This article was downloaded by: [Bond University] On: 23 January 2015, At: 18:18 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
Counselling Psychology Quarterly Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/ccpq20
The contribution of anxiety and depression to fatigue among a sample of Australian university students: suggestions for university counsellors a
b
Vicki Bitsika , Christopher F. Sharpley & Ryan Bell a
a
Bond University , Queensland
b
University of New England , Coolangatta, Queensland, Australia Published online: 11 Sep 2009.
To cite this article: Vicki Bitsika , Christopher F. Sharpley & Ryan Bell (2009) The contribution of anxiety and depression to fatigue among a sample of Australian university students: suggestions for university counsellors, Counselling Psychology Quarterly, 22:2, 243-255, DOI: 10.1080/09515070903216929 To link to this article: http://dx.doi.org/10.1080/09515070903216929
PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &
Downloaded by [Bond University] at 18:18 23 January 2015
Conditions of access and use can be found at http://www.tandfonline.com/page/termsand-conditions
Counselling Psychology Quarterly Vol. 22, No. 2, June 2009, 243–255
RESEARCH REPORT The contribution of anxiety and depression to fatigue among a sample of Australian university students: suggestions for university counsellors Vicki Bitsikaa*, Christopher F. Sharpleyb and Ryan Bella a
Bond University, Queensland; bUniversity of New England, Coolangatta, Queensland, Australia
Downloaded by [Bond University] at 18:18 23 January 2015
(Final version received 18 February 2008) Responses to the Zung Self-Rating Anxiety Scale (SAS: Zung, W. (1971). A rating instrument for anxiety disorders. Psychosomatics, 12, 371–379), the Self-Rating Depression Scale (SDS: Zung, W. (1973). From art to science: The diagnosis and treatment of depression. Archives of General Psychiatry, 29, 328–337) and the Fatigue Severity Scale (FSS) developed by Krupp and colleagues (Krupp, L.B., LaRocca, N.G., Muir-Nash, J., & Steinberg, A.D. (1989). The fatigue severity scale: Application to patients with multiple sclerosis and systemic lupus erythematosus. Archives of Neurology, 46, 1121–1123) were collected from 200 Australian university students to explore the links between these three disorders. Reliability data were satisfactory for all three scales and there were no significant gender or age-related differences between total scale scores. Factor analyses revealed a 5-factor solution for the SAS, a 6-factor solution for the SDS and a single factor for the FSS. There were 8 major and meaningful correlations found and these were entered into a regression of the SAS and SDS factor scores upon the single factor of the FSS. Fatigue factor scores were most powerfully predicted by psychomotor agitation, pain and resultant fatigue and cognitive and emotional arousal factor scores from the SAS and SDS. These data argue for an arousal/anxiety-fatigue-depression progression in disease that may be developmental or accumulative, with extreme levels of psychomotor arousal, resultant muscle fatigue and pain, plus concurrent elevated emotional state and cognitive arousal contributing to an eventual depletion of physical resources, leaving the individual in extreme fatigue. Implications for diagnosis and treatment by counsellors are discussed. Keywords: anxiety; depression; fatigue; factor structure; counselling
Introduction Several previous studies have indicated that university students report elevated feelings of fatigue compared to the general population (e.g., Bray & Born, 2004; McMichael & Hetzel, 1974; Schaufeli Martinez, Pinto, Salanova, & Bakker, 2002). While fatigue may be caused or exacerbated by the specific academic demands experienced by students, such as assignments and examinations (Lacey et al., 2000) and the transition from high school to university (Bray & Born, 2004) as well as the delay in circadian rhythm noted in late adolescence (Abbott, 2003; Carskadon,
*Corresponding author. Email:
[email protected] ISSN 0951–5070 print/ISSN 1469–3674 online ß 2009 Taylor & Francis DOI: 10.1080/09515070903216929 http://www.informaworld.com
