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Article(id=1322168073998680436, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322167989802217791, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.04.022, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1734278400000, receivedDateStr=2024-12-16, revisedDate=1769529600000, revisedDateStr=2026-01-28, acceptedDate=null, acceptedDateStr=null, onlineDate=1793159033536, onlineDateStr=2026-10-28, pubDate=1771948800000, pubDateStr=2026-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793159033536, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793159033536, creator=13701087609, updateTime=1793159033536, updator=13701087609, issue=Issue{id=1322167989802217791, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='4', pageStart='529', pageEnd='704', issueExtLink='null', onlineDate='null', pubDate='1771948800000', pubDateStr='2026-02-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793159013461, creator='13701087609', updateTime=1793159013461, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=673, endPage=677, ext={EN=ArticleExt(id=1322168074246144373, articleId=1322168073998680436, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Application progress of digital technology in mental health assessment for the elderly, columnId=1220689485315101342, journalTitle=Chinese Nursing Research, columnName=Review Articles, runingTitle=null, highlight=null, articleAbstract=

This study reviewed the application progress of technologies such as ChatGPT,big data technology, mobile wearable devices,and machine learning in the mental health assessment of the elderly,and discussed the challenges encountered and future development directions in the application process,so as to provide guidance and a basis for the subsequent development of elderly psychological assessment technologies or tools.

, authors=Qin ZHOU, Siliang WU, Wei JIANG, Yao LEI, Juan MIAO, authorsList=Qin ZHOU, Siliang WU, Wei JIANG, Yao LEI, Juan MIAO, authorCompany=null, correspAuthors=Qin ZHOU, authorNote=null, correspAuthorsNote=
ZHOU Qin, E-mail:
, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322168074577494390, articleId=1322168073998680436, tenantId=1146029695717560320, journalId=1220038315760373764, language=CN, title=数字化技术在老年心理健康测评中的应用进展, columnId=1220689485482873504, journalTitle=护理研究, columnName=科研综述, runingTitle=null, highlight=null, articleAbstract=

综述GhatGPT、大数据技术、移动可穿戴设备和机器学习等技术在老年心理健康测评中的应用进展，并探讨了应用过程中面临的挑战和未来发展方向，以期为后续老年心理测评技术或工具的开发提供指导和依据。

, authors=周琴, 吴思亮, 蒋维, 雷瑶, 缪娟, authorsList=周琴, 吴思亮, 蒋维, 雷瑶, 缪娟, authorCompany=null, correspAuthors=周琴, authorNote=null, correspAuthorsNote=
ZHOU Qin, E-mail:
周琴，副主任护师，本科，E-mail：
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数字化技术在老年心理健康测评中的应用进展
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周琴 , 吴思亮 , 蒋维 , 雷瑶 , 缪娟
护理研究 | 科研综述 2026,40(4): 673-677
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护理研究 |科研综述 2026 , 40 (4) : 673 -677
数字化技术在老年心理健康测评中的应用进展
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周琴 , 吴思亮, 蒋维, 雷瑶, 缪娟
作者信息
  • 1湖南中医药高等专科学校附属第一医院（湖南省直中医医院），湖南 412000
通讯作者:
ZHOU Qin, E-mail:
周琴，副主任护师，本科，E-mail：
Application progress of digital technology in mental health assessment for the elderly
Qin ZHOU , Siliang WU, Wei JIANG, Yao LEI, Juan MIAO
Affiliations
  • 1The First Affiliated Hospital of Hunan Traditional Chinese Medicine College(Hunan Province Directly Affiliated TCM Hospital）, Hunan 412000 China
    • Corresponding Author:
    ZHOU Qin, E-mail:
出版时间: 2026-02-25 doi: 10.12102/j.issn.1009-6493.2026.04.022
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摘要
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综述GhatGPT、大数据技术、移动可穿戴设备和机器学习等技术在老年心理健康测评中的应用进展，并探讨了应用过程中面临的挑战和未来发展方向，以期为后续老年心理测评技术或工具的开发提供指导和依据。

数字化技术  /  老年人  /  心理健康测评  /  综述

This study reviewed the application progress of technologies such as ChatGPT,big data technology, mobile wearable devices,and machine learning in the mental health assessment of the elderly,and discussed the challenges encountered and future development directions in the application process,so as to provide guidance and a basis for the subsequent development of elderly psychological assessment technologies or tools.

