University of Groningen
Neurophysiological signature(s) of visual hallucinations across neurological and perceptual
Dauwan, Meenakshi
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Dauwan, M. (2019). Neurophysiological signature(s) of visual hallucinations across neurological and perceptual: and non-invasive treatment with physical exercise. Rijksuniversiteit Groningen.
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CHAPTER
1 Brain Center Rudolf Magnus, University Medical
Center Utrecht, The Netherlands
2 Amsterdam UMC, VU University Medical Center,
Department of Clinical Neurophysiology and MEG Center, The Netherlands
3 Department of Psychiatry, University of Hong Kong,
China
4 Alzheimer Center, VU University Medical Center,
Amsterdam, The Netherlands
5 University of Groningen, University Medical Center
Meenakshi Dauwan1,2 Marieke JH Begemann1,* Margot IE Slot1,* Edwin HM Lee3 Philip Scheltens4 Iris EC Sommer1,5,6
10
Physical exercise
improves quality of life,
depressive symptoms,
and cognition across
chronic brain disorders: a
transdiagnostic systematic
review and meta-analysis
of randomized controlled
ABSTRACT
:H SHUIRUPHG D PHWDDQDO\VLV WR V\QWKHVL]H HYLGHQFH RQ WKH HIÀFDF\ DQG VDIHW\ of physical exercise as an add-on therapeutic intervention for quality of life (QoL), depressive symptoms and cognition across six chronic brain disorders: Alzheimer’s disease, Huntington’s disease, Multiple Sclerosis, Parkinson’s disease, Schizophrenia and Unipolar Depression.
122 studies (=k) (n=7231) were included. Exercise was superior to treatment as usual in improving QoL (k=64, n=4334, ES=0.40, p GHSUHVVLYHV\PSWRPV k=60, n=2909, ES=0.78, p WKHFRJQLWLYHGRPDLQVDWWHQWLRQ ZRUNLQJPHPRU\ k=21, n=1313, ES=0.24, p H[HFXWLYHIXQFWLRQLQJ k=14, n=977, ES=0.15, p=.013), memory (k=12, n=994, ES=0.12, p=.038) and psychomotor speed (k=16, n=896, ES=0.23, p=.003). Meta-regression showed a dose-response effect for exercise time (minutes/week) on depressive symptoms (ȕ=.007, p=.012). 69% of the studies that reported on safety, found no complications.
([HUFLVHLVDQHIÀFDFLRXVDQGVDIHDGGRQWKHUDSHXWLFLQWHUYHQWLRQVKRZLQJDPHGLXP sized effect on QoL and a large effect on mood in patients with chronic brain disorders, with a positive dose-response correlation. Exercise also improved several cognitive GRPDLQVZLWKVPDOOEXWVLJQLÀFDQWHIIHFWV
1. INTRODUCTION
Chronic brain disorders are associated with reduced quality of life (QoL) (Berrigan et al., 2016; Karow et al., 2016; Ready et al., 2008; van Uem et al., 2015), high prevalence of low mood and depression, stress sensitivity and cognitive dysfunction (Feinstein, 2011; Pfeiffer, 2016). These sequelae are interdependent, as depressive mood and cognitive LPSDLUPHQWDUHWZRPDLQIDFWRUVLQÁXHQFLQJ4R/ %HUULJDQHWDO %ULVVRVHWDO 2016; Conde-Sala et al., 2016; Feinstein, 2011; Pfeiffer, 2016; Ready et al., 2008; van Uem HWDO ZKLOHFRJQLWLRQLVQHJDWLYHO\LQÁXHQFHGE\GHSUHVVLRQ 3LURJRYVN\7XUN et al., 2016). Moreover, these general sequelae are associated with various adverse consequences such as poor treatment compliance, loss of independence and even mortality (Adamson et al., 2015). In treatment of brain disorders, current clinical SUDFWLFHWHQGVWRIRFXVRQLPSURYLQJGLVHDVHVSHFLÀFV\PSWRPV HJWUHPRUDQGULJLGLW\ in Parkinson’s disease, psychosis in schizophrenia). Notably, however, patients with brain disorders regard QoL and depressive mood as more important for their health VWDWXVWKDQGLVHDVHVSHFLÀFSK\VLFDODQGPHQWDOV\PSWRPV )D\HUVDQG0DFKLQ Therefore, improvement of these common features should become an important target in treatment of chronic brain disorders.
Exercise therapy may positively affect QoL, depression and cognition across disorders. A leading example is stroke, in which physical exercise has shown favorable effects in improving a wide range of symptoms, such that it has now been incorporated and recommended in guidelines as part of the standard treatment (Billinger et al., 2014; Excellence, 2016; Foundation, 2010; Party, 2016; Winstein et al., 2016). In contrast, UHVHDUFKRQWKHHIÀFDF\RISK\VLFDOH[HUFLVHLQWUHDWPHQWRIRWKHUEUDLQGLVRUGHUVLV still in its infancy and therefore not part of the standard care. Although several studies have investigated the effect of physical exercise in different chronic brain disorders such as Alzheimer’s disease (AD) (Groot et al., 2016; Lamb et al., 2018), multiple sclerosis (MS) (Campbell et al., 2018; Demaneuf et al., 2018; Etoom et al., 2018), Parkinson’s disease (PD) (da Silva et al., 2018; Fayyaz et al., 2018), Schizophrenia (Sz) (Dauwan et
KDYHEHHQKLJKO\GLYHUVH /DP $VDFRQVHTXHQFHFXUUHQWHYLGHQFHIRUHIÀFDF\ of exercise therapy is still disputed and exercise is not part of the regular care offer for patients with aforementioned disorders in most countries.
Of note, the above-mentioned chronic brain disorders share underlying SDWKRSK\VLRORJLFDOPHFKDQLVPV$VVXFKQHXURLQÁDPPDWLRQ )URKPDQHWDO Heneka et al., 2015; Hirsch and Hunot, 2009; Steiner et al., 2013), imbalance in same neurotransmitter (e.g. dopamine in Sz and PD (Lotharius and Brundin, 2002; Stepnicki et al., 2018), serotonin in Sz and UD (Lopez-Figueroa et al., 2004)) and growth factors (e.g. brain-derived neurotrophic factor; BDNF) (Angelucci et al., 2005; Parain et al., 1999), and disturbed connectivity (e.g. in default-mode network) (Bonavita et al., 2011; Garrity et al., 2007; Greicius et al., 2004; Tessitore et al., 2012) have been implicated in the pathophysiology of many of these brain disorders. Furthermore, a recent genome-wide association study (GWAS) showed high degree of genetic overlap among many SV\FKLDWULFGLVRUGHUVVWDWLQJWKDWWKHGLIIHUHQWSV\FKLDWULFGLVRUGHUVGRQRWUHÁHFW independent diseases but rather represent different overlapping phenotypes of the same clinical spectra (Anttila et al., 2018).
Physical exercise has been reported to protect and restore the brain by inducing changes in neuroplasticity (van Praag, 2009; Voss et al., 2013c). Neuroplasticity involves neurogenesis (i.e. the formation of new neurons), angiogenesis (i.e. growth of new blood vessels), and synaptic plasticity (i.e. changes in the connections between neurons) through production and upregulation of neurotrophic factors (i.e. growth factors that induce development, function and survival of neurons) such as BDNF, vascular HQGRWKHOLDOJURZWKIDFWRU 9(*) DQGLQVXOLQOLNHJURZWKIDFWRU ,*) &KLHIÀ HWDO 9RVVHWDOF 3K\VLFDOH[HUFLVHDOVRPRGXODWHVQHXURLQÁDPPDWLRQ 6YHQVVRQ HW DO ,W HOHYDWHV WKH H[SUHVVLRQ RI DQWLLQÁDPPDWRU\ F\WRNLQHV (i.e. immunomodulatory signaling molecules), and reduces the expression of pro-LQÁDPPDWRU\F\WRNLQHVLQWKLVZD\FKDQJLQJWKHSURLQÁDPPDWRU\VWDWHRIWKHEUDLQ LQWRDQDQWLLQÁDPPDWRU\PRGHDQGWKXVPHGLDWLQJQHXURSURWHFWLRQ 6YHQVVRQHWDO 2015). These physical exercise induced neuroprotective changes have been described
in many of the above-mentioned disorders (Dauwan et al., 2015; Hirsch et al., 2016; Knöchel et al., 2012).
7KHDIRUHPHQWLRQHGVKRZVKRZGLVHDVHVSHFLÀFUHVHDUFKKDVGHHPSKDVL]HGDQG limited our understanding of substantial commonalities that exist across disorders. Considering the overlap in pathophysiology and clinical picture across chronic brain disorders, commonalities across disorders outweigh the differences indicating that WUDQVGLDJQRVWLFDQGGLVHDVHVSHFLÀFWUHDWPHQWVPLJKWEHDWOHDVWHTXDOO\HIIHFWLYH Therefore, by targeting the common functional relationships across disorders with WUDQVGLDJQRVWLFWUHDWPHQWVERWKGLVHDVHVSHFLÀFDQGFRPPRQVKDUHGIDFWRUVFDQEH targeted during treatment. Physical exercise can be such a transdiagnostic treatment for chronic brain disorders.
The objective of this study is to quantitatively review the effect of additional physical exercise on QoL, depressive symptoms and cognition across the above-mentioned disorders. In addition, we aim to estimate the safety of exercise in aforementioned groups. There are of course more chronic brain disorders in which exercise therapy may be effective, but for reasons of feasibility we restricted this review to six different brain disorders of various origins.
2. METHOD
2.1 Literature search
This meta-analysis was performed according to the Preferred Reporting for Systematic Reviews and Meta-analysis (PRISMA) Statement.(Moher et al., 2009) A systematic search was performed in Pubmed (Medline), Embase, PsychInfo and Cochrane Database of Systematic Reviews (independently by MD, MS, and EL), using combinations of the following search terms: ‘Alzheimer’, ‘AD’, ‘Huntington’, ‘HD’, ‘multiple sclerosis’, ‘MS’, ‘Parkinson’, ‘PD’, ‘PDD’, ‘schizophrenia’, ‘psychosis’, ‘psychotic’, ‘depression’, ‘depressive’, ‘mood’, ‘affective’, ‘exercise’, ‘physical’, ‘training’, ‘endurance’, ‘aerobic’, ‘anaerobic’, ‘resistance’, ‘sport’ and ‘yoga’ (Table S1), with no year or language limits.
necessary, corresponding authors were contacted to provide full text details of the study outcome measures.
