Probabilistic Modelling of Gait for Robust Passive Monitoring in Daily Life

Passive monitoring in daily life may provide valuable insights into a person's health throughout the day. Wearable sensor devices play a key role in enabling such monitoring in a non-obtrusive fashion. However, sensor data collected in daily life reflect multiple health and behavior-related factors together. This creates the need for a structured principled analysis to produce reliable and interpretable predictions that can be used to support clinical diagnosis and treatment. In this work we develop a principled modelling approach for free-living gait (walking) analysis. Gait is a promising target for non-obtrusive monitoring because it is common and indicative of many different movement disorders such as Parkinson's disease (PD), yet its analysis has largely been limited to experimentally controlled lab settings. To locate and characterize stationary gait segments in free-living using accelerometers, we present an unsupervised probabilistic framework designed to segment signals into differing gait and non-gait patterns. We evaluate the approach using a new video-referenced dataset including 25 PD patients with motor fluctuations and 25 age-matched controls, performing unscripted daily living activities in and around their own houses. Using this dataset, we demonstrate the framework's ability to detect gait and predict medication induced fluctuations in PD patients based on free-living gait. We show that our approach is robust to varying sensor locations, including the wrist, ankle, trouser pocket and lower back.

Estimating the Effect of Early Treatment Initiation in Parkinson's Disease Using Observational Data

Background

Both patients and physicians may choose to delay initiation of dopamine replacement therapy in Parkinson's disease (PD) for various reasons. We used observational data to estimate the effect of earlier treatment in PD. Observational data offer a valuable source of evidence, complementary to controlled trials.

Method

We studied the Parkinson's Progression Markers Initiative cohort of patients with de novo PD to estimate the effects of duration of PD treatment during the first 2 years of follow‐up, exploiting natural interindividual variation in the time to start first treatment. We estimated the Movement Disorder Society–Unified Parkinson's Disease Rating Scale (MDS‐UPDRS) Part III (primary outcome) and several functionally relevant outcomes at 2, 3, and 4 years after baseline. To adjust for time‐varying confounding, we used marginal structural models with inverse probability of treatment weighting and the parametric g‐formula.

Results

We included 302 patients from the Parkinson's Progression Markers Initiative cohort. There was a small improvement in MDS‐UPDRS Part III scores after 2 years of follow‐up for patients who started treatment earlier, and similar, but nonstatistically significant, differences in subsequent years. We found no statistically significant differences in most secondary outcomes, including the presence of motor fluctuations, nonmotor symptoms, MDS‐UPDRS Part II scores, and the Schwab and England Activities of Daily Living Scale.

Conclusion

Earlier treatment initiation does not lead to worse MDS‐UPDRS motor scores and may offer small improvements. These findings, based on observational data, are in line with earlier findings from clinical trials. Observational data, when combined with appropriate causal methods, are a valuable source of additional evidence to support real‐world clinical decisions. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study

Background

Wearable sensors have been used successfully to characterize bradykinetic gait in patients with Parkinson disease (PD), but most studies to date have been conducted in highly controlled laboratory environments.

Objective

This paper aims to assess whether sensor-based analysis of real-life gait can be used to objectively and remotely monitor motor fluctuations in PD.

Methods

The Parkinson@Home validation study provides a new reference data set for the development of digital biomarkers to monitor persons with PD in daily life. Specifically, a group of 25 patients with PD with motor fluctuations and 25 age-matched controls performed unscripted daily activities in and around their homes for at least one hour while being recorded on video. Patients with PD did this twice: once after overnight withdrawal of dopaminergic medication and again 1 hour after medication intake. Participants wore sensors on both wrists and ankles, on the lower back, and in the front pants pocket, capturing movement and contextual data. Gait segments of 25 seconds were extracted from accelerometer signals based on manual video annotations. The power spectral density of each segment and device was estimated using Welch’s method, from which the total power in the 0.5- to 10-Hz band, width of the dominant frequency, and cadence were derived. The ability to discriminate between before and after medication intake and between patients with PD and controls was evaluated using leave-one-subject-out nested cross-validation.

Results

From 18 patients with PD (11 men; median age 65 years) and 24 controls (13 men; median age 68 years), ≥10 gait segments were available. Using logistic LASSO (least absolute shrinkage and selection operator) regression, we classified whether the unscripted gait segments occurred before or after medication intake, with mean area under the receiver operator curves (AUCs) varying between 0.70 (ankle of least affected side, 95% CI 0.60-0.81) and 0.82 (ankle of most affected side, 95% CI 0.72-0.92) across sensor locations. Combining all sensor locations did not significantly improve classification (AUC 0.84, 95% CI 0.75-0.93). Of all signal properties, the total power in the 0.5- to 10-Hz band was most responsive to dopaminergic medication. Discriminating between patients with PD and controls was generally more difficult (AUC of all sensor locations combined: 0.76, 95% CI 0.62-0.90). The video recordings revealed that the positioning of the hands during real-life gait had a substantial impact on the power spectral density of both the wrist and pants pocket sensor.

Conclusions

We present a new video-referenced data set that includes unscripted activities in and around the participants’ homes. Using this data set, we show the feasibility of using sensor-based analysis of real-life gait to monitor motor fluctuations with a single sensor location. Future work may assess the value of contextual sensors to control for real-world confounders.

Measuring Parkinson's disease over time: The real-world within-subject reliability of the MDS-UPDRS

Background

An important challenge in Parkinson's disease research is how to measure disease progression, ideally at the individual patient level. The MDS‐UPDRS, a clinical assessment of motor and nonmotor impairments, is widely used in longitudinal studies. However, its ability to assess within‐subject changes is not well known. The objective of this study was to estimate the reliability of the MDS‐UPDRS when used to measure within‐subject changes in disease progression under real‐world conditions.

Methods

Data were obtained from the Parkinson's Progression Markers Initiative cohort and included repeated MDS‐UPDRS measurements from 423 de novo Parkinson's disease patients (median follow‐up: 54 months). Subtotals were calculated for parts I, II, and III (in on and off states). In addition, factor scores were extracted from each part. A linear Gaussian state space model was used to differentiate variance introduced by long‐lasting changes from variance introduced by measurement error and short‐term fluctuations. Based on this, we determined the within‐subject reliability of 1‐year change scores.

Results

Overall, the within‐subject reliability ranged from 0.13 to 0.62. Of the subscales, parts II and III (OFF) demonstrated the highest within‐subject reliability (both 0.50). Of the factor scores, the scores related to gait/posture (0.62), mobility (0.45), and rest tremor (0.43) showed the most consistent behavior.

Conclusions

Our results highlight that MDS‐UPDRS change scores contain a substantial amount of error variance, underscoring the need for more reliable instruments to forward our understanding of the heterogeneity in PD progression. Focusing on gait and rest tremor may be a promising approach for an early Parkinson's disease population. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.