digital technology  /  the elderly  /  mental health assessment  /  review
周琴, 吴思亮, 蒋维, 雷瑶, 缪娟. 数字化技术在老年心理健康测评中的应用进展. 护理研究, 2026 , 40 (4) : 673 -677 . DOI: 10.12102/j.issn.1009-6493.2026.04.022
Qin ZHOU, Siliang WU, Wei JIANG, Yao LEI, Juan MIAO. Application progress of digital technology in mental health assessment for the elderly[J]. Chinese Nursing Research, 2026 , 40 (4) : 673 -677 . DOI: 10.12102/j.issn.1009-6493.2026.04.022
正文
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根据世界卫生组织2019年公布的数据，由于身体、认知等各方面功能的退化，高达20%的老年人存在各种心理健康问题[1]。根据世界卫生组织的数据，全球约有7.0%的老年人受到抑郁症的影响，而焦虑症影响了3.8%的老年人[2-3]。此外，老年抑郁症的患病率在某些地区高达10%~15%，并且老年抑郁症的常见症状包括持续的心境低落、行动和思维迟缓，以及对日常活动失去兴趣[1]。但因社会忽视、沟通障碍、社会孤立、医疗关注不足及家庭知识缺失等各种原因[4]，老年人的心理健康问题常被忽视，直接或间接导致身体合并症状[5]，往往会引起医疗费用的增加。心理健康测评是目前最常用的诊断方法[6-9]，但传统测评工具主要依赖于临床访谈和标准化量表，被测者易受先前评估经历等的影响，导致测评结果无法如实反映其内心想法。同时，传统测评工具不仅易受评估者主观因素的影响，而且耗时耗力，增加临床医护人员的工作负担。数字化技术的应用为老年心理健康测评提供了新的方法和工具。数字化技术通过在线问答、虚拟现实场景、人工智能分析等手段，可提高心理健康测评的准确性和效率[10]。通过实时收集数据、自动分析结果并提供个性化反馈[11-12]，使心理测评更加便捷、客观，同时有助于心理健康管理及干预。本研究对现有的数字化老年心理健康测评技术进行综述，以期为后续心理健康测评技术或工具的开发提供指导和依据。
1　数字化技术在老年心理健康测评中的应用
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1.1　ChatGPT
ChatGPT是2022年11月开发的一款高度先进的大型语言模型，旨在针对基于文本的查询生成类似人类的自然语言响应[13]。有研究采用ChatGPT的情绪觉察水平量表（LEAS）评分评估主我情绪（main character emotion）和客体情绪（other character emotion）得分，并由2名心理学家独立对每个回答的情境适用性进行评分[14]。有研究表明，ChatGPT除了具备理论和语义知识外，还能从场景行为描述中识别和描述情绪，并以深层次、多维度的综合方式进行反映[15-18]。有学者提出，ChatGPT在心理健康中还具备提高诊断和预后的准确性、促进对精神疾病机制的理解[19]等作用。通过LEAS评估了ChatGPT的情绪觉察性能（emotional awareness performance），ChatGPT 在所有测试量表中表现更好[19-20]。
尽管ChatGPT有巨大的应用潜力，但还是存在部分局限性。据报道，ChatGPT有时会提供虚幻的反应[20]。在基于知识的互动中，更容易识别出错误的答案，而情绪意识的反应本质上是主观的，很难确定其正确性。因此，应该通过进一步的研究解决非人性化、隐私和可访问性问题，以实现人工智能的好处，同时降低其潜在风险。ChatGPT使用可能受到年龄和文化适应的限制，未来研究应以开发对文化和年龄敏感的人工智能技术为目标，还应注意在精神卫生保健中使用人工智能的伦理准则，以确保技术不断进步。
1.2　机器学习
机器学习可以通过提供标准化的评估流程支持专业人员基于大量临床数据（包括声音和视觉表达数据）进行决策（例如诊断、预后、治疗等）[21]。有研究通过构建机器学习竞赛模型，根据临床访谈预测抑郁症的严重程度[21-23]，从而开发新工具以支持精确抑郁症诊断的研究。这些研究利用了机器学习算法在处理特征数量、多重共线性和噪声方面的已知优势[24]，并结合了数据预处理、特征工程和模型选择等关键步骤[2]，以提高预测模型的准确性和稳定性[2]。基于参数高效调优方法的深度预训练语言模型，通过在抑郁与焦虑访谈语料库（DAIC-WOZ）的实验，提出了1种融合从基于前缀的抑郁适应编码器和通用编码器中提取的嵌入新方法，该方法可以在预测抑郁严重程度方面实现先进性能，结果显示，前缀调整是一种实用且强大的方法，有助于开发用于自动评估病人抑郁严重程度的有效工具。
