Pain-related bias in the classification of emotionally ambiguous facial expressions in mothers of children with chronic abdominal pain

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PAIN 153 (2012) 674–681

www.elsevier.com/locate/pain

Pain-related bias in the classification of emotionally ambiguous facial expressions in mothers of children with chronic abdominal pain Christina Liossi a,b,⇑, Paul White c, Natasha Croome a, Popi Hatira d a

School of Psychology, University of Southampton, Southampton, UK Pain Control Service, Great Ormond Street Hospital for Children, London, UK c Department of Mathematical Sciences, University of the West of England, Bristol, UK d Department of Psychology, University of Crete, Rethymno, Greece b

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

a r t i c l e

i n f o

Article history: Received 26 May 2011 Received in revised form 19 November 2011 Accepted 8 December 2011

Keywords: Bias Children Chronic abdominal pain Emotion recognition Mothers

a b s t r a c t This study sought to determine whether mothers of young people with chronic abdominal pain (CAP) compared to mothers of pain-free children show a pain recognition bias when they classify facial emotional expressions. One hundred demographically matched mothers of children with CAP (n = 50) and control mothers (n = 50) were asked to identify different emotions expressed by adults in 2 experiments. In experiment 1, participants were required to identify the emotion in a series of facial images that depicted 100% intensity of the following emotions: Pain, Sadness, Anger, Fear, Happiness, and Neutral. In experiment 2, mothers were required to identify the predominant emotion in a series of computerinterpolated (‘‘morphed’’) facial images. In this experiment, pain was combined with Sad, Angry, Fearful, Happy, and Neutral facial expressions in different proportions—that is, 90%:10%, 70%:30%, 50%:50%, 30%:70%, 10%:90%. All participants completed measures of state and trait anxiety, depression, and anxiety sensitivity. In experiment 1, there was no difference in the performance of the 2 groups of mothers. In experiment 2, it was found that overall mothers of children with CAP were classifying ambiguous emotional expressions predominantly as pain. Mean response times for CAP and control groups did not differ significantly. Mothers of children with CAP did not report more anxiety, depression, and anxiety sensitivity compared to control mothers. It is concluded that mothers of children with CAP show a pain bias when interpreting ambiguous emotional expressions, which possibly contributes to the maintenance of this condition in children via specific parenting behaviours. Ó 2011 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

1. Introduction Chronic abdominal pain (CAP) is a common paediatric problem, affecting 7% to 25% of young people in school-based or community samples [25,38,59] and accounting for 2% to 4% of paediatric consultations [47]. Currently accepted pathophysiological models of CAP implicate 3 major mechanisms for this syndrome: altered intestinal motility, altered intestinal sensory thresholds, and psychosocial factors [30]. The biopsychosocial model conceptualizes chronic pain as a centrally mediated phenomenon, shaped jointly by physiological, psychological, social, and environmental variables [19]. In the context of this theoretical framework, social learning theory [3] has been used to describe the relationship between a child’s pain expe-

⇑ Corresponding author. Address: School of Psychology, University of Southampton, Highfield, Southampton S017 1BJ, UK. Tel.: +44 023 8059 4645; fax: +44 023 8059 4597. E-mail address: [email protected] (C. Liossi).

rience and family factors [10]. According to the theory, learning occurs through parental modeling and parental reinforcement of pain behaviors. Empirical investigations have demonstrated that parental protective responses to pain (eg, frequent attending to pain symptoms, granting permission to avoid regular activities) function as positive reinforcement and are associated with increased somatic symptoms [53], greater functional disability [39], greater child health care use [54], more frequent school absences [7], and slower recovery from surgery [20]. Parental minimization (eg, shifting focus away from pain behaviours, criticizing the child’s pain as excessive), is also associated with increased somatic symptoms [12]. These patterns have been further demonstrated in laboratory investigations that have examined the relationship between parental responses and child pain behaviour [10,55]. Information-processing models are useful frameworks for generating and testing hypotheses about the origins of human behaviour and may be useful in informing our understanding of parental responses to their children’s pain episodes. Beck’s schema model of cognitive processing, in particular, suggests that maladaptive

0304-3959/$36.00 Ó 2011 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.pain.2011.12.004

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schemas or beliefs can bias the processing of information through interpretive, attentional, and memory processes, which in turn help to maintain the schemas [5,26]. In the context of CAP, it is proposed that parents of CAP patients have the tendency to construe emotionrelevant information in an overly pain-related manner and are constantly alert to the possibility of pain. Indeed, parents of adolescents with chronic pain describe their parenting experience as dominated by vigilance and alertness to their children’s pain [27]. If parents have an interpretation bias, they may be more likely to interpret emotional signals such as facial expressions as pain related, and they may interact with their children in ways that they believe will promote pain management by, for example, inquiring about the presence of pain or engaging in protective behaviours, and therefore they may inadvertently contribute to the maintenance of their child’s pain. Facial emotion recognition is impaired in several disorders, such as depression [31] and anxiety [49], and has been associated with particular parenting behaviours [2]. The current study investigated the above proposition. More specifically, we tested the following 2 hypotheses: first, that mothers of children with CAP will detect the presence of pain in emotionally ambiguous faces at a lower percentage level than mothers of pain-free children; and second, that mothers of children with CAP will make their classification as ‘‘pain’’ more quickly than mothers of pain-free children.

