JMIR Publications

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, signalling 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 utilize numerous relevant pain cues.

Objective:

The present review aimed to identify observable cues of pain, which could be used for digital pain monitoring. 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 utilized. 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 could identify seven categories of behavioral and physiological cues associated with pain. Several sub-categories for each main category of pain cues were identified, each involving between one and 28 relevant specific pain cues. Addressing RQ2, most of the reported pain cues were assessed via human observation. A few studies additionally included facial recognition software or combined human observation with 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. Clinical Trial: Na