Using Active and Passive Smartphone Data to Enhance Adolescents’ Emotional Awareness in Forensic Outpatient Setting: A Qualitative Feasibility and Usability Study
Notice bibliographique
Résumé
BACKGROUND: Delinquent behavior in adolescence is a prevalent issue, often associated with difficulties across multiple life domains, which in turn perpetuates negative life outcomes. While current treatment programs show partial success in improving behavioral changes and reducing recidivism, comprehensive conclusions regarding the overall efficacy of these interventions have yet to be established. In forensic outpatient settings, the discrepancy between adolescents' limited emotional awareness and the predominant emphasis on cognitive reflection, combined with low treatment adherence, may be factors that undermine treatment efficacy. New technologies, such as smartphone apps, may offer a solution by integrating real-life data into treatment to improve emotional and behavioral patterns. The low-threshold use of smartphone data can be useful in addressing these treatment challenges. OBJECTIVE: This study aimed to explore the feasibility and usability of Feelee (Garage2020), a smartphone app that integrates active emoji and passive behavioral data, as a potential addition to treatment for adolescents in a forensic outpatient setting. METHODS: We conducted a prepilot study with adolescents (n=4) who used the Feelee app over a 2-week period. App usage included completing a brief emoji survey 3 times a day (active data) and allowing Feelee to track the call logs, Bluetooth devices in proximity, cell tower IDs, app usage, and phone status (passive data). During treatment sessions, both adolescents and clinicians reviewed and discussed the active and passive data. Semistructured interviews were conducted with adolescents and clinicians (n=7) to gather experiences and feedback on the feasibility and usability of incorporating smartphone data into treatment. RESULTS: The study showed that adolescents (n=3) succeeded in using Feelee for the full 2 weeks, and data were available for discussion in at least 1 session per participant. Both adolescents and clinicians (n=7) stated that Feelee was valuable for viewing, discussing, and gaining insight into their emotions, which facilitated targeted actions based on the Feelee data. However, neither adolescents nor clinicians reported increased engagement in treatment as a result of using Feelee. Despite technical issues, overall feedback on the Feelee app, in addition to treatment, was positive (n=7). However, further improvements are needed to address the high battery consumption and the inaccuracies in the accelerometer. CONCLUSIONS: This qualitative study provides an in-depth understanding of the potential benefits of integrating active and passive smartphone data for adolescents in a forensic outpatient setting. Feelee appears to contribute to a better understanding of emotions and behaviors, suggesting its potential value in enhancing emotional awareness in treatment. Further research is needed to assess Feelee's clinical effectiveness and explore how it enhances emotional awareness. Recommendations from adolescents and clinicians emphasize the need for prepilot studies to address user issues, guiding technical improvements and future research in forensic outpatient settings.
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 | 0,015 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».