2.2 Inclusion criteria
By consensus (between MD, MS, EL, and IS), the following studies were included: 1. Randomized controlled trials (RCTs) investigating the effect of any type of physical exercise as an add-on intervention on QoL, depressive symptoms and/or cognition 2. Studies investigating whole-body, or upper- or lower-body exercise (i.e. organ VSHFLÀFH[HUFLVHVXFKDVUHVSLUDWLRQPXVFOHRUSHOYLFPXVFOHWUDLQLQJZHUHH[FOXGHG 3. Studies including patients with a diagnosis of AD, HD, MS, (idiopathic) PD, Sz (Dauwan et al., 2015) and UD (according to a diagnostic interview) in both the intervention and control group (i.e. mixed study populations were excluded)
5&7VZLWKDFURVVRYHUGHVLJQSURYLGLQJGDWDIRUWKHÀUVWVWXG\SHULRG
5. Studies investigating combined interventions when the control group received the same non-exercise component of the intervention (e.g. exercise + medication versus medication only)
6. Studies investigating rehabilitation programs, provided that physical exercise constituted a main part of the program
6WXGLHVUHSRUWHGVXIÀFLHQWLQIRUPDWLRQWRFRPSXWHFRPPRQHIIHFWVL]H (6 VWDWLVWLFV (i.e. mean and standard deviations (SDs), exact F-, p-, t-, or z-values) or corresponding authors could provide these data upon request
8. If multiple publications were retrieved that described the same cohort, only the sample with largest overall sample size and/or original data was included
2.3 Exclusion criteria
1. Studies investigating same type of physical exercise in both the intervention and control group
$EVWUDFWVRIVWXGLHV ZLWKRXWIXOOWH[WDYDLODEOH ZLWKLQVXIÀFLHQWLQIRUPDWLRQDERXW the physical exercise intervention and/or outcome measures to calculate ES and untraceable corresponding information of the authors
2.4 Outcome measures
The outcome measures included pre- and post-intervention assessments (i.e. PHDVXUHGGLUHFWO\DIWHUÀQLVKLQJWKHLQWHUYHQWLRQDQGWKXVGRHVQRWLQFOXGHIROORZXS measurements) of QoL, depressive symptoms and/or cognition. For measurements of depressive symptoms, observer-rated scales were preferred over self-rated questionnaires because of its higher validity (Cuijpers et al., 2010).
Based on the cognitive domains and/or cognitive tests investigated across studies and GLVRUGHUVWKHIROORZLQJVL[FRJQLWLYHGRPDLQVZHUHFODVVLÀHG DWWHQWLRQDQGZRUNLQJ memory (A&WM), executive functioning (EF), memory (M), psychomotor speed (PS), YHUEDOÁXHQF\ 9) DQGJOREDOFRJQLWLRQ *& 7DEOH6 7RFRPELQHVWXGLHVDFURVV disorders, the most stringent control group per disorder (i.e. Treatment as usual 7$8 DOORZLQJWUHDWPHQWVVXFKDVHJGLVHDVHVSHFLÀFPHGLFDWLRQUHDGLQJQHZVSDSHUV educational sessions but no active treatments such as occupational therapy) was used as a reference group.
2.5 Assessment of risk of bias
According to the Cochrane Handbook of Systematic Reviews of Interventions (Higgins and Green, 2011), risk of bias was assessed for all eligible studies regarding selection bias, detection bias, attrition bias and reporting bias. Attrition bias was divided into assessment of incomplete outcome data (i.e. drop-out and exclusions) and intention-to-treat (ITT) analysis as ITT is considered the least biased method to measure intervention effects in RCTs (Higgins and Green, 2011). Performance bias was not assessed, as it is usually not possible to blind study participants to whether or not exercise intervention is performed.
2.6 Data analysis
All analyses were performed using Comprehensive Meta-Analysis Version 2.0. Per outcome measure, the effect of additional exercise (versus control group) was TXDQWLÀHGIRUHDFKVWXG\XVLQJ+HGJHV·JEDVHGRQFKDQJHVFRUHV HQGRIWUHDWPHQW minus baseline). When these were not reported, pre- and post-treatment means and SDs, or exact F-, p-, t-, or z-values were used. For studies that did not report exact SDVWKHVHZHUHFDOFXODWHGXVLQJWKH FRQÀGHQFHLQWHUYDOV 6' VTUW 1 >XSSHU limit-lower limit]/[2*1.96]) or standard error (SE) (SD= SE*sqrt(N)).
To achieve a single pair-wise comparison between exercise and TAU, if a study investigated two or more types of exercise intervention, groups were combined for the main analysis (Deeks et al., n.d.) but studied separately in the moderator analysis (see further). The ES of the individual intervention groups were combined to calculate a composite ES by incorporating the ES and variance of each individual intervention while taking into account the correlation among the different interventions (Borenstein et al., 2009a). Likewise, when a study used more than one questionnaire to measure QoL or depressive symptoms, or multiple neuropsychological tests to measure a cognitive domain, a composite ES was calculated. As the correlation among interventions or test measures was mostly not reported, a correlation of .5 was taken for all the computations to avoid under- and overestimation of the overall ES (Borenstein et al., 2009a).
Studies were combined in meta-analysis to calculate a mean weighted ES for each outcome measure (see supplementary for formulas). A random-effects model was considered appropriate given the heterogeneity across studies and diagnoses. Moreover, a random-effects model allows generalization of the results on population level (Borenstein et al., 2009b). ES were interpreted according to Cohen (Cohen, ZLWKDQ(6RILQGLFDWLQJDVPDOOHIIHFWDPHGLXPDQGDODUJHHIIHFW First, analyses were performed including all suitable studies per outcome measure. 6XEVHTXHQWO\DQDO\VHVZHUHUHSHDWHGE\H[FOXGLQJRXWOLHUVWXGLHVGHÀQHGDVVWXGLHV with standardized residual z-scores of ES exceeding ±1.96 (p WZRWDLOHG VKRZQ in Figure 2-4), studies with small total sample sizes (n EHFDXVHRIKLJKULVNRI
sampling error in effect estimates (Sterne et al., 2011) and studies with high risk of ELDV LHFRQVLGHULQJWKHDLPRIWKHPHWDDQDO\VLVWRVWXG\5&7VVWXGLHVFODVVLÀHGDV having high risk of bias on randomization and allocation concealment were excluded). ES with p ZHUHFRQVLGHUHGVLJQLÀFDQW+HWHURJHQHLW\RIUHVXOWVDFURVVVWXGLHVZDV assessed by calculating the Q-statistic and I2-statistic. Q-statistic tests the existence
of heterogeneity and displays a chi-square distribution with k-1 degrees of freedom (k=number of studies). QYDOXHVKLJKHUWKDQWKHGHJUHHVRIIUHHGRPLQGLFDWHVLJQLÀFDQW between-studies variability. I2 describes the percentage of total variation across studies
due to heterogeneity rather than chance. I2-values of 25%, 50%, and 75% are considered
as low, moderate and high heterogeneity, respectively (Higgins et al., 2003).
Potential publication bias was investigated by visual inspection of the funnel plots, with asymmetrical funnel plots indicating publication bias. When appropriate, the funnel plot asymmetry was tested with Egger’s test (p WZRWDLOHG (JJHUHWDO 1997). Additionally, Rosenthal’s fail-safe number (NR ZDVFDOFXODWHGIRUVLJQLÀFDQW(6 HVWLPDWLQJWKHQXPEHURIXQSXEOLVKHGVWXGLHVZLWKQRQVLJQLÀFDQWUHVXOWVQHHGHGWR EULQJWKHREVHUYHGUHVXOWWRQRQVLJQLÀFDQFH 5RVHQWKDO
2.6.1 Moderator analyses
6XEJURXS DQDO\VHV ZHUH SHUIRUPHG IRU ¶W\SH RI H[HUFLVH· FODVVLÀHG DV DHURELF resistance, or neuromotor exercise (e.g. yoga) according to the American College of Sports Medicine (ACSM) Guideline (Garber et al., 2011a)
6LQFHDQLQVXIÀFLHQWQXPEHURIVWXGLHVH[DPLQHGWKHHIIHFWRIÁH[LELOLW\H[HUFLVHRQO\ analysis was not feasible for this type of exercise.
Random effects meta-regression analyses were conducted to evaluate the effect of the following continuous moderator variables using the unrestricted maximum likelihood model:
- Exercise time (minutes/week)
- Total length of the intervention period (weeks)
If a study reported a range for any of these variables, the mean value of the variable was calculated from the upper and lower bounds. To include each pair-wise comparison separately in these sensitivity analyses, for studies with multiple intervention groups but one shared control group, the total number of participants in the control group were evenly divided up among the comparisons (Deeks et al., n.d.).
6LQFHDODUJHQXPEHURIWKHLQFOXGHGVWXGLHVGLGQRWSURYLGHVXIÀFLHQWLQIRUPDWLRQ about the intensity and safety of the exercise intervention and most of the included studies (80%) investigated supervised exercise intervention, a sub- or meta-regression analysis was not possible to investigate the effect of these parameters. The intensity and safety of the exercise interventions was assessed qualitatively.
