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Relationship between sleep disturbance, depression and anxiety in the 12 months following a cardiac event a
ab
ac
Michael R. Le Grande , Alun C. Jackson , Barbara M. Murphy Neil Thomason
&
d
a
Heart Research Centre, The Royal Melbourne Hospital, P.O. Box 2137, Melbourne, Victoria 3050, Australia b
Centre on Behavioural Health, University of Hong Kong, Hong Kong, China
Click for updates
c
Department of Psychology, The University of Melbourne, Melbourne, Australia d
Department of Philosophy, The University of Melbourne, Melbourne, Australia Published online: 11 May 2015.
To cite this article: Michael R. Le Grande, Alun C. Jackson, Barbara M. Murphy & Neil Thomason (2015): Relationship between sleep disturbance, depression and anxiety in the 12 months following a cardiac event, Psychology, Health & Medicine, DOI: 10.1080/13548506.2015.1040032 To link to this article: http://dx.doi.org/10.1080/13548506.2015.1040032
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.
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Psychology, Health & Medicine, 2015 http://dx.doi.org/10.1080/13548506.2015.1040032
Relationship between sleep disturbance, depression and anxiety in the 12 months following a cardiac event
Downloaded by [New York University] at 05:26 25 July 2015
Michael R. Le Grandea* Neil Thomasond
, Alun C. Jacksona,b, Barbara M. Murphya,c and
a Heart Research Centre, The Royal Melbourne Hospital, P.O. Box 2137, Melbourne, Victoria 3050, Australia; bCentre on Behavioural Health, University of Hong Kong, Hong Kong, China; c Department of Psychology, The University of Melbourne, Melbourne, Australia; dDepartment of Philosophy, The University of Melbourne, Melbourne, Australia
(Received 24 November 2014; accepted 1 April 2015) We aimed to assess the prevalence of sleep disturbance in a cardiac patient population over a 12-month period and assess its relationship with treatment adherence, self-efficacy, anxiety and depression. A total of 134 patients consecutively admitted to two Australian hospitals after acute myocardial infarction (31%), or to undergo bypass surgery (29%) or percutaneous coronary intervention (40%) were interviewed at six weeks and four and 12 months. Sleep disturbance was measured using a recode of the Beck Depression Inventory (v.2) item 16. Anxiety and depression were assessed by the Hospital Anxiety and Depression Scale. Sleep disturbance was highly prevalent (69%) at 6 weeks but was not associated with 12-month psychological outcomes. Path analysis revealed that sleep disturbance at 4 months was, however, associated with reduced treatment adherence and self-efficacy, and higher anxiety and depression scores at 12 months. The high prevalence of sleep disturbance in this study and its association with psychological outcomes may have adverse prognostic implications and possibly impede cardiac rehabilitation efforts. Keywords: cardiac patients; sleep patterns; affective disorders; longitudinal study
Introduction In the general population, both short and long sleep duration have been associated with cardiovascular and all-cause mortality (Alcántara, Peacock, Davidson, Hiti, & Edmondson, 2014; Azevedo Da Silva et al., 2014; Ferrie et al., 2007). In patients recovering from cardiac events such as acute myocardial infarction (AMI) or coronary artery bypass graft surgery (CABGS), the reported prevalence of sleep disorders is considerably higher than in the general population (Banack et al., 2014; Ludka, Galkova, Spinar, & Kara, 2012). There is also evidence that sleep disorders are associated with anxiety and depression in cardiac rehabilitation patients (Banack et al., 2014; Hayano et al., 2012). Further, it is known that depression is associated with treatment non-adherence (DiMatteo, Lepper, & Croghan, 2000) and reduced self-efficacy in cardiac patients (Howarter, Bennett, Barber, Gessner, & Clark, 2014; Steca et al., 2013). It is therefore feasible that sleep disorders may directly or indirectly hamper cardiac rehabilitation objectives and patient recovery.
*Corresponding author. Email: [email protected] © 2015 Taylor & Francis
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M.R. Le Grande et al.
