1

Breezing metabolic rate trackerCase Studies

www.breezing.com www.breezing.co

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Case #1: Gabriel P.’s case

+ 2 years

(- 40 kg)

June 2015

- 88 lbs

+5 years

+ 97 lbs(80 kg)176 lbs

(124 kg)273 lbs

(+ 44 kg)

(84 kg)185 lbs

1. Why did Gabriel gain 97 lbs (44 kg)?

2. How did Gabriel lose 88 lbs (40 kg)?

www.breezing.co

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Mifflin - St Jeor equation: Man:REE(M-StJ) = [10 * weight (kg)] + [6.25 * height (cm)] - [5 * age (y)] + 5

Why did Gabriel gain 97 lbs (44 kg) in 5 years?

• He used a calorie calculator to estimate Total Burn: 2100 kCal/day

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Estimated Total Burn: 2100 kcal/dayFirst True Total Burn: 1900 kcal/day

Difference between Estimated - True Burn: 200 kCal/day

How does this difference translate to weight?

[(200(kCal/day)*7*52weeks/year)]/[3500kCal/lbs]= + 20 lbs/year 5 yrs. ~100 lbs Total ~ 45 kg

Why did Gabriel gain 97 lbs (44 kg) in 5 years?

+5 years

+ 97 lbs80 kg176 lbs

124 kg273 lbs

+ 44 kg

Measuring Energy Expenditure was a key point in explaining why Gabriel gained weight

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How did Gabriel lose 88 lbs (40 kg)?*

+ 2 years

- 40 kg

- 88 lbs

124 kg273 lbs

84 kg185 lbs

1400 kcal/day - 1900 kcal/day- 500 kcal/day ~Gabriel expected a deficit of 3500 kcal per week equivalent to a loss of 1 lb per week (52 lbs/year).

Gabriel’s actual weight loss was 44lbs/year, a total of 88 lbs in 2 years

Energy Balance Equation

Energy Storage = Energy Intake - Total Energy Expenditure

Initial approach

*Dr. Pablo Pelegri (MD), Dr. Liliana Balsells (MD), Buenos Aires, Argentina; Breezing’s user experience team.

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1400 kcal/day - 1900 kcal/day- 500 kcal/day ~

Energy Balance Equation Components

Energy Storage = Energy Intake - Total Energy Expenditure

200 kcal/day1700 kcal/day

=

Resting Energy Expenditure represents a large percentage (75-95%) of Total Energy Expenditure

- [ + ]Resting Activity

Knowing Resting Energy Expenditure was a key point for Gabriel

(89%)

7

How many cases like Gabriel’s are out there?

124 kg273 lbs

www.breezing.co

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Characteristics of the population

Dr. Craig Stump, MD

www.breezing.co

Case #2: Clinical study in an overweight and obese population*

* Most of participants had T2 Diabetes, or were at risk of Diabetes

9

Difference of Calculated REE* – True (measured) REE

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

-1000

-500

0

500

1000

1500Calculated REE - Measured REE

Female

Male

Diffe

renti

al R

estin

g En

ergy

Exp

endi

ture

(kCa

l/Day

)

Study Participant Number

Group A

Group B

Group C

Dr. Craig Stump, MD

Group C

42%

* Predictive Equation (Harris-Benedict)

42% of the cases in the pilot study group (overweight and T2 diabetes)

had slower metabolic rates than what the equation predicted

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Why we can’t use equations to calculate REE ?

An actual REE value (from indirect calorimetry measurement) can differ from an estimated REE value (from the Harris-Benedict calculation).

The results show that for people of same gender and weight (e.g. men and 63 kg) the difference in actual REE values can be as high as 520 kCal/day.

If, for instance, subject A’s goal is to maintain weight, and the estimated REE (1640 kcal/day) is higher than the body’s actual REE (1480 kcal/day), a calorie recommendation based on the REE estimate will lead to weight gain.

Therefore, accurately measuring REE is crucial in establishing an effective weight management plan.

Plot from J. Arthur Harris and Francis G. Benedict, A Biometric Study of Human Basal Metabolism, Proc Natl Acad Sci U S A. 1918 December; 4(12): 370–373.Criscione, L. & Durr-Gross, M. Eating healthy and dying obese. Vitasanas GmbH, http://www.vitasanas.ch, ISBN: 978-3-0033-02225-6 (2010).

