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
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".