1 INTRODUCTION

Occupational burnout is a chronic condition that develops gradually as a result of prolonged exposure to work stress and insufficient resources for recovery (Aronsson et al., 2017; Maslach et al., 2001; Schaufeli & Enzmann, 1998). While the exact definition of burnout is still under debate, it is the most often operationalized according to the Maslach Burnout Inventory, including three factors: emotional exhaustion, depersonalization/cynicism and lack of professional efficacy (Eckleberry-Hunt et al., 2018). Emotional exhaustion refers to depletion of emotional resources, cynicism and depersonalization refer to the negative attitudes towards one's work and the need to emotionally distance from work, while the lack of professional efficacy refers to the feelings of insufficiency at work (Bakker et al., 2002; Schutte et al., 2000).

It has been estimated that from a quarter to a third of the working population experience burnout symptoms (Ahola et al., 2006; He et al., 2017; Shanafelt et al., 2012) and of those 2% have severe burnout (Ahola et al., 2006). As a common and persistent condition, with typical recovery times from months to several years (Bernier et al., 1998; Leone et al., 2008) burnout reduces quality of life (Wu et al., 2011), damages personal health (Mohren et al., 2003) and affects society by increasing health care costs (Ahola et al., 2008; Honkonen et al., 2006) and by reducing productivity (Ahola et al., 2008; Dewa et al., 2014).

The most prominent symptoms of occupational burnout are an experience of persistent exhaustion, fatigue and loss of positive attitude towards one's work (Schaufeli et al., 2009). In addition, individuals with burnout often report poor sleep and not feeling refreshed when waking up (Ekstedt et al., 2006; Sonnenschein et al., 2007). Amongst the most common complaints are also lowered mood and cognitive capability, especially difficulties in memory and concentration (Maslach et al., 2001; Österberg et al., 2009). Corresponding to the subjective complaints of cognition, several behavioural studies (for a review, see Deligkaris et al., 2014) have indeed shown that burnout is associated with deficiencies of attention (Linden et al., 2005; Sandström et al., 2005; van Dam et al., 2011), verbal and nonverbal memory (Sandström et al., 2005), working memory (Diestel et al., 2013; Jonsdottir et al., 2013; Oosterholt et al., 2012), processing speed (Jonsdottir et al., 2013; Österberg et al., 2009) and executive function (Diestel et al., 2013; Schmidt et al., 2007; see, however, also Luijtelaar et al., 2010; Castaneda et al., 2011; McInerney et al., 2012, for conflicting findings).

Furthermore, these subjective complaints and cognitive impairments are in accordance with structural brain imaging studies indicating morphological differences between individuals with burnout and their controls. Savic (2015) found pronounced cortical thinning in the medial frontal cortex, as well as increased amygdala and decreased caudate volumes in individuals with occupational stress. Moreover, the perceived stress of the participants was positively correlated with the amygdala volumes, as was the decreased caudate with an impairment of fine motor skills in the burnout group (Savic, 2015). Blix et al. (2013), in turn, found reduced grey matter volumes in the anterior cingulate cortex and in the dorsolateral prefrontal cortex in burnout. Also, caudate and putamen volumes were reduced, and importantly, inversely correlated to the degree of perceived stress (Blix et al., 2013).

In addition to morphological differences, the functional connectivity and activation patterns in cortical, limbic and even motor structures have been found deviant in burnout. In one functional magnetic resonance imaging study (fMRI) study, burnout was associated with stronger functional connectivity between the amygdala and the anterior cingulate cortex, the dorsolateral prefrontal cortex and the motor cortex, and with weaker connectivity from the amygdala to the cerebellum and the insular cortex (Golkar et al., 2014). Notably, the connectivity between the amygdala and the anterior cingulate cortex correlated with the reduced ability to down-regulate the response to emotional stress in burnout (Golkar et al., 2014). Another fMRI study (Tei et al., 2014) found reduced activity in emotion- and empathy-related brain areas, that is, in anterior insula, inferior frontal gyrus and temporoparietal junction. Further, this reduction was correlated with the degree of emotional exhaustion as measured with the Maslach Burnout Inventory (Tei et al., 2014). Furthermore, burnout has been associated with a reduction in memory-related dorsolateral prefrontal cortex activity, as well as with a reduction in the activity of right middle frontal gyrus, involved in empathy and perspective-taking (Durning et al., 2013).

