583372 research-article2015
HPQ0010.1177/1359105315583372Journal of Health PsychologyJones et al.
Review
A systematic review of the effectiveness of interventions using the Common Sense Self-Regulatory Model to improve adherence behaviours
Journal of Health Psychology 1–19 © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1359105315583372 hpq.sagepub.com
Christina J Jones, Helen E Smith and Carrie D Llewellyn
Abstract This systematic review assessed the effectiveness of the Common Sense Self-Regulatory Model in the design of interventions to improve adherence behaviours. Of nine eligible studies, six reported improvements in adherence behaviours and three showed moderate to large effects on return to work and lifestyle recommendations. Four studies stated how Common Sense Self-Regulatory Model constructs were addressed in the intervention and five measured illness perceptions as outcomes. Evidence was found for targeting cure/control perceptions in studies aimed at improving adherence behaviours. Future studies need to measure illness perceptions pre- and post-intervention to enable mediational analyses to assess the effect of Common Sense Self-Regulatory Model interventions on improving health outcomes.
Keywords adherence, Common Sense Self-Regulatory Model, illness perceptions, illness representations, interventions
Introduction The issue of non-adherence to medical advice is well documented; it is estimated that 25–50 per cent of medicines prescribed for chronic illness are not taken as prescribed (DiMatteo, 2004; Sabaté, 2003). Non-adherence to medical advice has serious implications for patient outcomes and society as a whole. It is reported that the odds of a favourable health outcome for patients who adhere to medical advice are three times greater than for those who do not adhere (DiMatteo et al., 2002). In the United Kingdom, drug costs are estimated to account for 10 per
cent of all National Health Service (NHS) expenditure and unused or unwanted medication costs the NHS around £100 million a year (Department of Health, 2008).
Brighton and Sussex Medical School, UK Corresponding author: Christina J Jones, Division of Primary Care and Public Health, Brighton and Sussex Medical School, 321 Mayfield House, Brighton BN1 9PH, UK. Email: [email protected]
Downloaded from hpq.sagepub.com at CALIFORNIA STATE UNIV FRESNO on July 23, 2015
2
Journal of Health Psychology
Figure 1. The Common Sense Self-Regulatory Model (modified from Leventhal et al., 1984).
Non-adherence falls into two broad categories: intentional and unintentional. Unintentional non-adherence occurs when the patient is willing to follow medical advice but factors beyond their control prevent this, such as poor recall of instructions or financial barriers. Contrastingly, intentional non-adherence is when the patient purposely chooses not to follow medical advice (Nunes et al., 2009). Reasons for patient reluctance to adhere are likely to be multi-faceted and may result from poor communication with health-care professionals leading to confusion and concern about their illness and treatment. To understand individuals’ health-related choices, theoretical work in psychology has produced a variety of Social Cognition Models such as the Health Belief Model (Rosenstock, 1966), the Protection Motivation Theory (Rogers, 1983) and the Theory of Planned Behaviour (Ajzen, 1991) to name only a few. More recently, interest has moved towards models of self-regulation (Sutton, 2001), which are based upon problem-solving models and suggest that individuals respond to illness or symptoms in the same way they would approach other problems (Ogden, 1997). This shift is important as proximal predictors of health actions and self-regulatory processes have shown to offer promising explanations for long-term health behaviour change (Abraham et al., 1998; Sniehotta et al., 2005). One psychological model of self-regulation which has been used extensively to help understand illness and treatment perceptions is
the Common Sense Self-Regulatory Model (CS-SRM) (Rogers, 1983; Rosenstock, 1966).
