Mindfulness‐based interventions for adults with type 2 diabetes mellitus

Objectives

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

To assess the effects of mindfulness‐based interventions for adults with type 2 diabetes mellitus (T2D).

Background

Description of the condition

Type 2 diabetes (T2D) is a disorder resulting from an interaction between genetic, psychological and environmental factors (Snel 2012). It is characterised by a progressive loss of beta‐cell insulin secretion and is accompanied by underlying skeletal muscle and hepatic insulin resistance (Chacińska 2019). Inadequately managed T2D often leads to an array of macro‐ and microvascular complications. 

The goals of treatment for diabetes are to prevent or delay complications and to optimise quality of life (American Diabetes Association 2021a). Atherosclerotic cardiovascular disease (ASCVD) ‐ defined as coronary heart disease (CHD), cerebrovascular disease or peripheral arterial disease, is the leading cause of morbidity and mortality for people with diabetes. In the past decade, risk‐factor modification in people with diabetes resulted in decreased CHD risk and lowered ASCVD morbidity and mortality among U.S. adults with diabetes (Gaede 2008). People with diabetes are also at increased risk of heart failure (Cavender 2015). Moreover, T2D and its treatments can have a negative impact on quality of life (Bradley 2018).

Physical exercise has been shown to improve glycaemic control and to reduce visceral adiposity and triglyceride levels in people with T2D (Nield 2007; Thomas 2006). No high‐quality data were available for the efficacy of dietary interventions on T2D treatment (Nield 2007).  However, more recent data from the Look Ahead (Wing 2013) and DiRECT (Lean 2018) studies suggest that significant improvements in glycaemic control, as well as sustained weight loss can be achieved in people with T2D with intensive lifestyle interventions and total dietary replacement.  In people with an impaired glucose tolerance, interventions that include both diet and physical activity were reported to delay, or even reduce, the incidence of T2D (Hemmingsen 2017). In line with these findings, international diabetes care guidelines recommend diet, physical activity, and behavioural therapy to achieve and maintain at least a 5% weight loss, with greater weight loss possibly being beneficial for better glycaemic control and management of cardiovascular risk (American Diabetes Association 2021b).

However, individuals with T2D with comorbid obesity often cite discomfort as a barrier to exercise (Korkiakangas 2009), and similar findings have been reported in regard to diet modulation (Tomiyama 2010). Also, other factors such as an inability to recognise and respond to internal cues of hunger (Smith 2006), and impaired emotional regulation may lead to overeating through behaviours such as emotional‐ and stress‐eating (Elfhag 2005).

The emotional burden of T2D is thought to induce negative thoughts, which lead to suboptimal diabetes‐related self‐care (Safren 2014). Individuals with T2D have a two‐ to three‐fold increased prevalence of a comorbid affective disorder when compared to healthy age‐matched individuals (Anderson 2001; Rajput 2016), and this contributes to the increased hospitalisation and loss of daily functional skills and activities in people with T2D and comorbid major depression (Egede 2007). Although mechanistically unclear, it is thought that comorbid affective disorders may lead to impaired immunity, exacerbated inflammatory processes, and contribute to social adversity (Moulton 2015). Psychological distress can lead to catecholamine‐driven sympathetic hyperactivity with negative sequelae for glycaemia and cardiometabolic risk (Thayer 2010). Comorbidity with depression and anxiety has been associated with an increase in all‐cause mortality in men with T2D (but not in women), compared to individuals without either T2D or affective disorders (Naicker 2017).

Description of the intervention

Meditation refers to a broad set of practices that involve intentional self‐regulation of attention for a variety of aims including inducing relaxation, improving well‐being and emotional balance (Baer 2003; Cahn 2006; Lutz 2008). Mindfulness meditation originated from Eastern/Buddhist meditation practices and is the practice of paying attention to the present moment experience with an orientation of curiosity, openness, acceptance, non‐reactivity and non‐judgemental (Baer 2003; Bishop 2004). There are two aspects of mindfulness meditation important for the awareness that arrives when practising mindfulness meditation (Kabat‐Zinn 2013). The first is self‐regulation of attention, which involves sustained attention, attention switching, and the inhibition of elaborative processing (Bishop 2004). The other is an orientation with an attitude of curiosity and acceptance, regardless of whether an experience is seen as pleasant or unpleasant. Therefore, mindfulness meditation cultivates both awareness and equanimity, which is an even‐minded mental state or dispositional tendency towards all experiences and objects, regardless of their affective valence (Schuman‐Olivier 2020).  

Mindfulness‐based programs (MBPs) involve systematic and sustained training in formal and informal mindfulness meditation practices for both teacher and participants. This is distinct from general mindfulness‐based interventions (MBIs), which still cultivate mindfulness meditation, but do not follow the same teacher‐training process or other MBP standards (Crane 2017; Kabat‐Zinn 2003). The practice of mindfulness meditation includes encouraging individuals to attend to internal experiences occurring in each moment, such as bodily sensations, thought and emotions or to pay attention to aspects of the environment, such as sights and sounds (Baer 2003). Some examples of commonly used formal mindfulness meditation practices include the body scan, mindful movement, and sitting meditation (Crane 2017), while informal practices may include paying attention to the present moment while washing dishes, walking, or eating (Birtwell 2019). While there are no widely agreed definitions of formal and informal mindfulness meditation practices, general formal practices can be viewed as practices where practitioners intentional set aside time to engage in the practice of MM, whereas informal practices incorporate mindfulness in everyday activities (Birtwell 2019). MBIs may combine other types of meditations or therapies (e.g. compassion, loving kindness or cognitive behavioural therapy) to support mindfulness meditation practice (Schuman‐Olivier 2020).