Also, electrophysiological studies on burnout have provided evidence on burnout related alterations in various aspects of information processing. Majority of the evidence links burnout to deviant attentional control and also emotional information processing. Individuals with burnout have been found to have shorter P3a latencies for angry and longer P3a latencies for happy emotional utterances, suggesting that individuals with burnout may be more sensitive for negative as opposed to positive emotional information (Sokka et al., 2014). Also orienting to task-relevant stimuli has been found decreased, as indicated by diminished P300 responses to targets (Luijtelaar et al., 2010) and, in another study, the more anterior distribution of visual P3b for task-relevant stimuli (Sokka et al., 2016). In both studies this has been interpreted as representing deficits in cognitive control needed to monitor and update information in working memory (van Luijtelaar et al., 2010; Sokka et al., 2016). Orienting to task-irrelevant auditory distractors has also been found decreased, particularly in conditions of high task load (Sokka et al., 2016). In burnout the increasing task load appears to reserve the attentional resources for task-relevant processing, reducing the residual attentional capacity for orienting to task-irrelevant, but potentially significant, information. Moreover, severe burnout, as opposed to controls and those with mild burnout has been associated with attenuated P3a responses related to rapid shifting of attention between tasks, together with an increased error rate in task performance (Sokka et al., 2017), again reflecting inefficiency of attentional allocation.

Event-related potentials also suggest that burnout is associated with altered error monitoring processes. The error related negativity (ERN) reflecting rapid automatic evaluation of error response has been found enhanced in two studies (Gajewski et al., 2017; Golonka et al., 2017). In the other one, also the error positivity (Pe), reflecting conscious error recognition was found decreased (Golonka et al., 2017). In the other study the participants had less severe, subclinical burnout and their Pe responses did not differ between groups (Gajewski et al., 2017). Finally, in later stages of error processing, following negative feedback, the feedback-related negativity (FRN) has been found enhanced, and the subsequent P3-like feedback related positivity (FRP) reduced (Gajewski et al., 2017). Some of these findings could also be explained by deviations in attentional control, but direct evidence is lacking.

It has been suggested that the altered cognitive function in burnout could result from the prolonged physiological state of stress with changes in hormonal and endocrine function known to be harmful for brain (McEwen, 2012, 2016). For instance, occupational stress has been shown to speed up the rate of physiological ageing (Ahola et al., 2012; Savic, 2015). Also learning shapes brain structures and information processing (review May, 2011), and in occupational burnout the alterations in cognition could be seen as an adaptation to stressful, and emotionally burdening environment. Further, vulnerability for the effects of stress has been attributed to a genetic predisposition (Afifi et al., 2010), providing explanation for why some individuals are more sensitive to the effects of stress than others. All the aforementioned studies on the association between burnout and brain function are, however, cross-sectional, that is, the comparisons have been made between individuals with burnout and their matched controls. Hence, it is not possible to define whether these structural and functional alterations have been caused by the prolonged psychophysiological state manifesting as occupational burnout, or preceded and possibly even contributed to the occurrence burnout. The scarce findings on the permanency of the burnout-related cognitive deficits suggest that the alterations in cognitive capabilities are, if not permanent, at least long lasting. van Dam et al. (2011) demonstrated that impaired cognitive performance in burnout patients could not be reversed by immediate motivational interventions. Moreover, in another study, the cognitive performance did not improve even after 10 weeks of intervention, though the individuals with burnout reported positive changes in burnout symptoms, general health, and cognitive difficulties (Oosterholt et al., 2012). It could be argued, however, that a 10-week intervention is too short for a condition that has been developing for many years. Indeed, after the same individuals were again tested 1.5 years after the intervention, a substantial recovery from burnout symptoms as well as significant recovery of cognitive performance, namely that of short-term memory and attention were found (Österberg et al., 2012).

