Why static plans fail when life happens
The standard playbook for mountain athletes is brutal: a coach writes you a 12-week plan in a spreadsheet, you grind through it, you check off boxes. The plan does not know that Tuesday's intervals were impossible because you flew red-eye, or that Saturday's hike turned into a six-hour scramble with 1500 metres of vertical, or that you caught a cold and wrote off a week.
A static plan keeps prescribing as if none of that happened. The athlete either feels guilty for falling behind, or grinds anyway and arrives overcooked. Both outcomes are worse than the plan had no business being.
An auto-sync Strava training plan solves this differently. Each session you complete is read into the algorithm within minutes. The next session, the next week, the next phase - all reshape around what you actually did, not what the spreadsheet said you should have done.
What TTM reads from Strava
Five signals from each Strava activity feed the algorithm. Each one drives a different part of the plan.
How the plan adapts: four scenarios
Concrete examples are easier than abstract rules. Here are four common situations and what the algorithm does in each.
The plan you complete shapes the plan you get. That is the difference between a static spreadsheet and a connected training app.
What Strava cannot show us
Honesty matters. Strava is excellent for what athletes did - duration, intensity, vertical, type. It is silent on three things that matter for training:
- Sleep and HRV. Strava does not currently expose sleep score or heart rate variability through its API for most users. Both are real signals of recovery state.
- Perceived effort. A session that feels like Z3 but reads as Z2 (because of heat, dehydration, illness) is more fatiguing than the data suggests.
- Pain, niggles, and injury risk. A knee that is starting to complain does not show up in heart rate data until it is too late.
TTM's AI coach (Ridge) covers this gap with short conversational check-ins, not with another wearable to buy. After key sessions, Ridge asks how it felt, and that perceived effort is added to the data the algorithm sees. It is a deliberate hybrid: machine-readable data for the bulk of the picture, human-readable input for what the machines miss.
Setting up the Strava connection
One-time OAuth flow when you join. We read your activities, we never publish back to your Strava feed. Existing Strava activities going back several months are read in on first connection, which means the algorithm has historical context from day one - it does not start from zero.
For the deeper rationale on why heart rate zones (and therefore Strava's HR data) matter so much for mountain athletes specifically, see our heart rate zones for mountaineering guide.
How TTM uses your Strava data, in one paragraph
The short version
- Each Strava session is read by the algorithm and converted into a Mountain Training Score that captures cardiovascular load plus mountain-specific factors (vertical, activity type).
- Your fitness and fatigue trends (MF, MFat, MForm) update with every session, exponentially weighted so recent training matters more than ancient history.
- Next week's plan is recalculated from that updated state - protecting the aerobic base, reshaping intensity, advancing or holding phase as needed.
- Ridge's check-ins add perceived effort and recovery context the data alone cannot show.
The result is a plan that earns its name. Not a 12-week spreadsheet you tried to keep up with - an actual coach that read your week and adjusted.