Factors influencing participant enrolment in a diabetes prevention program in general practice: lessons from the Sydney diabetes prevention program

Diabetes has been recognised as a significant contributor to global disease burden [1]. The rising prevalence of diabetes worldwide, coupled with the evidence of the costs and complications associated with this condition means prevention is an important strategy to reduce disease burden [1, 2].Obesity, physical inactivity and poor nutrition are major modifiable lifestyle risk factors for diabetes, making lifestyle interventions an obvious area to target for diabetes prevention [1].

The efficacy of intensive lifestyle interventions in preventing or delaying the onset of diabetes amongst high risk individuals has been well established in a number of large randomised controlled trials [3‐7]. Meta-analyses of these trials has shown that lifestyle intervention can reduce the incidence of diabetes by around 50% [8] and is at least as effective as drug treatment [9]. There are, however, a number of challenges in translating the findings of these large trials to achieve population health benefits. The first challenge is how to deliver such programs in a sustainable way in the community context and as part of routine service delivery. The second challenge is how best to engage high risk individuals to participate in such interventions.

Recent replication trials have demonstrated that it is feasible to implement community based diabetes prevention programs and early results have been promising [10‐17]. However, the reach of diabetes prevention programs has rarely been reported in the literature. A number of efficacy and replication trials have not provided any information on enrolment rates amongst eligible participants [4, 6, 12, 18‐23]. In other trials the proportion of eligible participants who agreed to enroll has varied widely from a third to 100 percent [3, 5, 7, 10, 11, 13‐16]. Even less is known about factors influencing enrolment in such programs. Understanding factors influencing enrolment is critical to improving the reach and population health impact of diabetes prevention programs.

This paper examines the predictors of enrolment in a diabetes prevention program conducted in general practice in New South Wales (NSW), Australia from 2008 to 2011. The findings will provide new insights into those individuals who are more or less likely to enrol in diabetes prevention programs informing the development of targeted recruitment strategies, particularly in the primary health care setting.

A total of 4055 individuals were screened, of which 1821 were eligible to participate in the study. Approximately two-thirds of eligible participants (n = 1238) enrolled in the program (Figure 1). The number of individuals enrolled by each GP varied considerably, with over a third of GPs (n = 77) not recruiting any and the majority (58.8%) enrolling between 1–20 participants. The mean AUSDRISK score of enrolled individuals was significantly higher than those who chose not to enrol in the program. Enrolled participants were more likely to have a family history of diabetes or history of high glucose levels and be physically inactive. However, individuals who did not enrol in the program were more likely to be male, of Aboriginal or Torres Strait Islander background, born in a country with high diabetes risk, smoke, take blood pressure lowering medication and consume low amounts of fruit and vegetables (Table 2). Interestingly, waist circumference, age and socioeconomic status (SEIFA index) were not associated with enrolment rates.

All of the factors found to be significant predictors of enrolment in univariate analysis remained independent predictors of enrolment in the multivariate model after adjusting for other variables and clustering by GPs, with the exception of gender (OR: 0.80, P = 0.100) and Aboriginal and Torres Strait Islander decent (OR: 0.53, P = 0.068), which were no longer significant predictors of enrolment (Table 3). In particular, physically inactive individuals (OR: 1.48, P = 0.004), those with a family history of diabetes (OR: 1.67, P = 0.000) and those with a history of high blood glucose levels (OR: 1.48, P = 0.001) were significantly more likely to enrol in the program. However, high risk individuals who smoked (OR: 0.52, P = 0.000), were born in a country with high diabetes risk (OR: 0.52, P = 0.000), were taking blood pressure lowering medications (OR: 0.80, P = 0.040) and consumed little fruit and vegetables (OR: 0.76, P = 0.047) were significantly less likely to take up the program. Overall, the model was able to correctly classify 69.7% of individuals, suggesting that the variables in the model are useful in accounting for the variation in enrolment rates.

This study provides important new insights into the factors influencing enrolment in diabetes prevention programs. Approximately one third of eligible individuals chose not to enrol in the lifestyle modification program, despite undergoing the initial screening process. Our findings suggest that some high risk individuals including those with a family history of diabetes, previous history of high blood glucose and physically inactive individuals are more likely to enrol in diabetes prevention programs. However, other high risk individuals including smokers, those born in a country with high diabetes risk, individuals taking blood pressure lowering medications and low consumers of fruit and vegetables are significantly less likely to take up such programs.

To our knowledge, few previous diabetes prevention studies have examined predictors of enrolment. In line with our findings, The DE-PLAN study in Greece [16] reported that program participation was independently associated with glucose intolerance and the site of recruitment. In contrast to our findings, Narayan et al. [27] found that after controlling for age, men were more likely to decline to take part compared with women, and after controlling for age and sex, people declining were more likely to have a lower weight and waist circumference. Little information was provided about other variables included in the models, making direct comparisons difficult.

