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Neuropsychological

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Depressive symptoms in acute stroke: A cross-sectional

study of their association with sociodemographics and clinical factors

Siren E. Kouwenhoven ^a^b , Caryl L. Gay ^c^d , Linda N.

Bakken ^b^e & Anners Lerdal ^a^d

a Department of Nursing Science , University of Oslo , Oslo , Norway

b Department of Health Sciences , Buskerud University College , Drammen , Norway

c Department of Family Health Care Nursing , University of California , San Francisco , CA , USA

d Department of Research , Lovisenberg Diakonale Hospital , Oslo , Norway

e Department of Behavioural Sciences in Medicine, Faculty of Medicine , University of Oslo , Norway Published online: 31 May 2013.

To cite this article: Siren E. Kouwenhoven , Caryl L. Gay , Linda N. Bakken & Anners Lerdal (2013): Depressive symptoms in acute stroke: A cross-sectional study of their association with sociodemographics and clinical factors, Neuropsychological Rehabilitation: An International Journal, DOI:10.1080/09602011.2013.801778 To link to this article: http://dx.doi.org/10.1080/09602011.2013.801778

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Depressive symptoms in acute stroke: A cross- sectional study of their association with

sociodemographics and clinical factors

Siren E. Kouwenhoven^1,2, Caryl L. Gay^3,4, Linda N. Bakken^2,5, and Anners Lerdal^1,4

1Department of Nursing Science, University of Oslo, Oslo, Norway

2Department of Health Sciences, Buskerud University College, Drammen, Norway

3Department of Family Health Care Nursing, University of California, San Francisco, CA, USA

4Department of Research, Lovisenberg Diakonale Hospital, Oslo, Norway

5Department of Behavioural Sciences in Medicine, Faculty of Medicine, University of Oslo, Norway

The aim of the study was to estimate the prevalence of post-stroke depression (PSD) in the acute phase following first-ever stroke, and to identify its sociodemographic and clinical correlates. Data were collected in a cross-sectional correlational study from face-to-face interviews using structured questionnaires and patients’ medical records. The sample consisted of 109 patients with first-ever stroke. Depressive symptoms after stroke were measured with Beck Depression Inventory II. Mild, moderate or severe depressive symptoms were reported by 27% of the participants. PSD was uniquely associated with

Correspondence should be addressed to Siren E. Kouwenhoven, PhD, Department of Nursing Science, University of Oslo, Postboks 1130 Blindern, 0318 Oslo, Norway. Email:

[email protected]

This paper stems from the research project on post-stroke fatigue for which Hesook Suzie Kim is the project director and Grethe Eilertsen, Anners Lerdal, and Heidi Ormstad are the prin- cipal researchers. This study is funded by the Research Council of Norway and Buskerud Uni- versity College. The authors acknowledge the support and assistance provided by various staff members of Buskerud Hospital in Drammen and Aker Hospital in Oslo, Norway in carrying out this research project, especially Gunn Pedersen who worked as a research assistant and partici- pated in data collection.

#2013 Taylor & Francis

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post-stroke fatigue, sleep latency and sleep disturbance. Patients with PSD also reported slightly more bodily pain. Stroke type, stroke location, and the sociodemographic characteristics we examined were unrelated to PSD. Further research is needed to assess the role sleep changes, fatigue and bodily pain might have in relation to depression in the acute phase after stroke.

Keywords:Stroke; Depression; Fatigue; Sleep; Pain; Acute phase.

INTRODUCTION

Post-stroke depression (PSD) is considered to be one of the most prevalent and serious emotional phenomena after stroke (Gaete & Bogousslavsky, 2008; Hackett, Yapa, Parag, & Anderson, 2005; Robinson, 2006). PSD may complicate and delay stroke rehabilitation and signals a poorer outcome and increased functional dependence compared to non-depressed stroke survivors (Gainotti, Antonucci, Marra, & Paolucci, 2001; Hadidi, Treat-Jacobson, & Lindquist, 2009; van de Weg, Kuik, & Lankhorst, 1999). PSD may also become a chronic condition and is associated with suicidal thoughts and mortality (Brønnum-Hansen, Davidsen, & Thorvaldsen, 2001; House, Anderson, & Hackett, 2001; Pohjasvaara, Vataja, Leppavuori, Kaste, & Erkinjuntti, 2001). Several studies have found that stroke survivors suffering from PSD at an early stage are particularly vulnerable to chronic depression (Berg, Palomaki, Lehtihalmes, Lonnqvist, & Kaste, 2003;

Townend et al., 2007), reduced possibility of achieving independence in activities of daily living (ADL) (Lai, Duncan, Keighley, & Johnson, 2002), ineffective use of rehabilitation services, and longer stays at the rehabilitation unit (Gillen, Tennen, McKee, Gernert-Dott, & Affleck, 2001).

