Yoga as part of a package of care versus non‐standard care for schizophrenia
Abstract
This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:
To examine the effects of yoga as part of a package of care versus non‐standard care for schizophrenia.
Background
Description of the condition
Schizophrenia is a relatively common mental disorder with a lifetime prevalence of 0.3% to 0.6% and an incidence of 10.2 to 22.0 per 100,000 people (McGrath 2008). Typical presentation is in early adulthood or late adolescence, and this disorder affects around 0.30% to 0.66% of people at some point in their life (McGrath 2008). Schizophrenia is characterised by a constellation of symptoms that can present in a wide variety of ways depending on the individual. Core features or symptoms can broadly be divided into positive symptoms and negative symptoms. Positive symptoms include delusions, hallucinations, disorganised speech, and disorganised behaviour. Negative symptoms include anhedonia (lack of pleasure), alogia (reduced speech), affective flattening (lack of emotional responsiveness), amotivation and social withdrawal (Owen 2016). Additionally, while they are not included in the current International Classification of Diseases (ICD‐10) or Diagnostic and Statistical Manual (DSM5) diagnostic systems as diagnostic criteria, characteristic cognitive deficits are widely recognised in schizophrenia and are the target of considerable clinical and research attention (Carbon 2014).
Schizophrenia has been identified as a serious public health concern, ranking 11th in the causes of years lived with disability worldwide (Global Burden of Disease Study 2013).The mainstay of treatment is antipsychotic medication (Owen 2016). A recent review highlighted that antipsychotic medication is associated with an increased risk for several physical diseases, including obesity, dyslipidaemia, diabetes mellitus, thyroid disorders and hyponatraemia, cardiovascular, respiratory tract, gastrointestinal, haematological, musculoskeletal and renal diseases, as well as movement and seizure disorders (Correll 2015). Although antipsychotic medication is effective in reducing positive symptoms, usually within the early stages of treatment (Leucht 2013), it is of less benefit for negative symptoms (Fusar‐Poli 2015) and cognitive deficits (Nielsen 2015). Unfortunately, it is the negative and cognitive symptoms that cause most long‐term disability (Vancampfort 2011; Vancampfort 2012). The side‐effect profile and inherent limitations of antipsychotics as well as patient preference to avoid this route where possible, have resulted in additional non‐pharmacological interventions being utilised as either an adjunct or alternative to medication therapy (Kern 2009). Low‐cost treatments that decrease negative symptoms, reduce cognitive deficits and promote mental and physical quality of life and functional recovery are warranted.
Description of the intervention
Yoga originates from India as an ancient Hindu practice incorporating physical postures with breathing exercises seeking to bring about a balance between the mental and physical state (Bussing 2012; Ross 2012; Sherman 2012). The principles behind its practice were first described by Pantajali, and were believed to allow the mind and the body to be prepared for spiritual development (Ross 2012). In the Western world, yoga has now been widely adopted as both a method of relaxation and exercise. Hatha yoga is the most widely adopted practice used in the Western world Collins 1998). Its use of postures (asanas) improves strength, flexibility, co‐ordination and endurance and its use of breathing exercises (pranayama) improves respiratory control and concentration. Mantra yoga is another well‐known and widely practiced form of Hindu yoga and focuses on the use of chants to achieve mental and spiritual transformation (Sherman 2012).
With its increasing popularity, research into the effect of yoga on both physical and mental health has identified key benefits of yoga. It has been shown to both reduce stress and improve cognitive function in healthy individuals (Bangalore 2012), and also to be useful as a complementary therapy for many health conditions, resulting in better blood pressure control and improvements in mental health conditions including depression and anxiety disorders (Bussing 2012).
Yoga's benefit for other mental health conditions has lead to research into the role of yoga as a potential complementary therapy for the management of schizophrenia. A systematic review of randomised control trials indicated that yoga could also be of benefit as an add‐on treatment to standard care by reducing both positive and negative symptoms of schizophrenia and improving the health‐related quality of life of people with schizophrenia (Vancampfort 2012), although the evidence is limited, as only three trials were included. A further review echoed the possible improvements in quality of life, but highlighted that long‐term benefits are not known and the safety of the intervention was not reported (Cramer 2013). Recently, it was demonstrated that yoga also improves the cognitive sub‐domain of long‐term memory in people with schizophrenia (Dauwan 2016).
Exercise is a subset of physical activity that is planned, structured, and repetitive and has, as a final or an intermediate objective, the "improvement or maintenance of physical fitness" (Caspersen 1985). Exercise is intentional and has an aim of increasing one or more of the components of physical fitness (i.e. cardiorespiratory fitness, muscular strength and endurance, body composition, flexibility and neuromotor fitness), or physical activity, or improving a physical indicator such as blood pressure.
