How gender affects the decoding of facial expressions of pain
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Peter J. Göller
,
Philipp Reicherts
Abstract
Objectives
Gender has been suggested to play a critical role in how facial expressions of pain are perceived by others. With the present study we aim to further investigate how gender might impact the decoding of facial expressions of pain, (i) by varying both the gender of the observer as well as the gender of the expressor and (ii) by considering two different aspects of the decoding process, namely intensity decoding and pain recognition.
Methods
In two online-studies, videos of facial expressions of pain as well as of anger and disgust displayed by male and female avatars were presented to male and female participants. In the first study, valence and arousal ratings were assessed (intensity decoding) and in the second study, participants provided intensity ratings for different affective states, that allowed for assessing intensity decoding as well as pain recognition.
Results
The gender of the avatar significantly affected the intensity decoding of facial expressions of pain, with higher ratings (arousal, valence, pain intensity) for female compared to male avatars. In contrast, the gender of the observer had no significant impact on intensity decoding. With regard to pain recognition (differentiating pain from anger and disgust), neither the gender of the avatar, nor the gender of the observer had any affect.
Conclusions
Only the gender of the expressor seems to have a substantial impact on the decoding of facial expressions of pain, whereas the gender of the observer seems of less relevance. Reasons for the tendency to see more pain in female faces might be due to psychosocial factors (e.g., gender stereotypes) and require further research.
Introduction
The decoding of facial expressions of pain plays an important role in social interactions as well as in clinical settings. Research shows that facial expressions of pain can be differentiated from facial expressions of other affective states (e.g., fear, anger) above chance level [1], [2], [3]; however, this decoding ability differs substantially between individuals. Several studies have tried to investigate which factors might impact the decoding ability of facial expressions of pain [1, 2, 4], [5], [6], [7], [8], [9], [10], [11]. One factor, that has been investigated quite frequently – not only in the context of pain but also for other affective states – is “gender”. On the one hand, the gender of the observer (“gender observer”) has been found to impact the decoding of facial expressions, with females often showing better decoding performances compared to males [6, 12], [13], [14], [15]. On the other hand, the gender of the person being observed (“gender expressor”) has also been found to have an impact, with female facial expressions being recognized faster and being rated as more intense [7, 16], [17], [18], [19]. With regard to the expression of pain, the results are more ambiguous. Although some studies demonstrate better decoding performances in female observers [20, 21], other studies failed to replicate this finding [6, 22]. In contrast, the influence of the gender of the expressor has been analyzed more often and most studies found that pain intensity was rated higher for female compared to male faces [2, 7, 9, 23], [24], [25]. However, there are also studies which failed to demonstrate any effects for the “gender expressor” on the decoding of facial expressions of pain [7, 26].
In light of these mixed findings, we aimed at further elucidating gender effects on the decoding of facial expressions of pain, by taking into consideration both the gender of the observer and the gender of the expressor. Moreover, we also wanted to differentiate between two aspects of the decoding process, namely: (I) differentiating pain from other affective states (pain recognition) and (II) inferring the intensity of the expressor’s experience (intensity decoding). Thus, we wanted to investigate whether gender (“gender observer” and “gender expressor”) impacts both of these decoding aspects.
In the present study, we decided to use computer-generated facial expressions to allow for highly controllable stimuli. Real facial expressions of pain come with the challenge that aspects such as age, attractiveness and the intensity of the expression are difficult to control and to modulate, which can impact the decoding and may interact with gender-effects. Therefore, here we used computer-generated avatars from a previous study that proved to be perceived as valid pain faces by observers [23]. These pain expressions were interleaved with expressions of other affective states in order to assess “pain recognition” namely the facial expressions of anger and disgust as those were found to be frequently confused with pain and are perceived similarly to pain in terms of valence and arousal [2, 8, 27], [28], [29].
In summary, the aim of two consecutive studies was to examine how gender (“gender observer” & “gender expressor”) affects the intensity decoding (arousal, valence, and pain intensity rating) as well as the pain recognition of facial expressions of pain, using computer-generated facial expressions of pain as well as facial expressions of anger and disgust as distractors.