3. RESULTS
A total of 400 articles investigating the effect of any type of exercise intervention for patients with chronic brain disorders were retrieved from the literature search (AD:
k=40, HD: k=6, MS: k=137, PD: k=124, Sz: k=29, UD: k VHHÀJXUH
A descriptive overview of these studies is provided in Table S3. Of these, 163 studies IXOÀOOHGWKHLQFOXVLRQFULWHULDDQGZHUHHOLJLEOHIRUPHWDDQDO\VLV $JXLDUHWDO Ahmadi et al., 2013, 2010b, 2010a; Allen et al., 2010; Arcoverde et al., 2014; Ashburn et al., 2007; Battaglia et al., 2013; Belton, 2014; Belvederi Murri et al., 2015; Bernhardt et al., 2012; Bhatia et al., 2017; Bjarnadottir et al., 2007; Blumenthal et al., 1999, 2007; Brenes et al., 2007; Briken et al., 2014; Bulguroglu et al., 2015; Busse et al., 2017, 2013; Çakt et al., 2010; Canning et al., 2014, 2012; Carneiro et al., 2015; Carroll et al., 2017; Carta et al., 2008; Carter et al., 2014, 2015; Chan et al., 2012; Cholewa et al., 2013; Chou et al., 2004; Clarke et al., 2016; Coghe, et al., 2018; Comelia et al., 1994; Conradsson et al., 2015; Cugusi et al., 2015; Dalgas et al., 2010; Danielsson et al., 2014; de Oliveira et al., 2016; K J Dodd et al., 2011; Doose et al., 2015; Doulatabad et al., 2013; Duff et al., 2018; Duncan and Earhart, 2014; Ebrahimi et al., 2015; Feys et al., 2016; Foster et al., 2013; Garrett et al., 2012; Goodwin et al., 2011; Hebert et al., 2012; Ho et al., 2016; Hoang et al., 2015; Hoffman et al., 2008; Hoffmann et al., 2015; Hogan et al., 2014; Holthoff et al., 2015; Huang et al., 2015; Ikai et al., 2013; Jäckel et al.,
2015; Kaltsatou et al., 2014; Kargarfard et al., 2012; Kemoun et al., 2010; Kerling et al., 2015; Kerse et al., 2010; Keus et al., 2007; Khalil et al., 2013; Khan et al., 2008; Khatri et al., 2001; Kimhy et al., 2015; Kinser et al., 2014; Kooshiar et al., 2015; Laupheimer et al., 2011; Lautenschlager et al., 2015; Lavretsky et al., 2011; Learmonth et al., 2017, 2012; Legrand, 2015; Legrand and Neff, 2016; Liao et al., 2015; Lin et al., 2015; Loh et al., 2016; Louie et al., 2015; Luttenberger et al., 2015; Maci et al., 2012; Marzolini et al., 2009; Mather et al., 2002; McCullagh et al., 2008; Miller et al., 2011; Mota-Pereira et al., 2011; Nabkasorn et al., 2006; Negahban et al., 2013; Ni et al., 2016a, 2016b; Niemi et al., 2016; Nilsagård et al., 2013; O’Donnell and Coote, 2011; Oertel-Knöchel et al., 2014a; Ohman et al., 2016; Oken et al., 2004; Ozgen, 2016; Park et al., 2014; Paul et al., 2014; Petajan et al., 1996; Pfaff et al., 2014; Picelli et al., 2016; Pilu et al., 2007; Plow et al., 2014; Poliakoff et al., 2013; Prakhinkit et al., 2014; Prathikanti et al., 2017; Prosperini et al., 2013; Quinn et al., 2016, 2014; Qutubuddin et al., 2013; Rahnama et al., 2011; Razazian et al., 2016; Rietberg et al., 2014; Roach et al., 2011; Rolland et al., 2007; Romberg et al., 2005; Romenets et al., 2015; Salhofer-Polanyi et al., 2013; Sandroff et al., 2017, 2016; Sangelaji et al., 2014; Santos et al., 2017; Schmitz-Hübsch et al., 2006; Schuch et al., 2015; Shahidi et al., 2011; Sharma et al., 2017, 2015; Silva-Batista et al., 2016; Sims et al., 2006; Singh et al., 2001, 2005, 1997; Siqueira et al., 2016; Stack et al., 2012; Steinberg et al., 2009; Storr et al., 2006; Straudi et al., 2014; Sutherland et al., 2001; Suttanon et al., 2012; Tallner et al., 2012; Tarakci et al., 2013; Teri et al., 2003; Thompson et al., 2013; Tickle-Degnen et al., 2010; Tolahunase et al., 2018; Tsang et al., 2012, 2006; Veale et al., 1992; Venturelli et al., 2011; Vergara-Diaz et al., 2018; Vermöhlen et al., 2017; Visceglia and Lewis, 2011; Vreugdenhil et al., 2012; Wade et al., 2003; Yágüez et al., 2011; Yang et al., 2015; Yeung et al., 2012, 2017; Zhang et al., )RUW\RQHVWXGLHVSURYLGHGLQVXIÀFLHQWLQIRUPDWLRQWRFRPSXWHFRPPRQHIIHFW VL]H7KHUHIRUHDÀQDOWRWDORIVWXGLHVFRXOGEHFRPELQHGLQPHWDDQDO\VLV5LVN of bias of all the eligible studies is shown in Table S4 with a corresponding elaborative assessment of the studies included in the meta-analysis.
Figure 1. 35,60$ÁRZFKDUWRIWKHOLWHUDWXUHVHDUFK
AD: Alzheimer’s disease, HD: Huntington’s disease, MS: Multiple Sclerosis, PD: Parkinson’s disease, Sz: Schizophrenia, UD: Unipolar Depression.
3.1 Quality of Life
Sixty-four studies (n=4334) examined the effect of exercise on QoL. Exercise showed DVLJQLÀFDQWPHGLXPVL]HHIIHFW (6 &,WRp ÀJXUHWDEOH 1). Heterogeneity was high (Q(63)=250.18, p I2=75%), indicating that 75% of
WKHGLVSHUVLRQVHHQLQÀJXUHUHÁHFWVGLIIHUHQFHLQWKHWUXHHIIHFWVL]HVZKLOHWKH remaining 25% can be attributed to random sampling error. Five studies (Battaglia et al., 2013; Kargarfard et al., 2012; Liao et al., 2015; Maci et al., 2012; Santos et al., ZHUHLGHQWLÀHGDVRXWOLHUVVL[VWXGLHV %DWWDJOLDHWDO %XVVHHWDO Canning et al., 2012; Carroll et al., 2017; Maci et al., 2012; Visceglia and Lewis, 2011) had small sample sizes (n DQGDQRWKHUIRXUVWXGLHV (EUDKLPLHWDO +RJDQHW DO 5RPHQHWVHWDO 6WRUUHWDO ZHUHFODVVLÀHGDVKDYLQJKLJKULVNRI ELDV$IWHUH[FOXVLRQ(6GHFUHDVHGEXWUHPDLQHGVLJQLÀFDQW k=51, n=3895, ES=0.31, 95%CI 0.19 to 0.43, p +HWHURJHQHLW\GHFUHDVHGEXWUHPDLQHGPRGHUDWHWR high (Q(50)=159.13, p I2=69%). Funnel plot and Egger’s test indicated potential
publication bias before (t(62)=5.00, p NR=1898), and after exclusion of the studies (t(49)=3.39, p NR=847) but with very high fail-safe numbers (table 1). Within disorder analysis showed a positive effect of exercise on QoL in patients with MS, PD and Sz (Table 2).
