ORIGINAL ARTICLE
The Bidirectional Relationship Between Pain Intensity and Sleep Disturbance/Quality in Patients With Low Back Pain Saad M. Alsaadi, MSc,* James H. McAuley, PhD,wz Julia M. Hush, PhD,y Serigne Lo, PhD,w Delwyn J. Bartlett, PhD,8 Roland R. Grunstein, PhD,8 and Chris G. Maher, PhDw
Objectives: This study investigated the bidirectional relationship between the intensity of low back pain (LBP) and sleep disturbance. Further, the study aimed to determine whether any relationship is dependent on pain duration, symptoms of depression and anxiety, and the method of sleep assessment (subjective vs. objective). Materials and Methods: Eighty patients with LBP completed a sleep diary. A subgroup of 50 patients additionally wore an electronic device (Armband) to measure sleep for 7 consecutive days. Pain intensity was assessed twice daily using a sleep diary. Depression and anxiety symptoms were assessed at baseline using the Depression Anxiety Stress Scale questionnaire. Generalized estimating equations (GEE) with an exchangeable correlation structure were used to examine the relationship between day-time pain intensity and sleep. Results: The GEE analysis showed that a night of poor sleep quality, difficulty falling sleep (assessed by the sleep diary), waking after sleep onset, and low sleep efficiency (assessed by the sleep diary and Armband) were followed by a day with higher pain intensity. Further, a day with higher pain intensity was associated with a decrease in the subsequent night’s sleep quality, an increase in sleep latency (assessed by the sleep diary), waking after sleep onset (assessed by both measures), and low sleep efficiency (assessed by the Armband). Discussion: The findings demonstrate that there is a bidirectional relationship between sleep and pain intensity in patients with LBP. The relationship is independent of pain duration and baseline symptoms of depression and anxiety and somewhat dependent on the method of sleep measurement (sleep diary or Armband). Future research is needed to determine whether targeting sleep improvement in patients with LBP contributes to pain reduction. Key Words: low back pain, sleep, pain intensity, bidirectional relationship, reciprocal
(Clin J Pain 2014;30:755–765)
Received for publication February 6, 2013; revised February 5, 2014; accepted October 27, 2013. From *Department of Physiotherapy, King Fahd Hospital of the University, The University of Dammam, Khobar, Saudi Arabia; wThe George Institute for Global Health, Faculty of Medicine; 8The Woolcock Institute of Medical Research, The University of Sydney; yDepartment of Health Professions, Faculty of Human Sciences, Macquarie University, Sydney, Australia; and zNeuroscience Research Australia and the School of Medical Sciences, the University of New South Wales, Randwick, Australia. Supported by a grant from the University of Sydney, Sydney Medical School, Sydney, Australia. C.G.M. fellowship is funded by the Australian Research Council, Canberra, Australian Capital Territory, Australia. The authors declare no conflict of interest. Reprints: Saad M. Alsaadi, MSc, Department of Physical Therapy, King Fahd Hospital of the University. The University of Dammam. PO Box 40035, Khobar 31952, Saudi Arabia (e-mail: [email protected]). Copyright r 2014 by Lippincott Williams & Wilkins
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L
ow back pain (LBP) is a common health condition, with annual prevalence estimates of around 40% of the adult population.1 For many patients the most troubling aspect of LBP is functional disability,2,3 which is associated with increased health care seeking4 and is difficult to manage effectively.5 In a qualitative study, patients with LBP reported that among other aspects of impaired quality of life, sleep disturbance was a major concern.3 Indeed, there is quantitative evidence that sleep disturbance is a common feature of LBP, with >50% of LBP patients reporting reduced sleep quality because of their pain.6–10 A recent systematic review concluded that sleep disturbance in patients with LBP is associated with increased functional disability and high levels of psychological distress.11 Although a relationship between LBP and sleep disturbance has been reported in several studies,6,8,12 the direction of this relationship is not well understood. It is commonly assumed that pain disturbs sleep13,14 although recent evidence from both laboratory-based experiments15–17 and clinical studies13,18 suggests sleep disruption is an important modulator of pain. For example, induced sleep disturbance by reduction in total sleep time (TST) or deprivation of sleep stages in healthy pain-free volunteers has been found to lead to reports of musculoskeletal pain19 and decreased tolerance to noxious stimuli.20 In addition the analgesic effect of recovery from sleep disturbance has been reported to be greater than the effect of common analgesic drugs.17 Clinical studies have also found a significant relationship between improved sleep quality and pain intensity in patients with musculoskeletal21 or osteoarthritic pain22 and comorbid insomnia. These findings have led researchers to propose that pain and sleep disturbance have a bidirectional, or reciprocal, relationship in which pain leads to a disturbed night’s sleep, aggravating pain the following day.23 A vicious cycle of pain and sleep disturbance thus develops, adversely affecting an individual’s quality of life, including physical activity, emotional well-being, and social integration.24 Although several studies have examined this hypothesis in patients with chronic pain, the findings are mixed. Affleck et al25 used the time-series method over 30 days in a group of 50 women diagnosed with fibromyalgia to monitor the effect of sleep disturbance on pain the following day. The investigators found that a night of self-reported disturbed sleep was associated with increased pain the following day, and that a painful day was associated with greater self-reported sleep disturbance the following night. O’Brien et al,26 examined a sample of 22 women with chronic pain over a 14-day period using a sleep diary and actigraphy measurements. They found that a night of sleep disturbance was associated with increased pain intensity the following day, and that a day of severe pain was associated www.clinicalpain.com |
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with a night of disturbed sleep, but only when sleep disturbance was measured using subjective (sleep diary) methods and not objective (actigraphy) methods.26 Conversely, Lewandowski et al27 found that only objective assessments of the number of minutes spent awake predicted subsequent day-time pain in adolescents with and without chronic pain and that day-time pain was not associated with either objectively or subjectively assessed disturbed sleep. Finally, Tang et al28 found that while pain did not affect the subsequent night’s sleep in a group of patients with chronic pain and concomitant insomnia, subjectively assessed sleep quality was a significant predictor of next day pain, but only for pain assessed during the morning. In summary, the literature contains conflicting findings on the direction of the relationship between pain and sleep. This relationship seems to vary and be dependent on the pain condition investigated. To date no study has investigated the direction of the relationship between pain and sleep in a sample of patients with LBP. The literature also suggests that the relationship between pain and sleep is associated with the presence of a comorbid sleep disorder28 and with symptoms of anxiety or depression.29 The relationship between pain and sleep is further believed to be dependent upon the method used to measure sleep (subjective vs. objective).30 The influence of these factors on the relationship between pain and sleep in patients with LBP has not been investigated. LBP is a common and costly condition with most contemporary treatments having limited treatment effectiveness.31 Clarifying the relationship between pain and sleep in patients with LBP is critical, potentially providing unique targets for intervention and improved pain management. The purpose of this study was to investigate the direction of the relationship between pain and sleep in a group of patients with LBP. We were also interested in whether this relationship was dependent upon the duration of pain, the method used to measure sleep disturbance, and/ or presence of symptoms of depression/anxiety.
MATERIALS AND METHODS Participants Participants with nonspecific LBP were recruited from physical therapy clinics in the Sydney metropolitan area and from the community through advertisements. Inclusion criteria were: age between 18 and 79 years with a primary symptom of LBP (pain between the 12th rib and buttock crease) with or without leg pain; and possessing sufficient fluency in the English language. Exclusion criteria were: LBP caused by a serious spinal pathology; nerve root compromise (evidenced by at least 2 of the following signs: myotomal weakness, dermatomal sensory loss, or hyporeflexia of the lower limb reflexes),32,33 which was assessed by a trained physical therapist; spinal surgery within the preceding 6 months; diagnosed with either a sleep disorder (eg, obstructive sleep apnea, insomnia) or a mental health condition (eg, depression), according to a medical report; and rotating night shift workers. Patients undertaking sleep treatment or using psychiatric medications were also excluded. There was no restriction on LBP duration, intensity of pain, or kind of treatment that the patient was receiving for LBP. Approval for the study was obtained from the University of Sydney Human Research Ethics Committee,
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Australia. All participants signed informed consent forms before participation.