Downloaded by [Bond University] at 18:18 23 January 2015
244
V. Bitsika et al.
Acebo, & Jenni, 2004), only about half of patients who report fatigue to their medical practitioner have experienced any reduction in their symptoms six months later (Sharpe & Wilks, 2002). Because fatigue can decrease cognitive performance on learning and assessment tasks that are typical of the university student’s lifestyle (Shaufell et al., 2002), as well as decrease overall well-being, an understanding of the range of causal factors that may contribute to students’ symptomatology when they present with fatigue to a university counselling service will inform counsellors and provide directions for their most effective intervention strategies. Although fatigue often has a physical basis such as infection, emotional, behavioural and psychological factors can prolong fatigue (Surawy, Hackman, Hawton, & Sharpe, 1995). Fatigue or fatigue-like symptoms have also been associated with anxiety and depression (Chen, 1986; Surawy et al., 1995; Van der Linden, Chalder, Hickie, Koschera, Sham, & Wessely, 1999), with data indicating that those who experience emotional stress and anxiety are also more likely to experience fatigue than people who do not feel stressed or anxious (Merikangas & Angst, 1994). As confirmation of the interrelationship between fatigue and depression, both the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) (American Psychiatric Association [APA], 2000) and International Classification of Diseases (ICD-10) (World Health Organization [WHO], 1992) list fatigue as one of the major somatic symptoms of depression, and numerous cross-sectional studies have shown positive associations between fatigue and depression (e.g., Beck, 1972; Huibers, Leone, Amelsvoort, Kant, & Knottnerus, 2007; Skapinakis, Lewis & Mavreas, 2004). Comorbidity rates between fatigue-specific disorders such as Chronic Fatigue Syndrome, and depressive disorders such as Clinical Depression, are over 55% (Fischler, D’Haenen, & Cluydts, 1996). This strong association between fatigue and depression has caused some researchers to suggest that fatigue and depression are similar manifestations of the same psychiatric disorder (Huibers et al., 2007). However, while certain symptoms such as exhaustion, sleep problems, and reduced activity are common in patients presenting with both depression and fatigue, other symptoms relating to guilt, self-esteem, and flat affect are specific only to depression (Beck, 1972). In spite of a number of studies (e.g., Lavidor, Weller, & Babkoff, 2002; Manu, Matthews, Lane, Tenneh, Hesselbrock, Mendola, & Affleck, 1989) designed to investigate the relationship between depression and fatigue, the detailed nature of the association between these states is still not well understood. Specifically, it remains unclear whether fatigue causes depression, whether depression predisposes a patient to fatigue, whether a third risk factor underlies both conditions, or whether the association is caused by an overlap in the criteria that define fatigue and depression. A repeated measures analysis conducted by Huibers et al. (2007) showed that depression had a strong impact on fatigue and fatigue had a strong impact on depression and, while the impact of depression on fatigue increased significantly over time, the impact of fatigue upon depression did not similarly increase over time. Other studies investigating the causal relationship between depression and fatigue have suggested that this relationship is bi-directional, as proposed by the circular model of depression and fatigue suggested by Skapinakis et al. (2004), wherein fatigue is a physiological response to the derelict mood common in depression and the symptoms of physical deconditioning and social isolation found in fatigue may increase an individual’s symptoms of depression.
Downloaded by [Bond University] at 18:18 23 January 2015
Counselling Psychology Quarterly
245
Similar symptomatology overlap exists between anxiety and fatigue. Both the DSM-IV-TR (APA, 2000) and ICD-10 (WHO, 1992) include becoming easily fatigued as one of the major somatic symptoms of many anxiety disorders. Chan, Chang, Leung, and Mak (2007) noted that anxiety accounted for 34.1% of the total variance in predicting the degree of physical fatigue symptoms in 73 patients with lung cancer, and that fatigue was exacerbated by symptoms of anxiety. Christensen, Hjortso, Mortensen, Riis-Hansen, and Kehlet (1986) reported correlations of 0.68 between self-reported measures of fatigue and state-anxiety and 0.66 between fatigue and trait anxiety. Wilson (2001) suggested that the anxiety-fatigue relationship was an outcome of ‘‘adrenal fatigue’’ occasioned by prolonged sympathetic nervous system arousal which led to insufficient biosyntheis of the adrenal catecholamines and cortisol. Primary symptoms of adrenal fatigue have been reported as tiredness and lethargy, particularly in the later day/evening, and it has been described as a significant risk factor for depression (Wilson, 2001). The presence of fatigue as a major symptom in people with anxiety is of special interest, as it deviates from other common features of this condition. Because anxiety is characterized by activation of the autonomic nervous system, the somatic symptoms of anxiety appear to be related to psychomotor agitation responses such as restlessness, irritability and muscle tension (APA, 2000). Symptoms of fatigue, however, tend to indicate lethargy, which is more common in depressive disorders. Similarly, while most of the somatic criteria for depression indicate symptoms of lassitude (i.e., fatigue, decrease in the amount of interest in daily activities, marked weight gain or loss), most depressive disorders also list symptoms of psychomotor agitation (a primary feature of anxiety) as a major symptom. Some researchers (Diaconu & Turecki, 2007; Sapolsky, 2004) have suggested that the psychomotor agitation observed in depression may be a symptom of anxiety caused by the lowered mood of depression (i.e., people being anxious about being depressed), and that the symptoms of fatigue observed in individuals with anxiety may be a symptom of mental exhaustion caused by the increased cognitive load of being anxious (Van Praag, 2005). Whatever the cause, this overlapping symptomatology may highlight a deeper relationship between depression, anxiety and fatigue than has previously been described. Like fatigue, anxiety and depression have been reported as higher among university students than the rest of the community (McLennan, 1992) and the pressures of university study have been inculcated in this elevated score (Tanaka & Huba, 1987). There is no doubt that university students encounter a number of new demands that can combine to cause anxiety and later depression, including academic pressure, financial concerns, social and sexual challenges and sleep deprivation (Scott & O’Hara, 1993). Anxiety and depression among students has also been shown to adversely influence their academic performance and contribute to learning difficulties (Dyrbye, Thomas, & Shanafelt, 2006). Kitzrow (2003) noted that 28% of freshmen reported being overwhelmed and 8% were depressed, and that these mental health problems can affect the interpersonal relationships of the unhappy student and also the academic performance of that person. Specifically in terms of depression, Tjia, Givens, and Shea (2005) found that over 15% of students surveyed in a medical school were depressed and 20.4% reported suicidal ideation but only 26.5% of those students who were depressed had received treatment for their mental state. A recent survey of the mental health needs of 939 students from a large Midwestern university found that 18.2% of females and 9.2% of males reported
