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Adding exercise to usual care in patients with hypertension, type 2 diabetes mellitus and/or cardiovascular disease: a protocol for a systematic review with meta-analysis and trial sequential analysis

Systematic Reviews volume 8, Article number: 330 (2019) Cite this article

Abstract

Background

Hypertension, type 2 diabetes mellitus and cardiovascular disease are among the leading causes of mortality globally. Exercise is one of the commonly recommended interventions/preventions for hypertension, type 2 diabetes mellitus and cardiovascular disease. However, the previous reviews have shown conflicting evidence on the effects of exercise. Our objective is to assess the beneficial and harmful effects of adding exercise to usual care for people with hypertension, type 2 diabetes mellitus and/or cardiovascular disease.

Methods

This protocol for a systematic review was undertaken using the recommendations of The Cochrane Collaboration, the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) and the eight-step assessment procedure suggested by Jakobsen et al. We plan to include all relevant randomised clinical trials and cluster-randomised trials assessing the effects of adding exercise to usual care for people with hypertension, type 2 diabetes mellitus and/or cardiovascular disease. We will search the Cochrane Central Register of Controlled Trials (CENTRAL), Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica database (EMBASE), Latin American and Caribbean Health Sciences Literature (LILACS), Science Citation Index Expanded on Web of Science, Chinese Biomedical Literature Database (CBM), China National Knowledge Infrastructure (CNKI), Chinese Science Journal Database (VIP) and BIOSIS. We will systematically assess the risks of random errors using Trial Sequential Analysis as well as risks of bias of all included trials. We will create a ‘Summary of Findings’ table in which we will present our primary and secondary outcomes, and we will assess the quality of evidence using the Grading of Recommendations Assessment, Development and Evaluation (GRADE).

Discussion

The present systematic review will have the potential to aid patients, clinicians and decision-makers recommending exercise and thereby, benefit patients with hypertension, type 2 diabetes mellitus and/or cardiovascular disease.

Systematic review registration

PROSPERO CRD42019142313

Peer Review reports

Background

Description of the conditions

Hypertension is the most important modifiable risk factor for cardiovascular disease globally [1, 2]. Hypertension is defined as a condition with an office systolic blood pressure equal to or above 140 mm Hg and/or diastolic blood pressure equal to or above 90 mm Hg [3]. Complications to hypertension account for 9.4 million deaths every year and contribute to 45% of deaths due to heart disease and 51% of deaths due to stroke [4]. From 2000 to 2010, the prevalence of hypertension has decreased by 2.6% in high-income countries while it has increased by 7.7% in low- and middle-income countries [5]. Despite the existence of effective blood pressure lowering drugs and preventive strategies, the burden of hypertension is constantly raising due to unhealthy lifestyle, ageing population [6] enhanced by lack of awareness and insufficient screening, treatment and control [7].

Cardiovascular disease is a common term for disorders of the heart or blood vessels which includes cerebrovascular disease, congenital heart disease, rheumatic heart disease, deep vein thrombosis, pulmonary thrombosis, coronary artery disease such as myocardial infarction, and heart failure [8]. It is the leading cause of death globally with 17.9 million deaths in 2016 [9]. Between 2007 and 2017, the age-standardised death rate due to cardiovascular disease decreased by about 10% globally [10]. However, it has escalated among low- and middle-income countries. Currently, more than three quarters of cardiovascular disease-related deaths occur in low- and middle-income countries [9]. The risk of cardiovascular disease is high among people with raised blood pressure (hypertension), raised blood glucose level and hyperlipidemia [8].

Patients with type 2 diabetes mellitus have defective insulin secretion and/or inadequate ability to utilise insulin produced by the body [11]. From 1980 to 2014, the prevalence of type 2 diabetes mellitus has doubled worldwide (4.7% vs 8.5%) [11]. The World Health Organization (WHO) estimates that by 2030, type 2 diabetes mellitus will be the 7th leading cause of death with predominant burden among urban dwellers from low- and middle-income countries [12, 13]. Long-term complications of type 2 diabetes mellitus lead to retinopathy, neuropathy and nephropathy, [11] and increase the risk of cardiovascular events by more than twofold [14] which heightens the risk of mortality and deteriorates the quality of life.

A substantial burden of cardiovascular disease in low- and middle-income countries may partly through hypertension, and type 2 diabetes mellitus be attributable to unhealthy lifestyle; physical inactivity, increased consumption of processed food, alcohol and tobacco abuse, high salt intake along with ageing population (decreasing infectious disease and famine) [15, 16]. Moreover, lack of adequate healthcare services which are often inaccessible and unaffordable for people living in this region create conducive environment for raising the burden of non-communicable diseases [17, 18]. Thus, it is important to ensure a comprehensive optimal lifestyle strategy to address the increasing burden of hypertension, type 2 diabetes mellitus and/or cardiovascular disease in low- and middle-income countries.

Description of the intervention

Physical activity may be defined as ‘the movement of skeletal muscle that results in energy expenditure’ (Table 1) [19]. On the basis of major physiological effects, physical activity may be classified into aerobic, anaerobic, muscle strengthening activity, bone strengthening activity, flexibility, balance training activity and body mind therapies such as yoga or Tai Chi [20].

Table 1 Classification of physical activity based on MET
Full size table

Exercise may be defined as ‘planned, structured and repetitive physical activities designed to improve or maintain physical fitness, physical performance and health’ [19]. Exercise is one of the most commonly recommended lifestyle interventions for people with hypertension, type 2 diabetes mellitus and/or cardiovascular disease [3, 19, 21]. Exercise occurs in various forms (Table 2). The intention with various forms of exercise in patients with hypertension, type 2 diabetes and/or cardiovascular disease is to enhance healthy structural, functional and biochemical characteristics eventually halting mortality [21,22,23]. A meta-epidemiological study suggested that structured exercise intervention was equally effective as drug intervention for prevention of mortality in the secondary prevention of coronary heart disease, rehabilitation after stroke, treatment of heart failure and prevention of diabetes [24]. Exercise seems to lower blood pressure, blood glucose, serum cholesterol and body weight, and seems to enhance insulin sensitivity, oxygen uptake and cardiac output [21, 23, 25, 26]. Physical inactivity is estimated as being the principal cause for approximately on third of both type 2 diabetes mellitus and ischaemic heart disease burden [27]. The recommended type and duration of exercise for patients with hypertension, type 2 diabetes mellitus and/or cardiovascular disease differ among regulatory bodies [3, 19, 21, 26, 28,29,30,31]. However, combination of aerobic and resistance exercises ranging from moderate to vigorous intensities has been recommended (Table 3). The optimal exercise for each patient may differ with individual progression of disease hence may need to be personalised [26].

Table 2 Common forms of exercise
Full size table
Table 3 Recommended level of exercise for patient with hypertension, type 2 diabetes mellitus and cardiovascular disease
Full size table

It should be noted that people in low- and middle-income countries may indulge in leisure time exercise, i.e. sports and exercise just as individuals from high-income countries [32]. However, their engagement in occupational and transportation or even household physical activity may surpass the recommended level of physical activity. Therefore, the importance of exercise for prevention of hypertension, type 2 diabetes mellitus and cardiovascular disease may be different in low- and middle-income countries.

Why do we need this review?

