Body weight is a strong predictor of postural stability
Introduction
Obesity is associated with serious medical complications that impair quality of life [1], [2], [3]. It also modifies body geometry, increases the mass of the different segments [4], [5], and imposes functional limitations pertaining to the biomechanics of activities of daily living that may predispose the obese to injury [6]. One of these limitations relates to balance control. Proper balance control is a critical factor in terms of fall prevention because balance impairments have been identified as important risk factors for falling [7].
The suggestion was made that, when an obese person is submitted to a small and normal forward oscillation, an abnormal distribution of body fat in the abdominal area (centre of mass position relative ankle joint) yields to an increased restabilizing ankle torque needed to regain balance [8]. This suggests that, when submitted to daily postural stresses and perturbations, obese persons, particularly those with an abnormal distribution of body fat in the abdominal area, may be at higher risk of falling than lightweight individuals because they have to generate ankle torque more rapidly and with a much higher rate of torque development to recover balance.
There are a number of studies with obese boys providing support to this association between body weight and balance stability. For obese boys aged 10–21, Goulding et al. [9] reported a significant negative relationship between body weight, body mass index, percentage of fat and total fat mass and a clinical balance score (Bruininks–Oseretsky). Compared to non-obese prepubertal boys, obese boys also showed greater sway areas and variability in the medial/lateral direction [10]. In addition, more obese children suffer from traumatic accidents to anterior teeth than non-obese children suggesting that they experience more forward fall [11]. Altogether, these studies support the idea that overweight can lead to poorer balance control. With young adults, Chiari et al. [12] investigated more closely the relationship between body sway oscillations and various anthropometric parameters. Their subjects had body mass index ranging from 17.8 to 31.0 kg/m2 (mean body weight of 65.6 ± 13.6 kg), which corresponds to an underweight/overweight range [1]. Body weight correlated with mean speed of the center of pressure (r = 0.43 for conditions with vision). Because simple correlations were used, it is not possible to determine the unique contribution of body weight independent of other anthropometric parameters likely to contribute to this relationship. For instance, body height and foot length also have been suggested to affect balance control [12], [13]. More recently, Teasdale et al. [14] showed that measures of postural stability (i.e., CP speed and range in antero-posterior and lateral axes) were improved in obese and morbid obese men after losing weight (mean weight loss of 12.3 and 71.3 kg, respectively). These authors observed a strong linear relationship between the magnitude of the weight loss and the improvement in balance control thus providing support to the suggestion that weight could be an important predictor of postural stability.
Therefore, the aim of the present study was to further investigate the relative contribution of body weight to balance control using a stepwise multiple regression model. In addition to body weight, body height, foot length, and age were included in the statistical model.
Section snippets
Subjects and postural stability protocol
Fifty-nine male individuals were tested in this study (age range 24–61 years). Table 1 presents physical characteristics of the subjects and shows that they covered a wide range of anthropometric characteristics, particularly for body weight and BMI (59.2–209.5 kg and 17.4–63.8 kg/m2, respectively). Subjects with known neurological disorders and cognitive impairments were excluded. By definition, all morbid obese subjects had some health problems (for instance, sleep disorders, diabetes,
Results
Mean values for all balance control parameters (with and without vision) analyzed are shown in Table 2. Forward stepwise multiple regression analyses were conducted for all CP variables. Table 3 presents results of the stepwise multiple regression analyses for the CP speed with and without vision.
With vision, the stepwise multiple regression indicated that, among all parameters, body weight was the only significant predictive factor of CP speed (p < 0.001). It accounted for 52% of the variance of
Discussion
The main objective of the study was to determine whether body weight could predict balance stability. With and without vision, body weight alone contributed to more than 50% of the variance observed for CP speed when controlling for age, body height and foot length. Without vision, the final statistical model predicted 63% of the variance observed for CP speed (55% with vision). Table 4 points to an interesting observation. With and without vision, body weight explained considerable variance
Acknowledgments
Supported by a NSERC collaborative health grant and NSERC discovery grants. Thanks are expressed to François Bégin for his help in data collection.
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