Internal Clock Progression: A Continuous Load Distribution Framework for Resistance and Multimodal Training

Traditional resistance training paradigms are structured around discontinuous loading cycles and high-intensity peaks. This paper formalizes Internal Clock Progression (ICP), a parametric framework that reframes training as a continuous signal integration process. By distributing workload across time via "Diagonal Progression," ICP maximizes Effective Training Time (ETT) while operating below the threshold of systemic exhaustion—a concept defined as "Stealth Adaptation." The framework features an ICP Logic Core and a Formal Validator Layer (FVL) to bridge mathematical progression with biological feedback, providing a robust substrate for complex, multi-disciplinary athletic environments.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-16
DOI
https://doi.org/10.5281/zenodo.20231553
Primary Topic
Sports Performance and Training
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Internal Clock Progression: A Continuous Load Distribution Framework for Resistance and Multimodal Training

Francesco Zampi
Zenodo (CERN European Organization for Nuclear Research)
Sports Performance and Training
preprint

Internal Clock Progression: A Continuous Load Distribution Framework for Resistance and Multimodal Training

Francesco Zampi
preprint en

Abstract

Traditional resistance training paradigms are structured around discontinuous loading cycles and high-intensity peaks. This paper formalizes Internal Clock Progression (ICP), a parametric framework that reframes training as a continuous signal integration process. By distributing workload across time via "Diagonal Progression," ICP maximizes Effective Training Time (ETT) while operating below the threshold of systemic exhaustion—a concept defined as "Stealth Adaptation." The framework features an ICP Logic Core and a Formal Validator Layer (FVL) to bridge mathematical progression with biological feedback, providing a robust substrate for complex, multi-disciplinary athletic environments.

Zenodo (CERN European Organization for Nuclear Research)
Sports Performance and Training
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.