We have identified several previous reviews of randomised clinical trials that have assessed the effects of exercise in patients with hypertension, type 2 diabetes mellitus and/or cardiovascular disease but with different conclusions (Table 4).

Table 4 Overview of existing reviews
Full size table

A review published in 2004 reported that exercise-based cardiac rehabilitation for coronary heart patients reduced all-cause mortality and cardiac mortality irrespective of type of cardiac rehabilitation (exercise only or in combination of psychosocial/educational intervention) or duration of the exercise [35]. Another Cochrane review published in 2016 on exercise-based cardiac rehabilitation exercise compared with no exercise reported that the exercise-based cardiac rehabilitation reduced the risk of cardiovascular mortality but not the risk of all-cause mortality [34], while the most recent update of the review in 2018 reported that exercise-based cardiac rehabilitation did not influence total mortality nor cardiac mortality [33]. Similarly, another meta-analysis reported that there was no significant difference between exercise (short term; ≤ 12 months or long term) compared with usual care on all-cause mortality or overall hospital admissions among patient with heart failure [41]. Another Cochrane review on the effectiveness of exercise-based rehabilitation after heart failure [37] and stable angina [38] reported uncertainty of the effects of exercise on prevention of mortality due to lack of sufficient randomised clinical trials with longer follow-up reporting mortality. Likewise, a Cochrane review on physical training after stroke [39] reported improvement in movement, exercise capacity and quality of life. However, the trials in the review did not report on mortality. Another meta-analysis among patients with heart failure showed no significant difference in mortality between different intensities of exercise [40].

The blood pressure lowering effect of various kinds of exercise has been reported by previous meta-analysis [42,43,44]. A recent meta-analysis reported that exercise interventions appear to be as equally effective as most antihypertensive medications on reducing systolic blood pressure [58] in patients with hypertension. However, across the whole population, the reduction in systolic blood pressure was greater among those receiving antihypertensive medication as compared with individuals who adopted structured exercise regimens [58].

Likewise, previous meta-analysis of randomised clinical trials on effect of different kinds of exercises among people with type 2 diabetes mellitus has recurrently reported reductions of blood glucose levels (glycated haemoglobin HbA1c) [25, 48, 50,51,52], while another meta-analysis showed supervised exercise among people with type 2 diabetes mellitus leads to reduction of systolic and diastolic blood pressure and lipid profiles irrespective of type of exercise involved [49].

A systematic review on Tai Chi exercise as an intervention for patient with cardiovascular disease was inconclusive on effectiveness of the therapy due to lack of high-quality randomised clinical trials [36]. Similar insufficiency of evidence on impact of yoga [45,46,47, 54] and Tai Chi [53, 57] among people with hypertension and type 2 diabetes mellitus exists. Two recent meta-analyses showed that Tai Chi is effective in reducing fasting blood glucose and glycated haemogloin (HbA1c) [55, 56] among patients with type 2 diabetes mellitus. However, majority of the trials included in both the reviews were of poor methodological quality and showed publication bias.

We have not identified any systematic review considering both risks of systematic errors and random errors assessing the effects of adding exercise to usual care in patients with hypertension, type 2 diabetes mellitus and/or cardiovascular disease for prevention of mortality. Furthermore, the existing reviews are outdated. No prior systematic review has attempted to assess the effect of adding exercise in usual care especially for low- and middle-income countries for prevention of mortality. Thus, we also aim to conduct a subgroup analysis for assessing effect of adding exercise to usual care on prevention of mortality for low- and middle-income countries.

Methods

Objective

To assess the beneficial and harmful effects of adding exercise to usual care for people with hypertension, type 2 diabetes mellitus and/or cardiovascular disease.

Methods

This protocol for a systematic review has been developed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) guidelines for reporting systematic reviews evaluating interventions in healthcare [59, 60] (see Additional file 1).

Criteria for considering studies for this review

Types of studies

Randomised clinical trials and cluster-randomised trials irrespective setting, trial duration, publication status, publication year and language. We will not include quasi-randomised studies or observational studies.

Types of participants

We will include adult participants (as defined by trialists) with hypertension (as defined by trialists), type 2 diabetes mellitus (as defined by trialists) and/or any cardiovascular disease (as defined by the trialists) or cardiovascular disease as defined by WHO. It includes cerebrovascular disease, congenital heart disease, rheumatic heart disease, deep vein thrombosis, pulmonary thrombosis, coronary artery disease such as myocardial infarction, and heart failure [8].

Types of interventions

  • Experimental intervention: exercise (or similar terms used by the trialists) see Table 2.

  • Control intervention: no exercise (primary comparison) or usual care (or similar terms defined by the trialists).

  • Co-interventions: any co-intervention, if the co-intervention is intended to be delivered similarly to the intervention and control groups.

Outcomes

Primary outcomes

  1. 1.

    All-cause mortality.

  2. 2.

    Proportion of participants with a serious adverse event defined as any untoward medical occurrence that resulted in death; was life threatening; was persistent; or led to significant disability, nephrotoxicity, superinfection, need for respiratory support, need for circulatory support or prolonged hospitalisation [61]. As we expect the trialists’ reporting of serious adverse events to be heterogeneous and not strictly according to the International Council for Harmonisation guideline for Good Clinical Practice (ICH-GCP) recommendations, we will include the event as a serious adverse if the trialists either (1) use the term ‘serious adverse event’ but not refer to ICH-GCP or (2) report the proportion of participants with an event we consider fulfils the ICH-GCP definition (e.g. myocardial infarction or hospitalisation). If several of such events are reported, then we will choose the highest proportion reported in each trial.

  3. 3.

    Quality of life measured on any valid scale (e.g. Short Form-36).

Secondary outcomes

  1. 1.

    Cardiovascular mortality (as defined by trialists).

  2. 2.

    Blood pressure (both systolic and diastolic).

  3. 3.

    Microvascular complications (retinopathy, nephropathy, neuropathy, diabetic foot).

  4. 4.

    Myocardial infarction (as defined by trialists).

  5. 5.

    Stroke (as defined by trialists).

Exploratory outcomes

  1. 1.

    Body weight (kg).

  2. 2.

    Exercise capacity assessed by validated outcome measure (e.g.VO2 max, 6-min walk test).

For all outcomes, we will use the trial results reported to the longest follow-up.

Search methods for identification of studies

Electronic searches

We will search the Cochrane Central Register of Controlled Trials (CENTRAL), Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica database (EMBASE), Latin American and Caribbean Health Sciences Literature (LILACS), Science Citation Index Expanded on Web of Science, Chinese Biomedical Literature Database (CBM), China National Knowledge Infrastructure (CNKI), Chinese Science Journal Database (VIP) and BIOSIS to identify relevant trials. The preliminary search strategy for MEDLINE (Ovid) is given in Appendix. We will search all databases from their inception to the present.

Searching other resources

The reference lists of relevant publications will be checked for any unidentified randomised trials.

Further, we will search for ongoing trials on the following:

Additionally, we will by hand search conference abstracts from cardiology and diabetes conferences for relevant trials. We will consider unpublished and grey literature trials relevant to the review, if we identify such trials.

Data collection and analysis

We will perform the review following the recommendations of The Cochrane Collaboration [62]. The analyses will be performed using STATA 16 and Trial Sequential Analysis [63].

