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Cochrane Database of Systematic Reviews Protocol - Intervention

Self‐management education programmes for rheumatoid arthritis

Authors' declarations of interest

Version published: 14 October 2014 Version history

https://doi.org/10.1002/14651858.CD011338
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Abstract

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

To assess the benefits and harms of self‐management education programmes for people with rheumatoid arthritis (RA).

Background

Description of the condition

Rheumatoid arthritis (RA) is a chronic, systemic, inflammatory, autoimmune disease, characterised by joint pain, swelling and stiffness (Bell 1998). RA affects synovial joints as well as a number of other tissues and organs, such as the heart, lungs and eyes (NICE 2009). Common symptoms include pain, fatigue, swelling, and tenderness in joints; morning stiffness, functional limitations (disability) and depression has also been shown to be higher than in the general population (Dickens 2003; St Clair 2004; Hewlett 2005). RA can also be associated with a high risk of cardiovascular disease (Van Doornum 2002; Michaud 2011). Functional status is associated with work disability and healthcare resource use as shown in data from a US national cohort (Allaire 2009).

Tumour necrosis factor (TNF) inhibitors and other biological agents have heralded a so‐called therapeutic revolution, transforming the outlook for patients with rheumatoid arthritis (Singh 2009; Lethaby 2013). However, improved disease outcomes preceded biological agents, reflecting early use of conventional drugs, ambitious treatment goals, and better management of comorbidities (Scott 2010). Because of the partial effects of treatment, the nature of pain and disability, and the unpredictability that people with arthritis face on a daily basis, however, self‐management education programmes for patients have become a complement to traditional medical treatment (Guillemin 2005).

Description of the intervention

Self‐management education programmes are an approach increasingly used in chronic disease care to improve self efficacy and wellness behaviors in the affected individual (Lorig 1993). Barlow et al. defined self‐management as "the ability to manage the symptoms, treatment, physical and psychosocial consequences, and lifestyle changes inherent in living with a chronic condition" (Barlow 2000). Self‐management education programmes are distinct from simple patient education or skills training, in that they are designed to allow people with chronic conditions to take an active part in the management of their own condition. These programmes typically emphasise self‐efficacy and problem‐solving (Bodenheimer 2002). Self‐management education programmes are complex behavioural interventions typically comprising a package of psychosocial and cognitive‐behavioral interventions and specifically targeted at patient education and behaviour modification (Kroon 2014).

Programmes in RA can vary in the content used to educate patients about their condition and how they can best manage their symptoms. Substantial variation exists in the delivery of self‐management education programmes, such as the mode (face‐to face, Internet, telephone), the audience (group, individual), the duration (single session, several months, ongoing), the frequency (once a week, once every two months) and the personnel (healthcare professionals, lay leaders) (Foster 2007). RA self‐management education programmes can also be delivered by different healthcare providers including nurses, psychologists, physical therapists, occupational therapists, podiatrists, social workers and dieticians (Hauptig 1999; Osborne 2007; Grondal 2008; Sjöquist 2010; Graham 2012).

How the intervention might work

A variety of terms in the literature are used to describe self‐management, including self‐care, self‐monitoring, self‐help and social support (Walker 2003). It is thought that the self‐management education programmes are best delivered in a prescriptive and structured format, providing both disease‐specific education and self‐management strategies, and at a minimum the following core steps provide a platform for behavior change (Lorig 2003; Osborne 2004):

  1. engaging in activities that promote health and prevent adverse sequelae;

  2. interacting with healthcare providers;

  3. improved self‐monitoring of physical and emotional status; and

  4. managing the effects of illness on a person’s ability to function in important roles and on emotions, self esteem, and relationships with others (Von Korff 1997).

The skills required for these tasks include problem solving, decision making, finding and utilising resources, forming partnerships with healthcare workers and taking action (Lorig 2003).

Assessing the characteristics and impact of self‐management education programmes

As outlined in a previous review of self‐management education programmes for osteoarthritis (Kroon 2014), studies of self‐management education programmes have varied widely in their attempts to quantify the potential impact of these programmes on participant health and well‐being. This has resulted in significant heterogeneity in outcome assessment across studies and has contributed to inconsistencies in reported effectiveness of programmes. Understanding which outcomes are most relevant to assessment of the effectiveness of self‐management education programmes is required, so that programmes can be assessed systematically on the basis of outcomes that we know are important to participants.

