• Bastian Greshake Tzovaras
  • Enric Senabre Hidalgo
  • Karolina Alexiou
  • Lukaz Baldy
  •  Basile Morane
  • Ilona Bussod 
  • Melvin Fribourg 
  • Katarzyna Wac
  • Gary Wolf
  • Mad Ball


Background: Wearables have been used widely for monitoring health in general, and recent research results show that they can be used to predict infections based on physiological symptoms. To date, evidence has been generated in large, population-based settings. In contrast, the Quantified Self and Personal Science communities are composed of people who are interested in learning about themselves individually by using their own data, which are often gathered via wearable devices.

Objective: This study aims to explore how a cocreation process involving a heterogeneous community of personal science practitioners can develop a collective self-tracking system for monitoring symptoms of infection alongside wearable sensor data.

Methods: We engaged in a cocreation and design process with an existing community of personal science practitioners to jointly develop a working prototype of a web-based tool for symptom tracking. In addition to the iterative creation of the prototype (started on March 16, 2020), we performed a netnographic analysis to investigate the process of how this prototype was created in a decentralized and iterative fashion.

Results: The Quantified Flu prototype allowed users to perform daily symptom reporting and was capable of presenting symptom reports on a timeline together with resting heart rates, body temperature data, and respiratory rates measured by wearable devices. We observed a high level of engagement; over half of the users (52/92, 56%) who engaged in symptom tracking became regular users and reported over 3 months of data each. Furthermore, our netnographic analysis highlighted how the current Quantified Flu prototype was a result of an iterative and continuous cocreation process in which new prototype releases sparked further discussions of features and vice versa.

Conclusions: As shown by the high level of user engagement and iterative development process, an open cocreation process can be successfully used to develop a tool that is tailored to individual needs, thereby decreasing dropout rates.


