Evaluating the Usability of Inertial Measurement Units for Measuring and Monitoring Activity Post-Stroke: A Scoping Review

TL;DR

This review examines how well inertial measurement units can track activity in stroke survivors, highlighting usability challenges and the need for better user feedback.

Contribution

The study provides a comprehensive overview of IMU usability in stroke populations, emphasizing gaps in user experience and feedback interpretation.

Findings

Most stroke participants and clinicians reported a positive user experience with IMUs.

Many users faced difficulties due to stroke-related impairments.

Few studies evaluated how users interpret and engage with IMU feedback.

Abstract

Stroke is the most common cause of disability in adults, resulting in declines in overall activity. Inertial measurement units (IMUs) allow for the monitoring of activity patterns in various settings, informing clinical interventions and patient self-management. This review aimed to synthesize existing research evaluating the usability of IMUs for monitoring activity in people with stroke. This scoping review was conducted according to PRISMA guidelines. The MEDLINE, Embase, and CINAHL databases were searched for publications evaluating the usability of IMUs for monitoring activity post-stroke. Fourteen publications met the inclusion criteria. Most studies were conducted in chronic stroke with data collected in real-world conditions. Usability findings indicated that most stroke participants and clinicians reported a positive user experience; however many reported difficulties with devices due to stroke-related impairments. Importantly, the interpretation of this finding was impacted by poorly reported impairments of stroke participants. Only a few studies evaluated end-user experiences (people with stroke and clinicians) in interpreting and engaging with feedback based on data from IMUs. Future usability studies in stroke populations should aim to understand participant characteristics that influence device engagement, with a focus on user experience in interpreting device-generated metrics.

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