Digital Devices for Assessing Motor Functions in Mobility Impaired and Healthy Populations: A Systematic Literature Review (Preprint)

Abstract

With the advent of smart sensing technology, mobile and wearable devices can provide continuous and objective monitoring and assessment of motor function outcomes.

We aimed to describe the existing scientific literature on wearable and mobile technologies that are being used or tested for assessing motor functions in mobility-impaired and healthy adults, and to evaluate the degree to which these devices provide clinically valid measures of motor function in these populations.

A systematic literature review was conducted by searching Embase, MEDLINE®, CENTRAL (January 1, 2015 – June 24, 2020), the United States and European Union clinical trial registries, and the United States Food and Drug Administration website using predefined study selection criteria. Study selection, data extraction, and quality assessment were performed by two independent reviewers.

Ninety-one publications representing 87 unique studies were included. The most represented clinical conditions were Parkinson’s disease (k = 51 studies), followed by stroke (k = 5), Huntington’s disease (k = 5), and multiple sclerosis (k = 2). A total of 42 motion-detecting devices were identified, and the majority (k = 27; 64%) were created for the purpose of healthcare-related data collection, though approximately 25% were personal electronic devices (e.g., smartphones, watches) and 11% were entertainment consoles (e.g., Microsoft Kinect/Xbox™, Nintendo Wii™). The primary motion outcomes were related to gait (k = 30), gross motor movements (k = 25), and fine motor movements (k = 23). As a group, sensor-derived motion data showed a mean sensitivity of .83 (SD = 7.27), mean specificity of .84 (SD = 15.40), mean accuracy of .90 (SD = 5.87) in discriminating between diseased individuals and healthy controls, and mean Pearson’s r validity coefficient of .52 (SD = 0.22) relative to clinical measures. We did not find significant differences in the degree of validity between in-lab and at-home sensor-based assessments, nor between device class (i.e., healthcare-related device, personal electronic devices, entertainment consoles).

Sensor-derived motion data can be leveraged to classify and quantify disease status for a variety of neurological conditions. However, most of recent research on digital clinical measures is derived from proof-of-concept studies with considerable variation in methodological approaches, and much of the reviewed literature has focused on clinical validation, with less than a quarter of the studies performing analytical validation. Overall, future research is crucially needed to further consolidate that sensor-derived motion data may lead to the development of robust and transformative digital measurements intended to predict, diagnose, and quantify neurological disease state and its longitudinal change.