Automated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson's Disease Severity.

Introduction: Technology holds the potential to track disease progression and response to neuroprotective therapies in Parkinson's disease (PD). The sit-to-stand (STS) transition is a frequently occurring event which is important to people with PD. The aim of this study was to demonstrate an automatic approach to quantify STS duration and speed using a real-world free-living dataset and look at clinical correlations of the outcomes, including whether STS parameters change when someone withholds PD medications. Methods: Eighty-five hours of video data were collected from 24 participants staying in pairs for 5-day periods in a naturalistic setting. Skeleton joints were extracted from the video data; the head trajectory was estimated and used to estimate the STS parameters of duration and speed. Results: 3.14 STS transitions were seen per hour per person on average. Significant correlations were seen between automatic and manual STS duration (Pearson rho - 0.419, p = 0.042) and between automatic STS speed and manual STS duration (Pearson rho - 0.780, p < 0.001). Significant and strong correlations were seen between the gold-standard clinical rating scale scores and both STS duration and STS speed; these correlations were not seen in the STS transitions when the participants were carrying something in their hand(s). Significant differences were seen at the cohort level between control and PD participants' ON medications' STS duration (U = 6,263, p = 0.018) and speed (U = 9,965, p < 0.001). At an individual level, only two participants with PD became significantly slower to STS when they were OFF medications; withholding medications did not significantly change STS duration at an individual level in any participant. Conclusion: We demonstrate a novel approach to automatically quantify and ecologically validate two STS parameters which correlate with gold-standard clinical tools measuring disease severity in PD.

Understanding how people with Parkinson's disease turn in gait from a real-world in-home dataset.

INTRODUCTION: Turning in gait digital parameters may be useful in measuring disease progression in Parkinson's disease (PD), however challenges remain over algorithm validation in real-world settings. The influence of clinician observation on turning outcomes is poorly understood. Our objective is to describe a unique in-home video dataset and explore the use of turning parameters as biomarkers in PD. METHODS: 11 participants with PD, 11 control participants stayed in a home-like setting living freely for 5 days (with two sessions of clinical assessment), during which high-resolution video was captured. Clinicians watched the videos, identified turns and documented turning parameters. RESULTS: From 85 hours of video 3869 turns were evaluated, averaging at 22.7 turns per hour per person. 6 participants had significantly different numbers of turning steps and/or turn duration between "ON" and "OFF" medication states. Positive Spearman correlations were seen between the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale III score with a) number of turning steps (rho = 0.893, p < 0.001), and b) duration of turn (rho = 0.744, p = 0.009) "OFF" medications. A positive correlation was seen "ON" medications between number of turning steps and clinical rating scale score (rho = 0.618, p = 0.048). Both cohorts took more steps and shorter durations of turn during observed clinical assessments than when free-living. CONCLUSION: This study shows proof of concept that real-world free-living turn duration and number of turning steps recorded can distinguish between PD medication states and correlate with gold-standard clinical rating scale scores. It illustrates a methodology for ecological validation of real-world digital outcomes.

Effects of Parkinson's disease and dopamine on digit span measures of working memory.

RATIONALE: Parkinson's disease (PD) impairs working memory (WM)-the ability to maintain items in memory for short periods of time and manipulate them. There is conflicting evidence on the nature of the deficits caused by the disease, and the potential beneficial and detrimental effects of dopaminergic medication on different WM processes. OBJECTIVES: We hypothesised that PD impairs both maintenance and manipulation of items in WM and dopaminergic medications improve this in PD patients but impair it in healthy older adults. METHODS: We tested 68 PD patients ON and OFF their dopaminergic medication, 83 healthy age-matched controls, and 30 healthy older adults after placebo and levodopa administration. We used the digit span, a WM test with three components (forwards, backwards, and sequence recall) that differ in the amount of manipulation required. We analysed the maximum spans and the percentage of lists correctly recalled, which probe capacity of WM and the accuracy of the memory processes within this capacity, respectively. RESULTS: PD patients had lower WM capacity across all three digit span components, but only showed reduced percentage accuracy on the components requiring manipulation (backwards and sequence spans). Dopaminergic medication did not affect performance in PD patients. In healthy older adults, levodopa did not affect capacity, but did impair accuracy on one of the manipulation components (sequence), without affecting the other (backwards). CONCLUSIONS: This suggests that the deficit of maintenance capacity and manipulation accuracy in PD patients is not primarily a dopaminergic one and supports a potential "overdosing" of intact manipulation mechanisms in healthy older adults by levodopa.