Smartphone motor testing to distinguish idiopathic REM sleep behavior disorder, controls, and PD

Abstract

ObjectiveWe sought to identify motor features that would allow the delineation of individuals with sleep study-confirmed idiopathic REM sleep behavior disorder (iRBD) from controls and Parkinson disease (PD) using a customized smartphone application.MethodsA total of 334 PD, 104 iRBD, and 84 control participants performed 7 tasks to evaluate voice, balance, gait, finger tapping, reaction time, rest tremor, and postural tremor. Smartphone recordings were collected both in clinic and at home under noncontrolled conditions over several days. All participants underwent detailed parallel in-clinic assessments. Using only the smartphone sensor recordings, we sought to (1) discriminate whether the participant had iRBD or PD and (2) identify which of the above 7 motor tasks were most salient in distinguishing groups.ResultsStatistically significant differences based on these 7 tasks were observed between the 3 groups. For the 3 pairwise discriminatory comparisons, (1) controls vs iRBD, (2) controls vs PD, and (3) iRBD vs PD, the mean sensitivity and specificity values ranged from 84.6% to 91.9%. Postural tremor, rest tremor, and voice were the most discriminatory tasks overall, whereas the reaction time was least discriminatory.ConclusionsProdromal forms of PD include the sleep disorder iRBD, where subtle motor impairment can be detected using clinician-based rating scales (e.g., Unified Parkinson's Disease Rating Scale), which may lack the sensitivity to detect and track granular change. Consumer grade smartphones can be used to accurately separate not only iRBD from controls but also iRBD from PD participants, providing a growing consensus for the utility of digital biomarkers in early and prodromal PD.

Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice

“…Given the low sampling frequency of recordings (8 kHz), we decided to exclude recordings with phonations less than 2 seconds, as done by previous studies (see Arora et al, 2015;Arora et al, 2018a;Arora et al, 2018b). After screening out non-usable recordings, we processed 2759 recordings from 1483 PD participants, and 15321 recordings from 8300 control participants.…”

Continuous Real-World Gait Monitoring in Idiopathic REM Sleep Behavior Disorder

“…frequency-based features, detrended fluctuation analysis, etc.) rather than more clinically relevant, specific gait characteristics were quantified [17].…”

Predicting motor, cognitive & functional impairment in Parkinson's

“…We analysed smartphone tests performed at 18‐monthly clinic visits and up to four times a day for a maximum of seven days at home, within a 3‐month period of their clinic visit (see Fig. for device details) . Smartphone tests assess: (1) Voice (participants hold the phone to their ear, take a deep breath, and say “aaah” at a comfortable and steady tone and level, for as long as possible); (2) Balance and (3) Gait (with the phone in a trouser pocket or arm band, participants stand still and then walk a distance of 20 yards before turning and walking back); (4) Dexterity (participants tap alternately between two buttons on the screen at a comfortable rate); (5) Non‐cued reaction time (participants press on a button as it appears on the screen, keeping their finger down whilst it is there and lifting their finger off as it disappears); (6) Rest and (7) Postural tremor (participants hold the smartphone in the hand most affected by tremor if they have tremor, or their dominant hand if they do not, while their hand is at rest or held outstretched in front of them).…”

“…Altogether 998 statistical features were extracted from each smartphone recording as previously described . These features help characterize different motor symptoms associated with Parkinson's.…”

“…We chose random forests as they are relatively robust to outliers and noisy features. Moreover, in previous smartphone‐based studies, random forests have been shown to be useful in distinguishing participants with RBD and Parkinson's from controls, and in predicting MDS‐UPDRS part III motor examination scores . To try and avoid model overfitting, we used cross validation (CV) which involves splitting the dataset into non‐overlapping training and test sets.…”