SLEEP AND CHRONIC PAIN

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Promoting Sleep Quality in Chronic Pain Patients

Christine Song
Edson College of Nursing and Health Innovation, Arizona State University

Author Note
Christine Song is registered nurse at Honor Health Hospital Arizona and a Doctoral of Nursing
Practice student at Arizona State University.
She has no known conflict of interest to disclose.
Correspondence should be addressed to Christine Song, Edson College of Nursing and Health
Innovation, Arizona State University, Health North Suite 301, PO Box 873020. Tempe,
AZ 85287-3020. Email: hsong@asu.edu

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Abstract

Objective: Chronic low back pain in adults is a global health and economic problem. Many with
back pain experience compromised sleep. While Cognitive Behavioral Therapy (CBT) is a gold
standard in improving sleep among individuals with pain, this approach requires trained staff.
The sleep hygiene education and meditation techniques, components of CBT, were utilized in
patients with chronic low back pain to improve sleep quality.
Methods: Twenty patients with chronic back pain volunteered to receive sleep hygiene
education and meditation videos to practice for 12 weeks and participate in 4-weekly phone
calls. Participants were assessed at baseline and post-treatment with the Pittsburgh Sleep Quality
Index (PSQI). Participants were patients at a local pain clinic with chronic low back pain without
untreated mental illness, sleep apnea, or restless leg syndrome. Informed consent was obtained
from participants, along with demographic data. Participants received a brochure with education
information to engage daily for 12 weeks. Participants were then contacted weekly by phone to
review the learned information.
Results: 13 participants completed the post-intervention questionnaire (35 % attrition rate).
Mean age was 55.15 yrs. and most were female (n=11). Paired t-test demonstrated that change in
pre and post PSQI score, and Medication Use did not show statistical significance (p=0.372;
p=0.502). However, Subjective Sleep Quality had clinical significance (p=.022) suggesting
individuals thought their sleep have improved.
Discussion: Sleep hygiene education and meditation techniques is an approach for individuals
considering non-invasive and cost-effective approach to improve sleep.
Keywords: sleep hygiene education, sleep quality, chronic low back pain

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Improving Chronic Back Pain and Sleep Quality
Obtaining adequate sleep every night is essential for every individual’s optimal health as
it can affect hormone levels, mood, and even weight. Characterized by an inability to initiate or
maintain sleep, insomnia is a common sleep disorder that affects many people and is associated
with the development of chronic diseases such as diabetes, cardiovascular disease, and
depression (Centers for Disease Control and Prevention [CDC], 2014; CDC, 2018). Insomnia is a
substantial burden for the healthcare system affecting nearly 25% of the general population with
an annual cost over $100 billion (Koffel et al., 2018).
Consequently, various organizations strive to raise public awareness of insomnia and
continue to develop evidence-based policies (American Academy of Sleep Medicine [AASM],
2020; CDC, 2017). However, management of insomnia among people who have existing comorbidities such as chronic pain is challenging as sleep has a bidirectional relationship with pain;
pain interferes with sleep, and lack of sleep can cause pain (Marshanasky et al., 2018).
Utilization of appropriate instruments and comprehensive assessment can help restore sleep
among chronic pain patients (Marshanasky et al., 2018; Martel et al, 2018).
Background and Significance
Insomnia has high associated costs; individuals with insomnia are more likely to use sick
leave and seek medical care (Dopheide, 2020). Loss of productivity can be disabling for many
individuals, especially for individuals in school or work. Along with the high prevalence of
insomnia, low back pain is a costly problem that affects up to 80% of adults at some point in
their life (Hajihasani et al., 2019). Individuals with chronic pain are more susceptible to
disrupted sleep. Delayed treatment and poor management can lead to various consequences,
including increased healthcare costs and adverse effects such as anxiety or depression. Among

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those who received treatment, many remained symptomatic despite using a benzodiazepine, a
medication with adverse effects when administered long-term (Sato et al., 2019).
Clinicians often address the severity of low back pain by prescribing medications like
non-steroid anti-inflammatory drugs (NSAIDs), muscle relaxants, and narcotics to help alleviate
pain (Zgierska et al., 2016). Such an approach complicates care for individuals who take
medications to improve their sleep. Long-term use of opioids and muscle relaxants can be a
double-edged sword with adverse effects such as drowsiness, sleepiness, respiratory depression,
and even death (Marshansky et al., 2018; Tang et al., 2019). Despite the various risk of drugdrug interactions and adverse effects, individuals can become reluctant to explore different
avenues, especially non-pharmacological approaches, to reduce pain.
Internal Evidence
A pain clinic located in the Southwestern United States provides a continuum of care for
individuals who have chronic back pain, neuropathy, or have undergone an operation for their
chronic pain. Out of 638 patients seen by two providers at the clinic in January 2021, 451
patients had a low back pain diagnosis (L. Baker, personal communication, February 10, 2021).
The two most common causes of low back pain were lumbar degenerative disc disease and
lumbar spondylosis.
In addition to inadequate pain control, a well identified problem at the pain clinic is the
prevalence of compromised sleep quality. The current approach to poor sleep in an individual is
sleep hygiene counseling and education based on individual needs (L. Baker, personal
communication, February 10, 2021). While sleep disturbance is recognized in this patient
population, the exact prevalence and extent of the insomnia are unclear as the pain clinic
primarily focuses on pain assessment and management. Currently, the reported barrier to

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managing sleep quality includes a lack of appropriate instruments, time, and management plans
to provide comprehensive sleep management. Therefore, intervention is warranted to minimize
the adverse effects of additional medication and provide safe and effective education.
This inquiry has led to the following clinically relevant PICOT question, “Among adults
with chronic low back pain (P), does cognitive behavioral therapy (I), compared to medication
alone (C), improve sleep quality (O) over a 12-week period (T)?”
Search Strategy
To answer the clinical question, an extensive search was completed through the
Cumulative Index to Nursing and Allied Health Literature (CINAHL), PubMed, and PsychINFO
databases. Keywords for the search included: insomnia, sleep disorder, cognitive behavioral
therapy, cognitive therapy, intervention, chronic pain, pain, and back pain. All search limits
were set to include publication within the last 5 years and English language.
A total of 57 articles were selected for further review. The publications were reviewed
thoroughly for inclusion and exclusion criteria. Inclusion criteria were studies that studied adult
participants and utilized CBT or behavioral change treatment as an intervention. Additionally,
the intervention must have targeted chronic pain, sleep disorder, or both. Studies from various
countries, settings, and treatment delivery methods were considered. Exclusion criteria were
studies that only investigated adolescents or pediatric populations and articles that propose trials
but have not completed participant recruitment. Studies that examined other medical conditions
other than chronic pain and sleep disorder were also excluded. After careful review through rapid
critical appraisal (RCA), 10 studies were chosen for the literature review. Of these, nine
publications were randomized controlled trials (RCT) and one cohort study (see Appendix A,
Table 1).

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Critical Appraisal & Synthesis of Evidence
The RCA process developed by Melnyk and Fineout-Overholt (2019) was utilized to
evaluate the quality of the 10 articles selected for this literature review. Most of the studies were
high level evidence, including 9 RCTs and one cohort study (see Appendix A, Table 2). All
studies received funding; however, only two studies recognized potential researcher bias. Two
studies had large sample sizes and the rest had small sample sizes. However, small sample size
was acceptable given the strict inclusion criteria. The intervention period ranged from 4 weeks to
12 weeks. However, follow-up data were collected both post-treatment and several weeks after
intervention completion in most studies. Follow-up data allowed to determine the long-term
effects of the intervention. The sample characteristics were relatively comparable among ten
studies. The majority of participants in nine studies were middle-aged females, and one study
primarily focused on females. All studies were conducted in outpatient settings.
The location of the study conducted varied widely. Three out of 10 studies were
conducted in the USA, while the rest were in various countries, including Sweden, Norway,
Japan, and the United Kingdom. Such heterogeneity provided insight into the effectiveness of
cognitive-behavioral education in other cultures. Regardless of the country, all studies produced
positive outcomes in the participants.
There is compelling evidence to suggest that CBT focusing on pain is effective in
producing better sleep quality and even improved mood (Blake et al., 2015; Espie et al., 2019;
Sato et al., 2019). The benefits of improved sleep among individuals who have chronic pain are
numerous. Enabling individuals to recognize their current health behaviors and promote positive
lifestyle changes could reduce medication use; improve anxiety, depression, and sleep
characteristics.

