Weight loss prediction and wellness journey
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While lifestyle accounts for up to 3/4 of healthcare costs, most people do not find exercise fun. Some have tried numerous diet and exercise programs with the primary goal of weight loss, only to fail and become discouraged, resulting in worse health outcomes over time. In this paper, we explore crucial elements in consumer engagement that can lead to improved health outcomes. We deploy data mining algorithms to understand the impacts of various interventions, such as social network, visual dashboard, event processing, games and challenges, on health outcomes using data obtained from a clinical trial. We explain what worked, what did not work, and why. More importantly, we describe salient attributes of social health games that are crucial in both consumer engagement and health outcomes. David Kil is the founder and CEO of HealthMantic, focusing on lifestyle-medical informatics and sensor-based health gaming. Prior to founding HealthMantic, he was Chief Scientist at SKT Americas and Chief Science Officer at Humana, responsible for the development and deployment of healthcare informatics applications. At SKTA, he founded the iWell project and built an integrated wellness platform consisting of sensors, platform, and informatics. The system underwent a successful clinical trial at PeaceHealth with promising results. At Humana, he led the design and development efforts in enterprise knowledge engine, predictive modeling, and outcomes analytics while working with Samsung on U-Health initiatives. The enterprise insight engine won the best-of-breed technology award from the ComputerWorld magazine. He co-authored a book entitled “Pattern Recognition and Prediction with Applications to Signal Characterization” by Springer-Verlag, published over 30 papers, and holds 8 US/European patents. He graduated from the University of Illinois at Urbana-Champaign with BSEE/Chemistry (Highest Honor and Bronze Tablet), the Polytechnic University of New York (MSEE), and Arizona State University (MBA).
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Weight loss prediction and wellness journey
- 1. O’Reilly Strata Rx Oct 16 –17, 2012 WEIGHT LOSS PREDICTION AND WELLNESS JOURNEY David Kil Frances Shin Brigitte Piniewski, MD Jin Hahn, MD Kristen Chan, MASc CEO, HealthMantic CSO, HealthMantic CMO, PeaceHealth Labs Stanford University Advisor, HealthMantic
- 2. Outline • Why has weight loss been so difficult? • What’s the right approach? • Lessons learned
- 3. What is the best way to improve fitness?
- 4. Is it a myth? Calories in = Calories out 4
- 5. Why do people drop out?
- 6. The right approach? Our health journey
- 7. PeaceHealth clinical trial 500-Person Randomized Controlled Trial
- 8. Web service features Customizable health summary page Zite-like personalized reading section Goals Virtual Virtual CoachCoach Social/coach nudging based on CEP Challenges, gam Coach es, friends, & role models
- 9. Physical activity Social connections influence physical activity Steps per day
- 10. Weight loss People lost statistically significant weight
- 11. Disease burden Better health outcomes for high risk participants
- 12. Taking a closer look PeaceHealth results BMI Social nudging Biomarkers Activities Tracking systems Real time feedback
- 13. BMI changes vs. steps per day Relationship is not as strong as expected BMI % Change 3.2% weight loss 8600 steps a day Steps per day (1000 steps)
- 14. Improving SN improves health Social nudging plays an important role Emerging influencers 87%role model request accepted 78%friend request accepted
- 15. Passing the smell test Relationships among activities, biometrics and biomarkers Steps per day: Time (y) vs. users Steps per day: 10/12/03 Time (y) vs users 11/03/13 11/06/21 SPD in K steps 5 BMI change 15 0 10 -5 5 Correlation between SPD & HDL = 0.25 -10 0 0 50 100 150 200 250 LDL: = -0.059 HDL: = 0.251 TC: = -0.001 TG: = -0.085 SPD, BMI = -0.24 40 50 20 10 100 0 0 0 -20 0 Correlation between 0 -40 -10 -50 -100 -60 SPD & BMI = –0.24 -20 -5 -80 -100 -200 -30 5 10 15 5 10 15 5 10 15 5 10 15 BMI -10 FPG: = -0.023 HbA1c: = -0.062 TG/HDL: = -0.143 TG/HDL vs. BMI : = 0.197 4 4 -15 50 0 2 2 0 0 0 -1 Correlation between BMI -20 -2 -4 -2 -4 5 10 15 -2 & TG/HDL = –0.20 -50 -6 -6 SPD (K steps) 5 10 15 5 10 15 5 10 15 -20 -10 0 SPD (K steps) BMI % change
