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AI for Qualitative Analysis of Quantified Self

Evidano6 min read

This post explains how AI-enabled qualitative research methods can amplify personal science and assess epistemic agency in self-tracking. The primary keyword is "qualitative analysis of quantified self" and the audience is qualitative researchers and UX/health research teams who want concrete methods and tooling. According to Frontiersin.org (published Aug 18, 2026), a conceptual literature review by Riggare et al. classified the academic use of "quantified self" and produced a reproducible framework you can apply to transcripts, logs, and n-of-1 records.

Key Takeaways

The Frontiers review by Riggare et al., available at Frontiersin.org (Aug 18, 2026), shows that self-tracking in health spans low- to high-epistemic-agency practices and that distinguishing agency depends on identifying who originated the question.

  • A literature search in Web of Science and PubMed performed in April–May 2025 returned 721 records, and Riggare et al. retained 241 full papers for analysis (Frontiersin.org, Aug 18, 2026).
  • Among those papers, 46 used self-tracking as the data-collection method and were classified by agency level as 17 low-agency, 10 mixed-agency, and 19 high-agency articles (Frontiersin.org, Aug 18, 2026).
  • Riggare et al. operationalize agency with a four-phase personal science cycle: Questioning, Observing, Reasoning, Discovering (framework described in Frontiersin.org, Aug 18, 2026).
  • The review highlights a practical distinction: "who owns the question" determines whether self-tracking is measurement only or personal science (Riggare et al., Frontiersin.org, Aug 18, 2026).

What happened and how the review measured agency

The Frontiersin.org review (Aug 18, 2026) applied a four-phase framework to classify epistemic agency across studies that used the search term "quantified self" in April–May 2025.

According to Riggare et al. (Frontiersin.org, Aug 18, 2026), the authors merged design and observing into a four-phase cycle (Questioning, Observing, Reasoning, Discovering) to reflect how personal scientists actually iterate in practice.

According to Frontiersin.org (Aug 18, 2026), studies were coded conservatively by two authors and disagreements (13% at abstract screening, 17% at detailed review stages) were resolved by consensus, yielding 241 papers for full analysis and 46 papers that used self-tracking as the data source.

The Frontiers review also cites Catherine Elgin when defining epistemic agency: "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" (Catherine Elgin, quoted in Riggare et al., Frontiersin.org, Aug 18, 2026).

Findings Snapshot

DateMetricValueImplication
April–May 2025Records returned for "quantified self"721Large, heterogeneous corpus requiring conceptual sorting (Frontiersin.org, Aug 18, 2026)
April–May 2025Full papers retained241Filtered to peer-reviewed empirical work for concept-level analysis (Frontiersin.org, Aug 18, 2026)
Aug 18, 2026Papers using self-tracking as data collection46Subset analyzed for epistemic agency across four phases (Frontiersin.org, Aug 18, 2026)
Aug 18, 2026Agency breakdown (low/mixed/high)17 / 10 / 19High-agency cases often include the self-tracker as co-author and sharing of methods (Frontiersin.org, Aug 18, 2026)
2018 and 2024 (publication years)Peak and uptick38 (2018 peak); 13 (2024); 14 (2021)Field peaked in 2018 then diversified; 2024 shows renewed interest (Frontiersin.org, Aug 18, 2026)

Implications for qualitative researchers and health UX teams

Qualitative researchers should treat self-tracking artifacts as mixed-method n-of-1 data that require an agency lens to interpret, according to Riggare et al. (Frontiersin.org, Aug 18, 2026).

According to the Frontiers review (Aug 18, 2026), when participants originate questions (high agency) their logs and reflections function as both data and analytic insight, changing coding choices and validation expectations.

According to Riggare et al. (Frontiersin.org, Aug 18, 2026), UX teams designing self-tracking studies must document who designed goals and who interprets outputs because the review found that data-sharing dominates low-agency studies while sharing of methods and learnings dominates high-agency reports.

