Mårtensson et al. BMC Psychiatry(2024) 24:389
https://doi.org/10.1186/s12888-024-05842-9

BMC Psychiatry

RESEARCH

Open AccessA network analysis of exhaustion disorder
symptoms throughout treatment
Gustav Mårtensson1*, Fred Johansson2, Monica Buhrman1, Fredrik Åhs3 and Jakob Clason van de Leur1
Abstract
Background Stress-induced Exhaustion Disorder (ED) is associated with work absenteeism and adverse healthoutcomes. Currently, little is known regarding how the symptoms of ED are interrelated and whether the patternsof symptoms influence treatment outcomes. To this end, the current study applied network analyses on ED patients
participating in a multimodal intervention.
Methods The first aim of the study was to explore the internal relationships between exhaustion symptoms andidentify symptoms that were more closely related than others. A second aim was to examine whether the baseline
symptom network of non-responders to treatment was more closely connected than the baseline symptom networksof responders, by comparing the sum of all absolute partial correlations in the respective groups’ symptom network.
This comparison was made based on the hypothesis that a more closely connected symptom network before
treatment could indicate poorer treatment outcomes. Network models were constructed based on self-rated ED
symptoms in a large sample of patients (n = 915) participating in a 24-week multimodal treatment program with a
12-month follow-up.
Results The internal relations between self-rated exhaustion symptoms were stable over time despite markedly
decreased symptom levels throughout participation in treatment. Symptoms of limited mental stamina and negative
emotional reactions to demands were consistently found to be the most closely related to other ED symptoms.
Meanwhile, sleep quality and irritability were weakly related to other exhaustion symptoms. The symptom network
for the full sample became significantly more closely connected from baseline to the end of treatment and 12-monthfollow-up. The symptom network of non-responders to treatment was not found to be more closely connected than
the symptom network of responders at baseline.
Conclusions The results of the current study suggest symptoms of limited mental stamina and negative emotional
reactions to demands are central ED symptoms throughout treatment, while symptoms of irritability and sleep quality
seem to have a weak relation to other symptoms of ED. The implications of these findings are discussed in relation to
the conceptualization, assessment, and treatment of ED.
Trial registration The clinical trial was registered on Clinicaltrials.gov 2017-12-02 (Identifier: NCT03360136).
Keywords Exhaustion due to persistent non-traumatic stress, Stress-induced exhaustion disorder, Exhaustion
disorder, Clinical burnout, Network analysis, Network theory, Network connectivity*Correspondence:
Gustav Mårtensson
martensson.gustav@gmail.com
1
Department of Psychology, Uppsala University, Box 1225,
Uppsala 751 42, Sweden

2

Department of Health Promotion Science, Sophiahemmet University,
Valhallavägen 91, Stockholm SE-114 28, Sweden
3
Department of Psychology and Social Work, Mid Sweden University,
Kunskapens väg 1, Östersund SE-831 40, Sweden© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use,sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) andthe source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in thisarticle are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not includedin the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you willneed to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The
Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available
in this article, unless otherwise stated in a credit line to the data.