2490

2290

2090

1890

1690

1490

1290

109035 45 55 65 75 85 95 105

2000 kCal/day

1640 kCal/day

1480 kCal/day520 kCal/day

64 kg

REE

(kCa

l/day

)

Weight (kg)

A

Data from seminal Harris-Benedict’s work

Harris-Benedict Equation

11www.breezing.co

The risk of using calorie intake recommendations from an equation-based REE value

Cont

rol G

roup

Inte

rven

tion

Grou

p

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Case #2: Clinical study in an overweight and obese population – Six-month study design

•The participants from the control group had an iPad with My Fitness Pal App to track calorie intake, an activity tracker to track steps and floors, and a weight scale.

• Each participant in the control group was recommended a 500-calorie deficit intake based on the Harris Benedict Equation

• The intervention group had the same gadgets as the control group, as well as a Breezing Tracker.

• Both groups were followed up with a Standard-of-Care procedure for 6 months, and were reached by e-mail every 2-3 weeks with general health information.

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Case #2: Weight & Body Mass changes

Observation: Weight change is accounted from 1st day the participant use MFP (baseline period) up to 6 months after the study

123456789

1011121314151617181920

-50 -40 -30 -20 -10 0 10 20 30

Series2Series1

Weight change* (lbs)

Parti

cipa

nts

Control Group (CG)Intervention Group (IG)

Other results:Weight loss Greater Than 6 lbs:CG: 40% (8/20) vs IG: 68% (13/19)

Intervention Group: 17 of 19 participants (89%) lost weight, 1 stayed steady and 1 (5%) gained 1.9 lbs.

Control Group: 11 of 20 participants (55%) lost weight, 1 stayed steady and 8 (40%) gained 2+ lbs.

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1 2

-250

-200

-150

-100

-50

0

Case #2: Weight & Body Mass Index (BMI) changesW

eigh

t cha

nge

aver

age

(lb)

Group

Control Intervention

*Statistical significant (p= 0.03)

The Intervention group’s total weight loss was 3 times greater than the control group

The difference in BMI changes in the intervention group was significantly different with respect to the control group

The intervention group’s drop of BMI from 35.5 resulted in a change from Obese Class II Group to Obese Class I Group

Control Intervention

BMI:-1.9

BMI:-0.5

The control group’s drop of BMI from 36.9 was not large enough to move out of Obese Class II Group

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Control GroupIntervention Group

Percentage of calorie intake completed days (%)

Parti

cipa

nts

www.breezing.co

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101112131415161718

0 10 20 30 40 50 60 70 80 90 100

Case #2: Calorie Intake Completed Days*

* Completed days represent calorie intake values with equal or 25%+ of recommended calorie intake

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Perc

enta

ge a

vera

ge o

f ca

lorie

inta

ke co

mpl

eted

da

ys (%

)

GroupControl Intervention

Statistically significant (p= 0.05)

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1 20

10

20

30

40

50

60The Intervention group had 70% more completed entries of daily calorie intake than the control group

Case #2: Calorie Intake Complete Days*

* Completed days represent calorie intake values with equal or 25%+ of recommended calorie intake

Case #2: Calorie Intake Entries

SENDCMBAGPNDJMJGDSAD

LJJSJJ

GVRDGCFV

WNAvg MFP

SDAvg MFP+B

CBDTVVLPYSSGARDLSBAAOFJF

AMMHMSBBJHJSJG

Total Measures

0 25 50 75 100 125 150

Number of Entries

Parti

cipa

nts

Cont

rol G

roup

Inte

rven

tion

Grou

pMy Fitness Pal (MFP)’s Volume Entries (including diet, activity, weight, comments)Breezing Entries

63 = MFP’s entry average79 = MFP’s entry average

Intervention Group: 25% more entries than control group

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Case #2: Benefits of weight loss in blood parameters

Intervention group had a better outcome for HDL cholesterol (increased HDL cholesterol with a significant difference of p = 0.037 with respect to the control group

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1011121314151617181920

-25 -20 -15 -10 -5 0 5 10 15 20 25

Series2Series1Controls

Intervention

HDL change

Diastolic Blood Pressure Intervention group had a better outcome for reduction of diastolic blood pressure: a decrease with a significant difference of p = 0.07 with respect to the control group

