DÄ internationalArchive38/2018The Association Between Resilience and Mental Health in the Somatically Ill

Original article

The Association Between Resilience and Mental Health in the Somatically Ill

A systematic review and meta-analysis

Dtsch Arztebl Int 2018; 115(38): 621-7; DOI: 10.3238/arztebl.2018.0621

Färber, F; Rosendahl, J

Background: Resilience refers to an individual’s positive adaptation to the experience of adversity. The maintenance of mental health is commonly considered a sign of successful coping with adverse conditions. The goal of the present meta-analysis was to investigate the association between resilience and mental health in patients with a somatic illness or health problem.

Methods: Studies were included if they reported measures of association between resilience, as assessed using a version of Wagnild and Young’s Resilience Scale, and self-reported mental health. A systematic literature search was conducted in the Medline, Web of Science, PsycInfo, PubPsych, and ProQuest databases and in the dissertation catalogue of the German National Library. In addition, a manual search was carried out. The study was registered with PROSPERO (registration number: CRD42017054822).

Results: 55 studies involving a total of 15 003 patients were included in the meta-analysis. Assuming a random-effects model, the weighted mean Pearson correlation between resilience and mental health was r = 0.43 (95% confidence interval [0.39; 0.48], p<0.001). This association was robust, although the heterogeneity among individual effect sizes was substantial (I2 = 89.6%). Correlations tended to be weaker in unpublished studies than in published ones.

Conclusion: Despite substantial heterogeneity across studies, the findings suggest a strong association between resilience and mental health in the somatically ill. In clinical practice, a lack of resilience as a resource for successful coping might indicate a need for psychosocial support during treatment for somatic illness.

LNSLNS

In psychology, resilience is the term used to describe an individual’s positive adaptation in the face of adversity, i.e. one’s success in dealing healthily with significant stressors.

The definitions of psychological resilience distinguish between resilience as a personality trait and resilience as a dynamic process (4). These definitions also represent the two major lines of psychological resilience research: the approach in personality psychology on the one hand, and the approach in developmental psychology on the other hand (5). In developmental psychology, resilience is primarily studied in children and adolescents who showed positive development despite having experienced considerable hardship or trauma (6); examples for this approach include the longitudinal studies of the research groups around Emmy Werner (7) and Ann Masten (8). By contrast, the concept of resilience as a personality trait is typically encountered in the literature on resilience in adults (2). Rooted in a psychoanalytical research tradition, it originates from the concept of ego-resiliency introduced by Block and Block (9) in the 1950ies. Current research on the personality psychology approach (10, 11) uses the term “trait resilience“ (12), describing resilience as a relatively stable personality trait, in contrast to the processual approach of developmental psychology.

A number of instruments is available to measure resilience. Of these, the Resilience Scale by Wagnild and Young (13) is the most commonly used measure (2, 14); it has been translated into numerous languages. When comparing the psychometric properties of instruments designed to measure psychological resilience as a personality trait, the most convincing evidence was found for the Resilience Scale with regard to theoretical foundation, reliability and validity (15, 16).

Wagnild and Young’s Resilience Scale measures the level of resilience as a positive personality characteristic in terms of a personal resource that enhances individual adaptation (13). The items can be attributed to the two underlying factors “personal competence“ and „acceptance of self and life“ (13). The following items are attributed to the first factor:

  • Self-reliance
  • Independence
  • Mastery
  • Resourcefulness
  • Perseverance.

The second factor is described by the items:

  • Adaptability
  • Balance
  • Flexibility and balanced perspective of life (13, 17).

The Resilience Scale consists of 25 items and a 7-point Likert-type scale (values from 1–7). Today, various short versions are available, including a German version with 13 items (18) (Table 1).

German version of the Resilience Scale, shortened 13-item version (RS-13) (18)
German version of the Resilience Scale, shortened 13-item version (RS-13) (18)
Table 1
German version of the Resilience Scale, shortened 13-item version (RS-13) (18)

In the definitions of both concepts of resilience—as a process and as a personality trait—two aspects are key:

  • A preceding experience of adversity
  • Subsequent positive adaption (1).

Positive adaption is understood as the maintenance of mental health or relatively rapid recovery after temporary disturbances (19).

