1. Fighting Weight Problems and Insulin Resistance
with the Metabolic Health Monitor App for
Patients in the Setting of Limited Access to
Health Care in Rural America*
Zsolt Ori, MD, MS, FACP
Ori Diagnostic Instruments, LLC
Durham, NC, USA
zsolt.ori56@gmail.com
Ilona Ori, JD
Ori Diagnostic Instruments, LLC
Durham, NC, USA
ilona@uchicago.edu
*Research supported by Ori Diagnostic Instruments, L.L.C; U.S. patent pending:
application numbers 14541033 and 62372363

2. Rural America has:
• Higher rate of obesity than average
• Higher rate of poverty
• Higher rate of sedentariness
• Less access to health care provider
• Fewer doctors
• Higher rate of prediabetes prevalence
• Less academic research
“Non intellecti nulla est curatio morbi...”
“Without proper understanding, there will be no cure for disease...”
--Cornelius Gallus (i.e. 70-26) Roman poet, orator and politician

3. From Cybernetics to Self-directed Cyber-therapy in the Era of Mobile
Communication and Computing
- The Age of “Apps”-
Cybernetics
Norbert Wiener (1894 – 1964)
• Communication & computing
John von Neuman (1903 – 1957)
• Feedback & statistical
estimation
Kálmán Rudolf Emil (1930 – 2016)
• Control
Norbert Wiener (1894 – 1964) & Andrey Kolmogorov (1903–1987)
Cyber-therapy - the Metabolic Health
Monitor App
• Mobile communication and computer
devices
• Feedback- Objective metrics with use
of personalized mathematical
modeling
• Self-Control - Adaptive predictive
control and individualized dynamic
behavior change models
• Coaching by remote healthy lifestyle
team and telemedicine
• Social media networking

4. Ori, 2015: Self-adaptive model of the human energy metabolism (SAM-HEM)
𝜚 𝐶 · ∆𝐺 𝑘+1 = 𝐶𝐼 𝑘 + 𝜈 𝑘
∗
· 𝐺𝑁𝐺𝑃𝑘 + 𝐺𝑁𝐺𝐹𝑘 − 𝜇 𝑘
∗
· 𝐷𝑁𝐿 𝑘 − 𝐺3𝑃𝑘 − 𝐶𝑎𝑟𝑏𝑂𝑥 𝑘 − 𝐸𝐹𝑠 𝑘 − 𝜑 𝑘
∗
1𝑎
𝜚 𝐹 · ∆𝐹𝑘+1 = 𝑟𝐹𝐹𝐴 · 𝐹𝐼 𝑘 + 𝜇 𝑘
∗
· 𝐷𝑁𝐿 𝑘 + 𝑟𝐺 · ∆𝐹𝑘+1 −𝐹𝑎𝑡𝑂𝑥 𝑘 (1𝑏)
𝜚 𝑃 · ∆𝑃𝑘+1 = 𝑃𝐼 𝑘 − 𝜈 𝑘
∗
· 𝐺𝑁𝐺𝑃𝑘 − 𝑃𝑟𝑜𝑡𝑂𝑥 𝑘 (1𝑐)
Ori, 2016: Self-adjusting input output model (SIO-HEM) of the human energy metabolism
ϱ 𝐿 · ∆𝐿 𝑘+1 = 𝐶𝐼 𝑘 + 𝑟𝐺𝐹 · 𝐹𝐼 𝑘 + 𝑃𝐼 𝑘 − 𝜎 𝑘 + 𝜔 𝑘 − 𝐶𝑎𝑟𝑏𝑂𝑥 𝑘 − 𝑃𝑟𝑜𝑡𝑂𝑥 𝑘 1a
ϱ 𝐹 · ∆𝐹𝑘+1 = 𝑟𝐹𝐹𝐴 · 𝐹𝐼 𝑘 + 𝜎 𝑘 − 𝐹𝑎𝑡𝑂𝑥 𝑘 (1b)
ϱ 𝑃 · ∆𝑃𝑘+1 = 𝑃𝐼 𝑘 − 𝐺𝑁𝐺𝑃𝑘 − 𝑃𝑟𝑜𝑡𝑂𝑥 𝑘 (1c)
Ori, 2016: Self-adjusting R-ratio input output model of the human energy metabolism (RIO-HEM) in canonical form
𝐿 𝑘+1 = 𝐿 𝑘 +
1
ϱ 𝐿
∗
𝑘
∙ 𝐶𝐼 𝑘 + 𝑟𝐺𝐹 · 𝐹𝐼 𝑘 + 𝑃𝐼 𝑘 − 𝜎 𝑘 + 𝜔 𝑘 −
ϱ 𝐿
∗
𝑘
∙ 𝑅 𝑘
∗
ϱ 𝐿
∗
𝑘
∙ 𝑅 𝑘
∗
+ ϱ 𝐹
∗
𝑘
∙ 𝑇𝐸𝐸 𝑘 (1a)
𝐹𝑘+1 = 𝐹𝑘 +
1
ϱ 𝐹
∗
𝑘
∙ 𝑟𝐹𝐹𝐴 · 𝐹𝐼 𝑘 + 𝜎 𝑘 −
ϱ 𝐹
∗
𝑘
ϱ 𝐿
∗
𝑘
∙𝑅 𝑘
∗
+ϱ 𝐹
∗
𝑘
∙ 𝑇𝐸𝐸 𝑘 (1b)
𝑃𝑘+1 = 𝑃𝑘 +
1
ϱ 𝑃
∙ 𝑃𝐼 𝑘 − 6.25 · ϱ 𝑃∙ 𝑁𝑒𝑥𝑐𝑟𝑘 (1𝑐)