Procedure Participating physical therapists informed patients about the study and provided them with comprehensive information about the study procedures. Potential participants from the community were provided with comprehensive information about the study through the post or email. Those who indicated an interest in participating in the study were subsequently contacted by a member of the research team, a trained physical therapist, who screened them for eligibility, and arranged an assessment time at the sleep clinic of the Woolcock Institute of Medical Research, the University of Sydney, Australia. During their visit to the clinic, each potential participant was screened for neurological signs, signed a consent form, and completed a series of self-report questionnaires, described below. After each participant completed the self-reported questionnaires, the researcher provided a Pittsburgh Sleep Diary (sleep diary),34 to complete over 7 consecutive nights. A subgroup of the participants was also provided with an electronic device (Armband) to collect objective measures of sleep parameters. In addition to written instructions, the researcher explained to participants how to complete the sleep diary and how to remove and refit the Armband. Participants using the Armband were instructed to keep it attached to their arm at all times, except when bathing or showering. Participants were requested to maintain their usual sleep-wake habits, use of medications, and normal consumption of alcohol, caffeine, and tobacco during the study period. At the end of the visit, an appointment was made for each participant to return the sleep diary and the Armband after the study period. Participants were followed up in the course of the study, at least once, either by phone calls or through SMS text messages to ensure that the sleep diary was being completed and the Armband was being worn as instructed. All participants were reimbursed for their time and transportation expenses.
Participant Assessment Self-reported Questionnaires During their first visit to the sleep clinic all participants completed the following self-reported questionnaires: (1) Demographic information (age, sex, body mass index [kg/m2], country of birth, educational level, employment status, smoking status, and whether seeking care for LBP or taking medication). (2) Brief Pain Inventory.35,36 (3) Depression, Anxiety, and Stress Scale (DASS-21).37 (4) Roland and Morris Disability Questionnaire (RMDQ).38 (5) Fatigue Severity Scale.39 (6) Pittsburgh Sleep Quality Index (PSQI).40 (7) Insomnia Severity Index (ISI).41 (8) Epworth Sleepiness Scale (ESS).42
Daily Subjective Sleep Assessment—Sleep Diary Reports
The Pittsburgh Sleep Diary34 was used to collect participants’ daily reports of sleep, for 1 week. The sleep diary consists of items completed at bedtime and in the morning. Bedtime items were: (1) the timing of meals; the consumption of (2) caffeine, (3) alcohol, and (4) tobacco; (5) medication use; (6) kind and duration of exercises; and (7) the amount and length of day-time naps. Items completed r
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immediately after waking in the morning related to the previous night’s sleep were: (1) time went to bed, (2) lights out time, (3) sleep onset latency (SOL), (4) time of final waking, (5) method of final waking, (6) number of night wakings, waking after sleep onset (WASO), (6) duration of WASO in minutes, (7) reason(s) for WASO, (8) sleep quality, (9) mood, and (10) alertness on final waking. Sleep quality, mood, and alertness were rated on a 0 to 10 numerical rating scale (NRS). Diary sleep efficiency (SE) was calculated according to the following formula28: SE ¼ ½TSTmin /ðSOLmin þWASOmin þTSTmin Þ100; where TST is the total sleep time in minutes; SOL is sleep onset latency and calculated as the minutes from lights out until falling asleep; WASO is wake after sleep onset and calculated as the total minutes of wakings after sleep onset and before final waking.
Daily Objective Sleep Assessment—SenseWear-Pro3, BodyMedia Armband The sleep parameters (TST, SOL, WASO, and SE) of participants were assessed using the Armband (registered trademark of the SenseWear-Pro3, BodyMedia Monitoring System, Pittsburgh, PA) for 7 consecutive nights. The Armband is a light-weight electronic instrument worn on the right upper arm. The Armband software (SenseWear Professional Software version 6.1) uses a scoring algorithm to determine whether the patient is either asleep or awake for each epoch of 60 seconds by considering the average variations in body movements, differential and proportional changes in heat flux, skin temperature and the galvanic skin response.43 The Armband has been recently reported to be a valid instrument to estimate sleep parameters in patients with sleep apnea.44 The criterion-related validity of the Armband in assessing sleep patterns in patients with LBP was examined by comparing its performance to the gold standard sleep measure, polysomnography (PSG) (S.M.A., J.H.M., J.M.H., D.J.B., Z.M.M., R.R.G., G.C.D. & C.G.M., unpublished data, February 2013). The Armband showed high agreement (85%) with PSG in detecting sleep/wake episodes. It was also shown to be a valid instrument for assessing TST (intraclass correlation coefficient [ICC] = 0.76), wake after sleep onset (ICC = 0.65), and SE (ICC = 0.52), but not for assessing SOL (ICC = 0.13). In addition, the study found consistency between the Armband and an Actiwatch in detecting sleep/ wake episodes and estimating sleep parameters. Armband data were only considered for analysis purposes if they matched the sleep diary reports of “lights out” and “awake time,” and discarded if 14),41,49 indicating subclinical insomnia. The mean score of the ESS (7.3) was within the normal range (ie, 2 minutes (P = 0.03) assessed by the sleep diary only. Both pain intensity upon waking and average day-time pain were significantly associated with SE assessed by the Armband, whereas only pain intensity upon waking (not average day-time pain) was significantly associated with SE assessed by the sleep diary. In this analysis, for every 1-point increase (0 to 10 scale) in pain upon waking, SE assessed by the Armband decreased by 1% (P = 0.004) and for every 1-point increase in average day-time pain, subsequent SE decreased by 0.55 (P = 0.05). Among all assessed sleep parameters, both pain upon waking and average day-time were significantly associated with increases in minutes of WASO assessed by both the sleep diary and Armband. The results indicated that for a every 1-point increase in pain intensity upon waking, WASO increased by 2.46 minutes (P = 0.03) assessed by the r
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sleep diary and increased by 5.83 minutes (P = 0.005) assessed by the Armband. Likewise, for a 1-point increase in average day-time pain, WASO increased by 2.50 minutes (P = 0.003) and by 4.33 minutes (P = 0.003) assessed by the sleep diary and Armband, respectively. Finally, neither pain upon waking nor the average day-time pain were found to be significantly associated with the subsequent night’s TST measured by either the sleep diary and the Armband.
Multivariate Models Including pain duration and baseline depression and anxiety symptoms to the GEE models showed that sleep and pain were not significantly related to pain duration or symptoms of psychological distress (Tables A1 and A2).
DISCUSSION The results of the current study demonstrated that pain intensity and sleep disturbance have a bidirectional relationship for patients with LBP, where a night of poor sleep quality, difficulty falling sleep, WASO, and low SE were, on an average, followed by a day with higher pain intensity. Likewise a day with high pain intensity was followed, on an average, by a decrease in the subsequent night’s sleep quality and SE, and by an increase in the duration of SOL and minutes of WASO. The bidirectional relationship was independent of pain duration and symptoms of psychological distress. However, the pain-sleep bidirectional relationship was dependent on several factors, including the particular sleep parameter that was assessed, whether the parameter was assessed by subjectively (the sleep diary) or objectively (Armband) and the time-point of the assessment of pain intensity. Although the bidirectional relationship between pain and sleep disturbance found by this study supports the findings of some previous research,25,26 other studies have www.clinicalpain.com |
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TABLE 3. Relationships Between Pain: 1. At Wake, 2. Average Day-time and Next Night’s Sleep
Independent Variables 1. Pain at wake Effect of b (95% CI), P GEE included observations Effect of b (95% CI), P GEE included observations
Effect of b (95% CI), P GEE included observations 2. Average day-time pain Effect of b (95% CI), P GEE included observations Effect of b (95% CI), P GEE included observations
Effect of b (95% CI), P GEE included observations
Dependent Variables 1. Sleep quality 0.49 ( 0.61 to 0.38), P < 0.001 535/560 2. Sleep diary reports SE (%) 0.78 ( 1.47 to 0.10), P = 0.02 530/560
TST (min) 0.59 ( 6.36 to 5.16), P = 0.83 531/560
WASO (min) 2.46 (0.82-4.11), P = 0.03
SOL (min) 1.02 ( 1.30 to 3.35), P = 0.39 313/329
TST (min) 1.58 ( 7.77 to 4.61), P = 0.61 317/329
WASO (min) 5.83 (1.71-9.94), P = 0.005
2. Sleep diary reports SE (%) 0.28 (0.73 to 0.15), P = 0.20 534/560
SOL (min) 0.68 ( 0.43 to 1.8), P = 0.22 535/560
TST (min) 1.06 ( 4.45 to 6.57), P = 0.45 535/560
WASO (min) 2.50 (0.83-4.09), P = 0.003
3. Armband sleep records SE (%) 0.55 ( 1.11 to 0.00),* P = 0.05 321/329
SOL (min) 0.27 ( 1.47 to 2.01), P = 0.76 317/329
TST (min) 0.45 ( 5.35 to 6.26), P = 0.87 321/329
WASO (min) 4.33 (1.48-7.18), P = 0.003
3. Armband sleep records SE (%) 1.08 ( 1.81 to 0.35), P = 0.004 317/329
SOL (min) 2.07 (0.11-4.03), P = 0.03 531/560
497/560
317/329
1. Sleep quality 0.20 ( 0.35 to 0.05), P = 0.008 539/560
500/560
321/329
*Value of 0 is due to rounding process. CI indicates confidence interval; GEE, generalized estimating equations; SE, sleep efficiency; SOL, sleep onset latency; TST, total sleep time; WASO, wake after sleep onset.
not reported similar findings. Tang et al28 and Lewandowski et al27 investigated the pain-sleep relationship in heterogenous samples of patients with chronic pain and did not find day-time pain to be a significant predictor of poor sleep quality or quantity. Similarly, Raymond et al51 who studied the pain-sleep relationship in patients with acute burn-related pain did not find day-time pain to be a reliable predictor of subsequent poor sleep. Some of the discrepancy in these findings may be related to timing of pain assessment. In our study we found that in general pain measured at waking was more strongly associated with the following night’s sleep quality and increased minutes of wake after sleep onset than mean day-time pain intensity assessed before sleep. This finding may be related to unmeasured variables (eg, inflammation or an individual’s chronotypes).52 Unfortunately we were unable to investigate this finding further in this study and further research is needed to clarify this relationship.
