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Submitted 10 February 2018
Accepted 16 June 2018
Published 11 July 2018
Corresponding author
Masahiro Banno,
solvency@med.nagoya-u.ac.jp
Academic editor
Andrew Gray
Additional Information and
Declarations can be found on
page 17
DOI 10.7717/peerj.5172
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2018 Banno et al.
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Exercise can improve sleep quality: a
systematic review and meta-analysis
Masahiro Banno1,2, Yudai Harada2, Masashi Taniguchi3,4, Ryo Tobita3,
Hiraku Tsujimoto5, Yasushi Tsujimoto6,7, Yuki Kataoka5,8and Akiko Noda9,10
1Department of Psychiatry, Seichiryo Hospital, Nagoya City, Aichi Prefecture, Japan
2Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya City, Aichi Prefecture,
Japan
3Division of Physical Therapy, Rehabilitation Units, Shiga University of Medical Science Hospital, Otsu City,
Shiga Prefecture, Japan
4Department of Physical Therapy, Graduate School of Medicine, Kyoto University, Kyoto City,
Kyoto Prefecture, Japan
5Hospital Care Research Unit, Hyogo Prefectural Amagasaki General Medical Center, Amagasaki City, Hyogo
Prefecture, Japan
6Department of Healthcare Epidemiology, School of Public Health in the Graduate School of Medicine, Kyoto
University, Kyoto City, Kyoto Prefecture, Japan
7Department of Nephrology and Dialysis, Kyoritsu Hospital, Kawanishi City, Hyogo Prefecture, Japan
8Department of Respiratory Medicine, Hyogo Prefectural Amagasaki General Medical Center, Amagasaki City,
Hyogo Prefecture, Japan
9Chubu University Graduate School of Life and Health Sciences, Kasugai City, Aichi Prefecture, Japan
10 Clinical Laboratory Technical Education Center, Chubu University, Kasugai City, Aichi Prefecture, Japan
ABSTRACT
Background. Insomnia is common. However, no systematic reviews have examined
the effect of exercise on patients with primary and secondary insomnia, defined as both
sleep disruption and daytime impairment. This systematic review and meta-analysis
aimed to examine the effectiveness/efficacy of exercise in patients with insomnia.
Methods. We searched the Cochrane Central Register of Controlled Trials, MEDLINE,
Embase, PsycINFO, World Health Organization International Clinical Trials Registry
Platform, and ClinicalTrials.gov to identify all randomized controlled trials that
examined the effects of exercise on various sleep parameters in patients with insomnia.
All participants were diagnosed with insomnia, using standard diagnostic criteria
or predetermined criteria and standard measures. Data on outcome measures were
subjected to meta-analyses using random-effects models. The Cochrane Risk of Bias
Tool and Grading of Recommendations, Assessment, Development, and Evaluation
approach were used to assess the quality of the individual studies and the body of
evidence, respectively.
Results. We included nine studies with a total of 557 participants. According to
the Pittsburgh Sleep Quality Index (mean difference [MD], 2.87 points lower in the
intervention group; 95% confidence interval [CI], 3.95 points lower to 1.79 points
lower; low-quality evidence) and the Insomnia Severity Index (MD, 3.22 points lower
in the intervention group; 95% CI, 5.36 points lower to 1.07 points lower; very low-
quality evidence), exercise was beneficial. However, exercise interventions were not
associated with improved sleep efficiency (MD, 0.56% lower in the intervention group;
95% CI, 3.42% lower to 2.31% higher; moderate-quality evidence). Only four studies
noted adverse effects. Most studies had a high or unclear risk of selection bias.
How to cite this article Banno et al. (2018), Exercise can improve sleep quality: a systematic review and meta-analysis. PeerJ 6:e5172;
DOI 10.7717/peerj.5172
Discussion. Our findings suggest that exercise can improve sleep quality without
notable adverse effects. Most trials had a high risk of selection bias. Higher quality
research is needed.
Subjects Epidemiology, Evidence Based Medicine, Psychiatry and Psychology, Statistics
Keywords Meta-analysis, Exercise, Physical activity, Sleep disorders, Systematic reviews
INTRODUCTION
Approximately 30% of the general population experience sleep disruption, while 10%
experience both sleep disruption and daytime dysfunction consistent with a diagnosis of
insomnia as defined by the National Institutes of Health (National Institutes of Health,
2005). Patients with insomnia are at high risk of developing hypertension, atherosclerosis,
and acute myocardial infarction (Laugsand et al., 2011;Fernandez-Mendoza et al., 2012;
Nakazaki et al., 2012). Insomnia is strongly correlated with mental illness and poses an
additional risk for depression as well as suicidal ideation and behavior (Baglioni et al., 2011;
Bjorngaard et al., 2011;Pigeon, Pinquart & Conner, 2012). Pharmacotherapy is an effective
treatment for patients with insomnia. However, the use of hypnotics is associated with
increased mortality (Kripke, 2016), and the frequency of falls and hip fractures increases
when hypnotics are used in elderly individuals (Allain et al., 2005). Cognitive behavioral
therapy for insomnia (CBT-I), the first-line treatment for insomnia (Morin et al., 2006),
requires frequent monitoring and has a high cost (Passos et al., 2012).
