Abstract
The “sense of coherence” incorporates the notion that when life seems comprehensive, manageable, and meaningful for an individual, even under tremendous adversity, this accounts for stamina and confidence. Its absence is associated with mental health problems, including depression. The current analysis aimed to explore the relationship between the sense of coherence and depression through a network analysis approach in a sample of 181 people with depression. The mean age of the individuals in the sample was 35.59 (11.50) years, and 76.8% were females (n = 139). All participants had at least one previous depressive episode; the duration of the current episode was 30.30 (77.30) days. Sense of coherence was assessed using the 13-item Sense of Coherence Scale (SOC-13). The severity of depression was quantified with the Beck Depression Inventory (BDI-I). The correlations between the two scales, three networks (i.e., SOC-13; BDI-I and joint SOC-13/BDI-I), and their centrality indices were calculated. The mean SOC-13 was 40.56 ± 9.99, and the mean BDI-I was 49.90 ± 9.26. There was a strong correlation-concordance (− 0.65; 95% CI: − 0.72 to − 0.55) index between the two scales. We identified detailed relationship dynamics between symptoms, clusters, and domains through the novel network analysis approach. The analysis of SOC-13 nodes revealed the pivotal role of social relationships in the network. However, in the depression network, we found a role for affection (in contrast to neglect) and joy (as opposed to boredom). In conclusion, solid and sustainable personal relationships in distress and adversity stand against the burden of depression.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
The ability to overcome adversity and thrive has inspired many storytellers and songwriters over the past several decades; however, it took some time until it started to catch the attention of psychiatrists and psychologists. In the 1970s and 1980s, while interviewing Holocaust survivors, Aaron Antonovsky recognized these individuals’ outstanding resilience (Mittelmark, 2022).
Based on his observations, Antonovsky developed a “salutogenic” view of mental health that has gained attraction over the past decades and has contributed to the understanding of psychological functioning. Instead of trying to understand the origin of diseases (as in pathogenesis), the salutogenic model focuses on factors that initiate and sustain health and well-being (Mittelmark, 2022). One essential part of the salutogenic health model is what he describes as a “sense of coherence,” with the notion that the effective management of stress and finding of meaning maintain health and promote overall well-being and resilience (Mittelmark, 2022; Antonovsky & Sagy, 1986).
Research on and definitions of the sense of coherence have evolved through extensive investigations of stress and coping mechanisms and have been refined through cross-cultural studies (Mittelmark, 2022; Antonovsky & Sagy, 1986). Antonovsky finally defined the sense of coherence as “a global orientation that expresses the extent to which one has a pervasive, enduring though dynamic feeling of confidence that the stimuli from one’s internal and external environments in the course of living are structured, predictable, and explicable the resources are available to one to meet the demands posed by these stimuli; and these demands are challenges, worthy of investment and engagement” (Mittelmark, 48,2,3,; Antonovsky, 1993).
In Antonovsky’s definition, we find the three core elements of the sense of coherence: comprehensibility, manageability and meaningfulness (Antonovsky, 1993). Comprehensibility allows one to appreciate reality without becoming overwhelmed, enabling one to predict coming events and plan accordingly. Manageability refers to the ability to use available resources and means and to build and maintain relationships. Meaningfulness refers to the ability to see a life purpose and discern positivity in adversity (Mittelmark, 2022; Antonovsky, 1987). The sense of coherence can be conceptualized as a complex system of cognitive (comprehensibility), behavioral (manageability), and motivational (meaningfulness) dimensions shaped by interactions (Portoghese, 2024).
Depression is a recurrent debilitating disorder characterized by a persistent feeling of sadness or loss of interest; it also impairs education, relationships, and employment and is associated with obesity, cardiac disease, and early death, including suicide (Marwaha et al., 2023). Depression is one of the leading causes of disability, both in terms of years lost and excess mortality (Herrman et al., 2019). The biomedical models conceptualize depression as a disorder of widely distributed neural networks with abnormalities in neurotransmitters (Marwaha et al., 2023; Moncrieff et al., 2023). Although a biopsychosocial approach to treating depression (including psychological interventions and social support) is recommended, medication is often considered essential for more severe cases (Marwaha et al., 2023).
