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How to Individualise intensity targets - That Triathlon Show

In this interview, originally published on the Endurance Innovation podcast, Michael Liberzon interviews Mikael Eriksson on topics related to acquiring and applying information and knowledge in an endurance sports context.

In this episode you'll learn about:

The role of social media, short-form vs. long-form content, and red flags for misinformation
Decision making and evaluation of potential training interventions as well as new products, technology, and metrics
Understanding relevance, validity, accuracy and precision
What data and which metrics should you focus on in your training process?
Data, information, knowledge and wisdom

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Shownotes

How to filter information on social media

04:34 -

I approach social media cautiously, recognising its potential for both distraction and utility. While I don’t typically use it to educate myself, I do consume it, often finding that logging out helps me stay away for weeks or months. However, when I need to post something and don’t log out again, I can waste more time there than I’d like.
Regarding the flow of information, selecting credible sources is crucial. Social media, while not ideal for depth or nuance, does have some good accounts. However, short-form content like Instagram posts or Twitter updates often lacks the balance necessary to fully understand a topic.
For instance, accounts like Knowledge is Watt and YLMSportScience share summaries of sports science research. While their information is useful, it’s limited without understanding the broader context.
In evaluating sources, I prioritise long-form content—podcasts, blogs, and detailed YouTube videos—over short snippets. Nuance is often missing from the latter, and the largest accounts frequently achieve their reach through sensationalism or clickbait rather than accuracy.
To assess the trustworthiness of a source, I consider several factors:
- Credentials: Does the individual or account have the expertise to discuss the topic? Credentials aren’t foolproof; even highly qualified people can spread misinformation, but they’re a good starting point.
- Content Quality: Is the content balanced, presenting pros and cons? Sensationalised claims or oversimplifications are red flags. Clickbait headlines, especially those making grandiose promises, often accompany misinformation.
- Engagement Tactics: Polarising or antagonistic behaviour is often a strategy to drive attention rather than a sign of credibility.
- Track Record: Review past posts for consistency and reliability. An account that mixes credible advice with outlandish claims (e.g., treating depression by shining light into your ears) is less trustworthy. While some posts may seem valid, a history of misinformation undermines credibility.
In the fitness and health sphere, many accounts share general truths—like eating healthily or sleeping well—but may intersperse these with dubious statements.
Evaluating these accounts requires effort, especially if you’re not an expert in the topic. Checking their historical claims and overall consistency can help discern their reliability.
I believe it’s crucial to address the challenges people face when seeking accurate information to improve their training or overall experience in their sport.
Unfortunately, misinformation is rampant, often presented in a polished and sophisticated manner that makes it deceptively believable.
This is particularly problematic in an era dominated by social media, where flashy, attention-grabbing content often overshadows more rigorous, evidence-based insights.
Social media platforms are designed to maximise user engagement, prioritising posts that generate clicks and interactions. This model inherently favours sensationalised content over substantiated, in-depth information.
As a result, even well-intentioned users can be led astray by content that looks scientific but lacks credibility or proper grounding.
Given these realities, I firmly believe that social media should not be a primary source for robust research—especially in areas as intricate as our sport.
For those truly looking to deepen their understanding and optimise their performance, it’s essential to seek out reliable, expert-led resources beyond the noise of social media algorithms.

Where to find information

14:04 -

I draw inspiration and gather new ideas primarily through conversations with coaches and athletes. These discussions, whether in person or virtual, are invaluable, particularly with the athletes I coach, as they often bring fresh perspectives or ideas they’ve encountered.
Regular meetings with my team at Scientific Triathlon also provide a collaborative platform where we exchange insights and explore new topics.
Hosting a podcast has been another rich source of creativity. It allows me to interact with thought leaders, coaches, and athletes who are constantly innovating.
In addition to conversations, I actively seek knowledge through various media. I’m an avid podcast listener and occasionally watch informative content on YouTube. Books, even those unrelated to triathlon, provide a broader creative lens and help generate new concepts.

