Skip to main content
NCBI home page
Wiley - PMC COVID-19 Collection logoLink to Wiley - PMC COVID-19 Collection
. 2022 Dec 2:10.1002/jclp.23468. Online ahead of print. doi: 10.1002/jclp.23468

Impact of facemasks on psychotherapy: Clinician's confidence and emotion recognition

Marco Bani 1,✉, Stefano Ardenghi 1, Giulia Rampoldi 1, Selena Russo 1, Maria Grazia Strepparava 1,2
PMCID: PMC9877818  PMID: 36459660

Abstract

Objectives

Facial emotion recognition is a key component of human interactions, and in clinical relationships contributes to building and maintaining the therapeutic alliance with patients. The introduction of facemasks has reduced the availability of facial information in private and professional relationships. This study aimed to assess the impact of facemasks on clinicians' perception of clinical interactions as well as their ability to read facial expressions.

Methods

In this cross‐sectional study, a purposive sample of 342 clinical psychologists or psychotherapists completed an online survey including the assessment of burnout, alexithymia, emotion dysregulation, and self‐perceived ability to build effective relationships and communication with patients with/without facemasks. Participants were randomly assigned to the standardized facial emotion recognition task Diagnostic Analysis of Nonverbal Accuracy FACES 2‐Adult Faces including 24 faces representing anger, fear, sadness, and happiness.

Results

Facemasks impaired the self‐perceived ability of clinicians to build effective relationships and communicate with patients and reduced satisfaction in clinical encounters. The ability of clinicians to recognize facial emotions is significantly reduced for masked happy and angry faces, but not for sad and afraid ones. The perceived difficulty in building good relationships and communication with patients had a positive correlation with alexithymia and emotion dysregulation; higher levels of discomfort when wearing facemasks had a positive correlation with burnout and emotion dysregulation.

Conclusion

Facemasks reduced clinicians' self‐confidence in clinical encounters with patients wearing facemasks, but their facial emotion recognition performance was only partially impaired.

Keywords: clinical encounters, clinical psychologists, DANVA‐AF, emotion recognition, facemasks

1. INTRODUCTION

1.1. Predictors and outcomes of facial emotion recognition

According to the emotion‐as‐social‐information model (Van Kleef, 2009), the emotion perception produces inferential processes that inform humans cognitions, actions, and thus, effective communication and interaction. Moreover, accurate facial emotion recognition allows observers to mimic others affective expressions, increasing liking and facilitating the establishing of a functional relationship and affiliation (Hess & Fischer, 2013; Hess, 2021). However, establishing a successful relationship and nonverbal communication of emotional states may be challenging while wearing a facemask.

Some sociodemographics influence emotion‐recognition accuracy. Emotional cues activate different brain areas in men and women, with women showing higher emotion perception than men (Fischer et al., 2018; Knyazev et al., 2010; Lee et al., 2005; McClure, 2000). Furthermore, older adults showed lower emotion‐recognition accuracy relative to younger subjects because of differences in neuron density and neurotransmitter balance (Ruffman et al., 2008). Age differences may be more pronounced when the emotion‐recognition task difficulty is increased, as in the case of masked faces (Orgeta, 2010). There are also some different psychological factors that affect emotion‐recognition accuracy. Alexithymia has been frequently associated with difficulties in decoding and understanding facial emotions (Grynberg et al., 2012) with a role of changes in brain regional activity (van der Velde et al., 2013) and attentional avoidance of others' facial eye regions (Fujiwara, 2018). Different studies have highlighted that alexithymic deficits are pronounced when emotional facial expressions are masked, ambiguous, or in general difficult to extract (Brewer et al., 2015; Parker et al., 2005; Reker et al., 2010). Moreover, previous experimental evidence indicated that individuals with burnout symptoms had an altered ability to processing emotional cues (Bianchi et al., 2020; Sokka et al., 2014). Colonnello et al. (2021) reported that healthcare professionals with burnout showed a reduction in the accuracy of facial emotion recognition, with a tendency to misclassify negative emotional cues as positive. To date, the relationship between the self‐emotion regulation and the recognition of others' emotions by facial expression is less studied (Trusova & Fedyukovich, 2020) and needs more investigation.

Emotion recognition competence is associated with better interpersonal outcomes in educational, work, and medical settings (Schmid Mast & Hall, 2018). This ability is relevant in everyday life, but even more, it plays a key role in the health care context. Through the information collected by decoding facial expressions, healthcare professionals can reliably read others' intentions, motivations, and feelings to make the most appropriate interventions. For example, higher levels of emotion recognition ability have been linked with higher interpersonal skill ratings in medical students (Hall et al., 2015) and higher levels of perceived cooperation in negotiations (Schlegel et al., 2018). Accurate recognition of a patient's emotions is associated with positive medical outcomes (Hall, 2011), better adherence to treatment (DiMatteo et al., 1986), greater accuracy in depression and anxiety diagnosis (Robbins et al., 1994), and increased patient satisfaction (DiMatteo et al., 1980). However, in the literature, there is a lack of studies that investigate the association between facial emotion recognition and professionals' self‐efficacy and satisfaction. The available studies are predominantly focused on patient satisfaction and on professionals' emotional intelligence associated with job satisfaction (McKenna et al., 2020).

1.2. Facial emotion recognition in clinical psychology and the impact of facemasks during COVID‐19

In clinical psychology, the facial emotion recognition competence represents a crucial skill and a component of the therapeutic alliance (Datz et al., 2019; Machado et al., 1999). Recently, a study focused on the non‐verbal synchrony of facial expressions showed that absolute synchrony (defined as an emotional interaction within 2 s) was positively related to the therapeutic alliance (Yokotani et al., 2020) and required a deep ability to read facial expressions. In a facial emotion recognition task, clinical psychologists performed better than psychopharmacologists (Dalkiran et al., 2017), suggesting that working more on relational aspects contributed to improving the ability to recognize emotions in facial expressions. In a cross‐cultural study (Hutchison et al., 2018), US and Japanese clinical psychology trainees were assessed in their ability to recognize facially expressed emotions. The results showed that there were no group or gender differences.

