Journal of Medical Internet Research最新文献_第7页

The Relationship Between Physician Self-Disclosure and Patient Acquisition in Digital Health Markets: Cross-Sectional Study. 数字医疗市场中医生自我披露与患者获取的关系：横断面研究。

IF 6 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research

Pub Date : 2026-01-29 DOI: 10.2196/84963

Quanchen Liu, Pengqing Yin, Jing Fan

Background: Online health communities have evolved into digital marketplaces where physicians have to compete for patients. Existing research examines physician-patient dynamics through a patient-centric lens, treating physicians as passive recipients of ratings and reviews, while the strategic role of physician self-disclosure remains unexamined. This gap constrains a comprehensive understanding of how physicians can actively shape patient decisions, making the investigation of strategic self-disclosure imperative.Objective: This study aims to investigate the relationship between physician self-disclosure breadth (scope of information) and depth (detailed expertise) and patient decision-making, as well as whether regional digital health care level (DHL) moderates these relationships.Methods: We conducted a cross-sectional analysis of observational data to test these relationships. Data were collected from China's online health care platform Haodf from September to December 2024. Self-disclosure breadth (including clinical performance, academic experience, and social reputation), self-disclosure depth (including expertise coverage, richness, and granularity), and patient decision-making (total visits) were captured through manual content coding and quantitative measurement. We used structured content analysis to extract the disclosure components, informational scope, and descriptive details of each profile. Then, using validated operational formulas, we calculated the composite indices for disclosure breadth and depth based on the coded dimensions. The study generated 1798 final physician samples with complete data across 14 focal variables. The hypotheses were tested using an ordinary least squares regression model, and 4 robustness checks were conducted, including variable substitution and different resampling techniques.Results: In the primary ordinary least squares regression models, self-disclosure breadth was significantly and positively associated with patient visits (β=0.255, 95% CI 0.054-0.456; P=.01), as was self-disclosure depth (β=0.098, 95% CI 0.030-0.167; P=.005). The breadth×DHL interaction was positive and significant (β=0.261, 95% CI 0.061-0.461; P=.01). Similarly, the depth×DHL interaction was positive and significant (β=0.070, 95% CI 0.002-0.138; P=.045). It should be noted that the association for self-disclosure breadth was stronger than that of self-disclosure depth. DHL strengthened the relationship between the disclosure strategies with patient visits. This contextual amplification indicates that DHL serves as a critical boundary condition, determining the degree to which physician self-disclosure strategies translate into patient acquisition outcomes.Conclusions: This study reconceptualizes physicians as strategic agents shaping patient decision-making through purposeful self-disclosure. Different from exist

背景：在线健康社区已经演变成数字市场，医生们必须为病人竞争。现有的研究通过以患者为中心的视角来审视医患动态，将医生视为评级和评论的被动接受者，而医生自我披露的战略作用仍未得到检验。这一差距限制了对医生如何积极塑造患者决策的全面理解，使得战略自我披露的调查势在必行。目的：本研究旨在探讨医师自我披露广度（信息范围）和深度（详细专业知识）与患者决策的关系，以及区域数字医疗水平（DHL）是否调节了这些关系。方法：我们对观察数据进行了横断面分析，以检验这些关系。数据于2024年9月至12月从中国在线医疗平台浩特收集。通过人工内容编码和定量测量获取自我披露广度（包括临床表现、学术经历和社会声誉）、自我披露深度（包括专业知识覆盖、丰富度和粒度）和患者决策（总访问量）。我们使用结构化内容分析来提取每个概要文件的公开组件、信息范围和描述性细节。然后，利用经过验证的运算公式，基于编码维度计算披露广度和披露深度的综合指数。该研究产生了1798个最终医生样本，其中包含14个焦点变量的完整数据。使用普通最小二乘回归模型对假设进行检验，并进行4次稳健性检验，包括变量替换和不同的重采样技术。结果：在初级普通最小二乘回归模型中，自我披露广度与患者就诊呈显著正相关（β=0.255, 95% CI 0.054 ~ 0.456， P= 0.01），自我披露深度与患者就诊呈显著正相关（β=0.098, 95% CI 0.030 ~ 0.167， P= 0.005）。breadth×DHL交互作用为正且显著（β=0.261, 95% CI 0.061 ~ 0.461； P= 0.01）。同样，depth×DHL相互作用为正且显著（β=0.070, 95% CI 0.002-0.138； P= 0.045）。值得注意的是，自我表露广度的相关性强于自我表露深度。DHL加强了信息披露策略与患者访问量之间的关系。这种背景放大表明DHL是一个关键的边界条件，决定了医生自我披露策略转化为患者获得结果的程度。结论：本研究将医生重新定义为通过有目的的自我披露来塑造患者决策的战略代理人。与现有研究将医生视为评级和评论的被动接受者不同，我们的研究表明，医生可以通过自我披露的广度和深度战略性地塑造患者获得。本研究通过证明自我披露是一种可行的患者获取机制，其中DHL是一个关键的边界条件，为数字健康市场带来了新的见解。研究结果具有现实意义：(1)医生可以利用基于证据的信息披露策略；(2)平台应该实施情境适应性特征；(3)政策制定者应该优先考虑数字基础设施投资，以提高医生的竞争力和患者决策质量。

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引用次数: 0

Patient and Care Team Perspectives of Barriers to and Facilitators for the Implementation of a Digital Health Program for Depression in Primary Care: Qualitative Study. 患者和护理团队对初级保健中抑郁症数字健康计划实施的障碍和促进因素的看法：定性研究。

IF 6 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research

Pub Date : 2026-01-29 DOI: 10.2196/72003

Andrea Nederveld, Elise A Robertson, Angela M Lanigan, Elisabeth F Callen, Tarin L Clay, Ben Fehnert, Lambros Chrones, Michael L Martin, Margaret McCue, Christina M Hester, Melissa K Filippi

