Pub Date : 2026-03-27DOI: 10.1038/s41746-026-02580-y
Nina de Lacy,Zach Boyd
{"title":"The doctor is not in, but the Chatbot is: Utah's experience regulating mental health AI.","authors":"Nina de Lacy,Zach Boyd","doi":"10.1038/s41746-026-02580-y","DOIUrl":"https://doi.org/10.1038/s41746-026-02580-y","url":null,"abstract":"","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"58 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-26DOI: 10.1038/s41746-026-02540-6
Michael Zeosky, Eucharist Kun, Siddharth Reddy, Devansh Pandey, Liaoyi Xu, Joyce Y Wang, Chenfei Li, Ryan S Gray, Carol A Wise, Nao Otomo, Vagheesh M Narasimhan
Scoliosis is the most common developmental spinal deformity, but its genetic underpinnings remain only partially understood. To identify scoliosis-related loci, we utilized dual energy X-ray absorptiometry (DXA) scans from 57,588 individuals in the UK Biobank (UKB), and quantified spinal curvature using deep learning-based vertebral segmentation and landmarking to measure cumulative horizontal displacement. On a subset of 150 individuals, our automated image-derived curvature measurements showed a correlation of 0.83 with clinical Cobb angle assessments, supporting their validity as a proxy for scoliosis severity. To connect spinal curvature to genetics, we conducted a genome-wide association study (GWAS). Our quantitative imaging phenotype identified 2 novel loci associated with scoliosis in a European population. These loci are in SEM1/SHFM1 and on an lncRNA on chr 3 located between EDEM1 and GRM7. Genetic correlation analysis revealed significant overlap between our image-based GWAS and ICD-10-based GWAS in the UKB and the Biobank of Japan. We show that our quantitative GWAS identifies more genome-wide significant loci than a case-control scoliosis dataset with ten times the sample size. Our results illustrate the potential of quantitative imaging phenotypes to uncover genetic associations that are challenging to capture with medical records alone and identify new loci for functional follow-up.
{"title":"Deep learning-based precision phenotyping of spine curvature identifies novel genetic risk loci for scoliosis in the UK Biobank.","authors":"Michael Zeosky, Eucharist Kun, Siddharth Reddy, Devansh Pandey, Liaoyi Xu, Joyce Y Wang, Chenfei Li, Ryan S Gray, Carol A Wise, Nao Otomo, Vagheesh M Narasimhan","doi":"10.1038/s41746-026-02540-6","DOIUrl":"https://doi.org/10.1038/s41746-026-02540-6","url":null,"abstract":"<p><p>Scoliosis is the most common developmental spinal deformity, but its genetic underpinnings remain only partially understood. To identify scoliosis-related loci, we utilized dual energy X-ray absorptiometry (DXA) scans from 57,588 individuals in the UK Biobank (UKB), and quantified spinal curvature using deep learning-based vertebral segmentation and landmarking to measure cumulative horizontal displacement. On a subset of 150 individuals, our automated image-derived curvature measurements showed a correlation of 0.83 with clinical Cobb angle assessments, supporting their validity as a proxy for scoliosis severity. To connect spinal curvature to genetics, we conducted a genome-wide association study (GWAS). Our quantitative imaging phenotype identified 2 novel loci associated with scoliosis in a European population. These loci are in SEM1/SHFM1 and on an lncRNA on chr 3 located between EDEM1 and GRM7. Genetic correlation analysis revealed significant overlap between our image-based GWAS and ICD-10-based GWAS in the UKB and the Biobank of Japan. We show that our quantitative GWAS identifies more genome-wide significant loci than a case-control scoliosis dataset with ten times the sample size. Our results illustrate the potential of quantitative imaging phenotypes to uncover genetic associations that are challenging to capture with medical records alone and identify new loci for functional follow-up.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":""},"PeriodicalIF":15.1,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147513847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sepsis has heterogeneous clinical trajectories, but conventional severity scores offer only static risk estimates. Timely, dynamic prediction could enable personalized intervention. In this multicenter retrospective study of 47,936 ICU patients meeting Sepsis-3 criteria from one institutional and two public datasets (MIMIC-III, eICU; sensitivity in MIMIC-IV), group-based trajectory modeling identified latent recovery patterns. An ensemble machine-learning model incorporating dynamic physiological variability was trained, temporally validated, and externally tested; clinical impact was assessed following implementation. Three trajectories emerged: rapid recovery (41.5%), slow recovery (36.4%), and clinical deterioration (22.1%). In the final binary classification task, AUROC was 0.92 (development), 0.89 (internal), 0.84 (MIMIC-III) and 0.77 (eICU); median warning time before deterioration was 17.6 h (Overall pooled across all cohorts). Reduced heart rate variability (SD < 10 bpm) predicted mortality (adjusted HR 2.17). Implementation reduced ICU stay by 1.8 days, machanical ventilation by 2.3 days, and 28-day mortality by 5.7%. This externally validated trajectory-based model offers accurate, early risk stratification for sepsis, supporting proactive, individualized critical care.