The main aim of the current secondary analysis was to report the direct and indirect effects of sleep disturbance upon medication adherence, patient self-efficacy, anxiety and depression and risk of recurrent coronary heart disease (CHD) over the course of 12 months in cardiac patients. Methods Participants
Downloaded by [New York University] at 05:26 25 July 2015
Subjects were patients who had participated in the control arm controlled trial undertaken to assess the effectiveness of programme of cognitive behavioural therapy and motivational behaviours compared with usual medical care (see Murphy
of a larger randomised an eight-week group interviewing on health et al., 2013). Eligible
Study population 3870
Eligible 797
Ineligible 3073
Included 437
Excluded 350
6 weeks 275
Withdrawn 162
Control – 6 weeks 136
Attend 4m 112
Incomplete data 2
Not attend 4mth 22 Incomplete data 1
Attend 12mth 107
Figure 1.
Not attend 12mth 4
Flowchart of participants throughout the study.
Treatment 139
Psychology, Health & Medicine
3
patients for the trial were those consecutively admitted to one of two hospitals after AMI, or to undergo CABGS, or percutaneous coronary intervention (PCI). Figure 1 presents a flowchart of participants throughout the study. Excluded patients were those who had an additional psychiatric illness (n = 58), were unable to attend the programme (n = 80), refused participation (n = 136) or were missed prior to hospital discharge (n = 77). The sample for the current study comprised the 134 of the 136 control group patients who had complete sleep data. Measures
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Assessment of sleep The Beck Depression Inventory (BDI version 2) item 16 was used to classify patients’ sleeping behaviour. This item has 7 responses which were recoded as following: 0 1a 2a 3a 1b 2b 3b
I have not experienced any change in my sleeping pattern (=usual sleeping pattern); I sleep somewhat more than usual or I sleep a lot more than usual or I sleep most of the day (=sleep more than usual); I sleep somewhat less than usual or I sleep a lot less than usual or I wake up 1–2 hours early and can’t get back to sleep (=sleep less than usual);
Responses that indicated either less sleep or more sleep than usual were coded as having a sleep disturbance. A similar method of classifying sleep disturbance based on the BDI (version 1) has been undertaken with college students (Nyer et al., 2013). Socio-demographic, medical and behavioural data Patients’ socio-demographic details were collected at baseline and included age, gender, partner status, country of birth, main language spoken at home, employment status and current or last occupation. Medical data were obtained from patients’ medical records and included diagnosis, BMI, history of high cholesterol, history of hypertension, history of diabetes, positive family history of CVD, smoking status and previous AMI. Medication adherence was assessed by the 5-item Medication Adherence Report Scale (Horne & Weinman, 1999). Two year risk for recurrent CHD was calculated at each time point using the Framingham algorithms for men and women with established CVD (D’Agostino et al., 2000). Self-efficacy was measured using the Rohrbaugh Self-efficacy Scale (Rohrbaugh et al., 2004). Anxiety and depression were assessed using the 14-item Hospital Anxiety and Depression Scale (HADS) (Zigmond & Snaith, 1983). The HADS depression scale was used in preference to the BDI v2 because it is independent from the sleep measure which was derived from an item of the BDI v2. Analyses Descriptive statistics, including means and standard deviations, were calculated for continuous variables. Categorical variables are expressed in contingency tables including frequency distribution (n) and proportion (%) and were compared using chi-square
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statistics or Fisher’s exact test. The significance level was set at p < .05 for all analyses. All data were analysed using SPSS version 20 (IBM Corp, 2011). The explanatory model was tested through path analysis. This procedure is an extension of multiple regression and depicts the direct dependencies among a set of variables while considering all relationships simultaneously in the model. Dependent variables in the model included these 12-month outcomes: Two-year risk for recurrent CHD, HADS anxiety and depression scores and self-efficacy. Explanatory variables included these measures assessed at 4 months: Presence or absence of a sleep disorder and medication adherence. In addition, possible confounding variables age, sex and diagnosis were included in the model. Path analysis was performed with Mplus (version 7, Los Angeles, CA, USA). The fit of the models was assessed using different indexes: The