• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

相似文献

1
Identifying Current Feelings of Mild and Moderate to High Depression in Young, Healthy Individuals Using Gait and Balance: An Exploratory Study.使用步态和平衡识别年轻健康个体中轻度、中度至高度抑郁的当前感受:一项探索性研究。
Sensors (Basel). 2023 Jul 23;23(14):6624. doi: 10.3390/s23146624.
2
Identifying Individuals Who Currently Report Feelings of Anxiety Using Walking Gait and Quiet Balance: An Exploratory Study Using Machine Learning.使用行走步态和安静平衡识别当前报告焦虑感的个体:一项使用机器学习的探索性研究。
Sensors (Basel). 2022 Apr 20;22(9):3163. doi: 10.3390/s22093163.
3
Association between Self-Reported Prior Night's Sleep and Single-Task Gait in Healthy, Young Adults: A Study Using Machine Learning.自我报告前一晚睡眠与健康年轻成年人单任务步态的关联:一项使用机器学习的研究。
Sensors (Basel). 2022 Sep 29;22(19):7406. doi: 10.3390/s22197406.
4
Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.在流行地区,服用抗叶酸抗疟药物的人群中,叶酸补充剂与疟疾易感性和严重程度的关系。
Cochrane Database Syst Rev. 2022 Feb 1;2(2022):CD014217. doi: 10.1002/14651858.CD014217.
5
Quantifying effects of age on balance and gait with inertial sensors in community-dwelling healthy adults.利用惯性传感器量化年龄对社区居住健康成年人平衡和步态的影响。
Exp Gerontol. 2016 Dec 1;85:48-58. doi: 10.1016/j.exger.2016.09.018. Epub 2016 Sep 22.
6
Instrumental assessment of balance and gait in depression: A systematic review.抑郁障碍患者平衡和步态的仪器评估:系统评价。
Psychiatry Res. 2020 Feb;284:112687. doi: 10.1016/j.psychres.2019.112687. Epub 2019 Nov 10.
7
Do depressive symptoms affect balance in older adults with mild cognitive impairment? Results from the "gait and brain study".抑郁症状是否会影响轻度认知障碍老年人的平衡?“步态与大脑研究”的结果。
Exp Gerontol. 2018 Jul 15;108:106-111. doi: 10.1016/j.exger.2018.04.004. Epub 2018 Apr 10.
8
Letter to the Editor: CONVERGENCES AND DIVERGENCES IN THE ICD-11 VS. DSM-5 CLASSIFICATION OF MOOD DISORDERS.给编辑的信:《ICD-11 与 DSM-5 心境障碍分类的趋同与分歧》
Turk Psikiyatri Derg. 2021;32(4):293-295. doi: 10.5080/u26899.
9
Predictors of self-reported negative mood following a depressive mood induction procedure across previously depressed, currently anxious, and control individuals.先前抑郁、当前焦虑和对照组个体在抑郁情绪诱导程序后自我报告的负面情绪的预测因素。
Br J Clin Psychol. 2014 Sep;53(3):348-68. doi: 10.1111/bjc.12053. Epub 2014 Apr 28.
10
Validity and reliability of an IMU-based method to detect APAs prior to gait initiation.一种基于惯性测量单元的方法在步态起始前检测姿势调整的有效性和可靠性。
Gait Posture. 2016 Jan;43:125-31. doi: 10.1016/j.gaitpost.2015.08.015. Epub 2015 Sep 25.

引用本文的文献

1
Gait in depression: a bibliometric analysis and knowledge mapping of research trends over the past 20 years.抑郁症中的步态:过去20年研究趋势的文献计量分析与知识图谱
Front Psychiatry. 2025 Aug 20;16:1457176. doi: 10.3389/fpsyt.2025.1457176. eCollection 2025.
2
Identifying Predictors of Neck Disability in Patients with Cervical Pain Using Machine Learning Algorithms: A Cross-Sectional Correlational Study.使用机器学习算法识别颈痛患者颈部功能障碍的预测因素:一项横断面相关性研究。
J Clin Med. 2024 Mar 28;13(7):1967. doi: 10.3390/jcm13071967.
3
Detecting Psychological Interventions Using Bilateral Electromyographic Wearable Sensors.使用双侧肌电可穿戴传感器检测心理干预。
Sensors (Basel). 2024 Feb 22;24(5):1425. doi: 10.3390/s24051425.

