Respiratory muscle training for obstructive sleep apnoea

Objectives

This is a protocol for a Cochrane Review (intervention). The objectives are as follows:

To assess the effects of respiratory muscle training (RMT) for people with obstructive sleep apnoea (OSA).

Background

Description of the condition

Obstructive sleep apnoea (OSA) is an intermittent blocking of the upper airways soft tissues during sleep. This causes airflow reduction (hypopnoea) or cessation (apnoea), which in turn causes intermittent hypoxia and disruption of sleep architecture.

Male patients present snoring and report daytime sleepiness and unsatisfactory rest and women mainly experience low energy or fatigue, tiredness, initial insomnia and morning headaches (Evans 2014; Fietze 2019; Nigro 2018; Theorell‐Haglöw 2018).

OSA has been also associated with an increase in risk for traffic accidents (Gottlieb 2018; Tregear 2009), occupational injuries (Hirsch 2016), deterioration in cognitive function (Beaudin 2021; Bubu 2020; Seda 2021), metabolic diseases (Patinkin 2017), cardiovascular diseases (Hou 2018; Sarkar 2018), and mortality (Butler 2019; Marshall 2008).

OSA has been associated as a risk factor for COVID‐19 infection and for increasing the risk of hospitalisation and of developing respiratory failure in people with COVID‐19 (Ekiz 2020; Maas 2020; McSharry 2020; Memtsoudis 2020; Miller 2021; Najafi 2020; Peker 2021; Strausz 2021; Tufik 2020).

Obesity is probably the single most important risk factor for OSA both for adults (Carneiro 2018; Hnin 2018), and for children (Andersen 2019). "It is estimated that over 70% of people with OSA are obese, and the prevalence of OSA among obese people may be as high as 45%" (Romero‐Corral 2010). Obesity is a growing problem all over the world and the incidence and prevalence of OSA will increase in parallel with it (Blüher 2019; Garvey 2015). Also, socioeconomic status could be a risk factor for OSA and coupled with obesity and disparities in health care could influence the association between OSA and racial/ethnic minorities (Guglielmi 2019).

Diagnosis of OSA includes polysomnographic sleep studies, the gold standard, or home sleep apnoea testing in people presenting a combination of symptoms including excessive daytime sleepiness, loud snoring, witnessed apnoea episodes, or non‐dipping nocturnal blood pressure diagnosed hypertension (Caples 2021; Jonas 2017; Kapur 2017; Randerath 2018). The diagnosis and classification of severity of the disease is based in the Apnoea–Hypopnoea Index (AHI): mild (AHI 5 to 14); moderate (AHI 15 to 30) and severe (AHI > 30) for adults (Hudgel 2016) and mild (AHI 2 to 5), moderate (AHI 6 to 10) and severe (AHI > 10) for children (Dehlink 2016; Kaditis 2016).

Published reviews found wide variation in the reported OSAs prevalence, caused in part by substantial methodological heterogeneity in population prevalence studies, including differences in diagnostic threshold to define cut‐off level for the AHI or the inclusion or not of excessive daytime sleepiness as necessary diagnostic criteria (Lozo 2016; Senaratna 2017). In adults, prevalence of OSA with excessive daytime sleepiness could range between 3% to 18% of men and between 1% and 17% of women (Fietze 2019; Franklin 2015; Jonas 2017; Mirrakhimov 2013; Sunwoo 2018; van der Spuy 2018). However, prevalence in women is probably under‐diagnosed, given the widespread belief that OSA is a condition rare in women and that the symptomatology in women differs from the considered classical symptoms: snoring and daytime somnolence (Garvey 2015). In children, prevalence could range from 1% to 6% (Bixler 2009; Kaditis 2016; Li 2010; Tsukada 2018).

Benjafield 2019 gathered available data on prevalence for OSA to estimate global burden of the disease in the world and estimated that “936 million adults aged 30‐69 years have mild to severe obstructive sleep apnoea and 425 million aged 30 to 69 years have moderate to severe obstructive sleep apnoea globally”. They considered that prevalence of OSA can be increasing due to obesity pandemic and the ageing of the population.

Description of the intervention

The term respiratory muscle training (RMT) refers to several specific exercises of the diaphragm and accessory respiratory muscles to improve their function. They may include training of inspiratory and expiratory muscles, or both, with the goal of increasing their strength or endurance (Illi 2012).