Yoga can therefore be considered a form of exercise. While purist yoga‐only programmes are often delivered, yoga also has the potential to be delivered as a part of a multi‐modal intervention, i.e. part of a package of care. This could be alongside other exercise, which could consist of any other activity which falls under the umbrella term of exercise, encompassing broad categories of skill‐related fitness, health‐related fitness, body‐mind fitness, as well as physical activities which are not specifically fitness focused. Yoga could also be combined with expressive therapies or talking therapies. Multi‐modal interventions could consist of two components or a diverse mix of more than two elements; for example, yoga combined with other exercise combinations such as Tai‐chi plus art therapy.
Expressive therapies include broad categories of art therapy, dance therapy, drama therapy, music therapy and writing therapy. These represent different approaches, but the uniting principal is that these forms of therapy take place within a patient‐therapist relationship.
In art therapy the patient is directed to use a range of art materials to make images, and the focus is on the relation between the image, the creator, and the therapist (Crawford 2007). Dance therapy is also sometimes referred to as dance‐movement therapy (DMT) (Payne 2006), and has been used as a healing ritual since early human history, although there is no one particular therapeutic dance (Ren 2013). Drama therapists use games, storytelling and role‐play (Crawford 2007). Music therapy is often perceived as a psychotherapeutic method in the sense that it addresses intra‐ and inter‐psychic, as well as social processes by using musical interaction as a means of communication, expression, and transformation (Mössler 2011). Writing therapy uses the act of writing and processing the written word as a therapeutic tool.
Talking therapies can be considered to consist of, but are not limited to; talking treatments, counselling, psychological therapies or treatments and psychotherapies. Cognitive behaviour therapy (CBT) is one of the most well‐recognised talking therapies. In CBT, links are made between the person’s feelings and patterns of thinking which underpin their distress. The participant is encouraged to take an active part in their therapy by using the following techniques (Jones 2012).
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Challenging their habitual patterns of thinking.
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Examining the evidence for and against their distressing beliefs.
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Using reasoning abilities and personal experience to develop rational and personally acceptable alternative explanations and interpretations Alford 1994) and to test these alternative explanations in real‐world situations(Tarrier 1993).
As CBT has latterly developed into a 'catch all' term for a variety of similar interventions, we will incorporate the criteria developed by Jones 2012 in this review.
How the intervention might work
Yoga has been identified as having a role in regulating the autonomic nervous system (Vancampfort 2012), decreasing sympathetic tone, and creating a reaction the opposite to 'fight or flight' reaction. There is a subsequent effect on the limbic system and hypothalamic pituitary axis leading to a reduction in blood cortisol levels. This leads to a regulation of heart rate and blood pressure, which has obvious cardiovascular benefits (Damodaran 2002). Yoga also focuses on relaxed breathing and this internal concentration is thought to reduce stress by minimising mental focus on external stressors or threats (Bangalore 2012). The decrease in cortisol levels is also thought to have an effect on the better control of blood glucose, cholesterol and total lipids. Since antipsychotic medication for the treatment of schizophrenia is associated with dyslipidaemia, diabetes and obesity (Correll 2015; Vancampfort 2015), yoga may be a useful adjuvant to therapy to minimise these effects (Bangalore 2012). The improvement in the physical health of these patients could have a direct benefit to their mental health. Yoga is also identified to have a role in improving sleep (Collins 1998). There is also thought to be a role of oxytocin, a hormone related to improved mood, analogues of which have been suggested as a possible treatment for schizophrenia (Bangalore 2012; Feifel 2011). It has been identified that plasma levels of oxytocin are higher in people after the practice of yoga (Vancampfort 2012).
Mechanisms explaining the beneficial effects of exercise in people with schizophrenia are not fully elucidated at this time. At present, the plausible mechanisms for change in positive and negative symptoms through exercise fall into one of two broad testable hypotheses: (1) biochemical changes such as increased levels of neurotransmitters (e.g. endorphins, dopamine or serotonin) which could be tested in schizophrenia‐like animal models, and (2) psychological changes such as social support, sense of autonomy, improved perceptions of competence, enhanced body image, self‐efficacy and distraction (Vancampfort 2014). Cardio‐metabolic and neurochemical pathways between skeletal muscle, the spinal cord, and the brain offer plausible, testable mechanisms that might help explain the effects of exercise on brain health in people with schizophrenia. Previous research demonstrated that changes in hippocampal volume and cortical thickening (or less thinning) following aerobic exercise were correlated with improvements in aerobic fitness measured by change in maximum oxygen consumption (Vancampfort 2014). The underlying mechanisms of brain volume increases resulting from improved aerobic fitness are still unknown, but it was shown (Kimhy 2015) that increased production of brain‐derived neurotrophic growth factors (BDNF) probably plays a role. More interventional and longitudinal exploration is needed of the underlying mechanisms for brain health improvements in patients with schizophrenia following exercise. Future research could investigate whether exercise, for example, reduces the inflammatory status of the brain by increasing levels of the anti‐inflammatory cytokine interleukin‐10.