Background: Depression is pervasive, and rates are rising in the United States. Most people with depression receive care from primary care clinicians, but gaps in the quality of care exist. Team-based approaches to depression care have been shown to aid in treatment and management; yet, challenges exist in implementation. Digital health apps have been shown to be effective in improving depression symptoms and enhancing patient engagement in some populations. Many, however, do not share data with clinical care teams.Objective: This study aimed to understand the barriers to and facilitators for implementation of a digital health program that supports coordinated use by clinical care teams and patients, via a mobile app and care team-facing web interface, for depression in primary care.Methods: This study was part of a larger intervention study that included 4 primary care practices: 2 intervention and 2 control sites. The intervention sites used a patient-facing mobile app and a care team-facing web interface, and the control sites continued usual care. The study team conducted interviews from May to October 2021. Patient and care team participants were recruited toward the end of their study involvement. Separate semistructured interview guides were developed for patient and care team participants. Interviews were recorded and transcribed. Data were coded using Atlas.ti.9, and data analysis was completed using a grounded theory approach.Results: Interviews with patient (n=8) and care team (n=8) participants revealed 3 main topics for program implementation: app/interface usability, tracking, and program recommendations. For app/interface usability, overall, navigation for both patient and care team participants was simple and straightforward. Although app content was relevant, patient participants desired additional educational resources and information to aid in their depression treatment and management. In terms of tracking, care team participants indicated that data obtained via the interface enabled them to monitor patients in between visits; and in some circumstances, these data facilitated conversations with patients about treatment plans. Tracking medication adherence differed among patient participants due to established routines of taking medications consistently, lack of motivation to track, or lack of interest in tracking. Care team participants reported the ability to respond more quickly to side effects. Patients commented on tracking difficulties: confusing response options, insufficient goal attainment response options, not being able to provide details or write notes, and no ability to change goals. Some patient and care team participants perceived that tracking encouraged communication with one another; others perceived tracking as having no impact on shared decision-making.Conclusions: Results suggest implementation

背景：抑郁症在美国非常普遍，而且发病率正在上升。大多数抑郁症患者接受初级保健临床医生的护理，但护理质量存在差距。以团队为基础的抑郁症治疗方法已被证明有助于治疗和管理；然而，在执行方面存在挑战。在某些人群中，数字健康应用已被证明在改善抑郁症状和提高患者参与度方面是有效的。然而，许多医院不与临床护理团队共享数据。目的：本研究旨在了解实施数字健康计划的障碍和促进因素，该计划通过移动应用程序和面向护理团队的web界面，支持临床护理团队和患者协调使用初级保健中的抑郁症。方法：本研究是一项大型干预研究的一部分，该研究包括4个初级保健实践：2个干预点和2个对照点。干预站点使用面向患者的移动应用程序和面向护理团队的web界面，而对照站点继续进行常规护理。研究小组于2021年5月至10月进行了访谈。患者和护理团队的参与者在研究结束时被招募。为患者和护理团队参与者制定了单独的半结构化访谈指南。采访被记录下来并记录下来。使用Atlas.ti对数据进行编码。9、数据分析采用扎根理论方法完成。结果：对患者（n=8）和护理团队（n=8）参与者的访谈揭示了项目实施的3个主要主题：应用程序/界面可用性、跟踪和项目建议。在应用程序/界面可用性方面，总体而言，患者和护理团队参与者的导航都简单明了。尽管应用程序的内容是相关的，但患者参与者希望获得额外的教育资源和信息，以帮助他们治疗和管理抑郁症。在跟踪方面，护理团队参与者表示，通过接口获得的数据使他们能够在两次访问之间监测患者；在某些情况下，这些数据有助于与患者讨论治疗计划。跟踪药物依从性在患者参与者之间存在差异，这是由于持续服药的既定惯例，缺乏跟踪的动机或缺乏跟踪的兴趣。护理小组的参与者报告说，他们能够更快地对副作用做出反应。患者评论了追踪困难：反应选项混乱，目标实现反应选项不足，无法提供细节或写笔记，无法改变目标。一些患者和护理团队参与者认为，跟踪鼓励了彼此之间的沟通；其他人认为跟踪对共同决策没有影响。结论：结果表明，在初级保健实践中实施抑郁症治疗和管理的数字健康计划可以影响患者的药物依从性，为药物优化提供更快的周转时间，鼓励目标设定，并促进患者和护理团队成员之间的沟通。程序增强可以优化患者和护理团队成员的参与。

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引用次数: 0

Detection of Antithrombotic-Related Bleeding in Older Inpatients: Multicenter Retrospective Study Using Structured and Unstructured Electronic Health Record Data. 老年住院患者抗血栓相关出血的检测：使用结构化和非结构化电子健康记录数据的多中心回顾性研究

IF 6 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research

Pub Date : 2026-01-29 DOI: 10.2196/77809

Claire Coumau, Frederic Gaspar, Mehdi Zayene, Elliott Bertrand, Lorenzo Alberio, Christian Lovis, Patrick E Beeler, Fabio Rinaldi, Monika Lutters, Marie-Annick Le Pogam, Chantal Csajka

Background: Bleeding complications are a major contributor to adverse drug events among older inpatients, particularly in those treated with antithrombotic agents. Timely and accurate detection of bleeding events is essential for improving drug safety surveillance and clinical risk management.Objective: The study aimed to develop and validate automated algorithms for detecting major bleeding (MB) and clinically relevant nonmajor bleeding (CRNMB) events from electronic medical records (EMRs) by combining structured data-based rule models and a natural language processing (NLP) approach, and to evaluate their performance and generalizability against a manually reviewed gold standard and an external dataset.Methods: We conducted a multicenter retrospective study using routinely collected EMR data from 3 Swiss university hospitals. Patients 65 years or older who received at least one antithrombotic agent and were hospitalized between January 2015 and December 2016 were included. To detect MB and CRNMB events, rule-based algorithms were developed using structured data (International Statistical Classification of Diseases, 10th Revision, German Modification [ICD-10-GM] codes, laboratory values, transfusion records, and antihemorrhagic prescriptions), with variables and cutoff values defined according to adapted International Society on Thrombosis and Haemostasis definitions and expert consensus. In parallel, a supervised NLP model was applied to discharge summaries from one hospital. A manual review of 754 EMRs served as the reference standard for internal validation, and the algorithm performance of the structured data algorithms (SDA), NLP, and their combination (SDA+NLP) was evaluated against this manually reviewed gold standard using standard performance metrics. External validation was performed on an independent dataset from the Lausanne University Hospital to assess model robustness and generalizability.Results: Among 36,039 inpatient stays, SDA identified 8.26% (n=2979) as MB and 15.04% (n=5419) as CRNMB cases. ICD-10-GM codes alone detected 28.5% (n=849) of MB and 31.48% (n=1706) of CRNMB cases, while laboratory data contributed most to event detection (n=1994, 66.94% for MB and n=3663, 67.60% for CRNMB). Integrating SDA with NLP improved detection, identifying 12.2% (920/7513) of MB and 27.4% (2062/7513) of CRNMB cases at 1 hospital. The combined model achieved the best performance (sensitivity 0.84, positive predictive value 0.51, F1-score 0.64). External validation on Lausanne University Hospital 2021-2022 data (n=24,054 stays) confirmed the algorithms' reproducibility; the prevalence of MB decreased while CRNMB increased, reflecting evolving clinical practices and antithrombotic use patterns.Conclusions: Our integrated approach, combining SDA with NLP, enhances the detection of hemorrhagic events in older hospitalized patient

