# Can Popular High-Intensity Interval Training (HIIT) Models Lead to Impossible Training Sessions?

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## Abstract

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## 1. Introduction

#### 1.1. The Skiba Model

_{balance}= 0), the athlete has reached exhaustion [19] and cannot pursue the effort without prior recovery.

_{balance}along the repeated exercise and recovery bouts [9,12]. In the Skiba model latest iteration [15], work performed below the CP allows replenishing W′ curvilinearly, at a rate proportional to the difference between the actual power output during recovery and the CP. The following set of equations describes the W′ depletion and its recovery. The rate of W′ depletion is always proportional to the difference between the power output ($P$) and the CP. Thus,

#### 1.2. The Coggan Model

#### 1.3. Practical and Theoretical Value of the Models

## 2. Materials and Methods

#### 2.1. Fictitious Athletes’ Profiles

#### 2.2. Combinations of HIIT Parameters

#### 2.3. Exhaustion, According to the Skiba Model

#### 2.4. Exhaustion, According to the Coggan Model

## 3. Results

## 4. Discussion

#### 4.1. Variations in the Percentage of Impossible Sessions

#### 4.2. Limitations of the Skiba Model

#### 4.3. Limitations of the Coggan Model

#### 4.4. Applicability of the Models

#### 4.5. Limitations

#### 4.6. Future Perspective

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Simulated HIIT sessions using the Skiba and Coggan-Modified models for each fictitious athlete profile. The x-axis refers to the interval duration of each training session, and the y-axis to the power output expressed as a percentage of the athlete’s maximal power output over the work interval duration. The black dots above the solid line correspond to sessions impossible to realize in practice. These sessions require, on every work interval, to surpass the maximal power output over the work interval duration. The relative percentage of the 6198 HIIT impossible sessions in practice for each model and each athlete profile is also reported on each respective graph.

**Figure 2.**Using the Skiba model, percentage of impossible sessions within subsets of (

**A**) Prescribed work intervals power output expressed in percent of the MAP, (

**B**) Interval durations, (

**C**) Total time spent at target intensity, and (

**D**) Rest durations. Each athlete profile is represented by line type (Time-Trialist: solid line, All-Rounder: dotted line, Sprinter: dashed line). For reference, the blue line corresponds to 30% of impossible sessions.

**Figure 3.**Using the Coggan-Modified model, percentage of impossible sessions within subsets of (

**A**) Prescribed work intervals power output expressed in percentage of the MAP, (

**B**) Interval durations, (

**C**) Total time spent at target intensity, and (

**D**) Rest durations. Each athlete profile is represented by a different line type (Time-Trialist: solid line, All-Rounder: dotted line, Sprinter: dashed line). For reference, the blue line corresponds to 30% of impossible sessions.

**Table 1.**Estimated physiological characteristics of each fictitious athlete. The endurance, anaerobic capacity, and MAP were used as parameters in the Péronnet–Thibault (1989) continuous exercise model to obtain each athlete’s power profile. The CP and W′ are derived from the fictitious athletes’ respective power profiles.

Profile | Endurance $\left(\frac{\Delta \%\mathrm{MAP}}{\Delta \mathrm{log}\left(\mathbf{T}\right)}\right)$ | Anaerobic Capacity (J/kg) | MAP (W/kg) | Critical Power (CP; W) | Anaerobic Reserve (W′; kJ) |
---|---|---|---|---|---|

Time-Trialist | −8 | 1400 | 25 | 304 | 21.5 |

All-Rounder | −10 | 1600 | 25 | 293 | 27.1 |

Sprinter | −12 | 1800 | 23 | 259 | 32.3 |

**Table 2.**Average power sustained over various durations for each fictitious cyclist, derived using the Péronnet–Thibault (1989) continuous exercise model.

Performance Duration | Sprinter (W) | All-Rounder (W) | Time-Trialist (W) |
---|---|---|---|

1 s | 1251 | 1115 | 978 |

15 s | 968 | 876 | 777 |

30 s | 782 | 719 | 647 |

45 s | 665 | 623 | 567 |

1 min | 589 | 561 | 516 |

2 min | 457 | 454 | 431 |

3 min | 411 | 419 | 403 |

4 min | 389 | 402 | 390 |

5 min | 375 | 391 | 382 |

10 min | 317 | 341 | 342 |

20 min | 276 | 307 | 315 |

30 min | 257 | 290 | 301 |

45 min | 239 | 274 | 289 |

60 min | 227 | 264 | 281 |

90 min | 211 | 249 | 269 |

2 h | 199 | 239 | 261 |

4 h | 172 | 214 | 241 |

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**MDPI and ACS Style**

Briand, J.; Tremblay, J.; Thibault, G.
Can Popular High-Intensity Interval Training (HIIT) Models Lead to Impossible Training Sessions? *Sports* **2022**, *10*, 10.
https://doi.org/10.3390/sports10010010

**AMA Style**

Briand J, Tremblay J, Thibault G.
Can Popular High-Intensity Interval Training (HIIT) Models Lead to Impossible Training Sessions? *Sports*. 2022; 10(1):10.
https://doi.org/10.3390/sports10010010

**Chicago/Turabian Style**

Briand, Jérémy, Jonathan Tremblay, and Guy Thibault.
2022. "Can Popular High-Intensity Interval Training (HIIT) Models Lead to Impossible Training Sessions?" *Sports* 10, no. 1: 10.
https://doi.org/10.3390/sports10010010