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criteria were the same as for the experimental group. Eligibility for the study was ascertained initially with a structured telephone interview and was confirmed when the investigators met the participants in person. From the 82 mothers who contacted the research group, on the basis of inclusion/exclusion criteria, 32 individuals were excluded from the CAP group (13 mothers had an anxiety or mood disorder; 10 mothers experienced chronic pain themselves; 3 mothers had a partner with chronic pain; 5 mothers had a child with a chronic medical condition; and 1 mother regularly received nonprescription medication) and 13 from the healthy control group (2 mothers had an anxiety or mood disorder; 1 mother experienced chronic pain herself; for 5 mothers, one of their children had a chronic medical condition; for 4 mothers, one of their children had been diagnosed with attention deficit hyperactivity disorder; and 1 mother regularly received nonprescription medication). On the basis of the above criteria, 50 participants were recruited to the CAP group [mean ± standard deviation (SD) age, 39.5 ± 2.8 years] and 50 to the control group (age 39. 2 ± 3.1 years). Table 1 lists the participants’ demographic characteristics and their children’s medical characteristics. All participants had either fullor part-time employment and had a high education level (more than 12 years of education). Most participants were married or in a relationship. 2.2. Questionnaires

2. Methods 2.1. Participants Participants were recruited via posters and press announcements from the south of England. To minimise the possibility of inducing a report bias to participants, the study was advertised as one ‘‘looking at psychological aspects of chronic abdominal pain,’’ and no information about the study’s hypotheses was included in the information and consent forms. No monetary compensation was provided to study participants. In order to study a homogenous group of participants in terms of emotion recognition abilities, only mothers were recruited for this study. Numerous studies have shown male–female differences in emotion recognition abilities corresponding to a Cohen’s d of about .40 [23,24,51], and despite efforts to include fathers in paediatric pain research, there is an overall preponderance of mothers participating in studies [41]. Inclusion criteria for the experimental group were as follows: (a) having a biological or adopted child diagnosed by a general practitioner and/or a consultant paediatrician or gastroenterologist with CAP (the American Academy of Pediatrics and the North American Society for Pediatric Gastroenterology, Hepatology and Nutrition Committee on Chronic Abdominal Pain has recently recommended that the term ‘‘chronic abdominal pain’’ be used to describe ‘‘long-lasting, intermittent or constant abdominal pain that is functional or organic’’ [48], and this definition was adopted in the present investigation); and (b) age between 20 and 45 years (research suggests that older adults do not identify emotions and cognitive states in the same way as younger adults do—for instance, subtle age differences have been found in emotion recognition skills, with older adults demonstrating relatively consistent deficits in identifying anger and sadness over a number of studies) [43]. Exclusion criteria were as follows: (a) having been diagnosed with a psychiatric disorder within the past 5 years, (b) experiencing any type of chronic pain, (c) having a partner that experienced any type of chronic pain, (d) taking any psychotropic or analgesic medication on a regular basis, and (e) having a child, apart from the index child, with a chronic medical or psychiatric condition. For the control group, inclusion criteria were (a) having a biological or adopted child, and (b) age 20–45 years. Exclusion

The questionnaire battery was used to characterise the sample and assess cognitive and emotional aspects of participants’ functioning, including anxiety, depression, fear of arousal symptoms, and their index child’s disability levels. The State-Trait Anxiety Inventory (STAI) is a 40-item self-report measure consisting of two 20-item scales assessing both trait and state levels of anxiety [46]. All items are representative of various symptoms of anxiety, and participants are asked to rate on a 4point Likert-type scale how they generally feel for the trait scale, and how they feel right now for the state scale. Responses range from not at all/almost never (1) to very much so/almost always (4). Positive items are reverse scored, and the scores are summed to yield overall state and trait anxiety scores. The STAI has been shown to have good levels of internal consistency [46] and to be highly reliable, with the ability to discriminate between highand low-stress situations [36]. The Beck Depression Inventory II (BDI-II) is a 21-item self-report measure that assesses the severity of depressive symptoms [4]. Participants are asked to rate the levels at which they have experienced several symptoms of depression over the last 2 weeks, with responses assessed on a 4-point Likert-type scale ranging from 0 to 3, with 3 being reflective of higher levels of depressive symptoms. Scores for each item are summed to yield an overall index of depression, with scores of 13 and below indicative of an absence of depression, 14–19 of mild depression, 20–28 of moderate depression, and 29 and above suggestive of severe depression. The BDI-II has been demonstrated to have good levels of reliability and high internal consistencies of 0.91 and above [4,14]. The Anxiety Sensitivity Index (ASI) is a 16-item self-report questionnaire measuring fear of bodily sensations relating to anxiety [40]. Each item is representative of symptoms of anxiety sensitivity, with responses assessed on a 5-point Likert-type scale ranging from very little (0) to very much (4). Scores for each item are summed to yield an overall index of anxiety sensitivity, with possible scores ranging from 0 to 64. Factor analysis has revealed 3 factors measured by the ASI: physical concerns, mental incapacitation concerns and social concerns [58]. The ASI has been shown to have high levels of internal consistency and good test–retest reliability [40].