目前对使用机器学习确定晚年抑郁症治疗反应预测因子的初步研究表明，临床和结构磁共振成像的整合提高了预测能力，及时选择晚发性抑郁症（LLD）治疗方案对于维持生活质量和认知功能至关重要，机器学习与多模态数据相结合是开发非侵入性、精确疾病管理方法的一个有效途径。
1.3　大数据技术
智能大数据分析是数据科学、大数据和人工智能时代的一种不断发展的模式。大数据可以通过实验数据或观察数据的形式，例如，实验研究人员可以使用跟踪设备频繁地捕捉参与者在一定时期内的生理和心理反应。1989年，Carol Ryff提出了心理健康量表（SPWB），该工具评估了心理健康的自我接纳、与他人的积极关系、自主性、环境掌控、生活目标和个人成长6个维度。有研究将组合数据分析工具与SPWB结合，将个人视为一个整体，并通过系统方法进行研究，对心理问卷数据的分析，特别是从组合数据分析角度与心理幸福感合作中，为医疗保健专业人员提供了相关信息，以更加个性化的方式实施治疗[25]。随着大数据技术的快速发展，其在老年心理健康领域的应用变得日益广泛。例如，通过实时监测和分析老年人的健康数据，可以及时发现心理健康问题的早期迹象，并评估干预措施的效果。此外，大数据分析有助于为政策制定者提供科学依据，从而优化老年人健康服务的政策设计。
1.4　智能可穿戴设备
近年来，可穿戴技术在医疗保健和临床应用中发展迅速，如智能眼镜、智能手环、植入式智能贴片和电子纺织品等，为焦虑、抑郁等精神疾病的个体化诊断和治疗管理提供了新的方法和思路[26-28]。研究表明，利用可穿戴设备实时收集与抑郁和焦虑相关的生物信号参数，能够监测、评估并诊断焦虑和抑郁症状，例如通过记录活动减少和睡眠行为改变评估抑郁症状[29-31]。然而，可穿戴设备无法检测到与焦虑和抑郁相关的所有症状。一方面，可穿戴设备无法检测多种生理数据，例如体重和食欲变化等[29]；另一方面，可穿戴设备无法评估主观症状，如社交互动、生活方式改变。尽管可穿戴人工智能可用于为焦虑和抑郁提供许多干预措施，如个性化的正念、冥想和生物反馈疗法，但缺乏用于治疗目的的基于人工智能的可穿戴设备。尽管可穿戴设备能够通过心率和其他变量反映心理状态（如紧张或焦虑），但可能会出现假阳性结果，进而加剧个体的焦虑或抑郁。在开发基于人工智能的可穿戴设备之前，应注重减少上述可穿戴设备技术导致的检测误差。
2　数字化技术在老年心理健康测评中应用的影响因素
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2.1　技术障碍
在数据信息化时代背景下，技术障碍成为老年人在使用数字化工具进行心理健康测评时面临的主要挑战之一[32-33]。当前，数字化技术程序较为复杂，部分老年人对智能设备的操作不够熟练，导致其在使用过程中产生困惑和挫败感[34]。此外，网络稳定性也是关键问题之一，许多老年人居住的环境可能缺乏稳定的互联网服务，进而导致在线测评工具无法正常运作，影响测评的连续性和准确性。数字化技术的安全性同样是老年人关注的重点，部分老年人担忧因操作不当导致个人信息泄露或设备损坏。同时，对于某些敏感数据，老年人可能更加关心隐私保护问题。这些技术障碍不仅影响老年人使用数字化技术的意愿，也可能影响他们心理健康测评结果的准确性和可靠性。
2.2　认知障碍
随着年龄增长，老年人可能会出现记忆力减退、注意力不集中、信息处理速度下降等认知功能衰退现象[35]，这些认知障碍可能影响其学习和使用数字化技术的能力。此外，认知障碍还可能降低老年人对数字化技术的接受度，他们担忧自己无法掌握新工具的使用方法，进而对数字化工具产生抵触情绪，不愿尝试或持续使用。此外，认知障碍可能致使老年人难以精准理解并回答测评问题，进而影响测评结果的准确性。
2.3　社会障碍
社会对老年人所持有的刻板印象，可能会影响他们使用数字化工具的信心[36]。例如，社会或许会认为老年人不适宜使用高科技产品，这种观念可能会对老年人的自我认同和使用意愿产生影响。这种社会层面的障碍，可能会使老年人产生被边缘化的感受，进而减少对数字化工具的使用。此外，老年人会缺乏必要的社会资源，诸如家庭成员的协助、社区组织的扶持或专业人员的指引，这些资源的匮乏可能会使老年人在使用数字化工具时感到孤立与无助。因此，社会障碍会致使老年人难以获得必要的支持与鼓励，进而影响他们的心理健康和社会福祉[36]。
2.4　教育障碍
教育水平欠缺可能会导致老年人缺乏必要的数字技能，进而使其难以有效运用数字化工具。这种教育层面的障碍，可能会使老年人在使用数字化工具时产生困难与挫败感，进而减少使用频率或彻底放弃使用。例如，老年人或许会由于缺乏基本的计算机操作知识，而难以打开应用程序，又或者因为不熟悉互联网浏览，而无法寻找相关的测评资源。此外，教育障碍可能使老年人在使用数字化工具时面临社会参与受限的问题[35]，这种参与受限可能导致老年人产生孤立感和被排斥感，进而影响其心理健康与社会福祉。
