(untitled) (Architecture Image Studies, 2025)
Background: Handgrip strength (HGS) is a widely used biomarker of musculoskeletal capacity and physiological aging. Traditional hydraulic devices such as the Jamar provide peak-force values but cannot capture dynamic neuromuscular characteristics. Digital IoT-based dynamometers offer high-resolution force–time data that may enhance functional assessment. Objective: This study examined the agreement between SoundBody IoT Dynamometer and the Jamar device and evaluated the added value of dynamic waveform-derived indicators for functional risk screening. Methods: Data from 312 adults were analyzed. Peak-force values were obtained using Jamar, while SoundBody recorded continuous force–time curves. Agreement was assessed using correlations, ICC, Bland–Altman plots, and proportional bias analysis. SoundBody-derived metrics included fatigue slope, variability, time-to-peak, asymmetry, and AUC. Age-stratified models evaluated their predictive utility. Results: SoundBody peak force strongly correlated with Jamar (r = 0.87; ICC = 0.83), although it underestimated peak force by 5 kg. Dynamic indicators showed greater sensitivity to aging than peak force, with older adults exhibiting steeper fatigue slopes, higher variability, and greater asymmetry. Models incorporating dynamic metrics explained more variance in functional decline than those using peak force alone. Conclusion: IoT-based dynamometry provides meaningful neuromuscular insights beyond peak-force assessment. SoundBody’s dynamic indicators support early detection of age-related deterioration and functional risk, positioning it as a promising digital standard for comprehensive grip-strength evaluation.