5. Three proposed metabolic metrics and trajectories
that uniquely identify the individual’s metabolic
trend
The daily energy density of the fat mass change ϱ 𝐹
∗
𝑘
:
ϱ 𝐹
∗
𝑘
=
11.72
𝐾𝑐𝑎𝑙
𝑔𝑟𝑎𝑚
𝑖𝑓 ∆𝐹𝑘 > 0
9.4
𝐾𝑐𝑎𝑙
𝑔𝑟𝑎𝑚
𝑖𝑓 ∆𝐹𝑘 ≤ 0
The daily energy density of the lean mass change ϱ 𝐿
∗
𝑘
:
ϱ 𝐿
∗
𝑘
=
ϱ 𝐹
∗
𝑘
𝐸 𝑘 − 𝐷 𝑘
∆𝐿 𝑘+1 = 𝐷 𝑘 · ∆𝐹𝑘+1 + 𝐸 𝑘 ∙ 𝐶𝐼 𝑘 + 𝐹𝐼 𝑘 + 𝑃𝐼 𝑘 − 𝑇𝐸𝐸 𝑘
The daily ratio of lean body mass change velocity and fat
mass change velocity or R ratio 𝑅 𝑘
∗
:
𝑅 𝑘
∗
=
∆𝐿 𝑘+1
∆𝐹𝑘+1
Change
in lean
mass
Change
in fat
mass
Change in
energy
balance
ϱ 𝐿
∗
𝑘
ϱ 𝐿
∗
𝑘
𝑅 𝑘
∗

6. The R-Ratio, a Novel Surrogate Market of the Insulin
Resistance
 Insulin resistance, and the compensatory hyperinsulinemia that
follows, appear to be caused primarily by excess body fat, particularly
around the abdomen and organs, which leads to inflammation and
elevated blood glucose levels.
 Energy balance experiments and clinical experience demonstrate also
that the ability to gain or lose weight depends not only on the energy
balance but also on the state of insulin sensitivity/ resistance and
along with it on the body’s fat mass itself.
ϱ 𝐿
∗
𝑘
· 𝑅 𝑘
∗
+ ϱ 𝐹
∗
𝑘
· ∆𝐹𝑘+1 = 𝐶𝐼 𝑘 + 𝐹𝐼 𝑘 + 𝑃𝐼 𝑘 − 𝑇𝐸𝐸 𝑘

7. My Fitness Pal Metabolic Health Monitor app
Tracks macronutrient calorie intake
(carbohydrate, fat, protein)
6 million food items
Daily entries needed (5-20 min a day)
Calculates the arhythmical sum of ingested calories,
does not deal with missing data
Tracks macronutrient calorie intake
(carbohydrate, fat, protein)
www.ars.usda.gov
Biweekly one-day calorie counting only
Estimates past utilized macronutrient intakes
Predicts future utilized macronutrient intakes
Tracks Physical Activity
Continuous measurement is needed
Tracks Physical Activity
Continuous measurement is needed
Tracks Weight
Frequent measurements encouraged
Tracks Weight
Biweekly body composition measurement
Estimates past and predicts future changes of
Fat mass
Lean mass
Protein mass
Calculates the arhythmical sum of ingested calories
minus calorie expenditure
Estimation and Prediction of the daily
Total Energy Calorie Balance
Fat balance
Carbohydrate balance
Protein balance

