ArticleJMIR formative research2026
Digital Health Monitoring and Intervention Suite for Stress in Frontline Nurses: Prospective Cohort Trial.
Article in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05923398 (Digital Interventions to Understand and Mitigate Stress Response), which is not on this map. Not yet cited in PubMed.
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Digital Interventions to Understand and Mitigate Stress Response: Process & Content Evaluation to Move From Feasibility (SRL-4) to Simulated Demonstration (SRL-6)
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Authors and funding
23 authors.
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Abstract
Background: Stress among health care workers (HCWs) contributes to burnout, workforce attrition, and adverse patient outcomes. Although virtual reality (VR), psychoeducation, ecological momentary assessments (EMAs), and wearables have independently shown promise in stress research, no integrated digital suite has combined controlled stress induction, intervention delivery, and longitudinal real-world monitoring in HCWs. Objective: This study aimed to evaluate the feasibility, engagement, and preliminary effectiveness of a multimodal Digital Health Monitoring and Intervention suite for Stress framework integrating VR simulation, psychoeducation, EMAs, and wearable biometrics. We examined (1) the impact of VR simulation and psychoeducation on stress outcomes and (2) associations between physiological and self-reported mental health outcomes. Methods: Ninety-nine nurses (mean age 33.7, SD 8.9 yr, 87% female) were enrolled in 2023. We conducted a single-arm prospective cohort study (NCT05923398). Using convenience sampling, participants were recruited from social media advertisements, flyers, and email notices distributed through professional listservs. Participants completed ≥2-week baseline monitoring, a single VR session (2 runs separated by a brief psychoeducation intervention), and 12-week follow-up. In-VR stress was assessed using the Subjective Units of Distress Scale (SUDS) and 4-item Moral Injury Outcome Scale (MIOS-4), with synchronous heart rate variability. Longitudinal outcomes included weekly and biweekly EMAs alongside 70 wearable-derived features. Paired t tests, aligned rank transform ANOVA, and Pearson correlations informed study objectives, with P values adjusted for multiple comparisons. Qualitative content analysis classified emotional responses during and after VR. Results: VR significantly increased subjective stress across checkpoints in both runs, with attenuation in Run B relative to Run A (all P<.001). No significant heart rate variability differences were observed between runs (P=.15). During VR, 92% (91/99) of participants felt stressed, 36% (36/99) reported anxiety or nervousness, and 51% (50/99)-78% (77/99) endorsed anger, guilt, shame, and/or betrayal. Most (59/99, 60%) HCWs returned to an emotional baseline post-VR, although 12% (12/99) reported lingering distress. Immediate reliable improvements in anger, guilt, shame, and/or betrayal occurred for 50% (50/99)-75% (74/99) of participants post intervention. Anxiety (mean -0.53, SD 2.34; P=.03) and stress (mean -3.05, SD 11.35; P=.01) decreased 2 weeks post intervention, but were not sustained at 12 weeks. Increased sleep restlessness was the only wearable feature showing significant changes (mean 2.46%, SD 5.43; Padj<.001). In-VR stress correlated with 12-week real-world stress (SUDS: r=0.57-0.58; MIOS-4: r=0.58-0.61; all P<.01). Data completion exceeded 90%, with 71% achieving full compliance. Conclusions: This study moves beyond single-tool interventions to demonstrate the feasibility and preliminary effectiveness of an integrated, multimodal stress platform within a single coordinated framework. This trial demonstrates high engagement, short-term symptom responsiveness, ecological validity, and emotional safety. The framework provides a scalable model for proactive stress identification, skills training, and implementation in high-risk occupational settings. Randomized controlled trials are needed to establish sustained efficacy and optimize deployment for real-world implementation.
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