578303 research-article2015
HPQ0010.1177/1359105315578303Journal of Health PsychologySchoth et al.
Article
Anxiety sensitivity is associated with attentional bias for pain-related information in healthy children and adolescents
Journal of Health Psychology 1–11 © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1359105315578303 hpq.sagepub.com
Daniel E Schoth, Lucy Golding, Emily Johnson and Christina Liossi
Abstract This investigation explored the association between anxiety sensitivity and attentional bias for threatening information in children and adolescents (N = 40). Participants completed a pictorial version of the visualprobe task, featuring pain-related, health-threat and general-threat images presented for 500 and 1250 ms. Regression analyses revealed significant associations between anxiety sensitivity and attentional bias towards pain-related images presented for 500 ms and between state anxiety and attentional bias towards generalthreat images presented for 1250 ms. These results suggest that in children and adolescents, anxiety sensitivity is associated with attentional bias for negative information of personal relevance.
Keywords anxiety, children, cognitive processing, health psychology, quantitative methods
Anxiety sensitivity (AS) is an individual difference variable conceptualised as a fear of anxietyrelated sensations (e.g. trembling, palpations, shortness of breath), stemming from the belief that such sensations may have negative somatic, psychological and/or social consequences (Reiss and McNally, 1985). In children and adolescents, AS has been associated with anxiety symptoms (Essau et al., 2010; Schmidt et al., 2010), social anxiety disorder (Alkozei et al., 2014), depression (Muris et al., 2001a) and post-traumatic stress disorder (Kılıç et al., 2008) and is predictive of panic attacks (Hayward et al., 2000). Research also highlights a relationship between AS and the experience of pain. Children with current pain report higher AS than those without pain (e.g. Lipsitz et al., 2004; Tsao et al., 2009), and in children with chronic pain, heightened AS
has been associated with reduced health-related quality of life (Mahrer et al., 2012) and increased fear of pain (Martin et al., 2007; Muris et al., 2001b). Child AS is also associated with pain catastrophising, which in turn is associated with higher post-operative pain (Esteve et al., 2014). It has been postulated that the relationship between AS and pain may be mediated by cognitive biases, in part due to a tendency to allocate attentional resources towards pain-related information which exacerbates the emotional
University of Southampton, UK Corresponding author: Daniel E Schoth, Academic Unit of Psychology, University of Southampton, Highfield, Southampton SO17 1BJ, UK. Email: [email protected]
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response to pain itself (Keogh and Cochrane, 2002). Indeed, AS itself has been conceived as a form of cognitive bias, entailing vigilance for somatic symptoms (Zvolensky and Forsyth, 2002) and misrepresentations of physical cues (Waszczuk et al., 2013). Considering this, research has reported attentional biases towards physical threat words (Keogh et al., 2001), anxiety symptomatology words (Hunt et al., 2006) and health-threat images (Lees et al., 2005) in adults with high AS. To date however only one investigation has explored the association between AS and attentional biases in children. Hunt et al. (2007) used a visual-probe task with anxiety symptomatology, social threat and positive-word conditions, presented at supraliminal (1000 ms; i.e. available to conscious awareness) and subliminal (14 ms, followed by a 986 ms mask; i.e. below the level of conscious awareness) exposure durations. Irrespective of exposure duration, children high in physical AS, relative to those low in physical AS, showed attentional bias towards all word categories. Despite the view that AS is fundamentally related to the experience of pain (Keogh and Cochrane, 2002), research to date has not explored whether AS in children is specifically associated with attentional bias towards painrelated information or is generalised to healththreat (i.e. information which represents poor well-being or an unhealthy state; similar to adults high in AS; see Lees et al., 2005) or even to other types of threat. Moreover, it remains unknown whether attentional biases in children with heightened AS generalise to emotional images as well as emotional words. For certain stimuli, images may have higher ecological validity than single words (e.g. a picture of an aggressive dog compared to the word dog). To address these questions, and to extend the knowledge generated by Hunt et al. (2007), who used linguistic stimuli, this study used painrelated, health-threat and general-threat images in a visual-probe task. The time-course of attentional bias may also be more fully explored, as Hunt et al. (2007) used only a single supraliminal presentation time. Presentation times of 500 and 1250 ms were used by Lees et al. (2005) in
their investigation of adults and are also commonly adopted in the broader anxiety attentional bias literature (for a review, see Bar-Haim et al., 2007). These two supraliminal durations were therefore used in this investigation in an exploratory manner. A distinction between different components of attention has been made (Allport, 1989), and in the visual-probe task bias towards threatening information at 500 and 1250 ms correspond to initial orienting of attention and maintained attention, respectively. This study therefore aimed to explore the time-course and specificity of attentional biases towards painrelated information in a community-based sample of children and adolescents. Based on the results of Hunt et al. (2007), significant positive associations were hypothesised between AS and attentional bias towards all threatening stimuli (i.e. pain-related, health-threat and generalthreat images).
Materials and methods Participants Participants were recruited from the south of England via press announcements and word of mouth. Inclusion criteria were as follows: (a) aged 8–17 years 11 months, (b) English as a first language and (c) having normal or corrected-tonormal vision. Exclusion criteria were as follows: (a) having been diagnosed with any form of chronic or recurrent pain, (b) severe learning disability, (c) the presence of a psychiatric or neurological condition and (d) serious medical illness. Eligibility criteria were determined via an initial telephone interview. On the basis of these criteria, two individuals were excluded who reported the experience of recurrent pain. In total, 40 young people meeting the eligibility criteria were recruited (mean age = 12.75 years; standard deviation (SD) = 3.26; range = 8–17 years; 29 females).
Measures Associations have been found in children between AS and trait and state anxiety (e.g. r = .55 and .27, respectively; Rabian et al., 1999)
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Schoth et al. and AS and depression (e.g. r = .49; Muris et al., 2001a). Considering this, the following selfreport measures were used to characterise this sample and allowed us to determine whether any attentional biases found are associated with AS specifically or also with other individual difference variables known to be correlated with AS. The Child Anxiety Sensitivity Index (CASI; Silverman et al., 1991) is an 18-item measure of fear of anxiety-related sensations for children. Participants rate the extent to which they agree with each item (e.g. It scares me when my heart beats fast) on a 3-point scale (none, some, a lot). Total scores range from 18 to 54, with higher scores indicating higher levels of AS. There is support for three lower-order factors, corresponding to physical concerns, psychological concerns and social concerns (McLaughlin et al., 2007), although there is variability between studies in the items comprising these subscales. The total score was used in this investigation. The CASI is commonly used in research with children and adolescents, with support for its internal consistency (.87) and test-retest reliability in clinical and non-clinical samples (.79 and .76, respectively; Silverman et al., 1991). Cronbach’s alpha in this investigation was .85. The State–Trait Anxiety Inventory for Children (STAIC; Spielberger, 1973) features a 20-item measure of state anxiety assessing current anxiety and a 20-item measure of trait anxiety assessing anxiety proneness. For each subscale, participants rate their level of agreement for each item on a 3-point scale. For the state subscale, participants indicate how they feel at that moment (e.g. I feel … very worried, worried or not worried), while for the trait subscale, participants indicate their level of agreement for each statement (e.g. I get upset at home) on a 3-point scale (hardly ever, sometimes, often). Scores for both subscales range between 20 and 60, with higher scores indicating higher levels of anxiety. The internal consistency of both state (.82–.87) and trait subscales (.78–.81) has been supported (Spielberger, 1973). Cronbach’s alphas in this investigation were .79 and .83 for state and trait subscales, respectively.
