中国全科医学

• •

改良2分钟原地踏步与6分钟步行试验在社区人群中的一致性评价

王子雯1,高刚强2,王仲1*,吴悠2,3*   

  1. 1.102218 北京市,清华大学临床医学院 北京清华长庚医院全科健康医学研究实验室 2.518055 深圳市,清华大学医院管理研究院 3.100084 北京市,清华大学医疗管理学院
  • 收稿日期:2025-11-05 接受日期:2025-12-19
  • 通讯作者: 王仲,主任医师;E-mail:wangzhong523@vip.163.com 吴悠,副教授;E-mail:youwu@tsinghua.edu.cn
  • 基金资助:
    国家自然科学基金资助项目 (72404158);中国中医药科技发展中心科研专项“中西医协同慢病管理项目”(CXZH2024136)

Consistency Evaluation of the Improved 2-Minute Step Test and the 6-Minute Walk Test in Community Populations

WANG Ziwen1, GAO Gangqiang2, WANG Zhong1*, WU You2, 3*   

  1. 1.Laboratory of general practice and health sciences, Beijing Tsinghua Changgung Hospital, Tsinghua University School of Clinical Medicine, Beijing, 102218, China 2.Institute for Hospital Management, Tsinghua University, Shenzhen, 518055, China 3.School of Healthcare Management, Tsinghua University, Beijing, 100084, China
  • Received:2025-11-05 Accepted:2025-12-19
  • Contact: Zhong Wang, Chief Physician; E-mail: wangzhong523@vip.163.com You Wu, Associate Professor; E-mail: youwu@tsinghua.edu.cn

摘要: 背景 随着社区健康管理需求的不断增长,心肺耐力评估在基层健康服务中的重要性日益凸显。直接测定最大摄氧量依赖专业设备、操作复杂,6分钟步行测试(6MWT)对场地条件要求较高,均限制了其在社区环境中的应用。因此,亟需开发一种受空间和设备条件限制较小、适用于基层场景的高效心肺耐力评估方法。目的 评估改良2分钟踏步测试(MS-2MST)相对于6分钟步行测试(6MWT)在社区人群中用于心肺耐力评估的相关性与一致性。方法 采用方便抽样方法,于2025年9月—2025年11月选取北京市昌平区天通苑北社区25名居民作为实验对象,分别完成6MWT与MS-2MST测试。其中,MS-2MST测试过程采用自主研发的可穿戴多模态监测系统,记录有效抬腿次数、平均抬腿度数、总运动量等运动参数;6MWT采用标准6 min步行试验方法进行评估,记录距离、转弯数等参数,两种运动方式均记录静息心率、运动结束后瞬时心率、运动结束 2 min 后心率。采用 Pearson 相关分析探讨不同变量间的相关性。将6MWT距离、MS-2MST有效抬腿次数按数值大小降序排列,并按三分位数分为高、中、低3组,采用χ2 检验进行一致性分析。采用多元线性回归分析探讨 6MWT 距离与 MS-2MST 总运动量之间的关系及其影响因素。结果 MS-2MST运动结束后瞬时心率及运动结束后心率变化量高于6MWT组(P<0.001);运动结束2 min后,MS-2MST组瞬时心率及心率变化量高于6MWT组(P<0.05);MS-2MST组疲劳评分在高于6MWT组(P<0.05)。Pearson相关分析结果显示,MS-2MST有效抬腿次数及总运动量均与6MWT步行距离呈正相关(r=0.664,P<0.001;r=0.724,P<0.001)。两组运动结束后瞬时心率(r=0.850,P<0.001)和心率变化量(r=0.775,P<0.001)、运动结束 2 min 后的瞬时心率(r=0.816,P<0.001)和心率变化量(r=0.693, P<0.001)、运动过程中的平均心率(r=0.848,P<0.001)和平均心率变化量(r=0.759,P<0.001)均呈正相关性;两种运动过程中疲劳评分呈正相关性(r=0.577,P=0.003)。一致性分析显示,MS-2MST有效抬腿次数及总运动量与6MWT距离均具有一致性(Kappa=0.459,P=0.001;Kappa=0.579,P<0.001)。多元线性回归分析结果表明,MS-2MST 总运动量(B=0.040,95%CI=0.022~0.059,P<0.001)与年龄(B=2.657,95%CI=0.697~4.618,P=0.011)是6MWT距离的独立影响因素。结论 借助可穿戴设备精确采集运动数据的MS-2MST测试,可作为简便高效的心肺功能评估手段,为基层骨骼肌健康筛查和个体化运动干预提供可行方案。

