MotorBrain: A mobile app for the assessment of users' motor performance in neurology. (May 2017)

• Record Type: Journal Article
• Authors: Vianello, Andrea; Chittaro, Luca; Burigat, Stefano; Budai, Riccardo
• Is Part Of: Computer methods and programs in biomedicine. Volume 143(2017)
• Page Start: 35
• Page End: 47
• Publication Date: 2017-05
• Subjects: Mobile applications -- Motor skills -- Neurology -- Aging -- Data collection; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.02.012
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We propose a smartphone app (MotorBrain) for the assessment of motor performance. A sample of healthy participants carried out the six motor tests offered by MotorBrain. Data collected by the app allowed measuring performance differences due to aging. MotorBrain supports the building of a normative database for neurological studies. Abstract: Background and Objective: Human motor skills or impairments have been traditionally assessed by neurologists by means of paper-and-pencil tests or special hardware. More recently, technologies such as digitizing tablets and touchscreens have offered neurologists new assessment possibilities, but their use has been restricted to a specific medical condition, or to stylus-operated mobile devices. The objective of this paper is twofold. First, we propose a mobile app (MotorBrain) that offers six computerized versions of traditional motor tests, can be used directly by patients (with and without the supervision of a clinician), and aims at turning millions of smartphones and tablets available to the general public into data collection and assessment tools. Then, we carry out a study to determine whether the data collected by MotorBrain can be meaningful for describing aging in human motor performance. Methods: A sample of healthy participants (N = 133) carried out the motor tests using MotorBrain on a smartphone. Participants were split into two groups (Young, Old) based on their age (less than or equal to 30 years, greater than or equal to 50 years, respectively). The data collected by the app characterizes accuracy, reaction times, and speed of movement. It was analyzed to investigate differences between the two groups. Results: The app does allow measuring differences in neuromotor performance. Data collected by the app allowed us to assess performance differences due to the aging of the neuromuscular system. Conclusions: Data collected through MotorBrain is suitable to make meaningful distinctions among different kinds of performance, and allowed us to highlight performance differences associated to aging. MotorBrain supports the building of a large database of neuromotor data, which can be used for normative purposes in clinical use.