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Record W2071976249 · doi:10.1089/tmj.2011.0230

A Usability Study of Patients Setting Up a Cardiac Event Loop Recorder and BlackBerry Gateway for Remote Monitoring at Home

2012· article· en· W2071976249 on OpenAlexaffabout
Jane Sparkes, Ruta Valaitis, Ann McKibbon

Bibliographic record

VenueTelemedicine Journal and e-Health · 2012
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsUsabilityEvent (particle physics)Set (abstract data type)BluetoothComputer scienceMobile deviceReceiptPsychologyHuman–computer interactionWorld Wide WebTelecommunicationsWireless

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.066
GPT teacher head0.445
Teacher spread0.379 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2012
Admission routes2
Has abstractyes

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