246
V. Bitsika et al.
Downloaded by [Bond University] at 18:18 23 January 2015
being depressed and 7.1% of females and 3.9% of males were anxious (Soet & Sevig, 2006). These data suggest that the presentation of fatigue symptoms by a student who is seeking counselling may also warrant a clinical investigation of anxiety and depression symptoms by the counsellor. Thus, research on the interrelationships between anxiety, depression and fatigue among university students might be informative in helping counsellors build hypothetical models of the contributions of anxiety and depression to student fatigue and thence assist counsellors in developing more effective intervention strategies. This research would be more profitable if it was done at the level of factor structures which encompass symptom groupings as well as overall raw scores which ignore specific symptomatology. Therefore, this study was designed to explore the underlying nature of the relationship between anxiety, depression and fatigue by focussing upon the relationships between the factor structures from inventories that tap these variables.
Method Participants Two-hundred undergraduate students from Bond University, Queensland, Australia, participated in the study (age range ¼ 17–54 years, M ¼ 23.6, SD ¼ 7.24; 104 females and 96 males). Participants were from all major Faculties of the university (Humanities/Social Sciences/Education ¼ 55%, Law ¼ 12%, Health & Medicine ¼ 5%, Business and IT ¼ 28%).
Measures Background data on age, gender and degree studied were obtained via brief questions. Anxiety was assessed by the Zung Self-Rating Anxiety Scale (SAS) (Zung, 1971) and depression was measured by the Zung Self-Rating Depression Scale (SDS) (Zung, 1965). The Zung SAS is a brief, self-report questionnaire which measures the presence and magnitude of anxiety-based symptoms. Based upon DSM criteria, the SAS contains 20 items that assess both physiological (e.g., muscle tremors, physical pain, urinary frequency, sweating, face flushing, insomnia) and psychological (e.g., nervousness, fear, mental disintegration, panic, apprehension, restlessness, nightmares) symptoms commonly associated with anxiety. Each item is scored on a 4-point scale in relation to whether the person has experienced each specific symptom ‘‘none or a little of the time’’ (rating ¼ 1), ‘‘some of the time’’ (2), ‘‘a good part of the time’’ (3), or ‘‘most or all of the time’’ (4) during the last two weeks. There are positively- and negatively-worded items to reduce response bias and identify inconsistencies in responses. Raw scores sum to a total that ranges from 20 to 80, with higher total scores reflecting a more anxious individual than lower total scores. The SAS correlates 0.75 with the Hamilton Anxiety Scale (Zung, 1971) and has been shown to significantly discriminate between normal adult samples and patients with anxiety disorders (Zung, 1971). Reliability data are 0.71 (split half: Zung, 1971) and 0.79 (coefficient alpha), the latter from a previous Australian sample of 552 participants (Sharpley & Rogers, 1985). Zung set a cutoff point raw score of 36, above which he described participants as having anxiety that
Downloaded by [Bond University] at 18:18 23 January 2015
Counselling Psychology Quarterly
247
‘‘was clinically significant’’ (Zung, 1980, p. 18), although this does not equate to an Anxiety Disorder according to DSM criteria. The Zung SDS (Zung, 1973) is also a 20-item self-report questionnaire which measures the presence and magnitude of depressive symptoms. It assesses psychological (e.g., sadness, crying, suicidal ideation, confusion, hopelessness, emptiness, irritability, indecisiveness, dissatisfaction, self-deprecation) and physiological (e.g., psychomotor agitation, insomnia, anorexia, weight loss, decreased libido, constipation, fatigability) symptoms commonly associated with depression. The same 4-point scale as the SAS is used and there are positively- and negatively-worded items to reduce response bias and identify inconsistencies in responses. Raw scores range from 20 to 80, with higher scores reflecting a more depressed individual. Zung (1973) noted that a total raw score exceeding 40 was indicative of clinically significant depression. The SDS was constructed according to the DSM-II (APA, 1968) criteria for depression, giving a high content and face validity. The SDS has high concurrent validity (Zung, 1965) and Schaefer et al. (1985) showed that the SDS was superior to the Beck Depression Inventory and the MMPI-D scale in assessing depression in male psychiatric patients. The reliability of the SDS has been reported as between 0.73 (split half ) and 0.90 (coefficient alpha) (Zung, 1965, 1973). Fatigue was assessed via the Fatigue Severity Scale (FSS) (Krupp, LaRocca, Muir-Nash, & Steinberg, 1989). The FSS is a brief, self-report questionnaire which measures the presence and magnitude of physical fatigue in an individual via nine items that relate to different aspects of physical fatigue. Respondents rate their agreement with each item on a 7-point Likert-type scale ranging from 1 (‘‘strong disagreement’’) to 7 (‘‘strong agreement’’). Responses to the questions are then averaged, yielding a score between 1 and 7 to reflect the subject’s overall level of fatigue. According to the test’s authors, an average FSS score of 3.3 or higher was shown to correlate with moderate to severe fatigue. In an initial validation study, the FSS was found to easily distinguish between healthy controls and individuals with fatigue-related illnesses such as MS or lupus. The FSS was also moderately correlated with a single-item visual analogue scale of fatigue intensity. In all patients, clinical improvement in fatigue was associated with reductions in scores on the FSS. The FSS has been shown to be a better predictor of severe fatigue in clinical samples than either the Visual Analogue Scale for Fatigue (VAS) or the Fatigue Impact Scale (FIS), with scores on the FSS most closely associated with the intensity of selfreported fatigue (Krupp et al., 1989). Procedure Participants were recruited by soliciting during lectures and through informal advertisements placed in the university. Participants completed the survey questionnaires either in class or privately in an office on university premises dedicated to this process. Once completed, the questionnaires were stored in a secure location before coding for subsequent data analysis. Ethical approval was obtained from the Bond University Human Research Ethics Committee
Results Reliability coefficients (Cronbach’s alpha) for the SAS, SDS and FSS were 0.85, 0.84 and 0.91 respectively, allowing further exploration of these data. There were no
