nursing-nutraceuticals-2018-keynote

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academies
Nov 12-13, 2018 | Paris, France
Joint Event
Nutraceuticals and Food Sciences
International Conference on 27th International Conference on
Nursing and Healthcare &
Journal of Food science and Nutrition | Volume 1
Introduction: The author applied his knowledge from
mathematics, physics, traditional engineering, and computer
science to conduct a big data analytics of food consumption
and PPG for type 2 diabetes (T2D) patients.
Methods: The focus on this paper was specifically applying
energy theory from physics and engineering. He used
both optical physics and signal wave processing to develop
his PPG prediction model. He realized weight is merely a
physical representation of internal energy exchange in the
human body. The energy infusion includes food and others,
whereas energy diffusion comprises of exercise/activity and
others. The major goal is to avoid having energy imbalance
(disequilibrium); otherwise, the excessive (left over) energy
will damage a person’s internal organs.
Results: The 4,066 food/meals (3,651 meals and 415 snacks)
in the selected period of 1,217 days (6/1/2015 - 9/30/2018)
indicate the average values for daily glucose as 118.5 mg/
dL and daily carbs/sugar intake as 14.8 grams per meal. The
food/meal database contains ~8 million, while the patient’s
metabolism data is ~1.5 million.
By applying both energy theory and wave theory, he found
a “preliminary” glucose-energy perturbation range of -7% to
17% resulting from left-over energy. In order to further narrow
down the variance, he identified a few practical methods to
improve both food intake and exercise in order to “wear-off”
the excessive glucose-energy.
Conclusion: The author did not discover any major new
findings during this research process. However, he adjusted
some methods regarding energy infusion through food intake
and energy consumption by walking. As a result, this set of
practical tips can guide T2D patients on further improving
their PPG conditions.
Speaker Biography
Gerald C Hsu received an honorable PhD in mathematics and majored in engineering
at MIT. He attended different universities over 17 years and studied seven academic
disciplines. He has spent 20,000 hours in T2D research. First, he studied six metabolic
diseases and food nutrition during 2010-2013, then conducted research during 2014-
2018. His approach is “math-physics and quantitativemedicine” based onmathematics,
physics, engineering modelling, signal processing, computer science, big data analytics,
statistics, machine learning, and AI. His main focus is on preventive medicine using
prediction tools. He believes that the better the prediction, the more control you have.
e:
Gerald C Hsu
EclaireMD Foundation, USA
Applying energy theory to re-examine the relationship between food/exercise and
postprandial plasma glucose (PPG)