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Summary of facts from the study (Case #2)

1. Breezing users had:

i) Effectively lost more weight (89% vs 55% in the control group)

ii) Completed 70% more calorie intake enties in the calorie counting app

iii) More comprehensive use of calorie counter app via entry volumes of diet, activity, weight, and comments.

iv) Better HDL cholesterol and Diastolic Blood Pressure parameter outcomes

2. How does knowing Correct Calories Burned relate to Weight Loss?

89% efficiency of weight loss (IG) vs. 55% efficiency of weight loss (CG)

5% of weight gain (IG) vs. 40% of weight gain (CG)

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HbA1c reduction

1

3

5

7

9

11

13

15

17

19

-3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1

Series2Series1

Controls Intervention

ControlsIntervention

Case#2 (cont.): General weight loss effect in T2 diabetes

The weight reduction resulted in a reduction of glycated hemoglobin in both groups (p < 0.1)

Since both groups had a relatively high rate of weight loss (89%-IG and 55%-CG), there was not significant difference between groups in regard to improvements of glycated hemoglobin (both groups did improved the T2 diabetes parameter)

CONCLUSION: weight loss has an intervention effect on lessening T2 diabetes symptoms and decreasing the risk of developing diabetes

Between groups: no difference

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What about pregnancy?

To learn more watch: https://www.youtube.com/watch?v=tHS-pegE_gQ

www.breezing.co

0 20 25 30 35 40

900

1200

1500

1800

2100

2400

After birth1540

1890 (+/-150) 1680 (+/- 50)

RE

E (R

MR

) (kc

al/d

ay)

Pregnancy week

1830 (+/- 30)

Baseline REE = 1,200 kCal/day

Cold/Flu

April 8th, 2015

Study case #3: Resting Energy Expenditure during pregnancy*

Jan 8th, 2015

day

How does the profile connect

to other body parameters?*Dr. Corrie Whisner, American Society of Nutrition's Public Information Committee

* D. Jackemeyer, BSW, Application Scientist, Arizona State University22

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Comparison of REE with Weight

Mifflin - St Jeor equation:

Woman:REE(M-StJ) = [10 * weight (kg)] + [6.25 * height (cm)] - [5 * age (y)] - 161

✗

-50 20 25 30 35 400

30

60

90

Pregnancy week

53 %(+/- 2)

RE

E C

hang

e (%

)

57 (+/-13) 40 (+/- 4)

Cold/Flu

After birth41%

0 20 25 30 35 4042444648505254565860

Wei

ght (

kg)

Pregnancy weeks

After birth

53

Baseline ~ 44 kg

Cold/Flu

REE does not follow the simple math of “higher mass -> higher metabolic rate” from the equation

Risk of underfeeding

Risk of over-feeding

Comparison of REE with Body Composition

Mifflin - St Jeor equation:

Woman:REE(M-StJ) = [A * FFM (kg)] + [B * FM (kg)] + C

✗0 20 25 30 35 40

10

20

30

40

50

60

11 kg

18 kg

53 kg

42 kg

39 kg

57 kg Weight (kg) Fat Mass (kg) Lean Body Mass (kg)

Bod

y (T

otal

/Fat

/Lea

n)M

ass

(kg)

Pregnancy weeks

53 kg44 kg

36.5 kg

7.5 kg

FFM

FM

-50 20 25 30 35 400

30

60

90

Pregnancy week

53 %(+/- 2)

RE

E C

hang

e (%

)57 (+/-13) 40

(+/- 4)

Cold/Flu

After birth41%

REE does not follow the simple math of: “the higher the Free Fat Mass (FFM) or the more Fat Mass (FM), the higher metabolic rate” from an equation.

24www.breezing.co

Dr. St Jeor, creator of MifflinSt Jeor’s REE

predictive equations

Picture of Dr. Sachiko St. Jeor at FNCE 2015, October 5th, using Breezing Tracker

https://www.facebook.com/breezing.co

Dr. St Jeor is now a Breezing’s advocate

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“ The use of predicative equations for estimating REE are only ESTIMATIONS”

“We are much more complex as individuals and the complexity is addressed only with a breath-

based REE measurement”

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What about weight management in sports?