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In our study we either controlled for or investigated the influence of several factors believed to moderate the painsleep relationship, including pain duration and pain condition, time-point of pain assessment and preexistence of a sleep disorder.28 In addition, findings of the current study and previous reports have shown that the sleep-pain relationship can also be dependent on the specific sleep disturbance parameter that is assessed. For example we found relationships between pain and objective SE and wake after sleep onset, but not between pain and TST or objective SOL or subjective SE. These findings highlight the importance of not relying on single broad measures of sleep disturbance to report relationships between pain and sleep and support current recommendations to combine the subjective and objective sleep measurement to achieve comprehensive sleep assessment.11 It is well known that the subjective method, such as the sleep diary reflects a patient’s perception of sleep quality and quantity, whereas the objective method (eg, the r
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Armband) estimates sleep parameters based on the patient’s biometric data. It is, therefore, not surprising that the painsleep bidirectional relationship in this sample was dependent on the method of sleep assessment. In accordance with the previous studies,25,26,28,51 we found a patient’s appraisal of sleep quality and the subjective evaluation of a night’s sleep, was strongly related to both the previous and next day’s pain. Sleep quality is a poorly understood construct46 and several factors may contribute to its perception.53 Tang et al28 emphasized in their recent work, that people with disturbed sleep tend to underestimate their sleep quality despite an adequate amount of sleep duration.54 There is some evidence that sleep quality is more closely related to disturbance of sleep architecture (sleep stages)55 that could be caused by pain. This hypothesis is supported by evidence from experimental studies where induced muscular pain during slow wave sleep causes the reporting of unrefreshed sleep (poor sleep quality) without altering sleep duration.13,56 Atkinson et al57 used PSG to investigate the sleep architecture in a small group of patients with LBP and found a large reduction in slow wave sleep (stages 3 and 4) and REM sleep latency. A study on patients with fibromyalgia also found that slow wave sleep disturbance was associated with poor sleep quality and an exacerbation of day-time pain.58 However, the impact of LBP on slow wave sleep disruption is currently unknown; hence further research to delineate the role of sleep architecture in reporting sleep quality and pain ratings in the LBP population may prove to be worthwhile. An interesting finding was that the average day-time pain was followed by a longer sleep duration (TST; Table 3). This is in contrast to the sleep diary finding and seems somewhat counter intuitive. A potential explanation may be that prolonged sleep duration is secondary to sleep disturbance. Edwards et al59 argued that a night with longer sleep duration may be an indirect indicator of poor sleep continuity than good sleep quality, which consequently predicts pain the following day. Indeed other sleep parameters, particularly number of minutes of wake after sleep onset and sleep quality support this hypothesis. Therefore, it may be that a day with more severe pain leads to a fragmented night’s sleep, which consequently produces increased sleep duration. Finally our data suggest that patients’ depression and anxiety symptoms did not have a meaningful moderating effect on the relationship between sleep-pain. It is, however, important to note that the study enrolment criteria excluded patients receiving care for a mental health condition, therefore excluding patients diagnosed with depression. In addition, the depression and anxiety levels, measured by the depression and anxiety subscales of the DASS-21 (Table 1), indicate that our sample’s overall depression and anxiety are within the normal range. It is also worth pointing out that both sleep quality and daily mood were assessed at the same time using same likert-scale (11-point scale), which would cause a linear association between these 2 explanatory factors. We therefore conducted the Pearson product-moment correlation coefficient to explore their collinearity. There was a strong correlation between daily mood and night’s sleep quality (r = 0.70, P < 0.001). Therefore and to avoid violation of collinearity assumption we did not include daily assessed mood in the multivariate analysis. The findings of the current study have several clinical implications. First, clinicians do not usually consider sleep assessment for patients with LBP.6 The bidirectional relationship of sleep and pain identified by the current study r
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indicates that untreated sleep disturbance has the potential to lead to a vicious circle of pain and sleep disturbance, which can complicate LBP management and potentially predispose the patient to developing psychological distress.24 It therefore seems prudent to include routine sleep assessment and consider its management within evidence based management strategies for LBP patients who have not responded to traditional treatment approaches. Various sleep treatment options, including cognitive-behavioral therapy or triazolam, have been reported to contribute to pain reduction through sleep improvement.60 Therefore, future research to determine the effectiveness of sleep management in patients with LBP is warranted. Second, a number of studies have reported that opioids, which are often prescribed for pain relief, cause a variety of sleep disturbances, including fragmentation of the sleep architecture (reduction in slow wave sleep) and an increase in sleep disturbance during the middle of the night.14,61 On the basis of the current study findings, it is plausible that these side effects may influence pain intensity and therefore reduce the effectiveness of opioid medicines. Participants’ intake of medications, as collected by the sleep diary, showed that only 2 participants (2.6%) used opioids at 2 occasions within the study period. Therefore, we were unable to examine the effect of opioids on pain-sleep relationship. Future research to determine the impact of such medications on pain-sleep relationship seems worthwhile. The current study had several limitations that need to be considered when interpreting the findings. First, both sleep quality and pain intensity upon waking were assessed on an 11-point NRS at the same time. This approach may have inflated the prediction of pain upon waking by the previous night’s sleep quality. Second, the study participants endorsed low to moderate levels of pain, were nonobese, showed overall good sleep patterns (based on both the sleep diary and Armband) and did not present with significant psychological distress or fatigue. The results may not therefore generalize to clinical populations of patients with LBP attending a specialist pain clinic. Although inclusion of both patients with acute and chronic LBP may provide a representative sample of patients in primary care, the chronicity of pain can interact distinctively with sleep disturbance.24 Therefore, future research to explore how LBP chronicity impacts pain-sleep relationship is required. As we could not include daily assessed mood, relying on baseline psychological distress is a limitation of the study. Although 44% of study participants were taking medication for their pain we did not control the role of medication in our analyses. Some pain medications have been found to have an important role in sleep quality. Finally, participants were followed up to ensure that the sleep diary was being completed, however as the sleep diary was in paper form it was difficult to confirm if it was completed at the time specified which could have affected the precision of sleep estimates. The study had also several strengths. It is the first evaluation of the bidirectional relationship between pain and sleep in patients with LBP. The study excluded people known to have preexisting sleep disorders or those known to have been diagnosed with depression, which may confound the reporting of sleep disturbance. Therefore, reported sleep disturbance in the study was more likely to be secondary to LBP. Finally, it has been reported that around half of the patients with LBP do not seek care for their pain,4 therefore, to maximize the representativeness of the study’s findings for the LBP population, the study included those seeking care and those not currently seeking care for their LBP. www.clinicalpain.com |
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APPENDIX
TABLE A1. Relationships Between Sleep Parameters and Pain: 1. At Waking, 2. Average Day-time*
Independent Variables
Dependent Variables
Effect of b (95% CI), P
GEE Included Observation
0.26 ( 0.35 to 0.16), P < 0.001
535/560
1. Pain at waking 1. Sleep quality (0-10) 2. Sleep diary sleep reports SE (%) SOL (min) TST (min) WASO (min) 3. Armband sleep records SE (%) SOL (min) TST (min) WASO (min)
0.018 0.009 0.000 0.005
( 0.037 to 0.002), P = 0.07 (0.001-0.018), P = 0.03 ( 0.002 to 0.002), P = 0.89 ( 0.001 to 0.012), P = 0.09
530/560 531/560 531/560 497/560
0.020 0.003 0.000 0.005
( 0.035 to 0.004), P = 0.01 ( 0.004 to 0.009), P = 0.41 ( 0.002 to 0.002), P = 0.91 (0.001-0.010), P = 0.03
317/329 317/329 317/329 317/329
2. Average day-time pain 1. Sleep Quality (0-10) 2. Sleep diary sleep reports SE (%) SOL (min) TST (min) WASO (min) 3. Armband sleep records SE (%) SOL (min) TST (min) WASO (min)
0.10 ( 0.19 to 0.02), P = 0.01
539/560
0.001 0.001 0.001 0.006
( 0.014 to 0.012), P = 0.87 ( 0.006 to 0.008), P = 0.76 ( 0.001 to 0.002), P = 0.33 (0.002-0.009), P = 0.003
534/560 535/560 535/560 500/560
0.01 0.000 0.001 0.004
( 0.021 to 0.002), P = 0.11 ( 0.004 to 0.005), P = 0.88 ( 0.002 to 0.003), P = 0.62 (0.001-0.007), P = 0.01
321/329 317/329 321/329 321/329
*Adjusted for pain duration, baseline depression and anxiety symptoms. CI indicates confidence interval; GEE, generalized estimating equations; SE, sleep efficiency; SOL, sleep onset latency; TST, total sleep time; WASO, wake after sleep onset.
