(Yet Another) AI ChatGPT Coach

[Josue_Teixeira]

HI! Congratulations!

I think there’s some inconsistency in the RPE in the workouts imported from intervals.icu.

[Clive_King]

Nice find - that does look a bit odd.

[Josue_Teixeira]

The biggest problem, in my view, is negatively influencing AI.

[Josue_Teixeira]

“That was a high-intensity session, hitting an IF of 0.766, which shows excellent work capacity! However, the workout was highly inefficient due to major power fluctuations (VI 1.25), which likely caused your poor subjective feel of 3/10. Moving forward, we need to prioritize smoothing out your power delivery to maximize the effectiveness of this hard work.”

This variation is an over-under workout from TrainerRoad, and the RPE was 7.

[Clive_King]

Hi All,

Planned workouts are now available. Hopefully Zones and Polarised values are also fixed as well.

These will appear in weekly report or if you ask for events separately (default is 14 days).

Planned Workouts (Dec 24 – Jan 7)

Date	Session	Load	Notes
Dec 24	Recovery Ride	35	Light spin
Dec 25	Rest / Walk	10	Optional active recovery
Dec 26	Endurance Ride	90	2 h Z2 steady
Dec 27	Tempo Builder	80	2×20 min @ 85 % FTP
Dec 28	Long Endurance	130	3 h Z2–low Z3
Dec 29	Recovery Spin	35	HR < 120 bpm
Dec 30	Endurance Dev.	120	3 h Z2 base ride
Dec 31	Mobility + Spin	35	Recovery + core
Jan 1	Tempo Builder	85	3×15 min @ 85 % FTP
Jan 2	Rest / Yoga	10	HRV check
Jan 3	Progressive Endurance	129	2.5 h base + tempo
Jan 4	Group Ride	70	2 h Z2 + Z4 surges
Jan 5	Recovery Spin	25	Light movement
Jan 6	Base	100	Steady aerobic
Jan 7	Tempo	75	Controlled threshold

Forecast:
CTL → 50.4 ATL → 8.0 TSB → +42.4 (very fresh)
→ Suggests readiness for higher-volume endurance block in 2–3 weeks.

Be aware that forecast is based on best practice horizon days (28 days) - i only have two weeks for example in my planned events right now.

• Keep the 28-day projection active to monitor trends (CTL/ATL decay or build).
• Actively manage the next 14 days — adjust intensity, rest days, and fuelling based on readiness.
• Re-run the weekly report every 7 days — URF automatically slides the 28-day horizon forward.

All the best

Clive

[Samantha_Lane]

@Clive_King

The major challenge I encounter daily with AI and LLM’s is the plausibility of answers it feels it must provide, any gap it just makes up an answer, a guess or something that pleases.

Have you tried claude?
I like to chat to claude about books and I find that if it doesn’t know something, instead of making any old thing up like chatGPT does, it will ask a question, or talk more generally.
There’s an article on the anthropic website explaining how they tested the effect of anti-hallucination training by inserting probes into the neural net and discovered that claude won’t make a statement unless it know with a high certainty

“Refusal to answer is actually the default behavior in Claude,” reveals groundbreaking research from Anthropic that has identified a specific neural circuit that prevents the AI assistant from fabricating information when responding to user queries."

It does sometimes make a thing up but not like chatGPT. The only problem is, is when you’ve had your free allowance of claude for the day, it cuts you off for a few hours.

I know absolutely nothing about making apps with claude or any LLM so…

[Clive_King]

You raise a valid point, I’m not fixed to ChatGPT, I can interface with any LLM that has an API that has similar capability to ChatGPT. A future thought. Thanks

[Clive_King]

Hi all,

Regards the forecast model, I’ve changed the model to auto detect the number of planned activities and forecast against this, however its flexible and you can specify other models. for e.g.

7-day forecast

run_future_forecast(context, forecast_days=7)

14-day forecast

run_future_forecast(context, forecast_days=14)

Auto-mode (adapts to number of planned event days)

run_future_forecast(context, forecast_days=“auto”)

28-day mesocycle forecast

run_future_forecast(context, forecast_days=28)

What this means in real terms is that if you run a weekly report and dont specify any forecast days it will default to auto.

Forecast (Staging Forecast Model v2)

The new short-range forecast behaves correctly:

• Auto-mode detected 15 days of planned events
• CTL decline only ≈ 3 points (71 → 69)
• ATL tracks recovery curve (55 → 61)
• TSB stabilises near +8 = mild freshness

This confirms the new forecast kernel (run_future_forecast auto/14d) is functioning — the model limits horizon dynamically to planned-event count.

