Continuous mobility monitoring: what is currently missing for a widespread deployment in clinical and research settings?

Mobility has been recognised as “the sixth vital sign” and its study and quantification usually occur in laboratory or clinical settings. However, it has been shown that free-living gait characteristics have better discriminative validity, especially in diseases characterised by specific mobility dysfunctions as in Parkinson’s Disease [1]. Continuous mobility monitoring could indeed detect, measure, and eventually predict mobility loss for providing essential information for personalized treatment. Therefore, a low-cost, easy-to-use and accurate approach that uses a technology that can operate in “real-world” scenario is mandatory for this aim; wearable inertial sensors are certainly ideal candidates. However, their widespread deployment in clinical and research settings can be influenced by a number of factors that might be grouped in four different categories, hereafter called “domains”: 

     Concurrent validity – factors related to the validity of the measurements;
     Human factors – factors related to the context of data capture, perception of the user towards the technology, data security and privacy, effect of monitoring outside clinical settings;
     Wearability & usability for the user – e.g., size, location, fixation modality, charging frequency;
     Data capture process – e.g., whether a calibration procedure, device programming, or anthropometric information are required for an appropriate data capture.

Although different solutions have been proposed in the literature, when these have been tested and/or compared, such domains were either considered in isolation [2]-[3] or only in a subset [4]-[5]. This poses serious limitations when a specific wearable solution has to selected for a prolonged time and all these domains should be considered, accounting for their relative importance, which has to be yet established. The aim of this study is to define an objective methodology for combining these domains, allowing optimal sensor choice for continuous mobility monitoring. A decision matrix is proposed to establish the relevant importance of different domains and subsequently rank different sensor solutions. A purposely developed questionnaire is used to define such matrix using responses gathered by selected participants with a variety of backgrounds. 

This work was supported by the Mobilise-D project that has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 820820. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA). Content in this publication reflects the authors’ view and neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.