The Children’s Depression Inventory – Short version (CDI-S; Kovacs, 2003) is a 10-item measure of the severity of depressive symptoms. For each item, participants pick one statement that describes them best (e.g. I am sad once in a while, I am sad many times or I am sad all the time), with statements rated on a scale of 0 to 2. Total scores range from 0 to 20, with higher scores indicative of more severe depression symptomology. The CDI-S is a valid screening instrument for depression, with acceptable internal consistency (.70; Allgaier et al., 2012). Cronbach’s alpha in this investigation was .77.
Experimental stimuli The visual-probe task featured three experimental conditions (pain-related, health-threat, general-threat) and two neutral filler conditions (neutral faces, neutral objects). Each image condition included eight image pairs. All images were 265 pixels wide × 250 pixels high. Painrelated images feature a model depicting a facial expression of pain, which were paired with another image of the same model with a neutral expression. Health-threat images feature objects threatening to health and well-being, paired with images of neutral objects (e.g. an ambulance paired with a white van, a wheelchair paired with an office chair). General-threat images reflect both natural and man-made sources of threat, which were paired with images of neutral objects and events (e.g. a house on fire paired with a house not on fire, a vicious dog paired with a tame dog). For health- and general-threat categories, the objects depicted in their respective pairs were matched for size, orientation and colour. For the filler stimuli, neutral faces feature image pairs of the same model depicting two similar emotionless expressions. Neutral objects depict pairs of similar neutral objects (e.g. chairs, cups), matched for size, colour and orientation. Pain-related images were derived from the Montréal Pain and Affective Face Clips database (Simon et al., 2008), while healththreat and general-threat images were collected from an online image database. All experimental
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stimuli have been previously used by Schoth and Liossi (2013). Schoth and Liossi conducted an analysis of the valence and arousal of a large set of experimental images, including the ones used in this investigation, via a computerised version of the Self-Assessment Manikin (Lang, 1980). A total of 10 independent participants (5 females; mean age = 25.70 years; SD = 3.34) rated the general-threat images as significantly more arousing and less pleasant than the healththreat and pain-related images (see http://links. lww.com/CJP/A45).
at the University of Southampton. Informed assent was provided by the participant and informed consent by the accompanying parent. Participants first completed the visual-probe task. Questionnaire measures were presented in a new randomised order for each participant and were completed last to avoid potential priming effects on the visual-probe task (Segal and Gemar, 1997). The total experimental duration was 45 minutes.
Visual-probe task
Results were analysed in IBM SPSS Statistics 21 for Windows. Practice and buffer trials were excluded from final analysis, along with trials with incorrect responses. Manual response times less than 300 ms and greater than 1100 ms were identified as outliers and removed. Mean response times were then calculated for each participant separately; any response time >3 SDs away from this individual mean were also removed as outliers. In line with other studies using the visual-probe task (Schoth et al., 2012), attentional bias scores were calculated for each image condition at both presentation times via the following equation: attentional bias score = ((ElPr − ErPr) + (ErPl − ElPl))/2, where E is the emotional image, P is the probe, r is the right position and l is the left position. A positive bias score indicates a shift of attention towards the location of emotional stimuli relative to neutral stimuli. A negative bias score indicates a shift of attention away from the location of emotional stimuli towards neutral stimuli. A score of 0 denotes equal attentional engagement of both emotional and neutral stimuli. A general linear model, in the form of an analysis of covariance (ANCOVA), was used to explore attentional bias scores across the different image and presentation time conditions, while also testing for a linear relationship with AS. In line with current thinking (e.g. Altman and Royston, 2006), AS was included as a continuous covariate of interest in the ANCOVA, rather than as a dichotomised variable. Oneway ANCOVAs and linear regression were used where appropriate to clarify significant
The visual-probe task was run on a PC with a 15-in colour monitor and featured 320 experimental trials (i.e. 64 trials per image condition). Participants initially completed 12 practice trials featuring neutral image pairs unused in the main experiment. Three short breaks were provided during the task. Each trial began with the presentation of a fixation cross for 500 ms. A randomly selected image pair was then presented horizontally for either 500 or 1250 ms. The distance between the inner edges of the two images was 40 mm. Immediately following the removal of the image pair, a dot-probe was displayed in either the left or right location. Participants indicated the location of the dotprobe as quickly and accurately as possible using a two-button response box. Across the task, the two stimuli presentation times were applied in a randomised fashion across all trials. Each image pair was presented eight times (four times for 500 ms and four times for 1250 ms). Within each presentation time, each image in their respective pair appeared twice in the left location and twice in the right location. The dotprobe position was fully counterbalanced across each image condition. All text, fixation crosses and probes were presented in white against a black background.
Procedure Ethical approval for this investigation was obtained from the Research Ethics Committee
Data reduction and analytic plan
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Schoth et al. Table 1. Means (SD) for attentional bias indices and self-report measures (N = 40) and Pearson’s correlation coefficients. Measure
Attentional bias index Pain-related images (500 ms) Pain-related images (1250 ms) Health-threat images (500 ms) Health-threat images (1250 ms) General-threat images (500 ms) General-threat images (1250 ms) Self-report measure CASI CDI-S STAIC state STAIC trait
Mean (SD)
CASI Pearson’s correlation coefficients
4.01 (31.24)
.350*
CDI-S Pearson’s correlation coefficients .156
STAIC state Pearson’s correlation coefficients
STAIC trait Pearson’s correlation coefficients
.344*
.199
3.69 (34.26)
−.278
−.071
−.186
−.186
1.63 (33.74)
.155
.061
−.043
.298
2.40 (29.95)
.299
.296
.027
.266
5.82 (36.19)
−.217
−.468**
.026
−.285
3.27 (38.15)
29.68 (6.16) 2.03 (2.39) 28.45 (3.82) 33.80 (6.25)
.373*
.236 .479** .457**
.257
.296 .419**
.517**
.227
.159
SD: standard deviation; CASI: Childhood Anxiety Sensitivity Index; CDI-S: Children’s Depression Inventory – Short version; STAIC: State–Trait Anxiety Inventory for Children. *Correlation is significant at the .05 alpha level. **Correlation is significant at the .01 alpha level.
effects. Effect sizes for ANCOVA and regression were quantified using partial eta-squared η2p and R2, respectively. For all analyses, the alpha level was set at .05, two-tailed.