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TABLE A2. Relationships Between Pain: 1. At Wake, 2. Average Day-time and Subsequent Night’s Sleep*
Independent Variables 1. Pain at wake Effect of b (95% CI), P GEE included observations Effect of b (95% CI), P GEE included observations
Effect of b (95% CI), P GEE included observations 2. Average day-time pain Effect of b (95% CI), P GEE included observations Effect of b (95% CI), P GEE included observations
Effect of b (95% CI), P GEE included observations
Dependent Variables 1. Sleep quality 0.48 (0.58 to 0.36), P < 0.001 535/560 2. Sleep diary reports SE (%) 0.68 (1.41 to 0.04), P = 0.06 530/560
SOL (min) 2.03 ( 0.01 to 4.08), P = 0.05 531/560
TST (min) 0.36 ( 5.90 to 6.03), P = 0.98 531/560
WASO (min) 2.08 (0.30-3.86), P = 0.02
3. Armband sleep records SE (%) 1.07 ( 1.80 to 0.35), P = 0.003 317/329
SOL (min) 1.12 ( 0.98 to 3.22), P = 0.29 313/329
TST (min) 1.30 ( 7.64 to 5.04), P = 0.68 317/329
WASO (min) 5.54 (1.37-9.70), P = 0.009
2. Sleep diary reports SE (%) 0.19 (0.64 to 0.25), P = 0.40 534/560
SOL (min) 0.60 ( 0.54 to 1.75), P = 0.30 535/560
TST (min) 2.40 ( 2.45 to 7.26), P = 0.33 535/560
WASO (min) 2.11 (0.55-3.66), P = 0.008
3. Armband sleep records SE (%) 0.54 ( 1.11 to 0.04), P = 0.06 321/329
SOL (min) 0.28 ( 1.30 to 1.87), P = 0.72 317/329
TST (min) 0.86 ( 5.01 to 6.74), P = 0.77 321/329
WASO (min) 4.04 (1.15-6.94), P = 0.006
497/560
317/329
1. Sleep Quality 0.18 (0.32 to 0.03), P = 0.01 539/560
500/560
321/329
*Adjusted for pain duration, baseline anxiety and depression symptoms. CI indicates confidence interval; GEE, generalized estimating equations; SE, sleep efficiency; SOL, sleep onset latency; TST, total sleep time; WASO, wake after sleep onset.
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REFERENCES 1. Hoy DG, Bain C, Williams G, et al. A systematic review of the global prevalence of low back pain. Arthritis Rheum. 2012;64: 2028–2037. 2. Manchikanti L, Singh V, Datta S, et al. Comprehensive review of epidemiology, scope, and impact of spinal pain. Pain Physician. 2009;12:E35–E70. 3. Hush JM, Refshauge K, Sullivan G, et al. Recovery: what does this mean to patients with low back pain? Arthritis Rheum. 2009;61:124–131. 4. Ferreira ML, Machado G, Latimer J, et al. Factors defining care-seeking in low back pain—a meta-analysis of population based surveys. Eur J Pain. 2010;14:747 e1–747 e7. 5. Balague F, Mannion AF, Pellise F, et al. Non-specific low back pain. Lancet. 2012;379:482–491. 6. Tang NKY, Wright KJ, Salkovskis PM. Prevalence and correlates of clinical insomnia co-occurring with chronic back pain. J Sleep Res. 2007;16:85–95. 7. O’Donoghue GM, Fox N, Heneghan C, et al. Objective and subjective assessment of sleep in chronic low back pain patients compared with healthy age and gender matched controls: a pilot study. BMC Musculoskelet Disord. 2009;10:122. 8. van de Water AT, Eadie J, Hurley DA. Investigation of sleep disturbance in chronic low back pain: an age- and gendermatched case-control study over a 7-night period. Man Ther. 2011;16:550–556. 9. Marty M, Rozenberg S, Duplan B, et al. Quality of sleep in patients with chronic low back pain: a case-control study. Eur Spine J. 2008;17:839–844. 10. Alsaadi SM, McAuley JH, Hush JM, et al. Erratum to: prevalence of sleep disturbance in patients with low back pain. Eur Spine J. 2012;21:554–560. 11. Kelly GA, Blake C, Power CK, et al. The association between chronic low back pain and sleep: a systematic review. Clin J Pain. 2011;27:169–181. 12. Marin R, Cyhan T, Miklos W. Sleep disturbance in patients with chronic low back pain. Am J Phys Med Rehabil. 2006;85:430–435. 13. Moldofsky H. Sleep and pain. Sleep Med Rev. 2001;5:385–396. 14. Onen SH, Onen F, Courpron P, et al. How pain and analgesics disturb sleep. Clin J Pain. 2005;21:422–431. 15. Smith MT, Edwards RR, McCann UD, et al. The effects of sleep deprivation on pain inhibition and spontaneous pain in women. Sleep. 2007;30:494–505. 16. Roehrs T, Hyde M, Blaisdell B, et al. Sleep loss and REM sleep loss are hyperalgesic. Sleep. 2006;29:145–151. 17. Onen SH, Alloui A, Gross A, et al. The effects of total sleep deprivation, selective sleep interruption and sleep recovery on pain tolerance thresholds in healthy subjects. J Sleep Res. 2001;10:35–42. 18. Haack M, Scott-Sutherland J, Santangelo G, et al. Pain sensitivity and modulation in primary insomnia. Eur J Pain. 2012;16:522–533. 19. Kundermann B, Spernal J, Huber MT, et al. Sleep deprivation affects thermal pain thresholds but not somatosensory thresholds in healthy volunteers. Psychosom Med. 2004;66:932–937. 20. Tiede W, Magerl W, Baumgartner U, et al. Sleep restriction attenuates amplitudes and attentional modulation of painrelated evoked potentials, but augments pain ratings in healthy volunteers. Pain. 2010;148:36–42. 21. Currie SR, Wilson KG, Pontefract AJ, et al. Cognitivebehavioral treatment of insomnia secondary to chronic pain. J Consult Clin Psychol. 2000;68:407–416. 22. Vitiello MV, Rybarczyk B, Von Korff M, et al. Cognitive behavioral therapy for insomnia improves sleep and decreases pain in older adults with co-morbid insomnia and osteoarthritis. J Clin Sleep Med. 2009;5:355–362. 23. Lautenbacher S, Kundermann B, Krieg J-C. Sleep deprivation and pain perception. Sleep Med Rev. 2006;10:357–369. 24. Lavigne GJ, Nashed A, Manzini C, et al. Does sleep differ among patients with common musculoskeletal pain disorders? Curr Rheumatol Rep. 2011;13:535–542.