Selection of studies

Two review authors (AR and EEN) will independently screen titles and abstracts. We will retrieve all relevant full-text study reports and publications. Two review authors (AR and EEN) will independently screen the full text and identify and record reasons for exclusion of the ineligible studies. We will resolve any disagreement through discussion or, if required, we will consult a third author (JCJ). Trial selection will be displayed in an adapted flow diagram as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [59].

Data extraction and management

Two authors (AR and EEN) will extract data independently from included trials. Disagreements will be resolved by discussion with a third author (JCJ). We will assess duplicate publications and companion papers of a trial together to evaluate all available data simultaneously (maximise data extraction, correct bias assessment). We will contact the trial authors by email to specify any additional data, which may not have been reported sufficiently or at all in the publication.

Trial characteristics

Bias risk components (as defined below), trial design (parallel, cluster, factorial or crossover), number of intervention arms, length of follow-up, estimation of sample size and inclusion and exclusion criteria.

Participant characteristics and diagnosis

Number of participants with cardiovascular disease (as defined by trialists); number of participants with type 2 diabetes mellitus (as defined by trialists); number of randomised participants; number of analysed participants; number of participants lost to follow-up/withdrawals/crossover; age range (mean or median) and sex ratio. We will additionally report the proportion of participants in the compared groups who receive exercise (or similar term used by trialists).

Exercise characteristics

Exercise characteristics are as follows: type of exercise, duration of exercise, frequency of exercise and follow-up period.

Co-intervention characteristics

Co-intervention characteristics are as follows: type of co-intervention, duration of co-intervention and frequency of co-intervention.

Individual patient data

We will ask those responsible for the included trials to supply individual patient data. Notes: Funding of the trial and notable conflicts of interest of trial authors will be extracted, if available. We will note in the ‘Characteristics of included studies’ table if outcome data were not reported in a usable way. Two review authors (AR and EEN) will independently transfer data into the Review Manager file. Disagreements will be resolved through discussion, or if required, we will consult with a third author (JCJ).

Assessment of risk of bias in included studies

We will use the instructions given in the Cochrane Handbook for Systematic Reviews of Interventions in our evaluation of the methodology and hence the risk of bias of the included trials.

We will classify the trials according to the following criteria.

Random sequence generation

  • Low risk: If sequence generation was achieved using computer random number generator or a random number table. Drawing lots, tossing a coin, shuffling cards and throwing dice were also considered adequate if performed by an independent adjudicator.

  • Unclear risk: If the method of randomisation was not specified, but the trial was still presented as being randomised.

  • High risk: If the allocation sequence is not randomised or only quasi-randomised. These trials will be excluded.

Allocation concealment

  • Low risk: If the allocation of patients was performed by a central independent unit, on-site locked computer, identical-looking numbered sealed envelopes, drug bottles or containers prepared by an independent pharmacist or investigator.

  • Uncertain risk: If the trial was classified as randomised but the allocation concealment process was not described.

  • High risk: If the allocation sequence was familiar to the investigators who assigned participants.

Blinding of participants and treatment providers

  • Low risk: If the participants and the treatment providers were blinded to intervention allocation and this was described.

  • Uncertain risk: If the procedure of blinding was insufficiently described.

  • High risk: If blinding of participants and the treatment providers was not performed.

Blinding of outcome assessment

  • Low risk of bias: If it was mentioned that outcome assessors were blinded and this was described.

  • Uncertain risk of bias: If it was not mentioned if the outcome assessors in the trial were blinded or the extent of blinding was insufficiently described.

  • High risk of bias: If no blinding or incomplete blinding of outcome assessors was performed.

Incomplete outcome data

  • Low risk of bias: If missing data were unlikely to make treatment effects depart from plausible values. This could be either (1) there were no drop-outs or withdrawals for all outcomes or (2) the numbers and reasons for the withdrawals and drop-outs for all outcomes were clearly stated and could be similar to both groups. Generally, the trial is judged as at a low risk of bias due to incomplete outcome data if drop-outs are less than 5%. However, the 5% cut-off is not definitive.

  • Uncertain risk of bias: If there was insufficient information to assess whether missing data were likely to induce bias on the results.

  • High risk of bias: If the results were likely to be biased due to missing data either because the pattern of drop-outs could be different in the two intervention groups or the trial used improper methods in dealing with the missing data (e.g. last observation carried forward).

Selective outcome reporting

  • Low risk of bias: If a protocol was published before or at the time the trial began and the outcomes specified in the protocol were reported. If there was no protocol or the protocol was published after the trial has begun, reporting of all-cause mortality and serious adverse events will grant the trial a grade of low risk of bias.

  • Uncertain risk of bias: If no protocol was published and the outcome all-cause mortality and serious adverse events were not reported on.

  • High risk of bias: If the outcomes in the protocol were not reported on.

For profit bias

  • Low risk of bias: If the trial appeared to be free of other components of for-profit bias.

  • Unclear risk of bias: If it was unclear whether the trial was free of for-profit bias.

  • High risk of bias: If there was a high risk of for-profit bias.

Other risks of bias

  • Low risk of bias: If the trial appears to be free of other components (for example, academic bias or for-profit bias) that could put it at risk of bias.

  • Unclear risk of bias: If the trial may or may not be free of other components that could put it at risk of bias.

  • High risk of bias: If there are other factors in the trial that could put it at risk of bias (for example, authors conducted trials on the same topic, for- profit bias, etc.).

Overall risk of bias

  • Low risk of bias: The trial will be classified as overall ‘low risk of bias’ only if all of the bias domains described in the above paragraphs are classified as ‘low risk of bias’.

  • High risk of bias: The trial will be classified as ‘high risk of bias’ if any of the bias risk domains described in the above are classified as ‘unclear’ or ‘high risk of bias’.

These components enable classification of randomised trials with low risk of bias and high risk of bias. The latter trials tend to overestimate positive intervention effects and underestimate negative effects [64,65,66,67,68,69,70]. We will classify a trial as being at overall ‘low risk of bias’, only if all bias domains are classified as ‘low risk of bias’. We will classify a trial as being at overall ‘high risk of bias’, if any of the bias domains are classified as ‘unclear’ or ‘high risk of bias’.

We will assess the domains ‘blinding of outcome assessment’, ‘incomplete outcome data’ and ‘selective outcome reporting’ for each outcome result. Thus, we can assess the bias risk for each outcome assessed in addition to each trial. Our primary conclusions will be based on the results of our primary outcomes at overall low risk of bias.

Differences between the protocol and the review

We will conduct the review according to this published protocol and report any deviations from it in the ‘Differences between the protocol and the review’ section of the systematic review.

Measures of treatment effect

Dichotomous outcomes

We will calculate risk ratios (RRs) with 95% confidence interval (CI) for dichotomous outcomes, as well as the Trial Sequential Analysis-adjusted CIs (see below).

Continuous outcomes

We will calculate the mean differences (MDs) with 95% CI for continuous outcomes as well as the Trial Sequential Analysis-adjusted CIs (see below). We will consider assessing health-related quality of life across different scales with standardised mean difference [62].

Dealing with missing data

We will, as first option, contact all trial authors to obtain any relevant missing data (i.e. for data extraction and for assessment of risk of bias, as specified above).

