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
Bibliographic record
Abstract
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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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".