In the period of the coronavirus disease (COVID‐19) global pandemic, wearing facemasks was a key strategy to prevent the spread of this severe respiratory disease (Chu et al., 2020). However, even if this preventive measure had a direct positive medical impact, facemasks covered about 60%−70% of the human face that is crucial for emotional expression, and thus emotion recognition (Carbon, 2020; Grahlow et al., 2021). Many studies have reported that facial emotion‐recognition accuracy declined from unmasked to masked target faces (Bani et al., 2021; Carbon, 2020; Grahlow et al., 2021; Grundmann et al., 2021; Marini et al., 2021). The COVID‐19 pandemic has had a deep impact on the clinical activity of mental health professionals (Hüfner et al., 2020), introducing the need for video consultations (Boldrini et al., 2020) in the first part of the pandemic, and requiring the use of a facemask (for both the clinician and for the patient) for in‐person sessions. Recently, a study explored the perception of attention deficit hyperactivity disorder (ADHD) patients and their therapists in two different settings: face‐to‐face with the therapist wearing a facemask, or via videoconferencing (Wyler et al., 2021). The results showed that patients and therapists rated the two settings as similar, even if videoconferencing was perceived as less deep. Another study on a sample of Italian therapists (Mancinelli et al., 2021) explored their self‐perceptions when providing on‐line interventions during the first peak of the COVID‐19 pandemic and reported an overall positive self‐perception, despite greater fatigue and a shift toward a more directive and talkative behavior during sessions to compensate for physical distance. However, little is known about facial emotion recognition among mental health clinicians, in particular their attitude towards the use of facemasks in clinical settings and their perception of the impact of facemasks on the quality of clinical encounters. Therefore, it is relevant to know the perceptions of clinicians regarding the use of facemasks in clinical consultations and their ability to recognize facial expressions with or without a facemask. This is particularly relevant, as the use of facemasks will remain in many healthcare settings and will be a part of clinical activity for clinicians as well as for patients. Therefore, there's a need to develop practical strategies to handle the impact of facemasks on clinical work.

1.3. Aims

The first aim of this study was to assess clinicians' experience and perception of facemasks wearing during clinical consultations. In particular, we compared (1) clinicians' perceived self‐efficacy in building a functional relationship, (2) their perceived self‐efficacy in communicating with patients, and (3) their satisfaction with clinical encounters when wearing versus not wearing facemasks. Participants' mask‐related discomfort was also explored. We expected that wearing facemasks would be associated with lower perceived self‐efficacy in building functional relationships and in communicating with patients, and with lower satisfaction with clinical encounters. We furthermore expected high levels of mask‐related discomfort among psychotherapists during clinical encounters.

The second aim was to assess the clinicians' ability to recognize emotions on static masked faces. We expected that facemasks would significantly impair the participants' ability to recognize emotions on static faces beyond the effect of gender and age, and that being male and older would be associated with more misattributions in a facial emotion recognition task.

The third aim was to explore the association of relevant psychological variables (alexithymia, emotion dysregulation, and level of burnout) with (a) the ability to recognize emotions on both static masked and unmasked faces, (b) the change of perceived self‐efficacy and satisfaction of clinicians during the clinical encounters with their patients after facemasks were introduced, and (c) the mask‐related discomfort of participants. We expected that alexithymia, emotion dysregulation, and level of burnout would be negatively associated with (a) the ability to recognize emotions on static masked and unmasked faces and (b) professionals' self‐efficacy and satisfaction during clinical encounters after having introduced facemasks, and (c) positively with the mask‐related discomfort of clinicians during encounters with patients.

2. METHODS

2.1. Participants and procedure

A purposive sample of 2752 Italian clinical psychologists and psychotherapists were invited to take part in the study by an invitation email with a link to the study survey. Participants were contacted through scientific associations of clinical psychologists and psychotherapists of different theoretical orientations, as well as the public register of clinicians. The inclusion criteria were (i) being a clinical psychologist or a psychotherapist (psychologist or psychiatrist according to the Italian rules) (ii) being sufficiently proficient in Italian to complete the survey. Data were collected during the COVID‐19 pandemic (December 2020 to January 2021) when wearing masks were compulsory in public spaces and during all in‐person clinical activities. The study received ethical approval by the Ethical Committee of the University of Milano—Bicocca (study n° 542 Prot. 0061750/20). Participation was voluntary and anonymous. In a between‐subject design, after providing informed consent digitally, participants were randomly allocated to either a masked or unmasked version of a standardized facial emotion recognition task described in detail in the next section and completed the on‐line survey (using the LimeSurvey platform).

2.2. Measures

The survey completion required about 15 min and included sociodemographic, professional, and educational information; psycho‐attitudinal variables (alexithymia, emotion dysregulation, and burnout); three ad hoc items to explore the change of participants' perceived self‐efficacy and satisfaction in building a functional relationship and in communicating with patients after introducing facemasks during the clinical encounters and one ad hoc item to assess the level of discomfort of clinicians in wearing facemasks in the clinical setting. A facial emotion recognition task was also performed.

The Toronto Alexithymia Scale (TAS‐20; Bressi et al., 1996) was used to assess the levels of alexithymia and consists of 20 items rated on a 5‐point Likert scale ranging from 1 (“Strongly disagree”) to 5 (“Strongly agree”). The TAS‐20 total score was obtained by summing the scores of each domain of the TAS‐20: difficulty in identifying feelings, difficulty in describing feelings, and externally‐oriented thinking. Higher TAS‐20 total scores reflect greater difficulty in the cognitive processing of emotion. In this study, Cronbach's alpha of the TAS‐20 total score was α = 0.77.

