The primary keyword for this post is "epistemic agency in self-tracking" and this article shows how that concept maps to actionable research workflows for qualitative teams. Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. According to the Frontiersin.org review by Riggare et al. (published 18 August 2026), personal science and Quantified Self practices vary widely in who controls the question, which matters for interpretation and ethics. This post explains, with concrete numbers from the Frontiersin.org review, how AI-enabled qualitative research can surface agency patterns, and how teams can convert those insights into reproducible thematic and cross-segment analyses.
Key Takeaways
According to the Frontiersin.org review (Riggare et al., 18 August 2026), an agency-based framework applied to the quantified self literature distinguishes low, mixed, and high epistemic agency and shows 46 self-tracking studies spanning that spectrum. Frontiersin.org
- A Web of Science and PubMed search in April–May 2025 returned 721 records and, after screening, 241 full papers were retained for analysis, as reported by Riggare et al. (Frontiersin.org, 18 August 2026).
- Riggare et al. (Frontiersin.org, 18 August 2026) found 46 papers used self-tracking as the data-collection method and classified those into 17 low-agency, 10 mixed-agency, and 19 high-agency studies.
- Riggare et al. (Frontiersin.org, 18 August 2026) reported a peak of QS publications in 2018 with 38 articles and noted an uptick in 2024 with 13 articles.
- Riggare et al. (Frontiersin.org, 18 August 2026) frame the core distinction as “who owns the question, ” a decisive factor between measurement-only studies and participatory personal science.
What Happened: how the Frontiers review characterized agency
Answer: Riggare et al. (Frontiersin.org, 18 August 2026) applied a four-phase personal science framework (Questioning, Observing, Reasoning, Discovering) and three agency levels (low, mixed, high) to classify studies that used self-tracking as a data source.
According to Riggare et al. (Frontiersin.org, 18 August 2026), the authors merged the conventional personal science phases into four phases because they found design and observing activities were tightly interwoven in practice.
According to Riggare et al. (Frontiersin.org, 18 August 2026), low-agency studies treat participants as data-collectors where only Observing is attributable to individuals, mixed-agency studies show partial participant involvement, and high-agency studies display full cycles of self-directed inquiry.
According to the Frontiersin.org review (Riggare et al., 18 August 2026), examples of high-agency personal science include N-of-1 protocols where the self-tracker owned the question, performed measurements, analyzed results, and sometimes coauthored the paper.
Findings Snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| April–May 2025 | Records returned by keyword "quantified self" (Web of Science and PubMed) | 721 records | Large, heterogeneous corpus that required screening, according to Riggare et al. (Frontiersin.org, 18 August 2026) |
| April–May 2025 | Full papers retained after screening | 241 papers | Corpus for conceptual analysis and taxonomy, according to Riggare et al. (Frontiersin.org, 18 August 2026) |
| Range through 2024 | Papers using self-tracking as data collection (analyzed by agency) | 46 papers | Subset suitable for agency classification and n-of-1 type analysis, according to Riggare et al. (Frontiersin.org, 18 August 2026) |
| Through 2024 | Agency breakdown of 46 self-tracking papers | 17 low, 10 mixed, 19 high | Shows a meaningful presence of self-directed personal science in the literature, according to Riggare et al. (Frontiersin.org, 18 August 2026) |
| 2018, 2021, 2024 | Publication counts highlighted | 2018: 38; 2021: 14; 2024: 13 | A peak in 2018 and a 2024 uptick suggest changing terminology and research interest, according to Riggare et al. (Frontiersin.org, 18 August 2026) |
| Screening process | Reviewer disagreement rates | 13% disagreement at abstract stage (n=80); 17% in detailed categorization (n=8) | Conservative coding approach with dual review and author adjudication, according to Riggare et al. (Frontiersin.org, 18 August 2026) |
Implications for qualitative researchers and research teams
Answer: The Frontiersin.org review (Riggare et al., 18 August 2026) implies qualitative teams must distinguish between self-tracking as raw measurement and personal science as participant-led inquiry when designing methods and interpreting results.
According to Riggare et al. (Frontiersin.org, 18 August 2026), studies labeled with "quantified self" in the literature are heterogeneous, so researchers should not assume uniform participant agency from keyword alone.
According to Riggare et al. (Frontiersin.org, 18 August 2026), when participants own the Questioning phase, researchers should plan for mixed data types and support participant reasoning and discovery, including coauthorship or shared methods documentation.