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C. Liossi et al. / PAIN 153 (2012) 674–681 Table 1 Demographic characteristics and questionnaire scores of a group of mothers with children with chronic abdominal pain (CAP), a group of mothers with pain-free children (Control), and characteristics of their children.a

a

CAP (n = 50)

Control (n = 50)

P

Mothers’ characteristics Ethnic background = white Age (y) Relationship status = in a relationship Years in education No. of children State anxiety Trait anxiety Beck Depression Inventory Anxiety Sensitivity Index

45 39.5 ± 2.8 39 13.5 ± 5.2 2.5 ± 1.8 36.74 ± 9.82 40.28 ± 11.74 8.30 ± 5.98 20.72 ± 9.07

43 39. 2 ± 3.1 41 13.1 ± 4.9 2.6 ± 1.5 34.46 ± 10.40 38.82 ± 8.66 6.36 ± 5.27 19.04 ± 9.87

.758 .613 .802 .693 .763 .262 .423 .088 .378

Child characteristics Index child’s age Pain duration (y) Functional Disability Index (parent version)

11.7 ± 2.3 1.7 ± 0.9 18.68 ± 6.48

12.1 ± 1.9 – –

.345

Data are presented as mean ± SD.

The parent version of the Functional Disability Index is a 15item self-report measure of child functional disability, with the parent responding with respect to their child’s experience [52]. Items measure the level of difficulty the child has, or would have, experienced performing various tasks over the last few days, with responses assessed on a 5-point Likert scale ranging from no trouble (0) to impossible (4). The Functional Disability Index has been shown to have high levels of internal consistency, and test–retest reliability estimates exceeding 0.60 have been demonstrated over a 3-month interval in a sample of patients with CAP [52]. In addition, the construct validity of the measure has been demonstrated by its ability to discriminate healthy patients from those with abdominal pain [52]. 2.3. Experimental stimuli The investigation consisted of 2 experiments and still images from the Montreal Pain and Affective Dynamic Pain Stimuli [45] (4 male and 4 female actors; mean ± SD age 24.4 ± 7.5 years) were utilised in both cases. In experiment 1, participants were required to identify the emotion in a series of facial images that depicted 100% intensity of the following emotions: Pain, Sadness, Anger, Happiness, Fear, and Neutral. In experiment 2, mothers were required to identify the predominant emotion in a series of computer-interpolated (‘‘morphed’’) facial images. This technique allows a range of ambiguous facial expressions to be presented in a measured manner. A computer generated program FaceMorph [1], based on the Beier–Neely algorithm [6], constructed morphing stages between the 2 end-point facial expressions (one of which was always Pain) of the 8 different actors. In this experiment, Pain was combined with Sad, Angry, Fearful, Happy, and Neutral facial expressions in different proportions—that is, 90%:10%, 70%:30%, 50%:50%, 30%:70%, 10%:90%. For example, one continuum combined Pain and Neutral as follows: 90% Pain:10% Neutral, 70% Pain:30% Neutral, 50% Pain:50% Neutral, 30% Pain:70% Neutral, 10% Pain:90% Neutral (Fig. 1). For both experiments, all images

90% neutral 10% pain

70% neutral 30% pain

50% neutral 50% pain

30% neutral 70% pain

10% neutral 90% pain

Fig. 1. Example stimuli utilised in the emotion recognition task, experiment 2.