The ability to regulate emotions was measured using the Italian 20‐item version of the Difficulties in Emotion Regulation Scale (DERS‐20; Lausi et al., 2020). It includes 20 items answered on a 5‐point Likert scale ranging from 1 (“Does not describe me well”) to 5 (“Describes me very well”). Higher DERS‐20 total scores reflect greater difficulties in emotion regulation. In this study, Cronbach's alpha of the DERS‐20 total score was α = 0.89.

The Copenhagen Burnout Inventory (CBI; Fiorilli, 2015) includes 19 items evaluating the respondents' level of burnout on three domains: personal burnout, work burnout, patient‐related burnout. According to the Italian validation of the CBI, all items were rated on a 5‐point Likert scale ranging from 1 (“Never”) to 5 (“Always”); the CBI total score was obtained by summing the scores of the three domains and greater scores reflect greater risk of burnout. In this study, Cronbach's alpha of the CBI was α = 0.86.

Three pairs of items explored participants' perceived self‐efficacy in building a functional relationship, in communicating with patients, and the satisfaction with the quality of the therapeutic relationship after introducing facemasks: (1) “How well did you feel able to build a good relationship with patients before facemasks were introduced?/How well do you feel able to build a good relationship with patients since facemasks have been introduced?”; (2) “How effective did you feel in communicating with patients before facemasks were introduced?”/“How effective do you feel in communicating with patients since facemasks have been introduced?”; (3) “How satisfied were you with the quality of the relationship with patients before facemasks were introduced?”/“How satisfied are you with the quality of the relationship with patients since facemasks have been introduced?.” All items were rated on a seven‐point Likert scale ranging from “1 = Not at all” to “7 = Very much.”

Each pair of items was combined to measure the perceived difference in (1) being able to build a good relationship with patients (Relational difficulties), (2) effectiveness in communicating with patients (Communication difficulties), and (3) satisfaction with the quality of the therapeutic relationship when not wearing versus wearing facemasks (Relational dissatisfaction). Each score was computed by subtracting the score of the “since facemasks have been introduced” item from the “before facemasks were introduced” item, with higher scores indicating greater professional/relational difficulties with facemask wearing during consultations.

One item rated on a 7‐point Likert scale assessed the level of discomfort in wearing facemasks during clinical encounters (“What is the level of stress/discomfort you feel when wearing the facemask during encounters with your patients?”).

Facial emotion recognition ability was measured with the Diagnostic Analysis of Nonverbal Accuracy FACES 2‐Adult Faces (DANVA2‐AF). It includes 24 photos of adult faces, with each face showing one of four emotions: fear, happiness, sadness, and anger. For each emotion, there are three high and three low‐intensity stimuli. The photos are presented for 2 s. Using a forced‐choice format, participants respond by selecting the best emotion label for each photo. The test scores reflect the number of errors made in identifying emotions. A modified version of the DANVA2‐AF was created digitally adding a light blue surgical mask to each photo (Bani et al., 2021).

2.3. Statistical analysis

Statistical analyses were performed with SPSS 24.0. Descriptive analysis, skewness, and kurtosis were calculated to explore the normality of the distribution of continuous and ordinal variables. Wilcoxon signed ranks test was used to compare the scores of the ad‐hoc items, whereas ANCOVAs controlled for gender and age were used to compare the DANVA2‐AF scores. The test statistic Z and the p value are reported for the former, while the F statistic and p‐value are indicated for the latter. The p value for significance was set according to the Bonferroni correction for each analysis (p = 0.007). Pearson's r, Spearman's rho, and partial correlations were calculated to explore the relationship between variables.

3. RESULTS

3.1. Sample features

Out of the 435 participants who accessed the survey 80 gave incomplete answers and were therefore not included in the analysis. Additionally, the answers of 13 subjects were removed as outliers. Table 1 shows the characteristics of the 342 study participants included in the analysis (response rate = 12.5%). Out of 342, 172 (50.3%) were allocated to the unmasked condition while 170 (49.7%) to the masked one.

Table 1.

Sample characteristics

Gender N (%)
Female 277 (81%)
Male 65 (19%)
Profession
Psychologist/psychotherapist 273 (79.8%)
Psychiatrist 23 (6.7%)
Other 46 (13.5%)
Licensed psychotherapist
Yes 281 (82.2%)
No 19 (5.6%)
In training 42 (12.3%)
Self‐reported theoretical framework
Psychodynamic/psychoanalytic 111 (32.5%)
Family systems paradigm 33 (9.6%)
Cognitive behavioral therapy 169 (49.4%)
Other 29 (8.5%)
Mean (SD)
Age 46.39 (12.21)
Years of clinical practice 16.54 (12.36)
Percentage of online sessions 27.80 (27.55)
Hours of clinical practice a week 22.84 (11.17)
Open in a new tab

Abbreviation: SD, standard deviation.

3.2. Experience and perception toward the use of facemasks during clinical sessions

Participants reported that introducing facemasks in the clinical setting significantly reduced their confidence of being able to build a good relationship with patients, to be effective in communicating with patients, and their satisfaction with the quality of the relationship with patients. Wilcoxon signed‐rank test results are reported in Table 2. The ability to communicate effectively with patients was the relational aspect of clinical consultation that seemed most affected by facemask wearing, with a score of 1.02.

Table 2.

Perception and differences of relational dimensions in clinical settings with and without facemasks

No facemasks Facemasks Z
Mean (SD) Mean (SD) Δ Mean p
Relational difficulties 5.92 5.09 0.83 −10.358 <0.001
(1.07) (1.14)
Communication difficulties 5.85 4.84 1.02 −10.057 <0.001
(0.99) (1.17)
Relational dissatisfaction 5.84 5.15 0.69 −11.805 <0.001
(1.04) (1.18)
Open in a new tab

Abbreviation: SD, standard deviation.

