Aug 2, 2010 - z Department of Medicine, University of Toronto, Canada ... College Hospital, 76 Grenville Street, 8th Floor, Room 815, Toronto, Ontario, ...
Osteoarthritis and Cartilage 18 (2010) 1365e1371
The multidimensionality of sleep quality and its relationship to fatigue in older adults with painful osteoarthritis G.A. Hawker yzx *, M.R. French y, E.J. Waugh yk, M.A.M. Gignac {#, C. Cheung y, B.J. Murray zyy y Women’s College Research Institute, Women’s College Hospital, Canada z Department of Medicine, University of Toronto, Canada x Department of Health Policy, Management and Evaluation, University of Toronto, Canada k Department of Physical Therapy, University of Toronto, Canada { Toronto Western Research Institute, University Health Network, Canada # Dalla Lana School of Public Health, University of Toronto, Canada yy Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
a r t i c l e i n f o
s u m m a r y
Article history: Received 24 February 2010 Accepted 2 August 2010
Objective: To evaluate subjective sleep quality and its relationship to fatigue in older adults with osteoarthritis (OA). Method: In a community cohort with hip/knee OA, subjective sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) and fatigue was measured by the Profile of Mood States e Fatigue subscale (POMS-F). Correlates of sleep quality and fatigue were determined by standardized interviews including socio-demographics, OA severity (Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) summary score), comorbidity, depression (Center for Epidemiologic Studies Depression Scale, CES-D), stressful life events, daytime napping, symptoms of restless legs syndrome (RLS) and prior sleep disorder diagnoses. Logistic regression examined correlates of poor sleep (PSQI score > 5). Linear regression evaluated the relationship between poor sleep and fatigue, and the effect of napping on this relationship. Results: In 613 respondents, mean age was 78 years, 78% were female, 11% had concomitant fibromyalgia, and 26% had 3þ comorbid conditions. Responses indicated moderate OA severity. Seventy percent reported poor sleep; 25% met criteria for RLS and 6.5% reported a diagnosed sleep disorder. Independent correlates of poor sleep were: greater arthritis severity (adjusted odds ratio (OR) per unit increase in WOMAC score ¼ 1.03, P5 is associated with 98.7% sensitivity and 84.4% specificity to discriminate individuals with insomnia from controls31. We also included three questions recommended for diagnosis of RLS in epidemiologic studies by the International Restless Legs Syndrome Study Group32. An affirmative response to each of: ‘Do you have unpleasant sensations in your legs combined with an urge or need to move your legs?’, ‘Do these feelings or symptoms occur mainly or only at rest and do they improve with movement?’ and ‘Are these feelings or symptoms worse in the evening or night than in the morning?’ is associated with 87.5% sensitivity and 96% specificity for RLS compared to diagnostic interviews32. Average number of days/week with naps and average minimum duration of naps were also assessed. Finally, participants were asked if they had ever received a physician diagnosis of a sleep disorder, and if so, whether they were currently being treated for this condition. Statistical analysis All analyses were performed using the Statistical Analysis System (SAS) Version 9.1 (SAS Institute, Cary, NC). Statistical significance was considered at a two-tailed level of 0.05. Descriptive statistics were performed on all data. Logistic regression was used to determine the correlates of ‘poor sleep’, defined as a PSQI global score >5. Variables of interest were: socio-demographics (age, sex, income); arthritis pain and disability (WOMAC