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. Author manuscript; available in PMC: 2022 Nov 1.
Published in final edited form as: Mindfulness (N Y). 2021 Sep 1;12(11):2672–2680. doi: 10.1007/s12671-021-01729-y

The role of mindfulness and relaxation in improved sleep quality following a mind-body and activity program for chronic pain

James Doorley 1,2, Jonathan Greenberg 1,2, Matthew Stauder 1,3, Ana-Maria Vranceanu 1,2
PMCID: PMC8653783  NIHMSID: NIHMS1744190  PMID: 34900019

Abstract

Objectives:

Poor sleep quality is prevalent among individuals with chronic pain and contributes to increased physical and emotional dysfunction. However, treatments that improve sleep quality among individuals with chronic pain are scant. A previously developed mind-body activity program for chronic pain has been shown to be feasible and associated with improvements in pain and physical and emotional function. Using secondary data-analysis, the purpose of this study was to understand whether participants also experienced significant and sustained improvements in sleep quality over time and whether these improvements were explained by change in two core treatment targets, relaxation and mindfulness.

Methods:

Participants with heterogenous chronic pain (N = 82) were randomized to a mind-body activity intervention with (GetActive-Fitbit; n=41) or without (GetActive; n=41) a Fitbit device. Sleep quality was measured with the PSQI, mindfulness with the CAMS-R, and relaxation with the relaxation subscale of the MOCS-A. Mediation was tested via mixed-models analysis.

Results:

Both intervention groups experienced significant and comparable improvements in sleep quality from baseline to post-treatment, which were sustained through a 3-month follow-up. Mindfulness and relaxation also improved significantly over time and these improvements were associated with improved sleep quality. Mindfulness and relaxation fully mediated improvement in sleep quality (medium to large effect sizes).

Conclusions:

Results suggest that, despite not targeting sleep explicitly, the two mind-body activity programs hold promise for sustainably improving sleep quality among patients with chronic pain. Targeting mindfulness and relaxation may facilitate these improvements.

Keywords: Sleep, chronic pain, mind-body, mindfulness, relaxation

Chronic pain and sleep are bidirectionally associated and place individuals on a disability spiral of greater pain, sleep disturbance, and emotional and physical dysfunction (Finan et al., 2013; Holfeld & Ruthig, 2014; Lautenbacher et al., 2006; Yoo et al., 2007). Research suggests that 67%−88% of individuals with chronic pain also suffer from poor sleep quality (Morin et al., 2006; Smith et al., 2001) and these individuals are at increased risk for additional comorbidities, including depression, obesity, and type 2 diabetes (Finan & Smith, 2013; Knutson et al., 2006). Thus, interventions to improve sleep quality among individuals with chronic pain are a priority (Finan et al., 2013).

Cognitive Behavioral therapy for Insomnia (CBT-I) is efficacious in improving objective and subjective sleep quality among people with chronic pain and sleep dysfunction (Finan et al., 2014; Greenberg et al., 2019, 2020; Ong et al., 2008), yet its effects on important pain outcomes like pain intensity and physical function are modest (Finan et al., 2014). Moreover, a full course of CBT-I is intense and may not be necessary for individuals with milder sleep disturbances. Additional interventions that can improve both sleep and pain outcomes, are minimally burdensome, and can be generalized for individuals across the spectrum of sleep problems are needed.

Previous research demonstrated that participation in two identical group-based mind-body and activity programs, one without (GetActive) and one with a Fitbit (GetActive-Fitbit), was associated with improved pain and emotional and physical functioning among individuals with chronic musculoskeletal pain (Greenberg et al., 2020). The programs teach a variety of skills including mindfulness and relaxation, two overlapping yet distinct skills (Luberto et al., 2020), which show potential in improving both pain-related outcomes and sleep quality (Alvaro et al., 2013; Feldman et al., 2007; Ong et al., 2008). Mindfulness may be particularly effective in improving sleep quality by reducing physiological reactivity and promoting acceptance in response negative emotions (e.g., anxiety, stress, frustration), cognitions (e.g., rumination), and pain (Alvaro et al., 2013; Davis et al., 2015; Ong et al., 2008; Winbush et al., 2007). Relaxation training induces the state of relaxation response, characterized by heightened parasympathetic relative to sympathetic nervous system activation and perceptions of calmness. Relaxation training may be effective at reducing sleep onset latency among patients with medical and psychiatric diagnoses (Cannici et al., 1983; Lichstein et al., 2000; Ong et al., 2008), and may be particularly helpful for patients with chronic pain who report high presleep arousal (Smith et al., 2000). Taken together, these results suggest that mindfulness and relaxation each influence pain- and sleep-related outcomes. However, it is not currently known whether improvements in both mindfulness and relaxation can explain improvements in sleep quality among people with chronic pain.