The term “post-stroke depression” can refer to both depressive symptoms measured with different screening tools, and depressive disorder diagnosed according to the standardised diagnostic criteria (DSM/ICD) (Hackett et al., 2005; Kouwenhoven, Kirkevold, Engedal, & Kim, 2011). One benefit of focusing on depressive symptoms rather than diagnosed depressive disorders is the ability to examine depression on a continuum of severity, including sub- clinical levels of depression. The aetiology of PSD has been explained in two ways: (1) as a pathophysiological mechanism related to the brain injury, particularly its site and location, and (2) as a psychological reaction to loss and social and psychological changes following stroke (Bhogal, Teasell, Foley, &

Speechley, 2004; Carson et al., 2000). Whyte and Mulsant support both explanations and emphasise that PSD is a multifactorial phenomenon (Whyte & Mulsant, 2002).

Estimates of the prevalence of post-stroke depression in the acute phase vary considerably across studies. However, most studies find the prevalence

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of PSD to be 20 – 40% during the months after stroke (Gaete & Bogous- slavsky, 2008; Kouwenhoven et al., 2011). In a systematic review of observational studies, Hackett and others estimated the frequency of depressive symptoms to be 33% in the first five years following stroke. However, they described variations in the pooled frequency when studies were grouped by method of mood assessment (Hackett et al., 2005). Several longitudinal studies have found the incidence of PSD to be higher within 1 month than at later stages during the first year after stroke (Leentjens, Aben, Lodder, &

Verhey, 2006; Morrison, Pollard, Johnston, & MacWalter, 2005; Niederma- ier, Bohrer, Schulte, Schlattmann, & Heuser, 2004).

PSD has been associated with different factors such as stroke-related consequences including functional or cognitive impairment, and stroke severity (Hackett & Anderson, 2005; Johnson, Minarik, Nystrom, Bautista, &

Gorman, 2006; Ouimet, Primeau, & Cole, 2001), pain, particularly stomach pain and bodily discomfort (Lee, Tang, Yu, & Cheung, 2007), past history of depression or other personal psychiatric history, living alone and being socially isolated (Ouimet et al., 2001). Several studies have investigated a possible association between PSD in the acute phase and sociodemographics such as age, gender, level of formal education, cohabitation status and person- ality factors, but the findings are ambiguous (Kouwenhoven et al., 2011).

Others have shown that depressive symptoms are related to fatigue after stroke (Carlsson, Moller, & Blomstrand, 2003; de Coster, Leentjens, Lodder, & Verhey, 2005; Lerdal et al., 2011; van der Werf, van den Broek, Anten, & Bleijenberg, 2001). Studies have also found a relationship between pre-stroke fatigue and fatigue after stroke, and therefore, to avoid confounding these related constructs, it has been recommended that future studies of post-stroke fatigue control for pre-stroke fatigue levels (Choi- Kwon, Han, Kwon, & Kim, 2005; Lerdal et al., 2011).

The association between sleep changes and depression (Beck & Steer, 1996; Kellermann et al., 1999; Williams, Weinberger, Harris, Biller, &

Clark, 1999), as well as between sleep changes and stroke (House et al., 1991; House, Dennis, Warlow, Hawton, & Molyneux, 1990) is well known.

Depression has been most strongly associated with sleep – wake disturbances, with a sleep problem such as insomnia or hypersomnia being one of the symptom categories for depressive disorder (Berg, Palomaki, Lehtihalmes, Lonnqvist, & Kaste, 2001; Gonzalez-Torrecillas, Mendlewicz, & Lobo, 1995). Sleep problems also constitute a key part of many screening tools for depression (Beck & Steer, 1971; Chalder et al., 1993; Krupp, LaRocca, Muir-Nash, & Steinberg, 1989). Sleep changes are frequent among stroke survivors, and stroke can affect sleep in several ways (House et al., 1990). Some studies have found a higher prevalence of sleep disturbance such as sleep breathing disorders (Lerdal et al., 2009; Naess, Waje-Andreassen, Thomas- sen, Nyland, & Myhr, 2006) and sleep – wake disturbance (Kleinman et al.,

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2000; Schepers, Visser-Meily, Ketelaar, & Lindeman, 2006; van de Port, Kwakkel, Schepers, Heinemans, & Lindeman, 2007) in stroke survivors compared to the general population. The findings of numerous awakenings, a high percentage of wake time, and a considerably variation of sleep time in the results of Bakken and colleagues, indicate that either hypersomnia or insomnia may occur in the acute phase after first-ever stroke (Roelcke et al., 1997).