Exercise is a nonpharmacological therapy for insomnia, is readily available, and costs
less than other nonpharmacological treatments for insomnia; notably, its effects depend
upon exercise type and evaluation methodology (Youngstedt, O’Connor & Dishman, 1997;
Driver & Taylor, 2000;Youngstedt, 2005). Recent randomized controlled trials (RCTs)
have confirmed that exercise has positive effects on sleep quality, sleep onset latency,
total sleep time, sleep efficiency, and insomnia severity (Passos et al., 2010;Reid et al.,
2010;Hartescu, Morgan & Stevinson, 2015). Epidemiological studies have clarified the
association between exercise and decreased complaints of insomnia (De Mello, Fernandez
& Tufik, 2000;Youngstedt & Kline, 2006), as well as a relationship between lower levels of
physical activity and a greater prevalence of insomnia (Morgan, 2003). Among the main
symptoms of insomnia, such as difficulty initiating sleep (DIS) or early morning awakening
(EMA) (Lichstein et al., 2003), EMA is more frequently observed in older adults than other
symptoms (Kim et al., 2000). These symptoms are associated with circadian core body
temperature. Patients with DIS have a delayed core body temperature rhythm, whereas
those with EMA have an advanced rhythm (Lack et al., 2008). However, experimental
investigations of the effects of exercise on sleep in individuals with insomnia are lacking.
The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5)
and the third edition of the International Classification of Sleep Disorders (ICSD-3)
made major revisions to their definitions of insomnia. The DSM-5 and ICSD-3 abolished
the distinction between primary and secondary insomnia. The revision was based on
the findings that insomnia: (1) often accompanies another disease, (2) is preceded by a
Banno et al. (2018), PeerJ , DOI 10.7717/peerj.5172 2/23
comorbid condition, (3) persists even after effective treatment for the comorbid condition,
and (4) exacerbates the symptoms of the comorbid condition (Riemann et al., 2015).
Previous systematic reviews and/or meta-analyses investigated the effects of exercise
in people with sleep complaints or chronic insomnia (Passos et al., 2012), undefined
populations (Kubitz et al., 1996;Youngstedt, O’Connor & Dishman, 1997;Kredlow et al.,
2015), and patients with sleep problems (Montgomery & Dennis, 2002;Montgomery &
Dennis, 2004;Yang et al., 2012). A previous review also examined the effects of exercise
on sleep in specific subpopulations (e.g., cancer survivors) (Mercier, Savard & Bernard,
2017). However, no previous systematic reviews have examined the effect of exercise in
patients with primary and secondary insomnia as defined by having both sleep disruption
and daytime impairment. Investigating the effect of exercise in patients with primary and
secondary insomnia would be beneficial in clinical practice since DSM-5 and ICSD-3
abolished the distinction between the two.
Study objectives
This review aimed to examine the effects of exercise in patients with insomnia.
MATERIALS AND METHODS
This systematic review was conducted according to the PRISMA statement (Liberati et al.,
2009). Table S1 shows the PRISMA 2009 checklist. The detailed methods are described in
CRD42016046064 in the National Institute for Health Research PROSPERO register.
Eligibility criteria
Study type
We included all published and unpublished RCTs, including those that were only abstracts
or letters. Crossover trials and cluster-, quasi-, and non-randomized trials were excluded.
Studies in any language from any country were accepted for screening. Studies were
included regardless of the follow-up period.
Participants
Participants included those diagnosed with insomnia using any standard diagnostic criteria
such as DSM, International Classification of Diseases, ICSD, Research Diagnostic Criteria
(RDC) for insomnia, or predetermined criteria and standard measures (i.e., Pittsburgh Sleep
Quality Index (PSQI); Buysse et al., 1989), Insomnia Severity Index (ISI) (Bastien, Vallieres
& Morin, 2001), and a sleep questionnaire). The American Academy of Sleep Medicine
developed standard definitions for insomnia disorders, such as the RDC for insomnia
(Edinger et al., 2004). We utilized the PSQI and ISI in our inclusion criteria because
both are appropriate screening tools for insomnia (Chiu et al., 2016). All participants
had insomnia-related daytime impairments or were screened using sleep questionnaires
including items about such impairments. Recent RCTs were beyond the scope of this review
because participants in these studies did not have insomnia-related daytime impairments
(Gebhart, Erlacher & Schredl, 2011;Chen et al., 2016;Tan et al., 2016).
The cutoff value for the PSQI global score used to diagnose a sleep disorder was defined
by the trial list. If a study did not specify a cutoff value, we surmised that a PSQI global score
Banno et al. (2018), PeerJ , DOI 10.7717/peerj.5172 3/23
>5 would be considered insomnia (Backhaus et al., 2002). We included patients of any age,
sex, race, and setting, but excluded those with sleep apnea syndrome. We also checked
the inclusion criteria for insomnia and the sleep questionnaire to determine whether the
screening process selected those with daytime impairment.
Interventions
The interventions were predetermined exercise programs. Interventions of any intensity,
duration, and frequency were included. We included exercise in combination with
medication if participants in the intervention and control groups were taking the same
medication. We excluded interventions recommending that patients increase physical
activity or encouraging improvement in self-efficacy through CBT, a mind-body bridging
program, a mindfulness meditation program, massage therapy, or breathing techniques
without physical activity. We examined the following interventions and comparisons:
(1) Exercise versus non-exercise and non-medication control; and
(2) Exercise plus medication versus medication alone.
