A Usability Study of Patients Setting Up a Cardiac Event Loop Recorder and BlackBerry Gateway for Remote Monitoring at Home
Bibliographic record
Abstract
This article reports on a usability study of remote noninvasive cardiac testing in homes. We studied the Vitaphone 3100BT (Bluetooth®) event loop recorder (Vitaphone GmbH, Mannheim, Germany) and paired BlackBerry® Curve™ 8520 smartphone (Research In Motion, Ltd., Waterloo, ON, Canada). This application requires independent device set-up by patients in their own homes following receipt by mail out of the kit (instructions plus the event loop recorder and smartphone). The case studies of five participants, each with varying experience with technology, were documented as they interacted with the devices. Participants were videotaped following written instructions as they performed a "think aloud" procedure while completing 20 device set-up tasks. Interviews provided insight into how the independent device set-up and processes could be improved. This study concluded that gender, age, and familiarity with technology seemed to influence the participants' abilities to successfully set up these devices and that sending the kit by mail appeared to be an acceptable strategy to provide remote noninvasive cardiac diagnostic services. This study provides a foundation for future research assessing usability of mobile healthcare technology.
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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".