While resilience as a resource is discussed in the context of a variety of adverse conditions or stressful situations (2), dealing with physical illness and health problems is the focus of this paper. Just as traumatic experiences and chronic stress, illnesses can precede the development of mental disorders (20). This risk of developing mental health problems is one and a half to two times as high in individuals living with chronic physical disease compared to both healthy individuals and the general population. In addition, subclinical symptoms of mental distress are commonly observed in patients with physical illness (21).

When patients develop symptoms of mental distress, these can have an impact on the course of disease, the compliance of the affected individual, and the success of treatment (21). Thus, understanding the factors which may help patients to successfully cope with physical illness is key. Systematic reviews reported negative associations between resilience and symptoms of mental distress in patients with physical illness (22), cancer (23), or chronic disease (24). A positive association was found between resilience and quality of life.

Meta-analyses of the association between resilience and mental health as an indicator of positive adaption to stress (2527) reported significant positive associations. However, these data refer to a wide range of stressful situations. In view of the diversity of adversities for which resilience is discussed as a protective factor (2), it would seem reasonable to differentiate between the respective stressors.

Thus, the aim of this study is to provide a systematic overview of the association between resilience and mental health in the physically ill and integrate the evidence by means of meta-analysis.

Methods

A detailed description of the methodology is provided in the eMethods section. Research questions, inclusion criteria and methods were pre-specified in a review protocol (PROSPERO International prospective register of systematic reviews; registration number: CRD42017054822).

Studies meeting the criteria listed in Table 2 were included in the analyses. A systematic literature search was conducted in the Medline, Web of Science, PsycInfo, PubPsych, and ProQuest databases and in the dissertation catalogue of the German National Library. In addition, a manual search was undertaken. Study selection and data extraction were performed by the two authors (FF, JR); any disagreements were resolved by consensus discussion.

Inclusion criteria
Inclusion criteria
Table 2
Inclusion criteria

Potential risk of bias was assessed based on the reliability of the instruments used, response rates, and completeness of reporting. The Pearson product-moment correlation coefficient was chosen as effect size measure. The weighted mean effect size for the association between resilience and mental health was calculated assuming a random-effects model. The magnitude of effect sizes is determined according to the conventions by Cohen (28). Hence, correlations from 0.10 are classed as a small effect, from 0.30 as a medium effect and above 0.50 as a large effect. In addition, the impact of potential publication bias on the determined mean effect was evaluated and subgroup, meta-regression and sensitivity analyses were performed.

Results

Study selection

Initially, the literature search identified 5592 studies; of these, n = 55 studies with i = 57 reported samples fulfilled the inclusion criteria (Figure). These contained data on k = 95 associations between resilience und measures of mental health.

Flowchart of study selection
Flowchart of study selection
Figure
Flowchart of study selection

Study characteristics

An overview of the included studies is provided in eTable 1. Altogether, studies from 18 countries with 15 003 patients published between 2006 and 2018 were taken into account. In addition to 49 published studies, 6 unpublished studies were included. 51% of study participants were female; the average age was 57.3 years.

In 23 samples, patients with chronic illness were studied and in 22 samples patients with acute critical illness. Four samples assessed patients undergoing a medical intervention, e.g. a surgical procedure. In 5 other samples, a health problem, such as high blood pressure, was present, while 3 samples were comprised of individuals with heterogeneous health problems, e.g. elderly patients with chronic illness.

At the time of data collection in the included studies, the respective health problem was present in 46 samples, while it had occurred in the past in 10 samples (in one study the point in time was unclear).

In 24 studies, psychological resilience was measured using the original or a translated version of the Resilience Scale with 25 items. In the remaining studies, short versions with 10 to 18 items were used, particularly often the version with 13 items (Table 1).

In most studies, mental health was measured using questionnaires assessing mental distress, i.e. by means of scales on anxiety (k = 30), depression (k = 40) or distress (k = 5). In the remaining cases, scales on mental quality of life (k = 17), on emotional functioning (k = 2) or on emotional wellbeing (k = 1) were used. The instruments used are listed in eTable 2.

