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Breezing Case Studies

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Real case studies on metabolism tracking.

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Breezing Case Studies

  1. 1. Breezing metabolic rate tracker Case Studies www.breezing.com 1www.breezing.co
  2. 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)? 2www.breezing.co
  3. 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 3www.breezing.co
  4. 4. Estimated Total Burn: 2100 kcal/day First 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 lbs 80 kg 176 lbs 124 kg 273 lbs + 44 kg Measuring Energy Expenditure was a key point in explaining why Gabriel gained weight 4www.breezing.co
  5. 5. How did Gabriel lose 88 lbs (40 kg)?* + 2 years - 40 kg - 88 lbs 124 kg 273 lbs 84 kg 185 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 5 *Dr. Pablo Pelegri (MD), Dr. Liliana Balsells (MD), Buenos Aires, Argentina; Breezing’s user experience team.
  6. 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%) 6
  7. 7. How many cases like Gabriel’s are out there? 124 kg 273 lbs 7www.breezing.co
  8. 8. Characteristics of the population Dr. Craig Stump, MD 8 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. 9. Difference of Calculated REE* – True (measured) REE -800 -600 -400 -200 0 200 400 600 800 1000 1200 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 Calculated REE - Measured REE Female Male DifferentialRestingEnergyExpenditure(kCal/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 9
  10. 10. Why we can’t use equations to calculate REE ? 10  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 1090 35 45 55 65 75 85 95 105 2000 kCal/day 1640 kCal/day 1480 kCal/day 520kCal/day 64 kg REE(kCal/day) Weight (kg) A Data from seminal Harris-Benedict’s work
  11. 11. 11www.breezing.co The risk of using calorie intake recommendations from an equation-based REE value
  12. 12. ControlGroup InterventionGroup 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. 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 -50 -40 -30 -20 -10 0 10 20 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Series2 Series1 Weight change* (lbs) Participants 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. 13www.breezing.co
  14. 14. -250 -200 -150 -100 -50 0 1 2 Case #2: Weight & Body Mass Index (BMI) changesWeightchangeaverage(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 14 The control group’s drop of BMI from 36.9 was not large enough to move out of Obese Class II Group
  15. 15. Control Group Intervention Group Percentage of calorie intake completed days (%) Participants 15www.breezing.co 0 20 40 60 80 100 1 3 5 7 9 11 13 15 17 19 Case #2: Calorie Intake Completed Days* * Completed days represent calorie intake values with equal or 25%+ of recommended calorie intake
  16. 16. Percentageaverageof calorieintakecompleted days(%) Group Control Intervention Statistically significant (p= 0.05) 16www.breezing.co 0 10 20 30 40 50 60 1 2 The 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
  17. 17. Case #2: Calorie Intake Entries 0 25 50 75 100 125 150 SE ND CM BA GP ND JM JG DS AD LJ JS JJ GV RD GC FV WN Avg MFP SD Avg MFP+B CB DT VV LP YS SG AR DL SB AA OF JF AM MH MS BB JH JS JG Total Measures Number of Entries Participants ControlGroupInterventionGroup My Fitness Pal (MFP)’s Volume Entries (including diet, activity, weight, comments) Breezing Entries 63 = MFP’s entry average 79 = MFP’s entry average Intervention Group: 25% more entries than control group www.breezing.co
  18. 18. Case #2: Benefits of weight loss in blood parameters 18 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 -25 -15 -5 5 15 25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Series2 Series1Controls 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. 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) 19www.breezing.co
  20. 20. HbA1c reduction 20 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1 3 5 7 9 11 13 15 17 19 Series2 Series1 Controls Intervention Controls Intervention 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. 21. What about pregnancy? To learn more watch: https://www.youtube.com/watch?v=tHS-pegE_gQ 21www.breezing.co
  22. 22. 0 20 25 30 35 40 900 1200 1500 1800 2100 2400 After birth 1540 1890 (+/-150) 1680 (+/- 50) REE(RMR)(kcal/day) 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 University 22
  23. 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 40 0 30 60 90 Pregnancy week 53 % (+/- 2) REEChange(%) 57 (+/-13) 40 (+/- 4) Cold/Flu After birth 41% 0 20 25 30 35 40 42 44 46 48 50 52 54 56 58 60 Weight(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 23
  24. 24. 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) Body(Total/Fat/Lean) Mass(kg) Pregnancy weeks 53 kg 44 kg 36.5 kg 7.5 kg FFM FM -50 20 25 30 35 40 0 30 60 90 Pregnancy week 53 % (+/- 2) REEChange(%) 57 (+/-13) 40 (+/- 4) Cold/Flu After birth 41% 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
