Theory‐based behavioural interventions for prediabetic state and people with diabetes mellitus
Abstract
This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:
To assess the effects of theory‐based behavioural interventions for prediabetic state and people with diabetes mellitus.
Background
Description of the condition
Diabetes mellitus is a metabolic disorder resulting from a defect in insulin secretion, insulin action, or both. A consequence of this is chronic hyperglycaemia (that is elevated levels of plasma glucose) with disturbances of carbohydrate, fat and protein metabolism. Long‐term complications of diabetes mellitus include retinopathy, nephropathy and neuropathy. The risk of cardiovascular disease is increased. For a detailed overview of diabetes mellitus, please see under 'Additional information' in the information on the Metabolic and Endocrine Disorders Group in The Cochrane Library (see 'About', 'Cochrane Review Groups (CRGs)'). For an explanation of methodological terms, see the main glossary in The Cochrane Library.
Diabetes is a growing public health concern worldwide. In 1997, the estimated number of people with diabetes worldwide was 124 million, of these 97% were those with diabetes mellitus type 2. It is projected that the number will double by the year 2010 (Amos 1997). Although diabetes type 2 accounts for the majority of this problem, the incidence of type1 diabetes mellitus is also increasing (Onkamo 1999). With a lesser degree of hyperglycaemia, prediabetes, also known as non‐diabetic hyperglycaemia, is a condition which includes impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or a combination of the two. Compared to individuals with normal glucose tolerance, those in a prediabetic state are at greater risk of developing diabetes (de Vegt 2001; Edelstein 1997), diabetes‐related complications (Pan 1993; Temelkova‐Kurktschiev 2000) and mortality (DECODE Study Group 2001; Lowe 1997).
Description of the intervention
'Lifestyle' modification has been shown to have an important role in the treatment and prevention of diabetes as well as its complications. Changes in diet, physical activity and weight control can prevent the development of diabetes in people at high risk (Knowler 2002; Tuomilehto 2001). Achievement of good glycaemic control in people with diabetes is crucial for preventing diabetes‐related complications (UKPDS 33; Valeri 2004). Diets with low glycaemic index as well as exercise have shown beneficial effects on glycaemic control in patients with diabetes (Thomas 2006; Thomas 2009). However, the majority of people with diabetes or pre‐diabetes fail to adopt or maintain favourable behaviours such as sufficient physical activity and appropriate diet (Lindstrom 2006; Nathan 1995; UKPDS 34).
The process of changing behaviours is complex. In an attempt to better understand determinants of people's behaviour and to help them to adopt and maintain healthy behaviours, many theories of behaviour change have been proposed (Table 1). For example, the trans‐theoretical model (TTM) (Prochaska 1992) is a theory by which behavioural change is seen as a process rather than a single event. Individuals engaging in a new behaviour move in an orderly progression from pre‐contemplation (either not willing to change or may not recognize the problem), contemplation (considering a change), preparation (planning to change), action (engaging in a new behaviour) and maintenance (sustaining their change over time). Behaviour change is considered as a dynamic process; individuals may move back and forth between stages. Relapse can occur at any point, and thus individuals may make several attempts at behaviour change before progressing to action and maintenance stages (Prochaska 1992). The theory of reasoned action (Ajzen 1908) holds that human behaviour is under voluntary control; intention to act is the most immediate determinant of behaviour and all other factors will be mediated through this. Behavioural intentions are thought to be influenced by two factors, namely attitude and subjective norm. Ajzen's theory of planned behaviour (TPB), is essentially an extension of the theory of reasoned action that includes perceived behavioural control, considered as a third influence on behavioural intentions (Armitage 2001). The social cognitive theory is another example of a theory of behavioural change which postulates that behaviour is influenced by constant interaction between environment and personal characteristics of an individual, and behavioural outcome. The health choices individuals make are related to outcome expectancies (whether an action will lead to a particular outcome) and self‐efficacy (whether they believe that they can change). The confidence in their ability to perform health behaviours influences which behaviour they will engage in (Bandura 1977).