T a b le 1 : Re su lt s o f m ai n a n d s u b gro u p a n al ys e s a cro ss d is o rd e rs St udies (N) Pa ti e n ts (I G / CG ) Mean ag e ( y r s) (r a n g e ) Ex e r c is e ti m e (m in /w k ) (r a n g e ) In ter v en ti on dur a ti on (w e e k s) (r a n g e ) Hed g es’ g 9 5 % C I p -V a lu e Q -s ta ti st ic (d f) I 2 (% ) Eg ge r ’s test N R Qo L 6 4 2 3 4 9 / 19 8 5 53 .3 ( 1 5 .4 -78 .0 ) 11 6 .5 0 ( 4 0 .0 -41 2 .5 ) 1 2 .2 0 ( 4 .0 -52 .0 ) 0.4 0 0. 2 7 to 0. 5 2 < .000 1 Q (6 3 )= 2 5 0 .1 8, p < .000 1 75 t( 6 2 )= 5 .0 0 , p < .000 1 18 9 8 Wit h ou t ou tl ie rs 51 2 0 91/ 18 0 4 54.6 ( 1 5 .4 -78 .0 ) 11 2 .4 9 ( 4 0 .0 -36 0 .0) 1 3 .43 ( 4 .0 -52 .0 ) 0. 3 1 0. 1 9 to 0. 4 3 < .000 1 Q (5 0 )= 1 5 9 .1 3 , p < .000 1 69 t( 4 9 )= 3 .39 , p< .0 1 0 84 7 Subg roup anal ys is - A e ro b ic ex er cis e 9 2 5 7 / 2 5 0 0.45 0. 1 6 to 0.7 5 .0 0 3 Q (8) = 2 7 .3 6 , p= .0 0 1 71 - N e u ro m o to r ex er cis e 1 0 2 5 4 / 2 1 5 0. 3 5 0 .07 t o 0. 6 4 .0 1 3 Q (9) = 2 2 .6 3 , p= .0 0 7 60 - Re si st an ce ex er cis e 6 11 8 / 1 0 9 0. 5 7 0. 20 to 0. 94 .0 0 3 Q( 5)= 4 .1 9 , p= .5 23 0 - A ll t y p e s o f ex er cis e 8 2 8 8 / 27 5 0. 3 7 0.0 8 to 0. 6 7 .0 1 4 Q(7 )= 2 6. 9 3 , p < .000 1 74 De pressiv e sy mptoms 6 0 1 6 3 5 / 12 7 4 54. 7 ( 1 5 .4 -83.0 ) 12 8 .7 5 ( 4 0 .0 -30 0.0 0 ) 1 3 .3 1 ( 1 .4 -52 .0 ) 0. 7 8 0. 58 to 0. 9 8 < .000 1 Q (5 9 )= 3 6 7 .9 0 , p < .000 1 84 t( 58 )= 6 .1 0 , p < .000 1 39 3 7 Wit h ou t ou tl ie rs 43 1 3 6 4 / 10 6 6 54. 3 ( 1 5 .4 -83.0 ) 11 8 .1 4 ( 4 0 .0 -21 0 .0 ) 1 4 .6 1 ( 1 .4 -52 .0 ) 0.4 7 0. 3 2 to 0. 6 2 < .000 1 Q (4 2 )= 1 3 0 .5 5 , p < .000 1 68 t( 4 1 )= 3. 9 7 , p< .0 0 1 10 8 8 Subg roup anal ys is - A e ro b ic ex er cis e 17 4 9 3 / 4 1 5 0.4 0 0. 1 6 to 0. 65 .0 0 1 Q( 1 6 )= 4 9 .4 1 , p < .000 1 68
ti n ue d St udies (N) Pa ti e n ts (I G / CG ) Mean ag e ( y r s) (r a n g e ) Ex e r c is e ti m e (m in /w k ) (r a n g e ) In ter v en ti on dur a ti on (w e e k s) (r a n g e ) Hed g es’ g 9 5 % C I p -V a lu e Q -s ta ti st ic (d f) I 2 (% ) Eg ge r ’s test N R o to r 8 17 6 / 1 4 3 0. 5 5 0 .18 t o 0. 9 1 .0 0 1 Q( 7 )= 7. 9 0 , p= .3 4 2 11 4 7 5 / 6 9 0. 9 6 0. 4 4 to 1. 4 8 <.0 0 1 Q(3 )= 6 .2 2 , p= .1 02 52 f 2 1 3 5 / 1 3 9 0 .0 6 -0 .53 t o 0. 6 4 .8 5 4 Q( 1 )=2 .3 5 , p= .1 2 5 57 g 2 1 7 9 4 / 5 1 9 5 5 .8 ( 2 4 .6 -82 .0 ) 11 8 .5 7 ( 6 0 .0 -36 0 .0) 1 5 .3 6 ( 3 .0 -10 4 .0 ) 0.2 4 0.0 6 to 0. 4 1 .0 09 Q( 20 )= 4 0 .8 3 , p= .0 0 4 51 t( 19 )= 2 .1 4 , p= .0 4 6 55 tl ie r 1 4 5 4 7 / 3 7 6 5 7. 8 ( 2 4 .6 -82 .0 ) 10 0 .18 ( 6 0 .0 -18 0 .0 ) 1 2 .8 2 ( 6 .0 -24 .0 ) 0.25 0.0 8 to 0. 4 2 .0 04 Q( 1 3 )=2 0 .8 3 , p= .0 7 6 3 8 t(12 )= 0 .7 5 , p= .4 6 6 8 2 8 7 / 1 8 4 0 .0 6 -0 .1 6 t o 0. 2 9 .5 7 5 Q (7) = 1 3 .2 7, p= .0 6 6 47 o to r 8 2 4 1 / 1 7 1 0. 3 9 0. 17 to 0 .60 .0 0 1 Q (7 )=6.8 4 , p= .4 4 6 0 g 1 4 5 9 6 / 3 8 1 5 6 .3 ( 2 4 .6 -7 8.8 ) 1 6 5.0 ( 6 0.0 -48 0 .0 ) 17 .7 1 ( 3 .0 -52 .0 ) 0. 1 5 0.0 3 to 0. 2 7 .0 1 3 Q( 1 3 )= 1 2 .3 0 , p= .5 03 0 t(12 )= 0 .4 8 , p= .6 4 1 tl ie r 1 0 5 6 5 / 3 51 5 2 .3 ( 2 4 .6 -78 .0 ) 17 3 .3 ( 6 0 .0 -48 0 .0 ) 2 0 .4 0 ( 3 .0 -52 .0 ) 0. 1 7 0.0 4 to 0. 2 9 .0 09 Q( 9 )= 4 .5 8 , p= .8 6 9 0t (8 )= 1 .5 4 , p= .1 63
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Ta b le 1 : C o n ti n ue d St udies (N) Pa ti e n ts (I G / CG ) Mean ag e ( y r s) (r a n g e ) Ex e r c is e ti m e (m in /w k ) (r a n g e ) In ter v en ti on dur a ti on (w e e k s) (r a n g e ) Hed g es’ g 9 5 % C I p -V a lu e Q -s ta ti st ic (d f) I 2 (% ) Eg ge r ’s test N R Subg roup anal ys is - A e ro b ic ex er cis e 7 3 1 6 / 2 4 1 0.20 0.0 6 to 0. 3 5 .0 0 7 Q( 6 )= 1 .9 2 , p= .9 2 7 0 - N e u ro m o to r ex er cis e 3 1 6 4 / 1 1 8 0 .0 8 -0 .1 3 t o 0. 2 9 .4 6 5 Q (2) = 5 .4 1 , p= .0 6 7 63 Mem o r y 1 2 6 0 9 / 3 8 5 51 .9 ( 2 4 .6 -7 8.8 ) 13 9 .3 8 ( 6 0 .0 -36 0 .0) 1 3 .5 0 ( 3 .0 -36 .0 ) 0. 1 2 0 .07 t o 0. 2 4 .038 Q( 11 )= 1 0 .7 4 , p= .4 65 0t (1 0 )= 0 .5 9 , p= .5 6 8 W it h o u t o u tl ie r 9 5 8 2 / 3 5 7 5 4 .7 ( 2 4 .6 -7 8.8 ) 14 3 .3 3 ( 6 0 .0 -36 0 .0) 1 6 .1 1 ( 3 .0 -36 .0 ) 0 .0 9 -0 .0 3 t o 0. 2 1 .1 2 7 Q (8 )= 4 .8 1 , p = .777 0t (7 )= 0 .9 0 , p= .3 9 9 Subg roup anal ys is - A e ro b ic ex er cis e 7 3 9 4 / 2 6 2 0 .1 1 -0 .0 2 t o 0. 2 4 .10 7 Q (6 )= 2 .9 4 , p= .8 17 0 - N e u ro m o to r ex er cis e 4 17 9 / 8 4 0 .1 4 -0 .1 0 t o 0. 3 8 .2 5 4 Q (3 )= .4 4 , p = .933 0 P sy c homotor Sp e e d 1 6 5 0 9 / 3 8 7 5 3 .1 ( 2 4 .6 -7 8.8 ) 11 5.0 ( 6 0.0 -18 0 .0 ) 1 3 .8 8 ( 3 .0 -36 .0 ) 0.2 3 0.0 8 to 0. 3 8 .0 0 3 Q( 1 5 )= 1 9 .0 2 , p= .2 1 3 21 t( 1 4 )= 2 .3 6 , p= .0 3 5 42 W it h o u t o u tl ie r 1 0 4 5 4 / 332 5 3 .0 ( 2 4 .6 -7 8.8 ) 11 2 .5 ( 6 0 .0 -18 0 .0 ) 2 8.8 6 ( 9 .0 -36 .0 ) 0. 1 4 0 .0 0 5 t o 0. 2 7 .04 2 Q( 9 )= 8 .5 6 , p= .4 79 0t (8 )= 1 .0 2 , p= .3 3 8 Subg roup anal ys is
ti n ue d St udies (N) Pa ti e n ts (I G / CG ) Mean ag e ( y r s) (r a n g e ) Ex e r c is e ti m e (m in /w k ) (r a n g e ) In ter v en ti on dur a ti on (w e e k s) (r a n g e ) Hed g es’ g 9 5 % C I p -V a lu e Q -s ta ti st ic (d f) I 2 (% ) Eg ge r ’s test N R 8 3 3 8 / 2 4 7 0 .0 9 -0 .07 t o 0. 2 4 .2 7 6 Q (7 )= 7. 0 4 , p= .4 2 5 1 o to r 2 6 0 / 2 6 0 .32 -0 .0 8 t o 0.7 1 .1 1 6 Q (1 )= 0 .6 6 , p= .4 1 6 0 6 3 0 3 / 2 3 7 6 6 .7 ( 4 9 .6 -7 8.8 ) 17 6 .2 5 ( 6 0 .0 -48 0 .0 ) 2 0 .1 7 (9 .0 -52 .0 ) 0 .2 4 -0 .07 t o 0. 5 5 .1 3 4 Q (5 )=1 4 .3 6 , p= .0 1 4 65 t( 4 )= 3 .0 9 , p= .0 3 7 3 tl ie r 5 2 8 8 / 2 2 2 6 5 .7 ( 4 9 .6 -7 8.8 ) 19 3 .5 0 ( 6 0 .0 -48 0 .0 ) 2 1 .8 0 (9 .0 -52 .0 ) 0 .0 6 -0 .1 5 t o 0. 2 7 .5 6 9 Q (4) =5 .5 5 , p= .2 36 28 t( 3 )= 2 .4 8 , p= .0 8 9 1 5 3 7 6 / 3 4 9 7 1 .1 ( 5 0 .4 -84 .0 ) 15 7. 8 6 ( 4 5 .0 -48 0 .0 ) 1 9 .1 3 ( 4 .0 -52 .0 ) 0 .3 0 -0 .0 3 t o 0. 6 3 .0 7 6 Q (1 4 )= 60. 7 9 , S 77 t( 1 3 )= 0 .1 1 , p= .9 17 1 0 32 1 / 29 9 6 9 .4 ( 5 0 .4 -82 .0 ) 16 3 .8 9 ( 4 5 .0 -48 0 .0 ) 2 1 .9 0 ( 8 .0 -52 .0 ) 0. 3 9 0.0 9 to 0. 6 8 .0 1 0 Q( 9 )= 2 6. 1 5 , p= .0 0 2 66 t( 8 )=1.1 4 , p= .2 8 6 4 1 4 8 / 1 3 1 0 .2 2 -0 .1 5 t o 0. 58 .2 4 6 Q (3 )= 7.2 3 , p= .0 6 4 59 1 2 6 / 1 3 1 .45 0. 5 6 to 2. 3 4 .0 0 1 l g ro u p; d f= d e gr e e s o f f re e d o m ; I G = In te rve n ti o n g ro u p; N R = R o se n th al ’s f ai l-sa fe n u m b e r; y rs= ye ar s; m in /w k = m in u te s p e r we e k