The first MBP to be developed was a standardised intervention named Mindfulness‐Based Stress Reduction (MBSR), which integrates Buddhist insight meditation, other contemplative practices such as yoga, and modern psychological therapies about stress and stress coping (Schuman‐Olivier 2020). MBSR involves eight weeks of group classes (approximately two hours per class), an all‐day class, and 45 minutes of home practice daily which includes mindful coping strategies as well as formal meditation. During the group classes, three key mindfulness meditation practices (body scan, mindful breathing and mindful yoga) are introduced and taught, and expanded upon and refined in group discussions. Participants also practise Hatha yoga. Questions asked and difficulties raised by the participants are used to illustrate and fine‐tune the meditation instructions. Didactic material is also presented on the relationship of stress to illness (Kabat‐Zinn 1982). 

Other mindfulness‐based interventions and programs have been developed, some adapting the MBSR regimen. For example, Mindfulness‐Based Cognitive Therapy (MBCT) is a mindfulness‐based program developed by Teasdale, Segal and Williams to incorporate aspects of cognitive behavioural therapy (Segal 2013). There is now a considerable variety of MBIs, some of which may be very brief in duration, or delivered over the Internet or through mobile devices (Van Dam 2018). The intensity of MBPs may be a barrier to participation. Brief MBIs, some of which have been delivered as a single five‐minute session, have been developed in an attempt to improve accessibility and convenience (Howarth 2019). MBIs have been adapted to suit a particular population and may differ quite significantly in core structure, form, dose and delivery method compared to MBPs (Crane 2017). Additionally, a number of psychotherapies and behavioural therapies draw on mindfulness principles but do not formally employ meditation practices, such as Acceptance and Commitment Therapy (Hayes 1999). This review focusses only on interventions that utilise formal mindfulness meditation practices as a main component of the intervention and where it is the intention of the intervention to cultivate mindfulness as a primary aim. 

Adverse effects of the intervention

A meta‐analysis of adverse events from two types of MBIs – MBSR and MBCT ‐ reported that adverse events that are related to these interventions were minimal, and there was no difference found between control and intervention groups (Wong 2018). However, less than one in five randomised controlled trials (RCTs) monitored for adverse events. Other reviews on MBIs similarly concluded that adverse events were minimal or absent. Deterioration of symptoms, which was not clinically significant, has been reported, as well as an increase in anxiety or triggering of memory of an assault (Baer 2019). However, adverse events are seldom reported as highlighted by a previous systematic review which showed that only 19% of included trials had studied potential adverse events (Goyal 2014). A temporary increase in the experience of negative emotions such as depression and anxiety has been reported, which is possible when one explores inner experiences (Wong 2018). A meta‐ethnographic study reported that during the course of learning new coping strategies using mindfulness, participants became aware of their previously maladaptive coping habits and that this was perceived to be overwhelming; additionally, present‐moment awareness was occasionally frightening (Malpass 2012). 

How the intervention might work

Mindfulness‐based interventions have been shown to reduce body weight (O'Reilly 2014), stress (Chiesa 2009), symptoms of depression (Klainin‐Yobas 2012) and anxiety (Treanor 2011), and improve general psychological health (Hofmann 2011), although trials were not specifically conducted in people with T2D. There is evidence from systematic reviews that mindfulness meditation in the general population can improve obesity‐related eating behaviours and increase physical activity (Carriere 2018; Katterman 2014; Noordali 2017; Olson 2015; Rogers 2017; Ruffault 2017) by teaching participants to become more accepting of the physical discomfort of portion control and physical exercise. Reductions in catecholamines have been described after the practice of mindfulness (Kopf 2014). According to recent systematic reviews, MBIs may improve some aspects of quality of life in people with T2D, although this evidence is limited (Bogusch 2019; Ni 2020). Further to the direct effect on mental health, MBIs have also been reported to improve adoption and maintenance of diabetes‐related treatments, which is often challenging (Rushforth 2016).

Why it is important to do this review

'Lifestyle' interventions including diet and/or exercise modulation are cornerstone therapies for the prevention and management forT2D. To be efficacious, these therapies rely on adoption and sustained adherence to elicit and maintain cardiometabolic improvements. Further, many studies have reported that individuals withT2D suffer from disease‐related barriers to exercise adoption and diet modulation (Vijan 2005; Wahl 2018). It has been hypothesised that mindfulness‐based interventions may serve as the link between behaviour change adoption and long‐term adherence (Priya 2018).

The use of meditation has tripled amongst adults in the USA between 2012 and 2017 (Clarke 2018). Several systematic reviews on MBI for T2D have been conducted (Bogusch 2019; Noordali 2017), but since then further RCTs on this topic have been published. The most recent meta‐analysis only included trials that used MBPs (namely, MBSR and MBCT) (Ni 2020). It is important to also evaluate a range of MBIs for clinical effectiveness in order to inform practice. MBIs that do not require the same time commitment as MBPs, or which are delivered online provide individuals with T2D improved accessibility and convenience which may be important in the context of inability to attend for face‐to‐face classes due to distance, lack of trained instructors locally, or fatigue or other symptoms posing a barrier to participation in more intense programs. To date, these lower‐intensity MBIs have not yet been evaluated for effectiveness.  Additionally, while hyperglycaemia and insulin resistance remain hallmark features of T2D, individuals with T2D suffer disproportionally from CVD, which remains the leading cause of death globally and in T2D (Morrish 2001). Currently, only two systematic reviews on mindfulness‐based interventions. MBIs included cardiovascular risk factors such as blood pressure (Massey 2019; Noordali 2017) and these were narrative reviews, not meta‐analyses. Therefore, it is also important to evaluate MBIs for their effects in improving CVD risk factors, in order to inform clinical practice.

Objectives

To assess the effects of mindfulness‐based interventions for adults with type 2 diabetes mellitus (T2D).

Methods

Criteria for considering studies for this review

Types of studies

We will include randomised controlled trials (RCTs).

Types of participants

We will include studies on adults (older than 18 years) with type 2 diabetes mellitus (T2D).

We will exclude studies that also include participants without T2D e.g. gestational diabetes, type 1 diabetes unless the outcomes for the subgroup of people with T2D are reported separately.