The purpose of this study was to examine the changes in burnout status and information processing after a 5-year follow-up period. We contacted the participants who took part in our initial study on occupational burnout (Sokka et al., 2014) and re-recorded their event related potentials to a standard tone/ta-ta/, nine different linguistically relevant deviants and three rare emotionally uttered syllables in a passive paradigm, as had been done 5 years before. At the time of the initial study, there were no existing brain research studies on the topic, that is, actual brain recordings of individuals with burnout. The hypotheses were drawn on the basis of subjective complaints, behavioural studies on effects of cognition, and general knowledge on the effects of stress on physiology. Several research paradigms were selected to tap different aspects of cognitive processing. The paradigm reported here had originally been developed for evaluation of central speech–sound representations (mismatch negativity, MMN) and attention allocation (P3a) to emotional information content of speech (Pakarinen et al., 2014), and its earlier version had been shown reliable in a study with repeated measurements (Pakarinen et al., 2009). For investigating burnout, this paradigm was particularly convenient as it allows investigation of basic auditory processing (obligatory responses P1, N1 and P2), the linguistic processing (MMN responses to deviants) and attention allocation to emotional information (P3a responses to emotional rare sounds; Pakarinen et al., 2014) in a short 30-min measurement time. More specifically, the P1-N1-P2 complex is known to reflect earliest stages of cortical processing (gaiting, detection and classification) of acoustic features, including many of the spectral and temporal cues that are related to speech perception and also change with exposure and experience (Tremblay et al., 2001; Wagner et al., 2013). The MMN for speech sounds represents not only processing of sound features, but also phoneme identity (Näätänen, 2001; Näätänen et al., 1997) and the MMN-P3a complex represents the sequence of attention capture (Escera & Corral, 2007). At the time of the initial study the nature of the study was explorative: what information could the electrophysiological brain research methods reveal about information processing in burnout and would it concur with the subjective complaints and the behavioural findings.

At the time of the follow up recording the hypotheses were based on the results of the initial study (Sokka et al., 2014), as well as to the assumptions how the recovery or prolongation might affect cognitive processing. We expected that the central auditory processing of speech sounds, including the modelling of basic auditory attributes such as sound intensity, or frequency and linguistic information such as vowel identity, as indicated by N1, P2 and the MMN signals would be comparable in individuals with burnout and their controls. That is, no amplitude nor latency differences were expected between groups. Further, as in Sokka et al. (2014) we expected to find burnout-related differences in attentional processes, namely in involuntary attention allocation to emotionally valenced information as reflected in P3a responses to the emotionally uttered, rarely occurring happy, angry and sad syllables. Specifically, we expected to see an increased P3a latency for the happy rare stimulus, and decreased P3a latency for the angry stimulus in the group with prolonged burnout. Moreover, we expected that the event related potential (ERP) responses of those who had recovered from burnout would be comparable to the responses measured from the control group. Hence, we expected that any between-group differences seen in auditory information processing would be associated with the burnout status in a way that a recovery from burnout would diminish the difference between the controls and individuals with burnout, and that prolongation of the burnout would maintain difference reported by Sokka et al. (2014). Alternatively, we expected that the prolongation of burnout would further amplify the difference between the burnout and the control group, as a consequence of the continuous adverse stress physiology. These hypotheses were also supported by the emerged studies indicating morphological (Blix et al., 2013; Savic, 2015), and functional (Durning et al., 2013; Golkar et al., 2014; Tei et al., 2014) studies suggesting alterations in stress regulation, and cognitive and emotional processing.

2 METHODS

2.1 Participants

In the initial study carried out by Sokka et al. (2014), the participants comprised of 67 currently working individuals. The participants were customers of the Occupational Health Centre of the city of Helsinki or employees of the city of Helsinki. Majority (59) were recruited through advertisements at an occupational healthcare station and intranet sites of these organizations. Eight participants were referred to the study by a physician, psychologist, or nurse. For a revisit, 31 returned to the laboratory. In addition, nine participants did not take part in the laboratory measurements, but completed an interview, including a Shirom–Melamed Burnout Measure (SMBM; Shirom & Melamed, 2006) by telephone. Four participants were reached, but they did not take part in the laboratory study nor complete the telephone interview. Twenty-three participants from the initial study were not reached.