comparative fit index and the root mean square error of approximation (RMSEA). A good model fit is indicated by a comparative fit index ≥0.90 (Hu & Bentler, 1999) and values of RMSEA close to 0 (Browne & Cudeck, 1993). Results Patients’ characteristics Patients’ ages ranged from 35 to 75 years, with a mean (SD) of 59.9 (9.3) years. Other patient characteristics are shown in Table 1. Significant differences were found between the 107 patients who completed the study at 12 months and the 27 participants who withdrew from the study by 12 months. Those who withdrew were more likely to be younger (age < 60) (χ2 = 5.90, n = 134, df = 1, p = 0.015), have no private health insurance (χ2 = 16.1, n = 117, df = 1, p < 0.001), have a manual occupation (χ2 = 7.98, n = 132, df = 1, p = 0.005), have a history of depression (χ2 = 5.73, n = 116, df = 1, p = 0.017) and have a history of diabetes (χ2 = 13.75, n = 125, df = 1, p < 0.001). There were no significant difference in proportion of sleep disturbance reported at 6 weeks (χ2 = 0.011, n = 134, df = 1, p = 0.918). Predictors of clinical anxiety and depression at 12 months Sleep disturbance at 6 weeks was not associated with anxiety (χ2 = 0.36, df = 1, p = 0.546) or depression at 12 months (χ2 = 2.66, df = 1, p = 0.103). Presence of a sleep disturbance at 4 months was associated with both anxiety (χ2 = 3.94, df = 1, p = 0.039) and depression (χ2 = 6.27, df = 1, p = 0.012) at 12 months. Three quarters of patients who were anxious at 12 months (n = 29) had been sleep disturbed at 4 months, while 90% of depressed patients at 12 months (n = 11) had been sleep disturbed at 4 months. Path analysis A path analytic model showing the significant (p < 0.05) associations between predictor variables at 4 months and outcome variables at 12 months is shown in Figure 2. Sleep disturbance was negatively associated with medication adherence and was also directly associated with lower self-efficacy and higher anxiety and depression scores at 12 months. Self-efficacy at 12 months which was partly determined by sleep disturbance, anxiety and depression was associated with lower risk scores for recurrent CHD. All model fit statistics indicated an excellent fit to the data (CFI = 1.00, TLI = 1.00,
Psychology, Health & Medicine Table 1.
Socio-demographic and medical characteristics.
Characteristics at 6 weeks (n = 134) Variables
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Socio-demographic Male gender Age ˃ 60 years Event type AMI CABGS PCI Lives alone Married/de facto Private health insurance Australian born In workforce Non-manual occupation Education > year 10 Clinical HADS depressed HADS anxious History of depression Family history CVD History of diabetes Total cholesterol above cut-off 6-min walk test above cut-off Body mass index ≥30 Waist girth above cut-off Current smoker
n
%
113 72
84 54
41 40 53 19 97 80 88 44 88 96
31 30 39 14 72 60 66 33 66 72
19 34 10 66 31 40 51 57 90 26
14 25 8 49 23 30 38 43 67 19
Notes: AMI: Acute myocardial infarction; CABGS: coronary artery bypass graft surgery; PCI: percutaneous coronary intervention; HADS: hospital anxiety and depression scale; CVD: cardiovascular disease.
SRMR = 0.01, RMSEA = 0.00), and the model explained 44% of the variation in the outcome variables. Discussion At 6 weeks post-hospitalisation, over two-thirds of our sample reported some form of sleep disturbance. This early sleep disturbance was not predictive of 12 months psychological outcomes. Our analyses indicated that the presence of a sleep disturbance at 4 months, however, could have a potential detrimental effect on cardiac rehabilitation efforts via reduction of treatment adherence, lower self-efficacy, and increased depression and anxiety. It is already known that cardiac patients with sleep disturbances have daytime sleepiness, fatigue and, as a result, reduced physical activity and find it more difficult to lose weight than those without a sleep disorder (Skobel et al., 2014). Our analysis which controlled for age, sex and type of cardiac patient (CABGS, AMI or PCI) suggests that these pathways resulting from earlier sleep disturbance may potentially result in lower patient self-efficacy and increased risk of further heart problems at 12 months following the initial cardiac event.
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Figure 2. Path diagram showing the relationships and effects of 4-month sleep disturbance, medication adherence and age on 12-month HADS depression and anxiety, self-efficacy and 2 year risk of recurrent CHD. Numbers represent standardised coefficients. Statistical significant path coefficients (p < 0.05) are represented. Analysis was conducted on patients who had sleep data available at 4 months and outcomes data available at 12 months (n = 107).
