Evaluating the feasibility of using an electronic patient-reported outcome (ePRO) smartphone application (app) and biosensor by patients with cancer undergoing systemic treatments.
Bibliographic record
Abstract
TPS1599 Background: Almost half of the nearly 370,000 patients with cancer who receive chemotherapy in the United States each year experience Emergency Department (ED) visits and unplanned hospital inpatient (IP) stays during treatment, largely due to poorly controlled symptoms. Recent studies have shown that utilizing PRO information in oncology practice can improve symptom management and patient outcomes. This study aims to examine the feasibility and usability of a PRO app paired with a biosensor to identify patients who are at high risk for ED and IP visits. Methods: This prospective, pragmatic, observational study will evaluate the feasibility and usability of a clinic-provided smartphone app and smartwatch biosensor for monitoring patients undergoing systemic cancer treatment. Eligible patients are 18–80 years old, ECOG PS 0–2, have a biopsy-proven solid tumor diagnosis of cancer (excluding non-melanoma skin cancer), and are scheduled to receive the first dose of intravenous (IV) or oral cancer therapy as an initial or new line of treatment. Patients should be able to provide informed consent, wear the biosensor daily, and complete the app ePRO survey and questionnaires in English. Study exclusion criteria include receiving radiation or hormone therapy only, residing in a skilled nursing facility, participating in another clinical trial, current pregnancy, and wearing pacemakers, implantable cardioverter defibrillators, cochlear implants, and/or neurostimulator devices. The app collects PROs (PRO-CTCAE), app usability and satisfaction (modified mHealth App Usability Questionnaire [mMAUQ]) and patient satisfaction with the biosensor (modified Quebec User Evaluation of Satisfaction with Assistive Technology [QUEST 2.0]). The study is divided into two phases: (1) vanguard (N = 30); (2) operational (N = 70). Patients will be asked to wear the biosensor and enter PROs into the app daily for a 2-week (vanguard) or 6-week period (operational). The vanguard sample size allows for the recruitment of ̃10 patients at each of the three participating oncology community clinics as is standard for initial device and software testing and development. Study endpoints for feasibility include: (1) vanguard – patient recruitment and protocol adherence, completeness of data capture, app usability, user satisfaction of biosensor; (2) operational – validity of self-reported hospital visits, feasibility of using electronic case report forms. Data collected from the vanguard will inform modifications to the app for the operational phase. The operational phase sample size is sufficient to assess data capture completion and clinical trial recruitment procedures in diverse practice settings (e.g., low volume vs. high volume, rural vs. urban). 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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".