CS-SRM The CS-SRM is a framework for examining individuals’ beliefs about their illness (illness representations) and health behaviours (i.e. adherence to medication/treatment advice, dietary and lifestyle recommendations). Leventhal et al. (1980; 1984) proposed that patients form both cognitive representations (beliefs about their illness) and emotional representations (emotional responses to their illness) and develop coping mechanisms accordingly. Both internal stimuli, such as symptoms, and external stimuli, such as information about risk, contribute to the beliefs that people form. Leventhal et al. (1980; 1984) suggested that the processing of this information is fed back on a continuous loop allowing modification of the representations and the coping mechanisms employed (Figure 1). Five cognitive dimensions of this process were originally identified. These were: identity (the label used to describe the condition and related symptoms), consequences (the anticipated effects and outcome of the condition), timeline acute/chronic (the length of time that the individual believes their condition will last), personal and treatment control/cure (the extent to which individuals believe they will recover from their condition or control it through treatment) and
Downloaded from hpq.sagepub.com at CALIFORNIA STATE UNIV FRESNO on July 23, 2015
3
Jones et al. cause (the individuals perception of what lead to the onset of the condition). Three further cognitive dimensions were later added which address emotional representations (emotional perceptions related to the illness), cyclical timeline beliefs (perceptions related to fluctuation in symptoms and changeability of the illness) and coherence (the extent to which an individual has a coherent understanding of their condition) (Moss-Morris et al., 2002). It is thought that an understanding of the individual’s perceptions about these cognitive dimensions may determine how and why they adhere to medical advice. A recent metaanalysis exploring whether mental representations derived from the CS-SRM predict adherence in patients with chronic illness demonstrated a weak relationship between CS-SRM mental representations and adherence (effect sizes of r −.02 to .12) (Brandes and Mullan, 2014). The strongest predictors of adherence among these chronically ill patients were control beliefs (treatment and personal) however, the effect sizes remained weak (r = .12). This led study authors to conclude that mental representations may not be the best predictors of adherence (Brandes and Mullan, 2014). Since then, a further systematic review has been conducted exploring the extent to which CS-SRM domains are associated with self-management behaviours in children with chronic illness (Law et al., 2014). This review reported high variability in results between studies, but some indication that control beliefs were again more strongly associated with self-management than other CS-SRM domains (Law et al., 2014). Authors recommended that control beliefs, especially those regarding treatment control, should be the target for interventional studies looking to improve self-management in adolescents with chronic illness. Although systematic reviews and metaanalyses exist looking at the predictive ability and association of CS-SRM domains and adherence or self-management, the majority of studies included are cross-sectional in design. To date, there has been no systematic review of the evidence to support the use of the CS-SRM in
the design of interventions to improve adherence. Previous systematic reviews and metaanalysis have proposed that certain CS-SRM domains, for example, control beliefs, should be the target of interventional studies due to their apparent predictive abilities. However, the evidence of CS-SRM intervention success in improving adherence has not been documented. In order to determine whether the CS-SRM and its related domains are a useful target for the design of interventional studies to improve adherence, the evidence must first be assessed. The purpose of this systematic review was therefore to identify studies that have specifically used the CS-SRM to develop interventions targeted at improving adherence to health-care behaviours (both medication and dietary/lifestyle), and assess intervention effectiveness.
Method Two search methods were used to identify studies using the CS-SRM as the theoretical basis for interventions to improve adherence behaviours (specifically medication/treatment adherence and dietary/lifestyle recommendations). A keyword search (Appendix 1) of electronic databases including PsycINFO (1894–December 2014), OVID-MEDLINE (1950–December 2014), EMBASE (1980–December 2014) and British Nursing Index (1985–December 2014) was followed by a manual search of the reference lists of eligible studies identified by the search described above.
Selection strategy Studies were included if they fulfilled all six criteria (1) interventional in design, (2) intervention aimed at improving adherence to medical advice including medication/treatment or dietary/lifestyle advice, (3) adherence measured as a behavioural outcome (i.e. not intentions to adhere or proxy/surrogate measures of adherence, (4) intervention designed to address any of the constructs derived from the CS-SRM, (5) no other additional established psychological model informed the intervention and (6)
Downloaded from hpq.sagepub.com at CALIFORNIA STATE UNIV FRESNO on July 23, 2015
4
Additional records identified through other sources (n = 8)
Records screened (n = 3360)
Records excluded at title/abstract abstract (n = 3304)
Full-text articles assessed for eligibility (n = 56)
Full-text articles excluded (n = 47) No intervention (n =19) Not CS-SRM component to intervention (n =16) No behavioural outcome measure of adherence (n = 7) Protocol only (n = 3) Mixed model intervention (n = 2)
Included
Screening
Records identified through database searching (n = 3352)
Eligibility
Identification
Journal of Health Psychology
Studies included in systematic review (n = 9)
Figure 2. PRISMA flow diagram for identification of intervention studies using the Common Sense SelfRegulatory Model to improve adherence (see Appendix 2 for PRISMA checklist).