scores); obesity (BMI > 30 kg/m2); comorbidity (self-reported diagnosis of FMS; number of other comorbid conditions e 0e1, 2, 3þ); depressed mood (CES-D scores), positive and negative life events, RLS, and napping (2 and 0 vs 1þ, respectively. Each correlate was examined bivariately; those associated with poor sleep at a P-value 0.10 were then evaluated simultaneously in multivariable regression analysis to determine independent correlates. As WOMAC subscale and summary scores were highly correlated, subsequent multivariable analysis used the WOMAC summary score as the measure of arthritis severity. Due to the known relationship between sleep and FMS, we conducted a sensitivity analysis in which we excluded individuals with co-occurring FMS. T-tests were used to compare mean values for self-reported fatigue for those with vs without ‘poor sleep’. The independent relationship of ‘poor sleep’ to self-reported fatigue was then evaluated using linear regression, controlling for the following previously identified correlates of fatigue: age, sex, arthritis severity (WOMAC summary scores), number of comorbid conditions, FMS, depressed mood, and stressful life events. We then tested for an interaction between poor sleep and regular daytime napping (defined as napping 4 times/week vs less often) to examine whether regular napping moderated the effect of poor sleep on fatigue. We hypothesized that the effect of poor sleep on fatigue
G.A. Hawker et al. / Osteoarthritis and Cartilage 18 (2010) 1365e1371
would be less among regular vs less frequent nappers, suggesting a restorative effect. Ethics The procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (Women’s College Hospital Research Ethics Board, Toronto, Canada) and with the Helsinki Declaration of 1975, as revised in 2000. All subjects gave informed consent to participate in this research. Results Sample characteristics Of the original cohort of 2,411, 805 were deceased, 470 were ineligible or unable to participate, 370 had refused further follow-up and 93 were lost to follow-up, leaving 673 individuals. These individuals were approached for participation in 2007. Of these, eight had died, one was ineligible, 24 were unable to participate due to illness, 29 could not be located and 14 refused. As well, 16 new participants were added to the cohort; these individuals were acquaintances of the original cohort participants, met eligibility criteria and had similar demographic characteristics as the original cohort (data not shown), and requested participation in the study, providing a sample of 613 for this analysis. Compared with the original cohort, the 613 participants were younger, more likely to be female and living with others, and had higher income and education. Participant characteristics are shown in Table I. Mean age was 77.8 years (range 59e98) three-quarters were female and 37.7% had an Table I Characteristics of respondents (n ¼ 613*) Characteristic Age (years) e mean (SD) Female e n (%) Level of education e n (%) High school Post-secondary Annual household income e n (%) $20,000 $21,000e$40,000 >$40,000 Living circumstances e n (%) Living alone, independently Living with others, independently Institution (e.g., long term care, etc.) BMI (kg/m2) e mean (SD) Obese (BMI > 30 kg/m2) e n (%) OA Severity e mean (SD) WOMAC pain scale/20 WOMAC physical function scale/68 WOMAC summary score/96 Undergone total hip or knee joint replacement Comorbid medical conditions e n (%) Number of comorbidities None 1e2 3þ Physician-diagnosed FMS LES e n (%) Positive life events: 1 event Negative life events: >2 events Depressed mood (CES-D score)/20 e mean (SD) Score 16 indicating possible depression e n (%) *
The denominator is provided when less than 613.