| Theory | Summary |
| Theory of reasoned action | Human behaviour is under voluntary control. Intention to act is the most immediate determinant of behaviour and all other factors influencing behaviour will be mediated through this. Behavioural intentions are thought to be influenced by two factors, namely attitude and subjective norm. |
| Theory of planned behaviour | An extension of the theory of reasoned action that includes measures of control belief and perceived behavioural control, considered as being a third influence on behavioural intentions. |
| Trans‐theoretical model | Behaviour change is a process rather than a single event. People do not change all at once, rather they move through a series of five stages: pre‐contemplation, contemplation, preparation, action and maintenance. |
| Social cognitive theory | Health choices people make are related to outcome expectancies (whether an action will lead to a particular outcome) and self‐efficacy (whether they believe that they can change). |
Although theories of behavioural change differ in the specific factors that are hypothesized to guide people’s behaviours, they all stipulate determinants for behavioural change. These theories specify how to change behaviour by identifying antecedents to behavioural change. Hence, general principles of these theories have been applied to intervention programmes to support health‐related behavioural change in people with prediabetic stage and diabetes mellitus.
Adverse effects of the intervention
Theory‐based interventions are not generally considered to be associated with any serious adverse event. However, the implementation of dietary intervention may cause hypoglycaemic events or deficiencies in nutritional status if restrictive measures are used. Diet control may also affect the quality of life. Additionally, physical activity may cause traumatic injury or adverse effects on cardiovascular system in people with insufficient training.
How the intervention might work
It is hypothesised that the knowledge of which theoretical constructs best predict behavioural change informs the design of an intervention, allowing the efforts to be concentrated on changing those psychological constructs central to behavioural change. A theory‐based intervention specifies what works for whom and under what conditions of the program delivery. Indeed, theories can help to identify effective methods for lifestyle interventions and may then improve health outcomes in people with prediabetes and diabetic patients.
Why it is important to do this review
A previous review of behaviour interventions found that education improves self‐management in diabetic patients which in turn ameliorates glycaemic control (Peyrot 1999). Nevertheless, there has been little information on distinct benefits of different types of theory‐based behaviour interventions. Although theory‐based interventions have been advanced, they are not evaluated rigorously. They may be so poorly defined that it can be difficult to replicate or compare them with other interventions. Some studies suggest that theory‐based interventions are associated with larger and longer effects than those with no explicit basis in theory (Hillsdon 2005), but the effectiveness of theory‐based interventions aiming at behaviour modification in people with diabetes and those in a prediabetic state remains controversial (Kim 2004; Kinmonth 2008; Kirk 2004).
By identifying the relevant literature systematically then critically appraising and analysing the retrieved information, the review will synthesize evidence for the effects of behavioural interventions on lifestyle modification and disease outcomes. The availability of this information is critical to informing service and resource allocation decisions related to patient education.
Objectives
To assess the effects of theory‐based behavioural interventions for prediabetic state and people with diabetes mellitus.
Methods
Criteria for considering studies for this review
Types of studies
Randomised controlled clinical trials.
Types of participants
We will study people with diabetes (type 1 or type 2) and prediabetes (either impaired fasting glucose (IFG) or impaired glucose tolerance (IGT)) as defined by either the ADA or WHO criteria. Participants from primary care, outpatient clinics, community and hospital settings are eligible for inclusion.To be consistent with changes in classification and diagnostic criteria of diabetes mellitus through the years, the diagnosis should have been established using the standard criteria valid at the time of the beginning of the trial (for example ADA 1999; WHO 1980; WHO 1985; WHO 1998). Ideally, diagnostic criteria should have been described. If necessary, authors' definition of diabetes mellitus will be used. Diagnostic criteria will be eventually subjected to a sensitivity analysis.
Types of interventions
Intervention
Theory‐based behaviour interventions including the trans‐theoretical model, the theory of planned behaviour and the social cognitive theory, aimed at lifestyle modification and diabetes control.
Control
Usual care or general education.
Types of outcome measures
Primary outcomes
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behaviour changes: measured by a proportion of participants with predefined changes in target behaviour (exercise and diet);
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change in glycosylated haemoglobin A1C (HbA1c) level;
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development of diabetes mellitus (incidence), particularly for trials in people with prediabetes.
Secondary outcomes
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anthropometric measures: body weight, body mass index, waist circumference and waist‐to‐hip ratio;
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stage of behaviour change (for example, stage changes or progression from one stage to another stage with regard to the trans‐theoretical model);
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self‐efficacy (changes in measures of self‐efficacy);
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quality of life;
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morbidity (retinopathy, nephropathy, neuropathy, erectile dysfunction, amputation, and cardiovascular disease (angina pectoris, myocardial infarction, stroke, peripheral vascular disease));
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mortality (all‐cause mortality; diabetes related mortality (death from myocardial infarction, stroke, peripheral vascular disease, renal disease, hyper‐ or hypoglycaemia or sudden death));
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costs.