Results Descriptive statistics and correlations Means and SDs for attentional bias scores and self-report measures, along with correlations between them, are presented in Table 1. AS was positively correlated with attentional bias for pain-related images at 500 ms, r = .350, p = .027 and bias for general-threat images at 1250 ms, r = .373, p = .018. State anxiety was positively correlated with bias for pain-related images at 500 ms, r = .344, p = .030 and bias for generalthreat images at 1250 ms, r = .517, p = .001. Depression was negatively correlated with bias
for general-threat images at 1250 ms, r = -.468, p = .002.
Visual-probe analysis A small percentage of data was lost due to errors (M = 2.95%, SD = 2.48) and outliers (M = 3.45%, SD = 2.71). The overall mean response time was 591.51 ms (SD = 98.39). A 3 (image condition; pain-related, health-threat, general-threat) × 2 (presentation time; 500 ms, 1250 ms) ANCOVA, with AS entered as a covariate, was conducted on attentional bias scores. Significant main effects were not found for AS, F(1, 38) = 3.47, p = .070, η2p=.084; image condition, F(2, 76) = .782, p = .461, η2p=.020; or presentation time, F(1, 38) = .207, p = .651, η2p=.005. A significant two-way interaction was found for image condition × presentation time, F(2, 76) = 7.41, p = .001, η2p=.163 but not for AS × image condition, F(2, 76) = .715, p = .492, η2p=.018 or
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AS × presentation time, F(1, 38) = 1.85, p = .670, η2p=.005. A significant three-way interaction was found for AS × image condition × presentation time, F(2, 76) = 7.57, p = .001, η2p=.166. To explore the significant three-way interaction further, one-way ANCOVAs were conducted for pain-related, health-threat and general-threat images separately, with presentation time (500 ms, 1250 ms) as an independent variable and AS as a covariate. The AS × presentation time interaction was significant for pain-related images, F(1, 38) = 8.82, p = .005, η2p=.188 and general-threat images, F(1, 38) = 5.82, p = .021, η2p=.133 but not for healththreat images, F(1, 38) = .340, p = .563, η2p=.009. Linear regressions were used to follow-up the significant interactions for pain-related and general-threat images, guided by the results of the correlational analyses. As bias for painrelated images at 500 ms was significantly correlated with both AS and state anxiety, both variables were included in a backward elimination linear regression to determine which statistically predict bias scores. The final model revealed AS only to be statistically predictive of bias for pain-related images at 500 ms, F(1, 38) = 5.30, p = .027. AS is a significant statistical predictor of bias for pain-related images presented for 500 ms, Y(intercept) = −48.67, β = .350, t(38) = 2.30, p = .027, R2 = .122 (unstandardised beta = 1.78, standard error (SE) = .771, 95% confidence interval (CI) of unstandardised beta [0.22, 3.34]). Bias for general-threat images at 1250 ms was significantly correlated with both AS and state anxiety, and therefore, both variables were included in a backward elimination linear regression to determine which statistically predict bias scores. The final model revealed state anxiety only to be statistically predictive of bias for general-threat images presented for 1250 ms, F(1, 38) = 13.87, p = .001. State anxiety is a significant statistical predictor of bias for general-threat images presented for 1250 ms, Y(intercept) = −143.56, β = .517, t(38) = 3.73, p = .001, R2 = .267 (unstandardised beta = 5.16, SE = 1.39, 95% CI of unstandardised beta [2.36, 7.97]).
Discussion Providing partial support for the adopted hypothesis, in this sample of community-based young people, regression analysis revealed a significant association between AS and attentional bias towards pain-related images presented for 500 ms. A positive association between AS and bias towards facial expressions of pain in children is unsurprising, especially because pain expressions convey signals of underlying disease or injury (Williams, 2002), and AS has been associated with fear of pain in young people (Muris et al., 2001b). Transient and acute pains are also commonly experienced by children (e.g. minor injuries when playing games), thus making the stimuli personally relevant to a degree. However, a significant association was found at 500 ms only, a presentation time related to initial orienting of attention and hypervigilance for threat (Bradley et al., 2000). This association was not maintained at 1250 ms, and in fact, a non-significant negative correlation was found between AS and bias scores at this longer presentation time. Although nonsignificant results should be interpreted with caution, this does at least suggest AS in children is not associated with difficulties disengaging attention from pain-related images. Disengaging attention away from pain-related images to neutral images may reflect attempts to regulate emotional response, although specific investigation into this possibility is needed. These results add to those reported by Hunt et al. (2007), showing children with heightened AS bias towards pain-related images. However, we were unable to replicate a general bias towards emotional information as correlational analyses revealed no significant associations between AS and bias towards health-threat images. While a significant correlation was found between AS and bias towards generalthreat images presented for 1250 ms, the final model of a backward elimination linear regression revealed only state anxiety to make a significant contribution to this model. However, different stimuli categories were used by Hunt and colleagues to this study, and evidence also
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Schoth et al. suggests some differences in the precise cortical regions involved in processing emotional words and images (Kensinger and Schacter, 2006). Furthermore, Hunt and colleagues used the physical AS subscale of the CASI, whereas in this investigation, we used the total CASI score, as it represents the global-order AS factor and therefore takes into consideration different types of fears, including fears of panic-related somatic, cognitive and social cues. Although it could be speculated that healththreat images may reflect the concerns of children with heightened AS, and therefore, AS would be associated with bias towards such stimuli, it is important to note that none of the children recruited to this study were experiencing any serious medical illness. Unlike the symptom of pain, the objects depicted in the health-threat category (e.g. wheelchair, ambulance) were not personally relevant to the children recruited. Research has shown personal relevance and negative experience to influence patterns of bias in children (LoBue, 2010), and lack of direct experience with the health-threat objects depicted may have influenced the pattern of results observed. Another potentially important difference between the image categories is that only the pain-related category featured facial expressions, and facial expressions are known to rapidly capture attention (Smith, 2012). Furthermore, emotional facial expressions may be more universally interpretable than images of objects and scenes (Waters et al., 2010). Regression analysis revealed a significant association between state anxiety and bias towards general-threat images presented for ms. General-threat images, which for 1250 example included images of fire, vicious dogs and guns, depict the most imminent sense of threat out of the three image categories included in this study. This finding is therefore in agreement with research showing both evolutionary and man-made threats are associated with biased attentional processing (e.g. Brosch and Sharma, 2005) and also with adult ratings showing the general-threat images to be significantly more arousing and less pleasant than the pain-related and health-threat images.
