As told previously, we needed to go back to the drawing board to rethink some essential flows in the app. When app features leave room for discussion or interpretation, and thus the need for our users to contact me to shine my light on the subject, it means we designed them wrong.
That’s certainly the case for our calibration tool. So that’s what this post is about: a redesign for our fitness calibration.
_ Fitness calibration
When you’re an established runner and training for a certain goal (often a race), calibrating your fitness is essential for the app to function well. Why? Because through this tool, you’re telling us about your current fitness level, via an all-out effort, which we then relate to your recent training load.
What do we get out of this? We’ll be able to
- define your training zones/paces
- match a training plan
- estimate your room for progress (historical training load vs future training load)
- predict your race paces
What parameters are essential in our current flow? And where does it often go wrong?
- When talking about fitness, it implies that we’re focusing on a current value. The question is: “How fit are you now?”. It’s not: “How fit were you?”. You might’ve run a world record three years ago; it means nothing if you haven’t run since or if your training load halved. Often users select a personal best, which does not necessarily reflect their current fitness level.
- We need an all-out effort to which we can relate your training load. Ideally you ran a race during the last 6 weeks, often that’s not the case so we’ll accept an estimation as well (bit tricky tough). But why all-out? Because that’s 100%. My junior coach sometimes instructed me to run at 80%, which just meant to not give it my all. But nobody knows what an 80% effort exactly is. So, we need 100%.
- What’s all-out? That term itself leaves room for discussion. We’ve used ChatGPT quite a lot during this ‘conceptual phase’ in the redesign process. I asked the AI-bot what all-out means: “It means giving everything you have and not holding back, using all your resources, energy, and abilities to achieve success.”. But some of our users interpreted it as ‘reaching your max speed’ or ‘running as far/long as I can’. That’s not only not what we meant; those interpretations can also cause acute/overuse injuries.
- We require a minimum distance of 3000m for this calibration. Short(er) distances are troublesome because of 1) your anaerobic speed reserve and/or capacity – we need an aerobic effort and 2) GPS misreadings – the longer the distance, the lesser the impact of, for example, 100m of erroneous data. When you haven’t raced in a while and you have to estimate your fitness, we often see users run a 10k time trial. As a coach, that’s not what I like to see, because we don’t need you to run this far and recovering from a 10k time trial takes more time than from a 5k time trial.
- Historical data so we don’t have to estimate your current training load. This means linking Strava or other platforms.
If one of these parameters isn’t interpreted as we require, we’ll not be able to provide you with a tailored training plan.
_ The new flow
The current flow doesn’t guide you well enough, leaving room for the above-mentioned errors. With the redesign, we’ll address those imperfections.
For starters, we’ll ask you if you ran a race during the past 6 weeks, a simple yes or no question.
If the answer is yes, we’ll then ask you if the recovery went well (no injury or sickness since). If the answer is still yes, we’ll let you select your data and then calibrate your fitness.
Haven’t raced the past 6 weeks, or sustained an injury/illness? A new flow will start. We’ll ask you that if you were to run a race today, what your estimated finish time could be. Three possible answers:
- ‘I think mm:ss on distance x’, after this we’ll calibrate your fitness
- ‘I don’t know’, where we’ll propose to schedule a 4km assessment run
- ‘I haven’t got the fitness to run a 5k race’, where we’ll redirect you to a rebuilding fitness plan
This future flow leaves little to no room for interpretation, resulting in better tailored training plans and less support questions.
In short: a better user experience.
And that’s what it’s all about.
Thanks for sharing the insights and transparency on the improvements! I enjoyed reading the post, and looking forward to the improvements in the flow! Keep up the good work.