  1. Chiauzzi E, Rodarte C, DasMahapatra P. Patient-centered activity monitoring in the self-management of chronic health conditions. BMC Med 2015 Apr 09;13:77 [FREE Full text] [CrossRef] [Medline]
  2. Vayena E, Brownsword R, Edwards SJ, Greshake B, Kahn JP, Ladher N, et al. Research led by participants: a new social contract for a new kind of research. J Med Ethics 2016 Apr;42(4):216-219 [FREE Full text] [CrossRef] [Medline]
  3. Allen BL, Ferrier Y, Cohen AK. Through a maze of studies: health questions and ‘undone science’ in a French industrial region. Environ Sociol 2016 Oct 08;3(2):134-144. [CrossRef]
  4. Wolf GI, De Groot M. A conceptual framework for personal science. Front Comput Sci 2020 Jun 30;2:00021. [CrossRef]
  5. White paper: design and implementation of participant-led research in the quantified self community. Quantified Self.   URL: [accessed 2020-09-02]
  6. De Groot M, Drangsholt M, Martin-Sanchez F, Wolf G. Single subject (N-of-1) research design, data processing, and personal science. Methods Inf Med 2017;56(6):416-418. [CrossRef] [Medline]
  7. Daza EJ, Wac K, Oppezzo M. Effects of sleep deprivation on blood glucose, food cravings, and affect in a non-diabetic: an N-of-1 randomized pilot study. Healthcare (Basel) 2019 Dec 25;8(1):6 [FREE Full text] [CrossRef] [Medline]
  8. Lupton D. ‘It’s made me a lot more aware’: a new materialist analysis of health self-tracking. Media Int Aust 2019 Apr 26;171(1):66-79. [CrossRef]
  9. Heyen NB. From self-tracking to self-expertise: the production of self-related knowledge by doing personal science. Public Underst Sci 2020 Feb;29(2):124-138 [FREE Full text] [CrossRef] [Medline]
  10. Mück JE, Ünal B, Butt H, Yetisen AK. Market and patent analyses of wearables in medicine. Trends Biotechnol 2019 Jun;37(6):563-566. [CrossRef] [Medline]
  11. Karjalainen J, Viitasalo M. Fever and cardiac rhythm. Arch Intern Med 1986 Jun;146(6):1169-1171. [Medline]
  12. Kamišalić A, Fister I, Turkanović M, Karakatič S. Sensors and functionalities of non-invasive wrist-wearable devices: a review. Sensors (Basel) 2018 May 25;18(6):1714 [FREE Full text] [CrossRef] [Medline]
  13. Wac K. From quantified self to quality of life. In: Digital Health. Basel, Switzerland: Springer International Publishing; Jan 2018.
  14. Clim A, Zota RD, Tinica G. Big Data in home healthcare: a new frontier in personalized medicine. Medical emergency services and prediction of hypertension risks. Int J Healthc Manag 2018 Nov 28;12(3):241-249. [CrossRef]
  15. Zheng J, Shen Y, Zhang Z, Wu T, Zhang G, Lu H. Emerging wearable medical devices towards personalized healthcare. In: Proceedings of the 8th International Conference on Body Area Networks. 2013 Presented at: 8th International Conference on Body Area Networks; Sep 30-Oct 2, 2013; Boston, United States. [CrossRef]
  16. Mizuno A, Changolkar S, Patel MS. Wearable devices to monitor and reduce the risk of cardiovascular disease: evidence and opportunities. Annu Rev Med 2021 Jan 27;72:459-471. [CrossRef] [Medline]
  17. Sana F, Isselbacher EM, Singh JP, Heist EK, Pathik B, Armoundas AA. Wearable devices for ambulatory cardiac monitoring: JACC state-of-the-art review. J Am Coll Cardiol 2020 Apr 07;75(13):1582-1592 [FREE Full text] [CrossRef] [Medline]
  18. Kourtis LC, Regele OB, Wright JM, Jones GB. Digital biomarkers for Alzheimer’s disease: the mobile/ wearable devices opportunity. NPJ Digit Med 2019;2:9 [FREE Full text] [CrossRef] [Medline]
  19. Tyler J, Choi SW, Tewari M. Real-time, personalized medicine through wearable sensors and dynamic predictive modeling: a new paradigm for clinical medicine. Curr Opin Syst Biol 2020 Apr;20:17-25 [FREE Full text] [CrossRef] [Medline]
  20. Sen-Crowe B, McKenney M, Elkbuli A. Utilizing technology as a method of contact tracing and surveillance to minimize the risk of contracting COVID-19 infection. Am J Emerg Med 2021 Jul;45:519 [FREE Full text] [CrossRef] [Medline]
  21. Seshadri DR, Davies EV, Harlow ER, Hsu JJ, Knighton SC, Walker TA, et al. Wearable sensors for COVID-19: a call to action to harness our digital infrastructure for remote patient monitoring and virtual assessments. Front Digit Health 2020 Jun 23;2:8. [CrossRef]
  22. Jeong H, Rogers JA, Xu S. Continuous on-body sensing for the COVID-19 pandemic: gaps and opportunities. Sci Adv 2020 Sep 2;6(36):eabd4794 [FREE Full text] [CrossRef] [Medline]
  23. Bruggeman L. Researchers investigate whether wearable apps could unveil hidden coronavirus cases. ABC News.   URL: https:/​/abcnews.​​Health/​researchers-investigate-wearable-apps-unveil-hidden-coronavirus-cases/​story?id=69925541 [accessed 2021-02-06]
  24. Quer G, Radin JM, Gadaleta M, Baca-Motes K, Ariniello L, Ramos E, et al. Wearable sensor data and self-reported symptoms for COVID-19 detection. Nat Med 2021 Jan;27(1):73-77. [CrossRef] [Medline]
  25. Mishra T, Wang M, Metwally A, Bogu G, Brooks A, Bahmani A, et al. Early detection of COVID-19 using a smartwatch. medRxiv 2020:20147512 (forthcoming). [CrossRef]
  26. Natarajan A, Su H, Heneghan C. Assessment of physiological signs associated with COVID-19 measured using wearable devices. NPJ Digit Med 2020 Nov 30;3(1):156 [FREE Full text] [CrossRef] [Medline]
  27. Smarr BL, Aschbacher K, Fisher SM, Chowdhary A, Dilchert S, Puldon K, et al. Feasibility of continuous fever monitoring using wearable devices. Sci Rep 2020 Dec 14;10(1):21640 [FREE Full text] [CrossRef] [Medline]
  28. Spinuzzi C. The methodology of participatory design. Tech Commun 2005 May;52(2):163-174 [FREE Full text]
  29. Sanders EB, Stappers PJ. Probes, toolkits and prototypes: three approaches to making in codesigning. CoDesign 2014 Mar 06;10(1):5-14. [CrossRef]