Mårtensson et al. BMC Psychiatry(2024) 24:389

BackgroundWork-related stress and exhaustion, commonly reported
by workers, are associated with work absenteeism andadverse health outcomes [1–4]. The costs of work-related
stress to Western societies have been estimated to be as
high as 187 billion dollars due to losses in productivity
and medical expenses [5]. Patients with exhaustion due
to persistent non-traumatic stress as their primary complaint report various somatic and psychiatric symptoms,
and there is a lack of international consensus on how
this heterogeneous condition should be diagnosed andunderstood [6–10]. In Sweden, the diagnostic criteria ofstress-induced Exhaustion Disorder (ED) (SE-ICD-10;
F43.8 A) are utilized to diagnose and guide the treatmentof exhaustion due to persistent non-traumatic stress. The
ED diagnosis is characterized as a reaction to a prolonged
period of persistent non-traumatic stress, resulting in alack of psychological energy and an increased need forrecovery, and for many, symptoms of cognitive deficiencies, irritability, poor sleep quality, and several somatic
symptoms [11]. The prevalence of ED has increased rapidly since its introduction in 2005, and it is today one of
the most common psychiatric disorders in Sweden, raising some concerns about the current diagnostic conceptualization of ED [12, 13]. Regardless of the specificdiagnostic construct of ED, there is an apparent need toincrease the understanding of the symptoms that underlie this condition, in Sweden and globally [6, 7, 14].
The network theory of mental disorders is a new perspective on mental illness which challenges the traditional assumption that psychiatric symptoms are causedby distinct underlying disorders [15]. Instead, network
theory proposes that mental disorders could develop andbe maintained by complex causal relationships between
the psychiatric symptoms themselves [15–17]. Considering the heterogeneous nature of exhaustion due to persistent non-traumatic stress, perhaps the network theory
could improve the understanding of the condition byfocusing on interactions between symptoms of exhaustion rather than attempting to understand it as a discretedisease entity.
Studies based on the network theory have largely usedstatistical network analysis methods to estimate andvisualize complex internal relations between symptoms
involved in a disorder [18]. Indices of network centrality have been used to discover symptoms that are closelyinterrelated with other symptoms in the network [19,20]. For example, researchers have found that fatigue isa centrally connected symptom of depression, along with
the DSM-V depression symptoms of loss of interest,
depressed mood, and concentration problems [21, 22].
These four central symptoms are strongly correlated withother depressive symptoms and are also the best predictors for the onset of major depression [22]. Identifying

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central symptoms may be of clinical importance, as it
has been suggested that these symptoms could represent
important treatment targets [17, 18].
Network connectivity, the sum of all absolute partial
correlations in a symptom network corresponding to adisorder, has been suggested to influence symptom severity and the longitudinal course of psychiatric disorders[18, 23]. The underlying assumption of this hypothesisis that higher connectivity suggests stronger reinforcing relationships between symptoms, causing disorders
to persist [18]. Some studies have retroactively founda lower baseline network connectivity in symptom networks for groups of patients who later improved fromdepression, compared to those who did not improve [23–25]. However, other studies have found no association
between baseline network connectivity and treatment
response [26, 27].
The current study used network analyses in a large
clinical sample of ED patients, to explore the internal
relationships between ED symptoms. Our first aim was
to investigate the centrality of ED symptoms to identify
symptoms that can potentially play a key role in maintaining ED and thus represent important treatment targets. Our second aim was to determine if there weredifferences in the pre-treatment network connectivity
between responders and non-responders to treatment, aspredicted by the connectivity hypothesis.

MethodDesign and participantsParticipants data was collected as part of an open clinical
trial of a 24-week multimodal intervention (MMI) witha 12-month follow-up. The study was conducted at two
healthcare centres (PBM Sweden AB) in Stockholm, Sweden, from October 2017 through December 2020. The
clinical trial was approved by the Regional Ethical Review
Board in Stockholm (Approval Nr. 2016/1834- 31/2) andfollowed the ethical principles of the Declaration of Helsinki. All participants provided written consent before
inclusion.
The participants were recruited via referrals fromhealthcare services in the Stockholm area. Inclusion criteria were (1) an ED diagnosis confirmed through assessment by a team of three different clinicians (a licensed
psychologist, a licensed physiotherapist, and an M.D.),(2) a self-rated score of at least 4.5 points on the ShiromMelamed Burnout Questionnaire [28], and (3) age 18
to 64. Exclusion criteria were (1) substance abuse, (2)moderate-high suicidal risk, and (3) severe psychiatric illness (for example severe schizophrenia, untreated PTSD
or bipolar disorder). The participants’ use of medication
was not restricted. The sample includes data from 915
participants. The 24-week MMI was a standardized multidisciplinary treatment based on a cognitive behaviouralMårtensson et al. BMC Psychiatry(2024) 24:389

model. It includes various components, for example,
individual and group-based CBT, applied relaxation,
medical treatment, physical exercise, physiotherapy, andreturn-to-work planning. Four articles have previously
been published based on other aspects of this data, more
specifically on symptom and return-to-work outcomes,
sub-groups, predictors of improvement and constructvalidity of the Karolinska Exhaustion Disorder Scale(KEDS) [29–32].
The participants answered surveys, which included
self-rating of ED symptoms, at five separate time points
throughout the trial, spanning roughly 19.5 months intotal: (1) at the initial assessment, (2) the start of treatment (≈ 1.5 months following assessment), (3) halfway
through treatment (3 months following the start of treatment), (4) the end of treatment (6 months following thestart of treatment), (5) at a 12-months follow-up (following the end of treatment).
Measurements