measured 10.5 cm wide by 7.11 cm high (ie, 372 pixels by 252 pixels) and were in full colour with single-coloured backdrops. Images were presented against a black background. 2.4. Procedure Ethical approval for this study was obtained from the research ethics committee of the School of Psychology, University of Southampton. All participants provided informed consent before inclusion into the study, in compliance with institutional regulations. Participants were tested individually in a quiet room in their own house. Participants initially completed the emotion recognition task, which was modelled on a version used in previous research [42]. In this task, after the presentation of an image, participants are asked to indicate the emotion seen by pressing a labelled key on the keyboard as soon as they identify it. Each trial started with a central fixation cross (+) (in size 28 font, approximately 5 mm by 5 mm) shown for 500 ms, which was replaced by the display of an image for 2 s accompanied by labels naming the different emotions: Happiness, Sadness, Anger, Pain, Fear, No emotion. The task was to select the label that best describes the emotional expression shown. Images were presented in a new random order to every participant. The position of the different response labels did not vary from trial to trial to avoid inconsistent response times, which have been reported in experimental studies that have varied the position of the response labels [42]. After 2 s, the image disappeared while the words remained on screen until the participant made a choice and clicked next to continue. Participants who clicked Next without having made a choice were cued, ‘‘Please make a choice.’’ Participants were seated approximately 0.5 m away from the monitor. Participants were instructed to press the key to indicate the emotion and to respond as soon as they made a decision, and response times were recorded. In experiment 1, there were 15 practice trials, followed by 4 buffer trials and a block of 48 trials. Each emotion was depicted once by each of 8 actors; therefore, there were 48 experimental trials. The task lasted approximately 5 min. In experiment 2, there were 10 practice trials, followed by 3 buffer trials and a block of 200 trials. In experiment 2, there were 5 emotional combinations (Pain:Sadness; Pain:Anger; Pain:Fear; Pain:Happiness; Pain:Neutral)  5 percentage combinations of pain with another emotion (90%:10%, 70%:30%, 50%:50%, 30%:70%, 10%:90%)  8 actors, resulting in 200 stimuli. Experiment 2 lasted approximately 10 min; the overall duration of the study was 60 min. In both emotion recognition tasks, all text and fixation crosses were presented in white against a black background. The task

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was developed on the Presentation software package (Neurobehavioral Sciences) and run on a laptop computer with a 15-inch monitor. To avoid potential priming on the emotion recognition tasks, all participants completed the self-report measures after the emotion recognition tasks [44]. To control for potential order effects, questionnaires were presented in a new random order for each participant. 2.5. Data analytic plan The sample size for this investigation was chosen to be sufficiently large to have a power of 0.8 for minimally detecting a medium effect size when conducting Bonferroni-corrected comparisons. It was estimated that for 2 equal-sized groups, a total sample size of 100 would result in satisfying both criteria and would provide a power of at least 0.9 for uncovering medium-sized effects. Before each analysis, a multivariate assessment of potentially outlying and potentially influential response profiles of participants was undertaken by the squared Mahalanobis distance from group centroids [50]. In all instances, no discrepant observations were found. More generally, through the result and consequences of the central limit theorem, standard analysis of variance (ANOVA) techniques are generally regarded as being robust to the overcapitalisation of type I errors, providing there is no pronounced skewness arising from outlying observations, and providing there are more than 20 degrees of freedom in univariate analyses [50]. The relatively large sample sizes under the design (n = 50 per group) ensured that this last requirement was satisfied. Moreover, standard ANOVA for repeated-measures designs are based on a sphericity assumption. Violation of this assumption, coupled with the use of an unadjusted F test, may lead to an undesirable inflation of the type I error rate. For this reason, the sphericity assumption was assessed by Mauchly’s test, and additionally the Geisser–Greenhouse conservative lower bound adjusted F test was used in validating statistical conclusions. In all instances in the following, there was complete agreement in statistical conclusions drawn from by the unadjusted F test and adjusted F tests, and on this basis, and for parsimony, only unadjusted F tests are reported. Observed effect size for ANOVA are reported by partial eta squared ðg2p Þ, and for 2-group comparisons Cohen’s d, defined as the absolute difference in means relative to the pooled SD of the 2 groups [13]. Differences in demographic and medical characteristics, questionnaire measures, and response times between groups were explored with v2 and t tests for categorical and continuous variables, respectively. For experiment 1, an analysis of variance for a 2  6 mixed design was used, with Group as a between-subjects factor with 2 levels (Control, CAP), Emotion as a repeatedmeasures factor with 6 levels (Pain, Anger, Sadness, Fear, Happiness, Neutral), and the number of emotions correctly identified (out of 8) as the dependent variable. In experiment 2, the data were analysed by an ANOVA for a 2  5  5 mixed design, with Group as a between-subjects factor at 2 levels (Control, CAP), Pain Percentage as a repeated-measures factor with 5 levels (90% Pain; 70% Pain; 50% Pain; 30% Pain; 10% Pain), Emotion as a repeated-measures factor with 5 levels (Anger, Sadness, Fear, Happiness, Neutral), and number of pain responses given as a dependent variable. In this analysis, a large 3-way interaction was anticipated, and therefore, planned separate analyses were conducted for each emotion with Group as a 2-level between-subjects factor and Pain Percentage (90% Pain; 70% Pain; 50% Pain; 30% Pain; 10% Pain) as a repeated-measures factor. The P values given for the 2-sample tests undertaken are from 2-sided tests. To examine the relative contribution of maternal depression, anxiety sensitivity, and state and trait anxiety (STAI1 and STAI2 respectively) to the total