Palomaki and others found that after the acute phase depression is related to insomnia (Palomaki et al., 2003). To our knowledge, only one study has investigated the association between sleep difficulties and depression during the acute period after stroke (Lerdal & Kottorp, 2011).

Building on this previous research, the aim of this study was to estimate the prevalence of PSD during the acute phase following first-ever stroke and to identify its sociodemographic and clinical correlates. The specific research questions were:

. What is the prevalence of depressive symptoms in the first two weeks after a first-ever stroke?

. What are the sociodemographic factors (age, gender, education, employment, and partner status) associated with PSD in the acute phase?

. Do depressive symptoms in the acute phase following stroke differ by stroke characteristics (lesion location or stroke type)?

. What other clinical characteristics (fatigue, sleep disturbance, physical functioning, bodily pain, and functional ability to perform activities of daily living) are associated with PSD in the acute phase?

METHODS Sample and procedures

Patients with first-ever clinical presentation of stroke admitted to one of two hospitals in Eastern Norway were invited to participate in a longitudinal fatigue study in 2007 and 2008. Data were collected from medical records and standardised interviews using validated questionnaires within 15 days of hospitalisation for first-ever stroke. Detailed procedures for recruitment and inclusion have been previously reported (Lerdal et al., 2011). Of 152 eli- gible patients, 109 (72%) consented and had sufficient data for this analysis.

Measures

Depressive Symptoms. The Beck Depression Inventory II (BDI-II; Beck

& Steer, 1996) was used to measure depressive symptom severity, with inter- viewer assistance when needed. The BDI-II is a well-known and frequently

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used questionnaire for measuring depressive symptoms in stroke patients (Carson et al., 2000; Hackett & Anderson, 2005; Kouwenhoven et al., 2011), and several studies have used the original BDI or BDI-II as a screening tool in the acute phase after stroke (Berg et al., 2001, 2003; Gonzalez-Torre- cillas et al., 1995; House et al., 1990, 1991; Kellermann et al., 1999; Leentjens et al., 2006; Williams et al., 1999). The BDI-II consists of 21 groups of four statements listed in order of symptom severity (0 – 3), and patients select one from each group. Scores range from 0 to 63, with higher scores indicating greater severity of depressive symptoms. Scores of 0 to 13 indicate no depression (minimal depressive symptoms), 14 – 19 mild depression, 20 – 28 moderate depression, and 29 – 63 severe depression (Beck & Steer, 1971, 1996). The Cronbach’sacoefficient for this sample was .86.

Sociodemographic variables. Data on age (years), gender, and cohabitation status (married/living with a partner or not) were collected from the patient’s medical record, while data on level of formal education (less than 12 years versus 12 years or more) and work status were collected from a questionnaire. Those reporting paid work or self-employment were categorised as employed; full-time homemakers and those on disability or old age pension were categorised as not employed.

Stroke-related variables. The type of stroke, lesion location, and date of hospital admission were obtained from the patient’s medical records.

Pre-stroke fatigue. Pre-stroke fatigue was assessed retrospectively using two items from the Fatigue Questionnaire (Chalder et al., 1993) “Did you experience fatigue before you had your stroke?” (yes/no) and if yes, “How long did you experience fatigue?” (, 1 week,,3 months, 3 – 6 months and.6 months). Patients who reported fatigue lasting longer than 3 months before the stroke were classified as having pre-stroke fatigue.

Post-stroke fatigue. Fatigue during the acute phase following stroke was assessed using the Fatigue Severity Scale (FSS; Krupp et al., 1989). The 9-item FSS is one of the most frequently used fatigue instruments in stroke populations (Lerdal et al., 2009; Naess et al., 2006; Schepers et al., 2006;

van de Port et al., 2007) and has well-established validity and reliability among patients with chronic illness (Kleinman et al., 2000; Krupp et al., 1989; Roelcke et al., 1997). A 7-item version (FSS-7) with better psychometric properties (Lerdal & Kottorp, 2011) was used for this study. Patients are asked to respond to statements about their fatigue on a 7-point Likert scale ranging from strongly disagree to strongly agree. A mean score from the items is computed and ranges from 1 to 7, with higher scores indicating

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higher levels of fatigue. The internal consistency of the FSS-7 in the present study was adequate (Cronbachacoefficient¼.88).

Physical functioning and bodily pain. The SF-36 is a questionnaire that consists of 36 items and measures eight dimensions of physical and mental issues related to health-related quality of life (QoL) during the past week.