We excluded the following intervention: Exercise combined with another treatment
(e.g., CBT).
Outcome measures
The following primary outcomes were measured:
1. Sleep quality according to the PSQI;
2. Sleep efficiency defined by the percentage of time spent in bed asleep as measured
objectively by a sleep device (e.g., polysomnography [PSG], actigraphy) or by
reports/diaries kept by a partner or nursing staff; and
3. Insomnia severity according to a standard measure (ISI).
Secondary outcomes were as follows:
1. Quality of life (QOL) as measured by standardized questionnaires with established
reliability and validity, such as the Short Form 36 (SF-36);
2. Sleep onset latency as measured objectively by sleep devices (e.g., PSG, actigraphy) or
reports/diaries maintained by a partner or nursing staff;
3. Total sleep time as measured objectively by a sleep device (e.g., PSG, actigraphy) or
reports/diaries maintained by a partner or nursing staff;
4. All adverse events (defined by the trial list);
5. Sleepiness during daily life according to a self-report using a standardized measure,
e.g., the Epworth Sleepiness Scale (ESS);
6. Current sleepiness according to a self-report using a standardized measure, e.g., the
Stanford Sleepiness Scale (SSS);
7. Wake after sleep onset (WASO) as measured objectively by a sleep device (e.g., PSG,
actigraphy) or reports/diaries maintained by a partner or nursing staff;
8. Anxiety according to a standardized questionnaire with established reliability and
validity (e.g., State-Trait Anxiety Inventory); and
9. Depression according to a standardized questionnaire with established reliability and
validity (e.g., Beck Depression Inventory).
Banno et al. (2018), PeerJ , DOI 10.7717/peerj.5172 4/23
We consulted an expert in sleep medicine (AN) and experts in exercise therapy (MT
and RT) and selected the moderator (primary and secondary outcomes, prioritization of
outcomes, and subgroup analysis items) in terms of clinical importance.
Search methods for study identification
Electronic searches
To identify relevant trials, we searched the following electronic databases on October 9,
2016 and updated the electronic searches on October 4, 2017:
1. The Cochrane Central Register of Controlled Trials (CENTRAL);
2. MEDLINE via EBSCOhost;
3. Embase; and
4. PsycINFO via PsycNET.
See Appendix S1 for details about the search strategies.
Searches of other resources
We also searched the following registries to identify completed but unpublished trials and
investigate reporting bias.
1. World Health Organization International Clinical Trials Registry Platform; and
2. ClinicalTrials.gov.
See Appendix S1 for details of the search strategies.
We also manually searched reference lists in clinical guidelines on exercise for insomnia
and in related guidelines (Morgenthaler et al., 2006;Bauer et al., 2007;Wilson et al., 2010;
NICE, 2012;Bauer et al., 2013;NICE, 2013;University of Texas at Austin School of Nursing,
2014;Bauer et al., 2015;NICE, 2015;Qaseem et al., 2016), reference lists of extracted studies,
and articles citing the extracted studies.
We contacted authors if the extracted studies lacked the necessary information.
Data collection and analysis
Study selection
Two of the five authors (MB, YH, HT, YT, and YK) independently screened the titles and
abstracts of the articles identified in the search. Two of the five authors were assigned
to each article to reduce the burden on each author. They assessed eligibility based on a
full-text review. Disagreement was resolved by discussion; if necessary, YK or YT (if YK
and an author other than YT were the two authors) or MB (if YK and YT were the two
authors) provided arbitration. We followed a pre-defined protocol to screen the abstracts
and full texts and used pre-defined criteria in the registered protocol. One lead author
(MB) checked all included studies and the exclusion criteria for all records subjected to the
full-text screening procedure. Therefore, the decision would not differ systematically.
Data extraction and management
The data were extracted on prespecified forms that were piloted using a random sample
of 10 studies. Two of the four authors (MB, HT, YT, and YK) independently extracted the
data. MB and another author were assigned to each article to reduce the burden on each
author. We contacted the authors of studies lacking sufficient information as necessary.
Differences in data extraction opinions were resolved by discussion and arbitrated by YK
Banno et al. (2018), PeerJ , DOI 10.7717/peerj.5172 5/23
or YT (if YK was the other author) when necessary. See Appendix S2 for details of the
extracted information.
Assessment of risk of bias of the included studies
Two of the four authors (MB, HT, YT, and YK) independently assessed the risk of bias of
the included studies using the Cochrane Risk of Bias Tool (Higgins & Green, 2011). MB
and another author were assigned to each article to reduce the burden on each author.
Differences in opinion about the assessment of risk of bias were resolved by discussion and
through arbitration by YK or YT (if YK was the other author) as necessary.
Measures of treatment effect
For continuous outcomes (sleep quality, sleep efficiency, insomnia severity, QOL, sleep
onset latency, total sleep time, sleepiness during daily life, current sleepiness, WASO,
anxiety, and depression), the standardized mean difference (SMD) or mean difference
(MD) with 95% CI was calculated as recommended by the Cochrane handbook (Higgins &
Green, 2011). We used MD when data including meta-analyses were derived from the same
indicator. We used SMD when data including meta-analyses were derived from different
indicators or we compared the data in the meta-analysis with data in a previous study using
SMD. Adverse events were narratively summarized since the definition of these outcomes
varied among studies.
