- Research article
- Open Access
Response bias to a randomised controlled trial of a lifestyle intervention in people at high risk of cardiovascular disease: a cross-sectional analysis
BMC Public Health volume 18, Article number: 1092 (2018)
Research evaluating lifestyle interventions for prevention of cardiovascular disease (CVD) may not reach those most at risk. We compared the response rate to a randomised controlled trial (RCT) of a lifestyle intervention by CVD risk, ethnicity and level of deprivation.
Primary care patients with a QRisk2 score ≥ 20% were invited to participate in a RCT of an intensive lifestyle intervention versus usual care. This cross-sectional analysis compares anonymised data of responders and non-responders with multiple logistic regression, using adjusted odds ratios (AORs) for QRisk2 score, ethnicity, Index of Multiple Deprivation (IMD 2010) quintile, age and sex.
From 60 general practices, 8902 patients were invited and 1489 responded. The mean age was 67.3 years and 21.0% were female. Of all patients invited, 69.9% were of white ethnic background, 13.9% ethnic minority backgrounds and 16.2% had no ethnicity data recorded in their medical records. Likelihood of response decreased as QRisk2 score increased (AOR 0.82 per 5 percentage points, 95% CI 0.77–0.88). Black African or Caribbean patients (AOR 0.67; 95% CI 0.45–0.98) and those with missing ethnicity data (AOR 0.55; 95% CI 0.46–0.66) were less likely to respond compared to participants of white ethnicity, but there was no difference in the response rates between south Asian and white ethnicity (AOR 1.08; 95% CI 0.84–1.38). Patients residing in the fourth (AOR 0.70; 95% CI 0.56–0.87) and fifth (AOR 0.52; 95% CI 0.40–0.68) most deprived IMD quintile were less likely to respond compared to the least deprived quintile.
Evaluations of interventions intended for those at high risk of CVD may fail to reach those at highest risk. Hard to reach patient groups may require different recruitment strategies to maximise participation in future trials. Improvements in primary care ethnicity data recording is required to aid understanding of how successfully study samples represent the target population.
ISRCTN, ISRCTN84864870. Registered 15 May 2012, https://doi.org/10.1186/ISRCTN84864870.
Cardiovascular disease (CVD) is the most common cause of mortality in developed nations . Modifiable risk factors for CVD include tobacco use, physical inactivity, obesity and raised low-density lipoprotein (LDL) cholesterol [2,3,4]. Those most at risk are older males, of south Asian ethnic background, with lower educational attainment and lower socioeconomic status [5, 6]. Lifestyle intervention trials for primary prevention of CVD are a research priority [7,8,9], but previous trials have reported low participation rates [10, 11].
Factors associated with increased participation in intervention trials to increase walking and physical activity include white ethnicity, living in more affluent areas, middle age, female sex and university education [11, 12], although methodological factors can lead to different participation biases across trials. Whilst some previous trials have observed that those of poorest health are the most likely to respond [11, 13,14,15], others report that participants are healthier and more active than non-participants [10, 14, 16, 17]. Participation of ethnic minorities is important as they are at higher risk for CVD and type 2 diabetes . Failure to recruit subjects at highest risk of disease may limit representativeness, underestimate effect sizes, and could lead to the implementation of interventions which increase rather than decrease health inequalities. Yet, there are few opportunities to study participation biases as those who do not respond to invitations to participate typically have not given consent to medical data access .
In this study we tested the hypothesis that people who have a lower risk of CVD are more likely to respond to an invite to participate in a randomised controlled trial (RCT) evaluating an intensive lifestyle intervention for reducing weight and increasing physical activity. We also tested whether potential sociodemographic markers of response such as white ethnicity and living in more affluent areas corresponded to a greater likelihood of response.
Setting and design
We used a cross-sectional design. The sample was derived from the target population invited for eligibility screening to participate in a RCT assessing the effectiveness of an enhanced MOtiVational intErviewing InTervention (MOVE IT) for reducing weight and increasing physical activity in people at high risk of CVD. MOVE IT compares the effectiveness of motivational interviewing (MI) and cognitive behaviour therapy (CBT) behaviour change techniques in group, individual and usual care arms. The study population consisted of patients at high risk of CVD in primary care from 12 south London Clinical Commissioning Groups (CCGs) representing an ethnically-diverse resident population of about 3 million . Further details are described in the trial protocol . Ethical approval for the MOVE IT trial was granted by the Dulwich ethics committee (12/LO/0917), including permission to extract anonymised data for all patients invited to participate. Data were not extracted if the patient record included an informed dissent code, indicating that patient data should not be shared with a third party.