Diagnostic criteria for diabetes mellitus

In order to be consistent with changes in the classification of, and diagnostic criteria for diabetes mellitus over the years, the diagnosis should be established using the standard criteria valid at the time of the study commencing (for example, Alberti 1998;  American Diabetes Association 2003; American Diabetes Association 2017). Ideally, the diagnostic criteria should have been described. We will use the study authors' definition of diabetes mellitus if necessary.

Diagnostic criteria for overweight and obese people

We will define participants with a body mass index (BMI) between 25 kg/m²and 29.9kg/m²as overweight and people with a BMI 30 kg/m² or greater as obese.

Changes in diagnostic criteria may have produced significant variability in the clinical characteristics of the participants included as well as in the results obtained (which will be investigated through sensitivity analysis).

Types of interventions

Mindfulness‐based interventions are designed to train individuals to cultivate mindfulness and incorporate its practice into daily life. For the purpose of this review, mindfulness‐based interventions will include all studies which implement an intervention that describes mindfulness meditation as the main component, utilise formal mindful meditation techniques such as the body scan, mindful breathing or mindful movement, and where the primary aim of the intervention is to cultivate mindfulness. Mindfulness must be defined as both present‐moment awareness and non‐judgement. Interventions that are based on mindfulness meditation techniques as the main component, and which include additional components such as psychotherapy will be included (including MBCT). MBIs can be of any type of delivery or frequency. We will exclude interventions described as yoga or tai chi, even though mindfulness is recognised as a central feature of these practices, unless mindfulness was specified as a main component or focus of the practice (e.g. mindful yoga). We will also exclude psychotherapy interventions as a main component that incorporate a mindfulness component. These include Acceptance and Commitment Therapy (ACT), and Dialectical Behavioural Therapy (DBT). These therapies are more commonly associated with traditional cognitive behavioural therapy, although they draw on ‘mindful’ principles within a larger suite of techniques but without an explicit focus on mindfulness meditation practice (Schuman‐Olivier 2020; Shonin 2013; Van Dam 2018). We will also exclude interventions that do not have a primary aim of cultivating mindfulness, such as Mindful Self Compassion which has the primary aim of cultivating self‐compassion (Neff 2013). These interventions are considered to be mindfulness‐informed interventions rather than mindfulness based (Schuman‐Olivier 2020). 

We plan to investigate the following comparisons of intervention versus control/comparator.

Intervention

(a) Mindfulness‐based interventions (MBIs)

(b) MBIs plus any other therapy

Comparisons

• Minimal intervention e.g. single session at baseline compared to (a)

  • Usual care

  • Waitlist control

  • Active control

  • Psychosocial interventions

• Minimal intervention plus any other therapy compared to (b)

  • Usual care plus any other therapy

  • Waitlist control plus any other therapy

  • Active control plus any other therapy

  • Psychosocial interventions plus any other therapy

Concomitant interventions will have to be the same in both the intervention and comparator groups to establish fair comparisons.

If a study includes multiple arms, we will include any arm that meets the review inclusion criteria.

Minimum duration of intervention

We will include studies of any duration.

Minimum duration of follow‐up

We will include studies with any length of follow‐up.

We will define any follow‐up period going beyond the original time frame for the primary outcome measure as specified in the power calculation of the study's protocol as an extended follow‐up period (also called 'open‐label extension study') (Buch 2011; Megan 2012).

Summary of specific exclusion criteria

We will exclude studies of the following category of participants or interventions.

• Gestational diabetes only

• Type 1 diabetes only

• Prediabetes only

• Intervention where mindfulness was not specified as a main component or focus of the practice

Types of outcome measures

We will not exclude a study if it fails to report one or several of our primary or secondary outcome measures. If none of our primary or secondary outcomes is reported in the study, we will not include the study, but will provide some basic information in the 'Characteristics of awaiting classification' table.

We will investigate the following outcomes using the methods and time points specified below.

Primary outcomes

• Health‐related quality of life

• Complications of diabetes

• Adverse events

Secondary outcomes

• All‐cause mortality

• Socioeconomic effects

• Cardiovascular risk factors

• Glycaemic control

Method of outcome measurement

• Health‐related quality of life: evaluated by validated self‐report scales. These may include psychological outcomes (anxiety, depression, diabetes‐related distress and other) evaluated by validated self‐report scales such as the Depression, Anxiety and Stress Scale 21 (Henry 2005).

• Complications of diabetes: defined as vascular complications (Ischaemic heart disease, peripheral vascular disease, with or without amputation, stroke), neuropathy (autonomic/peripheral), nephropathy, with or without dialysis/transplant, and retinopathy, with or without blindness.

• Adverse events: such as worsening of depressive symptoms.

• All‐cause mortality: defined as death from any cause.

• Socioeconomic effects: such as direct costs defined as admission or readmission rates; average length of stay; visits to general practitioner; accident or emergency visits; medication consumption; indirect costs defined as resources lost due to illness by the participant or their family member.

• Cardiovascular risk factors: defined as systolic and diastolic blood pressure, weight, body mass index (BMI), waist circumference, hip circumference, lipid levels.

• Glycaemic control: evaluated using glycosylated haemoglobin A1c (HbA1c).

Timing of outcome measurement

• For all‐cause mortality and adverse events: any time after participants were randomised to the intervention/comparator groups.

• For all other outcome measures: short term (up to 24 weeks follow‐up), medium term (follow‐up more than 24 weeks and up to 52 weeks), long term (follow‐up longer than 52 weeks).

Search methods for identification of studies

Electronic searches

We will search the following sources from the inception of each database to the date of search and will place no restrictions on the language of publication:

• Cochrane Central Register of Controlled Trials (CENTRAL) via the Cochrane Register of Studies Online (CRSO);

• MEDLINE (Ovid MEDLINE ALL 1946 to Daily Update);

• CINAHL EBSCO (Cumulative Index to Nursing and Allied Health Literature);

• PsycINFO

• Web of Science Clarivate (Science Citation Index, Social Science Citation Index, Emerging Citation Index)

• ClinicalTrials.gov (www.clinicaltrials.gov);

• World Health Organization International Clinical Trials Registry Platform (ICTRP) (www.who.int/trialsearch/);

For detailed search strategies, see Appendix 1. We will not include Embase in our search, as RCTs indexed in Embase are now prospectively added to CENTRAL via a highly sensitive screening process (Cochrane 2021).