{"title":"Machine learning predicts sepsis deterioration trajectories.","authors":"Rui Zhang,Fang Long,Zhanqi Zhao,Jingyi Wu,Ruoming Tan,Wen Xu,Lei Li,Yun Long,Hongping Qu","doi":"10.1038/s41746-026-02565-x","DOIUrl":"https://doi.org/10.1038/s41746-026-02565-x","url":null,"abstract":"Sepsis has heterogeneous clinical trajectories, but conventional severity scores offer only static risk estimates. Timely, dynamic prediction could enable personalized intervention. In this multicenter retrospective study of 47,936 ICU patients meeting Sepsis-3 criteria from one institutional and two public datasets (MIMIC-III, eICU; sensitivity in MIMIC-IV), group-based trajectory modeling identified latent recovery patterns. An ensemble machine-learning model incorporating dynamic physiological variability was trained, temporally validated, and externally tested; clinical impact was assessed following implementation. Three trajectories emerged: rapid recovery (41.5%), slow recovery (36.4%), and clinical deterioration (22.1%). In the final binary classification task, AUROC was 0.92 (development), 0.89 (internal), 0.84 (MIMIC-III) and 0.77 (eICU); median warning time before deterioration was 17.6 h (Overall pooled across all cohorts). Reduced heart rate variability (SD < 10 bpm) predicted mortality (adjusted HR 2.17). Implementation reduced ICU stay by 1.8 days, machanical ventilation by 2.3 days, and 28-day mortality by 5.7%. This externally validated trajectory-based model offers accurate, early risk stratification for sepsis, supporting proactive, individualized critical care.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"11 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-26DOI: 10.1038/s41746-026-02526-4
Laura Luisa Bielinski,Rebekka Büscher,Severin Hennemann,Carmen Henning,Simon H Kohl,Caroline Meyer,Annika S Reinhold,Lena Sophia Steubl,Ingrid Titzler,Lea Vogel,Anna-Carlotta Zarski,Carmen Schaeuffele
Blended care (BC), combining face-to-face therapy with digital components, is gaining momentum in the field of mental health, yet lacks conceptual clarity. This perspective paper outlines a dimensional conceptualization of BC and introduces the B-FIT (Blend-Focus-Integration-Timing) framework. We highlight the need to refine the theoretical foundations of BC, strengthen the evidence base for its effectiveness, and integrate stakeholder perspectives to inform future research and support the successful implementation of BC.
{"title":"The present and future of blended care: current research and introduction to the B-FIT framework.","authors":"Laura Luisa Bielinski,Rebekka Büscher,Severin Hennemann,Carmen Henning,Simon H Kohl,Caroline Meyer,Annika S Reinhold,Lena Sophia Steubl,Ingrid Titzler,Lea Vogel,Anna-Carlotta Zarski,Carmen Schaeuffele","doi":"10.1038/s41746-026-02526-4","DOIUrl":"https://doi.org/10.1038/s41746-026-02526-4","url":null,"abstract":"Blended care (BC), combining face-to-face therapy with digital components, is gaining momentum in the field of mental health, yet lacks conceptual clarity. This perspective paper outlines a dimensional conceptualization of BC and introduces the B-FIT (Blend-Focus-Integration-Timing) framework. We highlight the need to refine the theoretical foundations of BC, strengthen the evidence base for its effectiveness, and integrate stakeholder perspectives to inform future research and support the successful implementation of BC.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"20 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-25DOI: 10.1038/s41746-026-02566-w
Jun-Seok Sohn, Byeong-Gwan Ha, SoHyun Park, Jae-Jin Kim, Eojin Lee, Hyangkyeong Oh, San Lee, Eunjoo Kim
Mental health chatbots have proliferated rapidly, yet their effectiveness remains unclear. This systematic review and meta-analysis included randomized controlled trials comparing chatbots with any control condition for depressive and/or anxiety outcomes. PubMed, Embase, PsycINFO, Scopus and Web of Science were searched from January 2017 to October 2025. Risk of bias was assessed using the revised Cochrane tool. Pooled effect sizes (Hedges’ g) were calculated using random-effects models. Of the 39 eligible studies, 38 (n = 7,401) were analyzed for depression and 34 (n = 7,621) for anxiety. Chatbots produced statistically significant reductions in depressive (g = 0.31, 95% CI [0.17, 0.46]) and anxiety symptoms (g = 0.28, 95% CI [0.05, 0.51]) compared with controls. Subgroup analyses for depressive symptoms showed larger effects in clinical and subclinical than in nonclinical samples (p = 0.001). Contemporary chatbots thus appear to alleviate depressive and anxiety symptoms, especially in individuals with greater depressive severity. (PROSPERO registration: CRD42024598761).
