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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.
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Journal of Alzheimer’s Disease 60 (2017) 137–150
DOI 10.3233/JAD-170375
IOS Press
Pain Assessment in Dementia: Evaluation
of a Point-of-Care Technological Solution
Mustafa Ateea,, Kreshnik Hotia,b, Richard Parsonsaand Jeffery D. Hughesa
aSchool of Pharmacy, Curtin University, Bentley, WA, Australia
bDivision of Pharmacy, Faculty of Medicine, University of Pristina, Pristina, Kosovo
Handling Associate Editor: Alba Malara
Accepted 26 June 2017
Abstract. 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 = 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.
Keywords: Automated, dementia, ePAT, facial recognition technology, FACS, older people, pain assessment, psychometric
evaluation, reliability, validation
Pain is a frequent symptom in residential aged care
with up to 80% of residents experiencing pain at some
point of time, whilst at least 50% of those with demen-
tia have pain on a regular basis [1, 2]. Pain often is
under-detected and undertreated particularly in those
with moderate to severe dementia who can no longer
self-report pain due to the neurodegenerative changes
associated with the condition [3]. In these individuals,
pain may manifest as a behavioral disturbance(s) [4],
which could be inappropriately treated with a range
of psychotropic medications such as benzodiazepines
and antipsychotics [5].
Correspondence to: Mustafa Atee, School of Pharmacy,Curtin
University, PO Box U1987, Perth 6845, WA, Australia. Tel.: +61 8
9266 7369; Fax: +61 8 9266 2769; E-mail: Mustafa.Atee@curtin.
An alternative communication channel to report
pain for these residents is through non-verbal sig-
nals. Non-verbal communications were recognized
by the American Geriatric Society (AGS) in 2002 as
indicators of pain, and since have been recommended
for inclusion in behavioral (also known as observa-
tional) pain assessment tools [1]. Of these tools, none
use objective facial measures, and consequently are
all dependent on the subjective knowledge, skills and
training level of raters. This is problematic given the
fact that there is a high staff turnover in the aged care
industry and inconsistencies exist among health-care
professionals in detecting pain [6, 7].
Facial expressions are key non-verbal pain related
behaviors that have been included in many observa-
tional pain scales [8]. Existing scales often contain
abstract and abstruse descriptors such as grimac-
ing, which are difficult to identify as pain related
ISSN 1387-2877/17/$35.00 © 2017 – IOS Press and the authors. All rights reserved
This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC 4.0).
138 M. Atee et al. / Pain Assessment in Dementia
expressions by assessors. The Facial Action Cod-
ing System (FACS) offers a more objective way of
describing and measuring these facial expressions
[9]. FACS is an anatomical catalogue and taxonomy
of facial expressions, which contains 52 Action Unit
(AU) codes [9]. FACS is regarded the gold standard of
evaluating facial expressions including those related
to pain [10]. Despite its efficiency in categorizing
expressions, manual facial decoding is laborious,
time consuming, and inconvenient, because it uses
video recordings and requires lengthy training and
experts’ input; hence making it unrealistic for clinical
applications [10, 11]. A recent systematic review sup-
ported the notion of using FACS in pain assessment,
including automated pain assessment FACS-related
systems [12].
Patients with Alzheimer’s disease (the most com-
mon form of dementia) have been shown to display an
increase in the frequency and intensity of facial activ-
ity as measured by Action Units (AUs) of the FACS
[13]. Kunz et al. reported that pain related AU codes
(AU4,6/7,9/10) are three times more frequent in peo-
ple with dementia compared to healthy controls [13],
perhaps due to their impaired operant learning and
reinforcement ability to mask negative expressions
such as pain [14–16].
With the advancement of technologies including
computer vision and smart devices (e.g., Android
and iOS smartphones), automation may reduce the
reliance on human raters, making pain assessment
less prone to error and less subjective. The purpose
of this paper is to briefly describe a new pain assess-
ment tool, namely the electronic Pain Assessment
Tool (ePAT) [17] that integrates these technologies
to benefit patients with cognitive impairment and to
evaluate its psychometric properties compared to the
Abbey Pain Scale (APS).
Pain assessment tools
Abbey Pain Scale (standard care)
The APS was used as a comparator in this study
because it is frequently used in clinical practice
and is also endorsed by the Australian Pain Society
in their recommended management strategies [18].
The instrument has good psychometric properties in
older adults with dementia as reported in a number
of systematic reviews in the literature [11, 19–23].
The instrument consists of six subscales, namely:
vocalization, facial expressions, change in body lan-
guage, behavioral change, physiological change, and
physical change [24]. Each subscale is scored on an
ordinal rating range of 0–3 to indicate intensity. A
score of 0 indicates absence of pain while mild = 1,
moderate =2, and severe= 3. A total pain score (sum
of subscales) of 0–2 indicates no pain, 3–7 mild
pain, 8–13 moderate pain, and 14 and above indi-
cates severe pain [24]. Figure 1 shows the Abbey Pain
The electronic Pain Assessment Tool (ePAT)
(new tool)
Purpose: The ePAT is a multimodal pain scale
designed to assist clinicians and health care work-
ers assess pain in people with moderate to severe
dementia at the point of care.
Developers: The ePAT has been developed by a
research team at Curtin University, Western Aus-
tralia, in collaboration with the Swiss company, nViso
SA which is located at the Swiss Federal Institute of
Technology in Lausanne (EPFL).
Content (Images 1–6): The ePAT utilizes auto-
mated facial recognition technology to detect the
presence of facial micro-expressions indicative of
the presence of pain, which, when combined with
a range of behavioral and physical features based
on the other five domains of the AGS, can be used
to assess pain severity. Using a 10 second video of
the subject’s face, the ePAT maps the face and auto-
matically identifies in real-time the presence of pain
related facial micro-expressions (otherwise known as
Action Units).
The ePAT consists of 42 descriptor items dis-
tributed across six domains in the following order
[Domain 1: The Face (9 items), Domain 2: The
Voice (9 items), Domain 3: The Movement (7 items),
Domain 4: The Behavior (7 items), Domain 5: The
Activity (4 items) and Domain 6: The Body (6 items)].
Each domain represents a subclass that evaluates a
certain dimension of pain (refer to Table 1 for more
Platform used and method of administration: The
technology is packaged in a software app that can
be used across a range of mobile smart devices. The
current Android version of the app tested during
this study was installed on a Samsung Note 3 (SM-
N9005) device. The ePAT is also an observational
(informant-based) tool, which can be administered by
a care worker or clinician (user) using a smart device.
M. Atee et al. / Pain Assessment in Dementia 139
Fig. 1. The Abbey Pain Scale. Source: Abbey J, De Bellis A, Piller N, Esterman A, Giles L, Parker D, Lowcay B. Funded by the JH & JD
Gunn Medical Research Foundation 1998–2002.
140 M. Atee et al. / Pain Assessment in Dementia
Image 1. Face detection and tracking in the ePAT App during a clinical encounter.
Image 2. Facial features extraction of the ePAT App.