Instruments used to measure mental health
Instruments used to measure mental health
eTable 2
Instruments used to measure mental health

Risk of bias within studies

Reliability of resilience scales was reported for 32 samples by stating Cronbach’s α; values varied between α = 0.85 and α = 0.97 with a mean of α = 0.91. The reliability of scales measuring mental health was reported for 47 associations and ranged between α = 0.60 and α = 0.97, with a mean of α = 0.85. An increased risk of potential bias through lack of measuring accuracy affecting the measured effect sizes (Cronbach’s α<0.80) was found for k = 9 associations (9%), while for k = 40 associations (42%) a low risk and for k = 46 associations (48%) an unclear risk was found (eTable 3).

Risk of bias within studies
Risk of bias within studies
eTable 3
Risk of bias within studies

Results of individual studies and synthesis of results

The measures of association in the individual studies varied between r = 0.03 (e11) and r = 0.73 (e16) and showed considerable heterogeneity (Q [56] = 538.97, p<0.001, I2 = 89.6%). Five individual effect sizes were not significantly different from zero. All other study effects showed a significant positive association between resilience and mental health. In 11 studies a small effect, in 25 studies a medium effect, and in 21 studies a large effect was identified (28). The weighted mean correlation across all studies was r = 0.43; (95% confidence interval: [0.39; 0.48]; p<0.001) (eTable 1).

Risk of bias across studies

Visual evaluation of the funnel plot revealed a deviation of the distribution of studies from the funnel shape which one would normally expect in the absence of publication bias. Assuming a random-effects model, the trim-and-fill analysis indicated that 10 studies were missing. The mean effect size adjusted for these studies is r = 0.39 (eFigure). The left-sided test for asymmetry of the funnel plot using Egger’s regression test was not significant (p = 0.062). According to the classic Fail-safe N analysis, 31 159 studies with null results would be required for the mean correlation to exceed the significance level of α>0.05.

Funnel Plot of Fisher´s Z by standard error
Funnel Plot of Fisher´s Z by standard error
eFigure
Funnel Plot of Fisher´s Z by standard error

Additional analyses

In addition, a search for intervening variables was conducted which could have an effect on the association between resilience and mental health and could thus help to explain the heterogeneity of study effects. A significant negative effect of sample size was identified: The larger the study sample, the weaker was the association between resilience and mental health (p = 0.045). Furthermore, a trend towards significant differences was found for publication status (p = 0.056): In unpublished studies, weaker associations were reported compared to those in published studies.

Other important intervening variables could not be identified (eTable 4). The calculated mean effect was robust in sensitivity analyses to alternative approaches with regard to individual inclusion criteria and methods of analysis (eTable 5).

Results of subgroup analyses and meta-regression
Results of subgroup analyses and meta-regression
eTable 4
Results of subgroup analyses and meta-regression
Results of sensitivity analyses
Results of sensitivity analyses
eTable 5
Results of sensitivity analyses

Discussion

Altogether, 57 samples of patients with various physical illnesses and health problems from 55 studies were included in the meta-analysis. 95 associations between a version of the Resilience Scale (13) and a positive or negative measure of mental health were taken into account. Across the samples, a significant effect of r = 0.43 was determined, representing a moderate correlation. The higher the resilience in individuals with physical illness, the better they considered their mental health to be. This key result of our meta-analysis is consistent with previous findings on the association between higher resilience and better mental health in other contexts.

Other meta-analyses (2527), which did not define specific criteria for the studied sample and consequently included data from a variety of situations, found correlations in the moderate-effect range and evidence of high heterogeneity of effect sizes.

Overall, our meta-analysis comprises—despite a narrower choice of resilience scales used and stronger restrictions on the stress context in the included studies—more studies than some of the already available reviews on the association between resilience and mental health (26, 27, 29). Consequently, its representativeness for the studied area of resilience research can be regarded as good.

The Resilience Scale proved to be a reliable instrument in the included studies. The short forms appear to be especially well suited to economically measure resilience as a personality trait. With regard to measuring resilience, the issue of a potential tautology of resilience and depression is the subject of ongoing discussion. It is argued that some items of the Resilience Scale are merely positively worded depression-specific statements. If this were the case, the specific association between resilience and depression should be significantly stronger compared to the association between resilience and anxiety. However, this was not confirmed in sensitivity analyses.

In addition, there are obvious conceptual differences: The concept of resilience includes comparatively stable personality characteristics, such as self-reliance, perseverance, adaptability, balance, and flexibility. By contrast, depression as a temporary state compromises mental symptoms, such as feeling of inner void, loss of energy, self-doubt, fears, and physical symptoms. Here, the respective instruments differ significantly in the items they include.

