Pub Date : 2026-03-13DOI: 10.1038/s41746-026-02523-7
Dakshayani Rajappan,Ruoyu Yin,Laura Martinengo,Lorainne Tudor Car
This scoping review aimed to explore the technical and health content-related features that digital mental health interventions (DMHIs) for older adults should entail to facilitate their future design, development, and implementation. We included peer-reviewed expert opinion papers, experimental studies and their protocols on DMHIs for older adults. We searched PubMed, Embase, PsycINFO, Web of Science and Google Scholar. A total of 98 studies were included, comprising 81 experimental studies and 17 expert opinion papers. The DMHIs reported in experimental studies and their protocols included mobile apps, online platforms, and videoconferencing tools, targeting depression, anxiety and grief. However, experts highlighted three main challenges faced by older adults: functional limitations, limited digital literacy, and restricted access to technology. This review provides considerations for the development of future DMHIs, including co-design with older adults, content adaptation, gamification, stakeholder involvement, and privacy and data security. Further research is needed to evaluate these considerations for real-world settings.
本综述旨在探讨老年人数字心理健康干预（DMHIs）应具备的技术和健康内容相关特征，以促进其未来的设计、开发和实施。我们纳入了同行评议的专家意见论文、实验研究及其老年人DMHIs的协议。我们搜索了PubMed， Embase, PsycINFO， Web of Science和b谷歌Scholar。共纳入98项研究，包括81项实验研究和17篇专家意见论文。DMHIs在实验研究中报告，他们的协议包括移动应用程序、在线平台和视频会议工具，针对抑郁、焦虑和悲伤。然而，专家们强调了老年人面临的三个主要挑战：功能限制、有限的数字素养和有限的技术获取。本综述为未来DMHIs的发展提供了考虑因素，包括与老年人共同设计、内容适应、游戏化、利益相关者参与以及隐私和数据安全。需要进一步的研究来评估这些考虑因素在现实世界的设置。
{"title":"Designing digital mental health interventions for older adults: a scoping review.","authors":"Dakshayani Rajappan,Ruoyu Yin,Laura Martinengo,Lorainne Tudor Car","doi":"10.1038/s41746-026-02523-7","DOIUrl":"https://doi.org/10.1038/s41746-026-02523-7","url":null,"abstract":"This scoping review aimed to explore the technical and health content-related features that digital mental health interventions (DMHIs) for older adults should entail to facilitate their future design, development, and implementation. We included peer-reviewed expert opinion papers, experimental studies and their protocols on DMHIs for older adults. We searched PubMed, Embase, PsycINFO, Web of Science and Google Scholar. A total of 98 studies were included, comprising 81 experimental studies and 17 expert opinion papers. The DMHIs reported in experimental studies and their protocols included mobile apps, online platforms, and videoconferencing tools, targeting depression, anxiety and grief. However, experts highlighted three main challenges faced by older adults: functional limitations, limited digital literacy, and restricted access to technology. This review provides considerations for the development of future DMHIs, including co-design with older adults, content adaptation, gamification, stakeholder involvement, and privacy and data security. Further research is needed to evaluate these considerations for real-world settings.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"5 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147446931","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 : 2026-03-12DOI: 10.1038/s41746-026-02478-9
Geng Li, Yang Liu, Antao Chen
Working memory training (WMT) is widely used to enhance cognitive task performance, yet traditional delivery poses challenges for sustained implementation; computerised working memory training (CWMT) provides a scalable digital format with standardised delivery and integrated monitoring and feedback. We conducted the first meta-analysis integrating behavioural and neuroimaging evidence to quantify the behavioural effects and neural correlates of CWMT, drawing on 45 neuroimaging studies. Multivariate meta-analysis indicated a moderate benefit of CWMT relative to controls (Hedges’ g = 0.503, 95% CI [0.363–0.642]). Seed-based d mapping (SDM) identified training-related decreases in activation in the left angular gyrus (L-AG), bilateral superior frontal gyrus (SFG), right inferior parietal lobule (R-IPL), left cerebellum, and right middle frontal gyrus (R-MFG), a pattern compatible with reduced recruitment following training. Moderator analyses showed significant effects of task type, training compliance, total training dose, and age, but not cognitive status and sex. Moreover, CWMT-induced brain activation changes were associated with behavioural improvements, and significant co-activation was observed among the brain regions identified in the overall analysis. Together, these findings provide convergent evidence that CWMT is associated with improved cognitive task performance and reproducible modulation of task-related activation, supporting its relevance as a scalable digital approach to cognitive health.