published in English. Studies were excluded if the behavioural outcome measure was not confirmed (i.e. enrolment at rehabilitation but attendance not confirmed). Using the search criteria described, 3360 studies were identified for possible inclusion. Of these, 3304 were excluded after screening of the title and abstract. The full texts of 56 studies were accessed to determine eligibility. Of these, 47 failed to meet the eligibility criteria, the majority lacking an interventional study design (Figure 2). The remaining nine studies were reviewed for study design, participant characteristics, intervention and outcomes. Meta-analysis of these studies was attempted. However, given the heterogeneity in participants, interventions,
sample size, outcomes and length of follow-up periods, the results did not provide a meaningful synthesis of the literature. The trials are discussed in a narrative review and effects sizes are presented.
Assessment of impact of intervention In an attempt to assess impact of the intervention, Cohen’s d effect sizes were calculated for all studies where this was not provided in the published paper (Cohen, 1992). Where adherence was measured as a continuous outcome (e.g. number of days medication was used), the means and pooled standard deviation were used to calculate the effect. Where adherence was
Downloaded from hpq.sagepub.com at CALIFORNIA STATE UNIV FRESNO on July 23, 2015
5
Jones et al. measured by dichotomous outcomes (e.g. attendance at appointment), odds ratios were calculated before converting to Cohen’s d using a recommended calculation (Borenstein et al., 2009). Comparing the effect sizes of the interventions enabled us to minimise the impact sample size had on the statistical significance of results.
Classification of behaviour change techniques It is well recognised that the underreporting of behaviour change techniques limits the replicability of interventions so we used the Behaviour Change Technique Taxonomy (v1) to document the active content of interventions (Michie et al., 2013). The taxonomy includes 93 hierarchically clustered behaviour change techniques and we have previous experience of classifying intervention content using this taxonomy (Jones et al., 2014).
Quality assessment of studies To assess the methodological quality and reporting of studies, the Cochrane Collaboration’s tool for assessing risk of bias was used (Higgins and Green, 2008). All studies were assessed for risk of bias on six domains: sequence generation, allocation concealment, blinding, adequate description of incomplete outcome data, selective outcome reporting and any other potential sources of bias. The studies were rated as having ‘high’, ‘low’ or ‘unclear’ risk of bias independently by all authors and any disagreements (n = 2) were resolved by discussion.
2002), one each in Ireland (Keogh et al., 2011), The Netherlands (Theunissen et al., 2003) and United States (Gould, 2011). Studies spanned two decades; six studies were published in the 2000s and three since 2010. All studies included participants of both genders and focused on patient populations. Illnesses researched were myocardial infarction (Broadbent et al., 2009; Petrie et al., 2002), intermittent claudication (Cunningham et al., 2012), type 2 diabetes (French et al., 2008; Keogh et al., 2011), hypertension (Theunissen et al., 2003), end-stage renal disease (Karamanidou et al., 2008), acute cardiac events (Gould, 2011) and elderly medicine users (Elliott et al., 2008). All studies targeted adult patients, and two of the studies invited family members to partake in the intervention (Broadbent et al., 2009; Keogh et al., 2011).
Design characteristics The sample size of the studies ranged from 39 to 492 (mean = 162, median = 108). All of the studies were randomised controlled trials. The follow-up period post-intervention ranged from 1 day to 12 months. The majority of studies used a two-arm trial design but two studies compared two types of intervention against a control (French et al., 2008; Theunissen et al., 2003). Two of the nine studies reported adequate power based on a power calculation (Cunningham et al., 2012; Gould, 2011). Two of the nine studies not reporting power calculations had relatively large (n > 200) sample sizes (Elliott et al., 2008; French et al., 2008).