There are three main types of RMT: normocapnic hyperpnoea (rebreathing), flow resistive loading, and pressure threshold loading (Figure 1). The first one aims to promote muscle endurance and the last two to improve muscle strength (Silva 2013).

1.

a. Flow resistive loading, b. pressure thershold loading, c. normoncapnic hyperpnoea (rebreathing).

• Normocapnic hyperpnoea requires individuals to maintain high target levels of ventilation and corresponds to endurance training. It is a physically demanding and relatively time‐consuming (typically 30 minutes per session) mode of respiratory muscle training, which requires a high degree of motivation from the individual (McConnell 2004).

• Flow resistive loading is performed using a device with an orifice with a valve with a diameter that can be changed to allow different resistance and training loads (Romer 2003 ; Stanford 2020).

• Pressure threshold loading uses a device with a valve that remains closed until the individual generates enough pressure to allow inspiration, and it is necessary to tailor the adequate training load for each individual (McConnell 2005).

Proposers of this intervention consider that it is essential to monitor the individual respiratory pattern during the training to ensure an adequate training load (McConnell 2005).

How the intervention might work

It has been found that there is a significantly lower functional performance and higher fatigability for inspiratory muscles in patients with OSA (Chien 2010; Wilcox 1990).

Among the pathophysiological components that can play a role in OSA, research findings point to the role of the tone of respiratory muscles in keeping the width of upper airways and pharyngeal patency (Carlisle 2017; Herkenrath 2018). Moreover, upper and lower airway muscle function is relatively diminished during sleep and may potentially cause airway obstruction (Jordan 2010).

Lower respiratory muscle movements result in stiffening of the pharyngeal walls and keeps the patency of the upper airways (Hsu 2020).

Respiratory muscle training attempts pharyngeal, intercostal, and diaphragm muscle strengthening, usually against a specific resistance, and increasing passive stiffness of pharyngeal locomotor muscles, which may reduce the tendency for upper airway collapse during sleep (Erturk 2020; How 2007).

It has been observed using a threshold loading device that when expiratory muscles are activated the hyoid bone rises vertically from its rest position and since the hyoid bone is related with muscles, ligaments and fascia of the pharynx, jaw and skull, and it has been suggested that respiratory muscle training might be a useful treatment for OSA (Dos Santos Silva 2015; Wheeler 2007).

Why it is important to do this review

Even though continuous positive airway pressure (CPAP), administered via a face mask connected to a machine, aimed to keep the airways open during sleep, is usually considered the first treatment option for OSA, in many cases adherence to the treatment is very low (Bakker 2019; Mehrtash 2019; Rotenberg 2016). Poor compliance with CPAP is probably due to the side effects of the treatment, which include discomfort, nasal congestion, mask leaks, claustrophobia, abdominal bloating and inconvenience of regular usage (Askland 2020). Although they exist, other options for some patients with OSA, such as surgical options or oral appliances (Carvalho 2016), respiratory muscle training is non‐invasive, inexpensive, and has no major risks. It could be a safe and acceptable option for many patients with OSA and economically accessible for people and countries with lower incomes.

Until now there has not been published any systematic review on the efficacy and safety of respiratory muscle training in patients with obstructive sleep apnoea. This review aims to fill that gap.

Objectives

To assess the effects of respiratory muscle training (RMT) for people with obstructive sleep apnoea (OSA).

Methods

Criteria for considering studies for this review

Types of studies

We will include randomised controlled trials (RCTs). We will include studies reported in full text, those published as an abstract only and unpublished data.

Types of participants

We will include adults or children (aged 18 years or younger), or both with a diagnosis of obstructive sleep apnoea (OSA), defined for adults as five or more episodes of apnoea or hypopnoea per hour of sleep by polysomnography (PSG) or portable monitoring (Type I to Type III sleep monitors), and two or more episodes for children.

We will exclude studies in which participants have been diagnosed with OSA using only level IV sleep monitors because current evidence is not very strong for the stand‐alone use of level IV portable monitors (PMs) in clinical practice (Abrahamyan 2018).

We will also include studies with participants with other co‐morbidities such as chronic obstructive pulmonary disease (COPD), but we will exclude studies where the included participants experience other types of sleep disordered breathing, such as central sleep apnoea, or patients with recent stroke (within six months), or past history of stroke with residual disability.