According to Riggare et al. (Frontiersin.org, 18 August 2026), ethical and infrastructure implications include needs for data security, methods transparency, and training, particularly because many personal science projects operate outside formal oversight.
How Evidano Helps
Problem: Heterogeneous agency makes synthesis slow and error prone
Answer: Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
According to the Frontiersin.org review (Riggare et al., 18 August 2026), the literature spans low to high agency which creates friction for synthesis across studies and for extracting who performed each inquiry phase.
Evidano feature mapping: When agency is unclear, Evidano's thematic coding and cross-segment analysis can label Questioning, Observing, Reasoning, and Discovering activities automatically from methods sections and participant text.
Problem: Manual n-of-1 synthesis misses patterns across cases
Answer: Evidano's AI can accelerate n-of-1 and personal science synthesis by extracting timelines, metrics, and participant reflections at scale.
According to the Frontiersin.org review (Riggare et al., 18 August 2026), high-agency personal science often blends subjective logs and objective sensor data, which requires integrated qualitative and quantitative coding.
Evidano feature mapping: Evidano ingests documents and spreadsheets and performs thematic, frequency, and cross-segment analyses to expose recurring methods, tools, and reasoning strategies across multiple personal science reports. See the Evidano features page for relevant capabilities.
Problem: Documentation and reproducibility are uneven in personal science publications
Answer: Evidano provides reproducible pipelines for coding, visualization, and AI chat over your dataset so teams can audit how agency labels were assigned.
According to Riggare et al. (Frontiersin.org, 18 August 2026), many high-agency projects included authorship by the self-tracker as an indicator of the Discovering phase, which is a reproducible cue that should be extractable from text.
Evidano feature mapping: Evidano's visualizations (word clouds, co-occurrence networks, hierarchical codes) and AI chat transcripts create shareable artifacts that document the reasoning stage in a way that supports peer review and community learning.
FAQ: epistemic agency in self-tracking
What is epistemic agency in self-tracking?
Answer: Epistemic agency in self-tracking means the individual exercises control over knowledge production by posing questions, collecting and analyzing data, and articulating discoveries, as defined by Riggare et al. (Frontiersin.org, 18 August 2026).
According to Catherine Elgin as quoted in the Frontiersin.org review (Riggare et al., 18 August 2026), “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.”
According to Riggare et al. (Frontiersin.org, 18 August 2026), the review operationalizes that idea into four inquiry phases: Questioning, Observing, Reasoning, Discovering.
How did Riggare et al. classify agency in the literature?
Answer: Riggare et al. (Frontiersin.org, 18 August 2026) classified agency by attributing each of the four phases to participants or researchers and then assigning low, mixed, or high agency labels.
According to Riggare et al. (Frontiersin.org, 18 August 2026), the conservative coding rule was to assign the lower level when elements were ambiguous, and dual review with adjudication was used to resolve disagreements.
Which studies count as high-agency personal science?
Answer: According to Riggare et al. (Frontiersin.org, 18 August 2026), high-agency studies are those where individuals initiated the question, performed self-directed observing, carried out or meaningfully participated in reasoning, and articulated discoveries, often as coauthors.
According to Riggare et al. (Frontiersin.org, 18 August 2026), common tools in high-agency cases included consumer wearables, continuous glucose monitors, EEG headbands, and DIY data exports, combined with participant-led analysis.
Can AI tools safely help analyze personal science data?
Answer: AI tools can help if they are configured for privacy, reproducibility, and researcher oversight, a point implied by Riggare et al. (Frontiersin.org, 18 August 2026) when they discuss infrastructure and ethical needs for personal science.
According to Riggare et al. (Frontiersin.org, 18 August 2026), personal science raises data security and validity concerns which institutional support or clear guidelines should address; Evidano's data handling policies are available on our data security page.
Conclusion & Next Steps
Answer: The Frontiersin.org review (Riggare et al., 18 August 2026) shows that epistemic agency in self-tracking is measurable and meaningful for how we interpret self-tracking evidence.
According to Riggare et al. (Frontiersin.org, 18 August 2026), the decisive distinction is "who owns the question, " which determines whether a dataset represents measurement only or participant-led knowledge production.
For qualitative teams, Evidano can speed reproducible coding, surface agency signals across methods sections and participant text, and produce visual artifacts that document reasoning and discovery.
Next step: analyze a small set of self-tracking reports using an AI-enabled pipeline to quantify agency patterns and produce an audit trail; Try Evidano for free.
Topics
- epistemic agency in self-tracking
- personal science qualitative analysis
- quantified self epistemic agency
- AI-enabled qualitative research
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