Interestingly, state anxiety was associated with bias towards general-threat images at 1250 ms only, and not also at 500 ms. Although few studies like ours have specifically explored biases in community-based children nonselected for anxiety, Waters et al. (2004) also reported evidence of bias towards fear-related images (e.g. vicious dogs, guns) at 1250 ms in non-selected children. It has been argued that non-anxious individuals have a higher threshold for detecting threat than anxious individuals (Mogg and Bradley, 1998). While no evidence of hypervigilance was shown, the longer presentation time used allows for greater processing and elaboration of stimuli, with biases subsequently stemming from maintained attention towards threat (Donaldson et al., 2007). State but not trait anxiety was associated with attentional bias towards general-threat images. Little research has explored the specific role state anxiety has upon children’s attentional biases. Recruiting non-clinical children, Heim-Dreger et al. (2006) reported a positive correlation between state anxiety and attentional bias for threatening faces measured via the emotional Stroop task but not the visualprobe task. It has been suggested that state anxiety moderates the relationship between trait anxiety and attentional bias (e.g. Eysenck, 1997). Although a positive correlation was found between state and trait anxiety in this study, this was not statistically significant (.23) and was lower than that reported in a large study of children and adolescents (aged 8–16 years) using the STAIC (.54; Lau et al., 2006). Comparison to other studies exploring biases in children is limited due to the tendency of researchers not to report correlations between questionnaires. Overall, it is clear that further research is needed exploring how the interaction between state and trait anxiety may influence patterns of attentional bias in children and adolescents (Puliafico and Kendall, 2006). Correlational analysis showed a significant negative correlation between depression and attentional bias for general-threat images presented for 500 ms. Participants reporting higher levels of depression showed greater avoidance
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of such images. This is consistent with research showing children with depression exhibit an avoidance of sad facial expressions (Gibb et al., 2009; Johnson Harrison and Gibb, 2014), possibly as an emotion regulation strategy. AS was positively correlated with both state and trait anxiety, relationships that have been reported in former research (e.g. Rabian et al., 1999; Silverman et al., 1991). Depression was also positively correlated with trait anxiety, which is in line with evidence highlighting the comorbidity of these two individual difference variables in children (Garber and Weersing, 2010). The results of this study must be considered in light of some limitations. First, a relatively small sample size was recruited. Using GPower (Erdfelder et al., 1996), examination of achieved power in the three-way ANCOVA revealed only >68% power to detect significant effects should they exist. Second, a community-based sample of children and adolescents was recruited, who self-presented to the research team. The generalisability of these results therefore needs to be demonstrated through replication in more representative samples among children and adolescents in other settings and from the general population. Third, the sample consisted of children between 8 and 17 years of age. Although a broad age range creates the opportunity to examine developmental influences from childhood to adolescence, due to the small sample, we may not have been able to detect age effects. Fourth, participants did not rate the personal relevance or valence and arousal of the images used, and therefore, the role of these factors in the pattern of results reported may only be speculated. This decision was made in order to keep the investigation to a reasonable duration and to avoid fatigue, especially in younger children. Schoth and Liossi (2013) provide valence and arousal ratings from a group of adults, and although these results should be generalised with caution to this sample, research has shown children, adolescents and adults provide similar ratings of valence and arousal for emotional images (McManis et al., 2001). Fifth, the aim of this study was to examine threat-processing in
children, and therefore, we decided to limit the design to the inclusion of threat-related stimuli only. However, we cannot rule out the possible emotional valence effects of positive stimuli upon attentional biases, an effect that has been demonstrated in a previous study (Hunt et al., 2007). To date, only two studies have explored the association between AS and attentional bias in children and adolescents. Further research is therefore needed in this field, which is also apparent as attentional biases have been implicated in the development and/or maintenance of pain (Eccleston and Crombez, 2007) and anxiety (Bar-Haim, 2010) disorders, and the experience of AS in children has been associated with both. Before the possible clinical implications of biases in children with heightened AS are explored, however, it is important to replicate these results in other samples to determine their generalisability and also to extend beyond crosssectional designs which provide no information on causality. Future research could also directly compare attentional biases for pain-related pictorial and linguistic stimuli in children, as limited research with adults with high AS suggests biases to be more pronounced towards images than words (Lees et al., 2005). In conclusion, the results of this investigation add to those reported by Hunt et al. (2007), showing AS is associated with attentional bias towards pain-related images in children and adolescents. Acknowledgements We would like to thank Dr. Jin Zhang, PhD (Senior Experimental Officer, Academic Unit of Psychology, University of Southampton) for developing the programme used in this study, and for her continued technical support. We would also like to thank the anoymous reviewers for their helpful comments on an earlier version of the manuscipt.
Funding This research received no specific grant from any funding agency in the public, commercial or not-forprofit sectors.
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Kovacs M (2003) Children’s Depression Inventory (CDI): Technical Manual Update. New York: Multi-Health Systems. Lang PJ (1980) Behavioral treatment and bio-behavioral assessment: Computer applications. In: Sidowski JB, Johnson JH and Williams EA (eds) Technology in Mental Health Care Delivery Systems. Norwood, NJ: Ablex Publishing, pp. 119–137. Lau JY, Eley TC and Stevenson J (2006) Examining the state–trait anxiety relationship: A behavioural genetic approach. Journal of Abnormal Child Psychology 34(1): 18–26. Lees A, Mogg K and Bradley BP (2005) Health anxiety, anxiety sensitivity, and attentional biases for pictorial stimuli and linguistic health threat cues. Cognitive Emotion 19(3): 453–462. Lipsitz JD, Masia-Warner C, Apfel H, et al. (2004) Anxiety and depressive symptoms and anxiety sensitivity in youngsters with noncardiac chest pain and benign heart murmurs. Journal of Pediatric Psychology 29(8): 607–612. LoBue V (2010) What’s so scary about needles and knives? Examining the role of experience in threat detection. Cognition & Emotion 24(1): 180–187. McLaughlin EN, Stewart SH and Taylor S (2007) Childhood anxiety sensitivity index factors predict unique variance in DSM-IV anxiety disorder symptoms. Cognitive Behaviour Therapy 36(4): 210–219. McManis MH, Bradley MM, Berg WK, et al. (2001) Emotional reactions in children: Verbal, physiological, and behavioral responses to affective pictures. Psychophysiology 38(2): 222–231. Mahrer NE, Montaño Z and Gold JI (2012) Relations between anxiety sensitivity, somatization, and health-related quality of life in children with chronic pain. Journal of Pediatric Psychology 37(7): 808–816. Martin AL, McGrath PA, Brown SC, et al. (2007) Anxiety sensitivity, fear of pain and pain-related disability in children and adolescents with chronic pain. Pain Research and Management 12(4): 267–272. Mogg K and Bradley BP (1998) A cognitive-motivational analysis of anxiety. Behaviour Research and Therapy 36(9): 809–848. Muris P, Schmidt H, Merckelbach H, et al. (2001a) Anxiety sensitivity in adolescents: Factor structure and relationships to trait anxiety and symptoms of anxiety disorders and depression. Behaviour Research and Therapy 39(1): 89–100.
