complex barriers to access, use, and adopt digital health technologies (DHTs). These barriers can contribute to digital health inequities. Therefore, literature-based design principles called DEMIGNED have been developed to support the design and evaluation of DHTs for this rapidly increasing population. OBJECTIVE: This study aims to apply the DEMIGNED principles in usability evaluation methods to (1) capture usability problems on a mobile website providing information resources for people visiting a memory clinic, including those living with subjective cognitive decline (SCD), mild cognitive impairment (MCI), or dementia, and (2) investigate the realness of usability problems captured by the DEMIGNED principles in expert testing, specifically for mobile websites that act as a means of providing DHTs. METHODS: First, a heuristic evaluation was conducted, with the DEMIGNED principles serving as domain-specific guidelines, with 3 double experts (experienced in both usability and dementia) and 2 usability engineering experts. Second, think-aloud sessions were conducted with patients visiting a memory clinic who were living with SCD, MCI, or dementia. RESULTS: The heuristic evaluation resulted in 36 unique usability problems. A representative sample of 7 people visiting a memory clinic participated in a think-aloud session, including 4 (57%) with SCD, 1 (14%) with MCI, and 2 (29%) with dementia. The analysis of the think-aloud sessions revealed 181 encounters with usability problems. Of these encounters, 144 (79.6%) could be mapped to 18 usability problems identified in the heuristic evaluation. The remaining 37 (20.4%) encounters from the user testing revealed another 10 unique usability problems. Usability problems frequently described in the think-aloud sessions encompassed difficulties with using the search function, discrepancies between the user's expectations and the content organization, the need for scrolling, information overload, and unclear system feedback. CONCLUSIONS: By applying the DEMIGNED principles in expert testing, evaluators were able to capture 79.6% (144/181) of all usability problem encounters in the user testing of a mobile website for people visiting a memory clinic, including people living with dementia. Regarding unique usability problems, 50% (18/36) of the unique usability problems identified during the heuristic evaluation were captured by the user-testing sessions. Future research should look into the applicability of the DEMIGNED principles to other digital health functionalities to increase the accessibility of digital health and decrease digital health inequity for this complex and rapidly increasing population.
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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 | 0.022 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".