The present findings should be interpreted in the light of some limitations. Analysis of dropouts revealed that our 12-month sample may have varied from typical cardiac rehabilitation populations in a number of ways, e.g. less likely to have a history of depression. There was no difference in sleep disturbance patterns between dropouts and completers. This was a small sample restricted to the control group, so the study was underpowered to examine some covariates due to small numbers. Sleep disturbances in this study were self-reported. It is possible that respondents may have interpreted the response ‘I have not experienced any change in my sleeping pattern’ as a continuation of disturbed sleep rather than a proxy of a normal (i.e. healthy) sleeping pattern. It is recognised that standardised sleep instruments such as the Berlin Questionnaire (Netzer, Stoohs, Netzer, Clark, & Strohl, 1999) or objective measures such as portable home sleep monitoring or polysomnography are superior instruments for accurately measuring sleep disorders.
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In summary, this study found that sleep disturbance, whether it be short sleep or long sleep, is highly prevalent in a cardiac patient population. The persistence of self-reported sleep disturbance and its impact upon treatment adherence, self-efficacy and psychological outcomes over 12 months should be of some concern to cardiac rehabilitation efforts. It is not surprising that many authors are now advocating routine screening for sleep disturbances (Banack et al., 2014; Kauta, Keenan, Goldberg, & Schwab, 2014; Nunes et al., 2014) during the early recovery phase for most cardiac patients. Disclosure statement Potential and actual conflict of interest: none declared.
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ORCID Michael R. Le Grande
http://orcid.org/0000-0001-5902-6131
References Alcántara, C., Peacock, J., Davidson, K. W., Hiti, D., & Edmondson, D. (2014). The association of short sleep after acute coronary syndrome with recurrent cardiac events and mortality. International Journal of Cardiology, 171, e11–e12. Azevedo Da Silva, M., Singh-Manoux, A., Shipley, M. J., Vahtera, J., Brunner, E. J., Ferrie, J. E., … Nabi, H. (2014). Sleep duration and sleep disturbances partly explain the association between depressive symptoms and cardiovascular mortality: The Whitehall II cohort study. Journal of Sleep Research, 23, 94–97. Banack, H. R., Holly, C. D., Lowensteyn, I., Masse, L., Marchand, S., Grover, S. A., & Da Costa, D. (2014). The association between sleep disturbance, depressive symptoms, and healthrelated quality of life among cardiac rehabilitation participants. Journal of Cardiopulmonary Rehabilitation and Prevention, 34, 188–194. Browne, M. W., & Cudeck, C. (1993). Alternative ways of assessing model fit. Sociological Methods & Research, 21, 230–258. D’Agostino R. B., Russell M. W., Huse D. M., Ellison, R. C., Silbershatz, H., Wilson, P. W., & Hartz, S. C. (2000). Primary and subsequent coronary risk appraisal: New results from the Framingham study. American Heart Journal, 139, 272–281. DiMatteo, M., Lepper, H. S., & Croghan, T. W. (2000). Depression is a risk factor for noncompliance with medical treatment. Archives of Internal Medicine, 160, 2101–2107. Ferrie, J. E., Shipley, M. J., Cappuccio, F. P., Brunner, E., Miller, M. A., Kumari, M. & Marmot, M. G. (2007). A prospective study of change in sleep duration: Associations with mortality in the Whitehall II cohort. Sleep, 30, 1659–1666. Hayano, J., Carney, R. M., Watanabe, E., Kawai, K., Kodama, I., Stein, P. K., … Blumenthal, J. A. (2012). Interactive associations of depression and sleep apnea with adverse clinical outcomes after acute myocardial infarction. Psychosomatic Medicine, 74, 832–839. Horne, R., & Weinman, J. (1999). Patients’ beliefs about prescribed medicines and their role in adherence to treatment in chronic physical illness. Journal of Psychosomatic Research, 47, 555–567. Howarter, A. D., Bennett, K. K., Barber, C. E., Gessner, S. N., & Clark, J. M. R. (2014). Exercise self-efficacy and symptoms of depression after cardiac rehabilitation. The Journal of Cardiovascular Nursing, 29, 168–177. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1–55. Ibm Corp IBM SPSS Statistics for Windows. (2011). Version 20.0. Armonk, NY: Author. Kauta, S. R., Keenan, B. T., Goldberg, L., & Schwab, R. J. (2014). Diagnosis and treatment of sleep disordered breathing in hospitalized cardiac patients: A reduction in 30-day hospital readmission rates. Journal of clinical sleep medicine, 10, 1051–1059.
