Associations and condition sensitivity of smartphone-and force-plate-derived sway metrics during VR-CTSIB

Smartphone-based sensors may provide a complement to laboratory-based force-plate balance assessment; however, validation studies have not established whether smartphone-force-plate associations and agreement vary across combined manipulations of visual input and support-surface reliability, or which smartphone-derived metrics are most sensitive to sensory challenges. Therefore, this study compared smartphone-derived and force-plate-derived sway metrics during virtual reality-based Clinical Test of Sensory Interaction and Balance (VR-CTSIB) conditions. This secondary analysis included 40 adults (20 with chronic ankle instability (CAI) and 20 healthy controls). Participants completed 30-s double-leg stance trials under six VR-CTSIB conditions combining firm or foam surfaces with NormVision, DisplayOff, and SwayRef visual environments. Sway data were collected using a force plate and a smartphone-based head-mounted display. Root mean square (RMS) and mean absolute value (MAV) were calculated in the mediolateral (ML) and anterior-posterior (AP) directions. Pearson correlations with false discovery rate correction were used to examine device associations, Bland-Altman analyses assessed agreement, and Cohen's dz quantified condition sensitivity. In the primary analyses, ML smartphone metrics showed larger correlation coefficients with force-plate measures than AP metrics. The largest correlations were observed in the firm NormVision condition for mediolateral RMS with force-plate mediolateral velocity RMS (r = 0.705, FDR-adjusted p < 0.001) and ML MAV with force-plate ML velocity MAV (r = 0.679, FDR-adjusted p < 0.001). Force-plate outcomes demonstrated greater condition sensitivity than smartphone-derived outcomes. Among smartphone metrics, ML MAV and RMS in the SwayRef condition showed the largest effects (dz = 0.895 and 0.876, respectively). Bland-Altman analyses showed systematic bias, with agreement varying across metrics and conditions. Overall, smartphone-derived sway metrics may provide complementary information during virtual reality-based balance assessment, particularly in the ML direction. However, associations, agreement, and condition sensitivity varied by metric and sensory condition, indicating that smartphone- and force-plate-derived measures should not be interpreted as interchangeable.