Presentation Title

Utilization of Mobile Technology to Assess Gait and Mobility Post-Stroke: A Systematic Review

Abstract

Background & Objectives: Extremity weakness, fatigue, and postural instability often contribute to mobility deficits in persons after stroke. While mobile technologies have become increasingly utilized to track many health-related parameters, little is known about the use of such devices to examine gait and mobility post-stroke. The purpose of this systematic review was to identify how mobile technologies are being used to assess gait and mobility in this population.

Methods: A systematic review was conducted using the databases OVID Medline, CINAHL, and Cochrane. Included studies were conducted on persons with stroke within any time frame that investigated the use of mobile technology concerning the assessment of walking post-stroke. Included studies were quality assessed using various rating scales. The data extracted included information regarding participants, gait parameters, sensor(s), and analysis.

Results: A total of 339 articles were reviewed, and 18 met the inclusion/exclusion criteria. Mobile technologies utilized to assess gait and mobility post-stroke included accelerometers, “activity monitors,” smartphones/apps, smart shoes, foot-switches, and pedometers. Among these mobile devices, the parameters of gait that were most widely assessed included measures of gait speed/velocity and cadence. Mobility measures included duration of activity and measures of step count.

Conclusion: The use of various mobile technologies has allowed researchers and clinicians a somewhat viable way of monitoring patients’ activity in a multitude of settings. The most commonly used devices were accelerometers, followed by pedometers. Additionally, the greatest captured and reported spatial-temporal parameter of gait was step count. Due to gait variations that are commonly seen in patients post-stroke, not all devices’ algorithms can accurately monitor the mobility of these patients. Therefore, additional research is needed to accurately draw upon device algorithms to fully capture mobility deficits that impact the quality of life in this patient population.

Primary Faculty Mentor Name

Denise Peters

Status

Graduate

Student College

College of Nursing and Health Sciences

Program/Major

Physical Therapy

Primary Research Category

Health Sciences

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Utilization of Mobile Technology to Assess Gait and Mobility Post-Stroke: A Systematic Review

Background & Objectives: Extremity weakness, fatigue, and postural instability often contribute to mobility deficits in persons after stroke. While mobile technologies have become increasingly utilized to track many health-related parameters, little is known about the use of such devices to examine gait and mobility post-stroke. The purpose of this systematic review was to identify how mobile technologies are being used to assess gait and mobility in this population.

Methods: A systematic review was conducted using the databases OVID Medline, CINAHL, and Cochrane. Included studies were conducted on persons with stroke within any time frame that investigated the use of mobile technology concerning the assessment of walking post-stroke. Included studies were quality assessed using various rating scales. The data extracted included information regarding participants, gait parameters, sensor(s), and analysis.

Results: A total of 339 articles were reviewed, and 18 met the inclusion/exclusion criteria. Mobile technologies utilized to assess gait and mobility post-stroke included accelerometers, “activity monitors,” smartphones/apps, smart shoes, foot-switches, and pedometers. Among these mobile devices, the parameters of gait that were most widely assessed included measures of gait speed/velocity and cadence. Mobility measures included duration of activity and measures of step count.

Conclusion: The use of various mobile technologies has allowed researchers and clinicians a somewhat viable way of monitoring patients’ activity in a multitude of settings. The most commonly used devices were accelerometers, followed by pedometers. Additionally, the greatest captured and reported spatial-temporal parameter of gait was step count. Due to gait variations that are commonly seen in patients post-stroke, not all devices’ algorithms can accurately monitor the mobility of these patients. Therefore, additional research is needed to accurately draw upon device algorithms to fully capture mobility deficits that impact the quality of life in this patient population.