JMIR Publications

ABSTRACT

Background:

Gait impairments are common in stroke survivors, negatively impacting their overall quality of life. Therefore, gait rehabilitation is often targeted during in-clinic rehabilitation. Now, with recent advancements in technology it is feasible to measure different gait parameters outside of the clinical setting, opening new possibilities for autonomous patient assessments. However, it is important to determine whether standardized assessments typically used in the clinic can be effectively transferred to the real-world.

Objective:

The goal of the validation study presented here was to compare the results of a sensor based 10-Meter Walk Test conducted in a supervised (ST) versus unsupervised (UST) clinical setting.

Methods:

In total, 21 stroke survivors (10 female, 11 males; Mean age: 63.9±15.5 years) were randomly assigned to one of two data collection sequences and tested over 4 consecutive days, alternating between ST and UST assessments. For both assessments participants were required to walk a set distance of 10-meters as fast as possible while data was collected using a single wearable sensor (Physilog®5) attached to each shoe. After each walking assessment, the participants completed the Intrinsic Motivation Inventory. Statistical analyses were conducted examining the mean speed, stride length, and cadence, across repeated measurements and between assessment conditions.

Results:

The intra-class correlation coefficient indicated good-to-excellent reliability for speed (ST: κ=0.93, P<.001; UST: κ=0.93, P<.001), stride length (ST: κ=0.92, P<.001; UST: κ=0.88, P<.001), and cadence (ST: κ=0.91, P<.001; UST: κ=0.95, P<.001), across repeated measurements for both ST and UST assessments. There was no significant effect of testing order (ie, Sequence A versus B). Comparing the ST and UST, there were no significant differences in speed (t39=–0.735, P=.47, 95% CI –0.06 to 0.03), stride length (z=0.835, P=.80), or cadence (t39=–0.501, P=.62, 95% CI –3.38 to 2.04) between the two assessments. The overall motivation did not show any significant differences between the ST versus UST conditions, however, the self-reported perceived competence increased during the unsupervised assessment from the first to the second measurement.

Conclusions:

Unsupervised gait assessments offer a reliable alternative to supervised assessments, with comparable results for gait speed, stride length, and cadence, with no differences in the overall motivation between the two. Enabling unsupervised assessments has implications for clinical rehabilitation including potential reductions in healthcare costs and more frequent and naturalistic assessments. Outside of the clinic there are also positive implications as unsupervised assessments can allow for more continuous tracking of rehabilitation while increasing healthcare accessibility through portable, easy-to-use, low-cost tools. Clinical Trial: Approved by the local ethics committee via waiver (Req-2020-00995)