"Impact of Fatigue on Balance and Sensor-Based Assessments of Proprioception: A Comparative Study Across Different Sensory Conditions

Document Type : Original Article

Authors

1 Faculty of physical therapy Kafrelsheik university, Egypt

2 Level 5 student, Faculty of physical therapy, kafrelsheik university, kafrelsheik, Egypt.

3 Physiotherapy program School of Health and Social work, University of Hertfordshire Egypt [UH-GAF]

4 Human Performance and Nutrition Research Institute Oklahoma State University, Stillwater, Oklahoma, 74078, USA

5 Basic Science Department Physical Therapy Faculty Kafrelsheik University Kafrelsheik , Egypt

6 Department of pediatric physical therapy, Faculty of allied medical science Physiotherapy, Middle East University, Am-man, Jordon

7 Department of Cardiovascular / Respiratory disorders and geriatrics department, Faculty of physical therapy, Benha University, Qalyubia, Egypt.

8 Department of Basic sciences for physical therapy, Faculty of Physical Therapy, Horus university, Egypt.

10.21608/ejpt.2025.364222.1212

Abstract

Fatigue is a prevalent condition that can impair balance and proprioception, increasing the risk of falls and accidents. The effects of fatigue on balance have been studied, but there is limited research using advanced sensor technologies to assess these effects in real-time. Background/Objectives: This study aims to investigate how fatigue impacts balance and proprioception through mobile sensor technology. Methods: A total of 251 participants (mean age = 19.83 ± 0.94 years) were classified into low (n = 211) and high (n = 40) fatigue groups based on the Fatigue Assessment Scale. Each participant performed the mini-CITSIB (Cognitive and Sensory Integration Balance) test under four conditions: eye open with a fixed surface, eye closed with a fixed surface, eye open with a foam surface, and eye closed with a foam surface. Mobile sensors (accelerometer, gyroscope, linear accelerometer, and magnetometer) were used to record movement and stability data. Key variables calculated included jerk, peak-to-peak, root mean square (RMS), range, standard deviation (SD), and the coefficient of detrended fluctuation analysis (coeff_dfa). Data were analyzed using SPSS software, with independent samples t-tests comparing the fatigue groups for each condition. Results: Significant differences in sensor readings were observed between low and high fatigue groups across all conditions. The high fatigue group showed greater variability in movement and lower values in jerk, peak-to-peak, and RMS, indicating reduced stability and more erratic movements. Coefficients of DFA for rotation and side bending were significantly higher in the high fatigue group, suggesting increased fluctuations in movement.

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