Pain in the Context of Virtual Neuropsychological Assessment of Older Adults
- PMID: 37644879
- PMCID: PMC10879923
- DOI: 10.1093/arclin/acad064
Pain in the Context of Virtual Neuropsychological Assessment of Older Adults
Abstract
Objective: Pain and cognitive impairment are prevalent and often co-occur in older adults. Because pain may negatively affect cognitive test performance, identification of pain in the context of neuropsychological evaluation is important. However, pain detection based on self-report presents challenges, and pain is often under-detected in this population. Alternative methods (e.g., video-based automatic coding of facial biomarkers of pain) may facilitate pain identification and thus enhance interpretation of neuropsychological evaluation results.
Method: The current study examined pain in the context of virtual neuropsychological assessment in 111 community-dwelling older adults, first seeking to validate the use of software developed to automatically code biomarkers of pain. Measures of pain, including self-report of acute and chronic pain and automatic coding of pain, were compared while participants completed neuropsychological testing.
Results: Self-reported pain was negatively associated with poorer performance on a measure of executive function (both acute and chronic pain) and a global cognitive screening measure (acute pain only). However, self-reported acute and chronic pain did not correlate significantly with most neuropsychological tests. Automatic coding of pain did not predict self-report of pain or performance on neuropsychological tests beyond the influence of demographic factors and psychological symptoms.
Conclusions: Though results were largely not significant, correlations warrant further exploration of the influence of pain on neuropsychological test performance in this context to ensure that pain does not influence test performance in individuals with higher levels of pain and in other samples.
Keywords: Cognitive impairment; Neuropsychological assessment; Pain.
© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.
Conflict of interest statement
None declared.
References
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- AiCure . (2021). AiCure makes code open source to advance digital biomarker development. New York, NY: AiCure. https://aicure.com/
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