As expressive therapy consists of broad categories of art therapy, dance therapy, drama therapy, music therapy and writing therapy, the effects of these treatments are diverse, and are not completely known. It is not fully understood whether the healing aspect of therapy is the process of the actual expressive therapy, the relationship that develops between the therapist and the patient, or most likely, a complex fusion of the two. Generally, research into the physiological and biochemical effects of these therapies in schizophrenia is in its infancy. From a social and emotional perspective, music therapy for example, can have particular motivating, relationship‐building, and emotionally expressive qualities that may help those who do not respond to verbal therapy (Rolvsjord 2001; Solli 2008) while dance therapy is associated with other therapeutic benefits. Body movement dance can stimulate and release feelings, enable communication and enhance non‐verbal contact. In addition, the non‐critical therapeutic setting can decrease anxiety (Ren 2013).
Talking therapies are a diverse set of treatments which can be considered under the following broad categories; Cognitive‐Behavioural, Humanistic, Insight‐Oriented, Postmodernist, Systemic and Others. They are therefore associated with diverse effects, some of which are not fully understood. Cognitive behavioural therapy (CBT), for instance, aims to remediate distressing emotional experiences or dysfunctional behaviour by changing the way in which the individual interprets and evaluates the experience or cognates on its consequence and meaning (Jones 2012). CBT uses normalisation techniques as well as behavioural techniques to reduce distress and improve functioning. It has also been proposed (Birchwood 2006) that CBT might focus upon the following:
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Distress reduction or the reduction of depression and problem behaviour associated with beliefs about psychotic symptomatology.
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The emotional and interpersonal difficulty in individuals at high risk of developing psychosis.
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Relapse prodromes to prevent relapse in psychosis.
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‘Comorbid’ depression and social anxiety, including the patient’s appraisal of the diagnosis and its stigmatising consequences.
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General stress reactivity, thereby increasing resilience to life stress and preventing psychotic relapse.
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Increasing self‐esteem and social confidence in people with psychosis.
Little research has been conducted on the effect of multi‐modal interventions which encompass yoga. When delivered as part of a package of care, it is not known whether the multi‐modal intervention dilutes, has an additive effect, or makes no difference to the effect of yoga. If yoga is combined with another form of exercise, the results may be different to yoga combined with expressive or talking therapies. Even combining yoga with another exercise, the effect could depend on frequency, intensity, time and type of exercise, and whether the focus is on skill‐related fitness, health‐related fitness, body‐mind fitness, or physical activities which are not specifically fitness focused. Expressive and talking therapies are so diverse, that if combined with yoga, their combined effect could not be generalised.
Why it is important to do this review
It was originally envisaged that one Cochrane review entitled 'Yoga for schizophrenia' would adequately map this area, but on closer evaluation it became apparent that the yoga comparison includes several distinct strands. A pragmatic decision was therefore taken to logically group comparisons into a series of independent reviews, outlined in Table 1, conducted by the same core group of authors, and to synthesise these into a future overview entitled 'Yoga for schizophrenia, an overview of Cochrane systematic reviews'.
| Review number | Review Title | Status |
| 1 | Yoga versus standard care for schizophrenia | |
| 2 | Yoga versus non‐standard care for schizophrenia | |
| 3 | Yoga as part of a package of care versus standard care | |
| 4 | Yoga as part of a package of care versus non‐standard care | Current protocol |
Due to a growing demand from patients to use alternative or adjunct treatment to their medication (Elkins 2005) and a prevalence of poor antipsychotic compliance (van Os 2009), adjunctive non‐pharmacological options are increasingly important. Yoga as a package of care is one such non‐pharmacologically‐based add‐on treatment in the management of people with schizophrenia. In resource‐constrained times the question arises ‐ is yoga delivered as a package of care more effective than another treatment ‐ i.e. non‐standard care for people with schizophrenia? This review will provide the most comprehensive answer possible to this question and may expedite the integration of yoga as a package of care into clinical practice.
Objectives
To examine the effects of yoga as part of a package of care versus non‐standard care for schizophrenia.
Methods
Criteria for considering studies for this review
Types of studies
We will consider all relevant randomised controlled trials (RCTs). We will include RCTs meeting our inclusion criteria and reporting useable data. We will also consider trials that are described as 'double‐blind' ‐ in which randomisation is implied ‐ and include or exclude once we have carried out a sensitivity analysis (see Sensitivity analysis). We will exclude quasi‐randomised studies, such as those that allocate the intervention by alternate days of the week. Where people are given additional treatments as well as yoga as a package of care, we will only include data if the adjunct treatment is evenly distributed between groups and it is only the yoga intervention that is randomised.
Types of participants
We will consider adults, however defined, with schizophrenia or related disorders, including schizophreniform disorder, schizoaffective disorder and delusional disorder, by any means of diagnosis, regardless of their gender, age or severity of their illness.
