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FAQs
Latest Questions
Training Plans
Why is my marathon plan mileage lower than expected?
Excepting cases of severe data errors, this will be intentional. TrainAsONE prioritises consistency and injury prevention over 'hero' long runs. Statistical evidence shows that total weekly volume is a much better predictor of marathon success than the distance of any single training run. TrainAsONE focuses on the minimum effective dose to achieve your goal safely, i.e. injury-free. Data shows that 'more' is not always 'better', and total consistency is far more important than high peak mileage. Here are some data-driven details to think about... Weekly Mileage Research into marathon finishers shows a surprising lack of correlation at certain levels. Specifically, there is no statistical difference in the lower range of peak weekly distances achieved by 3.5-hour marathon runners compared to 5-hour runners. If you are hitting 45 km/week, this does not mean that your 3:15 marathon target is not achievable - statistically it is. The AI prioritises the quality and specific physiological stimulus of your runs rather than just adding 'junk miles' to hit an arbitrary target number. The "Long Run" Myth There is no statistical difference in the longest training run of an elite athlete versus a 5-hour marathon runner. As an isolated metric, the single long run has no bearing on your finish time. The 'magic' happens in your total cumulative volume and mix of workout types. The 40% Rule Approximately 40% of runners suffer a significant injury during marathon training. Around half of those do not make it to the start line (Did Not Start - DNS) and of all marathon runners, around 12% Did Not Finish (DNF) due to injury. To ensure you make it to the start line, our AI monitors your 'niggles' and recovery metrics. If it detects a risk, it will hold back volume to try to prevent things escalating. What should I do? If you feel the volume is "low," it is because the system has calculated that this is the safest and most efficient path for your current fitness and injury profile. Trust the consistency; it is the total work done over months that builds a marathoner, not a few high-mileage weeks.
General
TrainAsONE Roadmap
What are the main areas of focus for 2026? Our roadmap for 2026 is split into two primary streams: the Algorithms (the "engine") and the User Experience (the "dashboard"). 1. Algorithms: The Evolution of Artemis • Artemis 2: This remains a top priority. We are in a phase of continuous refinement with the goal of making Artemis 2 our default training algorithm for all users as soon as possible. • Artemis 3 (Running Power): We are continuing a measured roll-out to early testers. We are aiming for a wider public beta-testing phase in the second half of 2026 . • Next-Gen R&D: While currently in the research phase, we are looking at two major frontiers: multi-modal training (integrating other sports) and explainability . For the latter, we want to help the AI "show its work" so you understand why your plan is the way it is, and the reasoning behind specific workouts. 2. User Experience: A New Priority We recognise that while our maths is industry-leading, the way you interact with it needs to be just as sophisticated. Consequently, UX is getting a much higher priority this year: • Mobile App Expansion: We are significantly expanding the functionality of the mobile app to bring it closer to parity with the web experience. • Web App Overhaul: A completely new version of the web app is in the works to make navigation and data visualisation more intuitive. • Ease-of-Use Features: We will be prioritising highly requested "quality of life" features, such as 'Workout Move' (easier rescheduling) and 'Custom Workouts' . • Desktop App: We are also exploring the potential for a dedicated desktop application for those who prefer deep-diving into their data on a larger screen.
Garmin & Devices
Getting Started
Why isn't the TrainAsONE CIQ app available for my new Garmin watch?
If you have a newly released Garmin device (such as a new Forerunner, Fenix, or Venu model) and cannot find the TrainAsONE app in the ConnectIQ (CIQ) store, here is why it happens and how to resolve it. 1. The "Future Compatible" Limitation When we build our ConnectIQ (CIQ) app, we utilise a setting that supposedly makes it available for all "future compatible" devices. Unfortunately, Garmin's automated system frequently fails to recognise brand-new hardware releases immediately upon launch. 2. Manual Manifest Updates Because Garmin’s automation is unreliable, we must manually update the app's internal "manifest" to include specific new device IDs. We often rely on our community to alert us when a new model isn't seeing the app. Once notified, we aim to push a new release to the Garmin store as quickly as possible. 3. Immediate Workaround: Garmin Calendar Sync You do not need the CIQ app to get your workouts onto your device. In fact, most users prefer the native experience of Garmin Connect Sync: - Seamless Integration: This pushes your TrainAsONE sessions directly into your watch’s native "Training Calendar" widget. - Universal Compatibility: It works on nearly every Garmin wearable, even those not yet officially supported by the CIQ app. - How to Enable: Visit your TrainAsONE Services, click "Connect new service", and select Garmin.
Training Plans
Why are the pace ranges for some of my workout steps so wide?