A salutogenic approach and understanding of depressive disorders might help individuals identify strategies for promoting mental health and preventing depressive disorders. In particular, the sense of coherence seems to decrease in persons with mental health problems (Schafer et al., 8,10,; Mattisson et al., 2014), with an increasing magnitude from childhood to young adulthood (Schafer et al., 2023). A poor sense of coherence is particularly strongly related to internalizing disorders, such as anxiety and depression (Schafer et al., 2023; Ristkari et al., 2006; Konttinen et al., 2008). Furthermore, a low sense of coherence seems to predict the onset of depression (Sairenchi et al., 2011). However, the exact underlying mechanisms are unclear (Sairenchi 2011), and some evidence suggests a mediating role (between personality structure and depression) of the different factors of the sense of coherence (Kövi et al., 2017; Pallant & Lae 2002).
The current study aimed to analyse the relationship between a sense of coherence and depression to gain insight into how these two psychological constructs might interact with each other, especially how well-being, mental health, and particularly depression are affected. The study sets itself apart from other studies by implementing a network analytical approach. By visualizing and statistically modelling the relationship, we expect to gain detailed insight into the structure and dynamics of the relationship between the sense of coherence and depression, thus allowing us to identify its nuances and mechanisms.
Methods
Study design and population
We designed a prospective, randomized, controlled study to evaluate the effect of Internet Cognitive Behavioural Therapy (ICBT) (Rauen, 2020). The local ethics committee of the Canton of Zurich in Switzerland approved the study (BASEC-Nr. 2013-0542); it was registered after receiving approval (clinicaltrials.gov NCT02112266). The study was conducted in accordance with the Declaration of Helsinki, and all the subjects provided their electronic informed consent before participation.
Patients with depressive symptoms were recruited online and assessed for demographic and clinical baseline characteristics. The inclusion criteria were age between 18 and 65 years and at least two weeks of moderate to severe depressive episodes. The exclusion criteria were low German proficiency, suicidal ideation, alcohol or substance use, history of psychotic symptoms or bipolarity, and current treatment (Rauen, 2020).
We used demographic and clinical baseline characteristics for the current secondary analysis. A total of 839 people started the questionnaire; 319 reported having a current depressive disorder. Of those, 181 participants who completed the questionnaires without missing items were included in the current analysis.
Clinical assessments
Beck Depression Inventory-I (BDI-I). The BDI-I was developed to measure the severity of depression in adults (Beck et al., 1961). The BDI-I is a self-report questionnaire with 21 items, each with a choice of four statements. The statements describe symptom severity along an ordinal continuum from absent (0) to very severe (3): with a range from 0 to 63, higher scores are considered indicative of more severe depression. The BDI-I has good psychometric properties and is widely used in research and clinical practice (Beck et al., 1988). The BDI-I scale used in the present study is the validated German version; it has similar psychometric properties to the original scale, with an alpha of 0.89 (Kammer, 1983).
Sense of Coherence Scale (SOC-13). The SOC-13 was developed to measure a person’s ability to deal with stress and adversity (Antonovsky, 1987). It is a self-report questionnaire with 13 items, each evaluating a series of statements regarding resources and dispositions. Each item is rated on a seven-point Likert scale ranging from absent (seldom) to marked (frequent); five items (“1,” “2,” “3,” “7,” and “10”) are inversely rated. The SOC-13 concentration ranges from 0 to 78, with higher values indicating a greater sense of coherence (Antonovsky, 1993). This scale has shown good psychometric properties and is widely used in research practice (Eriksson & Contu, 2022). We used the validated German version of the SOC-13 scale, which also has good psychometric properties, with an alpha of 0.85 (Singer & Brähler, 2007; Schumacher et al., 2000).
