Correlation of Clinical Scores with Remote Assessments of Accelerometry Output of a Low-Cost Quantitative Continuous Measurement of Movements

This dataset accompanies the research poster titled “Correlation of clinical scores with remote assessments of accelerometry output of a low-cost quantitative continuous measurement of movements.” It focuses on the reliability and validity of using wearable accelerometers to remotely evaluate motor function in individuals with Parkinson’s disease (PD) and compares those results to standard clinical scores obtained through in-person assessment. The study collected accelerometry data from participants performing predefined motor tasks. These data were then processed to extract movement features such as amplitude, frequency, and signal stability. Clinical motor scores were concurrently collected using established neurological scales. The dataset includes both raw and summarized data from individuals with PD and age-matched typically developing (TD) controls. The primary analysis involved assessing the internal consistency and test-retest reliability of the accelerometer-derived features using Cronbach’s Alpha and Intraclass Correlation Coefficients (ICC). High internal consistency (e.g., Cronbach’s Alpha = 0.918 for PD group) and strong reliability in average measures (ICC = 0.918 for PD group) support the use of remote motor assessments as a valid tool for clinical or research purposes. This description style is in line with other published Mendeley Data entries such as: Yoon et al. (2021) – Wearable-based gait and balance features dataset for fall risk prediction in elderly (https://data.mendeley.com/datasets/dyhgxdkf5y/1) Giggins et al. (2022) – Accelerometer-derived physical activity and balance metrics in Parkinson’s disease (https://data.mendeley.com/datasets/xmzz5mfxv9/2) The dataset is intended for researchers interested in neurodegenerative disease monitoring, remote assessment technologies, movement disorders, and wearable sensor applications. It can be reused for meta-analyses, validation studies, and the development of digital health tools, particularly in resource-limited or telehealth contexts.本数据集配套于题为《临床评分与低成本定量连续运动监测的加速度计输出远程评估相关性》（Correlation of clinical scores with remote assessments of accelerometry output of a low-cost quantitative continuous measurement of movements.）的研究海报。本数据集聚焦于利用可穿戴加速度计（wearable accelerometers）远程评估帕金森病（Parkinson’s disease, PD）患者运动功能的信度与效度，并将该结果与面对面评估获得的标准临床评分进行对比。 本研究从完成预设运动任务的受试者处采集加速度计数据，随后对这些数据进行处理以提取运动特征，包括幅度、频率及信号稳定性。同时采用成熟的神经学量表采集临床运动评分。本数据集包含帕金森病患者与年龄匹配的典型发育（typically developing, TD）对照组受试者的原始数据与汇总数据。 本研究的核心分析采用克朗巴哈α系数（Cronbach’s Alpha）与组内相关系数（Intraclass Correlation Coefficients, ICC），评估加速度计提取特征的内部一致性与重测信度。结果显示，帕金森病组的内部一致性较高（如克朗巴哈α系数为0.918），且平均测量值的信度较强（组内相关系数为0.918），证实了远程运动评估可作为临床或研究场景中的有效工具。 本数据集的描述风格与已发布的其他Mendeley Data数据集条目一致，例如：Yoon等人（2021）——用于老年人跌倒风险预测的基于可穿戴设备的步态与平衡特征数据集（https://data.mendeley.com/datasets/dyhgxdkf5y/1）；Giggins等人（2022）——帕金森病患者加速度计提取的身体活动与平衡指标数据集（https://data.mendeley.com/datasets/xmzz5mfxv9/2）。 本数据集面向关注神经退行性疾病监测、远程评估技术、运动障碍及可穿戴传感器应用的研究人员，可被复用用于元分析、效度验证研究以及数字健康工具的开发，尤其适用于资源受限或远程医疗场景。