We are interested in making sure that information is as relevant as possible to the current care of people with schizophrenia, so aim to highlight the current clinical state clearly (acute, early post‐acute, partial remission, remission), as well as the stage (prodromal, first episode, early illness, persistent), and whether the studies primarily focused on people with particular problems (for example, negative symptoms, treatment‐resistant illnesses).
Types of interventions
1. Yoga as part of a package of care
We will include packages of care that combine yoga with another therapy, however defined by the study. Yoga can incorporate any of the major subtypes such as Mantra, Laya, Hatha and Raja and also include any of the combination of definitions including breathing exercises, meditation and body postures. We recognise that a package of care could include many diverse approaches that could be considered inappropriate to synthesise together. We propose the following combinations of interventions, but recognise that this may not be entirely inclusive.
1.1 Yoga plus other exercise (not including yoga)
Yoga is combined with another type of exercise. We have used the term 'other exercise' as yoga is also a type of exercise. 'Other exercise' can include broad categories of exercise focused on health‐related fitness (such as aerobic programme), mind and body fitness (such as Tai'chi) and other physical activity not necessarily focused on fitness. We recognise that these categories may not fully map this area and propose to keep each of the above categories separate as they represent quite different approaches.
1.2 Yoga plus talking therapy
Yoga is combined with a talking therapy to form a package of care. Talking therapy can include broad categories of Cognitive‐Behavioural, Humanistic, Insight‐oriented, Postmodernist, Systemic and Other. We propose to keep each of the above categories separate as they represent quite different approaches.
1.3 Yoga plus expressive therapies
Yoga is combined with expressive therapies. This can include broad categories of art therapy, dance therapy, drama therapy, music therapy and writing therapy. We propose to keep each of the above categories separate as they represent quite different approaches.
1.4 Yoga plus combination of above
2. Non‐standard care or approaches
It is accepted that non‐standard care could be considered an ambiguous term. We propose the following interventions (as described above), but recognise that this may not be entirely inclusive.
2.1 Other exercise (not including yoga)
2.2 Talking therapy
2.3 Expressive therapies
2.4 Combination of above
Should at least five trials in any of these areas of non‐standard care become available, we will carry out an independent review.
Adjunctive treatment to standard care
The yoga packages of care and non‐standard care interventions described above are in addition to the care participants would normally receive (standard care) or had previously received for the management of their schizophrenia (for example antipsychotic medication). This can also include waiting‐list control.
Types of outcome measures
We aim to divide all outcomes into short term (less than six months), medium term (seven to 12 months) and long term (over 12 months).
We will endeavour to report binary outcomes recording clear and clinically meaningful degrees of change (e.g. global impression of much improved, or more than 50% improvement on a rating scale ‐ as defined within the trials) before any others. Thereafter, we will list other binary outcomes and then those that are continuous. Of note, to ensure uniformity with the portfolio of yoga reviews under construction, the following outcomes are consistent with reviews outlined in Table 1
Primary outcomes
1. Mental state
1.1 Clinically important response in mental state (as defined by individual studies)
1.2 Average endpoint score on mental state scales
1.3 Average change scores on mental state scales
2. Global state
2.1 Relapse
2.2 Clinically important change in global state (as defined by each study)
2.3 Any change in global state
2.4 Average endpoint or change scores from global state scales
3. Social functioning
3.1 Clinically important change in social functioning (as defined by individual studies)
3.2 Average endpoint score on social functioning scales
3.3 Average change scores on social functioning scales
4. Adverse effects
4.1 Any clinically important adverse effects
Secondary outcomes
5. Quality of life
5.1 Clinically important change in quality of life functioning (as defined by individual studies)
5.2 Average endpoint score on quality of life scales
5.3 Average change scores on quality of life scales
6. Cognitive functioning
6.1 Clinically important change in cognitive functioning (as defined by individual studies)
6.2 Average endpoint score on cognitive functioning scales
6.3 Average change scores on cognitive functioning scales
7. Leaving the study early
7.1 Any reason
7.2 Due to adverse effects of intervention
7.3 Due to lack of engagement with intervention
7.4 Due to death (suicide, natural causes, other)
8. Costs of care
8.1 Direct costs of care
8.2 Indirect costs of care
9. Effect on standard care
9.1 Reduction in reported adverse effects of standard care
9.2 Change in the level of standard care required to manage condition
10. Physical health
10.1 Clinically important change in physical health (as defined by individual studies)
10.2 Any change in physical health
11. Service use
11.1 Acute hospital admissions
11.2 Length of stay in hospital
12. Disability
12.1 Important change in disability (as defined by individual studies)
13. Daily living
13.1 Clinically important change in daily living skills (as defined by individual studies)
13.2 Average endpoint score daily living scales
13.3 Average change scores on daily living scales
'Summary of findings' table
We will use the GRADE approach to interpret findings (Schünemann 2011); and will use GRADEpro GDT to export data from our review to create a 'Summary of findings' table. These tables provide outcome‐specific information concerning the overall certainty of evidence from each included study in the comparison, the magnitude of effect of the interventions examined, and the sum of available data on all outcomes we rate as important to patient care and decision making. We aim to select the following main outcomes for inclusion in the 'Summary of findings' table:
1. Mental state: clinically important response in mental state (as defined by studies)
2. Leaving the study early
3. Social functioning: clinically important change in social functioning (as defined by individual studies) (as defined by studies)
4. Quality of life: clinically important change in quality of life functioning (as defined by individual studies)
5. Physical health: clinically important change in physical health (as defined by individual studies)
6. Adverse effects: clinically important adverse effect
7. Costs of care ‐ direct and indirect
If data are not available for these pre‐specified outcomes but are available for ones that are similar, we will present the closest outcome to the pre‐specified one in the table but take this into account when grading the finding.