Note: Higher scores indicating greater professional/relational difficulties/dissatisfaction with facemask wearing during consultations.

3.3. Ability to recognize emotions on static masked faces

Table 3 reports the score difference in masked versus unmasked conditions in the total number of errors, the numbers of errors in the high/low intensity conditions, and the number of errors for each of the four emotions of the DANVA2‐AF. Higher errors in all the masked sets were found (Fs(1, 330) > 119.584, ps < 0.001, η 2s > 0.266) beyond the effect of gender (Fs(1, 330) <0 .015, ps > 0.152) and age (Fs(1, 330) > 6.112, ps < 0.014, η 2s > 0.018), except for Sadness and Fear (Table 3). Follow‐up Pearson's correlations showed that older participants showed higher errors in both the masked and unmasked DANVA2‐AF conditions than younger participants (range Pearson's rs = 0.151−0.244, ps < 0.005).

Table 3.

ANCOVA for masked and unmasked conditions and emotion type

No mask Mask No mask versus mask Gender Age
DANVA2‐AF errors Mean (SD) Mean (SD) F p η 2 F p η 2 F p η 2
Total 4.19 8.94 217.899 <0.001 0.398 0.022 0.882 ‐ 36.652 <0.001 0.100
(2.25) (2.47)
High intensity 1.26 3.62 156.547 <0.001 0.322 0.099 0.754 ‐ 17.441 <0.001 0.050
(1.09) (1.60)
Low intensity 2.93 5.33 119.584 <0.001 0.266 0.253 0.615 ‐ 28.506 <0.001 0.080
(1.64) (1.47)
Happy 0.54 2.93 341.245 <0.001 0.508 2.059 0.152 ‐ 8.573 0.002 0.030
(0.78) (1.08)
Sad 1.32 1.71 6.483 0.011 0.019 0.015 0.903 ‐ 6.112 0.014 0.018
(0.97) (1.12)
Fear 0.86 1.19 4.552 0.034 0.014 0.477 0.490 ‐ 19.728 <0.001 0.056
(0.88) (1.16)
Anger 1.38 3.32 164.692 <0.001 0.333 0.860 0.354 ‐ 19.135 <0.001 0.055
(1.02) (1.16)
Open in a new tab

Abbreviations: DANVA2‐AF, Diagnostic Analysis of Nonverbal Accuracy FACES 2‐Adult Faces; SD, standard deviation.

3.4. Association between alexithymia, emotion dysregulation, and level of burnout with emotion recognition task and mask‐related perceptions and experiences

Errors at the emotion recognition task in the unmasked conditions were positively correlated with alexithymia. No significant correlations between performances at the emotion recognition task in the masked condition and the variables considered emerged (Table 4).

Table 4.

Means, SD, and Pearson's correlation coefficients between emotion recognition task, alexithymia, emotion dysregulation, and level of burnout in the masked and unmasked conditions

Variable 1 2 3 4
1. DANVA2‐AF 1 0.064 −0.001 0.080
2. TAS‐20 0.150* 1 0.408*** 0.282***
3. DERS‐20 −0.023 0.371*** 1 0.311***
4. CBI −0.017 0.148 0.289*** 1
M ± SD 6.41 ± 3.38 39.00 ± 11.36 34.09 ± 9.28 43.23 ± 9.49
Open in a new tab

Note: Coefficients above the diagonal refer to the masked condition while the values below it refer to the unmasked condition.

Abbreviations: CBI Copenhagen Burnout Inventory; DANVA2‐AF, Diagnostic Analysis of Nonverbal Accuracy FACES 2‐Adult Faces; DERS, Difficulties in Emotion Regulation Scale; SD, standard deviation; TAS‐20, Toronto Alexithymia Scale.

*

p < 0.05.

**

p < 0.01.

***

p < 0.001.

When looking at facemask wearing‐related variables, participants with higher levels of discomfort when wearing a facemask during consultations showed higher levels of emotion dysregulation and burnout. Furthermore, statistically significant positive correlations emerged between relational and communication difficulties with patients wearing facemasks during consultations and both alexithymia and emotion dysregulation (see coefficients above the diagonal in Table 5). Discomfort wearing a facemask during consultations completely accounted for the correlations between relational and communication difficulties with patients wearing facemasks during consultations and both alexithymia and emotion dysregulation levels (rs < −0.047, p > 0.062).

Table 5.

Spearman's correlation coefficients between facemask‐wearing‐related variables and alexithymia, emotion dysregulation, and burnout

TAS‐20 DERS‐20 CBI
Relational difficulties 0.111* 0.148* 0.066
Communication difficulties 0.115* 0.169** 0.088
Relational dissatisfaction −0.026 −0.014 0.068
Discomfort when wearing a facemask during consultations 0.068 0.116** 0.279***
Open in a new tab

Abbreviations: CBI Copenhagen Burnout Inventory; DERS, Difficulties in Emotion Regulation Scale; TAS‐20, Toronto Alexithymia Scale.

*

p < 0.05.

**

p < 0.01.

***

p < 0.001.

4. DISCUSSION

To our knowledge, this is one of the first studies to explore the confidence of clinicians on the use of facemasks in clinical encounters and their performance on facial emotion recognition in a wide sample of clinical psychologists and psychotherapists.

