Towards Better Supporting Human Aspects in Mobile eHealth Applications

eHealth apps raise awareness of health to ensure they are more accessible during emergencies, usable in home-based disease management and effectively support personalized care through sensing/interaction. However, despite the huge number of these apps available, many do not adopt end-user human aspects in development and deployment – causing them to be ineffective and non-inclusive of diverse users. This project focuses on creating a more integrated approach for eHealth app development that addresses the human aspects of users.

The project aims to determine how different aspects are being addressed by developers now, which ones are missing or poorly handled, and which are the most important for different end user groups. It then explores different ways to improve support for users, developers and other stakeholders of eHealth apps in the form of improved actionable guidelines, work-flow framework, best practice examples and evaluation techniques.

Project stages

The team initially carried out a preliminary background study to identify the challenges in app development and related future needs. It was uncovered that:

  • Metadata based app classification methods are beneficial, but these do not work if the app description is unavailable.
  • The Model Driven Development (MDD) technique has the potential to address the challenges of non-trivial mobile app development because it simplifies the process, reduces complexity, increases abstraction level, helps achieve scalable solutions and maximizes cost-effectiveness and productivity.

In the second phase, this projects investigated a Reverse Engineering-based Approach to Classify mobile apps based on the data that exists inside the app (REACT) to overcome the limitations of meta-data based app classification approaches.

To replicate and validate the proposed REACT method, our researchers used an extensive set of Android apps (24,652 apps in total). Although our analysis shows some limitations in REACT procedure and implementations, we identified the root cause of failures and marked future enhancement ideas. Moreover, the REACT approach can identify the eHealth app development patterns, especially when mined with our extracted hundred thousand keywords from medical dictionaries.

In the third phase, the team investigated MDD-based mobile app development schemes via a Systematic Literature Review (SLR) to identify key benefits, limitations, gaps and future research potential in this domain.

The formal search protocol identified a total of 1,042 peer-reviewed academic research papers from four major software engineering databases, i.e., ACM digital library, IEEE Xplore, Springer link, and Science direct. These papers were subsequently filtered, and 55 high-quality relevant studies were selected for analysis, synthesis and reporting.

Our researchers identified the popularity of different applied MDD approaches, supporting tools, artefacts and evaluation techniques. The analysis found that architecture, domain model and code generation are the most crucial purposes in MDD-based app development. Three qualities – productivity, scalability and reliability – can benefit from these modelling strategies. We also summarized the key collective strengths, limitations and gaps from the studies, and made several future recommendations to guide future researchers, developers and stakeholders.

Throughout the research and SLR review process, we realized a much greater need for improved approaches supporting HCIs in eHealth app development, not only in MDD-based approaches but also in overall existing methods. Thus, we reviewed a set of existing eHealth apps, recent literature, current development procedures and evaluation guidelines. Outcomes of these analyses led us to narrow down the topic via a viable gap analysis.

In the final stage, we look at current user and developer engagement to support human aspects in the eHealth app domain. We collected and analysed data from 240 usable survey responses and 25 detailed interviews to better understand how app developers support human aspects in eHealth apps, corresponding challenges, and their impact on end-users. Our analysis found that human aspects dominate practical and effective eHealth app usage. However, several factors hinder appropriate aspect identification, addressing, and management, e.g., less research and education, poor practice culture, restrictions from vendors, expert shortage, insufficient technical support for developers, disengagement of users from app development process etc. We sorted identified aspects, their impacts and best practice examples into four categories considering mobile app development life-cycle phases. We then provide a set of findings to help app developers better address these aspects in eHealth apps. Now, we are triangulating the survey/interview results with our current findings to develop our proposed solutions in the forms of improved actionable guidelines, work-flow framework, best practice examples and evaluation techniques.

Our solution will help to better incorporate human aspects in eHealth apps development, ultimately helping end-users to have more effective apps. An interesting future futuristic work for this research project is to try out the proposed solutions with various eHealth app stakeholders in the form of feedback, ultimately on example projects and the generated apps

Project Lead

  • Project Lead: Md. Shamsujjoha
  • Principal Supervisor: Australian Laureate Fellow Prof. John Grundy
  • Associate Supervisors: Dr. Li Li, Dr. Hourieh Khalajzadeh, Dr. Qinghua Lu (Data61)

Publications from PhD Projects (to date)

  1. Md. Shamsujjoha, John Grundy, Li Li, Hourieh Khalajzadeh, and Qinghua Lu, “Developing Mobile Applications via Model Driven Development: A Systematic Literature Review”, Information and Software Technology, Eelsevier, vol: 140, no: 106693, pp:1-24, year: Apr-2021, ISSN: 0950-5849, DOI: 10.1016/j.infsof.2021.106693, (Based on PhD research works), [PDF File]
  2. Md. Shamsujjoha, John Grundy, Li Li, Hourieh Khalajzadeh, and Qinghua Lu, “Human-Centric Issues in eHealth App Development and Usage: A Preliminary Assessment”, 28th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), IEEE, pp:506-510, year: 2021, e-ISBN: 978-0-7695-4889-0, DOI: 10.1109/SANER50967.2021.00055 (Based on PhD research works), [PDF File]
  3. Md. Shamsujjoha, John Grundy, Li Li, Hourieh Khalajzadeh, and Qinghua Lu, “Checking App Behavior Against App Descriptions: What If There are No App Descriptions?”, 29th IEEE/ACM International Conference on Program Comprehension (ICPC), IEEE/ACM, ISSN-2643-7171, pp:422-432, year: 2021, DOI: 10.1109/ICPC52881.2021.00050 (Based on PhD research works) [PDF File]