Total cohort (n ¼ 613) 77.82 (6.95) 476 (77.7)
463 (75.53%) 150 (24.47%) 221/586 (37.71%) 306 (52.22%) 59 (10.07%) 230/591 343 18 28.61 188
(38.92%) (58.04%) (3.05%) (5.81) (33.16%)
9.01 (3.93) 34.53 (14.18) 46.09 (19.30)
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annual income $20,000. Mean WOMAC scores indicated moderateto-severe OA; 39% had undergone hip or knee joint replacement surgery. Twenty-six percent reported 3þ comorbid conditions, 10.8% reported physician-diagnosed FMS (65/66 were female), and 23% had CES-D scores 16 indicating possible clinical depression. Few participants reported 1þ positive life events in the prior year (11.7%), whereas 42.7% reported > two negative life events. Self-reported sleep quality, fatigue, sleep disorders and napping Participants’ mean global PSQI score was 8.0/21 (range 1e19); 70% had a global score >5 indicating poor sleep quality (Table II). PSQI subscale scores were highest for sleep latency (difficulty falling asleep) followed by sleep disturbances (interrupted sleep). Participants’ mean fatigue score was 11.6/20 (Standard deviation (SD) 5.8). One-quarter of participants met criteria for RLS and 159 (25.9%) reported currently using medications for sleep. Only 39/596 (6.5%) reported a physician-diagnosed sleep disorder; of these, 26 (66.7%) were receiving treatment. Most participants napped at least once/week (433/510, 84.9%); 218 (42.7%) napped 4þ times weekly. Correlates of poor sleep quality In unadjusted analyses, the odds of reporting poor sleep was greater among those who were female, had lower income, greater arthritis pain or disability, greater comorbidity, higher BMI, a diagnosis of FMS, greater scores for depressed mood and negative life events, RLS symptoms, and reported napping 4þ times/week (Table III). In multivariable analyses, independent correlates of poor sleep were: greater arthritis severity (WOMAC summary score; adjusted odds ratio (OR) ¼ 1.03, P < 0.0001), having 3þ comorbid conditions (adjusted OR ¼ 1.88; P ¼ 0.03), depressed mood (CES-D score; adjusted OR ¼ 1.09, P < 0.0001), and symptoms of RLS (adjusted OR ¼ 1.87; P ¼ 0.02) (Table III). Adjusting for use of sleep medications, or excluding individuals with concomitant FMS or those who were taking sleep medications, did not substantively alter our results (data not shown). Table II Self-reported sleep quality, fatigue, sleep disorders and napping in 613* cohort participants Characteristic Self-reported fatigue (POMS-F)/20 Met criteria for RLS Using medications for sleep
Total cohort (n ¼ 613) 11.59 (5.80) 152 (24.80%) 159 (25.98%)
Napping No Yes, once a week on average Yes, 2e3 times a week on average Yes, 4þ times a week on average
77/510 40 175 218
Self-reported diagnosis of a sleep disorder Currently receiving treatment
39/596 (6.54%) 26/39 (66.67%)
(15.10%) (7.84%) (34.31%) (42.75%)
237 (38.66%)
77/590 359 154 66
(13.05%) (60.84%) (26.10%) (10.77%)
72 (11.75%) 262 (42.74%) 11.35 (8.64) 141 (23.0%)
PSQI Global score,/21, Mean (SD), median Poor sleep (score >5) Sleep latency,/3, Mean (SD), median Sleep duration,/3, Mean (SD), median Sleep duration >7 h Habitual sleep efficiency,/3, Mean (SD), median Sleep efficiency >85%y Sleep disturbances,/3, Mean (SD), median Sleep medications,/3, Mean (SD), median Daytime dysfunction,/3, Mean (SD), median Subjective sleep quality,/3, Mean (SD), median * y
7.95 429 1.71 0.84 222 1.17 225 1.42 0.72 0.90 1.19
(3.94), 8 (69.98%) (0.94), 2 (0.80), 1 (36.22%) (1.13), 1 (36.70%) (0.50), 1 (1.20), 0 (0.82), 1 (0.84), 1
Denominators are provided when less than 613. Sleep efficiency refers to the proportion of time spent in bed that is spent asleep.
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G.A. Hawker et al. / Osteoarthritis and Cartilage 18 (2010) 1365e1371
Table III Correlates of poor global sleep quality as determined by multivariable logistic regression (n ¼ 577) Dependent variable ¼ poor vs normal sleep quality (Pittsburgh Global Sleep Quality Score >5 vs 5)
Independent variable
Unadjusted OR (95 % Confidence interval) Age (per year) Sex (female) Annual household income $20,000 (reference) $21,000e$40,000 >$40,000 WOMAC pain score (per unit increase) WOMAC physical function score (per unit increase) WOMAC summary score (per unit increase) Comorbidity 2 events) RLS (yes) Napping (4 times per week)
P value
Adjusted OR (95% Confidence interval)
P value
1.03 (1.00e1.05) 2.27 (1.53e3.37)
0.07