{"title":"Systematic review and meta analysis of chatbots in the management of depressive and anxiety symptoms","authors":"Jun-Seok Sohn, Byeong-Gwan Ha, SoHyun Park, Jae-Jin Kim, Eojin Lee, Hyangkyeong Oh, San Lee, Eunjoo Kim","doi":"10.1038/s41746-026-02566-w","DOIUrl":"https://doi.org/10.1038/s41746-026-02566-w","url":null,"abstract":"Mental health chatbots have proliferated rapidly, yet their effectiveness remains unclear. This systematic review and meta-analysis included randomized controlled trials comparing chatbots with any control condition for depressive and/or anxiety outcomes. PubMed, Embase, PsycINFO, Scopus and Web of Science were searched from January 2017 to October 2025. Risk of bias was assessed using the revised Cochrane tool. Pooled effect sizes (Hedges’ g) were calculated using random-effects models. Of the 39 eligible studies, 38 (n = 7,401) were analyzed for depression and 34 (n = 7,621) for anxiety. Chatbots produced statistically significant reductions in depressive (g = 0.31, 95% CI [0.17, 0.46]) and anxiety symptoms (g = 0.28, 95% CI [0.05, 0.51]) compared with controls. Subgroup analyses for depressive symptoms showed larger effects in clinical and subclinical than in nonclinical samples (p = 0.001). Contemporary chatbots thus appear to alleviate depressive and anxiety symptoms, especially in individuals with greater depressive severity. (PROSPERO registration: CRD42024598761).","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"57 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147506086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-25DOI: 10.1038/s41746-026-02350-w
Yu-Hsuan Lin, Nicholas Meyer, Ta-Wei Guu
{"title":"The untapped potential of ballistographic technology in behavioural sleep medicine","authors":"Yu-Hsuan Lin, Nicholas Meyer, Ta-Wei Guu","doi":"10.1038/s41746-026-02350-w","DOIUrl":"https://doi.org/10.1038/s41746-026-02350-w","url":null,"abstract":"","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"18 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147506682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-25DOI: 10.1038/s41746-026-02559-9
Yuqi Wu, Guangya Wan, Jingjing Li, Shengming Zhao, Lingfeng Ma, Tianyi Ye, Mike Zhang, Ion Pop, Yanbo Zhang, Jie Chen
Large Language Models (LLMs) offer promising opportunities to support mental healthcare workflows, yet they often lack the structured clinical reasoning needed for reliable diagnosis and may struggle to provide the emotionally attuned communication essential for patient trust. Here, we introduce WiseMind, a novel multi-agent framework inspired by the theory of Dialectical Behavior Therapy designed to facilitate psychiatric assessment. By integrating a “Reasonable Mind" Agent for evidence-based logic and an “Emotional Mind" Agent for empathetic communication, WiseMind effectively bridges the gap between instrumental accuracy and humanistic care. Our framework utilizes a Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5)-guided Structured Knowledge Graph to steer diagnostic inquiries, significantly reducing hallucinations compared to standard prompting methods. Using a combination of virtual standard patients, simulated interactions, and real human interaction datasets, we evaluate WiseMind across three common psychiatric conditions. WiseMind outperforms state-of-the-art LLM methods in both identifying critical diagnostic nodes and establishing accurate differential diagnoses. Across 1206 simulated conversations and 180 real user sessions, the system achieves 85.6% top-1 diagnostic accuracy, approaching reported diagnostic performance ranges of board-certified psychiatrists and surpassing knowledge-enhanced single-agent baselines by 15-54 percentage points. Expert review by psychiatrists further validates that WiseMind generates responses that are not only clinically sound but also psychologically supportive, demonstrating the feasibility of empathetic, reliable AI agents to conduct psychiatric assessments under appropriate human oversight.