= - [ + ]Resting Activity

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Emily's goal:

• Needed to to reach 160 lbs by competition day •Bottom Line: Needed to lose 10 lbs in 2 months

Case #4 – Weight management in sports*

* Rich Wenner, athletes’ coach & Amber Yudell, nutritionist, Arizona State University

www.breezing.co

29The results include all four module data from Breezing AppResting Energy Expenditure (REE) (indirect calorimetry) Activity (manually entered), and assessed with HR monitor (PulseONE)Diet (manually entered), and assessed with MyFitnessPal Weight (manually entered)

Case #4 – Weight management in sports

Weight (Lbs)

Resting Energy Expenditure (kcal/day)

9/5/15 6:56

4/5/15 5:59

12/4/15 8:23

11/4/15 7:12

7/4/15 6:20

27-03-2015 07:08

14-03-2015 07:01

10/3/15 6:18

9/3/15 6:37

8/3/15 8:38

7/3/15 7:24

11/2/15 4:54

10/2/15 4:58

9/2/15 6:48

8/2/15 7:41

7/2/15 6:20

0

500

1000

1500

2000

2500Activity (kcal/day)

9/5/15 0:00

7/5/15 0:00

4/5/15 0:00

15-04-2015 00:00

11/4/15 0:00

17-03-2015 00:00

14-03-2015 00:00

12/3/15 0:00

10/3/15 0:00

8/3/15 0:00

13-02-2015 00:00

11/2/15 0:00

9/2/15 0:00

7/2/15 0:00

0

200

400

600

800

1000

1200

1400

1600

Calorie Intake (kcal/day)

8/5/15 0:00

6/5/15 0:00

4/5/15 0:00

15-04-2015 00:00

12/4/15 0:00

17-03-2015 00:00

14-03-2015 00:00

10/3/15 0:00

7/3/15 0:00

11/2/15 0:00

9/2/15 0:00

7/2/15 0:00

0

500

1000

1500

2000

2500

7/5/15 8:12

5/5/15 5:47

15-04-2015 07:18

12/4/15 12:04

18-03-2015 05:41

16-03-2015 07:32

12/3/15 7:16

9/3/15 7:31

4/3/15 19:36

12/2/15 21:05

10/2/15 9:03

7/2/15 7:34

154156158160162164166168170172

Average: 1680 (sd: 130)

Average: 500 (sd: 290)

Average: 1720 (sd: 110)

TEE=REE+Act=2180 kcal/dayIntake= 1720 kcal/dayDeficit= -460 kcal/day

Competition day

-10lbs/9 weeks

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http://instagify.com/media/980460235926117550_1581604454

Emily J achieved her weight goal of 160 lbs in 2 months, and her life’s weightlifting record (70 kg, 5Kg over previous personal record)!

She can rescue someone with her own weight now!

Case #4 – Weight management in sports

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What about hypothyroidism?

= - [ + ]Resting Activity

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Old REE measure initially brought by the Breezing user (2600 kcal/day)

Case #5 – Weight management in Hypothyroidism

-= [ + ]

✓ The user thought that he should be losing weight!

Case with Cytomel (Thyroid T3) - 25mcg/day

2050 kcal/day~ - 600 kcal/daysmall kcal/day

www.breezing.co

The new Breezing user got REE measurements for from Feb. 2nd to March 26th 2015 – Total: 52 days

REE Mean

- 1SD

+1SD

Case #5 – Weight management in Hypothyroidism*

0 10 20 30 40

1000

1200

1400

1600

1800

2000

2200

2400

RE

E (k

cal/d

ay)

Days of testing (#)

REE Mean: 1730 kcal/day (SD: 200)Relative Variability (68prob., =+/-1SD): +/- 11.5%

-= [ + ]

✓High variability was observed due to the use of fast release of T3 hormone✓Higher metabolic rate was detected right after T3 hormone intake✓ Despite the REE variability, an average REE value could still be defined

33* Breezing’s user experience team. Advise from Dr. John Henried, MD, Sacramento, CA

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-= [ + ]

2050 kcal/day - 1830 kcal/day (+/- 200 kCal/day) 0 kcal/day ~

Expected weight maintenance

100 kcal/day1730 kcal/day

Applying the Breezing’s REE averaged measure to Energy Balance:

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Actual weight profile

23-03-2015 15:52 19-03-2015 09:51 17-03-2015 13:10 13-03-2015 06:56220

225

230

235

240

245

250

255

260

241.3 241.3 241.9 241.9

Day/Time

Wei

ght (

lbs)

✓ Weight profile showed less than 2% change, which corroborated the Energy Balance analysis from Breezing

✓ The REE average values adjusted the energy balance equation, despite the potential hormonal variability.

