Nutrinaut learning library
Learn With Nutrinaut
Practical app comparisons, concrete examples and the limits of consumer data. Choose an Android workflow, read a field note, or open the matching tool to apply the method.
Start with the task you want to finish
Check my own wearable export
Open the CSV workspace → map your columns → inspect timestamp, duration and duplicate checks.
Need context? Check the source, check the dates and compare sleep coverage.
Turn my recipe into portions and a shopping list
Audit the recipe basis → open Recipe & Grocery → save or print the worksheet.
Before interpreting a food total, check diary completeness. For a recommendation, check its evidence and record the context.
Compare food numbers on the same basis
Compare two fictional labels → scale your own label → check the arithmetic.
For meals rather than products, compare the calorie and protein contribution of a logged day.
Choosing an Android health app
Choose by your existing device, available records and daily questions. These are first-party comparisons by Nutrinaut’s founder, with official sources, dated pricing and explicit tradeoffs—not independent product tests.
Bevel Alternative for Android: What to Compare Before Switching
Check Bevel’s current Android status, its free and paid features, and the practical tradeoffs of an Android alternative.
Read the comparison → Android comparison · AthlyticAthlytic Alternative for Android: Recovery Data, HRV and Daily Context
Build a first-sync checklist and distinguish an Android HRV observation from an Apple Health value or a proprietary recovery score.
Read the comparison → Android comparison · WHOOPWHOOP Alternatives for Android: Use Your Existing Watch or Buy Dedicated Hardware?
Separate hardware, membership and software costs. Decide what an existing compatible watch can—and cannot—replace.
Read the comparison → Android comparison · Selection guideAndroid Recovery Apps Compared: Choose by Your Watch, Data and Goals
Compare six approaches by your existing device, the data available to share and the task you want to finish—without a universal winner.
Read the comparison →Wearables & sleep
Start with dates, duration and data sources before comparing a trend or reading a summary score.
Google Fit, Google Health and Health Connect: What Actually Changes Before 2027
Choose your data route, understand the Fit API transition and leave with a checklist for the records you want to keep.
Open the interactive route guide →Field note 14 · Reading sleep totalsTime Asleep, Time in Bed and the Night Window
Compare sleep categories, gaps and a separate in-bed total with an interactive coverage visual.
Read field note → Interactive lab · Data provenanceNutrinaut Data Forensics Lab
Map columns from your own CSV, preview the data and inspect timestamp, duration and possible duplicate checks locally.
Open interactive lab → Field note 13 · Calendar checksOne Night, Two Dates: Reading Wearable Sleep Around Clock Changes
Midnight, travel and clock changes can give one observation several date labels. Here is how to check the interval before changing the diary.
Read field note → Field note 12 · Training inputsBefore You Trust a Workout Calorie Estimate, Check the Minutes
An estimate can look precise while using the wrong minutes. Check the duration, its source and its definition before deciding what the calorie number means.
Read field note → Field note 07 · Wearable comparisonApple Watch and Garmin Readiness Disagree: What to Check First
Check scales, timestamps and missing records when two wearables disagree. Includes a downloadable comparison worksheet.
Read field note → Field note 01 · Wearable dataBefore You Trust a Wearable Trend, Check Its Source
Check sources and dates, separate daily summaries from workouts, and work through three missing-night scenarios.
Read field note → Field note 06 · RecoveryHow to Read Recovery Signals Without a Readiness Verdict
Separate a wearable measurement, a baseline, its context and the decision that follows.
Read field note →Food & calculations
Check diary completeness, portion sizes and arithmetic before interpreting a food or energy estimate.
One Pot, Different Portions: Audit a Recipe Before Logging It
Match ingredient basis, weigh the finished yield and separate whole-pack shopping cost from the amount used.
Read field note → Field note 15 · Calorie categoriesCan You Add Workout Calories to Your Watch’s Daily Total?
Use a calorie ledger to check categories, time windows and whether a workout is already included.
Read field note → Field note 10 · Diary completenessAn Empty Food Log Is Not a Calorie Deficit
Check what was recorded before interpreting a low average. Missing, pending and zero are not interchangeable.
Read field note → Field note 09 · Food arithmeticWhy Calories and Macros Do Not Always Match
Four worked examples and a repeatable check for the gap between a calorie total and its protein, carbohydrate and fat.
Read field note → Field note 03 · Label arithmeticOne Label, Three Portions: A Serving-Size Audit
Use the FDA sample label to see exactly how a one-, one-and-a-half- or whole-container portion changes the numbers.
Read field note → Field note 04 · Model sensitivityWhy Two Energy Estimates Can Both Look Plausible
A calculation audit showing how activity assumptions, not a hidden change in metabolism, can widen an energy estimate.
Read field note → Field note 05 · Food loggingWhat AI Meal Estimates Can—and Cannot—Tell You
Follow an estimate through portion and ingredient changes to the final saved meal.
Read field note →AI & context
Separate an observation from its interpretation, and check what evidence actually supports a recommendation.
When Should an AI Insight Stop?
Change one evidence condition at a time and see when a narrow statement still follows.
Open interactive lab → Field note 11 · AI evidenceBefore You Follow an AI Insight: Check the Evidence
Six questions, three worked examples and a downloadable card for checking the reasoning behind an app recommendation.
Read field note → Field note 08 · Reading your dataHow to Read Health-App Data Without Jumping to Conclusions
Six checks, three everyday examples and a reusable summary for making sense of food logs, wearable trends and AI insights.
Read field note → Field note 02 · Lifestyle tagsUse Lifestyle Tags as Context, Not Proof of Cause
Keep yes, no and unknown separate with a downloadable 14-day journal.
Read field note →