# Smartphone Accelerometer for Gait Assessment: Validity and Reliability in Healthy Adults
## 4 Discussion  
The results of this study demonstrate a high degree of agreement between the smartphone accelerometer system and the GAITRite system across various walking conditions. Intraclass correlation coefficients (ICCs) ranged from 0.778 to 0.999, highlighting that despite methodological differences, both systems can reliably assess key gait parameters such as walking distance, velocity, and cadence. These results suggest that smartphone accelerometer systems, despite their portability and ease of use, offer comparable accuracy to traditional systems such as GAITRite, thus broadening the applications of gait analysis across clinical, home, and community settings. This finding is consistent with prior research that has validated the effectiveness of smartphone-based systems for gait analysis in both controlled and real-world environments [13; 20]. The GAITRite system is widely recognized as the gold standard in gait analysis, particularly for its precision in measuring spatiotemporal gait parameters. It has been effectively utilized in clinical settings, especially in the evaluation of gait abnormalities in patients with neurodegenerative conditions such as Parkinson's disease and Alzheimer's disease, as well as in post-surgical rehabilitation [5; 8]. However, its use is constrained by the need for controlled environments and the relatively high costs associated with the system. In contrast, the smartphone accelerometer system provides significant advantages in terms of portability and the potential for use in non-clinical environments. Recent technological advancements have equipped smartphones with improved accelerometers and gyroscopes, which can now accurately measure basic gait parameters such as walking speed, distance, and cadence in real time [21; 22]. Studies have demonstrated that smartphone-based systems show high reliability, even when validated against gold-standard tools such as GAITRite [20], making them particularly useful for continuous real-world monitoring and for assessing patient progress over time, especially in orthopedic and stroke rehabilitation [23]. In this study, the Bland-Altman analysis revealed minimal differences between the two systems, with mean discrepancies in distance ranging from 4.79 cm to 9.53 cm and cadence differences ranging from \(-0.16\) to \(-0.73\) steps per minute across different walking speeds. Such minor variations have been reported in prior validation studies of smartphone-based gait analysis systems [24], further reinforcing the viability of smartphone systems in providing accurate and continuous gait assessments. When comparing the two systems across different walking speeds, performance varied slightly. Both systems exhibited high reliability in moderate, stable walking conditions, as ICCs often exceeded 0.90 for parameters such as walking speed and cadence [23; 24]. However, at faster walking speeds, the smartphone system displayed minor discrepancies, particularly in step length and gait variability, likely due to signal noise and minor integration drift during data collection [19]. At slower walking speeds, the smartphone system also struggled to capture certain joint-specific kinematic data, such as stride duration and stance time, which were better measured by GAITRite [21]. Cadence remained one of the most consistent variables across both systems, regardless of walking speed. Differences in cadence between the systems were found to be negligible, as supported by similar studies [20]. However, step length exhibited more pronounced discrepancies at both slow and fast walking speeds, with the smartphone system occasionally overestimating or underestimating distance due to increased body sway or instability during measurement [22]. Gait variability, particularly in measures such as stride length or time asymmetry, was better captured by the GAITRite system in controlled environments.