The pressing need for objective measures in evaluating chronic pain in research and practice highlights the role that neuro information systems (NeuroIS) research plays in designing smart clinical decision support systems. A first step in such a research agenda involves identifying practical stimuli-task paradigms that can reliably detect chronic pain from physiological measures such as eye movements. In this study, we propose and test a new stimuli-task paradigm. Our results show that our proposed stimuli-task paradigm can detect differences in the information-processing behavior of people with and without chronic pain. The results also show that our proposed stimuli-task paradigm can reliably predict self-reported subjective pain experience from eye movements. These findings provide support for our proposed stimuli- task paradigm. They also show that the eye-tracking variables that we selected to test our proposed paradigm are effective in capturing the impact of chronic pain on visual attention, suggesting that eye movements have the potential to serve as reliable biomarkers of chronic pain. Hence, our results support the potential for eye movements to aid in efforts to develop smart information systems that can detect the presence and/or the severity of chronic pain from an individual’s ocular behavior.
Chronic Pain and Eye Movements: A NeuroIS Approach to Designing Smart Clinical Decision Support Systems.
AIS Transactions on Human-Computer Interaction, 15(3), 268-291.
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