Participants and case definition
Participating practices screened primary care databases for eligible patients using either EMIS (EMIS Health, Leeds, UK) or Vision (In Practice Systems, London, UK) medical records systems, two of the clinical software programmes most commonly used in UK primary care. The risk of CVD was calculated using QRisk2 (QResearch, Nottingham, UK), a validated predictive tool for identifying the percentage risk of having a fatal or non-fatal cardiovascular event in the next 10 years . Registered patients with a QRisk2 score estimated on medical records to be ≥20% and aged 40–74 years were invited to participate via a standardised letter from their general practitioner (see Additional file 1) which also included a participant information sheet (see Additional file 2). Patients were given a choice of response methods: either returning a reply slip in a stamped and addressed envelope following which a research assistant would telephone to arrange an appointment time, or to telephone the research team directly. Patients were excluded from the invitation if their medical records included a Read code (a coded thesaurus of clinical terms used in UK primary care databases) for past diagnosis of CVD, diabetes, kidney disease, chronic obstructive pulmonary disease, disabling neurological disorder, severe mental illness, registered blind or housebound, currently pregnant, advanced cancer or a body mass index > 50 kg/m2.
The measures collected anonymously for patients invited to participate were QRisk2 score, ethnicity, postcode, age (at time of screening) and sex. QRisk2 score was estimated on medical records via a batch calculator which uses an algorithm of risk factors . The variables used by the QRisk2 algorithm are age, sex, ethnicity, deprivation calculated from postcode data, smoking status, diabetes status, rheumatoid arthritis status, chronic kidney disease status, atrial fibrillation status, hypertensive treatment status, family history of CVD, body mass index (BMI), systolic blood pressure and the ratio of high-density lipoprotein cholesterol to total cholesterol. The algorithm uses age- and sex-based national averages for missing data values.
Self-report ethnicity data on medical records includes a wide variety of categories, some of which are not clearly defined. Where possible, data were grouped into white, black African or Caribbean, south Asian, other Asian, other/mixed or missing. South Asian and other Asian are coded separately as a higher CVD risk is associated with south Asian ethnicity . The category other/mixed incorporates any ethnicity which is reported and does not fit into the previous categories, as well as indication of mixed ethnic background.
Postcode data were collected in order to calculate Index of Multiple Deprivation 2010 (IMD 2010; based on Lower Layer Super Output Area (LSOA)) . The IMD 2010 incorporates seven domains: income deprivation, employment deprivation, health deprivation and disability, education deprivation, crime, barriers to housing and services, and living environment. All small areas in England are ranked, from 1 (most deprived) to 32,482, and data can be grouped into quintiles.
Data are summarised as means and standard deviations (SD), or as percentages. The median IMD ranks are compared between responders and non-responders. In the unadjusted model, the odds of response were calculated for each explanatory variable separately. In the adjusted model, the odds of response were calculated via logistic regression adjusting for potential confounding by QRisk2 score, ethnicity, IMD quintile, age and sex in Stata version 14 (StataCorp, Texas, USA). General practice was included as a random effect in the model to allow for clustering by practice. Adjusted odds ratios (AORs) for age are presented per 5 year increase and for QRisk2 scores per 5 percentage point increase to provide a better comparison of the strength of the relationships.
General practice and participant recruitment
We invited all 302 general practices with a patient list size of 5000 or more in the 12 south London CCGs, and 9 general practices with a < 5000 patient list size recruited to increase participation; 134 general practices agreed to participate and were recruited between June 2013 and December 2014. The medical records data of 1,154,050 patients were screened for eligibility and 17,618 patients were potentially eligible and invited to screening for eligibility, representing the target study population of whom 3515 patients responded (response rate 20.0%). See Fig. 1 for the patient response and anonymised data collection flow chart.
