Work

Evaluation of Inter-Method Reliability Between Palarum PUP Smart Socks® and the Opal v1 Mobility Lab System Among Ambulatory Adults

Year: 2026

Type: article

Abstract: Early mobilization (EM) can mitigate hospital-acquired deconditioning by preserving prehospital functional status. EM measurement is challenging due to concerns such as accuracy and patient comfort. Palarum PUP® Smart Socks were designed for the hospital setting to track step count and time out of bed. The purpose of this study was to evaluate the inter-method reliability of the Palarum PUP® Smart Socks compared with the Opal v1 Mobility Lab system. Using a cross-sectional design, participants wore Smart Socks and six mobility sensors while completing three trials of a 20 m walk at a self-selected pace. Step count, stride length, gait speed, and duration were recorded. Differences between the two measurement systems were assessed using paired t-tests, agreement was evaluated using Bland–Altman analyses, and reliability was assessed using intraclass correlation coefficients (ICCs) with 95% confidence intervals. Fifty-three participants were enrolled (mean age 37.9 ± 16.7 years; 64% female; 75% White; BMI 28.2 ± 5.9 kg/m2). ICCs demonstrated excellent reliability for step count and duration (ICC > 0.90) moderate reliability for gait speed (ICC = 0.52), and poor reliability for stride length (ICC = 0.30). Reliability was statistically significant for step count, gait speed, duration, and stride length (all p < 0.01). Findings support the reliability of Palarum PUP® Smart Socks among ambulatory adults. By overcoming many limitations of traditional wearables in hospital settings, Smart Socks may be particularly well suited for EM programs. However, further validation is needed among hospitalized adults who may have slower gait speed and/or altered gait patterns.

Source: Sensors

DOI: 10.3390/s26185730

Authors: Pamela J. Wright, Alicia Flach, Kelsie M. Bryant, Kesheng Wang

Institutions: University of South Carolina, Virginia Commonwealth University

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,137