Statistical analysis
Descriptive statistics (proportion, mean, and standard deviation) are presented for patient demographics and baseline and outcome characteristics. The BDI-I and the SOC-13 internal consistency were examined using Cronbach’s alpha coefficient. We calculated the Pearson correlation coefficient between the BDI-I score and the SOC-13 score. Considering the differences in the scales’ ratings, we calculated the z scores for both scales before examining the level of accuracy and precision between the BDI-I and the SOC-13 with the concordance correlation coefficient (Lin, 1989; King & Chinchilli, 2001). To evaluate the agreement between the two scales, we used the Bland‒Altman plot. The difference between the two scales was plotted on the y-axis, while the mean was plotted on the x-axis. The confidence intervals and limits of agreement for both scales were calculated (Bland & Altman, 1986; Carkeet, 2015).
In network models, variables (i.e., items) are presented as nodes connected via edges, representing undirected regularized partial correlations (Borsboom, 2021). The network models of the scales (BDI-I and SOC-13) were calculated using the “extended Bayesian information criterion” (EBIC) and the “least absolute shrinkage and selection operator” (LASSO) regularization methods implemented within a single Gaussian random field network. For the degree of shrinkage, we used a low hyperparameter (gamma = 0.0) to maximize the stability of the network and balance sensitivity and specificity (Epskamp et al., 2018). To test the accuracy and stability of the network parameters, we estimated confidence intervals for the edge weights and the correlation stability coefficient using nonparametric bootstrapping (Epskamp & Fried, 2018).
The topological properties of the networks were described using centrality measures: strength, closeness, betweenness, expected influence, and bridge influence. The strength sums the absolute edge weights of the edges per node. Closeness quantifies the distance between one node and all other nodes by averaging the shortest path lengths. Betweenness, on the other hand, quantifies how frequently a node lies on the shortest path connecting two other nodes. The expected influence, in contrast to strength, considers the sign of the edge weight; it quantifies the variance influenced by the surrounding nodes. Bridge influence quantifies the variance of a node accountable for another construct or dimension, in our case, how sense of coherence determines depressive symptoms (Borsboom, 2021; Jones et al., 2021; McNally, 2021). To identify the outstanding nodes (i.e., items), we normalized the centrality measures and identified the nodes above the 95th percentile.
Statistical analyses and figures were generated using RStudio (2023.12.1 + 402); the statistical software R (4.3.2); and the R packages tidyverse (2.0.0), ltm (1.2-0), blandr (0.5.1), pwr (1.3-0), qgraph (1.9.8), bootnet (1.6), and networktools (1.5.1).
Results
Demographic characteristics of the sample
The mean age of the individuals in the sample was 35.59 (11.50) years (range 18–63), and three-quarters of the participants were females (76.8%, n = 139). The participants had an average education of 15.18 (5.23) years; approximately two-thirds had a professional education (i.e., either a completed apprenticeship or a higher education college/university degree) (62.4%, n = 113); most participants were either employed (48.0%, n = 87) or were in training/formation (24.3%, n = 44). All participants had at least one previous depressive episode; the duration of the current episode was 30.30 (77.30) days (for further details, see Table 1).
Psychometric characteristics of the sample
The mean BDI-I sum score was 49.90 (9.26) points, reflecting moderate to severe depression. The internal consistency of the BDI-I is excellent, with a Cronbach’s alpha of 0.84. The mean SOC-13 sum score was 40.56 (9.99) points. The internal consistency of the SOC-13 is good, with a Cronbach’s alpha of 0.72. The correlation index between the BDI-I score and SOC-13 score was − 0.65 (95% CI: − 0.72 to − 0.55). The Bland‒Altman plot showed good overlap between the two scales, with only two (1.05%) outliers. A post hoc analysis yielded a statistical power of 0.99, indicating a high probability of producing accurate results (Cohen 2013) (Fig.1).