Dichotomous outcomes

We will not impute missing values for any outcomes in our primary analysis. In two of our sensitivity analyses (see paragraph below), we will impute data.

Continuous outcomes

We will primarily analyse scores assessed at single time points. If only changes from baseline scores are reported, we will analyse the results together with follow-up scores. If standard deviations (SDs) are not reported, we will calculate the SDs using trial data, if possible. We will not use intention-to-treat data if the original report did not contain such data. We will not impute missing values for any outcomes in our primary analysis. In our sensitivity analysis (see paragraph below) for continuous outcomes, we will impute data.

Assessment of heterogeneity

We will primarily investigate forest plots to visually assess heterogeneity. We will secondly assess the presence of statistical heterogeneity by chi-square test (threshold P < 0.10) and measure the quantities of heterogeneity by the I2 statistics [71, 72].

We will investigate possible heterogeneity through subgroup analyses. Ultimately, we may decide that a meta-analysis should be avoided [62].

Assessment of reporting biases

We will use a funnel plot to assess reporting bias if ten or more trials are included. We will visually inspect funnel plots to assess the risk of bias. We are aware of the limitations of a funnel plot (i.e. a funnel plot assesses bias due to small sample size). From this information, we assess possible reporting bias. For dichotomous outcomes, we will test asymmetry with the Harbord test [73] if τ2 is less than 0.1 and with the Rücker test if τ2 is greater than 0.1. For continuous outcomes, we will use the regression asymmetry test [74] and the adjusted rank correlation test [75].

Unit of analysis issues

We will only include randomised clinical trials and cluster-randomised trials. For trials using crossover design, only data from the first period will be included [62, 76]. We will include cluster-randomised trials after adjusting the original sample size of the trial to the effective sample size using the intracluster correlation coefficient from the ‘design effect’ [62]. Therefore, there will not be any unit of analysis issues.

Data synthesis

Meta-analysis

We will undertake this meta-analysis according to the recommendations stated in the Cochrane Handbook for Systematic Reviews of Interventions [62], Keus et al. [77] and the eight-step assessment suggested by Jakobsen et al. [78]. We will use the statistical software Review Manager 5.3 provided by Cochrane to analyse data [79].

We will assess our intervention effects with both random effects meta-analyses [80] and fixed effects meta-analyses [81]. We will use the more conservative point estimate of the two [78]. The more conservative point estimate is the estimate closest to zero effect. If the two estimates are similar, we will use the estimate with the highest P value.

We use three primary outcomes, and therefore, we will consider a P value of 0.025 as the threshold for statistical significance [78]. For secondary and exploratory outcomes, we will consider a P value of 0.05 as the threshold for statistical significance as we only consider these results as hypothesis generating. We will investigate possible heterogeneity through subgroup analyses. Ultimately, we may decide that a meta-analysis should be avoided because of unexpected high heterogeneity [62]. We will use the eight-step procedure to assess if the thresholds for significance are crossed [78]. Our primary conclusion will be based on the results from the primary outcomes at low risk of bias [78]. Where multiple trial groups are reported in a single trial, we will include only the relevant groups. If two comparisons are combined in the same meta-analysis, we will have the control group to avoid double-counting [62]. Trials with a factorial design will be included.

Trial Sequential Analysis

Traditional meta-analysis runs the risk of random errors due to sparse data and repetitive testing of accumulating data when updating reviews. We wish to control the risks of type I errors and type 2 errors. We will, therefore, perform Trial Sequential Analysis on the outcomes, in order to calculate the required information size (that is the number of participants needed in a meta-analysis to detect or reject a certain intervention effect) and the cumulative Z-curve’s breach of relevant trial sequential monitoring boundaries [63, 82,83,84,85,86,87,88,89,90]. A more detailed description of Trial Sequential Analysis can be found in the Trial Sequential Analysis manual and at http://www.ctu.dk/tsa/ [88]. For dichotomous outcomes, we will estimate the required information size based on the observed proportion of patients with an outcome in the control group (the cumulative proportion of patients with an event in the control groups relative to all patients in the control groups), a relative risk reduction of 25%, an alpha of 2.5% for primary outcomes, an alpha of 5% for secondary and exploratory outcomes, a beta of 10%, and diversity as suggested by the trials in the meta-analysis. For continuous outcomes, we will in the Trial Sequential Analysis use the observed SD, a mean difference of the observed SD/2, an alpha of 2.5% for primary outcomes, an alpha of 5% for secondary and exploratory outcomes, and a beta of 10%.

Subgroup analysis and investigation of heterogeneity

Subgroup analysis

We will perform the following subgroup analysis when analysing the primary outcomes (all-cause mortality, serious adverse event and quality of life):

  1. 1.

    Trials at high risk of bias compared with trials at low risk of bias

  2. 2.

    Different types of exercise (Table 2)

  3. 3.

    High-income countries compared with low- and middle-income countries

  4. 4.

    Men compared with women

  5. 5.

    Different disease group: hypertension, type 2 diabetes mellitus and/or cardiovascular disease

We will use the formal test for subgroup interactions in Review Manager [91].

Sensitivity analysis

To assess the potential impact of the missing data for dichotomous outcomes, we will perform the two following sensitivity analyses on both the primary and secondary outcomes:

‘Best-worst-case’ scenario: we will assume that all participants lost to follow-up in the exercise group have survived, had no serious adverse events and had a higher quality of life (see paragraph below). We will assume the opposite for all participants lost to follow-up in the control group.

‘Worst-base-case’ scenario: we will assume that all participants lost to follow-up in the exercise have not survived, had serious adverse events and had a lower quality of life (see paragraph below). We will assume the opposite for all participants lost to follow-up in the control group. We will present results of both scenarios in our review. When analysing quality of a ‘beneficial outcome’ will be the group mean plus two standard deviations (SDs) of the group mean and a ‘harmful outcome’ will be the group mean minus two SDs of the group mean [78]. We will present results of this scenario in our review. Other post hoc sensitivity analyses might be warranted if unexpected clinical or statistical heterogeneity is identified during the analysis of the review results [78].

‘Summary of Findings’ table

We will create a ‘Summary of Findings’ table using each of the pre-specified outcomes (all-cause mortality, serious adverse event, quality of life, cardiovascular death, blood pressure, microvascular complications and myocardial infarction, stroke). We will use the five GRADE considerations (bias risk of the trials, consistency of effect, imprecision, indirectness and publication bias) to assess the quality of a body of evidence as it relates to the studies which contribute data to the meta-analyses for the pre-specified outcomes [78, 92,93,94]. We will assess ‘imprecision’ using Trial Sequential Analysis; otherwise, we will use methods and recommendations described in Chapter 8 (Section 8.5) and Chapter 12 of the Cochrane Handbook for Systematic Reviews of Interventions [62].

Discussion

Our planned methodology has several strengths. We have based the protocol on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) checklist [59, 60]. We have pre-defined our methodology based on the Cochrane Handbook for Systematic Reviews of Interventions [62], Keus et al. [77], the eight-step assessment as suggested by Jakobsen et al. [78], Trial Sequential Analysis [63] and the GRADE assessment [94, 95]. Through our pre-defined methodology, we consider both risks of random errors and systematic errors.