ED symptoms were measured using the Karolinska
Exhaustion Disorder Scale (KEDS). KEDS is a self-rating
questionnaire with nine questions, which are rated on aseven-point scale ranging from zero to six points, with a
total sum score ranging from 0 to 54. A total score of 19
or above indicates “at risk of ED” [33]. Cronbach’s alphafor KEDS at the initial assessment of the sample was 0.75.
Each item in KEDS represents a specific ED symptom
corresponding to the Swedish diagnostic criteria. The
items, along with their respective zero- and six-point
choices, are [33]: Ability to concentrate (I do not haveany difficulty concentrating, and can read, watch TV andconverse normally; I cannot concentrate on anything atall). Memory (I remember names, dates, and what I am
supposed to do; Every day, I forget important things orwhat I have promised to do). Physical stamina (I feel theway I usually do and perform my daily physical activities or exercise as usual; I feel very weak and cannot evenmove short distances). Mental stamina (I have just as
much energy as usual. I do not have any particular difficulty performing my daily activities; I do not have theenergy to do anything). Recovery (I do not have to rest
during the day; No matter how much I rest, it feels as if
I am unable to recharge my batteries). Sleep (I sleep welland long enough. I usually feel thoroughly rested when I
wake up after a night’s sleep; I sleep superficially or restlessly every night. I never feel thoroughly rested after anight’s sleep). Hypersensitivity to sensory impressions (Ido not think that my senses are more sensitive than usual;Sound, light or other sensory impressions bother me somuch that I withdraw in order to give my senses a chance
to rest). Experience of demands (I do what I am supposed
to do or want to do without experiencing it as especiallydemanding or difficult; l experience nearly everything as

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demanding and cannot handle it at all). Irritation andanger (I do not feel that I am especially easily irritated; I
am often furious and have to make an enormous effort inorder to restrain myself).
The construct validity of KEDS has, however, beenquestioned in a recent psychometric article, which warrants further examination of the ED symptomatology as
described by KEDS [32].
Differentiating responders and non-responders

To test the hypothesis that higher network connectivity,
the sum of all absolute partial correlations in the networks, could be associated with a worse treatment outcome, the participants were categorized as responders or
non-responders to treatment. Treatment response wasdefined as fulfilling the criteria of clinically significant
change [34]. To be classified as responders, participants
had to (1) show a reduction in KEDS score from the initial assessment to the 12-month follow-up of 9 points or
more, which indicates a reliable change [34], and (2) scorebelow the cut-off of 19 points on KEDS at the 12-monthfollow-up [33]. This resulted in a responder group of 328
participants and a non-responder group of 455. Due tomissing data at the 12-month follow-up, 132 participants
could not be assigned to either group.
Statistical analysis
Network estimation and stability

Unregularized weighted networks of the KEDS items ateach time point were estimated using partial Spearman
correlation matrices (Fig. 1). Confidence intervals aroundthe edge weights for the network at the initial assessment
were estimated using a non-parametric bootstrap with
10,000 samples (Fig. 2) [35, 36].
Node centralities for symptoms in all networks were
estimated using the centrality indices strength (the sumof absolute partial correlations connected to a node);closeness (the inverse average length of the shortest distance to all other nodes); betweenness centrality (number of times the node is located on the shortest distance
between two other nodes) (Fig. 3) [19, 37]. The stability of
the centrality indices for the network at the initial assessment was analysed using case-dropping bootstrap, generating 10,000 bootstrap samples consisting of subsets of
the original data [36]. The results were summarized using
correlation stability coefficients, indicating what proportion of the data that could be removed while retaining acorrelation of at least r = 0.7 with the centrality indicesfrom the original network, and are presented with a 95%
confidence interval. A correlation stability coefficient of
0.5 or above is considered stable [36]. All analyses werecomplete case analyses, using the data available at each
respective time point. This was deemed appropriate
given the low attrition rate.

