Conversational agent interventions in diabetes care: a systematic review
Bibliographic record
Abstract
This systematic review aimed to evaluate the effectiveness, acceptability, and safety of conversational agent (CA) interventions in diabetes care. CAs are artificial intelligence driven tools that simulate human-like dialogue and have emerged as promising supports for self-management in chronic disease. We searched six electronic databases from inception to June 2024 and identified 16 eligible studies involving 9076 participants across 13 countries. Included studies varied in design, population, diabetes type, and intervention duration. Eleven studies assessed effectiveness, with most reporting improvements in glycemic control (e.g., HbA1c reductions of 0.3 % to 1.0 %), medication adherence, health behaviours (e.g., diet, physical activity), or mental health outcomes (e.g, anxiety). Thirteen studies examined acceptability and found that most users had positive emotional and motivational responses, though some expressed dissatisfaction with repetitive or impersonal interactions. Only four studies addressed safety, and while adverse events were rare, mechanisms such as clinical escalation protocols were inconsistently applied. Most studies were rated as weak in methodological quality, with small samples and limited use of control groups. In conclusion, CAs show promise as scalable, patient-centered tools for diabetes care. However, rigorous research is needed to better understand their clinical impact, safety, and suitability for diverse patient populations.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".