We returned to general practices to extract anonymous data following the requirements of ethical approval. The original list of potentially eligible patients who had been invited to participate had been deleted from primary care databases at many general practices; 60 practices were still able to provide the original anonymised data totaling 8902 patients (50.5% of the target population). No exclusions of data were made based on informed dissent for data sharing with a third party as no informed dissent codes were identified during the search and data extraction. Socio-demographic data and QRisk2 scores were extracted. Table 1 shows the CCGs from which anonymised data were collected and the deprivation and ethnicity Census data of each local authority . General practice deprivation varies markedly within and between the CCGs, but population-weighted general practice deprivation scores (IMD 2010) of those practices from which anonymised data were extracted did not differ significantly from that of all other practices in south London (t (440) = 0.57, p = 0.57) .
The QRisk2 and sociodemographic data of responders (n = 1489) and non-responders (n = 7413), and the results of the adjusted logistic regression, are presented in Table 2. The mean QRisk2 score of all patients invited to participate was 25.2%, and 69.9% were of white ethnic background (13.9% were of non-white ethnic background and 16.2% had no ethnicity data recorded). The median (interquartile range) IMD 2010 rank for all patients was 13,489 (7618–22,804). The mean age of all patients at the time of invite was 67.3 (5.7) years, and 20.7% were female.
As CVD risk increased the odds of response decreased (AOR 0.82 per 5 percentage points; 95% CI 0.77–0.88); for every 5 point increase in QRisk2 score the odds of responding decreased by 18%. Response was lower in patients of black African or Caribbean ethnicity (AOR 0.67; 95% CI 0.45–0.98) and those with missing ethnicity data (AOR 0.55; 95% CI 0.46–0.66) compared to white ethnicity. The odds of response from Asian and other ethnic backgrounds were not significantly different to that of patients of white ethnicity. A median test found the IMD ranks of responders (15,314; 9285–24,774) was significantly higher than non-responders (12,854; 7411–22,330) (p < 0.001). The odds of response in the fourth (AOR 0.70; 95% CI 0.56–0.87) and fifth (AOR 0.52; 95% CI 0.40–0.68) most deprived quintiles were significantly lower than the least deprived quartile. Response was higher with increasing age (AOR 1.19 per 5 years; 95% CI 1.12–1.26); the odds of responding increased by 19% for each 5 year increase in age. Odds of responding were higher in male compared to female patients (AOR 1.25; 95% CI 1.08–1.45).
Pairwise comparisons were conducted to explore differences in response between each of the non-white ethnic groups, but there were no significant differences. Response rates of patients with missing ethnicity data were significantly lower than that of patients of south Asian ethnicity (p = 0.002). Due to small numbers of invitations to patients of non-white ethnicity, a sensitivity analysis was undertaken to investigate predictors of response to invitation with ethnicity removed from the model. Lower CVD risk (p < 0.001), lower levels of deprivation (p = 0.001), older age (p < 0.001) and male sex (p = 0.015) remained significant predictors of response.
In this cross-sectional study of response to an invitation to take part in a RCT of an intensive lifestyle intervention for primary prevention of CVD, we found that likelihood of response reduced with increasing CVD risk in a population who were all at high risk of CVD. Black African or Caribbean patients were less likely to respond than those of white ethnicity, although there were small numbers of black African or Caribbean patients invited. South Asian patients were as likely to respond as those of white ethnicity. We also observed high rates of missing ethnicity data on medical records and this group was less likely to respond compared to patients of white or south Asian ethnicity. Likelihood of response also reduced with increasing deprivation. Older age and male sex predicted greater rates of response.
Strengths and limitations
The main strengths of the study are the large sample size and the opportunity to assess response bias in a multi-ethnic and socio-economically varied setting, which few lifestyle intervention trials have achieved . Access to anonymised CVD risk and sociodemographic data of large numbers of non-responders, including those who may typically be difficult to reach and unlikely to provide research data, is a unique aspect of this study. As a consequence of this methodology we did not have informed consent to access medical records for further information and so there is a risk of residual confounding by employment status, education and comorbidities . We retrieved data from approximately half of the participating general practices, but as there was no difference in mean general practice level deprivation compared with all other practices in south London it is unlikely this was a significant source of bias. Other studies have reported more detailed data on a smaller number of non-participants, including qualitative feedback on reason for non-participation [11, 15, 24,25,26], but the current study benefits from an enhanced reach and greater power to demonstrate response biases.
