Pain in severe dementia: A comparison of a fine-grained assessment approach to an observational checklist designed for clinical settings

Abstract

Background: Fine-grained observational approaches to pain assessment (e.g. the Facial Action Coding System; FACS) are used to evaluate pain in individuals with and without dementia. These approaches are difficult to utilize in clinical settings as they require specialized training and equipment. Easy-to-use observational approaches (e.g. the Pain Assessment Checklist for Limited Ability to Communicate-II; PACSLAC-II) have been developed for clinical settings. Our goal was to compare a FACS-based fine-grained system to the PACSLAC-II in differentiating painful from non-painful states in older adults with and without dementia. Method: We video-recorded older long-term care residents with dementia and older adult outpatients without dementia, during a quiet baseline condition and while they took part in a physiotherapy examination designed to identify painful areas. Videos were coded using pain-related behaviours from the FACS and the PACSLAC-II. Results: Both tools differentiated between painful and non-painful states, but the PACSLAC-II accounted for more variance than the FACS-based approach. Participants with dementia scored higher on the PACSLAC-II than participants without dementia. Conclusion: The results suggest that easy-to-use observational approaches for clinical settings are valid and that there may not be any clinically important advantages to using more resource-intensive coding approaches based on FACS. We acknowledge, as a limitation of our study, that we used as baseline a quiet condition that did not involve significant patient movement. In contrast, our pain condition involved systematic patient movement. Future research should be aimed at replicating our results using a baseline condition that involves non-painful movements. Significance: Examining older adults with and without dementia, a brief observational clinical approach was found to be valid and accounted for more variance in differentiating pain-related and non-pain-related states than did a detailed time-consuming fine-grained approach.