How to use Suunto Guides with AI Endurance

How to use Suunto Guides with AI Endurance


Published Feb 17, 2022 · Updated Sep 4, 2024

We show you in a few simple steps how to connect Suunto Guides with your AI Endurance account. You can get live workout instructions that are optimized to you by our AI. You can also easily determine your training zones and thresholds via a simple ramp test that utilizes your heart rate variability (HRV) data.

How to connect Suunto Guides

Suunto Guides
  • From AI Endurance: go to the AI Endurance Apps page. From Suunto: go to your Suunto App profile -> Partner Services -> Connect AI Endurance
  • Click on Suunto.
  • Agree for AI Endurance to import your data and export workouts to your Suunto Guides

Synchronisation between AI Endurance and Suunto is automatic: whenever you request a new plan or modify your workouts, we will automatically forward these changes to Suunto Guides.

We import your Suunto history so our AI can learn which training has worked best for you in the past. AI Endurance predicts your future performance if you stick to your plan. We regularly assess your progress and if necessary let the AI re-calculate your optimal plan for your Suunto Guides.

Connect your Suunto to AI Endurance: Go to your Suunto App profile -> Partner Services -> Connect AI Endurance

How to determine your zones and thresholds

We automatically update your zones and thresholds from your in-activity HRV data using DFA a1. Your Suunto watch automatically records your HRV data if you wear a high quality heart rate strap such as the Suunto Smart Sensor.

If you want to assess your zones and thresholds right away you can perform a simple ramp test in Suunto Guides with only a few steps:

DFA alpha 1 ramp test
  • Go to the AI Endurance Apps page
  • Click "Push Ramp Test to Suunto"
  • A ramp test will be scheduled for today on your Suunto Guides immediately
  • After the test, you will be prompted to confirm your zones with your newly detected HRV values in the AI Endurance dashboard

It is important for constructive training to have your training zones set correctly. AI Endurance takes out the guesswork by continuously monitoring your training zones. By utilizing the most recent research in HRV, we can set these parameters without the need of a physiology lab. You only need to wear a heart rate strap that records high quality HRV data.

Share on:

More Blog Posts


Real-time readiness with alphaHRV and AI Endurance

Real-time readiness with alphaHRV and AI Endurance

by Stefano Andriolo, Markus Rummel and Iñigo Tolosa. We present a new real time feature of evaluating readiness to train based on in-activity heart rate variability (HRV) measurements during the warm up of your activity. You can use this feature in the newest version of the alphaHRV Garmin Connect IQ app at no additional cost.

Published Aug 28, 2024
Calculate cycling power without a power meter

Calculate cycling power without a power meter

Power meters are costly and we often can't afford one on every bike we own. AI Endurance calculates cycling power from activities without a power meter using heart rate, cadence and DFA alpha 1. The results are generally more accurate than speed based estimates such as Strava's estimated power. All you need is a heart rate monitor and ideally a cadence sensor on your bike and AI Endurance will estimate your power for every ride.

Published Oct 21, 2021
Your heart rate variability recovery model

Your heart rate variability recovery model

Before every workout you should know if you're actually ready for it. Everyone responds differently to stress, bad sleep and exercise fatigue - our new recovery model makes data driven decisions about when you should train and when you shouldn't - based on heart rate variability (HRV).

Published Sep 12, 2022
Determining the power  and DFA alpha 1 relationship accurately

Determining the power  and DFA alpha 1 relationship accurately

by Stefano Andriolo. Building on previous work, we refine a method to accurately determine the relationship between DFA alpha 1 and power. This method can be used to track fitness and thresholds of an athlete. We find in some cases ramp detection tends to overestimate thresholds, a finding mirrored in recent physiological papers. On the other hand, thresholds based on clustering of DFA alpha 1 values tend to agree well with this new method. We propose a hybrid lab and everyday workout experiment to further study the relationship.

Published Apr 26, 2024