16 Chapter 1 1 barriers such as difficulties understanding study content, challenges with abstract thinking, language or literacy issues, anxiety towards research or the research team, feelings of stigmatization, and limited exposure to technology and the internet (4951). Studies that do succeed in involving socio-economically disadvantaged groups often limit their involvement to later development stages, such as assessment of an app or patient portals on usability or readability of health information. To our knowledge there currently is no clear methodology for involving people with low socio-economic position or limited health literacy in the participatory design process of an eHealth intervention. Therefore, there is a need for best practice examples and guidance on how to effectively involve people with limited health literacy. Hence, to ensure that eHealth benefits all people in need and is accessible in terms of skills and motivation, it is essential to involve socio-economically disadvantaged groups throughout all stages of development using participatory design. By doing so we can help bridge the gap between those who have access to digital health technologies and those that do not, rather than creating a range of interventions that are largely unused by those who could benefit the most. Challenge four: limited evidence on the effectiveness of eHealth The lack of evidence on the effectiveness of eHealth solutions presents a significant challenge to their widespread adoption and implementation of eHealth. Generally, practice and clinical decision making nowadays is based on evidence-based medicine. Therefore, demonstrating the effectiveness and clinical benefits of eHealth interventions has become a crucial aspect of the transition towards digital healthcare (52). It plays a vital role in distinguishing useful and beneficial eHealth interventions from potentially harmful ones, and even influences reimbursement decisions (53). Randomized controlled trials (RCTs) based on the fundaments of creating two comparable groups to assess the true effect of an intervention, lie at the top of the scientific evidence pyramid. However, despite its robust evidence, there is growing uncertainty as whether the plethora of RCT evidence on eHealth actually translates into improvements in patient outcomes and care. One reason and challenge is formed by the fact that RTCs typically focus on well-defined, homogeneous populations, employ blinding techniques, follow specific protocols, and incorporate controlled clinical elements. These measures aim to ensure comparability between groups and attribute any differences in outcomes to the intervention itself. Paradoxically, these strict criteria limit the external generalizability of study findings, meaning that they may not readily apply to real-world practice or the broader population. They run the risk of overlooking the complexity of contextual factors that exist in everyday healthcare settings, creating a disconnect between research outcomes and practical application. Hence, there is an increasing need for real-world evaluation and generation of real-world evidence in terms of the effectiveness of eHealth technologies.
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