How to use diagnostics after FIT ingestion

Once FIT ingest finishes, the next job is not to start tuning controls immediately.

The first job is to decide whether the selected dataset is strong enough to continue.

This article walks through the main diagnostics that answer that question.

If you need the ingest steps themselves, read Ingest FIT files for calibration.

What these diagnostics are for

After ingest, you are trying to answer:

Does this dataset contain enough relevant, usable FIT data to continue, or should I improve the FIT folder first?

Check for these conditions:

  • the dataset contains enough usable activity
  • the terrain coverage is relevant to your real use
  • heart-rate coverage is good enough to support calibration
  • the grade, speed, and heart-rate relationships are strong enough to support the next tabs

This article is not about selecting a folder or starting ingest. It starts after ingest has already completed.

Step 1: Start with the FIT File Ingest Summary

Always begin with the FIT File Ingest Summary.

This is the shortest path to answering whether the dataset is viable at all.

Pay the most attention to:

  • Files analyzed
  • Ingest Gate
  • the overall amount of usable activity
  • whether heart-rate coverage looks strong enough to support calibration
  • whether the altitude context looks like the kind of trips you care about
  • whether the accepted usable data looks thin or substantial

Use this section to ask:

  • do I have enough usable activity to continue
  • do I have enough heart-rate coverage for calibration
  • does the altitude context match the trips I want to plan

If these top-level numbers are weak or the summary shows warnings, improve the FIT folder before using later tabs.

For folder-building guidance, go back to:

Step 2: Read Grade (%) vs speed before anything else shape-related

The Grade (%) vs speed (mph) chart is the first relationship chart to read.

It shows how observed moving-window speed behaves across grade.

Check for:

  • speed falling as uphill grade increases
  • a visible center band rather than pure scatter
  • enough spread across grade to support shape fitting
  • no dominance by outlier activity types

Use it to ask:

  • do uphill and downhill trends move in the expected direction
  • is the grade range broad enough for Speed Shape
  • does the point cloud look like hiking or backpacking activity

If this chart is chaotic, narrow, or dominated by unrelated activity, improve the dataset first.

Step 3: Use Grade (%) vs Heart Rate to judge strain by terrain

The Grade (%) vs. Heart Rate (bpm) chart helps you see how cardiovascular strain changes across grade.

Check for:

  • heart rate rising as uphill grades steepen
  • a lower-strain trough near easier grades
  • enough structure to support ΔHR Shape and Effort Intent
  • no obvious sign that HR coverage is too sparse

Use this chart to ask:

  • does the HR trend across grade look usable?
  • does the dataset appear rich enough to support later ΔHR review?
  • are there big coverage holes that make the curve hard to trust?

You do not need a perfect curve. You need a dataset with a usable trend.

Step 4: Use Speed vs Heart Rate as the final sanity check

The Speed (mph) vs. Heart Rate (bpm) chart is a cross-check on the overall movement and strain story.

Use it to ask:

  • does speed rise into a plausible HR range rather than behaving randomly
  • do the bands look like one activity family instead of mixed activity types
  • does this plot support continuing into the next tabs

This chart is most useful when the grade-based charts are borderline and you want one more cross-check before continuing.

Step 5: Check the altitude distribution last

The Altitude (ft) distribution chart appears lower on the page, so it works best as the final context check after you have already reviewed the main movement and HR relationships.

Check for:

  • where most valid samples cluster
  • whether the median altitude matches your intended trip style
  • whether the spread reflects your real use
  • whether there are obvious altitude outliers

Use this decision rule:

  • if the altitude band lines up with the trips you care about, keep going
  • if the dataset is dominated by a very different altitude regime from your intended planning use, be cautious about over-trusting the result

This does not automatically invalidate the calibration, but it tells you what kind of world the dataset is really describing.

Recommended reading order

Use this order:

  1. Read the FIT File Ingest Summary.
  2. Review Grade (%) vs speed.
  3. Review Grade (%) vs. Heart Rate.
  4. Use Speed vs. Heart Rate as a final sanity check.
  5. Check the altitude distribution as the final context check.

That sequence moves from ingest status to core movement charts to altitude context.

Signs the dataset is ready to continue

Continue when you have:

  • enough usable activity to support a pattern
  • usable heart-rate coverage
  • an altitude regime that roughly matches your intended use
  • grade-speed and grade-HR relationships that support the next tabs
  • no obvious sign that the folder is dominated by irrelevant activities

You do not need a perfect dataset. You need one that supports the next review steps.

Signs you should improve the dataset first

Be cautious if:

  • the usable activity looks thin
  • heart-rate coverage is weak
  • the altitude band is clearly unlike the trips you care about
  • the grade-speed relationship looks too noisy or too narrow
  • the HR plots look sparse, erratic, or physiologically implausible

If the ingest summary shows a Calibration note about estimated distance from GPS track points, TRIPS recovered the dataset from a less complete FIT export. That does not automatically block calibration, but it lowers confidence when the dataset is already thin or noisy.

In those cases, improving the FIT folder is more productive than forcing the calibration through later tabs.

What to do next

If the diagnostics support continuing, go to:

If the diagnostics do not support continuing, go back to:

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