The raw scores are converted into sub-scales for each of the dimensions ranging from 0 (lowest) to 100 (highest perceived QoL). The SF-36 has demonstrated satisfactory reliability and validity (Loge, Kaasa, Hjermstad,

& Kvien, 1998; Ware, Snow, & Kosinski, 2002) and has been suggested as the preferred instrument for measuring disability in stroke patients (Weimar et al., 2002). Two of the dimensional scales were used in this study: Bodily Pain (BP) and Physical Functioning (PF) (Ware et al., 2002). In the current sample, the Cronbach’s a coefficient for the Physical Functioning Scale and the Bodily Pain Scale was .94 and .85, respectively.

Activities of daily living. Functional ability was assessed with the Per- sonal Activities of Daily Life (ADL – P) Barthel Index (BI; Mahoney &

Barthel, 1965). Patients rated their functional abilities in performing 10 personal activities of daily living, such as eating and bathing. A total score can range from 0 (complete dependence) to 20 (complete independence) (Wade &

Collin, 1988). The Norwegian version of the BI has demonstrated satisfactory validity and reliability in stroke patients (Laake et al., 1995). Cronbach’sa for the BI was .91 in this sample.

Sleep quality. Subjective experience of sleep quality in the previous month was assessed with the 19-item Pittsburgh Sleep Quality Index (PSQI; Buysse, Reynolds, Monk, Berman, & Kupfer, 1989). The PSQI asks respondents how often they experienced sleep-related problems in the past month on a scale of 0 (not during the past month) to 3 (three or more times a week) The PSQI yields global scores that range from 0 to 21. Higher scores indicate poorer sleep quality, and a global score.5 has been found to distinguish good and poor sleepers (Buysse et al., 1989). In addition to the global score, the PSQI yields 7 component scores addressing sleep quality (1 item), sleep latency (2 items), sleep duration (1 item), habitual sleep efficiency (calculation based on 3 items), sleep disturbance (9 items), use of sleeping medication (1 item), and daytime dysfunction (2 items). Com- ponent scores range from 0 to 3, with higher scores indicating greater sleep disturbance. The current study reports both the global score and the 7 component scores as measures of the patient’s subjective sleep experience. The Cronbach’s a coefficient across the 7 component scores was .64 in this sample.

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Statistical analysis

Descriptive statistics were used to summarise sociodemographic and clinical characteristics. Each participant’s depression classification was determined by their BDI-II scores as specified above. Demographic and clinical associations with depression classification were evaluated using Fishers Exact Test or analysis of variance with Scheffe´ post hoc tests. Cohen’s d was used to estimate the clinical significance of mean differences, with values of .50 – .79 being considered moderate effects and values≥.80 being considered strong effects (Cohen, 1992). All sociodemographic and clinical variables associated with depression classification in bivariate analyses were evaluated for inclusion in a multivariable model. Unadjusted associations were determined using simple logistic regression models using each variable to predict mild, moderate, or severe depression (BDI-II score≥14); patients without depression (BDI-II score≤13) served as the reference group.

Pre-stroke fatigue was forced into the model to control for the potentially confounding effect of pre-existing fatigue. All variables significant in the unadjusted models were initially included in the multivariable model, and backwards step-wise regression was used to eliminate variables (except for pre-stroke fatigue) that did not significantly contribute to the model. A two- tailed alpha level of .05 was used for all statistical tests. Data were analysed using SPSS for Windows version 18.0.

Ethics

The study was approved by the Regional Medical Research Committee of Health East of Norway, the Norwegian Data Inspectorate and the hospital approval units for security of personal data. Informed written consent was obtained from all participants.

RESULTS Prevalence of depressive symptoms

Patient sociodemographic characteristics in relation to depression classification in the acute phase are shown in Table 1. The sample of 109 participants had a mean score on the BDI-II of 9.1 (SD7.0, range 0 – 29). Of the 109 participants, 79 (73%) had scores≤13 and were classified as having no depression, 22 (20%) were classified as having mild depression, 5 (5%) as having moderate depression, and the remaining 3 participants as having severe depression. However, given that the last 3 participants each obtained a BDI-II score of 29 (just over the threshold for severe depression) and the small numbers of participants in the moderate and severe classifications,

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these two groups were combined for subsequent analyses. Depression, fatigue and global PSQI scores did not differ between the three interviewers.

Sociodemographic factors

The sample consisted of 66 men (61%) and 43 women (39%). The age range was 29 – 91 years with a mean age of 68.3 years (SD13.1). Fifty-seven (72%) of the participants had less than 12 years of formal education and at the time of the stroke, 25% were employed. Most of the participants lived in paired relationships (75%). None of the sociodemographic characteristics we examined were associated with the severity of depression symptoms during the acute phase after stroke (Table 1).