Limitations

A key weakness of the present meta-analysis is founded in the design of the included studies, because associations identified in cross-sectional studies do not allow to draw causal inferences about the association between resilience and mental health.

Overall, very few prospective studies were available; of these, only two studies reported associations between resilience at a certain point in time and mental health at a later point in time. However, for reasons of methodological consistency, this review focused on cross-sectional associations. A further limitation stems from the substantial statistical heterogeneity of the individual study effects which makes it more difficult to generalize the results. However, all studies found positive associations; in 81% of samples, these were interpreted as moderate to strong.

Signs of publication bias represent another limiting factor. For example, unpublished studies found smaller effects compared with published studies. However, the calculated mean correlation was only slightly overestimated, because the adjusted mean effect is also significant and of medium size.

As yet, there is no common understanding of the stability or variability of resilience. While one line of research views resilience as a personality trait, another considers it to be a dynamic and changeable process and thus proposes different, outcome-based methods of operationalization and measurement (30).

Conclusion

Despite the limitations outlined above, the results of this meta-analysis indicate that in the context of physical illness or health problems a higher level of resilience is associated with better mental health.

In view of the breadth and diversity of psychological resilience research, the homogeneity of the primary studies with regard to the underlying conceptualization of resilience as a personality trait and the use of the Resilience Scale as the instrument (13) represents a strength of this review.

Given the numerous isolated findings in this field of research, a need for synthesis was identified. This study is the first to provide a statistical integration of the effects from 55 studies. With the higher validity of its findings compared to individual studies, the study makes a contribution to resilience research. Taking a meta-analytical approach, it ensures, in contrast to the existing reviews, a more objective selection and an integration of the results backed by statistical analysis. Prospective studies designed to clarify the causal nature of the association between resilience and mental health should be the focus of future research.

In view of the practice of medical psychology, this review provides starting points for a more targeted psychosocial support to help patients cope with physical illness. The appearance of symptoms of mental distress leads to increased care requirements as part of the medical management and can influence the course of illness and the successful outcome of treatment (21).

By screening patients for their levels of resilience, it would be possible to identify patients with low resilience early in clinical practice and to offer them more support in the form of external resources (5, 25).

Conflict of interest statement
The authors declare that no conflict of interest exists.

Manuscript received on 23 April 2018, revised version accepted on
12 July 2018

Translated from the original German by Ralf Thoene, MD.

Corresponding author
PD Dr. phil. med. habil. Jenny Rosendahl

Institut für Psychosoziale Medizin und Psychotherapie

Universitätsklinikum Jena

Stoystr. 3

07743 Jena, Germany

jenny.rosendahl@med.uni-jena.de

Supplementary material
For eReferences please refer to:
www.aerzteblatt-international.de/ref3818