Searching other resources

We will attempt to identify other potentially eligible studies or ancillary publications by searching the reference lists of included studies, systematic reviews, meta‐analyses, and health technology assessment reports. We will also contact the authors of included studies to obtain additional information on the retrieved studies and establish whether we may have missed further studies.

We will not use abstracts or conference proceedings for data extraction unless full data are available from study authors because this information source does not fulfil the CONSORT requirements (Scherer 2018; Schulz 2010). We will present information on abstracts or conference proceedings in the 'Characteristics of studies awaiting classification' table. We defined grey literature as records detected in ClinicalTrials.gov or the WHO ICTRP.

Data collection and analysis

Selection of studies

Two review authors (CE, AS or IAK) will independently screen the abstract, title, or both, of every record retrieved by the literature searches. We will obtain the full text of all potentially relevant records. We will resolve disagreements through consensus or by recourse to a third review author (MA). If we cannot resolve a disagreement, we will categorise the study as 'awaiting classification' and will contact the study authors for clarification. We will present an adapted PRISMA flow diagram to show the process of study selection (Liberati 2009). We will list all articles excluded after full‐text assessment in a 'Characteristics of excluded studies' table and will provide the reasons for exclusion.

Data extraction and management

For studies that fulfil our inclusion criteria, two review authors (CE, AS, MA, RM, MP, IAK) will independently extract key information on participants, interventions and comparators. We will describe interventions according to the 'template for intervention description and replication' (TIDieR) checklist (Hoffmann 2014; Hoffmann 2017).

We will report data on efficacy outcomes and adverse events using standardised data extraction sheets from the CMED Group. We will resolve disagreements by discussion or, if required, by consultation with a third review author (MA or CE). We will provide information, including the study identifier for potentially relevant ongoing trials in the 'Characteristics of ongoing trials' table and as a table entitled 'Matrix of study endpoint (publications and trial documents)'. We will attempt to find the protocol for each included study and will report in the matrix table the primary, secondary, and other outcomes from these protocols, alongside the data from the study publications. Data extraction will be performed using Microsoft Excel^® and stored online in an open repository (Zenodo).

We will email all authors of included studies to enquire whether they would be willing to answer questions regarding their studies and will present the results of this survey. We will thereafter seek relevant missing information on the study from the primary study author(s), if required.

Dealing with duplicate and companion publications

In the event of duplicate publications, companion documents, or multiple reports of a primary study, we will maximise the information yield by collating all available data, and we will use the most complete data set aggregated across all known publications. We will list duplicate publications, companion documents, multiple reports of a primary study, and trial documents of included trials (such as trial registry information) as secondary references under the study ID of the included study. Furthermore, we will list duplicate publications, companion documents, multiple reports of a study, and trial documents of excluded trials (such as trial registry information) as secondary references under the study ID of the excluded study.

Data from clinical trials registers

If data from included trials are available as study results in clinical trials registers, such as ClinicalTrials.gov or similar sources, we will make full use of this information and extract the data. If there is also a full publication of the study, we will collate and critically appraise all available data. If an included study is marked as completed in a clinical trial register but no additional information (study results or publication, or both) is available, we will add this study to the 'Characteristics of studies awaiting classification' table.

Assessment of risk of bias in included studies

Two review authors (CE, AS, MA, RM, MP) will independently assess the risk of bias for each included study. We will resolve disagreements by consensus or by consulting a third review author (MA or CE). In the case of disagreement, we will consult the remainder of the review author team and make a judgment based on consensus. If adequate information is unavailable from the study publications, study protocols, or other sources, we will contact the study authors for more detail to request missing data on risk of bias items.

We will use the Cochrane risk of bias assessment tool (Higgins 2019b), to assign assessments of low, high, or unclear risk of bias (for details, see Appendix 2; Appendix 3). We will evaluate individual bias items as described in the Cochrane Handbook for Systematic Reviews of Interventions, according to the criteria and associated categorisations contained therein (Higgins 2019b).

Summary assessment of risk of bias

We will present a risk of bias graph and a risk of bias summary figure. We will distinguish between self‐reported and investigator‐assessed and adjudicated outcome measures. We will consider the following self‐reported outcomes.

• Health‐related quality of life

• Adverse events

• Cardiovascular risk factors

We will consider the following outcomes to be investigator‐assessed.

• Complications of diabetes

• Adverse events

• All‐cause mortality

• Socioeconomic effects

• Cardiovascular risk factors

• HbA1c

Risk of bias for a study across outcomes
Some risk of bias domains, such as selection bias (sequence generation and allocation sequence concealment), affect the risk of bias across all outcome measures in a study. In case of high risk of selection bias, we will mark all endpoints investigated in the associated study as being at high risk. Otherwise, we will not perform a summary assessment of the risk of bias across all outcomes for a study.

Risk of bias for an outcome within a study and across domains

We will assess the risk of bias for an outcome measure by including all entries relevant to that outcome (i.e. both study‐level entries and outcome‐specific entries). We consider low risk of bias to denote a low risk of bias for all key domains, unclear risk to denote an unclear risk of bias for one or more key domains and high risk to denote a high risk of bias for one or more key domains.

Risk of bias for an outcome across studies and across domains

To facilitate our assessment of the certainty of evidence for key outcomes, we will assess risk of bias across studies and domains for the outcomes included in the summary of findings table. We will define the evidence as being at low risk of bias when most information comes from studies at low risk of bias, unclear risk of bias when most information comes from studies at low or unclear risk of bias, and high risk of bias when a sufficient proportion of information comes from studies at high risk of bias.