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25. Affleck G, Urrows S, Tennen H, et al. Sequential daily relations of sleep, pain intensity, and attention to pain among women with fibromyalgia. Pain. 1996;68:363–368. 26. O’Brien EM, Waxenberg LB, Atchison JW, et al. Intraindividual variability in daily sleep and pain ratings among chronic pain patients: bidirectional association and the role of negative mood. Clin J Pain. 2011;27:425–433. 27. Lewandowski AS, Palermo TM, De la Motte S, et al. Temporal daily associations between pain and sleep in adolescents with chronic pain versus healthy adolescents. Pain. 2010;151:220–225. 28. Tang NK, Goodchild CE, Sanborn AN, et al. Deciphering the temporal link between pain and sleep in a heterogeneous chronic pain patient sample: a multilevel daily process study. Sleep. 2012;35:675–687A. 29. O’Brien EM, Waxenberg LB, Atchison JW, et al. Negative mood mediates the effect of poor sleep on pain among chronic pain patients. Clin J Pain. 2010;26:310–319. 30. Wilson KG, Watson ST, Currie SR. Daily diary and ambulatory activity monitoring of sleep in patients with insomnia associated with chronic musculoskeletal pain. Pain. 1998;75:75–84. 31. Vos T, Flaxman AD, Naghavi M, et al. Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2013;380:2163–2196. 32. Henschke N, Maher CG, Refshauge KM, et al. Prognosis in patients with recent onset low back pain in Australian primary care: inception cohort study. BMJ. 2008;337:154–157, a171. 33. Hancock MJ, Maher CG, Latimer J, et al. Assessment of diclofenac or spinal manipulative therapy, or both, in addition to recommended first-line treatment for acute low back pain: a randomised controlled trial. Lancet. 2007;370:1638–1643. 34. Monk TH, Reynolds CF III, Kupfer DJ, et al. The Pittsburgh Sleep Diary. J Sleep Res. 1994;3:111–120. 35. Cleeland CS, Ryan KM. Pain assessment: global use of the Brief Pain Inventory. Ann Acad Med Singapore. 1994;23: 129–138. 36. Keller S, Bann CM, Dodd SL, et al. Validity of the Brief Pain Inventory for use in documenting the outcomes of patients with noncancer pain. Clin J Pain. 2004;20:309–318. 37. Brown TA, Chorpita BF, Korotitsch W, et al. Psychometric properties of the Depression Anxiety Stress Scales (DASS) in clinical samples. Behav Res Ther. 1997;35:79–89. 38. Roland M, Morris R. A study of the natural history of back pain. Part I: development of a reliable and sensitive measure of disability in low-back pain. Spine. 1983;8:141–144. 39. Krupp LB, LaRocca NG, Muir-Nash J, et al. The Fatigue Severity Scale. Application to patients with multiple sclerosis and systemic lupus erythematosus. Arch Neurol. 1989;46: 1121–1123. 40. Buysse DJ, Reynolds CF III, Monk TH, et al. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28:193–213. 41. Bastien CH, Vallie`res A, Morin CM. Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Med. 2001;2:297–307. 42. Johns MW. A new method for measuring daytime sleepiness: the Epworth Sleepiness Scale. Sleep. 1991;14:540–545. 43. Sunseri M, Liden C, Farringdon J, et al. The Sensewear Armband as a sleep detection device, BodyMedia internal white paper. Available at: http://www.bodymedia.com/ Professionals/Whitepapers/The-SenseWear-armband-as-a-SleepDetection-Device. Accessed July 21, 2013. 44. Sharif MM, BaHammam AS. Sleep estimation using BodyMedia’s SenseWear(TM) armband in patients with obstructive sleep apnea. Ann Thorac Med. 2013;8:53–57. 45. Williamson A, Hoggart B. Pain: a review of three commonly used pain rating scales. J Clin Nurs. 2005;14:798–804. 46. Harvey AG, Stinson K, Whitaker KL, et al. The subjective meaning of sleep quality: a comparison of individuals with and without insomnia. Sleep. 2008;31:383–393. r
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47. Parruti G, Tontodonati M, Rebuzzi C, et al. Predictors of pain intensity and persistence in a prospective Italian cohort of patients with herpes zoster: relevance of smoking, trauma and antiviral therapy. BMC Med. 2010;8:58. 48. Backhaus J, Junghanns K, Broocks A, et al. Test-retest reliability and validity of the Pittsburgh Sleep Quality Index in primary insomnia. J Psychosom Res. 2002;53:737–740. 49. Smith S, Trinder J. Detecting insomnia: comparison of four self-report measures of sleep in a young adult population. J Sleep Res. 2001;10:229–235. 50. Johns MW. Sensitivity and specificity of the Multiple Sleep Latency Test (MSLT), the maintenance of wakefulness test and the epworth sleepiness scale: failure of the MSLT as a gold standard. J Sleep Res. 2000;9:5–11. 51. Raymond I, Nielsen TA, Lavigne G, et al. Quality of sleep and its daily relationship to pain intensity in hospitalized adult burn patients. Pain. 2001;92:381–388. 52. Jankowski KS. Morning types are less sensitive to pain than evening types all day long. Eur J Pain. 2013;17:1068–1073. 53. Harvey AG, Tang NK. (Mis)perception of sleep in insomnia: a puzzle and a resolution. Psychol Bull. 2012;138:77–101.
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54. Means MK, Edinger JD, Glenn DM, et al. Accuracy of sleep perceptions among insomnia sufferers and normal sleepers. Sleep Med. 2003;4:285–296. 55. Riedel BW, Lichstein KL. Objective sleep measures and subjective sleep satisfaction: how do older adults with insomnia define a good night’s sleep? Psychol Aging. 1998;13:159–163. 56. Dijk D-J. Slow-wave sleep deficiency and enhancement: implications for insomnia and its management. World J Biol Psychiatry. 2010;11(suppl 1):22–28. 57. Atkinson J, Ancoli-Israel S, Slater M, et al. Subjective sleep disturbance in chronic back pain. Clin J Pain. 1988;4:225–232. 58. Roizenblatt S, Moldofsky H, Benedito-Silva AA, et al. Alpha sleep characteristics in fibromyalgia. Arthritis Rheum. 2001;44:222–230. 59. Edwards RR, Almeida DM, Klick B, et al. Duration of sleep contributes to next-day pain report in the general population. Pain. 2008;137:202–207. 60. Roehrs TA, Workshop P. Does effective management of sleep disorders improve pain symptoms? Drugs. 2009;69(suppl 2):5–11. 61. Benyamin R, Trescot AM, Datta S, et al. Opioid complications and side effects. Pain Physician. 2008;11:S105–S120.
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The Bidirectional Relationship Between Pain Intensity and Sleep Disturbance/Quality in Patients With Low Back Pain Saad M. Alsaadi, MSc,* James H. McAuley, PhD,wz Julia M. Hush, PhD,y Serigne Lo, PhD,w Delwyn J. Bartlett, PhD,8 Roland R. Grunstein, PhD,8 and Chris G. Maher, PhDw
Objectives: This study investigated the bidirectional relationship between the intensity of low back pain (LBP) and sleep disturbance. Further, the study aimed to determine whether any relationship is dependent on pain duration, symptoms of depression and anxiety, and the method of sleep assessment (subjective vs. objective). Materials and Methods: Eighty patients with LBP completed a sleep diary. A subgroup of 50 patients additionally wore an electronic device (Armband) to measure sleep for 7 consecutive days. Pain intensity was assessed twice daily using a sleep diary. Depression and anxiety symptoms were assessed at baseline using the Depression Anxiety Stress Scale questionnaire. Generalized estimating equations (GEE) with an exchangeable correlation structure were used to examine the relationship between day-time pain intensity and sleep. Results: The GEE analysis showed that a night of poor sleep quality, difficulty falling sleep (assessed by the sleep diary), waking after sleep onset, and low sleep efficiency (assessed by the sleep diary and Armband) were followed by a day with higher pain intensity. Further, a day with higher pain intensity was associated with a decrease in the subsequent night’s sleep quality, an increase in sleep latency (assessed by the sleep diary), waking after sleep onset (assessed by both measures), and low sleep efficiency (assessed by the Armband). Discussion: The findings demonstrate that there is a bidirectional relationship between sleep and pain intensity in patients with LBP. The relationship is independent of pain duration and baseline symptoms of depression and anxiety and somewhat dependent on the method of sleep measurement (sleep diary or Armband). Future research is needed to determine whether targeting sleep improvement in patients with LBP contributes to pain reduction. Key Words: low back pain, sleep, pain intensity, bidirectional relationship, reciprocal
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Received for publication February 6, 2013; revised February 5, 2014; accepted October 27, 2013. From *Department of Physiotherapy, King Fahd Hospital of the University, The University of Dammam, Khobar, Saudi Arabia; wThe George Institute for Global Health, Faculty of Medicine; 8The Woolcock Institute of Medical Research, The University of Sydney; yDepartment of Health Professions, Faculty of Human Sciences, Macquarie University, Sydney, Australia; and zNeuroscience Research Australia and the School of Medical Sciences, the University of New South Wales, Randwick, Australia. Supported by a grant from the University of Sydney, Sydney Medical School, Sydney, Australia. C.G.M. fellowship is funded by the Australian Research Council, Canberra, Australian Capital Territory, Australia. The authors declare no conflict of interest. Reprints: Saad M. Alsaadi, MSc, Department of Physical Therapy, King Fahd Hospital of the University. The University of Dammam. PO Box 40035, Khobar 31952, Saudi Arabia (e-mail: [email protected]). Copyright r 2014 by Lippincott Williams & Wilkins
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L