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T a b le 2 : Re su lt s p e r d is o rd e r f o r a ll o u tc o m e m e as u re s Ou tc o m e m e a sur e S tudi es (N) Pa ti e n ts (IG /C G ) Hed g es’ g 9 5 % C I p -v a lu e Q -s ta ti st ic ( d f) I 2 (% ) Eg ge r ’s te st 1 N R Qo L Al zh ei m e r’ s d is e as e 5 2 3 4 /22 4 0 .4 0 -0 .1 0 to 0 .9 1 .1 1 9 4 S 83 t(3 )= 1 .3 0 , p= .2 83 Wi th out out lier 4 2 27 /2 17 0 .2 2 -0 .2 4 t o 0 .6 8 .3 4 5 Q (3 )-1 5 .9 0 , p = .0 0 1 8 1 t( 2 )= 0 .4 7, p = .6 8 8 H u n ti n g to n ’s d is e as e 3 3 5 /32 0 .3 1 -0 .2 5 t o 0 .8 8 .2 8 0 Q (2 )= 3 .3 9 , p = .1 8 4 4 1 t( 1 )=5 .0 5 , p = .1 2 4 Wi th out out lier 2 2 6 /2 3 0 .0 5 -0 .4 6 t o 0 .5 6 .8 5 0 Q (1 )= 0 .2 4 , p = .6 2 6 0 M u lt iple S c le ro sis 2 5 9 0 9 /6 4 1 0 .4 1 0 .2 4 t o 0 .5 8 < .000 1 Q (2 4 )= 7 2 .6 1 , p < .000 1 6 7 t( 2 3 )= 2 .2 0 , p = .0 3 8 3 8 0 Wi th out out lier 21 74 9 /5 51 0. 3 9 0.25 t o 0. 5 4 <.0 0 0 1 Q (2 0 )= 3 4 .9 9 , p = .0 20 4 3 t( 1 9 )= 1.1 5 , p = .2 6 3 P ar k in so n ’s d ise as e 1 9 8 87 /8 5 2 0. 3 1 0.0 8 t o 0. 5 4 .0 0 9 Q (1 8 )= 8 1 .45 , p < .0 0 0 1 7 8 t( 1 7 )= 2 .9 4 , p = .0 0 9 5 9 Wi th out out lier 1 4 8 2 5 /7 9 3 0 .18 -0 .0 4 t o 0 .4 1 .1 1 2 4 S 7 5 t(12 )= 2 .0 5 , p = .0 6 3 S c hizophr e ni a 5 1 3 0 /8 8 0 .8 9 0 .2 2 t o 1 .5 5 .0 0 9 Q (4 )= 2 1 .0 2 , p < .0 0 0 1 8 1 t( 3 )= 1 .6 7, p = .1 94 Wi th out out lier 3 110 /7 2 0 .4 3 -0 .1 3 t o 0 .9 9 .1 3 0 Q (2 )= 6 .3 5 , p = .0 4 2 6 8 t( 1 )= 0 .1 1 , p = .9 3 1 U n ipol ar De pr e ssion 7 1 5 4 /1 4 8 0. 34 -0 .0 4 t o 0.7 2 .0 82 Q( 6 )= 1 0. 0 8 , p= .0 0 4 6 9 t( 5 )= .6 4 , p= .5 5 2 De pres siv e s y mptoms Al zh ei m e r’ s d is e as e 5 2 6 4 /254 0 .8 0 0 .1 2 t o 1 .4 9 .0 2 2 Q (4 )= 4 8 .1 5 , p < .0 0 0 1 9 2 t( 3 )= 3 .4 6 , p = .0 4 1 2 4 Wi th out out lier 3 2 3 7 /22 7 0 .05 -0 .1 6 to 0 .2 4 .6 5 3 Q (2 )= 2 .3 8 , p = .3 05 1 6 t( 1 )= 0 .0 05, p = .9 9 7 H u n ti n g to n ’s d is e as e 2 2 4 /2 4 0 .4 0 -0 .7 6 t o 1 .5 6 .4 9 6 Q (1 )= 4 .0 3 , p = .0 4 5 7 5 M u lt iple S c le ro sis 1 4 3 2 7 /2 4 9 0.45 0. 1 2 to 0. 7 9 .0 0 7 Q( 1 3 )= 4 7 .7 8 , p < .0 0 0 1 7 3 t( 1 2 )= 2 .3 0, p = .04 0 6 9 Wi th out out lier 1 3 2 91/ 2 3 1 0.2 3 0.06 t o 0.4 0 .0 1 0 Q (12 )= 9 .61 , p = .6 5 0 0 t( 1 1 )= 3 .5 9 , p = .0 0 4 1 8 P ark ins o n ’s dis e as e 5 11 6/ 1 0 0 0 .05 -0. 3 6 to 0. 45 .8 2 2 Q (4 )= 7. 9 1 , p = .09 5 4 9 t( 3 )= 0 .8 3, p = .4 6 9 Wi th out out lier 3 8 9 /77 -0 .0 4 -0 .6 3 t o 0 .5 5 .8 9 5 Q (2 )=6.2 2 , p = .0 4 5 6 8 t( 1 )= 0 .2 0 , p = .8 7 4
ti n ue d m e a sur e S tudi es (N) Pa ti e n ts (IG /C G ) Hed g es’ g 9 5 % C I p -v a lu e Q -s ta ti st ic ( d f) I 2 (% ) Eg ge r ’s te st 1 N R ni a 2 4 6 /2 1 0. 7 3 0.20 t o 1 .2 6 .0 0 7 Q( 1 )= 0 .8 9 , p= .3 4 7 0 lier 14 2 /1 5 0. 6 2 0.0 4 t o 1 .1 9 .0 3 7 Q(0)= 0 .0 0 , p= 1 .0 0 0 0 pr e ssion 3 2 8 5 8 /6 2 6 1 .0 8 0 .7 8 t o 1 .3 8 < .0 0 0 1 Q (3 1 )= 2 1 0 .9 6 , p < .0 0 0 1 8 5 t(30 )= 4 .8 3, p< .0 0 0 1 2 0 2 4 lier s 23 736 /5 2 3 0 .8 8 0 .6 2 t o 1 .1 4 < .0 0 0 1 Q (2 2 )= 1 0 1 .9 6 , p < .0 0 0 1 7 8 t( 2 1 )= 4 .1 8 , p < .0 0 1 9 8 0 o r k in g m e m o r y is e as e 3 4 4 /4 3 0 .2 8 -0 .1 3 t o 0 .6 9 .18 5 Q (2 )= 2 .3 0 , p .3 17 1 3 t( 1 )= 5 .2 9 , p = .1 1 9 le ro si s 5 11 7 /9 5 0 .2 3 -0 .0 4 t o 0 .4 9 .0 8 9 Q (4) = 4 .1 6 , p = .3 8 4 4 t( 3 )= 0 .6 7, p = .5 5 0 lier 4 11 2 /9 0 0 .2 4 -0 .07 t o 0 .5 6 .1 3 4 Q (3 )= 4 .1 6 , p = .2 4 5 2 8 t( 2 )= 1 .1 6 , = .3 6 5 d ise as e 5 8 9 /8 2 0. 5 0 0.20 t o 0. 8 0 .0 0 1 Q (4 )= 2 .6 3, p = .6 22 0 t( 3 )= 0 .05, p = .9 6 2 lier s 2 5 7 /54 0 .4 1 -0 .1 2 to 0 .94 .1 2 9 Q (1 )= 1 .6 1 , p = .2 05 38 ni a 4 3 7 3 /1 8 4 0 .0 7 -0. 4 1 t o 0. 5 5 .7 7 6 Q(3 )= 1 4. 5 7, p= .0 02 79 t( 2 )= 0 .5 4 , p= .6 4 2 lier 3 3 6 5 /1 7 4 0 .0 4 -0 .5 1 t o 0 .60 .8 7 9 Q (2 )= 1 4 .3 2 , p = .0 0 1 86 t( 1 )= 1 .4 4, p = .3 86 e p re ss io n 4 17 1 /1 1 5 0 .2 2 -0 .2 4 t o 0 .6 8 .3 51 Q (3 )= 8 .7 2 , p = .0 33 6 6 t( 2 )= 1 .1 4 , p = .3 7 3 lier 3 1 6 3 /1 07 0 .1 7 -0 .3 6 t o 0 .70 .5 4 0 Q (2 )= 7. 7 9 , p = .0 2 0 7 4 t( 1 )= 0 .7 8 , p = .5 7 8 func ti onin g is e as e 3 7 8 /8 2 0 .0 3 -0 .5 8 t o 0 .6 4 .9 2 1 Q (2 )= 5 .2 1 , p = .0 7 4 6 2 t( 1 )=0 .000 5 , p = 1 .000 lier 2 7 1 /7 5 -0 .1 7 -0 .8 6 t o 0 .5 2 .6 2 8 Q (1 )=3 .1 6 , p = .0 76 6 8 le ro sis 4 7 6 /5 6 0 .1 5 -0. 1 8 t o 0. 4 7 .3 7 0 Q(3 )=2 .0 0 , p= .5 7 2 0 t(2 )= 0. 4 9 , p= .6 73