The Arthritis Self‐Efficacy Scale (ASES) was the first arthritis‐specific instrument developed to measure the effects of arthritis self‐management programmes (Lorig 1989). It consists of three subscales (pain, function and other symptoms) and includes efficacy expectation items that ask individuals how certain they are that they can perform a specific activity, for example, walking 100 feet on flat ground in 7 seconds; as well as performance attainment items, for example, how certain they are that they can control their fatigue or deal with the frustration of arthritis. Although these items capture an individual’s ability to self‐manage and therefore are useful in measuring outcomes of self‐management education programmes, the validity of the ASES as a true self‐efficacy measure has been questioned (Brady 1997; Brady 2011). Although the ASES includes items pertaining to efficacy expectations, it does not ask about an individual’s confidence that different behaviours will produce the desired outcome (outcome expectations); an integral component of Bandura’s theory of self‐efficacy (Bandura 1977). In addition, the function subscale items appear to capture perceived physical function rather than self‐efficacy belief.

The Health Education Impact Questionnaire (heiQ) was developed to capture key indicators of effective self‐management interventions from the patient perspective (Osborne 2007). It includes eight independent domains, namely: health‐directed behaviour; positive and active engagement in life; emotional well‐being; self‐monitoring and insight; constructive attitudes and approaches; skill and technique acquisition; social integration and support; and health service navigation.The constructs used in the heiQ have been shown as robust across a range of settings (Nolte 2007; Nolte 2009).

Why it is important to do this review

The ability of interventions based on self‐regulation to improve physical, psychological and behavioural outcomes has been demonstrated among populations with chronic diseases, e.g., coronary heart disease (Chodosh 2005; Coleman 2008), asthma and renal disease (Christensen 2002). However, while self‐management education support is recognised as a core component of patient‐centered services for chronic illness care and recommended in guidelines for people with RA, self‐management education programmes are underutilised by people with arthritis, and there is little scientific evidence to support their effectiveness. A previous Cochrane review by Riemsa et al. assessed the effectiveness of “education programmes” for RA patients and showed that the benefits of such education on disability, joint counts, patients’ global assessment, psychological status, and depression were small and short‐lived (Riemsma 2003). However, this review was published more than 10 years ago, and despite the publication of many trials since that time, it has not been updated.

To be useful in practice, evidence should show that self‐management education programmes improve an individual's ability to self‐manage, and improve functional, psychological, and social outcomes for people with RA.

Objectives

To assess the benefits and harms of self‐management education programmes for people with rheumatoid arthritis (RA).

Methods

Criteria for considering studies for this review

Types of studies

We will include randomised controlled trials (RCTs), or quasi‐RCTs in which group assignments were determined through methods other than true randomisation (i.e. alternate assignment).

Only trials that are published as full articles or are available as a full trial report will be included.

Types of participants

We will include studies involving adults (over 18 years old, but with no upper age limit) diagnosed with clinical confirmation of the diagnosis of RA or meeting the RA criteria of the 1987 American College of Rheumatology Classification (ACR) (Arnett 1988), or the 2010/2009 ACR criteria for classification of RA (Aletaha 2010), whichever is used by the authors of the studies

Studies involving people with conditions other than RA (i.e. mixed populations) will be included only if outcomes for people with RA are presented separately or if separate data are available upon request from the study authors.

Types of interventions

To be consistent with a recent review of self‐management education programmes for people with osteoarthritis (Kroon 2014), we will consider the following interventions:

Experimental treatment: self‐management education programmes
Content

We will include structured self‐management education programmes as defined by Lorig et al. (Lorig 2003) and Osborne et al. (Osborne 2004), that are judged as being primarily educational and address self‐management of RA, or arthritis in general.

Programme components that directly address self‐management may include fostering skills in managing RA, such as problem solving, goal setting, decision making, self‐monitoring, coping with the condition, or interventions to manage pain or improve physical and psychological functioning.

We will exclude interventions that are judged as treating the participants as passive recipients of care (e.g. provision of information alone). Studies that focus solely on exercises will also be excluded.

Modalities (mode, audience, duration, frequency, personnel)

Structured programmes delivered by health professionals, or a combination of lay leaders and health professionals both will be included irrespective of whether the programme was delivered to a group of participants or on an individual basis. We will exclude studies describing interventions for self‐management that are not delivered within a structured format or some form of organised content delivery.

