Cost effectiveness review of text messaging, smartphone application, and website interventions targeting T2DM or hypertension
Bibliographic record
Abstract
Digital health interventions have been shown to be clinically-effective for type 2 diabetes mellitus (T2DM) and hypertension prevention and treatment. This study synthesizes and compares the cost-effectiveness of text-messaging, smartphone application, and websites by searching CINAHL, Cochrane Central, Embase, Medline and PsycInfo for full economic or cost-minimisation studies of digital health interventions in adults with or at risk of T2DM and/or hypertension. Costs and health effects are synthesised narratively. Study quality appraisal using the Consensus on Health Economic Criteria (CHEC) list results in recommendations for future health economic evaluations of digital health interventions. Of 3056 records identified, 14 studies are included (7 studies applied text-messaging, 4 employed smartphone applications, and 5 used websites). Ten studies are cost-utility analyses: incremental cost-utility ratios (ICUR) vary from dominant to €75,233/quality-adjusted life year (QALY), with a median of €3840/QALY (interquartile range €16,179). One study finds no QALY difference. None of the three digital health intervention modes is associated with substantially better cost-effectiveness. Interventions are consistently cost-effective in populations with (pre)T2DM but not in populations with hypertension. Mean quality score is 63.0% (standard deviation 13.7%). Substandard application of time horizon, sensitivity analysis, and subgroup analysis next to transparency concerns (regarding competing alternatives, perspective, and costing) downgrades quality of evidence. In conclusion, smartphone application, text-messaging, and website-based interventions are cost-effective without substantial differences between the different delivery modes. Future health economic studies should increase transparency, conduct sufficient sensitivity analyses, and appraise the ICUR more critically in light of a reasoned willingness-to-pay threshold.Registration: PROSPERO (CRD42021247845).
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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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.017 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".