The methodology of recruitment to the MOVE IT trial relied upon general practice database calculations of QRisk2 score, and these calculations were used in the current analysis. We did not seek ethical approval to assess the underlying data used in the QRisk2 calculations. It may be that large proportions of clinical data such as blood pressure, weight and cholesterol:HDL ratio were missing, which has been demonstrated previously , in which case they would be replaced with age- and sex-weighted averages. Additionally, where clinical data is present, accuracy of QRisk2 scores may be compromised by the length of time since clinical data had been collected. In future analyses, the extraction of more detailed data from medical records would allow for a sensitivity analysis to explore the effect of missing or outdated data on outcomes.
The use of a 20% QRisk2 score as a screening criteria led to invitations to participate being sent to older, and more male, patients as age and male sex are given a large weighting in the QRisk2 algorithm . Similarly, south Asian ethnicity contributes to an increased QRisk2 score and black African or Caribbean ethnicity to a lower QRisk2 score compared to white ethnicity. Therefore the use of an absolute QRisk2 score as a screening tool in recruitment contributed to limited demographic difference within the target population. Alternative recruitment strategies may include the specification of individual risk factors in the search strategy , a relative CVD risk score incorporating a comparison with the average score for an individual of the same age, sex and ethnicity, or a lifetime CVD risk score which tends to identify younger patients [29, 30]. These approaches may assist in identifying patients whose modifiable, rather than non-modifiable, risk factors suggest they would benefit from the intervention as well as increasing the likelihood of a more diverse and representative study sample.
Interpretation and comparison with other studies
Previous studies have found both higher levels of self-reported health and greater self-reported activity levels [10, 14, 16, 17], as well as lower levels of self-reported health, lower activity levels and a higher CVD risk profile in participants compared to non-participants [11, 13,14,15]. These contradictory findings may relate to variations in recruitment methodology, the particular target population involved and the aims of the trial. As the current study assessed those who did not respond to an invitation and who are by definition hard to reach, we could not compare self-reported physical activity or health problems in responders and non-responders, but had the advantage of reporting a standardised CVD risk algorithm score. Already feeling healthy is a frequently cited cause of non-participation [11, 15, 24,25,26], but using a QRisk2 cutoff score of 20% in the current study meant that all those invited to participate would be considered at high risk of CVD , a fact which was communicated to patients in the invitation letter. Those patients at the highest risk level may not respond due to a number of reasons which could be explored through qualitative work.
Previous studies have found greater response rates in those of white ethnic background and those residing in more affluent areas [11, 12], but the generalisability of available data is limited due to the majority of participants in previous trials being white, middle-aged females, and the lack of information on ethnicity and deprivation available in published trials . Our findings contribute to concerns that research in general fails to reach socially disadvantaged groups . However, the trial did reach patients of south Asian ethnic backgrounds as much as patients of white ethnic backgrounds, which is a promising finding given higher CVD risk in this group .
Ability to undertake an analysis of anonymised primary care data is limited by the large amount of missing ethnicity data on general practice databases, and this impacted the data collected for the current study. Providing self-report ethnicity data has been found to be less likely in ethnic minority populations in the United States , and ethnic minority patients are less likely to provide ethnicity information to health care providers due to concerns over how the information may be used . In a study of hospital patients with cancer in England, there were only small differences in proportion of missing ethnicity data recorded in secondary care between self-report ethnic backgrounds, and much larger differences were found between different hospitals . The missing ethnicity data in primary care may be related to either general practice ethnicity-recording processes or patient reluctance to divulge information, and further research is needed to explore this and which patient groups are more likely to have missing ethnicity data. Increasing ethnicity recording in primary care is vital for understanding the representativeness of study samples. Patients reluctant to provide optional self-report ethnicity data in primary care may similarly be at reduced likelihood of responding to a RCT invitation, as our findings may allude to. Increasing awareness in certain subgroups of the population through varied approaches to recruitment, such as employing telephone or email reminders for non-responders , or the assistance of a recruitment mediator who is a member of the subgroup , could help to improve study representativeness amongst ethnic minority and less affluent groups.