Downloaded by [Bond University] at 18:18 23 January 2015
248
V. Bitsika et al.
significant correlations between any of the dependent variables and age. In order to determine if the data from the SAS, SDS and FSS could be collapsed across genders, MANOVA on these three dependent variables was performed, showing no significant main effects (Wilks Lambda ¼ 0.966, F(3, 196) ¼ 2.268, p ¼ 0.082, partial eta squared ¼ 0.034), allowing male and female data to be combined for further analysis. The total sample’s mean (and SD) for the SAS was 36.670 (8.314), for the SDS it was 39.425 (8.580) and for the FSS it was 33.675 (12.199). Pearson correlations between three total DV scores were: SAS-SDS, r ¼ 0.786 ( p 5 0.001), SAS-FSS, r ¼ 0.610 ( p 5 0.001) and SDS-FSS, r ¼ 0.597 ( p 5 0.001). The significant relationships between all three sets of total scores suggest that further examination of their underlying factor structures is justified. Consequently, three factor analyses were performed. For the SAS, Exploratory Principal Component Analysis was performed. The case:item ratio of 10 : 1 was considered acceptable (Tabachnik & Fidell, 2001). In addition, over 30% of the item:item correlations were 0.3 or greater, the Kaisser-Meyer-Olkin (KMO) Measure of Sampling Adequacy was 0.845 (exceeding the minimum recommended value of 0.6 (Kaiser, 1970)) and Bartlett’s Test of Sphericity was significant ( p ¼ 0.000), thus supporting the factor analysis of these data. Principal Component Analysis with Varimax Rotation on the 20 SAS items and analysis of the screeplot produced a ‘‘1þ4’’ factor solution with eigenvalues greater than 1 that accounted for a total of 54.50% of the variance. Examination of the items that loaded on each factor suggested that factor one (28.18% of the variance) represented items forming a ‘‘Cognitive & emotional arousal’’ factor; factor two (8.65%) was labelled ‘‘Physiological arousal-1’’; factor three (6.78%) was termed ‘‘Physiological arousal-2’’; factor four (5.87%) was ‘‘Pain & fatigue’’; and factor five (5.02%) was ‘‘Sleep/digestive problems’’. The SDS had 24% of item-item correlations at 0.3 or greater, the KMO Measure of Sampling Adequacy was 0.824 and Bartlett’s Test of Sphericity was significant ( p ¼ 0.000), thus supporting this procedure. Principal Component Analysis, Varimax Rotation and analysis of the screeplot produced a ‘‘1 þ 5’’ factor solution that accounted for a total of 60.18% of the variance and with eigenvalues greater than 1. Factor one (28.45% of the variance) was labelled ‘‘Fatigue, confusion, pessimism’’; factor two (8.62%) was ‘‘Psychomotor agitation’’; factor three (6.90%) was ‘‘Digestive issues’’; factor 4 (5.58%) was ‘‘Somatic depression’’; factor five (5.58%) was ‘‘Diurnal enjoyment’’; and factor six was labelled as ‘‘Positive’’ factor (5.05%) because it consisted of two inverse correlations with negatively-worded items. Factor analysis of the FSS (case:item ratio ¼ 22 : 1) showed that 67% of the items correlated at 0.3 or greater with each other, the KMO Measure of Sampling Adequacy was 0.913 and Bartlett’s Test of Sphericity was significant ( p ¼ 0.000), thus supporting this procedure. Principal Component Analysis, Varimax Rotation and analysis of the screeplot produced a single factor that explained 59.06% of the variance and had an eigenvalue of 5.316. This was the only factor to have an eigenvalue of 1.0 or more, further supporting the single factor model. This factor loaded heavily (0.832 to 0.890) on FSS items 4 to 9, but only 0.327 and 0.431 on items 1 and 2. Item 3 loading was 0.746. Examination of the content of items 4 to 9 versus items 1 and 2 showed that the former subgroup of items tapped responses on the effects of fatigue (e.g., ‘‘My fatigue prevents sustained physical functioning’’), whereas items 1 to 3 were not so easily identified in a homogenous way. Therefore, it was decided to accept that the FSS had a single factor solution and that this factor
Downloaded by [Bond University] at 18:18 23 January 2015
Counselling Psychology Quarterly
249