number of pain responses, multiple regression analyses were conducted for both groups combined and for each group separately, with depression, anxiety sensitivity, and state and trait levels of anxiety as predictor variables and the total number of pain responses as the criterion variable. All variables were included in the models. The same statistical conclusions were obtained when stepwise forward regression, backward elimination, or best subsets model building was used—that is, BDI, ASI, STAI1, and STAI2 were not, either singly or in any combination, predictive of the total number of pain responses, either within groups or across groups. The regression models did not have the problems associated with multicollinearity, and all variance inflation factors were smaller than 1.3. All analyses were conducted with SPSS software, version 17 (SPSS, Chicago, IL). 3. Results 3.1. Participant characteristics Mean self-report data for questionnaires completed by group are presented in Table 1. All questionnaires in this investigation demonstrated acceptable to high levels of internal consistency (>0.75) [11]. The 2 groups of mothers did not differ in relationship status (v2 = 0.063, df = 1, P = .802), ethnicity (v2 = 0.095, df = 1, P = .758), age (t = 0.507, df = 98, P = .6127), years in education (t = 0.395, df = 98, P = .693), number of children (t = 0.301, df = 98, P = .763), age of index child (t = 0.948, df = 98, P = .345), state anxiety (t = 1.127, df = 98, P = .262), trait anxiety (t = 0.805, df = 98, P = .423), depression (t = 1.721, df = 98, P = .088), and anxiety sensitivity (t = 0.886, df = 98, P = .378). 3.2. Classification of emotions 3.2.1. Experiment 1 Mean values and SD for the number of emotions (out of 8) correctly identified in experiment 1 are listed in Table 2. Analysis of the data by a 2-way mixed ANOVA showed that there was a statistically significant difference in the mean number of emotions correctly identified, F(5, 490) = 123.78, MSE = 0.369, P < .001, g2p = 0.558, but no difference between groups either as a main effect, F(1, 98) = 0.383, MSE = 0.435, P = .538, g2p = 0.004, or as an interaction between Group and type of Emotion, F(5, 490) = 0.170, MSE = 0.369, P = .974, g2p = 0.002. A post hoc pairwise application of the paired samples t test with Bonferroni-corrected levels of observed significance gave a logically consistent set of results with a homogeneous grouping between Fear and Sadness (P = .263) showing the lowest means, followed by a second homogeneous group between Pain and Anger (P = .169), all which had lower mean values when compared with the mean identification of Neutral images (P < .001), which in turn was significantly lower than the corresponding mean in the Happiness condition (P < .001). Mean response times for CAP (mean ± SD, 2060 ± 1101 s) and Control (2188 ± 1299 s) groups did not differ significantly (t = 0.531, df = 98, P = .587). Table 2 Mean and standard deviation for the number of correctly identified emotions (out of 8) per group in experiment 1. Emotion

Pain Anger Sadness Fear Happiness Neutral

Mean

Standard deviation

Control

CAP

Control

CAP

6.82 7.10 6.28 6.08 7.94 7.40

6.82 7.04 6.32 6.06 7.84 7.34

0.748 0.364 0.730 0.724 0.240 0.535

0.919 0.402 0.653 0.767 0.370 0.557

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A multiple regression analysis for both groups combined, and for each group separately, showed that maternal depression, anxiety sensitivity, and state and trait levels of anxiety were not predictive of the total number of pain responses for experiment 1 (Supplementary Tables A1 and A2). 3.2.2. Experiment 2 Summary statistics for number of pain responses (out of 8) by Group, Pain Percentage, and Emotion are listed in Table 3 and also depicted graphically in Fig. 2. In experiment 2, there was a significant 3-way interaction between Group, Pain Percentage, and Emotion on the number of pain responses, F(16, 1568) = 16.083, MSE = 1.059, P < .001, g2p = 0.141. For the Anger stimulus combinations, there was a significant 2way interaction between Pain Percentage and Group, F(4, 392) = 91.843, MSE = 0.926, P < .001, g2p = 0.484 on the number of pain responses, along with a main effect for Pain Percentage, F(4, 392) = 732.449, MSE = 0.926, P < .001, g2p = 0.882, and a main effect for Group, F(1, 98) = 283.854, MSE = 0.944, P < .001, g2p = 0.743. Application of the independent samples t test identified that the mean number of pain responses was significantly higher in CAP than Control for Pain 50%:Anger 50% (t = 13.943, df = 98, P < .001, d = 2.79), for Pain 30%:Anger 70% (t = 12.532, df = 98, P < .001, d = 2.51), and to a lesser extent for Pain 10%:Anger 90% (t = 3.269, df = 98, P = .001, d = 0.65), but no statistically significant differences for Pain 90%:Anger 10% (P = .281, d = 0.22) or Pain 70%:Anger 30% (P = .151, d = 0.29). For the Sadness stimulus combinations, there was a significant interaction between Pain Percentage and Group on number of pain responses, F(4, 392) = 34.203, MSE = 1.112, P < .001, g2p = 0.259, and significant main effects for Pain Percentage, F(4, 392) = 704.187, MSE = 1.112, P < .001, g2p = 0.878, and a main effect for Group, F(1, 98) = 192.941, MSE = 0.964, P < .001, g2p = 0.663. A post hoc application of the independent samples t test showed that the mean number of pain responses was higher in CAP than Control for Pain 10%:Sadness 90% (t = 5.079, df = 98, P < .001, d = 1.02), for Pain 30%:Sadness 70% (t = 7.045, df = 98, P < .001, d = 1.41), and for Pain 50%:Sadness 50% (t = 10.897, df = 98, P < .001, d = 2.17), but there was no significant difference between the groups for Pain 70%:Sadness 30% (t = 1.938, df = 98, P = .055, d = 0.39) or for Pain 90%:Sadness 10% (t = 1.693, df = 98, P = .094, d = 0.34). For the Fear stimulus combinations, there was a significant 2way interaction between Pain Percentage and Group on number of pain responses, F(4, 392) = 40.644, MSE = 1.608, P < .001, g2p = 0.293, and significant main effects for Pain Percentage, F(4, 392) = 444.203, MSE = 1.608, P < .001, g2p = 0.819, and Group, F(1, 98) = 207.69, MSE = 1.715, P < .001, g2p = 0.679. A post hoc application of the independent samples t test showed that the

