Tailored mobile text messaging interventions targeting type 2 diabetes self-management: A systematic review and a meta-analysis
Bibliographic record
Abstract
Objectives This study aimed to identify, assess and summarize available scientific evidence on tailored text messaging interventions focused on type 2 diabetes self-management. The systematic review concentrated on message design and delivery features, and tailoring strategies. The meta-analysis assessed the moderators of the effectiveness of tailored text messaging interventions. Methods A comprehensive search strategy included major electronic databases, key journal searches and reference list searching for related studies. PRISMA and Cochrane Collaboration's guidelines and recommended tools for data extraction, quality appraisal and data analysis were followed. Data were extracted on participant characteristics (age, gender, ethnicity), and interventional and methodological characteristics (study design, study setting, study length, choice of modality, comparison group, message type, format, content, use of interactivity, message frequency, message timing, message delivery, tailoring strategies and theory use). Outcome measures included diet, physical activity, medication adherence and glycated hemoglobin data (HbA1C). Where possible, a random effects meta-analysis was performed to pool data on the effectiveness of the tailored text messaging interventions and moderator variables. Results The search returned 13 eligible trials for the systematic review and 11 eligible trials for the meta-analysis. The majority of the studies were randomized controlled trials, conducted in high-income settings, used multi-modalities, and mostly delivered informative, educational messages through an automated message delivery system. Tailored text messaging interventions produced a substantial effect ( g = 0.54, 95% CI = 0.08–0.99, p < 0.001) on HbA1C values for a total of 949 patients. Subgroup analyses revealed the importance of some moderators such as message delivery ( Q B = 18.72, df = 1, p = 0.001), message direction ( Q B = 5.26, df = 1, p = 0.022), message frequency ( Q B = 18.72, df = 1, p = 0.000) and using multi-modalities ( Q B = 6.18, df = 1, p = 0.013). Conclusions Tailored mobile text messaging interventions can improve glycemic control in type 2 diabetes patients. However, more rigorous interventions with larger samples and longer follow-ups are required to confirm these findings and explore the effects of tailored text messaging on other self-management outcomes.
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 | 0.029 | 0.059 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.047 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".