Muris P, Vlaeyen J and Meesters C (2001b) The relationship between anxiety sensitivity and fear of pain in healthy adolescents. Behaviour Research and Therapy 39(11): 1357–1368. Puliafico AC and Kendall PC (2006) Threat-related attentional bias in anxious youth: A review. Clinical Child and Family Psychology Review 9(3–4): 162–180. Rabian B, Embry L and MacIntyre D (1999) Behavioral validation of the Childhood Anxiety Sensitivity Index in children. Journal of Clinical Child Psychology 28(1): 105–112. Reiss S and McNally RJ (1985) Expectancy model of fear. In: Reiss S and Bootzin RR (eds) Theoretical Issues in Behavior Therapy. San Diego, CA: Academic Press, pp. 107–121. Schmidt NB, Keough ME, Mitchell MA, et al. (2010) Anxiety sensitivity: Prospective prediction of anxiety among early adolescents. Journal of Anxiety Disorders 24(5): 503–508. Schoth DE and Liossi C (2013) Specificity and timecourse of attentional bias in chronic headache: A visual-probe investigation. The Clinical Journal of Pain 29(7): 583–590. Schoth DE, Delgado Nunes V and Liossi C (2012) Attentional bias towards pain-related information in chronic pain; a meta-analysis of visual-probe investigations. Clinical Psychology Review 32(1): 13–25. Segal ZV and Gemar M (1997) Changes in cognitive organisation for negative self referent material following cognitive behaviour therapy for depression: A primed Stroop study. Cognition & Emotion 11(5–6): 501–516. Silverman WK, Fleisig W, Rabian B, et al. (1991) Childhood anxiety sensitivity index. Journal of Clinical Child and Adolescent Psychology 20(2): 162–168. Simon D, Craig KD, Gosselin F, et al. (2008) Recognition and discrimination of prototypical dynamic expressions of pain and emotions. Pain 135(1–2): 55–64. Smith ML (2012) Rapid processing of emotional expressions without conscious awareness. Cerebral Cortex 22(8): 1748–1760. Spielberger CD (1973) Manual for the State–Trait Anxiety Inventory for Children. Palo Alto, CA: Consulting Psychologists Press. Tsao JC, Allen LB, Evans S, et al. (2009) Anxiety sensitivity and catastrophizing associations with pain and somatization in non-clinical children. Journal of Health Psychology 14(8): 1085–1094.
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HPQ0010.1177/1359105315578303Journal of Health PsychologySchoth et al.
Article
Anxiety sensitivity is associated with attentional bias for pain-related information in healthy children and adolescents
Journal of Health Psychology 1–11 © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1359105315578303 hpq.sagepub.com
Daniel E Schoth, Lucy Golding, Emily Johnson and Christina Liossi
Abstract This investigation explored the association between anxiety sensitivity and attentional bias for threatening information in children and adolescents (N = 40). Participants completed a pictorial version of the visualprobe task, featuring pain-related, health-threat and general-threat images presented for 500 and 1250 ms. Regression analyses revealed significant associations between anxiety sensitivity and attentional bias towards pain-related images presented for 500 ms and between state anxiety and attentional bias towards generalthreat images presented for 1250 ms. These results suggest that in children and adolescents, anxiety sensitivity is associated with attentional bias for negative information of personal relevance.
Keywords anxiety, children, cognitive processing, health psychology, quantitative methods
Anxiety sensitivity (AS) is an individual difference variable conceptualised as a fear of anxietyrelated sensations (e.g. trembling, palpations, shortness of breath), stemming from the belief that such sensations may have negative somatic, psychological and/or social consequences (Reiss and McNally, 1985). In children and adolescents, AS has been associated with anxiety symptoms (Essau et al., 2010; Schmidt et al., 2010), social anxiety disorder (Alkozei et al., 2014), depression (Muris et al., 2001a) and post-traumatic stress disorder (Kılıç et al., 2008) and is predictive of panic attacks (Hayward et al., 2000). Research also highlights a relationship between AS and the experience of pain. Children with current pain report higher AS than those without pain (e.g. Lipsitz et al., 2004; Tsao et al., 2009), and in children with chronic pain, heightened AS
has been associated with reduced health-related quality of life (Mahrer et al., 2012) and increased fear of pain (Martin et al., 2007; Muris et al., 2001b). Child AS is also associated with pain catastrophising, which in turn is associated with higher post-operative pain (Esteve et al., 2014). It has been postulated that the relationship between AS and pain may be mediated by cognitive biases, in part due to a tendency to allocate attentional resources towards pain-related information which exacerbates the emotional
University of Southampton, UK Corresponding author: Daniel E Schoth, Academic Unit of Psychology, University of Southampton, Highfield, Southampton SO17 1BJ, UK. Email: [email protected]
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response to pain itself (Keogh and Cochrane, 2002). Indeed, AS itself has been conceived as a form of cognitive bias, entailing vigilance for somatic symptoms (Zvolensky and Forsyth, 2002) and misrepresentations of physical cues (Waszczuk et al., 2013). Considering this, research has reported attentional biases towards physical threat words (Keogh et al., 2001), anxiety symptomatology words (Hunt et al., 2006) and health-threat images (Lees et al., 2005) in adults with high AS. To date however only one investigation has explored the association between AS and attentional biases in children. Hunt et al. (2007) used a visual-probe task with anxiety symptomatology, social threat and positive-word conditions, presented at supraliminal (1000 ms; i.e. available to conscious awareness) and subliminal (14 ms, followed by a 986 ms mask; i.e. below the level of conscious awareness) exposure durations. Irrespective of exposure duration, children high in physical AS, relative to those low in physical AS, showed attentional bias towards all word categories. Despite the view that AS is fundamentally related to the experience of pain (Keogh and Cochrane, 2002), research to date has not explored whether AS in children is specifically associated with attentional bias towards painrelated information or is generalised to healththreat (i.e. information which represents poor well-being or an unhealthy state; similar to adults high in AS; see Lees et al., 2005) or even to other types of threat. Moreover, it remains unknown whether attentional biases in children with heightened AS generalise to emotional images as well as emotional words. For certain stimuli, images may have higher ecological validity than single words (e.g. a picture of an aggressive dog compared to the word dog). To address these questions, and to extend the knowledge generated by Hunt et al. (2007), who used linguistic stimuli, this study used painrelated, health-threat and general-threat images in a visual-probe task. The time-course of attentional bias may also be more fully explored, as Hunt et al. (2007) used only a single supraliminal presentation time. Presentation times of 500 and 1250 ms were used by Lees et al. (2005) in
their investigation of adults and are also commonly adopted in the broader anxiety attentional bias literature (for a review, see Bar-Haim et al., 2007). These two supraliminal durations were therefore used in this investigation in an exploratory manner. A distinction between different components of attention has been made (Allport, 1989), and in the visual-probe task bias towards threatening information at 500 and 1250 ms correspond to initial orienting of attention and maintained attention, respectively. This study therefore aimed to explore the time-course and specificity of attentional biases towards painrelated information in a community-based sample of children and adolescents. Based on the results of Hunt et al. (2007), significant positive associations were hypothesised between AS and attentional bias towards all threatening stimuli (i.e. pain-related, health-threat and generalthreat images).