List of information sources that Mikael uses

18:27 -

I enjoy exploring different podcasts, especially those featuring athletes or coaches. One of my favourites is The Triathlon Hour. It’s packed with long-form interviews, particularly in its earlier episodes, which delved deeply into training discussions.
While the focus has shifted slightly over time, it still offers excellent content. For running, I’m a fan of the Magnus and Marcus on Coaching podcast; it’s a brilliant resource.

How to use the information we get

20:44 -

Background Research and Reflection:
- Start by researching the training method comprehensively. Understand its mechanisms, principles, and the results it claims to deliver. Reflect on this knowledge in light of your own experiences, anecdotes, or insights from your network—fellow athletes, coaches, or colleagues. Consider whether the method aligns with successful practices you’ve seen or challenges those norms.
Historical Context:
- Training methods often reappear over time with slight refinements, but the core principles are rarely new. Explore the history of the method and its effectiveness during past cycles. For instance, trends like low-carb diets or extreme high-volume training have come and gone; reviewing their historical outcomes can provide valuable insight.
Contextual Relevance:
- Tailor the method to your specific circumstances. Are you a professional athlete, an age-grouper, or someone training for a unique event like an Ironman? Consider your fitness level, age, gender, and even environmental factors. Evaluate if research supporting the method is conducted on athletes similar to you. Studies often focus on specific demographics, which might not make them universally applicable.
Experimentation with Reflection:
- After thorough evaluation, trial the method. But remember, the cost of trying something isn’t just failure; it’s the opportunity cost of what else you could have done. Compare the new intervention to your current approach and evaluate its relative potential. This involves setting clear goals and tracking metrics such as performance in key workouts, heart rate-to-power ratios, or pre- and post-testing results.
Analysis and Adjustment:
- Reflect on the outcomes post-trial. While formal testing (like a 20-minute power test) is helpful, even less structured measures like improvements in specific workouts can provide valuable feedback. Be mindful of uncertainties: feelings of improved performance are useful but less reliable than quantifiable data. Keep in mind that not every athlete has the same level of self-awareness regarding their performance metrics.

On Seeking Guidance and Filtering Information:

Coaches are invaluable in this process. They bring a wealth of experience from working with diverse athletes and staying informed about evolving practices.
If you’re self-coached, immerse yourself in books, podcasts, and reputable sources—but apply critical thinking. Building expertise takes time, and while it’s feasible to do independently, a coach streamlines the process significantly.

On Experimentation:

Trying new methods is essential, but it requires informed decision-making. Due diligence before implementation is crucial. Also, accept that no trial is perfect; variables can’t always be isolated.
When multiple factors change simultaneously, acknowledge the increased uncertainty in assessing the results. Be strategic, reflective, and patient, ensuring that any trial aligns with your long-term goals.