According to Frontiersin.org (Aug 18, 2026), ethics and infrastructure matter: personal science often sits outside institutional oversight, so researchers should pre-specify data security, consent, and analytic transparency when working with self-trackers.

How Evidano helps research teams apply the agency framework

Problem: Heterogeneous artifacts and inconsistent agency metadata → Solution: Thematic, frequency, and cross-segment analysis

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 (Aug 18, 2026), agency depends on whether participants posed the question (Questioning) and participated in Reasoning and Discovering; Evidano lets teams tag transcripts, time-series logs, and diary notes with phase labels (Questioning, Observing, Reasoning, Discovering) so you can quantify agency across cases.

Evidano feature mapping: Problem: missing phase metadata → Feature: custom code hierarchies and cross-segment frequency analysis (see Evidano features).

Problem: Manual n-of-1 synthesis is slow → Solution: AI-assisted synthesis and AI chat over documents

According to Riggare et al. (Frontiersin.org, Aug 18, 2026), high-agency personal science often integrates subjective notes with objective streams; Evidano accelerates that synthesis with AI thematic extraction and timeline alignment so emergent hypotheses from individual cases scale to cross-case patterns.

Evidano feature mapping: Problem: slow synthesis of multi-modal logs → Feature: AI chat over your documents and temporal alignment visualizations (see Evidano features).

Problem: Privacy and non-institutional research → Solution: PII redaction and secure workflows

According to Frontiersin.org (Aug 18, 2026), many personal science projects operate outside formal oversight, raising data-security needs; Evidano supports encrypted storage and PII redaction to preserve participant privacy during analysis.

Evidano feature mapping: Problem: ad hoc data sharing and privacy risk → Feature: transcription with PII redaction and secure, encrypted workspaces (see Evidano data security).

FAQ: qualitative analysis of quantified self

What is epistemic agency in self-tracking research?

Answer: Epistemic agency is the capacity of the self-tracker to act as producer of knowledge rather than only a data source.

According to Riggare et al. (Frontiersin.org, Aug 18, 2026), epistemic agency is measured across four phases (Questioning, Observing, Reasoning, Discovering) and high agency requires identifiable participation in all four phases.

How do I code for agency in qualitative transcripts and logs?

Answer: Code each unit for who initiated the phase: participant-originated Questioning, participant-driven Observing, participant Reasoning, and participant Discovering.

According to Frontiersin.org (Aug 18, 2026), conservative coding assigns the lower agency level when evidence is ambiguous, and dual-review improves reliability (the review reported 13% disagreement at abstract screening and 17% disagreement in detailed categorization).

Can AI detect high-agency personal science cases automatically?

Answer: AI can flag candidate high-agency cases but human verification is required.

According to Riggare et al. (Frontiersin.org, Aug 18, 2026), high-agency articles commonly include participant co-authorship or explicit method sharing; AI-assisted keyword and authorship checks can surface these markers for manual review.

Is personal science the same as qualitative research?

Answer: No, personal science differs in that the researcher and subject are the same person and the aim is actionable self-knowledge.

According to Frontiersin.org (Aug 18, 2026), qualitative research seeks conceptual generalization while personal science prioritizes individual actionability, so analytic choices and validity criteria should differ accordingly.

Conclusion & Next Steps

The Frontiers review (Riggare et al., Frontiersin.org, Aug 18, 2026) provides a practical agency framework you can apply to transcripts, logs, and n-of-1 records to distinguish measurement from self-directed knowledge production.

Teams using qualitative methods should tag phase ownership and combine subjective notes with objective streams to surface high-agency personal science cases for deeper analysis.

To prototype that workflow, map your corpus to the four-phase schema, run thematic and cross-segment analyses, and then validate AI-flagged high-agency cases with human review.

Ready to try an AI workflow built for qualitative and personal science analysis? Try Evidano for free.

Topics

  • qualitative analysis of quantified self
  • personal science analysis
  • AI qualitative research
  • quantified self epistemic agency

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