Guide

Intervention Log

Use the intervention log to record the protocol you chose, the session or race it was tied to, and any notes that matter later. The goal is simple: capture what you tried so Threshold can compare it against how you responded.

This is the core dataset behind intervention insights. The more consistently you log protocols, the faster the platform can separate useful patterns from one-off experiments.

Baseline Tracker

Your baseline settings hold the stable information that makes later comparisons more meaningful: altitude, heart-rate zones, fueling anchors, hydration targets, and supplements you use regularly.

These inputs help Threshold interpret logs in context instead of treating every intervention as if it happened in a vacuum.

Race Profile

Save the race you are training for once, then link interventions to that event as you log them. Keep the race date and course basics current so your history stays tied to the right build.

Race context makes it easier to review a full preparation cycle rather than isolated sessions.

Insight Engine

When enough useful data exists, Threshold turns your intervention history, baseline trends, and race context into short insight cards. If there is not enough evidence yet, the product should tell you what to log next instead of pretending certainty.

The point of the insight engine is to help you make a decision, not to flood you with commentary.

Self-Selection Explorer

The explorer lets you choose one input variable and one outcome variable, then see the relationship in a simple chart with a plain-English interpretation. It works best when you already have a decent amount of history for the variables you pick.

This is useful when you want to test your own theory against your own data instead of waiting for the platform to surface a pattern automatically.

Research Library

The research library is where you can browse studies relevant to running from the mile through marathon+, plus gravel and triathlon training, without digging through academic formatting. Use search and filters to narrow the library to the topics you actually care about.

It gives context for why a protocol is worth testing, while your own logs show whether it worked for you.