Healthy Clinical Trial
Official title:
Effects of Different Cardiorespiratory Training Program on Endurance Performance
Conventional training methods are typically administered in a fixed progressive manner, which
can lead to sub-optimal responses and injuries. Artificial intelligence (i.e. CURATE.AI) can
be harnessed to personalise physical training strategies. Using a single participant training
profile, a parabolic/quadratic response to the intervention can be generated to identify the
training intensity needed to optimise training outcomes. Previous studies showed CURATE.AI
could dynamically modulate drug dosing in oncology. Extending the utility of results to human
performance, this study will adapt CURATE.AI with the goal of optimising endurance
performance through individualised training regimes.
Up to 20 participants will be recruited and randomised into two groups to undergo a
calibration phase, which involves performing 3 sessions of exercise sessions per week over 2
weeks per intensity (low, moderate and high) in a crossover study design. Exercise sessions
will be interspersed with a 2.4 km time trial, a VO2peak test and 2 weeks of wash out period.
The utility phase will divide participants into two groups to undergo 3 exercise sessions per
week, totalling to 12 exercise sessions. Either an AI-led training or a conventional training
programme will be performed to compare the differences in training outcomes. Blood plasma
will be obtained at selected time points in both phases to evaluate the effects of training
on blood lipid profiles.
Findings from this study can potentially optimise efficacy and efficiency of endurance
performance through personalised training with AI.
Status | Not yet recruiting |
Enrollment | 20 |
Est. completion date | December 31, 2022 |
Est. primary completion date | December 31, 2022 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | Male |
Age group | 21 Years to 35 Years |
Eligibility |
Inclusion Criteria: - Do not smoke or use tobacco products (including shisha) - BMI < 30 - Medically fit - 2.4 km run time of 10 to 14 minutes in the past 12 months Exclusion Criteria: •Individuals with a history of existing musculoskeletal injury or respiratory diseases |
Country | Name | City | State |
---|---|---|---|
Singapore | National University of Singapore | Singapore |
Lead Sponsor | Collaborator |
---|---|
Wang Yongtai Raymond | The N1 Institute for Health, Singapore |
Singapore,
Rashid MBMA, Toh TB, Hooi L, Silva A, Zhang Y, Tan PF, Teh AL, Karnani N, Jha S, Ho CM, Chng WJ, Ho D, Chow EK. Optimizing drug combinations against multiple myeloma using a quadratic phenotypic optimization platform (QPOP). Sci Transl Med. 2018 Aug 8;10(453). pii: eaan0941. doi: 10.1126/scitranslmed.aan0941. — View Citation
Wang H, Lee DK, Chen KY, Chen JY, Zhang K, Silva A, Ho CM, Ho D. Mechanism-independent optimization of combinatorial nanodiamond and unmodified drug delivery using a phenotypically driven platform technology. ACS Nano. 2015 Mar 24;9(3):3332-44. doi: 10.1021/acsnano.5b00638. Epub 2015 Feb 23. — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Changes in 2.4 km completion time following first cycle in phase 1 | Time taken to complete 2.4 km | Baseline and Week 4 of phase 1 (phase 1 first cycle consist of 42 days) | |
Primary | Changes in 2.4 km completion time following second cycle in phase 1 | Time taken to complete 2.4 km | Baseline and Week 9 of phase 1 (phase 1 second cycle consist of 35 days) | |
Primary | Changes in 2.4 km completion time following third cycle in phase 1 | Time taken to complete 2.4 km | Baseline and Week 14 of phase 1 (phase 1 third cycle consist of 21 days) | |
Primary | Changes in 2.4 km completion time following phase 2 | Time taken to complete 2.4 km | Baseline and Week 6 of phase 2 (phase 2 consist of 49 days) | |
Secondary | Changes in VO2peak following first cycle in phase 1 | Measurement of oxygen uptake under maximal work capacity | Baseline and Week 4 of phase 1 (phase 1 first cycle consist of 42 days) | |
Secondary | Changes in VO2peak following second cycle in phase 1 | Measurement of oxygen uptake under maximal work capacity | Baseline and Week 9 of phase 1 (phase 1 second cycle consist of 35 days) | |
Secondary | Changes in VO2peak following third cycle in phase 1 | Measurement of oxygen uptake under maximal work capacity | Baseline and Week 14 of phase 1 (phase 1 third cycle consist of 21 days) | |
Secondary | Changes in VO2peak following phase 2 | Measurement of oxygen uptake under maximal work capacity | Baseline and Week 6 of phase 2 (phase 2 consist of 49 days) | |
Secondary | Resting blood lipids panel (triglycerides, cholesterols, phospholipids, sphingolipids, inflammatory lipids, testosterone, cortisol, endocannabinoids) in phase 1 | Lipid biomarkers related to metabolism, inflammation, fitness, health, and pain | Week 1 of phase 1 (phase 1 consist of 98 days) | |
Secondary | Changes in blood lipids panel (triglycerides, cholesterols, phospholipids, sphingolipids, inflammatory lipids, testosterone, cortisol, endocannabinoids) pre- and post- training following first cycle in phase 1 | Lipid biomarkers related to metabolism, inflammation, fitness, health, and pain | Week 2 and 3 of phase 1 (phase 1 first cycle consist of 42 days) | |
Secondary | Changes in blood lipids panel (triglycerides, cholesterols, phospholipids, sphingolipids, inflammatory lipids, testosterone, cortisol, endocannabinoids) pre- and post- training following second cycle in phase 1 | Lipid biomarkers related to metabolism, inflammation, fitness, health, and pain | Week 7 and 8 of phase 1 (phase 1 second cycle consist of 35 days) | |
Secondary | Changes in blood lipids panel (triglycerides, cholesterols, phospholipids, sphingolipids, inflammatory lipids, testosterone, cortisol, endocannabinoids) pre- and post- training following third cycle in phase 1 | Lipid biomarkers related to metabolism, inflammation, fitness, health, and pain | Week 12 and 13 of phase 1 (phase 1 third cycle consist of 21 days) | |
Secondary | Changes in resting blood lipids panel (triglycerides, cholesterols, phospholipids, sphingolipids, inflammatory lipids, testosterone, cortisol, endocannabinoids) in phase 2 | Lipid biomarkers related to metabolism, inflammation, fitness, health, and pain | Week 1 and 7 of phase 2 (phase 2 consist of 49 days) | |
Secondary | Changes in blood lipids panel (triglycerides, cholesterols, phospholipids, sphingolipids, inflammatory lipids, testosterone, cortisol, endocannabinoids) pre- and post- training in phase 2 | Lipid biomarkers related to metabolism, inflammation, fitness, health, and pain | Week 2, 4 and 5 of phase 2 (phase 2 consist of 49 days) |
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