Our planned methodology also has limitations. We will pool data from all trials regarding adding different types of exercise to usual care for individuals with hypertension, type 2 diabetes mellitus and/or cardiovascular disease, which potentially will lead to clinical and statistical heterogeneity. To take this potential heterogeneity into account, we have pre-defined several subgroup and sensitivity analysis to assess whether or not the intervention effects differ between conditions and trials, and we will ultimately decide if meta-analysis should be avoided. Another limitation is that we do not search for non-randomised studies, and hence we may overlook harms [96]. If the present review finds solid evidence for benefits, then a more thorough investigation of potential harms seems warranted.

With this systematic review, we seek to provide the clinicians and decision-makers with a reliable evidence adjusted for bias, sparse data, and multiple testing regarding the beneficial and harmful effects of adding exercise to usual care for people with hypertension, type 2 diabetes mellitus and/or cardiovascular disease.

Availability of data and materials

Not applicable

Abbreviations

GRADE:

Grading of Recommendations Assessment, Development and Evaluation

ICH-GCP:

International Council for Harmonisation guideline for Good Clinical Practice Good Clinical Practice

HbA1C:

Glycated haemoglobin

MET:

Metabolic Equivalent of Tasks

SD:

Standard deviation

WHO:

World Health Organization

References

  1. Olsen MH, Angell SY, Asma S, Boutouyrie P, Burger D, Chirinos JA, et al. A call to action and a lifecourse strategy to address the global burden of raised blood pressure on current and future generations: the Lancet Commission on hypertension. Lancet 2016;S0140-6736(16):31134-5. https://0-doi-org.brum.beds.ac.uk/10.1016/S0140-6736(16)31134-5.

    Article  Google Scholar 

  2. Ettehad D, Emdin CA, Kiran A, Anderson SG, Callender T, Emberson J, et al. Blood pressure lowering for prevention of cardiovascular disease and death: a systematic review and meta-analysis. Lancet. 2015;387:957–67.

    Article  PubMed  Google Scholar 

  3. Williams B, Mancia G, Spiering W, Agabiti Rosei E, Azizi M, Burnier M, et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension: the Task Force for the management of arterial hypertension of the European Society of Cardiology and the European Society of Hypertension. J Hypertens. 2018;36(10):1953–2041.

    Article  CAS  PubMed  Google Scholar 

  4. WHO. A global brief on hypertension: silent killer, global public health crisis. Geneva, Switzerland: World Health Organization (WHO), 2013 Contract No.: WHO/DCO/WHD/2013.2.

  5. Mills KT, Bundy JD, Kelly TN, Reed JE, Kearney PM, Reynolds K, et al. Global disparities of hypertension prevalence and control. Circulation. 2016;134(6):441–50.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Bloch MJ. Worldwide prevalence of hypertension exceeds 1.3 billion. J Am Soc Hypertens. 2016;10(10):753–4.

    Article  PubMed  Google Scholar 

  7. Beaney T, Schutte AE, Tomaszewski M, Ariti C, Burrell LM, Castillo RR, et al. May Measurement Month 2017: an analysis of blood pressure screening results worldwide. Lancet Glob Health. 2018;6(7):e736–e43.

    Article  PubMed  Google Scholar 

  8. WHO. Global atlas on cardiovascular disease prevention and control. Switzerland, Geneva: World Health Organization (WHO); 2011.

    Google Scholar 

  9. Roth GA, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, et al. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1736–88.

    Article  Google Scholar 

  10. Dicker D, Nguyen G, Abate D, Abate KH, Abay SM, Abbafati C, et al. Global, regional, and national age-sex-specific mortality and life expectancy, 1950-2013;2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1684–735.

    Article  Google Scholar 

  11. WHO. Global report on diabetes. Geneva, Switzerland: World Health Organization (WHO); 2016.

    Google Scholar 

  12. Wild S, Roglic G, Green A, Sicree R, King H. Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care. 2004;27(5):1047–53.

    Article  PubMed  Google Scholar 

  13. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants. Lancet. 2016;387:1513–30.

    Article  Google Scholar 

  14. Emerging Risk Factors Collaboration, Sarwar N, Gao P, Seshasai SRK, Gobin R, Kaptoge S, et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375(9733):2215–22.

    Article  CAS  Google Scholar 

  15. Stanaway JD, Afshin A, Gakidou E, Lim SS, Abate D, Abate KH, et al. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1923–94.

    Article  Google Scholar 

  16. Allen L, Williams J, Townsend N, Mikkelsen B, Roberts N, Foster C, et al. Socioeconomic status and non-communicable disease behavioural risk factors in low-income and lower-middle-income countries: a systematic review. Lancet Glob Health. 2017;5(3):e277–e89.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Di Cesare M, Khang Y-H, Asaria P, Blakely T, Cowan MJ, Farzadfar F, et al. Inequalities in non-communicable diseases and effective responses. Lancet. 2013;381(9866):585–97.

    Article  PubMed  Google Scholar 

  18. Rijal A, Adhikari TB, Khan JAM, Berg-Beckhoff G. The economic impact of non-communicable diseases among households in South Asia and their coping strategy: a systematic review. PLoS One. 2018;13(11):e0205745.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Fletcher GF, Ades PA, Kligfield P, Arena R, Balady GJ, Bittner VA, et al. Exercise standards for testing and training. Circulation. 2013;128(8):873–934.

    Article  PubMed  Google Scholar 

  20. Physical Activity Guidelines Advisory Committee. Physical Activity Guidelines Advisory Committee Report. Physical Activity Guidelines Advisory Committee. Physical Activity Guidelines Advisory Committee Report. Washington, DC: U.S.: Department of Health and Human Services; 2008.

    Google Scholar 

  21. Colberg SR, Sigal RJ, Yardley JE, Riddell MC, Dunstan DW, Dempsey PC, et al. Physical activity/exercise and diabetes: a position statement of the American Diabetes Association. Diabetes Care. 2016;39(11):2065–79.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Nystoriak MA, Bhatnagar A. Cardiovascular effects and benefits of exercise. Front Cardiovasc Med. 2018;5:135.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Sharman JE, La Gerche A, Coombes JS. Exercise and cardiovascular risk in patients with hypertension. Am J Hypertens. 2014;28(2):147–58.

    Article  PubMed  Google Scholar 

  24. Naci H, Ioannidis JPA. Comparative effectiveness of exercise and drug interventions on mortality outcomes: metaepidemiological study. BMJ. 2013;347:f5577.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Schwingshackl L, Missbach B, Dias S, König J, Hoffmann G. Impact of different training modalities on glycaemic control and blood lipids in patients with type 2 diabetes: a systematic review and network meta-analysis. Diabetologia. 2014;57(9):1789–97.

    Article  CAS  PubMed  Google Scholar 

  26. Kemps H, Kränkel N, Dörr M, Moholdt T, Wilhelm M, Paneni F, et al. Exercise training for patients with type 2 diabetes and cardiovascular disease: what to pursue and how to do it. A position paper of the European Association of Preventive Cardiology (EAPC). Eur J Prev Cardiol. 2019;26(7):709–27.

    Article  PubMed  Google Scholar 

  27. WHO. Global health risks: mortality and burden of disease attributable to selected major risks. Geneva: World Health Organization (WHO); 2009.

