Acceptance of New Technology: A Usability Test of a Computerized Adaptive Test for Fatigue in Rheumatoid Arthritis
Bibliographic record
Abstract
BACKGROUND: Little is known about the acceptance and usability of computerized adaptive tests (CATs) among patients with rheumatoid arthritis (RA). The main difference between completing a CAT and a traditional questionnaire concerns item presentation. CATs only provide one item at a time on the screen, and skipping forward or backward to review and change already given answers is often not possible. OBJECTIVE: The objective of this study was to examine how patients with RA experience a Web-based CAT for fatigue. METHODS: In individual sessions, participants filled in the CAT while thinking aloud, and were subsequently interviewed about their experience with the new instrument. The technology acceptance model (TAM) was used to structure the results. RESULTS: The participants were 15 patients with RA. They perceived the CAT as clear, brief, and easy to use. They were positive about answering one question per screen, the changing response options, layout, progress bar, and item number. There were 40% (6/15) of the participants that also mentioned that they experienced the completion of the CAT as useful and pleasant, and liked the adaptive test mechanism. However, some participants noted that not all items were applicable to everybody, and that the wordings of questions within the severity dimension were often similar. CONCLUSIONS: Participants perceived the "CAT Fatigue RA" as easy to use, and also its usefulness was expressed. A 2.0 version has been improved according to the participants' comments, and is currently being used in a validation study before it will be implemented in daily clinical practice. Our results give a first indication that CAT methodology may outperform traditional questionnaires not merely on measurement precision, but also on usability and acceptance valuation.
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 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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".