Health Education Research Advance Access originally published online on March 21, 2005
Health Education Research 2005 20(6):676-687; doi:10.1093/her/cyh022
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A causal modelling approach to the development of theory-based behaviour change programmes for trial evaluation
1 General Practice and Primary Care Research Unit, Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, Forvie Site, Robinson Way, Cambridge CB2 2SR, 2 School of Psychology, William Guild Building, University of Aberdeen, Aberdeen AB24 2UB, 3 Rookery Medical Centre, Newmarket CB8 8NW and 4 MRC Epidemiology Unit, Elsie Widdowson Laboratory, Cambridge CB1 9NL, UK
5 Correspondence to: W. Hardeman; E-mail: wendy.hardeman{at}phpc.cam.ac.uk
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Abstract |
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Theory-based intervention programmes to support health-related behaviour change aim to increase health impact and improve understanding of mechanisms of behaviour change. However, the science of intervention development remains at an early stage. We present a causal modelling approach to developing complex interventions for evaluation in randomized trials. In this approach a generic model links behavioural determinants, causally through behaviour, to physiological and biochemical variables, and health outcomes. It is tailored to context, target population, behaviours and health outcomes. The development of a specific causal model based on theory and evidence is illustrated by the ProActive programme, supporting increased physical activity among individuals at risk of Type 2 diabetes. The model provides a rational guide to appropriate measures, intervention points and intervention techniques, and can be tested quantitatively. Causal modelling is critically compared to other approaches to intervention development and evaluation, and research directions are indicated.
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Introduction |
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Interventions supporting changes in health-related behaviours can contribute significantly to preventing a range of common chronic conditions, such as cardiovascular disease, Type 2 diabetes and some cancers. However, the science of intervention development and evaluation remains at an early stage (Ory et al., 2002
). Interventions may be carefully developed, but weakly evaluated, or elegant trial designs used to evaluate poorly specified interventions. This highlights the need to link intervention development with evaluation and design issues during early phases of intervention development. This linkage is particularly important when the intervention is complex, as in most behaviour change programmes.
Complex interventions are those that consist of a number of components that may act both independently and inter-dependently (Medical Research Council, 2000). A framework for the development and evaluation of complex interventions has recently been developed by the UK Medical Research Council (MRC) (Campbell et al., 2000; Medical Research Council, 2000), and emphasizes the importance of the early phases of developing the intervention, measures and trial design (Table I). Details of how to achieve phase 1 (review of theory and evidence) and phase 2 (modelling) of the framework are lacking. This paper presents a causal modelling approach to the development of theory-based interventions for randomized controlled trial evaluation, focusing on the first two phases. We use the term ‘causal modelling’ to mean the development of a specific causal model to guide the design of a programme to support behaviour change for trial evaluation. The specific causal model can then be populated with data from the trial or from other relevant studies, and tested quantitatively. This paper describes the development and application of a specific causal model.
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A range of approaches to intervention development and evaluation have been described from public health and social science perspectives. They employ mapping techniques of various kinds to guide intervention development, including logic models (Conrad et al., 1999
), and matrices for objectives, intervention methods and strategies (Bartholomew et al., 2001). Causal models that span behavioural determinants, behaviours, health outcomes and their correlates may complement these approaches. A comprehensive causal modelling approach starts with a simple generic model (Table II) linking behavioural and disease determinants in a causal pathway. It contains four levels: behavioural determinants, behaviour, physiological and biochemical variables, and health outcomes.
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The generic model is further specified for each application. Appropriate intervention and measurement points and behaviour change techniques are indicated in one graphical representation. The specific model is tailored to characteristics of the target population, social context, target behaviour and health or disease outcomes. Theory and evidence are used to guide the selection of behavioural determinants, intervention and measurement points, and behaviour change techniques. However, existing intervention studies rarely justify the theory selected and we therefore formulated criteria to inform this important step.
The approach is illustrated by a case study of the primary-care-based ProActive programme, aimed at increasing physical activity among individuals at high risk of Type 2 diabetes and currently under trial in Cambridgeshire, UK (Williams et al., 2004). The questions posed in developing the programme were: what intervention techniques might be most effective in supporting increases in physical activity and how could their efficacy be tested?
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Methods |
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Development of the ProActive model drew iteratively on epidemiology and psychology. The methods used in developing the causal model are summarized in Table III.
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Contribution of epidemiology
Epidemiology informed the causal model from behaviour to health outcome in four ways:
- (1) Defining the health outcome (Type 2 diabetes), its importance and its predictors.
- (2) Defining the target group.