Covariates, effect modifiers and confounders
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compliance;
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co‐morbidities;
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age;
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socioeconomic status.
Timing of outcome measurement
Outcomes will be assessed in the short term (12 weeks to less than 24 weeks after treatment), medium (24 weeks to 12 months after treatment) and long term (more than 12 months after treatment).
Search methods for identification of studies
Electronic searches
We will use the following sources for the identification of trials:
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Cochrane Library (most recent issue);
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MEDLINE (until recent);
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EMBASE (until recent);
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Global Health (until recent);
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PsycINFO (until recent);
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PsyExtra (until recent);
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CINAHL (until recent).
We will also search databases of ongoing trials: 'Current Controlled Trials' (www.controlled‐trials.com ‐ with links to other databases of ongoing trials).
For detailed search strategies please see under Appendix 1.
Additional key words of relevance may be detected during any of the electronic or other searches. If this is the case, electronic search strategies will be modified to incorporate these terms. Studies published in any language will be included, and articles will be translated when necessary.
Searching other resources
We will try to identify additional studies by searching the reference lists of included trials and (systematic) reviews, meta‐analyses and health technology assessment reports noticed.
Data collection and analysis
Selection of studies
To determine the studies to be assessed further, two authors (SN, PC) will independently scan the abstract, title or both sections of every record retrieved. All potentially relevant articles will be investigated as full text. Interrater agreement for study selection will be measured using the kappa statistic (Cohen 1960). Differences will be marked and if these studies are later on included, the influence of the primary choice will be subjected to a sensitivity analysis. Where differences in opinion exist, they will be resolved by a third party. If resolving disagreement is not possible, the article will be added to those 'awaiting assessment' and authors will be contacted for clarification. An adapted PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) flow‐chart of study selection will be attached (Liberati 2009).
Dealing with duplicate publications
In the case of duplicate publications and companion papers of a primary study, we will try to maximise yield of information by simultaneous evaluation of all available data. In cases of doubt, the original publication (usually the oldest version) will obtain priority.
Data extraction and management
For studies that fulfil inclusion criteria, two authors (SN, PC) will independently extract relevant population and intervention characteristics using standard data extraction templates (for details see 'Characteristics of included studies' and Table 2, Appendix 2) with any disagreements to be resolved by discussion, or if required by a third party. Any relevant missing information on the trial will be sought from the original authors of the article, if required.
| study ID | intervention (I) | [n] screened | [n] randomised | [n] safety | [n] ITT | [n] finishing study | [%] of randomised | comments |
| ID1 | I: behavioural intervention C: usual care | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | |
| ID2 | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | ||
| ID3 | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | ||
| ID3 | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | ||
| ID4 | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | ||
| ID5 | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | ||
| ID6 | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | ||
| ID7 | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | ||
| ID8 | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | ||
| ID9 | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: |
ITT: intention‐to‐treat
Assessment of risk of bias in included studies
Two authors (PC, TA) will assess each trial independently. Possible disagreement will be resolved by consensus, or with consultation of a third author (SN) in case of disagreement. Interrater agreement for key bias indicators (e.g. allocation concealment, incomplete outcome data) will be calculated using the kappa statistic (Cohen 1960). In cases of disagreement, the rest of the group will be consulted and a judgement will be made based on consensus. Assessment of the risk of bias of included studies will be done using the Collaboration's risk of bias tool, based on the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2008).
Measures of treatment effect
Primary analysis will be based on the association between theory‐based intervention and the principal outcome measures. These will include either continuous variables (e.g. HbA1cand anthropometric measures ) or dichotomous outcomes (e.g. proportion of participants with behavioural change). For continuous variables, weighted mean differences (WMD) between the intervention and control groups will be used to analyse the size of the intervention effects. When the information is provided, we will use an intention‐to‐treat analysis. If results for continuous outcomes are presented on different scales, we plan to use standardised mean differences (SMD). Dichotomous data will be expressed as relative risks.
Unit of analysis issues
We will report data in natural units for each study. We will present results in terms of absolute difference (mean or proportion of outcome in intervention group minus control at study completion).
For cluster randomisation, analyses will be performed at the same level as the allocation, using measurements from each cluster. Then, the sample size will be the number of clusters and analyses will proceed as if the trial was individually randomised.