Action: the user was switched to a slow release thyroid hormone to control the T3 levels in blood to avoid spikes due to fast release

35www.breezing.co

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• The breath measurement of Resting Energy Expenditure (REE) is important to manage weight in a variety of different health-related situations, including obesity, type 2 diabetes, hormonal problems, pregnancy as well as in fitness training.

• The importance on breath analysis for REE is similar to a blood pressure measurement for management of blood pressure.

• Calorie intake based on Resting Energy Expenditure measurement can be accurately prescribed to manage weight successfully.

• Attempts to use an equation, instead of a measurement for Resting Energy Expenditure, produce mere estimations (guess).

Conclusions from Case#1 – Case#5

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Blood pressure management Weight and wellness management

37

How we can increase metabolism and reverse sedentary lifestyles without drastically altering our schedules?

What about High Intensity Intermittent Training (HIIT)?

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Case #6: Study with High Intensity Intermittent Training (HIIT)*

Total time = 4min

20s 20s 20s 20s 20s 20s 20s 20s

10s 10s 10s 10s 10s 10s 10s

Troy Anderson, Trainer

* In collaboration with Barb Ainsworth (Former ACSM President), Troy Anderson (CPT), and D. Jackemeyer (ASU)

39

Blood measurements• Blood glucose• Blood ketones

Metabolic measurements• REE• IEE pre exercise• IEE post exercise• IEE 1hr post exercise• IEE 2hr post exercise

Body composition• % Muscle mass• % Fat mass• %LBM• Weight• BMI

Intervention

19 subjects

Control

11 subjects

Intervention

24 subjects

Control

10 subjects

Total enrolled

34 subjects

Random allocation 4 subjects withdrew

1 moved to control

HIIT*

No Training

*3 HIIT sessions per week for 6 weeks

Case #6: Study Design*

HIIT

CONTROL

* In collaboration with Barb Ainsworth (Former ACSM President), Troy Anderson (CPT), and D. Jackemeyer (ASU)

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Case #6: Quantification of the amount of exercise

Example: lifting work of 20 lbs and 1.06 m with thruster movement

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 0

10

20

30

40

50

60

70

80

90

100

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Work and Power (Avgs) -- repeat ascending liftsErica - ef11 (per Kg Body Weight)

J x 10 -̂1 reps WattsW

ork

(J) &

reps Pow

er (W)

Session number

* In collaboration with Barb Ainsworth (Former ACSM President), Troy Anderson (CPT), and D. Jackemeyer (ASU)

41

* Squatting work of 36-55 lbs and 0.53 m with up & down

-1500

-1000

-500

0

500

1000

1500

2000

2500

3000

3500Devon

REE

(KCa

l/day

)

REE:BL REE:S1 REE:BL REE:S1

*

RE

E/ I

EE

(kC

al/d

ay)

IEE

(kCa

l/day

)

HIIT day

HIIT dayNo HIIT day

No HIIT day

Case #6: Quantifying Momentary Energy Expenditure before and after exercising*

Can we detect a difference in metabolism between a High Intensity Interval Training (HIIT) day vs a No-HIIT day ?

* In collaboration with Barb Ainsworth (Former ACSM President), Troy Anderson (CPT), and D. Jackemeyer (ASU)

42

Case #6: Effect of HIIT on individual’s energy expenditure throughout training sessions

-350-280-210-140-70

070

140210280350

Aver

aged

iE

E (im

m p

ost)

** * HIIT Control No HIIT

A B C

*D

Aver

aged

IE

E imm

pos

t

Averaged change of pre- and post- energy expenditure (iEE = EEpost - EEpre) was significantly different:

HIIT day vs. NO HIIT day (HIIT group) HIIT day (HIIT group) vs. CONTROL (Control group)

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Is higher immediate post-exercise IEE change related to muscle mass increase ?