Participants signed an informed consent after reading the written briefing and the opportunity to discuss the study. The study was approved by the ethical board of the Helsinki University Hospital and was conducted in accordance with the Declaration of Helsinki. All the participants met all the same exclusion and inclusion criteria as in the initial study. The exclusion criteria were excessive use of alcohol or drugs, diagnosed severe psychiatric or neurological disorders, schizophrenia in first grade family members and other diagnosed illnesses that are of organic origin and known to result in fatigue. Also, all participants were working at the time of the revisit and none of them had received any treatment or interventions targeted at occupational burnout.

In order to evaluate whether the burnout status had effect on participation, the SMBM scores for those who agreed to a telephone interview (mean SMBM 3.3, SD 1.3, range 1.7–5.7) and for those who took part in the laboratory study (mean SMBM 3.4, SD 1.1, range 1.4–5.3) were tested for equivalence with the two one-sided tests (TOST) procedure as in Lakens et al. (2018). The smallest effect size of interest (SESOI) was set to 1.0, on the basis on literature indicating that physiological correlates of burnout can be found between groups with a difference of 1 point score (Grossi et al., 2003, see also Gerber et al., 2018, for psychometric properties of SMBM, indicating a population mean of 3.24 and clinically significant cut-off of 4.4). Two-sample Welch's t test, using equivalence bounds of ± 1.0 revealed a t₁₂ = −1.9, p = .04, indicating equivalence.

In order to study the longitudinal effects of occupational burnout, the participants taking part to the laboratory study were divided into four groups on the basis of their reported burnout symptoms (Figure 1) using Finnish version of the Maslach Burnout Inventory–General Survey (MBI-GS; Kalimo et al., 2006). MBI-GS was selected due to the comparability with the initial sample. Participants in the control group (N = 14) scored at or below 2.5 on both measurement occasions. Similarly, participants assigned to the prolonged burnout group (N = 8) scored above the 2.5 cut on both measurement occasions. Those assigned to the recovered group (N = 8) reported initially symptoms above the cut-off point but by the time of the second measurement their experienced level of burnout had decreased to a score at or below the 2.5 points. One participant had acquired burnout (N = 1) between the measurement occasions. The cut-off point of 2.5, corresponding to the midpoint of mild-burnout category, was chosen to produce balanced and sufficiently sized groups for further analyses. The higher than normative cut-off was selected to have more clear and strong symptomatology at the burnout group, as the score of 1.5 signifies an average frequency of symptoms on only monthly, while at the upper end of mild burnout (scores approaching 3.5) symptoms are experienced nearly weekly (Kalimo et al., 2006). By raising the cut-off, the participants in the burnout group experienced symptoms more regularly, and those who experienced burnout symptoms only monthly basis fell into the control group. Furthermore, with this cut-off, balanced and barely sufficient group sizes were obtained.

After excluding the data for two controls with excessive artefacts in the electroencephalography (EEG) recording, and the sole participant who had acquired occupational burnout between the measurement occasions, the final dataset comprised of 28 participants: 12 controls (mean age at second measurement 51.5 years, SD = 6.8 years, 1 male, 1 left-handed), 8 recovered participants (mean age at second measurement 54.1 years, SD = 4.6 years, 1 male, all right-handed) and 8 participants with prolonged burnout (mean age at second measurement 50.3 years, SD = 9.4 years, 1 male, 1 left-handed). The resulting groups did not differ in terms of age, gender, education and working experience.