Mårtensson et al. BMC Psychiatry(2024) 24:389

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Fig. 1 Unregularized partial correlation networks for all time points of KEDS self-rating. The visual layout based on self-ratings at the initial assessment isused for all networks. Blue edges represent positive partial correlations. Edge weights with an absolute value below ρ = 0.1 are omitted. The size and saturation on all visual edges are relative to the maximum edge weight in any of the networks (ρ = 0.37) and represent the strength of the partial correlations

Network visualization

The visualized networks consist of nine nodes, each representing an item of the KEDS, which are connected byweighted edges, each representing partial Spearman
correlation. To limit the number of edges and increase
the interpretability of the visual networks, edge weights
below ρ = 0.1 are hidden in the visualization of the networks. The width and saturation of edges reflect thestrength of the partial correlation between nodes, in proportion to the maximum correlation in any of the networks (ρ = 0.37).

The placement of the nodes in the visualization of the
networks is based on the Fruchterman-Reingold algorithm that was implemented on the networks from theinitial assessment and then kept constant for the following time points to increase visual comparability between
networks [38]. The algorithm places nodes with strong
partial correlations close to each other, and nodes with ahigh absolute sum of partial correlations near the middleof the visual network.

Mårtensson et al. BMC Psychiatry(2024) 24:389

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Fig. 2 Edge weights for the network at the initial assessment and the variation of the bootstrap estimates of edge weights. The x-axis shows the partial
Spearman´s correlation coefficient. The overlapping red and black lines indicate the initial assessment’s edge weights and the bootstrapped samples’
average edge weights, respectively. The shaded area represents 95% confidence intervals around the edge weights

Comparison of the overall network structure over time

Differences between the network from the initial assessment and the network at the end of treatment, as wellas the 12-month follow-up, were analysed using the
Network Comparison Test [39]. The Network Comparison Test is a permutation test, that tests if a specific difference between two networks (e.g., a differencein an edge weight) is significantly different from what
would be expected by repeated random rearrangement of participants between the two groups. We used
the Network Comparison Test to analyse differences inboth overall connectivity and individual edge weights
between networks at different time points, using 10,000
permutations.
Comparison of network connectivity between responders
and non-responders

When comparing the network connectivity of the
responder and non-responder groups at baseline, thegroups were matched on total KEDS scores to rule outthat group differences in initial symptom levels could
explain differences in connectivity or treatment outcome.
A subset of participants, 207 in total, that could not bematched due to differences in group size and baseline
total scores of the responder and non-responder group,
were randomly selected for exclusion from the analysis.
After matching, both groups consisted of n = 288 participants with identical baseline KEDS total scores (whilestill differing in individual items scores).
Unregularized networks based on Spearman partial correlations were estimated for each group, and thedifference in network connectivity was then tested using
the Network Comparison Test generating 10,000 randomly rearranged pairs of groups.
Networks were estimated, visualized, and comparedusing the R-version 4.2.1 and the packages bootnet [36],qgraph [38] and NetworkComparisonTest [39].

Results
Sample characteristics

Our sample included 915 participants who completed theinitial assessment. The mean (SD) age was 43 years (9.4),
86% were women and 71% had a university education(Table 1). The follow-up rate was 99% at the start of treatment, 97% a mid-treatment, 95% after finishing treatment and 86% at the 12-month follow-up (Table 2). Meanscores on the KEDS decreased during treatment, both forthe total score and the individual item scores (Table 2).
Visualization of networks

Graphical networks based on partial Spearman correlations of KEDS items for all the time points are presented
in Fig. 1.
The consistently strongest edges were between memory and the ability to concentrate, and between recovery
and mental stamina. Mental stamina and the experienceof demands both had strong relations to other ED symptoms for most symptom networks. Irritability and sleep
both lack strong relationships to other ED symptoms inmost networks.

