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.