The majority of those invited to the trial were above working age, and likelihood of response increased with age. Burden of time is a common reason for non-participation in lifestyle interventions [11, 24,25,26], and older invitees are more likely to be retired and may have more flexibility to participate. Other studies have found that participation rate increases with age in working age populations , but reduces into older age possibly as patients become more frail [11, 16]. Tailoring a lifestyle intervention for primary prevention of CVD may necessitate changing the setting and target population from primary care to the workplace, and making the intervention more desirable by reducing time burden, increasing flexibility and using digital technologies.
It has previously been reported that females are more likely to participate in lifestyle intervention trials , however reviews of the literature indicate many lifestyle intervention trials have recruited female only samples [12, 32]. The higher response rate of males in the current study, secondary to the larger proportion of males invited, may reflect public opinion that CVD is a predominantly male disease despite also being the leading cause of death in females [5, 40]. Previous studies assessing participation in trials of those who already have CVD [41, 42], and in a survey regarding cardiovascular risk factors , also found lower response in females. In a small sample of those providing feedback on the reasons for non-participation, females were more likely to mention caring responsibilities as a barrier . Increasing public awareness of CVD risk in females, as well as providing flexible appointments as previously mentioned, may be required to increase female participation.
The findings of this study are specific to the RCT under study, and should not be conflated with likelihood of response to similar interventions in clinical practice. Reasons for non-response may be research-specific, or in combination with lack of motivation or interest in the particular intervention. As participants were not contacted for this analysis, we could not explore patient perspectives on study-specific materials such as the invitation letter sent by the patient’s general practice. Further research is required to gain a greater understanding of the influence of study-specific invitation procedures, patient perspectives of research in general and willingness to undergo an intervention.
We have demonstrated that it is possible to ethically assess response bias to a RCT with the use of anonymised patient data. As a result, we have highlighted the risk that RCTs of lifestyle interventions may fail to recruit the highest risk patients, ethnic minority patients and those residing in more socioeconomically deprived areas, which could result in implementation of interventions which increase health inequalities. Such analyses are limited by missing ethnicity data on primary care databases. Improvements in ethnicity reporting would aid understanding of whether RCTs have successfully recruited a representative sample. Future RCTs of lifestyle interventions should aim to proactively minimise recruitment biases and report on the representativeness of their samples as part of a process evaluation.
Adjusted odds ratio
Cognitive behaviour therapy
Clinical Commissioning Groups
Index of Multiple Deprivation
Low density lipoprotein
Lower layer super output area
- MOVE IT:
MOtiVational intErviewing InTervention
Randomised controlled trial
World Health Organization. Global status report on noncommunicable diseases 2010. Geneva: World Health Organization; 2011.
Shaper AG, Pocock SJ, Walker M, et al. British Regional Heart Study: cardiovascular risk factors in middle-aged men in 24 towns. Br Med J (Clin Res Ed). 1981;283:179–86.
Clair C, Rigotti NA, Porneala B, et al. Association of smoking cessation and weight change with cardiovascular disease among adults with and without diabetes. JAMA. 2013;309:1014–21.
Heart Protection Study Collaborative Group. MRC/BHF heart protection study of cholesterol lowering with simvastatin in 20 536 high-risk individuals: a randomised placebo controlled trial. Lancet. 2002;360(9326):7–22.
Backholer K, Peters SA, Bots SH, et al. Sex differences in the relationship between socioeconomic status and cardiovascular disease: a systematic review and meta-analysis. J Epidemiol Community Health. 2017;71:550–7.
Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ. 2008;336:1475–82.
NHS England. NHS five year forward view. 2014. https://www.england.nhs.uk/wp-content/uploads/2014/10/5yfv-web.pdf. Accessed 12 Mar 2015.
Perk J, De Backer G, Gohlke H, et al. European guidelines on cardiovascular disease prevention in clinical practice (version 2012). Eur Heart J. 2012;33:1635–701.
Pearson TA, Palaniappan LP, Artinian NT, et al. American heart association guide for improving cardiovascular health at the community level, 2013 Update. Circulation. 2013;127:CIR-0b013e31828f8a94.