[Marco_Saraceno]

I’ve tried the feature, though it created the workout as notes in intervals with a wrong syntax.
I had to modify manually

[Clive_King]

Yes, this is tricky, the create/update feature needs more thought and work. I’m stateless by design so I only know what chatgpt tells me. I have it presently configured to be smart depending on what chatgpt sends me. We are at the limitations of chatgpt here; I have to guess what it sends me based on the event title, etc

Category	Type	Trigger Keywords	Description
RACE_A	Ride	a race, a-race, priority race, main event, race, competition, event, gran fondo, marathon, triathlon	Top-priority or general race (cycling-focused unless otherwise matched).
RACE_A	Run	Same as above + includes “run”	Running race (e.g., marathon).
RACE_A	Swim	Same as above + includes “swim”	Swimming race (e.g., triathlon swim leg).
RACE_B	Ride	b race, b-race	Secondary priority race.
RACE_C	Ride	c race, c-race	Low-priority or practice race.
WORKOUT	Ride	ride, bike, zwift, trainer, tempo, interval, ftp, endurance, climb	Default cycling workout (outdoor).
WORKOUT	VirtualRide	Contains virtual	Indoor/virtual cycling session (e.g., Zwift).
WORKOUT	MountainBikeRide	Contains mountain	MTB workout.
WORKOUT	GravelRide	Contains gravel	Gravel workout.
WORKOUT	Run	run, jog, tempo run, track	Running workout.
WORKOUT	TrailRun	Includes trail	Trail running workout.
WORKOUT	Swim	swim, laps, pool	Pool swimming workout.
WORKOUT	OpenWaterSwim	Includes open	Open-water swimming.
WORKOUT	WeightTraining	weight, gym, strength, lifting, resistance, powerlifting, squat, deadlift, bench	Strength/weight training session.
WORKOUT	Yoga	core, mobility, yoga, stretch, pilates, rehab, balance	Mobility, yoga, or core work.
WORKOUT	Hike	hike, walk, trek	Hiking or brisk walking.
NOTE	Other	rest, recovery, off, easy, relax	Recovery or rest days.
HOLIDAY	Other	holiday, vacation, break, travel	Non-training days due to travel or holidays.
SICK	Other	sick, ill, flu, cold	Illness or sickness days.
INJURED	Other	injury, injured, rehab	Injury or rehabilitation days.
SET_EFTP	Ride	ftp test, max hr, threshold, fitness test	Fitness or FTP test event.
PLAN	Other	plan, schedule, block	Planning or training block marker.
(default)	Other	(none matched)	Falls back to NOTE or PLAN depending on context.

Based on this I asked it to list my planned events, then create an action plan for the week then asked it to add to calendar ahead and it did add these correctly.

Date	Session	Type	Duration	Load (TSS)
Dec 24	Active Recovery / Rest	Walk / Spin	40 min	15
Dec 25	Light Aerobic Maintenance	Ride	60 min	35
Dec 26	Foundation Rebuild	Ride	90 min	55
Dec 27	Optional Group Ride / Walk	Ride	90 min	40
Dec 28	Recovery / Rest	Walk / Spin	30 min	10
Dec 29	Planned: Recovery Spin	Ride	60 min	35

It created in intervals:

I hope that helps.

[Clive_King]

Hi ohmax,

I’ve been double checking HR only athletes and confirm the following effect on metrics:

Summary

Metric	Depends on Power?	HR-only Behavior	Effect on Reports
ZQI (Zone Quality Index)	Prefers power zones (power_z*), falls back to HR zones (hr_z*)	Works fine with HR zones — will compute % high-intensity time from hr_z5–z7	Accurate, but HR zones yield less precision (delayed physiological response).
Polarisation Index	Prefers zone_dist_power, else zone_dist_hr, else IF fallback	Works fully with HR zones (hr_z1–z3)	Still valid, but tends to underestimate high-intensity exposure due to HR lag.
Seiler Polarisation (Z1+Z3)/(2×Z2)	Needs either power or HR zones	Works with HR (hr_z1–z3)	OK — just note slower HR kinetics makes Z3 time “blurrier”.
Fat Ox Efficiency (FatOxEfficiency)	Uses IF (intensity factor, power-derived)	Falls back to default IF = 0.7	You’ll get a static aerobic bias (≈0.63) instead of dynamic value.
ZQI (fallback)	Uses zone_dist_hr if power missing	Works	HR-only training still gets a realistic ZQI.
Stress Tolerance, Monotony, Strain, FatigueTrend, ACWR	Based on icu_training_load (TSS)	Unaffected	Still valid, as load comes from planned/estimated TSS.
Metabolic metrics (FOxI, CUR, GR, MES)	Derived from IF (power proxy)	Degraded precision	Fixed assumptions replace real power-derived metabolic balance.
RecoveryIndex	Based on monotony	Unaffected	Still valid.
CTL/ATL (from Tier-3)	Based on load time series	Unaffected	HR-only athletes still get valid fatigue/load curves.

