Acceptability criteria of precision medicine: Lessons from patients' experiences with the GUIDE-IBD trial regarding the use of mobile health technology

Abstract

Abstract Background Research about mobile health technologies for IBD reveals that these devices are mainly used to predict or self-report disease activity. However, in the near future these tools can be used to integrate large data sets into machine learning for the development of personalized treatment algorithms. The impact of these technologies on patients’ wellbeing and daily lives has not yet been investigated. Methods We conducted 10 qualitative interviews with patients who used the XXXX mHealth technology. This is a special smartphone app for patients to record patient reported outcomes and a wearable to track physical activity, heart rate, and sleep quality. For data analysis, we used interpretative phenomenological analysis (IPA). This method is ideally suited for studying people’s lived experiences. Results The analysis of the data revealed 11 themes that were mentioned by at least 3 participants. These themes were: Self-tracking with wearable devices as normality; variable value of the data from the wearable; risk of putting people under pressure; stimulus to reflect about their own well-being and illness; risk of psychological distress; discussion about app data in the medical consultation is very brief or nonexistent; easier to be honest with an app than with a doctor; questionnaires do not always adequately capture the patient's condition; need for support; and the possibility to look at the data retrospectively. Conclusions Patients identified benefits, risks, and potentials for improvement, which should be considered in the further development of the devices and patient reported outcome scales, and in the implementation in usual care.