本文引用的文献

1
Data augmentation for depression detection using skeleton-based gait information.基于骨骼步态信息的抑郁症检测数据增强。
Med Biol Eng Comput. 2022 Sep;60(9):2665-2679. doi: 10.1007/s11517-022-02595-z. Epub 2022 Jul 13.
2
Identifying Individuals Who Currently Report Feelings of Anxiety Using Walking Gait and Quiet Balance: An Exploratory Study Using Machine Learning.使用行走步态和安静平衡识别当前报告焦虑感的个体:一项使用机器学习的探索性研究。
Sensors (Basel). 2022 Apr 20;22(9):3163. doi: 10.3390/s22093163.
3
Is This the Real Life, or Is This Just Laboratory? A Scoping Review of IMU-Based Running Gait Analysis.这是现实生活,还是仅仅是实验室?基于惯性测量单元的跑步步态分析的范围综述。
Sensors (Basel). 2022 Feb 23;22(5):1722. doi: 10.3390/s22051722.
4
Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach.使用源自被动感知数据的行为标记物预测情绪状态:数据驱动的机器学习方法。
JMIR Mhealth Uhealth. 2021 Mar 22;9(3):e24465. doi: 10.2196/24465.
5
Salivary Biomarkers of Stress, Anxiety and Depression.压力、焦虑和抑郁的唾液生物标志物
J Clin Med. 2021 Feb 1;10(3):517. doi: 10.3390/jcm10030517.
6
Skipping breakfast and mood: The role of sleep.不吃早餐与情绪:睡眠的作用。
Nutr Health. 2021 Dec;27(4):373-379. doi: 10.1177/0260106020984861. Epub 2021 Jan 11.
7
Caffeine-Containing, Adaptogenic-Rich Drink Modulates the Effects of Caffeine on Mental Performance and Cognitive Parameters: A Double-Blinded, Placebo-Controlled, Randomized Trial.含咖啡因、适应原丰富的饮品调节咖啡因对精神表现和认知参数的影响:一项双盲、安慰剂对照、随机试验。
Nutrients. 2020 Jun 29;12(7):1922. doi: 10.3390/nu12071922.
8
Instrumental assessment of balance and gait in depression: A systematic review.抑郁障碍患者平衡和步态的仪器评估:系统评价。
Psychiatry Res. 2020 Feb;284:112687. doi: 10.1016/j.psychres.2019.112687. Epub 2019 Nov 10.
9
See your mental state from your walk: Recognizing anxiety and depression through Kinect-recorded gait data.从步态看精神状态:利用 Kinect 记录的步态数据识别焦虑和抑郁。
PLoS One. 2019 May 22;14(5):e0216591. doi: 10.1371/journal.pone.0216591. eCollection 2019.
10
Data-driven biological subtypes of depression: systematic review of biological approaches to depression subtyping.基于数据驱动的抑郁症生物学亚型:抑郁症亚型生物学方法的系统评价。
Mol Psychiatry. 2019 Jun;24(6):888-900. doi: 10.1038/s41380-019-0385-5. Epub 2019 Mar 1.

使用步态和平衡识别年轻健康个体中轻度、中度至高度抑郁的当前感受:一项探索性研究。

Identifying Current Feelings of Mild and Moderate to High Depression in Young, Healthy Individuals Using Gait and Balance: An Exploratory Study.

机构信息

Honors Department, Clarkson University, Potsdam, NY 13699, USA.

Department of Kinesiology, Indiana University, Bloomington, IN 47405, USA.

出版信息

Sensors (Basel). 2023 Jul 23;23(14):6624. doi: 10.3390/s23146624.

DOI:10.3390/s23146624
PMID:37514917
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10384769/
Abstract

Depressive mood states in healthy populations are prevalent but often under-reported. Biases exist in self-reporting of depression in otherwise healthy individuals. Gait and balance control can serve as objective markers for identifying those individuals, particularly in real-world settings. We utilized inertial measurement units (IMU) to measure gait and balance control. An exploratory, cross-sectional design was used to compare individuals who reported feeling depressed at the moment ( = 49) with those who did not ( = 84). The Quality Assessment Tool for Observational Cohort and Cross-sectional Studies was employed to ensure internal validity. We recruited 133 participants aged between 18-36 years from the university community. Various instruments were used to evaluate participants' present depressive symptoms, sleep, gait, and balance. Gait and balance variables were used to detect depression, and participants were categorized into three groups: not depressed, mild depression, and moderate-high depression. Participant characteristics were analyzed using ANOVA and Kruskal-Wallis tests, and no significant differences were found in age, height, weight, BMI, and prior night's sleep between the three groups. Classification models were utilized for depression detection. The most accurate model incorporated both gait and balance variables, yielding an accuracy rate of 84.91% for identifying individuals with moderate-high depression compared to non-depressed individuals.

摘要

健康人群中抑郁情绪状态较为普遍,但往往报告不足。在健康个体中,自我报告抑郁存在偏差。步态和平衡控制可以作为识别这些个体的客观标志物,特别是在现实环境中。我们使用惯性测量单元 (IMU) 来测量步态和平衡控制。采用探索性横断面设计比较了当下感到抑郁的个体(=49 人)和未感到抑郁的个体(=84 人)。采用观察性队列研究和横断面研究的质量评估工具来确保内部有效性。我们从大学社区招募了 133 名年龄在 18-36 岁之间的参与者。使用各种仪器评估参与者的当前抑郁症状、睡眠、步态和平衡。步态和平衡变量用于检测抑郁,参与者被分为三组:无抑郁、轻度抑郁和中重度抑郁。使用 ANOVA 和 Kruskal-Wallis 检验分析参与者的特征,在三组之间未发现年龄、身高、体重、BMI 和前一晚睡眠有显著差异。使用分类模型进行抑郁检测。最准确的模型同时纳入了步态和平衡变量,对于识别中重度抑郁个体与无抑郁个体的准确率为 84.91%。