Recruiting patients for clinical trials is only half of the battle. Retaining those eligible patients over the course of the study to ensure reliable data is another common industry hurdle. In this blog, we explore patient engagement in clinical trials, including steps to improve it. 

Patient engagement in clinical trials is a concept that refers to a patient’s participation, perception, and reception of the clinical study. The relationship between the patient and care team is an important domain of patient engagement. The care environment also plays an important role in how well the patient is engaged during the trial.  

Poor patient engagement can manifest in clinical trials in many ways. Most impactful are medication nonadherence and study dropout.   

Medication adherence is the process by which patients take the prescribed regimen and follow predefined study procedures. Poor medication adherence (or overestimating adherence) can negatively impact the study power and lead to an inaccurate estimate of the benefit/risk ratio. Unfortunately, this is a widespread phenomenon in healthcare. Real-world nonadherence rates can exceed 50%​¹​ in some populations, and this real-world prevalence often translates to clinical research – despite the fact that researchers generally assume adherence is ideal² in clinical trial settings. In fact, significant evidence actually shows the troubling rates of nonadherence​²​ in clinical trials.  

Study dropout refers to patients withdrawing from participating in a study before it is completed. Too many dropouts can also drastically reduce study power, causing significant implications on the validity and generalizability of clinical trial results. The prevalence of high dropout rates varies depending on the study design, intervention, and indication, but, generally, a dropout rate between 20%​³​ and 30% is not uncommon. While global trial recruitment rates have improved over the years, retention rates have still struggled, especially in indications like CNS and oncology​⁴​ ​⁵​.  

A failed clinical trial due to weak study power – whether from a high rate of nonadherence or dropouts – is costly, not only to the researcher investing in the new therapy but also to the patients who need the new treatment. So, what can be done? 

Patient engagement strategies  

To minimize the impact of medication nonadherence and study dropout, researchers aim to design studies that are engaging. To understand these strategies, let’s briefly review how patient engagement can be defined, according to a concept analysis of scientific literature​⁶​: 

• Personalization: Personalizing resources to the patient’s interests, circumstances, and capabilities.  
• Access: Improving the patient’s ability to obtain information, including adapting resources to the patient’s location and background.  
• Commitment: Stimulating intrinsic cognitive or emotional forces through social support, intellectual resources, and other means of encouragement. 

• Therapeutic alliance: Improving the quality of clinical interactions, communication, empathy and mutual understanding. 

Optimizing patient engagement strategies can tie back to any one of these four domains. Most notably, clinical trial researchers can improve patient engagement during the conduct of the trial by: 

1. Maintaining clear and consistent communication throughout the course of the trial.  

2. Complementing visits with technology to make the trial less burdensome, including mobile apps, wearables, and telemedicine. 

3. Seeking feedback from patients to understand their experiences and refine communications, resources, and interventions to meet their needs.  

Role of patient individual characteristics with engagement 

To date, many patient engagement strategies have been developed based on patient populations, not necessarily the individual patient​⁷​. However, not every member of a specific ethnic group, gender, educational status, or other demographic or socioeconomic domain will respond to a single prescribed engagement strategy.  

In fact, it’s proven that certain personality traits​⁸​ might be associated with a lack of adherence. Type A personalities (anxious, hyperactive, and hostile, associated with strong fear) admit to missing doses, while patients with type D personalities (a distressed personality that includes negative affectivity and social inhibition) are less compliant in general. Reviewing the available patient engagement approaches, it appears that patient traits or beliefs are not considered and that multilevel intervention are recommended​⁵​. In light of this, the first step to improving patient engagement could be better understanding individual patient personality traits and monitoring their engagement during the study.    

Cognivia is a clinical trial technology company that develops solutions that help researchers understand patients at an individual level. We capture characteristics relating to patient engagement, like motivation, belief in medicine, and traits, to identify the risk of poor engagement (manifesting as nonadherence or dropout risk). As patient motivations, conditions, and relationships with the environment evolve throughout the course of the study, we can monitor the patient’s status and offer recommendations for interventions that reduce the risk of nonadherence or dropout.  

Compl-AI​⁹​ designed by Cognivia provides a risk factor (of dropout or non-adherence) for each patient screened, before enrolment in the trial. Because “one does not fit all”, Compl-AI risk factor supports a personalized patient engagement strategy, by indicating the type of support that would help the patient to be more compliant or to stay in the clinical study.  

Conclusion 

Patient engagement is an important part of the equation in clinical trials. Poor engagement can lead to increased rates of medication nonadherence and dropouts, making efficacy and safety more difficult to determine. Ultimately, more engaged patients result in more meaningful clinical trials. Monitoring patient engagement over the course of the trial and offering the right resources to the right patient at the right time will help improve patient outcomes.  

References 

​​1. Shiovitz TM, Bain EE, McCann DJ, et al. Mitigating the Effects of Nonadherence in Clinical Trials. The Journal of Clinical Pharmacology. 2016;56(9):1151-1164. doi:10.1002/jcph.689 

​ Enhancing, and Accounting for Medication Adherence in Clinical Trials. Clin Pharmacol Ther. 2014 Jun;95(6):617-26. Doi:10.1038/clpt.2014.59 

​3. Bell ML, Kenward MG, Fairclough DL, Horton NJ. Differential dropout and bias in randomised controlled trials: when it matters and when it may not. BMJ. 2013;346(jan21 1):e8668-e8668. doi:10.1136/bmj.e8668 

​4. Recruitment Rates Rising, but Retention Rates Fall, According to New Study. Accessed July 3, 2023. https://www.centerwatch.com/articles/24543-recruitment-rates-rising-but-retention-rates-fall-according-to-new-study 

​5. Mansoor Malik, Suneeta Kumari, Partam Manalai. Treatment Nonadherence: An Epidemic Hidden in Plain Sight. Psychiatric Times. 2020;37(3). 

​6. Higgins T, Larson E, Schnall R. Unraveling the meaning of patient engagement: A concept analysis. Patient Educ Couns. 2017;100(1):30-36. doi:10.1016/j.pec.2016.09.002 

​7. Patients are people: a new take on patient-centricity. https://cognivia.com/patients-are-people-a-new-take-on-patient-centricity/ 

​8. Rychter A, Miniszewska J, Góra-Tybor J. Personality traits favourable for non-adherence to treatment in patients with chronic myeloid leukaemia: role of type A and D personality. Biopsychosoc Med. 2023;17(1):1. doi:10.1186/s13030-023-00261-w 

​9. Compl-AI – Cognivia. Accessed July 3, 2023. https://cognivia.com/compl-ai-2/