The user must be trained on the use of the tool and be
familiar with the patient undergoing assessment. The
user needs to navigate from one domain to another to
complete the assessment.
Scoring: The ePAT uses a hybrid model in which
the Face domain is fully automated while other
domains (Domains 2–6) are questionnaire-based
checklists manually completed by the assessor, using
the mobile device. Similar to the Pain Assessment
Checklist for Seniors with Limited Ability to Com-
municate (PACSLAC), a binary (2-point) format
is adopted to evaluate the presence (score= 1) or
absence (score =0) of pain related behaviors on each
of the 42 items. Magnitude of pain is measured by
obtaining a cumulative score across all items. Total
pain score, cumulated over all domains, can range
from 0–42, with the corresponding band categories
of pain intensity (no pain, mild, moderate, severe) to
be explored in this study.
Conceptual foundation: The tool was developed
on the basis of the definition of pain as “an unpleas-
ant sensory and emotional experience associated
with actual or potential tissue damage, or described
in terms of such damage” [25]. There is also a
great need for developing novel and innovative pain
assessment instruments for non-verbal people with
dementia as evident in the current literature. A meta-
review by Lichtner et al. suggested that new pain
assessment tools need to be developed on an inno-
vative conceptual basis [26]. In addition, a review by
M. Atee et al. / Pain Assessment in Dementia 141
Image 3. Detection of facial Action Units (AUs) codes in the ePAT App.
Image 4. Domain 5 of the ePAT; The Activity.
Hadjistavropoulos et al. has strongly recommended
that including a FACS-based pain expression should
be considered by researchers for future development
and refinement of pain instruments for older adults
with dementia [27].
Therefore, we considered these main principles in
designing the ePAT:
1) Objectivity
(a) Through integration of FACS into the tool
(b) Via automation: this is achieved by using a
deep learning algorithm, with the purpose
of reducing proxy rating error associated
with human judgement
(c) Use of a binary (yes/no) approach to the
identification of the presence of non-facial
pain cues
2) Comprehensiveness
(a) Inclusion of AGS items in the tool to
identify subtle behavioral changes based
on pain items specifically geared towards
older persons with dementia
3) Portability and smart device interoperability
142 M. Atee et al. / Pain Assessment in Dementia
Image 5. Domain 6 of the ePAT; The Body.
Image 6. Total score screen of the ePAT App depicting to pain intensity score.
(a) Smart device capabilities (such as high
computational efficiency, e.g., processing
power, digitization, and in-built cameras)
and their popular use (due to reasonable
costs, use with various platforms, e.g.,
Android, iOS) make them suitable to facil-
itate pain assessment at the point-of-care.
A comparative account between the ePAT and APS
is summarized in Table 1.
All clinical assessments were performed in accor-
dance with principles outlined in the Declaration
of Helsinki, Alzheimer’s Australia Guidelines, and
clauses for undertaking research in cognitively
impaired individuals by the Australian National
Statement for Ethical Conduct in Human Research.
Ethical approval (HREC: HR10/2014) was granted
by Human Research Ethics Committee, Curtin Uni-
versity, Western Australia and by ethics review
boards of participating facilities. Informed consent
could not be elicited from residents with demen-
tia due to their impaired cognitive capacity. Thus,
proxy consents were provided by relatives or an
authorized representative of the cognitively impaired
residents prior to participation. Proxies were noti-
fied that they could revoke their consent at any time
M. Atee et al. / Pain Assessment in Dementia 143
without affecting the quality of care or the rela-
tionship of participants with those working in the
aged care facility. Verbal explanations using very
simple language (e.g., “we are checking whether
you have any pain today by taking a short video
of you”) were also used to explain the study to the
Design and setting
The study was a prospective observational study
which involved residents from three metropolitan
aged care homes (ACHs) in Perth, Western Australia.
Residents were eligible to enroll if they met the fol-
lowing criteria: (1) age greater than 60 y, (2) living
in a designated dementia unit of the ACH, (3) had a
diagnosis of dementia, (4) their cognitive score based
on the Mini-Mental State Examination (MMSE): <
19 or Psychogeriatric Assessment Scale–Cognitive
Impairment Scale (PAS-CIS): > 10, and (5) possessed
a documented history of a chronic pain condition
such as osteoarthritis or currently suffer from acute
(e.g., urinary tract infection), recurrent (e.g., gout) or
incidental pain (e.g., pressure sores).
Residents were excluded from the study if they
could not partially or completely exhibit any facial
expression (for example as a result of a facial palsy),
were clinically too unwell, or where it was inappro-
priate for them to be assessed for pain, as determined
by the treating doctor.
Protocol plan
The study was conducted over a 13-week period in
each of the three participating ACHs. The study was
initiated at Aged Care Home 1 (ACH 1) from March-
July 2015, then Aged Care Home 2 (ACH 2) from
October 2015-January 2016, and Aged Care Home 3
(ACH 3) from January-April 2016. The choice of 13
weeks was made to allow adequate time for testing
to occur under various conditions and while resi-
dents were doing their routine activities (i.e., at rest
and upon movements, e.g., walking, repositioning,
bathing, etc.).
Each resident was independently evaluated using
the two assessment tools during routine care. The
APS (i.e., standard care) was administered by a staff
member (nurse or carer) employed by the facility as
part of normal care, while the ePAT (the new tool)
was administered, in most instances, by the primary
researcher (MA), although health care profession-
als (e.g., registered nurse), personal care workers, or
nursing and occupational therapy students also con-
ducted some assessments. All raters were blinded to
each other’s assessments. With the exception of the
health science students, those involved in performing
the assessments were already experienced in using
APS or ePAT. Practical training on the use of the ePAT
and the APS was delivered by the primary investiga-
tor to health science students. Paired pain assessments
were undertaken during various levels of activities
such as walking, after toileting or showering to induce
nociceptive painful experiences, and during resting to
mimic non-nociceptive periods.
Pain ratings were conducted mainly during day-
time between 8 am and 6 pm. Ratings were
undertaken indoors in multiple locations (e.g., activ-
ity room, resident’s room, dining room) inside the
ACHs. In cases where the ePAT assessor was unfa-
miliar with the resident, care staff not involved with
the study were consulted to answer various questions
about residents’ behaviors (e.g., sleeping/eating pat-
tern). Both ePAT and APS assessments were brief
in nature and they were administered either concur-
rently or within 2-3 min of each other. The order
in which the assessments were delivered was ran-
dom to minimize the possibility of any learning
Statistical analysis
Standard descriptive statistics were used to sum-
marize the study participants and number of
assessments conducted (frequencies and percentages
for categorical variables, means, standard deviations,
and ranges for continuous variables).
Concurrent validity was assessed using the Pear-
son’s correlation coefficient between the overall pain
scores assigned by the APS and ePAT instruments,
and separately for observations made at rest and fol-
lowing movement. The correlation is not a measure
of exact agreement, as the instruments are based on
different scoring mechanisms, but a strong correla-
tion would indicate that the ePAT is equivalent to
the APS up to a scaling factor. A refinement of the
Pearson’s correlation coefficient was also calculated,
following the method of Lam et al. [28], using a SAS
macro described by Hamlett [29]. This refinement
took into account the repeated measurements made
on each participant in case agreement between APS
and ePAT differed between participants.
