Products/AI & Machine Learning/Symptune

Symptune

You Changed Your Protocol. Did It Work?

AI & Machine Learningaimachine-learningReviewed
Symptune

Our Take

If you're tweaking your TRT protocol and wondering "did that timing change actually help?" — you probably have a spreadsheet for doses, another app for sleep, and a notes app for how you felt. Symptune folds all of that into one sequence so you can finally see cause and effect across weeks, not just isolated daily logs. It builds your personal baseline before it starts making claims, which is honestly the move most health apps skip. For anyone running blind between quarterly labs, this is the missing layer.

A daily check-in app that tracks protocol inputs (dose, timing, supplements, sleep) alongside daily state metrics (energy, mood, sleep, libido, recovery) to surface which changes actually moved the needle over time.

Key Features
Daily 20-second check-ins tracking energy, mood, sleep, libido, recovery, Protocol input logging (dose, timing, supplements, sleep shifts), Sequence-based organization - connecting what changed to how you felt after, Baseline creation at day 1, Pattern detection as data accumulates (first trends by day 7), Personalized insights based on repeated check-ins over time, Protocol comparison tools, Longitudinal trend analysis, Honest data sufficiency messaging - tells you when there isn't enough data
Problem It Solves
Protocol optimization happens with disconnected logs - dose timing, sleep, supplements, training, labs, and daily state live in separate places. The missing layer is sequence: what changed first, what followed, and what kept repeating.
Target Customer
People actively adjusting TRT, hormone therapy, supplementation, or sleep protocols who want to understand what actually drove their results with the same rigor they apply to training or labs.
Use Cases
Dialing in TRT - tracking what changed after each dose timing/frequency adjustment, Running labs without context - connecting lab results to check-in history, Tracking without clarity - seeing what changed before and after each protocol shift
Pricing Details
Free tier available - 'Start tracking free' and 'Build Your Baseline Free' mentioned
Differentiator
Pattern detection not diagnosis - focuses on sequence and context rather than isolated entries; honest about when data is insufficient; never fabricates insights or claims cause-and-effect without evidence; builds personal baseline before insights
Why Now
People optimizing health protocols operate on incomplete information - labs happen quarterly but symptoms change daily; connecting what changed to how you actually feel is genuinely hard without a structured record

Key Facts

Category
AI & Machine Learning
Pricing
Free
Discovered via
betalist

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Symptune — SLAYREPORT