Development of an App for Symptom Management in Women With Breast Cancer Receiving Maintenance Aromatase Inhibitors: Protocol for a Mixed Methods Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Patients with postmenopausal nonmetastatic estrogen receptor-positive breast cancer often experience a reduced quality of life after primary treatment. The disease and treatment trajectory consists of surgery followed by chemotherapy or radiation therapy. Upon this, maintenance hormone therapy with an aromatase inhibitor can result in several physical and psychosocial symptoms. Optimal symptom control during maintenance therapy is central to maintaining the patient's quality of life. OBJECTIVE: This study aims to (1) develop an electronic symptom management tool for patients with postmenopausal early breast cancer receiving maintenance aromatase inhibitors with an endocrine aspect and (2) assess the feasibility, acceptability, and usability of the pilot version of the Bone@BC app. Furthermore, longitudinally, symptom prevalence and quality of life for patients with postmenopausal nonmetastatic estrogen receptor-positive breast cancer will be explored. METHODS: This study follows a multistage research plan. In stage 1, a systematic literature review to establish an overview of aromatase inhibitor-related symptoms reported by postmenopausal women with nonmetastatic estrogen receptor-positive breast cancer will be completed. In stage 2, a comprehensive overview of symptoms related to aromatase inhibitors (letrozole, exemestane, and anastrozole) will be performed (eg, by reviewing medical leaflets and guidelines). In stage 3, an electronic app with a user-friendly Patient Concern Inventory list to comprise symptoms and concerns will be developed. Last, in stage 4, a convergent mixed methods feasibility study of the pilot version of the Bone@BC app will be conducted. A total of 45 patients with postmenopausal nonmetastatic estrogen receptor-positive breast cancer will use the app daily for symptom identification and respond to 6 serial patient-reported outcome measurements for 12 weeks. Finally, semistructured interviews will be performed. The primary outcome includes consent rate, attrition rate, retention rates, technical issues, and adherence, assessed using preestablished criteria on feasibility and a mixed methods approach for exploring acceptability. A patient advisory board consisting of 5 women with breast cancer is recruited to include their perspectives and experiences in the planning, organization, implementation, and dissemination of the research throughout the project. RESULTS: At the time of submitting this paper (January 2024), a total of 23 patients have been included in the stage 2 medical audit over the recruitment period of 3 months (November 2022 to February 2023), and 19 patients have been enrolled in stage 2, the semistructured patient interviews. CONCLUSIONS: This protocol describes a study investigating the feasibility, acceptability, and usability of the symptom management tool Bone@BC developed for patients with breast cancer with an endocrine aspect. TRIAL REGISTRATION: ClinicalTrails.gov NCT05367830; https://clinicaltrials.gov/ct2/show/NCT05367830. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49549.
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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 | 0.038 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.055 | 0.011 |
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".