144 M. Atee et al. / Pain Assessment in Dementia
Table 1
Comparison between the observational pain tools APS and ePAT
Number of domains 6 6
Tool item domains & number of
descriptors per domain
Vocalization (1 item) The Face (9 items)
(e.g., whimpering, groaning, crying) [see facial expressions below]
Facial expressions (1 item) The Voice (9 items)
[e.g., looking tense, frowning grimacing, looking frightened] [noisy Pain Sounds, e.g., ouch, ah, mm, requesting help repeatedly,
groaning, moaning, crying, screaming, loud talk, howling, sighing]
Change#in body language (1 item)
The Movement (7 items)
[e.g., fidgeting, rocking, guarding part of body, withdrawn]
[altered or random leg/arm movement, restlessness, freezing, guarding/
Behavioral change#(1 item)
touching body part, moving away, abnormal (altered)
sitting/standing/walking, pacing/wandering]
[e.g., increased confusion, refusing to eat, alteration in usual patterns]
The Behavior (7 items)
Physiological change#(1 item)
[introvert (unsocial) or altered behavior, verbally offensive, aggressive, fear
or extreme dislike of touch, people, inappropriate behavior, confused,
[e.g., temperature, pulse or blood pressure outside normal
The Activity (4 items)
limits, perspiring, flushing or pallor]
[resisting care, prolonged resting, altered sleep cycle, altered routines]
Physical change#(1 item)
The Body (6 items)
[e.g., skin tears, pressure areas, arthritis, contractures,
previous injuries]
[profuse sweating, pale/flushed (red-faced), feverish/cold, rapid breathing,
painful injuries, painful medical conditions]
Facial expressions Abstract description Specific annotation
Emotion-like expressions Action Unit (AU) codes of Facial Action Coding System (FACS)
(frowning, grimacing, looking frightened, AU4: Brow Lowering
looking tense) AU6: Cheek Raising
AU7: Tightening of Eyelids
AU9: Wrinkling of Nose
AU10: Raising of Upper Lip
AU12: Pulling at Corner Lip
AU20: Horizontal Mouth Stretch
AU25: Parting Lips
AU43: Closing Eyes
Scoring format Ordinal Binary
[4 point scale (absent =0, mild = 1, moderate= 2, severe = 3) for each of the 6
[2 point scale (no =0, yes = 1) for each item in each domain]
Scoring procedure Pen and paper recording Automated facial recognition for the Face domain technology (with an
option of manual recording by the user) and touch screen electronic
completion of other 5 domains using a smart device
Total pain score range 0–18 0–42
#Change refers to observer assessed change compared to previous assessment (relies on having familiarity with the person being assessed).
M. Atee et al. / Pain Assessment in Dementia 145
Discriminant validity investigated whether the
agreement between APS and ePAT depended on the
conditions (at rest or with movement). This was
explored by using the difference in pain scores (APS
minus ePAT) as the dependent variable in a random
effects regression model, with the timing (rest or with
movement) as the independent variable and the sub-
ject number as the random effect. Naming the subject
as a random effect in this model took into account
any correlations between the repeated measures made
on each study participant. The p-value associated
with timing indicated its influence on the agreement
between measures.
Inter-rater reliability was assessed by classifying
the pain scores for APS and ePAT into four cate-
gories from no pain to mild, moderate, and severe
pain. Agreement between the measures according to
these categories was then assessed using the Cohen’s
kappa statistic. The standard (unweighted) kappa is a
measure of exact agreement within categories, while
the weighted kappa gives some weight to small dis-
Internal consistency between the two measures was
calculated using Cronbach’s alpha. This assesses the
extent to which two or more measures are essentially
measuring the same construct [30]. It was used in this
study to compare the overall APS and ePAT scores.
Values of Cronbach’s alpha above 0.7 are indicative
of a good agreement between measures [30].
Statistical analyses were performed using the SAS
version 9.2 software (SAS Institute Inc, Cary, NC,
USA, 2008).
Demographic characteristics
A total of 40 residents were recruited into the
study from the three aged care homes. The average
age of the participants was 79.7 y (SD: 9.1; range:
60 to 98 y). The majority of residents were females
(70%) and Caucasians (n= 39), with the remaining
participant being Asian. The residents had a range of
chronic pain conditions as a result of arthritis (e.g.,
osteoarthritis, rheumatoid arthritis and gout), previ-
ous injuries and/or surgeries, skin tears and sores,
dental disorders (e.g., sore gums associated with
gingivitis), and neuropathic pain (e.g., post-herpetic
neuralgia). Seventy percent of the cohort had one or
more documented chronic pain diagnoses. A number
of participating residents were bed-ridden, immobile
or had limited mobility. All residents were identified
as having moderate to severe cognitive impairment
based on a PAS-CIS score in the range of 10–15
and 16–21, respectively. Eighty-seven percent of res-
idents had severe impairment. MMSE scores were
unable to be completed for most residents due to
severe impairments and were only recorded for eight
residents with a mean of 14.0 ±3.9. More than half
(57.5%) of the sample had a diagnosis of Alzheimer’s
dementia while 25% reported to have an unspecified
type of dementia. Other documented dementias were
frontotemporal dementia (7.5%), Lewy body demen-
tia (2.5%), Parkinsonian’s dementia (5%), and mixed
dementia (2.5%). Refer to Table 2 for further details.
Table 2
Resident demographics and pain characteristics
Number (%) Mean (SD)
Age (y) 79.7 (9.1)
(Median: 79.0, range: 60–98)
Female 29 (70)
Male 11 (30)
Country of birth
Australia 16 (40)
Czech Republic 1 (2.5)
England 13 (32.5)
Ireland 1 (2.5)
Lithuania 1 (2.5)
Mauritius 1 (2.5)
Scotland 3 (7.5)
Unknown 4 (10)
Caucasians 39 (97.5)
Asian 1 (2.5)
Primary language
English 38 (95)
French 1 (2.5)
Lithuanian 1 (2.5)
Limited 18 (45)
Immobile 4 (10)
Bed-ridden 2 (5)
Cognitive performance
MMSE (range: 8–17) 8 (20) 14.0 (3.9)
PAS-CIS (range: 10–15) 5 (12.5)
PAS-CIS (range: 16–21) 35 (87)
Diagnosis of dementia
Alzheimer’s disease 23(57.5)
Frontotemporal dementia 3(7.5)
Lewy Body dementia 1 (2.5)
Parkinson’s dementia 2 (5)
Mixed (Alzheimer’s/Vascular 1 (2.5)
Unspecified 10 (25)
Number of documented chronic
painful diagnoses
0 12 (30)
1 10 (25)
2 9 (22.5)
3 4 (10)
5 4 (10)
146 M. Atee et al. / Pain Assessment in Dementia
Table 3
Pain assessment data for the three participating aged care homes
Aged Care Homes
ACH 1 ACH 2 ACH 3 Combined
Study period Mar 2015 – Jul 2015 Oct 2015 – Jan 2016 Jan 2016 – Apr 2016 Mar 2015 – Apr 2016
Sample size 8 15 17 40
% males 50% 40% 12% 30%
Total No. of ePAT assessments 40 127 186 353
No. of ePAT assessments during rest 22 70 118 209
No. of ePAT assessments upon movement 18 57 69 144
Table 4
Number of assessments completed by each assessor
Number of assessments ACH 1 ACH2 ACH 3 Total
per staff classification
CN#1 116 0 117
RN#11 0 156 167
EN#23 11 0 34
CW#1 0 30 31
MA* 36 127 186 349
Total 80 254 372 706
CN, clinical nurse; CW, care worker; RN, registered nurse;
EN, enrolled nurse; HSS, health science student; MA, primary
investigator. #completed APS assessments, *completed ePAT
assessments, @students did a total of four APS and four ePAT
Pain assessment data
Pain assessments for residents were undertaken
during routine care while at rest or with movement.