loaded most heavily on items 4 to 9 and was thus labelled as ‘‘Effects of fatigue’’. The decision to omit items 1 and 2 from the factor structure of the FSS was supported by the finding that the overall Cronbach’s alpha increased if items 1 and 2 were deleted. Item 3 appears to be statistically related to the single factor but not in obvious content rationale terms. In order to examine the nature of the relationships between the factor structures of the SAS, SDS and FSS, Pearson correlations were performed on the 5 SAS factors, the 6 SDS factors and the single FSS factor. Table 1 shows the correlation matrix from this analysis and indicates that, although there were many significant correlations at the p 5 0.01 level (as might be expected from a sample size of 200), only some of these were meaningful in terms of variance explained between the two correlates. By applying the rule set by Cohen (1988) regarding interpretation of obtained r values and selecting those which fulfil the criteria of being ‘‘large’’ (i.e., above 0.50), the eight underscored correlations shown in Table 1 are most likely to represent meaningful relationships (as well as accounting for at least 25% of the variance). These are shown in Table 2, with the factor descriptions previously obtained, and underline a relationship between arousal, agitation and fatigue, pain, confusion and pessimism. To test if the apparent powerful relationship between fatigue and other factors was one that could be predictive, multiple regression was performed upon the six SAS and SDS factors that are shown in Table 2, with the factor scores from the FSS as the dependent variable. Correlations between the DV (FSS Fac1) and the independent variables were all greater than 0.458 except for SAS Fac3, which was 0.293; there were multiple correlations of 0.3 or greater among the independent variables, but these were not greater than 0.7; and examination of the Tolerance values indicated that they were 0.411 or greater. Together, these data indicate that collinearity was not a source of invalidity in this analysis. Similarly, examination of the Normal Probability Plot revealed an almost straight diagonal line from lower left to upper right, showing no major deviations from normality. The scatterplot was roughly rectangular, with no outliers. The R Square value was 0.443, and the ANOVA was significant ( p 5 0.0005), indicating that just over 44% of FSS Fac1 scores were predicted by the SAS and SDS factors entered in the equation. Beta values showed that SDS Fac2 made the strongest unique contribution to FSS Fac1 scores ( ¼ 0.310, t ¼ 3.703, p 5 0.0005), followed by SAS Fac4 ( ¼ 0.243, t ¼ 3.089, p ¼ 0.002) and SDS Fac1 ( ¼ 0.205, t ¼ 2.764, p ¼ 0.006). No other independent variables were significant contributors to the FSS Fac1 scores.
Discussion The previously-reported association between fatigue and anxiety and depression scale total scores was confirmed here. In addition, examination of the factor structures of the three inventories used in this study produced more detailed evidence of the nature of that overall relationship. The factor analyses of the SAS, SDS and FSS produced structures that are congruent with the symptomatology described in DSM and ICD nomenclature and thus hold face validity, further supporting this approach. The SAS showed a structure that included cognitive, emotional and physiological arousal, with aspects of this triad that were orthogonal, suggesting that the theoretical basis of this disorder that considers all three aspects
0.490**
0.303** 0.240**
SAS Fac3 0.489** 0.385** 0.337**
SAS Fac4 0.388** 0.403** 0.132 0.365**
SAS Fac5
*p 5 0.05, **p 5 0.01. Underscored values represent meaningful relationships.
Fac1 SAS Fac1 SAS Fac2 SAS Fac3 SAS Fac4 SAS Fac5 SDS Fac1 SDS Fac2 SDS Fac3 SDS Fac4 SDS Fac5 SDS Fac6
SAS Fac2 0.575** 0.330** 0.508** 0.417** 0.268**
SDS Fac1
Table 1. Pearson correlations of factors for SAS, SDS and FSS.
0.710** 0.523** 0.273** 0.578** 0.435** 0.507**
SDS Fac2 0.146* 0.359** 0.172* 0.173* 0.197** 0.257** 0.267**
SDS Fac3 0.371** 0.446** 0.226** 0.215* 0.111 0.334** 0.378** 0.215**
SDS Fac4
Downloaded by [Bond University] at 18:18 23 January 2015
0.498** 0.270** 0.321** 0.289** 0.141* 0.412** 0.363** 0.224** 0.302**
SDS Fac5
0.328** 0.270** 0.326** 0.642** 0.217** 0.397** 0.459** 0.184** 0.118 0.248**
SDS Fac6
0.458** 0.355** 0.293** 0.553** 0.391** 0.485** 0.577** 0.154* 0.306** 0.238** 0.463**