ow back pain (LBP) is a common health condition, with annual prevalence estimates of around 40% of the adult population.1 For many patients the most troubling aspect of LBP is functional disability,2,3 which is associated with increased health care seeking4 and is difficult to manage effectively.5 In a qualitative study, patients with LBP reported that among other aspects of impaired quality of life, sleep disturbance was a major concern.3 Indeed, there is quantitative evidence that sleep disturbance is a common feature of LBP, with >50% of LBP patients reporting reduced sleep quality because of their pain.6–10 A recent systematic review concluded that sleep disturbance in patients with LBP is associated with increased functional disability and high levels of psychological distress.11 Although a relationship between LBP and sleep disturbance has been reported in several studies,6,8,12 the direction of this relationship is not well understood. It is commonly assumed that pain disturbs sleep13,14 although recent evidence from both laboratory-based experiments15–17 and clinical studies13,18 suggests sleep disruption is an important modulator of pain. For example, induced sleep disturbance by reduction in total sleep time (TST) or deprivation of sleep stages in healthy pain-free volunteers has been found to lead to reports of musculoskeletal pain19 and decreased tolerance to noxious stimuli.20 In addition the analgesic effect of recovery from sleep disturbance has been reported to be greater than the effect of common analgesic drugs.17 Clinical studies have also found a significant relationship between improved sleep quality and pain intensity in patients with musculoskeletal21 or osteoarthritic pain22 and comorbid insomnia. These findings have led researchers to propose that pain and sleep disturbance have a bidirectional, or reciprocal, relationship in which pain leads to a disturbed night’s sleep, aggravating pain the following day.23 A vicious cycle of pain and sleep disturbance thus develops, adversely affecting an individual’s quality of life, including physical activity, emotional well-being, and social integration.24 Although several studies have examined this hypothesis in patients with chronic pain, the findings are mixed. Affleck et al25 used the time-series method over 30 days in a group of 50 women diagnosed with fibromyalgia to monitor the effect of sleep disturbance on pain the following day. The investigators found that a night of self-reported disturbed sleep was associated with increased pain the following day, and that a painful day was associated with greater self-reported sleep disturbance the following night. O’Brien et al,26 examined a sample of 22 women with chronic pain over a 14-day period using a sleep diary and actigraphy measurements. They found that a night of sleep disturbance was associated with increased pain intensity the following day, and that a day of severe pain was associated www.clinicalpain.com |
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with a night of disturbed sleep, but only when sleep disturbance was measured using subjective (sleep diary) methods and not objective (actigraphy) methods.26 Conversely, Lewandowski et al27 found that only objective assessments of the number of minutes spent awake predicted subsequent day-time pain in adolescents with and without chronic pain and that day-time pain was not associated with either objectively or subjectively assessed disturbed sleep. Finally, Tang et al28 found that while pain did not affect the subsequent night’s sleep in a group of patients with chronic pain and concomitant insomnia, subjectively assessed sleep quality was a significant predictor of next day pain, but only for pain assessed during the morning. In summary, the literature contains conflicting findings on the direction of the relationship between pain and sleep. This relationship seems to vary and be dependent on the pain condition investigated. To date no study has investigated the direction of the relationship between pain and sleep in a sample of patients with LBP. The literature also suggests that the relationship between pain and sleep is associated with the presence of a comorbid sleep disorder28 and with symptoms of anxiety or depression.29 The relationship between pain and sleep is further believed to be dependent upon the method used to measure sleep (subjective vs. objective).30 The influence of these factors on the relationship between pain and sleep in patients with LBP has not been investigated. LBP is a common and costly condition with most contemporary treatments having limited treatment effectiveness.31 Clarifying the relationship between pain and sleep in patients with LBP is critical, potentially providing unique targets for intervention and improved pain management. The purpose of this study was to investigate the direction of the relationship between pain and sleep in a group of patients with LBP. We were also interested in whether this relationship was dependent upon the duration of pain, the method used to measure sleep disturbance, and/ or presence of symptoms of depression/anxiety.
MATERIALS AND METHODS Participants Participants with nonspecific LBP were recruited from physical therapy clinics in the Sydney metropolitan area and from the community through advertisements. Inclusion criteria were: age between 18 and 79 years with a primary symptom of LBP (pain between the 12th rib and buttock crease) with or without leg pain; and possessing sufficient fluency in the English language. Exclusion criteria were: LBP caused by a serious spinal pathology; nerve root compromise (evidenced by at least 2 of the following signs: myotomal weakness, dermatomal sensory loss, or hyporeflexia of the lower limb reflexes),32,33 which was assessed by a trained physical therapist; spinal surgery within the preceding 6 months; diagnosed with either a sleep disorder (eg, obstructive sleep apnea, insomnia) or a mental health condition (eg, depression), according to a medical report; and rotating night shift workers. Patients undertaking sleep treatment or using psychiatric medications were also excluded. There was no restriction on LBP duration, intensity of pain, or kind of treatment that the patient was receiving for LBP. Approval for the study was obtained from the University of Sydney Human Research Ethics Committee,
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Australia. All participants signed informed consent forms before participation.
Procedure Participating physical therapists informed patients about the study and provided them with comprehensive information about the study procedures. Potential participants from the community were provided with comprehensive information about the study through the post or email. Those who indicated an interest in participating in the study were subsequently contacted by a member of the research team, a trained physical therapist, who screened them for eligibility, and arranged an assessment time at the sleep clinic of the Woolcock Institute of Medical Research, the University of Sydney, Australia. During their visit to the clinic, each potential participant was screened for neurological signs, signed a consent form, and completed a series of self-report questionnaires, described below. After each participant completed the self-reported questionnaires, the researcher provided a Pittsburgh Sleep Diary (sleep diary),34 to complete over 7 consecutive nights. A subgroup of the participants was also provided with an electronic device (Armband) to collect objective measures of sleep parameters. In addition to written instructions, the researcher explained to participants how to complete the sleep diary and how to remove and refit the Armband. Participants using the Armband were instructed to keep it attached to their arm at all times, except when bathing or showering. Participants were requested to maintain their usual sleep-wake habits, use of medications, and normal consumption of alcohol, caffeine, and tobacco during the study period. At the end of the visit, an appointment was made for each participant to return the sleep diary and the Armband after the study period. Participants were followed up in the course of the study, at least once, either by phone calls or through SMS text messages to ensure that the sleep diary was being completed and the Armband was being worn as instructed. All participants were reimbursed for their time and transportation expenses.
Participant Assessment Self-reported Questionnaires During their first visit to the sleep clinic all participants completed the following self-reported questionnaires: (1) Demographic information (age, sex, body mass index [kg/m2], country of birth, educational level, employment status, smoking status, and whether seeking care for LBP or taking medication). (2) Brief Pain Inventory.35,36 (3) Depression, Anxiety, and Stress Scale (DASS-21).37 (4) Roland and Morris Disability Questionnaire (RMDQ).38 (5) Fatigue Severity Scale.39 (6) Pittsburgh Sleep Quality Index (PSQI).40 (7) Insomnia Severity Index (ISI).41 (8) Epworth Sleepiness Scale (ESS).42
Daily Subjective Sleep Assessment—Sleep Diary Reports
The Pittsburgh Sleep Diary34 was used to collect participants’ daily reports of sleep, for 1 week. The sleep diary consists of items completed at bedtime and in the morning. Bedtime items were: (1) the timing of meals; the consumption of (2) caffeine, (3) alcohol, and (4) tobacco; (5) medication use; (6) kind and duration of exercises; and (7) the amount and length of day-time naps. Items completed r
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immediately after waking in the morning related to the previous night’s sleep were: (1) time went to bed, (2) lights out time, (3) sleep onset latency (SOL), (4) time of final waking, (5) method of final waking, (6) number of night wakings, waking after sleep onset (WASO), (6) duration of WASO in minutes, (7) reason(s) for WASO, (8) sleep quality, (9) mood, and (10) alertness on final waking. Sleep quality, mood, and alertness were rated on a 0 to 10 numerical rating scale (NRS). Diary sleep efficiency (SE) was calculated according to the following formula28: SE ¼ ½TSTmin /ðSOLmin þWASOmin þTSTmin Þ100; where TST is the total sleep time in minutes; SOL is sleep onset latency and calculated as the minutes from lights out until falling asleep; WASO is wake after sleep onset and calculated as the total minutes of wakings after sleep onset and before final waking.