The first aim was to assess clinicians' experiences and confidence on facemask wearing during clinical consultations. Overall, clinicians reported that the use of facemasks in clinical encounters significantly reduced their confidence to communicate with patients, but also the perceived capacity to build a good relationship with them; moreover, the use of facemasks reduced the satisfaction in the relationship with patients. These results confirm the concerns expressed by many clinicians in some commentaries about the impact of facemasks in clinical encounters (Hüfner et al., 2020; Mehta et al., 2020; Thirthalli et al., 2020; Veluri, 2020) and gives empirical support to them. Recently, some authors have shown that a low perception of depth and quality in therapeutic relationships contributes to burnout (Zarzycka et al., 2022) highlighting the need of a monitoring of the clinicians' perception of therapeutic relationships when wearing facemasks. However, a recent cross‐sectional study collecting the perspective of clinicians and ADHD patients on the experience of face‐to‐face sessions (wearing facemask) and videoconferencing did not find differences in terms of satisfaction and therapeutic alliance for both patients and clinicians (Wyler et al., 2021). Moreover, clinicians seemed to be more concerned than patients about the negative impacts of wearing facemasks, but further studies should verify if patients with different clinical conditions are differently impacted by the use of facemasks. In fact, previous studies have shown that some patients, namely patients with borderline personality disorders (Daros et al., 2013), somatoform disorders (Pedrosa Gil et al., 2009), and patients at risk of psychosis (Comparelli et al., 2014; Seo et al., 2020) have more difficulties in facial emotion recognition; the use of facemasks could amplify this pattern.

The second aim was to assess the clinicians' ability to recognize emotions on static masked faces. According to previous studies on the general population or healthcare students (Bani et al., 2021; Carbon, 2020; Marini et al., 2021), facemasks also disrupt the clinician's ability to recognize emotions both for high and low intensity emotions. However, contrary to a previous study and using the same task (Bani et al., 2021) there was no difference in emotion recognition for fear and sadness, only for happiness and anger. Clinicians may be more skilled at recognizing facial emotions than lay people, as suggested by previous studies (Dalkiran et al., 2017), and this ability mitigates the disruptive effect of facemasks for some emotions, in particular for unpleasant emotions such as sadness and fear. However, a comparison with a paired group of nonclinicians should be used to confirm this hypothesis. According to our prediction and previous studies (Ruffman et al., 2008), the number of errors in the emotion recognition task increased with age, consistently with the decline in cognitive skills (Fölster et al., 2014; Orgeta, 2010); interestingly, the clinical experience does not compensate for this decline in the emotion recognition task and younger clinicians with less clinical experience had higher scores in the emotion recognition task. It is important to note that facial recognition is only one side of the complex emotion reading process, and clinicians with more experience can compensate for the decline in facial emotion recognition with other relational skills and also using paraverbal and nonverbal signals (Machado et al., 1999; Soma et al., 2020). Contrary to our expectations, there was no difference in emotion recognition between male and female participants; previous studies observed better performance in female participants (Grundmann et al., 2021).

The third aim was to explore the association of alexithymia, emotion dysregulation, and burnout with facial emotion recognition, mask‐related discomfort, and perceived self‐confidence and satisfaction during clinical encounters. Our predictions were partially confirmed as in the unmasked condition worse performance on the emotion recognition task was positively correlated with alexithymia, but not with emotion dysregulation and burnout. This result is consistent with those of a previous study (Augustin et al., 2020) in a sample of patients with burnout syndrome, who reported higher levels of alexithymia but no impairment in facial emotion recognition. On the contrary, a recent study on healthcare professionals reported worse performance in facial recognition of unmasked dynamic faces for participants with higher levels of burnout (Colonnello et al., 2021).

In our study greater discomfort with wearing facemasks was related to higher levels of burnout. Furthermore, clinicians with higher levels of alexithymia and emotion dysregulation reported a higher perceived difficulty in building good relationships and communication with patients wearing facemasks. These associations, however, were entirely explained by reported discomfort with wearing facemasks during consultation indicating that clinicians' own experiences and sensations related to facemask wearing better account for their perception of communication and relational difficulties with patients. As previous studies have highlighted that facemasks can increase the level of distress in professionals (Campagne, 2021; Rosner, 2020; Unoki et al., 2021), clinicians (particularly those with a pre‐existing risk of burnout) should consider taking more time between sessions to take off the facemask and recover physical and mental resources for subsequent sessions or to alternate in‐person sessions with facemasks and online sessions without facemasks.

Recent findings have shown that facemasks impact the acoustic voice signal, affecting intelligibility in both directions, that is, the ability to understand what a person said and the ability to be understood (Nguyen et al., 2021). Although both surgical and KN95 masks reduced the voice frequencies, surgical masks should be preferred as they have a weaker impact. Furthermore, the authors advanced that masks affected mostly the transmission of consonants, and suggested to hyperarticulate the speech, speak slowly, and increase speech pausing to compensate for the impact of wearing a facemask. These strategies should be considered also in clinical consultations when either the healthcare professional and the patient wear a facemask.

4.1. Limitations

Our sample is primarily composed of female participants, reflecting the gender distribution of Italian clinicians. This study lacks the patient's perspective on the use of facemasks, and it would be interesting to verify which kind of patients suffer more from the limited accessibility of facial expressions as well as the level of concordance between clinicians and patients. Another limitation is related to the use of an emotion recognition task with static photos. The complexity of emotion expression and reading in clinical encounters cannot be fully depicted with a simple recognition task, and for this reason further investigations are needed to confirm our results, using dynamic masked faces and less restrictive tasks (e.g., using free labeling vs. forced choice or including more distractors among the answer alternatives) or including also paraverbal cues (such as the tone of the voice). Furthermore, an eye tracker could help to verify which non‐verbal cues are used by clinicians to categorize a masked facial expression. Moreover, previous studies have shown better performance in emotion recognition with younger rather than older target faces (Grundmann et al., 2021); the task used in the present study included only young target faces. Finally, a wider sample size could help to verify the role of age and clinical experience on emotion recognition.

