A Mobile Technology for Collecting Patient-Reported Physical Activity and Distress Outcomes: Cross-Sectional Cohort Study
Bibliographic record
Abstract
BACKGROUND: Electronic patient-reported outcome (PROs) provides a fast and reliable assessment of a patient's health-related quality of life. Nevertheless, using PRO in the traditional paper format is not practical for clinical practice due to the limitations associated with data analysis and management. A questionnaire app was developed to address the need for a practical way to group and use distress and physical activity assessment tools. OBJECTIVE: The purpose of this study was to assess the level of agreement between electronic (mobile) and paper-and-pencil questionnaire responses. METHODS: We validated the app version of the distress thermometer (DT), International Physical Activity Questionnaire (IPAQ), and Patient Health Questionnaire-9 (PHQ-9). A total of 102 participants answered the paper and app versions of the DT and IPAQ, and 96 people completed the PHQ-9. The study outcomes were the correlation of the data between the paper-and-pencil and app versions. RESULTS: A total of 106 consecutive breast cancer patients were enrolled and analyzed for validation of paper and electronic (app) versions. The Spearman correlation values of paper and app surveys for patients who responded to the DT questionnaire within 7 days, within 3 days, and on the same day were .415 (P<.001), .437 (P<.001), and .603 (P<.001), respectively. Similarly, the paper and app survey correlation values of the IPAQ total physical activity metabolic equivalent of task (MET; Q2-6) were .291 (P=.003), .324 (P=.005), and .427 (P=.01), respectively. The correlation of the sum of the Patient Health Questionnaire-9 (Q1-9) according to the time interval between the paper-based questionnaire and the app-based questionnaire was .469 for 14 days (P<.001), .574 for 7 days (P<.001), .593 for 3 days (P<.001), and .512 for the same day (P=.03). These were all statistically significant. Similarly, the correlation of the PHQ (Q10) value according to the time interval between the paper-based questionnaire and the app-based questionnaire was .283 for 14 days (P=.005), .409 for 7 days (P=.001), .415 for 3 days (P=.009), and .736 for the same day (P=.001). These were all statistically significant. In the overall trend, the shorter the interval between the paper-and-pencil questionnaire and the app-based questionnaire, the higher the correlation value. CONCLUSIONS: The app version of the distress and physical activity questionnaires has shown validity and a high level of association with the paper-based DT, IPAQ (Q2-6), and PHQ-9. The app-based questionnaires were not inferior to their respective paper versions and confirm the feasibility for their use in clinical practice. The high correlation between paper and mobile app data allows the use of new mobile apps to benefit the overall health care system. TRIAL REGISTRATION: ClinicalTrials.gov NCT03072966; https://clinicaltrials.gov/ct2/show/NCT03072966.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".