ArticleFrontiers in human neuroscience2026
Sex and age differences of postural control in community-dwelling older adults.
Article in Frontiers in human neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: Force platforms are widely used to assess postural stability and fall risk in older adults. However, traditional parameters often capture overlapping phenomena and fail to fully reflect underlying control mechanisms. This study evaluated combining of six partly independent parameters to distinguish sex and age-related differences in postural control among community-dwelling elderly. Methods: A total of 4,588 adults aged 65-95 years were assessed using static posturography under non-visual conditions. Six time-domain parameters, reflecting torque control, positional control and anticipatory control of the center point of force. Romberg's quotient was included for comparison. Results: Females exhibited greater stability, whereas males relied more on corrective force moments and showed larger sway amplitudes. Classification trees predicted sex with 71% accuracy using three parameters. Aging was associated with increased anteroposterior sway amplitude and a reduction in the critical time for transition between open- and closed-loop control. Additional age-sensitive parameters included mediolateral velocity zero-crossing rate and steady-phase duration. Age could be predicted within ±5 years for both sexes. Romberg's quotient could discriminate age in 30% and sex differences in 60% of participants, only. Conclusion: Postural stability is influenced by both sex and age. The identified combination of parameters provides a framework for estimating the "biological age" of postural control and investigate balance impairments. Age-related decline appears consistent within a 5-year range, bur does not exclude the effect of lifestyle or comorbid factors. This study demonstrates that use of multidimensional data vectors with the implementation of AI-modeling can improve the predictive accuracy and clinical applicability of posturography.
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