Methods
The two studies were conducted as online studies via Soscisurvey software (www.soscisurvey.de) [30]. In both studies, videos of facial expressions displayed by male and female avatars were shown to male and female observers. Both study protocols were approved by the ethics committee of the University of Bamberg (#2020-05/15).
Study 1 (valence and arousal ratings; “intensity decoding”)
Participants
119 participants (66 female, median age: 20–24 years) were recruited via e-mail at the University of Augsburg and the University of Bamberg. The sample included mostly students of psychology and medicine who received course credit for participating. All participants provided written informed consent.
Procedure
The study was carried out online via Soscisurvey software (www.soscisurvey.de) [30] in June – September 2020. Video clips of 2 different avatars (1 male & 1 female) displaying facial expressions of pain as well as of 6 basic emotions (anger, disgust, happiness, fear, surprise, sadness) were created, which were presented in a randomized order. After each video clip, participants were asked to provide two ratings, namely valence and arousal ratings. We started off by providing detailed written instructions, followed by a familiarization trial. The completion of this online task took between 30 and 40 min. The participants could adjust the size of the video clips individually, so that the video clips filled the screen and could be easily observed by the participants (this data was also recorded). As explained in the introduction, we focused on facial expressions of pain and compared them with facial expressions of anger and disgust.
Stimulus material
The faces of the avatars were modelled with the software FaceGen Modeller Core 3.5 (Version of 2019). Dynamic facial expressions were created with the software FACSGen3 (Version of 2019). This software allows to create facial responses based on the Facial Action Coding System (FACS) [31]. FACS is an anatomically based system that separates facial responses and muscle contractions into so called Action Units (AUs). FACSGen3 allows to animate each AU separately, at levels of intensity from 0 to 100. Regarding the facial expressions of pain, we used the same expressions as described in detail in Meister et al. 2021. In short, three variations of facial expressions of pain were created that stem from empirically identified variations in facial expressions of pain [1, 32]. For the present study, we additionally modelled facial expressions representing six basic emotions (anger, disgust, happiness, fear, surprise, sadness). In order to mirror the approach used for the facial expression of pain we also wanted to use variations in facial expressions for the other emotional stimulus categories. Given that there is no empirical study which systematically investigated variations in facial expressions of six basic emotions, we selected given variants already programmed in FACSGen3. For further analyses, we only focused on the facial expressions of pain, anger, and disgust.
The three created variants for pain, anger and disgust are displayed in Figure 1. All in all, 1 male “gender expressor” and 1 female “gender expressor” of the avatars were animated with identical facial activity patterns. The animation of the facial expressions always followed the same time-course. It started off with a neutral expression, followed by a 1 s increase in Action Unit intensities. The full-blown expression was kept stable for 0.5 s, followed by a 1 s decrease and ending again with a neutral expression. Thus, the complete facial expression lasted for 2.5 s. Each video clip started with a fixation cross in the middle of the screen for 1 s to focus participants’ attention, followed by the appearance of the avatar’s neutral facial expression. In total 42 video clips (2 avatars x 7 types of facial expressions x 3 variations) were presented in a randomized order.
Different types of facial expressions of pain, anger and disgust, created for the male and female “gender expressor” of the avatars (FaceGen modeller core 3.5 (version of 2019) + FACSGen3 (version of 2019)) the intensity of the shown action units is displayed in brackets (ranging from 0 to 100). Pictures show the full-blown expressions.
Rating scales
After each video clip, participants were asked to rate valence and arousal of the expressed affective state. Valence and arousal were assessed using Self-Assessment Manikin (SAM [33]) that appeared in the middle of the computer screen (valence on the upper half, arousal on the lower half). Ratings were performed by mouse click on the manikins or spaces in-between, resulting in 9 categories (i.e., “maximum positive” to “maximum negative” for valence and “maximum” to “no” for arousal). The participants had unlimited time to provide their ratings. Only after participants provided the two ratings, they were able to click on the button “continue” and the next fixation cross, followed by the appearance of the next avatars neutral facial expression appeared and the next facial expression unfolded. To familiarize subjects with the rating procedure, two practice trials were conducted at the beginning of the study.
