Baseline adjustment increases accurate interpretation of posturographic sway scores

•Regression to the mean affects studies with repeated measures design.•We measured 62 subjects' postural steadiness with eyes open (EO) and closed (EC).•Postural sway was quantified by its amplitude, velocity and frequency.•We compared three linear regression models on how to interpret the sway scores.•Adjusting for baseline score increases the detection accuracy between EO and EC.

Postural steadiness may be quantified using posturographic sway measures. These measures are commonly used to differentiate between a person's baseline balance and balance related to some physiological condition. However, the difference in sway scores between the two conditions may be difficult to detect due to large inter-subject variation. We compared detection accuracy provided by three models that linearly regress a sway measure (mean distance, velocity, or frequency) on the effect of eye closure on balance (eyes open (EO) vs. eyes closed (EC)). In Model 1 the dependent variable is a single sway score (EO or EC), whereas in Models 2 and 3 it is a change score (EO-EO or EC-EO). The independent variable is always the group (group = 0: EO or group = 1: EC). Model 3 also accounts for the regression to the mean effect (RTM), by considering the baseline value (EO) as a covariate. When differentiating between EO and EC conditions, 94% accuracy can be achieved when using mean velocity as sway measure and either Model 2 or 3. Thus by adjusting for baseline score one increases the accurate interpretation of posturographic sway scores.