  30. Foth M, Axup J. Participatory design and action research: identical twins or synergetic pair? In: Expanding Boundaries in Design: Proceedings Ninth Participatory Design Conference. Canada: Computer Professionals for Social Responsibility; 2006.
  31. Madey G, Freeh V, Tynan R. The open source software development phenomenon: an analysis based on social network theory. In: Proceedings of the Eighth Americas Conference on Information Systems. 2002 Presented at: Eighth Americas Conference on Information System; 2002; Dallas   URL:
  32. Kozinets R. Netnography. In: The International Encyclopedia of Digital Communication and Society. Hoboken, New Jersey: Wiley; Feb 11, 2015.
  33. Tzovaras BG, Angrist M, Arvai K, Dulaney M, Estrada-Galiñanes V, Gunderson B, et al. Open Humans: a platform for participant-centered research and personal data exploration. Gigascience 2019 Jun 01;8(6):giz076 [FREE Full text] [CrossRef] [Medline]
  34. Open Humans Community. Slack.   URL: [accessed 2021-02-22]
  35. Quantified Self.   URL: [accessed 2021-02-22]
  36. Open COVID19 initiative. Just One Giant Lab.   URL: [accessed 2021-02-22]
  37. OpenHumans / quantified-flu. GitHub.   URL: [accessed 2021-02-22]
  38. Zhao J, Wang T, Fan X. Patient value co-creation in online health communities: Social identity effects on customer knowledge contributions and membership continuance intentions in online health communities. J Serv Manag 2015 Mar 16;26(1):72-96. [CrossRef]
  39. Costello L, McDermott M, Wallace R. Netnography: range of practices, misperceptions, and missed opportunities. Int J Qual Methods 2017 Apr 04;16(1):160940691770064. [CrossRef]
  40. Zainal Z. Case study as a research method. J Kemanusiaan 2007;5(1):1 [FREE Full text]
  41. Preece J. Etiquette, empathy and trust in communities of practice: stepping-stones to social capital. J Univers Comput Sci 2004;10(3):294-302 [FREE Full text] [CrossRef]
  42. Quantified Flu.   URL: [accessed 2021-02-22]
  43. OpenHumans / qf-heartrate-apple-health. GitHub.   URL: [accessed 2021-02-22]
  44. Mamykina L, Smaldone AM, Bakken SR. Adopting the sensemaking perspective for chronic disease self-management. J Biomed Inform 2015 Aug;56:406-417 [FREE Full text] [CrossRef] [Medline]
  45. Hekler EB, Klasnja P, Chevance G, Golaszewski NM, Lewis D, Sim I. Why we need a small data paradigm. BMC Med 2019 Jul 17;17(1):133 [FREE Full text] [CrossRef] [Medline]
  46. Kraut R, Resnick P. Building Successful Online Communities. Cambridge, MA: The MIT Press; 2016.
  47. Bot BM, Suver C, Neto EC, Kellen M, Klein A, Bare C, et al. The mPower study, Parkinson disease mobile data collected using ResearchKit. Sci Data 2016 Mar 03;3:160011 [FREE Full text] [CrossRef] [Medline]
  48. Torous J, Lipschitz J, Ng M, Firth J. Dropout rates in clinical trials of smartphone apps for depressive symptoms: a systematic review and meta-analysis. J Affect Disord 2020 Feb 15;263:413-419. [CrossRef] [Medline]
  49. Meyerowitz-Katz G, Ravi S, Arnolda L, Feng X, Maberly G, Astell-Burt T. Rates of attrition and dropout in app-based interventions for chronic disease: systematic review and meta-analysis. J Med Internet Res 2020 Sep 29;22(9):e20283 [FREE Full text] [CrossRef] [Medline]
  50. Vaghefi I, Tulu B. The continued use of mobile health apps: insights from a longitudinal study. JMIR Mhealth Uhealth 2019 Aug 29;7(8):e12983 [FREE Full text] [CrossRef] [Medline]
  51. Call SA, Vollenweider MA, Hornung CA, Simel DL, McKinney WP. Does this patient have influenza? J Am Med Assoc 2005 Feb 23;293(8):987-997. [CrossRef] [Medline]
  52. Dugas AF, Valsamakis A, Atreya MR, Thind K, Manchego PA, Faisal A, et al. Clinical diagnosis of influenza in the ED. Am J Emerg Med 2015 Jun;33(6):770-775 [FREE Full text] [CrossRef] [Medline]
  53. Addeo F, Paoli A, Esposito M, Bolcato M. Doing social research on online communities: The benefits of netnography. Athens J Soc Sci 2019 Dec 05;7(1):9-38. [CrossRef] [Medline]
  54. Jong S. Netnography: researching online populations. In: Handbook of Research Methods in Health Social Sciences. Singapore: Springer; 2019.
  55. Sack W, Détienne F, Ducheneaut N, Burkhardt J, Mahendran D, Barcellini F. A methodological framework for socio-cognitive analyses of collaborative design of open source software. Comput Supported Coop Work 2006 Jun 10;15(2-3):229-250. [CrossRef]
  56. Goodyear-Smith F, Jackson C, Greenhalgh T. Co-design and implementation research: challenges and solutions for ethics committees. BMC Med Ethics 2015 Nov 16;16:78 [FREE Full text] [CrossRef] [Medline]
  57. Wiggins A, Wilbanks J. The rise of citizen science in health and biomedical research. Am J Bioeth 2019 Aug 24;19(8):3-14. [CrossRef] [Medline]
  58. Grant AD, Wolf GI, Nebeker C. Approaches to governance of participant-led research: a qualitative case study. BMJ Open 2019 Apr 02;9(4):e025633 [FREE Full text] [CrossRef] [Medline]
  59. Mahr D, Dickel S. Citizen science beyond invited participation: nineteenth century amateur naturalists, epistemic autonomy, and big data approaches avant la lettre. Hist Philos Life Sci 2019 Oct 07;41(4):41. [CrossRef] [Medline]
  60. Haklay M. Citizen science and volunteered geographic information: overview and typology of participation. In: Crowdsourcing Geographic Knowledge. Dordrecht, Netherlands: Springer; 2013.
  61. Ferretti F. Mapping do-it-yourself science. Life Sci Soc Policy 2019 Jan 14;15(1):1 [FREE Full text] [CrossRef] [Medline]
  62. Delfanti A. Users and peers. From citizen science to P2P science. J Sci Commun 2010;09(01):09010501. [CrossRef]

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