1. Smartphone App–Based Assessment of Gait During Normal and Dual-Task Walking: Demonstration of Validity and Reliability
- Author
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Manor, Brad, Yu, Wanting, Zhu, Hao, Harrison, Rachel, Lo, On-Yee, Lipsitz, Lewis, Travison, Thomas, Pascual-Leone, Alvaro, and Zhou, Junhong
- Subjects
Information technology ,T58.5-58.64 ,Public aspects of medicine ,RA1-1270 - Abstract
BackgroundWalking is a complex cognitive motor task that is commonly completed while performing another task such as talking or making decisions. Gait assessments performed under normal and “dual-task” walking conditions thus provide important insights into health. Such assessments, however, are limited primarily to laboratory-based settings. ObjectiveThe objective of our study was to create and test a smartphone-based assessment of normal and dual-task walking for use in nonlaboratory settings. MethodsWe created an iPhone app that used the phone’s motion sensors to record movements during walking under normal conditions and while performing a serial-subtraction dual task, with the phone placed in the user’s pants pocket. The app provided the user with multimedia instructions before and during the assessment. Acquired data were automatically uploaded to a cloud-based server for offline analyses. A total of 14 healthy adults completed 2 laboratory visits separated by 1 week. On each visit, they used the app to complete three 45-second trials each of normal and dual-task walking. Kinematic data were collected with the app and a gold-standard–instrumented GAITRite mat. Participants also used the app to complete normal and dual-task walking trials within their homes on 3 separate days. Within laboratory-based trials, GAITRite-derived heel strikes and toe-offs of the phone-side leg aligned with smartphone acceleration extrema, following filtering and rotation to the earth coordinate system. We derived stride times—a clinically meaningful metric of locomotor control—from GAITRite and app data, for all strides occurring over the GAITRite mat. We calculated stride times and the dual-task cost to the average stride time (ie, percentage change from normal to dual-task conditions) from both measurement devices. We calculated similar metrics from home-based app data. For these trials, periods of potential turning were identified via custom-developed algorithms and omitted from stride-time analyses. ResultsAcross all detected strides in the laboratory, stride times derived from the app and GAITRite mat were highly correlated (P
- Published
- 2018
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