- (3) Identification of the target behaviour and the likely impact of achievable behaviour change on physiological and biochemical variables, and health outcome.
- (4) Development and validation of precise objective measures of the target behaviour.
- (2) Defining the target group.
Contribution of psychology
Psychology informed the causal model from behavioural determinants to behaviour, and contributed in four ways:
- (1) Defining theory-based behavioural determinants.
- (2) Defining intervention points.
- (3) Defining techniques to support behaviour change.
- (4) Developing measures of change in behavioural determinants.
- (2) Defining intervention points.
Defining theory-based behavioural determinants
- Expert meetings. At the first of two expert meetings the project team (authors), psychologists, a sociologist, epidemiologists and primary care practitioners considered the applicability of a wide range of theories from sociology, social, health and clinical psychology to ProActive. After the meeting five criteria were defined for theory selection: (1) use in interventions aimed at similar target behaviours, (2) applicability to the target group, (3) clear definition of causal, testable pathways between behavioural determinants and behaviour, (4) strength of evidence about predictive validity, and (5) clear guidelines for measurement.
- Systematic reviews. To inform the first criterion, systematic reviews were conducted of interventions aimed at weight gain prevention and family-based interventions to prevent and treat obesity (Hardeman et al., 2000; McLean et al., 2003).
- Focus groups and individual consultations. To inform the second criterion, focus groups and interviews were conducted with seven individuals at risk of Type 2 diabetes, aged 30–45 years.
- Team meetings. At several project meetings the utility of a range of theories was reviewed in relation to all five criteria.
Defining intervention points
Selection of intervention points to facilitate behaviour change and habit formation was informed by both the selected theory and a review of evidence by psychologists at the second expert meeting.
Defining techniques to support behaviour change
Selection of behaviour change techniques was based on (1) the underlying theory, (2) techniques used in other interventions aimed at similar target behaviours and (3) a review of evidence about effectiveness of techniques. We reviewed techniques proposed by authors of the selected theory and conducted systematic reviews (Hardeman et al., 2000, 2002; McLean et al., 2003). Clinical, social and health psychologists also reviewed empirical evidence for effective behaviour change techniques at a second expert meeting, without detailed consideration of underlying theories.
Developing measures of change in behavioural determinants
A pilot study was conducted among 213 participants aged 35–75 years in the Ely observational cohort study into Type 2 diabetes, to identify salient beliefs and predictors of intention towards increasing physical activity (Sutton et al., 2003). Subsequently, a questionnaire was developed to assess change in salient beliefs over time.
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Results |
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Epidemiology
Defining the health outcome and its importance
Type 2 diabetes is a common and burdensome chronic disease. Its prevalence is well over 5% in many developed countries and is rising (Williams et al., 2002
). The disorder is managed by a regimen including dietary change, increase in physical activity and taking multiple medication, which may include injections of insulin. Effective management of hyperglycaemia and related abnormalities including hypertension and dyslipidaemia can reduce the complications of diabetes, including premature cardiovascular disease, retinopathy and nephropathy (UK Prospective Diabetes Study Group, 1998; Gaede et al., 2003). Labelling, treatment and complications can impact on functional status, well-being and quality of life (Johnston et al., 1984; de Grauw et al., 1999; UK Prospective Diabetes Study Group, 1999). The incidence of diabetes can be predicted by various anthropometric, physiological and biochemical variables, including age, family history, blood glucose, insulin and proinsulin (Wareham et al., 1999), and these risk predictors were included in the causal model.
Defining the target group
Adults with a family history of Type 2 diabetes constitute a high-risk population, identifiable through inclusion of their parents with diabetes on GP registers. They have a 3-fold increased risk of developing diabetes compared to those without a family history. This risk is magnified by physical inactivity, weight gain and aging. At least 40% of the excess risk associated with weight gain might be avoided if Body Mass Index did not exceed 30 kg/m2 (Sargeant et al., 2000). The study therefore recruited at-risk individuals by virtue of their family history, sedentary lifestyle and middle age. As family members may share disease risk, which could indicate shared behaviours, environmental or genetic determinants (Hippisley-Cox et al., 2002), we intervened on all willing family members living together, so that more family members might potentially benefit from the programme.
