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The presented work is part of a project aimed at designing and developing a digital health ecosystem focused on improving the monitoring and treatment of depression. This ecosystem integrates various physiological indicators with subjective mood and activity records to provide valuable insights for therapeutic interventions. After conducting a comparative analysis of existing software related to health digital ecosystems that integrate data from different sources, several limitations were identified and addressed in our design. This paper presents the first steps in the design of one of the key touchpoints of the overall service: the web interface for healthcare professionals. The design strategy prioritizes real-world testing of functionalities at an early stage through autonomous user testing, before undertaking user experience design. The need to develop fully functional early prototypes led to the use of Low-Code and Visual Programming Language techniques. These techniques minimize the need for textual coding and increase the accessibility of functional programming for teams without extensive programming expertise, as shown in the paper. From a technical standpoint, the Minimum Viable Product (MVP) facilitated data collection from multiple sources, including wearable devices, app-generated data, subjective mood and activity logs, and qualitative diary entries. Open tools from Google were used to collect and store subjective data, a third-party platform integrated with Google for objective data, and Looker Studio was integrated for visualization and comparison between subjective and objective data. From a collaboration and teamwork perspective, seamless integration of front-end and back-end functionalities was achieved without requiring traditional coding approaches, facilitating multidisciplinary collaboration between designers and developers. From a product perspective, iterative refinement based on co-design and user feedback gathered through evaluations involving healthcare professionals allowed for early validation of the concept’s feasibility in a real-world setting and informed decision-making.

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Visual Programming for Minimum Viable Product in a Health Design Project

  • 会议论文

  • 发表日期：

    2025年

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  • Teresa Blanco,
  • Natalia Carrera,
  • Diego Cajal,
  • Eduardo Gil,
  • Roberto Casas

摘要

The presented work is part of a project aimed at designing and developing a digital health ecosystem focused on improving the monitoring and treatment of depression. This ecosystem integrates various physiological indicators with subjective mood and activity records to provide valuable insights for therapeutic interventions. After conducting a comparative analysis of existing software related to health digital ecosystems that integrate data from different sources, several limitations were identified and addressed in our design. This paper presents the first steps in the design of one of the key touchpoints of the overall service: the web interface for healthcare professionals. The design strategy prioritizes real-world testing of functionalities at an early stage through autonomous user testing, before undertaking user experience design. The need to develop fully functional early prototypes led to the use of Low-Code and Visual Programming Language techniques. These techniques minimize the need for textual coding and increase the accessibility of functional programming for teams without extensive programming expertise, as shown in the paper. From a technical standpoint, the Minimum Viable Product (MVP) facilitated data collection from multiple sources, including wearable devices, app-generated data, subjective mood and activity logs, and qualitative diary entries. Open tools from Google were used to collect and store subjective data, a third-party platform integrated with Google for objective data, and Looker Studio was integrated for visualization and comparison between subjective and objective data. From a collaboration and teamwork perspective, seamless integration of front-end and back-end functionalities was achieved without requiring traditional coding approaches, facilitating multidisciplinary collaboration between designers and developers. From a product perspective, iterative refinement based on co-design and user feedback gathered through evaluations involving healthcare professionals allowed for early validation of the concept’s feasibility in a real-world setting and informed decision-making.