CONSORT-EHEALTH: Improving and Standardizing Evaluation Reports of Web-based and Mobile Health Interventions
Bibliographic record
Abstract
BACKGROUND: Web-based and mobile health interventions (also called "Internet interventions" or "ehealth/mhealth interventions") are tools or treatments, typically behaviorally based, that are operationalized and transformed for delivery via the Internet or mobile platforms. These include electronic tools for patients, informal caregivers, healthy consumers, and health care providers. The "Consolidated Standards of Reporting Trials" (CONSORT) was developed to improve the suboptimal reporting of randomized controlled trials (RCTs). While broadly the CONSORT statement can be applied to provide guidance on how ehealth and mhealth trials should be reported, RCTs of web-based interventions pose very specific issues and challenges, in particular related to reporting sufficient details of the intervention to allow replication and theory-building. OBJECTIVE: To develop a checklist, dubbed CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile HEalth Applications and onLine TeleHealth), as an extension of the CONSORT statement that provides guidance for authors of ehealth and mhealth interventions. METHODS: A literature review was conducted, followed by a survey among ehealth experts and a workshop. RESULTS: An instrument and checklist was constructed as an extension of the CONSORT statement. The instrument has been adopted by the Journal of Medical Internet Research (JMIR) and authors of ehealth RCTs are required to submit an electronic checklist explaining how they addressed each subitem. CONCLUSIONS: CONSORT-EHEALTH has the potential to improve reporting and provides a basis for evaluating the validity and applicability of ehealth trials. Subitems describing how the intervention should be reported can also be used for non-RCT evaluation reports. As part of the development process, an evaluation component is essential, therefore feedback from authors will be solicited, and a before-after study will evaluate whether reporting has been improved.
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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 | 0.672 | 0.816 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.025 |
| Bibliometrics | 0.025 | 0.026 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".