On the relationship between body sway and body kinematics durino standing balance in ankylosing spondilytis subjects

Introduction The control of posture is maintained by a complex sensorimotor system, which integrates information from the visual, vestibular, and somatosensory systems [1]. Postural alterations in the ankylosing spondilytis (AS) subjects may reflect changes in the spine which becomes rigid from the occiput to the sacrum. AS patients stand in a stooped position and are unable to see the horizon. Characterizing AS-related changes in postural steadiness may provide information useful in identifying suitable rehabilitation therapies. In the present work is presented an evaluation of AS posture through the analysis of the displacement of the center-of-pressure (CoP) and of the trunk–hip–knee–ankle joints and pelvis rotations. Methods Twelve normal and 12 AS subjects (respectively age: 45 ± 18 years, BMI: 22 ± 2; age: 49 ± 1 years, BMI: 25 ± 3) were recruited. Six cameras BTS Sr.l. motion capture system (60–120 Hz) synchronized with 2 Bertec force plates (FP4060-10) were used. A modified version of Benedetti et al. [2]. The following anatomical landmarks were calibrated: anterior and posterior superior iliac spines, medial epicondyles and medial malleolus, greater trochanters. The subjects were asked to stand for 60 s in an upright position with the feet 30° apart and their arms along the body and to look at a small black circular target placed about 1 meter from the eyes, at 7 different heights: eye height (e.h.), ±10°, ±20°, ±30° than eye level. Roemberg Test was also performed. Sway area (sa), Ellipsis 95% (E), the CoP coordinate time series, the CoP coordinate time series in antero-posterior (AP) and medio-lateral (ML) directions, mean velocity, mean velocity in AP and ML directions [1]. A posture model was developed to describe the reciprocal position of the body segment with 9 angles on the sagittal plane. The hip joint center position was reconstructed as Harrington [3]. Pearson correlation coefficients (R Statistic) and 1-way Anova (Matlab) were computed for all measures using the data pooled from the two age groups. Results These data show a very nice correlation between kinematics and posturographic variables when considering either the increment (ivth) or reduction (rvth) of the visual target height. In particular a strong correlation was found between the following variables: the head protrusion and both E and sa, the knee flexion-extension (FE) and both the path in AP and the mean velocity in AP, the trunk FE and both the path in ML and the mean velocity in ML (R = 0.8, rvth); the pelvis FE and both the path in AP and the mean velocity in AP, the knee FE and both the path in AP and the mean velocity in AP (R = 0.9, ivth). In Table 1 have been reported the results of the 1-way Anova.