All modes of delivery will be included such as face‐to‐face interventions and interventions delivered by post, internet, or telephone, provided they incorporate an iterative process of interaction between the participant and programme facilitators. Programmes that incorporate education for carers or relatives will also be included provided the intervention is primarily intended for the person with RA.

Comparator

Studies that compare outcomes of people with RA assigned to a self‐management education programme versus those of individuals who did not receive a self‐management educational intervention (i.e. information only, no treatment, usual care, waiting list control, or alternative interventions not considered self‐management (i.e. education programmes alone)) will be eligible for inclusion.

We will include studies that include a co‐interventions, provided that the comparison group received the same co‐intervention.

We will exclude studies that compare one type of self‐management programme versus another.

Types of outcome measures

We selected outcome measures on the basis of their relevance to self‐management and as recommended by international experts of self‐management assessment (Mulligan 2005; Osborne 2007).

Main outcomes

The following outcomes were selected as the most relevant and will be included in the 'Summary of findings' tables:

  • Self‐management of RA (participant’s self‐monitoring and insight into living with RA)

  • Participant’s positive and active engagement in life (including return to work, fulfilling his or her role within the family)

  • Self‐reported function (e.g. function as measured on the Health Assessment Questionnaire (HAQ))

  • Pain, e.g. as measured on the Pain visual analogue scale

  • Fatigue, e.g. as measured on a visual analogue scale or other self‐report measures (Hewlett 2007)

  • Quality of life (including participant‐reported general health status)

  • Withdrawals (including withdrawals related or unrelated to the study intervention (dropouts) and individuals lost to follow‐up)

Other outcomes

We will also include the following outcomes, which are relevant to the impact of self‐management:

  • Emotional distress (including depression, anxiety, stress)

  • Health‐directed activity (including adherence, exercise, diet, relaxation)

  • Social integration and support (including social participation, social network, social input)

  • Health service navigation (visits to healthcare professionals, emergency room visits, hospital admissions, length of stay)

  • Skill and technique acquisition (including knowledge about the condition and how symptoms can be managed)

  • Constructive attitude and approaches (including changes in perceived impact of RA on participant’s life)

  • Participant satisfaction

  • Disease activity indice (including number of swollen and painful joints, patient global assessment score, self report‐physician global assessment, acute phase reactants)

  • Serious adverse events defined as: inpatient hospitalisation, life‐threatening events or death

  • Adverse effects associated with the intervention

In keeping with the previous review of self‐management education programmes for people with osteoarthritis (Kroon 2014), we considered whether to include self‐efficacy as a separate outcome. Arthritis‐specific self‐efficacy measures, particularly the ASES (Lorig 1989), are commonly used outcome measures in trials of self‐management education programmes. However, as these tools may not comprehensively capture all aspects of self‐efficacy theory and may include items measuring performance attainment, we considered an important or meaningful distinction between the outcome of ’self‐management’ and ’self efficacy’ to be insufficient to justify treating these as separate outcomes in the review. We therefore included arthritis self‐efficacy scales within the primary outcome of self‐management.

Timing of outcome assessment

We will use data from the outcome assessment conducted immediately (up to six weeks from the start of the intervention), intermediate (measured at greater than six weeks up to and including a year after the intervention) and longer‐term outcomes (longer than one year after the intervention).

Search methods for identification of studies

Electronic searches

We will search the following electronic databases for primary studies from inception up to the date of the search:

  • The Cochrane Central Register of Controlled Trials (CENTRAL) in The Cochrane Library

  • MEDLINE via Ovid

  • EMBASE via Ovid

  • CINAHL via EBSCOhost

  • PsycINFO

The 'optimal' sensitive search strategies designed to identify clinical trials will be used as described by Lefebvre 2011. Search queries will combine free text words and controlled vocabulary. The search strategy will be based on synonyms of (“rheumatoid arthritis” OR “arthritis”) AND (“health promotion” OR “patient education” OR “behavior therapy” OR “self management” OR “psychological adaptation”).The Cochrane Musculoskeletal Review Group's Trials Search Co‐ordinator will help to develop each search equation. The electronic search strategy for MEDLINE is outlined in Appendix 1. This search strategy will be adapted for other databases.