Stroke characteristics

Stroke characteristics by depression classification are presented in Table 2.

Most of the participants (80%) had suffered an infarction, 6% a haemorrhage, while 14% of the strokes had an unknown cause. With relation to stroke location, the sample was equally distributed between right (38%) and left (36%) hemispheric strokes, and 26% were bilateral strokes. Neither stroke type nor location was found to be related to depressive symptom severity.

However, the number of participants with haemorrhagic stroke was small (n¼7) and therefore the analysis of stroke type lacked sufficient power to detect a significant difference. In addition, the number of participants with an unknown stroke location was nearly 30%, thereby limiting the power of the stroke location analysis. Participants were assessed a mean of 4.47 days

TABLE 1

Patient sociodemographic characteristics by depression classification (N¼109)

Demographic variables

No depression (n¼79)

Mild depression (n¼22)

Moderate/severe

depression(n¼8) p values

Age, mean (SD) 68.6 (11.9) 68.0 (15.5) 66.1 (18.9) .877

Gender,n(%) .381^∗

Male 51 (65%) 11 (50%) 4 (50%)

Female 28 (35%) 11 (50%) 4 (50%)

Education,n(%) .720^∗

,12 years 57 (72%) 17 (77%) 5 (63%)

≥12 years 22 (28%) 5 (23%) 3 (38%)

Work status,n(%) .930

Working 19 (24%) 6 (27%) 2 (25%)

Not working 60 (76%) 16 (73%) 6 (75%)

In paired relationship,n(%) 57 (72%) 13 (59%) 4 (50%) .245^∗

∗Fischer’s Exact Test.

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(SD¼3.18, range 1 – 15 days) after their first-ever stroke. Within this narrow time frame, depression classification was unrelated to the number of days since the stroke occurred.

Other clinical characteristics

Patients with mild or moderate to severe depressive symptoms reported significantly more fatigue (Table 2) and sleep disturbance (Table 3) than those without depression. The mean differences in post-stroke fatigue ratings (FSS-7 scores) between patients in the “no depression” group and the “mild depression” and “moderate/severe depression” groups corresponded to strong effect sizes (Cohen’s d¼.91 and .95, respectively). Patients with moderate/severe depressive symptoms also reported more bodily pain than

TABLE 2

Patient clinical characteristics by depression classification (N¼109)

Clinical characteristics

No depression

Mild depression

Moderate/severe depression

p values

(n¼79) (n¼22) (n¼8)

Stroke characteristics

Stroke type,n(%) .978^a

Infarct 63 (80%) 17 (77%) 7 (88%)

Haemorrhage 5 (6%) 2 (9%) 0 (0%)

Unknown 11 (14%) 3 (14%) 1 (12%)

Location (n¼77),n(%) .188^a

Right hemisphere 22 (40%) 6 (40%) 1 (17%)

Left hemisphere 17 (30%) 6 (40%) 5 (83%)

Bilateral 17 (30%) 3 (20%) 0 (0%)

Pre-stroke factors

Fatigue,n(%) .009^a,b

No 62 (79%) 12 (55%) 3 (38%)

Yes 17 (21%) 10 (45%) 5 (63%)

Post-stroke factors

Fatigue (FSS-7), M (SD) 3.6 (1.4) 4.9 (1.5) 4.9 (1.0) ,.001^b Physical Functioning (SF-36),

M (SD)

62.3 (33.1) 54.3 (30.2) 55.0 (33.6) .535 Bodily Pain (SF-36), M (SD) 78.0 (30.6) 73.0 (30.0) 51.3 (23.4) .057 ADL – P (Barthel Index)^c, M (SD) 17.9 (3.9) 18.4 (3.1) 15.6 (5.4) .224 FSS-7¼Fatigue Severity Scale – 7 item version, scores range 1 – 7, higher scores represent more fatigue; ADL-P¼Activities of Daily Living – Personal, scores range 1 – 20, higher scores represent more independency; SF-36 scores range 1 – 100, higher scores represent better health

aFischer’s Exact Test;

bMild and moderate/severe depression.no depression;

cn¼100.

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those with no depression (Cohen’sd¼.89), although the omnibus test did not reach statistical significance. The mild depression and moderate/severe depression groups also differed from the no depression group with respect to perceived sleep quality (both Cohen’s d¼.78 for PSQI global scores), with sleep latency being the dimension of sleep that most distinguished the groups (Cohen’s d¼.86 and .69, respectively). Physical functioning and ADL abilities were unrelated to depression classification during the acute phase after stroke.