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AASM (2020) also recognizes Cognitive Behavioral Therapy (CBT) as first-line
approach to improve sleep quality. CBT is a psychotherapy that entails behavioral, cognitive, and
educational components (Edinger et al., 2021; Espie et al., 2019). It is an engaging therapy that
challenges individuals to recognize the negative thoughts and beliefs towards their health
condition (Hanscom et al., 2015). The primary difference between CBT and traditional clinicianguided education is that CBT is patient-centered. Such an approach helps change individuals’
perspectives towards a problem, identify barriers, and make positive changes. When an
individual voluntarily makes the change, its effect lasts longer (Blom et al., 2016).
However, CBT is a rigorous therapy that requires an experienced clinician. The evidence
suggests that the core components of CBT, sleep hygiene, dysfunctional thought, and
relaxational technique, produce positive outcomes such as improved mood (Blake et al., 2015;
Espie et al., 2019; Sato et al., 2019). Sleep hygiene education and meditation techniques are less
labor-intensive strategies to improve sleep. According to Zengin and Aylaz (2019), sleep hygiene
education helped increase sleep quality and decreased fatigue among individuals receiving
chemotherapy. Sleep hygiene education and relaxation exercises have also improved sleep
among postmenopausal women who are suffering from insomnia (Duman & Timur Taşhan,
2018). Given the bidirectional relationship between chronic pain and sleep, individuals with
chronic pain can benefit from cost-effective education to improve sleep quality and potentially
decrease the need for additional medication. This inquiry has led to the following evaluation
questions, “Among adults with chronic low back pain, does CBT-based education improve sleep
quality over 12 weeks?”
Theoretical Framework and Implementation Framework

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The Cognitive Model (CM) was chosen as the conceptual framework for this project for
its applicability (see Appendix B, Figure 1). Aaron Beck first formulated the CM over 45 years
ago, and CM-based psychotherapies are often utilized synonymously to CBT (Knapp & Beck,
2008). Early in Beck’s CM journey, he identified the Cognitive Triad, which consists of three
elements of the belief system: negative representations of the self, the personal world, and the
future (see Appendix B, Figure 2). These three factors influence each other and form either a
positive or negative outlook in an individual. Such a discovery prompted researchers to develop
therapies and theoretical frameworks that promote positive thoughts and behaviors.
According to Beck Institute (n.d.), the CM consists of these six key elements: situation,
automatic thought and images, reactions, emotion, physical, and physiological (see Appendix B,
Figure 1). The CM gives insight into how one’s perceptions or spontaneous thoughts about the
environment or situation can influence emotional, physical, and psychological reactions. All six
elements continuously interact with one another. This interactive model explains why an
individual in physical pain can develop negative thoughts about the situation and respond with
negative emotions and behaviors. The goal of CBT is to help the individual recognize this
cognitive process and thereby promote positive responses. Therefore, utilizing the core aspect of
CBT may positively impact individuals’ health outcomes
The Model for Change to Evidence-Based Practice (Rosswurm & Larrabee, 1999) is
appropriate for this project in developing and implementing an intervention. This model is a
frequently utilized framework for quality improvement projects as it provides a systematic
guidance for change in practice (see Appendix B, Figure 3). It involves six steps that generally
progress successively; however, the researcher can revise the prior steps at any point if needed
(Rosswurm & Larrabee, 1999).

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The initial step of the framework helped identify the need for change. The fieldwork helped
identify problems, gaps, and issues in the current practice. The next step involved assessing the
problems to current practice, potential intervention, and the benefit of the change. Next, the
writer completed literature synthesized for its quality. Then, the writer further defined the
proposed change and designed the implementation and study. The writer encountered the barrier
that CBT requires a trained and qualified clinician, and the intervention was re-considered. The
writer reexamined the current literature and evidence-based practice, and intervention was reconsidered. When planning was complete, the execution of the study began, and the writer
evaluated the result to determine whether to reject or adopt the practice change. These steps were
appropriate to yield a high-quality improvement project and allow future studies with necessary
modifications. Therefore, this theoretical model worked seamlessly in developing a plan for
patients with chronic pain who also experience s poor sleep quality.
Methods
Privacy and Confidentiality
The project approval was received from the Arizona State University’s Institutional Review
Board in September 2021. The participants were recruited through project flyers which provided
details about the expectation of the project, eligibility, and contact information of the project lead.
Willing individuals signed the informed consent after receiving further information about the
background, significance, intervention, screening tool, and the risk and benefit of the project. The
privacy of the participants was protected by including no identifiable patient information. All
documents were linked to the last four digits of the patient’s phone number to allow for paired
analysis at the completion of the project. The collected data were kept on a password-protected
electronic device that is only accessible by the project lead. The data was managed and stored

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until the study was completed and published.
Inclusive and Exclusive Criteria
Inclusive criteria were existing patients at the pain clinic aged 18 or older. Individuals
diagnosed with chronic low back pain, lumbar degenerative disc disease, or lumbar spondylosis
were eligible to participate. An in-person encounter for the initial assessment was necessary.
Individuals who scheduled a telemedicine encounter were excluded. In addition, individuals with
an unmanaged mental disorder or untreated obstructed sleep apnea and restless leg syndrome
were excluded. Such parameters were necessary to exclude health conditions that can contribute
to compromised sleep.
Project Procedure
Participants were invited to engage in daily sleep hygiene practices and meditation
activities for 12 weeks to improve sleep quality. Sleep quality was assessed utilizing the
Pittsburg Sleep Quality Index (PSQI) at the beginning of the project and 12 weeks after the
initial encounter. In addition to the screening tool, demographic data and their contact
information was collected to perform weekly follow-up phone calls for the next four weeks.
Completing the PSQI and filling out the initial information took about 10 minutes. Completing
the PSQI and filling out the initial information took about 10 minutes. After this, participants
were provided with a sleep hygiene education brochure with recommended activities to help
improve their sleep to practice daily. Participants received face-to-face education regarding these
instructions and had an opportunity to ask questions. The education included the significance of
sleep among individuals with chronic pain, sleep hygiene education recommendations, and links
to meditation videos.

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After the initial encounter, participants were contacted by phone weekly for four weeks to
review any questions or concerns and to assess their ability to engage in the provided activities.
Participants were encouraged to review the sleep education brochure during this time. Each
phone call lasted between 5 to 10 minutes. Participants had the right not to answer any question
and to stop participation at any time. Participants were expected to return to the clinic after 12
weeks to complete another PSQI questionnaire. This visit was coordinated with a regular followup visit to the clinic. However, if participant was unable to return in-person, the project lead read
all the instructions and questions on the PSQI questionnaire for the participant to answer.
Data Collection and Outcome Measurement
The measurable outcome was improved sleep quality through behavioral changes. This
links to the CM framework that positive thought yields desired behaviors. Demographic
information collected was participant’s age, gender, primary diagnoses, and status of mental
disorder, if applicable. The PSQI questionnaire result assessed baseline sleep quality. Then, at
the end of the project, a repeat PSQI questionnaire result evaluated the efficacy of sleep
education. The PSQI is a tool that is easy to use and understand, measures sleep quality, and
distinguishes good and poor sleepers (Buysse et al., 1989). The PSQI comprises seven
components: Subjective Sleep Quality, Sleep Latency, Sleep Duration, Habitual Sleep Efficacy,
Sleep Disturbances, Use of Sleep Medication, Daytime Dysfunction. The score ranges from 0-to
3 for each component, with a total possible score of 21. A score of 5 or more indicated poor
sleep quality (Buysse et al.,1989). According to Buysse et al. (1989), the global PSQI tool
demonstrated a sensitivity of 89.6% and specificity of 86.5% in identifying good and poor
sleepers.
Budget and Funding

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The total expense for this project was $375 (see Appendix A, table 3). Expense items include
stationeries, brochures, survey print-out, gas for transportation, and intellectus software. No
funding was received for the project.
Results
A total of 20 individuals volunteered to participate in the project; 13 participants
completed the post-intervention survey. The attrition rate was 35%. The mean age of participants
who completed the project was 55.15 years. Out of 13 participants, 84.62% (n=11) were female
participants and 84.62% (n=11) were Caucasians. Such an outcome is consistent with the
literature where most participants were female with a mean age of 47.95 yrs. (Blake et al., 2015;
Burke et al., 2019; Espie et al., 2019; Lami et al., 2018; McCrae at el., 2019; Nordin et al., 2016;
Sato et al., 2019; Smitherman et al.2016; Vedaa et al., 2020; Zgierska et al., 2016).
The data analysis with paired t-tests and descriptive statistics showed a slight reduction in
total PSQI score, indicating improvement but without statistical significance (p=.372). Similarly,
The Use of Sleep Medication component also had some improvement but without statistical
significance (p=.502). The result had good internal reliability with Cronbach’s alpha coefficient
of .87.
While the overall PSQI score did not have statistical significance, the Subjective Sleep
Quality component indicated clinical significance (p=.022) with Cronbach’s alpha coefficient
of .90, indicating excellent internal reliability. Before the intervention, the mean PSQI score was
1.62 with the Standard Deviation (SD) of 0.77. After the intervention, the mean PSQI was 1.27
with an SD of 0.83. Many participants also commented that they were sleeping better during the
subsequent visit to complete the questionnaire. Such findings indicate that participants believed