Types of interventions

We will include studies comparing respiratory muscle training of any duration with one of the following control groups.

1. Sham therapy, no intervention or waiting list.

2. Continuous positive airway pressure (CPAP).

3. Any other active intervention.

4. Combination therapy: respiratory muscle training added to CPAP versus CPAP alone or CPAP plus sham respiratory muscle training.

We will not include studies in which respiratory muscle training is part of a multi‐component intervention in which there is no possibility to assess the separate effect of adding or no respiratory muscle training.

We will include the following co‐interventions provided they are not part of the randomised treatment: exercise for weight loss and diet and sleep recommendations.

Types of outcome measures

We will assess outcomes at longest time measurement points reported in primary studies, and we will pool data for the short, intermediate and long term, defined as follows.

• Short term: up to three months.

• Intermediate term: from three months to two years.

• Long term: more than two years.

We will analyse the following outcomes in the review, but we will not use them as a basis for including or excluding studies.

Primary outcomes

1. Daytime sleepiness, measured by a validated scale or questionnaire, such as the Epworth Sleepiness Scale (ESS).

2. Cognitive function, measured by a validated scale or questionnaire, such as the Mini Mental State Examination (MMSE).

3. Serious adverse events.

Secondary outcomes

1. Quality of life, measured by a validated scale or questionnaire, such as the Short Form Survey (SF‐36).

2. Sleep quality, measured by a validated scale or questionnaire, such as the Pittsburgh sleep quality index (PSQI).

3. Mortality.

4. Apnoea‐Hypopnoea Index (AHI), defined as the number of episodes of apnoea hypopnoea per hour of sleep, measured objectively by polysomnography or portable monitoring.

5. Snoring frequency measured by the Snoring Index (number of snore events per hour of sleep) or the snoring intensity measured by sleep studies.

6. Changes in sleep efficiency and sleep architecture (percentage and absolute duration of deep sleep and rapid eye movement (REM) sleep).

For numerical outcomes, we consider as minimum clinically important differences (MCID) the following ones: for daytime sleepiness, three points if measured by the ESS (Patel 2017; Weaver 2001); for cognitive function 1.6 points if measured by MMSE (Watt 2021);for sleep quality, three points if measured by the PSQI (Hughes 2009); for AHI five points (Kim 2017).

Search methods for identification of studies

Electronic searches

We will identify studies from searches of the following databases and trial registries.

1. Cochrane Airways Trials Register (Cochrane Airways 2019), via the Cochrane Register of Studies, all years to date.

2. Cochrane Central Register of Controlled Trials (CENTRAL), via the Cochrane Register of Studies, all years to date;.MEDLINE Ovid SP 1946 to date.

3. Embase Ovid SP 1974 to date.

4. US National Institutes of Health Ongoing Trials Register ClinicalTrials.gov (www.clinicaltrials.gov).

5. World Health Organization International Clinical Trials Registry Platform (apps.who.int/trialsearch).

The proposed MEDLINE search strategy is listed in Appendix 1. This will be adapted for use in the other databases. Population search terms are based on the standard Cochrane Airways search strategy for sleep apnoea. Intervention search terms were identified by manually reviewing the MeSH terms and keywords from a small sample of relevant studies and related systematic reviews, and analysing MeSH terms with the Yale MeSH Analyzer (Grossetta Nardini 2021). The search strategy was developed by the Cochrane Airways Information Specialist in collaboration with the authors, and was peer‐reviewed by another Cochrane Information Specialist using the PRESS checklist (McGowan 2016).

All databases and trials registries will be searched from their inception to the present, and there will be no restriction on language or type of publication. Hand‐searched conference abstracts and grey literature will be identified through the Cochrane Airways Trials Register and the CENTRAL database.

Searching other resources

We will check the reference lists of all primary studies and review articles for additional references. We will search relevant manufacturers' websites for study information.

We will search on PubMed for errata or retractions from included studies published in full text, and report the date this was done within the review.