Data quality management
To ensure sufficient data quality, we inspected the ratings following suggestions for data gathered in online studies [34], [35], [36]. First criterion was that we checked the size in which the videos were displayed (the experimental set-up allowed for the adjustment of the video-size to fit the computer screen). If the video size was set below 3% of the screen those participants were excluded because it is questionable whether the facial responses could really be seen. Second criteria was the anomaly index (IBM SPSS 28) which allows the identification of unusual cases based on their rating behaviour (e.g., a very high arousal rating for a neutral facial expression). If a participant showed high anomaly index across more than three variables, the participant was excluded. Third criterion was, that there was almost no variation/variance in the response behaviour (this means that the same rating was used over and over again). After inspection of the data, 2 participants were excluded based on these criteria. Furthermore, in order to have gender balance for the analyses, which is important to ensure homoscedasticity and to keep the risk of an alpha error as low as possible [37], 13 women were randomly excluded. Thus, 104 participants (53 female, median age category: 20–24 years) were entered into the analyses.
Statistical analysis
In order to investigate how gender affects the intensity decoding (valence and arousal) of facial expressions, we conducted 3 multivariate analyses of variance (ANOVA) with repeated measurements separately for facial expressions of pain, anger and disgust. In the first ANOVA (pain) “gender expressor” was entered as the within-subject factor and “gender observer” was entered as the between subject factor, with valence and arousal ratings for the facial expressions of pain being entered as the multivariate outcome variables. To investigate whether potential gender effects are specific for pain or might also be found for decoding of facial expressions of other negative affective states, the analyses of variance was additionally conducted for facial expressions of anger and disgust. Significance was assumed at an alpha level ≤0.05. Data were analysed using SPSS (version 28.0).
Study 2
In study 2 we wanted to investigate potential gender differences, not only in intensity decoding but also in pain recognition of pain. Thus, the same video clips as in study 1 were presented again, however this time allowing for assessing pain recognition.
Participants
263 participants (164 female; median age 20–24) were recruited through e-mail at the University of Augsburg and the University of Bamberg. The sample included mostly students of psychology and medicine who received course credit for participating. All participants provided written informed consent.
Procedure
Study 2 was also carried out online via Soscisurvey software (www.soscisurvey.de) [30] in November 2020 – February 2021. We used the same video clips as in study 1. Here, each video clip was shown several times because this time each video was paired with ratings for each of the 7 affective states (anger, disgust, sadness, surprise, fear, happiness, pain ratings) displayed by two avatars (1 male & 1 female), resulting in 294 presentations (2 Avatars x 7 facial expressions (anger, disgust, sadness, surprise, fear, happiness, pain) x 3 variants of each facial expression x 7 affective state ratings). All video clips were presented in pseudo-randomized order in 3 blocks á 98 video clips. As in study 1 and as explained in the introduction, we focused on the facial expressions of pain and compared it with facial expressions of anger and disgust for further analyses (the remaining affective states can be found in the Appendix). The study started with informed consent, instructions, and a familiarization trial. The study took about 50–70 min.
Stimulus material
We used the same video clips as in study 1.
Rating scales
After each video clip participants were asked to rate the intensity of one of 7 affective states (pain, anger, disgust, happiness, sadness, surprise, fear) using an 11-point-Likert Scale from e.g., 0 – “no pain” to 10 – “extremely strong pain”. The accompanying instruction was e.g., “Please rate the intensity of “PAIN” in this facial expression”, with the affective state printed in bold letters. The same type of rating was presented for the six basic emotions (anger, disgust, happiness, fear, surprise, sadness). The ratings were obtained after each video clip by mouse-click. The participants had unlimited time for providing the rating and the study only continued once the participants had given a rating.
Data quality
As in study 1, we inspected the data following suggestions for data-inspections of online studies [34], [35], [36] using the same 3 criteria. After inspection of the data, 23 participants were excluded based on these criteria. Furthermore, in order to have approximately gender balance, which is important to ensure homoscedasticity and to keep the risk of an alpha error as low as possible [37], 38 women were randomly excluded. Thus, 202 participants (111 female, median age: 20–24 years) were entered into the analyses.
