Systematic review and evaluation of web-accessible tools for management of diabetes and related cardiovascular risk factors by patients and healthcare providers
Bibliographic record
Abstract
OBJECTIVE: To identify and evaluate the effectiveness, clinical usefulness, sustainability, and usability of web-compatible diabetes-related tools. DATA SOURCES: Medline, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials, world wide web. STUDY SELECTION: Studies were included if they described an electronic audiovisual tool used as a means to educate patients, care givers, or clinicians about diabetes management and assessed a psychological, behavioral, or clinical outcome. DATA EXTRACTION: Study abstraction and evaluation for clinical usefulness, sustainability, and usability were performed by two independent reviewers. RESULTS: Of 12,616 citations and 1541 full-text articles reviewed, 57 studies met inclusion criteria. Forty studies used experimental designs (25 randomized controlled trials, one controlled clinical trial, 14 before-after studies), and 17 used observational designs. Methodological quality and ratings for clinical usefulness and sustainability were variable, and there was a high prevalence of usability errors. Tools showed moderate but inconsistent effects on a variety of psychological and clinical outcomes including HbA1c and weight. Meta-regression of adequately reported studies (12 studies, 2731 participants) demonstrated that, although the interventions studied resulted in positive outcomes, this was not moderated by clinical usefulness nor usability. LIMITATION: This review is limited by the number of accessible tools, exclusion of tools for mobile devices, study quality, and the use of non-validated scales. CONCLUSION: Few tools were identified that met our criteria for effectiveness, usefulness, sustainability, and usability. Priority areas include identifying strategies to minimize website attrition and enabling patients and clinicians to make informed decisions about website choice by encouraging reporting of website quality indicators.
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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 | 0.026 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".