Usability Evaluation of a Mobile Phone–Based System for Remote Monitoring and Management of Chemotherapy-Related Side Effects in Cancer Patients: Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: As most chemotherapy is administered in the outpatient setting, patients are required to manage related side effects at home without direct support from health professionals. The Advanced Symptom Management System (ASyMS) has been developed to facilitate the remote monitoring and management of chemotherapy-related toxicity in patients with cancer, using patient-reported outcomes questionnaires and a clinician alerting system. OBJECTIVE: This study aims to evaluate the usability of the ASyMS, a mobile phone-based technology, from the perspective of Canadian patients with cancer receiving chemotherapy to identify existing design, functionality, and usability issues and elicit their views, experiences, and satisfaction with the ASyMS. METHODS: We used a mixed-method approach to data collection with user-based testing, a think-aloud technique, semistructured interviews, and short answer questionnaires with a purposive sample of 10 patients with cancer. Participants attended usability testing sessions at the Centre for Global eHealth Innovation, University Health Network, and performed specific tasks on the ASyMS device. The test was videorecorded and each task was timed during the test. After the usability sessions, participants completed a posttest questionnaire and participated in a semistructured qualitative interview. A thematic analysis was used to code and categorize the identified issues into themes that summarized the type and frequency of occurrence. RESULTS: The thematic analysis generated 3 overarching themes as follows: ASyMS user-friendliness; usefulness of ASyMS (content quality and richness); and intention to use. Results from the posttest questionnaire indicated that 80% (8/10) of participants had great motivation to use the ASyMS, 70% (7/10) had positive perceptions of the successful use of the ASyMS, and all (10/10, 100%) had a positive attitude toward using the ASyMS in the future. Most identified design and functionality issues were related to the navigation of the ASyMS device and a desire for a more attractive design with advanced functionality and features. The main general design recommendations were as follows: enhance the readability of the screen; implement advance options (eg, search option); and support better navigation. CONCLUSIONS: The ASyMS has shown positive perceptions of patients in usability testing and qualitative interviews. An evaluation of the effects of the ASyMS on symptom outcomes in a clinical trial is needed.
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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".