Compounding effects

31:00 -

When I start a new training phase, I often notice a compounding effect where improvements in one area lead to positive changes in others. For instance, as my training progresses and I begin to build fitness, my sleep quality tends to improve significantly.
Sleep, as we all know, is crucial not only for overall well-being but also for optimising training performance. Better sleep enhances recovery and helps maintain motivation, particularly regarding nutrition.
Eating well becomes a natural extension of this momentum, creating a feedback loop where these factors—training, sleep, and nutrition—synergise to support further progress.
Interestingly, it’s not always the specific training intervention itself that drives improvement; sometimes, it’s the peripheral effects like better habits or renewed enthusiasm that make the difference.
Trying something new in training can be a catalyst—it sparks motivation, which in turn influences other critical aspects like sleep and nutrition. This interplay is a fascinating aspect of endurance training, where everything is interconnected and small adjustments can cascade into significant outcomes.
When discussing how to approach new training methods, I emphasised the importance of being deliberate and structured. My framework involves five key steps:
- Identifying the Method: Deciding on a training method or intervention that aligns with specific goals.
- Understanding Its Purpose: Knowing why you’re implementing it and what it’s supposed to achieve.
- Trialling It: Actually applying the method in your training.
- Reviewing It: Setting success metrics in advance to evaluate its effectiveness.
- Reassessing the Timeline: Deciding when to review progress and being prepared to extend the trial period if results are inconclusive.
A critical aspect of step five is avoiding passivity. It’s easy to adopt a new method because it seems novel and exciting, but without monitoring its actual impact, it can become an automatic routine.
Many athletes, myself included, can fall into the trap of moving on to the next shiny idea without ever knowing if the previous one truly worked. Having a clear plan for evaluation—knowing when and how to assess progress—is vital to avoid wasting time and effort.
Finally, the process reminded me of the importance of self-awareness and accountability. As someone who has been self-coached for a long time, I know how easy it is to “go with the flow” and neglect structured testing or evaluation.
Staying disciplined about monitoring progress ensures that each decision in training contributes to long-term improvement.

How to implement technology

34:37 -

First, even if a technology is new to a specific context—like endurance sports—it might not be entirely new. For example, when continuous glucose monitors like SuperSapiens entered endurance sports, they had already been extensively used in medical contexts.
This highlights the importance of analysing the historical background where applicable, even if it's not directly within the sport. Often, technologies introduced into sports are adaptations of existing technologies or slight variations of them.
Second, in the initial evaluation phase, four critical concepts must be considered: relevance, validity, accuracy, and precision.
- Relevance: This asks whether the technology or metric genuinely addresses a meaningful problem. For instance, many wearables today claim to improve recovery. While they measure certain metrics, it's essential to question if those measurements translate into actionable and helpful insights. If the device doesn’t address a real need, its relevance is questionable.
- Validity: This examines whether the device measures what it claims to measure. A great example is running power meters. These don’t measure running power directly; they estimate it based on other data like accelerations and weight, applying a model. There’s even disagreement about whether they represent metabolic or mechanical power. A lack of validity doesn’t necessarily render a metric useless but does affect its interpretation and applicability.
- Accuracy: This considers how close the device's measurements are to the actual values. For example, if your power meter shows 300 watts when you're actually outputting 350 watts, it lacks accuracy. While accuracy is important, it can sometimes be less critical for everyday training if other factors like precision are strong.
- Precision: This is about consistency. If a device consistently shows 300 watts every time under the same conditions, it is precise, even if not accurate. Precision is critical for day-to-day training because it allows for reproducibility and trend analysis over time.
Among these factors, I think relevance is foundational—if the technology isn’t relevant, it’s unlikely to be used. However, precision often stands out in practical scenarios since training relies heavily on repeatable and reliable data.
Even a less valid metric (like running power, which doesn’t directly measure power) can still be useful in certain applications, such as pacing on hilly courses or uneven terrains, provided it is precise and conditions remain constant.
I believe it’s crucial to take the time to analyse and understand its potential value before making a purchase. This diligence can save money and ensure you're investing in something meaningful for your needs.
Some factors, such as precision—whether the device consistently measures what it claims—can often be assumed based on the manufacturer's claims.
However, the relevance and validity of the measurements, and sometimes even the accuracy, require deeper scrutiny. These elements often depend on understanding how the device functions and whether its methods align with your goals.
This may involve technical knowledge in fields like physics, biology, medicine, or engineering.
As devices become more advanced, particularly with the integration of AI, the challenge grows. Many modern tools rely on proprietary algorithms to interpret raw data, making it harder to discern their accuracy.
For instance, power meters like Stryd or tools like the Core body temperature sensor use models to estimate metrics indirectly, such as core body temperature.
They don’t measure it directly; instead, they rely on data like skin temperature, heat flux, and heart rate to estimate core temperature.
Take the Core sensor as an example. While it doesn’t measure core temperature directly, its model provides valuable insights if used correctly.
Personally, I recently decided to purchase one after years of interest because I gathered sufficient evidence that it’s precise and relevant for my needs, especially for heat training or preparing for hot races.
While it doesn’t match the accuracy of ingestible temperature pills and has a consistent offset, it’s a reliable tool as long as you work within its constraints.
In my decision-making process, I applied a structured framework to evaluate the device, considering factors like relevance, precision, and consistency.
While no device is perfect, the Core sensor provides enough consistent and actionable data to justify its use, especially when preparing for thermal stress in training.
For many users, its consistent offset makes it suitable for trend analysis and training adaptation, which are the primary use cases for such a tool. This approach—focusing on consistency and practicality—is, I believe, the best way to assess these technologies.

