The Effect of the Virtual Reality–Based Biofeedback Intervention DEEP on Stress, Emotional Tension, and Anger in Forensic Psychiatric Inpatients: Mixed Methods Single-Case Experimental Design
Notice bibliographique
Résumé
BACKGROUND: Decreasing aggression through stress reduction is an important part of forensic psychiatric treatment. DEEP is an experience-based virtual reality intervention that uses biofeedback to train diaphragmatic breathing and increase relaxation. Although DEEP has shown promising results in reducing stress and anxiety in students and adolescents in special education, it has not been examined in forensic psychiatric populations. OBJECTIVE: This study aimed to evaluate DEEP's potential to reduce stress, emotional tension, and anger in forensic psychiatric inpatients. METHODS: A mixed methods, alternating treatment, single-case experimental design was conducted with 6 Dutch forensic inpatients. For 20 days, participants engaged in 4 DEEP sessions. Experience sampling was used for continuous monitoring of stress, emotional tension, and anger twice daily. A repeated linear mixed model was used as a primary statistical approach for analyzing the experience sampling data as well as visual analyses. Finally, semistructured interviews were conducted with participants and health care professionals to compare quantitative with qualitative results. RESULTS: Of the 6 participants, 3 (50%) completed all 4 DEEP sessions, while the other 3 (50%) missed one session due to technical difficulties or absence from the inpatient clinic. P1 showed a significant reduction of stress after session 2 (β=-.865; P=.005). No significant changes over time were found, although an experienced effect was reported during the interviews. P2 showed no significant results. They reported the sessions as being repetitive, with no experienced effect. P3 showed a momentary increase of emotional tension after the first session (β=-.053; P=.002), but no changes were observed over time. No experienced effects were reported in the interview. P4 did not show significant results over time, and was hesitant to report clear experienced effects. P5 showed a significant decline of emotional tension (β=-.012; P=.006), stress (β=-.014; P=.007), and anger (β=-.007; P=.02) over time. They also reported short-term experienced effects in the interview. P6 showed a significant decline of stress over time (β=-.029; P<.001) and reported experiencing substantial effects. Finally, health care professionals reported a relaxing effect of DEEP in their patients but did not expect many long-term effects because no clear behavioral changes were observed. CONCLUSIONS: DEEP shows promise in teaching deep breathing techniques to forensic psychiatric inpatients, potentially decreasing stress, emotional tension, and anger in some patients. However, DEEP is not a one-size-fits-all intervention that supports every patient because the effectiveness on the outcome measures varied among participants. To increase effectiveness, emphasis should be put on supporting patients to transfer deep breathing skills into their daily lives. This highlights the importance for the structural integration of DEEP into current treatment protocols.
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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 | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».