Objective assessment of fall risk in Parkinson's disease using a body-fixed sensor worn for 3 days

Abstract

Background: Patients 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 materials: 107 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.

Results: The 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/significance: These 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.

Figure 1. Examples of vertical acceleration signals of a PD faller (left) and a non-faller (right).

Figure 1A shows a 3-day raw acceleration signal. Figure 1B and 1C show the time and frequency domains of a 30 second signal (derived from the raw signal), respectively. The acceleration pattern of the PD faller (male, 61 yrs old) is less smooth compared to the PD non-faller (male, 74 yrs old) (Figure 1B). The peak amplitude of the dominant frequency is lower and wider in the faller compared to the non-faller, indicating of a less consistent, more variable gait pattern (figure 1C). Figure 1D shows an example of 3-day vertical acceleration signal in the frequency domains. The PD faller has a less consistent gait pattern, as reflected by the lower amplitude and wider spectrum. Similar findings are observed on a group level (recall Table 3).

Figure 2. Time spent in different activities in a PD faller and non-faller (right).

Figure 2A shows a general, descriptive example of the time spent walking, standing, lying and sitting in two subjects as a function of time over the 72 hour recordings. Figure 2B shows the percent time spent in each of these activities. Note that on a group level, walking amounts were similar in the fallers and non-fallers (see also Table 2). Please note that although this figure is based on previously validated measures , we do not extract any quantitative measures from it. The figure is included here to illustrate how the present approach can be extended further in future work. In the current study, the analyses focused on walking bouts that were one minute or longer in order to robustly identify walking and, ultimately, the quality of these walking bouts (recall the methods and Table 2).

Figure 3. Survival curve showing the time to first fall among all subjects who reported no falls in the year prior to the study.

Based on fall history, all of these subjects had a relatively low risk of future falls. However, the anterior-posterior width of the peak in the frequency domain, a measure of gait variability derived from the 3-day recording, was associated with time to first fall. When subjects were classified as those having a relatively high (above the median) or low (below the median) width, those with a high width experienced a fall sooner (Log rank test: p = 0.0034, Wilcoxon test: p = 0.0029), compared to those with a relatively low width.

Figure 4. Survival curve showing that gait speed while off was not significantly associated with the time to first fall (Log rank test: p = 0.688, Wilcoxon test: p = 0.697) among subjects who reported no falls in the year prior to testing.

Please compare to Figure 3.