Faces of Pain in Dementia: Learnings From a Real-World Study Using a Technology-Enabled Pain Assessment Tool.
Pain is common in people living with dementia (PLWD), including those with limited verbal skills. Facial expressions are key behavioral indicators of the pain experience in this group. However, there is a lack of real-world studies to report the prevalence and associations of pain-relevant facial mi...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/88396 |
| _version_ | 1848765015424761856 |
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| author | Atee, Mustafa Hoti, Kreshnik Chivers, Paola Hughes, Jeffery D |
| author_facet | Atee, Mustafa Hoti, Kreshnik Chivers, Paola Hughes, Jeffery D |
| author_sort | Atee, Mustafa |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Pain is common in people living with dementia (PLWD), including those with limited verbal skills. Facial expressions are key behavioral indicators of the pain experience in this group. However, there is a lack of real-world studies to report the prevalence and associations of pain-relevant facial micro-expressions in PLWD. In this observational retrospective study, pain-related facial features were studied in a sample of 3,144 PLWD [mean age 83.3 years (SD = 9.0); 59.0% female] using the Face domain of PainChek®, a point-of-care medical device application. Pain assessments were completed by 389 users from two national dementia-specific care programs and 34 Australian aged care homes. Our analysis focused on the frequency, distribution, and associations of facial action units [AU(s)] with respect to various pain intensity groups. A total of 22,194 pain assessments were completed. Of the AUs present, AU7 (eyelid tightening) was the most frequent facial expression (48.6%) detected, followed by AU43 (closing eyes; 42.9%) and AU6 (cheek raising; 42.1%) during severe pain. AU20 (horizontal mouth stretch) was the most predictive facial action of higher pain scores. Eye-related AUs (AU6, AU7, AU43) and brow-related AUs (AU4) were more common than mouth-related AUs (e.g., AU20, AU25) during higher pain intensities. No significant effect was found for age or gender. These findings offer further understanding of facial expressions during clinical pain in PLWD and confirm the usefulness of artificial intelligence (AI)-enabled real-time analysis of the face as part of the assessment of pain in aged care clinical practice. |
| first_indexed | 2025-11-14T11:28:32Z |
| format | Journal Article |
| id | curtin-20.500.11937-88396 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | eng |
| last_indexed | 2025-11-14T11:28:32Z |
| publishDate | 2022 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-883962022-05-10T03:59:59Z Faces of Pain in Dementia: Learnings From a Real-World Study Using a Technology-Enabled Pain Assessment Tool. Atee, Mustafa Hoti, Kreshnik Chivers, Paola Hughes, Jeffery D PainChek® action units aged care artificial intelligence dementia facial expressions pain real-world Pain is common in people living with dementia (PLWD), including those with limited verbal skills. Facial expressions are key behavioral indicators of the pain experience in this group. However, there is a lack of real-world studies to report the prevalence and associations of pain-relevant facial micro-expressions in PLWD. In this observational retrospective study, pain-related facial features were studied in a sample of 3,144 PLWD [mean age 83.3 years (SD = 9.0); 59.0% female] using the Face domain of PainChek®, a point-of-care medical device application. Pain assessments were completed by 389 users from two national dementia-specific care programs and 34 Australian aged care homes. Our analysis focused on the frequency, distribution, and associations of facial action units [AU(s)] with respect to various pain intensity groups. A total of 22,194 pain assessments were completed. Of the AUs present, AU7 (eyelid tightening) was the most frequent facial expression (48.6%) detected, followed by AU43 (closing eyes; 42.9%) and AU6 (cheek raising; 42.1%) during severe pain. AU20 (horizontal mouth stretch) was the most predictive facial action of higher pain scores. Eye-related AUs (AU6, AU7, AU43) and brow-related AUs (AU4) were more common than mouth-related AUs (e.g., AU20, AU25) during higher pain intensities. No significant effect was found for age or gender. These findings offer further understanding of facial expressions during clinical pain in PLWD and confirm the usefulness of artificial intelligence (AI)-enabled real-time analysis of the face as part of the assessment of pain in aged care clinical practice. 2022 Journal Article http://hdl.handle.net/20.500.11937/88396 10.3389/fpain.2022.827551 eng http://creativecommons.org/licenses/by/4.0/ fulltext |
| spellingShingle | PainChek® action units aged care artificial intelligence dementia facial expressions pain real-world Atee, Mustafa Hoti, Kreshnik Chivers, Paola Hughes, Jeffery D Faces of Pain in Dementia: Learnings From a Real-World Study Using a Technology-Enabled Pain Assessment Tool. |
| title | Faces of Pain in Dementia: Learnings From a Real-World Study Using a Technology-Enabled Pain Assessment Tool. |
| title_full | Faces of Pain in Dementia: Learnings From a Real-World Study Using a Technology-Enabled Pain Assessment Tool. |
| title_fullStr | Faces of Pain in Dementia: Learnings From a Real-World Study Using a Technology-Enabled Pain Assessment Tool. |
| title_full_unstemmed | Faces of Pain in Dementia: Learnings From a Real-World Study Using a Technology-Enabled Pain Assessment Tool. |
| title_short | Faces of Pain in Dementia: Learnings From a Real-World Study Using a Technology-Enabled Pain Assessment Tool. |
| title_sort | faces of pain in dementia: learnings from a real-world study using a technology-enabled pain assessment tool. |
| topic | PainChek® action units aged care artificial intelligence dementia facial expressions pain real-world |
| url | http://hdl.handle.net/20.500.11937/88396 |