Responsible data selection method for algorithmic personalization of health apps: a case study on promoting mental health - Takeaways - MDSpire

Responsible data selection method for algorithmic personalization of health apps: a case study on promoting mental health

  • By

  • Esra Cemre Su de Groot

  • Ujwal Gadiraju

  • Olya Kudina

  • Loes Keijsers

  • Manon H. J. Hillegers

  • Willem-Paul Brinkman

  • June 24, 2026

  • 0 min

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  • 1

    The study proposes a stepwise method for Responsible Data Selection (ReDS) to address ethical implications in algorithmic personalization of mHealth.

  • 2

    The ReDS method emphasizes the importance of using ethically less risky data while personalizing health applications for user engagement.

  • 3

    A case study involving 1181 adolescents demonstrated the ReDS method by personalizing coping strategy challenges based on cognitive behavioral therapy.

  • 4

    The analysis revealed that tiredness data could serve as a less sensitive alternative to emotion data for personalization objectives.

  • 5

    The findings highlight the need for developers to incorporate ethical considerations explicitly in the development of personalization algorithms.

Original Source(s)

Responsible data selection method for algorithmic personalization of health apps: a case study on promoting mental health

  • by Esra Cemre Su de Groot, Ujwal Gadiraju, Olya Kudina, Loes Keijsers, Manon H. J. Hillegers, Willem-Paul Brinkman

  • June 24, 2026

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