Data for training and coaching

45:57 -

Understanding the metrics you're working with is crucial. You need to evaluate their relevance, validity, accuracy, and precision to determine whether they make sense in your context.
Many devices today automatically capture and calculate a plethora of metrics, but that doesn't mean you need to focus on all of them.
Attempting to track and optimise everything will likely dilute your effectiveness. Instead, narrow your focus and master a handful of key metrics that truly matter.
For triathlon and endurance sports, some essential metrics stand out. For example, on the bike, power and heart rate are vital, while pace is a priority in running, and in swimming, speed may take precedence.
Subjective measures like Rate of Perceived Exertion (RPE) and detailed workout comments are also invaluable. These tools provide context that raw data alone cannot offer.
For instance, you might see a decrease in heart rate relative to power during training, but without RPE or notes, you wouldn't know if that was due to fatigue, nutrition, or another factor.
For self-coached athletes, logging workout comments and RPE can significantly enhance your ability to evaluate training interventions. It gives you the ability to connect how you felt with the data, enabling a more nuanced understanding of your performance.

Speed on the bike

48:23 -

When it comes to cycling performance, it’s critical to focus on speed rather than power as the ultimate measure of success, especially in a race context. While power influences speed, the results sheet only reflects how fast you were, not how much power you produced.
If you're consistently producing good power but achieving relatively low speed, this discrepancy needs investigation. It could be due to poor aerodynamics, mechanical inefficiencies, or equipment issues—like a dirty chain.
I’ve encountered situations where a rider dramatically improved their performance simply by cleaning and properly lubing their chain, which had been causing unnecessary resistance. Over the course of a long event like an Ironman, these inefficiencies can result in substantial time losses.
In training, I typically prescribe workouts based on a mix of power, perceived effort (RPE), and heart rate. Speed is less commonly used, though there are scenarios where it becomes essential.
For instance, if you’re simulating race conditions, focusing on achieving maximum speed for a given power output can reveal areas for improvement, such as pacing strategy or bike handling. This could involve pushing harder on hills or improving efficiency on descents.
Speed acts as a valuable metric to evaluate overall cycling efficiency. By observing it alongside power, you can identify performance limiters and potential gains, such as refining aerodynamics or addressing mechanical friction.
However, speed is often more insightful when comparing multiple workouts rather than analysing a single session unless you're conducting specific aerodynamic testing or a workout designed with speed as the main objective.
Introducing novelty in training—like basing a session on speed or RPE rather than power—can also be beneficial. It challenges athletes to think differently about their performance and adapt to varying conditions, which can be both mentally refreshing and physically beneficial.

Parameter use cases

51:50 -

While these metrics aim to provide a comprehensive view of training stress, they fail important tests of relevance and validity.
For instance, TSS and its derivatives are seen as flawed due to their susceptibility to extreme fluctuations based on external factors such as the type of ride or event, e.g., using a wetsuit for swimming, which can inflate the metric significantly.
Additionally, the transferability between different disciplines like cycling, running, and swimming is poor.
For example, a very stochastic ride or hilly run can result in inflated TSS, which does not accurately reflect the true effort or stress involved in those activities.
Furthermore,the interplay between the three sports in a triathlon context is particularly challenging for TSS. The use of equipment in swimming, such as wetsuits and different pool lengths, adds complexity and further reduces its validity.
Despite these, TSS might be more applicable in certain contexts like ultra-cycling or gravel riding, where steadier, longer efforts are involved.