8. Additional functioning in MHM
but non-existing in My Fitness Pal
Macronutrient Oxidation
• Estimates past and predicts future changes of
• Fat oxidation
• Carbohydrate oxidation
• Protein oxidation
Utilized macronutrient energy intake
• Estimates past and predicts future changes of
• Utilized fat energy
• Utilized carbohydrate energy
• Utilized protein energy
Relative measurement of insulin resistance
• R = Lean mass change/ Fat mass change

9. Advantages of the current modeling of the
metabolism
• individualized model through self-adjusting mathematical features
• calibration possible
• inverse calculation from body composition change to model input
• intra- and interindividual comparison
• metric for value based purchasing
• metric for medical research of insulin resistance
• metric for clinical use and services
• metric for behavior model development
• allows for sharing metrics
• provides confidence intervals for the metabolic calculations
• choosing the best working diet and exercise regime for the user

10. Proof of Feasibility Through Simulation Studies
• Minnesota starvation and overfeeding experiment [26]:
–SAM-HEM: the average daily prediction errors of
• fat mass change estimates: 0.44±1.16 g/day
• fat free mass: -2.6±64.98 g/day
–SIO-HEM/RIO/HEM: the average daily prediction errors of
• fat mass change estimates: 3.72±6.63 g/day
• fat free mass: -86.54±383.3 g/day

11. Proof of Feasibility through Simulation Studies
• Effects of brief perturbations in energy balance on indices of
glucose homeostasis in healthy lean men [23]:
– The calculated correlation coefficient between HOMA-IR and R ratio
change estimation ∆ 𝑅7 𝑑𝑎𝑦 across all experimental phases was
-0.78898 with a P value of 5.41105∙10−10.
• Dietary Weight Loss and Exercise Effects on Insulin Resistance in
Postmenopausal Women [29]:
– The calculated correlation coefficient between HOMA-IR and R ratio
change estimation ∆ 𝑅1 𝑦𝑒𝑎𝑟 across all experimental phases was
-0.8383 with a P value of 0.0093.

12. The psychological processes of self-directed weight management and the
loops of information flow, emphasizing control by feedback

13. Motivation improvement by MHM
• MHM helps to dole out the individual's metabolic health goal
into small achievable daily sub-goals and allows for a minimal
contact approach (weekly text messages)
• Repeated positive experiences from achieving the small sub-
goals successfully with no frustration will motivate the user

14. Conclusion
• MHM offers tools for objective observation of the actual state and trends and
trajectories of an individual’s metabolic processes. The loop of feedback of
information gained by the proposed MHM app allows for non-judgmental
observation and self-organization of the complex biological system of the human
energy metabolism.
• The MHM supported framework for improving fat mass and insulin resistance
through improved diet and exercise appears to be quite applicable in the
resource limited setting that exists in rural America. Our proposal would facilitate
to gain control and self-manage weigh and insulin resistance.
• We offer the MHM app for free for volunteers in rural America for self-treatment
and for result data gathering to help forge a battle against obesity and insulin
resistance in rural America.

15. OPEN INVITATION FOR COLLABORATION ON
FURTHER DEVELOPMENTS
• for clinical use to treat obesity, pre-diabetes and insulin resistance using
coaching and the minimal contact approach
• For academia to study insulin resistance related research and nutrition
• for dynamic behavioral model development regarding lifestyle change
• for insurance companies to create “Value Based Incentive Programs”
• government policy making regarding prevention of obesity, pre-diabetes,
diabetes and education
• future product development
– For measurement of body composition, intra and extracellular water mass
– Camera tools for food recognition and analysis
– Body scan for body volume measurements
– Home portable RMR measurement
– Well calibrated PAE measurement

16. Metabolic Health Monitor App and Web site
development
• www.FinelyFit.com
• Joint product development with TC2 Labs