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Related Experiment Video

Updated: Oct 20, 2025

An Application for Pairing with Wearable Devices to Monitor Personal Health Status
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Using an Individual-Centered Approach to Gain Insights From Wearable Data in the Quantified Flu Platform: Netnography

Bastian Greshake Tzovaras1,2, Enric Senabre Hidalgo1, Karolina Alexiou

  • 1Center for Research & Interdisciplinarity, INSERM U1284, Université de Paris, Paris, France.

Journal of Medical Internet Research
|September 10, 2021
PubMed
Summary

This study shows that a collaborative, co-created system effectively tracks infection symptoms using wearable data. This approach enhances user engagement and personalizes health monitoring for better self-tracking outcomes.

Keywords:
COVID-19citizen sciencecocreationnetnographic analysisself-trackingsymptom trackingwearable devices

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Area of Science:

  • Digital Health
  • Personalized Medicine
  • Human-Computer Interaction

Background:

  • Wearable devices are widely used for general health monitoring and predicting infections via physiological symptoms.
  • Evidence for wearable-based infection prediction has primarily come from large, population-based studies.
  • The Quantified Self and Personal Science communities focus on individual self-learning using personal data, often from wearables.

Purpose of the Study:

  • To explore the co-creation of a collective self-tracking system for infection symptom monitoring.
  • To integrate personal science practitioners' insights with wearable sensor data for infection symptom tracking.

Main Methods:

  • Engaged in a co-creation and design process with personal science practitioners to develop a web-based symptom tracking tool prototype.
  • Utilized a netnographic analysis to investigate the decentralized and iterative development process of the prototype.
  • Initiated prototype creation on March 16, 2020, focusing on iterative development.

Main Results:

  • The Quantified Flu prototype enabled daily symptom reporting and integrated data with wearable metrics like heart rate, body temperature, and respiratory rate.
  • Demonstrated high user engagement, with 56% (52/92) becoming regular users reporting data for over 3 months.
  • Netnographic analysis revealed the prototype evolved through continuous co-creation, where new releases spurred feature discussions.

Conclusions:

  • High user engagement and iterative development confirm the success of open co-creation for developing tailored health tools.
  • Co-creation processes can lead to tools that meet individual needs, potentially reducing user dropout rates.
  • This approach facilitates personalized health monitoring and symptom tracking through community involvement.