Statistical analysis
In order to investigate how the factors “gender observer” and the “gender expressor” influence the intensity decoding of pain, an analysis of variance (ANOVA) with repeated measurements was conducted, with the within-subject factor “gender expressor” and the between-subject factor “gender observer” and pain intensity ratings for the video clips showing facial expressions of pain as the outcome variable. As in study 1, we investigated whether potential gender effects are specific to pain or can also be found for facial expressions of anger and disgust. Thus, we conducted the same analysis, this time entering the intensity ratings of anger and disgust for facial expressions of anger and disgust, respectively, as outcome variables.
Moreover, we conducted receiver operating characteristic (ROC) curves [38], [39], [40] to analyze gender differences in the pain recognition of facial expressions of pain. We created ROC-Curves separately for female and male observers (“gender observer”) and for female and male avatars (“gender expressor”). The ROC-Curves were calculated using the reported intensity of pain as hit for the facial expression of pain whereas the facial expressions of anger and disgust were used as disruptive influences, according to the principle of the signal detection theory, where the hit rate and false positive rate are used to estimate the recognition ability [40]. We then used the area under the curve (AUC) to obtain an indication of the gender differences in the pain recognition of the facial expression of pain for the “gender expressor” and “gender observer”. An AUC-rating of 0.5 indicated a chance performance on the decoding task.
Significance was assumed at an alpha level ≤0.05. Data were analysed using SPSS (version 28.0).
Results
Study 1
Gender effects on intensity decoding of facial expressions of pain
There was a significant main effect of the “gender expressor” on valence and arousal ratings of facial expressions of pain (F(2, 101)=13.62, p<0.001). As univariate outcomes showed, the “gender expressor” significantly affected both valence (F(1, 102)=16.37, p<0.001) as well as arousal ratings (F(1, 102)=21.50, p<0.001) of facial expressions of pain. As can be seen in Figure 2A, female avatars displaying facial expressions of pain were rated as more negative and more arousing than male avatars.
Valence and arousal ratings (mean, standard deviation).
Ratings are given seperately for pain (A), anger (B) and disgust (C). Moreover, ratings are given seperately for male and female observers (gender observer) and for male and female avatars (gender expressor).
There was no significant main effect of the “gender observer” on valence and arousal ratings of facial expressions of pain (F(2, 101)=1.86, p=0.161). There was also no significant interaction effect between “gender expressor” and “gender observer” on the valence and arousal ratings of the facial expressions of pain (F(1, 102)=0.35, p=0.705).
Gender effects of anger & disgust intensity decoding
Both for the facial expressions of anger (F(2, 101)=9.44, p<0.001) and for the facial expressions of disgust (F(2, 101)=19.71, p<0.001), we found significant main effects for the “gender expressor” on valence and arousal ratings. As univariate outcomes showed the “gender expressor” significantly affected valence (F(1, 102)=16.82, p<0.001) and arousal (F(1, 102)=9.32, p=0.003) ratings for anger as well as valence (F(1, 102)=35.65, p<0.001) and arousal ratings (F(1, 102)=22.96, p<0.001) for disgust. As can be seen in Figure 2B, and C, female expressions were always rated as more arousing and more negative than male expressions. We also found a significant main effect for “gender observer” for the facial expressions of anger (F(1, 102)=3.85, p=0.024), but not for the facial expression of disgust (F(1, 102)=2.01 p=0.139). As can be seen in Figure 2B, female observers rated especially anger expressions as more negative than male observers. With regard to interaction effects between gender observer and gender expressor, no significant interactions were found for facial expressions of anger (F(2, 101)=0.77 p=0.465), but only for facial expressions of disgust (F(2, 101)=5.27 p=0.007); with the pattern of perceiving female expressions as more negative being more pronounced in male observers.
Study 2
Gender effects on intensity decoding of facial expressions of pain
Analogue to study 1, we found a significant main effect for the “gender expressor” (F(1, 200)=7.49, p=0.007) on intensity ratings for facial expressions of pain. As can be seen in Figure 3, the pain intensity was rated higher when a female avatar displayed the facial expression of pain compared to a male avatar. Also comparable to study 1, the “gender observer” again did not significantly impact the intensity ratings of facial expressions of pain (F(1, 200)=2.98, p=0.086). Moreover, the interaction effect between “gender expressor” and “gender observer” also missed the level of significance (F(2, 199)=3.48, p=0.063).