3 RESULTS

The scores for the MBI-GS and its subscales MBI-Exh, MBI-Cyn and MBI-Eff together with the scores for the BDI2, BAI and BNSQ are presented in Table 2. All the scores correlated significantly with each other (R₅₄ = .29–.90, p < .05), except for the correlation between BAI and BNSQ (R₅₄ = .22, ns.; Table 3). The strongest correlations (R ≥ 70) were observed between the MBI-GS and its subscales MBI-EXh (R₅₄ = .90, p < .001), MBI-Cyn (R₅₄ = .88, p < .001) and MBI-Eff (R₅₄ = .70, p < .001), and between MBI-GS and BDI2 (R₅₄ = .76, p < .001). The weakest correlations (R₅₄ ≤ 35) were seen between BNSQ and MBI-Cyn (R₅₄ = .29, p < .05), MBI-Eff (R₅₄ = .30, p < .05) and BDI2 (R₅₄ = .35, p < .05), as well as between BAI and MBI-Eff (R₅₄ = .29, p < .005). Table 4 presents the results for the between group comparisons for burnout, anxiety, depression and sleep problems, separately for the first and the second measurement. The MBI-GS scores for the control group were lower than for the recovered group on the first measurement only (t_15.4 = −6.73, p < .001). The control group had lower scores as compared with the prolonged group on both measurement occasions (t_13.2 = −6.93, and t_15.2 = −6.83, p < .001, for first and second measurements, respectively). Finally, the recovered group had lower scores than the prolonged group on the second measurement only (t_13.9 = −5.15, p < .001). Of the three subscales, the MBI-Eff was the only one that did not follow the same logic as the MBI-GS, as there was no difference in the scores between the control and the recovered group in the first measurement occasion (t_11.7 = −1.83, ns.). When comparing the anxiety and depression symptoms between groups, the control group had lower BAI and BDI2 scores on the first measurement occasion as compared to the recovered (BAI: t_16.8 = −2.21, p < .05; BDI: t_12.0 = −4.08, p < .01) but this difference was no longer seen in the second measurement. The control group scored lower than the prolonged group on BAI on the first measurement occasion only (t_12.3 = −2.56, p < .05), whereas for the BDI2 the control group scored lower on both measurement occasions (first measurement: t_11.1 = −3.61, p < .01; second measurement: t_12.3 = −2.93, p < .05). The groups did not differ with respect to their BNSQ scores at any of the paired comparisons (t_14.8 = −1.53 – t_13.8 = .4, ns.). The MBI-GS scores at the time of the first measurement strongly predicted the score in the follow-up measurement (R₂₈ = .69, p < .05; R² = .48, 95% population CI for the R = .44–.85).

The standard, the deviant, and three deviant–minus–standard waveforms for the nine deviant tones are presented in Figure 2 and the ERP amplitudes and latencies together with the standard deviations (SD) are given in Table 5. Estimated marginal means from the mixed effects models for amplitudes are given in the Table 6/appendix. When modelling for the obligatory response amplitudes for the standard tones a statistically significant main effect of burnout group was found for the N1 (estimated marginal means for the control group 2.8; recovered group 1.3 and prolonged group 2.9 uV; F_2,31.4 = 3.33, p < .05). Pairwise comparisons revealed that in the second measurement, the N1 responses were larger (that is, more negative) for the recovered group as compared with the control (t_34.0 = −2.53, p < .05, Cohen's d = .9) and the prolonged burnout (t_33.2 = −2.50, p < .05, Cohen's d = .82) groups. For the P2 amplitude an interaction of burnout group and time (F_2,28.7 = 3.35, p < .05) was found. Pairwise comparisons revealed that this resulted from the decreased P2 amplitudes for the recovered group from the first measurement to the second (estimated marginal means 5.5 vs. 4.4 uV; t_35.0 = 2.62, p < .05, Cohen's d = .58).

When modelling for the MMN amplitudes for the deviants (Figure 2), both a statistically significant main effect of burnout group (estimated marginal means for the control group −.6; recovered group −1.95 and prolonged group −1.4 uV; F_2,32.4 = 3.44, p < .05), and an interaction of measurement time and burnout group (F_2,28.7 = 7.1, p < .01) were found for the noise deviant. Pairwise comparisons revealed that, in the first measurement, the MMN responses for the noise deviant were smaller (i.e., more positive) for the control group as compared with the recovered (estimated marginal means −.4 vs. −2.7 uV t_40.4 = 3.16, p < .01, Cohen's d = .73) and the prolonged (estimated marginal means −.4 vs. −2.1 uV; t_40.4 = 2.35, p < .05, Cohen's d = .25) groups. The difference however was not present anymore at the second measurement, as the MMN amplitudes for the noise deviants had decreased from the first measurement to the second measurement for both the recovered (estimated marginal means −2.7 vs. −1.2 uV; t_36.0 = −2.97, p < .01, Cohen's d = .66) and for the prolonged burnout groups (estimated marginal means −2.1 vs. −.7 uV; t_33.0 = −3.02, p < .01, Cohen's d = .67).