Identifying the target behaviour, and its impact on physiological and biochemical variables, and health outcome
Physical inactivity accounts for at least 11.7% of all deaths in developed countries (Murray and Lopez, 1996) and the rise in obesity in many countries is associated with a decline in physical activity. Physical activity can make a clinically important impact on the prevention of diabetes. A consistent direct relationship exists between sedentary living and risk of Type 2 diabetes (Hamman, 2002). Three trials among individuals with impaired glucose tolerance in China (Pan et al., 1997), Finland (Tuomilehto et al., 2001) and the USA (Diabetes Prevention Program Research Group, 2002) have established that intensive approaches to change lifestyle, including physical activity, can delay or prevent progression to diabetes by up to 58% over 3 years (Diabetes Prevention Program Research Group, 2002) and possibly longer (Pan et al., 1997), and are cost-effective relative to pharmacological therapy (Herman et al., 2003). While these trials focused on structured activity, the Ely observational cohort study suggested that an increase in overall energy expenditure was beneficial: an increase equivalent to 0.5 hours walking per day was associated with a reduction in risk of the metabolic cardiovascular syndrome (physiological and biochemical variables) of about one-third (Wareham et al., 1998) and of undiagnosed diabetes or impaired glucose tolerance of about one-fifth (Wareham et al., 2000). The relationship is continuous with no threshold. Such increases in physical activity are achievable in sedentary populations in community settings (Hillsdon et al., 1995; Simons-Morton et al., 1998; Sallis and Owen, 1999). Interventions aimed at unsupervised physical activity, emphasizing leisure physical activity of low intensity, can be associated with large effects (Dishman and Buckworth, 1996). At consultations our target group preferred unorganized activities above structured, formal activities such as going to the gym.
Development and validation of objective measures of the target behaviour
Most behavioural studies rely on self-report of exercise. In trials this may inflate differences between groups due to recall bias or reduce the study's power to detect a difference between groups due to imprecision of measurement. Furthermore, self-reports tend to focus on leisure-time exercise and do not quantify changes in overall energy expenditure (Wareham and Rennie, 1998). Valid estimation of total energy expenditure is possible in free-living individuals using heart rate monitoring with individual calibration (Wareham et al., 1997). This method increases the precision of measuring the quantitative link between physical activity and different diseases, and was used in the Ely study which demonstrated the importance of overall daily activity, rather than fitness alone. Overall physical activity in the causal model was measured by individually calibrated heart rate monitoring over 4 days preceded by submaximal VO2 fitness testing (Wareham et al., 2000) and by self-report (Wareham et al., 2002). Objectively measured physical activity was expressed as the Physical Activity Ratio (PAR) (Schutz et al., 2001).
Psychology
Theory-based behavioural determinants
The systematic reviews identified no commonly used theory to inform choice of behavioural determinants. Underlying theories were varied and rarely specified.
Focus groups and interviews with at-risk individuals showed that few were aware that family history increased their risk of developing diabetes and Pierce (Pierce, 1996) reported that 66% of people with a parental history perceived little risk of developing diabetes themselves. Experts proposed at their first meeting that the theory should specify determinants of intention to change, as risk awareness and associated motivation to adopt preventative measures could not be assumed among the offspring of people with diabetes. Behavioural models (e.g. operant theory) and cognitive-behavioural models (e.g. cognitive-behavioural therapy) were not selected as the organizing theory, because they do not clearly specify how to strengthen motivation and primarily focus on problem behaviours. Social cognition models that specify determinants of motivation were chosen for further consideration.
After the expert meeting the project team reviewed the social cognition theories in more detail. The Theory of Planned Behaviour [TPB (Ajzen, 1991)] was selected to underpin the causal model, as it clearly specifies causal links between determinants of intention and behaviour, there is good evidence to support the theory's predictive validity, and construct measurement is clearly specified, which allows testing of the theory's causal pathways (Godin and Kok, 1996; Sutton, 1998; Armitage and Conner, 2001). In a population of similar age, the model's three main components (attitude, subjective norm and perceived behavioural control) explained 48% of the variance in intention to increase physical activity and this intention was on average only moderately strong (French et al., 2005). In a meta-analysis, intention and perceived behavioural control explained on average 27% of the variance in physical activity (Hagger et al., 2002).
Intervention points
Informed by the TPB, the beliefs predicting intention to change were defined as the first intervention point. According to the theory, change in these beliefs would lead to changes in intention and behaviour. As the intervention was delivered to individuals in family groups, it was possible to identify and target individual salient beliefs rather than working with the modal salient beliefs of the target group, which did not adequately represent those of the individual (Sutton et al., 2003). Thus, the TPB was used in a novel, although theoretically appropriate, way. The importance of building on individuals' own reasons for change was corroborated by interviews with at-risk individuals.
Experts identified a limitation of the TPB: a strong intention alone does not always lead to behaviour change (Sheeran, 2002; Sutton, 2004). Thus, intention and behaviour were identified as further intervention points.