工作记忆训练（Working memory training， WMT）被广泛用于提高认知任务绩效，但传统的方法难以持续实施；计算机化工作记忆训练（CWMT）提供了一种可扩展的数字格式，具有标准化的交付和集成的监测和反馈。我们进行了第一个整合行为和神经影像学证据的荟萃分析，以量化CWMT的行为效应和神经相关因素，借鉴了45项神经影像学研究。多因素荟萃分析显示，CWMT相对于对照组有中等疗效（Hedges ' g = 0.503, 95% CI[0.363-0.642]）。基于种子的d映射（SDM）发现了训练相关的左角回（L-AG）、双侧额上回（SFG）、右顶叶下小叶（R-IPL）、左小脑和右额叶中回（R-MFG）的激活减少，这种模式与训练后招募减少相一致。调节分析显示任务类型、训练依从性、总训练剂量和年龄有显著影响，但认知状态和性别没有显著影响。此外，cwmt诱导的大脑激活变化与行为改善有关，并且在总体分析中发现的大脑区域之间观察到显着的共激活。总之，这些发现提供了一致的证据，证明CWMT与改善的认知任务表现和任务相关激活的可重复调节有关，支持其作为认知健康的可扩展数字方法的相关性。
{"title":"Meta-analysis of computerised working memory training: behavioural gains, training parameters, transfer mechanisms, and neural correlates","authors":"Geng Li, Yang Liu, Antao Chen","doi":"10.1038/s41746-026-02478-9","DOIUrl":"https://doi.org/10.1038/s41746-026-02478-9","url":null,"abstract":"Working memory training (WMT) is widely used to enhance cognitive task performance, yet traditional delivery poses challenges for sustained implementation; computerised working memory training (CWMT) provides a scalable digital format with standardised delivery and integrated monitoring and feedback. We conducted the first meta-analysis integrating behavioural and neuroimaging evidence to quantify the behavioural effects and neural correlates of CWMT, drawing on 45 neuroimaging studies. Multivariate meta-analysis indicated a moderate benefit of CWMT relative to controls (Hedges’ g = 0.503, 95% CI [0.363–0.642]). Seed-based d mapping (SDM) identified training-related decreases in activation in the left angular gyrus (L-AG), bilateral superior frontal gyrus (SFG), right inferior parietal lobule (R-IPL), left cerebellum, and right middle frontal gyrus (R-MFG), a pattern compatible with reduced recruitment following training. Moderator analyses showed significant effects of task type, training compliance, total training dose, and age, but not cognitive status and sex. Moreover, CWMT-induced brain activation changes were associated with behavioural improvements, and significant co-activation was observed among the brain regions identified in the overall analysis. Together, these findings provide convergent evidence that CWMT is associated with improved cognitive task performance and reproducible modulation of task-related activation, supporting its relevance as a scalable digital approach to cognitive health.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"196 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394090","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 : 2026-03-12DOI: 10.1038/s41746-026-02532-6
Tejas S Athni,Arjun Mahajan,Dylan Powell
{"title":"Redesigning leadership for clinical AI deployment.","authors":"Tejas S Athni,Arjun Mahajan,Dylan Powell","doi":"10.1038/s41746-026-02532-6","DOIUrl":"https://doi.org/10.1038/s41746-026-02532-6","url":null,"abstract":"","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"8 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147439650","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 : 2026-03-12DOI: 10.1038/s41746-026-02528-2
Yonatan E. Brand, Aron S. Buchman, Felix Kluge, Luca Palmerini, Clemens Becker, Andrea Cereatti, Walter Maetzler, Beatrix Vereijken, Alison J. Yarnall, Lynn Rochester, Silvia Del Din, Arne Mueller, Jeffrey M. Hausdorff, Or Perlman