The standard, the emotional, and the emotional–minus–standard waveforms for the rare emotional stimuli are presented in Figure 3. Interaction plots for averaged MMN and P3a mean amplitudes and peak amplitude latencies for the rare emotional stimuli are presented in Figure 4.

When modelling for the MMN amplitudes for the rare emotional sounds, a significant interaction of burnout group and time was found for the MMN amplitude for the happy stimulus (F_2,28.1 = 3.75, p < .05). Pairwise comparisons revealed that in the second measurement the MMN amplitudes were larger (i.e., more negative) for both the control group (estimated marginal means −3.8 vs. −1.8 uV; t_38.7 = −1.97, p < .05, Cohen's d = 1.11) and for the recovered (estimated marginal means −4.1 vs. −1.8 uV; t_33.7 = −2.36, p < .05, Cohen's d = 1.37) group as compared with the prolonged group. This resulted from the amplitude decrement from the first measurement to the second one in the prolonged group (estimated marginal means −4.8 vs. −1.8 uV; t_32.6 = −3.29, p < .01, Cohen's d = 1.13).

When modelling for the P3a amplitudes for the rare emotional stimuli a significant interaction of burnout group and time was found for the happy stimulus (F_2,28.9 = 3.40, p < .05). Pairwise comparisons revealed that, on the second measurement, the P3a amplitudes were larger in the prolonged burnout group as compared with the control group (estimated marginal means 10.0 vs. 6.8 uV; t_36.8 = −2.04, p < .05, Cohen's d = .83).

Analyses of the peak latencies for the MMN and P3a responses for the rare emotional stimuli revealed an interaction of burnout group and measurement time (F_2,29.5 = 3.52, p < .05) for the P3a latency for the sad stimulus. Pairwise comparisons indicated that for the prolonged group, the P3a for the sad stimulus peaked earlier on the second measurement than on the first measurement (estimated marginal means 269 vs. 292 ms; t_32.7 = 2.30, p < .05, Cohen's d = −2.56). Also, significantly earlier P3a responses were seen for the recovered group as compared to the control group at the second measurement (estimated marginal means 269 vs. 269 ms; t_58.7 = 2.48, p < .05, Cohen's d = 1.39).

4 DISCUSSION

Here, we examined the association of prolongation and recovery from burnout with cognitive function, namely auditory information processing and attention allocation. After 5 years of follow-up, a large portion of the participants were still suffering from various burnout symptoms (Table 2). Half, that is, 8 of the 16 participants had recovered (MBI-GS score below 2.5), while the other half still had burnout symptoms more often than monthly (MBI-GS score at or more than 2.5). This is in line with earlier findings suggesting that for many, the recovery process can take years (Bernier et al., 1998; Leone et al., 2008) and require active interventions and modifications to the work conditions (Hätinen et al., 2009; Kalimo et al., 2003). According to a Dutch cohort study, half of the participants had recovered from burnout at 1 year follow-up, but more than a third of the participants had burnout still after 3 years (Leone et al., 2008). In the same study, the recovery from burnout was predicted by mainly work-related factors such as low exhaustion, high professional efficacy, and absence of conflicts with colleagues (Leone et al., 2008). In our sample, the initial burnout score strongly predicted the degree of burnout 5 years later as 48% of the variance in burnout scores at the follow-up measurement was explained with the burnout scores measured at the first visit. Also this testifies for the perseverance of burnout in the absence of an active intervention.