背景：出血并发症是老年住院患者药物不良事件的主要原因，特别是那些使用抗血栓药物治疗的患者。及时准确地发现出血事件对于改善药物安全监测和临床风险管理至关重要。目的：本研究旨在通过结合结构化数据规则模型和自然语言处理（NLP）方法，开发和验证用于从电子病历（emr）中检测大出血（MB）和临床相关非大出血（CRNMB）事件的自动算法，并根据人工审查的金标准和外部数据集评估其性能和泛化性。方法：我们进行了一项多中心回顾性研究，使用常规收集的瑞士3所大学医院的电子病历数据。纳入了在2015年1月至2016年12月期间接受至少一种抗血栓药物治疗并住院的65岁或以上患者。为了检测MB和CRNMB事件，使用结构化数据（国际疾病统计分类，第十次修订，德国修改[ICD-10-GM]代码，实验室值，输血记录和抗出血处方）开发了基于规则的算法，变量和截止值根据改编的国际血栓和止血学会定义和专家共识定义。同时，一个监督的NLP模型应用于一家医院的出院总结。对754份emr进行人工审查，作为内部验证的参考标准，并使用标准性能指标根据人工审查的金标准评估结构化数据算法（SDA）、NLP及其组合（SDA+NLP）的算法性能。对来自洛桑大学医院的独立数据集进行外部验证，以评估模型的稳健性和泛化性。结果：在36039例住院患者中，SDA鉴定出8.26% （n=2979）为MB， 15.04% （n=5419）为CRNMB。ICD-10-GM代码单独检出MB病例28.5% (n=849)， CRNMB病例31.48% (n=1706)，实验室数据对事件检出贡献最大（n=1994, MB 66.94%, CRNMB 3663, 67.60%）。将SDA与NLP相结合提高了检出率，在1家医院中发现12.2%（920/7513）的MB和27.4%（2062/7513）的CRNMB病例。联合模型的敏感性为0.84，阳性预测值为0.51，f1评分为0.64。洛桑大学医院2021-2022年数据（n=24,054次住院）的外部验证证实了算法的可重复性；MB的患病率下降而CRNMB增加，反映了临床实践和抗血栓药物使用模式的演变。结论：我们的综合方法，结合SDA和NLP，提高了抗血栓药物治疗的老年住院患者出血事件的检测，表明其在药物安全监测和临床风险管理方面的潜在用途。

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引用次数: 0

Collaborative and Cooperative Hospital "In-House" Medical Device Development and Implementation in the AI Age: The European Responsible AI Development (EURAID) Framework Compatible With European Values. 人工智能时代的协同和合作医院“内部”医疗器械开发和实施：符合欧洲价值观的欧洲负责任的人工智能开发（EURAID）框架。

IF 6 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research

Pub Date : 2026-01-29 DOI: 10.2196/80754

Anett Schönfelder, Maria Eberlein-Gonska, Manfred Hülsken-Giesler, Florian Jovy-Klein, Jakob Nikolas Kather, Elisabeth Kohoutek, Thomas Lennefer, Elisabeth Liebert, Myriam Lipprandt, Rebecca Mathias, Hannah Sophie Muti, Julius Obergassel, Thomas Reibel, Ulrike Rösler, Moritz Schneider, Larissa Schlicht, Hannes Schlieter, Malte L Schmieding, Nils Schweingruber, Martin Sedlmayr, Reinhard Strametz, Barbara Susec, Magdalena Katharina Wekenborg, Eva Weicken, Katharina Weitz, Anke Diehl, Stephen Gilbert

The last years have seen an acceleration in the development and uptake of artificial intelligence (AI) systems by "early adopter" hospitals, caught between the pressures to "perform" and "transform" in a struggling health care system. This transformation has raised concerns among health care providers as their voices and location-specific workflows have often been overlooked, resulting in technologies that fail to integrate meaningfully into routine care and worsen rather than improve care processes. How can positive AI implementation be carried out in health care, aligned with European values? Based on a perspective that spans all stakeholders, we have created EURAID (European Responsible AI Development), a practical, human-centric framework for AI development and implementation based on agreed goals and values. We illustrate this approach through the co-development of a narrow-purpose "in-house" AI system, designed to help bridge the AI implementation gap in real-world clinical settings. This example is then expanded to address the broader challenges associated with complex, multiagent AI systems. By portraying all key stakeholders across the AI development life cycle and highlighting their roles and contributions within the process, real use cases, and methods for achieving iterative consensus, we offer a unique practical approach for safe and fast progress in hospital digital transformation in the AI age.

过去几年，“早期采用者”医院加速开发和采用人工智能（AI）系统，在挣扎中的医疗保健系统中面临“表现”和“转型”的压力。这种转变引起了卫生保健提供者的关注，因为他们的声音和特定地点的工作流程往往被忽视，导致技术无法有效地融入日常护理，并且恶化而不是改善护理流程。如何在符合欧洲价值观的情况下，在医疗保健领域实施积极的人工智能？基于跨越所有利益相关者的视角，我们创建了EURAID（欧洲负责任的人工智能开发），这是一个基于商定目标和价值观的人工智能开发和实施的实用、以人为中心的框架。我们通过共同开发一个窄用途的“内部”人工智能系统来说明这种方法，旨在帮助弥合现实世界临床环境中人工智能实施的差距。然后将这个例子扩展到解决与复杂的多智能体AI系统相关的更广泛的挑战。通过描绘人工智能开发生命周期中的所有关键利益相关者，并强调他们在流程、实际用例和实现迭代共识的方法中的角色和贡献，我们为人工智能时代医院数字化转型的安全和快速进展提供了一种独特的实用方法。