Physical activity and mobility are critical for healthy aging and predict diverse health outcomes. While wrist-worn accelerometers are widely used to monitor physical activity, estimating gait metrics from wrist data remains challenging. We extend ElderNet, a self-supervised deep-learning model previously validated for walking-bout detection, to estimate gait metrics from wrist accelerometry. Validation involved 819 older adults (Rush-Memory-and-Aging-Project) and 85 individuals with gait impairments (Mobilise-D), from six medical centers. In Mobilise-D, ElderNet achieved an absolute error of 8.82 cm/s and an intra-class correlation of 0.87 for gait speed, outperforming state-of-the-art methods (p < 0.001) and models using a lower-back sensor. ElderNet outperformed (percentage error; p < 0.01) competing approaches in estimating cadence and stride length, and better (p < 0.01) classified mobility disability (AUC = 0.80) than conventional gait or physical activity metrics. These results demonstrate the potential of ElderNet a scalable tool for gait assessment using wrist-worn devices in aging and clinical populations.
{"title":"Continuous assessment of daily-living gait using self-supervised learning of wrist-worn accelerometer data","authors":"Yonatan E. Brand, Aron S. Buchman, Felix Kluge, Luca Palmerini, Clemens Becker, Andrea Cereatti, Walter Maetzler, Beatrix Vereijken, Alison J. Yarnall, Lynn Rochester, Silvia Del Din, Arne Mueller, Jeffrey M. Hausdorff, Or Perlman","doi":"10.1038/s41746-026-02528-2","DOIUrl":"https://doi.org/10.1038/s41746-026-02528-2","url":null,"abstract":"Physical activity and mobility are critical for healthy aging and predict diverse health outcomes. While wrist-worn accelerometers are widely used to monitor physical activity, estimating gait metrics from wrist data remains challenging. We extend ElderNet, a self-supervised deep-learning model previously validated for walking-bout detection, to estimate gait metrics from wrist accelerometry. Validation involved 819 older adults (Rush-Memory-and-Aging-Project) and 85 individuals with gait impairments (Mobilise-D), from six medical centers. In Mobilise-D, ElderNet achieved an absolute error of 8.82 cm/s and an intra-class correlation of 0.87 for gait speed, outperforming state-of-the-art methods (p < 0.001) and models using a lower-back sensor. ElderNet outperformed (percentage error; p < 0.01) competing approaches in estimating cadence and stride length, and better (p < 0.01) classified mobility disability (AUC = 0.80) than conventional gait or physical activity metrics. These results demonstrate the potential of ElderNet a scalable tool for gait assessment using wrist-worn devices in aging and clinical populations.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"76 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394031","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 : 2026-03-11DOI: 10.1038/s41746-026-02466-z
Jannis Kraiss,Felix Fiß,Farid Chakhssi,Fatma Betül Aktas,Jurrijn Alexander Koelen,Jorge Piano Simões
This meta-analysis aimed to code active cognitive behavioral elements in mental health apps and to examine the association between these elements and improvements in depression and anxiety. Trials evaluating mental health apps were coded based on 34 pre-registered elements. 169 trials with 1137 timepoints were included (N = 41,807; mean age = 34.3 years; 72.9% female). Psychoeducation, relaxation, mindfulness, and self-monitoring were used most frequently. Bivariate mixed-effect meta-regression models showed that many elements were weakly to moderately effective. Desensitization, stimulus control, and activity scheduling were most strongly and robustly associated with improvements in depression and exposure-based elements with improvements in anxiety. Ineffective elements included graded tasks and personal strengths, but in sum, there was considerable variation in the frequency and impact of active elements. Interventions incorporating a greater number of elements were more effective. This meta-analysis provides insight into how active elements in mental health apps are associated with therapeutic change, informing future interventions.