Even though there is overlap with burnout and depressive (for a review, see Bianchi et al., 2015), and anxiety (e.g., Rössler et al., 2015) symptoms, empirical evidence supports burnout as a distinguished disorder from depression and anxiety disorder (review and meta-analysis: Koutsimani et al., 2019). As shown in Table 3, and in line with existing literature (Bianchi et al., 2015; Rössler et al., 2015), also in this study the burnout symptoms were strongly associated with symptoms of depression, and also moderately with anxiety. At the time of the initial measurement, both burnout groups had also more depressive and anxiety symptoms than the control group (Table 4). Moreover, after recovery from burnout, also the depression and anxiety symptoms had reverted to the level the controls. Yet, also divergence over time between the anxiety, depression and burnout symptoms was observed for the study groups: For the group who still had burnout at the time of the second measurement, the depression symptoms persisted, but the level of anxiety no longer differed from the controls (Table 4). Most importantly, the two burnout groups differed from each other only with respect to the burnout scores in the second measurement as defined, and no differences were observed in their anxiety nor depression symptoms at either one of the measurement occasions.

The difficulty in distinguishing between depression, anxiety and burnout poses also challenges for the modelling approach, that is, to the treatment of depression and anxiety scores. As some proportion of the depression and anxiety scores can be considered attributable to burnout, instead of actual depression and anxiety, assigning anxiety and burnout scores as covariates may also render the predictive power for the burnout inflated. On the positive side, the observed divergence in all these three scores over time and between groups (Table 2) reduces the risk for this issue. Also leaving out the depression and anxiety scores would be problematic as comorbid depression and anxiety can have a confounding effects. Here, the former was considered as smaller risk than the latter, and also conforming to the previous studies, the depression and anxiety scores were treated as covariates in the model.

The association of burnout and cognitive processing was examined by modelling the relationship between the burnout status and the N1, P2, MMN and P3a responses reflecting central auditory information processing, including also the effects of depressive and anxiety symptoms in the model. This modelling approach, using Beck's depression and anxiety inventory scores (BDI2 and BAI) as covariates addresses the question of what is the relationship between the burnout scores (MBI-GS) and the aforementioned ERPs, given the scores observed for depression and anxiety. Majority of the findings are related to the prolongation of the burnout. When examining the processing of the rare emotional utterances, burnout-related alterations in the deviance detection as well as in the attention allocation were found. More specifically, and as seen in Figure 4, the prolongation of the burnout was associated with a decrease in MMN amplitude and an increase in P3a amplitude for the happy stimulus in the second measurement. The MMN decrements are typically thought to reflect impaired detection of the sound change, whereas the increments of the P3a amplitude are attributed to increased distractibility (see, for instance, Horváth et al., 2008).

The prolonged group had also earlier P3a responses to the sad stimulus in the second measurement as compared with the first measurement, suggesting that the prolongation of burnout may have accelerated orienting to negative emotional stimuli. However, despite this significant decrease of P3a latency from first to second recording, the P3a peaks were not significantly earlier than those of the controls. On the other hand, at the second measurement, the P3a latencies for the sad stimulus were earlier for the recovered group than for the control group. This confusing result may at least partly arise from the instability of the P3a peak latency measurement. The latency estimation as such is less stable as compared to the mean amplitude, and the P3a peak amplitudes were considerably varying in time for the both burnout groups, and in particular for the prolonged group (see Figure 3) further affecting latency estimates.

Some suggestive evidence of an association between prolonged burnout and sensitivity to a change in noise level was also found. In the initial measurement, the MMN amplitudes for the noise deviant were larger for both burnout groups as compared with the control group. Furthermore, the amplitudes for the control group did not change between the measurements, whereas the amplitudes for the both burnout groups had reverted to the control level at the second measurement. The result could be interpreted as an increased sensitivity to a change in the tone of voice in the earlier stages of burnout, as in the speech context the noise deviant carries meaning on the tone of voice, and the increased noisiness can be perceived as a hiss-like character or whispering effect in speech (Thibault & Depalle, 2004).