eMethods, eBox, eTables, eFigure:
www.aerzteblatt-international.de/18m0621

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Radke J, Franke GH: Motivationale Inkongruenz in der somatischen Rehabilitation. In: Arbeitskreis Klinische Psychologie in der Rehabilitation BDP, Mariolakou A, Nowik D, Schwabe A, Spreen-Ledebur M, Groll J (eds.): Rehabilitation – Balance zwischen müssen und wollen Beiträge zur 35 Jahrestagung des Arbeitskreises Klinische Psychologie in der Rehabilitation vom 28–30102016 im Bildungszentrum Erkner. Berlin: Deutscher Psychologen Verlag 2016; 147–57.
e40.
Ramírez-Maestre C, Esteve R, Ruiz-Párraga G, Gómez-Pérez L, López-Martínez AE: The key role of pain catastrophizing in the disability of patients with acute back pain. Int J Behav Med 2017; 24: 239–48 CrossRef MEDLINE
e41.
Rebagliati GAA, Sciume L, Iannello P, et al.: Frailty and resilience in an older population. The role of resilience during rehabilitation after orthopedic surgery in geriatric patients with multiple comorbidities. Funct Neurol 2016; 31: 171–7.
e42.
Robottom BJ, Gruber-Baldini AL, Anderson KE, et al.: What determines resilience in patients with Parkinson‘s disease? Parkinsonism Relat Disord 2012; 18: 174–7.
e43.
Ruiz-Párraga GT, López-Martínez AE, Esteve R, Ramírez-Maestre C, Wagnild G: A confirmatory factor analysis of the Resilience Scale adapted to chronic pain (RS-18): new empirical evidence of the protective role of resilience on pain adjustment. Qual Life Res 2015; 24: 1245–53 CrossRef MEDLINE
e44.
Runkewitz K, Kirchmann H, Strauß B: Anxiety and depression in primary care patients: predictors of symptom severity and developmental correlates. J Psychosom Res 2006; 60: 445–53 CrossRef MEDLINE
e45.
Schumacher A, Sauerland C, Silling G, Berdel WE, Stelljes M: Resilience in patients after allogeneic stem cell transplantation. Support Care Cancer 2014; 22: 487–93 CrossRef MEDLINE
e46.
Suffeda A, Meissner W, Rosendahl J, Guntinas-Lichius O: Influence of depression, catastrophizing, anxiety, and resilience on postoperative pain at the first day after otolaryngological surgery. A prospective single center cohort observational study. Medicine (Baltimore) 2016; 95: e4256 CrossRef MEDLINE PubMed Central
e47.
Torma LM, Houck GM, Wagnild GM, Messecar D, Jones KD: Growing old with fibromyalgia. Nurs Res 2013; 62: 16–24 MEDLINE
e48.
Vinaccia S, Quiceno JM: Calidad de vida relacionada con la salud y factores psicológicos: un estudio desde la enfermedad pulmonar obstructiva crónica – EPOC. Terapia Psicológica 2011; 29: 65–76 CrossRef
e49.
Vinaccia S, Quiceno JM: Resiliencia y calidad de vida relacionada con la salud en pacientes con insuficiencia renal crónica – IRC. Revista Argentina de Clínica Psicológica 2011; 20: 201–11.
e50.
Wallhäußer-Franke E, Delb W, Balkenhol T, Hiller W, Hörmann K: Tinnitus-related distress and the personality characteristic resilience. Neural Plast 2014: 370307 MEDLINE PubMed Central
e51.
Wallhäußer-Franke E, Repik I, Delb W, Glauner A, Hormann K: Langzeit-Entwicklung von akutem Tinnitus. Laryngo-Rhino-Otol 2015; 94: 759–69 CrossRef MEDLINE
e52.
Wang ZY, Liu L, Shi M, Wang L: Exploring correlations between positive psychological resources and symptoms of psychological distress among hematological cancer patients: a cross-sectional study. Psychol Health Med 2016; 21: 571–82 CrossRef MEDLINE
e53.
Wick K, Bauer S, Malessa C, Settmacher U, Strauß B: Zusammenhang zwischen Belastungsfaktoren und Ressourcen bei Patienten auf der Warteliste für ein Spenderorgan: Nieren- und leberinsuffiziente Patienten im Vergleich. Psychother Psychosom Med Psychol 2015; 65: 311–20 CrossRef MEDLINE
e54.
Wolfram M: Zusammenhang zwischen Resilienz, dyadisches Coping und psychischer Belastung bei Krebspatienten und deren Partnern. Medizinische Projektarbeit. Jena: Friedrich-Schiller-Universität 2017.
e55.
Yang YL, Liu L, Li MY, Shi M, Wang L: Psychological disorders and psychosocial resources of patients with newly diagnosed bladder and kidney cancer: a cross-sectional study. PLoS One 2016; 11: e0155607 CrossRef MEDLINE PubMed Central
