Clinical trial NCT06630637: Monitoring Intermittent Fasting for Human Optimization Using Wearable Technology: An N-of-1 Study
Official title: Intermittent Fasting Monitoring for Human Optimization Utilizing Wearable Technology: an N-of-1 Prospective Study
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Intermittent Fasting
Healthy
Digital Health
Keywords: Continuous Glucose Monitoring (CGM)
Intermittent Fasting (IF)
Ketone Measurement
Wearable Technology
Exercise Routines
Heat/Cold Interventions
Diet and Supplements
Human Optimization
N-of-1
Single Participant
Personalized Health
Digital Health
Sensors
Lead sponsor: Institute for Digital Medicine (WisDM)
Sponsor class: OTHER
First posted: 2024-10-08
Enrollment: 1
Sex: MALE
Minimum age (years): 21
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: This is an N-of-1 trial involving a single participant.
Primary purpose: BASIC_SCIENCE

Brief summary: 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\&#39;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.


Eligibility criteria: Inclusion Criteria:

* The principal investigator is the sole participant in this N-of-1 trial: a healthy individual with no chronic diseases or conditions affecting metabolic health.

Exclusion Criteria:

* The principal investigator is the sole participant in this N-of-1 trial.

Primary outcomes:
measure: Metabolic biomarker analysis: total cholesterol levels
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: low density lipoprotein (LDL) cholesterol levels
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: high density lipoprotein (HDL) cholesterol levels
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: triglycerides levels
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: apolipoprotein B (ApoB) levels
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: apolipoprotein A1 (ApoA1) levels
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: C reactive protein (CRP) levels
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: interleukin 1β (IL- 1β) levels
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: interleukin 6 (IL- 6) levels
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: interleukin 8 (IL- 8) levels
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: interleukin 10 (IL- 10) levels
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: interleukin 12 (IL- 12) levels
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: interleukin 17 (IL- 17) levels
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: omega 3:6 ratio
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: Microtubule-associated protein 1A/1B-light chain 3 (LC3) levels
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: Sequestosome 1 (p62/SQSTM1) levels
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: Beclin-1 levels
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: Red Blood Cells (RBCs)
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: White Blood Cells (WBCs)
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: Hemoglobin (Hb)
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: Hematocrit (Hct)
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: Platelets (PLT)
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: Blood Urea Nitrogen (BUN)
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: Creatinine
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: Estimated Glomerular Filtration Rate (eGFR)
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: Sodium
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: Potassium
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: Chloride
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: Alanine Aminotransferase (ALT)
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: Aspartate Aminotransferase (AST)
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: Alkaline Phosphatase (ALP)
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: Total Bilirubin
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Metabolic biomarker analysis: Albumin
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Microbial diversity
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Taxonomic abundance
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Concentrations of metabolites
timeFrame: Pre and post 48 hour fasts (2 fasts per month for 12 months)
measure: Glycemic and ketone monitoring for adherence assessment
timeFrame: 10 times per day for up to 12 months
measure: Wearable health metrics analysis for physiological monitoring
timeFrame: Daily for up to 12 months
measure: Artificial intelligence (AI)-enhanced visual data representation for activity monitoring
timeFrame: Once a month for up to 12 months
Secondary outcomes:
Other outcomes:
measure: Qualitative assessment of participant experience
timeFrame: Day one, day 8, at study completion (12 months)
Locations:
Study officials:
affiliation: The Institute for Digital Medicine (WisDM)
name: Dean Ho, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Mandal S, Simmons N, Awan S, Chamari K, Ahmed I. Intermittent fasting: eating by the clock for health and exercise performance. BMJ Open Sport Exerc Med. 2022 Jan 7;8(1):e001206. doi: 10.1136/bmjsem-2021-001206. eCollection 2022.
citation: Patterson RE, Sears DD. Metabolic Effects of Intermittent Fasting. Annu Rev Nutr. 2017 Aug 21;37:371-393. doi: 10.1146/annurev-nutr-071816-064634. Epub 2017 Jul 17.
citation: Levy E, Chu T. Intermittent Fasting and Its Effects on Athletic Performance: A Review. Curr Sports Med Rep. 2019 Jul;18(7):266-269. doi: 10.1249/JSR.0000000000000614.
citation: Tinsley GM, La Bounty PM. Effects of intermittent fasting on body composition and clinical health markers in humans. Nutr Rev. 2015 Oct;73(10):661-74. doi: 10.1093/nutrit/nuv041. Epub 2015 Sep 15.
citation: Wang P, Tadeo X, Chew HSJ, Sapanel Y, Ong YH, Leung NYT, Chow EK, Ho D. N-of-1 health optimization: Digital monitoring of biomarker dynamics to gamify adherence to metabolic switching. PNAS Nexus. 2024 May 30;3(6):pgae214. doi: 10.1093/pnasnexus/pgae214. eCollection 2024 Jun.