In a multivariable model adjusting for other associated symptoms (Table 4), the only symptoms uniquely associated with a mild, moderate or severe level of depression were post-stroke fatigue, sleep latency (difficulty falling asleep) and sleep disturbance (difficulty maintaining sleep). These three symptoms were associated with depression classification even when controlling for whether the patient had experienced pre-stroke fatigue. The overall multivariable model was significant,x²(4)¼35.60,p,.001, and explained 40.5% of the variance in depression classification. Although controlling for pre-stroke fatigue is recommended when evaluating post-stroke fatigue, excluding pre- stroke fatigue from the model and only retaining significant predictors of depression classification did not alter the strength of the final model,x²(3)¼ 35.37, p,.001; Nagelkerke R²¼.403, or the predictors (post-stroke fatigue OR¼2.35, 95% CI 1.48, 3.74; sleep onset latency OR¼2.07, 95%

CI 1.21, 3.56; sleep disturbance OR¼2.79, 95% CI 1.08, 7.24).

To determine whether the model improved using a more homogeneous sample, the regression analysis was repeated without the seven haemorrhagic

TABLE 3

Patient sleep scores by depression classification (N¼109)

PSQI scores

No depression (n¼79)

Mild depression (n¼22)

Moderate/severe

depression(n¼8) p values

Global score, M (SD) 6.1 (3.3) 8.7 (3.4) 8.8 (5.0) .002^a

Global score.5,n(%) 38 (48%) 19 (86%) 6 (75%) .003^a

Component scores, M (SD)

Subjective sleep quality 0.82 (0.66) 1.18 (0.80) 1.0 (0.7) .097

Sleep latency 1.01 (0.88) 1.81 (1.10) 1.63 (1.06) .001^a

Sleep duration 0.52 (0.81) 0.55 (0.80) 0.63 (0.92) .938

Habitual sleep efficiency 0.86 (1.12) 0.78 (0.87) 1.63 (1.51) .152

Sleep disturbance 1.20 (0.54) 1.59 (0.50) 1.50 (0.76) .010^a

Use of sleep medication 0.77 (1.10) 1.55 (1.37) 0.75 (1.39) .025^a Daytime dysfunction 0.90 (0.69) 1.27 (0.88) 1.63 (1.06) .011^b PSQI¼Pittsburgh Sleep Quality Index, Higher scores represents worse sleep quality; global scores range 1 – 21, and component scores range 0 – 3.

aMild depression.no depression,

bModerate/severe depression.no depression.

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stroke patients. Although the sleep predictors changed slightly (sleep quality was retained instead of sleep disturbance), the overall strength of the model, x²(4)¼33.76, p,.001, Nagelkerke R²¼.410, was similar to the one reported with the full sample in Table 4.

DISCUSSION Prevalence of depressive symptoms

We found the prevalence of mild, moderate or severe depressive symptoms to be 27% during the acute phase (within two weeks) after stroke. Among the studies reporting such statistics, several different observer rated scales and self-report questionnaires are used, each with a different cut-off point.

Thus, Kouwenhoven and colleagues reported that the prevalence across the different measures ranged from 5 to 54% during the acute phase (Kouwenho- ven et al., 2011). The finding in our study is generally consistent with those of other studies screening for acute-phase depressive symptoms with the original BDI. Berg and associates (2001) indicated a prevalence of 27% two weeks after stroke, House and colleagues (1990) reported a prevalence of 32% 1 month after stroke, and Williams and others (1999) found a higher prevalence of 39% at 1 month post-stroke. This fairly consistent pattern of results across

TABLE 4

Logistic regression analysis of symptoms that distinguish patients with mild/moderate/

severe depression (n¼30) from those with no depression (n¼79)

Univariate analysis Multivariate analysis

Symptoms OR 95%CI p OR 95%CI p

Fatigue symptoms

Pre-stroke fatigue 3.65 1.49 – 8.92 .005 1.32 0.43 – 4.06 .627 Post-stroke fatigue 2.18 1.47 – 3.24 ,.001 2.25 1.37 – 3.69 .001 Sleep symptoms

Sleep quality 1.89 1.02 – 3.51 .038

Sleep latency 2.21 1.39 – 3.49 .001 2.04 1.19 – 3.50 .010

Sleep disturbance 3.41 1.50 – 7.76 .003 2.74 1.05 – 7.17 .040

Sleep medication 1.45 1.03 – 2.03 .033

Daytime dysfunction 2.14 1.22 – 3.74 .008

All symptoms identified in univariate analyses as being associated with mild/moderate depressive symptoms were evaluated for inclusion in the multivariable model; only the three significant predictors and the covariate pre-stroke fatigue were retained in the final model. Patients with no depression served as the reference group. Higher symptom scores indicate more fatigue or sleep disturbance. The NagelkerkeR²for the multivariable model was .405.

