Appropriate Use and Operationalization of Adherence to Digital Cognitive Behavioral Therapy for Depression and Anxiety in Youth: Systematic Review
Notice bibliographique
Résumé
BACKGROUND: Digital, self-guided cognitive behavioral therapy (CBT) interventions circumvent many barriers to in-person therapy for young people (aged 12-24 years), although adherence to these interventions is low. The absence or insufficient disclosure of recommendations or instructions for appropriate use may account for this. As such, many young people may not self-administer these interventions appropriately or receive the optimal degree of treatment. OBJECTIVE: This systematic review aims to synthesize the literature on digital CBT for depression and anxiety in young people to describe how appropriate use has been defined and communicated to users as instructions for use, to describe how adherence has been measured, and to determine the associations between adherence and treatment outcomes. METHODS: A systematic review was conducted with 2 reviewers (SHL and MRA) extracting data independently. Overall, 4 electronic databases (Embase, MEDLINE, PsycINFO, and Cochrane Library) were searched in April 2021 for studies that met the following inclusion criteria: participants aged between 12 and 24 years, evaluated a digital CBT intervention targeting depression or anxiety, and reported instructions or recommendations for use or measures of adherence. Studies that evaluated non-CBT interventions or cognitive- or behavioral-only interventions were excluded. Methodological quality was assessed using the Cochrane Risk of Bias Tool and the Integrated Quality Criteria for the Review of Multiple Study Designs. RESULTS: There were 32 manuscripts that met the inclusion criteria, of which 28 (88%) were unique studies (N=16,578 youths). Definitions of appropriate use varied among the different interventions in terms of intended recipients, duration and frequency of use, and the features used to support engagement and adherence to appropriate use definitions. Reporting of appropriate use definitions in studies was inconsistent, with no study systematically describing components of appropriate use or providing information on how recommendations for use were relayed to users. Most often, definitions of appropriate use were derived from the study protocol and descriptions of intervention features. Adherence was mostly operationalized as the degree of intervention completion; however, reporting of adherence data was heterogeneous. There was little evidence of an association between degree of use and outcomes in the 9 studies that examined this. CONCLUSIONS: Definitions of appropriate use are unique to each digital CBT intervention. However, statements of appropriate use are not systematically reported in the literature. Furthermore, the extent to which recommendations for use are communicated to users is not routinely reported. Despite unique definitions of appropriate use, adherence was most often generically operationalized as the degree of intervention completion and was not consistently associated with outcomes. We proposed a framework to promote systematic reporting of definitions of appropriate use for digital interventions to provide guidance to users and to assist the development of appropriate and nuanced measures of adherence. TRIAL REGISTRATION: PROSPERO CRD42020208668; https://tinyurl.com/4bu2yram.
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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 | 0,023 | 0,100 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,009 | 0,010 |
| Bibliométrie | 0,010 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| 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 ».