Mårtensson et al. BMC Psychiatry(2024) 24:389

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Fig. 3 Strength, closeness and betweenness centrality for ED symptoms at all time points. The numbers on the y-axis represent the z-values of centrality
indices. Concen: ability to concentrate, phys.st: physical stamina, ment.st: mental stamina, recov: recovery, sensory: hypersensitivity to sensory impressions, exp.dem: experience of demands, irr.ang: irritation and anger

Edge weights

Partial Spearman’s correlations for all ED symptoms
based on KEDS-rating at the initial assessment are presented in Fig. 2, along with 95% confidence intervals.
In general, the 95% confidence intervals around edge
weights were fairly large, indicating that differences inedge weights should be interpreted with caution.
Node centrality

Centrality indices for all the time points are presented inFig. 3. The correlation stability coefficient at the initialassessment for strength centrality was 0.60, closenesscentrality 0.52 and betweenness centrality 0.13, indicating that closeness and strength centrality were stable,
while betweenness centrality was unstable.
The ability to concentrate, mental stamina, recovery
and the experience of demands all had similarly high values in strength centrality. Mental stamina and the experience of demands had the highest values in betweenness
centrality and closeness centrality. Irritability andsleep both had consistently low values in all indices ofcentrality.

Mårtensson et al. BMC Psychiatry(2024) 24:389

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Table 1 Pretreatment characteristics of the sample (N = 915)
Total (n = 915)

Respond- Non-reers
spond(n = 328) ers(n = 455)

Demographical variables
Age, mean (SD)
43.0 (9.4)
42.2 (9.6)
Women, n (%)
789 (86)282 (86)
Marital status, n(%)-Single or other
280 (31)91 (28)-Married/living together
572 (63)
223 (68)-Partner (living apart)
63 (7)
14 (4)
Education, n(%)- Elementary school and/or
232 (25)
72 (22)
secondary school- University < 3 years
142 (16)52 (16)- University ≥ 3 years
500 (55)
188 (57)- Other
41 (4)
16 (5)
Household income, n(%)- 0–250 000 SEK/year
76 (8)
19 (6)- 250 000–500 000 SEK/year
308 (34)
97 (30)- 500 000–1000 000 SEK/year 391 (43)149 (45)- > 1000 000 SEK/year
140 (15)63 (19)Percentage of working/studying full-time, n (%)- 0%
507 (55)
185 (56)- 1–25%90 (10)30 (9)- 26–50%
163 (18)54 (16)- 51–75%
39 (4)
14 (4)- 76–100%
116 (13)45 (14)43.6 (9.4)
396 (87)
144 (32)270 (59)
41 (9)
120 (26)
71 (16)249 (55)
15 (3)
47 (10)159 (35)
190 (42)59 (13)250 (55)
52 (11)81 (18)20 (4)
52 (11)

Note 132 participants could not be assigned to the responder or non-respondergroup, due to missing data at the 12-month follow-up

Network comparisonsComparison of networks from different time points

The network at the initial assessment was compared on
network structure and network connectivity using the
Network Comparison Test, to the networks at the end oftreatment and the 12-month follow-up, respectively. No
significant differences in individual edge weights could
be found between the initial assessment and the end oftreatment or the 12-month follow-up. However, the network connectivity had increased from the initial assessment (3.56) to the end of treatment (3.91, p < 0.1) andthe higher level of connectivity was also retained at the
12-month follow-up (3.90, p < 0.1).
Comparison of networks of responders and non-responders

Symptom networks based on KEDS-self rating were estimated for the matched groups consisting of 288 responders and 288 non-responders to treatment. The network
connectivity at the initial assessment was 3.84 for the
responder group and 3.49 for the non-responder group.
This difference was not statistically significant (p = 0.31).