It is not uncommon to see pace targets with a broad range, particularly in sessions like Progression runs. While other platforms often provide narrow, fixed targets, TrainAsONE uses a more sophisticated, data-driven approach. 1. Evidence-Based Ranges (Not Arbitrary Margins) Most training apps apply a simple, "one-size-fits-all" margin (like 5% or 15 seconds per km). TrainAsONE avoids this because it isn't scientifically sound. Our ranges represent a Confidence Interval of physiological benefit. The spread shows the window of pace that the AI calculates will provide the intended training effect for you today. A wide range simply means the AI has identified a broader "benefit window" where you can achieve the workout's goal without unnecessary strain. 2. The Impact of "Noisy" Data The AI builds your profile based on your history. If your recent data is "noisy" — perhaps due to running on varied terrain (hills vs. flats), extreme weather, or inconsistent GPS data — it becomes mathematically harder to pinpoint a single, narrow pace. In these cases, the AI’s regression model widens. Rather than "guessing" at a precise target that the data doesn't support, the system provides a wider window that it knows will still result in the correct training stimulus. 3. Workout Specificity (Progression vs. Intervals) The type of workout dictates the width of the range: - High Specificity (Intervals/Threshold): These sessions have "tighter" ranges because the physiological adaptations occur at very specific intensities. - Low Specificity (Progression Runs): There is less scientific evidence suggesting that Progression runs require a pinpoint intensity to be effective. Because the "benefit window" is naturally broader for these runs, the AI reflects that flexibility in the plan. 4. How to follow a wide-range workout When you see a broad pace target, use it to your advantage: - Reduce Training Stress: You don’t need to obsess over your watch. As long as you are within the range, you are "winning" the workout. - Listen to Your Body: If you feel fresh, aim for the faster end of the range. If you’re feeling the effects of a long week, stay toward the slower end. - Focus on Consistency: As you record more "clean" data (runs on consistent terrain with good sensor data), the AI’s confidence will increase, and these ranges will naturally tighten over time.
Training Plans
Why is my heart rate target the same (or nearly the same) for different pace steps?
It can be confusing to see a workout — like a Progression Run — where the target pace increases at every step while the heart rate target remains (nearly) identical. While it looks like a glitch, it is actually a result of the AI’s rigorous analysis of your data. 1. The Goal: Finding Your 'Sweet Spot' TrainAsONE constantly seeks the 'Sweet Spot' — the optimal combination of pace and heart rate that delivers the maximum physiological benefit with the minimum amount of unnecessary strain. If the system believes a specific heart rate range is your ideal 'operating window' for the entire session, it will keep it steady while using pace to drive the workload. 2. The Technical Reason: 'Flat Regression' The AI builds your targets by looking at your historical data. If the system observes a Flat Regression , it means that in your recent runs, changes in your pace resulted in very little detectable change in your heart rate. Because your heart rate is barely moving relative to your speed in that specific intensity zone, the AI cannot confidently prescribe a different HR for each step. Instead, it provides: - Variable Pace Targets to ensure you are hitting the intended intensity. - A Constant Heart Rate Range to act as a safe 'ceiling' for that effort. 3. Handling Data Noise Human physiology is volatile. Factors like heat, caffeine, stress, or even a loose heart rate strap can create 'noisy' data. If the data is too scattered for the AI to find a clear, linear relationship between a specific pace and a specific heart rate, it defaults to a proven, safe range rather than 'guessing' at targets it can't mathematically justify. 4. How to Run These Sessions If you see a flat heart rate target across multiple pace steps, follow this simple strategy: - Prioritise the Pace: Focus on hitting the speed targets for each step. This ensures you get the intended training stimulus. - Use HR as a Ceiling: Treat the heart rate range as a safety buffer. As long as you stay within that broad range, the workout is a success. - Manual Refinement: If you prefer a 'stepped' feel, you can aim for the lower end of the HR range for the first step and gradually move toward the upper end as the pace increases. The Evolution of Your Plan: As you record more 'clean' data and your fitness profile matures, TrainAsONE will continue to refine these relationships. Over time, as the AI gains higher confidence in your cardiovascular 'fingerprint', it will begin to prescribe more granular, stepped heart rate targets.
General
Why did my race prediction get slower after I ran a Personal Best?
It can be surprising (and even a bit discouraging) to smash a Personal Best only to see your predicted finish times for future events get slower. However, this is actually a sign of the AI's sophistication — it is reacting to your real-world physiological state rather than just your "paper" potential. 1. The "Race Tomorrow" Principle Most running apps provide a "best-case scenario" prediction based on your fastest recent segments. TrainAsONE is different: it predicts what you could achieve if you had to race tomorrow. If you just ran an all-out 10k yesterday, your body is currently in a state of high fatigue. The AI knows that if you tried to race that same distance again 24 hours later, you would be slower. As you recover over the coming days, your prediction will "climb" back up as the fatigue clears, eventually reflecting your new, higher fitness level. 2. Predictions don't "drive" your training A common misconception is that a slower prediction leads to "slower" or "worse" training. This is not the case. The prediction and the training plan are separate outputs. The AI doesn't give you a recovery run because the prediction dropped; instead, both the recovery run and the lower prediction are simultaneous reactions to the high workload detected from your recent effort. 3. "Clearing the Decks" for Recovery When you see your training sessions get shorter or slower after a big effort, the system is "clearing the decks." It is prioritising the reduction of your Acute Workload to protect you from injury. Once the AI sees your heart rate and pace stabilise during these easier sessions, it regains "confidence" in your recovery. At that point, it will begin ramp your predictions back up to reflect your new "floor" of fitness. 4. Accuracy and the Margin of Error Standard sports science formulas (like the Riegel or Vickers models) often have an error margin of 8% to 10% (or worse as race distance increases). TrainAsONE uses your second-by-second physiological data to be far more precise. If your actual race time is within a five percentage points of the AI's prediction, the model is performing with high accuracy. While there can be outliers — perhaps you had a "superhero" day or the data was noisy — the system will recalibrate, and it does not mean that next time it will be the same story.