In the present study, we conducted secondary data analysis to examine whether self-reported sleep quality improved following participation in these programs, whether improvements were sustained at 3-month follow-up, and whether improvements in mindfulness and relaxation mediated improvements in sleep quality. First, we hypothesized that participation in a group mind-body and activity program would be associated with significant and sustained improvements in sleep quality, controlling for intervention group. Second, we hypothesized that improvements in mindfulness and relaxation would mediate improvements in sleep, controlling for intervention group.

Methods

Participants

We recruited 82 patients with heterogeneous chronic musculoskeletal pain via direct referrals from the Massachusetts General Hospital (MGH) Pain Clinic and hospital-wide email lists (see Table 1 for participant socio-demographics). Detailed information on inclusion and exclusion criteria, procedures, and intervention development have been reported previously (Greenberg, 2019). Participants were mostly female (n=54, 66%), white (n=66, 80%), non-Hispanic (n=72, 88%), and predominantly college-educated (4 years of college; n=17, 21%; or obtained a Graduate/Professional degree; n=28, 34%). Approximately one-fifth of the sample (n=17; 21%) endorsed full-time employment (see Table 2 for participant medical and mental health history). All procedures were conducted in an outpatient clinic between July 2018 and September 2019.

Table 1.

Participant Socio-Demographics Variables

Total (N = 82)
Sex n (%)
 Female 54 (65.85%)
 Male 28 (34.15%)
Racial background
 White 66 (80.48%)
 Black or African-American 7 (8.54%)
 Bi/multiracial 4 (4.87%)
 Asian 3 (3.66%)
 American Indian or Alaska Native 2 (2.44%)
Ethnicity
 Non-Hispanic or Latino/Latina 72 (87.80%)
 Hispanic or Latino/Latina 8 (9.75%)
 Not reported 2 (2.44%)
Marital Status
 Single, never married 28 (34.15%)
 Married 23 (28.05%)
 Separated/Divorced 16 (19.51%)
 Living with significant other 11 (13.41%)
 Widowed 4 (4.87%)
Annual Household Income
 Less than $10,000 18 (21.95%)
 $10,000 – less than $20,000 14 (17.07%)
 $20,000 – less than $35,000 12 (14.63%)
 $35,000 – less than $50,000 9 (10.97%)
 $50,000 – less than $75,000 7 (8.54%)
 $75,000 or greater 17 (20.73%)
 Not reported 5 (6.10%)
Education
 High school graduate or GED 11 (13.41%)
 Some college/Associate degree 26 (31.71%)
 Completed 4 years of college 17 (20.73%)
 Graduate/professional degree 28 (34.15%)
Employment
 Employed full-time 17 (20.73%)
 Employed part-time 11 (13.41%)
 Student (full-time or part-time) 3 (3.70%)
 Self-employed 1 (1.22%)
 Retired 18 (21.95%)
 Unemployed 18 (21.95%)
 Disability 12 (14.63%)
 Worker’s Compensation 2 (2.44%)
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Table 2.