We will not restrict the search according to language of publication or publication status.

Searching other resources

We will handsearch the reference lists of selected trials and systematic reviews identified from electronic searches. We will contact authors and field experts for any additional published or unpublished data. To identify trials in progress, we will use the WHO International Clinical Trials Registry Platform (www.apps.who.int/trialsearch); ClinicalTrials.gov (www.clinicaltrials.gov) to identify research in progress.

We will contact authors of active or completed trials for provisional results if they have not yet been published.

Data collection and analysis

Selection of studies

We will remove duplicate records from the selected references. Using the inclusion and exclusion criteria (see Criteria for considering studies for this review), two independent review authors will screen the titles and abstracts identified by the searches to choose potentially relevant studies. Two independent review authors will obtain and screen the full length articles of the selected titles and abstracts to check their eligibility and decide on their inclusion. We will resolve disagreements by discussion, and will request the assistance of a third independent review author when disagreement cannot be resolved.

If results of eligible trials are available in abstracts only, we will contact the trial authors to ask for a report of the trial results. If the data are unavailable we will include the trial in 'Studies awaiting assessment'.

We will link together multiple reports relating to the same trial or trials with potentially overlapping populations. If the possibility of overlapping populations cannot be excluded, we will correspond with the authors of these reports.

Data extraction and management

Two review authors will independently extract data from the included trials, including information about the study population, interventions, analyses, outcomes and sources of funding, using a standardised data extraction form specifically designed and piloted for this review.

In keeping with the previous review of self‐management education programmes for people with osteoarthritis (Kroon 2014), contextual factors and characteristics of the population relevant for addressing potential issues in health equity will be extracted using the PROGRESS‐Plus concept (place of residence; race, ethnicity and culture; occupation; sex; religion; education; socioeconomic status; social capital; age; disability; and sexual orientation) (Bambas 2004; Borkhoff 2011).

Health literacy of the population may be another important issue of relevance to health equity that is not currently captured in the PROGRESS‐Plus framework (Kroon 2014). The World Health Organization describes health literacy as “the cognitive and social skills which determine the motivation and ability of individuals to gain access to, understand and use information in ways which promote and maintain good health” (World Health Organization 1998). Health and social policies are emerging in both developing and developed countries, that highlight health literacy as a key determinant of a person’s ability to optimally manage his or her health and ensure equitable access to and use of services (Committee on Health Literacy 2004; Commonwealth of Aus 2009; UNECOSOC 2010). We will therefore also extract information regarding health literacy of the study population, if available, using the nine domains of the Health Literacy Questionnaire (HLQ) (Osborne 2013):

  • Feeling understood and supported by healthcare providers

  • Having sufficient information to manage my health

  • Actively managing my health

  • Social support for health

  • Appraisal of health information

  • Ability to actively engage with healthcare providers

  • Navigating the healthcare system

  • Ability to find good health information

  • Understand health information well enough to know what to do

To be consistent with the previous review (Kroon 2014), the following information will be collected for each self‐management education programme:

  • Intended audience (people with RA or arthritis)

  • Mode (delivered on a one‐to‐one basis or to groups of participants)

  • Personnel (led by healthcare professionals or by trained facilitators) and types of health professionals involved: nurse, psychologist, physical therapist, occupational therapist, podiatrist, social worker, and dietician

  • Delivery method (face‐to‐face, written, audio, video, phone, Internet)

  • Language (English or other languages)

  • Format (tailored to the individual’s needs or delivered in standard format)

  • Location (hospital, GP clinic, community setting, home)

  • Duration (number and frequency of sessions, hours per session, total duration of programme)

We will also extract information about the components of each self‐management education intervention using the eight domains described in the Health Education Impact Questionnaire (heiQ) (Osborne 2007). Each of these domains has been identified as an independent outcome indicator of effective self‐management interventions and has been found to be robust across settings. We will assess whether interventions were developed on the basis of an explicit theoretical framework (e.g. models of behavioural theory) or a set of principles (e.g. principles of adult education), and whether each of the following components was addressed within each programme:

  • Health‐directed activity

  • Positive and active engagement in life

  • Emotional distress

  • Self‐monitoring and insight

  • Constructive attitudes and approaches

  • Skill and technique acquisition

  • Social integration and support

  • Health service navigation

To assess the effects of an intervention, we will extract raw data for outcomes of interest (means and standard deviations for continuous outcomes and number of events for dichotomous outcomes) when available in the published reports.