mean number of pain responses was higher in CAP than the Control group (P < .001 in all instances)—specifically, for Pain 90%:Fear 10% (t = 3.859, df = 98, P < .001, d = 0.77), for Pain 70%:Fear 30% (t = 4.171, df = 98, P < .001, d = 0.83), for Pain 50%:Fear 50% (t = 8.396, df = 98, P < .001, d = 1.68), for Pain 30%:Fear 70% (t = 9.985, df = 98, P < .001, d = 2.00), and for Pain 10%:Fear 90% (t = 4.481, df = 98, P < .001, d = 0.90). For the Happiness stimulus combinations, there was a significant 2-way interaction between Pain Percentage and Group on number of pain responses, F(4, 392) = 17.025, MSE = 1.308, P < .001, g2p = 0.148, and significant main effects for Pain Percentage, F(4, 392) = 666.084, MSE = 1.308, P < .001, g2p = 0.872, and Group, F(1, 98) = 37.557, MSE = 1.363, P < .001, g2p = 0.277. A post hoc application of the independent samples t test showed that the mean number of pain responses was higher in CAP than the Control group for Pain 90%:Happiness 10% (t = 2.212, df = 98, P = .029, d = 0.44), for Pain 70%:Happiness 30% (t = 2.029, df = 98, P = .045, d = 0.41), and for Pain 50%:Happiness 50% (t = 7.017, df = 98, P < .001, d = 1.45), but no significant difference for Pain 30%:Happy 70% (t = 0.375, df = 98, P = .708, d = 0.07) or for Pain 10%:Happy 90% (t = 1.426, df = 98, P = .157, d = 0.29). For the Neutral stimulus combinations, there was a significant 2-way interaction between Pain Percentage and Group on number of pain responses, F(4, 392) = 30.359, MSE = 1.110, P < .001, g2p = 0.237, and significant main effects for Pain Percentage, F(4, 392) = 741.493, MSE = 1.110, P < .001, g2p = 0.883, and Group, F(1, 98) = 34.845, MSE = 1.880, P < .001, g2p = 0.262. A post hoc application of the independent samples t test showed that the mean number of pain responses was higher in CAP than the Control group for Pain 90%:Neutral 10% (t = 2.606, df = 98, P = .011, d = 0.52), for Pain 70%:Neutral 30% (t = 2.400, df = 98, P = .018, d = 0.48), for Pain 50%:Neutral 50% (t = 8.147, df = 98, P < .001, d = 1.63), but no significant difference for Pain 30%:Neutral 70% (t = 0.000, df = 98, P = 1.00, d = 0.00) or for Pain 10%:Neutral 90% (t = 0.962, df = 98, P = .339, d = 0.19). In experiment 2, mean ± SD response time for CAP (2496 ± 1206 s) and Control (2386 ± 1435 s) groups did not differ significantly (t = 0.414, df = 98, P = .597). A multiple regression analysis for both groups combined and for each group separately showed that maternal depression, anxiety sensitivity, and state and trait levels of anxiety were not predictive of the total number of pain responses for experiment 2 (Supplementary Tables B1 and B2). When we ran the regression model in mothers of children with CAP and mothers of pain-free children, combined with maternal depression, anxiety sensitivity, and state and trait levels of anxiety, and group entered in a stepwise fashion as predictor variables, the same statistical conclusions were obtained. Similarly, we obtained equivalent results when we used

Table 3 Mean and standard deviation of the number of pain responses (out of 8) by group, pain percentage, and emotion for experiment 2.