Participant Medical and Mental Health History

Total (N = 82)
Medical diagnosis present n (%)
 Yes 56 (68.29%)
 No 22 (26.83%)
History of mental health diagnosis 1
 None 41 (50.00%)
 Depression 32 (39.02%)
 Anxiety 31 (37.80%)
 PTSD 11 (13.41%)
 Bipolar 1 (1.22%)
 Panic Disorder 1 (1.22%)
Current mental health diagnosis
 None 49 (59.76%)
 Depression 23 (28.05%)
 Anxiety 24 (29.27%)
 PTSD 10 (12.20%)
 Bipolar 2 (2.44%)
 Panic Disorder 1 (1.22%)
Current psychiatric medication
 Yes 36 (43.90%)
 No 43 (52.44%)
Current pain medication
 Yes 59 (71.95%)
 No 21 (25.61%)
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Note.

1

Participants checked all mental health diagnoses that applied. Therefore, comorbidities are included in the frequencies for each diagnosis.

Procedure

Participants attended an initial clinic visit to undergo informed consent and complete baseline assessments. Participants were randomly assigned to one of two 10-week programs (GetActive and GetActive-Fitbit) that were identical in content and structure, though in one program (GetActive-Fitbit) participants received a Fitbit digital monitoring device. Participants attended their first 90-minute group session one week after their baseline assessment and returned weekly for the remaining 9 sessions. One week after the last group session, participants completed a post-intervention assessment with measures identical to those at baseline. Participants were compensated $30 for each assessment. All study procedures were approved by the Massachusetts General Hospital Internal Review Board.

Both the GetActive and GetActive-Fitbit programs involve 10 weekly 90-minute in-person sessions delivered by a clinical psychologist. Core skills were identical between programs and included hypothesized mechanisms of improvement in pain, emotional and physical function including mindfulness (e.g., mindful breathing and walking) and relaxation exercises (e.g., diaphragmatic breathing and progressive muscle relaxation). Detail description of the programs have been previously published (Greenberg et al., 2019).

Measures

Sleep quality.

We assessed sleep using the 19-item Pittsburgh Sleep Quality Index (PSQI), which assesses past-month sleep quality with 19 items (items rated by a bed-partner or roommate were excluded). Items are grouped into 7 component scores: sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction, which were summed to create a total score. Lower scores indicate better sleep quality (α = .68).

Mindfulness.

We assessed mindfulness using the 12-item Cognitive and Affective Mindfulness Scale-Revised (CAMS-R), which measures the ability to attend to the present moment nonjudgmentally. Higher scores indicate greater mindfulness. The CAMS-R has demonstrated acceptable convergent and discriminant validity with measures of mindfulness, distress, and well-being and is associated with self-esteem and unconditional self-acceptance (α = .77) (Feldman et al., 2007).

Relaxation.

We assessed relaxation using the 2-item relaxation subscale from the Measure of Current Status (MOCS-A) (e.g.,“I am able to use muscle relaxation techniques to reduce any tension I experience.” “I am able to use mental imagery to reduce any tension I experience.”). Item 10: which measures the ability to implement healthy coping skills (relaxation, awareness of tension, assertiveness, and coping confidence (α = .74) (Antoni et al., 2006).

Data Analyses

We conducted analyses using SPSS 26 (IBM, 2017) and RStudio 3.6.1 (R Core Team, 2019). We tested Hypothesis 1 (sleep will significantly and sustainably improve) using linear mixed effects models (i.e., mixed models) containing the baseline, post-intervention, and 3-month follow-up timepoints. We entered sleep quality as the dependent variable and time as the focal predictor. Similar to previous studies using these data (e.g., Greenberg et al., 2021, Grunberg et al., in press), we combined the GetActive and GetActive-Fitbit groups together in our analyses and controlled for group as a fixed effect. We chose this conservative approach to explore whether our findings varied between interventions and account for potential group differences. If significant change in sleep quality was observed across time (from baseline to 3-month follow-up), we planned post-hoc tests to assess change from baseline to post-intervention and from post-intervention to 3-month follow-up. Sleep quality was standardized for Hypothesis 1 analyses. Time and group are not continuous variables, and thus were not standardized.