Data collection and analysis

Selection of studies

We plan to use Cochrane’s Screen4Me workflow to help assess the search results. Screen4Me comprises three components: known assessments – a service that matches records in the search results to records that have already been screened in Cochrane Crowd and been labelled as an RCT or as Not an RCT; the RCT classifier – a machine learning model that distinguishes RCTs from non‐RCTs; and if appropriate, Cochrane Crowd (http://crowd.cochrane.org) – Cochrane’s citizen science platform where the Crowd help to identify and describe health evidence. More detailed information about the Screen4Me components can be found in the following publications: Marshall 2018, McDonald 2017, Noel‐Storr 2018, Thomas 2017.

Following this initial assessment, three review authors (MRE, IMA and ILA) will screen the titles and abstracts of the remaining search results independently and code them as 'retrieve' (eligible or potentially eligible/unclear) or 'do not retrieve'. We will retrieve the full‐text study reports of all potentially eligible studies and three review authors (MRE, IMA and ILA) will independently screen them for inclusion, recording the reasons for exclusion of ineligible studies. We will resolve any disagreement through discussion or, if required, we will consult a fourth review author (JRR). We will identify and exclude duplicates and collate multiple reports of the same study so that each study, rather than each report, is the unit of interest in the review. We will record the selection process in sufficient detail to complete a PRISMA flow diagram and 'Characteristics of excluded studies' table (Moher 2009).

Data extraction and management

We will use a data collection form for study characteristics and outcome data, which has been piloted on at least one study in the review. Two review authors (MRE and ILA) will extract the following study characteristics from included studies.

1. Methods: study design, total duration of study, details of any 'run‐in' period, number of study centres and location, study setting, withdrawals and date of study.

2. Participants: number (N), mean age, age range, gender, severity of condition, diagnostic criteria, baseline lung function, smoking history, inclusion criteria and exclusion criteria.

3. Interventions: intervention, comparison, concomitant medications and excluded medications.

4. Outcomes: primary and secondary outcomes specified and collected, and time points reported.

5. Notes: funding for studies and notable conflicts of interest of trial authors.

Two review authors (MRE and ILA) will independently extract outcome data from included studies. We will note in the 'Characteristics of included studies' table if outcome data were not reported in a usable way. We will resolve disagreements by consensus or by involving a third review author (JRR). One review author (MRE) will transfer data into RevMan Web. We will double‐check that data are entered correctly by comparing the data presented in the systematic review with the study reports. A second review author (JRR) will spot‐check study characteristics for accuracy against the study report.

Assessment of risk of bias in included studies

Two review authors (MRE and ILA) will assess risk of bias independently for each study using the risk of bias 2 (ROB2) tool outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2020; Higgins 2021). We will resolve any disagreements by discussion or by involving another author (JRR). We will assess the risk of bias for each relevant outcome according to the following domains:

1. bias arising from the randomisation process;

2. bias due to deviations from intended interventions;

3. bias due to missing outcome data;

4. bias in measurement of the outcome; and

5. bias in selection of the reported result.

Our effect of interest is assignment to the intervention at baseline and our main outcomes are those listed in the summary of findings table. We will reach a risk of bias judgement and assign one of three levels to each domain:

• low risk of bias;

• some concerns; or

• high risk of bias.

We will provide a quote from the study report together with a justification for our judgement in therRisk of bias table.

We will reach an overall risk‐of‐bias judgement for a specific outcome for each study according to the following criteria.

• Low risk of bias: the trial is judged to be at low risk of bias for all domains for this result.

• Some concerns: the trial is judged to raise some concerns in at least one domain for this result, but not to be at high risk of bias for any domain.

• High risk of bias: the trial is judged to be at high risk of bias in at least one domain for this result. Or the trial is judged to have some concerns for multiple domains in a way that substantially lowers confidence in the result.

Where information on risk of bias relates to unpublished data or correspondence with a trialist, we will note this in the risk of bias table.

When considering treatment effects, we will take into account the risk of bias for the studies that contribute to that outcome.

In case there are cluster‐randomised trials or cross‐over trials to be included in the review, the risk of bias of those studies will be assessed using the Rob2 CRT and the RoB 2.0 tool for cross‐over trials, respectively.

To manage the assessment of bias of included studies we will use templates and spreadsheets of the Rob2 tools (Sterne 2019).