4.2. Conclusions

Facemasks reduced clinicians' confidence in building good relationships and communicating with patients and their ability to recognize happy and angry faces. A regular check of the clinicians' level of distress and their perception of self‐confidence in clinical work should be done to detect early signs of risk of burnout. In addition, a proper time management plan should be considered to schedule live (with facemasks) and online (without facemasks) sessions.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

PEER REVIEW

The peer review history for this article is available at https://publons.com/publon/10.1002/jclp.23468.

ETHICS STATEMENT

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was also obtained from all participants.

ACKNOWLEDGMENT

This study was supported by the University of Milano ‐ Bicocca, grant 2020‐ATE‐0171.

Bani, M. , Ardenghi, S. , Rampoldi, G. , Russo, S. , & Strepparava, M. G. (2022). Impact of facemasks on psychotherapy: Clinician's confidence and emotion recognition. Journal of Clinical Psychology, 1–14. 10.1002/jclp.23468

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

REFERENCES

  1. Augustin, M. , Eichhammer, P. , Haas, J. , Schiekofer, S. , & Busch, V. (2020). Increased alexithymia but no profound emotion processing disorder in burnout syndrome. Journal of Nervous & Mental Disease, 208(10), 794–802. [DOI] [PubMed] [Google Scholar]
  2. Bani, M. , Russo, S. , Ardenghi, S. , Rampoldi, G. , Wickline, V. , Nowicki, S. , & Strepparava, M. G. (2021). Behind the mask: Emotion recognition in healthcare students. Medical science educator, 31(4), 1273–1277. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bianchi, R. , Laurent, E. , Schonfeld, I. S. , Bietti, L. M. , & Mayor, E. (2020). Memory bias toward emotional information in burnout and depression. Journal of health psychology, 25(10–11), 1567–1575. [DOI] [PubMed] [Google Scholar]
  4. Brewer, R. , Cook, R. , Cardi, V. , Treasure, J. , & Bird, G. (2015). Emotion recognition deficits in eating disorders are explained by co‐occurring alexithymia. Royal Society Open Science, 2(1), 140382. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Boldrini, T. , Schiano Lomoriello, A. , Del Corno, F. , Lingiardi, V. , & Salcuni, S. (2020). Psychotherapy during COVID‐19: How the clinical practice of Italian psychotherapists changed during the pandemic. Frontiers in Psychology, 11. 10.3389/fpsyg.2020.591170 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Campagne, D. M. (2021). The problem with communication stress from face masks. Journal of Affective Disorders Reports, 3, 100069. [Google Scholar]
  7. Carbon, C.‐C. (2020). Wearing face masks strongly confuses counterparts in reading emotions. Frontiers in Psychology, 11, 2526. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Chu, D. K. , Akl, E. A. , Duda, S. , Solo, K. , Yaacoub, S. , Schünemann, H. J. , Chu, D. K. , Akl, E. A. , El‐Harakeh, A. , Bognanni, A. , Lotfi, T. , Loeb, M. , Hajizadeh, A. , Bak, A. , Izcovich, A. , Cuello‐Garcia, C. A. , Chen, C. , Harris, D. J. , Borowiack, E. , … Schünemann, H. J. (2020). Physical distancing, face masks, and eye protection to prevent person‐to‐person transmission of SARS‐CoV‐2 and COVID‐19: A systematic review and meta‐analysis. The Lancet, 395(10242), 1973–1987. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Colonnello, V. , Carnevali, L. , Russo, P. M. , Ottaviani, C. , Cremonini, V. , Venturi, E. , & Mattarozzi, K. (2021). Reduced recognition of facial emotional expressions in global burnout and burnout depersonalization in healthcare providers. PeerJ, 9, e10610. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Comparelli, A. , De Carolis, A. , Corigliano, V. , Trovini, G. , Dehning, J. , Di Pietro, S. , De Pisa, E. , Galderisi, S. , & Girardi, P. (2014). Facial emotion recognition impairment is related to disorganisation in multi‐episode schizophrenia. Schizophrenia Research: Cognition, 1(2), 122–125. 10.1016/j.scog.2014.07.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Dalkiran, M. , Yuksek, E. , & Karamustafalioglu, O. (2017). Facial emotion recognition ability in psychiatrists, psychologist and psychological counselors. European Psychiatry, 41(S1), S157. [Google Scholar]
  12. Daros, A. R. , Zakzanis, K. K. , & Ruocco, A. C. (2013). Facial emotion recognition in borderline personality disorder. Psychological Medicine, 43(9), 1953–1963. 10.1017/s0033291712002607 [DOI] [PubMed] [Google Scholar]
  13. Datz, F. , Wong, G. , & Löffler‐Stastka, H. (2019). Interpretation and working through contemptuous facial micro‐expressions benefits the patient‐therapist relationship. International Journal of Environmental Research and Public Health, 16(24), 4901. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. DiMatteo, M. R. , Hays, R. D. , & Prince, L. M. (1986). Relationship of physicians' nonverbal communication skill to patient satisfaction, appointment noncompliance, and physician workload. Health Psychology, 5(6), 581–594. [DOI] [PubMed] [Google Scholar]
  15. DiMatteo, M. R. , Taranta, A. , Friedman, H. S. , & Prince, L. M. (1980). Predicting patient satisfaction from physicians' nonverbal communication skills. Medical Care, 18, 376–387. [DOI] [PubMed] [Google Scholar]
  16. Fiorilli, C. , De Stasio, S. , Benevene, P. , Iezzi, D. F. , Pepe, A. , & Albanese, O. (2015). Copenhagen burnout inventory (CBI): A validation study in an Italian teacher group. TPM‐Testing, Psychometrics, Methodology in Applied Psychology, 22(4), 537–551. [Google Scholar]
