EnduranceIQ
A reference guide to the methods embedded in your weekly report—structured metrics first, evidence-linked findings second. Nothing here substitutes clinical assessment or coach-written programming.
The weekly surface merges deterministic rule-engine findings with optional Claude Haiku prose generated only on the server from numeric aggregates: HR summaries by workout, intensity percentages, load indices, and rules-output snippets—never free-form athlete notes or Strava titles. Outputs pass validator gates before persistence so blocked prose falls back to template paragraphs grounded in the same findings JSON.
Each timed running interval contributes classified HR buckets grouped into easy (zones 1–2), moderate (zone 3), and hard (zones 4–5) using athlete-relative thresholds anchored on observed max HR or configured ceilings from onboarding data. Benchmark heuristic aligns loosely with polarised prescriptions—a directional compass rather than laboratory-derived physiology labels.
Acute stress aggregates roughly trailing-seven-day totals versus chronic rolling averages derived from synced endurance workloads with measurable strain proxies (runs contributing HR-derived strain estimates). Elevated ratios flag spikes needing pacing restraint relative to historical norms—not Garmin readiness scores.
Time-in-zone and TRIMP-weighted load share tell different stories. Two athletes can both spend 80% of their running time in Zone 1–2, yet if one athlete's hard sessions are much harder, their load share from Zone 4–5 will be significantly higher than their time share suggests. Looking at both metrics together catches this hidden polarization gap.
EnduranceIQ computes training impulse (TRIMP) per session using the Banister formula — either the full Karvonen heart-rate-reserve version when resting HR is provided, or an HR-max-only approximation otherwise. The sex-specific exponential weighting (Banister 1991) reflects physiological differences in cardiovascular response to exercise intensity. Sessions are bucketed using r-value thresholds: easy (r < 0.74), moderate (0.74–0.84), hard (≥ 0.84).
These load-share metrics are currently computed in the background (shadow mode). They will replace the time-based display after threshold tuning in Phase 1.4.
Zones approximate Daniels-style proportional anchors scaled against empirical maximum HR captured across uploads plus validated onboarding overrides where athletes capture lactate-threshold estimates elsewhere. Field zones tolerate noisy telemetry—prefer drift-aware pacing cues paired with perceived exertion rather than rigid BPM policing alone.
Canonical physiological thresholds demand metabolic lab testing; EnduranceIQ flags deviations versus heuristic envelopes rather than diagnosing physiology.
Heavy neuromuscular sessions overlapping endurance stimuli inside acute physiological recovery arcs elevate cumulative fatigue risk; EnduranceIQ highlights narrowly spaced resistance-plus-interval stacking scenarios surfaced via deterministic timestamps rather than subjective readiness guesses.
Programmable lifting prescriptions referencing plyometrics, heavy compounds, and injury-prevention circuits arrive alongside roadmap integrations tying biomechanical weaknesses to prescription tweaks—planned downstream phases populate richer workout widgets referencing citations similar to running insights below.
Each running pattern maps to a specific strength emphasis. The table below documents every pattern, its prescribed emphasis, the training rationale, and the supporting research. This section is intended for coach review before the feature goes wide.
Pattern
Low easy-zone load share (<60% TRIMP in Z1–2)
Emphasis
Single-leg economy + posterior chain
Why
Better running economy makes Zone 2 pace easier to sustain at lower HR, gradually shifting load share toward easy zones without dropping volume.
Evidence
2–4% running economy improvement after 8 weeks of heavy/explosive strength. Beattie et al. (2017) ↗; Blagrove et al. (2018) ↗
Pattern
Low cadence on intervals (<168 spm)
Emphasis
Plyometric + single-leg economy
Why
Ground-contact time determines cadence ceiling. Plyometric training shortens ground contact and improves the stretch-shortening cycle.
Evidence
Plyometric training improves running economy and ground contact mechanics. Saunders et al. (2006) ↗
Pattern
Long run HR drift (final third vs first third)
Emphasis
Posterior chain + core stability
Why
Late-race HR drift with pace decline indicates fatigue in the posterior chain and trunk stabilisers — key muscles for maintaining form when tired.
Evidence
Posterior chain strength reduces hamstring injury risk and maintains running mechanics under fatigue. Bourne et al. (2017) ↗; Blagrove et al. (2018) ↗
Pattern
Interference window (High severity)
Emphasis
Mobility only
Why
A high-severity interference finding means strength work preceded a quality run within the acute neuromuscular recovery window. Adding more load would compound the problem.
Evidence
Strength performed before quality running reduces neuromuscular quality of the run. Fyfe et al. (2014) ↗; Wilson et al. (2012) ↗
Pattern
Taper or high load ratio (>1.3)
Emphasis
Maintenance (25–30 min)
Why
During a taper or elevated-load week, the goal is to maintain neuromuscular readiness without adding new fatigue. A short maintenance block achieves this.
Evidence
Taper strategies that maintain stimulus while reducing volume preserve or improve performance. Mujika (2010) ↗
Pattern
Default (no specific pattern detected)
Emphasis
Single-leg economy + posterior chain + core stability
Why
In a normal training week with no detected running weaknesses, a well-rounded lower-body session addresses the most common injury-risk areas for runners.
Evidence
Strength training reduces running injury rates and improves economy in recreational marathon runners. Beattie et al. (2017) ↗; Blagrove et al. (2018) ↗
When you set a primary race, your weekly report and strength plan adapt to how close that race is. The transition from general preparation to taper to race week is driven by event-specific evidence — a 5K and an ultramarathon should not taper the same way.
Bosquet et al. (2007) found optimal taper duration is 14–21 days for most endurance events. Mujika & Padilla (2003) and Mujika (2010) refined this by event distance: shorter events benefit from shorter tapers because chronic fatigue is smaller; ultras need longer windows because chronic load and physiological recovery time scale up. Knechtle & Nikolaidis (2018) covers the recovery window for ultra-distance events specifically.
| Race type | Taper window | Race week | Sources |
|---|---|---|---|
| Ultramarathon | 28d | 7d | Knechtle & Nikolaidis (2018) ↗; Bosquet et al. (2007) ↗ |
| Ironman (full) | 21d | 7d | Mujika (2010) ↗; Bosquet et al. (2007) ↗ |
| Marathon | 21d | 7d | Bosquet et al. (2007) ↗; Mujika & Padilla (2003) ↗ |
| Ironman 70.3 | 14d | 7d | Mujika (2010) ↗ |
| Half marathon | 14d | 7d | Mujika & Padilla (2003) ↗ |
| 10K | 10d | 5d | Mujika (2010) ↗; Pyne, Mujika & Reilly (2009) ↗ |
| 5K | 7d | 4d | Mujika (2010) ↗ |
| Other endurance / unknown | 14d | 7d | Bosquet et al. (2007) ↗ |
Phases beyond taper (general preparation, specific preparation, pre-competition) are uniform across race types: 22 weeks, 12 weeks, and 6 weeks before race day respectively. After the race, the first 14 days are recovery; beyond that the athlete is back in transition until the next primary race is set.
Strava-derived session taxonomy mixes importer-normalised labels (`easy_run`, `long_run`, `interval`, …) with sport modality inference plus HR-relative adherence badges summarising drift versus endurance envelopes described earlier—classification informs downstream UX colouring rather than autonomous scheduling prescriptions yet.