From wellness to medical diagnostic apps: the Parkinson's Disease case
TL;DR: The design and development of the CloudUPDRS app and supporting system developed as a Class I medical device to assess the severity of motor symptoms for Parkinson’s Disease is presented.
Abstract: This paper presents the design and development of the CloudUPDRS app and supporting system developed as a Class I medical device to assess the severity of motor symptoms for Parkinson’s Disease. We report on lessons learnt towards meeting fidelity and regulatory requirements; effective procedures employed to structure user context and ensure data quality; a robust service provision architecture; a dependable analytics toolkit; and provisions to meet mobility and social needs of people with Parkinson’s.
Summary (2 min read)
- It is well understood that modern smartphones present unique opportunities for mobile healthcare.
- Yet, the vast majority of these apps do not conform to the safety, quality, performance and regulatory requirements set for medical devices and as such they can only be employed either to encourage a healthy lifestyle or for research purposes correspondingly, but are not tools for medical diagnosis.
- In contrast to this situation, this paper presents the design and development of the CloudUPDRS app and its associated information management and analytics platform, which meets the standards set for medical devices.
- In particular, the authors describe how CloudUPDRS achieves the accurate, precise, and repeatable assessment of motor symptoms for people with Parkinson’s (PwP), which clinicians can use with confidence.
- In this paper the authors present the key contributions of this work towards achieving this goal.
2 Background and Rationale
- PD symptoms are typically caused by the loss of neurones that produce dopamine, a key chemical messenger in the brain, decreased levels of which lead to abnormal brain activity (cf.  for more details).
- Since symptoms vary greatly independent of treatment and PD progresses at different rates in different individuals, it requires regular clinical monitoring and medication adjustment.
- Monitoring and adjustment however require hospital visits and assessment under the standard Movement Disorders Society’s Unified Parkinson’s Disease Rating Scale (mds-updrs) .
- Due to these constraints, such reviews are relatively infrequent, carried out typically only a few times per year.
- By adopting this approach, the authors also intend to capture in-depth medical intelligence supporting the discovery of longitudinal trends, promoting deeper understanding of the patterns of normal daily symptom variations, and predicting the onset of dyskinesias thus facilitating high-precision personalised targeting of treatment.
3 The CloudUPDRS app
- Specifically, the authors designed, developed and validated in a field study a prototype app on Android implementing Part III of the mds-updrs.
- Participants were also tested in the same areas of motor performance using the standard lab procedure outlined in mds-updrs and using bespoke biomedical data acquisition equipment to obtain a baseline for comparing the performance of the app.
- The app implements a comprehensive workflow partially depicted in Figure 1, which provides audio, video and textual guidance on how to conduct the actions required by the tests and automatically adapts to match the specifications of its host device.
- Overall, the CloudUPDRS system consists of the following elements: 1. PD patient smartphone apps for Android and iOS that carry out motor performance measurements and wellness self-assessment; conduct session management; securely transfer captured data to the CloudUPDRS service; and, present an interface providing guidance and feedback.
- Data-mining toolkit for medical intelligence incorporating quantitative and semi-structured data, and longitudinal analyses, clustering and classification; and a clinical user interface incorporating visualisation.
4 The CloudUPDRS Service Platform
- The CloudUPDRS service platform enables the secure capture, management and analysis of data collected by the app and provides effective communication of insights generated to clinicians enabling them to explore alternative treatment scenarios.
- The microservices architectural style is set in contrast to traditional monolithic web applications and aims to maximise opportunities for vertical decomposition and scaling-out, which are critical for high performance and service resilience in data intensive situations.
- In CloudUPDRS, microservices are loosely coupled and employ lightweight communication and coordination mechanisms such as the Consumer-Driven Contract pattern and implemented on Apache Thrift (cf. https://thrift.apache. org/) selected due to its highly efficient and compact protocol structure.
- System componentization follows the design displayed in Figure 2, enforced via versioning of published RESTful interfaces.
- While the data collection and signal processing APIs are implemented using python and django REST within an nginx/gunicorn container, semi-structured longitudinal analytics are implemented as Ruby bundles.
5 Lessons Learnt and Conclusions
- The pervasive computing community has invested significant effort in techniques for modelling and adapting to user context, which is critical for the interpretation of sensor data streams.
- Yet, when context modelling is not possible or incurs prohibitively high costs, an effective alternative is to bound context by imposing structure and thus predictability to user actions during sensing, an approach that was successfully applied with the CloudUPDRS app.
- While there are clearly situations when the development of new algorithms and techniques is required, in other cases there seems to be good reason to opt for a more traditional approach.
- In CloudUPDRS rather than optimise individual stages the authors engineer an end-to-end quality assurance strategy that they find to be more effective.
- It incorporates features of the user experience, which permit the user to initiate the repeat of tests when an external event has disrupted the session, to increasing the duration of individual tests so as to enable oversampling and cross-validation, to employing heuristics that allow us to quickly identify data quality problems in the captured signal.
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"From wellness to medical diagnostic..." refers methods in this paper
...Monitoring and adjustment however require hospital visits and assessment under the standard Movement Disorders Society’s Unified Parkinson’s Disease Rating Scale (mds-updrs) ....
"From wellness to medical diagnostic..." refers methods in this paper
...To facilitate this modus operandi, we have structured workflows implemented through microservices following the lambda architecture , which provides an intuitive model for the fusion of both types of data on the fly....
"From wellness to medical diagnostic..." refers background or methods in this paper
...In  tests were administered by an experienced clinician in the lab using an HTC Desire device and the collected sensor data were extracted and processed using standard biomedical data analysis software....
...Further pursuing this insight, in  we investigated the possibility to precisely quantify and implement the mds-updrs methodology as a smartphone app to enable the assessment of motor performance through tremor, gait and bradykinesia measurements obtained from standard sensors embedded in smartphones within a clinical setting....
...In CloudUPDRS we employ the data collection and analysis techniques described in  to develop an app with extended functionality that enables its independent but dependable use by PwP and their carers at home and in their communities....
...As discussed in the previous Section, in  we demonstrate the feasibility of using smartphones as a means to assess commonly occurring motor symptoms of PD in a clinical setting....