Forensic Psychiatric Outpatients’ and Therapists’ Perspectives on a Wearable Biocueing App (Sense-IT) as an Addition to Aggression Regulation Therapy: Qualitative Focus Group and Interview Study
Notice bibliographique
Résumé
BACKGROUND: Given the increased use of smart devices and the advantages of individual behavioral monitoring and assessment over time, wearable sensor-based mobile health apps are expected to become an important part of future (forensic) mental health care. For successful implementation in clinical practice, consideration of barriers and facilitators is of utmost importance. OBJECTIVE: The aim of this study was to provide insight into the perspectives of both psychiatric outpatients and therapists in a forensic setting on the use and implementation of the Sense-IT biocueing app in aggression regulation therapy. METHODS: A combination of qualitative methods was used. First, we assessed the perspectives of forensic outpatients on the use of the Sense-IT biocueing app using semistructured interviews. Next, 2 focus groups with forensic therapists were conducted to gain a more in-depth understanding of their perspectives on facilitators of and barriers to implementation. RESULTS: Forensic outpatients (n=21) and therapists (n=15) showed a primarily positive attitude toward the addition of the biocueing intervention to therapy, with increased interoceptive and emotional awareness as the most frequently mentioned advantage in both groups. In the semistructured interviews, patients mainly reported barriers related to technical or innovation problems (ie, connection and notification issues, perceived inaccuracy of the feedback, and limitations in the ability to personalize settings). In the focus groups with therapists, 92 facilitator and barrier codes were identified and categorized into technical or innovation level (n=13, 14%), individual therapist level (n=28, 30%), individual patient level (n=33, 36%), and environmental and organizational level (n=18, 20%). The predominant barriers were limitations in usability of the app, patients' motivation, and both therapists' and patients' knowledge and skills. Integration into treatment, expertise within the therapists' team, and provision of time and materials were identified as facilitators. CONCLUSIONS: The chances of successful implementation and continued use of sensor-based mobile health interventions such as the Sense-IT biocueing app can be increased by considering the barriers and facilitators from patients' and therapists' perspectives. Technical or innovation-related barriers such as usability issues should be addressed first. At the therapist level, increasing integration into daily routines and enhancing affinity with the intervention are highly recommended for successful implementation. Future research is expected to be focused on further development and personalization of biocueing interventions considering what works for whom at what time in line with the trend toward personalizing treatment interventions in mental health care.
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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 | 0,011 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,007 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,003 |
| 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 ».