We conducted multiple mediation analyses to test Hypothesis 2 (improvements in sleep quality will be mediated by improvements in mindfulness and relaxation). Mediation was determined if 1) mindfulness and relaxation independently changed across time (path a), 2) mindfulness and relaxation (in the same model) predicted change in sleep quality (path b), and 3) there was an indirect effect of time on improvements in sleep quality through mindfulness and relaxation (path a*b). For descriptive purposes, we also assessed whether the direct effect of time on sleep quality was no longer significant after including mindfulness and relaxation in the same model as proposed mediators (path c’). The direct effect of time on sleep quality (path c) will already be assessed in the context of Hypothesis 1. Mediation analyses were planned to target timepoints in which significant change in sleep occurred from Hypothesis 1 (baseline to post-intervention, post-intervention to 3-month follow-up, or both).

We used the RMediation package (Tofighi & MacKinnon, 2011) to test specificity models and calculate the Monte Carlo confidence interval (CI) of the indirect effects (path a*b). This approach, using mixed-effects modeling and follow-up specificity models, has five key advantages for identifying intervention mechanisms compared to traditional mediation approaches (Baron & Kenny, 1986). First, it controls for the random effects of intercepts to accommodate between-person differences in treatment targets at baseline. Second, it allowed us to examine changes in outcomes while controlling for group assignment. Third, intervention targets (mindfulness and relaxation) were modeled simultaneously in a multiple mediation framework to identify their unique influence on outcomes (sleep quality). Fourth, it allowed us to retain all data available for analyses, thus increasing the power of our models. Finally, it reduces potential problems due to multicollinearity and multiple comparisons (Chakraborty & Gu, 2009). For all models testing Hypothesis 2, we entered time and group as fixed effects and intercepts as random effects. All continuous variables (e.g., sleep quality, mindfulness, relaxation) were standardized and effect sizes of the indirect effects were calculated using “completely standardized indirect effects” (CSIE), with small, medium, and large effects corresponding standardized coefficients or approximately .01, .09, and .25 respectively (Preacher & Kelley, 2011).

Results

Descriptive Statistics

Table 2 displays descriptive statistics for primary study variables across all three timepoints. Baseline mean scores on sleep quality (M = 9.47, SD = 4.10), mindfulness (M = 31.21, SD = 6.75), and relaxation (M = 2.80, SD = 1.93) fell within expected ranges based on existing studies of adults with chronic pain (Gudenkauf et al., 2015; Marty et al., 2008; Vasiliou et al., 2019). Mean PSQI scores indicated greater sleep disturbance relative to adult community samples (Buysse et al., 2008; Knutson et al., 2006) and similar sleep disturbance relative to other adult samples with chronic illnesses (e.g., Neu et al., 2007; Osorio et al., 2006).

Hypothesis Testing

We found support for our first hypothesis that participation in a group mind-body physical activity program would be associated with significant and sustained improvement in sleep quality. Mixed models revealed that time was a significant predictor of sleep quality (b = −.14, SE = .06; t = −2.37, p = .019, CI = [−.27, −.03]) controlling for group, which was also a significant predictor of sleep quality (b = .73, SE = .18, t = 4.15, p = .000, CI = [.38, 1.08]). This suggests that sleep quality improved from baseline to 3-month follow-up (lower PSQI scores indicate greater sleep quality). Post-hoc tests revealed that change in sleep was only significant between baseline and post-intervention (b = −.25, SE = .10; t = −2.51, p = .014, CI = [−.44, −.05]) (i.e., the direct effect; path c) controlling for group, which was also a significant predictor (b = .71, SE = .18, t = 3.85, p = .000, CI = [.34, 1.07]). Change in sleep was not significant from post-intervention to 3-month follow-up (b = −.02., SE = .09; t = −.20, p = .842, CI = [−.19, .16]). This suggests pre-post improvements in sleep were sustained, but did not continue to improve 3-months later.

We also found support for our second hypothesis that improvements in mindfulness and relaxation would mediate improvements in sleep. For path a (testing whether treatment targets change significantly over time), separate mixed models showed that time was a significant predictor of both mindfulness (b = .47, SE = .10, t = 4.59, p < .001, CI = [.27, .68]) and relaxation (b = .89, SE = .11, t = 8.17, p < .001, CI = [.66, 1.09]). Group was not a significant predictor. This suggests mindfulness and relaxation improved significantly from baseline to post intervention regardless of group.

