Real-Time Data Collection Utilizing Mobile Technology in Clinical Research: A Secondary Analysis of a Prospective Cohort
Bibliographic record
Abstract
INTRODUCTION: Clinical research faces the challenge of declining physician participation in the pursuit of advancing evidence-based medicine. This secondary analysis focuses on the interactive mobile health (mHealth) application's utility as a real-time data collection tool in clinical settings, specifically targeting cow's milk protein allergy (CMPA) management. The study assesses the mHealth application's potential to alleviate data collection inefficiencies and improve physician engagement in clinical research. METHODS: The analysis utilized de-identified survey data from a prospective cohort of 61 physicians who employedan interactive mobile survey application over 12 weeks, documenting 808 patient visits. RESULTS: Of these physicians, 52 (85%) completed initial and follow-up surveys, with the predominant reason for exclusion being incomplete or non-submission of data. Of the 404 patient surveys collected, 75 (19%) were excluded primarily due to the absence of follow-up information. CONCLUSION: These results underscore the application's practicality in streamlining clinical data collection, evidenced by the high rate of survey completion and the efficiency of data management among participating physicians. The findings indicate that interactive mobile health applications aided in collecting and managing clinical data, with 85% of physicians completing surveys for initial and follow-up visits. This high completion rate suggests the potential for mobile applications to mitigate traditional barriers to physician participation, such as time constraints and complex data management. The study contributes empirical evidence to the potential of mobile technology in enhancing research efficiency and engagement among physicians in the context of CMPA management. While the results are insightful, further studies are encouraged to extend the utility of the interactive mobile survey application and similar technologies across diverse clinical research areas, reinforcing mobile technology's role in transforming clinical research practices.
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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 | 0.027 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".