Fan Jingyuan, Zhang Tao, Gu Fanbin, Wang Zhaoyang, Cai Chengfeng, Wang Honggang, Liu Xiaolin, Yang Jiantao, Qi Jian, Zhu Qingtang
Department of Microsurgery, Orthopedic Trauma and Hand Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
J Med Internet Res. 2025 Sep 3;27:e63308. doi: 10.2196/63308.
Shoulder pain is a highly prevalent musculoskeletal disorder that severely compromises patients' quality of life. The Constant-Murley Scale (CMS) is a well-established method for shoulder function evaluation. However, the necessity of clinician involvement constrains its utility in continuous monitoring. Recent improvements in human pose estimation and inertial sensors provide possibilities for automated functional assessment.
This study introduces an automated CMS assessment system that can provide objective measuring results using movement images and inertial sensor data (Mobile Constant) and aims to evaluate its reliability by comparison with standard results from human raters.
The Mobile Constant system integrated subjective symptom questionnaires, range-of-motion analysis, and strength assessment. Patients presenting with shoulder concerns were enrolled consecutively, with movement images and inertial sensor data collected from each participant. The dataset was structured as follows: patients recruited from February to November 2022 at our hospital formed the training set, those enrolled between December 2022 and February 2023 served as the internal validation set, and patients recruited from April to July 2025 at an independent hospital constituted the external validation set. Gold standard assessments were determined independently by 2 raters using standardized protocols. Six machine learning models (logistic regression, k-nearest neighbors, decision tree, support vector machine, random forest, and adaptive boosting) were developed. The reliability of the system was determined by comparison with human raters using differences, Cohen κ coefficients, and intraclass correlation coefficients (ICCs). Agreement across human raters was also evaluated by comparison between 4 independent clinicians.
Data from 141 patients with shoulder pain and stiffness were collected (training set: n=83, 58.9%; internal validation set: n=28; 19.9%; external validation set: n=30, 21.3%). For range-of-motion analysis, the Mobile Constant system showed fair to substantial reliability, achieving κ coefficients ranging from 0.498 to 0.819 and ICCs ranging from 0.898 to 0.956 in the internal validation set. In the external validation set, κ coefficients ranged from 0.198 to 0.699, and ICCs ranged from 0.584 to 0.922. For abduction strength assessment, the k-nearest neighbors model demonstrated substantial reliability, yielding a κ coefficient of 0.707 and an ICC of 0.759 in internal validation and higher agreement in external validation (κ=0.809; ICC=0.906).
The self-reported method for shoulder function evaluation demonstrated substantial agreement with experienced human raters. The proposed system enabled reliable patient-conducted assessment using mobile phone-integrated cameras and inertial sensors and exhibited strong potential for remote monitoring.
肩部疼痛是一种非常普遍的肌肉骨骼疾病，严重影响患者的生活质量。Constant-Murley量表（CMS）是一种成熟的肩部功能评估方法。然而，临床医生参与的必要性限制了其在连续监测中的应用。人体姿态估计和惯性传感器的最新进展为自动功能评估提供了可能性。
本研究介绍一种自动CMS评估系统，该系统可使用运动图像和惯性传感器数据（移动Constant）提供客观测量结果，并旨在通过与人工评分者的标准结果进行比较来评估其可靠性。
移动Constant系统整合了主观症状问卷、活动范围分析和力量评估。连续招募有肩部问题的患者，收集每位参与者的运动图像和惯性传感器数据。数据集的结构如下：2022年2月至11月在我院招募的患者组成训练集，2022年12月至2023年2月登记的患者作为内部验证集，2025年4月至7月在一家独立医院招募的患者构成外部验证集。由2名评分者使用标准化方案独立确定金标准评估。开发了六种机器学习模型（逻辑回归、k近邻、决策树、支持向量机、随机森林和自适应增强）。通过使用差异、Cohen κ系数和组内相关系数（ICC）与人工评分者进行比较来确定系统的可靠性。还通过4名独立临床医生之间的比较来评估人工评分者之间的一致性。
收集了141例肩部疼痛和僵硬患者的数据（训练集：n = 83，58.9%；内部验证集：n = 28，19.9%；外部验证集：n = 30，21.3%）。对于活动范围分析，移动Constant系统显示出从一般到高度的可靠性，在内部验证集中κ系数范围为0.498至0.819，ICC范围为0.898至0.956。在外部验证集中，κ系数范围为0.198至0.699，ICC范围为0.584至0.922。对于外展力量评估，k近邻模型显示出高度可靠性，在内部验证中κ系数为0.707，ICC为0.759，在外部验证中一致性更高（κ = 0.809；ICC = 0.906）。
自我报告的肩部功能评估方法与经验丰富的人工评分者显示出高度一致性。所提出的系统能够使用集成手机的摄像头和惯性传感器进行可靠的患者自我评估，并具有远程监测的强大潜力。