  17. Fischer, A. H. , Kret, M. E. , & Broekens, J. (2018). Gender differences in emotion perception and self‐reported emotional intelligence: A test of the emotion sensitivity hypothesis. PLoS One, 13(1), e0190712. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Fölster, M. , Hess, U. , & Werheid, K. (2014). Facial age affects emotional expression decoding. Frontiers in Psychology, 5, 30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Fujiwara, E. (2018). Looking at the eyes interferes with facial emotion recognition in alexithymia. Journal of Abnormal Psychology, 127(6), 571–577. [DOI] [PubMed] [Google Scholar]
  20. Grahlow, M. , Rupp, C. , & Derntl, B. (2021). The impact of face masks on emotion recognition performance and perception of threat. [DOI] [PMC free article] [PubMed]
  21. Grundmann, F. , Epstude, K. , & Scheibe, S. (2021). Face masks reduce emotion‐recognition accuracy and perceived closeness. PLoS One, 16(4), e0249792. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Grynberg, D. , Chang, B. , Corneille, O. , Maurage, P. , Vermeulen, N. , Berthoz, S. , & Luminet, O. (2012). Alexithymia and the processing of emotional facial expressions (EFEs): Systematic review, unanswered questions and further perspectives. [DOI] [PMC free article] [PubMed]
  23. Hall, J. A. (2011). Clinicians' accuracy in perceiving patients: Its relevance for clinical practice and a narrative review of methods and correlates. Patient Education and Counseling, 84(3), 319–324. [DOI] [PubMed] [Google Scholar]
  24. Hall, J. A. , Ship, A. N. , Ruben, M. A. , Curtin, E. M. , Roter, D. L. , Clever, S. L. , Smith, C. C. , & Pounds, K. (2015). Clinically relevant correlates of accurate perception of patients' thoughts and feelings. Health Communication, 30(5), 423–429. [DOI] [PubMed] [Google Scholar]
  25. Hess, U. (2021). Who to whom and why: The social nature of emotional mimicry. Psychophysiology, 58(1), e13675. [DOI] [PubMed] [Google Scholar]
  26. Hess, U. , & Fischer, A. (2013). Emotional mimicry as social regulation. Personality and social psychology review, 17(2), 142–157. [DOI] [PubMed] [Google Scholar]
  27. Hüfner, K. , Hofer, A. , & Sperner‐Unterweger, B. (2020). On the difficulties of building therapeutic relationships when wearing face masks. Journal of Psychosomatic Research, 138, 110226. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hutchison, A. , Gerstein, L. , & Kasai, M. (2018). A Cross‐Cultural comparison of US and Japanese trainees' Emotion‐Recognition ability. Japanese Psychological Research, 60(2), 63–76. [Google Scholar]
  29. Knyazev, G. G. , Slobodskoj‐Plusnin, J. Y. , & Bocharov, A. V. (2010). Gender differences in implicit and explicit processing of emotional facial expressions as revealed by event‐related theta synchronization. Emotion (Washington, D.C.), 10(5), 678–687. [DOI] [PubMed] [Google Scholar]
  30. Lausi, G. , Quaglieri, A. , Burrai, J. , Mari, E. , & Giannini, A. M. (2020). Development of the DERS‐20 among the Italian population: A study for a short form of the difficulties in emotion regulation scale. Mediterranean Journal of Clinical Psychology, 8(2). 10.6092/2282-1619/MJCP-2511 [DOI] [Google Scholar]
  31. Lee, T. M. C. , Liu, H. L. , Chan, C. C. H. , Fang, S. Y. , & Gao, J. H. (2005). Neural activities associated with emotion recognition observed in men and women. Molecular Psychiatry, 10(5), 450–455. [DOI] [PubMed] [Google Scholar]
  32. Machado, P. P. , Beutler, L. E. , & Greenberg, L. S. (1999). Emotion recognition in psychotherapy: Impact of therapist level of experience and emotional awareness. Journal of Clinical Psychology, 55(1), 39–57. [DOI] [PubMed] [Google Scholar]
  33. Mancinelli, E. , Gritti, E. S. , Schiano Lomoriello, A. , Salcuni, S. , Lingiardi, V. , & Boldrini, T. (2021). How does it feel to be online: Psychotherapists' self‐perceptions in telepsychotherapy sessions during the COVID‐19 pandemic in Italy. Frontiers in Psychology, 12. 10.3389/fpsyg.2021.726864 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Marini, M. , Ansani, A. , Paglieri, F. , Caruana, F. , & Viola, M. (2021). The impact of facemasks on emotion recognition, trust attribution and re‐identification. Scientific Reports, 11(1), 5577. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. McClure, E. B. (2000). A meta‐analytic review of sex differences in facial expression processing and their development in infants, children, and adolescents. Psychological Bulletin, 126(3), 424–453. [DOI] [PubMed] [Google Scholar]
  36. McKenna, J. , Webb, J. A. , & Weinberg, A. (2020). A UK‐wide analysis of trait emotional intelligence in occupational therapists. International Journal of Therapy And Rehabilitation, 27, 1–13. [Google Scholar]
  37. Mehta, U. M. , Venkatasubramanian, G. , & Chandra, P. S. (2020). The “mind” behind the “mask”: Assessing mental states and creating therapeutic alliance amidst COVID‐19. Schizophrenia Research, 222, 503–504. 10.1016/j.schres.2020.05.033 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Nguyen, D. D. , McCabe, P. , Thomas, D. , Purcell, A. , Doble, M. , Novakovic, D. , Chacon, A. , & Madill, C. (2021). Acoustic voice characteristics with and without wearing a facemask. Scientific Reports, 11, 5651. 10.1038/s41598-021-85130-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Orgeta, V. (2010). Effects of age and task difficulty on recognition of facial affect. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 65(3), 323–327. [DOI] [PubMed] [Google Scholar]