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Ludka, O., Galkova, L., Spinar, J., & Kara, T. (2012). Prevalence of sleep apnea in patients after acute myocardial infarction. Circulation, 125, e894. Murphy, B. M., Worcester, M. U., Higgins, R. O., Elliott, P. C., Le Grande, M. R., Mitchell, F., ... Goble, A. J. (2013). Reduction in 2-year recurrent risk score and improved behavioral outcomes after participation in the “Beating Heart Problems” self-management program. Journal of Cardiopulmonary Rehabilitation and Prevention, 33, 220–228. Netzer, N. C., Stoohs, R. A., Netzer, C. M., Clark, K., & Strohl, K. P. (1999). Using the Berlin Questionnaire to identify patients at risk for the sleep apnea syndrome. Annals of Internal Medicine, 131, 485–491. Nunes F. S., Danzi-Soares N. J., Genta P. R., Drager L. F., Cesar L. A., & Lorenzi-Filho G. (2014). Critical evaluation of screening questionnaires for obstructive sleep apnea in patients undergoing coronary artery bypass grafting and abdominal surgery. Sleep Breath, 19, 115–122. Nyer, M., Farabaugh, A., Fehling, K., Soskin, D., Holt, D., Papakostas, G. I., … Mischoulon, D. (2013). Relationship between sleep disturbance and depression, anxiety, and functioning in college students. Depression and Anxiety, 30, 873–880. Rohrbaugh, M. J., Shoham, V., Coyne, J. C., Cranford, J. A., Sonnega, J. S., & Nicklas, J. M. (2004). Beyond the “self” in self-efficacy: Spouse confidence predicts patient survival following heart failure. Journal of Family Psychology, 18, 184–193. Skobel, E., Kamke, W., Bonner, G., Alt, B., Purucker, H. C., Schwaab, B., … Kruger, S. (2014). Risk factors for, and prevalence of, sleep apnoea in cardiac rehabilitation facilities in Germany: The Reha-Sleep registry. European Journal of Preventive Cardiology. doi:10.1177/ 2047487314537916 Steca, P., Greco, A., Monzani, D., Politi, A., Gestra, R., Ferrari, G., … Parati, G. (2013). How does illness severity influence depression, health satisfaction and life satisfaction in patients with cardiovascular disease? The mediating role of illness perception and self-efficacy beliefs. Psychology & Health, 28, 765–783. Zigmond, A. S., & Snaith, R. P. (1983). The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica, 67, 361–370.
Psychology, Health & Medicine Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/cphm20
Relationship between sleep disturbance, depression and anxiety in the 12 months following a cardiac event a
ab
ac
Michael R. Le Grande , Alun C. Jackson , Barbara M. Murphy Neil Thomason
&
d
a
Heart Research Centre, The Royal Melbourne Hospital, P.O. Box 2137, Melbourne, Victoria 3050, Australia b
Centre on Behavioural Health, University of Hong Kong, Hong Kong, China
Click for updates
c
Department of Psychology, The University of Melbourne, Melbourne, Australia d
Department of Philosophy, The University of Melbourne, Melbourne, Australia Published online: 11 May 2015.
To cite this article: Michael R. Le Grande, Alun C. Jackson, Barbara M. Murphy & Neil Thomason (2015): Relationship between sleep disturbance, depression and anxiety in the 12 months following a cardiac event, Psychology, Health & Medicine, DOI: 10.1080/13548506.2015.1040032 To link to this article: http://dx.doi.org/10.1080/13548506.2015.1040032
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.
