Using Generative AI as My Marathon Training Coach

11

October

2024

No ratings yet.

I want to run a marathon next summer, so I turned to ChatGPT to help me out. Not with the running part, but mostly everything else. With generative AI, I can have a personal marathon coach that understands my needs, optimizes my training plan, and continually adapts as my fitness level evolves.

Setting Up My AI Coach

To create my AI coach, I used the MyGPT function offered by OpenAI. It allows you to customize a ChatGPT model to your liking by providing sets of instructions and information. I instructed it to act as my professional marathon coach who understands my goals, fitness level, and the specific challenges of marathon training. I explained my current abilities, body metrics, and general athletic history. I also provided my time goal, the marathon course info, and the date of the event. Moreover, I uploaded important data:

  • Running metrics for current, regular runs (distance, time, heart rate zone info, cadence, pace, split pace, effort level, etc.)
  • Apple Watch training data
  • Calendar data

This data ensures that the model can give more specific advice and training plans. For example, it uses my previous data to set pace targets considering terrain and what heart rate zone I should be in for a particular session.

Phased Training Plan

The AI-generated plan broke my preparation into phases, each with a specific focus. The initial phase was about building endurance with consistent mileage and easy runs. Later phases introduced speed work and tempo runs to improve stamina and pace. The AI explains the purpose behind each phase, which keeps me motivated and committed.

The phased approach means I’m not overwhelmed. Instead of seeing it as one long journey, I focus on each phase, trusting that the AI has mapped out the best way to build my fitness gradually. Each phase has unique challenges and goals, which helps me stay focused and structured. Here is an example of what it created:


Phase 2: Strength & Speed Building (Weeks 11–20)

Goal: Increase mileage, speed, and introduce hill work. Continue to improve cadence and manage heart rate.

Weekly Structure:

  • 5 days running:
    • 1 long run (gradually increasing distance)
    • 1 interval session (e.g., 4 x 1 km at 5:00/km)
    • 1 tempo run at marathon pace (5:40–5:50/km)
    • 2 easy runs (HR < 150 bpm)
  • 1–2 days cross-training or strength training focused on core and lower body strength.

Example Week 15:

  • Monday: Rest or cross-training
  • Tuesday: 8 km easy run (HR < 150 bpm)
  • Wednesday: Interval run: 5 x 1 km at 4:55–5:05/km with 2-min recovery
  • Thursday: Cross-training (strength-focused)
  • Friday: 8 km tempo run at marathon pace (5:40/km)
  • Saturday: 18 km long run (pace 6:10–6:30/km)
  • Sunday: Rest or mobility work

Long Run Progression:

  • Week 11: 18 km at 6:10/km
  • Week 14: 22 km at 6:00–6:20/km (HR under 160 bpm)
  • Week 17: 26 km at 6:10/km with last 3 km at marathon pace (5:40/km)
  • Week 20: 30 km long run (gradual build, HR < 160 bpm)

End of Phase Goal:

  • Cadence: Improve to around 165–170 SPM.
  • Long Runs: 30 km long run with a fast finish.
  • Tempo Pace: Consistent pacing at 5:40/km for up to 12 km.

How AI Adjusts Based on Real-Time Data

The AI adjusts based on real-time data from my Apple Watch, including cadence, heart rate, pace, and distance. If my heart rate is higher than usual during long runs, the AI might recommend more rest or adjust upcoming workouts. On strong days, it suggests more intensity or extra mileage. These adjustments keep my plan in sync with my body’s needs. The AI doesn’t just follow a set schedule—it adapts continuously to prevent burnout or injuries and to push me when I’m ready.

Personalized Insights & Accountability

The AI can provide insights into my performance, spotting patterns I might not notice. I can ask it to analyze my recent running data and give me advice if it is necessary to adjust the training plan. For example, when my pace stagnated, the AI suggested changes—more recovery days and different speed workouts. This helped me break through the plateau, improving my pace within weeks. These insights are invaluable and something I wouldn’t easily notice on my own.

Looking Ahead: My Marathon Journey Continues

With about eight months until the marathon, I’m excited to continue building on the progress I’ve made. Having a personalized plan gives me confidence that I will be well-prepared when race day arrives. Generative AI has transformed my training goals. It’s personalized, adaptive, and insightful. It helps me understand my capabilities, push limits, and gain confidence in my training approach.

Have you ever considered using AI to help with your fitness goals? I’d love to hear your thoughts or experiences—feel free to share in the comments below!

Please rate this

Digital adoption in the Histopathology industry

20

September

2024

No ratings yet.

I wanted to share some insights about a simple application of digital technologies in the histopathology industry.

Some basics about the field and industry:

  • Histopathology is the study of examining and diagnosing manifestations of diseases in the tissue(1). The discipline is commonly used to diagnose possible cancer formations.
  • Labs receive tissue samples that must undergo multiple processes to make it possible for a pathologist to evaluate the tissue structure. Examples of these stages are grossing, fixation, tissue processing, embedding, sectioning and staining.
  • The processes were developed in the 19th century and remain largely unchanged. However, technologies, materials, and lab organization have.

Diagnosis speed is crucial for quality patient care. Traditionally, labs have completed all processes manually which is both time consuming and psychically demanding for lab technicians. Turnaround time could take days, even weeks. To improve the throughput capacity, laboratories across the world (in the last two decades) have been acquiring specialized instruments to improve the efficiency of processes.

Two issues that many laboratories face is the cost of ownership and production organization. Laboratories, especially under public health care systems, face strong cost pressures from the purchasing department.

The non-digital solution has been to offer lease type deal which is more cash flow friendly, but the downside is that it usually offers much less production flexibility. Customers are often locked to semi-rigid contract and need to pay a monthly fee based on a fixed output quantity. Laboratories still need to pay based on the minimum slide requirement regardless of output schedule. For many laboratories, this becomes a dealbreaker.

The digital solution has been to connect instruments to healthcare specific systems (LIS) to record output, then invoice customers based on their usage. They pay more per patient, but the production flexibility is a benefit what many customers value. It is a small but powerful pricing innovation enabled by the digitalization of the instruments.

The Royal College of Pathologists. (n.d.). Histopathology. https://www.rcpath.org/discover-pathology/news/fact-sheets/histopathology.html#:~:text=Histopathology%20is%20the%20diagnosis%20and,clinicians%20manage%20a%20patient’s%20care.)

Requests for slides and blocks. (n.d.). https://www.utsouthwestern.edu/education/medical-school/departments/pathology/services/slides-and-blocks.html

Please rate this