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studies suggests that approximately one third of patients experiences PSD during the first month after stroke.

Sociodemographic correlates of PSD

As in prior studies we investigated several sociodemographic factors in relation to PSD in the acute phase after stroke. However, in contrast to other studies documenting a relationship between acute phase PSD and factors such as gender (Andersen, Vestergaard, Ingemann-Nielsen, & Laurit- zen, 1995; Leentjens et al., 2006; Paradiso & Robinson, 1998), level of formal education (Caeiro, Ferro, Santos, & Figueira, 2006), age (Berg et al., 2003;

Fure, Wyller, Engedal, & Thommessen, 2006) or living alone (Andersen et al., 1995), we found no statistically significant associations. This could be due to variability in the sample demographic characteristics across different studies or our sample may have been too small to detect subtle associations between PSD and demographic variables.

Stroke characteristics and PSD

The association between the location of the brain lesion and type of stroke, and the development and severity of PSD has been a matter of intense research and continuous controversy (Carson et al., 2000; Hackett & Ander- son, 2005; Whyte & Mulsant, 2002). In a systematic review on PSD and lesion location, Carson and others (2000) stated that there is no association between lesion location and risk of PSD. Our study supports this conclusion.

In addition, we found no relationship between stroke type and the development of depressive symptoms in early phase after stroke, although the small number of patients with haemorrhagic stroke in this sample limited the statistical power for this analysis.

Other clinical factors related to PSD

In the current study, we found that mild, moderate or severe depressive symptoms in the acute phase after stroke were significantly associated with percep- tions of poor sleep quality, sleep disturbance (trouble maintaining sleep), sleep latency (difficulty falling asleep), use of sleep medication and daytime dysfunction. To our knowledge, these associations with PSD have not been previously described. In a prior analysis of this sample, Bakken and others found that half the patients slept more or less than recommended as normal sleep time per night for an adult population, and that as many as 78% of the participants had disturbed sleep with more than nine awakenings per night during the acute phase (Bakken, Lee, Kim, Finset, & Lerdal, 2011).

The current study indicates that these sleep problems may be associated with mood changes as well.

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The relationship between changes in sleep and depression is well-known (Choi-Kwon et al., 2005; Lerdal et al., 2011; van der Werf et al., 2001), and has been defined as bi-directional (Buysse, 2004). On one hand, depression is one of the strongest risk factors for insomnia; on the other hand, current insomnia is also a risk factor for future depression. In a meta- analysis of 21 studies, Baglioni and colleagues found that non-depressed people with insomnia have a two-fold risk for developing depression compared to people with no sleep difficulties. Riemann (2009) stated that insomnia increases the risk of developing subsequent depression, as well as the duration of established depression, and relapse following treatment for depression.

Sleep changes are also frequent among stroke survivors, and stroke can affect sleep in several ways: sleep disturbance can be triggered by stroke, sleep-related breathing disorders are well-recognised risk factors of ischaemic stroke, and having a stroke can aggravate a pre-existing sleep disorder (Alberti, 2012; Das & Khan, 2012). Several studies have found a higher prevalence of sleep disturbance in stroke survivors compared to the general population.

More than 50% of stroke survivors suffer from sleep breathing disorders (Hermann & Bassetti, 2009; Ramar & Surani, 2010), while 20 – 40% have a sleep – wake disturbance (Hermann, Piccoli, & Bassetti, 2003; Leppavuori, Pohjasvaara, Vataja, Kaste, & Erkinjuntti, 2002; Terzoudi et al., 2009).

We also found mild, moderate or severe depressive symptoms were associated with significantly more fatigue. Fatigue has been considered one of the most critical concomitant symptoms associated with PSD. The two symptoms often coexist and share common experiences, which make it difficult to differ- entiate them as independent phenomena (de Coster et al., 2005). It might be difficult (or impossible) to separate the phenomenon of fatigue from depression. PSD could be explained and understood from a biomedical angle, as a psychological reaction or as a multifactorial condition (Aben &

Verhey, 2006; Whyte & Mulsant, 2002). Depression, sleep disturbance and fatigue seem to be strongly connected to each other and this could be seen as support for the multifactorial view of PSD.

Some studies have shown an association between PSD and cognitive impairment or pain in the acute phase after stroke (Hackett & Anderson, 2005; Lee et al., 2007; Ouimet et al., 2001). Other studies have found reduced physical condition, severe disability and physical dependence to be the most prevalent factors associated with depressive symptoms in the acute phase after stroke (Hackett, Anderson, House, & Halteh, 2008;

Hackett, Anderson, House, & Xia, 2008; Kouwenhoven et al., 2011).