Assessment of bias in conducting the systematic review

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

Measures of treatment effect

We will analyse dichotomous data as odds ratios (OR) and continuous data as the mean difference (MD) or standardised mean difference (SMD) with 95% confidence intervals (CIs). If data from rating scales are combined in a meta‐analysis, we will ensure they are entered with a consistent direction of effect (e.g. lower scores always indicate improvement).

We will describe skewed data narratively (for example, as medians and interquartile ranges (IQRs)for each group).

Where multiple trial arms are reported in a single study, we will include only the relevant arms. If two comparisons (e.g. drug A versus placebo and drug B versus placebo) are combined in the same meta‐analysis, we will either combine the active arms or halve the control group to avoid double‐counting.

If adjusted analyses are available (ANOVA or ANCOVA), we will use these as a preference in our meta‐analyses. If both change from baseline and endpoint scores are available for continuous data, we will use change from baseline unless there is low correlation between measurements in individuals. If a study reports outcomes at multiple time points we will use the last one available.

We will use intention‐to‐treat (ITT) or 'full analysis set' analyses where they are reported (i.e. those where data have been imputed for participants who were randomly assigned but did not complete the study) instead of completer or per protocol analyses.

Unit of analysis issues

For dichotomous outcomes, we will use participants, rather than events, as the unit of analysis (i.e. number of participants with one or more adverse event, rather than number of adverse events). However, if rate ratios are reported in a study, we will analyse them on this basis. We will only meta‐analyse data from cluster‐RCTs if the available data have been adjusted (or can be adjusted), to account for the clustering.

Dealing with missing data

We will contact investigators or study sponsors in order to verify key study characteristics and obtain missing numerical outcome data where possible (e.g. when a study is identified as an abstract only). Where this is not possible, and the missing data are thought to introduce serious bias, we will take this into consideration in the GRADE rating for affected outcomes.

Assessment of heterogeneity

We will use the I² statistic to measure heterogeneity among the studies in each analysis. If we identify substantial heterogeneity we will report it and explore the possible causes by pre‐specified subgroup analysis.

Assessment of reporting biases

If we are able to pool more than 10 studies, we will create and examine a funnel plot to explore possible small‐study and publication biases.

Data synthesis

We will undertake meta‐analyses only where this is meaningful; that is, if the treatments, participants and the underlying clinical question are similar enough for pooling to make sense. We will use random‐effects model.

The primary analysis will include studies at any risk of bias, and we will perform a sensitivity analysis to explore risk of bias.

We anticipate the following comparator groups: respiratory muscle training (RMT) versus sham therapy/no intervention/standard medical treatment/oropharyngeal exercises/non‐invasive ventilation.

Subgroup analysis and investigation of heterogeneity

We plan to carry out the following subgroup analyses.

1. Gender (females versus males).

2. Age (18 years and younger versus older than 18 years).

3. Type of respiratory muscle training (inspiratory versus expiratory muscle training).

4. Type of training (normocapnic hyperpnoea, flow resistive loading and pressure threshold loading).

5. Severity of OSA: mild versus moderate to severe (mild (AHI 5 to 14); moderate (AHI 15 to 30) and severe (AHI > 30) for adults and mild (AHI 2 to 5), moderate (AHI 6 to 10) and severe (AHI > 10) for children).

We will use the following outcomes in subgroup analyses.

1. Daytime sleepiness.

2. Morbidity (including accidents) and mortality.

3. Serious adverse events.

We will use the formal test for subgroup interactions in Review Manager (RevMan Web 2020).

Sensitivity analysis

We plan to carry out the following sensitivity analyses, removing from the primary outcome analyses studies with an overall high risk of bias judgement.

We will compare the results from a fixed‐effect model with the random‐effects model.

We will undertake a sensitivity analysis to investigate whether choice of summary statistic (odds ratio or risk ratio) is critical to the conclusions of the meta‐analysis.

Summary of findings and assessment of the certainty of the evidence

We will create separate summary of findings tables for each comparison in the review (i.e. one for RMT versus sham therapy, one for RMT versus standard medical treatment, one for RMT versus oropharyngeal exercises, one for RMT versus non‐invasive ventilation, and so on) using the following outcomes: daytime sleepiness, morbidity (including accidents) and mortality, quality of life, sleep quality, adverse events, apnoea hypo‐apnoea Index (AHI) and snoring. We will use the five GRADE considerations (risk of bias, consistency of effect, imprecision, indirectness and publication bias) to assess the certainty of a body of evidence as it relates to the studies that contribute data for the pre‐specified outcomes. Risk of bias assessment will be based on consensus decisions on overall risk of bias of the included studies, stored and presented in templates and spreadsheets of the RoB 2 tools (Sterne 2019).