Tully MA, Cupples ME, Chan WS, et al. Brisk walking, fitness, and cardiovascular risk: a randomized controlled trial in primary care. Prev Med. 2005;41:622–8.
Rogers A, Harris T, Victor C, et al. Which older people decline participation in a primary care trial of physical activity and why: insights from a mixed methods approach. BMC Geriatr. 2014;14:1.
Foster CE, Brennan G, Matthews A, et al. Recruiting participants to walking intervention studies: a systematic review. Int J Behav Nutr Phys Act. 2011;8:137.
Ives DG, Traven ND, Kuller LH, et al. Selection bias and nonresponse to health promotion in older adults. Epidemiology. 1994;5:456–61.
Harris TJ, Victor CR, Carey IM, et al. Less healthy, but more active: opposing selection biases when recruiting older people to a physical activity study through primary care. BMC Public Health. 2008;8:182.
Groeneveld IF, Proper KI, van der Beek AJ, et al. Factors associated with non-participation and drop-out in a lifestyle intervention for workers with an elevated risk of cardiovascular disease. Int J Behav Nutr Phys Act. 2009;6:80.
van Heuvelen MJ, Hochstenbach JB, Brouwer WH, et al. Differences between participants and non-participants in an RCT on physical activity and psychological interventions for older persons. Aging Clin Exp Res. 2005;17:236–45.
Golomb BA, Chan VT, Evans MA, et al. The older the better: are elderly study participants more non-representative? A cross-sectional analysis of clinical trial and observational study samples. BMJ open. 2012;2:e000833.
Go AS, Mozaffarian D, Roger VL, et al. Executive summary: heart disease and stroke statistics—2014 update. Circulation. 2014;129:399–410.
Glasgow RE, Vogt TM, Boles SM. Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am J Public Health. 1999;89:1322–7.
Office of National Statistics. Census 2011. https://www.ons.gov.uk/census/2011census/2011ukcensuses/ukcensusesdata. Accessed 10 Aug 2018.
Bayley A, de Zoysa N, Cook DG, et al. Comparing the effectiveness of an enhanced MOtiVational intErviewing InTervention (MOVE IT) with usual care for reducing cardiovascular risk in high risk subjects: study protocol for a randomised controlled trial. Trials. 2015;16:112.
Noble M, Wright G, Smith G, et al. Measuring multiple deprivation at the small-area level. Environ Plan. 2006;38:169–85.
Public Health England. National General Practice Profiles. https://fingertips.phe.org.uk/profile/general-practice. Accessed 21 July 2017.
van Dongen EJ, Duijzer G, Jansen SC, et al. Process evaluation of a randomised controlled trial of a diabetes prevention intervention in Dutch primary health care: the SLIMMER study. Public Health Nutr. 2016;19:3027–38.
Attwood S, Morton KL, Mitchell J, et al. Reasons for non-participation in a primary care-based physical activity trial: a qualitative study. BMJ Open. 2016;6:e011577.
Normansell R, Holmes R, Victor C, et al. Exploring non-participation in primary care physical activity interventions: PACE-UP trial interview findings. Trials. 2016;17:178.
Dalton AR, Bottle A, Okoro C, et al. Implementation of the NHS health checks programme: baseline assessment of risk factor recording in an urban culturally diverse setting. Fam Pract. 2010;28(1):34–40.
Gray BJ, Bracken RM, Turner D, et al. Predicted 10-year risk of cardiovascular disease is influenced by the risk equation adopted: a cross-sectional analysis. Br J Gen Pract. 2014;64:e634–40.
Cooney MT, Dudina AL, Graham IM. Value and limitations of existing scores for the assessment of cardiovascular risk: a review for clinicians. J Am Coll Cardiol. 2009;54(14):1209–27.
Hippisley-Cox J, Coupland C, Robson J, Brindle P. Derivation, validation, and evaluation of a new QRISK model to estimate lifetime risk of cardiovascular disease: cohort study using QResearch database. BMJ. 2010;341:c6624.
Rabar S, Harker M, O’flynn N, et al. Lipid modification and cardiovascular risk assessment for the primary and secondary prevention of cardiovascular disease: summary of updated NICE guidance. BMJ. 2014;349:g4356.