Network models of the scales (BDI-I and SOC-13)
The BDI-I scale network (see Fig. 2A) has an excellent stability index of 0.82. Due to the LASSO approach, low threshold associations were reduced to zero. Thus, the network presented is restricted to the salient associations. In the BDI-I, we can identify several overlapping communities of nodes: one related to exhaustion and coping (items L, M, O; Q); another related to self-contempt (items C, E; F, K; G, H; N); one related to hopelessness (items A; B; D and I); and one related to bodily sensations (items J; N P; R; S). The nodes had a mean strength of 0.74 (0.17); the node with the highest strength was node D (dissatisfaction). The nodes had a mean closeness of 0.88 (0.07); node D (dissatisfaction) had the highest closeness. The nodes had a mean betweenness of 0.45 (0.23), with node N (body image) having the highest betweenness. The nodes had an expected influence of 0.65 (0.23), with nodes B (pessimism) and D (dissatisfaction) having the highest influence within the network. The BDI-I nodes had a bridge influence of 0.22 (0.13), with node A (Sadness) having the highest influence on the SOC-13 network (for further details, see Fig. 2B).
The SOC-13 scale network (see Fig. 3A) has an excellent stability index of 0.79. Due to the LASSO approach, low threshold associations were reduced to zero. Thus, the network presented is restricted to the salient associations. Network, we could see four communities of nodes: one regarding trust in social relationships (Items 2 and 3); one concerning meaningfulness (Items 1, 4, 7, and 12); one concerning comprehensiveness (Items 5, 6, and 10); and one concerning manageability (Items 8, 9, 11, and 13). The nodes had a mean strength of 0.83 (0.16); the nodes with the highest strength were 06 (helplessness), 10 (bad luck), 12 (meaningless), and 13 (emotional control). The nodes had a mean closeness of 0.93 (0.07); again, nodes 06 (helplessness), 10 (bad luck), 12 (meaningless), and 13 (emotional control) had the highest closeness. The nodes had a mean betweenness of 0.56 (0.22), with node 12 (meaningless) having the highest betweenness. The nodes had an expected influence of 0.78 (0.21), with nodes 06 (helplessness), 10 (bad luck), 12 (meaningless), and 13 (emotional control) within the network. The SOC-13 nodes had a bridge influence of 0.35 (0.22), with node 01 (unattached) having the highest influence on the BDI-I network (for further details, see Fig. 3B).
Figure 4 displays the final network model of the BDI-I and SOC-13 scales with all items (i.e., 34 nodes). The network stability index was 0.78 (i.e., the maximum proportion of patients who could be dropped and still retained a correlation over 0.70 with the original estimate in 95% of the samples). In the joint BDI-I and SOC-13 network, depressive symptoms clustered around the SOC communities (see Fig. 4).
Discussion
Our analysis revealed a strong (inverse) correlation between the strength of the sense of coherence measured by the SOC and symptoms/severity of depression measured by the BDI-I. Our results confirm previous findings reporting that clinically depressed persons tend to have a lower sense of coherence (Carstens & Spangenberg, 1997; Valimaki et al., 2009). The novel network analysis approach identified detailed relationship dynamics between symptoms, communities, and domains (Borsboom & Cramer, 2013).
According to our findings, relationships (i.e., social and personal) are pivotal in the SOC-13 network, and previously known factors (comprehensiveness, manageability, and meaningfulness) are involved (Antonovsky, 1993). The notion that supporting and reliable social relationships play a significant role in maintaining mental health is largely known (Cohen & Wills, 1985). It can be regarded as a determining factor for health and well-being (House et al., 1988). Our analysis’s results overlap to some extent with previous results and strengthen the notion that the sense of coherence is a complex system of cognitive, behavioral, and motivational dimensions that is structurally divisible but functionally indivisible (Portoghese, 2024).