FSS
250 V. Bitsika et al.
Counselling Psychology Quarterly
251
Table 2. Factors which correlate at 0.5 (i.e., 25% of variance) or greater. Cognitive & emotional with with Physiological arousal-1 with Physiological arousal-2 with Pain & fatigue with with with SDSFac1: Fatigue, confusion, with pessimism
Downloaded by [Bond University] at 18:18 23 January 2015
SAS Fac1: arousal SAS Fac2: SAS Fac3: SAS Fac4:
SDS Fac1: Fatigue, confusion, pessimism (0.575**) SDS Fac2: Psychomotor agitation (0.710**) SDS Fac2: Psychomotor agitation (0.523**) SDS Fac1: Fatigue, confusion, pessimism (0.508**) SDS Fac2: Psychomotor agitation (0.578**) SDS Fac6: Positive (0.642**) FSS Fac1: Effects of fatigue (0.553**) SDS Fac2: Psychomotor agitation (0.507**)
of symptomatology as required for diagnosis is justified. The presence of pain and fatigue as a separate factor indicated the possible nature of this factor as being a link between the somatic manifestations of anxiety that include physical symptoms, and that these may, after sufficient time, lead to fatigue. The presence of sleep (and digestive) problems as a further separate factor suggests that fatigue may result from another cause – the loss of adequate sleep caused by persistent worry. The factor structure of the SDS also had a powerful fatigue nature, and linked fatigue with difficulties in understanding and solving problems and (a resultant?) pessimism, helplessness and hopelessness about any positive change in this state. In addition to issues surrounding digestion and sleep, there were two ‘‘positive’’ factors in the SDS, deriving from items that indicated the participant ‘‘felt best in the morning’’ and inverse associations with items that measured frequency of constipation and the belief that ‘‘others would be better off if I were dead’’. These factors do not in any way contradict the rest of the factor structure of the SDS but rather indicate that these items and the corresponding symptomatology underlying them were not typically experienced by this sample. These data may argue for a further exploration of the factor structure of depression among students versus non-students but this is not within the range of the present study. The single factor nature of the FSS was supportive of the focus that this sample maintained upon the debilitating effects of fatigue rather than its causes. All of the six items that loaded on this single factor were concerned with various aspects of fatigue’s effects upon the individual, including physical functioning, fulfilling duties and responsibilities, interfering with work, family and social life and as being most disabling. It may be that this scale taps only a limited range of the symptoms of fatigue and the further enlargement of item content to contain other aspects of fatigue might bear revisiting. When tested for their interrelationships, anxiety, fatigue and depression were significantly and meaningfully correlated at the total scale score level, as previously reported. The examination of relationships between factor scores was an extension of previous research and produced some data that verify the links between arousal and agitation (which may lead to) fatigue, pain and confusion. With prolonged fatigue and confusion, it is not unexpected that some sense of pessimism might develop, as shown by the correlation matrix. However, these relationships are not causal, and the regression equation examined this aspect more fully. When taken as the dependent variable, responses on the FSS single factor score were most powerfully predicted by psychomotor agitation (from the SDS), pain and fatigue
Downloaded by [Bond University] at 18:18 23 January 2015
252
V. Bitsika et al.
(SAS) and cognitive and emotional arousal (SAS). Although these predictors are not causally linked in a temporal fashion, they may have an accumulative effect. Thus, it might be suggested that experiencing extreme levels of sympathetic nervous system activity (manifested by psychomotor arousal), the resulting muscle fatigue and pain that follows from continual muscle tension (from psychomotor agitation) and the elevated emotional state and raised frequency of beta-wave activity (during cognitive arousal) may eventually drain the body of physical resources, resulting in fatigue that (as shown in the items that contributed to the single FSS factor) encompasses all aspects of the individual’s lifestyle and ability to deal effectively with day-to-day demands. The links between anxiety and depression that are shown by the overlapping symptomatology in the DSM and ICD nomenclature are also emphasized by these data. While it was the SAS item data on cognitive and emotional arousal that was a significant predictor of FSS factor scores, these symptoms are also tapped by the SDS, although with an emphasis upon the resulting pessimism. It could also be argued that prolonged fatigue would lead to a sense of pessimism about the future, as is shown in fatigue-related disorders such as Chronic Fatigue Syndrome. Although not the focus of this study, these data argue for further examination of the overlap between anxiety and depression, especially as they are constituted in the ICD as Mixed Anxiety and Depressive Disorder.
Implications for university counsellors These data suggest that, when students present with complaints of fatigue, counsellors could beneficially investigate several aspects of the symptomatology associated with anxiety and depression as well as addressing the fatigue itself. When the previously-mentioned lack of success in treating half of those general medical practice patients who present with fatigue is taken into account, counsellors clearly need to widen their investigations of fatigue to also assess anxiety and depression. Even if these disorders are not present at levels which indicate that formal diagnostic classification criteria are met (i.e., DSM, ICD), the specific symptoms that are mentioned by students would repay attention by the counsellor. From this initial examination of those anxiety and depression symptoms that are present in students with fatigue, counsellors could form causal models that focus upon the symptoms and thence lead to more targeted treatment goals and strategies. For example (drawing upon the major factors that predicted FSS scores in this study), if a student who reports being fatigued also shows those symptoms that are grouped as psychomotor agitation (i.e., being restless, fast heart beat, sweating), then counselling could focus upon these behaviours as outcomes of exaggerated sympathetic nervous system activation commonly associated with anxiety, thus leading the counsellor to (i) an examination of the student’s major sources of anxiety (e.g., difficulties understanding university learning content, inappropriate timeallocation to tasks, deficits in task skills, non-university sources of worry such as relationships, family tensions, financial problems, etc) and then (ii) to methods of reducing the psychomotor agitation induced by this anxiety (e.g., direct action to address skills deficits, time-management strategies, counselling for relationship problems, generalized anxiety reduction methods such as systematic desensitization, relaxation training, biofeedback).
