Use of Behavior Change Techniques in Digital HIV Prevention Programs for Adolescents and Young People: Systematic Review
Notice bibliographique
Résumé
BACKGROUND: HIV infections have caused severe public health and economic burdens to the world. Adolescents and young people continue to constitute a large proportion of newly diagnosed HIV cases. Digital health interventions have been increasingly used to prevent the rising HIV epidemic. Behavior change techniques (BCTs) are intervention components designed to modify the underlying processes that regulate behavior. The BCT taxonomy offers a systematic approach to identifying, extracting, and coding these components, providing valuable insights into effective intervention strategies. However, few reviews have comprehensively identified the use of BCTs in digital HIV interventions among adolescents and young people. OBJECTIVE: This study aimed to synthesize existing evidence on the commonly used BCTs in effective digital HIV prevention programs targeting adolescents and young people. METHODS: In total, 4 databases (PubMed, Embase, Cochrane Library, and APA PsycINFO) were searched, and studies from January 2008 to November 2024 were screened. Reference lists of relevant review studies were reviewed to identify any additional sources. Eligible randomized controlled trials with 1 of 3 HIV prevention outcomes (ie, HIV knowledge, condom-use self-efficacy, and condom use) were included. Basic study characteristics, intervention strategies, and study results were extracted and compared for data analysis. For the included interventions, BCTs were identified according to the BCT taxonomy proposed by Abraham and Michie in 2008, and the frequencies of BCTs used in these interventions were counted. RESULTS: Searches yielded 383 studies after duplicates were removed, with 34 (8.9%) publications finally included in this review. The most frequently used BCTs included prompting intention formation (34/34, 100%), providing information about behavior-health link (33/34, 97%), providing information on consequences (33/34, 97%), and providing instruction (33/34, 97%). Interventions with significant improvements in HIV knowledge (11/34, 32%) more frequently used BCTs with a provision nature, such as providing information about behavior-health link (11/11, 100%), information on consequences (11/11, 100%), encouragement (10/11, 91%), and instruction (10/11, 91%). Those with significant increases in condom-use self-efficacy (7/34, 20%) used BCTs toward initiating actions, such as prompts for intention formation (7/7, 100%), barrier identification (7/7, 100%), and practice (5/7, 71%). In addition, studies showing significant improvements in condom use (14/34, 41%) included BCTs focused not only on provision and initiation but also on behavioral management and maintenance, such as use follow-up prompts (5/14, 36%), relapse prevention (4/14, 29%), prompt self-monitoring of behavior (3/14, 21%), and prompt review of behavioral goals (3/14, 21%). CONCLUSIONS: This is the first systematic review that examined the use of BCTs in digital HIV prevention interventions for adolescents and young adults. The identified BCTs offer important reference for developing more effective digital interventions, with implications for enhancing their HIV knowledge, condom-use self-efficacy, and condom use in youth.
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 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 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,009 | 0,007 |
| Bibliométrie | 0,011 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 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) | 0,005 | 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 ».