The Performance of Digital Technologies for Measuring Tuberculosis Medication Adherence: A Systematic Review
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,030 | 0,140 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,011 | 0,010 |
| Bibliométrie | 0,013 | 0,014 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».