Intensity ratings of the facial expressions of pain, anger & disgust (mean, standard deviation). Ratings are given seperately for male and female observers (gender observer) and for male and female avatars (gender expressor).
Gender effects on intensity decoding of facial expressions of anger/disgust
Neither for anger (F(1, 200)=0.15, p=0.702), nor for disgust (F(1, 200)=1.88, p=0.172) did we find a significant main effects for “gender expressor”. As can be seen in Figure 3, the intensity of anger and disgust was not rated higher in female compared to male avatars. Moreover, also the factor “gender observer” did not yield significant outcomes for anger (F(1, 200)=0.002, p=0.965) or for disgust (F(1, 200)=0.22, p=0.639). Thus, gender did not significantly affect the anger and disgust intensity decoding in study 2.
Pain recognition of facial expressions of pain
As can be seen in Figure 4, all AUCs were significantly above chance level, indicating that facial expressions of pain were differentiated from facial expressions of anger/disgust above chance level. However, overall, the AUCs showed only poor pain recognition performances for differentiating facial expressions of pain from expressions of anger and disgust. The AUC for male (AUC=0.582, p=0.014; maximum Youden Index: J=0.123) and for female “expressors” (AUC=0.587, p=0.014; maximum Youden Index: J=0.138) showed comparable values. The same applied to the AUCs for male (AUC=0.574, p=0.015; maximum Youden Index was J=0.115) and female “observers” (AUC=0.594, p=0.014; maximum Youden Index: J=0.136). Thus, we found that neither the “gender expressor” nor the “gender observer” substantially affected the pain recognition performance for facial expressions of pain.
ROC (receiver operating characteristic)-curves of the gender observer and gender expressor for the facial expressions of pain.
Discussion
The aim of the present study was to investigate whether and how gender might impact the decoding of facial expressions of pain using highly controlled facial expressions displayed by computer generated avatars. We found that the gender of the expressor influenced the decoding process of facial expressions of pain, with expressions displayed by female faces being rated as more arousing, more negative and expressing more pain compared to male faces. In contrast, the gender of the decoder had no clear effect.
How does the gender of the expressor affect the decoding of facial expressions of pain?
Our findings suggest that the gender of the expressor has a relevant impact on the intensity decoding of pain. Across the two studies that we conducted, we assessed valence, arousal (Study 1) as well as pain intensity ratings (Study 2) for female and male faces showing facial expressions of pain. The gender effects we found were evident across all three ratings and across the two studies, that is arousal, valence and pain intensity were always rated higher for female’ compared to male facial expressions of pain. This is in line with the majority of previous studies, which also showed that observers rate the pain intensity as being higher in female compared to male faces [2, 9, 23], [24], [25, 41], [42], [43], [44]. Thus, the intensity decoding for facial expressions of pain seems to be different for female and male faces expressing pain.
A reason for this effect might be that gender stereotypes such as that women are more vulnerable to pain or the fact that women are more emotionally expressive might play a role [9, 14, 23, 43, 45], [46], [47], [48]. So far it is not clear whether these gender-biases transfer to a clinical context. Based on our findings one might expect that pain is overestimated in female patients or -which is even more critical – likely underestimated in male patients [49, 50], which could lead to differences in pain treatment of male and female patients [26, 45, 51, 52].
When interpreting gender differences in decoding affective facial expressions the question arises how affect-or in the present case pain-specific these findings are or whether they generalize to other negative affective states. In our studies, we found some evidence, that similar gender-effects can be found for anger and disgust, with female faces being perceived as more arousing and more negative compared to male faces. However, the intensity ratings of anger and disgust as assessed in study 2, did not show this gender effect. Finding higher ratings when judging facial expressions of various other (negative) affective states in females is in line with previous findings [2, 16, 17, 19, 53, 54]. Thus, it seems that the gender effects we found for intensity decoding of facial expressions of pain are not necessarily pain specific. Nevertheless, given that the gender expressor effects occurred only for facial expressions of pain across all three types of ratings (valence, arousal, pain intensity) suggests that these gender effects might be more robust for pain. We can only speculate on the reasons for the greater robustness of seeing more pain in female faces. Possibly, the gender stereotype of perceiving women as being more vulnerable and more in need of help than men [26, 48] might be more pronounced when judging affective states, such as pain, which have a stronger linkage to constructs of “vulnerability” or “weakness” compared to anger or disgust.