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Ta b le 2 : C o n ti n ue d Ou tc o m e m e a sur e S tudi es (N) Pa ti e n ts (IG /C G ) Hed g es’ g 9 5 % C I p -v a lu e Q -s ta ti st ic ( d f) I 2 (% ) Eg ge r ’s te st 1 N R Wi th out out lier 3 7 1 /5 1 0 .2 1 -0 .1 3 t o 0 .5 6 .2 2 3 Q (2 )= 0 .7 4 , p = .6 9 2 0 t( 1 )= 0 .8 2 , p = .5 6 4 P ar k in so n ’s d ise as e 2 2 4 /1 6 0 .2 8 -0 .2 5 t o 0 .8 0 .3 0 6 Q( 1 )= 0 .7 0 , p= .4 02 0 W it h o u t o u tl ie r 1 1 5 /8 0 .0 8 -0 .6 2 t o 0 .7 8 .8 27 S c hizophr e ni a 2 2 6 3 /1 2 5 0 .1 7 -0. 2 1 t o 0. 5 5 .3 8 6 Q( 1 )=2 .8 8 , p= .0 9 0 65 U n ip o la r D e p re ss io n 2 1 4 6 /9 1 0 .2 0 -0 .0 1 t o 0 .42 .0 6 5 Q (1 )= 0 .0 4 , p = .8 3 5 0 Mem o r y A lzheimer ’s dis e as e 3 1 2 7 /1 1 0 0 .0 5 -0. 1 8 to 0. 28 .666 Q (2 )= 0 .7 5, p = .6 8 8 0 t( 1 )= 0. 3 1 , p = .8 11 M u lt iple S c le ro sis 2 4 8 /3 0 0 .4 8 -0. 5 3 t o 1 .4 8 .3 5 2 Q( 1 )= 4 .6 4 , p= .0 3 1 7 8 S c h iz o p h re n ia 3 27 1 /1 3 5 0 .1 3 -0 .07 t o 0 .33 .2 0 1 Q (2 )= 0 .8 9 , p = .6 4 1 0 t( 1 )= 1 .0 1 , p = .4 9 6 Wi th out out lier 2 2 6 3 /1 2 5 0 .1 2 -0 .0 9 t o 0 .33 .2 5 0 Q (1 )= 0 .7 9 , p = .3 7 6 0 U n ipol ar De pr e ssion 3 1 5 4 /9 9 0 .1 7 -0. 0 4 t o 0. 3 8 .1 0 4 Q(2 )= 0 .7 7, p= .6 8 0 0 t( 1 )= 0. 6 9 , p= .6 1 5 Wi th out out lier 2 1 46/ 9 1 0 .1 6 -0 .05 to 0 .38 .1 3 6 Q (1 )= 0 .6 7, p = .4 1 3 0 P sy c homotor S p eed A lzheimer ’s dis e as e 3 1 2 7 /1 1 3 0 .4 9 -0. 3 2 to 1 .2 9 .2 3 7 Q (2 )= 1 0. 3 8 , p = .0 0 6 8 1 t( 1 )= 1 .6 2 , p = .3 5 2 M u lt iple S c le ro sis 6 1 3 3 /1 1 3 0 .2 4 -0 .0 0 8 to 0 .48 .0 58 Q( 5 )= 3 .2 2 , p= .6 6 7 0 t( 4 )= 0. 6 8 , p= .5 3 3 Wi th out out lier s 4 11 8 /9 9 0 .2 2 -0 .0 4 t o 0 .4 8 .0 9 9 Q(3 )=2 .6 3 , p= .4 5 2 0 t( 2 )= 0 .2 0 , p= .8 5 8 S c hizophr e ni a 2 7 7 /4 3 0.45 0.0 7 t o 0. 8 3 .0 20 Q( 1 )= 0 .0 2 , p= .8 8 6 0 Wi th out out lier 16 9 /3 3 0.4 4 0.0 2 t o 0. 85 .0 4 0 U n ip o la r D e p re ss io n 3 1 5 4 /9 9 0 .1 8 -0 .0 5 t o 0 .4 1 .1 33 Q (2 )= 1 .8 4 , p = .3 9 8 0 t( 1 )= 1 .7 4 , p = .3 32
n ti n ue d m e a sur e S tudi es (N) Pa ti e n ts (IG /C G ) Hed g es’ g 9 5 % C I p -v a lu e Q -s ta ti st ic ( d f) I 2 (% ) Eg ge r ’s te st 1 N R lier 2 1 46/ 9 1 0 .1 4 -0 .1 0 to 0 .38 .2 38 Q (1 )= 0 .4 8 , p = .4 8 7 0 H Q F \ s d is e as e 4 1 8 8 /1 7 8 0 .2 7 -0 .2 0 t o 0 .7 4 .2 6 4 Q (3 )= 1 2 .2 3 , p = .0 07 7 5 t( 2 )= 2 .9 2 , p = .1 0 0 gn it io n s d is e as e 1 0 2 99 /28 7 0. 2 1 -0 .2 1 to 0.6 3 .3 3 2 4 S 82 t( 8 )= 0 .1 9 , p= .8 5 3 lier s 72 7 1 /2 6 0 0. 3 2 0.0 2 t o 0. 6 3 .0 3 9 Q (6 )= 1 6. 6 3 , p = .0 1 1 6 4 t( 5 )= 0 .8 1 , p = .4 5 6 ’s d is e as e 2 2 4 /2 6 0 .1 4 -0 .4 0 t o 0 .6 8 .6 1 3 Q (1 )= 0 .1 5 , p = .7 0 2 0 dis e as e 3 5 3 /3 6 0 .7 1 -0.0 3 to 1 .45 .0 60 Q (2 )= 5 .5 1 , p = .0 6 4 6 4 t( 1 )= 0 .0 7, p = .9 5 7 lier s 12 6 /1 3 1 .45 0. 6 9 to 2 .2 1 < .0 0 0 1 l g ro u p; d f= d e gr e e s o f f re e d o m ; I G = In te rve n ti o n g ro u p; N R = R ose n th al ’s f ail -s afe numbe r c an n o t b e p e rf o rm e d f o r k
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Figure 2. Meta-analysis of the effect of physical exercise on quality of life. Effect sizes (ES) per study and the overall ES are in Hedges’ g with corresponding p-values and sample size of the intervention and control group. Standardized residual z-scores of ES were used to detect outlier studies.
3.2 Depressive symptoms
Sixty studies (n VKRZHGDVLJQLÀFDQWODUJHVL]HHIIHFWRIH[HUFLVHRQGHSUHVVLYH symptoms (ES=0.78, 95%CI 0.58 to 0.98, p ÀJXUH ZLWKKLJKKHWHURJHQHLW\ (Q(59)=367.90, p I2=84%; table 1). Excluding eight outliers (Mota-Pereira et al.,
2016; Sharma et al., 2017; Singh et al., 1997; Vreugdenhil et al., 2012), seven small studies (n &DUQHLURHWDO &KRXHWDO .LQVHUHWDO 0DFLHWDO Marzolini et al., 2009; Oertel-Knöchel et al., 2014b; Picelli et al., 2016) and two studies (Luttenberger et al., 2015; Romenets et al., 2015) with high risk of bias decreased the overall ES to a medium effect (k=43 n=2430, ES=0.47, 95%CI 0.32 to 0.62, p Heterogeneity reduced to moderate to high (Q(42)=130.55, p I2=68%). Funnel
plot and Egger’s test indicated potential publication bias (t(58)=6.10, p NR=3937), which remained after exclusion of the outliers (t(41)=3.97, p NR=1088; table 1). Within disorder analysis showed a positive effect of exercise on depressive symptoms in AD, MS, Sz and UD (Table 2).
3.3 Cognition
Of the 120 studies, 36 studies (AD: k=12, HD: k=3, MS: k=7, PD: k=7, Sz: k=3, UD: k=4), examining 2125 patients, evaluated cognitive functioning and were included.
3.3.1 Attention & Working memory
([HUFLVHVKRZHGDVLJQLÀFDQWVPDOOHIIHFWRQDWWHQWLRQ ZRUNLQJPHPRU\ k=21,
n=1313, ES=0.24, 95%CI 0.06 to 0.41, p ÀJXUH ZLWKPRGHUDWHKHWHURJHQHLW\
(Q(20)=40.83, p=.004; I2=51%). The funnel plot and Egger’s test indicated potential
publication bias (t(19)=2.14, p.046, NR WDEOH 7KH(6UHPDLQHGVLJQLÀFDQWDIWHU excluding one outlier study (Bhatia et al., 2017), four small studies (n 'XQFDQDQG Earhart, 2014; Oertel-Knöchel et al., 2014b; Picelli et al., 2016; Sandroff et al., 2016) and one study (Romenets et al., 2015) with high risk of bias (k=14, n=923, ES=0.25, 95%CI 0.08 to 0.42, p=.004). Heterogeneity turned low to moderate (Q(13)=20.83,
p=.076; I2 (JJHU·VWHVWZDVQRQVLJQLÀFDQW WDEOH
3.3.2 Executive functioning
Fourteen studies (n VKRZHGDVLJQLÀFDQWVPDOOHIIHFWRIH[HUFLVHRQH[HFXWLYH functioning (ES=0.15, 95%CI 0.03 to 0.27, p ÀJXUH 6WXGLHVZHUHKRPRJHQRXV (Q(13)=12.30, p=.503; I2 (JJHU·VWHVWZDVQRQVLJQLÀFDQW WDEOH $IWHUH[FOXGLQJ
DO 6DQGURIIHWDO (6UHPDLQHGVLJQLÀFDQW k=10, n=916, ES=0.17, 95%CI 0.04 to 0.29, p=.009). There were no studies with high risk of bias.
Figure 3. Meta-analysis of the effect of physical exercise on depressive symptoms. Effect sizes (ES) per study and the overall ES are in Hedges’ g with corresponding p-values and sample size of the intervention and control group. Standardized residual z-scores of ES were used to detect outlier studies.
3.3.3 Memory
Twelve studies (n=994) examined the effect of physical exercise on memory and VKRZHGDEHQHÀFLDOVPDOOHIIHFWRIH[HUFLVH LQYROYLQJPDLQO\DHURELFH[HUFLVH (6 95%CI 0.07 to 0.24, p ÀJXUH 6WXGLHVZHUHKRPRJHQRXV Q(11)=10.74, p=.465; I2 (JJHU·VWHVWZDVQRQVLJQLÀFDQW WDEOH $IWHUH[FOXGLQJRQHRXWOLHUVWXG\
(Briken et al., 2014) and one small study (Oertel-Knöchel et al., 2014b), ES was non-VLJQLÀFDQW k=9, n=939, ES=0.09, 95%CI -0.03 to 0.21, p=.127), while studies remained homogenous (table 1).
