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Postural control while standing in older adults with and without mild cognitive impairment: A linear and nonlinear
Khantamat Thammachat1, Sawit Na Songkhla2, Daruj Aniwattanapong3
1Human Movement Performance Enhancement Research Unit, Department of Physical Therapy, Faculty of Allied Health Sciences, Chulalongkorn University, Thailand.
Background:
Mild cognitive impairment (MCI) is a transitional stage between normal aging and dementia in which functional independence is generally preserved, although subtle deficits in standing postural control may occur. Studies using linear center of pressure (CoP) measures have reported inconsistent findings. Nonlinear analyses, which evaluate the temporal structure and complexity of postural dynamics, may better detect standing postural control impairments in MCI; however, their application in this population remains limited.
Research Question:
Is standing postural control impaired in older adults with MCI compared with those without MCI under different sensory conditions?
Methods:
Sixty-eight older adults (34 with MCI and 34 age- and sex-matched controls) participated. Postural control was assessed using a force platform under four conditions: firm surface-eyes open, firm surface-eyes closed, foam surface-eyes open, and foam surface-eyes closed. CoP data were analyzed using linear measures (range and velocity) and nonlinear analysis (sample entropy; SampEn).
Results:
The MCI group showed significantly higher total CoP velocity on a foam surface with eyes open compared with controls (109.29 ± 32.22 vs. 96.53 ± 16.01; p < 0.04). In contrast, mediolateral velocity on a foam surface with eyes closed was significantly lower in the MCI group (98.33 ± 21.62 vs. 112.53 ± 29.13; p < 0.03). Nonlinear analysis revealed significantly lower mediolateral SampEn during the foam-eyes-closed condition in the MCI group (7.03 ± 1.15 × 10⁻³ vs. 7.94 ± 1.54 × 10⁻³; p = 0.01).
Significance:
Older adults with MCI exhibit impaired postural control, particularly under sensory-challenging conditions. Combining linear and nonlinear analyses may enhance early detection of balance dysfunction associated with cognitive decline.