Besides the evaluation of the intensity of pain, decoding of facial expressions of pain also involves a categorical decision, namely the differentiation of pain from other types of affective states (pain recognition). For pain recognition, we found that facial expressions of pain were reliably distinguished from anger and disgust in both male and female faces above chance level. However, the area under the curve (AUC) shows that the discrimination performance was far from perfect. The reason for this could be the overlap in facial muscle movements (Action Units, AUs) between the facial expressions of pain, anger, and disgust. For instance, all three types of facial expression have in common that a “contraction of the eyebrows” (AU 4) is displayed [2, 28, 31, 55], which could have led to this low discrimination performance. Interestingly, the decoding performance was not affected by the gender of the expressor. Thus, although higher pain intensities were perceived in female compared to male faces, this did not lead to a better discrimination for pain in female faces.
How does the gender of the observer affect the decoding of facial expressions of pain?
Our findings suggest that the gender of the observer has no substantial impact on the intensity decoding of pain across the two studies. Neither for valence and arousal nor for intensity ratings did we find significant differences between male and female observers. This is in line with previous studies, which also found no clear differences between male and female observers for intensity decoding of facial expressions of pain [21, 22]. Similarly to the outcomes regarding pain expressions, we also found that the gender of the observer had no substantial impact on intensity decoding of anger and disgust, which corresponds to the results of previous findings (e.g., [2, 14]).
For “pain recognition”, namely differentiating pain from facial expressions of anger and disgust, we also found no difference in decoding performances between male and female observers. Although the pain recognition performance was only poor, both male and female observers could distinguish facial expressions of pain from anger and disgust above chance level. Previous studies have assumed that females might be more accurate in pain recognition of facial expressions of pain [20, 21, 45]. This assumption likely stems from findings on facial expressions of other affective states, where female observers proved to be better at differentiating between facial expressions of various affective states [6, 12, 13, 15, 19, 56, 57]. We can only speculate why females in the present study did not outperform males in “pain recognition”. One possibility could be that facial expressions of pain are simply different from expressions of other affective states and that females are not in advantage here to correctly recognize these. It is also possible that the type of stimulus material plays a crucial role. We used highly controlled computer-generated expressions and thus, the naturally occurring variations with regard to head posture, eye movement, order of single facial muscle movements, skin color etc. that can be relevant for distinguishing between affective states are missing. Thus, at least with regard to computer-generated facial expressions of pain, females are not better in distinguishing facial expressions of pain from anger and disgust.
Overall, our results regarding the gender difference of the observers in the perception of facial expressions of pain rather suggest that the gender differences can be neglected.
Limitations
There are also some limitations to our studies that we need to address. The studies were conducted as online studies, and thus, we could not control the context (lightning, noise, size of the screen, environment), or how focused participants were during the performance of the decoding task. However, the online study allowed us to successfully recruit a large sample even in times of the COVID-pandemic. In addition, we created female and male avatars with clear gender features and thus, they are not representative for the vast continuum of gender features in the general population.
Conclusions
We found evidence in agreement with previous findings, that the gender of the expressor has a substantial influence on the perception of facial expressions of pain. Observers evaluated a pain expression as being more intense, more arousing and more negative when displayed by a female compared to a male avatar. This gender expressor difference is independent of the gender of the observer. Indeed, based on our study, the influence of the gender of the observer on decoding facial expressions of pain can be neglected. Future studies should investigate whether and how this gender bias impact pain diagnostic and pain treatment.
Acknowledgments
We thank Thomas Frank for the IT-support in setting up the online-study.
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Research funding: Authors state no funding involved.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: Authors state no conflict of interest.