3　促进数字化技术在老年心理健康测评中应用的策略
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3.1　缩小数字鸿沟
缩小数字鸿沟是一个系统性的工程，需要政府、社会组织的共同努力[30]。政府可以设立专项基金，为经济困难的老年人提供补贴，或者为社区中心提供资金，用于购买和维护公共数字设备。社会组织可通过提供技术培训、建立社区支持网络缩小教育水平差距，例如定期举办免费技术培训课程，指导老年人使用智能手机和应用程序。科技公司可以开发适合老年人的简单易用的产品，同时提供用户友好的客户服务。例如，智能手机制造商可以设计大屏幕、大按钮的手机，配备语音助手功能，方便老年人操作。应用程序开发者可以设计界面简洁、操作直观的心理健康应用程序，提供详细的使用指南和在线客服支持。
3.2　适应性设计
对于老年人来说，需要考虑其视觉、听觉和认知特点，进行界面简洁、操作简单、功能明确的设计。信息过多、布局复杂可能让老年人感到困惑，设计时应精简元素，采用大字体和高对比度色彩，确保关键信息清晰可见。例如，心理健康应用的主界面可仅展示常用功能按钮，如开始测评、查看结果、联系专家，避免菜单和选项过多。此外，操作步骤应尽量简化，测评时可提供分步指导，每步均有明确说明和提示，助力老年人顺利完成测评。考虑到老年人的视觉和听觉需求，设计时应增设辅助功能，如应用程序可提供字体放大选项，便于视力不佳的老年人阅读。同时，语音指导功能也至关重要，可帮助听力正常的老年人通过听取指令操作应用，对于听力不佳的老年人，则提供文字提示。
3.3　培训医疗专业人员
医疗专业人员需要了解最新的数字化心理健康工具和技术，包括工作原理、应用场景和潜在益处。例如，可通过举办研讨会、开设在线课程及发表专业期刊文章，向医疗专业人员介绍研究成果和实践经验。医务人员还需掌握这些工具的使用方法，包括应用程序操作、测评结果解读及相应干预措施的提供。许多医疗专业人员可能对数字化技术持怀疑态度，担心其安全性和有效性。因此，需要通过教育和案例分享，改变观念，增强他们对数字化技术的信任，例如，根据相关研究，数字化工具在高中生心理健康评价中的应用显示了其在提高评价效率和准确性方面的优势。相较于传统方法，数字化工具能够快速收集和分析大量数据，为心理健康工作者提供更准确的评价结果，并能根据学生的心理状况提供个性化的干预方案。
3.4　跨学科合作
组建由心理健康专家、技术开发者、政策制定者及社区组织代表构成的跨学科团队，共同探讨并解决老年心理健康测评中的问题[37]。各合作方需共享资源，涵盖研究数据、技术工具及专业知识，以提升合作效率与效果。例如，心理健康专家可以提供老年心理健康的研究数据和评估工具，技术开发者可以提供技术解决方案，政策制定者可以提供政策支持和资金援助，社区组织可以提供实际操作经验和用户反馈。此外，建立一个开放的合作平台也是必要的，通过建立在线平台，促进各合作方之间的交流和合作。
3.5　持续评估和反馈
构建系统化数据平台，定期收集老年人使用数字化工具的相关数据，包括使用频率、满意度及效果评估等。定期评估数字化工具的效果，涵盖其对老年心理健康的影响、用户使用体验及满意度等。建立便捷的用户反馈机制，让老年人可以随时提出意见和建议。鼓励老年人参与评估和反馈过程，让他们成为数字化工具改进的参与者。通过持续评估与反馈机制，可确保数字化工具的有效运行与持续优化，提升老年人的使用体验，推动数字化技术在老年心理健康测评领域的广泛应用。
4　小结
收起
数字化技术在老年心理健康测评中展现出巨大潜力，借助ChatGPT、大数据技术、移动可穿戴设备及机器学习等技术，可实现更为精准、高效的评估。然而，这些技术的应用也面临技术接受度、设备和网络条件、数据隐私和成本等多方面的挑战。通过采取有效的策略，如技术培训、优化设计、数据保护和成本降低，可以充分发挥数字化技术的优势，提升老年心理健康测评水平，为老年人提供更优质的心理健康服务。

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doi: 10.12102/j.issn.1009-6493.2026.04.022
  • 接收时间：2024-12-16
  • 首发时间：2026-10-28
  • 出版时间：2026-02-25
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  • 收稿日期：2024-12-16
  • 修回日期：2026-01-28
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    1湖南中医药高等专科学校附属第一医院（湖南省直中医医院），湖南 412000

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