Assessment of heterogeneity
We first assessed heterogeneity by visual inspection of the forest plots. We also calculated
I2statistics and analyzed them according to recommendations in the Cochrane handbook
(0–40%, might not be important, 30–60%, may represent moderate heterogeneity, 50–
90% may represent substantial heterogeneity, and 75–100% may represent considerable
heterogeneity). When heterogeneity was detected (I2>50%), we attempted to identify
possible causes.
Data synthesis
We pooled the data using a random-effects model. The DerSimonian and Laird method was
used in the random-effects meta-analysis (DerSimonian & Laird, 1986). All analyses were
conducted using Review Manager software (RevMan 5.3; The Nordic Cochrane Centre,
The Cochrane Collaboration, Copenhagen, Denmark).
Subgroup analysis and investigation of heterogeneity
We further aimed to identify possible causes of heterogeneity. The following prespecified
subgroup analyses of the primary outcomes were planned: (1) sex; (2) primary and
secondary insomnia; (3) exercise duration: short-term (<2 months), medium-term (2
to <6 months), long-term (≥6 months); (4) exercise intensity: aerobic versus anaerobic
exercise; (5) exercise type: aerobic (walking), resistance, and aerobic plus resistance; and
(6) exercise setting or location: home, physical therapy center, hospital, or elsewhere.
Banno et al. (2018), PeerJ , DOI 10.7717/peerj.5172 6/23
Table 1 Summary of findings
Outcomes (time frame) Number of
participants (studies)
in follow-up
Quality of
evidence
(GRADE)
Relative
effect
(95% CI)
Anticipated
absolute effectsa
(95% CI)
Risk with
control
Risk difference
with exercise
Total PSQI score (8 wks to 6 mos) 361 ⊕ ⊕ – MD 2.87 point lower
Scale: 0 to 21 (6 RCTs) LOWb,c,d (3.95 lower to 1.79 lower)
Sleep efficiency (%) (1 d to 6 mos) 186 ⊕⊕⊕– MD 0.56% lower
assessed with: polysomnography and
actigraphy
(4 RCTs) MODERATEd(3.42 lower to 2.31 higher)
Scale: 0 to 100
Total ISI score(4–6 mos) 66 ⊕ – MD 3.22 point lower
Scale: 0 to 28 (2 RCTs) VERY LOWb,c,d,e,f (5.36 lower to 1.07 lower)
Sleep onset latency (minute)
(1 d to 6 mos)
206 ⊕ ⊕ – MD 1.9 minutes higher
(5 RCTs) LOWd,g (3.63 lower to 7.43 higher)
Total sleep time (minute)
(1 d to 6 mos)
206 ⊕ ⊕ – MD 4.32 minutes higher
(5 RCTs) LOWd,g (9.19 lower to 17.84 higher)
All adverse events (2–6 mos) 150 ⊕ –
(4 RCTs) VERY LOWb,c,d,h,i
Notes.
aThe risk in the intervention group (and its 95% CI) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI).
bParticipants were not blinded.
cThe outcome assessors were not blinded.
dSample size was small. Sample size did not meet criteria of optimal information size (OIS) (400). OIS was 400 if alpha =0.05, beta =0.2, delta =0.2.
eAllocation concealment was not done in 40% of participants.
fThere were incomplete outcome data in 40% of participants.
gThere were incomplete outcome data in 25% of participants.
hThere were incomplete outcome data in 50% of participants.
iAllocation concealment was not done in 30% of participants.
ISI, Insomnia Severity Index; MD, mean differences; OIS, optimal information size; GRADE, Grading of Recommendations, Assessment, Development, and Evaluation;
OR, odds ratio; PSQI, Pittsburgh Sleep Quality Index; RCTs, randomized controlled trials; RR, risk ratio.
GRADE working group grades of evidence: High quality, We are very confident that the true effect lies close to that of the estimate of the effect; Moderate quality, We are
moderately confident in the effect estimate: The true effect is likely to be close to the estimate of the effect, but a substantial difference is possible; Low quality, Our confidence
in the effect estimate is limited: The true effect may be substantially different from the estimate of the effect; Very low quality, We have very little confidence in the effect esti-
mate: The true effect is likely to be substantially different from the estimate of effect.
Sensitivity analysis
The following prespecified sensitivity analyses of the primary outcomes were planned:
(1) repeating the analysis but restricting it to studies with low risks of bias from random
sequence generation and allocation concealment, using the Cochrane Risk of Bias Tool
(Higgins & Green, 2011); (2) repeating the analysis using a fixed-effects model instead of
random-effects model; and (3) excluding studies with ‘‘a per-protocol analysis’’ or ‘‘analysis
including imputed data.’’
Summary of findings tables
The main results of our review are presented in the Summary of findings table (Table 1),
which includes an overall grading of the evidence related to each of the main outcomes using
Banno et al. (2018), PeerJ , DOI 10.7717/peerj.5172 7/23
the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE)
approach (Guyatt et al., 2011;Higgins & Green, 2011).
Registration
We registered the protocol in the National Institute for Health Research PROSPERO register
(http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42016046064).
RESULTS
Search results
After removing duplicates, we identified 4,085 records during the search conducted
in October 2016 and updated the electronic searches on October 4, 2017 (Fig. 1). We
included 17 trials in the qualitative synthesis and detected seven unpublished trials and one
completed trial without outcomes data (Chan et al., 2017). Ultimately, 557 participants in
nine trials were included in the quantitative synthesis.