The number of paired pain assessments per resi-
dent varied and ranged from 2 to 15. Overall, the
total number of paired pain assessments was 353
(Table 3). Those performing the pain assessments
(Table 4) included seven nurses (clinical nurse (CN);
n= 2, registered nurse (RN); n= 3, and enrolled nurse
(EN); n= 2), two care workers (CW), four health sci-
ences students (HSS), and the primary investigator
Concurrent validity
Pearson’s correlation coefficient to assess overall
agreement between APS and ePAT was 0.882 (95%
CI: 0.857–0.903). This was based on the 353 paired
assessments made on the 40 study participants. This
correlation indicates a very strong and positive rela-
tionship between the two scores. Figure 2 below
represents the ePAT pain scores graphed against the
APS scores, with black dots indicating pain score at
Fig. 2. Scatter plot of individual APS scores and ePAT scores. Black dots indicating pain score at rest and red dots pain score with movement.
Note that some dots represent more than one observation.
M. Atee et al. / Pain Assessment in Dementia 147
rest and red dots pain score with movement. Note that
some dots represent more than one observation.
In a similar fashion the ePAT pain scores and
the APS scores demonstrate significant correlation
both at rest (r= 0.880; 95% CI: 0.845–0.907) and
with movement (r= 0.894; 95% CI: 0.855–0.922).
The refinement to the standard correlation coeffi-
cient, which took into account the repeated measures
on each participant, led to adjusted correlation
coefficients at rest: r= 0.881; and with movement:
r= 0.894. As these figures differed only in the third
decimal place from the unadjusted figures, this sug-
gests that the repeated measurements on participants
had little impact on the correlation between the
Discriminant validity
Discriminant validity was assessed by comparing
ePAT scores to APS for the same resident at rest
and then after movement, e.g., movement in walk-
ing, repositioning, and toileting. As was the case
with APS scores, the ePAT pain scores increased
after residents were subjected to movement which
elicited pain. The random effects regression model
showed that the difference between ePAT and APS
scores was not significantly influenced by the tim-
ing of the assessment (at rest versus with movement;
p= 0.795).
Inter-rater reliability
Association between pain groups of APS and
ePAT was evaluated using a contingency table. A
preliminary analysis of the results (n= 229) led to
the following categorization of ePAT scores into
pain groups as follows: 1–6 = No pain; 7–11 = Mild
pain, 12–15 = Moderate pain, and 16–42 = Severe
pain. These cut-off scores were selected as they pro-
vided good agreement with the APS with respect
to these categories of pain. They were obtained by
cross-tabulating the raw ePAT scores against the APS
categories, and optimum cut-off scores were obtained
in a manner similar to a discriminant analysis. These
categories continued to give good agreement with the
APS categories for the full dataset (n= 353). Table 5
below showed the overall agreement.
The weighted kappa scores (Table 6) demonstrated
that there was moderate to good reliability based
on the following guide: Kappa 0.20 is consid-
ered poor; 0.0.21–0.40, fair; 0.41–0.60, moderate;
0.61–0.80, good, and 0.81–1.00, very good [31].
Table 5
Numbers shown in the cells are the number of assessments (per-
centage of the APS category)
APS category ePAT category Total
No pain Mild Moderate Severe
No pain 183 (95.3) 9 (4.7) 0 0 192
Mild 32 (23.4) 97 (70.8) 8 (5.8) 0 137
Moderate 0 5 (21.7) 14 (60.9) 4 (17.4) 23
Severe 0 0 1 (100) 0 1
Table 6
Inter-rater reliability data for ePAT versus Abbey Pain Scale
Activity Weighted Kappa 95% CI
All (Rest + Movement) n= 353 0.74 0.69–0.80
At rest n= 209 0.71 0.63–0.80
With movement n= 144 0.78 0.70–0.86
Internal consistency
The Cronbach’s alpha () statistic was used
to compare the overall APS and ePAT scores.
Cronbach’s was 0.925 and Pearson’s correlation
coefficient (r) was r= 0.882 (95% CI: 0.857 – 0.903).
Internal consistency was excellent overall for ePAT
versus APS.
The results of this study demonstrated that ePAT
offers a valid and reliable new method to assess pain
in people with moderate to severe dementia who
can no longer self-report their pain. We believe it
offers significant advantages over currently available
behavioral pain assessment tools. It utilizes auto-
mated facial recognition technology to identify the
presence of specific AUs which are associated with
pain. In addition, it utilizes binary answers to each
parameter rather than subjective 0–3 scoring of inten-
sity as with APS, therefore removing the subjectivity
associated with the assessment of the features of pain
and providing an objective and reproducible assess-
ment of pain facial expression for each individual.
Further, the ePAT app automatically calculates a pain
severity score once the user enters other non-facial
pain cues observed in the person.
The strong correlation demonstrated between
ePAT and APS in this study is very encouraging.
According to Herr et al. [19], an acceptable corre-
lation coefficient for a new pain assessment tool is
0.4–0.6, whereas ePAT achieved a correlation coeffi-
cient was 0.88 when compared with the APS. By
way of comparison, Lichtner et al., in their systematic
148 M. Atee et al. / Pain Assessment in Dementia
review, reported on the outcomes of concurrent valid-
ity assessments in which the scores of one tool were
compared with those of another, or with healthcare
professional ratings of pain or with self-reports (using
VAS scales) [26]. The results obtained from this
study are generally better than those reported from
other head to head comparisons of behavioral (obser-
vational) pain scales, or when such tools’ scores
are compared with observer pain ratings or self-
reports [26].