Techniques to support behaviour change
The authors of the TPB propose persuasive messages as the main technique to change beliefs (Ajzen and Fishbein, 1980). In ProActive, information is provided about the potential to prevent diabetes by increases in physical activity. TPB-based interventions in the literature used a wide range of techniques, but their choice was rarely justified (Hardeman et al., 2002). At the second meeting experts identified additional techniques with evidence of effectiveness to change beliefs: reinforcing positive beliefs and problem solving in relation to negative beliefs.
To bridge the ‘gap’ between intention and behaviour, other techniques, not informed by the TPB, were selected to impact directly on intention and behaviour. Systematic reviews identified goal setting, self-monitoring, reinforcement (Hardeman et al., 2000) and building family support (McLean et al., 2003) as potentially effective techniques. The experts identified additional techniques to support individuals in moving from intention to action: action planning (Gollwitzer, 1996), building social support, facilitating habit formation (Aarts et al., 1997) and preventing relapse (Marlatt and Gordon, 1985). These techniques can be effectively applied to support increased physical activity (Dishman et al., 1996; Sallis et al., 1999).
Measures of change in behavioural determinants
Salient beliefs towards increasing physical activity were identified in the Ely pilot study, to inform a TPB-based questionnaire assessing change in behavioural determinants (Sutton et al., 2003).
The final causal model, including intervention points, behaviour change techniques and associated measures, is shown in Table IV. Behavioural determinants are derived from the TPB and the model specifies that change in specific physical activity behaviours will affect total objectively measured physical activity, which in turn will impact on physiological and biochemical variables, either directly via increased energy expenditure or fitness, or indirectly via weight gain prevention. Changes in these variables would result in lower risk of developing Type 2 diabetes.
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Discussion |
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This paper has presented a causal modelling approach to the development of a theory-based programme for evaluation in a randomized controlled trial. It outlined the steps followed in the translation of a generic causal model into a specific model, in the context of a particular disease and behavioural programme for its prevention in a high-risk group. The specific model shows causal pathways, intervention components and measures in one graphical representation. The case study used theories and evidence from psychology and epidemiology. Psychological determinants of the target behaviour (physical activity, measured as energy expenditure) were identified from the TPB. Prior trial evidence was available for causal links from target behaviour to outcomes, but evidence for the pathways from psychological determinants to behaviour came mainly from cross-sectional studies among people unrepresentative of our target population and are therefore hypothesized causal pathways to be tested in the trial and against other data sets.
Patient-centred measures of well-being and quality of life are also being assessed as important outcomes in their own right (Kinmonth et al., 1998). Impact on well-being may occur through many pathways, e.g. relating to anxiety about risk status (Shaw et al., 1999) or the burden of physical activity, or to reassurance from engaging in preventative activity or the social and physiological effects of exercise (Fox, 1999).
Comparison with other approaches
Most published approaches to intervention development and evaluation are comprehensive and best compared to the overall MRC framework, of which causal modelling is only a component. The MRC framework has a strong clinical trials focus and includes five phases (see Table I) representing a continuum of increasing evidence. The RE-AIM evaluation model (Glasgow et al., 1999) focuses on public health impact of interventions, and includes reach, efficacy, adoption, implementation and maintenance, which are considered in MRC phases 3–5. Other approaches describe phases of intervention development, implementation and evaluation, each with particular strengths. Precede–Proceed (Green and Kreuter, 1999) specifies phases of needs assessment that map onto MRC phase 1. Intervention Mapping (Bartholomew et al., 2001) describes five phases of programme development, from definition of proximal programme objectives to anticipation of process and effect evaluation, and Logic Models (Conrad et al., 1999) link inputs and activities to programme products and outcomes, while communicating the logic (theory) behind the programme. The last two approaches describe activities for MRC phases 1–4.
The causal modelling approach is most similar to the Four-Model Approach (Bauman et al., 1991), which works from a theoretical model to an implementation model for the programme. The causal modelling approach differs from this by specifying steps involved in developing causal models and specifying measures along the causal pathways. The modelling described in this paper includes steps used in most approaches, e.g. needs assessment, use of theory and evidence from epidemiology and social and behavioural sciences, and specification of techniques and measures. It varies from other approaches in terms of working towards a concise, one-page representation of theory- and evidence-based causal pathways linked to intervention components and measures, and an explicit focus on statistical modelling.
Causal models extend the MRC framework and other approaches in four ways.
- (1) They guide the choice of intervention points and measures along hypothesized causal pathways. This helps to avoid the common problems of measuring variables that could not logically be affected by the intervention and omitting those that could.
- (2) They assist in the choice of behaviour change techniques, indicating how they are hypothesized to impact on behaviour and its determinants, and allowing quantitative hypothesis testing along the causal path. This makes it possible to examine why interventions are effective or not.