Table 2 summarizes the published studies included in the qualitative synthesis. Table S2
shows the characteristics of the seven unpublished trials. Table S3 shows the sleep
medications used in the included completed trials.
The bias risk of the quantitative synthesis is shown in Figs. 2A and 2B.
Primary outcomes
Sleep quality
Data from six trials comprising 361 participants that measured sleep quality were
pooled in our meta-analysis (Reid et al., 2010;Tang, Liou & Lin, 2010;Irwin et al., 2014;
Hartescu, Morgan & Stevinson, 2015;Chan et al., 2016;Tadayon, Abedi & Farshadbakht,
2016) (Fig. 3A). All trials measured PSQI and had an intervention period of eight weeks
to six months. There was a significant effect noted in favor of the intervention (MD, 2.87
points lower in the intervention group; 95% CI, 3.95 points lower to 1.79 points lower;
P<0.001; low-quality evidence). A lower score was more beneficial in PSQI. Substantial
heterogeneity was observed (Tau2=1.18; I2=68%).
Sleep efficiency
Data from four trials that examined sleep efficiency in 186 participants were pooled in our
meta-analysis (Passos et al., 2010;Afonso et al., 2012;Irwin et al., 2014;Hartescu, Morgan &
Stevinson, 2015) (Fig. 3B). All trials measured sleep efficiency with PSG and actigraphy and
had an intervention period of 1 day to 6 months. There was no significant improvement
in favor of the intervention (MD, 0.56% lower in the intervention group; 95% CI, 3.42%
lower to 2.31% higher; P=0.70; moderate-quality evidence). A higher percentage was
more beneficial for sleep efficiency. No statistical heterogeneity was indicated (Tau2<0.001;
I2=0%).
Insomnia severity
Data from two trials that measured insomnia severity in 66 participants were pooled in our
meta-analysis (Afonso et al., 2012;Hartescu, Morgan & Stevinson, 2015) (Fig. 3C). All trials
measured ISI and had an intervention period of four to six months. There was significant
Banno et al. (2018), PeerJ , DOI 10.7717/peerj.5172 8/23
Table 2 Summary of the published studies including qualitative synthesis.
Source Setting Patients, NAge Inclusion criteria Exercise type Exercise
frequency
Exercise
duration
Afonso et al. (2012) Elsewhere 61 50 to 65 years Postmenopausal women
with primary insomnia
meeting DSM-4
Aerobic (other aero-
bic)
2 session/wk 4 mos
Chan et al. (2016) Elsewhere 52 60 years or older Older adults with cognitive
impairment with CPSQI
>5
Aerobic (other aero-
bic)
2 session/wk 2 mos
Chan et al. (2017) Elsewhere Unknown 18 years or older Participants with mild to
moderate depression and
PSQI >5
Aerobic (other aero-
bic)
3 session/wk 8 wks
Guilleminault et al.
(1995)
At home 32 34 to 55 years Patients with psychophys-
iologic insomnia meeting
predetermined criteria
Aerobic (walking) 7 d/wk 4 wks
Hartescu, Morgan &
Stevinson (2015)
At home 41 40 years or older Inactive adults meeting
RDC for insomnia
Aerobic (walking) 5 d/wk 6 mos
Irwin et al. (2014) Elsewhere 123 34 to 55 years Older adults with chronic
and primary insomnia
meeting DSM-IV-TR and
ICSD-2
Aerobic (other aero-
bic)
1 d/wk 4 mos
Passos et al. (2010) Exercise laboratory 48 30 to 55 years Primary insomnia meeting
DSM-IV-TR and ICSD-2
Aerobic (walking,
other aerobic)
Acute One time
Reid et al. (2010) Elsewhere 17 55 years or older Older adults with insomnia
meeting predetermined cri-
teria
Aerobic (walking,
other aerobic)
4 times per wk 16 wks
Tadayon, Abedi &
Farshadbakht (2016)
At home 112 Mean 52.39 (SD
1.65) years
Postmenopausal women
with PSQI >5
Aerobic (walking) 7 d/wk 12 wks
Tang, Liou & Lin
(2010)
At home 71 Mean 51.80 (SD
12.13) years
Cancer patients with PSQI
>5
Aerobic (walking) 3 d/wk 8 wks
Notes.
Chan et al. (2017) was included in the qualitative synthesis but excluded in the quantitative synthesis because the trial did not include outcomes data for a meta-analysis.
DSM, Diagnostic and Statistical Manual of Mental Disorders; ICSD, International Classification of Sleep Disorders; PSQI, Pittsburgh Sleep Quality Index; RDC, research diagnostic criteria.
Banno et al. (2018), PeerJ , DOI 10.7717/peerj.5172 9/23
Figure 1 PRISMA 2009 flow diagram. CENTRAL: Cochrane Central Register of Controlled Trials; IC-
TRP, International Clinical Trials Registry Platform; RCTs, randomized controlled trials
Full-size DOI: 10.7717/peerj.5172/fig-1
improvement in favor of the intervention (MD, 3.22 points lower in the intervention group;
95% CI, 5.36 points lower to 1.07 points lower; P=0.003; very low-quality evidence). A
lower score was more beneficial in ISI. No statistical heterogeneity was indicated (Tau2
<0.001; I2=0%).