3.3.4 Psychomotor speed
([HUFLVH VKRZHG D VLJQLÀFDQW VPDOO HIIHFW RQ SV\FKRPRWRU VSHHG k=16, n=896, ES=0.23, 95%CI 0.08 to 0.38, p ÀJXUH +HWHURJHQHLW\DPRQJVWXGLHVZDV low (Q(15)=19.02, p=.213; I2=21%). Funnel plot and Egger’s test indicated potential
publication bias (t(14)=2.36, p=.035, NR=42). After excluding one outlier (Holthoff et al., 2015) and four small studies (Oertel-Knöchel et al., 2014b; Picelli et al., 2016; 6DOKRIHU3RODQ\LHWDO 6DQGURIIHWDO (6UHPDLQHGVLJQLÀFDQW k=10, n=786, ES=0.14, 95%CI 0.005 to 0.27, p=.042). Studies showed complete homogeneity and (JJHU·VWHVWZDVQRQVLJQLÀFDQW WDEOH
9HUEDOÁXHQF\
([HUFLVHVKRZHGQRVLJQLÀFDQWHIIHFWRQYHUEDOÁXHQF\ k=6, n=540, ES=0.24, 95%CI -0.07 to 0.55, p ÀJXUH DQGUHPDLQHGQRQVLJQLÀFDQWDIWHUH[FOXGLQJRQHRXWOLHU study (Holthoff et al., 2015) (k=5, n=510, ES=0.06, 95%CI -0.15 to 0.27, p=.569). Heterogeneity among studies was moderate to high (Q(5)=14.36, p=.014; I2=65%; table
1) but decreased after excluding the outlier (table 1). 3.3.6 Global cognition
Fifteen studies (n=725) showed a trend of exercise in improving global cognition (ES=0.30, 95%CI -0.03 to 0.63, p ÀJXUH (6LQFUHDVHGDQGVKRZHGVLJQLÀFDQFH (k=10, n=620, ES=0.39, 95%CI 0.09 to 0.68, p=.010) after excluding two outliers (Arcoverde et al., 2014; Venturelli et al., 2011), three small studies (Busse et al., 2013;
after exclusion of the studies (Q(9)=26.15, p=.002; I2=66%). Egger’s test was
non-VLJQLÀFDQW WDEOH
6HSDUDWHDQDO\VHVSHUGLVRUGHUVKRZHGEHQHÀFLDOHIIHFWVRIH[HUFLVHRQ$ :0LQ3' PS in Sz and on GC in AD and PD (Table 2).
The study by Oertel knöchel et al (Oertel-Knöchel et al., 2014a) and Maci et al (Maci et al., 2012) investigated physical exercise in combination with a cognitive intervention. Exclusion of these studies did not change results for any of the outcome measures.
Figure 4. Meta-analysis of the effect of physical exercise on the cognitive domains (from top to down) attention & working memory, executive functioning, memory, psychomotor speed, verbal ÁXHQF\DQGJOREDOFRJQLWLRQ(IIHFWVL]HV (6 SHUVWXG\DQGWKHRYHUDOO(6DUHLQ+HGJHV·JZLWK corresponding p-values and sample size of the intervention and control group. Standardized residual z-scores of ES were used to detect outlier studies.
3.4 Studies with ITT-analysis
Additional analyses with studies with only low or unclear risk of bias on ITT analyses showed even larger effect of exercise on both QoL (ES=0.56) and depressive symptoms (ES=0.90), while effect on the cognitive domain psychomotor speed remained small
3.5 Moderator analysis
6XEJURXSDQDO\VLVVKRZHGDVLJQLÀFDQWPHGLXPHIIHFWRIDHURELFDQGQHXURPRWRU exercise and a medium to large effect of resistance exercise on QoL and depressive symptoms. Furthermore, a comprehensive program including all types of exercises according to ACSM was also effective in improving QoL. For cognition, aerobic and QHXURPRWRUH[HUFLVHVVKRZHGVLJQLÀFDQWHIIHFWV WDEOH
Meta-regression analysis showed a small but positive dose-response effect for the amount of weekly exercise in min/week in reducing depressive symptoms (ȕ=0.007, 95%CI 0.002 to 0.013, p=.012; Table S6; Figure S1), indicating that every one minute increase in exercise intervention per week corresponds to an .007 unit increase is ES. 1RVLJQLÀFDQWHIIHFWZDVIRXQGIRUWKHPRGHUDWRUWRWDOOHQJWKRILQWHUYHQWLRQ UDQJH 1.4-104 weeks). Additional meta-regression results are shown in Table S6.
3.5.1 Intensity
With regard to intensity of the exercise intervention as possible moderator, 50 of the analyzed studies (41.0%) did not report any information. Of the remaining 59.0%, 18 studies (25.0%) investigated neuromotor exercises and therefore possibly could not report any intensity level. 36 studies (50.0%) applied low-to-moderate intensity of exercise, while 16 studies (22.2%) investigated moderate-to-high intensity exercise. Two studies (2.8%) investigated low-to-high intensity exercise (Figure S2).
3.5.2 Safety
6L[W\ÀYHVWXGLHV UHSRUWHGRQVDIHW\DVSHFWVRIWKHH[HUFLVHLQWHUYHQWLRQ )LJXUH6 )RUW\ÀYHRIWKHVHVWXGLHV IRXQGQRSK\VLFDOLQMXULHVUHODWHGWR exercise. Eighteen studies (27.7%) found physical injuries that were related to the exercise intervention. These consisted mainly of muscle/joint pain (17.5%), fall incidents (11.4%, all with complete recovery) and ankle sprain (1.9%). In 83.3% of these studies (k=15), physical injuries were short lasting and/or had no consequences for participation in and completion of the exercise intervention.
4. DISCUSSION
2QHKXQGUHGDQGWZHQW\WZRVWXGLHVLQFOXGLQJSDWLHQWVVKRZHGDVLJQLÀFDQW medium-size effect (ES=0.40) of exercise as an add-on therapeutic intervention on QoL (k=64, n=4334), a large effect (ES=0.78) on depressive symptoms (k=60, n DQGDVPDOOEXWVLJQLÀFDQWHIIHFW (6 RQLPSURYLQJIXQFWLRQLQ several cognitive domains. The effects for QoL and depression were well powered. The included number of patients was lower for cognition (k=36, n=2125), which makes WKHVHUHVXOWVPRUHVHQVLWLYHIRUQHZÀQGLQJV)URPWKHVWXGLHVWKDWUHSRUWHGRQVDIHW\ (k=18), low incidences of complications related to the exercise interventions were found, which had no lasting consequences for participation in and completion of the exercise interventions.
4.1 Current clinical practice
In present clinical practice, the role of physical exercise as an add-on therapy in the management of QoL, depressive symptoms and cognitive impairment in chronic brain disorders remains elusive (Hindle et al., 2013; Kalron and Zeilig, 2015; Ranjbar et al., 2015). Management guidelines sometimes suggest physical exercise in treatment of e.g. physical health, motor symptoms, falls and fatigue in chronic brain disorders but lack in clarity over the effectiveness of physical exercise on the studied symptoms (C. et al., 2015; California Department of Public Health, 2008; Keus et al., 2014; National Collaborating Centre for Mental Health, 2014; Netz, 2017; NICE, 2014; Vidal-Jordana and Montalban, 2017).
Chronic brain disorders commonly affect well-being and QoL. Therefore, improvement of QoL is a main care objective in these disorders. Depressed mood and cognitive inabilities are important contributors to reduce QoL. Currently, evidence for WUHDWPHQWGHVLJQHGVSHFLÀFDOO\WRWDUJHW4R/LVODFNLQJ0RVWWUHDWPHQWVIRUFKURQLF EUDLQGLVRUGHUVDOOHYLDWHGLVHDVHVSHFLÀFV\PSWRPVSURJUHVVLRQRUUHODSVH,QFRQWUDVW exercise therapy targets overall well-being, mood and cognition, independent of type
At present, physical exercise is not generally viewed as an effective intervention. For example, in a recent review, Kok et al. evaluated treatment of depression in older adults and stated that depressive symptoms can be effectively treated with antidepressants whereas physical exercise may not be a mainstream treatment modality, yet might be considered as a complementary therapy (Kok et al., 2017). In contrast, Turner HWDOVKRZHGWKDWWKHHIÀFDF\RIDQWLGHSUHVVDQWVLVVXEMHFWWRVHOHFWLYHSXEOLFDWLRQ of positive studies with a precipitous drop in ES to an overall ES of 0.32 when non-published FDA approved drug trials of antidepressants were combined with non-published drug trials (Turner et al., 2008).
For dementia, there are still no disease-modifying agents available and treatment is limited to amelioration of symptoms (Dunkel et al., 2012). The effects for cognition IRXQGLQRXUPHWDDQDO\VLVDUHVWDWLVWLFDOO\VPDOOEXWVLJQLÀFDQWDQGVLPLODURUODUJHU than effects of cognitive therapy (Clare and Woods, 2013; Huntley et al., 2015; Leung et al., 2015; Nair et al., 2016; Revell et al., 2015; Rilo et al., 2016) or drug treatment (Di Santo et al., 2013; Keefe et al., 2013; Shilyansky et al., 2016; Str??hle et al., 2015), which makes these effects relevant for cognitive outcomes.
4.2 Heterogeneity and moderators
7RRXUNQRZOHGJHWKLVLVWKHÀUVWPHWDDQDO\VLVWRDVVHVVWKHHIIHFWRISK\VLFDOH[HUFLVH interventions across chronic brain disorders. Since heterogeneity between studies is a valid reason of concern in meta-analyses, our study shows that when we consider brain disorders to share underlying mechanisms, it’s feasible to combine disorders and studies across disorders in a joint analysis. We found lower heterogeneities in the joint analysis compared to within disorder analysis. High heterogeneity across studies and disorders was accounted for by using the random-effects model and excluding outlier studies, small studies and studies with high risk of bias. As a consequence, for QoL and depressive symptoms, both heterogeneity and ES decreased, but exercise VWLOOVKRZHGDVLJQLÀFDQWPHGLXPHIIHFW0RGHUDWRUDQDO\VHVSHUIRUPHGWRDVVHVV potential sources of heterogeneity, showed moderate variability between studies that LQYHVWLJDWHGDHURELFH[HUFLVHVZKHUHDVVWXGLHVWKDWHYDOXDWHGWKHHIÀFDF\RIUHVLVWDQFH or neuromotor exercises on QoL and depressive symptoms showed higher ES and no
heterogeneity. Largest effects were found for resistance exercise. Better performance of resistance exercise on these outcomes might be mediated by an increase in peripheral blood levels of Insulin-growth-factor-1 (IGF-1), which can cross the blood-brain barrier and has been shown to regulate the effects of exercise on depression, learning, angiogenesis and hippocampal neurogenesis (Cotman et al., 2007; Voss et al., 2013c). As one study evaluated the role of resistance exercise only on cognition, this result should be interpreted with caution. Heterogeneity across studies assessing cognition was low or completely lacking for all but two cognitive domains (i.e. attention ZRUNLQJPHPRU\DQGJOREDOFRJQLWLRQ WKDWVKRZHGVLJQLÀFDQWUHVXOWV)RUFRJQLWLRQ neuromotor exercise resulted in higher effects than aerobic exercise. Neuromotor exercises involve multifaceted exercises that target different brain systems involved in the regulation of attention, balance, coordination, mood, motor functioning and cognition, amongst others. Hence, neuromotor exercises are suggested to improve V\QFKURQL]DWLRQEHWZHHQGLIIHUHQWEUDLQDUHDVZKLFKPLJKWH[SODLQWKHLUHIÀFDF\RQ a wide variety of clinical symptoms (Schmalzl et al., 2015).