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they were sleeping better than they did. This positive shift in their belief can improve their sleep
habits with continuous education.
Discussion
Overall, sleep hygiene education and meditation techniques did not have statistical
significance to the overall sleep quality index score. However, there was clinical significance.
Participants considered they were sleeping better than before the study. For patients who have
back pain and are willing to explore a non-invasive approach, the project intervention provides
cost-effect methods to encourage a suitable environment for sleep. If utilized accordingly,
improved sleep quality through the non-invasive way can reduce the need for providers to
prescribe additional medication with undesirable side effects or dependence. Reducing the need
for hypnotics or pain medication can prevent medical treatment related to medication-related
complications.
The feasibility and sustainability of the project intervention rely on patients, healthcare
providers, medical directors, and staff caring for the patients. In a busy clinic, additional
education or screening tools can be unfavorable. Hence, healthcare staff perspective assessment
and buy-in are necessary to deliver appropriate educational material. Implementing another
screening tool at the clinic is challenging as individuals complete several screening tools at each
visit. However, continuous use of the brochure is feasible as it provides standardized education
without requiring skilled staff.
Additional study is necessary to provide an innovative approach to improving sleep
quality among individuals with chronic pain. Limitations and barriers encountered identified are
external factors that could contribute to poor sleep quality. Participants verbalized traveling,
holiday gatherings, and moving were barriers to practicing sleep hygiene education and

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meditation techniques. In addition, when arranging follow-up questionnaires during their existing
provider visit, some individuals canceled or rescheduled their appointment. Missed appointments
resulted in a loss in follow-up by the project investigator.
Previous literature has demonstrated insignificant findings to determine the effectiveness
of sleep hygiene education and medication techniques among individuals with insomnia or other
comorbidities. The project finding aligns with other existing studies and their limitations. Despite
various limitations and barriers, additional investigation is warranted. Individuals at the pain
clinic recognize the importance of obtaining adequate sleep and its benefit to overall health
quality. Future studies can investigate more interactive education and individualized approach.

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back pain: Pilot findings from a randomized controlled trial. Pain Medicine, 17(10),
1865–1881. https://doi.org/10.1093/pm/pnw006

SLEEP AND CHRONIC PAIN

20
Appendix A
Evaluation and Synthesis Table

Table 1
Quantitative Evaluation Table
Citation

Blake et al.
(2015)
The impact of a
cognitive
behavioral pain
management
program on
sleep in patients
with chronic
pain: Results of
a pilot study.
Funding:
Brona M. Fullen
received a nonrestricted
educational
grant from
Pfizer

Theory/
Framewor
k
CM,
inferred

Design/ Method

Design: NonRCT/ cohort
study
Purpose:
To determine the
impact of a CBTpain management
program on sleep
in patients with
chronic pain
Sampling:
Purposive

Sample/ Setting

Major Variables
& Definitions

Measurement/
Instrumentation

Data Analysis

N=46
CG: n=22
IG: n=24
Demographic:
M age
(IG=47.7;
CG=46.8),
number of years
with pain
(IG=6.9; CG=
7.9; pvalue=0.80),
smoking,
employment
status.
Daily NSAID
use (IG n = 8,
CG n = 0)
Setting:
Outpatient

IV: CBT

PSQI,
Simmond's
functional tests,
HADS, WASO

All data
coded

DV1: Sleep
quality
DV2: Mood
DV3: Physical
function
Definition:
Multidisciplinar
y approach
(daily
physiotherapy,
gym-based
sessions,
hydrotherapy
pool); 3 days a
week for 6
hours for 4

Actigraphy

Pearson’s
correlation
coefficients
ANOVA
models

Findings/
Results
∆ 2 months
DV1: P =
0.04
DV2:
P<0.05

LOE; Decision
for practice/
application to
practice
LOE: level 3
Strength:
PSQI has
sensitivity:98.7
& specificity:
84.4;
Actigraphy is
cost-effective
& easy; CBT
help with
anxiety and
depression.
Weakness:
small sample
size, low
statistical
power. Follow
up limited to 2
months. More
IG utilized

Key: ANCOVA – Analysis of covariance; ANOVA – Analysis of variance; BPI – brief pain inventory; CBT – Cognitive behavioral therapy; CM – Cognitive
model; CSQ – coping strategies questionnaire; CG – control group; DCBT—digital cognitive behavioral therapy; DV—dependent variable; ESS – Epworth
sleepiness scale; f – female; FAM – Fear Avoidance Model; FM – fibromyalgia; GAD – Generalized anxiety disorder scale; GSII – Glasgow Sleep Impact
Index; HADS – Hospital anxiety and depression scale; ICM – Integrated care model; IG – intervention group; ITT – intention-to-treat analysis; IV –
independent variable; LOE – level of evidence; LBP – low back pain; LT – Learning theory; M – mean; N – number of participants in study; n – number of
participants in subset; NSAID– Nonsteroidal anti-inflammatory drug; PHQ-9 – patient health questionnaire-depression scale; PSQI – Pittsburgh Sleep Quality
Index; QOL – quality of life scale; RCT – randomized control trial; SES – self efficacy scale; SCT – Self-care Theory; UC – usual care; VAS – visual analog
scale; WASO – wake after sleep onset; y– year; ∆ – pretest to posttest change for intervention group; & – and

SLEEP AND CHRONIC PAIN
Citation

Theory/
Framewor
k

Design/ Method

Healthcare
Ireland
Country:
United
Kingdom

Sample/ Setting

Exclusion:
Declined to
participate

Major Variables
& Definitions

Measurement/
Instrumentation

Data Analysis

Findings/
Results

PROMIS, GSII,
PHQ-9, GAD

Linear mixedeffects model

∆ 8 weeks
& 24 weeks
DV1: P =
<0.001 (IG)

weeks with
post-program
review at 2 and
6 months.

Attrition:
2.17%

Bias: No

Espie et al.
(2019)

21

CM,
inferred

Design:
RCT

Effect of digital
cognitive
behavioral
therapy for
insomnia on
health,
psychological
well-being, and
sleep-Related
quality of life:
A randomized
clinical trial

Purpose:
To determine the
impact of a
DCBT- insomnia
management
program on sleep
in patients with
chronic pain
compared to
sleep hygiene
education.

Funding:

Sampling:
Convenience

N=1711
CG: n= 853
IG: n=858
Demographic:
f= 77.7%, M
age 48, White
(91.1%)
Setting:
outpatient
Exclusion: life
expectancy of
less than 6
months,
currently
receive
psychological
treatment for

IV: DCBT
DV1: Sleep
Quality
DV2: Physical
Function
DV3: Mood
Definition:
IG: DCBT
delivered via
Sleepio
program and
mobile app;

DV3:
P= <0.001
GAD
P<0.001
PHQ-9

LOE; Decision
for practice/
application to
practice
NSAID at
baseline
Feasibility:
Test is easily
duplicated for
future study
with larger
participants.
Clinician can
encourage CBT
for other health
conditions.
LOE: level 2
Strength:
Large sample
size; easily
deliverable
intervention;
included
individuals
who take
medications for
sleep problems
or for other
physical and
mental
problem.

Key: ANCOVA – Analysis of covariance; ANOVA – Analysis of variance; BPI – brief pain inventory; CBT – Cognitive behavioral therapy; CM – Cognitive
model; CSQ – coping strategies questionnaire; CG – control group; DCBT—digital cognitive behavioral therapy; DV—dependent variable; ESS – Epworth
sleepiness scale; f – female; FAM – Fear Avoidance Model; FM – fibromyalgia; GAD – Generalized anxiety disorder scale; GSII – Glasgow Sleep Impact
Index; HADS – Hospital anxiety and depression scale; ICM – Integrated care model; IG – intervention group; ITT – intention-to-treat analysis; IV –
independent variable; LOE – level of evidence; LBP – low back pain; LT – Learning theory; M – mean; N – number of participants in study; n – number of
participants in subset; NSAID– Nonsteroidal anti-inflammatory drug; PHQ-9 – patient health questionnaire-depression scale; PSQI – Pittsburgh Sleep Quality
Index; QOL – quality of life scale; RCT – randomized control trial; SES – self efficacy scale; SCT – Self-care Theory; UC – usual care; VAS – visual analog
scale; WASO – wake after sleep onset; y– year; ∆ – pretest to posttest change for intervention group; & – and

SLEEP AND CHRONIC PAIN
Citation

Theory/
Framewor
k

Design/ Method

Big Health Ltd;
Supported by
multiple
research centers
Country:
United
Kingdom

Cognitive
behavioral

Sample/ Setting

Major Variables
& Definitions

insomnia or
were expecting
treatment within
6 months, who
reported
suicidal
thoughts

CG: access to
website and
downloadable
booklet;
Recommend
bed routines and
use of alcohol
and caffeine

Attrition: 47%

Bias: Dr.Espie
is a cofounder,
chief medical
officer,
shareholder.
Receive salary
from Big Health
Ltd and is a
developer.

