Agency Matters: Epistemic Agency in Self-Tracking

According to the Frontiers in Public Health review by Riggare et al. (18 August 2026), an agency-based framework distinguishes low-agency measurement from high-agency personal science by asking who formulates the question and interprets results. See the original article at Frontiers in Public Health.

• Riggare et al. searched Web of Science and PubMed in April–May 2025 and retrieved 721 records, of which 241 full papers were retained for analysis.
• Riggare et al. classified 46 articles where self-tracking was the data-collection method: 17 low-agency, 10 mixed-agency, and 19 high-agency studies (analysis reported 18 August 2026).
• Riggare et al. identified a peak in publications using the term “quantified self” in 2018 with 38 articles and reported an uptick in 2024 with 13 articles.

Answer: Riggare et al. applied a four-phase framework (Questioning, Observing, Reasoning, Discovering) and three agency levels (low, mixed, high) to the quantified self literature to operationalize epistemic agency.

According to Riggare et al. (Frontiers in Public Health, 18 August 2026), the authors used Jaakkola's theory-synthesis approach to develop the analytic framework and searched Web of Science and PubMed for the exact phrase “quantified self” in April–May 2025, yielding 721 unique records.

According to Riggare et al., abstracts were screened to 241 full papers, and 46 primary research articles using self-tracking as the data-collection method were analyzed for which of the four inquiry phases the individual performed.

Riggare et al. defined agency levels conservatively: articles were assigned the lower level when evidence of individual participation in a phase was ambiguous, and in practice high-agency articles often showed the self-tracker as a co-author.

For conceptual clarity Riggare et al. borrow Catherine Elgin's definition of epistemic agency, quoting Elgin: "Epistemic agents should think of themselves as, and act as, legislating members of a realm of epistemic ends: they make the rules, devise the methods, and set the standards that bind them" (Elgin, quoted in Riggare et al., 2026).

Answer: The Riggare et al. framework gives qualitative teams a concrete rubric to code agency in self-tracking projects and to distinguish data contribution from knowledge production.

According to Riggare et al. (Frontiers in Public Health, 18 August 2026), the key diagnostic is authorial control: if the individual did Questioning, Observing, Reasoning, and Discovering, label the case high-agency personal science; if the individual only collected data, label it low-agency.

For qualitative research design, Riggare et al. recommend tracking who formulates the research question, who refines measurement definitions, who performs the analysis, and who authors dissemination. These four checkpoints map directly to common codebook categories used by qualitative software.

Using the framework helps avoid category errors: Riggare et al. show that many articles that use the phrase “quantified self” are actually low-agency studies in which participants function as sensor platforms rather than as knowledge producers.

Practical note: the review flags ethical and infrastructure gaps for personal science, including lack of formal oversight and the need for community documentation and data-security practices, aligning with citizen science discussions such as Den Broeder et al. (2016) on citizen science for public health.

Riggare et al.'s Frontiers review (18 August 2026) shows that epistemic agency in self-tracking is measurable and consequential: among 46 self-tracking studies they found 17 low-agency, 10 mixed-agency, and 19 high-agency examples.

For qualitative researchers and patient-led projects, the practical test is simple: who owns the question, who interprets the data, and who authors the discoveries.

AI-enabled qualitative analysis platforms can accelerate coding, produce auditable evidence of participant agency, and streamline reporting to journals and ethics boards.

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