*

Emotion

Group

Pain percentage, mean

Pain percentage, SD

90

70

50

30

10

90

70

50

30

10

Anger

Control CAP

6.94 6.80

6.56 6.74

4.08* 7.22

2.28* 6.06

0.34* 0.70

0.620 0.670

0.675 0.565

1.482 0.582

1.415 1.596

0.519 0.580

Sadness

Control CAP

6.68 6.90

6.78 7.02

4.16* 6.96

1.54* 3.84

0.24* 0.78

0.653 0.647

0.708 0.515

1.695 0.669

1.581 1.683

0.431 0.616

Fear

Control CAP

6.56* 7.06

6.60* 7.18

3.98* 7.00

1.56* 5.40

0.30* 0.80

0.733 0.550

0.808 0.560

2.495 0.495

1.831 2.010

0.463 0.639

Happiness

Control CAP

6.68* 7.02

6.42* 6.70

3.26* 5.58

1.38 1.50

0.26 0.40

0.913 0.589

0.731 0.647

1.454 1.830

2.079 0.886

0.443 0.535

Neutral

Control CAP

6.58* 6.96

6.24* 6.60

3.04* 5.82

1.42 1.42

0.40 0.50

0.785 0.669

0.771 0.728

2.020 1.320

1.797 0.928

0.535 0.505

Statistically significant mean difference by independent sample t test.

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679

Fig. 2. Number of pain responses by group as a function of Pain Percentage for Anger, Sadness, Fear, Happiness, and Neutral, and for all emotions combined.

as criterion variable the number of pain responses in the 50%:50% combination across all emotions.

4. Discussion The aim of this study was to test the theoretically derived proposition that mothers of children with CAP will detect the presence of pain in emotionally ambiguous faces at a lower percentage level than mothers of pain-free children. It was also predicted that the CAP group of mothers would make their classifications of emotion faster compared to the control group. In experiment 1, participants were required to identify the emotion in facial images that depicted 100% intensity of Pain, Sadness, Anger, Happiness, Fear, and Neutral. The results showed that there was no difference in the emotion recognition performance between the 2 groups of mothers. In line with the general emotion recognition literature [15], participants from both groups combined were better at recognising Happiness, followed by Neutral, Anger, Pain, Sadness, and Fear. Humans are best at classifying happy expressions because the smile makes the task easy [21]. On the other hand, people usually find that fear expressions are difficult to classify [29]. On the basis of systematic comparisons between studies, pain expression has been described to be unique and distinct from the 6 prototypical facial expressions of basic emotions [28]. There is evidence of reasonably accurate identification of pain expression in adults; however, the data on distinction of pain from other facial expressions, such as those of fear or anger, are scant [28], and experiment 1 adds to this literature. In experiment 2, mothers completed an emotion recognition task that required them to identify the predominant emotion in a series of morphed facial images. In line with our hypothesis, it

was found that overall mothers of children with CAP were classifying ambiguous emotional expressions predominantly as pain. Interestingly, when pain was combined with negative emotions, the bias appeared at a lower percentage level of pain (10%) compared to when combined with positive or neutral emotional expressions, where the bias appeared at a higher percentage level (50%), while there was a pain bias for all the fear stimulus combinations. Mean response times for CAP and control groups did not differ significantly, which confirms that both parent groups spent approximately the same amount of time making their judgements. Moreover, there was no difference in the performance of the 2 groups in experiment 1, which suggests that the differences observed in experiment 2 are not due to an overall deficit in the emotion-recognition ability of mothers of children with CAP. Humans are primed to read facial expressions and often read expressions into neutral faces, perceiving static facial features as indicative of emotional or personality traits [9]. Previous research into interpretation bias has demonstrated that ambiguity triggers the tendency to selectively impose schema-related interpretations on information [35]. In case of several competitive interpretations, it can be hypothesized that mothers of children with CAP draw conclusions (from the given information) in conformity with their schemas. In Beck’s view, schemata result from experience and guide new information along the processing lines that experience has formed. Biases in information processing result from systematic distortions in cognitive schemata that have subsequently been strengthened by perceptual sensitivity and memory biases for information congruent activated schema, at the time of encoding. It may be that sensitivity to pain expression is enhanced by repeated exposure to a family member in pain. Evidence from studies that have examined the agreement between patients’ reports of their own pain and that of health care professionals and relatives