McCrae et al.
(2019)

22
Measurement/
Instrumentation

Data Analysis

Findings/
Results

LOE; Decision
for practice/
application to
practice
Weakness:
single-blinded
RCT;
convenience
sampling;
white females;
questionnaires;
high attrition
rate; singleblinded RCT
Feasibility:
Clinician can
encourage
DCBT for
other health
conditions.
Highly
motivated
patient can
benefit from
the treatment.
DCBT; cost
effective.

CM,
inferred

Design: RCT
Purpose:
To examine the
effects of

N=113
CG: n=37
IG-Insomnia:
n=39
IG-Pain: n=37

IV: CBT
DV1: Sleep
DV2: Pain
Intensity

WASO, Sleep
Efficacy, Sleep
Quality, Sleep
Onset Latency

ANOVA

∆ 6 months
DV1:
IG-

LOE: level 2
Strength:
Cost-effective
and easy to

Key: ANCOVA – Analysis of covariance; ANOVA – Analysis of variance; BPI – brief pain inventory; CBT – Cognitive behavioral therapy; CM – Cognitive
model; CSQ – coping strategies questionnaire; CG – control group; DCBT—digital cognitive behavioral therapy; DV—dependent variable; ESS – Epworth
sleepiness scale; f – female; FAM – Fear Avoidance Model; FM – fibromyalgia; GAD – Generalized anxiety disorder scale; GSII – Glasgow Sleep Impact
Index; HADS – Hospital anxiety and depression scale; ICM – Integrated care model; IG – intervention group; ITT – intention-to-treat analysis; IV –
independent variable; LOE – level of evidence; LBP – low back pain; LT – Learning theory; M – mean; N – number of participants in study; n – number of
participants in subset; NSAID– Nonsteroidal anti-inflammatory drug; PHQ-9 – patient health questionnaire-depression scale; PSQI – Pittsburgh Sleep Quality
Index; QOL – quality of life scale; RCT – randomized control trial; SES – self efficacy scale; SCT – Self-care Theory; UC – usual care; VAS – visual analog
scale; WASO – wake after sleep onset; y– year; ∆ – pretest to posttest change for intervention group; & – and

SLEEP AND CHRONIC PAIN
Citation

treatments for
insomnia and
pain in adults
with comorbid
chronic
insomnia and
fibromyalgia:
clinical
outcomes from
the SPIN
randomized
controlled trial
Funding:
Funded by the
National
Institute of
Arthritis and
Musculoskeletal
and Skin
Diseases
Country:
USA
Bias: none

Theory/
Framewor
k

23

Design/ Method

Sample/ Setting

cognitive
behavioral
treatments for
insomnia and
pain in patients
with comorbid
fibromyalgia and
insomnia.

Demographic:
mean age: 53
f: (IG-Insomnia
& CG: 100%;
IG-Pain:
91.89%)

Sampling:
Purposive
sampling

Setting:
outpatient
Exclusion: <11
tender points,
other sleep
disorder,
medical and
psychiatric
condition
Attrition:
34.51%

Major Variables
& Definitions

Measurement/
Instrumentation

DV3: Mood

VAS

Definition:
IG: 8
audiotaped
sessions, 50
minutes each

Beck
Depression
Inventory

IG-Insomnia:
- Sleep
education
- Sleep hygiene
and stimulus
control
- Relaxation
- Cognitive
therapy

Ambulatory
polysomnograp
hy

IG-Pain:
- Pain education
- Progressive
muscle
relaxation Autogenic
relaxation
- Visual
imaginary
- Cognitive
therapy
- Review skills

Actigraphy

Data Analysis

Power
analysis

Findings/
Results
Insomnia
P = .02
IG-Pain
P= .06
DV2:
morning
pain
P = 0.06
DV3:
P>0.08

LOE; Decision
for practice/
application to
practice
use; excluded
individuals
with other
sleep disorder
Weakness:
Participants
were
compensated;
excluded
individuals
with medical
and psychiatric
condition;
female; selfreport
questionnaires
Feasibility:
CBT can be
beneficial for
both pain and
insomnia;
modifiable for
individual’s
health
condition.

Key: ANCOVA – Analysis of covariance; ANOVA – Analysis of variance; BPI – brief pain inventory; CBT – Cognitive behavioral therapy; CM – Cognitive
model; CSQ – coping strategies questionnaire; CG – control group; DCBT—digital cognitive behavioral therapy; DV—dependent variable; ESS – Epworth
sleepiness scale; f – female; FAM – Fear Avoidance Model; FM – fibromyalgia; GAD – Generalized anxiety disorder scale; GSII – Glasgow Sleep Impact
Index; HADS – Hospital anxiety and depression scale; ICM – Integrated care model; IG – intervention group; ITT – intention-to-treat analysis; IV –
independent variable; LOE – level of evidence; LBP – low back pain; LT – Learning theory; M – mean; N – number of participants in study; n – number of
participants in subset; NSAID– Nonsteroidal anti-inflammatory drug; PHQ-9 – patient health questionnaire-depression scale; PSQI – Pittsburgh Sleep Quality
Index; QOL – quality of life scale; RCT – randomized control trial; SES – self efficacy scale; SCT – Self-care Theory; UC – usual care; VAS – visual analog
scale; WASO – wake after sleep onset; y– year; ∆ – pretest to posttest change for intervention group; & – and

SLEEP AND CHRONIC PAIN
Citation

Sato et al.
(2019)
Effectiveness of
Internetdelivered
computerized
cognitive
behavioral
therapy for
patients with
insomnia who
remain
symptomatic
following
pharmacotherap
y: Randomized
controlled
exploratory trial
Funding:
Supported by
Grant-in-Aid for
Scientific
Research from
the Japan
Society for the

Theory/
Framewor
k

CM,
inferred

Design/ Method

Design: RCT
Purpose: exami
ne the
effectiveness of
our DCBT
program as an
adjunct to UC in
patients with
insomnia who
remain
symptomatic
following
hypnotics
Sampling:
Purposive

24
Sample/ Setting

Major Variables
& Definitions

Measurement/
Instrumentation

Data Analysis

N= 23
CG (UC): n=12
IG
(UC+DCBT):
n=11

IV: DCBT

Self-rated
questionnaire
(PSQI, HADS,
QOL)

Fisher exact
test

Demographic:
M age= 50;
nonsmoker.
PSQI>5.5 after
use of hypnotics
(100%)
Setting:
outpatient
Exclusion:
Severe anxiety
and depression,
psychosis,
organic mental
disorder, or
current high risk
of suicide,
substance
abuse, or
dependence

DV1: Sleep
characteristics
DV2: quality of
life
DV3: mood
Definition:
UCpharmacotherap
y & received
email
magazines with
general
information
about insomnia
and hypnotics 4
times over a 6week period.
CG received
UC and 5
weekly face-toface CBT for
insomnia

ANCOVA

Findings/
Results

LOE; Decision
for practice/
application to
practice

∆ 6 weeks:

LOE: level 2

DV1:

Strength:
No adverse
effect of
DCBT; costeffective;
studies
individual who
takes
benzodiazepine
s; no changes
of sleep
medication
during the
study;
participants
were blinded

IG:
P<0.001
∆12 weeks:
DV2:
P<0.01
DV3:
HADS (IG)
p<0.01

Weakness:
small sample
size; short
follow up;
subjective data;
assessors not
blinded

Key: ANCOVA – Analysis of covariance; ANOVA – Analysis of variance; BPI – brief pain inventory; CBT – Cognitive behavioral therapy; CM – Cognitive
model; CSQ – coping strategies questionnaire; CG – control group; DCBT—digital cognitive behavioral therapy; DV—dependent variable; ESS – Epworth
sleepiness scale; f – female; FAM – Fear Avoidance Model; FM – fibromyalgia; GAD – Generalized anxiety disorder scale; GSII – Glasgow Sleep Impact
Index; HADS – Hospital anxiety and depression scale; ICM – Integrated care model; IG – intervention group; ITT – intention-to-treat analysis; IV –
independent variable; LOE – level of evidence; LBP – low back pain; LT – Learning theory; M – mean; N – number of participants in study; n – number of
participants in subset; NSAID– Nonsteroidal anti-inflammatory drug; PHQ-9 – patient health questionnaire-depression scale; PSQI – Pittsburgh Sleep Quality
Index; QOL – quality of life scale; RCT – randomized control trial; SES – self efficacy scale; SCT – Self-care Theory; UC – usual care; VAS – visual analog
scale; WASO – wake after sleep onset; y– year; ∆ – pretest to posttest change for intervention group; & – and

SLEEP AND CHRONIC PAIN
Citation

Theory/
Framewor
k

Design/ Method

Promotion of
Science

25
Sample/ Setting

Major Variables
& Definitions

Measurement/
Instrumentation

Data Analysis

Findings/
Results

within the 12
months before
enrollment,
antisocial
personality
disorder, or
unstable
medical
condition

Country: Japan
Bias: none

LOE; Decision
for practice/
application to
practice
Feasibility:
Clinician can
encourage
DCBT for
other health
conditions.
DCBT has no
adverse effects.