Intervention characteristics Results Demographic characteristics Information on each of the nine studies is summarised in Table 1. Four of these studies were conducted in the United Kingdom (Cunningham et al., 2012; Elliott et al., 2008; French et al., 2008; Karamanidou et al., 2008), two in New Zealand (Broadbent et al., 2009; Petrie et al.,
All interventions were led by health professionals and three interventions took place over several sessions. Complex interventions, involving a health professional with the addition of an action or management plan, were frequently used (Broadbent et al., 2009; Cunningham et al., 2012; Gould, 2011; Karamanidou et al., 2008; Keogh et al., 2011; Petrie et al., 2002; Theunissen et al., 2003). The health professionals involved
Downloaded from hpq.sagepub.com at CALIFORNIA STATE UNIV FRESNO on July 23, 2015
Downloaded from hpq.sagepub.com at CALIFORNIA STATE UNIV FRESNO on July 23, 2015
Intervention = telephone call with pharmacist who followed a semi-structured interview schedule based on the CS-SRM Control = usual care
RCT
Elliott et al. (2008)
492 patients aged 75+ medicine users
Intervention = two-arms GP 15-minute consultation (1) discussing patients ideas about their hypertension and (2) discussing action plans Control = usual care
108 patients with RCT hypertension
Intervention = 3 × 30– 40 minute sessions with a psychologist discussing illness perceptions and developing action plans to manage post MI Control = usual care
Theunissen et al. (2003)
Non-adherence was lower in the intervention group (9%) compared to controls (16%, p = .032)
Adherence d = .34 Self-reported non- 4 weeks adherence (missing >1 dose in 7 days)
Reattribution, Not specified information about antecedents,
Return to work d = .44 Attendance at cardiac rehabilitation d = .45
Effect size (behaviour)
Adherence Health status, IPQ, Immediate, Both experimental conditions reported d = .12 action intentions, 1 month modest changes in action self-efficacy, illness representations adherence (p
HPQ0010.1177/1359105315583372Journal of Health PsychologyJones et al.
Review
A systematic review of the effectiveness of interventions using the Common Sense Self-Regulatory Model to improve adherence behaviours
Journal of Health Psychology 1–19 © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1359105315583372 hpq.sagepub.com
Christina J Jones, Helen E Smith and Carrie D Llewellyn
Abstract This systematic review assessed the effectiveness of the Common Sense Self-Regulatory Model in the design of interventions to improve adherence behaviours. Of nine eligible studies, six reported improvements in adherence behaviours and three showed moderate to large effects on return to work and lifestyle recommendations. Four studies stated how Common Sense Self-Regulatory Model constructs were addressed in the intervention and five measured illness perceptions as outcomes. Evidence was found for targeting cure/control perceptions in studies aimed at improving adherence behaviours. Future studies need to measure illness perceptions pre- and post-intervention to enable mediational analyses to assess the effect of Common Sense Self-Regulatory Model interventions on improving health outcomes.
Keywords adherence, Common Sense Self-Regulatory Model, illness perceptions, illness representations, interventions
Introduction The issue of non-adherence to medical advice is well documented; it is estimated that 25–50 per cent of medicines prescribed for chronic illness are not taken as prescribed (DiMatteo, 2004; Sabaté, 2003). Non-adherence to medical advice has serious implications for patient outcomes and society as a whole. It is reported that the odds of a favourable health outcome for patients who adhere to medical advice are three times greater than for those who do not adhere (DiMatteo et al., 2002). In the United Kingdom, drug costs are estimated to account for 10 per
cent of all National Health Service (NHS) expenditure and unused or unwanted medication costs the NHS around £100 million a year (Department of Health, 2008).
Brighton and Sussex Medical School, UK Corresponding author: Christina J Jones, Division of Primary Care and Public Health, Brighton and Sussex Medical School, 321 Mayfield House, Brighton BN1 9PH, UK. Email: [email protected]
Downloaded from hpq.sagepub.com at CALIFORNIA STATE UNIV FRESNO on July 23, 2015
2
Journal of Health Psychology
Figure 1. The Common Sense Self-Regulatory Model (modified from Leventhal et al., 1984).
