The effectiveness of text message-based self-management interventions for poorly-controlled diabetes: A systematic review
Bibliographic record
Abstract
BACKGROUND: Poorly controlled diabetes leads to debilitating complications at a significant cost to health systems. Text messaging is an ideal platform for the delivery of self-management interventions to patients with poorly controlled diabetes due to the ubiquity of mobile phones, and the ability of text messaging to reach people in their everyday lives when self-management of the condition is vital. This systematic review aimed to assess the effectiveness of short message service-based diabetes self-management interventions on glycaemic control in adults with poorly controlled diabetes. METHODS/DESIGN: MEDLINE, PubMed, EMBASE, The Cochrane Library and PsychINFO were searched from inception through to 23 January 2017 for randomised controlled trials investigating the use of text messaging based self-management interventions on haemoglobin A1c for patients with poorly controlled diabetes. RESULTS: Seven studies met the inclusion criteria and were included in the review. Three of the studies reported a significant decrease in haemoglobin A1c from baseline to follow-up in the intervention group compared to the control group. No clear relationship between positive outcomes and intervention dose, content and functionality was seen. DISCUSSION: Evidence supporting text messaging for improvements in glycaemic control in people with poorly controlled diabetes is mixed. Previous reviews have reported positive impacts on glycaemic control for short message service interventions in patients with diabetes; however, when limited to those with poorly controlled diabetes the evidence is less clear. Large-scale studies with robust methodology and longer-term follow-up are needed to further understand the impact of text-messaging-based self-management interventions for people with poorly controlled diabetes.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".