{"title":"Identifying what works in mental health apps through meta-regression analyses of 169 trials.","authors":"Jannis Kraiss,Felix Fiß,Farid Chakhssi,Fatma Betül Aktas,Jurrijn Alexander Koelen,Jorge Piano Simões","doi":"10.1038/s41746-026-02466-z","DOIUrl":"https://doi.org/10.1038/s41746-026-02466-z","url":null,"abstract":"This meta-analysis aimed to code active cognitive behavioral elements in mental health apps and to examine the association between these elements and improvements in depression and anxiety. Trials evaluating mental health apps were coded based on 34 pre-registered elements. 169 trials with 1137 timepoints were included (N = 41,807; mean age = 34.3 years; 72.9% female). Psychoeducation, relaxation, mindfulness, and self-monitoring were used most frequently. Bivariate mixed-effect meta-regression models showed that many elements were weakly to moderately effective. Desensitization, stimulus control, and activity scheduling were most strongly and robustly associated with improvements in depression and exposure-based elements with improvements in anxiety. Ineffective elements included graded tasks and personal strengths, but in sum, there was considerable variation in the frequency and impact of active elements. Interventions incorporating a greater number of elements were more effective. This meta-analysis provides insight into how active elements in mental health apps are associated with therapeutic change, informing future interventions.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"55 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383476","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 forget up to 80% of information conveyed during medical consultations. While clinicians may provide hand-written notes to patients during in-person appointments, such opportunities are limited in telehealth. Palliative care patients with complex information needs may benefit from consultation summaries. We developed a consultation summary application (CSA) to generate patient-facing summaries during video telehealth, in a palliative care context. Traditional research methods fall short in early identification and resolution of socio-technical factors, e.g., workflow compatibility, which impact the adoption of digital health innovations. Drawing on the Service Readiness Level Framework, we adopted a phased approach to generating evidence for the CSA. We conducted clinical simulations with seven clinician-simulated patient dyads involving the metastatic lung cancer scenario to examine and address usability and workflow integration issues prior to real-world implementation. Both clinicians and simulated patients perceived the CSA as a valuable tool to support palliative care patients with information recall and self-management. We recommend clinical simulation to de-risk real-world deployment, and optimise the digital health innovations.
{"title":"Using clinical simulation to evaluate a video telehealth consultation summary application.","authors":"Teresa O'Brien,Kit Huckvale,Olivia Metcalf,Wendy Chapman,Hasan Ferdous,Rashina Hoda,Peter Poon,Andy Li,Laura Bird,Isabella Hall,Emmy Trinh,Christopher Bain,Sam Georgy,Xiao Chen,Mahima Kalla","doi":"10.1038/s41746-026-02506-8","DOIUrl":"https://doi.org/10.1038/s41746-026-02506-8","url":null,"abstract":"Patients forget up to 80% of information conveyed during medical consultations. While clinicians may provide hand-written notes to patients during in-person appointments, such opportunities are limited in telehealth. Palliative care patients with complex information needs may benefit from consultation summaries. We developed a consultation summary application (CSA) to generate patient-facing summaries during video telehealth, in a palliative care context. Traditional research methods fall short in early identification and resolution of socio-technical factors, e.g., workflow compatibility, which impact the adoption of digital health innovations. Drawing on the Service Readiness Level Framework, we adopted a phased approach to generating evidence for the CSA. We conducted clinical simulations with seven clinician-simulated patient dyads involving the metastatic lung cancer scenario to examine and address usability and workflow integration issues prior to real-world implementation. Both clinicians and simulated patients perceived the CSA as a valuable tool to support palliative care patients with information recall and self-management. We recommend clinical simulation to de-risk real-world deployment, and optimise the digital health innovations.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"33 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147393772","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 : 2026-03-11DOI: 10.1038/s41746-026-02549-x
