Behavioral Theories and Motivational Features Underlying eHealth Interventions for Adolescent Antiretroviral Adherence: Systematic Review
Notice bibliographique
Résumé
BACKGROUND: eHealth systems provide new opportunities for the delivery of antiretroviral therapy (ART) adherence interventions for adolescents. They may be more effective if grounded in health behavior theories and behavior change techniques (BCTs). Prior reviews have examined the effectiveness, feasibility, and acceptability of these eHealth systems. However, studies have not systematically explored the use of health behavior theories and BCTs in the design of these applications. OBJECTIVE: The purpose of this review was to explore whether health behavior theories and BCTs were considered to ground designs of eHealth systems supporting adolescents' (10-24 years) ART adherence. More specifically, we examined which specific theories and BCTs were applied, and how these BCTs were implemented as design features. Additionally, we investigated the quality and effect of eHealth systems. METHODS: A systematic search was performed on IEEE Xplore, ACM, ScienceDirect, PubMed, Scopus, and Web of Science databases from 2000 to 2020. Theory use and BCTs were coded using the Theory Coding Scheme and the Behavior Change Technique Taxonomy version 1 (BCTTv1), respectively. Design features were identified using the lenses of motivational design for mobile health (mHealth). The number of BCTs and design features for each eHealth system and their prevalence across all systems were assessed. RESULTS: This review identified 16 eHealth systems aiming to support ART adherence among adolescents. System types include SMS text message reminders (n=6), phone call reminders (n=3), combined SMS text message and phone call reminders (n=1), electronic adherence monitoring devices (n=3), smartphone apps (n=1), smartphone serious games (n=1), gamified smartphone apps (n=1), leveraging existing social media (n=2), web-based applications (n=1), videoconferencing (n=1), and desktop applications (n=1). Nine were grounded in theory, of which 3 used theories extensively. The impact of adolescent developmental changes on ART adherence was not made explicit. A total of 42 different BCTs and 24 motivational design features were used across systems. Ten systems reported positive effects on 1 or more outcomes; however, of these ten systems, only 3 reported exclusively positive effects on all the outcomes they measured. As much as 6 out of 16 reported purely no effect in all the outcomes measured. CONCLUSIONS: Basic applications (SMS text messaging and phone calls) were most frequent, although more advanced systems such as mobile apps and games are also emerging. This review indicated gaps in the use of theory and BCTs, and particularly the impact of developmental changes on ART adherence was not adequately considered. Together with adopting a developmental orientation, future eHealth systems should effectively leverage health theories and consider developing more advanced systems that open the door to using BCTs more comprehensively. Overall, the impact of eHealth systems on adolescent ART adherence and its mediators is promising, but conclusive evidence on effect still needs to be provided.
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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 | 0,011 | 0,043 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,006 |
| Bibliométrie | 0,010 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».