Discussion
The present study aimed to investigate the internal
structure of ED symptoms and explore the relative connectedness of its symptoms using indices of network
centrality. These analyses revealed several noteworthy
findings, including limited mental stamina and negative experiences of demands consistently emerging as
the most central symptoms of ED within the full sample.
Conversely, irritability and sleep quality were found tobe the least central symptoms in the network structure.
Furthermore, the network structure appeared stablethroughout the study, as no differences in individual edge
weights were statistically significant when comparing
symptom networks from the initial assessment to the endof treatment and the 12-month follow-up. In addition,
the current study also aimed to determine if ED patients
who responded to an MMI had lower network connectivity before treatment but found no significant difference in network connectivity between responders andnon-responders.
Network structure and centrality

The network structure of ED symptoms was relatively
stable throughout the study. While the overall network

Table 2 Average Karolinska exhaustion disorder scales scores and missing data for all time points. MMI = Multimodal intervention. The
possible value of the individual items ranges between 0–6, and the total score between 0–54
Item scores
Ability to concentrate, M (SD)
Memory, M (SD)Physical stamina, M (SD)
Mental stamina, M (SD)
Recovery, M (SD)
Sleep, M (SD)Hypersensitivity to sensory impressions, M(SD)Experience of demands, M (SD)
Irritation and anger, M (SD)
Total scores, M (SD)Missing data, n (%)

Initial assessment
3.81(1.02)
3.84(1.28)
3.38(1.07)
3.94(0.89)
4.52(1.10)
3.95(1.44)
3.93(1.41)

Start of MMI
3.64(1.00)
3.59(1.29)
3.30(1.04)
3.72(0.89)
4.26(1.89)
3.71(1.49)
3.74(1.35)

Half-way point of MMI
3.07(1.13)
3.16(1.24)
2.79(1.10)
3.03(0.99)
3.49(1.14)
2.93(1.46)
3.17(1.38)

Finished MMI
2.49(1.24)
2.79(1.25)
2.32(1.21)
2.50(1.09)
2.99(1.14)
2.46(1.42)
2.74(1.40)

12-month follow-up
2.25(1.38)
2.53(1.28)
2.14(1.31)
2.28(1.24)
2.74(1.36)
2.34(1.55)
2.51(1.46)

4.08(0.88)
3.35(1.40)
34.81(6.17)
1 (< 1%)3.80(0.91)
3.09(1.31)
32.85(6.47)
9 (< 1%)3.08(1.08)
2.52(1.29)
27.24(7.55)
28 (3%)

2.47(1.18)
1.96(1.24)
22.72(8.14)
49 (5%)

2.25(1.25)
1.91(1.35)
20.95(9.04)
132 (14%)Mårtensson et al. BMC Psychiatry(2024) 24:389

connectivity for the full sample significantly increased
from the initial assessment to the end of treatment andthe 12-month follow-up, no changes in any individualedge weights were found to be statistically significant.
The centrality values of symptoms at different time points
are also largely consistent throughout the study. The consistency in network structure throughout multiple pointsof self-rating and a large reduction in symptom levels
suggests that the correlations between symptoms could
potentially represent meaningful relationships between
ED symptoms. While the network structure was stable, it
should be highlighted that the initial assessment and pretreatment networks are likely the most representative of
the ED population, since they are based on self-rated ED
symptoms before manipulation through MMI treatment.
Limited mental stamina and negative experiences ofdemands emerged as the most central and most densely
connected ED symptoms. That limited mental stamina would prove to be a central symptom was perhaps
unsurprising, since it is arguably the symptom that mostclosely represents the larger ED construct and is a cardinal criterion for diagnosis. Meanwhile, the high centrality values of experience of demands were more surprising
as it is one of the least explored ED symptoms in previous research [14]. According to network theory, central
symptoms may be important treatment targets, as they
are believed to be drivers of a potentially pathologicalself-sustaining network structure [15]. Negative reactions and lacking perceived resources when faced withdemands are related to aspects of perfectionism [40–42]
and previous research has found perfectionistic traits andbehaviours at work to be associated with exhaustion due
to persistent non-traumatic stress [43, 44]. An analysis
of predictors of change based on the same sample dataas the current study found that participants who scoredhigher on perfectionistic traits also reported higher
degrees of ED before treatment, and proportionally benefitted more from MMI treatment [30]. This finding, combined with the central position of negative experiences ofdemands in the symptom networks of the current study,
suggests that perfectionistic tendencies could represent
an important psychological process to target in treatment
for ED patients. Consequently, future research on clinical interventions for ED would potentially benefit fromfocusing on methods specifically targeting perfectionism
and the negative experience of demands, for example,
cognitive techniques related to self-criticism and exposure in vivo to the experience of not meeting demands.
The least central symptoms in all centrality indiceswere sleep quality and irritability. While research on
irritability in ED patients is limited, the marginal roleof sleep quality was surprising as previous research has
found sleep quality/insomnia to be an important predictor for the onset of ED, and improved sleep to be a key

