A Technical Note on the PainChek™ System: A Web Portal and Mobile Medical Device for Assessing Pain in People With Dementia

Atee, Mustafa; Hoti, Kreshnik; Hughes, Jeffery

doi:10.3389/fnagi.2018.00117

Cited by 44 publications

(62 citation statements)

References 47 publications

(76 reference statements)

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“…The ePAT was designed by Curtin University researchers after reviewing the literature of pain, dementia, geriatric care, and pain facial expressions. 30 , 31 , 61 ePAT is a smart device application (App) that uses a combination of a selected set of facial AU codes and common pain behaviors reported in the literature (eg, items included in the American Geriatric Society [AGS] Indicators of Persistent Pain [2002]) to assess pain at the point of care. 29 , 32 A predefined set of facial AUs were included in the tool because they were associated with pain.…”

Section: Methodsmentioning

confidence: 99%

“…The tool uses digitization, real-time automated facial recognition and decoding using a deep learning (AI) approach, as a means of identifying and evaluating pain. 61 Digitization and smart device technology serve as a platform to facilitate documentation, while automated FACS decoding is integrated in the tool with the view to improve objectivity through reducing human observation errors. 61 Automated facial analysis identifies subtle facial muscle movements called AUs, which represent the smallest building blocks responsible for exerting microexpressions, each of which lasts for 100–500 milliseconds.…”

Section: Methodsmentioning

confidence: 99%

“… 61 Digitization and smart device technology serve as a platform to facilitate documentation, while automated FACS decoding is integrated in the tool with the view to improve objectivity through reducing human observation errors. 61 Automated facial analysis identifies subtle facial muscle movements called AUs, which represent the smallest building blocks responsible for exerting microexpressions, each of which lasts for 100–500 milliseconds. 36 The automated facial assessment consists of three steps ( Figure 1 ): Face detection and tracking ( Figure 2 ) Localization and extraction of facial features ( Figure 3 ) Detection of facial AUs ( Figure 4 ).…”

Section: Methodsmentioning

confidence: 99%

“…The ePAT is composed of six domains (Face, Voice, Movement, Behavior, Activity, and Body), which contains a total of 42 items. 31 , 61 Table 1 describes the ePAT domains and the corresponding items along with their operational definitions and primary conceptual basis. Each domain contains a number of items.…”

Section: Methodsmentioning

confidence: 99%

“…These intricacies have inspired us to develop the electronic Pain Assessment Tool (ePAT). 30 , 31 , 61 In this study, we aimed to examine interrater reliability of ePAT as a means of evaluating pain in aged care residents with moderate-to-severe dementia. Further, we examined the relationship between facial scores (which are determined using automated facial analysis) and total pain scores.…”

Section: Introductionmentioning

confidence: 99%

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A novel pain assessment tool incorporating automated facial analysis: interrater reliability in advanced dementia

Atee¹,

Hoti²,

Parsons³

et al. 2018

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ObjectivesRegardless of its severity, dementia does not negate the experience of pain. Rather, dementia hinders self-reporting mechanisms in affected individuals because they lose the ability to do so. The primary aim of this study was to examine the interrater reliability of the electronic Pain Assessment Tool (ePAT) among raters when assessing pain in residents with moderate-to-severe dementia. Secondly, it sought to examine the relationship between total instrument scores and facial scores, as determined by automated facial expression analysis.Study designA 2-week observational study.SettingAn accredited, high-care, and dementia-specific residential aged care facility in Perth, Western Australia.ParticipantsSubjects were 10 residents (age range: 63.1–84.4 years old) predominantly with severe dementia (Dementia Severity Rating Scale score: 46.3±8.4) rated for pain by 11 aged care staff. Raters (female: 82%; mean age: 44.1±12.6 years) consisted of one clinical nurse, four registered nurses, five enrolled nurses, and one care worker.MeasurementsePAT measured pain using automated detection of facial action codes and recordings of pain behaviors.ResultsA total of 76 assessments (rest =38 [n=19 pairs], movement =38 [n=19 pairs]) were conducted. At rest, raters’ agreement was excellent on overall total scores (coefficient of concordance =0.92 [95% CI: 0.85–0.96]) and broad category scores (κ=1.0). Agreement was moderate (κ=0.59) on categorical scores upon movement, while it was exact in 68.4% of the cases. Agreement in actual pain category scores gave κw=0.72 (95% CI: 0.58–0.86) at rest and κw=0.69 (95% CI: 0.50–0.87) with movement. All raters scored residents with higher total scores post-mobilization compared to rest. More facial action unit codes were also detected during pain (mean: 2.5 vs 1.9; p<0.0012) and following mobilization (mean: 2.5 vs 1.7; p<0.0001) compared to no pain and rest, respectively.ConclusionsePAT, which combines automated facial expression analysis and clinical behavioral indicators in a single observational pain assessment tool, demonstrates good reliability properties, which supports its appropriateness for use in residents with advanced dementia.

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