Downloaded by [New York University] at 05:26 25 July 2015
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 & Conditions of access and use can be found at http://www.tandfonline.com/page/termsand-conditions
Psychology, Health & Medicine, 2015 http://dx.doi.org/10.1080/13548506.2015.1040032
Relationship between sleep disturbance, depression and anxiety in the 12 months following a cardiac event
Downloaded by [New York University] at 05:26 25 July 2015
Michael R. Le Grandea* Neil Thomasond
, Alun C. Jacksona,b, Barbara M. Murphya,c and
a Heart Research Centre, The Royal Melbourne Hospital, P.O. Box 2137, Melbourne, Victoria 3050, Australia; bCentre on Behavioural Health, University of Hong Kong, Hong Kong, China; c Department of Psychology, The University of Melbourne, Melbourne, Australia; dDepartment of Philosophy, The University of Melbourne, Melbourne, Australia
(Received 24 November 2014; accepted 1 April 2015) We aimed to assess the prevalence of sleep disturbance in a cardiac patient population over a 12-month period and assess its relationship with treatment adherence, self-efficacy, anxiety and depression. A total of 134 patients consecutively admitted to two Australian hospitals after acute myocardial infarction (31%), or to undergo bypass surgery (29%) or percutaneous coronary intervention (40%) were interviewed at six weeks and four and 12 months. Sleep disturbance was measured using a recode of the Beck Depression Inventory (v.2) item 16. Anxiety and depression were assessed by the Hospital Anxiety and Depression Scale. Sleep disturbance was highly prevalent (69%) at 6 weeks but was not associated with 12-month psychological outcomes. Path analysis revealed that sleep disturbance at 4 months was, however, associated with reduced treatment adherence and self-efficacy, and higher anxiety and depression scores at 12 months. The high prevalence of sleep disturbance in this study and its association with psychological outcomes may have adverse prognostic implications and possibly impede cardiac rehabilitation efforts. Keywords: cardiac patients; sleep patterns; affective disorders; longitudinal study
Introduction In the general population, both short and long sleep duration have been associated with cardiovascular and all-cause mortality (Alcántara, Peacock, Davidson, Hiti, & Edmondson, 2014; Azevedo Da Silva et al., 2014; Ferrie et al., 2007). In patients recovering from cardiac events such as acute myocardial infarction (AMI) or coronary artery bypass graft surgery (CABGS), the reported prevalence of sleep disorders is considerably higher than in the general population (Banack et al., 2014; Ludka, Galkova, Spinar, & Kara, 2012). There is also evidence that sleep disorders are associated with anxiety and depression in cardiac rehabilitation patients (Banack et al., 2014; Hayano et al., 2012). Further, it is known that depression is associated with treatment non-adherence (DiMatteo, Lepper, & Croghan, 2000) and reduced self-efficacy in cardiac patients (Howarter, Bennett, Barber, Gessner, & Clark, 2014; Steca et al., 2013). It is therefore feasible that sleep disorders may directly or indirectly hamper cardiac rehabilitation objectives and patient recovery.
*Corresponding author. Email: [email protected] © 2015 Taylor & Francis
2
M.R. Le Grande et al.
The main aim of the current secondary analysis was to report the direct and indirect effects of sleep disturbance upon medication adherence, patient self-efficacy, anxiety and depression and risk of recurrent coronary heart disease (CHD) over the course of 12 months in cardiac patients. Methods Participants
Downloaded by [New York University] at 05:26 25 July 2015
Subjects were patients who had participated in the control arm controlled trial undertaken to assess the effectiveness of programme of cognitive behavioural therapy and motivational behaviours compared with usual medical care (see Murphy
of a larger randomised an eight-week group interviewing on health et al., 2013). Eligible
Study population 3870
Eligible 797
Ineligible 3073
Included 437
Excluded 350
6 weeks 275
Withdrawn 162
Control – 6 weeks 136
Attend 4m 112
Incomplete data 2
Not attend 4mth 22 Incomplete data 1
Attend 12mth 107
Figure 1.
Not attend 12mth 4
Flowchart of participants throughout the study.
