Free Simple Wearable Report turns Oura Ring exports into lab-style summaries for LLM analysis

Simple Wearable Report, a free tool built by an Oura Ring community member, turns raw Oura export files into compact, lab-style summaries and offers an option to push those summaries to external AIs such as Gemini, Claude, or ChatGPT. That workflow matters because it makes wearable data both easier to scan like clinical notes and directly consumable by large language models for further analysis.

The creator designed the report to simplify sharing Oura data with primary care providers and to enable AI-assisted interpretation. In a hands — on test by Nina Raemont published May 27, 2026, the author uploaded recent Oura Ring exports to Simple Wearable Report, then fed the generated summary into Gemini and also queried Oura’s native Advisor to compare the two AIs’ responses to the same dataset.

Oura’s official app already provides sleep, cycle, health panel, and period summaries across weekly to annual views, but those in-app outputs can be harder to scan as a clinical snapshot. Simple Wearable Report intentionally mimics a lab-style output: short, dense sections that present scores, dates, and contributing datapoints so a clinician or model can quickly spot notable trends and anomalies.

When the author ran the workflow, Gemini identified a specific date with unusually high wellness and returned the readiness and sleep scores along with the datapoints that contributed to them. Gemini also produced numeric contribution scores for individual biometrics — for example, a resting heart rate contribution score of 7 out of 100 and a sleep debt contribution score of 11 out of 100 — metrics not surfaced as numeric ratings in the Oura app, which typically flags only notable issues.

Both the Oura Advisor and Gemini recommended adding more daytime movement, but their delivery and granularity differed. Oura’s in-house Advisor offered a gentle, high-level suggestion about short walks, while Gemini produced a blunt observation that step counts “fluctuate wildly from 0 to over 17,000 steps” and highlighted precise differences between great and just‑ok wellness days in resting heart rate and heart — rate variability.

For builders and clinicians the workflow highlights two clear opportunities: provide structured, exportable summaries that clinicians can scan quickly, and enable machine — readable exports so third — party models can compute numeric contribution scores and produce precise, model — driven comparisons. Those capabilities let different AIs surface complementary, actionable interpretations from the same wearable dataset. The tool remains free and user-built, and the author’s hands — on test demonstrates how pairing a curated summary with an LLM can yield both macro trends and highly granular analyses from Oura Ring data.