I’ve also implemented a factor of 0.93 against this.

Why This Exists — HR vs Power Equivalence

Heart rate (HR) is a lagging physiological response to external workload (power).
Over many studies (Seiler, Coggan, Jones, Esteve-Lanao), HR-zone time is found to underestimate mechanical load by about 5–10%, especially above aerobic threshold, due to:

• HR drift (cardiovascular strain increases even if power is stable),
• HR lag (30–90s delay),
• nonlinear HR–VO₂ relationship at higher intensities.

So HR-only training zones tend to slightly understate load distribution compared to power zones.

That’s where the ≈0.93 correction factor comes from.

It means:

“1 minute in HR zone ≈ 0.93 minutes of equivalent power-zone time.”

This normalizes total “time in zone” sums and ZQI/Polarisation ratios so that HR-derived data aligns better with power-based datasets.

So, in theory all should be good for HR only athletes.

BR

Clive

[Clive_King]

Hi Olirip

I may have fixed it for you. I’m now agnostic on (wellness) HRV source:

Vendor / Platform	Detected Columns or Keys	HRV Handling	Notes
Garmin	hrv, hrv_mean, hrvSDNN, restingHR, sleepScore, etc.	Direct numeric HRV (ms)	Native Intervals.icu support; no conversion needed
Whoop	recovery_score, whoop_recovery	Converts 0–100 recovery score → HRV ms (×1.2)	Converts recovery % scale into approximate ms
Oura Ring	oura_hrv, oura_rmssd, recovery_index	Direct numeric HRV (ms)	Handles both raw HRV and RMSSD fields
Apple Health / Watch	apple_hrv, daily_hrv	Direct numeric HRV (ms)	Detected alongside Oura/Fitbit class
Fitbit	fitbit_hrv, fitbit_rmssd, recovery_index	Direct numeric HRV (ms)	Merged with Apple/Oura under oura/apple/fitbit source
Polar	polar_hrv, polar_rmssd, recovery_status	Direct numeric HRV (ms)	Identified via Polar-specific fields
Coros	coros_hrv, recovery_level	Direct numeric HRV (ms)	Detected through REST API if present
Suunto	suunto_hrv, recovery_index	Direct numeric HRV (ms)	Optional support, treated as generic numeric
Generic / Unknown	Any column containing: hrv, rmssd, recovery, daily_hrv, hrv_status, etc.	Fallback to numeric coercion	Used when vendor can’t be inferred

Metric	Latest	7d Trend	Interpretation
HRV (ms)	57	-1.6	Slight downward trend (fatigue easing)
Resting HR	46 bpm	Normal	Consistent with baseline
TSB / Form	+18.3	Fresh	Ready for aerobic build

I dont have a whoop so cannot test it
BR

Clive

[SeaneSutton]

Thank you for your hard work. I am also trying this out at the moment. I was using KoopAI, but am thinking of switching to this in a couple weeks. I love the feedback and ability to interact which KoopAI is really missing in its current form. It created good plans, but no talk/chat beyond that on how the sessions went.

Thank you so much!

[Robert]

@Clive_King

m

I get the error; The season report failed to generate because the Intervals.icu dataset returned from Cloudflare was missing the required moving_time field — a critical value used by the renderer to compute total training hours and verify Tier-0 integrity.

It tells me to Add the “Moving Time” column to verify if it shows data.
Moving Time column is already there and it shows data.

Weekly Report also failed for the same root cause — the moving_time column is completely missing from your Intervals.icu activity dataset.

All other reports also fail because of the missing moving time.

I have a lot of rides without power and distance maybe that’s causing it?

[TracyChan]

I entered the weekly report with AI, and AI replied with a prompt asking, ‘Can you please explain what type of weekly report you want?’? Do you want to do project updates, performance summaries, or a specific area (business, finance, etc.)? Tell me what you need, and I will help you solve it! It seems that I have not been able to connect with ICU authorization again because I had been unable to obtain data before. I manually cancelled the authorization and attempted to reconnect. How can I resolve this issue? Thank you

[Clive_King]

open the ChatGPT app and go here. Can be better on the web as sometime the app miss behaves.

and login:

[TracyChan]

@Clive_King

ChatGPT应用，点击这里。

I couldn’t find this logout option. Could you tell me the detailed steps? Thank you

[Clive_King]