Banno et al. (2018), PeerJ , DOI 10.7717/peerj.5172 10/23
Figure 2 (A) Risk of bias graph (B) Risk of bias summary. (A) Review author judgments about the risk
for each bias item presented as percentages across all included trials. (B) Review author judgments about
the risk for each bias item in all included trials.
Full-size DOI: 10.7717/peerj.5172/fig-2
Banno et al. (2018), PeerJ , DOI 10.7717/peerj.5172 11/23
Figure 3 (A) Forest plot of comparison: Total PSQI score (B) Forest plot of comparison: Sleep efficiency (%) (C) Forest plot of comparison: To-
tal ISI score. (A) Total PSQI score was measured subjectively. IV, inverse variance; PSQI, Pittsburgh Sleep Quality Index (B) Sleep efficiency was
measured objectively by the devices (e.g., PSG, actigraphy). IV, inverse variance; PSG, polysomnograph (C) Total ISI score was measured subjec-
tively. ISI, Insomnia Severity Index; IV, inverse variance
Full-size DOI: 10.7717/peerj.5172/fig-3
Banno et al. (2018), PeerJ , DOI 10.7717/peerj.5172 12/23
Secondary outcomes
QOL
Five trials examined QOL, but the data were not subjected to the meta-analysis or assessed
by the GRADE approach because concepts of QOL measures differed among the trials. The
12-item medical outcomes study short form health survey version 2.0 (SF-12v2) or SF-36
had two types of scores (physical component summary and mental component summary).
Other QOL instruments had a single total score. Therefore, we did not calculate the SMD
of the QOL instruments. Significant effects in favor of the intervention were noted in all
trials (Figs. S1–S14;Table S4).
Sleep onset latency
Data from five trials that measured sleep onset latency (min) in 206 participants were
pooled for the meta-analysis (Guilleminault et al., 1995;Passos et al., 2010;Afonso et al.,
2012;Irwin et al., 2014;Hartescu, Morgan & Stevinson, 2015). All trials measured sleep
onset latency using PSG and actigraphy and had an intervention period of one day to six
months. There was no significant improvement in favor of the intervention (MD, 1.90 min
higher in the intervention group; 95% CI, 3.63 min lower to 7.43 min higher; P=0.50;
low-quality evidence). Shorter duration was more beneficial for sleep onset latency. No
statistical heterogeneity was indicated (Tau2< 0.001; I2=0%) (Fig. S15;Table S4).
Total sleep time
Data from five trials that examined total sleep time (min) in 206 participants were pooled
for the meta-analysis (Guilleminault et al., 1995;Passos et al., 2010;Afonso et al., 2012;Irwin
et al., 2014;Hartescu, Morgan & Stevinson, 2015). All trials measured total sleep time using
PSG and actigraphy and had an intervention period of one day to six months. There
was no significant improvement in favor of the intervention (MD, 4.32 min higher in
the intervention group; 95% CI, 9.19 min lower to 17.84 min higher; P=0.53; low-
quality evidence). Longer duration was more beneficial for total sleep time. No statistical
heterogeneity was indicated (Tau2< 0.001; I2=0%; Fig. S16;Table S4).
All adverse events (defined by the trial list)
Four trials comprising 150 participants measured adverse events. Three trials found no
adverse events in any of the participants (Reid et al., 2010;Afonso et al., 2012;Chan et al.,
2016). One trial described one adverse event, a mild sprained ankle, in the intervention
group (Hartescu, Morgan & Stevinson, 2015). Follow-up was two to six months (very
low-quality evidence).
Other secondary outcomes (Secondary outcomes not including Summary
of findings table)
Anxiety and depression were significantly ameliorated in favor of the intervention (Figs. S17
and S18;Table S4). ESS and WASO did not detect a significant effect in favor of the
intervention (Figs. S19 and S20;Table S4). None of the trials measured SSS (Fig. S21;
Table S4).
Banno et al. (2018), PeerJ , DOI 10.7717/peerj.5172 13/23
Additional analyses
We performed subgroup analyses of sleep quality because the outcome showed an I2>50%.
We conducted an ad-hoc subgroup analysis for exercise frequency (acute or regular) because
the underlying mechanisms may differ between acute exercise and regular exercise. We
also conducted an ad-hoc subgroup analysis of background variables (cancer patients,
postmenopausal women, and others). The exercise type subgroups differed significantly
(P<0.001; Table S4) and other pre-specified and ad-hoc subgroups of sleep quality did
not differ significantly (Table S4). Sleep efficiency did not improve significantly in favor
of the intervention with acute exercise (MD, 0.50% lower in the intervention group; 95%
CI, 8.31% lower to 7.31% higher; P=0.90) or regular exercise (MD, 0.56% lower in the
intervention group; 95% CI, 3.64% lower to 2.52% higher; P=0.72; Table S4). A higher
percentage was more beneficial for sleep efficiency.
We conducted sensitivity analysis by restricting the analyzed studies to those that had a
low risk of selection bias; however, the results were the same as those obtained in the original
analysis (Table S4). Moreover, the results did not change with the use of a fixed-effects
model instead of a random-effects model (Table S4). We were unable to estimate the ISI
results, as none of the trials showed a low risk of selection bias (Table S4).