{"title":"WiseMind: a knowledge-guided multi-agent framework for accurate and empathetic psychiatric diagnosis","authors":"Yuqi Wu, Guangya Wan, Jingjing Li, Shengming Zhao, Lingfeng Ma, Tianyi Ye, Mike Zhang, Ion Pop, Yanbo Zhang, Jie Chen","doi":"10.1038/s41746-026-02559-9","DOIUrl":"https://doi.org/10.1038/s41746-026-02559-9","url":null,"abstract":"Large Language Models (LLMs) offer promising opportunities to support mental healthcare workflows, yet they often lack the structured clinical reasoning needed for reliable diagnosis and may struggle to provide the emotionally attuned communication essential for patient trust. Here, we introduce WiseMind, a novel multi-agent framework inspired by the theory of Dialectical Behavior Therapy designed to facilitate psychiatric assessment. By integrating a “Reasonable Mind\" Agent for evidence-based logic and an “Emotional Mind\" Agent for empathetic communication, WiseMind effectively bridges the gap between instrumental accuracy and humanistic care. Our framework utilizes a Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5)-guided Structured Knowledge Graph to steer diagnostic inquiries, significantly reducing hallucinations compared to standard prompting methods. Using a combination of virtual standard patients, simulated interactions, and real human interaction datasets, we evaluate WiseMind across three common psychiatric conditions. WiseMind outperforms state-of-the-art LLM methods in both identifying critical diagnostic nodes and establishing accurate differential diagnoses. Across 1206 simulated conversations and 180 real user sessions, the system achieves 85.6% top-1 diagnostic accuracy, approaching reported diagnostic performance ranges of board-certified psychiatrists and surpassing knowledge-enhanced single-agent baselines by 15-54 percentage points. Expert review by psychiatrists further validates that WiseMind generates responses that are not only clinically sound but also psychologically supportive, demonstrating the feasibility of empathetic, reliable AI agents to conduct psychiatric assessments under appropriate human oversight.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"49 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147506085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-24DOI: 10.1038/s41746-026-02543-3
Annie Aitken, Abbey Sawyer, Akiko Iwasaki, Harlan M. Krumholz, Rory Preston, Paul Calcraft, Harry Leeming, Jenna Tosto-Mancuso, Amy Proal, Michael A. Osborne, David Putrino
Altered heart‑rate variability (HRV) and resting heart rate (HR) are common in many complex chronic conditions. Mobile and wearable technologies now provide real-time, valid measurements of HRV and HR, advancing symptom monitoring and management. The current study integrates a 60-s morning PPG assessment with evening symptom severity reports, yielding a high-density mobile health dataset (n = 4244) with an average of 125 biometric observations per participant. We examined whether within-person fluctuations in HR, HRV, and respiratory rate predicted daily changes in crash, fatigue, and brain fog symptoms and secondarily evaluated model predictive performance. Model fit and variance explained were highest in models that included morning biometrics in addition to prior-day symptom reports and covariates. Within-person increases in HR and decreases in HRV in the morning were associated with worsening symptom reports in the evening. Walk-forward cross-validation showed a statistically significant improvement in model performance when morning biometrics were added to prior-day symptom reports (AUC = 0.82–0.85 vs. 0.73–0.83). These findings represent the prospective utility of mobile health tools for precision monitoring and prediction of real-time symptom exacerbations in complex chronic illness.