Non-adherence falls into two broad categories: intentional and unintentional. Unintentional non-adherence occurs when the patient is willing to follow medical advice but factors beyond their control prevent this, such as poor recall of instructions or financial barriers. Contrastingly, intentional non-adherence is when the patient purposely chooses not to follow medical advice (Nunes et al., 2009). Reasons for patient reluctance to adhere are likely to be multi-faceted and may result from poor communication with health-care professionals leading to confusion and concern about their illness and treatment. To understand individuals’ health-related choices, theoretical work in psychology has produced a variety of Social Cognition Models such as the Health Belief Model (Rosenstock, 1966), the Protection Motivation Theory (Rogers, 1983) and the Theory of Planned Behaviour (Ajzen, 1991) to name only a few. More recently, interest has moved towards models of self-regulation (Sutton, 2001), which are based upon problem-solving models and suggest that individuals respond to illness or symptoms in the same way they would approach other problems (Ogden, 1997). This shift is important as proximal predictors of health actions and self-regulatory processes have shown to offer promising explanations for long-term health behaviour change (Abraham et al., 1998; Sniehotta et al., 2005). One psychological model of self-regulation which has been used extensively to help understand illness and treatment perceptions is
the Common Sense Self-Regulatory Model (CS-SRM) (Rogers, 1983; Rosenstock, 1966).
CS-SRM The CS-SRM is a framework for examining individuals’ beliefs about their illness (illness representations) and health behaviours (i.e. adherence to medication/treatment advice, dietary and lifestyle recommendations). Leventhal et al. (1980; 1984) proposed that patients form both cognitive representations (beliefs about their illness) and emotional representations (emotional responses to their illness) and develop coping mechanisms accordingly. Both internal stimuli, such as symptoms, and external stimuli, such as information about risk, contribute to the beliefs that people form. Leventhal et al. (1980; 1984) suggested that the processing of this information is fed back on a continuous loop allowing modification of the representations and the coping mechanisms employed (Figure 1). Five cognitive dimensions of this process were originally identified. These were: identity (the label used to describe the condition and related symptoms), consequences (the anticipated effects and outcome of the condition), timeline acute/chronic (the length of time that the individual believes their condition will last), personal and treatment control/cure (the extent to which individuals believe they will recover from their condition or control it through treatment) and
Downloaded from hpq.sagepub.com at CALIFORNIA STATE UNIV FRESNO on July 23, 2015
3
Jones et al. cause (the individuals perception of what lead to the onset of the condition). Three further cognitive dimensions were later added which address emotional representations (emotional perceptions related to the illness), cyclical timeline beliefs (perceptions related to fluctuation in symptoms and changeability of the illness) and coherence (the extent to which an individual has a coherent understanding of their condition) (Moss-Morris et al., 2002). It is thought that an understanding of the individual’s perceptions about these cognitive dimensions may determine how and why they adhere to medical advice. A recent metaanalysis exploring whether mental representations derived from the CS-SRM predict adherence in patients with chronic illness demonstrated a weak relationship between CS-SRM mental representations and adherence (effect sizes of r −.02 to .12) (Brandes and Mullan, 2014). The strongest predictors of adherence among these chronically ill patients were control beliefs (treatment and personal) however, the effect sizes remained weak (r = .12). This led study authors to conclude that mental representations may not be the best predictors of adherence (Brandes and Mullan, 2014). Since then, a further systematic review has been conducted exploring the extent to which CS-SRM domains are associated with self-management behaviours in children with chronic illness (Law et al., 2014). This review reported high variability in results between studies, but some indication that control beliefs were again more strongly associated with self-management than other CS-SRM domains (Law et al., 2014). Authors recommended that control beliefs, especially those regarding treatment control, should be the target for interventional studies looking to improve self-management in adolescents with chronic illness. Although systematic reviews and metaanalyses exist looking at the predictive ability and association of CS-SRM domains and adherence or self-management, the majority of studies included are cross-sectional in design. To date, there has been no systematic review of the evidence to support the use of the CS-SRM in
the design of interventions to improve adherence. Previous systematic reviews and metaanalysis have proposed that certain CS-SRM domains, for example, control beliefs, should be the target of interventional studies due to their apparent predictive abilities. However, the evidence of CS-SRM intervention success in improving adherence has not been documented. In order to determine whether the CS-SRM and its related domains are a useful target for the design of interventional studies to improve adherence, the evidence must first be assessed. The purpose of this systematic review was therefore to identify studies that have specifically used the CS-SRM to develop interventions targeted at improving adherence to health-care behaviours (both medication and dietary/lifestyle), and assess intervention effectiveness.