We will use the methods and recommendations described in Chapter 14 of the Cochrane Handbook for Systematic Reviews of Intervention (Schünemann 2021), using GRADEpro software (GRADEpro GDT). We will justify all decisions to downgrade the quality of studies using footnotes, and we will make comments to aid the reader's understanding of the review where necessary.

Appendices

Appendix 1. MEDLINE search strategy

Database: Ovid MEDLINE(R) ALL <1946 to April 22, 2021>
Search Strategy:
‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐
1 exp Sleep Apnea, Obstructive/ (22147)
2 (sleep$ adj33 (apnea$ or apnoea$)).tw. (40920)
3 (hypopnea$ or hypopnoea$).tw. (12640)
4 (OSA or SHS or OSAHS).ti,ab. (19711)
5 (sleep$ adj2 disordered adj2 breathing).tw. (6710)
6 or/1‐5 (51892)
7 Exercise/ (116794)
8 Exercise Therapy/ (42603)
9 Respiratory Muscles/ph [Physiology] (2125)
10 Resistance Training/mt [Methods] (4091)
11 Muscle Strength/ph [Physiology] (11421)
12 Breathing Exercises/ (3549)
13 (threshold adj2 (load or device*)).tw. (707)
14 (resist$ adj2 (load$ or device$)).tw. (3480)
15 isocapnic hyperpnea.tw. (84)
16 ((inspiratory or expiratory or ventilat$ or respiratory) adj3 (muscle or resistance) adj3 (train$ or strength$ or endur$ or exercis$)).tw. (3192)
17 (IMT or RMT or EMST or TIMT).ti,ab. (9436)
18 or/7‐17 (182864)
19 6 and 18 (665)
20 (controlled clinical trial or randomized controlled trial).pt. (616865)
21 (randomized or randomised).ab,ti. (667694)
22 placebo.ab,ti. (223134)
23 dt.fs. (2302354)
24 randomly.ab,ti. (356658)
25 trial.ab,ti. (639358)
26 groups.ab,ti. (2209715)
27 or/20‐26 (5054142)
28 Animals/ (6801686)
29 Humans/ (19182954)
30 28 not (28 and 29) (4782368)
31 27 not 30 (4400685)
32 19 and 31 (242)

Contributions of authors

M. Rueda‐Etxebarria: wrote the final draft of the protocol and will write the draft of the full review. He is the contact person with the editorial base and coordinated the contributions from the co‐authors and will be the guarantor of the final review.

I. Mugueta‐Aguinaga, I. Lascurain‐Aguirrebeña and JR. Rueda: drafted the clinical sections of the background and will select the studies for the full review and will do risk of bias evaluation and will extract the data.

Contribution of editorial team

Rebecca Fortescue (Coordinating Editor) edited the protocol; advised on methodology; approved the protocol prior to publication.

Iain Crossingham (Contact Editor): edited the protocol; advised on content.

Emma Dennett (Managing Editor): coordinated the editorial process; advised on content; edited the protocol.

Emma Jackson (Assistant Managing Editor): conducted peer review; edited the references and other sections of the protocol.

Elizabeth Stovold (Information Specialist): designed the search strategy; edited the search methods section; arranged for peer review of the search strategy

Sources of support

Internal sources

• M Rueda‐Etxebarria, Spain

  Receives salary from Osakidetza the Basque Country Health Service, Spain.

• I Mugueta‐Aguinaga, Spain

  Receives salary from Osakidetza the Basque Country Health Service, Spain.

• JR Rueda, Spain

  Receives salary from the University of the Basque Country, Spain.

• I Lascurain‐Aguirrebeña, Spain

  Receives salary from the University of the Basque Country, Spain.

External sources

• All, Spain

  The authors declare that no such funding was received for this systematic review

Declarations of interest

M. Rueda‐Etxebarria: non known.

I. Mugueta‐Aguinaga: none known.

I. Lascurain‐Aguirrebeña: none known.

JR. Rueda: none known.

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