Feasibility and patient acceptability of a commercially available wearable and a smart phone application in identification of motor states in parkinson’s disease

Abstract

In the quantification of symptoms of Parkinson’s disease (PD), healthcare professional assessments, patient reported outcomes (PRO), and medical device grade wearables are currently used. Recently, also commercially available smartphones and wearable devices have been actively researched in the detection of PD symptoms. The continuous, longitudinal, and automated detection of motor and especially non-motor symptoms with these devices is still a challenge that requires more research. The data collected from everyday life can be noisy and frequently contains artefacts, and novel detection methods and algorithms are therefore needed. 42 PD patients and 23 control subjects were monitored with Garmin Vivosmart 4 wearable device and asked to fill a symptom and medication diary with a mobile application, at home, for about four weeks. Subsequent analyses are based on continuous accelerometer data from the device. Accelerometer data from the Levodopa Response Study (MJFFd) were reanalyzed, with symptoms quantified with linear spectral models trained on expert evaluations present in the data. Variational autoencoders (VAE) were trained on both our study accelerometer data and on MJFFd to detect movement states (e.g., walking, standing). A total of 7590 self-reported symptoms were recorded during the study. 88.9% (32/36) of PD patients, 80.0% (4/5) of DBS PD patients and 95.5% (21/22) of control subjects reported that using the wearable device was very easy or easy. Recording a symptom at the time of the event was assessed as very easy or easy by 70.1% (29/41) of subjects with PD. Aggregated spectrograms of the collected accelerometer data show relative attenuation of low (<5Hz) frequencies in patients. Similar spectral patterns also separate symptom periods from immediately adjacent non-symptomatic periods. Discriminative power of linear models to separate symptoms from adjacent periods is weak, but aggregates show partial separability of patients vs. controls. The analysis reveals differential symptom detectability across movement tasks, motivating the third part of the study. VAEs trained on either dataset produced embedding from which movement states in MJFFd could be predicted. A VAE model was able to detect the movement states. Thus, a pre-detection of these states with a VAE from accelerometer data with good S/N ratio, and subsequent quantification of PD symptoms is a feasible strategy. The usability of the data collection method is important to enable the collection of self-reported symptom data by PD patients. Finally, the usability of the data collection method is important to enable the collection of self-reported symptom data by PD patients.

Use of Consumer Wearables in Health Research: Issues and Considerations

“…In the meantime, researchers may seek to develop their own methods of capturing the data they require, such as bespoke applications to which the raw wearable data, if available, can be transferred and processed to meet the needs of the study [29]. Nonetheless, researchers must consider that wearable companies' practices and ethos may change at any time, with implications for the products they sell and customer treatment.…”

Improving naturalistic neuroscience with patient engagement strategies

“…These limitations challenge the interpretation of these assessments over short time intervals. An increasing number of studies reports on the application of active, sometimes gamified, motor assessments either to assess naturalistic motor or cognitive symptoms ( Adams et al, 2023 ; Broeder et al, 2023 ; Crook-Rumsey et al, 2023 ; Liikkanen et al, 2023 ), or to improve symptoms due to training exercises ( Gallou-Guyot et al, 2022 ). They mostly report good feasibilities, some requiring remote support, good test accuracies ( Broeder et al, 2023 ), but also remaining challenges around the interpretation ( Page et al, 2022 ; Liikkanen et al, 2023 ).…”