The Impact of Digital Adherence Technologies on Health Outcomes in Tuberculosis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background Suboptimal tuberculosis (TB) treatment adherence may lead to unsuccessful treatment and relapse. Digital adherence technologies (DATs) may allow more person-centric approaches for supporting treatment. We conducted a systematic review (PROSPERO-CRD42022313166) to evaluate the impact of DATs on health outcomes in TB. Methods We searched MEDLINE, Embase, CENTRAL, CINAHL, Web of Science and preprints from medRxiv, Europe PMC, and clinicaltrials.gov for relevant literature from January 2000 to April 2023. We considered experimental or cohort studies reporting quantitative comparisons of clinical outcomes between a DAT and the standard of care in each setting. Results Seventy studies (total 58,950 participants) met inclusion criteria. They reported SMS-based interventions (k=18 studies), feature phone-based interventions (k=7), medication sleeves with phone calls (branded as “99DOTS,” k=5), video-observed therapy (VOT; k=17), smartphone-based interventions (k=5), digital pillboxes (k=18), ingestible sensors (k=1), and interventions combining 2 DATs (k=1). Overall, the use of DATs was associated with more frequent treatment success in TB disease (OR = 1.18 [1.06, 1.33]; I 2 = 66%, k = 46), a decrease in loss to follow up (OR = 0.71 [0.53, 0.94]; I 2 = 80%, k = 36) and an increase in adverse event reporting (OR = 1.53 [1.26, 1.86]; I 2 = 0%, k = 9). VOT was associated with an increased likelihood of treatment success in TB disease (OR 1.54 [1.09; 2.19]; I 2 = 0%, k = 8) and treatment completion in TB infection (OR 4.69 [2.08; 10.55]; I 2 = 0%, k = 2) as well as an increased frequency of adverse event reporting (OR = 1.79 [1.27; 2.52]; I 2 = 34%, k = 4). Other interventions involving smartphone technologies were associated with increased treatment success in TB disease (OR 1.98 [1.07; 3.65]; I 2 =56%, k = 5) and a decreased frequency of loss to follow up (OR = 0.31 [0.13; 0.77]; I 2 = 36%, k = 5). Digital pillboxes were also associated with an improvement in treatment success (OR = 1.32 [1.00; 1.73]; I 2 = 71%, k = 11). However, improved treatment success was only seen in high- and upper middle-income countries. SMS-based interventions, feature-phone interventions and 99DOTS were not associated with improvements in short-term clinical outcomes. Conclusion Certain DATs--notably VOT and smartphone-based interventions, in higher income settings and sometimes combined with other supportive measures—may be associated with improvements in treatment success and losses to follow-up, compared to standard care. However, evidence remains highly variable, and generalizability limited. Higher quality data are needed.
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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 | 0.024 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.042 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".