Real-time monitoring of barriers to patient engagement for improved rehabilitation: a protocol and representative case reports

Abstract

Purpose

There is ample evidence that patient engagement is of major clinical importance in rehabilitation, and it seems this engagement is based upon effective allocation of attention to the tasks during the rehabilitation session. It is possible to discern two types of barriers which hinder attentive engagement: (1) dysfunctional affective coping and (2) limited cognitive recruitment and specifically attention deficit. These barriers might be general for a given patient, due to pre-morbid or co-morbid dysfunctions. But more often they are evoked by tasks or challenges during the rehabilitation session which might be too complicated or stressing for the specific patient who copes with potentially grave impairments. These barriers hinder rehabilitation progress and should be monitored and overcome, by the therapist, throughout the session.

Methods

We have developed an easy-to-use tool for monitoring a patient’s attentive engagement in real-time throughout a rehabilitation session based on analysing the electrophysiological signal sampled from a simple headset. The tool then analyzes the dynamics of the marker over time to identify cognitive and affective barriers during the session. It enables the therapist to insert feedback regarding the patient’s functional performance and to combine it with the analysed barriers, in order to derive automatic recommendations for overcoming the cognitive and affective barriers (if identified) for significant enhancement of the rehabilitation session.

Results and conclusions

In this work we present the principles of the tool as well as three detailed case reports to demonstrate its potential usefulness.

IMPLICATIONS FOR REHABILITATION

• Cognitive and affective barriers hinder patient engagment and rehabilitation success.

• In this work we present an easy to use electrophysiology-based tool which monitors these barriers.

• Based on the measured barriers and patient’s performance, the tool derives treatment suggestions.

Keywords:

• rehabilitation
• engagement
• monitor
• barriers
• cognitive
• affective

Acknowledgements

The authors want to thank Reut Stark for fruitful discussions regarding this study and Dr. Gadi Bartur for technical support.

Disclosure statement

GS founded several companies in the field of EEG analysis. However, the present study is not related to these companies and their technology. It is based on developments in GS’s academic laboratory at Rambam Health Care Campus. AG has no conflict of interest to declare.