Principal component analysis.

We developed the Physical Capacity Score, a set of validated and commonly used physical tests for adults, administered through a custom-built hardware and software platform that enables automated data collection and analysis. This study aimed to evaluate the platform’s repeatability, examine age-related differences, and explore the relationships between different physical capacities in a sample of adults. A total of 812 participants (aged 18–68 years, 63.5% female) were recruited. Participants completed six physical tests: finger tapping, handgrip strength, single-leg stance, sit-and-reach, five-times sit-to-stand, and the YMCA 3-minute step test. Outcome data were standardized by gender (z-scores) and analyzed across age groups using ANOVA. Pearson’s correlation coefficient (r) was used to assess redundancy among outcomes, and Principal Component Analysis (PCA) was conducted. In a test-retest analysis, all variables demonstrated coefficient of variation (COV) 0.90, except for CoP path length (COV = 10.5%, ICC = 0.64). Correlations among outcomes were weak (r range: 0.036–0.373). While all physical capacities declined with age (p 2 values), we found differences in handgrip strength (η2 = 0.035), sit-and-reach (η2 = 0.050), finger tapping (η2 = 0.059), CoP path length (η2 = 0.095), lower-limb power (η2 = 0.148), and cardiorespiratory fitness (η2 = 0.389). The average PCA component scores revealed large age-related differences (η2 = 0.301). Our findings suggest that the developed platform is a valuable tool for assessing physical function, and all physical tests captured distinct aspects of physical capacities. This highlights the necessity of employing a comprehensive battery of tests to gain a holistic understanding of an individual’s physical health and detect age-related decline effectively.