Development and evaluation of a novel virtual agent-based app for patients with colorectal cancer: A mixed methods study
Bibliographic record
Abstract
Background and aim: Information support is an integral part of cancer care, but its provision can be problematic in busy health settings. The aim of this project was to develop and evaluate a health app to facilitate the provision of information support in newly diagnosed patients with colorectal cancer (CRC). Instead of delivering information using text, three animated embodied virtual agents (VAs) were deployed. The VAs were formulated after patients’ treating clinicians (male oncologist, female nurse and female pharmacist) to explore the role of familiarity, which has not been addressed in previous research. \n \nStudy methods: A multi-stage development process was followed for the app, which was provided to the study participants before the beginning of their treatment. A convergent parallel mixed methods design involving pre- and post-exposure questionnaires (adapted versions of the Toronto Information Needs Questionnaire and the System Usability Scale), app usage data and semi-structured interviews was deployed to evaluate the intervention. \n \nResults and discussion: The app was acceptable by the end users and had a good degree of usability (mean System Usability Scale score=73.89). The information content was appropriate and met patients’ demands to a moderate extent; this was because patients utilised other information sources (e.g., printed material) to address their needs. Incorporating supportive functions such as a medicinal calendar in addition to the information content emerged as an important aspect. \nThe inclusion of VAs was deemed to be appropriate. The VAs fostered a sense of presence, added trustworthiness to the information content and were perceived as more interactive than reading text. Having a VA representing a familiar clinician was favoured by most users. The vast majority of patients perceived the VAs as cartoon figures and suggested that they should be improved to look realistic in order to give the impression of having an exchange with a real person. Natural voices were preferred over synthetic speech. \n \nConclusion: VA-based mHealth interventions are an acceptable way of supporting patients with CRC. Appropriate consideration should be given to the requirements of the intended user audience to design acceptable interventions that reflect their needs.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 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".