When studies using imputed data or per-protocol analysis were excluded, PSQI (two
trials with 164 participants) did not exhibit a significant effect in favor of the intervention
(MD, 2.21 points lower in the intervention group; 95% CI, 5.34 points lower to 0.92 point
higher). A lower score was more beneficial for PSQI. Sleep efficiency (one trial with 48
participants) did not significantly improve in favor of the intervention (MD, 0.50% lower
in the intervention group; 95% CI, 8.31% lower to 7.31% higher). A higher percentage
was more beneficial for sleep efficiency. We were unable to estimate the ISI results because
no trials remained after exclusion of those with imputed data or per-protocol analysis
(Table S4).
We conducted an ad-hoc sensitivity analysis by excluding one study with acute exercise
because it was an experimental RCT. When the study with acute exercise was excluded,
sleep efficiency did not significantly improve in favor of the intervention (MD, 0.56% lower
in the intervention group; 95% CI, 3.64% lower to 2.52% higher). A higher percentage was
more beneficial for sleep efficiency.
DISCUSSION
The pooled results revealed that exercise improves PSQI and ISI scores. These results were
consistent across the included trials despite the indication of substantial heterogeneity in
the PSQI. The heterogeneity of PSQI seemed to be explained by exercise type. Whether
exercise improves QOL was inconclusive in our study, although exercise did have some
adverse effects which were of little importance. These results suggested that exercise was
an effective nonpharmacological treatment because improved sleep quality is one of the
primary treatment goals (Schutte-Rodin et al., 2008). Furthermore, a recent comprehensive
narrative review strongly recommended aerobic exercise in subjects with sleep disorders
(Chennaoui et al., 2015). Exercise can be as promising a nonpharmacological intervention
for patients with insomnia as CBT-I.
Banno et al. (2018), PeerJ , DOI 10.7717/peerj.5172 14/23
Results compared to those of prior studies
A three-point change in PSQI score was chosen to indicate a minimal clinically important
difference (MCID) (Hughes et al., 2009). Therefore, the effect of exercise on PSQI in favor
of the intervention (low-quality evidence) was considered small. A previous study (Yang
et al., 2012) found a small-to-moderate effect (SMD, 0.47; 95% CI [0.08–0.86]) of exercise
on PSQI among patients with sleep complaints, whereas our study found that exercise
exerts a large effect (SMD, 1.00; 95% CI [0.48–1.53]) on the PSQI. These results suggest
that exercise may provide more beneficial effects on PSQI in patients with insomnia than
in participants with sleep complaints. There is a possible ceiling and floor effect of exercise
on sleep in patients with sleep complaints compared to those with insomnia (Chennaoui et
al., 2015). For example, baseline total PSQI scores may be higher in patients with insomnia
than in those with sleep complaints, which may explain the differences in the results of
these studies.
Since a change in ISI score greater than 7 would be considered moderate improvement
(Morin et al., 2011), the effect of exercise on ISI (MD, 3.22 points lower in the intervention
group; 95% CI, 5.36 points lower to 1.07 points lower; very low-quality of evidence) in
favor of the intervention was considered small. The only previous study using PSG (Yang
et al., 2012) detected no change in sleep efficiency or onset latency, which was consistent
with results on these two parameters in our study.
In the present study, exercise did not improve sleep efficiency, sleep onset latency, or total
sleep time, and there was no evidence of heterogeneity across studies. The non-randomized
crossover study demonstrated an acute morning exercise decrease in the arousal index and
the number of stage shifts during the second half of the night in older individuals with
insomnia (Morita, Sasai-Sakuma & Inoue, 2017). A polysomnographic and subjective sleep
study found a significant decrease in sleep onset latency and wake time after sleep onset
as well as a significant increase in sleep efficiency following a six-month exercise training
program, but no significant differences were seen between morning and late-afternoon
exercise in chronic primary insomnia (Passos et al., 2011). Inconsistent subjective and
objective results regarding the effects of exercise on sleep, which may be related to variations
in exercise intensity, and time between exercise and sleep, were reported. Moreover, acute
exercise affects the endocrine system (Tuckow et al., 2006), metabolism (Scheen et al.,
1996), and core body temperature (Murphy & Campbell, 1997;Gilbert et al., 2004). Regular
exercise affects the endocrine system (Kern et al., 1995), metabolism (Scheen et al., 1996),
circadian rhythm and body core temperature (Murphy & Campbell, 1997). Sleep loss may
affect metabolism, the central nervous system, the endocrine system, inflammation, and
the autonomic nervous system (Chennaoui et al., 2015). Some studies have focused on the
sleep process in insomnia. Regular daytime exercise can increase melatonin secretion in and
improve the sleep quality of patients with insomnia (Taheri & Irandoust, 2018). Insomnia
can also result in cognitive dysfunction because sleep may restore cognitive function and
maintain attentional mechanisms (Taheri & Arabameri, 2012). Thus, the beneficial effects
of exercise on sleep efficiency and onset latency contribute to the interaction between
Banno et al. (2018), PeerJ , DOI 10.7717/peerj.5172 15/23
circadian rhythm and metabolic, immune, thermoregulatory, and endocrine effects. Future
trials to investigate the effects of exercise on sleep cycle and sleep process in patients with
insomnia are required.