For path b (treatment targets predicting improvement in sleep quality), a mixed model showed that both mindfulness (b = −.28, SE = .08, t = −3.52, p = .001, CI = [−.43, −.12]) and relaxation (b = −.16, SE = .07, t = −2.30, p = .038, CI = [−.29, −.02]) predicted unique variance in improved sleep quality (i.e., decreased PSQI scores) from baseline to post-intervention controlling for group, which was also a significant predictor (b = .63, SE = .18, t = 3.58, p = .001, CI = [.28, .98]). When adding mindfulness and relaxation as mediators in this model, the direct effect of time on sleep quality was no longer significant (path c’; b = .02, SE = .11, t = .22, p = .829, CI = [−.20, .25]), indicating the mindfulness and relaxation fully mediated the effects of time on sleep quality. For the a*b path (testing the indirect effect of time on sleep quality through mindfulness and relaxation) results from RMediation showed significant indirect effects of time on sleep quality mediated by mindfulness (CSIE = −.13, SE = .05, 95% CI = [−.23, −.05]) and relaxation (CSIE = −.14, SE = .07, 95% CI = [−.28, −.02]). These indirect effects fell in the medium to large range (Preacher & Kelley, 2011). Overall, results suggest improvements in mindfulness and relaxation mediated improvements in sleep quality (Figure 1).

Figure 1. Mediation model testing the indirect effects of time on improvement in sleep quality from baseline to post-intervention via improvements in mindfulness and relaxation.

Figure 1.

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Paths specificy level-1 mixed linear modeling (MLM) equations with standardized coefficients and standard errors in parentheses. *p < .05; ** p <.01; ***p <.001. 95% confidence intervals are reported for completely standardized indirect effects (CSIEs).

Discussion

Sleep difficulties frequently co-occur with chronic pain and exacerbate physical and emotional dysfunction. Evidence for effective treatments to improve sleep quality are limited among patients with chronic pain. This study examined whether participation in a mind-body and activity program with (GetActive-Fitbit) and without (GetActive) a Fitbit device was associated with improved self-reported sleep quality and whether mindfulness and relaxation mediated sleep improvements.

Our first hypothesis, that participation in both mind-body activity programs will be associated with significant and sustained improvement in sleep quality, was supported. While sleep was not an explicit treatment target, sleep quality significantly improved from baseline to post-intervention regardless of intervention group. Sleep did not change significantly from post-intervention to 3-month follow-up, suggesting improvements were sustained. These findings are consistent with a limited body of research suggesting that mind-body skills improve sleep quality among individuals with chronic pain (Kwekkeboom et al., 2010; Morone et al., 2008; Rybarczyk et al., 1999). The finding that improvements in sleep were sustained 3 months after intervention completion are particularly promising, given that benefits following mind-body interventions for chronic pain often to fade after intervention completion (Anheyer et al., 2017; Hilton et al., 2017; Martorella et al., 2017; Williams et al., 2012).

Our second hypothesis, that improvements in mindfulness and relaxation would mediate improvements in sleep, was also supported. Specifically, mindfulness and relaxation fully mediated improvements in sleep quality over time with medium to large effects for each (Preacher & Kelley, 2011). The fact that both mindfulness and relaxation uniquely mediated sleep improvements is notable. While both skills have considerable overlap, mindfulness and relaxation training have distinct neural mechanisms as well as key philosophical differences (Luberto et al., 2020; Sevinc et al., 2018), each of which may play a role in improving sleep quality for individuals with chronic pain. For example, a goal of relaxation training is to activate the relaxation response (i.e., greater parasympathetic relative to sympathetic nervous system activation), which may specifically reduce sleep-interfering physiological sensations (e.g., hyperarousal) (Rosen et al., 2000). In contrast, a “goal” of mindfulness training, insofar as goals are advocated, is nonjudgmental awareness of the present moment, including unpleasant internal experiences. Greater mindfulness may reduce unproductive cognitive engagement with such experiences that would otherwise impair sleep (e.g., rumination, catastrophizing) (Davis et al., 2015; Winbush et al., 2007). Thus, targeting mindfulness and relaxation may help promote both physiological and cognitive changes that improve sleep quality among individuals with chronic pain. It is also noteworthy that our mind-body programs taught mindfulness and relaxation as pain coping strategies, and these were not tailored to individual sleep concerns. This suggests that relaxation and mindfulness exercises may not need to have sleep content in order to help improve sleep. This finding has important implications for mind-body interventions for people with chronic illness where developing sleep specific relaxation and mindfulness exercises might make the programs more cumbersome and lengthy.