Measures of treatment effect

When at least two included studies are available for a comparison of a given outcome, we will try to express dichotomous data as a risk ratio (RR) or an odds ratio (OR) with 95% confidence intervals (CIs). For continuous outcomes measured on the same scale (e.g. weight loss in kg), we will estimate the intervention effect using the mean difference (MD) with 95% CIs. For continuous outcomes that measure the same underlying concept (e.g. health‐related quality of life) but use different measurement scales, we will calculate the standardised mean difference (SMD). We will express time‐to‐event data as a hazard ratio (HR) with 95% CIs.

Unit of analysis issues

We will take into account the level at which randomisation occurred, such as cross‐over studies, cluster‐randomised trials, and multiple observations for the same outcome. If more than one comparison from the same study is eligible for inclusion in the same meta‐analysis, we will either combine groups to create a single pair‐wise comparison, or we will appropriately reduce the sample size so that the same participants do not contribute data to the meta‐analysis more than once (splitting the 'shared' group into two or more groups). Although the latter approach offers some solution for adjusting the precision of the comparison, it does not account for correlation arising from inclusion of the same set of participants in multiple comparisons (Higgins 2019a).

We will attempt to re‐analyse cluster‐RCTs that have not appropriately adjusted for potential clustering of participants within clusters in their analyses. Variance of the intervention effects will be inflated by a design effect. Calculation of a design effect involves estimation of an intracluster correlation coefficient (ICC). We will obtain estimates of ICCs by contacting study authors, or by imputing ICC values using either estimates from other included studies that report ICCs or external estimates from empirical research (e.g. Bell 2013). We plan to examine the impact of clustering by performing sensitivity analyses.

Dealing with missing data

If possible, we will obtain missing data from the authors of included studies. We will carefully evaluate important numerical data such as screened, randomly‐assigned participants, as well as intention‐to‐treat and as‐treated and per‐protocol populations. We will investigate attrition rates (e.g. dropouts, losses to follow‐up, withdrawals), and we will critically appraise issues concerning missing data and use of imputation methods (e.g. last observation carried forward (LOCF)).

For studies in which the standard deviation (SD) of the outcome is not available at follow‐up, or we cannot re‐create it, we will standardise by the mean of the pooled baseline SD from studies that reported this information.

When included studies do not report means and SDs for outcomes, and we do not receive requested information from study authors, we will impute these values by estimating the mean and the variance from the median, the range and the size of the sample (Hozo 2005).

We will investigate the impact of imputation on meta‐analyses by performing sensitivity analyses, and we will report for every outcome which studies had imputed SDs.

Assessment of heterogeneity

In the event of substantial clinical or methodological heterogeneity, we will not report study results as the pooled effect estimate in a meta‐analysis.

We will identify heterogeneity (inconsistency) by visually inspecting the forest plots and by using a standard Chi² test with a significance level of α = 0.1 (Deeks 2021). In view of the low power of this test, we will also consider the I² statistic, which quantifies inconsistency across studies, to assess the impact of heterogeneity on the meta‐analysis (Higgins 2002; Higgins 2003).

When we find heterogeneity, we will attempt to determine possible reasons for this by examining individual study and subgroup characteristics.

Assessment of reporting biases

If we include 10 or more studies that investigate a particular outcome, we will use funnel plots to assess small‐study effects. Several explanations may account for funnel plot asymmetry, including true heterogeneity of effect with respect to study size, poor methodological design (and hence bias of small studies), and selective non‐reporting (Kirkham 2010). Therefore, we will interpret the results carefully (Sterne 2011).

Data synthesis

We plan to undertake (or display) a meta‐analysis only if we judge the participants, interventions, comparisons and outcomes to be sufficiently similar to ensure a result that is clinically meaningful. Unless good evidence shows homogeneous effects across studies of different methodological quality, we will primarily summarise data that are of low risk of bias using a random‐effects model (Wood 2008). We will interpret random‐effects meta‐analyses with due consideration for the whole distribution of effects and will present a prediction interval (Borenstein 2017; Borenstein 2009; Higgins 2009). A prediction interval requires 10 studies to be calculated and specifies a predicted range for the true treatment effect in an individual study (Riley 2011). For rare events (such as event rates below 1%) we will use the Peto odds ratio method, provided there is no substantial imbalance between intervention and comparator group sizes, and intervention effects are not exceptionally large. In addition, we will perform statistical analyses according to the statistical guidelines presented in the Cochrane Handbook for Systematic Reviews of Interventions (Deeks 2021).

Subgroup analysis and investigation of heterogeneity

We expect the following characteristics to introduce clinical heterogeneity, and we plan to carry out subgroup analyses for these, including investigation of interactions (Altman 2003).

• Participants:

  • gender.

• Intervention:

  • mode of delivery (face‐to‐ face versus no face‐to‐face);

  • type of mindfulness intervention: MBSR, MBCT, mindful yoga, other (not adapted from MBSR), brief intervention (<30 minutes on any one occasion, <100 minutes per week, up to four weeks duration)(Howarth 2019).

• Control: 

  • type of control (minimal intervention, usual care, wait list control, active control, psychosocial intervention).

 

Sensitivity analysis

When applicable, we plan to explore the influence of important factors on effect sizes, by performing sensitivity analyses in which we restrict the analyses to the following.

• Published studies.

• Studies with low risk of bias, as specified in the Assessment of risk of bias in included studies section.

• Very long or large studies, to establish the extent to which they dominate the results.

We will use of the following filters, if applicable: diagnostic criteria, imputation used, language of publication (English versus other languages), source of funding (industry versus other), or country (depending on data).

We will also test the robustness of results by repeating the analyses using different measures of effect size (i.e. RR, OR, etc.) and different statistical models (fixed‐effect and random‐effects models).