Daily Objective Sleep Assessment—SenseWear-Pro3, BodyMedia Armband The sleep parameters (TST, SOL, WASO, and SE) of participants were assessed using the Armband (registered trademark of the SenseWear-Pro3, BodyMedia Monitoring System, Pittsburgh, PA) for 7 consecutive nights. The Armband is a light-weight electronic instrument worn on the right upper arm. The Armband software (SenseWear Professional Software version 6.1) uses a scoring algorithm to determine whether the patient is either asleep or awake for each epoch of 60 seconds by considering the average variations in body movements, differential and proportional changes in heat flux, skin temperature and the galvanic skin response.43 The Armband has been recently reported to be a valid instrument to estimate sleep parameters in patients with sleep apnea.44 The criterion-related validity of the Armband in assessing sleep patterns in patients with LBP was examined by comparing its performance to the gold standard sleep measure, polysomnography (PSG) (S.M.A., J.H.M., J.M.H., D.J.B., Z.M.M., R.R.G., G.C.D. & C.G.M., unpublished data, February 2013). The Armband showed high agreement (85%) with PSG in detecting sleep/wake episodes. It was also shown to be a valid instrument for assessing TST (intraclass correlation coefficient [ICC] = 0.76), wake after sleep onset (ICC = 0.65), and SE (ICC = 0.52), but not for assessing SOL (ICC = 0.13). In addition, the study found consistency between the Armband and an Actiwatch in detecting sleep/ wake episodes and estimating sleep parameters. Armband data were only considered for analysis purposes if they matched the sleep diary reports of “lights out” and “awake time,” and discarded if 14),41,49 indicating subclinical insomnia. The mean score of the ESS (7.3) was within the normal range (ie, 2 minutes (P = 0.03) assessed by the sleep diary only. Both pain intensity upon waking and average day-time pain were significantly associated with SE assessed by the Armband, whereas only pain intensity upon waking (not average day-time pain) was significantly associated with SE assessed by the sleep diary. In this analysis, for every 1-point increase (0 to 10 scale) in pain upon waking, SE assessed by the Armband decreased by 1% (P = 0.004) and for every 1-point increase in average day-time pain, subsequent SE decreased by 0.55 (P = 0.05). Among all assessed sleep parameters, both pain upon waking and average day-time were significantly associated with increases in minutes of WASO assessed by both the sleep diary and Armband. The results indicated that for a every 1-point increase in pain intensity upon waking, WASO increased by 2.46 minutes (P = 0.03) assessed by the r
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sleep diary and increased by 5.83 minutes (P = 0.005) assessed by the Armband. Likewise, for a 1-point increase in average day-time pain, WASO increased by 2.50 minutes (P = 0.003) and by 4.33 minutes (P = 0.003) assessed by the sleep diary and Armband, respectively. Finally, neither pain upon waking nor the average day-time pain were found to be significantly associated with the subsequent night’s TST measured by either the sleep diary and the Armband.
Multivariate Models Including pain duration and baseline depression and anxiety symptoms to the GEE models showed that sleep and pain were not significantly related to pain duration or symptoms of psychological distress (Tables A1 and A2).
DISCUSSION The results of the current study demonstrated that pain intensity and sleep disturbance have a bidirectional relationship for patients with LBP, where a night of poor sleep quality, difficulty falling sleep, WASO, and low SE were, on an average, followed by a day with higher pain intensity. Likewise a day with high pain intensity was followed, on an average, by a decrease in the subsequent night’s sleep quality and SE, and by an increase in the duration of SOL and minutes of WASO. The bidirectional relationship was independent of pain duration and symptoms of psychological distress. However, the pain-sleep bidirectional relationship was dependent on several factors, including the particular sleep parameter that was assessed, whether the parameter was assessed by subjectively (the sleep diary) or objectively (Armband) and the time-point of the assessment of pain intensity. Although the bidirectional relationship between pain and sleep disturbance found by this study supports the findings of some previous research,25,26 other studies have www.clinicalpain.com |
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TABLE 3. Relationships Between Pain: 1. At Wake, 2. Average Day-time and Next Night’s Sleep
Independent Variables 1. Pain at wake Effect of b (95% CI), P GEE included observations Effect of b (95% CI), P GEE included observations
Effect of b (95% CI), P GEE included observations 2. Average day-time pain Effect of b (95% CI), P GEE included observations Effect of b (95% CI), P GEE included observations
Effect of b (95% CI), P GEE included observations
Dependent Variables 1. Sleep quality 0.49 ( 0.61 to 0.38), P < 0.001 535/560 2. Sleep diary reports SE (%) 0.78 ( 1.47 to 0.10), P = 0.02 530/560
TST (min) 0.59 ( 6.36 to 5.16), P = 0.83 531/560
WASO (min) 2.46 (0.82-4.11), P = 0.03
SOL (min) 1.02 ( 1.30 to 3.35), P = 0.39 313/329
TST (min) 1.58 ( 7.77 to 4.61), P = 0.61 317/329
WASO (min) 5.83 (1.71-9.94), P = 0.005
2. Sleep diary reports SE (%) 0.28 (0.73 to 0.15), P = 0.20 534/560
SOL (min) 0.68 ( 0.43 to 1.8), P = 0.22 535/560
TST (min) 1.06 ( 4.45 to 6.57), P = 0.45 535/560
WASO (min) 2.50 (0.83-4.09), P = 0.003
3. Armband sleep records SE (%) 0.55 ( 1.11 to 0.00),* P = 0.05 321/329
SOL (min) 0.27 ( 1.47 to 2.01), P = 0.76 317/329
TST (min) 0.45 ( 5.35 to 6.26), P = 0.87 321/329
WASO (min) 4.33 (1.48-7.18), P = 0.003
3. Armband sleep records SE (%) 1.08 ( 1.81 to 0.35), P = 0.004 317/329
SOL (min) 2.07 (0.11-4.03), P = 0.03 531/560
497/560
317/329
1. Sleep quality 0.20 ( 0.35 to 0.05), P = 0.008 539/560
500/560
321/329
*Value of 0 is due to rounding process. CI indicates confidence interval; GEE, generalized estimating equations; SE, sleep efficiency; SOL, sleep onset latency; TST, total sleep time; WASO, wake after sleep onset.
not reported similar findings. Tang et al28 and Lewandowski et al27 investigated the pain-sleep relationship in heterogenous samples of patients with chronic pain and did not find day-time pain to be a significant predictor of poor sleep quality or quantity. Similarly, Raymond et al51 who studied the pain-sleep relationship in patients with acute burn-related pain did not find day-time pain to be a reliable predictor of subsequent poor sleep. Some of the discrepancy in these findings may be related to timing of pain assessment. In our study we found that in general pain measured at waking was more strongly associated with the following night’s sleep quality and increased minutes of wake after sleep onset than mean day-time pain intensity assessed before sleep. This finding may be related to unmeasured variables (eg, inflammation or an individual’s chronotypes).52 Unfortunately we were unable to investigate this finding further in this study and further research is needed to clarify this relationship.