In regards to the discriminant validity, it should
be noted that pain scores associated with movement
were higher than those at rest in the same individual,
when assessed using either the APS or ePAT. Statisti-
cal analysis of the effect of timing of assessment (i.e.,
whether the assessment was undertaken at rest or on
movement) demonstrated no difference between the
two pain assessment tools. This means that whether
the measurements were taken at rest or with move-
ment had no influence on the relationship between
the ePAT and APS scores. Given that the APS is
one of a number of behavioral pain scales which
have been shown to demonstrate significant differ-
ences in scores pre- and post-interventions/events
(e.g., movement) [26], this suggests that ePAT is
also able to discriminate between pain at rest and
pain on movement as non-facial items of both tools
share same conceptual foundation (i.e., AGS). Other
tools that have proven discriminant validity include
the Certified Nursing Assistant Pain Assessment
Tool (CPAT), Checklist of Nonverbal Pain Indicator
(CNPI), Discomfort Scale-Dementia of Alzheimer
Type (DS-DAT), Pain Assessment Checklist for
Seniors with Limited Ability to Communicate (PAC-
SLAC), Mobilization – Observation – Behavior –
Intensity – Dementia Pain Scale (MOBID), Assess-
ment of Discomfort in Dementia (ADD), and the
Behavior Checklist [26]. In the future, it will be
important to assess whether ePAT can detect changes
in individuals’ pain scores post-intervention, both
pharmacological and non-pharmacological.
A test cannot be valid if it is not reliable, i.e., the
assessment tool must produce stable and consistent
results. In this study, ePAT demonstrated good inter-
rater reliability in comparison to APS when results
for each instrument were categorized as represent-
ing mild, moderate, or severe pain with weighted
kappa scores > 0.6. The inter-rater reliability of the
APS was found to be moderately good (0.335–0.475)
when tested by 26 nurses in 126 residents in a study
by Neville and Ostini in 2014 [32]. By compar-
ison the Pain Assessment in Advanced Dementia
Scale (PAINAD), for example, has also shown simi-
lar inter-rater reliability when compared head to head
with CNPI in the range of 0.31 at rest and 0.54
during movement [33]. Lints-Martindale et al. exam-
ined the inter-rater agreement (Cohen’s kappa) for
six observation pain tools; namely ADD, Nursing
Assistant-Administered Instrument to Assess Pain
in Demented Individuals (NOPAIN), Pain Assess-
ment for the Dementing Elderly (PADE), PACSLAC,
PAINAD, and CNPI. Employing an influenza
vaccination as the painful stimulus, they found that
agreement between the tools ranged from substan-
tial (i.e., = 0.61 to 0.80) to high levels of agreement
(i.e., = 0.81 to 1.0) [34]. The highest level of agree-
ment was obtained between the PACSLAC and the
CNPI. Interestingly, like the PACSLAC, the scor-
ing for the CNPI is binary. Assessors assign 1 if
a behavior is present and 0 if it is absent. As
there are six categories (namely: non-verbal vocaliza-
tions [e.g., moans, groans], facial grimaces/winces,
bracing [e.g., clutching or holding onto side rails],
restlessness, rubbing, and verbal vocal complaints
[e.g., “ouch”, “that hurts”], pain is scored on a scale of
0 to 6 [35]. PACSLAC utilizes a series of 60 questions
across four categories (namely: facial expressions,
activity/body movements, social/personality/mood
indicators) with a range of 0 to 60 [36]. Other tools
such as PAINAD and APS require the assessor both
to identify presence of a particular behavior (items)
and rate its intensity [24, 37].
The overall internal consistency of ePAT when
compared with APS was found to be excellent
(> 0.9), and in keeping with those of other obser-
vational pain tools. For example, PACSLAC (total
scale) range 0.82 to 0.87 [19, 38] and subscales
range 0.55 to 0.73; CNPI (both at rest and with
movement) = 0.54 [35], APS (total scale) range
0.71 to 0.81 [19], PADE Part 1, Physical (e.g., facial
expressions) range 0.76 to 0.88 [39]; DOLOPLUS
2= 0.82 [19]; and L ´
echelle Comportementale pour
Personnes Ag´
ees (ECPA) = 0.7 [19].
One major strength of this study was that it is
the first study of its kind (as far as we know) to
evaluate a pain assessment tool linked to automated
facial recognition technology and built into a smart
device for people with moderate to severe demen-
tia. Compared to other existing tools, this offers the
advantage of minimizing rater subjectivity in one of
the key AGS pain assessment domains, i.e., facial
expressions. Another strength is that pain scores were
obtained while participants were receiving their stan-
dard care. The latter was provided during the study at
M. Atee et al. / Pain Assessment in Dementia 149
all times and with minimal or no interruption. Stan-
dard care is believed to elicit nociceptive pain and also
offers a real world context as encountered in the res-
idential aged care setting, and with less potential for
recall bias from raters. Residents had various types of
dementias and pain diagnoses covered a wide spec-
trum of medical conditions. Also, pain measurements
were performed on a weekly basis for a period of 13
consecutive weeks to portray a clearer clinical picture
about the frequency and status of pain symptoms in
these subjects.
Limitations of this psychometric evaluation
include the following: (1) small sample size and non-
random selection of participants; however, a point
of saturation was reached with regard to correlation
findings; (2) homogenous nature of the sample in
terms of gender and ethnicity because of the over-
representation of Caucasian (n= 39) females (n= 28)
so that findings may be only applicable to this group;
(3) unequal number of assessments per resident over
the study period; (4) some participants might have
exhibited little or no pain-related behaviors even
in the presence of severe pain; (5) proxy reporting
and recall bias are possible because a care worker
could have a fallible memory and may not remem-
ber events accurately, which might affect the quality
and amount of information provided (although this
limitation also exists for APS); (6) these findings
were observed based on clinical pain, so agreement
between ePAT and APS may differ in experimentally-
induced pain modalities; (7) potential for judgement
subjectivity and interpretation bias when scoring non-
facial domains on ePAT (minimized by employing
a binary assessment) and all domains of the APS
which uses an ordinal scale; (8) despite using the
APS in the current study, there is currently no globally
accepted gold standard observational pain scale; and
(9) some pain behavioral cues are difficult to inter-
pret and they could be identified by raters as pain
where they actually related to other signs of men-
tal disorders such as depression. Our study design is
observational in nature where no intervention (e.g.,
analgesics) is given to subjects. Hence unless an
adequately powered, tightly controlled clinical trial
is employed with an intervention targeted towards
these behavioral problems, the confounding effect
is inevitable to occur. Rater-related limitations also
include the fact that only a small number of raters
completed the assessments, and there were a number
of novice raters. The impact of the latter was evalu-
ated by comparing the results with and without the
inclusion of their assessments, this had a negligible
impact on the results. Although it is also desirable
to conduct an additional multivariate analysis, we
consider the current analysis of variables provides
sufficient information to meet the objectives of the
This study demonstrates that ePAT has psychomet-
ric properties which make it suitable for use in people
with moderate to severe dementia. It has proven valid-
ity and reliability compared to APS, which is the
current gold standard for pain assessment in people
with dementia who cannot self-report pain in Aus-
tralia. We believe it offers a significant advantage in
that the facial expression assessment is automated,
providing an objective and reproducible evidence of
the presence of pain, in conjunction with non-facial
features. Further, the non-facial domain items, which
have been specifically geared towards older people
with dementia, only require Yes/No responses, rather
than judgements about their presence and intensity,
providing objective assessment and a point for future
reference. The fact that the tool is integrated into
a mobile application, which can store repeat pain
assessments for individuals, is highly advantageous
as it facilitates ongoing monitoring of patients’ pain
and the effectiveness of their management. Lastly, it
has been designed for use by healthcare professionals
and lay carers alike.