Attrition:
4.34%
Nordin et al.
(2016)
Effects of the
web behavior
change program
for activity and
multimodal pain
rehabilitation:
Randomized
controlled trial
Funding:
Financed by the
REHSAM
research project,
a cooperation

CM,
inferred

Design: RCT
Purpose:
To evaluate the
effects of
multimodal pain
rehabilitation in
combination with
the DCBT
compared with
MMR alone
among persons
with persistent
musculoskeletal
pain in primary
health care

N=109
CG (UC): n=49
IG (UC &
DCBT): n= 60
Demographic:
M age=43;
f=85% Pain
duration (CG=
78 months;
IG=79months)
Setting:
outpatient
Exclusion:

IV: DCBT
DV1: Pain
intensity
DV2: selfefficacy
DV3: coping
strategies
Definition:
UC= patientcentered
biopsychosocial
treatments with
at least three
health care
professionals; 2

Questionnaire
(VAS, SES,
CSQ)

Independentsamples t test,
MannWhitney U tes
t,
chi-square
test

DV1&
DV2
P=0.002
∆12 weeks
DV3:
P=0.003

ANOVA
*other
finding:
Satisfaction
∆ 4 months:
P<.001

LOE: level 2
Strength:
longer pain
duration and
higher level of
pain
Weakness: No
significance
found between
groups; bias in
sampling
method;
Questionnaire;
control group
also received

Key: ANCOVA – Analysis of covariance; ANOVA – Analysis of variance; BPI – brief pain inventory; CBT – Cognitive behavioral therapy; CM – Cognitive
model; CSQ – coping strategies questionnaire; CG – control group; DCBT—digital cognitive behavioral therapy; DV—dependent variable; ESS – Epworth
sleepiness scale; f – female; FAM – Fear Avoidance Model; FM – fibromyalgia; GAD – Generalized anxiety disorder scale; GSII – Glasgow Sleep Impact
Index; HADS – Hospital anxiety and depression scale; ICM – Integrated care model; IG – intervention group; ITT – intention-to-treat analysis; IV –
independent variable; LOE – level of evidence; LBP – low back pain; LT – Learning theory; M – mean; N – number of participants in study; n – number of
participants in subset; NSAID– Nonsteroidal anti-inflammatory drug; PHQ-9 – patient health questionnaire-depression scale; PSQI – Pittsburgh Sleep Quality
Index; QOL – quality of life scale; RCT – randomized control trial; SES – self efficacy scale; SCT – Self-care Theory; UC – usual care; VAS – visual analog
scale; WASO – wake after sleep onset; y– year; ∆ – pretest to posttest change for intervention group; & – and

SLEEP AND CHRONIC PAIN
Citation

Theory/
Framewor
k

between the
Swedish Social
Insurance
Agency, the
Ministry of
Health and
Social Affairs,
the Swedish
Association of
Local
Authorities and
Regions.

Design/ Method

Sampling:
purposive

26
Sample/ Setting

Major Variables
& Definitions

Reduced
Cognitive
ability, current
alcohol or drug
abuse, need
other medical
care, pregnancy

to 3 times a
week for 8
weeks

Measurement/
Instrumentation

Data Analysis

Findings/
Results

DCBT: 8 selfguided modules

Attrition:9.17
%

Feasibility:
utilize costeffective
intervention for
chronic pain
patients (>5
years), increase
adherence and
patient
satisfaction.
make them feel
more in control
with their
health.

Country:
Sweden
Bias: none

Zgierska et al.,
(2016)
Mindfulness
meditation and
cognitive
behavioral

ICM
CM,
inferred

Design: RCT
Purpose:
To assess
benefits of
mindfulness
meditation and
CBT for opioid-

N=35
CG (UC only):
n=14
IG (UC+CBT):
n=21
Demographic:

LOE; Decision
for practice/
application to
practice
patientcentered
treatment
provided by
healthcare
professionals.

IV: CBT
DV1: pain
intensity
DV2: physical
function

BPI, Oswestry
Disability
Index,
Biomarkers,
Opioid dose,
Thermal

linear mixed
model
ITT
Wilcoxon
tests

DV1: ∆ 8 &
26 weeks:
P = 0.045
thermal
stimuli
P < 0.05

LOE: level 2
Strength: RCT
study; no
participants
withdrew from

Key: ANCOVA – Analysis of covariance; ANOVA – Analysis of variance; BPI – brief pain inventory; CBT – Cognitive behavioral therapy; CM – Cognitive
model; CSQ – coping strategies questionnaire; CG – control group; DCBT—digital cognitive behavioral therapy; DV—dependent variable; ESS – Epworth
sleepiness scale; f – female; FAM – Fear Avoidance Model; FM – fibromyalgia; GAD – Generalized anxiety disorder scale; GSII – Glasgow Sleep Impact
Index; HADS – Hospital anxiety and depression scale; ICM – Integrated care model; IG – intervention group; ITT – intention-to-treat analysis; IV –
independent variable; LOE – level of evidence; LBP – low back pain; LT – Learning theory; M – mean; N – number of participants in study; n – number of
participants in subset; NSAID– Nonsteroidal anti-inflammatory drug; PHQ-9 – patient health questionnaire-depression scale; PSQI – Pittsburgh Sleep Quality
Index; QOL – quality of life scale; RCT – randomized control trial; SES – self efficacy scale; SCT – Self-care Theory; UC – usual care; VAS – visual analog
scale; WASO – wake after sleep onset; y– year; ∆ – pretest to posttest change for intervention group; & – and

SLEEP AND CHRONIC PAIN
Citation

therapy
intervention
reduces pain
severity and
sensitivity in
opioid-treated
chronic low
back pain: Pilot
findings from a
randomized
controlled trial
Funding: Funds
from the
University of
WisconsinMadison;
Clinical and
Translational
Science Award,
National
Institutes of
Health &
National
Institute on
Alcohol Abuse
and Alcoholism
Country: USA
Bias: none

Theory/
Framewor
k

Design/ Method

treated chronic
low back pain

Sampling:
Purposive

27
Sample/ Setting

Major Variables
& Definitions

Measurement/
Instrumentation

M age= 51.8;
white f=80%

DV3:
Medication use

Sensory
Analyzer

Setting:
outpatient

Definition:
UC= opioid
management by
regular
clinician;
pharmacotherap
y, safety,
treatment
progress
monitoring,
specialty care;
physical
therapy,
complementary
therapies for
pain and/or
mental health

Exclusion:
No daily opioid
use: prior
experience with
mindfulness
meditation
training,
inability to
consent for or
reliably
participate in
study activities;
diagnoses of
borderline
personality,
bipolar, or
delusional
disorders; or
current
pregnancy
Attrition: 0%

IG= UC and
2 hours per
week
manualized
training,
mindful
meditation at
least 6
days/week for at

Data Analysis

Findings/
Results

DV2:
P=0.434
DV3:
P=0.654

LOE; Decision
for practice/
application to
practice
the study;
included more
than selfquestionnaire;
involved
individuals
with severe
chronic opioids
use in large
dosage
Weakness:
small sample
size;
participants
received a
financial
renumeration
upon
completion;
individuals can
decline to
participate; no
significant
benefit found
in biomarkers;
non-blinding
Feasibility:
High retention

Key: ANCOVA – Analysis of covariance; ANOVA – Analysis of variance; BPI – brief pain inventory; CBT – Cognitive behavioral therapy; CM – Cognitive
model; CSQ – coping strategies questionnaire; CG – control group; DCBT—digital cognitive behavioral therapy; DV—dependent variable; ESS – Epworth
sleepiness scale; f – female; FAM – Fear Avoidance Model; FM – fibromyalgia; GAD – Generalized anxiety disorder scale; GSII – Glasgow Sleep Impact
Index; HADS – Hospital anxiety and depression scale; ICM – Integrated care model; IG – intervention group; ITT – intention-to-treat analysis; IV –
independent variable; LOE – level of evidence; LBP – low back pain; LT – Learning theory; M – mean; N – number of participants in study; n – number of
participants in subset; NSAID– Nonsteroidal anti-inflammatory drug; PHQ-9 – patient health questionnaire-depression scale; PSQI – Pittsburgh Sleep Quality
Index; QOL – quality of life scale; RCT – randomized control trial; SES – self efficacy scale; SCT – Self-care Theory; UC – usual care; VAS – visual analog
scale; WASO – wake after sleep onset; y– year; ∆ – pretest to posttest change for intervention group; & – and

SLEEP AND CHRONIC PAIN
Citation

Theory/
Framewor
k

Design/ Method

28
Sample/ Setting

Major Variables
& Definitions

Measurement/
Instrumentation

Data Analysis

IV: CBT

Actigraphy

ITT

DV1: Headache
frequency
DV2: Sleep
efficacy
DV3: Mood

Sleep diary

Definition:
IG= daily
practice of 5
instructions
(stimulus
control and
sleep
restriction) with
rationale,
treatment
provided by

