Pain is common among people with moderate to severe dementia, but inability of patients to self-report means it often goes undetected and untreated. We developed the electronic Pain Assessment Tool (ePAT) to address this issue. A point-of-care App, it utilizes facial recognition technology to detect facial micro-expressions indicative of pain. ePAT also records the presence of pain-related behaviors under five additional domains (Voice, Movement, Behavior, Activity, and Body). In this observational study, we assessed the psychometric properties of ePAT compared to the Abbey Pain Scale (APS). Forty aged care residents (70% females) over the age of 60 years, with moderate to severe dementia and a history of pain-related condition(s) were recruited into the study. Three hundred and fifty-three paired pain assessments (either at rest or post-movement) were recorded and analyzed. The ePAT demonstrated excellent concurrent validity (r = 0.882, 95% CI: 0.857–0.903) and good discriminant validity. Inter-rater reliability score was good overall (weighted κ= 0.74, 95% CI: 0.68–0.80) while internal consistency was excellent. ePAT has psychometric properties which make it suitable for use in non-communicative patients with dementia. ePAT also has the advantage of automated facial expression assessment which provides objective and reproducible evidence of the presence of pain.

Background: Pain in dementia is predominant particularly in the advanced stages or in those who are unable to verbalize. Uncontrolled pain alters the course of behaviors in patients with dementia making them perturbed, unsettled, and devitalized. Current measures of assessing pain in this population group are inadequate and underutilized in clinical practice because they lack systematic evaluation and innovative design.Objective: To describe a novel method and system of pain assessment using a combination of technologies: automated facial recognition and analysis (AFRA), smart computing, affective computing, and cloud computing (Internet of Things) for people with advanced dementia.Methods and Results: Cognification and affective computing were used to conceptualize the system. A computerized clinical system was developed to address the challenging problem of identifying pain in non-verbal patients with dementia. The system is composed of a smart device enabled app (App) linked to a web admin portal (WAP). The App “PainChek™” uses AFRA to identify facial action units indicative of pain presence, and user-fed clinical information to calculate a pain intensity score. The App has various functionalities including: pain assessment, pain monitoring, patient profiling, and data synchronization (into the WAP). The WAP serves as a database that collects the data obtained through the App in the clinical setting. These technologies can assist in addressing the various characteristics of pain (e.g., subjectivity, multidimensionality, and dynamicity). With over 750 paired assessments conducted, the App has been validated in two clinical studies (n = 74, age: 60–98 y), which showed sound psychometric properties: excellent concurrent validity (r = 0.882–0.911), interrater reliability (Kw = 0.74–0.86), internal consistency (α = 0.925–0.950), and excellent test-retest reliability (ICC = 0.904), while it possesses good predictive validity and discriminant validity. Clinimetric data revealed high accuracy (95.0%), sensitivity (96.1%), and specificity (91.4%) as well as excellent clinical utility (0.95).Conclusions: PainChek™ is a comprehensive and evidence-based pain management system. This novel approach has the potential to transform pain assessment in people who are unable to verbalize because it can be used by clinicians and carers in everyday clinical practice.

Background/Aims: Pain is common in aged care residents with dementia; yet it often goes undetected. A novel tool, the electronic Pain Assessment Tool (ePAT), was developed to address this challenging problem. We investigated the psychometric properties of the ePAT. Methods: In a 10-week prospective observational study, the ePAT was evaluated by comparison against the Abbey Pain Scale (APS). Pain assessments were blindly co-performed by the ePAT rater against the nursing staff of two residential aged care facilities. The residents were assessed twice by each rater: at rest and following movement. Results: The study involved 34 residents aged 85.5 ± 6.3 years, predominantly with severe dementia (Psychogeriatric Assessment Scale – Cognitive Impairment score = 19.7 ± 2.5). Four hundred paired assessments (n = 204 during rest; n = 196 following movement) were performed. Concurrent validity (r = 0.911) and all reliability measures (κ_w = 0.857; intraclass correlation coefficient = 0.904; α = 0.950) were excellent, while discriminant validity and predictive validity were good. Conclusion: The ePAT is a suitable tool for the assessment of pain in this vulnerable population.

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Background/Objective: People living with dementia (PLWD) in residential aged care homes (RACHs) are frequently prescribed psychotropic medications due to the high prevalence of neuropsychiatric symptoms, also known as behaviours and psychological symptoms of dementia (BPSD). However, the gold standard to support BPSD is using psychosocial/non-pharmacological therapies. This study aims to describe and evaluate services and neuropsychiatric outcomes associated with the provision of psychosocial person-centred care interventions delivered by national multidisciplinary dementia-specific behaviour support programs.Methods: A 2-year retrospective pre-post study with a single-arm analysis was conducted on BPSD referrals received from Australian RACHs to the two Dementia Support Australia (DSA) programs, the Dementia Behaviour Management Advisory Service (DBMAS) and the Severe Behaviour Response Teams (SBRT). Neuropsychiatric outcomes were measured using the Neuropsychiatric Inventory (NPI) total scores and total distress scores. The questionnaire version “NPI-Q” was administered for DBMAS referrals whereas the nursing home version “NPI-NH” was administered for SBRT referrals. Linear mixed effects models were used for analysis, with time, baseline score, age, sex, and case length as predictors. Clinical significance was measured using Cohen's effect size (d; ≥0.3), the mean change score (MCS; 3 points for the NPI-Q and 4 points for the NPI-NH) and the mean percent change (MPC; ≥30%) in NPI parameters.Results: A total of 5,914 referrals (55.9% female, age 82.3 ± 8.6 y) from 1,996 RACHs were eligible for analysis. The most common types of dementia were Alzheimer's disease (37.4%) and vascular dementia (11.7%). The average case length in DSA programs was 57.2 ± 26.3 days. The NPI scores were significantly reduced as a result of DSA programs, independent of covariates. There were significant reductions in total NPI scores as a result of the DBMAS (61.4%) and SBRT (74.3%) programs. For NPI distress scores, there were 66.5% and 69.1% reductions from baseline for the DBMAS and SBRT programs, respectively. All metrics (d, MCS, MPC) were above the threshold set for determining a clinically significant effect.Conclusions: Multimodal psychosocial interventions delivered by DSA programs are clinically effective as demonstrated by positive referral outcomes, such as improved BPSD and related caregiver distress.