e56.
Stroup DF, Berlin JA, Morton SC: Meta-analysis of observational studies in epidemiology: a proposal for reporting. JAMA 2000; 283: 2008–12 CrossRef
e57.
Vandenbroucke JP, von Elm E, Altman DG, et al.: Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. PLoS Med 2007; 4: e297.
e58.
Effective Public Health Practice Project: Quality Assessment Tool For Quantitative Studies. www.merst.ca/wp-content/uploads/2018/02/quality-assessment-tool_2010.pdf (Last accessed on 8 March 2018).
e59.
Myers L, Sirois MJ: Spearman correlation coefficients, differences between. In: Kotz S (ed.): Encyclopedia of statistical sciences. Hoboken: John Wiley & Sons 2006; 7901-3.
e60.
Walker DA: JMASM9: Converting kendall‘s tau for correlational or meta-analytic analyses. J Mod Appl Stat Methods 2003; 2: 525–30 CrossRef
e61.
Hedges LV, Olkin I: Statistical methods for meta-analysis (7. ed.). San Diego: Academic Press 1999.
e62.
Egger M, Smith GD, Schneider M, Minder C: Bias in meta-analysis detected by a simple, graphical test. Br Med J 1997; 315: 629–34 CrossRef
e63.
Duval S, Tweedie R: Trim and Fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics 2000; 56: 455–63 CrossRef
e64.
Rosenthal R: The ‘File drawer problem’ and tolerance for null results. Psychol Bull 1979; 86: 638–41 CrossRef
e65.
Hunter JE, Schmidt FL: Methods of meta-analysis correcting error and bias in research findings (2. ed.). Thousand Oaks, CA: Sage 2004.
Institute of Psychosocial Medicine and Psychotherapy, University Hospital Jena, Friedrich-Schiller University Jena: Francesca Färber, M.Sc., PD Dr. phil. med. habil. Jenny Rosendahl
Flowchart of study selection
Flowchart of study selection
Figure
Flowchart of study selection
Key messages
German version of the Resilience Scale, shortened 13-item version (RS-13) (18)
German version of the Resilience Scale, shortened 13-item version (RS-13) (18)
Table 1
German version of the Resilience Scale, shortened 13-item version (RS-13) (18)
Inclusion criteria
Inclusion criteria
Table 2
Inclusion criteria
The Clinical Perspective
Search strategies for the electronic database search
Search strategies for the electronic database search
eBox
Search strategies for the electronic database search
Funnel Plot of Fisher´s Z by standard error
Funnel Plot of Fisher´s Z by standard error
eFigure
Funnel Plot of Fisher´s Z by standard error
Instruments used to measure mental health
Instruments used to measure mental health
eTable 2
Instruments used to measure mental health
Risk of bias within studies
Risk of bias within studies
eTable 3
Risk of bias within studies
Results of subgroup analyses and meta-regression
Results of subgroup analyses and meta-regression
eTable 4
Results of subgroup analyses and meta-regression
Results of sensitivity analyses
Results of sensitivity analyses
eTable 5
Results of sensitivity analyses
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e1. Amler S, Sauerland MC, Deiters C, Buchner T, Schumacher A: Factors influencing life satisfaction in acute myeloid leukemia survivors following allogeneic stem cell transplantation: a cross-sectional study. Health Qual Life Outcomes 2015; 13: 11 MEDLINE PubMed Central
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e3.Brix C, Schleußner C, Füller J, Röhrig B, Strauß B: Fatigue und deren Determinanten in der Radioonkologie. Psychother Psychosom Med Psychol 2008; 59: 42–9 MEDLINE
e4.Chopp-Hurley JN, Brenneman EC, Wiebenga EG, Bulbrook B, Keir PJ, Maly MR: Randomized controlled trial investigating the role of exercise in the workplace to improve work ability, performance, and patient-reported symptoms among older workers with osteoarthritis. J Occup Environ Med 2017; 59: 550–6 CrossRef MEDLINE
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e7.Erim Y, Kahraman Y, Vitinius F, Beckmann M, Kroncke S, Witzke O: Resilience and quality of life in 161 living kidney donors before nephrectomy and in the aftermath of donation: a naturalistic single center study. BMC Nephrol 2015; 16: 10 MEDLINE PubMed Central