In line with earlier findings (Sokka et al., 2014), no support was found for an association between burnout and central sound change detection, as evaluated with the MMN amplitudes for the changes in sound density, frequency, intensity, sound source location, omission, consonant duration, vowel duration and the change of vowel identity in the deviant tones, in neither one of the measurement occasions. In a similar vein, the obligatory responses were comparable between the groups. Only in the second recording, the N1 and P2 amplitudes for the recovered group shifted towards negative. Possibly, this group, though recovered from burnout, may have been sensitized and thus more prone to acute stress (Shapero et al., 2014; Stam et al., 2000), suggested to increase responses to standard tones (Simoens et al., 2007).

As we used the cut-off of 2.5 the results cannot be directly compared with our initial study (Sokka et al., 2014). Using this higher cut-off, the participants in the burnout group experienced symptoms more regularly, and those who experienced burnout symptoms only monthly basis fell into the control group. Also, a higher cut-off is more likely to decrease the overlap between the depressive and burnout symptoms, as the conditions are more difficult to distinguish at the lower end of the symptom frequency (Bianchi et al., 2015). It is also important to note, when comparing the results of the initial study and the results here, that the subsample that took part in the follow-up measurement may not represent the same population as the initial study sample. Almost half of those who initially participated were no longer available for the follow-up recordings. On the other hand, those who returned to the laboratory did not differ in their burnout scores from those who completed the telephone interview only, suggesting that participation was not related to the burnout status.

In the original study (Sokka et al., 2014) burnout was associated with deviant attention allocation to the emotionally uttered, rarely presented stimuli. As indicated by the P3a peak latencies, involuntary attention was oriented faster towards the negative, that is, angry stimulus and more slowly towards the positive, happy stimulus as compared with the controls (Sokka et al., 2014). With the smaller study sample of the present study, this result was not replicated. That the majority of findings here were seen in the second measurement for the prolonged group may also reflect the fact that chances in such a primitive level of cognitive processing emerge very slowly and become visible after several years of burnout. On the other hand, the sample size also makes it difficult to detect small differences that were observable in the initial study with double the number of participants. Furthermore, as the rare deviant emotional utterances comprise of many relatively large acoustical changes, it is difficult to assess which parts of the findings are related to the rareness and suddenness of the sounds, which are related to the acoustical properties (i.e., relatively large acoustical differences from the standard) of the sound changes, and which might be associated with the emotional contents of those rare utterances. However, as, apart from the noise processing, no evidence of impairments of basic acoustic change detection were not found, the rareness and the emotional aspects should be more probable candidates to explain the differences.

Some cautious conclusions can be drawn. First, burnout is, at least in the absence of any corrective actions or interventions, a very pervasive condition. Secondly, in light of previous evidence (Sokka et al., 2014) and the results of the present study, it is reasonable to assume that the central auditory processing, namely the deviance detection for the basic acoustic features such as sound duration, intensity or frequency in the context of speech (the acoustic changes were embedded in speech stimuli) remains intact in burnout. Some suggestive evidence was found for differences in the deviant processing of noise, but this finding requires further investigation. Thirdly, as suggested by the decreased MMN for the rare happy stimulus, occupational burnout may be associated with altered processing of emotional contents of speech. And fourth, as suggested by the deviant P3a amplitudes and latencies for the rare emotional utterances found already previously (Sokka et al., 2014) and in this study, burnout is rather likely associated with altered attentional control. Finally, as majority of our findings were seen for the group with prolonged burnout, the effects are probably amplified with the continuation of the condition.

CONFLICT OF INTEREST

None of the authors have potential conflicts of interest to be disclosed.

AUTHOR CONTRIBUTION

S. Pakarinen: Conceptualization, Methodology, Writing–original draft, Supervision, Project administration, Funding acquisition; J. Lohilahti: Methodology, Formal analysis, Data Curation, Writing–review & Editing, Visualization; L. Sokka: Conceptualization, Methodology, Investigation, Data Curation; J. Korpela: Formal analysis, Resources; M. Huotilainen: Conceptualization, Writing–review & Editing, Funding acquisition; K. Müller: Conceptualization, Writing–review & Editing, Funding acquisition.