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引用次数: 0

Characterization of Models for Identifying Physical and Cognitive Frailty in Older Adults With Diabetes: Systematic Review and Meta-Analysis. 识别老年糖尿病患者身体和认知虚弱模型的特征：系统回顾和荟萃分析。

IF 6 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research

Pub Date : 2026-01-29 DOI: 10.2196/84617

Xia Wang, Shujie Meng, Xiang Xiao, Liu Lu, Hongyan Chen, Yong Li, Rong Zhang, Qiwu Jiang, Shan Liu, Ru Gao

Background: Physical frailty and cognitive frailty are increasingly recognized as critical geriatric syndromes among older adults with diabetes, contributing to adverse outcomes such as disability, hospitalization, and mortality. Early identification of individuals at high risk is therefore essential for timely prevention and intervention. Although a growing number of prediction models have been developed for this population, evidence regarding their methodological rigor, predictive performance, and generalizability remains fragmented.Objective: This study aims to evaluate and characterize existing models for detecting or predicting physical frailty and cognitive frailty in older adults with diabetes.Methods: PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang, and VIP databases were searched from their inception to December 2025. Retrospective, cross-sectional, and prospective studies that developed or validated models predicting frailty or cognitive frailty in older adults with diabetes were included. The Prediction Model Study Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability. Random effects meta-analyses using the Hartung-Knapp-Sidik-Jonkman method were conducted to synthesize model performance, including the pooled area under the receiver operating characteristic curve (AUC). Heterogeneity was explored through subgroup and sensitivity analyses. Small study effects were evaluated using funnel plots, the Egger test, and the Deeks funnel plot asymmetry test.Results: A total of 24 studies comprising 32 diagnostic models were included. The overall pooled analysis demonstrated an AUC of 0.851 (95% CI 0.820-0.882) with a 95% prediction interval of 0.710-0.992, sensitivity of 0.810 (95% CI 0.740-0.850), and specificity of 0.850 (95% CI 0.810-0.890). Statistical comparisons in the modeling approach revealed that logistic regression models achieved a significantly higher pooled AUC (0.850) compared with machine learning models (0.785; P=.003). Similarly, retrospective studies demonstrated superior performance, with an AUC of 0.900 compared with 0.843 for cross-sectional studies (P=.03). Conversely, no significant differences were observed across subgroups stratified by data source (P=.42), patient characteristics (P=.77), validation methods (P=.16), or specific outcomes (P=.94). The most common predictors identified were depression, age, and regular exercise; however, all included studies were assessed as having a high risk of bias.Conclusions: To our knowledge, this review provides the first comprehensive synthesis of models for risk stratification of physical frailty and cognitive frailty in older adults with diabetes. The findings indicate that existing models demonstrate satisfactory discrimination; specifically, CIs confirmed a robust average effect, while pr

背景：身体虚弱和认知虚弱越来越被认为是老年糖尿病患者的关键老年综合征，导致诸如残疾、住院和死亡等不良后果。因此，及早发现高危人群对于及时预防和干预至关重要。尽管针对这一人群开发了越来越多的预测模型，但有关其方法严谨性、预测性能和普遍性的证据仍然是碎片化的。目的：本研究旨在评估和表征现有的检测或预测老年糖尿病患者身体虚弱和认知虚弱的模型。方法：检索PubMed、Embase、Web of Science、中国知网（CNKI）、万方、VIP数据库，检索时间为建站至2025年12月。回顾性、横断面和前瞻性研究，这些研究开发或验证了预测老年糖尿病患者虚弱或认知虚弱的模型。使用预测模型研究偏倚风险评估工具（PROBAST）评估偏倚风险和适用性。采用hartung - knap - sidik - jonkman方法进行随机效应荟萃分析，综合模型性能，包括受试者工作特征曲线（AUC）下的汇总面积。通过亚组分析和敏感性分析探讨异质性。采用漏斗图、Egger检验和Deeks漏斗图不对称检验评估小研究效果。结果：共纳入24项研究，包括32种诊断模型。总体合并分析显示AUC为0.851 (95% CI 0.820-0.882)， 95%预测区间为0.710-0.992，敏感性为0.810 (95% CI 0.740-0.850)，特异性为0.850 （95% CI 0.810-0.890）。建模方法中的统计比较显示，逻辑回归模型的合并AUC（0.850）明显高于机器学习模型（0.785；P= 0.003）。同样，回顾性研究也表现出了更好的效果，其AUC为0.900，而横断面研究的AUC为0.843 （P=.03）。相反，按数据源（P= 0.42）、患者特征（P= 0.77）、验证方法（P= 0.16）或特定结果（P= 0.94）分层的亚组间无显著差异。最常见的预测因素是抑郁、年龄和定期锻炼；然而，所有纳入的研究都被评估为具有高偏倚风险。结论：据我们所知，这篇综述首次全面综合了老年糖尿病患者身体虚弱和认知虚弱的风险分层模型。研究结果表明，现有模型具有令人满意的区分能力；具体来说，ci证实了一个稳健的平均效应，而预测区间表明，在未来的设置中，性能虽然可变，但可能仍然是可以接受的。然而，临床应用目前受到高偏倚风险和有限的外部验证的限制。未来的研究必须优先考虑严格的、前瞻性的、遵循标准报告指南的多中心研究（例如，TRIPOD[透明报告个体预后或诊断的多变量预测模型]），以建立有效的、可推广的、临床可操作的预后工具。

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引用次数: 0

Tailored Internet-Delivered Mindfulness-Based Interventions for Patients With Hepatocellular Carcinoma After Transarterial Chemoembolization: Qualitative Study. 针对肝细胞癌经动脉化疗栓塞后患者量身定制的基于互联网的正念干预：定性研究。

IF 6 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research

Pub Date : 2026-01-29 DOI: 10.2196/78337

Zengxia Liu, Min Li, Yong Jia, Li Chen

Background: Patients with hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE) experience significant psychological distress, impacting outcomes. While mindfulness-based interventions (MBIs) are beneficial, access is limited. Internet-delivered MBIs (iMBIs) offer an accessible alternative; yet, qualitative understanding of patient experiences with tailored iMBIs for this specific population is lacking.

Objective: This study aimed to explore the facilitators and barriers of patients with HCC post TACE and participated in tailored iMBIs.