Method Two search methods were used to identify studies using the CS-SRM as the theoretical basis for interventions to improve adherence behaviours (specifically medication/treatment adherence and dietary/lifestyle recommendations). A keyword search (Appendix 1) of electronic databases including PsycINFO (1894–December 2014), OVID-MEDLINE (1950–December 2014), EMBASE (1980–December 2014) and British Nursing Index (1985–December 2014) was followed by a manual search of the reference lists of eligible studies identified by the search described above.
Selection strategy Studies were included if they fulfilled all six criteria (1) interventional in design, (2) intervention aimed at improving adherence to medical advice including medication/treatment or dietary/lifestyle advice, (3) adherence measured as a behavioural outcome (i.e. not intentions to adhere or proxy/surrogate measures of adherence, (4) intervention designed to address any of the constructs derived from the CS-SRM, (5) no other additional established psychological model informed the intervention and (6)
Downloaded from hpq.sagepub.com at CALIFORNIA STATE UNIV FRESNO on July 23, 2015
4
Additional records identified through other sources (n = 8)
Records screened (n = 3360)
Records excluded at title/abstract abstract (n = 3304)
Full-text articles assessed for eligibility (n = 56)
Full-text articles excluded (n = 47) No intervention (n =19) Not CS-SRM component to intervention (n =16) No behavioural outcome measure of adherence (n = 7) Protocol only (n = 3) Mixed model intervention (n = 2)
Included
Screening
Records identified through database searching (n = 3352)
Eligibility
Identification
Journal of Health Psychology
Studies included in systematic review (n = 9)
Figure 2. PRISMA flow diagram for identification of intervention studies using the Common Sense SelfRegulatory Model to improve adherence (see Appendix 2 for PRISMA checklist).
published in English. Studies were excluded if the behavioural outcome measure was not confirmed (i.e. enrolment at rehabilitation but attendance not confirmed). Using the search criteria described, 3360 studies were identified for possible inclusion. Of these, 3304 were excluded after screening of the title and abstract. The full texts of 56 studies were accessed to determine eligibility. Of these, 47 failed to meet the eligibility criteria, the majority lacking an interventional study design (Figure 2). The remaining nine studies were reviewed for study design, participant characteristics, intervention and outcomes. Meta-analysis of these studies was attempted. However, given the heterogeneity in participants, interventions,
sample size, outcomes and length of follow-up periods, the results did not provide a meaningful synthesis of the literature. The trials are discussed in a narrative review and effects sizes are presented.
Assessment of impact of intervention In an attempt to assess impact of the intervention, Cohen’s d effect sizes were calculated for all studies where this was not provided in the published paper (Cohen, 1992). Where adherence was measured as a continuous outcome (e.g. number of days medication was used), the means and pooled standard deviation were used to calculate the effect. Where adherence was
Downloaded from hpq.sagepub.com at CALIFORNIA STATE UNIV FRESNO on July 23, 2015
5
Jones et al. measured by dichotomous outcomes (e.g. attendance at appointment), odds ratios were calculated before converting to Cohen’s d using a recommended calculation (Borenstein et al., 2009). Comparing the effect sizes of the interventions enabled us to minimise the impact sample size had on the statistical significance of results.