?

IEEimm post Muscle change (%)

44

The difference between groups is significant at 80% level of confidence

Case #6: Muscle Mass (%) Change & immediate post-exercise Energy Expenditure Change (IEEimm post)

Group A: IEEimm post (HIIT with ≥6% muscle increase) = 241 kCal/day (SEM = 77)

Group B: IEEimm post (HIIT with (-1;4) % muscle increase) = 70 kCal/day (SEM = 58)

Difference A – B: 171 kCal/day

≥6% muscle increase

=

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Case #7: Personal tracking of resting and moment metabolism

0 20 40 240 2700

500

1000

1500

2000

2500R

EE

/ IE

E (k

Cal

/day

)

Day

HIIT HIIT

HIITHIITHIIT

HIIT

HIIT

Long-term RMR(REE) / IEE (MEE) tracking

Breezing personal parameter tracking of resting and High Intensity Interval Training (HIIT) interventions: REE and IEE (MEE) values over nine months,

including seven HIIT session.

Case #7: Breezing Personal Tracking for over nine months

Fat Oxidation Dominant

HIIT

HIIT

0 200 4000.65

0.70

0.75

0.80

0.85

0.90

0.95

RQ

Time (min)

Fat Oxidation Subordinant

One -day transient RQ during a fasting day

Case #7: Breezing Personal Tracking

Breezing personal parameter tracking of resting and High Intensity Interval Training (HIIT) interventions: RQ values for 2 HIIT sessions over 6 hours in a fasted individual.

48

HIIT

HIIT

Baseline: ~1,550 kCal/day

0 200 4000

400

800

1200

1600

2000

2400

HH:MM

Time (min)

HH:MM

19:00

RE

E/IE

E (k

Cal

/day

)

13:00

HIIT HIIT

One -day REE / IEE tracking

One -day cumulative EPOC

0 200 4000

5

10

15

20

25

Cum

ulat

ive

EP

OC

(kC

al)

Time (min)

Breezing personal parameter tracking of resting and High Intensity Interval Training (HIIT) interventions: REE and IEE, and corresponding cumulative EPOC parameters for 2 HIIT sessions over 6 hours in a fasted individual.

Breezing Personal Tracking

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Summary of Case #6 & Case#7

Personalized tracking of metabolism (REE, RQ) in connection with physical activity energy expenditure is possible

Metabolism change from our lifestyles changes can be quantified

Metabolism(kCal/day)

Resting (RMR)

Time

Moment (MEE)

FatCarbs

Energy Source (RQ)

49

0 30 60 300 330 3600

500

1000

1500

2000

2500

3000

3500

Day of Intervetion (#)R

EE

(kC

al/d

ay)

0 30 60 300 330 3600.6

0.7

0.8

0.9

1.0

Res

pira

tory

Quo

tient

0.6

0.7

0.8

0.9

1.0

0 30 60 300 330 36080

82

84

86

88

90

Wei

ght (

kg)

Day of intervention (#)

March 2014 to June 2015(ketogenic diet- higher fat)

Jan. 2015 to April 2015(ketogenic diet- lesser

fat.

Diet A: Ketogenic diet- higher fat:

Intake: 1800 cal/day, Fat: 1250 cal (140g), Protein: 360 cal (90g), Carb: 180 cal (45g).

Diet B: Ketogenic diet- lesser fat:

Intake: 1200 – 1400 cal/day Fat: 75 g,Protein: 80g,Carb: 5 days 50 g, 2 days 100g.

Diet A increased metabolic rate above 2,000 kcal/day level, and Respiratory Quotient (RQ) reflected diet composition.

Diet B did not change metabolic rate, it increased RQ 1, indicating only carbohydrate oxidation source.

Refs. for RQ values:0.60 to 0.80: mostly fat oxidation0.80 to 0.90: mixed source, fat and carb oxidation0.90 to 1.00: mostly carbohydrate oxidation or anaerobic metabolism increased.

Case #8: Long-Term Resting Energy

Expenditure monitoring on Ketogenic Diets

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By knowing true REE and adding this information to the user profile, we can make Activity Tracking (calories burned from different activities) more

accurate.

51

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In Conclusion