A feasibility study of a new smartphone application for patients with cancer.
Bibliographic record
Abstract
211 Background: Smartphone-applications have been used for self-care, but not all applications have a sufficient level of efficacy. We evaluated the feasibility of a new smartphone application (the App) which has two main functions—diary function and SNS function—and investigated the characteristics of active users. Methods: Patients who visited the Breast and Medical Oncology or GI Medical Oncology divisions at National Cancer Center Hospital were recruited between March and April in 2016. Participants could use the App for 28 days. Web-based survey was performed before and 28 days after using the App. The primary endpoint was Daily Active User (DAU) rate (the number of daily log-in user divided by the number of patients who completed installation). A mean DAU rate higher than 20% was predetermined as feasible. The secondary endpoints were daily diary function user rate, daily SNS function user rate, and overall user rate and change of Edmonton Symptom Assessment System Revised Japanese version (ESAS-r-j) before and after the App use. “Active user” was defined as a participant who used the AppeWe more than median active days for diary or SNS function. Duration of App use was counted from the date of installation to the date of last use, and overall user rate was calculated by Kaplan Meier method. Results: One-hundred and four patients (96%) completed the App installation and answered the first survey. All patients were followed for 28 days. The mean DAU rate was 50.9%. Active diary function user rate was 53% and active SNS function user rate was 40%. Overall user rate was 87.5%. Diary function was used more frequently by male patients than female, and non-breast cancer patients. SNS function was used more frequently by unemployed patients than others, and by patients younger than 40 y/o. We could evaluate the change of ESAS-r-j score in 80 patients who answered the second survey. There was a significant improvement of depression score among patients who were single and who had metastatic disease. Conclusions: The App was considered feasible. Gender, cancer type, job status, and age may influence the behavior of the App use. The App may improve patients’ psychological symptoms, but further investigations are warranted to reveal the clinical utility of the App.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".