Classification of behaviour change techniques It is well recognised that the underreporting of behaviour change techniques limits the replicability of interventions so we used the Behaviour Change Technique Taxonomy (v1) to document the active content of interventions (Michie et al., 2013). The taxonomy includes 93 hierarchically clustered behaviour change techniques and we have previous experience of classifying intervention content using this taxonomy (Jones et al., 2014).
Quality assessment of studies To assess the methodological quality and reporting of studies, the Cochrane Collaboration’s tool for assessing risk of bias was used (Higgins and Green, 2008). All studies were assessed for risk of bias on six domains: sequence generation, allocation concealment, blinding, adequate description of incomplete outcome data, selective outcome reporting and any other potential sources of bias. The studies were rated as having ‘high’, ‘low’ or ‘unclear’ risk of bias independently by all authors and any disagreements (n = 2) were resolved by discussion.
2002), one each in Ireland (Keogh et al., 2011), The Netherlands (Theunissen et al., 2003) and United States (Gould, 2011). Studies spanned two decades; six studies were published in the 2000s and three since 2010. All studies included participants of both genders and focused on patient populations. Illnesses researched were myocardial infarction (Broadbent et al., 2009; Petrie et al., 2002), intermittent claudication (Cunningham et al., 2012), type 2 diabetes (French et al., 2008; Keogh et al., 2011), hypertension (Theunissen et al., 2003), end-stage renal disease (Karamanidou et al., 2008), acute cardiac events (Gould, 2011) and elderly medicine users (Elliott et al., 2008). All studies targeted adult patients, and two of the studies invited family members to partake in the intervention (Broadbent et al., 2009; Keogh et al., 2011).
Design characteristics The sample size of the studies ranged from 39 to 492 (mean = 162, median = 108). All of the studies were randomised controlled trials. The follow-up period post-intervention ranged from 1 day to 12 months. The majority of studies used a two-arm trial design but two studies compared two types of intervention against a control (French et al., 2008; Theunissen et al., 2003). Two of the nine studies reported adequate power based on a power calculation (Cunningham et al., 2012; Gould, 2011). Two of the nine studies not reporting power calculations had relatively large (n > 200) sample sizes (Elliott et al., 2008; French et al., 2008).
Intervention characteristics Results Demographic characteristics Information on each of the nine studies is summarised in Table 1. Four of these studies were conducted in the United Kingdom (Cunningham et al., 2012; Elliott et al., 2008; French et al., 2008; Karamanidou et al., 2008), two in New Zealand (Broadbent et al., 2009; Petrie et al.,
All interventions were led by health professionals and three interventions took place over several sessions. Complex interventions, involving a health professional with the addition of an action or management plan, were frequently used (Broadbent et al., 2009; Cunningham et al., 2012; Gould, 2011; Karamanidou et al., 2008; Keogh et al., 2011; Petrie et al., 2002; Theunissen et al., 2003). The health professionals involved
Downloaded from hpq.sagepub.com at CALIFORNIA STATE UNIV FRESNO on July 23, 2015
Downloaded from hpq.sagepub.com at CALIFORNIA STATE UNIV FRESNO on July 23, 2015
Intervention = telephone call with pharmacist who followed a semi-structured interview schedule based on the CS-SRM Control = usual care
RCT
Elliott et al. (2008)
492 patients aged 75+ medicine users
Intervention = two-arms GP 15-minute consultation (1) discussing patients ideas about their hypertension and (2) discussing action plans Control = usual care
108 patients with RCT hypertension
Intervention = 3 × 30– 40 minute sessions with a psychologist discussing illness perceptions and developing action plans to manage post MI Control = usual care
Theunissen et al. (2003)
Non-adherence was lower in the intervention group (9%) compared to controls (16%, p = .032)
Adherence d = .34 Self-reported non- 4 weeks adherence (missing >1 dose in 7 days)
Reattribution, Not specified information about antecedents,
Return to work d = .44 Attendance at cardiac rehabilitation d = .45
Effect size (behaviour)
Adherence Health status, IPQ, Immediate, Both experimental conditions reported d = .12 action intentions, 1 month modest changes in action self-efficacy, illness representations adherence (p