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e15.Herrmann D, Scherg H, Verres R, von Hagens C, Strowitzki T, Wischmann T: Resilience in infertile couples acts as a protective factor against infertility-specific distress and impaired quality of life. J Assist Reprod Genet 2011; 28: 1111–7 CrossRef MEDLINE PubMed Central
e16.Jaenichen D, Brunkhorst FM, Strauß B, Rosendahl J: Körperliche und psychische Langzeitfolgen nach intensivmedizinischer Behandlung einer schweren Sepsis bei Patienten und Angehörigen. Psychother Psychosom Med Psychol 2012; 62: 335–43 CrossRef MEDLINE
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e23.Küch D, Rank C, Herbold D, Jacobi C, Kegel D, Franke GH: Resilienz, berufliche Belastungen und psychische Gesundheit bei Rehabilitanden der Orthopädie und Psychosomatik. Eine Querschnittstudie. Praxis Klinische Verhaltensmedizin und Rehabilitation 2016: 148–61.
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e25.Kurz K, Reißig A, Strauß B, Rosendahl J: Geschlechtsunterschiede in der psychischen Belastung von Patienten mit Lungenkarzinom und deren Partnern. Psychother Psychosom Med Psychol 2014; 64: 431–8 CrossRef MEDLINE
e26.Leontjevas R, Op de Beek W, Lataster J, Jacobs N: Resilience to affective disorders: a comparative validation of two resilience scales. J Affect Disord 2014; 168: 262–8 CrossRef MEDLINE
e27.Li MY, Yang YL, Liu L, Wang L: Effects of social support, hope and resilience on quality of life among Chinese bladder cancer patients: a cross-sectional study. Health Qual Life Outcomes 2016; 14: 73 CrossRef MEDLINE MEDLINE
e28. Liu JC, Chang LY, Wu SY, Tsai PS: Resilience mediates the relationship between depression and psychological health status in patients with heart failure: a cross-sectional study. Int J Nurs Stud 2015; 52: 1846–53 CrossRef MEDLINE
e29.Liu CL, Liu L, Zhang Y, Dai XZ, Wu H: Prevalence and its associated psychological variables of symptoms of depression and anxiety among ovarian cancer patients in China: a cross-sectional study. Health Qual Life Outcomes 2017; 15: 161 CrossRef MEDLINE PubMed Central
e30.Losoi H, Silverberg ND, Wljas M, et al.: Resilience is associated with outcome from mild traumatic brain injury. J Neurotrauma 2015; 32: 942–9 CrossRef MEDLINE
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e37.Popa-Velea O, Diaconescu L, Jidveian Popescu M, Truţescu C: Resilience and active coping style: effects on the self-reported quality of life in cancer patients. Int J Psychiatry Med 2017; 52: 124–36 CrossRef MEDLINE
e38.Quiceno JM, Vinaccia S: Resiliencia, percepción de enfermedad, creencia y afrontamiento espiritual religioso y calidad de vida relacionada con la salud en pacientes con diagnóstico de artritis reumatoide. Psicología desde el Caribe 2013; 30: 591–619.
e39.Radke J, Franke GH: Motivationale Inkongruenz in der somatischen Rehabilitation. In: Arbeitskreis Klinische Psychologie in der Rehabilitation BDP, Mariolakou A, Nowik D, Schwabe A, Spreen-Ledebur M, Groll J (eds.): Rehabilitation – Balance zwischen müssen und wollen Beiträge zur 35 Jahrestagung des Arbeitskreises Klinische Psychologie in der Rehabilitation vom 28–30102016 im Bildungszentrum Erkner. Berlin: Deutscher Psychologen Verlag 2016; 147–57.
e40. Ramírez-Maestre C, Esteve R, Ruiz-Párraga G, Gómez-Pérez L, López-Martínez AE: The key role of pain catastrophizing in the disability of patients with acute back pain. Int J Behav Med 2017; 24: 239–48 CrossRef MEDLINE
e41.Rebagliati GAA, Sciume L, Iannello P, et al.: Frailty and resilience in an older population. The role of resilience during rehabilitation after orthopedic surgery in geriatric patients with multiple comorbidities. Funct Neurol 2016; 31: 171–7.
e42.Robottom BJ, Gruber-Baldini AL, Anderson KE, et al.: What determines resilience in patients with Parkinson‘s disease? Parkinsonism Relat Disord 2012; 18: 174–7.
e43.Ruiz-Párraga GT, López-Martínez AE, Esteve R, Ramírez-Maestre C, Wagnild G: A confirmatory factor analysis of the Resilience Scale adapted to chronic pain (RS-18): new empirical evidence of the protective role of resilience on pain adjustment. Qual Life Res 2015; 24: 1245–53 CrossRef MEDLINE
