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PubMed2026

Immersive virtual reality-assisted anatomy training improves endotracheal intubation performance in simulation: a randomized controlled trial among Chinese non-anesthesiology residents.

INTRODUCTION: This study aimed to compare immersive virtual reality (IVR)-assisted versus conventional anatomy training for teaching endotracheal intubation (ETI) to novice non-anesthesiology residents enrolled in China's Standardized Residency Training program. METHODS: A total of 90 non-anesthesiology residents without prior ETI experience were randomly assigned to either an IVR group receiving IVR-assisted anatomy training (n = 45) or a control group receiving conventional anatomy training (n = 45). All participants underwent a standardized teaching protocol. The primary endpoint was residents' ETI performance on a simulator, assessed using both the Global Rating Scale (GRS) and a task-specific checklist. The secondary endpoints included changes in written multiple-choice question (MCQ) scores and residents' evaluations of the course. RESULTS: In practical ETI assessments on a manikin, the IVR group achieved significantly higher scores on the task-specific checklist than the control group (90.34 ± 2.89 vs. 87.20 ± 3.29; p < 0.001), whereas GRS scores were comparable between groups. Both groups showed significant post-training improvement in knowledge scores (p < 0.001), with the IVR group showing a greater gain in theoretical knowledge (54.0% vs. 36.3%; p < 0.001). Participants in the IVR group also expressed a stronger preference for their training method (80.8%) and reported higher levels of motivation, confidence, and enjoyment (all p < 0.05). CONCLUSION: IVR-assisted anatomy training enhances the effectiveness of ETI training for novice non-anesthesiology residents, offering an interactive, engaging, and reproducible approach within China's Standardized Residency Training framework.

Humans↗

PubMed2026

Game changer? Cognitive-motor effects of VR exergaming compared to video-based training.

BACKGROUND/OBJECTIVE: Virtual reality (VR) exergaming enhances several cognitive domains through multisensory engagement. Acute cognitive benefits of VR are established, but evidence for direct comparisons with non-immersive controls is limited. This study aimed to determine whether VR exercise provides additional cognitive and cognitive-motor benefits beyond a matched non-immersive active stick-fight video (SFV) intervention, and whether effects persist after training. METHODS: In this randomized quasi-experimental study, N = 55 healthy adults (VR: n = 30; SFV: n = 25; 25.5 ± 7.1 years; 41.8% female) completed an 8-week program (2 × 30 min/week), of VR or SFV matched in movement patterns, frequency, intensity and duration. Measurements included reaction time (RT), Stroop Test (versions 1-3), Letter Cancellation Test (LCT), Trail Making Test (TMT), Trail Walking Test (TWT) and Fitts task (difficulty level 1-4). Data were analyzed using mixed-design ANOVAs. RESULTS: Improvements were observed in Stroop reading (F(1,53) = 14.84, p < .001, η2 = 0.219), Stroop inhibition (F(1,53) = 10.99, p = .002, η2 = 0.172), and LCT (F(1,53) = 4.57, p = .037, η2 = 0.079). A time × group interaction was found for TMT (F(1,53) = 6.55, p = .031, η2 = 0.110), indicating greater changes following VR training. Both groups improved cognitive-motor performance (TWT: F(1,25) = 55.32, p < .001, η2 = 0.689; Fitts3: F(1,53) = 44.97, p < .001, η2 = 0.459), with greater gains for VR in Fitts3 (p = .006). CONCLUSION(S): Eight weeks of VR and SFV enhanced cognitive and cognitive-motor performance. VR provided domain-specific advantages in executive function, but these effects were not uniformly persistent. SFV sustained more improvements in real-world-relevant cognitive-motor tasks.

Humans↗

PubMed2026

Effectiveness and usability of artificial intelligence-powered assistive technologies in Supporting daily activities of children with cerebral palsy: a systematic review.

BACKGROUND: Cerebral Palsy (CP) is the main cause of motor disabilities in childhood, necessitating innovative approaches to rehabilitation and assistive technology (AT). Simultaneously, artificial intelligence (AI) is increasingly being integrated into devices to create more adaptive, personalized, and effective AT. This systematic review aimed to evaluate the effectiveness and usability of AI-powered assistive technologies designed to support daily activities and rehabilitation in children with CP. MATERIALS AND METHODS: Five databases, including Scopus, Web of Science, PubMed, Embase, and IEEE Xplore, were systematically searched, and 23 articles were included in the final analysis. Articles were identified, selected, and categorized into emerging thematic areas based on the primary function and application of the technology. RESULTS: Five key thematic topics were identified: 1) AI-driven motor rehabilitation and gait training for functional mobility; 2) intelligent assessment and monitoring systems for clinical decision support; 3) AI-supported communication, social interaction, and intention recognition tools; 4) gamified and virtual reality-based interventions to enhance engagement and usability; and 5) smart assistive systems supporting daily living and independent mobility. The findings demonstrate a strong trend toward the application of AI technologies in personalized, engaging, and data-driven interventions for children with CP. However, the field is predominantly in the proof-of-concept stage, with limitations including small sample sizes, lack of long-term clinical validation, challenges in user-centered design, and usability for children with CP. CONCLUSION: AI-powered assistive technologies hold significant potential for transforming the care of children with CP by enabling highly personalized and engaging interventions. To actualize this potential, future work must realize that practical application remains challenging owing to limited clinical validation, technological integration, and usability barriers for children with CP. Future research must prioritize user-centered design and multidisciplinary collaboration to ensure that AI and robotic advancements improve the usability and quality of life for children with CP.

Humans↗

PubMed2026

Effectiveness of artificial intelligence in nursing simulation education: A systematic review, meta-analysis and bibliometric visualization analysis.

OBJECTIVES: To synthesize the roles and core functions of AI in nursing simulation education for nursing students via systematic review, quantitatively evaluate its effects on students' knowledge and skill outcomes through meta-analysis, and map the research landscape and development trends of this field through bibliometric visualization analysis. DESIGN: Systematic review, meta-analysis and bibliometric visualization analysis. DATA SOURCES: Eight electronic databases: PubMed, Web of Science, MEDLINE, ERIC, Academic Search Complete, China National Knowledge Infrastructure (CNKI), Wanfang Database, VIP Chinese Science and Technology Journal Database (VIP) were employed to search studies from the time of construction to 16 December 2025. REVIEW METHODS: Studies meeting the inclusion criteria were screened. The revised Cochrane Risk of Bias tool (ROB 2) and Joanna Briggs Institute (JBI) critical appraisal checklists were used for quality assessment. Meta-analysis was performed with Review Manager 5.4, and bibliometric visualization analysis was conducted using VOSviewer 1.6.20 and Bibliometrix (based on R4.4.3). RESULTS: A total of 61 studies were included. AI primarily played two roles in nursing simulation education: peer-type new subject (n = 24) and direct mediator (n = 22). Meta-analysis showed that AI interventions significantly improved nursing students' knowledge (SMD = 1.49, 95% CI [0.55,2.43], p = 0.002) and skills (SMD = 0.66, 95% CI [0.02,1.31], p = 0.04). Bibliometric analysis identified that the United States of America and China were the two main contributing countries in this field, and the key motor themes included generative artificial intelligence, virtual patients, and geriatric care. CONCLUSIONS: AI exerts positive effects on nursing students' knowledge acquisition and skill enhancement in simulation education, with peer-type new subject and direct mediator as the dominant roles. Future research should focus on expanding AI applications in multi-specialty simulation scenarios, activating the data-driven value of machine learning, and strengthening international collaboration and standardization construction, so as to promote the sustainable development of AI-integrated nursing simulation education.

Humans↗