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Self-Quantification Systems to Support Physical Activity: From Theory to Implementation Principles

Author

Listed:
  • Paul Dulaud

    (Tech-CICO (Technologies for Cooperation, Interaction, and Knowledge, in Collectives), Université de Technologie de Troyes, 12 Rue Marie Curie, 10000 Troyes, France)

  • Ines Di Loreto

    (Tech-CICO (Technologies for Cooperation, Interaction, and Knowledge, in Collectives), Université de Technologie de Troyes, 12 Rue Marie Curie, 10000 Troyes, France)

  • Denis Mottet

    (Euromov Digital Health in Motion, Université de Montpellier, IMT Mines Alès, 700 av. Pic St Loup, 34090 Montpellier, France)

Abstract

Since the emergence of the quantified self movement, users aim at health behavior change, but only those who are sufficiently motivated and competent with the tools will succeed. Our literature review shows that theoretical models for quantified self exist but they are too abstract to guide the design of effective user support systems. Here, we propose principles linking theory and implementation to arrive at a hierarchical model for an adaptable and personalized self-quantification system for physical activity support. We show that such a modeling approach should include a multi-factors user model (activity, context, personality, motivation), a hierarchy of multiple time scales (week, day, hour), and a multi-criteria decision analysis (user activity preference, user measured activity, external parameters). This theoretical groundwork, which should facilitate the design of more effective solutions, has now to be validated by further empirical research.

Suggested Citation

  • Paul Dulaud & Ines Di Loreto & Denis Mottet, 2020. "Self-Quantification Systems to Support Physical Activity: From Theory to Implementation Principles," IJERPH, MDPI, vol. 17(24), pages 1-22, December.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:24:p:9350-:d:461787
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    References listed on IDEAS

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    1. Melanie Swan, 2009. "Emerging Patient-Driven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantified Self-Tracking," IJERPH, MDPI, vol. 6(2), pages 1-34, February.
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    Cited by:

    1. Yu-dong Zhang & Hui-long Zhang & Jia-qin Xie & Chu-bing Zhang, 2023. "The influence of self-quantification on individual’s participation performance and behavioral decision-making in physical fitness activities," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.

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