The region of birth of participants was the only measured socio-demographic characteristic predictive of enrolment rates in our study. Despite their higher diabetes risk, individuals born in the Indian sub-continent, Middle East, Southern Europe and North Africa were less likely to enrol in the program. This highlights the importance of having programs targeting high risk ethnic groups as these groups are less likely to enrol in mainstream programs. In line with this, intervention programs specifically targeting Arabic-speaking and Chinese-speaking people were provided as part of the SDPP [24].

While indigenous status and socio-economic status (SES) were not independent predictors of enrolment in this study, this may reflect a lack of power to detect an effect due to the small number of eligible participants of Aboriginal and Torres Strait Islander descent in the study (n = 81, 4.4%) and the limited variation in the SES of participants, with few participants (n = 61, 3.4%) from areas of high deprivation. Given the high prevalence of diabetes amongst Aboriginal and Torres Strait Islanders [28] and the poorer health status of low SES groups [29], further research is required to explore uptake of diabetes prevention programs in these populations. Finally, the lack of association between age and enrolment likely reflects the narrow age range for this study (50–65 years) and the limited age categories used in the AUSDRISK tool.

Our findings highlight the importance of an individual’s health risk profile in predicting enrolment rates in diabetes prevention programs. A family history of diabetes and a previous history of high blood glucose were strong independent predictors of program uptake, suggesting high awareness and motivation amongst these individuals. Similarly, those individuals not meeting physical activity recommendations of 2.5 hours of physical activity per week were also more likely to enrol in the program. .While the measure of physical activity was crude, consisting of a single question (“On average, would you say you do at least 2.5 hours of physical activity per week (for example, 30 minutes a day on 5 or more days a week)?) this has been found to be a significant predictor of diabetes risk. Given that family history, of high blood glucose and physical inactivity are important risk factors for diabetes [25] our findings highlight the potential value in targeting those high risk individuals who are likely to be most receptive. In contrast, smokers were significantly less likely to take up the program, despite their higher diabetes risk [30]. There is some evidence to suggest that smokers are less likely to participate in health promotion programs [31, 32], and less likely to participate in research studies [33, 34].

Low consumers of fruit and vegetables (less than daily) were also less likely to take up the program. It should be noted that the measure of fruit and vegetable consumption was crude (‘everyday’ versus ‘not every day’) and may not accurately reflect actual intake. Finally individuals taking anti-hypertensive medication were also less likely to enrol. The reason for this is uncertain. As few studies have examined diabetes risk perception and its relationship with enrolment in diabetes prevention programs, these findings require exploration in future research.

While primary health care has been identified as an important setting for chronic disease prevention [35], the overall low rates of enrolment by GPs in this study points to the difficulties in implementing these programs in general practice, raising the question of how best to engage GPs in prevention. It could also be that since the AUSDRISK tool had only recently been developed and implemented as a screening tool, there was limited awareness and use in general practice [36]. The low rates of engagement by GPs may reflect the fact that the program was a trial and not an ongoing service. Research suggests that better system support is required to engage general practice in prevention including adequate funding and reimbursement systems, the use of staff such as practice nurses and managers and referral brokers (who act to facilitate referrals between GPs and services), along with better practice systems such as patient registration and recall and reminder systems [37‐41]. Our findings also highlight the importance of appropriately briefing GPs on prevention programs so they can encourage participation amongst their high risk patients. The provision of prompt feedback to GPs on the progress of referred individuals is also likely to be important in encouraging future referrals.

This study has a number of limitations. Firstly, data were not collected on the total number of participants approached and the proportion who agreed to be screened. It is likely that a high proportion of individuals who were approached but declined screening may have been eligible to participate, which means that the enrolment rate is likely to be over-estimated in this sample. Only a limited amount of data were collected on potential participants who agreed to be screened (using the AUSDRISK tool), reducing the number of variables that could be examined in relation to enrolment rates. However, the model was able to correctly predict enrolment outcomes in 69% of individuals, suggesting that the variables examined were important predictors. Some of the screening questions, particularly those relating to physical activity levels and fruit and vegetable consumption were crude single item measures, and hence caution is required in interpreting these results. Further research is required to confirm these findings across a larger number of studies, using both quantitative and qualitative methods. In particular, qualitative interviews with eligible participants who decline to take part in diabetes prevention programs may provide important insights to complement these quantitative findings. This study was also unable to examine the contribution of individual versus GP/practice factors in influencing enrolment rates using multi-level analysis. This type of analysis was not possible in this study due to limited availability of data at the GP and practice level. Future research in the general practice setting should aim to examine predictors of enrolment at the practice, GP and individual level.

Our findings highlight that engaging both primary health care providers and high risk individuals to participate in diabetes prevention programs remains an ongoing challenge. At the individual level, our findings suggest that some high risk groups including those with a family history of diabetes, a history of high blood glucose and physically inactive individuals are more likely to enrol in lifestyle intervention programs and may be worth targeting initially due to their high risk status and likely receptivity. However, other high risk groups are likely to require targeted strategies, in particular smokers and those born in countries with high diabetes risk. Further research is required to better understand factors influencing the uptake of diabetes prevention programs in primary health care, in particular the influence of individual, GP and practice factors.