Summary of findings and assessment of the certainty of the evidence

We will present the overall certainty of the evidence for each outcome specified below, according to the GRADE approach, which takes into account issues related to internal validity (risk of bias, inconsistency, imprecision, publication bias) and external validity (such as directness of results). Two review authors (CE, AS) will independently rate the certainty of evidence for each outcome. We will resolve any differences in assessment by discussion or by consultation with a third review author (MA).

We will include an appendix entitled 'Checklist to aid consistency and reproducibility of GRADE assessments', to help with standardisation of the summary of findings tables (Meader 2014). Alternatively, we will use GRADEpro GDT software and will present evidence profile tables as an appendix (GRADEproGDT 2015). If meta‐analysis is not possible, we will present the results in a narrative format in the summary of findings table. We will justify all decisions to downgrade the certainty of the evidence by using footnotes, and we will make comments to aid the reader's understanding of the Cochrane Review when necessary.

Summary of findings table

We will present a summary of the evidence in a summary of findings table. This will provide key information about the best estimate of the magnitude of effect, in relative terms and as absolute differences for each relevant comparison of alternative management strategies; the numbers of participants and studies addressing each important outcome; and a rating of overall confidence in effect estimates for each outcome. We will create the summary of findings table using the methods described in the Cochrane Handbook for Systematic Reviews of Interventions (Schünemann 2019), along with Review Manager 5 software (Review Manager 2020).

Interventions presented in the summary of findings table will be MBSR, MBCT, mindful yoga, and other, the comparators will be minimal intervention, usual care, wait list control, and active control.

We will report the following outcomes, listed according to priority.

• Health‐related quality of life

• Complications of diabetes

• Adverse events

• All‐cause mortality

• Socioeconomic effects

• Cardiovascular risk factors

• HbA1c

What's new

Date	Event	Description
6 October 2016	Amended	Please note: This protocol template refers to the Cochrane Metabolic and Endocrine Disorders (CMED) Group and the official Cochrane MECIR (Methodological Expectations of Cochrane Intervention Reviews) standards (standards for the conductandstandards for the reportingof Cochrane intervention reviews) ‐ see 'Guidance' pane Please do not forget to check on your protocol draft with the help of the protocol submission form before checking in your finalprotocol draft for editorial approval! No standard items, appendices or title of this protocol template may be deleted without prior approval of the CMED Group!

Notes

We have based parts of the Methods, as well as Appendix 1, Appendix 2, and Appendix 3 of this Cochrane protocol, on a standard template established by the CMED Group.

Appendices

Appendix 1. Search strategies

Cochrane Central Register of Controlled Trials (Cochrane Register of Studies Online)

1. MESH DESCRIPTOR Diabetes Mellitus, Type 2 EXPLODE ALL TREES    2. (MODY or NIDDM or T2DM or T2D):TI,AB,KY 3. diabet*:TI,AB,KY  4. #1 OR #2 OR #3  5. MESH DESCRIPTOR Mindfulness 6. MESH DESCRIPTOR Mind‐Body Therapies 7. MESH DESCRIPTOR Meditation 8. mindfulnes*:TI,AB,KY 9. (mind‐body ADJ3 (therap* OR program* OR medicin*)):TI,AB,KY 10. meditation:TI,AB,KY 11. (relaxation ADJ3 (technique* or therap*)):TI,AB,KY 12. (MBSR or MBCT):TI,AB,KY 13. #5 OR #6 OR #7 OR #8 OR #9 OR #10 OR #11 OR #12 14. #4 AND #13

MEDLINE (Ovid SP)

1. exp Diabetes Mellitus, Type 2/     2. (MODY or NIDDM or T2DM or T2D).tw.     3. diabet*.tw.  4. or/1‐3  5. Mindfulness/ 6. Mind‐Body Therapies/ 7. Meditation/ 8. mindfulnes*.tw. 9. (mind‐body adj2 (therap* OR program* OR medicin*)).tw. 10. meditation.tw. 11. (relaxation adj2 (technique* or therap*)).tw. 12. (MBSR or MBCT).tw.  13. or/5‐12 14. 4 and 13 [Cochrane Handbook 2021 RCT filter ‐ sensitivity maximizing version] 15. randomized controlled trial.pt.  16. controlled clinical trial.pt.  17. randomi?ed.ab.  18. placebo.ab.  19. drug therapy.fs.  20. randomly.ab.  21. trial.ab.  22. groups.ab.  23. or/15‐22  24. exp animals/ not humans/  25. 23 not 24  26. 14 and 25

CINAHL (EBSCO)

1. MH "Diabetes Mellitus, Type 2+"    2. TI (MODY OR NIDDM OR T2DM OR T2D) OR AB (MODY OR NIDDM OR T2DM OR T2D)   3. TI (diabet*) OR AB (diabet*)  4. S1 OR S2 OR S3 5. MH "Mindfulness" 6. MH "Mind Body Techniques" 7. MH "Meditation" 8. TI (mindfulnes*) OR AB (mindfulnes*) 9. TI (mind‐body N2 (therap* OR program* OR medicin*)) OR AB (mind‐body N2 (therap* OR program* OR medicin*)) 10. TI (meditation) OR AB (meditation) 11. TI (relaxation N2 (technique* OR therap*)) OR AB (relaxation N2 (technique* OR therap*)) 12. TI (MBSR OR MBCT) OR AB (MBSR OR MBCT) 13. S5 OR S6 OR S7 OR S8 OR S9 OR S10 OR S11 OR S12 14. S4 AND S13 [Wong 2006 "therapy studies" filter ‐ SDSSGS version]  15. MH "treatment outcomes+" OR MH "experimental studies+" or random* 16. S14 AND S15

PsycINFO (Ovid)

1. Type 2 Diabetes/    2. (MODY or NIDDM or T2DM or T2D).tw.     3. diabet*.tw.  4. or/1‐3  5. Mindfulness/ 6. Mindfulness‐Based Interventions/ 7. Meditation/ 8. mindfulnes*.tw. 9. (mind‐body adj2 (therap* OR program* OR medicin*)).tw. 10. meditation.tw. 11. (relaxation adj2 (technique* or therap*)).tw. 12. (MBSR or MBCT).tw.  13. or/5‐12 14. 4 and 13 [Eady 2008 "PsycInfo Search Strategies" filter ‐ BS version]  15. control*.tw. OR random*.tw. OR exp Treatment/ 16. 14 and 15

