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Intermittent Fasting Monitoring for Human Optimization Utilizing Wearable Technology: an N-of-1 Prospective Study

ClinicalTrials.gov · COMPLETED, 2025

1 participants

Abstract

There is a growing interest in personalized health strategies, including diet, exercise, and fasting regimens, to enhance metabolic fitness. Recent research indicates that intermittent fasting (IF) is useful not only for weight management, but also for energy production and improvement of metabolic well-being.

IF can potentially reduce systemic inflammation and help to prevent chronic diseases. IF research can also help athletes sustain performance during fasting periods. This prospective study aims to investigate the effects of a human optimization program on an individual\'s metabolic health and overall well-being.

This research examines the correlations between continuous glucose monitoring, ketone measurement, exercise routines, heat/cold interventions, diet, supplements and intermittent fasting patterns. The study involves a single participant -the principal investigator- who will adhere to a personalized regimen involving wearable monitoring of metabolic changes, training and intermittent fasting. This research seeks to understand the potential relationships between these factors, contributing to the emerging knowledge on individualized health optimization.

Conditions: Intermittent Fasting, Healthy, Digital Health.

Primary outcome: Metabolic biomarker analysis: total cholesterol levels; Metabolic biomarker analysis: low density lipoprotein (LDL) cholesterol levels; Metabolic biomarker analysis: high density lipoprotein (HDL) cholesterol levels; Metabolic biomarker…

How strong is the evidence?

Evidence Hub reads this as a clinical trial, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 1 participants or records.

How the five levels of evidence work. The level is assigned automatically from the abstract and can be wrong, so check the paper.

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