Limitations and Future Research

Several limitations of this study are worth noting. First, in the absence of a control group, we cannot rule out confounding factors that may have contributed to changes in sleep quality (e.g., spontaneous improvement or regression to the mean). Second, sleep was measured using a self-report measure (PSQI). While this approach is common in studies of chronic pain (Heffner et al., 2011; O’Brien et al., 2010; Sayar et al., 2002), adopting a multi-method assessment approach, using self-report questionnaires, polysomnography, and actigraphy, is considered optimal, while costly (de la Vega & Miró, 2013). Third, since our outcome (sleep quality) improved significantly from baseline to post-test only, we employed multiple mediation using two time points. For mediational analyses with clinical trial data, some authors recommend each variable be measured at 3 separate time points (Goldsmith et al., 2018). Finally, the PSQI demonstrated slightly lower internal consistency in our sample relative to the conventional cut-off for acceptability. Of note, our observed alpha coefficient of .68 is similar to other studies testing the PSQI in medical populations, including patients with breast cancer (Fontes et al., 2017), chronic fatigue syndrome (Mariman et al., 2012), temporomandibular disorders (Rener-Sitar et al., 2014), and others (see meta-analysis by Mollayeva et al., 2016). It may be that patients with certain medical diagnoses respond in a more heterogenous fashion to PSQI items than other populations.

To address these limitations, researchers may seek to replicate and extend our findings by incorporating comparison (e.g., CBT for pain/insomnia) and control groups, including an objective measure of sleep, and assessing variables of interest at mid-treatment to enhance temporal precedence. Overall, future intervention studies may benefit from investigating the role of mindfulness and relaxation skills in improving sleep quality among individuals with chronic pain.

Table 3.

Total Scores for Primary Variables

Variable – Measure (Possible Range) Baseline (N = 82) Post-intervention (N = 72) 3-month follow-up (N = 58)
M (SD) Sample Range M (SD) Sample Range M (SD) Sample Range
Outcome
 Sleep Quality – PSQIa (0 – 21) 9.47 (4.10) 1 – 19 8.38 (4.23) 1 – 19 8.34 (4.21) 2 – 16
Hypothesized Mediators
 Mindfulness – CAMS-Rb (12 – 48) 31.21 (6.75) 19 – 48 34.68 (6.49) 21 – 48 -- --
 Relaxation – MOCS-Ac subscale (0 – 8) 2.80 (1.93) 0 – 8 4.67 (1.88) 1 – 8 -- --
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Note. Higher scores on the PSQI indicated lower sleep quality (i.e., more sleep disturbance). Higher scores on the CAMS-R and MOCS-A relaxation subscale indicated higher levels of mindfulness and relaxation, respectively. “--” = variables from this time point not included in analyses.

a

PSQI: Pittsburgh Sleep Quality Index

b

CAMS-R: Cognitive and Affective Mindfulness Scale - Revised

c

MOCS-A: Measure of Current Status

Funding Sources:

This study was funded by an R34 grant (1R34AT009356-01A1) to the senior author and a K23 award (1K23AT01065301A1) to the second author, both from the National Center for Complimentary and Integrative Health.

Footnotes

Disclosure Statement: All authors declare that they have no conflicts of interest.

Conflict of Interest Statement

The authors have no relevant financial or non-financial interests to disclose.

Ethics Statement

All procedures performed in this study were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was approved by Massachusetts General Hospital’s Institutional Review Board (IRB).

Informed Consent Statement

Informed consent was obtained from all individual participants included in the study.

Data Availability

Data will be made available upon request by contacting the corresponding author.

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Data Availability Statement

Data will be made available upon request by contacting the corresponding author.

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