The Performance of Digital Technologies for Measuring Tuberculosis Medication Adherence: A Systematic Review
Bibliographic record
Abstract
ABSTRACT Introduction Digital adherence technologies (DATs), such as phone-based technologies, and digital pillboxes, can provide more person-centric approaches to support tuberculosis (TB) medication adherence. We synthesized evidence addressing the performance of DATs for measuring tuberculosis medication adherence. Methods We conducted a systematic review (PROSPERO - CRD42022313526) which identified relevant published literature from January 2000 through April 2023 in five databases, and pertinent preprints. Studies reporting quantitative data on the performance of DATs for measuring adherence to medications for TB disease or infection, against a reference standard, with at least 20 participants using the DAT were included. Study characteristics and performance outcomes (e.g., sensitivity, specificity, positive and negative predictive values) were extracted. Article quality was assessed using the QUADAS-2 tool for diagnostic accuracy studies. Results Of 5692 studies initially identified by our systematic search, 13 met our inclusion criteria. These studies addressed the performance of medication sleeves with phone calls [branded as “99DDOTS”; N=4], digital pillboxes [N=5], ingestible sensors [N=2], artificial intelligence-based video observed therapy [N=1], and multifunctional mobile applications [N=1]. All but one involved persons with TB disease. For medication sleeves with phone calls, compared to urine analysis, reported sensitivity and specificity was 70-94% and 0-61%, respectively. For digital pillboxes, compared to pill count, reported sensitivity and specificity was 25-99% and 69-100%, respectively. For ingestible sensors, the sensitivity of dose detection was ≥95% in comparison to directly observed ingestion. Participant selection was the most frequent potential source of bias across articles. Conclusion Limited available data suggest suboptimal and variable performance of DATs for dose monitoring, with significant evidence gaps, notably in real-world programmatic settings. Future research should aim to improve understanding of the relationships of specific technologies, settings, user characteristics, and user engagement with DAT performance, and should measure and report performance in a more standardized manner. KEY MESSAGES What is already known on this topic Several cohort studies have suggested that digital adherence technologies (DATs) can both underestimate and overestimate medication ingestion among persons treated for tuberculosis. No previous review has synthesized available evidence in this regard. What this study adds Reports of DAT (medication sleeves with phone calls, digital pillboxes) implementation in real-world treatment settings consistently indicate suboptimal performance for measuring medication adherence. However, available evidence is limited in scope and quality. How this study might affect research, practice, or policy Suboptimal dose reporting from DATs potentially compromises their effectiveness, and program efficiency. Future clinical practice will be strengthened by rigorous technology evaluations that reflect more consistent use of reference standards, and clearer benchmarks for medication adherence.
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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 | 0.030 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".