Our results emphasize the importance of relationships in the salutogenic model in general and the sense of coherence in particular (Ejlertsson et al., 2013). Furthermore, in the context of overcoming depression, the therapeutic relationship is the most important single factor determining the efficacy and outcome of a psychological intervention for depression (Lambert & Barley, 2001; Steger & Kashdan, 2009). Nevertheless, the ability to commit to such a relationship might be severely impaired in a major depressive episode (Marshall & Harper-Jaques, 2008). Similarly, the ability to predict others’ behavior could be largely impaired due to symptoms of depression (Nestor et al., 2022).
By graphically representing the network model of SOC-13, we observed the nuclear position of social relationships in the network. Although the SOC-13 items reflecting relationships have relatively weak centrality indices, this apparent weakness might allow them to interact freely with the other nodes (Granovetter, 1973). This discrepancy from previous findings could be explained by the fact that, in contrast to our sample, the Sense of Coherence Scale was developed in nonclinical populations (Eriksson & Lindstrom, 2005), where an intact ability to relate can be expected. Moreover, the ability to maintain close relationships seems to be a critical factor in holocaust survivors (Armour, 2010). Furthermore, collective adversity brings up special emotional bonds (as beautifully shown in the Movie: “Marek Edelman… And There Was Love in the Ghetto”) (2024).
In the network of depressive symptoms, we observed several overlapping communities of symptoms. The first group had increased negative affect, and the second related to reduced positive affect. Together, they encompass the core symptom domains of a depressive disorder. A third community forms around the bodily sensations. A fourth community involved exhaustion and coping with obligations and daily activities. Finally, suicidal thoughts were related to several symptoms: fatigue, pessimism, social withdrawal, and dissatisfaction. Taken together, our results confirm previous results on depressive networks (Ma, 2022).
The cluster related to hopelessness reflects reduced positive affect—furthermore, the appearance of suicidal thoughts is closely related to feelings of hopelessness and despair. At the same time, the cluster related to an increased negative affect shows two cores. The first relates to a sense of rejection, including punishment and irritability; the second is self-contempt with failure and self-dislike. The passive form of self-dislike is closely related to bodily sensations. Thus, we believe that one reflects the agitated form of depression, while the other is the melancholic type (Koukopoulos & Koukopoulos, 1999).
Disability from depression results from fatigue, indecisiveness, and the consequent neglect of obligations (either work, educational, social, or personal) and recreational activities (Bruce, 2001). Furthermore, obligations and daily activities are considered a source of suffering, distress, or frustration, fuelling maladaptive thoughts and beliefs, especially in younger groups (Lee, 2018). It is thus not surprising that these factors are associated with social withdrawal and suicidal thoughts. Interventions that encourage the reconstitution of daily activities and self-appraisal through rewarding achievements effectively alleviate suffering in people with a depressive disorder (Cuijpers et al., 2011).
In general, bridge symptoms on the side of the SOC-13 Scale exerted a more significant influence than did those on the BDI-I side, with concern and neglect exercising the most influence on depressive symptoms. These patients are closely followed by symptoms of tediousness from obligations and daily activities. Thus, both reflect the loss of meaningfulness through depressive symptoms. We found that the feeling of loss and the lack of emotional control reflect the second line: the influence of manageability on depressive symptoms. Regarding items of the BDI-I, feelings of sadness influence the Sense of Coherence score; these symptoms are related to social withdrawal, thus spanning the core element of personal relationships.
Our study has several limitations that must be accounted for. First, we used solely self-assessment questionnaires without an external validation of the symptoms and diagnosis. The participants involved in the study actively exhibited help-seeking behavior and had at least one previous depressive episode; therefore, we can assume that they were familiar with the disorder and its symptoms. Furthermore, both questionnaires have proven validity and reliability (Carstens & Spangenberg, 1997; Välimäki et al., 2009).
