Contextual factors influencing implementation of tuberculosis digital adherence technologies: a scoping review guided by the RE-AIM framework
Notice bibliographique
Résumé
ABSTRACT Introduction Digital adherence technologies (DATs) may enable person-centered tuberculosis (TB) treatment monitoring; however, implementation challenges may undermine their effectiveness. Using the RE-AIM framework, we conducted a scoping review to identify contextual factors informing “reach” (DAT engagement by people with TB) and “adoption” (DAT uptake by healthcare providers or clinics). Methods We searched eight databases from January 1, 2000 to April 25, 2023 to identify all TB DAT studies. After extracting qualitative and quantitative findings, using thematic synthesis, we analyzed common findings to create meta-themes informing DAT reach or adoption. Meta-themes were further organized using the Unified Theory of Acceptance and Use of Technology, which posits technology use is influenced by perceived usefulness, ease of use, social influences, and facilitating conditions. Results 66 reports met inclusion criteria, with 61 reporting on DAT reach among people with TB and 27 reporting on DAT adoption by healthcare providers. Meta-themes promoting reach included perceptions that DATs improved medication adherence, facilitated communication with providers, made people feel more “cared for,” and enhanced convenience compared to alternative care models (perceived usefulness); and lowered stigma (social influences). Meta-themes limiting reach included literacy and language barriers and DAT technical complexity (ease of use); increased stigma (social influences); and suboptimal DAT function and complex cellular accessibility challenges (facilitating conditions). Meta-themes promoting adoption included perceptions DATs improved care quality or efficiency (perceived usefulness). Meta-themes limiting adoption included negative DAT impacts on workload or employment and suboptimal accuracy of adherence data (perceived usefulness); and suboptimal DAT function, complex cellular accessibility challenges, and insufficient provider training (facilitating conditions). Limitations of this review include the limited studies informing adoption meta-themes. Conclusion This review identifies diverse contextual factors that can inform improvements in DAT design and implementation to achieve higher engagement by people with TB and healthcare providers, which could improve intervention effectiveness. KEY MESSAGES What is already known on this topic Digital adherence technologies (DATs) are increasingly used to monitor TB treatment; however, systematic reviews suggest DATs have mixed effectiveness for improving TB outcomes and suboptimal accuracy for measuring medication adherence. Inadequate DAT “reach” (engagement by people with TB) and “adoption” (uptake by healthcare providers) may contribute to their limited effectiveness and accuracy. Understanding contextual factors influencing DAT reach and adoption may be critical to improve the design, implementation, and public health impact of TB DATs. What this study adds Our findings show people with TB value DATs when they improve adherence, enhance communication with providers, enhance convenience of care, and reduce stigma. People with TB are less likely to engage with DATs in settings with barriers to cellular accessibility or when DATs are not designed for their literacy level, are technically complex, have suboptimal function, or increase stigma. TB healthcare providers value DATs when they improve care quality or efficiency. Healthcare providers are less likely to engage in settings with barriers to cellular accessibility or when DATs increase workloads, threaten employment, provide inaccurate adherence data, or have suboptimal function. How this study might affect research, practice, or policy Our findings may inform future design of DATs to focus on what people with TB value, such as improved communication with providers and convenience of care. Our findings may also help to identify settings in which DATs are unlikely to be effective, such as locations where cellular accessibility barriers are substantial due to poor infrastructure.
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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 | 0,098 | 0,229 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,007 | 0,011 |
| Bibliométrie | 0,039 | 0,030 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,009 | 0,010 |
| Science ouverte | 0,005 | 0,008 |
| Intégrité de la recherche | 0,004 | 0,003 |
| 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 ».