4-days-a-week-experiment • 3 min read

5 day experiment

An experiment in attempting measurable hypertrophy through 4 days a week exercise, 2-3 days a week cardio.

By Rationalist

Experimental Report: Hypertrophy Response to a “Less is More” Approach

I will attempt to measure my hypertrophy response through a minimalist resistance training approach supplemented with cardiovascular training. This experiment seeks to clarify the efficacy of such a strategy in promoting hypertrophy and to quantify the results based on various metrics.


Hypothesis

A minimalist hypertrophy training approach, supplemented with cardiovascular training, will result in measurable muscle growth, fat loss, and improvements in cardiovascular fitness. Consistency in dietary tracking, workout intensity, and body metrics will drive these outcomes, while limiting training volume will not significantly hinder hypertrophy gains when intensity and recovery are optimized.


Methodology

Empirical Data Collection

Weight Change

  • Data will be collected via a smart scale to monitor fluctuations in weight, reflecting changes in fat and muscle mass.
  • The primary goal is to reach 10–15% body fat, corresponding to approximately 78 kg. After achieving this, weight gain will target 200 g per month to ensure muscle gain with minimal fat.
  • Average weight measurements will account for variables like hydration, muscle glycogen, and bloating for higher granularity.

Circumference and Skinfold Measurement

  • Frequency: Every two weeks.
  • Metrics: Circumference (e.g., arms, thighs, waist) and body fat percentage via calipers combined with weight data.
  • Goal: Track muscle growth and fat changes to corroborate weight data.
  • When body fat reaches 15–20%, weight loss of 200 g per week will commence.

Workout Tracking

  • Tool: Digital workout tracking app.
  • Resistance training volume and intensity will be tracked to assess the relationship between these factors and hypertrophy.
  • Structure: Fixed resistance training volume with flexible cardiovascular training.

Heart Rate Monitoring

  • Cardiovascular training will consist of 15 minutes of high-intensity interval training (HIIT), tracked with a Fitbit.
  • Heart rate data during both resistance and cardiovascular training will indicate the training’s impact on fitness levels.

Caloric Tracking

  • Tool: MacroFactor app.
  • Frequency: 5 days per week, with all meals weighed and logged using a food scale.
  • On two designated “free days,” caloric intake will exceed the prescribed limit for flexibility and metabolic adjustment.

Data Analysis

  • Tools: AI-driven analysis using research-focused models (e.g., LangChain) for data visualization, statistical analysis, and explanation.
  • Hypotheses generated by AI will cross-reference findings with established scientific literature.
  • Models will analyze potential contradictions between the data and current hypertrophy and nutritional research.

The Workout Plan

Resistance Training

  • Split: Upper/lower split with an emphasis on arm volume on lower-body days.
  • Schedule: 4 days per week.
  • Structure:
    • 5 movements per session, 3 working sets each.
    • Incorporation of at least one horizontal and one vertical movement per session.

Cardiovascular Training

  • Frequency: 3 days per week (on non-resistance days).
  • Structure:
    • 15 minutes of HIIT (45 seconds low-intensity, 15 seconds high-intensity).
    • Followed by 4 sets of 10–12 cable ab crunches for core conditioning.

Caloric Methodology

  • Caloric Adjustments: Calories will decrease by 150 every two weeks if weight loss stalls.
  • Macros:
    • Protein: ≥1.6 g/kg body weight.
    • Fat: ≥60 g/day.
    • Remaining calories allocated to carbohydrates.

Results (Preliminary Expectations)

  • Results will include metrics on weight, body fat percentage, muscle circumference, cardiovascular fitness, and training progression.
  • Analysis and findings will be ready for review by June 2025.

This experiment seeks to contribute personal insights into the minimalistic hypertrophy approach while providing data-driven conclusions about its feasibility and effectiveness.