SelfQuestionnaire
(Migraine
Disability
Assessment
Questionnaire,
Headache
Impact Test,
PHQ-9, ESS,
PSQI, GAD)

Findings/
Results

least
30 minutes/day

Smitherman et
al. (2016)
Randomized
controlled pilot
trial of
behavioral
insomnia
treatment for
chronic
migraine with
comorbid
insomnia
Funding:
Dr. Smitherman
received fund
from Migraine
Research

LT; CM,
inferred

Design: single
blinded RCT

Purpose:
To pilot-test the
efficacy of a
brief behavioral
insomnia
intervention for
adults with
chronic migraine
and comorbid
insomnia
Sampling:
Purposive

N=32 (1
dropout after
baseline
assessment)
CG=15
IG=16
Demographic:
M age = 30.8
years; f=90.3%;
white=80.6%
No significant
difference in M
age, gender,
race, disability,
depression
anxiety.
Setting:
outpatient
Exclusion:

Structured
Interviews

Generalized
linear models

DV1:
Headache
frequency
P=0.028
OR=0.40
Cl=95%
DV2:
P=0.001
DV3: No
significant
difference
between
group

LOE; Decision
for practice/
application to
practice
rate indicates
this is feasible
intervention for
patients in
clinic who are
motivated. Can
offer to
individuals
who are opioid
dependent
LOE: level 2
Strength: No
adverse effects
reported; both
groups yielded
reduction in
headache
frequency; high
adherence;
objective
monitoring
through
actigraphy;
included
difference race

Key: ANCOVA – Analysis of covariance; ANOVA – Analysis of variance; BPI – brief pain inventory; CBT – Cognitive behavioral therapy; CM – Cognitive
model; CSQ – coping strategies questionnaire; CG – control group; DCBT—digital cognitive behavioral therapy; DV—dependent variable; ESS – Epworth
sleepiness scale; f – female; FAM – Fear Avoidance Model; FM – fibromyalgia; GAD – Generalized anxiety disorder scale; GSII – Glasgow Sleep Impact
Index; HADS – Hospital anxiety and depression scale; ICM – Integrated care model; IG – intervention group; ITT – intention-to-treat analysis; IV –
independent variable; LOE – level of evidence; LBP – low back pain; LT – Learning theory; M – mean; N – number of participants in study; n – number of
participants in subset; NSAID– Nonsteroidal anti-inflammatory drug; PHQ-9 – patient health questionnaire-depression scale; PSQI – Pittsburgh Sleep Quality
Index; QOL – quality of life scale; RCT – randomized control trial; SES – self efficacy scale; SCT – Self-care Theory; UC – usual care; VAS – visual analog
scale; WASO – wake after sleep onset; y– year; ∆ – pretest to posttest change for intervention group; & – and

SLEEP AND CHRONIC PAIN
Citation

Theory/
Framewor
k

Design/ Method

Foundation; Dr.
Houle receives
unrestricted
grant funding
from Merck, Inc
Country: USA

Bias: none

Burke et al.
(2019)
An internetdelivered
cognitive
behavioural
therapy pain

SCT;
Holistic
Framewor
k

Design: RCT
Purpose:
To test the
efficacy of Spinal
Cord Injury Pain
Ireland (SPIRE)

29
Sample/ Setting

Major Variables
& Definitions

secondary
headache
disorder,
pregnancy or
breastfeeding,
untreated sleep
apnea; alcohol,
substance abuse
or dependence;
active bipolar
disorder,
psychiatric
hospitalization
within the last
year, recent
changes in
preventive
pharmacotherap
y
Attrition:21.87
%

three graduatelevel therapist,
acupressure
training, range
of motion
exercises.

N=69
CG= 34
IG=35

IV: CBT

Demographic:
M age= 51;
f=25%, M postinjury time= 16

Measurement/
Instrumentation

Data Analysis

Findings/
Results

LOE; Decision
for practice/
application to
practice
Weakness:
small sample
size; high
attrition rate;
short study (6week follow
up), study lack
details in
intervention
(time, duration,
accessibility)
Feasibility:
Useful to
address sleep
and other
comorbidities.
Effective in
different race
and gender.

DV1: Quality of
life
DV2: Pain
DV3: Mood
DV4: Sleep
quality

PSQI, BPI,
QOL, HADS

linear mixed
models
Cohen's d
Chi‐square

∆ 3 months
DV1:
P> 0.16

LOE: level 2

DV2:
P=0.15
DV3:

Strength:
included
individuals
who reported

Key: ANCOVA – Analysis of covariance; ANOVA – Analysis of variance; BPI – brief pain inventory; CBT – Cognitive behavioral therapy; CM – Cognitive
model; CSQ – coping strategies questionnaire; CG – control group; DCBT—digital cognitive behavioral therapy; DV—dependent variable; ESS – Epworth
sleepiness scale; f – female; FAM – Fear Avoidance Model; FM – fibromyalgia; GAD – Generalized anxiety disorder scale; GSII – Glasgow Sleep Impact
Index; HADS – Hospital anxiety and depression scale; ICM – Integrated care model; IG – intervention group; ITT – intention-to-treat analysis; IV –
independent variable; LOE – level of evidence; LBP – low back pain; LT – Learning theory; M – mean; N – number of participants in study; n – number of
participants in subset; NSAID– Nonsteroidal anti-inflammatory drug; PHQ-9 – patient health questionnaire-depression scale; PSQI – Pittsburgh Sleep Quality
Index; QOL – quality of life scale; RCT – randomized control trial; SES – self efficacy scale; SCT – Self-care Theory; UC – usual care; VAS – visual analog
scale; WASO – wake after sleep onset; y– year; ∆ – pretest to posttest change for intervention group; & – and

SLEEP AND CHRONIC PAIN
Citation

Theory/
Framewor
k

Design/ Method

30
Sample/ Setting

management
programme for
spinal cord
injury pain: A
randomized
controlled trial.

pain management
programme, an
internet‐delivered
CBT for spinal
cord injury pain

y; analgesic in
last 6 months
(61%)

Funding:
Supported by
The Irish
Society of
Chartered
Physiotherapists
Eastern Branch
Research
Bursary 2016
and the Health
Informatics
Society of
Ireland
Research
Bursary 2016.

Sampling:
purposive

Exclusion:
Mental health
issues requiring
active
psychiatric
management,
previous
completed a
CBT,
confounding co‐
morbidities
(cancer,
substance
misuse

Country:
Ireland
Bias: none

Setting:
outpatient

Attrition: 26%

Major Variables
& Definitions

Definition:
CBT= six
modules
delivered
weekly,
physiotherapist
engagement and
feedback, live
webinar, written
educational
information

Measurement/
Instrumentation

Data Analysis

Spearman's
rank
correlation
coefficient

Findings/
Results
HADSanxiety:
P=0.12
HADSDepression
P=0.16

LOE; Decision
for practice/
application to
practice
poor computer
skills; national
database
utilized.
Weakness:
low
recruitment
rate; high
attrition rate;
low rate of
intervention
completion;
non-blinded
RCT;
participants
underwent
other
concurrent
treatment;
participants
pre-selected
during
sampling
process.
Feasibility:
Clinician can

Key: ANCOVA – Analysis of covariance; ANOVA – Analysis of variance; BPI – brief pain inventory; CBT – Cognitive behavioral therapy; CM – Cognitive
model; CSQ – coping strategies questionnaire; CG – control group; DCBT—digital cognitive behavioral therapy; DV—dependent variable; ESS – Epworth
sleepiness scale; f – female; FAM – Fear Avoidance Model; FM – fibromyalgia; GAD – Generalized anxiety disorder scale; GSII – Glasgow Sleep Impact
Index; HADS – Hospital anxiety and depression scale; ICM – Integrated care model; IG – intervention group; ITT – intention-to-treat analysis; IV –
independent variable; LOE – level of evidence; LBP – low back pain; LT – Learning theory; M – mean; N – number of participants in study; n – number of
participants in subset; NSAID– Nonsteroidal anti-inflammatory drug; PHQ-9 – patient health questionnaire-depression scale; PSQI – Pittsburgh Sleep Quality
Index; QOL – quality of life scale; RCT – randomized control trial; SES – self efficacy scale; SCT – Self-care Theory; UC – usual care; VAS – visual analog
scale; WASO – wake after sleep onset; y– year; ∆ – pretest to posttest change for intervention group; & – and

SLEEP AND CHRONIC PAIN
Citation

Vedaa et al.
(2020)
Effects of
digital cognitive
behavioural
therapy for
insomnia on
insomnia
severity: a
large-scale
randomised
controlled trial.
Funding:
Norwegian
Research
Council;
Liaison
Committee for
Education,
Research and
Innovation in
Central Norway

Theory/
Framewor
k

CM,
inferred

Design/ Method

31
Sample/ Setting

Major Variables
& Definitions

Measurement/
Instrumentation

Data Analysis

Design: RCT

N=1721

IV: DCBT

Cohen's d

Purpose: To
investigate the
effect of a fully
automated DCBT
programme on
insomnia
severity, sleep–
wake patterns,
sleep medication
use, and daytime
impairment

CG: n=853
IG: n=868
Demographic:
M age =45
years, female
(68%), sleep
problem >6
years (66%)
Setting:
outpatient

DV: insomnia
severity
DV2:
Medication Use
DV3: Mood
Definition:
IG=Six fully
automated and
interactive
online sessions
designed to be
completed
within a 9-week
intervention
period

Sleep diary
(onset, wake
time, early
morning
awakening, total
sleep time, sleep
efficiency)

Sampling:
purposive

Exclusion:
ESS >10; selfreported regular
snoring and
breathing
problems with
difficulties
staying awake
during the day;
medical
comorbidities
(epilepsy,

Insomnia
Severity Index,
Bergen
Insomnia Scale
HADS

latent growth
models

Findings/
Results

LOE; Decision
for practice/
application to
practice
encourage CBT
among highly
motivated
participants.