Search methods for identification of studies
Electronic searches
As this review is part of a series of yoga reviews and an umbrella overview, the Cochrane Schizophrenia's Information Specialist has already searched the Group's trials register in February 2015 using the following search strategy:
*Yoga* in Title, Abstract and Index Terms of REFERENCE or in Interventions of STUDY
The Cochrane Schizophrenia Group maintains a register of trials. This is compiled by systematic searches of major resources (including AMED, BIOSIS, CINAHL, Embase, MEDLINE, PsycINFO, PubMed, and registries of clinical trials) and their monthly updates, handsearches, grey literature, and conference proceedings (see Group Module). There is no language, date, document type, or publication status limitations for inclusion of records into the register. A number of studies were identified as potentially suitable for inclusion in this review and the Information Specialist re‐ran this search to ensure no new studies were missed for this review.
Searching other resources
1. Reference searching
We will inspect references of all included studies for further relevant studies.
2. Personal contact
We will contact the first author of each included study for information regarding unpublished trials. We will note the outcome of this contact in the 'Included studies' or 'Studies awaiting classification' tables.
Data collection and analysis
Selection of studies
Review authors JB and DV will independently inspect citations from the searches and identify relevant abstracts; a post‐doctoral fellow JM will independently re‐inspect a random 20% sample of these abstracts to ensure reliability of selection. Where disputes arise, we will acquire the full‐text report for more detailed scrutiny. JB will then obtain and inspect full‐text reports of the abstracts or reports meeting the review criteria. JM will re‐inspect a random 20% of these reports in order to ensure reliability of selection. Where it is not possible to resolve disagreement by discussion, we will attempt to contact the authors of the study concerned for clarification.
Data extraction and management
1. Extraction
Review authors JB and JM will extract data from all included studies. In addition, to ensure reliability, DV will independently extract data from a random sample of these studies, comprising 10% of the total. We will attempt to extract data presented only in graphs and figures whenever possible, but will include only if two review authors independently obtain the same result. If studies are multi‐centre, then where possible we will extract data relevant to each. We will discuss any disagreement and document our decisions. If necessary, we will attempt to contact authors through an open‐ended request in order to obtain missing information or for clarification. CEA (see Acknowledgements) will help clarify issues regarding any remaining problems and we will document these final decisions.
2. Management
2.1 Forms
We will extract data onto standard, pre‐designed, simple forms.
2.2 Scale‐derived data
We will include continuous data from rating scales only if:
a) the psychometric properties of the measuring instrument have been described in a peer‐reviewed journal (Marshall 2000);
b) the measuring instrument has not been written or modified by one of the trialists for that particular trial; and
c) the instrument should be a global assessment of an area of functioning and not sub‐scores which are not, in themselves, validated or shown to be reliable. However there are exceptions, we will include sub‐scores from mental state scales measuring positive and negative symptoms of schizophrenia.
Ideally, the measuring instrument should either be i. a self‐report or ii. completed by an independent rater or relative (not the therapist). We realise that this is not often reported clearly; in 'Description of studies' we will note if this is the case or not.
2.3 Endpoint versus change data
There are advantages of both endpoint and change data: change data can remove a component of between‐person variability from the analysis; however, calculation of change needs two assessments (baseline and endpoint), which can be difficult to obtain in unstable and difficult‐to‐measure conditions such as schizophrenia. We have decided primarily to use endpoint data, and only use change data if the former are not available. If necessary, we will combine endpoint and change data in the analysis, as we prefer to use mean differences (MDs) rather than standardised mean differences (SMDs) throughout (Deeks 2011).
2.4 Skewed data
Continuous data on clinical and social outcomes are often not normally distributed. To avoid the pitfall of applying parametric tests to non‐parametric data, we will apply the following standards to relevant continuous data before inclusion.
