Running training plans can be genuinely useful, especially if you need a structure to get started. The problem shows up when a generic plan gets treated as an individual prescription and stays the same even after your reality has changed.
Training involves goal, volume, intensity, recovery, and individual response. That's why the ability to adjust a plan to the runner's context can matter more than simply following a fixed sequence.
1. Running is an individual response sport
One of the basic principles of sports training is biological individuality.
Two runners of the same age, same gender and same training time can have totally different responses to the same stimulus.
A 2014 study followed 61 runners subjected to the same standardized 12-week interval training program. The result varied drastically: some had +40% VO₂max gain, while others barely improved.
In 2022, Finnish researchers compared recreational runners who followed a fixed spreadsheet versus training adjusted daily by recovery status (using heart rate and HRV). After 8 weeks, the spreadsheet group had +5% improvement, while the individualized group achieved +12%.
2. The limit of a plan that never changes
A fixed spreadsheet doesn't know, on its own, what happened to you last week.
It doesn't know if you:
- slept less than usual;
- missed two workouts;
- ran a session faster than planned;
- had a much higher-volume week;
- felt the effort was harder than expected;
- had to adapt your routine.
That doesn't mean a spreadsheet is necessarily bad. A well-structured plan can work very well when your context stays close to what was expected.
The problem shows up when the plan and reality drift apart and neither one gets revisited.
The evidence on training load and injuries doesn't support a simple "more load = more injury" rule either. A 2022 systematic review pooling 36 studies and more than 23,000 runners found conflicting results for the association between distance, duration, frequency, intensity, and injuries — meaning no single training parameter, on its own, explains injury risk.
3. Lack of dynamic adjustment
Training isn't static. Bad days happen: sleep, stress, menstrual cycle, colds… and fixed spreadsheets don't react to these variables.
What science shows:
A study with elite players showed that the Rate of Perceived Exertion (RPE) was one of the best indicators of internal load and overload risk, surpassing external measures like heart rate, proving that how the body feels matters as much as the numbers.
More recently, a 2021 study followed adults in a 6-week aerobic training program and observed that some had large VO₂max gains (on average +15%) and power, while others improved only in metabolic efficiency, without VO₂max change. That is, equal stimuli generated different adaptations.
Practical conclusion: without continuous feedback, spreadsheets can't adapt to what you feel and respond to. Dynamic training, which integrates objective data (pace, heart rate) and subjective data (RPE, fatigue), is the path to safe progress.
4. What does training individually actually mean?
Individualizing training doesn't just mean writing a different plan for every person.
It means accounting for information relevant to the prescription, such as:
- goal;
- current level;
- training history;
- recent volume;
- availability;
- intensity;
- recovery;
- how workouts were actually executed;
- the runner's own perception.
The literature recognizes the importance of individualizing exercise prescription, but it also shows that individual training response is complex — "personalized" shouldn't automatically become a synonym for "better."
5. Where technology fits into this
Technology can make it easier to track information that would be hard to gather manually.
At Rai, the data available about a runner can include:
- pace;
- distance;
- heart rate;
- activity history;
- availability;
- how workouts were executed;
- feedback sent by the runner.
That information helps shape upcoming recommendations.
The goal isn't to replace training principles with technology. It's to use technology to apply those principles in a more individualized way and keep track of what happens from one week to the next.
When does a spreadsheet still make sense?
A spreadsheet can work very well when:
- the goal is clear;
- the runner understands the purpose of each workout;
- the plan fits their level;
- the load is compatible with their routine;
- there's someone to make adjustments when needed.
The question isn't "spreadsheets are bad" versus "AI is good." The question is how much personalization and follow-up you actually need.
How Rai is different
Rai doesn't start from a ready-made spreadsheet and just swap in your name.
It starts from the information available about you, builds a training structure that fits your goal, and uses actual execution and feedback to shape the upcoming weeks.
That ability to follow the process — not just generate a table — is what defines Rai.
Read also:
- How to Start Running from Zero in 8 Weeks
- Running Training Types: Practical Science to Evolve Safely
- Rai's Methodology
- Pace Calculator — find your ideal running pace
References
High responders and low responders: individual variability of training adaptation in endurance athletes (2014, PubMed)
Individualized endurance training based on recovery and training status improves adaptations (2022, PMC)
The association between running injuries and training parameters: a systematic review — 36 studies, 23,047 runners (2022, PubMed)
Use of RPE-based training load in soccer (2005, PubMed)
Responders and non-responders to aerobic exercise training beyond the evaluation of VO₂max (2021, Physiological Reports)
Individualization of training prescription: Importance, methods, pitfalls, and future directions (2019, PubMed)
Physiological adaptations to interval training and the role of exercise intensity (2016, PMC)
Effectiveness of recovery strategies after training and competition in endurance athletes (2024, Springer Open)
Pattern of energy expenditure during simulated competition (1993, PubMed)
Describing and understanding pacing strategies during athletic competition (2008, PubMed)
Regulation of pacing strategy during athletic competition (2011, PubMed)