Downloaded by [Bond University] at 18:18 23 January 2015
Counselling Psychology Quarterly
253
Similarly, issues of physical pain and resultant fatigue might best result in referral to medical or ancillary (e.g., physiotherapist) treatment and advice. If the physical pain arose from a disability, then counsellors should ensure that the student has access to the university’s disability support services. If the student also is embarrassed about their disability, then assertiveness training might help them address this issue more directly and productively. If the symptoms of fatigue are an outcome of cognitive or emotional arousal, then some aspects of cognitive therapy might be fruitfully applied with the student, as well as challenging those beliefs which may have led the student to have unrealistic expectations of others and resulting emotional disappointment. As mentioned, the predictors of fatigue that were exposed in this study may not be linked in terms of following one another in a progression of symptoms. Instead, they may be accumulative and may need to reach a critical threshold before triggering fatigue of a sufficient intensity as to provoke disturbance within the student. So, psychomotor arousal may result in muscle tension and pain, elevated emotional arousal and difficulties in relaxing from intense (and obsessional) thinking, all eventually contributing to a lack of energy and fatigue. This kind of flow-on of symptoms may be addressed by counsellors’ breaking the chain at any point in the series of symptomatology via cognitive challenge, physical relaxation or further exploration of the purpose and functions of the symptoms via Functional Analysis or Valued Outcome Analysis (e.g., Bitsika & Sharpley, 2006; Kohlenberg & Tsai, 1991). There are several limitations to this study. First, the sample is intentionally restricted to university students to maintain homogeneity because of their previouslyreported high levels of fatigue, anxiety and depression. Extension of these procedures to other samples would elucidate the generalizability of these findings. Second, the student sample is representative of a particular culture which, while not likely to be extremely different to that of other western nations, may contain some characteristics that limit generalization. Third, the data reported here were collected at a single point in time and therefore sampling from a repeated time-series might provide further clarification of the developmental process that the regression equation hinted at. Finally, any study such as this uses only one set of instruments. In this case, they were chosen on the basis of their validity and reliability but further exploration of the data from other tests of anxiety, depression and fatigue might be worthwhile to strengthen the concurrent validity and hopefully confirm the robustness of the findings. References Abbott, A. (2003). Restless nights, listless days. Nature, 425, 896–898. American Psychiatric Association. (1968). Diagnostic and statistical manual of mental disorders (2nd ed.). Washington, DC: American Psychiatric Association. American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (2nd ed.). IV – text revision. Washington, DC: American Psychiatric Association. Beck, A.T. (1972). Depression: Causes and treatment. Philadelphia: University of Pennsylvania Press. Bitsika, V., & Sharpley, C.F. (2006). Treating the client rather than the symptoms: Moving beyond manualised treatments in psychotherapy. Australian Journal of Guidance and Counselling, 16, 159–175.
Downloaded by [Bond University] at 18:18 23 January 2015
254
V. Bitsika et al.
Bray, S.R., & Born, H.A. (2004). Transition to university and vigorous physical activity: Implications for health and psychological well-being. Journal of American College Health, 52, 181–188. Carskadon, M.A., Acebo, C., & Jenni, O.G. (2004). Regulation of adolescent sleep: Implications for behavior. Annals of the New York Academy of Sciences, 1021, 276–291. Chan, W.H.C., Chang, A.M., Leung, S.F., & Mak, S.S.S. (2007). Reducing breathlessness, fatigue and anxiety in Chinese patients undergoing lung cancer radiotherapy in Hong Kong. Hong Kong Medicine Journal, 13, S4–7. Chen, M.K. (1986). The epidemiology of self-perceived fatigue among adults. Preventative Medicine, 15, 74–81. Christensen, T., Hjortso, N.C., Mortensen, E., Riis-Hansen, M., & Kehlet, H. (1986). Fatigue and anxiety in surgical patients. Acta Psychiatrica Scandinavica, 73, 76–79. