Upload
breezing-metabolism-tracker
View
495
Download
1
Embed Size (px)
Citation preview
1
Breezing metabolic rate trackerCase Studies
www.breezing.com www.breezing.co
2
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
3
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
www.breezing.co
4
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
www.breezing.co
5
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.
6
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
8
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
10
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
12
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.
13
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.
www.breezing.co
14
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
15
Control GroupIntervention Group
Percentage of calorie intake completed days (%)
Parti
cipa
nts
www.breezing.co
123456789
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
16
Perc
enta
ge a
vera
ge o
f ca
lorie
inta
ke co
mpl
eted
da
ys (%
)
GroupControl Intervention
Statistically significant (p= 0.05)
www.breezing.co
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
www.breezing.co
18
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
123456789
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
19
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)
www.breezing.co
20
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
21
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
23
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
26
“ 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”
www.breezing.co
27
What about weight management in sports?
= - [ + ]Resting Activity
www.breezing.co
28
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
30
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
www.breezing.co
31
What about hypothyroidism?
= - [ + ]Resting Activity
www.breezing.co
32
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
34
-= [ + ]
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:
www.breezing.co
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
36
• 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
www.breezing.co
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)?
38
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)
40
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)
www.breezing.co
43
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
=
45
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
www.breezing.co
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
50
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
www.breezing.co
In Conclusion