e44.Runkewitz K, Kirchmann H, Strauß B: Anxiety and depression in primary care patients: predictors of symptom severity and developmental correlates. J Psychosom Res 2006; 60: 445–53 CrossRef MEDLINE
e45.Schumacher A, Sauerland C, Silling G, Berdel WE, Stelljes M: Resilience in patients after allogeneic stem cell transplantation. Support Care Cancer 2014; 22: 487–93 CrossRef MEDLINE
e46.Suffeda A, Meissner W, Rosendahl J, Guntinas-Lichius O: Influence of depression, catastrophizing, anxiety, and resilience on postoperative pain at the first day after otolaryngological surgery. A prospective single center cohort observational study. Medicine (Baltimore) 2016; 95: e4256 CrossRef MEDLINE PubMed Central
e47.Torma LM, Houck GM, Wagnild GM, Messecar D, Jones KD: Growing old with fibromyalgia. Nurs Res 2013; 62: 16–24 MEDLINE
e48.Vinaccia S, Quiceno JM: Calidad de vida relacionada con la salud y factores psicológicos: un estudio desde la enfermedad pulmonar obstructiva crónica – EPOC. Terapia Psicológica 2011; 29: 65–76 CrossRef
e49.Vinaccia S, Quiceno JM: Resiliencia y calidad de vida relacionada con la salud en pacientes con insuficiencia renal crónica – IRC. Revista Argentina de Clínica Psicológica 2011; 20: 201–11.
e50.Wallhäußer-Franke E, Delb W, Balkenhol T, Hiller W, Hörmann K: Tinnitus-related distress and the personality characteristic resilience. Neural Plast 2014: 370307 MEDLINE PubMed Central
e51.Wallhäußer-Franke E, Repik I, Delb W, Glauner A, Hormann K: Langzeit-Entwicklung von akutem Tinnitus. Laryngo-Rhino-Otol 2015; 94: 759–69 CrossRef MEDLINE
e52.Wang ZY, Liu L, Shi M, Wang L: Exploring correlations between positive psychological resources and symptoms of psychological distress among hematological cancer patients: a cross-sectional study. Psychol Health Med 2016; 21: 571–82 CrossRef MEDLINE
e53.Wick K, Bauer S, Malessa C, Settmacher U, Strauß B: Zusammenhang zwischen Belastungsfaktoren und Ressourcen bei Patienten auf der Warteliste für ein Spenderorgan: Nieren- und leberinsuffiziente Patienten im Vergleich. Psychother Psychosom Med Psychol 2015; 65: 311–20 CrossRef MEDLINE
e54.Wolfram M: Zusammenhang zwischen Resilienz, dyadisches Coping und psychischer Belastung bei Krebspatienten und deren Partnern. Medizinische Projektarbeit. Jena: Friedrich-Schiller-Universität 2017.
e55.Yang YL, Liu L, Li MY, Shi M, Wang L: Psychological disorders and psychosocial resources of patients with newly diagnosed bladder and kidney cancer: a cross-sectional study. PLoS One 2016; 11: e0155607 CrossRef MEDLINE PubMed Central
e56.Stroup DF, Berlin JA, Morton SC: Meta-analysis of observational studies in epidemiology: a proposal for reporting. JAMA 2000; 283: 2008–12 CrossRef
e57.Vandenbroucke JP, von Elm E, Altman DG, et al.: Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. PLoS Med 2007; 4: e297.
e58.Effective Public Health Practice Project: Quality Assessment Tool For Quantitative Studies. www.merst.ca/wp-content/uploads/2018/02/quality-assessment-tool_2010.pdf (Last accessed on 8 March 2018).
e59.Myers L, Sirois MJ: Spearman correlation coefficients, differences between. In: Kotz S (ed.): Encyclopedia of statistical sciences. Hoboken: John Wiley & Sons 2006; 7901-3.
e60.Walker DA: JMASM9: Converting kendall‘s tau for correlational or meta-analytic analyses. J Mod Appl Stat Methods 2003; 2: 525–30 CrossRef
e61.Hedges LV, Olkin I: Statistical methods for meta-analysis (7. ed.). San Diego: Academic Press 1999.
e62. Egger M, Smith GD, Schneider M, Minder C: Bias in meta-analysis detected by a simple, graphical test. Br Med J 1997; 315: 629–34 CrossRef
e63.Duval S, Tweedie R: Trim and Fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics 2000; 56: 455–63 CrossRef
e64.Rosenthal R: The ‘File drawer problem’ and tolerance for null results. Psychol Bull 1979; 86: 638–41 CrossRef
e65.Hunter JE, Schmidt FL: Methods of meta-analysis correcting error and bias in research findings (2. ed.). Thousand Oaks, CA: Sage 2004.