Feasibility and usability of an electronic patient (pt)-reported outcome (ePRO) smartphone application (app) and biosensor for pts with cancer undergoing systemic therapy.
Bibliographic record
Abstract
444 Background: Almost half of the nearly 370,000 pts with cancer who receive chemotherapy in the United States each year experience Emergency Department (ED) visits and unplanned inpatient (IP) stays during treatment, largely due to poorly controlled symptoms. Recent studies show that using PROs in oncology practice can improve symptom management and pt outcomes. This study examines the feasibility and usability of a PRO app paired with a biosensor to identify pts at high risk of ED and IP visits during systemic therapy. Methods: ML41539 is an ongoing prospective observational study evaluating the feasibility and usability of a clinic-provided app and smartwatch biosensor for monitoring pts undergoing systemic cancer therapy (ISRCTN25569053). Key inclusion criteria are 18–80 years old, Eastern Cooperative Oncology Group Performance Status 0–2, biopsy-proven solid tumor diagnosis (excluding non-melanoma skin cancer), and scheduled to receive a first dose of intravenous or oral cancer therapy as an initial or new line of therapy. Exclusion criteria include receiving radiation or hormone therapy only, residing in a skilled nursing facility, or participating in another clinical trial. The app collects 15 common treatment-related symptoms (PRO-CTCAE) daily. Usability and satisfaction were assessed with the modified mHealth App Usability Questionnaire (mMAUQ: score 1-7; higher score indicates better app usability and satisfaction) and the modified Quebec User Evaluation of Satisfaction with Assistive Technology (mQUEST 2.0: score 1-5; higher score indicates better sensor satisfaction). We report planned analysis results of the first 32 pts in the vanguard phase of the trial. Pts wore the biosensor and recorded symptoms on the app for 2 weeks. Results: Thirty-two pts from three Washington State community oncology clinics consented to the vanguard phase. One pt was not onboarded; two dropped out before completing the 2-week observation period. The mean age was 60 years; 68% were women. The most common cancer types were breast (41%), colorectal (13%), endometrial (9%), and melanoma (9%). Of the 29 pts, 59% completed all daily ePRO assessments and 55% wore the sensor every day during the 2-week period. The overall adherence rate was 91% (370/406 assessments) for ePRO and 86% (349/406 biosensor days) for the biosensor. The average mMAUQ score was 6.25 (n = 26); the average mQUEST score was 4.02 (n = 25). Conclusions: Pts receiving systemic cancer therapy had relatively high adherence to a daily digital monitoring system that included an ePRO app and biosensor. Participants expressed moderately high usability of the app and satisfaction with the biosensor. The results support the feasibility of monitoring pts with an app and biosensor. Future studies to assess adherence and data completeness for full courses of systemic therapy are needed. Clinical trial information: ISRCTN25569053.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".