Use of Technology-Based Tools to Support Adolescents and Young Adults With Chronic Disease: Systematic Review and Meta-Analysis
Notice bibliographique
Résumé
BACKGROUND: With the large amount of material that is readily available on the internet, there are endless opportunities for electronic health-literate patients to obtain and learn new information. Although novel, a Web- or mobile-based program can be a powerful way to engage adolescents and young adults (AYAs). The ongoing engagement of AYAs with chronic disease is vital not only to empower them but also to ensure a smooth transition from pediatric to adult health care. OBJECTIVE: This study aimed to evaluate the current evidence on Web- or mobile-based interventions designed for AYAs. METHODS: This review was registered with PROSPERO: CRD42018096487. A systematic search of MEDLINE Complete, EMBASE, and CINAHL Complete was conducted on April 10, 2019, for studies that examined the perspectives of transition-age patients about technology-based interventions, the process involved in intervention development, or the evaluation of intervention efficacy. For each study, the comprehensiveness of reporting was appraised. The Downs and Black checklist was used for intervention efficacy trials, the Standards for Reporting Qualitative Research checklist was used for qualitative work, and a 16-item tool developed by Tong et al was used for questionnaire research. RESULTS: The search uncovered 29 relevant studies, which included qualitative studies (n=14), intervention efficacy studies (n=7), questionnaire studies (n=4), mixed qualitative and questionnaire studies (n=2), and a mixed qualitative and pilot randomized controlled trial study (n=1). The reporting comprehensiveness score of questionnaires was rated considerably lower (n=6, 13%-57% [2/16-8/14]) than the scores of intervention efficacy trials (n=8, 48%-85% [13/27-23/27]) and qualitative research (n=17, 40%-93% [8.5/21-19.5/21]). AYAs were receptive to obtaining information via a website or mobile app. An intervention was more likely to be perceived as useful by AYAs when there was a concerted effort to involve AYAs and subject matter experts in the process of intervention design, as opposed to relying solely on the AYAs or the experts alone. The preferred medium of intervention delivery varied greatly for AYAs, ranging from static text to audiovisual materials. However, AYAs considered being concise was the most important aspect. Across different conditions, AYAs were interested in receiving information on diverse topics, such as anxiety and stress management, dealing with insurance, and having social relationships. Patients also requested for disease-specific information, such as weather forecasts and pollen levels for patients with asthma and information related to the pretransplant period for organ transplant recipients. Meta-analyses showed no significant group differences across time on quality of life, self-efficacy, and self-management. CONCLUSIONS: Owing to the lack of intervention efficacy trials, no conclusion can be drawn if an intervention delivered via a mobile app is better than that delivered via a website. However, through this systematic review, it is confirmed that AYAs were receptive to receiving medical information electronically.
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 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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,012 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| 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 | 0,001 | 0,002 |
| 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».