Web of Science

1. AB=(diabet* OR MODY OR NIDDM OR T2DM OR T2D) AND (mindfulnes* OR meditation OR ("mind‐body" NEAR/3 (therap* OR program* OR medicin*)) OR (relaxation NEAR/3 (technique* OR therap*)) OR MBSR or MBCT) OR TI=(diabet* OR MODY OR NIDDM OR T2DM OR T2D) AND (mindfulnes* OR meditation OR ("mind‐body" NEAR/3 (therap* OR program* OR medicin*)) OR (relaxation NEAR/3 (technique* OR therap*)) OR MBSR or MBCT)   2. AB=(random* OR placebo OR trial OR groups OR "phase 3" or "phase3" or p3 or "pIII") OR TI=(random* OR placebo OR trial OR groups OR "phase 3" or "phase3" or p3 or "pIII")   3. #1 AND #2 Indexes=SCI‐EXPANDED, SSCI, ESCI Timespan=All years

WHO ICTRP Search Portal (Standard search)

diabet* AND mindfulnes* OR diabet* AND meditati* OR diabet* AND relax* OR diabet* AND MBSR* OR diabet* AND MBCT* OR diabet* AND mind* AND body* OR T2D* AND mindfulnes* OR T2D* AND meditati* OR T2D* AND relax* OR T2D* AND MBSR* OR T2D* AND MBCT* OR T2D* AND mind* AND body* OR

ClinicalTrials.gov (Advanced search)

Condition or disease: diabetes OR diabetic OR MODY OR NIDDM OR T2DM OR T2D  Intervention/treatment: mindfulness OR meditation OR "mind body" OR relaxation OR MBSR OR MBCT

Appendix 2. Risk of bias assessment

'Risk of bias' domains

Random sequence generation (selection bias due to inadequate generation of a randomised sequence) For each included study, we will describe the method used to generate the allocation sequence in sufficient detail to allow an assessment of whether it should produce comparable groups. Low risk of bias: study authors achieved sequence generation using computer‐generated random numbers or a random numbers table. Drawing of lots, tossing a coin, shuffling cards or envelopes, and throwing dice are adequate if an independent person performed this who was not otherwise involved in the study. We will consider the use of the minimisation technique as equivalent to being random. Unclear risk of bias: insufficient information about the sequence generation process. High risk of bias: the sequence generation method was non‐random or quasi‐random (e.g. sequence generated by odd or even date of birth; sequence generated by some rule based on date (or day) of admission; sequence generated by some rule based on hospital or clinic record number; allocation by judgment of the clinician; allocation by preference of the participant; allocation based on the results of a laboratory test or a series of tests; or allocation by availability of the intervention). Allocation concealment (selection bias due to inadequate concealment of allocation prior to assignment) We will describe for each included study the method used to conceal allocation to interventions prior to assignment, and we will assess whether intervention allocation could have been foreseen in advance of or during recruitment or changed after assignment. Low risk of bias: central allocation (including telephone, interactive voice‐recorder, internet‐based and pharmacy‐controlled randomisation); sequentially numbered drug containers of identical appearance; sequentially numbered, opaque, sealed envelopes. Unclear risk of bias: insufficient information about the allocation concealment. High risk of bias: used an open random allocation schedule (e.g. a list of random numbers); assignment envelopes used without appropriate safeguards; alternation or rotation; date of birth; case record number; any other explicitly unconcealed procedure. We will also evaluate study baseline data to incorporate assessment of baseline imbalance into the 'Risk of bias' judgement for selection bias (Corbett 2014). Chance imbalances may also affect judgements on the risk of attrition bias. In the case of unadjusted analyses, we will distinguish between studies that we rate as being at low risk of bias on the basis of both randomisation methods and baseline similarity, and studies that we judge as being at low risk of bias on the basis of baseline similarity alone (Corbett 2014). We will reclassify judgements of unclear, low, or high risk of selection bias as specified in Appendix 3.   Blinding of participants and study personnel (performance bias due to knowledge of the allocated interventions by participants and personnel during the study) We will evaluate the risk of detection bias separately for each outcome (Hróbjartsson 2013). We will note whether endpoints were self‐reported, investigator‐assessed, or adjudicated outcome measures (see below). Low risk of bias: blinding of participants and key study personnel was ensured, and it was unlikely that the blinding could have been broken; no blinding or incomplete blinding, but we judge that the outcome is unlikely to have been influenced by lack of blinding. Unclear risk of bias: insufficient information about the blinding of participants and study personnel; the study does not address this outcome. High risk of bias: no blinding or incomplete blinding, and the outcome is likely to have been influenced by lack of blinding; blinding of study participants and key personnel attempted, but likely that the blinding could have been broken, and the outcome is likely to be influenced by lack of blinding. Blinding of outcome assessment (detection bias due to knowledge of the allocated interventions by outcome assessment) We will evaluate the risk of detection bias separately for each outcome (Hróbjartsson 2013). We will note whether endpoints were self‐reported, investigator‐assessed, or adjudicated outcome measures (see below). Low risk of bias: blinding of outcome assessment is ensured, and it is unlikely that the blinding could have been broken; no blinding of outcome assessment, but we judge that the outcome measurement is unlikely to have been influenced by lack of blinding. Unclear risk of bias: insufficient information about the blinding of outcome assessors; the study did not address this outcome. High risk of bias: no blinding of outcome assessment, and the outcome measurement was likely to have been influenced by lack of blinding; blinding of outcome assessment, but likely that the blinding could have been broken, and the outcome measurement was likely to be influenced by lack of blinding. Incomplete outcome data (attrition bias due to quantity, nature or handling of incomplete outcome data) For each included study or each outcome, or both, we will describe the completeness of data, including attrition and exclusions from the analyses. We will state whether the study reported attrition and exclusions, and we will report the number of participants included in the analysis at each stage (compared with the number of randomised participants per intervention/comparator groups). We will also note if the study reported the reasons for attrition or exclusion, and whether missing data were balanced across groups or were related to outcomes. We will consider the implications of missing outcome data per outcome such as high dropout rates (e.g. above 15%) or disparate attrition rates (e.g. difference of 10% or more between study arms). Low risk of bias: no missing outcome data; reasons for missing outcome data unlikely to be related to true outcome (for survival data, censoring unlikely to introduce bias); missing outcome data balanced in numbers across intervention groups, with similar reasons for missing data across groups; for dichotomous outcome data, the proportion of missing outcomes compared with observed event risk was not enough to have a clinically relevant impact on the intervention effect estimate; for continuous outcome data, plausible effect size (mean difference or standardised mean difference) among missing outcomes was not enough to have a clinically relevant impact on observed effect size; appropriate methods, such as multiple imputation, were used to handle missing data. Unclear risk of bias: insufficient information to assess whether missing data in combination with the method used to handle missing data were likely to induce bias; the study did not address this outcome. High risk of bias: reason for missing outcome data was likely to be related to true outcome, with either imbalance in numbers or reasons for missing data across intervention groups; for dichotomous outcome data, the proportion of missing outcomes compared with observed event risk enough to induce clinically relevant bias in the intervention effect estimate; for continuous outcome data, plausible effect size (mean difference or standardised mean difference) among missing outcomes enough to induce clinically relevant bias in observed effect size; 'as‐treated' or similar analysis done with substantial departure of the intervention received from that assigned at randomisation; potentially inappropriate application of simple imputation. Selective reporting (reporting bias due to selective outcome reporting) We will assess outcome reporting bias by integrating the results of the appendix 'Matrix of study endpoints (publications and trial documents)' (Boutron 2014; Jones 2015; Mathieu 2009), with those of the appendix 'High risk of outcome reporting bias according to the Outcome Reporting Bias In Trials (ORBIT) classification' (Kirkham 2010). This analysis will form the basis for the judgement of selective reporting.   Low risk of bias: the study protocol was available and all the study's prespecified (primary and secondary) outcomes that were of interest to this review were reported in the prespecified way; the study protocol was unavailable, but it was clear that the published reports included all expected outcomes (ORBIT classification). Unclear risk of bias: insufficient information about selective reporting. High risk of bias: not all the study's prespecified primary outcomes were reported; one or more primary outcomes were reported using measurements, analysis methods, or subsets of the data (e.g. subscales) that were not prespecified; one or more reported primary outcomes were not prespecified (unless clear justification for their reporting was provided, such as an unexpected adverse effect); one or more outcomes of interest in the Cochrane Review were reported incompletely so that we cannot enter them into a meta‐analysis; the study report failed to include results for a key outcome that we would expect to have been reported for such a study (ORBIT classification). Other bias Low risk of bias: the study appears to be free from other sources of bias. Unclear risk of bias: information was insufficient to assess whether an important risk of bias existed; insufficient rationale or evidence that an identified problem introduced bias. High risk of bias: the study had a potential source of bias related to the specific study design used; the study was claimed to be fraudulent; or the study had some other serious problem.