  25. 25. 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. 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” 26www.breezing.co
  27. 27. What about weight management in sports? = - [ + ] Resting Activity 27www.breezing.co
  28. 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* 28 * Rich Wenner, athletes’ coach & Amber Yudell, nutritionist, Arizona State University www.breezing.co
  29. 29. The results include all four module data from Breezing App Resting 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) 0 500 1000 1500 2000 2500 Activity (kcal/day) 0 200 400 600 800 1000 1200 1400 1600 Calorie Intake (kcal/day) 0 500 1000 1500 2000 2500 154 156 158 160 162 164 166 168 170 172 Average: 1680 (sd: 130) Average: 500 (sd: 290) Average: 1720 (sd: 110) TEE=REE+Act=2180 kcal/day Intake= 1720 kcal/day Deficit= -460 kcal/day Competition day -10lbs/9 weeks 29
  30. 30. http://instagify.com/media/980460235926117 550_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 30www.breezing.co
  31. 31. What about hypothyroidism? = - [ + ] Resting Activity 31www.breezing.co
  32. 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/day small kcal/day 32www.breezing.co
  33. 33. 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 REE(kcal/day) 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. 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: 34www.breezing.co
  35. 35. Actual weight profile 241.3 241.3 241.9 241.9 220 225 230 235 240 245 250 255 260 23-03-2015 15:52 19-03-2015 09:51 17-03-2015 13:10 13-03-2015 06:56 Weight(lbs) Day/Time  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. 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 36www.breezing.co Blood pressure management Weight and wellness management
  37. 37. How we can increase metabolism and reverse sedentary lifestyles without drastically altering our schedules? 37 What about High Intensity Intermittent Training (HIIT)?
  38. 38. Case #6: Study with High Intensity Intermittent Training (HIIT)* 38 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. 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 39 * In collaboration with Barb Ainsworth (Former ACSM President), Troy Anderson (CPT), and D. Jackemeyer (ASU)
  40. 40. Case #6: Quantification of the amount of exercise Example: lifting work of 20 lbs and 1.06 m with thruster movement 1.0 1.5 2.0 2.5 3.0 3.5 4.0 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Power(W) Work(J)&reps Work and Power (Avgs) -- repeat ascending lifts Erica - ef11 (per Kg Body Weight) J x 10^-1 reps Watts Session number 40* In collaboration with Barb Ainsworth (Former ACSM President), Troy Anderson (CPT), and D. Jackemeyer (ASU)
  41. 41. * Squatting work of 36-55 lbs and 0.53 m with up & down Resting Pre exercise 0 hr post exercise 1 hr post exercise 2 hr post exercise -1500 -1000 -500 0 500 1000 1500 2000 2500 3000 3500 Devon REE(KCal/day) REE:BL REE:S1 REE:BL REE:S1 * REE/IEE(kCal/day)IEE(kCal/day) HIIT day HIIT day No HIIT day No HIIT day 41 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. 42. Case #6: Effect of HIIT on individual’s energy expenditure throughout training sessions -350 -280 -210 -140 -70 0 70 140 210 280 350 AveragediEE(immpost) ** * HIIT Control No HIIT A B C * D AveragedIEEimmpost 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) 42www.breezing.co
  43. 43. Is higher immediate post-exercise IEE change related to muscle mass increase ? 43 ? IEEimm post Muscle change (%)
  44. 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 = 44
  45. 45. Case #7: Personal tracking of resting and moment metabolism 45
  46. 46. 0 20 40 240 270 0 500 1000 1500 2000 2500 REE/IEE(kCal/day) Day HIIT HIIT HIIT HIITHIIT 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
  47. 47. Fat Oxidation Dominant HIIT HIIT 0 200 400 0.65 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. 48. HIIT HIIT Baseline: ~1,550 kCal/day 0 200 400 0 400 800 1200 1600 2000 2400 HH:MM Time (min) HH:MM 19:00 REE/IEE(kCal/day) 13:00 HIIT HIIT One -day REE / IEE tracking One -day cumulative EPOC 0 200 400 0 5 10 15 20 25 CumulativeEPOC(kCal) 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 48www.breezing.co
  49. 49. 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) Fat Carbs Energy Source (RQ) 49
  50. 50. 0 30 60 300 330 360 0 500 1000 1500 2000 2500 3000 3500 Day of Intervetion (#) REE(kCal/day) 0 30 60 300 330 360 0.6 0.7 0.8 0.9 1.0 RespiratoryQuotient 0.6 0.7 0.8 0.9 1.0 0 30 60 300 330 360 80 82 84 86 88 90 Weight(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 oxidation 0.80 to 0.90: mixed source, fat and carb oxidation 0.90 to 1.00: mostly carbohydrate oxidation or anaerobic metabolism increased. Case #8: Long-Term Resting Energy Expenditure monitoring on Ketogenic Diets 50
  51. 51. 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

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