Treatment 139
Psychology, Health & Medicine
3
patients for the trial were those consecutively admitted to one of two hospitals after AMI, or to undergo CABGS, or percutaneous coronary intervention (PCI). Figure 1 presents a flowchart of participants throughout the study. Excluded patients were those who had an additional psychiatric illness (n = 58), were unable to attend the programme (n = 80), refused participation (n = 136) or were missed prior to hospital discharge (n = 77). The sample for the current study comprised the 134 of the 136 control group patients who had complete sleep data. Measures
Downloaded by [New York University] at 05:26 25 July 2015
Assessment of sleep The Beck Depression Inventory (BDI version 2) item 16 was used to classify patients’ sleeping behaviour. This item has 7 responses which were recoded as following: 0 1a 2a 3a 1b 2b 3b
I have not experienced any change in my sleeping pattern (=usual sleeping pattern); I sleep somewhat more than usual or I sleep a lot more than usual or I sleep most of the day (=sleep more than usual); I sleep somewhat less than usual or I sleep a lot less than usual or I wake up 1–2 hours early and can’t get back to sleep (=sleep less than usual);
Responses that indicated either less sleep or more sleep than usual were coded as having a sleep disturbance. A similar method of classifying sleep disturbance based on the BDI (version 1) has been undertaken with college students (Nyer et al., 2013). Socio-demographic, medical and behavioural data Patients’ socio-demographic details were collected at baseline and included age, gender, partner status, country of birth, main language spoken at home, employment status and current or last occupation. Medical data were obtained from patients’ medical records and included diagnosis, BMI, history of high cholesterol, history of hypertension, history of diabetes, positive family history of CVD, smoking status and previous AMI. Medication adherence was assessed by the 5-item Medication Adherence Report Scale (Horne & Weinman, 1999). Two year risk for recurrent CHD was calculated at each time point using the Framingham algorithms for men and women with established CVD (D’Agostino et al., 2000). Self-efficacy was measured using the Rohrbaugh Self-efficacy Scale (Rohrbaugh et al., 2004). Anxiety and depression were assessed using the 14-item Hospital Anxiety and Depression Scale (HADS) (Zigmond & Snaith, 1983). The HADS depression scale was used in preference to the BDI v2 because it is independent from the sleep measure which was derived from an item of the BDI v2. Analyses Descriptive statistics, including means and standard deviations, were calculated for continuous variables. Categorical variables are expressed in contingency tables including frequency distribution (n) and proportion (%) and were compared using chi-square
Downloaded by [New York University] at 05:26 25 July 2015
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statistics or Fisher’s exact test. The significance level was set at p < .05 for all analyses. All data were analysed using SPSS version 20 (IBM Corp, 2011). The explanatory model was tested through path analysis. This procedure is an extension of multiple regression and depicts the direct dependencies among a set of variables while considering all relationships simultaneously in the model. Dependent variables in the model included these 12-month outcomes: Two-year risk for recurrent CHD, HADS anxiety and depression scores and self-efficacy. Explanatory variables included these measures assessed at 4 months: Presence or absence of a sleep disorder and medication adherence. In addition, possible confounding variables age, sex and diagnosis were included in the model. Path analysis was performed with Mplus (version 7, Los Angeles, CA, USA). The fit of the models was assessed using different indexes: The comparative fit index and the root mean square error of approximation (RMSEA). A good model fit is indicated by a comparative fit index ≥0.90 (Hu & Bentler, 1999) and values of RMSEA close to 0 (Browne & Cudeck, 1993). Results Patients’ characteristics Patients’ ages ranged from 35 to 75 years, with a mean (SD) of 59.9 (9.3) years. Other patient characteristics are shown in Table 1. Significant differences were found between the 107 patients who completed the study at 12 months and the 27 participants who withdrew from the study by 12 months. Those who withdrew were more likely to be younger (age < 60) (χ2 = 5.90, n = 134, df = 1, p = 0.015), have no private health insurance (χ2 = 16.1, n = 117, df = 1, p < 0.001), have a manual occupation (χ2 = 7.98, n = 132, df = 1, p = 0.005), have a history of depression (χ2 = 5.73, n = 116, df = 1, p = 0.017) and have a history of diabetes (χ2 = 13.75, n = 125, df = 1, p < 0.001). There were no significant difference in proportion of sleep disturbance reported at 6 weeks (χ2 = 0.011, n = 134, df = 1, p = 0.918). Predictors of clinical anxiety and depression at 12 months Sleep disturbance at 6 weeks was not associated with anxiety (χ2 = 0.36, df = 1, p = 0.546) or depression at 12 months (χ2 = 2.66, df = 1, p = 0.103). Presence of a sleep disturbance at 4 months was associated with both anxiety (χ2 = 3.94, df = 1, p = 0.039) and depression (χ2 = 6.27, df = 1, p = 0.012) at 12 months. Three quarters of patients who were anxious at 12 months (n = 29) had been sleep disturbed at 4 months, while 90% of depressed patients at 12 months (n = 11) had been sleep disturbed at 4 months. Path analysis A path analytic model showing the significant (p < 0.05) associations between predictor variables at 4 months and outcome variables at 12 months is shown in Figure 2. Sleep disturbance was negatively associated with medication adherence and was also directly associated with lower self-efficacy and higher anxiety and depression scores at 12 months. Self-efficacy at 12 months which was partly determined by sleep disturbance, anxiety and depression was associated with lower risk scores for recurrent CHD. All model fit statistics indicated an excellent fit to the data (CFI = 1.00, TLI = 1.00,
Psychology, Health & Medicine Table 1.