∆ 9 weeks:
DV1:
p<0·001

LOE: level 2

DV2:
p<0.001

Strength:
intervention is
cost effective
as it is fully
automated;
does not
require
participant and
assessor
interaction;
first large RCT
conducted in
non-English
speaking
country; no
adverse effects

OR=0.49
Cl=95%
DV3:
P<0.001

Weakness:
female
predominance;
only 65%

Key: ANCOVA – Analysis of covariance; ANOVA – Analysis of variance; BPI – brief pain inventory; CBT – Cognitive behavioral therapy; CM – Cognitive
model; CSQ – coping strategies questionnaire; CG – control group; DCBT—digital cognitive behavioral therapy; DV—dependent variable; ESS – Epworth
sleepiness scale; f – female; FAM – Fear Avoidance Model; FM – fibromyalgia; GAD – Generalized anxiety disorder scale; GSII – Glasgow Sleep Impact
Index; HADS – Hospital anxiety and depression scale; ICM – Integrated care model; IG – intervention group; ITT – intention-to-treat analysis; IV –
independent variable; LOE – level of evidence; LBP – low back pain; LT – Learning theory; M – mean; N – number of participants in study; n – number of
participants in subset; NSAID– Nonsteroidal anti-inflammatory drug; PHQ-9 – patient health questionnaire-depression scale; PSQI – Pittsburgh Sleep Quality
Index; QOL – quality of life scale; RCT – randomized control trial; SES – self efficacy scale; SCT – Self-care Theory; UC – usual care; VAS – visual analog
scale; WASO – wake after sleep onset; y– year; ∆ – pretest to posttest change for intervention group; & – and

SLEEP AND CHRONIC PAIN
Citation

Country:
Norway
Bias: Two
authors report
financial or
business
interests in
BeHealth
Solutions and
Pear
Therapeutics,
two companies
that develop and
disseminate
digital
therapeutics

Theory/
Framewor
k

Design/ Method

32
Sample/ Setting

bipolar disorder,
schizophrenia
or psychotic
disorders, or
recent cardiac
surgery); nighttime shift
workers

Attrition:
35.03%

Major Variables
& Definitions

Measurement/
Instrumentation

Data Analysis

Findings/
Results

LOE; Decision
for practice/
application to
practice
completed
questionnaire
at 9-week
follow up; over
5000
individuals
were initially
screened; only
self-report data
utilized
Feasibility:
DCBT can be a
cost effective
and lowintensity
modality to
help improve
sleep in short
period of time.

Key: ANCOVA – Analysis of covariance; ANOVA – Analysis of variance; BPI – brief pain inventory; CBT – Cognitive behavioral therapy; CM – Cognitive
model; CSQ – coping strategies questionnaire; CG – control group; DCBT—digital cognitive behavioral therapy; DV—dependent variable; ESS – Epworth
sleepiness scale; f – female; FAM – Fear Avoidance Model; FM – fibromyalgia; GAD – Generalized anxiety disorder scale; GSII – Glasgow Sleep Impact
Index; HADS – Hospital anxiety and depression scale; ICM – Integrated care model; IG – intervention group; ITT – intention-to-treat analysis; IV –
independent variable; LOE – level of evidence; LBP – low back pain; LT – Learning theory; M – mean; N – number of participants in study; n – number of
participants in subset; NSAID– Nonsteroidal anti-inflammatory drug; PHQ-9 – patient health questionnaire-depression scale; PSQI – Pittsburgh Sleep Quality
Index; QOL – quality of life scale; RCT – randomized control trial; SES – self efficacy scale; SCT – Self-care Theory; UC – usual care; VAS – visual analog
scale; WASO – wake after sleep onset; y– year; ∆ – pretest to posttest change for intervention group; & – and

SLEEP AND CHRONIC PAIN
Citation

Lami et al.
(2018)
Efficacy of
combined
cognitivebehavioral
therapy for
insomnia and
pain in patients
with
fibromyalgia: A
randomized
controlled trial
Funding:
Financially
supported by
the Spanish
Ministry of
Science and
Innovation and
Spanish
Ministry of
Economy and
Competitivenes
s
Country: Spain

Theory/
Framewor
k
Fearavoidance
model
CM,
inferred

Design/ Method

Design: RCT
Purpose: To
identify the
clinical benefits
of CBT for
management of
insomnia and
pain compared
with the usual
psychological
treatment
focused on pain
and the usual
medical care
regarding sleep
quality, pain and
other troubling
symptoms in
fibromyalgia
women
Sampling:
convenience

33
Sample/ Setting

Major Variables
& Definitions

Measurement/
Instrumentation

Data Analysis

N= 126
CG (UC): n= 42
IG1 (CBT-pain:
n=42
IG2 (CBTinsomnia &
pain): n=42
Demographic:
M age=50.19;
female (100%)

IV: CBT

Semi-structured
interview

ANOVA
ANCOVA
KruskalWallis test
Chi-square
test
Cohen’s d

Setting:
outpatient
Exclusion:
concomitant
medical
conditions
(inflammatory
rheumatic
disease, cancer,
recent surgery);
pregnancy;
mental
disorders with
severe
symptoms
(suicide
ideation,
schizophrenia,
personality

DV1: sleep
quality
DV2: selfefficacy
D3: use of
medication
D4:
Definition:
CBT=90minutes group
sessions weekly
for 9 weeks
CBT-pain:
based on FearAvoidance
Model of
Chronic Pain
CBT- insomnia
& pain involves
CBT-pain and
training in
cognitive,
affective, and
behavioral skills
for better

Sleep diary
PSQI
SES

Findings/
Results
CBTinsomnia
&pain
∆ posttreatment:
total sleep
quality,
using sleep
medication
p<0.01
∆ 3 months:
p>0.05
*other
finding
No
significant
improveme
nt on
anxiety and
depression
in three
groups

LOE; Decision
for practice/
application to
practice
LOE: level 2
Strength:
explored
hybrid therapy;
compared three
modalities;
only those
completed
treatment was
included in
analysis
Weakness: All
participants
were female;
high attrition
rate; treatment
only
demonstrated
short-term
benefits;
participants
had
fibromyalgia
diagnosis
rather than
generalized
chronic pain;

Key: ANCOVA – Analysis of covariance; ANOVA – Analysis of variance; BPI – brief pain inventory; CBT – Cognitive behavioral therapy; CM – Cognitive
model; CSQ – coping strategies questionnaire; CG – control group; DCBT—digital cognitive behavioral therapy; DV—dependent variable; ESS – Epworth
sleepiness scale; f – female; FAM – Fear Avoidance Model; FM – fibromyalgia; GAD – Generalized anxiety disorder scale; GSII – Glasgow Sleep Impact
Index; HADS – Hospital anxiety and depression scale; ICM – Integrated care model; IG – intervention group; ITT – intention-to-treat analysis; IV –
independent variable; LOE – level of evidence; LBP – low back pain; LT – Learning theory; M – mean; N – number of participants in study; n – number of
participants in subset; NSAID– Nonsteroidal anti-inflammatory drug; PHQ-9 – patient health questionnaire-depression scale; PSQI – Pittsburgh Sleep Quality
Index; QOL – quality of life scale; RCT – randomized control trial; SES – self efficacy scale; SCT – Self-care Theory; UC – usual care; VAS – visual analog
scale; WASO – wake after sleep onset; y– year; ∆ – pretest to posttest change for intervention group; & – and

SLEEP AND CHRONIC PAIN
Citation

Bias: none

Theory/
Framewor
k

Design/ Method

34
Sample/ Setting

Major Variables
& Definitions

disorder); other
organic sleep
disorder; severe
dependence of
hypnotic drugs;
irregularities in
circadian
rhythms at the
time of the
study

management of
sleep problems

Attrition:
42.86%

Measurement/
Instrumentation

Data Analysis

Findings/
Results

LOE; Decision
for practice/
application to
practice
require trained
psychologist.
Feasibility:
Can be useful
hybrid
approach to
patients with
chronic pain
who have
comorbid sleep
disorders. CBT
is modifiable
for individual’s
needs.