Ariel Yuhan Ong,Kyra L Rosen,Margaret Sui,Joseph C Kvedar
{"title":"\"Doing no harm\" in the digital age: navigating tradeoffs and operational considerations for privacy-preserving deep learning in medicine.","authors":"Ariel Yuhan Ong,Kyra L Rosen,Margaret Sui,Joseph C Kvedar","doi":"10.1038/s41746-026-02549-x","DOIUrl":"https://doi.org/10.1038/s41746-026-02549-x","url":null,"abstract":"","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"1 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147393829","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}
Prenatal assessment of fetal cardiac function is crucial for predicting neonatal outcomes, yet manual echocardiographic measurements are labor-intensive and subjective. We developed a fully automated artificial intelligence (AI) workflow for estimating fetal cardiac function parameters from echocardiograms. The workflow integrates a deep learning model for real-time detection and segmentation of cardiac structures, followed by quality control and geometric calculation. It was developed and validated using an internal dataset of 52,942 annotated images from 1940 normal fetal echocardiograms, with further testing on two external normal datasets (245 echocardiograms) and one internal abnormal dataset (83 echocardiograms). Performance was compared against manual and Fetal Heart Quantification (Fetal HQ) measurements, and a dynamic Z-score model referencing gestational age and fetal biometrics was established. The AI achieved accurate segmentation, with mean Dice similarity coefficients >92% and mean intersection-over-union >85% across all test datasets. It exhibited higher intraclass correction coefficients and R-values relative to experts than inter-observer variability, alongside smaller mean absolute error and limits of agreement. The mean individual equivalence coefficients of all cardiac function parameters were below zero, indicating lower variability than manual or Fetal HQ. These results demonstrate that our fully automated AI workflow enables accurate, efficient, and reproducible quantification of fetal cardiac function, supporting its potential for standardized clinical application.
{"title":"Automated interpretation of fetal cardiac function evaluation from the echocardiogram","authors":"Caixin Huang, Lihe Zhang, Baihong Xie, Yuting Jiang, Yunxiao Zhu, Xiaozhen Liu, Ting Lei, Miao He, Yafei Yan, Nan Wang, Hongning Xie","doi":"10.1038/s41746-026-02381-3","DOIUrl":"https://doi.org/10.1038/s41746-026-02381-3","url":null,"abstract":"Prenatal assessment of fetal cardiac function is crucial for predicting neonatal outcomes, yet manual echocardiographic measurements are labor-intensive and subjective. We developed a fully automated artificial intelligence (AI) workflow for estimating fetal cardiac function parameters from echocardiograms. The workflow integrates a deep learning model for real-time detection and segmentation of cardiac structures, followed by quality control and geometric calculation. It was developed and validated using an internal dataset of 52,942 annotated images from 1940 normal fetal echocardiograms, with further testing on two external normal datasets (245 echocardiograms) and one internal abnormal dataset (83 echocardiograms). Performance was compared against manual and Fetal Heart Quantification (Fetal HQ) measurements, and a dynamic Z-score model referencing gestational age and fetal biometrics was established. The AI achieved accurate segmentation, with mean Dice similarity coefficients >92% and mean intersection-over-union >85% across all test datasets. It exhibited higher intraclass correction coefficients and R-values relative to experts than inter-observer variability, alongside smaller mean absolute error and limits of agreement. The mean individual equivalence coefficients of all cardiac function parameters were below zero, indicating lower variability than manual or Fetal HQ. These results demonstrate that our fully automated AI workflow enables accurate, efficient, and reproducible quantification of fetal cardiac function, supporting its potential for standardized clinical application.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"76 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147381705","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 : 2026-03-10DOI: 10.1038/s41746-026-02534-4