Socio-demographic and medical characteristics.
Characteristics at 6 weeks (n = 134) Variables
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Socio-demographic Male gender Age ˃ 60 years Event type AMI CABGS PCI Lives alone Married/de facto Private health insurance Australian born In workforce Non-manual occupation Education > year 10 Clinical HADS depressed HADS anxious History of depression Family history CVD History of diabetes Total cholesterol above cut-off 6-min walk test above cut-off Body mass index ≥30 Waist girth above cut-off Current smoker
n
%
113 72
84 54
41 40 53 19 97 80 88 44 88 96
31 30 39 14 72 60 66 33 66 72
19 34 10 66 31 40 51 57 90 26
14 25 8 49 23 30 38 43 67 19
Notes: AMI: Acute myocardial infarction; CABGS: coronary artery bypass graft surgery; PCI: percutaneous coronary intervention; HADS: hospital anxiety and depression scale; CVD: cardiovascular disease.
SRMR = 0.01, RMSEA = 0.00), and the model explained 44% of the variation in the outcome variables. Discussion At 6 weeks post-hospitalisation, over two-thirds of our sample reported some form of sleep disturbance. This early sleep disturbance was not predictive of 12 months psychological outcomes. Our analyses indicated that the presence of a sleep disturbance at 4 months, however, could have a potential detrimental effect on cardiac rehabilitation efforts via reduction of treatment adherence, lower self-efficacy, and increased depression and anxiety. It is already known that cardiac patients with sleep disturbances have daytime sleepiness, fatigue and, as a result, reduced physical activity and find it more difficult to lose weight than those without a sleep disorder (Skobel et al., 2014). Our analysis which controlled for age, sex and type of cardiac patient (CABGS, AMI or PCI) suggests that these pathways resulting from earlier sleep disturbance may potentially result in lower patient self-efficacy and increased risk of further heart problems at 12 months following the initial cardiac event.
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Figure 2. Path diagram showing the relationships and effects of 4-month sleep disturbance, medication adherence and age on 12-month HADS depression and anxiety, self-efficacy and 2 year risk of recurrent CHD. Numbers represent standardised coefficients. Statistical significant path coefficients (p < 0.05) are represented. Analysis was conducted on patients who had sleep data available at 4 months and outcomes data available at 12 months (n = 107).
The present findings should be interpreted in the light of some limitations. Analysis of dropouts revealed that our 12-month sample may have varied from typical cardiac rehabilitation populations in a number of ways, e.g. less likely to have a history of depression. There was no difference in sleep disturbance patterns between dropouts and completers. This was a small sample restricted to the control group, so the study was underpowered to examine some covariates due to small numbers. Sleep disturbances in this study were self-reported. It is possible that respondents may have interpreted the response ‘I have not experienced any change in my sleeping pattern’ as a continuation of disturbed sleep rather than a proxy of a normal (i.e. healthy) sleeping pattern. It is recognised that standardised sleep instruments such as the Berlin Questionnaire (Netzer, Stoohs, Netzer, Clark, & Strohl, 1999) or objective measures such as portable home sleep monitoring or polysomnography are superior instruments for accurately measuring sleep disorders.
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In summary, this study found that sleep disturbance, whether it be short sleep or long sleep, is highly prevalent in a cardiac patient population. The persistence of self-reported sleep disturbance and its impact upon treatment adherence, self-efficacy and psychological outcomes over 12 months should be of some concern to cardiac rehabilitation efforts. It is not surprising that many authors are now advocating routine screening for sleep disturbances (Banack et al., 2014; Kauta, Keenan, Goldberg, & Schwab, 2014; Nunes et al., 2014) during the early recovery phase for most cardiac patients. Disclosure statement Potential and actual conflict of interest: none declared.
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ORCID Michael R. Le Grande
http://orcid.org/0000-0001-5902-6131
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