We found a positive dose-response effect for the weekly time spent on exercise in min/ week in reducing depressive symptoms, indicating that the more time spend on exercise SHUZHHNWKHODUJHUWKHUHGXFWLRQLQGHSUHVVLYHV\PSWRPV+RZHYHUQRVLJQLÀFDQW dose-response effect was found for the total length of the exercise intervention (i.e. the number of weeks spend on exercise), suggesting that both short- and long-term H[HUFLVHLQWHUYHQWLRQVPLJKWEHEHQHÀFLDOLQLPSURYLQJ4R/GHSUHVVLYHV\PSWRPVDQG FRJQLWLRQ3DWLHQWJURXSVUDQJHGLQPHDQDJHIURPWR\HDUVEXWQRVLJQLÀFDQW effect of this moderator was found on the outcome measures indicating that the effect of exercise on the examined outcome measure is not age-dependent.
Regarding exercise intensity, most of the studies that provided information on the intensity of the studied exercise intervention, applied moderate exercise intensity. Additionally, we found that risk of possible complications due to exercise is low, which should not be considered a limiting factor for exercise intervention.
cognition, the role of exercise intensity and safety could not be assessed quantitatively. One other explanation for the high heterogeneity could be the different questionnaires used in the separate studies. For both QoL and depressive symptoms, 13 different rating scales were used. For global cognition, six different tests were used.
4.3 Implications for clinical practice
Currently, physical exercise is not a standard part of the treatment of the six chronic brain disorders included in this study. Based on our work, it is likely that patients ZLWKDQ\RIWKHLQYHVWLJDWHGEUDLQGLVRUGHUVFRXOGEHQHÀWIURPDGGLWLRQDOSK\VLFDO exercise therapy. As safety issues and age constraints do not seem to be a limiting IDFWRUKHDOWKFDUHSURIHVVLRQDOVFRXOGXVHWKHSUHVHQWÀQGLQJVWRSURYLGHSDWLHQWVZLWK a tailored intervention in terms of type of exercise, exercise time and duration of intervention period. We showed a positive dose-effect interaction for exercise time, indicating that longer exercise programs are better for mood improvement. Most studies included in our meta-analysis assessed supervised exercise. Therefore, our results cannot be generalized to unsupervised exercise.
4.4 Implications for further research
Given the purpose and transdiagnostic character of the present study, we chose to compare exercise intervention only to TAU control condition. Evaluation of any differential effects of other components of the interventions such as adherence, setting (e.g. home-based vs. gym-based), monitoring of exercise sessions with instruments (e.g. heart rate meters), cost-effectiveness and comparison with other control groups (e.g. active control conditions) is required to provide detailed recommendations on physical exercise interventions for the clinical practice.
4.5 Strengths and limitations
The greatest strength of the present study is that it provides an up-to-date and H[WHQVLYHTXDQWLWDWLYHRYHUYLHZRIWKHOLWHUDWXUHUHJDUGLQJWKHHIÀFDF\RIGLIIHUHQW H[HUFLVHLQWHUYHQWLRQVLQSDWLHQWVZLWKFKURQLFEUDLQGLVRUGHUV6HFRQGRXUÀQGLQJV are largely in accordance with previous (quantitative) reviews that synthesized evidence RQWKHHIÀFDF\RISK\VLFDOH[HUFLVHLQWKHVWXGLHGEUDLQGLVRUGHUV &DPSEHOOHWDO
2018; Dauwan et al., 2015; Fayyaz et al., 2018; Firth et al., 2015; Schuch et al., 2016; Veronese et al., 2018). However, in contrast to previous work, we performed both transdiagnostic and within disorder analyses and evaluated the effect of several moderators providing evidence that physical exercise can be considered as an effective add-on and transdiagnostic treatment.
This study has some limitations. First, several studies could not be included in the cognitive meta-analyses, so that the overall effect of exercise on cognition was based RQIHZHUVWXGLHVWKDQWKHRWKHUPHWDDQDO\VHVPDNLQJWKHVHÀQGLQJVPRUHVXVFHSWLEOH to change over time (when more studies become available). Notably, a recent RCT of four month aerobic and resistance exercise of moderate to high intensity added to usual care found that physical exercise did not slow cognitive decline in patients with mild to moderate dementia (Lamb et al., 2018). The authors measured global cognition with Alzheimer’s disease assessment scale-cognitive subscale (ADAS-cog) and found a small average difference with uncertain clinical relevance. This study did QRWIXOÀOOWKHLQFOXVLRQFULWHULDRIRXUVWXG\WREHLQFOXGHGLQWKHTXDQWLWDWLYHUHYLHZ However, considering the fact that we included four RCTs (Holthoff et al., 2015; Maci et al., 2012; Ohman et al., 2016; Venturelli et al., 2011) with negative outcomes of H[HUFLVHRQJOREDOFRJQLWLRQLQ$' VHHÀJXUH DQGGLGQRWÀQGDVLJQLÀFDQWRYHUDOO effect of exercise on global cognition, we do not expect that adding this study would KDYHFKDQJHGRXUÀQGLQJV6HFRQGSXEOLFDWLRQELDVLVDQLPSRUWDQWSRVVLEOHGUDZEDFN in meta-analytical studies. Egger’s test showed potential publication bias for QoL and depressive symptoms. However, the fail-safe numbers of these tests were extremely large, increasing the validity of the results. Third, heterogeneity among studies was high, possibly due to combining studies with largely different interventions offered to different groups. However, heterogeneity values of the joint analysis were lower than the within disorder heterogeneities (Table 1 and 2), indicating consistency in studies across disorders so that joint analysis of disorders deemed sensible. Moreover, one RIWKHPDLQLQWHUVWXG\GLIIHULQJYDULDEOHVDJHGLGQRWDIIHFWWKHHIÀFDF\RIH[HUFLVH
measures, the risk of bias assessment indicated highest risk in terms of attrition. Incomplete outcome data and lack of ITT-analysis in studies could have biased the observed results. However, to account for possible attrition bias, we performed separate analyses on studies that performed ITT-analysis and thus had low risk of bias DQGVWXGLHVZLWKXQFOHDUULVNRIELDVRQ,77DQDO\VLV LHLQVXIÀFLHQWLQIRUPDWLRQWR judge). These results showed even higher effects of exercise on QoL and depressive symptoms, while effects on cognition remained similar for the cognitive domain PS, EXWWXUQHGWRQRQVLJQLÀFDQFHIRUWKHFRJQLWLYHGRPDLQV$ :0()DQG07KHODWWHU is likely due to the moderate to high heterogeneity among studies after inclusion of the study by (Bhatia et al., 2017). Finally, we randomly selected six brain disorders RIYDULRXVHWLRORJ\ HJQHXURGHJHQHUDWLYHQHXURGHYHORSPHQWDOLQÁDPPDWRU\ WR GHPRQVWUDWHWKHJHQHUDOL]DELOLW\RIHIÀFDF\RIH[HUFLVH6LQFHZHGLGQRWÀQGDQ\ RCTs evaluating the effect of physical exercise in bipolar disorder, we decided to only include unipolar depression in the present study. Other brain disorders, such as epilepsy, traumatic brain injury and migraine have been investigated as well, but given UHVWULFWLRQLQWLPHDQGFDSDFLW\ DVZHOODVZRUGFRXQW WKLVSDSHUZDVFRQÀQHGWRWKH chronic brain disorders summed above.
6. CONCLUSION
Additional therapy with physical exercise in patients with chronic brain disorders seems VDIHDQGKDVDPHGLXPVL]HGHIIHFWRQ4R/DQGDODUJHEHQHÀFLDOHIIHFWRQGHSUHVVLYH V\PSWRPVZLWKDSRVLWLYHGRVHUHVSRQVHFRUUHODWLRQ7KHHYLGHQFHIRUWKHHIÀFDF\ on cognition is small, but clinically relevant. Therefore, in order to improve the health status of patients with chronic brain disorders, add-on exercise therapy should be considered as an essential part of the treatment.
SUPPLEMENTAL MATERIAL
Table S4: Risk of bias assessment of studies eligible for meta-analysis
Random sequence B a se line imbalance B lindin g of ou tcome Incomplet e outcome dat a Alzheimer’s disease Aguiar 2014 Arcoverde 2014 Kemoun 2010 Lautenschlager 2015 Maci 2012 Ohman 2016a Roach 2011 Rolland 2007 Steinberg 2009 Teri 2003 Venturelli 2011 Vreugdenhil 2012
10
Busse 2013 Busse 2017 Khalil 2013 Quinn 2014 Quinn 2016 Thompson 2013 Ahmadi 2010a Ahmadi 2010b Ahmadi 2013 Bernhardt 2012 Briken 2014 Bulguroglu 2015 Cakit 2010 Carter 2014 Coghe 2018 Dalgas 2010b Dodd 2011 Doulatabad 2013 Ebrahimi 2015
Feys 2016 Hebert 2012 Hoang 2015 Hogan 2014 Jäckel 2015 Kargarfard 2012 Khan 2008 Kooshiar 2015 Learmonth 2012 Learmonth 2017 Louie 2015 McCullagh 2008 Miller 2011 Negahban 2013 Nilsagard 2013 O’Donnell 2011 Oken 2004 Ozgen 2016 Paul 2014 Petajan 1996 Plow 2014