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Informed consent: Informed consent has been obtained from all individuals included in this study.
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Ethical approval: Research involving human subjects complied with all relevant national regulations, institutional policies and is in accordance with the tenets of the Helsinki Declaration (as amended in 2013) and has been approved the ethics committee of the University of Bamberg (#2020-05/15).
| Valence (Study 1) | Arousal (Study 1) | Intensity (Study 2) | ||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Female observer | Male observer | Female observer | Male observer | Female observer | Male observer | |||||||||||||||||||
| Female expressor | Male expressor | Female expressor | Male expressor | Female expressor | Male expressor | Female expressor | Male expressor | Female expressor | Male expressor | Female expressor | Male expressor | |||||||||||||
| M | SD | M | SD | M | SD | M | SD | M | SD | M | SD | M | SD | M | SD | M | SD | M | SD | M | SD | M | SD | |
| Pain | 2.98 | 0.73 | 3.26 | 0.71 | 3.24 | 0.73 | 3.42 | 0.60 | 5.91 | 1.36 | 5.43 | 1.56 | 5.87 | 1.40 | 5.42 | 1.28 | 4.03 | 1.86 | 3.94 | 1.78 | 4.63 | 1.93 | 4.15 | 1.74 |
| Anger | 2.78 | 0.59 | 3.06 | 0.61 | 3.05 | 0.58 | 3.23 | 0.59 | 5.83 | 1.27 | 5.39 | 1.37 | 5.87 | 1.18 | 5.67 | 1.17 | 6.54 | 1.71 | 6.26 | 1.79 | 6.29 | 1.75 | 6.49 | 1.64 |
| Disgust | 2.97 | 0.51 | 3.13 | 0.58 | 2.88 | 0.56 | 3.39 | 0.60 | 5.45 | 1.15 | 5.03 | 1.40 | 5.80 | 1.20 | 5.22 | 1.23 | 6.35 | 1.88 | 6.29 | 1.77 | 6.32 | 1.77 | 6.10 | 1.66 |
| Happiness | 6.61 | 0.82 | 6.74 | 0.74 | 6.72 | 0.67 | 7.01 | 0.58 | 4.25 | 1.41 | 4.30 | 1.37 | 4.56 | 1.13 | 4.76 | 1.23 | 4.96 | 1.95 | 5.37 | 1.88 | 5.15 | 1.99 | 5.90 | 1.84 |
| Fear | 4.73 | 0.79 | 4.19 | 0.88 | 5.05 | 0.83 | 4.36 | 0.81 | 5.07 | 1.25 | 5.05 | 1.21 | 4.86 | 1.17 | 4.99 | 1.08 | 2.92 | 1.99 | 3.96 | 2.00 | 2.99 | 1.85 | 4.25 | 1.90 |
| Sadness | 3.32 | 0.59 | 3.42 | 0.68 | 3.42 | 0.57 | 3.54 | 0.49 | 4.54 | 1.30 | 4.45 | 1.22 | 4.52 | 1.05 | 4.36 | 1.04 | 4.64 | 2.29 | 4.52 | 2.08 | 4.96 | 1.89 | 4.81 | 2.28 |
| Surprise | 5.20 | 0.76 | 4.86 | 0.72 | 5.17 | 0.93 | 4.95 | 0.88 | 5.08 | 1.39 | 4.86 | 1.42 | 5.01 | 1.13 | 4.90 | 1.10 | 6.42 | 1.69 | 5.78 | 1.70 | 6.33 | 1.76 | 5.83 | 1.73 |
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© 2022 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Editorial Comment
- Chronic pain and health inequalities: why we need to act
- Systematic Reviews
- Resilience as a protective factor in face of pain symptomatology, disability and psychological outcomes in adult chronic pain populations: a scoping review
- Is intravenous magnesium sulphate a suitable adjuvant in postoperative pain management? – A critical and systematic review of methodology in randomized controlled trials
- Topical Review
- Pain assessment 3 × 3: a clinical reasoning framework for healthcare professionals
- Clinical Pain Researches
- The treatment lottery of chronic back pain? A case series at a multidisciplinary pain centre
- Parameters of anger as related to sensory-affective components of pain
- Loneliness in patients with somatic symptom disorder
- The development and measurement properties of the Dutch version of the fear-avoidance components scale (FACS-D) in persons with chronic musculoskeletal pain
- Observational Studies
- Can interoceptive sensitivity provide information on the difference in the perceptual mechanisms of recurrent and chronic pain? Part I. A retrospective clinical study related to multidimensional pain assessment
- Distress intolerance and pain catastrophizing as mediating variables in PTSD and chronic noncancer pain comorbidity
- Stress-induced headache in the general working population is moderated by the NRCAM rs2300043 genotype
- Does poor sleep quality lead to increased low back pain the following day?
