Interpreting progress

How improving / stable / declining classifications are derived.

Last updated April 17, 2026

When a subject has more than one session in the same module, QOOM compares the most recent session to the previous one and classifies change per metric.

Classifications

  • Improving — the metric moved in the direction that corresponds to better performance, by more than the stability threshold.
  • Stable — the metric changed by less than the stability threshold.
  • Declining — the metric moved in the direction that corresponds to worse performance, by more than the stability threshold.
  • Insufficient data — not enough history to classify, or the metric was excluded (e.g. value was outside plausible range).

Stability threshold

By default, a change of less than 3% is classified as stable. For some metrics (symmetry index, knee valgus, limb symmetry index), a smaller threshold is used because small changes in these metrics are more meaningful.

Direction semantics

The direction of "improvement" depends on the metric:

  • Higher is better — gait speed, hop distance, jump height, RSI, LSI
  • Lower is better — symmetry index, knee valgus, contact time, trunk lean
  • Range — cadence, squat tempo, landing knee flexion, concentric/eccentric duration (middle of the range is better)

Overall trend

An overall module trend is derived from the per-metric classifications:

  • Improving if more metrics improved than declined.
  • Declining if more metrics declined than improved.
  • Stable otherwise.

How to use trend information

  • Trends are more meaningful than single-session grades.
  • Consistent improvement over 3+ sessions is a stronger signal than a single improvement.
  • One declining session in isolation may reflect measurement variability — compare multiple sessions.
  • Always interpret trends in the context of what you changed in the subject's program between sessions.

What trends do not tell you

  • Why the metric changed. Could be real change, pacing, calibration, clothing, lighting, or measurement noise.
  • Whether the change is clinically significant.
  • Causality. A subject who added squats between sessions may or may not have improved because of the squats.

Use trends as one input into your professional assessment of the subject's progress.