Methods: From November 2020 to December 2022, 11 patients with HCC post TACE who had taken part in tailored iMBIs were purposively recruited from a tertiary hospital in Jilin Province. Data were collected through semistructured interviews lasting 30-60 minutes. The interviews were analyzed using conventional content analysis.

Results: Five main categories emerged from the analysis: (1) mindfulness mindset, including acceptance, calmness, and mood improvement; (2) improvement of physical discomfort, such as better sleep, pain relief, reduced gastrointestinal symptoms, and increased activity levels; (3) resistance to mindfulness practice, including perceived lack of effectiveness, unsuitable conditions, equipment limitations, and difficulty concentrating; (4) support and encouragement, involving social support, supervision, and professional guidance; and (5) accessibility and convenience characterized by restoration of life balance and user-friendly features of the practice. Each category encompassed several subcategories reflecting the diverse experiences of participants.

Conclusions: While iMBIs were generally perceived as convenient and accessible, challenges such as equipment limitations were noted. Future implementation should focus on enhancing supportive factors to improve adherence, minimizing barriers, and refining the design and delivery of iMBI programs.

Trial registration: Chinese Clinical Trial Registry ChiCTR1900027976; https://www.chictr.org.cn/showproj.html?proj=46657.

背景：肝细胞癌（HCC）接受经动脉化疗栓塞（TACE）的患者会经历显著的心理困扰，影响预后。虽然正念干预（mbi）是有益的，但获取途径有限。互联网传输的mbi （imbi）提供了一种可访问的替代方案；然而，针对这一特定人群的量身定制的imbi患者体验的定性理解是缺乏的。目的：本研究旨在探讨HCC患者TACE后的促进因素和障碍，并参与量身定制的imbi。方法：从2020年11月至2022年12月，在吉林省某三级医院有目的地招募11例参加过量身定制imbi的肝癌术后TACE患者。数据通过持续30-60分钟的半结构化访谈收集。访谈采用常规内容分析法进行分析。结果：从分析中得出五个主要类别：(1)正念心态，包括接纳、平静和情绪改善；(2)改善身体不适，如改善睡眠、缓解疼痛、减轻胃肠道症状和增加活动水平；(3)抗拒正念练习，包括感觉缺乏有效性、条件不合适、设备限制和难以集中注意力；(4)支持和鼓励，包括社会支持、监督和专业指导；(5)以恢复生活平衡和人性化为特征的可达性和便利性。每个类别都包含若干子类别，反映了参与者的不同经历。结论：虽然imbi通常被认为是方便和可访问的，但注意到设备限制等挑战。未来的实施应侧重于加强支持性因素，以提高依从性，最大限度地减少障碍，并改进iMBI项目的设计和交付。试验注册：中国临床试验注册中心ChiCTR1900027976；https://www.chictr.org.cn/showproj.html?proj=46657。

{"title":"Tailored Internet-Delivered Mindfulness-Based Interventions for Patients With Hepatocellular Carcinoma After Transarterial Chemoembolization: Qualitative Study.","authors":"Zengxia Liu, Min Li, Yong Jia, Li Chen","doi":"10.2196/78337","DOIUrl":"10.2196/78337","url":null,"abstract":"Background: Patients with hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE) experience significant psychological distress, impacting outcomes. While mindfulness-based interventions (MBIs) are beneficial, access is limited. Internet-delivered MBIs (iMBIs) offer an accessible alternative; yet, qualitative understanding of patient experiences with tailored iMBIs for this specific population is lacking.Objective: This study aimed to explore the facilitators and barriers of patients with HCC post TACE and participated in tailored iMBIs.Methods: From November 2020 to December 2022, 11 patients with HCC post TACE who had taken part in tailored iMBIs were purposively recruited from a tertiary hospital in Jilin Province. Data were collected through semistructured interviews lasting 30-60 minutes. The interviews were analyzed using conventional content analysis.Results: Five main categories emerged from the analysis: (1) mindfulness mindset, including acceptance, calmness, and mood improvement; (2) improvement of physical discomfort, such as better sleep, pain relief, reduced gastrointestinal symptoms, and increased activity levels; (3) resistance to mindfulness practice, including perceived lack of effectiveness, unsuitable conditions, equipment limitations, and difficulty concentrating; (4) support and encouragement, involving social support, supervision, and professional guidance; and (5) accessibility and convenience characterized by restoration of life balance and user-friendly features of the practice. Each category encompassed several subcategories reflecting the diverse experiences of participants.Conclusions: While iMBIs were generally perceived as convenient and accessible, challenges such as equipment limitations were noted. Future implementation should focus on enhancing supportive factors to improve adherence, minimizing barriers, and refining the design and delivery of iMBI programs.Trial registration: Chinese Clinical Trial Registry ChiCTR1900027976; https://www.chictr.org.cn/showproj.html?proj=46657.","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e78337"},"PeriodicalIF":6.0,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12902756/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146086107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assistive Robotics for Healthy Aging: A Foundational Phenomenological Co-Design Exercise. 健康老龄化的辅助机器人：基础现象学协同设计练习。

IF 6 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research

Pub Date : 2026-01-28 DOI: 10.2196/77179

Stephen Potter, Mark Hawley, Angela Higgins, Farshid Amirabdollahian, Mauro Dragone, Alessandro Di Nuovo, Praminda Caleb-Solly

Background: Assistive robotics for helping older people live well and stay independent has, to date, failed to fulfill its promise: there are few assistive robots in everyday use. In part, this failing can be attributed to inadequate or missing co-design activities that would ensure that these technologies and any services that incorporate them are developed with prospective end users, addressing their actual needs and wants, and not merely for them, and based on lazy assumptions about heterogeneous user groups.

Objective: This exercise aimed to address some of these limitations by taking a "phenomenological snapshot" of what it means to be an older person in the current sociotechnological context, and making this snapshot, along with the co-design materials developed, available to the wider assistive robotics community to provide solid foundational evidence for steering the development of assistive robotics in more productive directions.

Methods: Two rounds of co-design workshops have been conducted with older people and their caregivers, based on an innovative methodology that used personas and speculative designs to explore sensitive everyday difficulties faced by participants and highlight some of their general wishes for and concerns about assistive robotics. The data collected during the workshops were analyzed, and key themes were extracted.