For endpoint data from studies including fewer than 200 participants:
a) when a scale starts from the finite number zero, we will subtract the lowest possible value from the mean, and divide this by the standard deviation. If this value is lower than one, it strongly suggests that the data are skewed and we will present these data as 'other data'. If this ratio is higher than one but less than two, there is suggestion that the data are skewed: we will enter these data and test whether their inclusion or exclusion would change the results substantially. Finally, if the ratio is larger than two we will include these data, because it is less likely that they are skewed (Altman 1996; Higgins 2011).
b) if a scale starts from a positive value (such as the Positive and Negative Syndrome Scale (PANSS), which can have values from 30 to 210 (Kay 1986)), we will modify the calculation described above to take the scale starting point into account. In these cases skewed data are present if 2 SD > (S − S min), where S is the mean score and 'S min' is the minimum score.
Please note: we will enter all relevant data from studies of more than 200 participants in the analysis irrespective of the above rules, because skewed data pose less of a problem in large studies. We will also enter all relevant change data, as when continuous data are presented on a scale that includes a possibility of negative values (such as change data), it is difficult to tell whether or not data are skewed.
2.5 Common measurement
To facilitate comparison between trials we aim, where relevant, to convert variables that can be reported in different metrics, such as days in hospital (mean days per year, per week or per month) to a common metric (e.g. mean days per month).
2.6 Conversion of continuous to binary
Where possible, we will make efforts to convert outcome measures to dichotomous data. This can be done by identifying cut‐off points on rating scales and dividing participants accordingly into 'clinically improved' or 'not clinically improved'. It is generally assumed that if there is a 50% reduction in a scale‐derived score such as the Brief Psychiatric Rating Scale (BPRS) (Overall 1962), or the PANSS (Kay 1986), this could be considered as a clinically significant response (Leucht 2005). If data based on these thresholds are not available, we will use the primary cut‐off presented by the original authors.
2.7 Direction of graphs
Where possible, we will enter data in such a way that the area to the left of the line of no effect indicates a favourable outcome for yoga as a package of care. Where keeping to this makes it impossible to avoid outcome titles with clumsy double‐negatives (e.g. 'not un‐improved'), we will report data where the left of the line indicates an unfavourable outcome and note this in the relevant graphs.
Assessment of risk of bias in included studies
Review authors JB and JM will work independently to assess risk of bias by using criteria described in the Cochrane Handbook for Systematic Reviews of Interventions to assess trial quality (Higgins 2011a). This set of criteria is based on evidence of associations between potential overestimation of effect and the level of risk of bias of the article that may be due to aspects of sequence generation, allocation concealment, blinding, incomplete outcome data and selective reporting, or the way in which these 'domains' are reported.
If the raters disagree, we will make the final rating by consensus. Where inadequate details of randomisation and other characteristics of trials are provided, we will attempt to contact authors of the studies in order to obtain further information. We will report non‐concurrence in quality assessment, but if disputes arise regarding the category to which a trial is to be allocated, we will resolve this by discussion.
We will note the level of risk of bias in both the text of the review, Figures, and the 'Summary of findings' table/s.
Measures of treatment effect
1. Binary data
For binary outcomes we will calculate a standard estimation of the risk ratio (RR) and its 95% confidence interval (CI), as it has been shown that RR is more intuitive than odds ratios Boissel 1999); and that odds ratios tend to be interpreted as RR by clinicians (Deeks 2000). Although the number needed to treat for an additional beneficial outcome (NNTB) and the number needed to treat for an additional harmful outcome (NNTH), with their CIs, are intuitively attractive to clinicians, they are problematic to calculate and interpret in meta‐analyses (Hutton 2009). For binary data presented in the 'Summary of findings' table/s we will, where possible, calculate illustrative comparative risks.
2. Continuous data
For continuous outcomes we will estimate MD between groups. We prefer not to calculate effect size measures (SMD). However if scales of very considerable similarity are used, we will presume there is a small difference in measurement, and we will calculate effect size and transform the effect back to the units of one or more of the specific instruments.
Unit of analysis issues
1. Cluster trials
Studies increasingly employ 'cluster randomisation' (such as randomisation by clinician or practice), but analysis and pooling of clustered data poses problems. Authors often fail to account for intra‐class correlation in clustered studies, leading to a unit‐of‐analysis error whereby P values are spuriously low, CIs unduly narrow and statistical significance overestimated (Divine 1992). This causes type I errors (Bland 1997; Gulliford 1999).
Where clustering has been incorporated into the analysis of primary studies, we will present these data as if from a non‐cluster randomised study, but adjust for the clustering effect.
Where clustering is not accounted for in primary studies, we will present data in a table, with a (*) symbol to indicate the presence of a probable unit of analysis error. We will seek to contact first authors of studies to obtain intra‐class correlation coefficients (ICCs) for their clustered data and to adjust for this by using accepted methods (Gulliford 1999).