Materials and methods Participants Participants were recruited from the south of England via press announcements and word of mouth. Inclusion criteria were as follows: (a) aged 8–17 years 11 months, (b) English as a first language and (c) having normal or corrected-tonormal vision. Exclusion criteria were as follows: (a) having been diagnosed with any form of chronic or recurrent pain, (b) severe learning disability, (c) the presence of a psychiatric or neurological condition and (d) serious medical illness. Eligibility criteria were determined via an initial telephone interview. On the basis of these criteria, two individuals were excluded who reported the experience of recurrent pain. In total, 40 young people meeting the eligibility criteria were recruited (mean age = 12.75 years; standard deviation (SD) = 3.26; range = 8–17 years; 29 females).
Measures Associations have been found in children between AS and trait and state anxiety (e.g. r = .55 and .27, respectively; Rabian et al., 1999)
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Schoth et al. and AS and depression (e.g. r = .49; Muris et al., 2001a). Considering this, the following selfreport measures were used to characterise this sample and allowed us to determine whether any attentional biases found are associated with AS specifically or also with other individual difference variables known to be correlated with AS. The Child Anxiety Sensitivity Index (CASI; Silverman et al., 1991) is an 18-item measure of fear of anxiety-related sensations for children. Participants rate the extent to which they agree with each item (e.g. It scares me when my heart beats fast) on a 3-point scale (none, some, a lot). Total scores range from 18 to 54, with higher scores indicating higher levels of AS. There is support for three lower-order factors, corresponding to physical concerns, psychological concerns and social concerns (McLaughlin et al., 2007), although there is variability between studies in the items comprising these subscales. The total score was used in this investigation. The CASI is commonly used in research with children and adolescents, with support for its internal consistency (.87) and test-retest reliability in clinical and non-clinical samples (.79 and .76, respectively; Silverman et al., 1991). Cronbach’s alpha in this investigation was .85. The State–Trait Anxiety Inventory for Children (STAIC; Spielberger, 1973) features a 20-item measure of state anxiety assessing current anxiety and a 20-item measure of trait anxiety assessing anxiety proneness. For each subscale, participants rate their level of agreement for each item on a 3-point scale. For the state subscale, participants indicate how they feel at that moment (e.g. I feel … very worried, worried or not worried), while for the trait subscale, participants indicate their level of agreement for each statement (e.g. I get upset at home) on a 3-point scale (hardly ever, sometimes, often). Scores for both subscales range between 20 and 60, with higher scores indicating higher levels of anxiety. The internal consistency of both state (.82–.87) and trait subscales (.78–.81) has been supported (Spielberger, 1973). Cronbach’s alphas in this investigation were .79 and .83 for state and trait subscales, respectively.
The Children’s Depression Inventory – Short version (CDI-S; Kovacs, 2003) is a 10-item measure of the severity of depressive symptoms. For each item, participants pick one statement that describes them best (e.g. I am sad once in a while, I am sad many times or I am sad all the time), with statements rated on a scale of 0 to 2. Total scores range from 0 to 20, with higher scores indicative of more severe depression symptomology. The CDI-S is a valid screening instrument for depression, with acceptable internal consistency (.70; Allgaier et al., 2012). Cronbach’s alpha in this investigation was .77.
Experimental stimuli The visual-probe task featured three experimental conditions (pain-related, health-threat, general-threat) and two neutral filler conditions (neutral faces, neutral objects). Each image condition included eight image pairs. All images were 265 pixels wide × 250 pixels high. Painrelated images feature a model depicting a facial expression of pain, which were paired with another image of the same model with a neutral expression. Health-threat images feature objects threatening to health and well-being, paired with images of neutral objects (e.g. an ambulance paired with a white van, a wheelchair paired with an office chair). General-threat images reflect both natural and man-made sources of threat, which were paired with images of neutral objects and events (e.g. a house on fire paired with a house not on fire, a vicious dog paired with a tame dog). For health- and general-threat categories, the objects depicted in their respective pairs were matched for size, orientation and colour. For the filler stimuli, neutral faces feature image pairs of the same model depicting two similar emotionless expressions. Neutral objects depict pairs of similar neutral objects (e.g. chairs, cups), matched for size, colour and orientation. Pain-related images were derived from the Montréal Pain and Affective Face Clips database (Simon et al., 2008), while healththreat and general-threat images were collected from an online image database. All experimental
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stimuli have been previously used by Schoth and Liossi (2013). Schoth and Liossi conducted an analysis of the valence and arousal of a large set of experimental images, including the ones used in this investigation, via a computerised version of the Self-Assessment Manikin (Lang, 1980). A total of 10 independent participants (5 females; mean age = 25.70 years; SD = 3.34) rated the general-threat images as significantly more arousing and less pleasant than the healththreat and pain-related images (see http://links. lww.com/CJP/A45).
at the University of Southampton. Informed assent was provided by the participant and informed consent by the accompanying parent. Participants first completed the visual-probe task. Questionnaire measures were presented in a new randomised order for each participant and were completed last to avoid potential priming effects on the visual-probe task (Segal and Gemar, 1997). The total experimental duration was 45 minutes.
Visual-probe task
Results were analysed in IBM SPSS Statistics 21 for Windows. Practice and buffer trials were excluded from final analysis, along with trials with incorrect responses. Manual response times less than 300 ms and greater than 1100 ms were identified as outliers and removed. Mean response times were then calculated for each participant separately; any response time >3 SDs away from this individual mean were also removed as outliers. In line with other studies using the visual-probe task (Schoth et al., 2012), attentional bias scores were calculated for each image condition at both presentation times via the following equation: attentional bias score = ((ElPr − ErPr) + (ErPl − ElPl))/2, where E is the emotional image, P is the probe, r is the right position and l is the left position. A positive bias score indicates a shift of attention towards the location of emotional stimuli relative to neutral stimuli. A negative bias score indicates a shift of attention away from the location of emotional stimuli towards neutral stimuli. A score of 0 denotes equal attentional engagement of both emotional and neutral stimuli. A general linear model, in the form of an analysis of covariance (ANCOVA), was used to explore attentional bias scores across the different image and presentation time conditions, while also testing for a linear relationship with AS. In line with current thinking (e.g. Altman and Royston, 2006), AS was included as a continuous covariate of interest in the ANCOVA, rather than as a dichotomised variable. Oneway ANCOVAs and linear regression were used where appropriate to clarify significant
The visual-probe task was run on a PC with a 15-in colour monitor and featured 320 experimental trials (i.e. 64 trials per image condition). Participants initially completed 12 practice trials featuring neutral image pairs unused in the main experiment. Three short breaks were provided during the task. Each trial began with the presentation of a fixation cross for 500 ms. A randomly selected image pair was then presented horizontally for either 500 or 1250 ms. The distance between the inner edges of the two images was 40 mm. Immediately following the removal of the image pair, a dot-probe was displayed in either the left or right location. Participants indicated the location of the dotprobe as quickly and accurately as possible using a two-button response box. Across the task, the two stimuli presentation times were applied in a randomised fashion across all trials. Each image pair was presented eight times (four times for 500 ms and four times for 1250 ms). Within each presentation time, each image in their respective pair appeared twice in the left location and twice in the right location. The dotprobe position was fully counterbalanced across each image condition. All text, fixation crosses and probes were presented in white against a black background.