In our study, we used the BDI-I questionnaire instead of newer versions. The BDI-I does not include all of the DSM-IV diagnostic criteria for depressive disorder (in contrast to the BDI-II). We consider the BDI-I to be better suited for classifying subtypes of depression, such as melancholia or agitated depression (Beck et al., 1996). The scores on the BDI-I we found in our sample corresponded to a moderate to severe major depressive disorder, thus compensating for the limitations mentioned above for the BDI-I score. Furthermore, the analysis of the seven items corresponding to the BDI-I screening scale yielded similar severity ranges of depression as did the BDI-I sum score.
Another limitation is the high rate of missing values; we consider this to be within the expectation for an online survey. Including only complete datasets strengthens the analysis by reducing imputation bias (Böwing-Schmalenbrock, 2011). Our analysed sample included only 181 individuals; although this sample might seem small compared to that of other studies, it is sufficient to perform a robust network analysis (Guadagnoli & Velicer, 1988). This was confirmed by the bootstrap analysis we performed to test network stability (Epskamp & Fried, 2018). Our study included mainly middle-aged adults, with a few participants older than 60 years. Therefore, we could not determine whether the Sense of Coherence increased with age, as previously reported (Eriksson & Lindström, 2006), and we could not determine whether age has a protective effect (Dezutter et al., 2013).
Our results revealed a strong correlation between the sense of coherence and the severity of reported symptoms of depression. Network analysis confirmed the pivotal role of personal relationships in the presence of a sense of coherence. We observed the central role of dissatisfaction in the depressive network. The relationship between the two networks relies on the meaningfulness and manageability of daily living. We believe that social relationships and social withdrawal constitute the core action targets that might alleviate the severity of depression on the one hand and strengthen the sense of coherence on the other hand. In conclusion, positive relationships are crucial for treating and preventing depression through the provision of support, understanding, and a sense of connection.
Data availability
The datasets generated and/or analysed during the current study are not publicly available due to privacy and ethical restrictions but are available from the corresponding author upon reasonable request.
Abbreviations
- BDI-I:
-
Beck depression inventory I
- BDI-II:
-
Beck depression inventory II
- CI:
-
Confidence interval
- EBIC:
-
Extended bayesian information criterion
- ICBT:
-
Internet cognitive behavioural therapy
- LASSO:
-
Least absolute shrinkage and selection operator
- M:
-
Mean
- SD:
-
Standard deviation
- SOC:
-
Sense of coherence
- SOC-13:
-
Sense of coherence scale 13
References
(2024). Marek Edelman... In addition, There Was Love in the Ghetto.https://www.imdb.com/title/tt11064792/?ref_=nv_sr_srsg_4_tt_8_nm_0_q_love%2520ghe.
Antonovsky, A. (1987). The mystery of health: How people manage stress and stay well (p. 175). Jossey-Bass.
Antonovsky, A. (1993). The structure and properties of the sense of coherence scale. Social Science & Medicine,36(6), 725–733.
Antonovsky, H., & Sagy, S. (1986). The development of a sense of coherence and its impact on responses to stress situations. Journal of Social Psychology,126(2), 213–226.
Armour, M. (2010). Meaning making in survivorship: Application to Holocaust survivors. Journal of Human Behavior in the Social Environment,20(4), 440–468.
Beck, A. T., et al. (1961). An inventory for measuring depression. Archives of General Psychiatry,4(6), 561–571.
Beck, A. T., et al. (1996). Comparison of Beck Depression Inventories-IA and-II in psychiatric outpatients. Journal of Personality Assessment,67(3), 588–597.
Beck, A. T., Steer, R. A., & Carbin, M. G. (1988). Psychometric properties of the Beck Depression Inventory: Twenty-five years of evaluation. Clinical Psychology Review,8(1), 77–100.
