Immersive Virtual Reality eHealth Intervention to Reduce Anxiety and Depression in Pregnant Women: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Mental health during pregnancy is a critical factor influencing maternal and fetal outcomes. Anxiety and depression affect up to 30% of pregnant women, with significant consequences for maternal well-being and child development. Despite this, interventions during pregnancy remain limited, creating a need for innovative, accessible solutions. OBJECTIVE: This study aimed to evaluate the effectiveness of an immersive virtual reality (IVR) eHealth intervention in reducing anxiety and depression symptoms in women during pregnancy. METHODS: A 2-arm, randomized controlled trial was conducted across 5 primary care centers in Catalonia, Spain, between October 2021 and May 2024. The study included pregnant women (N=70) aged ≥18 years with moderate anxiety and depression symptoms (Edinburgh Postnatal Depression Scale [EPDS] scores: 9-12) at 12 to 14 weeks of gestation. They were randomly assigned (1:1) to an IVR intervention or standard care group. The intervention group engaged in daily 14-minute IVR mindfulness and relaxation sessions for 6 weeks. Mental health outcomes were assessed using the EPDS and State-Trait Anxiety Inventory. RESULTS: The intervention group demonstrated significant reductions in EPDS scores, with a mean decrease from 11.32 (SD 0.96) to 7.25 (SD 1.32; P<.001), compared to an increase in the control group from 11.32 (SD 0.94) to 16.23 (SD 1.25; P<.001). Similarly, State-Trait Anxiety Inventory scores improved markedly in the intervention group (mean decrease from 57.94, SD 5.23 to 35.03, SD 6.12; coefficient -30.47, 95% CI -45.23 to -15.72; P<.001), while the control group experienced a nonsignificant increase (from 66.10, SD 5.89 to 72.91, SD 6.34; P=.68). High adherence rates were observed, with 79% (26/33) of participants completing ≥30 sessions. Participant satisfaction was high, with 87% (29/33) reporting being "very satisfied" with the intervention. CONCLUSIONS: The IVR eHealth intervention significantly reduced symptoms of anxiety and depression, demonstrating its potential as an accessible and effective tool for mental health support during pregnancy. High adherence and satisfaction levels underscore its feasibility and acceptability. Future research should explore the long-term effects and scalability of IVR interventions in diverse settings. TRIAL REGISTRATION: ClinicalTrials.gov NCT05756205; https://clinicaltrials.gov/study/NCT05756205. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12912-023-01440-4.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".