The project described was supported by grant
funding and stipend scholarship from Alzheimer’s
Australia. The content is solely the responsibility
of the authors and does not necessarily represent
the official views of Alzheimer’s Australia Demen-
tia Research Foundation. The authors want to thank
aged care staff, residents, and their families for their
involvement in the project.
Authors’ disclosures available online (http://j-alz.
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... 19 In addition, healthcare facilities could adopt facial recognition technology to detect facial microexpressions related to pain while observers record the presence of other signs (e.g., whimpering and groaning; change in body language; and physical, physiologic, and behavioral changes). 21,22 The electronic Pain Assessment Tool 21,22 has good reliability to assess pain in patients with moderate-tosevere dementia. The electronic Pain Assessment Tool uses a 10-s video, mapping the face of a patient with dementia to automatically identify the presence of pain in real-time. ...
... 19 In addition, healthcare facilities could adopt facial recognition technology to detect facial microexpressions related to pain while observers record the presence of other signs (e.g., whimpering and groaning; change in body language; and physical, physiologic, and behavioral changes). 21,22 The electronic Pain Assessment Tool 21,22 has good reliability to assess pain in patients with moderate-tosevere dementia. The electronic Pain Assessment Tool uses a 10-s video, mapping the face of a patient with dementia to automatically identify the presence of pain in real-time. ...
... 27 Equipping healthcare professionals with the resources to understand the nonverbal communications of patients with dementia (such as "pain recognition") accords with a commitment by healthcare facilities for in-service education. 12,21,22,28 In addition, family caregivers should be supported with access to resources such as the Alzheimer's Association 7 and the Dementia Dictionary 23 websites that will help them understand and interpret the nonverbal communications of their loved ones with dementia. 29 Also, we need to raise awareness among the public about the potential changes and declines in verbal communications of patients with dementia. ...
Full-text available
In interactions with caregivers, patients with dementia have communication challenges that are common and worrisome to families. Family and professional caregivers find it challenging to “guess” or “interpret” what their patients with dementia are trying to tell them. In this creative controversy article, we discuss how family and professional caregivers can seek to understand and correctly interpret the nonverbal communications of patients with dementia (behaviors, actions, facial expressions, and vocal sounds). Equipping family and professional caregivers with the resources to interpret the nonverbal communications of patients with dementia requires a commitment to in-service and family education in healthcare facilities. Nurses could play a critical role in raising the awareness among the public about the potential changes and declines in verbal communications of the patients with dementia.
... Despite the abundance of such tools that contain facial expressions, only a few have objective facial item descriptors (11). Examples of these are PainChek R and the Pain Assessment Checklist for Seniors with Limited Ability to Communicate-II (PACSLAC-II), both of which contain fine-grained anatomically based facial items (e.g., nose wrinkling), derived from the Facial Action Coding System (FACS) (12)(13)(14). The FACS is a catalog of 46 facial action units (AUs), each produced by contraction and/or relaxation of a single or group of facial muscle(s) (13). ...
... This is complicated by the fact that professional caregivers are not superior in decoding pain facial expressions compared with non-professional caregivers (17). One way of overcoming these difficulties is using technology-enabled pain assessments, such as PainChek R (12). ...
... The PainChek R is a multimodal, multi-platform, and hybrid pain assessment tool system, which uses automated recognition and analysis of facial AUs, together with checklists of AGS-based and other recognized pain behaviors to identify and quantify pain in non-verbal adults, especially those living with advanced dementia (12). The system is regulatory cleared by Australia's Therapeutic Goods Administration, Health Canada, Singapore Health Sciences Authority, and European Conformity (CE) for this population (18). ...
Full-text available
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.
... Pain assessment scales can be challenging to apply to people with dementia due to the presence of mental and linguistic barriers, such as difficulty with verbal communication or abstract comprehension (Achterberg et al., 2020). People with dementia require the use of additional evaluation strategies, such as observations of pain-related behaviours and in-depth pain examinations (Atee et al., 2017;Felton et al., 2021). The challenges to identifying and assessing pain among people living with dementia can leave this population vulnerable to pain (Achterberg et al., 2020). ...
Full-text available
Aims and objectives: To identify the efficacy of non-pharmacological interventions designed to reduce pain in people with dementia. Background: Pain is prevalent among patients with dementia but frequently remains untreated. Although non-pharmacological interventions have been used to reduce pain in people with dementia, the efficacy of these interventions for pain management in people with dementia has not been thoroughly synthesised. Design: Systematic review and meta-analysis. Methods: The study was conducted in accordance with PRISMA guidelines and Cochrane criteria for systematic reviews. A comprehensive search was performed using the Academic Search Complete, CINAHL, Cochrane Library, EMBASE, MEDLINE, PubMed, OVID and Web of Science databases, from databases inception to 13 March 2022. The modified Cochrane risk-of-bias tool (ROB-2) was used to evaluate the methodological quality of each included study. Standardised mean differences (SMDs) with 95% confidence intervals (CIs) were synthesised using a random-effects model to assess the efficacy of non-pharmacological interventions for reducing pain in people with dementia (using Stata 16.0). Results: The final analysis assessed 12 studies, including 989 persons with dementia. Non-pharmacological interventions were found to reduce pain in 4-8 weeks after the interventions (SMD: -0.32; 95% CI: -0.62 to -0.02). However, the effects of intervention frequency and patient age remain unknown. Conclusions: Non-pharmacological interventions are effective for reducing pain in people with dementia. Further investigations remain necessary to explore the effectiveness of specific non-pharmacological therapies for pain reduction in people with dementia (e.g. aromatherapy, play activity, singing or robotic care). Relevance to clinical practice: The findings of this study can guide healthcare practitioners when considering the use of non-pharmacological pain management methods for people with dementia and may improve the implementation of these methods in clinical practice. Patient or public contributions: The study suggests non-pharmacological interventions to reduce pain and underlines the relevance of health provider's viewpoints. The types, duration and length of follow-up of non-pharmacological interventions can be offered based on patient's conditions and the standard of clinical practice.
... However, this meant measurement tools used in clinical practice but not trials may have been omitted. Second, given the methods adopted through Search 1 to identify potential measurement tools, more recent tools such as the ePAT were not included in the analysis [39]. Consideration of this and inclusion of forthcoming evidence on psychometric properties should be made to update the findings as new evidence evolves in the field. ...