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show that overall health care professionals tend to underestimate patients’ pain, while patients’ relatives are more likely to overestimate patients’ pain [28]. Underestimation is often attributed to a habituation process [56]. Parents have been shown to both overestimate and underestimate pain [57], and parental variables such as catastrophizing [22] are positively associated with higher pain attributions for their children. However, direct comparison of these results with ours is problematic, given that we used images of adults expressing various emotions and also a group of mothers of pain-free children as comparator instead of health care professionals, thus controlling for role differences. The ability to accurately infer the mental state of others from external cues such as facial emotional expressions is essential for regulating one’s own emotional state and guiding one’s own behaviour in a social context. Thus, facial emotion recognition can be considered a cognitive cornerstone for intact social functioning. Consequently, the capability to recognise the internal state from external cues can be seen as a cognitive basis of parenting and might promote empathy, trust, and prosocial behaviour [34]. In turn, misinterpretations due to impaired recognition are likely to result in altered parenting behaviours. For example, a mother with a pain-related bias may misinterpret an overall negative emotion expressed by her child, such as a combination of pain and sadness, as pain and proceed by labelling the emotion as pain, then offering sympathy and relief from chores. Additional research is needed to determine under what circumstances mothers’ emotion recognition biases emerge, whether they vary in intensity over time, and how they may impact parent–child interactions. To this end, it may be useful, following Palermo and Eccleston’s [37] suggestion and test models that delineate the processes underlying the connection between pain and functioning, to target specific child and family factors in interventions to optimise child functioning. A biased perception towards pain may be of particular relevance for family therapy in CAP. Given this pattern of maternal perception, when therapists deliver cognitive behavioural treatment may need to emphasise self-regulatory, metacognitive, and metaemotional interventions for parents. Thus, especially CAP mothers who show deficits in emotion recognition might benefit from cognitive training that focuses on improving the differentiation of subtle, ambiguous social cues of the emotional states of others in general and their children in particular. In this investigation, mothers of children with CAP did not report more anxiety, depression, and anxiety sensitivity compared to control mothers. Although the evidence is mixed, some studies have found significant parental psychopathology in families of youth with chronic or recurrent pain. Research on parents of children with CAP reveals elevated rates of anxiety and depression compared with parents of healthy controls [18,33]. However, strict inclusion and exclusion criteria were imposed in the present study to control for any other influences on emotion recognition ability. Some limitations of the present investigation need to be noted. First, in experiment 2, expressions mixing 2 emotion types that differed in intensity were shown. It is thus difficult to establish what the correct answer is, in that the results always have to be interpreted compared to the control group. For example, control mothers in most cases saw a 30% Pain:70% Sad face as Sad, whereas generally mothers of children with CAP viewed this as Pain. Although this response may be the result of an interpretation bias towards pain, the response as such is not fully incorrect. Also, facial expressions consisting of 2 different emotions may seem unnatural. Second, by means of static emotional facial expressions, it remains unclear whether mothers of children with CAP exhibit an altered detection threshold, especially for ambiguous stimuli, or whether they show differences in the accuracy of labelling specific emotions. Third, social interaction in everyday life is complex and dynamic—that is, we see a neutral face change into an angry one. People also normally

rely on more than one sensory modality when evaluating the emotional state of another person. Thus, future studies could extend the experimental setting and begin to examine the ability of mothers of children with CAP to recognise emotional states of others in more complex scenes, which are closer to interactions in everyday life. Such multimodal tasks could involve the integration of visual and auditory information. In defence of the present investigation, however, it has to be acknowledged that people are more accurate in recognizing facial expressions relative to other kinds of expressive information [17] and information from the face is privileged relative to other communication channels [8,16]. Fourth, we need to explore whether the effect observed in this study is attenuated when children’s facial expressions are used. It is possible that the classifications of adult faces by adults may differ from classifications of children faces by adults. Finally, even though every effort was made to minimise the possibility of the study context inducing a report bias to participants, it is still possible that independently or in combination with a perceptual difference in the classification of emotionally ambiguous facial expressions, mothers of children with chronic pain may tend to report pain more. In conclusion, there is increasing evidence for the role of parents in the maintenance of chronic pain in children [32], and the present investigation showed that mothers of children with CAP interpret ambiguous emotional information as pain. Merging these lines of evidence, we tentatively propose that emotion interpretation biases interfere with parenting and facilitate maladaptive behaviours, thereby contributing to the maintenance of CAP in young people. However, it is acknowledged that this is a theoretical supposition and prospective studies with interventions intended to modify parental responses to pain cues and assessing the effects on the child could address such critical issues. Conflict of interest statement The authors report no conflict of interest. Acknowledgments This study was funded by a research assistantship from the British Psychological Society. We thank Dr Daniela Simons for permission to use and morph static images from the Montreal Pain and Affective Dynamic Pain Stimuli; Drs Wendy Adams, Katy Grey, and Eric Graf for permission to use Face Morph and training in its use; and Dr Jin Zang for programming the emotion recognition task and technical support. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.pain.2011.12.004. References [1] Adams WJ, Gray KLH, Garner M, Graf EW. High-level face adaptation without awareness. Psychol Sci 2010;21:205–10. [2] Balge KA, Milner JS. Emotion recognition ability in mothers at high and low risk for child physical abuse. Child Abuse Negl 2000;24:1289–98. [3] Bandura A. Social learning theory. New York, NY: General Learning Press; 1977. [4] Beck A, Steer RA, Brown GK. Manual for Beck Depression Inventory-II. San Antonio, TX: Psychological Corporation; 1996. [5] Beck AT. Cognitive therapy and the emotional disorders. New York, NY: International Universities Press; 1976. [6] Beier T, Neely S. Feature based image metamorphosis. Proc SIGGRAPH 1992:35–92. [7] Brace MJ, Smith MS, McCauley E, Sherry DD. Family reinforcement of illness behavior: a comparison of adolescents with chronic fatigue syndrome, juvenile arthritis, and healthy controls. J Dev Behav Pediatr 2000;21:332–9. [8] Carrera-Levillain P, Fernandez-Dols JM. Neutral faces in context: their emotional meaning and their function. J Nonverbal Behav 1994;18:281–99.

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