Pain Cues in People With Dementia: Scoping Review

Abstract

Background: Individuals with dementia, especially those in later stages, have difficulties with verbally reporting their experience of pain. This results in both underassessment and undertreatment of pain, signaling the need for better pain recognition in persons with dementia. A promising form of pain assessment is digital monitoring, which can concurrently and more objectively detect and use numerous relevant pain cues. Objective: This review aimed to identify observable cues of pain, which could be used for digital pain monitoring. A total of 2 research questions (RQs) were formed as we set out to examine which digital cues offered a valid insight into pain in people with dementia (RQ1) and identify how these cues were originally measured (RQ2). Methods: A standard methodological approach for scoping reviews was used. Relevant research papers were chosen based on SCOPUS and Web of Science databases, and relevant data on pain cues were extracted from all papers that satisfied the inclusion criteria. The gathered data were analyzed using a thematic analysis, which involved categorizing the observable cues into higher-order categories. Results: Of the 3705 publications identified in the search, 34 satisfied the inclusion criteria and were closely examined. Addressing RQ1, we identified 7 categories of behavioral and physiological cues associated with pain, most frequently facial expressions (20/34, 59%) and body movements or expressions (15/34, 44%). Several subcategories for each main category of pain cues were also identified, each involving between 1 and 28 relevant specific pain cues. Addressing RQ2, 29/34 (85%) studies assessed pain cues via human observation only, while 5/34 (15%) combined human observation with either facial recognition software, PainChek app, or computer vision. Conclusions: The review provides a comprehensive list of the most relevant cues that signify pain in persons with dementia and offers a foundation for the use of artificial intelligence and digital monitoring for the screening of pain in dementia.