Full-text available
Purpose Detecting pain in older people with dementia is challenging. Consequentially, pain is often under-reported and under-treated. There remains uncertainty over what measures should be promoted for use to assess pain in this population. The purpose of this paper is to answer this question. Methods A search of clinical trials registered on the and ISRCTN registries was performed to identify outcome measures used to assess pain in people with dementia. Following this, a systematic review of published and unpublished databases was performed to 01 November 2021 to identify papers assessing the psychometric properties of these identified measures. Each paper and measure was assessed against the COSMIN checklist. A best evidence synthesis analysis was performed to assess the level of evidence for each measure. Results From 188 clinical trials, nine outcome measures were identified. These included: Abbey Pain Scale, ALGOPLUS, DOLOPLUS-2, Facial Action Coding System, MOBID-2, self-reported pain through the NRS or VAS/thermometer or Philadelphia Geriatric Pain Intensity Scale, PACSLAC/PACSLAC-2, Pain Assessment in Advanced Dementia (PAINAD), and Checklist for non-verbal pain behavior (CNPI). From these, 51 papers (5924 people with dementia) were identified assessing the psychometric properties of these measures. From these, there was strong- and moderate-level evidence to support the use of the facial action coding system, PACSLAC and PACSLAC-II, CNPI, DOLOPLUS-2, ALGOPLUS, MOBID, and MOBID-2 tools for the assessment of pain with people living with dementia. Conclusion Whilst these reflect measurement tools used in research, further consideration on how these reflect clinical practice should be considered. PROSPERO registration CRD42021282032
... PainChek ® scores above 6 (i.e. ⩾7) indicate the presence of pain (Atee et al., 2017a(Atee et al., , 2017b(Atee et al., , 2018. ...
Objective Younger-onset dementia accounts for about 5–10% of all dementias in Australia. Little data is available on neuropsychiatric symptoms in people with younger-onset dementia compared to those with older-onset dementia. This study aims to compare the types of neuropsychiatric symptoms and their clinico-demographic characteristics of people with younger-onset dementia and older-onset dementia who are referred to a specific dementia support service. Methods A 2-year retrospective observational cross-sectional analysis was undertaken on referrals with neuropsychiatric symptoms from Dementia Support Australia programmes. Neuropsychiatric symptoms were measured using the Neuropsychiatric Inventory total severity scores and distress scores. Contributing factors to neuropsychiatric symptoms for dementia groups were examined. Logistic regression was used to examine the relationship between individual neuropsychiatric symptoms and having older-onset dementia vs younger-onset dementia. Results Of the 15,952 referrals, about 5% ( n = 729, mean age: 60.7 years, standard deviation = 5.4) were individuals with younger-onset dementia. Referrals with older-onset dementia were more likely to be female (56%), whereas referrals with younger-onset dementia were more likely to be male (54%). There was a four times greater rate of frontotemporal dementia for those with younger-onset dementia (16.0%, n = 117) compared to those with older-onset dementia (2.8%, n = 427), χ ² (1) = 366.2, p < 0.001. Referrals with younger-onset dementia were more likely to be referred from community settings and those with older-onset dementia were more likely to be from residential aged care. Overall, there was no difference in the severity and distress of neuropsychiatric symptoms between the two groups. Contributing factors to neuropsychiatric symptoms were different between the groups, with pain being more frequently endorsed for individuals with older-onset dementia whereas communication difficulties were more commonly identified for those with younger-onset dementia. Conclusion Clinico-demographics of referrals with younger-onset dementia differ from those with older-onset dementia. There were some differences in the characteristics of neuropsychiatric symptoms between younger-onset dementia and older-onset dementia. Our findings have implications for service provision and support for people with dementia at different ages.
... A handful of studies have investigated the prevalence of painful syndromes in DLB patients, with findings ranging from 25-70% [3,23,37,100]. Notably, the majority of studies utilized non-standardized clinical interviews to assess pain prevalence, and no study has yet assessed pain in DLB patients using dementia-validated tools such as the Pain Assessment in Advanced Dementia (PAINAD) Scale or the electronic Pain Assessment Tool (ePAT) [101,102]. Two studies used the EQ-5D (which rates five dimensions of quality of life, including "pain or discomfort" at 3 possible levels) and Brief Pain Inventory, respectively: the former is reliable in mild dementia but has validity concerns in moderate-tosevere dementia, while the latter has not yet been validated in dementia [3,100,103]. Thus, it is currently difficult to draw any conclusions on pain prevalence in DLB. ...
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Parkinson’s disease-related pain has increasingly been investigated in research studies. Still, only a few studies have addressed the prevalence and clinical characteristics of pain in neurodegenerative disorders with atypical parkinsonism. The existing evidence, although scarce, suggests that, similarly as in Parkinson’s disease, individuals with neurodegenerative diseases with atypical parkinsonism might be predisposed to the development of persistent pain. Today, as the global population is aging and we face an epidemic of neurodegenerative disorders, under-treated pain is taking a great toll on an ever-rising number of people. Here, we provide an up-to-date review of the current knowledge on the prevalence of pain, its clinical features, and findings from experimental studies that might signpost altered pain processing in the most prevalent neurodegenerative disorders with atypical parkinsonism: multiple system atrophy, progressive supranuclear palsy, corticobasal syndrome, frontotemporal dementia, and dementia with Lewy bodies. Finally, we point out the current gaps and unmet needs that future research studies should focus on. Large-scale, high-quality clinical trials, coupled with pre-clinical research, are urgently needed to reveal the exact pathophysiological mechanisms underpinning heightened pain and pave the path for mechanistically-driven analgesic interventions to be developed, ultimately leading to an improvement in the quality of life of individuals with neurodegenerative disorders.
... Mild cognitive impairment would not likely impede informed consent, shared decision-making, or accurate selfreporting of pain and symptoms, but a surrogate may be needed for more advanced forms of dementia (102). Special tools such as Pain Assessment in Advanced Dementia have been developed to assist in the care of such patients (103). ...
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A clinical conundrum can occur when a patient with active opioid use disorder (OUD) or at elevated risk for the condition presents with cancer and related painful symptoms. Despite earlier beliefs that cancer patients were relatively unaffected by opioid misuse, it appears that cancer patients have similar risks as the general population for OUD but are more likely to need and take opioids. Treating such patients requires an individualized approach, informed consent, and a shared decision-making model. Tools exist to help stratify patients for risk of OUD. While improved clinician education in pain control is needed, patients too need to be better informed about the risks and benefits of opioids. Patients may fear pain more than OUD, but opioids are not always the most effective pain reliever for a given patient and some patients do not tolerate or want to take opioids. The association of OUD with mental health disorders (dual diagnosis) can also complicate delivery of care as patients with mental health issues may be less adherent to treatment and may use opioids for “chemical coping” as much as for pain control.
... Assessed as a reaction of any action that affects the body, especially pain like rigid, guarding, and tense, physical aggression, fidgeting, increased pacing/rocking, and mobility changes such as inactivity or motor restlessness. [24,31,40] Vocalization When we are in a bad situation, we shout for help as the same when babies feel in distress or pain, they would cry, so our voices in a different pattern (Moaning, crying, groaning, sighing, and gasping). Voice nature can noticed in verbal self-report. ...
