Requirements for mHealth and Augmented Reality Apps for Patient Education Regarding Colorectal Cancer Surgery: Focus Group Study
Bibliographic record
Abstract
Background: The purpose of preoperative informed consent is to provide patients with comprehensive information about their treatment, including risks and alternatives, to enable informed decision-making. However, studies have shown that patients are often unable to understand or remember important information. Mobile health (mHealth) and augmented reality (AR) apps have been identified as promising solutions to improve patient education and knowledge retention. Objective: This study aims to identify the essential requirements for an mHealth app to support informed decision-making for patients with colorectal cancer, with a specific focus on the potential of AR for visualization. This research explores the patient and physician perspectives on these requirements, particularly regarding information delivery and visualization to guide app design. Methods: A qualitative focus group study was conducted with groups of mostly patients with colorectal cancer and a physician's group. Topics related to patient education were discussed, guided by a semistructured interview guide covering personal experience; information content; context of use; and acceptance and presentation of content, which included presenting various visualizations in 2D, 3D, and AR. The interviews were transcribed and analyzed using qualitative content analysis. Results: We conducted 4 focus groups with patients (n=23) and 1 focus group with physicians (n=7), for a total of 30 participants. Relevant informational content for the app and its presentation was identified. Patients consistently expressed a desire for personalized, detailed, and visual information about their condition and treatment tailored to their specific case throughout the treatment journey, so they could prepare for the informed consent discussion after diagnosis, prepare for treatment, access guidance and track progress during hospitalization, and access information and resources during recovery after treatment. Patients demonstrated a strong preference for interactive 3D visualizations, while physicians favored simpler 2D images that could be easily integrated into their existing workflow. AR visualizations were seen as a potential tool to provide a general overview of anatomy and surgical approaches but more as a novelty feature and a supplement to more traditional visualizations. Conclusions: An ideal patient education app combines comprehensive content with interactive, customizable visualizations like 3D models and AR and should be accessible throughout a patient's treatment journey. This study highlights the need for a patient-centered design that balances detailed information with ease of understanding and considering different preferences for visualization modalities and levels of detail.
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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".