Results: Analysis of the workshop data gives access to the lived experience of older people and their caregivers, and their opinions about domestic robotics and assistive technologies more generally. The findings are organized thematically as everyday difficulties, the daily problems faced by older people; ideas for aging better, older people's own suggestions for how their lives could be improved; and living with technology, their preferences and requirements for assistive robots, along with their concerns about what the introduction of robots might mean, both for themselves and for society more widely.

Conclusions: We believe that our findings provide solid foundational evidence for the development of assistive robotics for older people. We are in the process of disseminating these results through various channels to the wider assistive robotics community; ultimately, the success of our activities will be demonstrated only through the development of acceptable, useful, and viable assistive robotics for older people.

背景：迄今为止，帮助老年人更好地生活和保持独立的辅助机器人尚未实现其承诺：日常使用的辅助机器人很少。在某种程度上，这种失败可以归因于不充分或缺失的协同设计活动，这些活动可以确保这些技术和包含它们的任何服务是与潜在的最终用户一起开发的，满足他们的实际需求和愿望，而不仅仅是为了他们，并且基于对异构用户组的懒惰假设。目的：本练习旨在通过对当前社会技术背景下老年人的含义进行“现象学快照”来解决其中的一些限制，并使该快照以及开发的协同设计材料可用于更广泛的辅助机器人社区，为指导辅助机器人技术向更富有成效的方向发展提供坚实的基础证据。对研讨会期间收集的数据进行了分析，并提取了关键主题。结果：通过对研讨会数据的分析，可以了解老年人及其护理人员的生活经验，以及他们对家用机器人和辅助技术的普遍看法。调查结果按主题组织为日常困难，老年人面临的日常问题；如何更好地老化的想法，老年人对如何改善生活的建议；与科技共存，他们对辅助机器人的偏好和要求，以及他们对机器人引入对他们自己和更广泛的社会可能意味着什么的担忧。结论：我们相信我们的发现为老年人辅助机器人的发展提供了坚实的基础证据。我们正在通过各种渠道向更广泛的辅助机器人社区传播这些结果；最终，我们的活动的成功将通过为老年人开发可接受的、有用的、可行的辅助机器人来证明。

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引用次数: 0

The Development and Use of AI Chatbots for Health Behavior Change: Scoping Review. 人工智能聊天机器人在健康行为改变中的发展和使用：范围审查。

IF 6 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research

Pub Date : 2026-01-28 DOI: 10.2196/79677

Lingyi Fu, Ryan Burns, Yuhuan Xie, Jincheng Shen, Shandian Zhe, Paul Estabrooks, Yang Bai

Background: Artificial intelligence (AI) chatbots are technologies that facilitate human-computer interaction through communication in a natural language format. By increasing cost-effectiveness, interaction, autonomy, personalization, and support, mobile health interventions can benefit health behavior change and make it more natural and intuitive.Objective: This study aimed to provide an up-to-date and practical overview of how text-based AI chatbots are designed, developed, and evaluated across 8 health behaviors, including their roles, theoretical foundations, health behavior change techniques, technology development workflow, and performance validation framework.Methods: In accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework, relevant studies published before March 2024 were identified from 9 bibliographic databases (ie, PubMed, CINAHL, MEDLINE, Embase, Web of Science, Scopus, APA PsycINFO, IEEE Xplore, and ACM Digital Library). Two stages (ie, title and abstract screening followed by full-text screening) were conducted to screen the eligibility of the papers via Covidence software. Finally, we extracted the data via Microsoft Excel software and used a narrative approach, content analysis, and evidence map to synthesize the reported results.Results: Our systematic search initially identified 10,508 publications, 43 of which met our inclusion criteria. AI chatbots primarily served 2 main roles: routine coach (27/43, 62.79%) and on-demand assistant (12/43, 27.91%), while 4 studies (4/43, 9.30%) integrated both roles. Frameworks like cognitive behavioral therapy (13/24, 54.17%) and behavior change techniques, such as goal setting, feedback and monitoring, and social support, guided the development of theory-driven AI chatbots. Noncode platforms (eg, Google Dialogflow and IBM Watson) integrated with social messaging platforms (eg, Facebook Messenger) were commonly used to develop AI chatbots (23/43, 53.49%). AI chatbots have been evaluated across 4 domains: technical performance (17/43, 39.53%), usability (17/43, 39.53%), engagement (37/43, 86.05%), and health behavior change (33/43, 76.74%). Evidence for health behavior changes remains exploratory but promising. Among 33 studies with 120 comparisons, 81.67% (98/120) showed positive outcomes, though only 35.83% (43/120) had moderate or larger effects (Hedges g or odds ratio or Cohen d>0.5). Most involved nonclinical (36/43, 83.72%) and adults (23/43, 53.49%), and a few were randomized controlled trials (14/43, 32.56%). Benefits were mainly seen in physical activity, smoking cessation, stress management, and diet, with limited evidence for other behaviors. Findings were inconsistent regarding the influence of long-term effects, intervention duration, modality, and engagement on health behavior change outcomes.<st

背景：人工智能（AI）聊天机器人是一种通过自然语言格式的通信促进人机交互的技术。通过提高成本效益、互动、自主、个性化和支持，移动卫生干预措施可有利于卫生行为改变，使其更加自然和直观。目的：本研究旨在为基于文本的人工智能聊天机器人的设计、开发和评估提供最新和实用的概述，包括它们的角色、理论基础、健康行为改变技术、技术开发流程和性能验证框架。方法：按照PRISMA-ScR （Preferred Reporting Items for Systematic Reviews and meta - analysis extension for Scoping Reviews）框架，从9个书目数据库（PubMed、CINAHL、MEDLINE、Embase、Web of Science、Scopus、APA PsycINFO、IEEE Xplore和ACM Digital Library）中检索2024年3月前发表的相关研究。通过covid - ence软件筛选论文的合格性分为两个阶段（即标题和摘要筛选，然后是全文筛选）。最后，我们通过Microsoft Excel软件提取数据，并采用叙述方法、内容分析和证据图对报告结果进行综合。结果：我们的系统检索最初确定了10,508篇出版物，其中43篇符合我们的纳入标准。AI聊天机器人主要扮演两个主要角色：日常教练（27/43,62.79%）和点选助手（12/43,27.91%），有4项研究（4/43,9.30%）将这两个角色整合在一起。认知行为疗法（13/24,54.17%）和行为改变技术（如目标设定、反馈和监控、社会支持）等框架指导了理论驱动型人工智能聊天机器人的发展。非代码平台（如谷歌Dialogflow和IBM Watson）与社交消息平台（如Facebook Messenger）集成在一起，通常用于开发人工智能聊天机器人（23/43,53.49%）。人工智能聊天机器人在4个领域进行了评估：技术性能（17/43,39.53%）、可用性（17/43,39.53%）、参与度（37/43,86.05%）和健康行为改变（33/43,76.74%）。健康行为改变的证据仍然是探索性的，但很有希望。33项研究共120组比较中，81.67%（98/120）的结果为阳性，但只有35.83%（43/120）的结果为中等或较大的效果（Hedges g or比值比或Cohen d >.5）。大多数涉及非临床（36/43,83.72%）和成人（23/43,53.49%），少数为随机对照试验（14/43,32.56%）。益处主要体现在体育锻炼、戒烟、压力管理和饮食方面，其他行为方面的证据有限。关于长期效果、干预持续时间、方式和参与对健康行为改变结果的影响，研究结果不一致。结论：探索性综合为开发和评估人工智能聊天机器人在健康行为改变中的作用提供了路线图，强调需要进一步研究成本、实施结果以及睡眠、体重管理、久坐行为和酒精使用等未被充分探索的行为。