As our definition of pain evolves, the factors implicit in defining and predicting pain status grow. These factors each have unique data characteristics and their outcomes each have unique target attributes. The clinical characterization of pain does not, as defined in the most recent IASP definition, require any tissue pathology, suggesting that the experience of pain can be uniquely psychological in nature. Predicting a persons pain status may be optimized through integration of multiple independent observations; however, how they are integrated has direct relevance towards predicting chronic pain development, clinical application, and research investigation. The current challenge is to find clinically-mindful ways of integrating clinical pain rating scales with neuroimaging of the peripheral and central nervous system with the biopsychocial environment and improving our capacity for diagnostic flexibility and knowledge translation through data modeling. This commentary addresses how our current knowledge of pain phenotypes and risk factors interacts with statistical models and how we can proceed forward in a clinically responsible way.
The prevalence of chronic pain increases with age, with up to 40% of older people and 80% of those in residential aged care are affected by pain. This chapter shall discuss the most common causes of pain in this population and the way aging affects perception of pain and pain assessment. The chapter will conclude by discussing the pharmacological management of pain in older people including guideline recommendations, changes in the pharmacokinetics and pharmacodynamics of analgesics, as well as the adverse effect profiles of those that are most commonly prescribed.
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There is evidence of under-detection and poor management of pain in patients with dementia, in both long-term and acute care. Accurate assessment of pain in people with dementia is challenging and pain assessment tools have received considerable attention over the years, with an increasing number of tools made available. Systematic reviews on the evidence of their validity and utility mostly compare different sets of tools. This review of systematic reviews analyses and summarises evidence concerning the psychometric properties and clinical utility of pain assessment tools in adults with dementia or cognitive impairment. We searched for systematic reviews of pain assessment tools providing evidence of reliability, validity and clinical utility. Two reviewers independently assessed each review and extracted data from them, with a third reviewer mediating when consensus was not reached. Analysis of the data was carried out collaboratively. The reviews were synthesised using a narrative synthesis approach. We retrieved 441 potentially eligible reviews, 23 met the criteria for inclusion and 8 provided data for extraction. Each review evaluated between 8 and 13 tools, in aggregate providing evidence on a total of 28 tools. The quality of the reviews varied and the reporting often lacked sufficient methodological detail for quality assessment. The 28 tools appear to have been studied in a variety of settings and with varied types of patients. The reviews identified several methodological limitations across the original studies. The lack of a ‘gold standard’ significantly hinders the evaluation of tools’ validity. Most importantly, the samples were small providing limited evidence for use of any of the tools across settings or populations. There are a considerable number of pain assessment tools available for use with the elderly cognitive impaired population. However there is limited evidence about their reliability, validity and clinical utility. On the basis of this review no one tool can be recommended given the existing evidence.
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Self-reporting is the most widely used pain measurement tool, although it may not be useful in patients with loss or deficit in communication skills. The aim of this paper was to undertake a systematic review of the literature of pain assessment through the Facial Action Coding System (FACS). The initial search found 4,335 references and, within the restriction «FACS», these were reduced to 40 (after exclusion of duplicates). Finally, only 26 articles meeting the inclusion criteria were included. Methodological quality was assessed using the GRADE system. Most patients were adults and elderly health conditions, or cognitive deficits and/or chronic pain. Our conclusion is that FACS is a reliable and objective tool in the detection and quantification of pain in all patients. Copyright © 2014 Elsevier España, S.L.U. All rights reserved.
Background: The key factor to improving pain management for cognitively impaired elderly patients is accurate pain assessment. Behavioural-observation methods are required for individuals who cannot communicate their pain verbally. A thorough understanding of the key components of behavioural pain assessment and the use of valid and reliable behavioural pain assessment tools would enhance the assessment of pain in this vulnerable population. Objectives: To identify the key components involved in behavioural pain assessment in cognitively impaired elderly people and to analyse the reported psychometric properties, feasibility and utility of behavioural pain assessment tools. Selection criteria: Studies using descriptive, correlation and comparative designs were included.Cognitively impaired elderly people older than 65 years in aged care, acute care or nursing home settings were included.Components measured in behavioural pain assessment; psychometric properties, feasibility and utility of behavioural pain assessment tools used to assess pain in cognitively impaired elderly people in acute or long-term care settings.Identification of behavioural criteria for assessment of pain and investigation of any aspect of the psychometric properties of behavioural pain assessment tools. Search strategy: An initial limited search of MEDLINE and CINAHL to find published studies between 1990 to 2010 in the English Language was undertaken, following an analysis of the text words contained in the title and abstract. A second search using all identified keywords and index terms was undertaken and extended to a further seven relevant databases. Thirdly, the reference lists of all identified reports and articles were searched for additional studies. Methodological quality: Studies selected for retrieval were assessed for inclusion by two independent reviewers for methodological validity using the Critical Appraisal Tool for Psychometric Studies adapted from Fallon, Westaway, and Moloney1. Data extraction: Quantitative data were extracted from included studies using the Data Extraction Tool for Psychometric Studies adapted from Fallon, Westaway, and Mahoney1. Data synthesis: As statistical pooling was not possible, evidence in relation to psychometric properties, was analysed and presented in narrative summary. Results: Twenty three studies were included in the review. No tool has been found suitable for use across both acute and long-term care settings. Nevertheless, three tools show the most promising outcomes and potential for use. Conclusions: Although behavioural measures may inform healthcare providers on the presence of pain in an individual, they do not provide information about the aetiology of pain. Hence, pain assessment should not depend solely on behavioural observation conducted using standardised behavioural pain assessment tools, but regarded as an essential component of a multifaceted approach to pain assessment. Clinicians may select tools which show promising qualities and pilot them in their respective clinical settings and populations. In particular, the MPS, the PACSLAC and the PAINAD are recommended for potential use in the cognitively impaired elderly in acute and long-term care settings.Several tools show promise for use in acute or long-term care settings. These tools require tool revisions to strengthen their psychometric properties. Instead of developing new tools, modification of existing tools and conducting further psychometric evaluations on them can provide more evidence of their psychometric properties.
Chronic pain is highly prevalent in the ageing population. Individuals with neurological disorders such as dementia are susceptible patient groups in which pain is frequently under-recognised, underestimated, and undertreated. Results from neurophysiological and neuroimaging studies showing that elderly adults are particularly susceptible to the negative effects of pain are of additional concern. The inability to successfully communicate pain in severe dementia is a major barrier to effective treatment. The systematic study of facial expressions through a computerised system has identified core features that are highly specific to the experience of pain, with potential future effects on assessment practices in people with dementia. Various observational-behavioural pain assessment instruments have been reported to be both reliable and valid in individuals with dementia. These techniques need to be interpreted in the context of observer bias, contextual variables, and the overall state of the individual's health and wellbeing. Copyright © 2014 Elsevier Ltd. All rights reserved.