The impact of digital adherence technologies on treatment outcomes, adherence, and patient-reported outcomes in tuberculosis: a systematic review and meta-analysis
Notice bibliographique
Résumé
BACKGROUND: Incomplete tuberculosis (TB) treatment adherence may lead to unsuccessful treatment and relapse. Digital adherence technologies (DATs) may allow more person-centric approaches for supporting treatment adherence. We conducted a systematic review (PROSPERO- CRD42022313166) to evaluate the impact of DATs on adherence, treatment outcomes and patient-reported outcomes in persons treated for TB. METHODS: We searched MEDLINE, Embase, CENTRAL, CINAHL, Web of Science and preprints from Europe PMC, and clinicaltrials.gov for relevant literature from January 2000 to March 2024. We considered experimental or cohort studies reporting quantitative comparisons of adherence, treatment outcomes and patient-reported outcomes between a DAT and the standard of care in each setting. We excluded studies where the technology was used only to log visit attendance or for “routine telephone calls” to patients. Risk of bias was assessed using the Cochrane risk of bias assessment tool and the Newcastle- Ottawa Scale. Pre-specified subgroup analyses considered study design, specific DAT interventions as well as income levels in the countries where studies were conducted. RESULTS: Seventy-six studies (total 86,586 participants) were included evaluating SMS-based interventions (k = 18 studies), feature phone-based interventions (k = 8), medication sleeves with phone calls (branded as “99DOTS,” k = 6), video-observed therapy (VOT; k = 18), smartphone apps (k = 7), digital pillboxes (k = 21), ingestible sensors (k = 1), and interventions combining two DATs (k = 2). Overall, the use of DATs was associated with a modest increase in treatment success in TB disease in both RCTs (OR = 1.14 [0.99, 1.30]; I2 = 57%, k = 34, very low certainty evidence) and observational studies (OR = 1.11 [0.94, 1.30]; I2 = 74%, k = 22, very low certainty evidence). Additionally, DAT use was linked to a significant increase in reporting of adverse events in RCTs (OR = 1.57 [1.25, 1.97]; I2 = 12%, k = 6, moderate certainty) while observational studies showed a similar but non-significant finding (OR = 1.39 [0.93, 2.09]; I2 = 0%, k = 3, moderate certainty). VOT was associated with an increased likelihood of treatment completion in TB infection (OR 4.69 [2.08; 10.55]; I2 = 0%, k = 2, low certainty evidence). VOT also increased frequency of adverse event reporting, as demonstrated in RCTs (OR = 1.9 [1.27; 2.84]; I2 = 0%, k = 3, moderate certainty evidence) and a similar but non-significant effect in observational studies (OR = 1.48 [0.91; 2.42]; I2 = 0%, k = 2, low certainty evidence). Other interventions involving smartphone apps were associated with increased treatment success in TB disease, with a significant effect observed in RCTs (OR 2.17 [1.07; 4.4]; I2 = 20%, k = 3, low certainty evidence) and a non-significant effect in observational studies (OR 1.51 [0.53; 4.3]; I2 = 60%, k = 3, very low certainty evidence). In contrast, interventions with 99DOTS were not associated with improvements in short-term clinical outcomes. There was substantial methodological heterogeneity among studies reporting on adherence. Few studies assessed patient-reported outcomes, though satisfaction was generally higher with DATs. CONCLUSION: Some DATs, notably VOT and smartphone apps, have been successfully used to support TB treatment. Although in many cases DATs did not improve clinical outcomes, they may improve efficiency and adherence, and may be preferred to traditional directly observed therapy by persons with TB. However, evidence remains highly variable, and generalizability limited. Higher quality data are needed. TRIAL REGISTRATION: PROSPERO- CRD42022313166
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,008 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, pas un consensus.
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