Kassavou A, Turner A, French DP. Do interventions to promote walking in groups increase physical activity? A meta-analysis. Int J Behav Nutr Phys Act. 2013;10:18.
Bonevski B, Randell M, Paul C, et al. Reaching the hard-to-reach: a systematic review of strategies for improving health and medical research with socially disadvantaged groups. BMC Med Res Methodol. 2014;14:42.
Wild SH, Fischbacher CM, Brock A, et al. Mortality from all causes and circulatory disease by country of birth in England and Wales 2001–2003. J Public Health. 2007;29:191–8.
Eisenhower A, Suyemoto K, Lucchese F, et al. “Which box should I check?”: examining standard check box approaches to measuring race and ethnicity. Health Serv Res. 2014;49(3):1034–55.
Baker DW, Cameron KA, Feinglass J, et al. Patients’ attitudes toward health care providers collecting information about their race and ethnicity. J Gen Intern Med. 2005;20(10):895–900.
Saunders CL, Abel GA, El Turabi A, et al. Accuracy of routinely recorded ethnic group information compared with self-reported ethnicity: evidence from the English Cancer patient experience survey. BMJ Open. 2013;3(6):e002882.
Treweek S, Lockhart P, Pitkethly M, et al. Methods to improve recruitment to randomised controlled trials: Cochrane systematic review and meta-analysis. BMJ Open. 2013;3:e002360.
Banks-Wallace J, Enyart J, Johnson C. Recruitment and entrance of participants into a physical activity intervention for hypertensive African American women. Adv Nurs Sci. 2004;27:102–16.
Kannel WB. The Framingham study: historical insight on the impact of cardiovascular risk factors in men versus women. J Gend Specif Med. 2001;5:27–37.
Lakerveld J, IJzelenberg W, van Tulder MW, et al. Motives for (not) participating in a lifestyle intervention trial. BMC Med Res Methodol. 2008;8(1):17.
Lloyd-Williams F, Mair F, Shiels C, et al. Why are patients in clinical trials of heart failure not like those we see in everyday practice? J Clin Epidemiol. 2003;56(12):1157–62.
Boshuizen HC, Viet AL, Picavet HSJ, et al. Non-response in a survey of cardiovascular risk factors in the Dutch population: determinants and resulting biases. Public Health. 2006;120(4):297–308.
We would like to thank the research team, healthy lifestyle facilitator team, patient volunteers, and south London Clinical Research Network who have supported the study, all general practices involved in the study across south London and all of the patients who are participating. We would also like to thank the Trial Steering Committee and the Data Ethics and Monitoring Committee for their oversight.
The MOVE IT study is funded by the Health Technology Assessment-National Institute for Health Research - Health Technology Assessment Programme (NIHR-HTA) (Project: 10/62/03) and work will be published in the Health Technology Assessment journal. Further information available at https://www.journalslibrary.nihr.ac.uk/HTA/#/. The funding body was not involved in the study design, collection of data, analysis of data, writing of the manuscript or the decision to submit the manuscript for publication.
This report presents independent research commissioned by the NIHR and part funded by the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views and opinions expressed by authors in this publication are those of the authors and do not necessarily reflect those of the NHS, the NIHR, MRC, CCF, NETSCC, the Health Technology Assessment programme or the Department of Health.
Availability of data and materials
Please contact the corresponding author to discuss availability of datasets analysed for the current study.
Ethics approval and consent to participate
The MOVE IT trial and this response bias study was granted ethics approval by the Dulwich ethics committee (12/LO/0917). Approval was granted by the Dulwich ethics committee to extract anonymised data for all patients invited to participate, unless an informed dissent code was present on their personal medical records, and the requirement to obtain consent from responders and non-responders to the original study for the use of their anonymised data was waived.
Consent for publication
The authors declare that they have no competing interests.
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Cite this article
Bayley, A., Stahl, D., Ashworth, M. et al. Response bias to a randomised controlled trial of a lifestyle intervention in people at high risk of cardiovascular disease: a cross-sectional analysis. BMC Public Health 18, 1092 (2018). https://doi.org/10.1186/s12889-018-5939-y
- Cardiovascular disease
- Physical activity
- Complex intervention
- Participation bias
- Primary care