1. open the app ChatGPT - Intervals ICU Coach V5 (Railway T2 Engine) in a web browser:
2. Open the drop down from the app bar:
   
   
3. Select “Privacy Settings”
4. select connected accounts and click login or logout/ login
   
   

[Clive_King]

Hi All,

It’s that time of year when we look back at the year. I’ve updated the summary report to produce a year’s review (last 365 days). Hope it works out for you

Annual Summary Report (Staging • Clive King)
Period: 27 Dec 2024 → 27 Dec 2025
Framework: URF v5.1 • Scope: Full-year executive review • Timezone: Europe/Zurich

---

Training Overview

Metric	Value	Status
Total Hours	814.0 h	Excellent consistency
Total TSS	35,934	Productive load
Distance	17,369 km	Strong annual volume
CTL / ATL / TSB	73 / 67 / +6	Positive balance
VO₂max (Garmin)	66 ml/kg/min	Elite aerobic capacity
FTP	300 W (4.12 W/kg)	Stable high fitness

Summary:
A robust, high-consistency year with strong aerobic development and stable form. You maintained training stress across the year without significant overload periods. CTL plateaued at 70–75 for much of Q4, signaling sustained endurance conditioning.

---

Adaptation Indicators

Domain	Metric	Result	Status	Coaching Insight
Fatigue Trend	−8.2 %	Recovering	Positive fatigue decay — sustainable training load
Fatigue Resistance	0.95	Stable	Strong long-duration endurance
Efficiency Factor	1.9	Consistent	Power-to-HR ratio stable
HRV Mean / Latest	48 / 61	Trending upward	Resilient stress–recovery response
Polarisation Index	0.35	Z2-heavy	Acceptable during base, but reduce Z2 during build
Stress Tolerance	2.0	Low	Keep intensity control and avoid spikes
FatOx Index (FOxI)	0.60	Balanced metabolism	Efficient substrate use at endurance loads

---

Periodisation Overview

Macrocycle: Base → Build → Peak → Base II
Seasonal Phases:

• Q1–Q2: High-volume base work; low fatigue accumulation
• Q3: Build progression with structured intensity blocks
• Q4: Controlled taper and recovery (Dec) with rising HRV and positive TSB

Interpretation:
The 2025 macrocycle showed disciplined control of intensity with endurance emphasis. The late-season Base II phase (Dec) positioned you ideally for a January transition into structured Build work.

---

Annual Totals by Quarter

Quarter	Hours	Distance (km)	TSS	Theme
Q1	198	4,120	8,400	Base / Endurance
Q2	210	4,550	9,200	Build / VO₂ Focus
Q3	204	4,400	9,050	Peak / Race Conditioning
Q4	202	4,300	9,284	Base II / Recovery Integration

Trend: Stable performance load with minimal regression. Seasonal CTL variability kept within 10 points, demonstrating exceptional load management.

---

Recovery & Wellness

Metric	Value	Trend
Resting HR	43 bpm	Stable
HRV 7d Trend	+3.3 ms	Improving
Sleep & HRV Sync (Garmin)	Active	Reliable input
TSB Rolling Mean	+6	Well-balanced fatigue–freshness

Insight: Sustained recovery discipline evident in HRV normalization post-build and consistent readiness markers.

---

2026 Transition Priorities

1. Maintain Aerobic Base: Continue Z1–Z2 ≥ 70 % of volume.
2. Reintroduce Build Phases: Add controlled Z3–Z4 blocks from mid-January.
3. Monitor Stress Tolerance: Avoid intensity stacking; target monotony ≤ 2.0.
4. Durability Focus: Long-ride cadence and fueling remain strengths — preserve weekend structure.
5. Polarisation Strategy: Increase Z1 recovery contrast before adding threshold work.

---

Executive Summary

“Clive maintained world-class consistency in 2025 — high aerobic durability, steady efficiency, and disciplined recovery.
HRV and CTL trends both confirm deep aerobic fitness without overreach.
Entering 2026, the focus should shift toward performance sharpening through structured intensity and recovery integration.”

[Ben_J_Marshall]

Hi Clive, thanks for the continued work on this.

Im seeing some strange results around polarisation which I think you worked on recently. I fall into a mixed category where I train with power data for indoor rides but HR only for outdoor rides. This means that most of my volume will be HR only and my sessions with power will tend to be a little skewed. At the moment my recent indoor sessions have been z2 focussed and i think this is what the reports are picking up. I get reported “zones_pct” with 100% in z1+z2 for the annual report which is not correct. I also see Seiler polarisation of 0.01 which i dont think is right either. Does your current URF code handle this mixed power/hr data at the moment?