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引用次数: 0

Implementing an Artificial Intelligence Decision Support System in Radiology: Prospective Qualitative Evaluation Study Using the Nonadoption Abandonment Scale-Up, Spread, and Sustainability (NASSS) Framework. 在放射学中实施人工智能决策支持系统：使用不采用放弃、扩大、传播和可持续性（NASSS）框架的前瞻性定性评估研究。

IF 6 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research

Pub Date : 2026-01-28 DOI: 10.2196/80342

Sundresan Naicker, Paul Schmidt, Bruce Shar, Amina Tariq, Ashleigh Earnshaw, Steven McPhail

Background: Medical imaging remains at the forefront of advancements in adopting digital health technologies in clinical practice. Regulator-approved artificial intelligence (AI) clinical decision support systems are commercially available and being embedded into routine practices for radiologists internationally. These decision support solutions show promising clinical validity compared to standard practice conditions; however, their implementation over time and implications on radiologists' practice are poorly understood.Objective: This paper aims to examine the real-world implementation of an AI clinical decision support tool in radiology through a qualitative evaluation across pre-, peri-, and postimplementation phases. Specifically, it seeks to identify the key contextual, organizational, and human factors shaping adoption and sustainability, to map these influences using the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework, and to generate insights that inform evidence-based strategies and policy for integrating AI safely and effectively into public hospital imaging services.Methods: This prospective study was conducted in a large public tertiary referral hospital in Brisbane, Queensland, Australia. One-to-one participant interviews were undertaken across the 3 implementation phases. Participants comprised radiology consultants, registrars, and radiographers involved in chest computed tomography studies during the study period. Interviews were guided by the NASSS framework to identify contextual factors influencing implementation.Results: A total of 43 semistructured interviews were conducted across baseline (n=16), peri-implementation (n=9), and postimplementation (n=18) phases, comprising 7 (16%) radiographers, 20 (47%) registrar radiologists, and 16 (37%) consultant radiologists. Across NASSS domains, 56 barriers and 18 enablers were identified at baseline, 55 and 14 during peri-implementation, and 82 and 33 postimplementation. Organizational barriers dominated early phases, while technological issues such as system accuracy, interoperability, and information overload became most prominent during and after rollout. Enablers increased over time, particularly within the technology and value proposition domains, as some clinicians adapted the AI as a secondary safety check. Trust and adoption remained constrained by performance inconsistency, weak communication, and medicolegal uncertainty.Conclusions: The implementation of AI decision support in radiology is as much an organizational and cultural process as a technological one. Clinicians remain willing to engage, but sustainable adoption depends on consolidating early positive experiences and addressing negative ones, embedding communication and training, and maintaining iterative feedback between users, vendors, and system leaders. Applying t

背景：在临床实践中，医学成像仍然处于采用数字健康技术的前沿。监管机构批准的人工智能（AI）临床决策支持系统已经商业化，并被嵌入到国际放射科医生的常规实践中。与标准实践条件相比，这些决策支持解决方案显示出有希望的临床有效性；然而，随着时间的推移，它们的实施和对放射科医生实践的影响却知之甚少。目的：本文旨在通过实施前、实施中和实施后阶段的定性评估，研究人工智能临床决策支持工具在放射学中的实际实施情况。具体而言，它试图确定影响采用和可持续性的关键环境、组织和人为因素，使用不采用、放弃、扩大、传播和可持续性（NASSS）框架来绘制这些影响，并产生见解，为基于证据的战略和政策提供信息，以便安全有效地将人工智能整合到公立医院成像服务中。方法：本前瞻性研究在澳大利亚昆士兰州布里斯班的一家大型公立三级转诊医院进行。在三个实施阶段进行了一对一的参与者访谈。参与者包括在研究期间参与胸部计算机断层扫描研究的放射学顾问、登记员和放射技师。访谈以NASSS框架为指导，以确定影响实施的背景因素。结果：在基线（n=16）、实施期间（n=9）和实施后（n=18）阶段共进行了43次半结构化访谈，包括7名（16%）放射技师、20名（47%）注册放射科医师和16名（37%）放射咨询医师。在NASSS领域，基线时确定了56个障碍和18个促进因素，实施期间确定了55个和14个，实施后确定了82个和33个。组织障碍在早期阶段占主导地位，而技术问题，如系统准确性、互操作性和信息过载，在推出期间和之后变得最为突出。随着时间的推移，推动因素越来越多，特别是在技术和价值主张领域，因为一些临床医生将人工智能作为二次安全检查。信任和采用仍然受到性能不一致、沟通薄弱和医学法律不确定性的限制。结论：人工智能决策支持在放射学中的实施既是一个技术过程，也是一个组织和文化过程。临床医生仍然愿意参与，但是可持续的采用依赖于巩固早期的积极经验和解决消极经验，嵌入沟通和培训，以及维护用户、供应商和系统领导者之间的迭代反馈。应用NASSS框架揭示了领域如何随时间动态交互，为寻求从试点转向常规、可信赖的人工智能集成的医院提供了对社会技术复杂性的理论见解和实践指导。