What metric Mikael uses instead of TSS

54:54 -

In terms of training, I prioritise volume by using metrics such as a three-month rolling average or a six-week rolling average of volume. While intensity may vary slightly depending on periods of training, it remains relatively consistent within what the athlete can tolerate.
There's always some form of intensity, whether it's race pace, threshold, or VO2 max intervals.
The programme will include different intensities throughout. For instance, if training for 15 hours per week, I ensure that intensity is balanced across different sessions, whether it’s through kilojoules or other metrics.
Volume, particularly the three-month rolling average, is a significant factor in tracking progress. In cycling, volume plays a crucial role, and I find this to be one of the easiest metrics to monitor effectively.

DIKW pyramid

56:40 -

The DIKW pyramid, which stands for Data, Information, Knowledge, and Wisdom, is a framework I find incredibly valuable in understanding how we process and apply facts in our daily lives. Here's a detailed breakdown of what it means and its relevance.

Data

Data is the raw, unprocessed fact. For example, "15 degrees Celsius" is just a number. On its own, it has no meaning because it lacks context. It could be the temperature outside, the temperature in a fridge, or even a measurement error. In isolation, data is meaningless.

Information

Information is organised and contextualised data. For instance, if we say, "The temperature outside is 15 degrees Celsius right now," we add context—answering questions like what, where, and when. This step gives basic meaning to data but still doesn’t fully unlock its potential.

Knowledge

Knowledge builds on information by adding understanding and personal context. Using the previous example, knowing it’s 15 degrees outside might help me decide how to dress for a bike ride. However, someone from a warmer climate with less cycling experience might not know how to dress appropriately despite having the same information. Knowledge is about connecting the data to experience, making it personally meaningful.

Wisdom

Wisdom is the application of knowledge. Suppose I have two friends visiting from warmer climates: one muscular and heat-resistant, the other slender and prone to cold.
Knowing their physiology, the route's challenges, and the weather, I can provide personalised clothing recommendations. Wisdom requires integrating knowledge from various fields—like physiology and environmental conditions—and making informed decisions.
It’s harder to define but evident when knowledge is consistently and effectively applied.

Practical Implications

We live in an era where information is abundant, but we often fail to convert it into knowledge and wisdom. For example, new devices and metrics provide processed data, but they rarely deliver actionable insights.
To bridge the gap, you must critically assess whether investing time in learning and mastering a tool is worth the opportunity cost. True benefits come only when you achieve wisdom—applying insights in ways that enhance results.

A Focused Approach

I’ve found that focusing on a few fundamental metrics and deeply understanding them leads to better outcomes than scattering attention across numerous metrics. This approach applies to training, technology, and even how we consume educational resources.
Many of the things we encounter, from social media to advanced devices, stop at the information level. Without progressing to wisdom, these resources remain underutilised.

Key Takeaways

Understand the DIKW framework: Recognise where you’re positioned on the pyramid when working with data or learning new concepts.
Aim for wisdom: Prioritise applying insights meaningfully rather than amassing information.
Assess opportunity costs: Consider whether time spent learning a tool or metric yields enough benefit to justify the effort.
Invest in higher-order thinking: Spend less time on data and information, and more on developing the skills to transform them into knowledge and wisdom.

LINKS AND RESOURCES:

Bernardo Gonçalves

Bernardo is a Portuguese elite cyclist and co-founder of SpeedEdge Performance, a company focused on optimising cycling and triathlon performance. He writes the shownotes for That Triathlon Show, and also produces social media content for each new episode.