    Google Scholar 

  28. Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL, et al. 2016 European Guidelines on cardiovascular disease prevention in clinical practice: the sixth joint task force of the European Society of Cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of 10 societies and by invited experts) developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur Heart J. 2016;37(29):2315–81.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Misra A, Nigam P, Hills AP, Chadha DS, Sharma V, Deepak KK, et al. Consensus physical activity guidelines for Asian Indians. Diabetes Technol Ther. 2012;14(1):83–98.

    Article  PubMed  Google Scholar 

  30. Pescatello LS, MacDonald HV, Lamberti L, Johnson BT. Exercise for hypertension: a prescription update integrating existing recommendations with emerging research. Curr Hypertens Rep. 2015;17(11):87.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Ainsworth BE, Haskell WL, Herrmann SD, Meckes N, Bassett DRJ, Tudor-Locke C, et al. 2011 Compendium of physical activities: a second update of codes and MET values. Med Sci Sports Exerc. 2011;43(8):1575–81.

    Article  PubMed  Google Scholar 

  32. WHO. Global recommendations on physical activity for health. Geneva: World Health Organization (WHO); 2010.

    Google Scholar 

  33. Powell R, McGregor G, Ennis S, Kimani PK, Underwood M. Is exercise-based cardiac rehabilitation effective? A systematic review and meta-analysis to re-examine the evidence. BMJ Open. 2018;8(3):e019656.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Anderson L, Thompson DR, Oldridge N, Zwisler AD, Rees K, Martin N, et al. Exercise-based cardiac rehabilitation for coronary heart disease. Cochrane Database Syst Rev. 2016;https://0-doi-org.brum.beds.ac.uk/10.1002/14651858.CD001800.pub3(1).

  35. Taylor RS, Brown A, Ebrahim S, Jolliffe J, Noorani H, Rees K, et al. Exercise-based rehabilitation for patients with coronary heart disease: systematic review and meta-analysis of randomized controlled trials. Am J Med. 2004;116(10):682–92.

    Article  PubMed  Google Scholar 

  36. Yeh GY, Wang C, Wayne PM, Phillips R. Tai chi exercise for patients with cardiovascular conditions and risk factors: a systematic review. J Cardiopulm Rehabil Prev. 2009;29(3):152–60.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Rees K, Taylor RRS, Singh S, Coats AJS, Ebrahim S. Exercise based rehabilitation for heart failure. Cochrane Database Syst Rev. 2004;https://0-doi-org.brum.beds.ac.uk/10.1002/14651858.CD003331.pub2(3).

  38. Long L, Anderson L, Dewhirst AM, He J, Bridges C, Gandhi M, et al. Exercise-based cardiac rehabilitation for adults with stable angina. Cochrane Database Syst Rev. 2018;https://0-doi-org.brum.beds.ac.uk/10.1002/14651858.CD012786.pub2(2).

  39. Saunders DH, Sanderson M, Hayes S, Kilrane M, Greig CA, Brazzelli M, et al. Physical fitness training for stroke patients. Cochrane Database Syst Rev. 2016;https://0-doi-org.brum.beds.ac.uk/10.1002/14651858.CD003316.pub6(3).

  40. Ismail H, McFarlane JR, Nojoumian AH, Dieberg G, Smart NA. Clinical outcomes and cardiovascular responses to different exercise training intensities in patients with heart failure: a systematic review and meta-analysis. JACC Heart Fail. 2013;1(6):514–22.

    Article  PubMed  Google Scholar 

  41. Davies EJ, Moxham T, Rees K, Singh S, Coats AJS, Ebrahim S, et al. Exercise training for systolic heart failure: Cochrane systematic review and meta-analysis. Eur J Heart Fail. 2010;12(7):706–15.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Whelton SP, Chin A, Xin X, He J. Effect of aerobic exercise on blood pressure: a meta-analysis of randomized, controlled trials. Ann Intern Med. 2002;136(7):493–503.

    Article  PubMed  Google Scholar 

  43. MacDonald HV, Johnson BT, Huedo-Medina TB, Livingston J, Forsyth KC, Kraemer WJ, et al. Dynamic resistance training as stand-alone antihypertensive lifestyle therapy: a meta-analysis. J Am Heart Assoc. 2016;5(10):e003231.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Cornelissen VA, Smart NA. Exercise training for blood pressure: a systematic review and meta-analysis. J Am Heart Assoc. 2013;2(1):e004473.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Cramer H, Haller H, Lauche R, Steckhan N, Michalsen A, Dobos G. A systematic review and meta-analysis of yoga for hypertension. Am J Hypertens. 2014;27(9):1146–51.

    Article  CAS  PubMed  Google Scholar 

  46. Chu P, Gotink RA, Yeh GY, Goldie SJ, Hunink MM. The effectiveness of yoga in modifying risk factors for cardiovascular disease and metabolic syndrome: a systematic review and meta-analysis of randomized controlled trials. Eur J Prev Cardiol. 2016;23(3):291–307.

    Article  PubMed  Google Scholar 

  47. Hagins M, States R, Selfe T, Innes K. Effectiveness of yoga for hypertension: systematic review and meta-analysis. Evid Based Complement Alternat Med. 2013;2013:649836.

    PubMed  PubMed Central  Google Scholar 

  48. Thomas D, Elliott EJ, Naughton GA. Exercise for type 2 diabetes mellitus. Cochrane Database Syst Rev. 2006;https://0-doi-org.brum.beds.ac.uk/10.1002/14651858.CD002968.pub2(3): CD002968.

  49. Hayashino Y, Jackson JL, Fukumori N, Nakamura F, Fukuhara S. Effects of supervised exercise on lipid profiles and blood pressure control in people with type 2 diabetes mellitus: a meta-analysis of randomized controlled trials. Diabetes Res Clin Pract. 2012;98(3):349–60.

    Article  CAS  PubMed  Google Scholar 

  50. Grace A, Chan E, Giallauria F, Graham PL, Smart NA. Clinical outcomes and glycaemic responses to different aerobic exercise training intensities in type 2 diabetes: a systematic review and meta-analysis. Cardiovas Diabetol. 2017;16(1):37.

    Article  Google Scholar 

  51. Snowling NJ, Hopkins WG. Effects of different modes of exercise training on glucose control and risk factors for complications in type 2 diabetic patients: a meta-analysis. Diabetes Care. 2006;29(11):2518–27.

    Article  PubMed  Google Scholar 

  52. Liu Y, Ye W, Chen Q, Zhang Y, Kuo C-H, Korivi M. Resistance exercise intensity is correlated with attenuation of HbA1c and insulin in patients with type 2 diabetes: a systematic review and meta-Analysis. Int J Environ Res Public Health. 2019;16(1):140.

    Article  CAS  PubMed Central  Google Scholar 

  53. Innes KE, Selfe TK. Yoga for adults with type 2 diabetes: a systematic review of controlled trials. J Diabetes Res. 2016;2016:6979370.

    Article  PubMed  CAS  Google Scholar 

  54. Cui J, Yan J-H, Yan L-M, Pan L, Le J-J, Guo Y-Z. Effects of yoga in adults with type 2 diabetes mellitus: a meta-analysis. J Diabetes Investig. 2017;8(2):201–9.

