Technology-Dependent Rehabilitation Involving Action Observation and Movement Imagery for Adults with Stroke: Can It Work? Feasibility of Self-Led Therapy for Upper Limb Rehabilitation after Stroke

Abstract

Background: Motor (re)learning via technology-dependent therapy has the potential to complement traditional therapies available to older adults living with stroke after hospital discharge and increase therapy dose. To date, little is known about the feasibility of technology-dependent therapy in a home setting for this population.

Objective: To develop a technology-dependent therapy that provides mental and physical training for older adults with stroke and assess feasibility. Specifically we ask, "Can it work"?

Design: Single group repeated measures.

Methods: 13 participants, aged 18 years and over, were recruited over a six-month period. All participants had mild upper limb impairment following a stoke and were no longer receiving intensive rehabilitation. All participants received 18 days of technology-dependent therapy in their own home. Information was gathered on recruitment and retention, usability, and suitability of outcome measures.

Results: 11 participants completed the study. The recruitment rate (number recruited/number canvassed; 10.7%) suggests 1907 participants would need to be canvassed to recruit the necessary sample size (n = 204) for a definitive trial designed to provide 90% power at 5% level of significance to detect a clinically meaningful difference of 5.7 points on the Action Research Arm Test. The usability of the application was rated as exceptional on the System Usability Scale. Effectiveness cannot be determined from this study; however, there was a trend for improvement in measures of upper limb function and emotional well-being. Limitations. The study was limited by a relatively small sample size and lack of control group.

Conclusions: This study demonstrated proof of concept of a technology-dependent therapy for upper limb rehabilitation following stroke. The data suggest a definitive trial is feasible, additional strategies to improve recruitment should be considered. Outcome measures aligned with the residual motor function of participants are required.

Figure 1

The schematic illustrates how an exercise session is created and executed. In a typical session a user would (a) select the exercise programme; (b) select an ADL category; (c) select an action(s) from within the category, using the top right-hand buttons to toggle between action or segment; (d) add the action(s) to the exercise session; (e) start the session; and (f) begin SIMULATe training (in this example, doing up buttons from a first-person visual perspective is presented). Note: prior to selecting the exercise program, the dominant hand is selected in the default settings.

Figure 2

An individual using the SIMULATe application at home to practice one of the ADLs in the library (pinching the ends of a tea towel and folding it over). The individual in this manuscript has given written informed consent to publish this photograph.

Figure 3

A schematic of the study design. Outcome measures include the action research arm test (ARAT) [54, 55], grip strength, positive and negative assessment scale (PANAS) [56], nine hole peg test (9-HPT), System Usability Scale (SUS) [57], and adherence and semistructured interviews.

Figure 4

CONSORT diagram illustrating participant recruitment and retention.

Figure 5

Box plots of the secondary outcome measures; (a) ARAT [55], (b) 9-HPT, (c) grip strength, and (d) PANAS [56]. Horizontal lines indicate the median values, boxes indicate the interquartile range, and vertical lines indicate the full range.