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mediating variable for recovery from ED in patients participating in CBT [45–47]. Supporting the results of thecurrent article however, a recently published psychometric article (partly based on the same sample as the current study) used confirmatory factor analysis on KEDS
responses and found the irritability and sleep items to be
weakly related to the unidimensional ED construct supposedly captured by KEDS [32]. Despite the weak specific relations to other symptoms and the ED construct,improved sleep has positive effects on a wide range ofpsychiatric complaints [48], which means that bettersleep quality would likely lead to improved health regardless of the specific condition.
It is worth noting that irritability and poor sleep arehighly prevalent in a wide range of psychiatric disorders
and are common reactions to stressors, such as grief andtrauma [21, 49]. Given the prevalence of these symptoms
in mental illness in general and the weak relationship of
irritability and sleep quality to other ED symptoms in thecurrent study and the psychometric article on KEDS [32],it seems adequate to question their diagnostic relevanceand specificity to ED. The lack of empirical evidence for
the ED diagnostic criteria has recently been highlighted[14]. Additionally, concerns have been raised about current ED diagnostic criteria being overly inclusive, which
increases the risk of overdiagnosing and biases towardcategorizing other mental disorders as ED [13]. Diagnostic constructs should be discriminatory, not comprehensive illness descriptions. Considering the findings in thecurrent study, future research should consider removing
irritability and sleep quality from the diagnostic criteria
and ED measurements such as KEDS as a way of increasing the specificity of the ED diagnosis and simplifying
differential diagnostics.
Network connectivity

The finding that symptom networks of responders compared to non-responders did not differ in network connectivity means that the current study could not findsupport for the connectivity hypothesis, which proposes that higher network connectivity could represent
stronger reinforcing feedback loops between symptoms
and therefore be a predictor of a worse treatment outcome [18, 23]. Interestingly the connectivity level was
higher for the responder group, although this differenceis uncertain as it was not statistically significant. Before
analysis, the groups were matched on baseline total
KEDS score, to rule out the possibility that differences inconnectivity could be explained by differences in baseline
symptom levels and variance. It is worth highlighting that
this meant a subset of the non-responder groups with themost severe initial symptoms were not included in theanalysis, which could have affected its outcome.

Mårtensson et al. BMC Psychiatry(2024) 24:389

The connectivity hypothesis has received some empirical support from a limited number of studies [23–25].
However, a study by Elovaino et al. [26] did not find a difference in network connectivity of symptoms of depression when comparing a large clinical sample and a groupof healthy controls. The fact that the network connectivity for the full sample significantly increased from the initial assessment to the end of treatment and the 12-monthfollow-up, as the overall symptom levels decreased, raises
further questions on network connectivity as an indicator of clinical severity and treatment outcome. Several
previous studies have also found an increase in network
connectivity following treatment [25, 50, 51]. McElroy
et al. [25] speculate that increased connectivity following treatment could suggest “positive spirals”, whereas
improvement in one symptom leads to improvements inothers and, thus, stronger correlations between symptoms. Another possible explanation for increased connectivity in the current study is that floor effects at theend of treatment cause the increased correlation between
symptoms, due to the low average symptom levels.
Strengths and limitations

A major strength of the current study is the quality of thesample data on which the network analyses were based.
It is the largest clinical sample of self-rated ED symptoms collected to date, with multiple time points spanning roughly 1.5 years, few dropouts, and a low degreeof missing data. All patients were professionally assessedby a team of clinicians before inclusion to confirm the
ED diagnosis. Women and individuals with a university
education were overrepresented in the sample, comparedto the Swedish population. However, based on previous
research and sick leave data, this is likely somewhat representative of the demographics of the ED population[11, 52]. The open clinical trial was conducted in a naturalistic setting, and the inclusion criteria were permissive regarding medications and comorbid illnesses, whichshould increase the external validity of our findings.
The current study has some important limitations. The
study has estimated symptom networks based on largescale group data, which risks masking considerable individual heterogeneity or sub-group differences in the data.
It is unclear to what extent aggregated group-level relationships between symptoms can be generalized to individuals [53]. Another limitation is that while the network
theory of mental disorders assumes causal relationships
between symptoms [15], the current study´s design does
not allow for conclusions on causality.
Network models based on partial correlations are sensitive to which variables are included [53]. First, failure
to include all relevant factors in a network risks creating
spurious connections due to confounder bias. Second, byconditioning on the potential common effects of different