Appendix 3. Selection bias decisions

Selection bias decisions for studies that reported unadjusted analyses: comparison of results obtained using method details alone versus results obtained using method details and study baseline informationa
Reported randomisation and allocation concealment methods	'Risk of bias' judgement using methods reporting	Information gained from study characteristics data	'Risk of bias' using baseline information and methods reporting
Unclear methods	Unclear risk	Baseline imbalances present for important prognostic variable(s)	High risk
		Groups appear similar at baseline for all important prognostic variables	Low risk
		Limited or no baseline details	Unclear risk
Would generate a truly random sample, with robust allocation concealment	Low risk	Baseline imbalances present for important prognostic variable(s)	Unclear riskb
		Groups appear similar at baseline for all important prognostic variables	Low risk
		Limited baseline details, showing balance in some important prognostic variablesc	Low risk
		No baseline details	Unclear risk
Sequence is not truly randomised or allocation concealment is inadequate	High risk	Baseline imbalances present for important prognostic variable(s)	High risk
		Groups appear similar at baseline for all important prognostic variables	Low risk
		Limited baseline details, showing balance in some important prognostic variablesc	Unclear risk
		No baseline details	High risk
aTaken from Corbett 2014; judgements highlighted in bold indicate situations in which the addition of baseline assessments would change the judgement about risk of selection bias compared with using methods reporting alone. bImbalance was identified that appears likely to be due to chance. cDetails for the remaining important prognostic variables are not reported.

Contributions of authors

All review authors contributed to, read and approved the final protocol draft.

Sources of support

Internal sources

• Salary, Australia

  CE, AS, MA and MP were supported by Western Sydney University as part of their normal academic duties.

• PhD Scholarship, Australia

  RM is supported by a PhD scholarship from the University of Melbourne (Grant number is not applicable).

External sources

• PhD scholarship ‐ Australian Rotary Health, Other

  RM is supported by a PhD scholarship from Australian Rotary Health (Grant number is not applicable).

• Endowment, Australia

  CE's salary is supported by an endowment from the Jacka Foundation of Natural Therapies

Declarations of interest

Carolyn C Ee (CE), Angelo Sabag (AS) and Mike Armour: as a medical research institute, NICM Health Research Institute receives research grants and donations from foundations, universities, government agencies, and industry. Sponsors and donors provide untied and tied funding for work to advance the vision and mission of the Institute. The project that is the subject of this article was not undertaken as part of a contractual relationship with any donor or sponsor.

Rita McMorrow: none known.

Milan Piya: Dr Milan K Piya has received honoraria for speaking for Novo Nordisk, UCB Australia and Shire Pharmaceuticals.
 

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