Oliver Toparti,Kunal Rajput,Ara Darzi,Saira Ghafur
{"title":"Cybersecurity in connected medical devices: a policy agenda for the NHS.","authors":"Oliver Toparti,Kunal Rajput,Ara Darzi,Saira Ghafur","doi":"10.1038/s41746-026-02534-4","DOIUrl":"https://doi.org/10.1038/s41746-026-02534-4","url":null,"abstract":"","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"237 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383661","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 : 2026-03-09DOI: 10.1038/s41746-026-02482-z
Jan Schaal,Tobias Leutritz,Marco Lindner,Alexander Zamzow,Joy Backhaus,Sarah König,Tobias Mühling
Virtual reality (VR) is increasingly used for assessment in educational and clinical settings. However, users' immersive competence (IC)-the ability to navigate and operate VR systems-may introduce bias unrelated to clinical skills or patient functioning. In this randomized controlled trial, 88 medical students received either general IC training, general+specific IC training, or no structured training before completing a VR-based assessment scenario. Multimodal data were collected, including electrodermal activity, cognitive-load ratings, procedural efficiency, and usability barriers. Specific IC training improved performance compared with control (28.3% ± 10.3% vs. 21.2% ± 10.8%, p = 0.010, d = 0.67), moderated by procedural efficiency and increased cognitive load. Prior 3D experience did not predict performance in the control group, likely due to a floor effect, but did in the specific training group. These findings indicate that IC is a causal, modifiable factor in VR-based assessments and should be considered to ensure fair and valid evaluations.
虚拟现实（VR）越来越多地用于教育和临床环境的评估。然而，用户的沉浸式能力（IC）——导航和操作VR系统的能力——可能会引入与临床技能或患者功能无关的偏见。在这项随机对照试验中，88名医学生在完成基于vr的评估方案之前，分别接受了普通IC培训、普通+特定IC培训或不接受结构化培训。收集多模式数据，包括皮电活动、认知负荷评分、程序效率和可用性障碍。与对照组相比，特定IC训练提高了表现（28.3%±10.3% vs. 21.2%±10.8%,p = 0.010, d = 0.67），但程序效率和认知负荷的增加对其有调节作用。先前的3D经验并不能预测控制组的表现，可能是由于地板效应，但在特定训练组中确实如此。这些发现表明，在基于虚拟现实的评估中，IC是一个因果关系，可改变的因素，应被考虑以确保评估的公平和有效。
{"title":"Immersive competence as a source of bias in virtual reality clinical assessment.","authors":"Jan Schaal,Tobias Leutritz,Marco Lindner,Alexander Zamzow,Joy Backhaus,Sarah König,Tobias Mühling","doi":"10.1038/s41746-026-02482-z","DOIUrl":"https://doi.org/10.1038/s41746-026-02482-z","url":null,"abstract":"Virtual reality (VR) is increasingly used for assessment in educational and clinical settings. However, users' immersive competence (IC)-the ability to navigate and operate VR systems-may introduce bias unrelated to clinical skills or patient functioning. In this randomized controlled trial, 88 medical students received either general IC training, general+specific IC training, or no structured training before completing a VR-based assessment scenario. Multimodal data were collected, including electrodermal activity, cognitive-load ratings, procedural efficiency, and usability barriers. Specific IC training improved performance compared with control (28.3% ± 10.3% vs. 21.2% ± 10.8%, p = 0.010, d = 0.67), moderated by procedural efficiency and increased cognitive load. Prior 3D experience did not predict performance in the control group, likely due to a floor effect, but did in the specific training group. These findings indicate that IC is a causal, modifiable factor in VR-based assessments and should be considered to ensure fair and valid evaluations.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"16 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147381234","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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