Bland, J. M., & Altman, D. (1986). Statistical methods for assessing agreement between two methods of clinical measurement. The Lancet,327(8476), 307–310.
Borsboom, D. (2021). Network analysis of multivariate data in psychological science. Nature Reviews Methods Primers,1(1), 58.
Borsboom, D., & Cramer, A. O. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annu Rev Clin Psychol,9, 91–121.
Bruce, M. L. (2001). Depression and Disability in Late Life: Directions for Future Research. The American Journal of Geriatric Psychiatry,9(2), 102–112.
Böwing-Schmalenbrock, M., & Jurczok, A. (2011). Multiple Imputation in Der Praxis: Ein Sozialwissenschaftliches Anwendungsbeispiel.
Carkeet, A. (2015). Exact parametric confidence intervals for bland–Altman limits of agreement. Optometry and Vision Science,92(3), e71-80.
Carstens, J. A., & Spangenberg, J. J. (1997). Major depression: A breakdown in sense of coherence? Psychological Reports,80(3_suppl), 1211–1220.
Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Academic.
Cohen, S., & Wills, T. A. (1985). Stress, social support, and the buffering hypothesis. Psychological Bulletin,98(2), 310.
Cuijpers, P., et al. (2011). Psychological treatment of depression in inpatients: A systematic review and meta-analysis. Clinical Psychology Review,31(3), 353–360.
Dezutter, J., et al. (2013). Sense of coherence, depressive feelings and life satisfaction in older persons: A closer look at the role of integrity and despair. Aging & Mental Health,17(7), 839–843.
Ejlertsson, G., et al. (2013). Family relations and work experiences relate to Salutogenic Health–a survey among Swedish employees in 2012: Göran Ejlertsson. The European Journal of Public Health,23(suppl_1), ckt123.
Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods,50(1), 195–212.
Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods,23(4), 617–634.
Eriksson, M., & Contu, P. (2022). The sense of coherence: Measurement Issues. The Handbook of Salutogenesis (pp. 79–91). Springer.
Eriksson, M., & Lindstrom, B. (2005). Validity of Antonovsky’s sense of coherence scale: A systematic review. J Epidemiol Community Health,59(6), 460–466.
Eriksson, M., & Lindström, B. (2006). Antonovsky’s sense of coherence scale and the relation with health: A systematic review. Journal of Epidemiology & Community Health,60(5), 376–381.
Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology,78(6), 1360–1380.
Guadagnoli, E., & Velicer, W. F. (1988). Relation of sample size to the stability of component patterns. Psychological Bulletin,103(2), 265.
Herrman, H., et al. (2019). Reducing the global burden of depression: A Lancet–World Psychiatric Association Commission. The Lancet,393(10189), e42–e43.
House, J. S., Landis, K. R., & Umberson, D. (1988). Social relationships and health. Science,241(4865), 540–545.
Jones, P. J., Ma, R., & McNally, R. J. (2021). Bridge centrality: A Network Approach to understanding Comorbidity. Multivariate Behav Res,56(2), 353–367.
Kammer, D. (1983). Eine Untersuchung der psychometrischen Eigenschaften des deutschen Beck-Depressionsinventars (BDI). Diagnostica,29, 48.
King, T. S., & Chinchilli, V. M. (2001). A generalized concordance correlation coefficient for continuous and categorical data. Statistics in Medicine,20(14), 2131–2147.
Konttinen, H., Haukkala, A., & Uutela, A. (2008). Comparing sense of coherence, depressive symptoms and anxiety, and their relationships with health in a population-based study. Social Science & Medicine,66(12), 2401–2412.
Koukopoulos, A., & Koukopoulos, A. (1999). Agitated depression as a mixed state and the problem of melancholia. Psychiatric Clinics of North America,22(3), 547–564.
Kövi, Z., et al. (2017). Sense of coherence as a mediator between personality and depression. Personality and Individual Differences,114, 119–124.