Although higher dependence in personal activities at six months post-stroke has been associated with low estimated sleep during the night and less estimated sleep during the day in the acute phase (Bakken, Kim, Finset, &

Lerdal, 2012), levels of ADL-P in the acute phase were not related to

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depressive symptoms. There was a slight association between PSD and bodily pain in our study, but it did not reach statistical significance. Age could be a confounding factor, as the mean age of our sample was 68.3 years. It is well known that there is a higher prevalence of both sleep changes (Crowley, 2011) and depression in older adults (Blazer, 2003; Folkehelseinstituttet, 2010; Lenze et al., 2001) compared to the general population.

An important consideration for future studies is that depression screening tools often include symptoms that are not specific to depression, and may be related to the stroke injury itself. As a result, the standard cutoff scores may not be valid for patients in the acute phase following stroke. For example, many stroke patients report fatigue, sleep disturbance, and concentration difficulties, and these symptoms may be unrelated to depression. However, these symptoms commonly observed in stroke patients can still be indicative of post-stroke depression and thus warrant attention and further evaluation.

Further research is needed to establish valid cutoff scores when screening patients for post-stroke depression.

Study limitations

The results of this study should be considered in light of several study limitations. The BDI-II is a screening tool that includes several symptoms that may be related to the stroke injury rather than to depression. As a result, the prevalence of depressive symptoms may have been overestimated in this sample.

Psychiatric interviews that take into account stroke-related symptoms would have greater validity. Although the differences between the depression groups on several fatigue and sleep measures represent moderate to strong effects, an estimated 60% of the variance in depression classification remains unexplained by the regression model. Additional research is needed to identify other factors which explain the remaining variance. As mentioned previously, the small number of participants with haemorrhagic strokes and the large number with missing data for stroke location limited the statistical power for the analyses of stroke characteristics. Data on stroke characteristics were collected from the patients’ medical records. A specific diagnostic examination from a validated classification, e.g., the TOAST criteria (Adams et al., 1993; Goldstein et al., 2001), would have obtained more accurate stroke data. We did not screen the patients for obstructive sleep apnoea. Thus we were not able to statistically control for obstructive sleep apnoea as a possible confounder for the reported relationships between sleep symptoms and depressive symptoms. In addition, the study sample contained few patients with severe depressive symptoms and the highest BDI-II score in this sample was 29. This could have been the result of sampling bias (i.e., the more severely depressed patients may have been less likely to participate in the study), limited time for severe depression to

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develop (particularly for those assessed within the first few days following stroke), and/or a true lack of severe depression in this population. Whatever the reason, the study findings are limited to the less severe levels of depression observed in this sample. Finally, information on pre-stroke symptoms was limited. The pre-stroke fatigue measure was dichotomous and measured retrospectively, and may not have been adequate for controlling for pre-existing levels of fatigue. Furthermore, information was not available on pre-stroke levels of depression or sleep disturbance, and therefore we cannot determine whether the depressive symptoms or sleep problems reported in this study were new or ongoing.

Clinical implications

Given the association between mood, sleep, fatigue, and bodily pain, health care professionals should pay attention to stroke patients’ sleep quality, fatigue levels, and experience of pain in the acute phase after stroke. The sleeping conditions at hospitals and rehabilitation units could be improved by minimising night-time lighting, noise, and interruptions by staff. Staff can also facilitate good sleep by varying the patient’s position between supine and side lying. Because pain interferes with sleep and mood, effective pain management should be emphasised. Helping stroke patients manage their daytime activities, including economising strategies, and to experience meaningful daytime activities, in addition to physical exercise, should also be incorporated during the acute phase.

CONCLUSION

Stroke survivors suffering from PSD at an early stage are particularly vulnerable for long-term consequences. It is therefore important to identify associated factors and clinical correlates. Our study found the prevalence of depressive symptoms to be 27%, which is in line with comparable studies. The only symptoms uniquely associated with depression in the acute phase after stroke was post-stroke fatigue, sleep latency and sleep disturbance. To our knowledge, these associations with PSD have not been previously described.

Health care professionals should pay attention to the importance of stroke patients’ sleep quality, fatigue levels, and experience of pain in the acute phase after stroke. Additional knowledge is needed in order to identify stroke survivors at risk for PSD so that depression can be prevented or treated. Further research is needed to assess the role sleep changes, fatigue and bodily pain might have in relation to depression in the acute phase after stroke.

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Manuscript received February 2013 Revised manuscript received May 2013 First published May 2013