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Record W2903704216 · doi:10.2196/humanfactors.9481

Complementing a Clinical Trial With Human-Computer Interaction: Patients’ User Experience With Telehealth

2018· article· en· W2903704216 on OpenAlexvenueno aff
Sakib Jalil, Trina Myers, Ian Atkinson, Muriel Soden

Bibliographic record

VenueJMIR Human Factors · 2018
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsTelehealthTelemedicineClinical trialHuman–computer interactionComputer scienceMedicineInternal medicineHealth care

Abstract

fetched live from OpenAlex

BACKGROUND: The use of telehealth to monitor patients from home is on the rise. Telehealth technology is evaluated in a clinical trial with measures of health outcomes and cost-effectiveness. However, what happens between a technology and the patients is not investigated during a clinical trial-the telehealth technology remains as a "black box." Meanwhile, three decades of research in the discipline of human-computer interaction (HCI) presents design, implementation, and evaluation of technologies with a primary emphasis on users. HCI research has exposed the importance of user experience (UX) as an essential part of technology development and evaluation. OBJECTIVE: This research investigates the UX of patients with type 2 diabetes mellitus (T2D) with a telehealth in-home monitoring device to manage T2D from home. We investigate how the UX during a clinical trial can be researched and what a clinical trial can learn from HCI research. METHODS: We adopted an ethnographic philosophy and conducted a contextual inquiry due to time limitations followed by semistructured interviews of 9 T2D patients. We defined the method as Clinical User-experience Evaluation (CUE). The patients were enrolled in a telehealth clinical trial of T2D; however, this research was an independent study conducted by information technologists and health researchers for a user-centered evaluation of telehealth. RESULTS: Key analytical findings were that patients valued the benefits of in-home monitoring, but the current device did not possess all functionalities that patients wanted. The results include patients' experiences and emotions while using the device, patients' perceived benefits of the device, and how patients domesticated the device. Further analysis showed the influence of the device on patients' awareness, family involvement, and design implications for telehealth for T2D. CONCLUSIONS: HCI could complement telehealth clinical trials and uncover knowledge about T2D patients' UX and future design implications. Through HCI we can look into the "black box" phenomenon of clinical trials and create patient-centered telehealth solutions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.140
GPT teacher head0.476
Teacher spread0.336 · 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
Published2018
Admission routes1
Has abstractyes

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