Procedure Ethical approval for this investigation was obtained from the Research Ethics Committee
Data reduction and analytic plan
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Schoth et al. Table 1. Means (SD) for attentional bias indices and self-report measures (N = 40) and Pearson’s correlation coefficients. Measure
Attentional bias index Pain-related images (500 ms) Pain-related images (1250 ms) Health-threat images (500 ms) Health-threat images (1250 ms) General-threat images (500 ms) General-threat images (1250 ms) Self-report measure CASI CDI-S STAIC state STAIC trait
Mean (SD)
CASI Pearson’s correlation coefficients
4.01 (31.24)
.350*
CDI-S Pearson’s correlation coefficients .156
STAIC state Pearson’s correlation coefficients
STAIC trait Pearson’s correlation coefficients
.344*
.199
3.69 (34.26)
−.278
−.071
−.186
−.186
1.63 (33.74)
.155
.061
−.043
.298
2.40 (29.95)
.299
.296
.027
.266
5.82 (36.19)
−.217
−.468**
.026
−.285
3.27 (38.15)
29.68 (6.16) 2.03 (2.39) 28.45 (3.82) 33.80 (6.25)
.373*
.236 .479** .457**
.257
.296 .419**
.517**
.227
.159
SD: standard deviation; CASI: Childhood Anxiety Sensitivity Index; CDI-S: Children’s Depression Inventory – Short version; STAIC: State–Trait Anxiety Inventory for Children. *Correlation is significant at the .05 alpha level. **Correlation is significant at the .01 alpha level.
effects. Effect sizes for ANCOVA and regression were quantified using partial eta-squared η2p and R2, respectively. For all analyses, the alpha level was set at .05, two-tailed.
Results Descriptive statistics and correlations Means and SDs for attentional bias scores and self-report measures, along with correlations between them, are presented in Table 1. AS was positively correlated with attentional bias for pain-related images at 500 ms, r = .350, p = .027 and bias for general-threat images at 1250 ms, r = .373, p = .018. State anxiety was positively correlated with bias for pain-related images at 500 ms, r = .344, p = .030 and bias for generalthreat images at 1250 ms, r = .517, p = .001. Depression was negatively correlated with bias
for general-threat images at 1250 ms, r = -.468, p = .002.
Visual-probe analysis A small percentage of data was lost due to errors (M = 2.95%, SD = 2.48) and outliers (M = 3.45%, SD = 2.71). The overall mean response time was 591.51 ms (SD = 98.39). A 3 (image condition; pain-related, health-threat, general-threat) × 2 (presentation time; 500 ms, 1250 ms) ANCOVA, with AS entered as a covariate, was conducted on attentional bias scores. Significant main effects were not found for AS, F(1, 38) = 3.47, p = .070, η2p=.084; image condition, F(2, 76) = .782, p = .461, η2p=.020; or presentation time, F(1, 38) = .207, p = .651, η2p=.005. A significant two-way interaction was found for image condition × presentation time, F(2, 76) = 7.41, p = .001, η2p=.163 but not for AS × image condition, F(2, 76) = .715, p = .492, η2p=.018 or
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AS × presentation time, F(1, 38) = 1.85, p = .670, η2p=.005. A significant three-way interaction was found for AS × image condition × presentation time, F(2, 76) = 7.57, p = .001, η2p=.166. To explore the significant three-way interaction further, one-way ANCOVAs were conducted for pain-related, health-threat and general-threat images separately, with presentation time (500 ms, 1250 ms) as an independent variable and AS as a covariate. The AS × presentation time interaction was significant for pain-related images, F(1, 38) = 8.82, p = .005, η2p=.188 and general-threat images, F(1, 38) = 5.82, p = .021, η2p=.133 but not for healththreat images, F(1, 38) = .340, p = .563, η2p=.009. Linear regressions were used to follow-up the significant interactions for pain-related and general-threat images, guided by the results of the correlational analyses. As bias for painrelated images at 500 ms was significantly correlated with both AS and state anxiety, both variables were included in a backward elimination linear regression to determine which statistically predict bias scores. The final model revealed AS only to be statistically predictive of bias for pain-related images at 500 ms, F(1, 38) = 5.30, p = .027. AS is a significant statistical predictor of bias for pain-related images presented for 500 ms, Y(intercept) = −48.67, β = .350, t(38) = 2.30, p = .027, R2 = .122 (unstandardised beta = 1.78, standard error (SE) = .771, 95% confidence interval (CI) of unstandardised beta [0.22, 3.34]). Bias for general-threat images at 1250 ms was significantly correlated with both AS and state anxiety, and therefore, both variables were included in a backward elimination linear regression to determine which statistically predict bias scores. The final model revealed state anxiety only to be statistically predictive of bias for general-threat images presented for 1250 ms, F(1, 38) = 13.87, p = .001. State anxiety is a significant statistical predictor of bias for general-threat images presented for 1250 ms, Y(intercept) = −143.56, β = .517, t(38) = 3.73, p = .001, R2 = .267 (unstandardised beta = 5.16, SE = 1.39, 95% CI of unstandardised beta [2.36, 7.97]).