Summary of the findings and recommendatons
We first performed a systematic review and meta-analysis of the effects of exercise on sleep
in patients with insomnia (diagnosed using criteria or screened with questionnaires). Our
findings suggest that the effects of exercise on sleep were greater in patients with insomnia
than in other populations and should be an effective nonpharmacological intervention.
Exercise interventions may alleviate symptoms in patients with insomnia without use of
hypnotics. The American Academy of Sleep Medicine report does not include exercise as
a viable recommendation for treating insomnia (Morgenthaler et al., 2006). Our findings
suggest that future clinical practice guidelines should include exercise as a recommendation
for treating patients with insomnia.
Strengths
The primary strength of this study was its careful and rigorous screening, extraction, and
scoring process. The secondary strength was the extensive subgroup analyses that explored
the heterogeneity of the results.
Limitations
Our study has several limitations. First, only four of the nine included trials examined
adverse effects (Reid et al., 2010;Afonso et al., 2012;Hartescu, Morgan & Stevinson, 2015).
Therefore, unreported outcomes and important unmeasured outcomes such as adverse
effects (for example, arrhythmia) may exist (Andersen et al., 2013). Second, most studies
had a high or unclear risk of selection bias, although our sensitivity analysis revealed that the
results were unchanged when studies were restricted to those that had a low risk of selection
bias (Table S4). In the future, trials with low risks of selection bias need to be conducted
verify our findings. Third, our review did not consider menopause in the meta-analysis
because none of the included studies reported subgroup data by postmenopausal status. In
the future, trials with subgroup data on postmenopausal women compared with women of
other age groups are needed to determine the effects of exercise in patients with insomnia.
CONCLUSIONS
Our findings suggest that exercise can improve sleep quality without notable adverse effects
in patients with insomnia. Most of the trials included in our review suggested a high risk
of selection bias in some domains. Therefore, higher quality research is needed to clarify
the effects of exercise on sleep in patients with insomnia.
ACKNOWLEDGEMENTS
We are grateful to Mr. Rui Afonso (Departamento de Psicobiologia, Universidade Federal de
São Paulo, Sao Paulo), Prof. Helena Hachul (Departamento de Psicobiologia, Universidade
Federal de São Paulo, Sao Paulo; Departamento de Ginecologia, Universidade Federal de
Banno et al. (2018), PeerJ , DOI 10.7717/peerj.5172 16/23
São Paulo, Sao Paulo), Dr. Iuliana Hartescu (Clinical Sleep Research Unit, Loughborough
University, Loughborough), Dr. Arun Kumar (Department of Physiology, Seth G. S.
Medical Collage & K. E. M. Hospital, Mumbai), Dr. Kathryn J. Reid (Department
of Neurology and Center for Circadian and Sleep Medicine, Northwestern University
Feinberg School of Medicine, Chicago, IL), Prof. Phyllis C. Zee (Department of Neurology
and Center for Circadian and Sleep Medicine, Northwestern University Feinberg School
of Medicine, Chicago, IL) and Assistant Professor Dr. Aileen WK Chan (The Nethersole
School of Nursing, The Chinese University of Hong Kong, Hong Kong), the authors of
the included study, for providing the detailed information. We thank Dr. Jessie Chan and
Prof. Cecilia Chan from Department of Social Work and Social Administration, Centre
on Behavioral Health, The University of Hong Kong, Hong Kong, for kindly providing
valuable unpublished data. We are grateful to Dr. Kiyomi Shinohara (Department of Health
Promotion and Human Behavior, Kyoto University Graduate School of Medicine/ School
of Public Health, Kyoto) for helping to make scope of the review and providing information
for PsycNet to enable screening in PsycINFO. We are grateful to Dr. Kazuhiro Uda (Office
for Infectious Control, National Center for Child Health and Development, Tokyo) for
helping collect references. We would like to thank Editage (http://www.editage.jp) for
English language editing.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
This work was supported by Japan Society for the Promotion of Science (KAKENHI Grant
Number 25282210), Nagoya University Academy of Psychiatry, and self-funding. The
funders had no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
Grant Disclosures
The following grant information was disclosed by the authors:
Japan Society for the Promotion of Science: 25282210.
Nagoya University Academy of Psychiatry.
Competing Interests
Masahiro Banno has received speaker honoraria from Dainippon Sumitomo, Eli Lilly, and
Otsuka; honoraria for a manuscript from Seiwa Shoten Co., Ltd, SENTAN IGAKU-SHA
Ltd and Kagakuhyoronsha Co., Ltd.; and travel fees from Yoshitomi Pharmaceutical
Industries Ltd. Yuki Kataoka received research funds from Eli Lilly. The other authors
declare no competing interests.
Author Contributions
•Masahiro Banno, Hiraku Tsujimoto, Yasushi Tsujimoto and Yuki Kataoka conceived and
designed the experiments, performed the experiments, analyzed the data, contributed
reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed
drafts of the paper, approved the final draft.
Banno et al. (2018), PeerJ , DOI 10.7717/peerj.5172 17/23
•Yudai Harada performed the experiments, authored or reviewed drafts of the paper,
approved the final draft.
•Masashi Taniguchi, Ryo Tobita and Akiko Noda conceived and designed the experiments,
authored or reviewed drafts of the paper, approved the final draft.
Data Availability
The following information was supplied regarding data availability:
The raw data are provided in a Supplemental File.
Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/10.7717/
peerj.5172#supplemental-information.
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