In case of cross‐over trials, a paired t‐test will be used to compare continuous data from a two‐period, two‐intervention cross‐over trials if neither carry‐over nor period effects are thought to be a problem. This method evaluates the value of ‘measurement on experimental arm (E)’ minus ‘measurement on control arm (C)’ separately for each participant. The mean and standard error of the difference are the building blocks of an effect estimate and a statistical test. The effect estimate may be included in a meta‐analysis using the generic inverse‐variance method in Review Manager. When results are available broken by the particular sequence each participant received, then analyses that adjust for period effects are straightforward (Higgins 2008).
Dealing with missing data
Relevant missing data will be obtained from authors, if feasible. Evaluation of important numerical data such as screened, randomised patients as well as intention‐to‐treat (ITT) and per‐protocol (PP) population will be carefully performed. Attrition rates, for example drop‐outs, losses to follow‐up and withdrawals will be investigated. Issues of missing data and techniques to handle these (for example last‐observation‐carried‐forward (LOCF)) will be critically appraised.
Assessment of heterogeneity
In the event of substantial clinical or methodological or statistical heterogeneity, study results will not be combined by means of meta‐analysis. Heterogeneity will be identified by visual inspection of the forest plots, by using a standard Chi2 test and a significance level of α = 0.1, in view of the low power of such tests. Heterogeneity will be specifically examined with the I2 statistic (Higgins 2002), where I2 values of 50% and more indicate a substantial level of heterogeneity (Higgins 2008). When heterogeneity is found, we will attempt to determine potential reasons for it by examining individual study and subgroup characteristics.
Assessment of reporting biases
Funnel plots will be used to assess for the potential existence of small study bias. There are a number of explanations for the asymmetry of a funnel plot (Sterne 2001). Therefore, we will carefully interpret results (Lau 2006).
Data synthesis
Data will be summarised statistically if they are available, sufficiently similar and of sufficient quality. Statistical analysis will be performed according to the statistical guidelines referenced in the newest version of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2008).
Subgroup analysis and investigation of heterogeneity
Subgroup analyses will be mainly performed if one of the primary outcome parameters demonstrates statistically significant differences between intervention groups. In any other case subgroup analyses will be clearly marked as a hypothesis generating exercise.
The following subgroup analyses are planned:
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different theory‐based intervention (trans‐theoretical model, theory of planned behaviour, social cognitive theory);
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period of time the outcome is evaluated (short, medium and long term);
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type of condition (prediabetes, type 1 and type 2 diabetes).
Sensitivity analysis
We will perform sensitivity analyses in order to explore the influence of the following factors on effect size:
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repeating the analysis excluding unpublished studies;
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repeating the analysis taking account of risk of bias, as specified above;
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repeating the analysis excluding any very long or large studies to establish how much they dominate the results;
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repeating the analysis excluding studies using the following filters: diagnostic criteria, language of publication, source of funding (industry versus other), country.
The robustness of the results will also be tested by repeating the analysis using different measures of effects size (relative risk, odds ratio etc.) and different statistical models (fixed‐ and random‐effects models).
| Theory | Summary |
| Theory of reasoned action | Human behaviour is under voluntary control. Intention to act is the most immediate determinant of behaviour and all other factors influencing behaviour will be mediated through this. Behavioural intentions are thought to be influenced by two factors, namely attitude and subjective norm. |
| Theory of planned behaviour | An extension of the theory of reasoned action that includes measures of control belief and perceived behavioural control, considered as being a third influence on behavioural intentions. |
| Trans‐theoretical model | Behaviour change is a process rather than a single event. People do not change all at once, rather they move through a series of five stages: pre‐contemplation, contemplation, preparation, action and maintenance. |
| Social cognitive theory | Health choices people make are related to outcome expectancies (whether an action will lead to a particular outcome) and self‐efficacy (whether they believe that they can change). |
| study ID | intervention (I) | [n] screened | [n] randomised | [n] safety | [n] ITT | [n] finishing study | [%] of randomised | comments |
| ID1 | I: behavioural intervention C: usual care | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | |
| ID2 | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | ||
| ID3 | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | ||
| ID3 | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | ||
| ID4 | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | ||
| ID5 | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | ||
| ID6 | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | ||
| ID7 | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | ||
| ID8 | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | ||
| ID9 | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | I: C: Total: | ||
| ITT: intention‐to‐treat | ||||||||
