Use of a mobile app to capture supplemental health information during pregnancy: Implications for clinical research
Bibliographic record
Abstract
PURPOSE: Mobile applications ("apps") may be efficient tools for improving the quality of clinical research among pregnant women, but evidence is sparse. We assess the feasibility and generalizability of a mobile app for capturing supplemental data during pregnancy. METHODS: In 2017, we conducted a pilot study of the FDA MyStudies mobile app within a pregnant population identified through Kaiser Permanente Washington (KPWA), an integrated healthcare delivery system. We ascertained health conditions, medications, and substance use through app-based questionnaires. In a post-hoc analysis, we utilized electronic health records (EHR) to summarize sociodemographic and health characteristics of pilot participants and, for comparison, a pregnant population identified using similar methods. RESULTS: Six percent (64/1070) of contacted women enrolled in the pilot study. Nearly half (23/53) reported taking medication for headaches and one-fourth for constipation (13/53) and nausea (12/53) each. Few instances (2/92) of over-the-counter medication use were identified in electronic dispensing records. One-quarter to one-third of participants with depression and anxiety/panic, respectively, reported recently discontinuing medications for these conditions. Eighty-eight percent of pilot participants reported White race (95%CI: 81-95%), versus 67% of the comparison population (N = 2065). More pilot participants filled ≥1 prescription for antianxiety medication (22% [95%CI: 13-35%]) and antidepressants (19% [95%CI 10-31%]) pre-pregnancy than the comparison population (10 and 9%, respectively). CONCLUSIONS: Mobile apps may be a feasible tool for capturing health data not routinely available in EHR. Pregnant women willing to use a mobile app for research may differ from the general pregnant population, but confirmation is needed.
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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 | 0.077 | 0.346 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".