We will contact the authors of all studies to obtain more information as needed.

If a study reported multiple time points within immediate, intermediate or longer‐term outcomes, only the longest time point will be extracted.

Assessment of risk of bias in included studies

We will evaluate the risk of bias of each included study according to the 'Risk of bias' tool recommended by The Cochrane Collaboration (Higgins 2011). Two independent review authors will examine seven specific domains (sequence generation, allocation concealment, blinding of participants or personnel, blinding of outcome assessors, incomplete outcome data, selective outcome reporting and other potential sources of bias (i.e. industry supported trials, single centre trials, baseline imbalance, specific study design).

We will score each of the criteria as 'high risk of bias', 'low risk of bias' or 'unclear risk of bias', depending on the information supplied in the report and according to the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). The review authors will also report the information used to establish the judgment.

We will classify studies as having a low risk of bias if they fulfilled a low risk of bias for all key domains and had no serious flaws. We will consider studies at high risk of bias if one or more items are rated at high risk of bias. . For studies with one or more key domains assessed as unclear, we will classify them as having an unclear risk of bias (Higgins 2011). We will resolve any disagreements independently by involving a third review author.

Measures of treatment effect

Point estimates and 95% confidence intervals (CIs) will be calculated for outcomes of individual RCTs whenever possible. For dichotomous data, we will express the results of each RCT as risk ratios (RRs). RRs are considered clinically relevant if the RR is smaller than 0.7 or larger than 1.5, in favour of the intervention or control group, respectively.

For continuous data, results will be summarised as mean difference (MDs) if the same tool has been used to measure the same outcome across the studies. Alternatively, we will calculate the standardised mean difference (SMDs) when studies measured the same outcome but with different tools. We will calculate the SMD by dividing the mean difference by the standard deviation of outcome among participants. SMDs greater than zero will indicate a beneficial effect in favour the experimental treatment. We will compute a 95% CI for the SMD. We will interpret the SMD as described in Cohen 1988, i.e. an SMD of 0.2 is considered to indicate a small beneficial effect, 0.5 a medium effect, and 0.8 a large effect (Cohen 1988).

If possible, we will sum up the estimates of the intervention effect via a meta‐analysis. The estimate of the common treatment effect is the weighted average of the individual estimates for each study.

If the meta‐analyses result in statistically significant overall estimates, we will transform these treatment effect measures (pooled estimate of RR or SMD) into measures which are clinically useful in daily practice, such as the number needed to treat for an additional beneficial outcome (NNTB) or harmful outcome (NNTH), and the absolute and/or relative improvement on the original units to express the final results of the review. We will back translate the results by multiplying SMD by the standard deviation from a representative study (Akl 2011).

Unit of analysis issues

For cross‐over trials, we will extract data from the first period only. Whenever possible, we will use results from an intention‐to‐treat analysis.

For studies containing more than two intervention groups, making multiple pair‐wise comparisons between all possible pairs of intervention groups possible, we will include the same group of participants only once in the (meta‐)analysis following the procedure recommended by The Cochrane Collaboration (www.cochranenet.org/openlearning/html/modA2‐5.htm).

Dealing with missing data

If faced with missing outcome data issues, we will contact the original authors to request data according to the procedure recommended by The Cochrane Collaboration (Higgins 2011a). If data are unavailable, we will consider methods of allowing for uncertainty due to missing data in meta‐analysis (Gamble 2005; Higgins 2008). We will perform sensitivity analyses to assess how sensitive results are to changes, and we will address the potential impact of missing data on the findings of the review in the Discussion section.

Assessment of heterogeneity

Before a meta‐analysis is conducted, studies will be assessed for similarities with respect to characteristics of the self‐management education programmes, comparison groups and outcomes. Studies judged by the review authors as being too different from each other will not be combined in the analysis but will be described separately in the text of the review.

For studies judged as sufficiently similar, statistical heterogeneity will be assessed visually by looking at the scatter of effect estimates on the forest plots and by determining the I2 statistic which describes the percentage of the variability in effect estimates that is due to heterogeneity rather than sampling error (Higgins 2002). We will interpret the value of the I2 statistic according to the following thresholds (Higgins 2011): 0% to 40%: might not be important; 30% to 60%: may represent moderate heterogeneity; 50% to 90%: may represent substantial heterogeneity; and 75% to 100%: considerable heterogeneity. We will also compute the 95%CI for the I2 statistic (Ioannidis 2007) as well as the between‐study variance Tau2, estimated from the random‐effects model (Rucker 2008).