    Article  CAS  PubMed  Google Scholar 

  55. Chao M, Wang C, Dong X, Ding M. The effects of Tai Chi on type 2 diabetes mellitus: a meta-analysis. J Diabetes Res. 2018;2018:7350567.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Xia T-W, Yang Y, Li W-H, Tang Z-H, Li Z-R, Qiao L-J. Different training durations and styles of tai chi for glucose control in patients with type 2 diabetes: a systematic review and meta-analysis of controlled trials. BMC Complement Altern Med. 2019;19(1):63.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Lee MS, Choi T-Y, Lim H-J, Ernst E. Tai chi for management of type 2 diabetes mellitus: a systematic review. Chinese Journal of Integrative Medicine. 2011;17(10):789–93.

    Article  PubMed  Google Scholar 

  58. Naci H, Salcher-Konrad M, Dias S, Blum MR, Sahoo SA, Nunan D, et al. How does exercise treatment compare with antihypertensive medications? A network meta-analysis of 391 randomised controlled trials assessing exercise and medication effects on systolic blood pressure. Br J Sports Med. 2018. https://0-doi-org.brum.beds.ac.uk/10.1136/bjsports-2018-099921:bjsports-2018-099921.

  59. Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4(1):1.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ. 2015;349:g7647.

    Article  Google Scholar 

  61. International conference on harmonisation of technical requirements for registration of pharmaceuticals for human use (ICH) adopts consolidated guideline on good clinical practice in the conduct of clinical trials on medicinal products for human use. International digest of health legislation. 1997;48(2):231-4.

  62. Higgins JP, Green S. Cochrane handbook for systematic reviews of interventions: the Cochrane Collaboration; 2011. Available from: http://handbook-5-1.cochrane.org/

    Google Scholar 

  63. Copenhagen Trial Unit. Trial sequential analysis [Available from: http://www.ctu.dk/tsa/.

  64. Moher D, Pham B, Jones A, Cook DJ, Jadad AR, Moher M, et al. Does quality of reports of randomised trials affect estimates of intervention efficacy reported in meta-analyses? Lancet. 1998;352(9128):609–13.

    Article  CAS  PubMed  Google Scholar 

  65. Kjaergard LL, Villumsen J, Gluud C. Reported methodologic quality and discrepancies between large and small randomized trials in meta-analyses. Ann Intern Med. 2001;135(11):982–9.

    Article  CAS  PubMed  Google Scholar 

  66. Wood L, Egger M, Gluud LL, Schulz KF, Jüni P, Altman DG, et al. Empirical evidence of bias in treatment effect estimates in controlled trials with different interventions and outcomes: meta-epidemiological study. BMJ. 2008;336(7644):601–5.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Lundh A, Lexchin J, Mintzes B, Schroll JB, Bero L. Industry sponsorship and research outcome. Cochrane Database Syst Rev. 2017;https://0-doi-org.brum.beds.ac.uk/10.1002/14651858.MR000033.pub3(2).

  68. Savovic J, Jones HE, Altman DG, Harris RJ, Juni P, Pildal J, et al. Influence of reported study design characteristics on intervention effect estimates from randomised controlled trials combined analysis of meta epidemiological studies. Health Technol Assess. 2012;16.

  69. Schulz KF, Chalmers I, Hayes RJ, Altman DG. Empirical evidence of bias: dimensions of methodological quality associated with estimates of treatment effects in controlled trials. JAMA. 1995;273(5):408–12.

    Article  CAS  PubMed  Google Scholar 

  70. Gluud LL. Bias in clinical intervention research. Am J Epidemiol. 2006;163(6):493–501.

    Article  PubMed  Google Scholar 

  71. Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557–60.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–58.

    Article  PubMed  Google Scholar 

  73. Harbord RM, Egger M, JA S. A modified test for small-study effects in meta-analyses of controlled trials with binary endpoints. Stat Med. 2006;25(20):3443–57.

    Article  PubMed  Google Scholar 

  74. Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. Br Med J. 1997;315:629–34.

    Article  CAS  Google Scholar 

  75. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50:1088–101.

    Article  CAS  PubMed  Google Scholar 

  76. Elbourne DR, Altman DG, Higgins JP, Curtin F, Worthington HV, Vail A. Meta-analyses involving cross-over trials: methodological issues. Int J Epidemiol. 2002;31(1):140–9.

    Article  PubMed  Google Scholar 

  77. Keus F, Wetterslev J, Gluud C, van Laarhoven CJHM. Evidence at a glance: error matrix approach for overviewing available evidence. BMC Med Res Methodol. 2010;10:90.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Jakobsen JC, Wetterslev J, Winkel P, Lange T, Gluud C. Thresholds for statistical and clinical significance in systematic reviews with meta-analytic methods. BMC Med Res Methodol. 2014;14:120.

    Article  PubMed  PubMed Central  Google Scholar 

  79. The Cochrane Collaboration. Review Manager. 5.3 ed 2014.

  80. DerSimonian R, Laird N. Meta-analysis in clinical trials revisited. Contemp Clin Trials. 2015;45(Pt A):139–45.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Demets DL. Methods for combining randomized clinical trial: strengths and limitations. Stat Med. 1986;6:341–50.

    Article  Google Scholar 

  82. Brok J, Thorlund K, Gluud C, Wetterslev J. Trial sequential analysis reveals insufficient information size and potentially false positive results in many meta-analyses. 2008;61:763–9. J Clin Epidemiol. 2008;61:763-769.

    Article  PubMed  Google Scholar 

  83. Wetterslev J, Thorlund K, Brok J, Gluud C. Trial sequential analysis may establish when firm evidence is reached in cumulative meta-analysis. J Clin Epidemiol. 2008;61:64–75.

    Article  PubMed  Google Scholar 

  84. Brok J, Thorlund K, Wetterslev J, Gluud C. Apparently conclusive meta-analyses may be inconclusive—trial sequential analysis adjustment of random error risk due to repetitive testing of accumulating data in apparently conclusive neonatal meta-analyses. Int J Epidemiol. 2008;38(1):287–98.

    Article  PubMed  Google Scholar 

  85. Thorlund K, Devereaux PJ, Wetterslev J, Guyatt G, Ioannidis JPA, Thabane L, et al. Can trial sequential monitoring boundaries reduce spurious inferences from meta-analyses? Int J Epidemiol. 2008;38(1):276–86.

    Article  PubMed  Google Scholar 

  86. Wetterslev J, Thorlund K, Brok J, Gluud C. Estimating required information size by quantifying diversity in random-effects model meta-analyses. BMC Med Res Methodol. 2009;9:86.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Thorlund K, Anema A, Mills E. Interpreting meta-analysis according to the adequacy of sample size. An example using isoniazid chemoprophylaxis for tuberculosis in purified protein derivative negative HIV-infected individuals. Clin Epidemiol. 2010;2:57–66.

    PubMed  PubMed Central  Google Scholar 

  88. Thorlund K, Wetterslev J, Brok J, Imberger G, C G. User manual for trial sequential analysis (TSA) [Available from: http://www.ctu.dk/tsa/files/TSA_manual.pdf2011.

  89. Imberger G, Gluud C, Boylan J, Wetterslev J. Systematic reviews of anesthesiologic interventions reported as statistically significant: problems with power, precision, and type 1 error protection. Anesthesia Analgesia. 2015;121(6):1611–22.

