Two days after the first interval session, the plan asked for an easy run. The pace did not matter. I was supposed to keep the effort controlled and let my heart rate decide how fast I ran.
The weather had other ideas.
It was 28°C and humid. The first few kilometres felt fine, but the run became increasingly difficult as it went on.
The data
| Metric | Result |
|---|---|
| Distance | Approximately 8 km |
| Average pace | 6:42/km |
| Average heart rate | 138 bpm |
| Maximum heart rate | 152 bpm |
| Average power | 230 W |
| Elevation gain | 39 m |
| Temperature | 28°C, humid |
| How it felt | Very demanding by the end |
The split pattern told the story. I started between roughly 6:13 and 6:29 per kilometre. Later kilometres slowed to around 6:50 and beyond, with one kilometre taking more than seven minutes.
The interesting part was that my heart rate did not rise dramatically as the pace dropped. It stayed close to 140 bpm for much of the second half.
What the coach saw
ChatGPT viewed the slower second half as sensible effort management rather than a collapse. I had reduced speed while keeping the internal load fairly stable.
The estimate was that the conditions had cost around 20 to 30 seconds per kilometre. That figure should not be treated as a precise calculation, but the broader point was useful. This was not a good day for judging race fitness from pace alone.
The coach rated the heart rate control positively and did not change the long term goal. It did, however, reduce the intensity planned for the following quality session so that I would still have enough energy for the weekend long run.
My takeaway
This run is a good example of why I wanted a more flexible process. If I had looked only at pace, I might have called it a poor session. The heart rate suggested something different. I had adjusted to the heat without turning an easy day into a race.
It still did not feel easy. That matters too. The data explained the run, but it did not cancel out my own experience of it.
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