Advancements in technology, such as smartphones and wearabledevices, can be used for collecting movement data through embeddedsensors. This paper focuses on linking Parkinson’s Disease severitywith data collected from mobile phones in the mPower study. As referencefor symptoms’ severity, we use the answers provided to part 2 ofthe standard MDS-UPDRS scale. As input variables, we use the featurescomputed within mPower from the raw data collected during 4 phonebasedactivities: walking, rest, voice and finger tapping. The features arefiltered in order to remove unreliable datapoints and associated to referencevalues. After pre-processing, 5 Machine Learning algorithms areapplied for predictive analysis. We show that, notwithstanding the noisedue to the data being collected in an uncontrolled manner, the regressedsymptom levels are moderately to strongly correlated with the actualvalues (highest Pearson’s correlation = 0.6). However, the high differencebetween the values also implies that the regressed values can not beconsidered as a substitute for a conventional clinical assessment (lowestmean absolute error = 5.4).

Internet of Things (IoT) can work as a useful tool for clinical research. We developed a software platform that allows researchers to publish clinical studies and volunteers to participate into them using an app and connected IoT devices. The platform includes a REST API, a web interface for researchers and an app that collects data during tasks volunteers are invited to contribute. Nine tasks have been developed: Forms, Positioning, Finger tapping, Pulse-oximetry, Peak Flow measurement, Activity tracking, Data query, Queen’s College step test and Six-minute walk test. These leverage sensors embedded in the phone, connected Bluetooth devices and additional APIs like HealthKit and Google Fit. Currently, the platform is used in two clinical studies by 25 patients: an asthma management study in the United Kingdom, and a neuropathic pain management study in Spain.

INTRODUCTION: The clinical assessment of Parkinson's disease (PD) symptoms can present reliability issues and, with visits typically spaced apart 6 months, can hardly capture their frequent variability. Smartphones and smartwatches along with signal processing and machine learning can facilitate frequent, remote, reliable and objective assessments of PD from patients' homes.

AIM: To investigate the feasibility, compliance and user experience of passively and actively measuring symptoms from home environments using data from sensors embedded in smartphones and a wrist-wearable device.

METHODS AND ANALYSIS: In an ongoing clinical feasibility study, participants with a confirmed PD diagnosis are being recruited. Participants perform activity tests, including Timed Up and Go (TUG), tremor, finger tapping, drawing and vocalisation, once a week for 2 months using the Mobistudy smartphone app in their homes. Concurrently, participants wear the GENEActiv wrist device for 28 days to measure actigraphy continuously. In addition to using sensors, participants complete the Beck's Depression Inventory, Non-Motor Symptoms Questionnaire (NMSQuest) and Parkinson's Disease Questionnaire (PDQ-8) questionnaires at baseline, at 1 month and at the end of the study. Sleep disorders are assessed through the Parkinson's Disease Sleep Scale-2 questionnaire (weekly) and a custom sleep quality daily questionnaire. User experience questionnaires, Technology Acceptance Model and User Version of the Mobile Application Rating Scale, are delivered at 1 month. Clinical assessment (Movement Disorder Society-Unified Parkinson Disease Rating Scale (MDS-UPDRS)) is performed at enrollment and the 2-month follow-up visit. During visits, a TUG test is performed using the smartphone and the G-Walk motion sensor as reference device. Signal processing and machine learning techniques will be employed to analyse the data collected from Mobistudy app and the GENEActiv and correlate them with the MDS-UPDRS. Compliance and user aspects will be informing the long-term feasibility.

ETHICS AND DISSEMINATION: The study received ethical approval by the Swedish Ethical Review Authority (Etikprövningsmyndigheten), with application number 2022-02885-01. Results will be reported in peer-reviewed journals and conferences. Results will be shared with the study participants.

This work aims to link finger tapping and drawing tests performed on mobile phone screens with clinical ratings of Parkinson's Disease (PD). Thirty PD patients were recruited and instructed to carry out these tests in their homes. Features were extracted and used to assess the validity of the data vis a vis clinical scales (MDS-UPDRS). Statistical tests show that several features correlate with clinical scores (max correlation 0.54) and significant differences between data collected before and after medication intake (p<0.05), demonstrating the clinical validity of smartphone data. The use of Machine Learning (ML) algorithms to regress Part-3 of MDS-UPDRS further supports the validity with an absolute mean error of 6.25 (over a 0-72 scale).

This paper investigates usability aspects of a mobile application aimed at monitoring symptoms of Parkinson’s disease (PD) patients. Thirty PD patients collected data through mobile-based questionnaires and activity tasks aimed at measuring motor and non-motor symptoms for a duration of two months. We report the results about usability conducted within this study. A combination of methods consisting of the uMARS questionnaire and interviews with PD patients inform the usability aspects of the mobile application. Results indicate that the app is overall received well and is usable (median uMARS score=4). Interviews reveal usability issues related to the size of textual instructions and buttons, and to the context of use of the app, particularly when the phone is used as a sensor. These findings highlight the need of co-design and preliminary testing when developing apps for PD.

This study validates a smartphone app for hand tremor assessment in Parkinson's disease (PD). Twenty-eight PD patients performed a weekly tremor test using the app while wearing a wrist-worn actigraphy device (GeneActiv), of which twenty-one yielded usable actigraphy data for comparative analysis. Features were extracted from both devices to compare smartphone application derived data against actigraphy measurements. Further analysis examined the validity of the smartphone data in ON versus OFF medication states and against clinical scales (MDS-UPDRS). Statistical tests show high correlations between the app and GeneActiv features whereas app-derived features show statistical significant differences on data between medication states (p < 0.05), and correlations (maximum correlation 0.47) with clinical scores (MDS-UPDRS). The findings support the clinical validity of smartphone-based tremor assessments in PD, which shows potential for ongoing symptom monitoring in individuals with PD from at-home environments.

Keeping track of symptoms is a familiar yet often complex task for people living with chronic conditions. In the context of Parkinson's disease, sensor-based technologies are becoming more common to track motor symptoms. These technologies typically rely on passive monitoring but can also be combined with active tests, in which users intentionally perform measuring tasks like finger-tapping or drawing. In this paper, we explore how people with Parkinson's experienced using such active tests through a smartphone app over the course of eight weeks. Drawing on 26 semi-structured interviews, our findings indicate that active tests impact bodily awareness, come with frictions of integration into daily life and may be reframed as motivations for exercises. Speculations on the resulting data suggest that these are partly seen as a useful resource for self-care, but also as a potential cause for anxiety and ambivalence when facing worsening symptoms and decline.