Effects of sampling frequency and interpolation preprocessing on inter- and intra-study comparability of stabilometric variables

•Impact of the sampling frequency on stabilometric variables is investigated.•Low sampling frequency affects inter- and intra-study comparability.•Interpolation refines all intra-study comparability and most inter-study comparability.•Application of interpolation technique would lead to proper interpretation of results. The center of pressure (CoP) is commonly assessed during static standing; however, instrumental measurement performance critically impacts this variable. We focused on sampling frequency and systematically identified the effects of sampling frequency and interpolation preprocessing on inter- and intra-study comparability of stabilometric variables. Stabilometric variables in four domains—positional, dynamic, frequency, and stochastic—were investigated. CoP data were collected at 1000 Hz, and the variables obtained from data downsampled to 100 Hz were treated as “real values.” Additionally, variables were obtained from low-sampling frequency measurement data, which were downsampled to 50 Hz and 20 Hz and upsampled to 100 Hz through interpolation applied to simulated 50-Hz and 20-Hz data. The inter-study comparability was assessed via the effect size and minimal detectable change with 95% certainty, and the intra-study comparability was assessed via the coefficient of determination. Although a sampling frequency of 20 Hz affected the intra-study comparability of one dynamic variable and four frequency variables, interpolation preprocessing restored the intra-study comparability of all variables. Moreover, low-sampling frequency conditions affected the inter-study comparability, except for positional variables. Interpolation preprocessing restored the inter-study comparability of most variables; however, the number of crossings of the CoP velocity in the interpolated 50-Hz and 20-Hz data as well as the short-term diffusion and scaling coefficients in the interpolated 20 Hz data were still affected. Although some variables require careful handling, upsampling improves the consistency of variables and simplifies the comparison and interpretation of the results, including low-sampling frequency measurement data.