Balancing research needs with software usability for digital health - developing software in academia
Digital health solutions are rapidly becoming a mainstay in research, transforming the way health data (e.g. wearables, questionnaires, environment) is collected, analysed, and used. These technologies, ranging from mobile health apps to digital therapeutics, offer huge potential to enhance the efficiency of research and improve health outcomes. However, researchers often face challenges of balancing the drive for cutting-edge research with the need for software that is usable, intuitive, and accessible to end-users (otherwise known as research participants).
When it comes to developing digital health software, researchers must juggle complex research-driven goals, such as ensuring scientific validity, rigour and data accuracy, with creating an interface that participants can easily navigate.
This task is further complicated by factors such as the limitations imposed by research funding, the expectations and varying approaches of principal investigators and funders, and the absence of a standardised approach to software development. This discussion will also draw upon the concept of a Research Viable Product (RVP), which I coined in a previous post.
Understanding research needs in digital health
Academic research has a distinct set of requirements that must be carefully integrated into digital health solutions. A great example would be the development of the DrinksRation app. At the fore are concerns about data integrity, participant privacy, and the overall scientific validity of the study. Rigorous data collection procedures must be in place to ensure that the information gathered is both reliable and relevant to the research objectives (keep in mind data minimisation here). Ethics also plays a crucial role, with strict protocols governing informed consent and participant confidentiality.
In digital health research, studies often focus on assessing the efficacy of an intervention, which requires precise tracking of participant outcomes (often through questionnaires). This focus on high-quality data often demands sophisticated software with features that support granular data collection, while also ensuring participant engagement.
For example, if an app is too cumbersome or difficult to use, data quality could suffer as participants may engage less frequently or inaccurately. These challenges highlight the importance of integrating and considering research-focused features into digital health platforms without losing sight of the need for usability. As discussed in my post on the Research Viable Product (RVP), achieving this balance is key to success in academic digital health projects.
The concept of Research Viable Product (RVP)
The concept of a Research Viable Product (RVP) is designed to meet the specific needs of research, offering a structured approach to software development that prioritises research goals. RVP takes inspiration from the Minimal Viable Product (MVP), a term commonly used in the tech industry to describe a version of a product that includes only its most essential features.
However, RVP differs significantly by focusing on the unique demands of research-driven projects, particularly in fields like digital health. Unlike the MVP, which aims for rapid market entry with minimal features, RVP ensures that all critical research elements, such as rigorous data collection, ethical compliance, and scientific validity, are embedded in the product from the very beginning.
One of the core differences between RVP and MVP is the emphasis on maintaining the integrity of research throughout the development process. MVPs may prioritise speed and functionality over comprehensive features, often sacrificing elements that are not immediately necessary for market testing. In contrast, an RVP must ensure that the software meets high academic standards, addressing requirements such as data robustness, participant privacy, and regulatory compliance. This is particularly important in digital health, where the software must support ethical and accurate data collection to evaluate the efficacy of interventions, as well as ensuring participant safety and privacy.
In reality, RVP provides a framework that bridges the gap between traditional product development and academic research priorities. It allows teams to develop software that not only functions well but is also suitable for academic validation and ethical scrutiny.
By integrating the essential elements of research from the start, an RVP approach helps academic teams avoid the need for major reworking later on, thus streamlining the path from concept to publication, and eventually, to real-world implementation in clinical settings. This balance between usability and research validity makes the RVP model particularly suited to the development of digital health tools.
Usability and participant engagement
In the world of digital health research, usability isn’t just an added bonus; it’s essential to deliver the effect. The success of a digital health tool often hinges on how intuitive and user-friendly it is for participants, who may have varying degrees of technological literacy. A well-designed, easy-to-use interface not only helps ensure that participants continue engaging with the software but also improves the quality of the data being collected.
Several best practices can enhance usability without compromising data accuracy. First, involving Public and Patient Involvement and Engagement (PPIE) from the start can offer valuable insights into how real users interact with the software. Agile development methodologies enable iterative improvements based on user feedback. Beyond functionality, attention must be paid to the naming of features and design choices, as these can significantly impact how users perceive and engage with the software. Additionally, user interactions should be assessed through suitable questionnaires and continuously monitored via logging and analytics to ensure data accuracy and user satisfaction.
Balancing both worlds: Practical strategies
Successfully navigating the divide between research needs and usability requires a careful, iterative approach. Academic research teams can greatly benefit from regular feedback from both researchers and participants. By incorporating this feedback into each phase of development, teams can refine both the research protocols embedded in the software and the overall user experience. Iterative development, grounded in real-world usage data, allows academic teams to stay agile, making necessary adjustments without derailing the research. This is critical to success of research software, and does not need to come at a cost.
Co-production and co-code production are key strategies for integrating the needs of both researchers and end-users. By involving a wide range of stakeholders, from developers to participants, throughout the process, teams can create software that is robust from a research perspective while also meeting the usability needs of participants. This collaborative approach ensures that academic digital health solutions can fulfil their dual mission: supporting rigorous research while remaining accessible and engaging to users.
Security and privacy considerations
Security and privacy are paramount in the development of digital health tools, especially within research. Adhering to privacy laws like GDPR in the UK/Europe or HIPAA in the United States is not optional; it is a legal requirement that, if neglected, could result in significant breaches and fines. The UK’s Information Commissioner’s Office (ICO) has reported multiple data breaches in the digital health space, underscoring the critical need for secure systems. This also includes proactively tackling cyber security. Also, it is also important to seek to develop Data Protection Impact Assessments on data that is being collected to ensure compliance.
Methods for ensuring robust data protection include implementing encryption, secure access controls, and regular security audits. At the same time, these security measures should not overly hinder the usability of the software. By integrating data protection measures seamlessly into the software’s architecture, academic teams can build systems that protect participant privacy without sacrificing functionality or user experience.
Conclusion
Balancing research needs with software usability is a challenging but essential task in the development of digital health tools within academia. As we have seen, the Research Viable Product framework provides a useful lens for achieving this balance, ensuring that academic software is not only scientifically rigorous but also user-friendly. By adopting an iterative development approach, involving stakeholders in the process, and adhering to strict security and privacy standards, researchers can create digital health tools that are both effective and engaging.
These are just some thoughts and rambling from my 10 years of developing software for research.