  40. Parker, P. D. , Prkachin, K. M. , & Prkachin, G. C. (2005). Processing of facial expressions of negative emotion in alexithymia: The influence of temporal constraint. Journal of Personality, 73(4), 1087–1107. [DOI] [PubMed] [Google Scholar]
  41. Pedrosa Gil, F. , Ridout, N. , Kessler, H. , Neuffer, M. , Schoechlin, C. , Traue, H. C. , & Nickel, M. (2009). Facial emotion recognition and alexithymia in adults with somatoform disorders. Depression and Anxiety, 26(1), E26–E33. 10.1002/da.20456 [DOI] [PubMed] [Google Scholar]
  42. Reker, M. , Ohrmann, P. , Rauch, A. V. , Kugel, H. , Bauer, J. , Dannlowski, U. , Arolt, V. , Heindel, W. , & Suslow, T. (2010). Individual differences in alexithymia and brain response to masked emotion faces. Cortex, 46(5), 658–667. [DOI] [PubMed] [Google Scholar]
  43. Robbins, J. M. , Kirmayer, L. J. , Cathebras, P. , Yaffe, M. J. , & Dworkind, M. (1994). Physician characteristics and the recognition of depression and anxiety in primary care. Medical Care, 32, 795–812. [DOI] [PubMed] [Google Scholar]
  44. Rosner, E. (2020). Adverse effects of prolonged mask use among healthcare professionals during COVID‐19. J Infect Dis Epidemiol, 6(3), 130. [Google Scholar]
  45. Ruffman, T. , Henry, J. D. , Livingstone, V. , & Phillips, L. H. (2008). A meta‐analytic review of emotion recognition and aging: Implications for neuropsychological models of aging. Neuroscience & Biobehavioral Reviews, 32, 863–881. [DOI] [PubMed] [Google Scholar]
  46. Schlegel, K. , Mehu, M. , van Peer, J. M. , & Scherer, K. R. (2018). Sense and sensibility: The role of cognitive and emotional intelligence in negotiation. Journal of Research in Personality, 74, 6–15. [Google Scholar]
  47. Schmid Mast, M. , & Hall, J. A. (2018). The impact of interpersonal accuracy on behavioral outcomes. Current Directions in Psychological Science, 27(5), 309–314. [Google Scholar]
  48. Sokka, L. , Huotilainen, M. , Leinikka, M. , Korpela, J. , Henelius, A. , Alain, C. , Müller, K. , & Pakarinen, S. (2014). Alterations in attention capture to auditory emotional stimuli in job burnout: An event‐related potential study. International Journal of Psychophysiology, 94(3), 427–436. [DOI] [PubMed] [Google Scholar]
  49. Soma, C. S. , Baucom, B. R. W. , Xiao, B. , Butner, J. E. , Hilpert, P. , Narayanan, S. , Atkins, D. C. , & Imel, Z. E. (2020). Coregulation of therapist and client emotion during psychotherapy. Psychotherapy research, 30(5), 591–603. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Seo, E. , Park, H. Y. , Park, K. , Koo, S. J. , Lee, S. Y. , Min, J. E. , Lee, E. , & An, S. K. (2020). Impaired facial emotion recognition in individuals at ultra‐high risk for psychosis and associations with schizotypy and paranoia level. Frontiers in Psychiatry, 11. 10.3389/fpsyt.2020.00577 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Trusova, A. V. , & Fedyukovich, E. I. (2020). Emotion recognition as a marker of emotional regulation deficits in substance use disorder: Review of current research.
  52. Thirthalli, J. , Manjunatha, N. , & Math, S. B. (2020). Unmask the mind! Importance of video consultations in psychiatry during COVID‐19 pandemic. Schizophrenia Research, 222, 482–483. 10.1016/j.schres.2020.06.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Unoki, T. , Sakuramoto, H. , Sato, R. , Ouchi, A. , Kuribara, T. , Furumaya, T. , Tatsuno, J. , Wakabayashi, Y. , Tado, A. , Hashimoto, N. , Inagaki, N. , & Sasaki, Y. (2021). Adverse effects of personal protective equipment among intensive care unit healthcare professionals during the COVID‐19 pandemic: A scoping review. SAGE Open Nursing, 7, 237796082110261. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. van der Velde, J. , Servaas, M. N. , Goerlich, K. S. , Bruggeman, R. , Horton, P. , Costafreda, S. G. , & Aleman, A. (2013). Neural correlates of alexithymia: A meta‐analysis of emotion processing studies. Neuroscience and Biobehavioral Reviews, 37(8), 1774–1785. [DOI] [PubMed] [Google Scholar]
  55. Van Kleef, G. A. (2009). How emotions regulate social life: The emotions as social information (EASI) model. Current Directions in Psychological Science, 18(3), 184–188. [Google Scholar]
  56. Veluri, N. (2020). Are masks impacting psychiatric inpatients' treatment. Psychiatry Research, 293, 113459. 10.1016/j.psychres.2020.113459 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Wyler, H. , Liebrenz, M. , Ajdacic‐Gross, V. , Seifritz, E. , Young, S. , Burger, P. , & Buadze, A. (2021). Treatment provision for adults with ADHD during the COVID‐19 pandemic: An exploratory study on patient and therapist experience with on‐site sessions using face masks vs. telepsychiatric sessions. BMC Psychiatry, 21(1), 237. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Yokotani, K. , Takagi, G. , & Wakashima, K. (2020). Nonverbal synchrony of facial movements and expressions predict therapeutic alliance during a structured psychotherapeutic interview. Journal of Nonverbal Behavior, 44(1), 85–116. [Google Scholar]
  59. Zarzycka, B. , Jankowski, T. , & Krasiczyńska, B. (2022). Therapeutic relationship and professional burnout in psychotherapists: A structural equation model approach. Clinical Psychology & Psychotherapy, 29(1), 250–259. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Articles from Journal of Clinical Psychology are provided here courtesy of Wiley

RESOURCES