Lambert, M. J., & Barley, D. E. (2001). Research summary on the therapeutic relationship and psychotherapy outcome. Psychotherapy: Theory Research Practice Training,38(4), 357–361.
Lee, S., et al. (2018). Activity diversity and its associations with Psychological Well-Being Across Adulthood. Journals of Gerontology. Series B, Psychological Sciences and Social Sciences,73(6), 985–995.
Lin, L. I. (1989). A concordance correlation coefficient to evaluate reproducibility. Biometrics,45(1), 255–268.
Ma, S., et al. (2022). The central symptoms of depression, anxiety, and somatization: A network analysis. All Life,15(1), 933–941.
Marshall, A. J., & Harper-Jaques, S. (2008). Depression and family relationships: Ideas for healing. Journal of Family Nursing,14(1), 56–73.
Marwaha, S., et al. (2023). Novel and emerging treatments for major depression. Lancet,401(10371), 141–153.
Mattisson, C., Horstmann, V., & Bogren, M. (2014). Relationship of SOC with sociodemographic variables, mental disorders and mortality. Scandinavian Journal of Public Health,42(5), 434–445.
McNally, R. J. (2021). Network Analysis of psychopathology: Controversies and challenges. Annu Rev Clin Psychol,17, 31–53.
Mittelmark, M. B. (2022). The handbook of salutogenesis. Springer.
Moncrieff, J., et al. (2023). The serotonin theory of depression: A systematic umbrella review of the evidence. Molecular Psychiatry,28(8), 3243–3256.
Nestor, B. A., Sutherland, S., & Garber, J. (2022). Theory of mind performance in depression: A meta-analysis. Journal of Affective Disorders,303, 233–244.
Pallant, J. F., & Lae, L. (2002). Sense of coherence, well-being, coping and personality factors: Further evaluation of the sense of coherence scale. Personality and Individual Differences,33(1), 39–48.
Portoghese, I. (2024). A network perspective to the measurement of sense of coherence (SOC): an exploratory graph analysis approach. Current Psychology. https://doi.org/10.1007/s12144-023-05567-0
Rauen, K. (2020). Internet cognitive behavioral therapy with or without face-to-face psychotherapy: A 12-weeks clinical trial of patients with depression. Frontiers in Digital Health,2, 4.
Ristkari, T., et al. (2006). Self-reported psychopathology, adaptive functioning and sense of coherence, and psychiatric diagnosis among young men–a population-based study. Social Psychiatry and Psychiatric Epidemiology,41(7), 523–531.
Sairenchi, T., et al. (2011). Sense of coherence as a predictor of onset of depression among Japanese workers: A cohort study. Bmc Public Health,11, 1–5.
Schafer, S. K., et al. (2023). The relationship between sense of coherence and mental health problems from childhood to young adulthood: A meta-analysis. Journal of Affective Disorders,325, 804–816.
Schumacher, J., Gunzelmann, T., & Brähler, E. (2000). Deutsche Normierung Der sense of coherence scale Von Antonovsky. Diagnostica,46(4), 208–213.
Singer, S., & Brähler, E. (2007). Die? Sense of Coherence Scale?: Testhandbuch zur deutschen Version. Vandenhoeck & Ruprecht.
Steger, M. F., & Kashdan, T. B. (2009). Depression and everyday social activity, belonging, and well-being. Journal of Counselling Psychology,56(2), 289.
Valimaki, T. H., et al. (2009). Caregiver depression is associated with a low sense of coherence and health-related quality of life. Aging & Mental Health,13(6), 799–807.
Funding
Open access funding provided by University of Zurich.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors have no competing interests to declare.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
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) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Kieraité, M., Novoselac, A., Bättig, J.J. et al. Relationship between sense of coherence and depression, a network analysis. Curr Psychol 43, 23295–23303 (2024). https://doi.org/10.1007/s12144-024-06034-0
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12144-024-06034-0