- “I had already tried that before going to the doctor” – exploring adolescents’ with knee pain perspectives on ‘wait and see’ as a management strategy in primary care; a study with brief semi-structured qualitative interviews
- Problematic opioid use among osteoarthritis patients with chronic post-operative pain after joint replacement: analyses from the BISCUITS study
- Worst pain intensity and opioid intake during the early postoperative period were not associated with moderate-severe pain 12 months after total knee arthroplasty – a longitudinal study
- Original Experimentals
- How gender affects the decoding of facial expressions of pain
- A simple, bed-side tool to assess evoked pressure pain intensity
- Effects of psychosocial stress and performance feedback on pain processing and its correlation with subjective and neuroendocrine parameters
- Participatory research: a Priority Setting Partnership for chronic musculoskeletal pain in Denmark
- Educational Case Report
- Hypophosphatasia as a plausible cause of vitamin B6 associated mouth pain: a case-report
- Short Communications
- Pain “chronification”: what is the problem with this model?
- Korsakoff syndrome and altered pain perception: a search of underlying neural mechanisms
Articles in the same Issue
- Frontmatter
- Editorial Comment
- Chronic pain and health inequalities: why we need to act
- Systematic Reviews
- Resilience as a protective factor in face of pain symptomatology, disability and psychological outcomes in adult chronic pain populations: a scoping review
- Is intravenous magnesium sulphate a suitable adjuvant in postoperative pain management? – A critical and systematic review of methodology in randomized controlled trials
- Topical Review
- Pain assessment 3 × 3: a clinical reasoning framework for healthcare professionals
- Clinical Pain Researches
- The treatment lottery of chronic back pain? A case series at a multidisciplinary pain centre
- Parameters of anger as related to sensory-affective components of pain
- Loneliness in patients with somatic symptom disorder
- The development and measurement properties of the Dutch version of the fear-avoidance components scale (FACS-D) in persons with chronic musculoskeletal pain
- Observational Studies
- Can interoceptive sensitivity provide information on the difference in the perceptual mechanisms of recurrent and chronic pain? Part I. A retrospective clinical study related to multidimensional pain assessment
- Distress intolerance and pain catastrophizing as mediating variables in PTSD and chronic noncancer pain comorbidity
- Stress-induced headache in the general working population is moderated by the NRCAM rs2300043 genotype
- Does poor sleep quality lead to increased low back pain the following day?
- “I had already tried that before going to the doctor” – exploring adolescents’ with knee pain perspectives on ‘wait and see’ as a management strategy in primary care; a study with brief semi-structured qualitative interviews
- Problematic opioid use among osteoarthritis patients with chronic post-operative pain after joint replacement: analyses from the BISCUITS study
- Worst pain intensity and opioid intake during the early postoperative period were not associated with moderate-severe pain 12 months after total knee arthroplasty – a longitudinal study
- Original Experimentals
- How gender affects the decoding of facial expressions of pain
- A simple, bed-side tool to assess evoked pressure pain intensity
- Effects of psychosocial stress and performance feedback on pain processing and its correlation with subjective and neuroendocrine parameters
- Participatory research: a Priority Setting Partnership for chronic musculoskeletal pain in Denmark
- Educational Case Report
- Hypophosphatasia as a plausible cause of vitamin B6 associated mouth pain: a case-report
- Short Communications
- Pain “chronification”: what is the problem with this model?
- Korsakoff syndrome and altered pain perception: a search of underlying neural mechanisms
