Recruiting patients into trials in general practice: insights from the ENERGISED trial

Background Recruiting patients into randomised controlled trials in general practice is challenging and carries a substantial risk of bias. The ENERGISED trial evaluated an mHealth physical activity intervention in patients with prediabetes or type 2 diabetes recruited through general practice. To minimise bias, the trial employed a systematic recruitment strategy in which general practitioners assessed the eligibility of patients from random stratified samples of their registers and sought consent from all those deemed eligible. This study aimed to analyse the recruitment process of the ENERGISED trial and identify sources of potential bias arising from general practitioners' eligibility assessments (selection bias) and patient consent (self-selection bias). Methods Patients with prediabetes or type 2 diabetes were randomly sampled from the registers of 28 Czech general practices using sex- and diagnosis-stratified lists. Eligibility was systematically assessed during routine visits, with general practitioners documenting reasons for ineligibility. All eligible patients were invited to participate, and reasons for non-consent were recorded. Logistic mixed-effects models were used to examine the influence of patient characteristics (age, sex, diagnosis) and general practitioner characteristics on eligibility and consent. Results Of 1,376 sampled patients, 1,138 (83%) were assessed, 792 (70% of assessed) were eligible, 348 (44% of eligible) consented and 343 were randomised. Older age was associated with lower odds of eligibility (OR 0.955, 95% CI 0.942–0.968; p < 0.001) and lower odds of consent among eligible patients (OR 0.972, 95% CI 0.958–0.986; p < 0.001). Ineligibility was most often due to digital barriers. Practices with older registered populations showed stronger age-related bias. Female practitioners and practices with more diabetes/prediabetes patients achieved significantly higher eligibility rates. Conclusions Systematic recruitment through general practice can reduce selection and self-selection bias, yet digital exclusion, particularly in older adults, persists. Future trials must proactively address digital literacy and age-related barriers to ensure representative participation in primary care research.