Page 9 of 11

nodes, network analysis is also sensitive to collider bias,
by which conditioning on a common effect could create
spurious relationships between nodes. Networks basedon ED diagnostic symptoms may be missing unidentified variables of importance in maintaining the disorder, which means there is a risk of biases. Arguably, thisrisk is higher when discussing ED as compared to otherdisorders, as the research on its diagnostic symptoms
is limited and it is not clear to what extent the current
diagnostic criteria of ED adequately describe the condition [14]. In future studies, network models based on
symptoms of exhaustion due to persistent non-traumaticstress could include a greater variety of variables than the
ED diagnostic criteria currently used in Sweden. Network models, including, for example, symptoms of anxiety and depression as well as theoretical constructs suchas perfectionism, could shed further light on what processes underlie exhaustion due to persistent non-traumatic stress and how it relates to other conditions andsymptoms.
It should also be emphasized that ED has been criticized for being a poorly validated diagnostic construct[13, 14]. The scope of the current article was to examine
the relative internal relatedness between these diagnostic symptoms. It is not possible from the findings of thecurrent article to draw conclusions on the validity of thediagnosis, and whether it should be understood as a diagnostic construct distinct from validated disorders withoverlapping symptoms.

Conclusion
This study found that network structure and internal relationships between ED symptoms were stable over time,
despite decreasing symptom levels. Symptoms of limited mental stamina and negative reactions to demands
emerged most strongly related to other ED symptoms.
Meanwhile, irritability and sleep quality were weakly
related to other ED symptoms. The study found no evidence supporting network connectivity as an indicator ofclinical severeness or likely treatment outcome.
In conclusion, these findings do have the potential toinform future developments of treatments, assessment
tools, and diagnostic conceptualizations of exhaustion
due to persistent non-traumatic stress, as they highlightthe centrality of limited mental stamina and negative
experiences of demands as well as the limited influenceof irritability and sleep quality in the symptom networkof ED.
Acknowledgements
Not applicable.
Author contributions
CRediT author statement (which is also included in the manuscript):
Gustav Mårtensson: Conceptualization; Formal analysis; Methodology;
Software; Validation; Visualization, Writing – Original Draft Fredrik Johansson:Mårtensson et al. BMC Psychiatry(2024) 24:389

Conceptualization; Data curation; Formal analysis; Methodology; Software;Supervision; Validation; Writing – Review & Editing Monica Buhrman:
Conceptualization; Investigation; Methodology; Project administration;
Resources; Supervision; Writing – Review & Editing Fredrik Åhs:Conceptualization; Methodology; Writing – Review & Editing Jakob Clason vande Leur: Conceptualization; Data curation; Funding acquisition; Investigation;
Methodology; Project administration; Resources; Supervision; Validation;
Writing – Original Draft.
Funding
Open access funding provided by Uppsala University. The study was funded
by a research grant (nr 2018 − 00154) from “FORTE: Swedish Research Councilfor Health, Working Life and Welfare.” FORTE had no influence in the design ofthe study, data collection, analysis, and interpretation of data and in writing
the manuscript.
Open access funding provided by Uppsala University.
Data availability
The patient data analysed in the study are not openly available due toreasons of sensitivity but are available from the corresponding author upon
reasonable request.

Declarations
Ethics approval and consent to participate
The clinical trial was approved by the Regional Ethical Review Board in
Stockholm (Approval Nr. 2016/1834- 31/2). All participants provided writtenconsent before inclusion in the clinical trial.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Received: 10 November 2023 / Accepted: 13 May 2024

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