Accelerometer-derived measures based on 3-day recordings are useful for evaluating fall risk as older adults perform daily living activities in their everyday home environment.

Frequency-derived measures are valid and sensitive estimates of stride-to-stride variability that can be used to assess the quality and consistency of walking in patients with PD in real-life settings.

BackgroundPatients with Parkinson's disease (PD) suffer from a high fall risk. Previous approaches for evaluating fall risk are based on self-report or testing at a given time point and may, therefore, be insufficient to optimally capture fall risk. We tested, for the first time, whether metrics derived from 3 day continuous recordings are associated with fall risk in PD.Methods and Materials107 patients (Hoehn & Yahr Stage: 2.6±0.7) wore a small, body-fixed sensor (3D accelerometer) on lower back for 3 days. Walking quantity (e.g., steps per 3-days) and quality (e.g., frequency-derived measures of gait variability) were determined. Subjects were classified as fallers or non-fallers based on fall history. Subjects were also followed for one year to evaluate predictors of the transition from non-faller to faller.ResultsThe 3 day acceleration derived measures were significantly different in fallers and non-fallers and were significantly correlated with previously validated measures of fall risk. Walking quantity was similar in the two groups. In contrast, the fallers walked with higher step-to-step variability, e.g., anterior-posterior width of the dominant frequency was larger (p = 0.012) in the fallers (0.78±0.17 Hz) compared to the non-fallers (0.71±0.07 Hz). Among subjects who reported no falls in the year prior to testing, sensor-derived measures predicted the time to first fall (p = 0.0034), whereas many traditional measures did not. Cox regression analysis showed that anterior-posterior width was significantly (p = 0.0039) associated with time to fall during the follow-up period, even after adjusting for traditional measures.Conclusions/SignificanceThese findings indicate that a body-fixed sensor worn continuously can evaluate fall risk in PD. This sensor-based approach was able to identify transition from non-faller to faller, whereas many traditional metrics were not successful. This approach may facilitate earlier detection of fall risk and may in the future, help reduce high costs associated with falls.

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Background Time to complete the Timed Up and Go (TUG), a test of mobility and fall risk, was recently associated with cognitive function. Objectives To assess whether different TUG subtasks are preferentially affected among older adults with mild cognitive impairment (MCI) and are specific to different cognitive abilities. Design Cross sectional study Setting Community and home setting Participants 347 older adults without dementia (mean 83.6±3.5yrs, 75% females, 19.3% MCI) participating in the Rush Memory and Aging Project. Measurements Subjects wore a small, light-weight sensor that measured acceleration and angular velocity while they performed the instrumented TUG (iTUG). Measures of iTUG were derived from 4 subtasks: walking, turning, sit-to-stand and stand-to-sit and compared between participants with no cognitive impairment (NCI) versus MCI. Results NCI and MCI did not differ in age, sex, years of education (p>0.44) or time to complete the TUG (NCI:7.6±3.7sec vs. MCI:8.4±3.7sec;p=0.12). MCI had less walking consistency (p=0.0091), smaller pitch range during transitions (p=0.005), lower angular velocity during turning, and required more time to complete the turn-to-walk (p=0.042). Gait consistency was correlated with perceptual speed (p=0.012) and turning was correlated with perceptual speed (p=0.024) and visual-spatial abilities (p=0.049). Conclusions MCI is associated with impaired performance on iTUG subtasks that cannot be identified when simply measuring overall duration of performance. Distinctive iTUG tasks were related to particular cognitive domains, demonstrating the specificity of motor-cognitive interactions. Using a single body worn sensor for quantify of mobility may facilitate our understanding of late-life gait impairments and their inter-relationship with cognitive decline.