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Erlbaum. Diaconu, G., & Turecki, G. (2007). Panic disorder and suicidality: Is comorbidity with depression the key? Journal of Affective Disorders, 104, 203–209. Dyrbye, L.N., Thomas, M.R., & Shanafelt, T.D. (2006). Systematic review of depression, anxiety, and other indicators of psychological distress among U.S. and Canadian medical students. Academic Medicine, 81, 354–373. Fischler, B., D’Haenen, H., & Cluydts, R. (1996). Comparison of 99m Tc HMPAO SPECT scan between chronic fatigue syndrome, major depression and healthy controls: An exploratory study of clinical correlates of regional cerebral blood flow. Neuropsychobiology, 34, 175–183. Huibers, M.J.H., Leone, S.S., Amelsvoort, L.G., Kant, I., & Knottnerus, J.A. (2007). Associations of fatigue and depression among fatigued employees over time: A 4-year follow-up study. Journal of Psychosomatic Research, 63, 137–142. Kaiser, H. (1970). A second generation Little Jiffy. Psychometrika, 35, 401–415. Kohlenberg, R.J., & Tsai, M. (1991). Functional analytic psychotherapy: Creating intense and curative therapeutic relationships. New York: Plenum. Krupp, L.B., LaRocca, N.G., Muir-Nash, J., & Steinberg, A.D. (1989). The fatigue severity scale: Application to patients with multiple sclerosis and systemic lupus erythematosus. Archives of Neurology, 46, 1121–1123. Kitzrow, M.A. (2003). The mental health needs of today’s college student: Challenges and recommendations. NASPA Journal, 41, 167–181. Lacey, K., Zaharia, M.D., Griffiths, J., Ravindran, A.V., Merali, Z., & Amisman, H. (2000). A prospective study of neuroendocrine and immune alterations associated with the stress of an oral academic examination among graduate students. Psychoneuroendocrinology, 25, 339–356. Lavidor, M., Weller, A., & Babkoff, H. (2002). Multidimensional fatigue, somatic symptoms and depression. British Journal of Health Psychology, 7, 67–75. Manu, P., Matthews, D.A., Lane, T.J., Tenneh, H., Hesselbrock, V., Mendola, R., & Affleck, G. (1989). Depression among patients with a chief complaint of chronic fatigue. Journal of Affective Disorders, 17, 165–172. McLennan, J. (1992). ‘University Blues’: Depression among tertiary students during an academic year. British Journal of Guidance and Counselling, 20, 186–192. McMichael, A.J., & Hetzel, B.S. (1974). An epidemiological study of the mental health of Australian university students. International Journal of Epidemiology, 3, 125–134. Merikangas, K., & Angst, J. (1994). Neurasthenia in a longitudinal cohort study of young adults. Psychological Medicine, 24, 1013–1024. Sapolsky, R.M. (2004). Why zebras don’t get ulcers (pp. 291–298). LLC: Henry Holt and Company.
Downloaded by [Bond University] at 18:18 23 January 2015
Counselling Psychology Quarterly
255
Schaefer, A., Brown, J., Watson, C., Plenel, D., DeMotts, J., Howard, M., et al. (1985). Comparison of the validities of the Beck, Zung and MMPI depression scales. Journal of Consulting and Clinical Psychology, 53, 415–418. Schaufeli, W.B., Martinez, I.M, Pinto, A.M., Salanova, M., & Bakker, A.B. (2002). Burnout and engagement in university students. Journal of Cross-Cultural Psychology, 33, 464–481. Scott, L., & O’Hara, M.W. (1993). Self-discrepancies in clinically anxious and depressed university students. Journal of Abnormal Psychology, 102, 282–287. Sharpe, M., & Wilks, D. (2002). ABC of psychological medicine: Fatigue. British Medical Journal, 325, 480–483. Sharpley, C.F., & Rogers, H.J. (1985). Naı¨ ve versus sophisticated item-writers for the assessment of anxiety. Journal of Clinical Psychology, 41, 58–62. Skapinakis, P., Lewis, G., & Mavreas, V. (2004). Temporal relations between unexplained fatigue and depression: Longitudinal data from an international study in primary care. Psychosomatic Medicine, 66, 330–345. Soet, J., & Sevig, T. (2006). Mental health issues facing a diverse sample of college students: Results from the College Student Mental Health Survey. NASPA Journal, 43, 410–431. Surawy, C., Hackman, A., Hawton, K., & Sharpe, M. (1995). Chronic fatigue syndrome: A cognitive approach. Behaviour Research and Therapy, 33, 535–544. Tabachnick, B.G., & Fidell, L.S. (2001). Using multivariate statistics (4th ed.). Boston: Allyn & Bacon. Tanaka, J.S., & Huba, G.J. (1987). Assessing the stability of depression in college students. Multivariate Behavioural Research, 22, 5–19. Tjia, J., Givens, J.L., & Shea, J.A. (2005). Factors associated with undertreatment of medical student depression. Journal of American College Health, 53, 219–224. Van der Linden, G., Chalder, T., Hickie, I., Koschera, A., Sham, P., & Wessely, S. (1999). Fatigue and psychiatric disorder: Different or the same? Psychological Medicine, 29, 863–868. Van Praag, H.M. (2005). Can stress cause depression? World Journal of Biological Psychiatry (supplement), 5–22. Wilson, J. (2001). Adrenal fatigue: The 21st-century stress syndrome. Santa Rosa, CA: Smart Publications. World Health Organization. (1992). The international statistical classification of diseases and related health problems (10th ed.). Geneva, Switzerland: World Health Organization. Zung, W. (1965). A self-rating depression scale. Archives of General Psychiatry, 12, 63–70. Zung, W. (1971). A rating instrument for anxiety disorders. Psychosomatics, 12, 371–379. Zung, W. (1973). From art to science: The diagnosis and treatment of depression. Archives of General Psychiatry, 29, 328–337. Zung, W. (1980). How normal is anxiety? Current concepts (entire edition). Durham, NC: Upjohn.
