Feasibility of the Social Media–Based Prevention Program “Leduin” for German Adolescents on Instagram: Mixed Methods Pilot Study
Notice bibliographique
Résumé
Background: Digital platforms, particularly social media, including Instagram, present unique opportunities for health promotion among adolescents due to their widespread use with interactive features supporting high user engagement. However, the feasibility of effectively utilizing platforms like Instagram for health interventions requires careful consideration of adolescent engagement patterns. Objective: This pilot study evaluated the leduin program-designed to foster essential life skills and functional social media use among adolescents-while also exploring the broader feasibility of using Instagram to deliver complex social and psychological interventions in this population. Methods: The study adapted the feasibility framework by Bowen et al and used a mixed methods approach. Quantitatively, Instagram interaction metrics of 99 participants (women: 62/99, 63%; men: 37/99, 37%; aged 14-18 years; mean age 15.2, SD 0.74 years) were analyzed descriptively (means, medians, SDs) and inferentially (Welch ANOVA, Kruskal-Wallis, Pearson and Spearman correlations, linear and segmented regression analyses) using RStudio. Metrics included story views, retention rates, feature engagement (eg, polls, question stickers, quizzes), and drop-off rates. Recruitment efforts were also analyzed descriptively. Qualitatively, 13 postprogram semistructured interviews were conducted with 11 women (11/13, 65%) and 6 men (6/13, 35%; mean age 15.29, SD 0.99 years). Participants were sampled to reflect varying engagement levels (6 high, 5 medium, 6 low). The mean interview duration was 25 minutes 11 seconds (SD 6 minutes 34 seconds). Content analysis, with high intercoder reliability (κ=0.90), comprehensively explored participants' experiences and the program's impact. Results: Quantitative results indicated that the recruitment process was challenging, with 101 schools and 10 youth centers contacted, resulting in a participation rate of 12.8% (99/775 students). On Instagram, story views ranged from 34 to 81 per post, with an average daily retention rate of 87.7% (SD 7.8%). By week 4, 76% of the total drop in views had occurred (mean views declined from 66.1 to 53.4); by week 6, 97.3% of the drop had been reached (declined from 66.1 to 49.9 views), indicating sustained viewer interest over the 14-week program. Features requiring minimal user effort, including polls (mean 56.8%-54.4%), quizzes (mean 56.6%), and sliders (mean 51.2%), showed significantly higher interaction rates than more demanding features such as challenges (mean 21.7%) and question stickers (mean 20.6%; P<.001). Qualitative findings revealed that adolescents valued the program, its design and methods for its relevance to their daily lives, and its support in developing essential life skills. Suggestions for improvements were made. Conclusions: The study underlines the potential of various Instagram features and content posting schedules for health interventions to meet adolescent preferences and interests. Challenges with reaching the target group effectively emphasize the need for targeted recruitment strategies and optimizing initial content to boost engagement, underscoring the critical implications for prevention research and policy in leveraging digital platforms to enhance adolescent health.
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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 | 0,014 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».