心率变异性（HRV）和静息心率（HR）改变在许多复杂的慢性疾病中很常见。移动和可穿戴技术现在提供实时、有效的心率波动和人力资源测量，推进症状监测和管理。目前的研究整合了60秒的早晨PPG评估和晚上症状严重程度报告，产生了一个高密度的移动健康数据集（n = 4244），平均每个参与者有125个生物特征观察。我们研究了人体内HR、HRV和呼吸频率的波动是否能预测碰撞、疲劳和脑雾症状的每日变化，并对模型的预测性能进行了二次评估。除了前一天的症状报告和协变量外，包括早晨生物特征的模型的模型拟合和方差解释最高。早晨人体内HRV升高和HRV降低与夜间症状报告恶化相关。前向交叉验证显示，在前一天的症状报告中加入早晨生物特征时，模型性能有统计学意义的改善（AUC = 0.82-0.85 vs. 0.73-0.83）。这些发现表明，移动医疗工具在复杂慢性疾病的实时症状恶化的精确监测和预测方面具有前瞻性的效用。
{"title":"Digital physiological biomarkers predict within-person symptom changes in complex chronic illness","authors":"Annie Aitken, Abbey Sawyer, Akiko Iwasaki, Harlan M. Krumholz, Rory Preston, Paul Calcraft, Harry Leeming, Jenna Tosto-Mancuso, Amy Proal, Michael A. Osborne, David Putrino","doi":"10.1038/s41746-026-02543-3","DOIUrl":"https://doi.org/10.1038/s41746-026-02543-3","url":null,"abstract":"Altered heart‑rate variability (HRV) and resting heart rate (HR) are common in many complex chronic conditions. Mobile and wearable technologies now provide real-time, valid measurements of HRV and HR, advancing symptom monitoring and management. The current study integrates a 60-s morning PPG assessment with evening symptom severity reports, yielding a high-density mobile health dataset (n = 4244) with an average of 125 biometric observations per participant. We examined whether within-person fluctuations in HR, HRV, and respiratory rate predicted daily changes in crash, fatigue, and brain fog symptoms and secondarily evaluated model predictive performance. Model fit and variance explained were highest in models that included morning biometrics in addition to prior-day symptom reports and covariates. Within-person increases in HR and decreases in HRV in the morning were associated with worsening symptom reports in the evening. Walk-forward cross-validation showed a statistically significant improvement in model performance when morning biometrics were added to prior-day symptom reports (AUC = 0.82–0.85 vs. 0.73–0.83). These findings represent the prospective utility of mobile health tools for precision monitoring and prediction of real-time symptom exacerbations in complex chronic illness.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"235 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147506083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Patients undergoing gynecologic laparoscopic surgery (GLS) often experience postoperative complications, including acute postoperative pain (APSP). This study aimed to assess the safety and efficacy of immersive virtual reality-based and aromatherapy-enhanced multisensory stimulation (IVR-MS) in patients undergoing GLS. This prospective, randomized controlled trial was registered at www.clinicaltrials.gov on 4/03/2025 (NCT06922838). Participants were randomly assigned to the IVR-MS group (received IVR combined with olfactory stimulation for enhanced multisensory stimulation), the IVR group (received IVR intervention), and the aromatherapy group (received lavender aromatherapy). From baseline to postoperative 24 hours, pain response, patient-controlled analgesia (PCA), anxiety, sleep quality, comfort level, rescue analgesic, and abdominal distension were evaluated in patients. Ultimately, 124 participants completed all analyses. Significant statistical differences were observed among the three groups in postoperative pain scores, PCA usage, anxiety levels, comfort, and sleep quality following the intervention. However, no significant differences were found in the classification of abdominal distension. Trial registration This trial was registered at www.clinicaltrials.gov (Registration Number: NCT06922838, Registration Date: april 03th, 2025).
{"title":"Effects of multisensory stimulation based on immersive virtual reality in postoperative neuropsychiatric recovery after gynecological laparoscopy","authors":"Jiang Liu, Yuxiu Liu, Liuna Bi, Li Zhao, Fuchan Hu, Mengyao Huang, Jingyuan Zhang, Xun Zhou, Ting Wang, Shirong Fang, Fengxian Zhang, Yuanjian Song","doi":"10.1038/s41746-026-02515-7","DOIUrl":"https://doi.org/10.1038/s41746-026-02515-7","url":null,"abstract":"Patients undergoing gynecologic laparoscopic surgery (GLS) often experience postoperative complications, including acute postoperative pain (APSP). This study aimed to assess the safety and efficacy of immersive virtual reality-based and aromatherapy-enhanced multisensory stimulation (IVR-MS) in patients undergoing GLS. This prospective, randomized controlled trial was registered at www.clinicaltrials.gov on 4/03/2025 (NCT06922838). Participants were randomly assigned to the IVR-MS group (received IVR combined with olfactory stimulation for enhanced multisensory stimulation), the IVR group (received IVR intervention), and the aromatherapy group (received lavender aromatherapy). From baseline to postoperative 24 hours, pain response, patient-controlled analgesia (PCA), anxiety, sleep quality, comfort level, rescue analgesic, and abdominal distension were evaluated in patients. Ultimately, 124 participants completed all analyses. Significant statistical differences were observed among the three groups in postoperative pain scores, PCA usage, anxiety levels, comfort, and sleep quality following the intervention. However, no significant differences were found in the classification of abdominal distension. Trial registration This trial was registered at www.clinicaltrials.gov (Registration Number: NCT06922838, Registration Date: april 03th, 2025).","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"38 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147496765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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