关键词: 可穿戴设备, 改良 2 分钟踏步测试, 6 分钟步行测试, 数字健康监测, 社区健康管理

Abstract: Background With the continuous growth in demand for community-based health management, the importance of assessing cardiorespiratory fitness in primary healthcare services has become increasingly prominent. Direct measurement of maximal oxygen uptake relies on specialized equipment and complex procedures, while the 6-minute walk test (6MWT) requires relatively large testing space, both of which limit their application in community settings. Therefore, there is an urgent need to develop an efficient cardiorespiratory fitness assessment method that is less constrained by space and equipment and suitable for primary care scenarios. Objective To evaluate the correlation and agreement between the modified 2-minute step test (MS-2MST) and the 6-minute walk test (6MWT) for assessing cardiorespiratory fitness in a community population. Methods Using convenience sampling, 25 residents from Tiantongyuan North Community, Changping District, Beijing, were recruited between September and November 2025. All participants completed both the 6MWT and the MS-2MST. During the MS-2MST, a self-developed wearable multimodal monitoring system was used to record exercise parameters, including the number of effective step-ups, mean step angle, and total exercise volume. The 6MWT was conducted according to the standard 6-minute walk test protocol, with walking distance and number of turns recorded. Resting heart rate, immediate post-exercise heart rate, and heart rate at 2 minutes post-exercise were recorded for both tests. Pearson correlation analysis was used to examine the relationships among variables. The 6MWT distance and the effective step-up count of the MS-2MST were ranked in descending order and divided into high, medium, and low groups based on tertiles, and agreement was analyzed using the chi-square test. Multiple linear regression analysis was performed to explore the relationship between 6MWT distance and MS-2MST total exercise volume and its influencing factors. Results The immediate post-exercise heart rate and post-exercise heart rate change were significantly higher in the MS-2MST than in the 6MWT (P<0.001). At 2 minutes post-exercise, the heart rate and heart rate change in the MS-2MST were also higher than those in the 6MWT (P<0.05). The fatigue score during the MS-2MST was higher than that during the 6MWT (P<0.05). Pearson correlation analysis showed that both the effective step-up count and total exercise volume of the MS-2MST were positively correlated with the 6MWT walking distance (r=0.664, P<0.001; r=0.724, P<0.001). Immediate post-exercise heart rate (r=0.850, P<0.001) and heart rate change (r=0.775, P<0.001), heart rate at 2 minutes post-exercise (r=0.816, P<0.001) and heart rate change (r=0.693, P<0.001), mean heart rate during exercise (r=0.848, P<0.001) and mean heart rate change (r=0.759, P<0.001) were all positively correlated between the two tests. Fatigue scores during the two tests were also positively correlated (r=0.577, P=0.003). Agreement analysis showed consistency between the MS-2MST effective step-up count and 6MWT distance (Kappa=0.459, P=0.001), as well as between MS-2MST total exercise volume and 6MWT distance (Kappa=0.579, P<0.001). Multiple linear regression analysis indicated that MS-2MST total exercise volume (B=0.040, 95%CI=0.022-0.059, P<0.001) and age (B=2.657, 95%CI=0.697-4.618, P=0.011) were independent influencing factors of the 6MWT distance. Conclusion The MS-2MST, combined with wearable devices for accurate acquisition of exercise data, can serve as a simple and efficient method for assessing cardiorespiratory fitness, providing a feasible approach for skeletal muscle health screening and individualized exercise interventions in primary community settings.

Key words: Wearable devices, Modified Standardized 2-minute step test, 6-minute walk test, Digital health monitoring, Community health

中图分类号: