Implementation of clinical research trials using web-based and mobile devices: challenges and solutions
Bibliographic record
Abstract
BACKGROUND: With the increasing implementation of web-based, mobile health interventions in clinical trials, it is crucial for researchers to address the security and privacy concerns of patient information according to high ethical standards. The full process of meeting these standards is often made more complicated due to the use of internet-based technology and smartphones for treatment, telecommunication, and data collection; however, this process is not well-documented in the literature. RESULTS: The Smart Heart Trial is a single-arm feasibility study that is currently assessing the effects of a web-based, mobile lifestyle intervention for overweight and obese children and youth with congenital heart disease in Southwestern Ontario. Participants receive telephone counseling regarding nutrition and fitness; and complete goal-setting activities on a web-based application. This paper provides a detailed overview of the challenges the study faced in meeting the high standards of our Research Ethics Board, specifically regarding patient privacy. CONCLUSION: We outline our solutions, successes, limitations, and lessons learned to inform future similar studies; and model much needed transparency in ensuring high quality security and protection of patient privacy when using web-based and mobile devices for telecommunication and data collection in clinical research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.127 | 0.354 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.032 | 0.038 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".