We have sought statistical advice and have been advised that the binary data from cluster trials presented in a report should be divided by a 'design effect'. This is calculated using the mean number of participants per cluster (m) and the ICC: thus design effect = 1 + (m − 1) * ICC (Donner 2002). If the ICC is not reported, we will assume it to be 0.1 (Ukoumunne 1999).
If cluster studies have been appropriately analysed and taken ICCs and relevant data documented in the report into account, synthesis with other studies will be possible using the generic inverse variance technique.
2. Cross‐over trials
A major concern of cross‐over trials is the carry‐over effect. This occurs if an effect (e.g. pharmacological, physiological or psychological) of the treatment in the first phase is carried over to the second phase. As a consequence, participants can differ significantly from their initial state at entry to the second phase, despite a wash‐out phase. For the same reason cross‐over trials are not appropriate if the condition of interest is unstable (Elbourne 2002). As both carry‐over and unstable conditions are very likely in severe mental illness, we will only use data from the first phase of cross‐over studies.
3. Studies with multiple treatment groups
Where a study involves more than two treatment arms, if relevant, we will present the additional treatment arms in comparisons. If data are binary we will simply add these and combine within the two‐by‐two table. If data are continuous we will combine data following the formula in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). Where additional treatment arms are not relevant, we will not reproduce these data.
Dealing with missing data
1. Overall loss of credibility
At some degree of loss of follow‐up, data must lose credibility (Xia 2009). We choose that, for any particular outcome, should more than 50% of data be unaccounted for we will not reproduce these data or use them within analyses. If, however, more than 50% of those in one arm of a study are lost, but the total loss is less than 50%, we will address this within the 'Summary of findings' table/s by down‐rating quality. Finally, we will also downgrade quality within the 'Summary of findings' table/s should the loss be 25% to 50% in total.
2. Binary
In the case where attrition for a binary outcome is between 0% and 50% and where these data are not clearly described, we will present data on a 'once‐randomised‐always‐analyse' basis (an intention‐to‐treat (ITT) analysis). Those leaving the study early are all assumed to have the same rates of negative outcome as those who completed, with the exception of the outcome of death and adverse effects. For these outcomes, the rate of those who stay in the study ‐ in that particular arm of the trial ‐ will be used for those who did not. We will undertake a sensitivity analysis to test how prone the primary outcomes are to change when data only from people who complete the study to that point are compared to the ITT analysis using the above assumptions.
3. Continuous
3.1 Attrition
We will use data where attrition for a continuous outcome is between 0% and 50%, and data only from people who complete the study to that point are reported.
3.2 Standard deviations
If standard deviations (SDs) are not reported, we will try to obtain the missing values from the authors. If these are not available, where there are missing measures of variance for continuous data, but an exact standard error (SE) and CIs available for group means, and either P value or t value available for differences in mean, we can calculate SDs according to the rules described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). When only the SE is reported, SDs are calculated by the formula SD = SE * √(n). The Cochrane Handbook for Systematic Reviews of Interventions presents detailed formulae for estimating SDs from P, t or F values, CIs, ranges or other statistics (Higgins 2011). If these formulae do not apply, we will calculate the SDs according to a validated imputation method which is based on the SDs of the other included studies (Furukawa 2006). Although some of these imputation strategies can introduce error, the alternative would be to exclude a given study’s outcome and thus to lose information. Nevertheless, we will examine the validity of the imputations in a sensitivity analysis that excludes imputed values.
3.3 Assumptions about participants who left the trials early or were lost to follow‐up
Various methods are available to account for participants who left the trials early or were lost to follow‐up. Some trials just present the results of study completers; others use the method of last observation carried forward (LOCF); while more recently, methods such as multiple imputation or mixed‐effects models for repeated measurements (MMRM) have become more of a standard. While the latter methods seem to be somewhat better than LOCF (Leon 2006), we feel that the high percentage of participants leaving the studies early and differences between groups in their reasons for doing so is often the core problem in randomised schizophrenia trials. We will therefore not exclude studies based on the statistical approach used. However, by preference we will use the more sophisticated approaches, i.e. we will prefer to use MMRM or multiple‐imputation to LOCF, and we will only present completer analyses if some kind of ITT data are not available at all. Moreover, we will address this issue in the item 'Incomplete outcome data' of the 'Risk of bias' tool.
Assessment of heterogeneity
1. Clinical heterogeneity
We will consider all included studies initially, without seeing comparison data, to judge clinical heterogeneity. We will simply inspect all studies for participants who are clearly outliers or situations that we had not predicted would arise and, where found, discuss such situations or participant groups.
2. Methodological heterogeneity
We will consider all included studies initially, without seeing comparison data, to judge methodological heterogeneity. We will simply inspect all studies for clearly outlying methods which we had not predicted would arise and discuss any such methodological outliers.
3. Statistical heterogeneity
3.1 Visual inspection
We will inspect graphs visually to investigate the possibility of statistical heterogeneity.