- (3) They inform the assessment of fidelity to theories, e.g. to what extent intervention providers targeted the behavioural determinants and applied specified techniques.
- (4) They enable statistical modelling of the relationships between measured behaviours and distant health outcomes. In ProActive, 1-year follow-up does not allow assessment of the relationship between physical activity and incidence of Type 2 diabetes. However, epidemiological studies using similar measures of physical activity in comparable populations have estimated quantitatively the association between energy expenditure and diabetes risk (Wareham et al., 1997
). This allows the effect of the intervention on diabetes incidence to be modelled from its effect on physical activity.
- (2) They assist in the choice of behaviour change techniques, indicating how they are hypothesized to impact on behaviour and its determinants, and allowing quantitative hypothesis testing along the causal path. This makes it possible to examine why interventions are effective or not.
Importance of multidisciplinary working
The approach highlights the importance of a multidisciplinary perspective. In the case study, theories, intervention points, techniques and measures were drawn from social, health and clinical psychology. Target groups at risk of common chronic diseases that might be prevented by behaviour change were identified from epidemiology. Studies with objective measures of overall physical activity provided quantitative evidence that everyday activities (walking, cycling) were important target behaviours in terms of public health impact on diabetes risk (Wareham et al., 2000). Broad epidemiological studies can also inform environmental, social, political, economic and geographical determinants of behaviours, and in the future will inform genetic determinants of risk.
Theoretical issues
There are no clearly agreed criteria for choosing theories to inform interventions. A wide range of theories exists with substantial overlap in terms of constructs (Fishbein et al., 2001; Nigg et al., 2002; Sutton, 2003). We therefore developed a set of five criteria (see Methods), and future studies might develop them in relation to disease group (acute, chronic), stage in disease trajectory (asymptomatic, symptomatic, relapse), target group (volunteers, those seeking help), target behaviour (adopting healthy behaviours, decreasing addictive behaviours) and setting (primary care, specialist setting). Links between behaviour change techniques and behavioural determinants are also poorly developed. Intervention development may therefore draw on theories that specify causal pathways to inform psychological determinants of behaviour and more dynamic theories of behaviour change (e.g. self-regulation theory) to inform intervention techniques.
Operationalization
Moving from a specific causal model to an intervention for evaluation in a trial requires further significant work to bridge the gap between theory and practice. In ProActive, the causal model was applied in an action research model, working with 15 volunteer high-risk individuals and their families. Intervention protocols and fidelity measures were developed, and the feasibility and acceptability of the intervention tested. Two versions of a year-long intervention, an intensive face-to-face intervention (delivered at participants' homes and by phone) and less intensive distance intervention (delivered by phone and correspondence), were developed and are being tested in the trial against brief advice about physical activity. The practicalities of an intervention are determined by feasibility of the underlying health service model and trial design, considerations that are part of the wider MRC framework. The health service model and trial design inform additional measures in a trial, such as questionnaires to assess satisfaction with the programme and to inform health economic analysis.
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Conclusion |
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As a rational approach to intervention development and evaluation, the causal modelling approach indicates testable pathways from behavioural determinants to health outcomes and logical intervention points, and links the pathways to behaviour change techniques and measures in the context of a specific target group, health outcome and target behaviour. The approach is generalizable to other target groups and interventions. Its utility needs to be tested in a range of contexts, and future research needs to build on the steps required to design specific causal models and develop a comprehensive framework for theory selection.
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Acknowledgments |
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We thank Vanessa Carty and Mairi Huntly for piloting the programme, David French for piloting the psychological measures, Mary Pierce for consulting the target group, and Claire Somerville for interviewing families. We also thank Derek Johnston, Shirley Pearce, Paul Little, Theresa Marteau, Susan Michie, Sally McIntyre, Charles Abraham, Nick Tarrier, Christine Barrowclough, Helen Brown, David Kanka and Ron Zimmern for their contribution to the development of ProActive. We are grateful to the Practice Team of the Rookery Medical Centre, Newmarket, Suffolk and to families registered at the practice for their contribution to the development of ProActive. ProActive is supported by the MRC/DoH Nutrition Initiative, Royal College of General Practitioners, NHS R & D, MRC Health of the Public Initiative and Diabetes UK (ISRCTN 61323766). The views expressed in this publication are those of the researchers and not necessarily those of the Department of Health or the MRC. W. H., S. G. and A. L. K. are funded by the NHS R & D infrastructure research capacity building programme.
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Received on August 12, 2004; accepted on January 31, 2005
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