Assessment of reporting biases

To assess the presence of small‐study effects, we will draw contour‐enhanced funnel plots for each meta‐analysis (Peters 2008). If the required statistical conditions are met (≥ 10 studies, no significant heterogeneity, and ratio of the maximal to minimal variance across studies > 4), we will use asymmetry tests (Ioannidis 2007). In cases of evidence of small‐study effects, we will perform sensitivity analyses according to Copas' selection model (Schwarzer 2010).

Data synthesis

Included studies will be grouped and assessed according to whether they compare self‐management education programmes versus:

  • an attention control group (i.e. participants received the same contact hours with programme providers, but the content delivered was unrelated to self‐management of their condition);

  • a group that received no treatment or usual care or were placed on a waiting list to attend the self‐management programme at a later date;

  • an information‐only group (i.e. educational materials, programme handbook);

  • a group that received an alternate intervention that was not a self‐management education programme (e.g. exercise or occupational therapy).

In keeping with the previous review of self‐management education programmes for people with osteoarthritis (Kroon 2014), we consider the first two comparisons to be the most important for addressing the objectives of this review. Evidence from physical therapy trials suggests that the quality of the therapeutic relationship influences clinical outcomes such as pain and physical function (Hall 2010; Pinto 2012). This effect may also apply to self‐management education programme providers.

An attention control may control for any effect of contact time with programme providers, and we consider this to be the comparator with the lowest risk of bias in determining the true effect of self‐management education programmes. We consider usual care to be an important comparator as well, as this reflects routine care. However comparisons versus usual care are generally unblinded (analogous to no treatment), while an attention control allows for blinding of participants (of utmost importance when outcomes are participant assessed) so is closer to a sham/placebo control.

If studies are considered to be sufficiently similar in terms of the self‐management education programme delivered and the comparison intervention provided, we will pool outcomes in a meta‐analysis using the random‐effects method (Deeks 2011).

To minimise outcome reporting bias, if data from more than one self‐efficacy scale are reported for a trial, we will extract data according to the following hierarchy:

  • Self‐efficacy on a visual analogue scale

  • Arthritis self‐efficacy scale mean score (ASES, ASES‐8, RASE, CDSES, URICA questionnaire)

  • Arthritis self‐efficacy subscale (pain or other symptoms)

  • Self‐efficacy on other scales (i.e. Arthritis Helplessness Index, heiQ self‐monitoring and insight)

If data on more than one self‐reported function scale are provided for a trial, we will extract data according to the following hierarchy:

  • Health Assessment Questionnaire (HAQ) or modified Health Assessment Questionnaire (M‐HAQ)

  • SF‐36 subscale "physical functioning" (SF‐36 PF)

  • Arthritis Impact Measurement Scales (AIMS‐function scale)

  • McMaster Toronto Arthritis Patient Preference (MACTAR)

  • Sickness Impact Profile 68 (SIP 68)

  • Michigan Hand Outcomes Questionnaire (MHQ‐physical function scale)

  • Disease Activities Questionnaire (DAQ)

  • Hannover Functional Ability Questionnaire (FFbH)

  • Impact of Rheumatic Diseases on General Health and Lifestyle (IRGL‐mobility scale)

If data on more than one self‐reported pain scale are provided for a trial, we will extract data according to the following hierarchy:

  • Pain visual analogue scale

  • Arthritis Impact Measurement Scales (AIMS2‐pain scales)

  • Patient’s global assessment

If data on more than one self‐reported fatigue scale are provided for a trial, we will extract data according to the following hierarchy:

  • Variations of a numerical rating (NRS) or visual analogue (VAS) fatigue scale

  • Short Form 36 (SF‐36) vitality subscale to measure fatigue

  • Multidimensional Assessment of Fatigue (MAF)

  • Profile of Mood States (POMS) vigour subscale

  • Functional Assessment of Chronic Illness Therapy Fatigue (FACIT‐F)

  • Checklist Individual Strength (CIS)

If data on more than one disease activity scale are provided for a trial, we will extract data according to the following hierarchy:

  • Number of swollen and painful joints (Tender and swollen joint count, Ritchie Articular Index, ACR Articular Index, EULAR Articular Index)

  • Mean disease activity composite score (DAS28/DAS/CDAI/SDAI /RADAI /ACR 50 20 70)

  • Patient global assessment score, Self‐Rated Global Health Scale, ACR patient global assessment of disease using 10‐cm visual analogue scale or Likert scale, Duration of early morning stiffness (in minutes)

  • Self report‐Physician global assessment of disease activity using 10‐cm visual analogue scale or Likert scale

  • Acute phase reactants (Erythrocyte sedimentation rate (ESR), C‐reactive protein (CR)

If data on more than one self‐reported quality of life scale are provided for a trial, we will extract data according to the following hierarchy:

  • Arthritis Impact Measurement Scales (AIMS) total score

  • Short Form 36 health related quality of life questionnaire (SF‐36)

  • Short Form 12 health related quality of life questionnaire (SF‐12)

  • EuroQoL Questionnaire (EuroQoL)

  • Sickness Impact Profile (SIP)

  • Nottingham Health Profile (NHP)

  • Assessment of Quality of Life instrument (AQoL)

  • Short form of the Arthritis Impact Measurement Scale 2 (EMIR17)

  • Other validated quality of life scores

If data on a quality of life scale are provided in both a multi‐question format and a visual analogue scale format, we will choose the first, as we judge this would provide a more accurate measure of quality of life and patient‐reported global health status.

If more than one measure of self‐reported psychological status is provided for a trial, we will extract data according to the following hierarchy:

  • Hospital Anxiety and Depression Scale (HAD)

  • The Center for Epidemiological Studies‐Depression Scale (CES‐D)

  • Zung Self‐Rating Depression Scale (ZSRDS)

  • Health distress

Subgroup analysis and investigation of heterogeneity

If sufficient data are available for the primary outcomes of the review, we will conduct a subgroup analysis to explore whether there is a relationship between any of the component domains addressed in the self‐management education programmes (based on the heiQ, see Data extraction and management) or between the types of health professionals involved (see Data extraction and management) and participant outcomes.

We will also use subgroup analyses to explore the potential impact of issues of health equity on participant outcomes. Based upon a previous review that found that the majority of studies included mainly Caucasian, educated, older females (Kroon 2014), we will classify studies according to whether the study population consisted mainly of Caucasian, educated, older females or populations drawn from minority groups within the community (e.g. culturally and linguistically diverse populations).

Sensitivity analysis

We will conduct a sensitivity analysis based upon whether participants were randomly allocated and group assignments had been adequately concealed.

'Summary of findings'

We will present the main outcomes of the review in 'Summary of findings' tables (self‐management, positive and active engagement in life, self‐reported function, pain, fatigue score, quality of life and withdrawals) to provide key information concerning the quality of evidence, the magnitude of effect of the interventions examined and the sum of available data on the main outcomes, as recommended by The Cochrane Collaboration (Schünemann 2011b). The ’Summary of findings’ tables provide an overall grading of the evidence related to each of the main outcomes based on the GRADE approach (Schünemann 2011a).

Outcomes pooled using SMDs will be re‐expressed as a mean difference by multiplying the SMD by a representative control group baseline standard deviation from a trial, using a familiar instrument. In the comments column, we will calculate the absolute percentage change and the relative percentage change; and, for outcomes with statistically significant differences between intervention groups, we will calculate the number needed to treat for an additional beneficial outcome (NNTB).

For dichotomous outcomes, the absolute risk difference will be calculated using the risk difference statistic in RevMan and the result expressed as a percentage; the relative percentage change will calculated as the risk ratio ‐1 and expressed as a percentage; and the NNT from the control group event rate and the risk ratio will be determined using the Visual Rx NNT calculator (Cates 2013).

For continuous outcomes, the absolute risk difference will be calculated as the mean difference between intervention and control groups in the original measurement units (divided by the scale), expressed as a percentage; the relative difference will be calculated as the absolute change (or mean difference) divided by the baseline mean of the control group from a representative trial. We will use the Wells calculator to obtain the NNTB for continuous measures (available at the Cochrane Musculoskeletal Group (CMSG) Editorial office; http://musculoskeletal.cochrane.org/).