3.2 Employing the I² statistic
We will investigate heterogeneity between studies by considering the I² statistic alongside the Chi² P value. The I² statistic provides an estimate of the percentage of inconsistency thought to be due to chance (Higgins 2003). The importance of the observed value of I² depends on the magnitude and direction of effects as well as the strength of evidence for heterogeneity (e.g. P value from Chi² test, or a confidence interval for I²). We will interpret an I² estimate greater than or equal to 50% and accompanied by a statistically significant Chi² statistic as evidence of substantial heterogeneity (Chapter 9. Cochrane Handbook for Systematic Reviews of Interventions) (Deeks 2011). When substantial levels of heterogeneity are found in the primary outcome, we will explore reasons for heterogeneity (Subgroup analysis and investigation of heterogeneity).
Assessment of reporting biases
Reporting biases arise when the dissemination of research findings is influenced by the nature and direction of results (Egger 1997). These are described in section 10.1 of the Cochrane Handbook for Systematic reviews of Interventions (Sterne 2011).
1. Protocol versus full study
We will try to locate protocols of included randomised trials. If the protocol is available, we will compare outcomes in the protocol and in the published report . If the protocol is not available, we will compare outcomes listed in the methods section of the trial report with actually reported results.
2. Funnel plot
We are aware that funnel plots may be useful in investigating reporting biases, but are of limited power to detect small‐study effects. We will not use funnel plots for outcomes where there are 10 or fewer studies, or where all studies are of similar size. In other cases, where funnel plots are possible, we will seek statistical advice in their interpretation.
Data synthesis
We understand that there is no closed argument for preference for use of fixed‐effect or random‐effects models. The random‐effects method incorporates an assumption that the different studies are estimating different, yet related, intervention effects. This often seems to be true to us and the random‐effects model takes into account differences between studies, even if there is no statistically significant heterogeneity. There is, however, a disadvantage to the random‐effects model: it puts added weight onto small studies, which often are the most biased ones. Depending on the direction of effect, these studies can either inflate or deflate the effect size. We choose to use random‐effects model for all analyses.
Subgroup analysis and investigation of heterogeneity
1. Subgroup analyses
1.1 Primary outcomes
Insert any sub‐group analysis here; or state that no sub‐group analysis is anticipated, with reasons why, or any additional information, as appropriate.
2. Investigation of heterogeneity
We will report if inconsistency is high. Firstly, we will investigate whether data have been entered correctly. Secondly, if data are correct, we will inspect the graph visually and remove outlying studies successively to see if homogeneity is restored. For this review we have decided that should this occur with data contributing to the summary finding of no more than 10% of the total weighting, we will present data. If not, we will not pool these data and will discuss any issues. We know of no supporting research for this 10% cut‐off but are investigating use of prediction intervals as an alternative to this unsatisfactory state.
When unanticipated clinical or methodological heterogeneity is obvious we will simply state hypotheses regarding these for future reviews or versions of this review. We do not anticipate undertaking analyses relating to these.
Sensitivity analysis
If there are substantial differences in the direction or precision of effect estimates in any of the sensitivity analyses listed below, we will not add data from the lower‐quality studies to the results of the higher‐quality trials, but will present these data within a subcategory. If their inclusion does not result in a substantive difference, they will remain in the analyses.
1. Implication of randomisation
If trials are described in some way as to imply randomisation, for the primary outcomes, we will pool data from the implied trials with trials that are randomised.
2. Assumptions for lost binary data
Where assumptions have to be made regarding people lost to follow‐up (see Dealing with missing data) we will compare the findings of the primary outcomes when we use our assumption compared with completer data only. If there is a substantial difference, we will report results and discuss them but continue to employ our assumption.
Where assumptions have to be made regarding missing SDs (see Dealing with missing data), we will compare the findings on primary outcomes when we use our assumption compared with completer data only. We will undertake a sensitivity analysis testing how prone results are to change when 'completer' data only are compared to the imputed data using the above assumption. If there is a substantial difference, we will report results and discuss them but continue to employ our assumption.
3. Risk of bias
We will analyse the effects of excluding trials that are at high risk of bias across one or more of the domains (see Assessment of risk of bias in included studies) for the meta‐analysis of the primary outcome.
4. Imputed values
We will also undertake a sensitivity analysis to assess the effects of including data from trials where we use imputed values for ICC in calculating the design effect in cluster‐randomised trials.
5. Fixed‐ and random‐effects
We will synthesise data using a fixed‐effect model however, we will also synthesise data for the primary outcome using a random‐effects model to evaluate whether this alters the significance of the results.
| Review number | Review Title | Status |
| 1 | Yoga versus standard care for schizophrenia | |
| 2 | Yoga versus non‐standard care for schizophrenia | |
| 3 | Yoga as part of a package of care versus standard care | |
| 4 | Yoga as part of a package of care versus non‐standard care | Current protocol |