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In our study we either controlled for or investigated the influence of several factors believed to moderate the painsleep relationship, including pain duration and pain condition, time-point of pain assessment and preexistence of a sleep disorder.28 In addition, findings of the current study and previous reports have shown that the sleep-pain relationship can also be dependent on the specific sleep disturbance parameter that is assessed. For example we found relationships between pain and objective SE and wake after sleep onset, but not between pain and TST or objective SOL or subjective SE. These findings highlight the importance of not relying on single broad measures of sleep disturbance to report relationships between pain and sleep and support current recommendations to combine the subjective and objective sleep measurement to achieve comprehensive sleep assessment.11 It is well known that the subjective method, such as the sleep diary reflects a patient’s perception of sleep quality and quantity, whereas the objective method (eg, the r
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Armband) estimates sleep parameters based on the patient’s biometric data. It is, therefore, not surprising that the painsleep bidirectional relationship in this sample was dependent on the method of sleep assessment. In accordance with the previous studies,25,26,28,51 we found a patient’s appraisal of sleep quality and the subjective evaluation of a night’s sleep, was strongly related to both the previous and next day’s pain. Sleep quality is a poorly understood construct46 and several factors may contribute to its perception.53 Tang et al28 emphasized in their recent work, that people with disturbed sleep tend to underestimate their sleep quality despite an adequate amount of sleep duration.54 There is some evidence that sleep quality is more closely related to disturbance of sleep architecture (sleep stages)55 that could be caused by pain. This hypothesis is supported by evidence from experimental studies where induced muscular pain during slow wave sleep causes the reporting of unrefreshed sleep (poor sleep quality) without altering sleep duration.13,56 Atkinson et al57 used PSG to investigate the sleep architecture in a small group of patients with LBP and found a large reduction in slow wave sleep (stages 3 and 4) and REM sleep latency. A study on patients with fibromyalgia also found that slow wave sleep disturbance was associated with poor sleep quality and an exacerbation of day-time pain.58 However, the impact of LBP on slow wave sleep disruption is currently unknown; hence further research to delineate the role of sleep architecture in reporting sleep quality and pain ratings in the LBP population may prove to be worthwhile. An interesting finding was that the average day-time pain was followed by a longer sleep duration (TST; Table 3). This is in contrast to the sleep diary finding and seems somewhat counter intuitive. A potential explanation may be that prolonged sleep duration is secondary to sleep disturbance. Edwards et al59 argued that a night with longer sleep duration may be an indirect indicator of poor sleep continuity than good sleep quality, which consequently predicts pain the following day. Indeed other sleep parameters, particularly number of minutes of wake after sleep onset and sleep quality support this hypothesis. Therefore, it may be that a day with more severe pain leads to a fragmented night’s sleep, which consequently produces increased sleep duration. Finally our data suggest that patients’ depression and anxiety symptoms did not have a meaningful moderating effect on the relationship between sleep-pain. It is, however, important to note that the study enrolment criteria excluded patients receiving care for a mental health condition, therefore excluding patients diagnosed with depression. In addition, the depression and anxiety levels, measured by the depression and anxiety subscales of the DASS-21 (Table 1), indicate that our sample’s overall depression and anxiety are within the normal range. It is also worth pointing out that both sleep quality and daily mood were assessed at the same time using same likert-scale (11-point scale), which would cause a linear association between these 2 explanatory factors. We therefore conducted the Pearson product-moment correlation coefficient to explore their collinearity. There was a strong correlation between daily mood and night’s sleep quality (r = 0.70, P < 0.001). Therefore and to avoid violation of collinearity assumption we did not include daily assessed mood in the multivariate analysis. The findings of the current study have several clinical implications. First, clinicians do not usually consider sleep assessment for patients with LBP.6 The bidirectional relationship of sleep and pain identified by the current study r
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indicates that untreated sleep disturbance has the potential to lead to a vicious circle of pain and sleep disturbance, which can complicate LBP management and potentially predispose the patient to developing psychological distress.24 It therefore seems prudent to include routine sleep assessment and consider its management within evidence based management strategies for LBP patients who have not responded to traditional treatment approaches. Various sleep treatment options, including cognitive-behavioral therapy or triazolam, have been reported to contribute to pain reduction through sleep improvement.60 Therefore, future research to determine the effectiveness of sleep management in patients with LBP is warranted. Second, a number of studies have reported that opioids, which are often prescribed for pain relief, cause a variety of sleep disturbances, including fragmentation of the sleep architecture (reduction in slow wave sleep) and an increase in sleep disturbance during the middle of the night.14,61 On the basis of the current study findings, it is plausible that these side effects may influence pain intensity and therefore reduce the effectiveness of opioid medicines. Participants’ intake of medications, as collected by the sleep diary, showed that only 2 participants (2.6%) used opioids at 2 occasions within the study period. Therefore, we were unable to examine the effect of opioids on pain-sleep relationship. Future research to determine the impact of such medications on pain-sleep relationship seems worthwhile. The current study had several limitations that need to be considered when interpreting the findings. First, both sleep quality and pain intensity upon waking were assessed on an 11-point NRS at the same time. This approach may have inflated the prediction of pain upon waking by the previous night’s sleep quality. Second, the study participants endorsed low to moderate levels of pain, were nonobese, showed overall good sleep patterns (based on both the sleep diary and Armband) and did not present with significant psychological distress or fatigue. The results may not therefore generalize to clinical populations of patients with LBP attending a specialist pain clinic. Although inclusion of both patients with acute and chronic LBP may provide a representative sample of patients in primary care, the chronicity of pain can interact distinctively with sleep disturbance.24 Therefore, future research to explore how LBP chronicity impacts pain-sleep relationship is required. As we could not include daily assessed mood, relying on baseline psychological distress is a limitation of the study. Although 44% of study participants were taking medication for their pain we did not control the role of medication in our analyses. Some pain medications have been found to have an important role in sleep quality. Finally, participants were followed up to ensure that the sleep diary was being completed, however as the sleep diary was in paper form it was difficult to confirm if it was completed at the time specified which could have affected the precision of sleep estimates. The study had also several strengths. It is the first evaluation of the bidirectional relationship between pain and sleep in patients with LBP. The study excluded people known to have preexisting sleep disorders or those known to have been diagnosed with depression, which may confound the reporting of sleep disturbance. Therefore, reported sleep disturbance in the study was more likely to be secondary to LBP. Finally, it has been reported that around half of the patients with LBP do not seek care for their pain,4 therefore, to maximize the representativeness of the study’s findings for the LBP population, the study included those seeking care and those not currently seeking care for their LBP. www.clinicalpain.com |
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APPENDIX
TABLE A1. Relationships Between Sleep Parameters and Pain: 1. At Waking, 2. Average Day-time*
Independent Variables
Dependent Variables
Effect of b (95% CI), P
GEE Included Observation
0.26 ( 0.35 to 0.16), P < 0.001
535/560
1. Pain at waking 1. Sleep quality (0-10) 2. Sleep diary sleep reports SE (%) SOL (min) TST (min) WASO (min) 3. Armband sleep records SE (%) SOL (min) TST (min) WASO (min)
0.018 0.009 0.000 0.005
( 0.037 to 0.002), P = 0.07 (0.001-0.018), P = 0.03 ( 0.002 to 0.002), P = 0.89 ( 0.001 to 0.012), P = 0.09
530/560 531/560 531/560 497/560
0.020 0.003 0.000 0.005
( 0.035 to 0.004), P = 0.01 ( 0.004 to 0.009), P = 0.41 ( 0.002 to 0.002), P = 0.91 (0.001-0.010), P = 0.03
317/329 317/329 317/329 317/329
2. Average day-time pain 1. Sleep Quality (0-10) 2. Sleep diary sleep reports SE (%) SOL (min) TST (min) WASO (min) 3. Armband sleep records SE (%) SOL (min) TST (min) WASO (min)
0.10 ( 0.19 to 0.02), P = 0.01
539/560
0.001 0.001 0.001 0.006
( 0.014 to 0.012), P = 0.87 ( 0.006 to 0.008), P = 0.76 ( 0.001 to 0.002), P = 0.33 (0.002-0.009), P = 0.003
534/560 535/560 535/560 500/560
0.01 0.000 0.001 0.004
( 0.021 to 0.002), P = 0.11 ( 0.004 to 0.005), P = 0.88 ( 0.002 to 0.003), P = 0.62 (0.001-0.007), P = 0.01
321/329 317/329 321/329 321/329
*Adjusted for pain duration, baseline depression and anxiety symptoms. CI indicates confidence interval; GEE, generalized estimating equations; SE, sleep efficiency; SOL, sleep onset latency; TST, total sleep time; WASO, wake after sleep onset.
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TABLE A2. Relationships Between Pain: 1. At Wake, 2. Average Day-time and Subsequent Night’s Sleep*
Independent Variables 1. Pain at wake Effect of b (95% CI), P GEE included observations Effect of b (95% CI), P GEE included observations
Effect of b (95% CI), P GEE included observations 2. Average day-time pain Effect of b (95% CI), P GEE included observations Effect of b (95% CI), P GEE included observations
Effect of b (95% CI), P GEE included observations
Dependent Variables 1. Sleep quality 0.48 (0.58 to 0.36), P < 0.001 535/560 2. Sleep diary reports SE (%) 0.68 (1.41 to 0.04), P = 0.06 530/560
SOL (min) 2.03 ( 0.01 to 4.08), P = 0.05 531/560
TST (min) 0.36 ( 5.90 to 6.03), P = 0.98 531/560
WASO (min) 2.08 (0.30-3.86), P = 0.02
3. Armband sleep records SE (%) 1.07 ( 1.80 to 0.35), P = 0.003 317/329
SOL (min) 1.12 ( 0.98 to 3.22), P = 0.29 313/329
TST (min) 1.30 ( 7.64 to 5.04), P = 0.68 317/329
WASO (min) 5.54 (1.37-9.70), P = 0.009
2. Sleep diary reports SE (%) 0.19 (0.64 to 0.25), P = 0.40 534/560
SOL (min) 0.60 ( 0.54 to 1.75), P = 0.30 535/560
TST (min) 2.40 ( 2.45 to 7.26), P = 0.33 535/560
WASO (min) 2.11 (0.55-3.66), P = 0.008
3. Armband sleep records SE (%) 0.54 ( 1.11 to 0.04), P = 0.06 321/329
SOL (min) 0.28 ( 1.30 to 1.87), P = 0.72 317/329
TST (min) 0.86 ( 5.01 to 6.74), P = 0.77 321/329
WASO (min) 4.04 (1.15-6.94), P = 0.006
497/560
317/329
1. Sleep Quality 0.18 (0.32 to 0.03), P = 0.01 539/560
500/560
321/329
*Adjusted for pain duration, baseline anxiety and depression symptoms. CI indicates confidence interval; GEE, generalized estimating equations; SE, sleep efficiency; SOL, sleep onset latency; TST, total sleep time; WASO, wake after sleep onset.
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