    Article  PubMed  Google Scholar 

  90. Imberger G, Thorlund K, Gluud C, Wetterslev J. False-positive findings in Cochrane meta-analyses with and without application of trial sequential analysis: an empirical review. BMJ Open. 2016;6(8):e011890.

    Article  PubMed  PubMed Central  Google Scholar 

  91. The Cochrane Collaboration. Review Manager (RevMan). 5.3 ed. Copenhagen: the Nordic Cochrane Centre: 2014.

  92. Schünemann HJ, Best D, Vist G, Oxman AD, Group GW. Letters, numbers, symbols and words: how to communicate grades of evidence and recommendations. CMAJ. 2003;169(7):677–80.

    PubMed  PubMed Central  Google Scholar 

  93. Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008;336(7650):924–6.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Guyatt GH, Oxman AD, Schüneman HJ. Tugwell P, A K. GRADE guidelines: a new series of articles in the Journal of Clinical Epidemiology. J Clin Epidemiol. 2011;64:380–2.

    Article  PubMed  Google Scholar 

  95. Castellini G, Bruschettini M, Gianola S, Gluud C, Moja L. Assessing imprecision in Cochrane systematic reviews: a comparison of GRADE and Trial Sequential Analysis. Syst Rev. 2018;7(1):110.

    Article  PubMed  PubMed Central  Google Scholar 

  96. Storebø OJ, Pedersen N, Ramstad E, Kielsholm ML, Nielsen SS, Krogh HB, et al. Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of adverse events in non-randomised studies. Cochrane Database Syst Rev. 2018;5(5):CD012069-CD.

    Google Scholar 

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Acknowledgements

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Funding

This protocol is part of the corresponding author’s PhD project. The PhD is supported by Danish Diabetes Academy, Denmark (Grant number: PhD012-18) and the Department of Regional Health Research, University of Southern Denmark, Denmark.

Author information

Authors and Affiliations

  1. Department of Internal Medicine, Holbæk Hospital, Holbæk, Denmark

    Anupa Rijal, Emil Eik Nielsen, Michael Hecht Olsen & Janus Christian Jakobsen

  2. Department of Regional Health Research, The Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark

    Anupa Rijal, Emil Eik Nielsen, Michael Hecht Olsen & Janus Christian Jakobsen

  3. Department of Endocrinology and Nephrology, Nordsjællands Hospital, Hillerød, Denmark

    Bianca Hemmingsen

  4. Welch Center for Prevention, Epidemiology, and Clinical Research, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, USA

    Dinesh Neupane

  5. Department of Cardiology and Endocrinology, Næstved-Slagelse-Ringsted (NSR) Hospital, Slagelse, Denmark

    Peter Haulund Gæde

  6. Center for Individualized Medicine in Arterial Diseases, Odense University Hospital, University of Southern Denmark, Odense, Denmark

    Michael Hecht Olsen

  7. Copenhagen Trial Unit, Rigshospitalet, Copenhagen, Denmark

    Janus Christian Jakobsen

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  1. Anupa Rijal
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Contributions

AR drafted the protocol. EEN, BH, DN, PHG, MHO and JCJ amended the protocol. All authors read and approved of the final manuscript.

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Correspondence to Anupa Rijal.

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Supplementary information

Additional file 1:

PRISMA-P 2015 Checklist for this protocol.

Appendix

Appendix

Preliminary search strategy Ovid _Medline (1946 to July 2019)

Cardiovascular diseases

  1. 1.

    exp Cardiovascular Diseases/

  2. 2.

    ((heart or cardiac or cardial or cardiopath* or cardiomyopath* or coronor* or myocord*) adj3 (ischem* or ischaem* or anoxia or hypoxia)).ab,ti

  3. 3.

    (coronary adj3 (insufficien* or occlus* or disease* or acute or atherosclero* or orteriosclero* or sclero* or cordiosclero* or constrict* or vasoconstrict* or obstruct* or stenosis* or thrombo*)).ab,ti

  4. 4.

    ((heart or myocard* or cardiac or cardial) adj3 infarct*).ab,ti

  5. 5.

    ((cerebrovascul* or brain or 'cerebral vascular' or cerebrovascular) adj3 (accident* or lesion or attack or ischem* or ischaem* or insult* or insuffucien* or arrest* or apoplex*)).ab,ti

  6. 6.

    (cva or stroke or angina*) .ab,ti

  7. 7.

    exp Hypertension/

  8. 8.

    (pressure adj3 (blood or systolic or diastolic or arterial or venous or pulse)).ab,ti

  9. 9.

    (hypertensive or antihypertensive or anti-hypertensive).ab,ti

  10. 10.

    1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9

Type 2 diabetes mellitus

  1. 11.

    exp Diabetes Mellitus, Type 2/

  2. 12.

    (('adult onset' or 'type 2' or 'type II' or 'non-insulin dependent' or 'noninsulin dependent' or 'insulin independent') adj3 diabet*).ab,ti

  3. 13.

    11 or 12

Exercise

  1. 14.

    exp Exercise/ or exp Exercise Therapy/

  2. 15.

    exp Sports/

  3. 16.

    exp Physical Exertion/

  4. 17.

    rehabilitat*.ab,ti

  5. 18.

    (physical* adj3 (fit* or train* or therap* or activit*)).ab,ti

  6. 19.

    (train* adj3 (strength* or aerobic* or exercise* or endurance* or weight*)).ab,ti

  7. 20.

    ((exercise* or fitness) adj3 (treatment or intervent* or program* or rehabilitat*)).ab,ti

  8. 21.

    ((exercise* adj3 (aerobic* or endurance* or combine* or resistance*)).ab,ti

  9. 22.

    (run* or bicycle* or treadmill* or ergometer* or walk* or swim*).ab,ti

  10. 23.

    exp Tai Ji/

  11. 24.

    (‘tai chi’ or ‘tai chi chuan’ or ‘ta'i chi’ or ‘tai ji’ or taijiquan).ab,ti

  12. 25.

    exp Yoga/th [Therapy]

  13. 26.

    14 or 15 or 16 or 17 or 18 or 19 or 20 or 21 or 22 or 23 or 24 or 25

  14. 27.

    10 or 13 and 26

  15. 28.

    random*.mp. [mp=title, abstract, original title, name of substance word, subject heading word, floating sub-heading heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier, synonyms]

  1. 29.

    blind*.mp. [mp=title, abstract, original title, name of substance word, subject heading word, floating sub-heading heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier, synonyms]

  1. 30.

    meta-analys*.mp. [mp=title, abstract, original title, name of substance word, subject heading word, floating sub-heading heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier, synonyms]

  2. 31.

    28 or 29 or 30

  3. 32.

    27 and 31

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Rijal, A., Nielsen, E.E., Hemmingsen, B. et al. Adding exercise to usual care in patients with hypertension, type 2 diabetes mellitus and/or cardiovascular disease: a protocol for a systematic review with meta-analysis and trial sequential analysis. Syst Rev 8, 330 (2019). https://0-doi-org.brum.beds.ac.uk/10.1186/s13643-019-1233-z

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  • DOI: https://0-doi-org.brum.beds.ac.uk/10.1186/s13643-019-1233-z

Keywords

Systematic Reviews

ISSN: 2046-4053