Discussion Providing partial support for the adopted hypothesis, in this sample of community-based young people, regression analysis revealed a significant association between AS and attentional bias towards pain-related images presented for 500 ms. A positive association between AS and bias towards facial expressions of pain in children is unsurprising, especially because pain expressions convey signals of underlying disease or injury (Williams, 2002), and AS has been associated with fear of pain in young people (Muris et al., 2001b). Transient and acute pains are also commonly experienced by children (e.g. minor injuries when playing games), thus making the stimuli personally relevant to a degree. However, a significant association was found at 500 ms only, a presentation time related to initial orienting of attention and hypervigilance for threat (Bradley et al., 2000). This association was not maintained at 1250 ms, and in fact, a non-significant negative correlation was found between AS and bias scores at this longer presentation time. Although nonsignificant results should be interpreted with caution, this does at least suggest AS in children is not associated with difficulties disengaging attention from pain-related images. Disengaging attention away from pain-related images to neutral images may reflect attempts to regulate emotional response, although specific investigation into this possibility is needed. These results add to those reported by Hunt et al. (2007), showing children with heightened AS bias towards pain-related images. However, we were unable to replicate a general bias towards emotional information as correlational analyses revealed no significant associations between AS and bias towards health-threat images. While a significant correlation was found between AS and bias towards generalthreat images presented for 1250 ms, the final model of a backward elimination linear regression revealed only state anxiety to make a significant contribution to this model. However, different stimuli categories were used by Hunt and colleagues to this study, and evidence also
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Schoth et al. suggests some differences in the precise cortical regions involved in processing emotional words and images (Kensinger and Schacter, 2006). Furthermore, Hunt and colleagues used the physical AS subscale of the CASI, whereas in this investigation, we used the total CASI score, as it represents the global-order AS factor and therefore takes into consideration different types of fears, including fears of panic-related somatic, cognitive and social cues. Although it could be speculated that healththreat images may reflect the concerns of children with heightened AS, and therefore, AS would be associated with bias towards such stimuli, it is important to note that none of the children recruited to this study were experiencing any serious medical illness. Unlike the symptom of pain, the objects depicted in the health-threat category (e.g. wheelchair, ambulance) were not personally relevant to the children recruited. Research has shown personal relevance and negative experience to influence patterns of bias in children (LoBue, 2010), and lack of direct experience with the health-threat objects depicted may have influenced the pattern of results observed. Another potentially important difference between the image categories is that only the pain-related category featured facial expressions, and facial expressions are known to rapidly capture attention (Smith, 2012). Furthermore, emotional facial expressions may be more universally interpretable than images of objects and scenes (Waters et al., 2010). Regression analysis revealed a significant association between state anxiety and bias towards general-threat images presented for ms. General-threat images, which for 1250 example included images of fire, vicious dogs and guns, depict the most imminent sense of threat out of the three image categories included in this study. This finding is therefore in agreement with research showing both evolutionary and man-made threats are associated with biased attentional processing (e.g. Brosch and Sharma, 2005) and also with adult ratings showing the general-threat images to be significantly more arousing and less pleasant than the pain-related and health-threat images.
Interestingly, state anxiety was associated with bias towards general-threat images at 1250 ms only, and not also at 500 ms. Although few studies like ours have specifically explored biases in community-based children nonselected for anxiety, Waters et al. (2004) also reported evidence of bias towards fear-related images (e.g. vicious dogs, guns) at 1250 ms in non-selected children. It has been argued that non-anxious individuals have a higher threshold for detecting threat than anxious individuals (Mogg and Bradley, 1998). While no evidence of hypervigilance was shown, the longer presentation time used allows for greater processing and elaboration of stimuli, with biases subsequently stemming from maintained attention towards threat (Donaldson et al., 2007). State but not trait anxiety was associated with attentional bias towards general-threat images. Little research has explored the specific role state anxiety has upon children’s attentional biases. Recruiting non-clinical children, Heim-Dreger et al. (2006) reported a positive correlation between state anxiety and attentional bias for threatening faces measured via the emotional Stroop task but not the visualprobe task. It has been suggested that state anxiety moderates the relationship between trait anxiety and attentional bias (e.g. Eysenck, 1997). Although a positive correlation was found between state and trait anxiety in this study, this was not statistically significant (.23) and was lower than that reported in a large study of children and adolescents (aged 8–16 years) using the STAIC (.54; Lau et al., 2006). Comparison to other studies exploring biases in children is limited due to the tendency of researchers not to report correlations between questionnaires. Overall, it is clear that further research is needed exploring how the interaction between state and trait anxiety may influence patterns of attentional bias in children and adolescents (Puliafico and Kendall, 2006). Correlational analysis showed a significant negative correlation between depression and attentional bias for general-threat images presented for 500 ms. Participants reporting higher levels of depression showed greater avoidance
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of such images. This is consistent with research showing children with depression exhibit an avoidance of sad facial expressions (Gibb et al., 2009; Johnson Harrison and Gibb, 2014), possibly as an emotion regulation strategy. AS was positively correlated with both state and trait anxiety, relationships that have been reported in former research (e.g. Rabian et al., 1999; Silverman et al., 1991). Depression was also positively correlated with trait anxiety, which is in line with evidence highlighting the comorbidity of these two individual difference variables in children (Garber and Weersing, 2010). The results of this study must be considered in light of some limitations. First, a relatively small sample size was recruited. Using GPower (Erdfelder et al., 1996), examination of achieved power in the three-way ANCOVA revealed only >68% power to detect significant effects should they exist. Second, a community-based sample of children and adolescents was recruited, who self-presented to the research team. The generalisability of these results therefore needs to be demonstrated through replication in more representative samples among children and adolescents in other settings and from the general population. Third, the sample consisted of children between 8 and 17 years of age. Although a broad age range creates the opportunity to examine developmental influences from childhood to adolescence, due to the small sample, we may not have been able to detect age effects. Fourth, participants did not rate the personal relevance or valence and arousal of the images used, and therefore, the role of these factors in the pattern of results reported may only be speculated. This decision was made in order to keep the investigation to a reasonable duration and to avoid fatigue, especially in younger children. Schoth and Liossi (2013) provide valence and arousal ratings from a group of adults, and although these results should be generalised with caution to this sample, research has shown children, adolescents and adults provide similar ratings of valence and arousal for emotional images (McManis et al., 2001). Fifth, the aim of this study was to examine threat-processing in
children, and therefore, we decided to limit the design to the inclusion of threat-related stimuli only. However, we cannot rule out the possible emotional valence effects of positive stimuli upon attentional biases, an effect that has been demonstrated in a previous study (Hunt et al., 2007). To date, only two studies have explored the association between AS and attentional bias in children and adolescents. Further research is therefore needed in this field, which is also apparent as attentional biases have been implicated in the development and/or maintenance of pain (Eccleston and Crombez, 2007) and anxiety (Bar-Haim, 2010) disorders, and the experience of AS in children has been associated with both. Before the possible clinical implications of biases in children with heightened AS are explored, however, it is important to replicate these results in other samples to determine their generalisability and also to extend beyond crosssectional designs which provide no information on causality. Future research could also directly compare attentional biases for pain-related pictorial and linguistic stimuli in children, as limited research with adults with high AS suggests biases to be more pronounced towards images than words (Lees et al., 2005). In conclusion, the results of this investigation add to those reported by Hunt et al. (2007), showing AS is associated with attentional bias towards pain-related images in children and adolescents. Acknowledgements We would like to thank Dr. Jin Zhang, PhD (Senior Experimental Officer, Academic Unit of Psychology, University of Southampton) for developing the programme used in this study, and for her continued technical support. We would also like to thank the anoymous reviewers for their helpful comments on an earlier version of the manuscipt.
Funding This research received no specific grant from any funding agency in the public, commercial or not-forprofit sectors.
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