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引用次数: 0

Efficacy of Brain-Computer Interface Therapy for Upper Limb Rehabilitation in Chronic Stroke: Systematic Review and Meta-Analysis of Randomized Controlled Trials. 脑机接口治疗对慢性脑卒中上肢康复的疗效：随机对照试验的系统评价和meta分析。

IF 6 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research

Pub Date : 2026-01-28 DOI: 10.2196/79132

HongJie Chen, GuoJun Yun

Background: Over 50% of people with chronic stroke experience persistent upper limb dysfunction. Brain-computer interface (BCI) therapy, creating a sensorimotor loop via neural feedback, is a promising alternative; yet, its optimal application remains unclear.Objective: This meta-analysis evaluates BCI's efficacy on motor function, tone, and activities of daily living (ADL) in chronic stroke and identifies optimal feedback modalities and intervention parameters.Methods: We systematically searched Cochrane Library, Embase, PubMed, Scopus, Web of Science, and Wanfang Data from inception to October 2025 for randomized controlled trials (RCTs) comparing BCI-based training to control interventions in adults with chronic stroke. Primary outcomes were upper limb motor function (Fugl-Meyer Assessment for upper extremity [FMA-UE], Action Research Arm Test [ARAT]), muscle tone (Modified Ashworth Scale [MAS]), and ADL (Modified Barthel Index [MBI], Motor Activity Log [MAL]). Screening, data extraction, and risk-of-bias assessment were performed independently. Meta-analysis used a random-effects model with Hartung-Knapp-Sidik-Jonkman adjustment. Pooled mean differences (MDs) with 95% CIs and 95% prediction intervals (PIs) were calculated. Subgroup analyses examined feedback modalities, intervention intensity, and follow-up effects. Sensitivity analysis was also conducted.Results: From 3529 records, 21 RCTs (650 participants) were included. BCI training significantly improved motor function (FMA-UE: MD 2.50, 95% CI 0.60-4.40; P=.01; 95% PI -2.52 to 7.22) and ADL performance (MBI: MD 8.38, 95% CI 2.23-14.53; P=.02; 95% PI -3.92 to 20.53; MAL: MD 2.09, 95% CI 0.42-3.76; P=.03; 95% PI -0.69 to 4.54). No significant effects were observed for fine motor skills (ARAT: MD 0.18, 95% CI -0.27 to 0.62; P=.30; 95% PI -3.64 to 3.99) or muscle tone (MAS: MD -0.48, 95% CI -1 to 0.03; P=.06; 95% PI -1.27 to 0.35). Subgroup analyses revealed that BCI-functional electrical stimulation (FES) yielded the greatest improvement in motor recovery (FMA-UE: MD 5, 95% CI 1.86-8.13; P=.01). The optimal intervention protocol was identified as 30-minute sessions, administered 4-5 times per week over 2 weeks (total of 10-12 sessions). However, benefits were not sustained at follow-up.Conclusions: Low- to moderate-certainty evidence suggests that BCI training, particularly the BCI-FES paradigm, can improve upper limb motor function and ADL in people with chronic stroke on average. However, wide prediction intervals indicate the effect may vary substantially across settings, ranging from negligible to beneficial. Subgroup analyses suggested a potential optimal protocol of 30-minute sessions, 4-5 times per week for 2 weeks, but these findings are limited by the small number of studies in each subgroup and the high risk of bias in several included trials. Therefore, this propose

背景：超过50%的慢性中风患者经历持续的上肢功能障碍。脑机接口（BCI）疗法，通过神经反馈创造一个感觉运动回路，是一个很有前途的选择；然而，它的最佳应用仍不清楚。目的：本荟萃分析评估脑机接口对慢性卒中患者运动功能、音调和日常生活活动（ADL）的影响，并确定最佳反馈模式和干预参数。方法：我们系统地检索了Cochrane Library、Embase、PubMed、Scopus、Web of Science和Wanfang Data，检索了从成立到2025年10月的随机对照试验（RCTs），比较了基于bci的训练与控制干预对成年慢性卒中患者的影响。主要结果为上肢运动功能（Fugl-Meyer上肢评估[FMA-UE]、动作研究臂测试[ARAT]）、肌肉张力（改良Ashworth量表[MAS]）和ADL（改良Barthel指数[MBI]、运动活动日志[MAL]）。筛选、数据提取和偏倚风险评估是独立进行的。meta分析采用Hartung-Knapp-Sidik-Jonkman调整的随机效应模型。计算95% ci和95%预测区间的合并平均差异（MDs）。亚组分析检查了反馈方式、干预强度和随访效果。并进行敏感性分析。结果：从3529条记录中，纳入21项随机对照试验（650名受试者）。BCI训练显著改善运动功能（FMA-UE: MD 2.50, 95% CI 0.60-4.40； P= 0.01; 95% PI -2.52至7.22）和ADL表现（MBI: MD 8.38, 95% CI 2.23-14.53； P= 0.02; 95% PI -3.92至20.53;MAL: MD 2.09, 95% CI 0.42-3.76； P= 0.03; 95% PI -0.69至4.54）。在精细运动技能（ARAT: MD 0.18, 95% CI -0.27至0.62；P= 0.30; 95% PI -3.64至3.99）或肌肉张力（MAS: MD -0.48, 95% CI -1至0.03；P= 0.06; 95% PI -1.27至0.35）方面未观察到显著影响。亚组分析显示，脑机接口功能电刺激（FES）对运动恢复的改善最大（FMA-UE: MD 5, 95% CI 1.86-8.13； P= 0.01）。最佳干预方案确定为每次30分钟，每周4-5次，持续2周（共10-12次）。然而，这些益处在随访中并未持续。结论：低到中等确定性的证据表明，BCI训练，特别是BCI- fes模式，可以改善慢性卒中患者的上肢运动功能和ADL。然而，较宽的预测间隔表明，在不同的设置中，效果可能有很大差异，从可以忽略到有益。亚组分析提示可能的最佳方案是每次30分钟，每周4-5次，持续2周，但这些发现受到每个亚组的研究数量较少和几个纳入试验的高偏倚风险的限制。因此，该方案应被视为初步的，需要在未来高质量的随机对照试验中进行验证。未来的研究还应侧重于确定最有可能受益的患者亚组，并制定维持长期收益的策略。试验注册：PROSPERO CRD420251063808；https://www.crd.york.ac.uk/PROSPERO/view/CRD420251063808。

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引用次数: 0