Key: ANCOVA – Analysis of covariance; ANOVA – Analysis of variance; BPI – brief pain inventory; CBT – Cognitive behavioral therapy; CM – Cognitive
model; CSQ – coping strategies questionnaire; CG – control group; DCBT—digital cognitive behavioral therapy; DV—dependent variable; ESS – Epworth
sleepiness scale; f – female; FAM – Fear Avoidance Model; FM – fibromyalgia; GAD – Generalized anxiety disorder scale; GSII – Glasgow Sleep Impact
Index; HADS – Hospital anxiety and depression scale; ICM – Integrated care model; IG – intervention group; ITT – intention-to-treat analysis; IV –
independent variable; LOE – level of evidence; LBP – low back pain; LT – Learning theory; M – mean; N – number of participants in study; n – number of
participants in subset; NSAID– Nonsteroidal anti-inflammatory drug; PHQ-9 – patient health questionnaire-depression scale; PSQI – Pittsburgh Sleep Quality
Index; QOL – quality of life scale; RCT – randomized control trial; SES – self efficacy scale; SCT – Self-care Theory; UC – usual care; VAS – visual analog
scale; WASO – wake after sleep onset; y– year; ∆ – pretest to posttest change for intervention group; & – and

SLEEP AND CHRONIC PAIN

35

Table 2
Synthesis Table
Study
Blake et al.
characteristic
Year
2015
RCT
Cohort Study
•
# of subjects
46
Theory Model
CM
•
FAM
ICM
LT
SCT
Intervention
4 weeks
Length
Measurement Actigraphy,
Tools PSQI,
HADS,
WASO,
QOL,

Setting
outpatient
Country

•

Burke et
al.
2019
•

Espie et
al.
2019
•

Lami et al.

Nordin et al.

Sato et al.

2018
•

McCrae et
al.
2019
•

2016
•

2019
•

Smitherman
et al.
2016
•

69
inferred

Vedaa et
al.
2020
•

Zgierska et
al.
2016
•

1771

126

113

99

23

31

1721

35

•

inferred
•

•

•

•

inferred

•

inferred
•

•
•
12 weeks
BPI,
HADS,
PSQI,
QOL,

•

8 weeks

9 weeks

8 weeks

PROMIS,
GSII,
PHQ-9,
GAD

PSQI,
SES, SCL

Actigraphy,
Sleep
Efficiency,
VAS

•

•

•

8 weeks
VAS, SES,
CSQ

•

6 weeks
PSQI,
HADS,
QOL

•

4 weeks
Actigraphy,
PSQI,
PHQ-9,
GAD

•

9 weeks
Insomnia
Severity
Index,
Bergen
Insomnia
Scale,
HADS

•

8 weeks
BPI,
Biomarkers,
Thermal
sensory
analyzer

•

Key: ANCOVA – Analysis of covariance; ANOVA – Analysis of variance; BPI – brief pain inventory; CBT – Cognitive behavioral therapy; CM – Cognitive
behavioral model; CSQ – coping strategies questionnaire; CG – control group; DCBT—digital cognitive behavioral therapy; DV—dependent variable; ESS –
Epworth sleepiness scale; f – female; FAM – Fear Avoidance Model; FM – fibromyalgia; GAD – Generalized anxiety disorder scale; GSII – Glasgow Sleep
Impact Index; HADS – Hospital anxiety and depression scale; ICM – Integrated care model; IG – intervention group; ITT – intention-to-treat analysis; IV –
independent variable; LOE – level of evidence; LBP – low back pain; LT – Learning theory; M – mean; N – number of participants in study; n – number of
participants in subset; NSAID– Nonsteroidal anti-inflammatory drug; PHQ-9 – patient health questionnaire-depression scale; PSQI – Pittsburgh Sleep Quality
Index; QOL – quality of life scale; RCT – randomized control trial; SES – self efficacy scale; SCT – Self-care Theory; UC – usual care; VAS – visual analog
scale; WASO – wake after sleep onset; y– year; ∆ – pretest to posttest change for intervention group; & – and

SLEEP AND CHRONIC PAIN
USA
Other
UK
Demographic
Mean age (y)
47.7
%f
58.7%
Condition
Pain
•
Type of Pain
General
Insomnia
Independent Variable
CBT sessions
•
DCBT
sessions
Dependent Variable
Sleep quality
•
Pain intensity
Self-efficacy
Medication
use
Physical
Function
Mood
•
QOL
•
Findings
Improved
•
Sleep Quality
Improved
Pain Intensity
Improved
•
Mood

36

Ireland

UK

Spain

51
25%

48
77.7%

50.19
100%

•

•
Spinal cord

•

•

•
•

•

•
•

•

Japan

53
97%

43
84.8%

50
78%

30.8
90.3%

•
FM
•

•
FM
•

•
General

•

•
Migraine
•

•

•

•
•
•
•
•

•

•

•

•

•
•

Norway
45
68%

•

•
•

•

•
•

•
•

•
LBP
•

•

•
•

•

•

51.8
80%

•

•

•

•

•

•
•

•

•

•

•
•

•

Sweden

•
•

•
•

•

•

•
•

Key: ANCOVA – Analysis of covariance; ANOVA – Analysis of variance; BPI – brief pain inventory; CBT – Cognitive behavioral therapy; CM – Cognitive
model; CSQ – coping strategies questionnaire; CG – control group; DCBT—digital cognitive behavioral therapy; DV—dependent variable; ESS – Epworth
sleepiness scale; f – female; FAM – Fear Avoidance Model; FM – fibromyalgia; GAD – Generalized anxiety disorder scale; GSII – Glasgow Sleep Impact
Index; HADS – Hospital anxiety and depression scale; ICM – Integrated care model; IG – intervention group; ITT – intention-to-treat analysis; IV –
independent variable; LOE – level of evidence; LBP – low back pain; LT – Learning theory; M – mean; N – number of participants in study; n – number of
participants in subset; NSAID– Nonsteroidal anti-inflammatory drug; PHQ-9 – patient health questionnaire-depression scale; PSQI – Pittsburgh Sleep Quality
Index; QOL – quality of life scale; RCT – randomized control trial; SES – self efficacy scale; SCT – Self-care Theory; UC – usual care; VAS – visual analog
scale; WASO – wake after sleep onset; y– year; ∆ – pretest to posttest change for intervention group; & – and

SLEEP AND CHRONIC PAIN
Reduced
Medication
Use
Improved
QOL

37
•

•
•

Key: ANCOVA – Analysis of covariance; ANOVA – Analysis of variance; BPI – brief pain inventory; CBT – Cognitive behavioral therapy; CM – Cognitive
model; CSQ – coping strategies questionnaire; CG – control group; DCBT—digital cognitive behavioral therapy; DV—dependent variable; ESS – Epworth
sleepiness scale; f – female; FAM – Fear Avoidance Model; FM – fibromyalgia; GAD – Generalized anxiety disorder scale; GSII – Glasgow Sleep Impact
Index; HADS – Hospital anxiety and depression scale; ICM – Integrated care model; IG – intervention group; ITT – intention-to-treat analysis; IV –
independent variable; LOE – level of evidence; LBP – low back pain; LT – Learning theory; M – mean; N – number of participants in study; n – number of
participants in subset; NSAID– Nonsteroidal anti-inflammatory drug; PHQ-9 – patient health questionnaire-depression scale; PSQI – Pittsburgh Sleep Quality
Index; QOL – quality of life scale; RCT – randomized control trial; SES – self efficacy scale; SCT – Self-care Theory; UC – usual care; VAS – visual analog
scale; WASO – wake after sleep onset; y– year; ∆ – pretest to posttest change for intervention group; & – and

SLEEP AND CHRONIC PAIN

38

Table 3
Budget and Funding
EXPENSE ITEMS

AMOUNT

Equipment

$30

Survey Print-Out

$50

Utensils

$10

Brochure (color printing)

$13

Delivery: Gas

$120

Evaluation: Intellectus Software

$150

TOTAL EXPENSE
TOTAL FUNDING

$373
$0

SLEEP AND CHRONIC PAIN

39

Appendix B
Models and Framework
Figure 1
Cognitive Model

Beck Institute (n.d.).

SLEEP AND CHRONIC PAIN
Figure 2
Cognitive Triad

McLeod (2019).

40

SLEEP AND CHRONIC PAIN
Figure 3
Rosswurm and Larrabee’s Model for evidence-based practice

Rosswurm & Larrabee (1999).

41

SLEEP AND CHRONIC PAIN

42