- 16. Disease risk vs. health outcomes Better health outcomes for high risk participants Lowering FPG 64.0% of high-risk people lowered FPG while 47.5% of the rest moved in the right direction Weight Loss Greater weight loss for the higher-risk subgroup 6.4 lbs vs. the rest 3.7 lbs. Seniors lost 7.8% weight Cholesterol 223 to 214 mg/dL little change for the rest Triglycerides 183 to 157 mg/dL little change for the rest
- 17. Good predictors for weight loss Social nudging & key service features SPD FDR= 0.54 20 Weight loss 18 No weight loss Feature Fisher’s ratio 16 Average steps per day 0.54 14 PDF comparison of steps per day # of users 12 # of goals 0.52 between those 10 # of competitions 0.36 withlargeweight loss 8 # of wellness games 0.35 and small weight 6 loss # of students 0.31 4 2 # of UGC nudging 0.22 received from friends 0 0 0.5 1 1.5 2 2.5 Average steps per day x 10 4 # of UGC nudging Ngoals FDR= 0.52 0.20 25 written Weight loss No weight loss # of family members 0.14 20 PDF comparison of the UGC = User generated content number of goals set # of users 15 between those with large weight loss and 10 small weight loss 5 0 0 5 10 15 20 25 30 # of goals
- 18. BRN is better at predicting weight loss As we add more features, performance improves Prediction accuracy improves as we add good features
- 19. Best predictor Success begets more success Lab data Steps Wellness score PA trends Social reputation Competition & games Weight Goal difficulty SN Goals activities
- 20. Why not better results? Lessons learned from listening to participants and data • Pedometer = blunt instrument • Real-time feedback vs. near real-time feedback • Full credit, not partial credit • 30% hunger factor
- 21. The right approach Gentler, more effective, shorter, personalized workout • When is enough enough? • Sufficient statistics concept • Motion sensing and classification • Exercise to improve flexibility, balance, strength, and cardio Full credit & real-time feedback • Games during rest for fun & relaxation • Where is the “fitness science” for the masses?
- 22. Personal journey Building a new fitness program with sensor tracking
- 23. My 2 year health data I ran a total of 2 marathons & 8 half marathons Min-by-min Activity Pattern: Co pa raw 20 ½ marathon 10 mph 3:20 No 6:40 device 10:00 Seoul 13:20 16:40 Madrid 20:00 23:20 0 mph 160 Steps per day (K) 40 155 Weight (lbs) 20 150 145 0 40 # friends 20 0 100 50 Wellness meter Social reputation score 0 10/05/13 10/07/02 10/08/21 10/10/10 10/11/29 11/01/18 11/03/09 11/04/28 11/06/17
- 24. My last half marathon run Are treadmills designed to torture prisoners? Short walk Long walk Run, run, and run some more
- 25. Can we differentiate? Cycling, elliptical and walk/run for full credit Dave tread ellipt cycle walk 04 Dec 2011.csv 3000 Parameters for real-time Magnitude 2000 feedback Treadmill Elliptical Cycling Walk • Duration 1000 • VO2max 0 1 2 3 4 5 6 • Speed/distance Sample index (30 Hz) 4 SPM, strides per min, and RPM over time x 10 • Calories 300 • HIIT parameters Activity intensity 200 • RPM parameters • … 100 0 0 1 2 3 4 5 6 Sample index (0.1172 Hz) 4 x 10 Activity Classification Decision stairs 800 cycling 600 elliptical 400 walk/run 200 no activity 0 1 2 3 4 5 6 0 0 20 40 60 80 100 120 140 160 180 200 4 x 10
- 26. Adding HIIT for greater effectiveness Helps build strength and endurance fast 800 600 Outside walk 400 High-intensity interval training 200 Elliptical 0 0 50 100 150 200
- 27. Recognition of motion building blocks Personalized fitness programs A library of 300 motions consisting of The Happy Body, Tai Chi, Qi-Gong, Resistance, Tibetan Rites, Martial Arts, Dynamic/Static Stretching, Cardio, Tabata, Yoga, Pilates, etc.
- 28. Importance of resistance training Improving body composition – fallacy of BMI Raw gyroscope sensor data after signal processing 5000 0 Roll Pitch Yaw -5000 0 2000 4000 6000 8000 10000 Combined accelerometer and gyroscope outputs 1200 Chin-ups Bicep curl Pull 1000 One-arm side pull downs 800 Accelerometer Bench press Gyroscope 600 400 200 0 0 500 1000 1500 2000 2500 3000 3500
- 30. Losing the last 10 – 15lbs The right way 1. Easy, short effective Weight & body fat/muscle % • No punishing workout • Fun motion interval training including free weights 2. Positive Health outcomes • Instant performance feedback • Sensible nutrition plan – no diet, more awareness • Healthy micro-habit formation 3. Financial ROI
- 31. Happy Body trial results 18 users, age range 27 – 65, ~6 months
- 33. Gentle Enough is enough Weight loss = 24.2 lbs
- 34. Lessons learned Easy on ramp, effective, science, enjoyable • Right way to improve health • Personalized and effective workout & relaxation plans • Body fat and muscle science (no BMI obsession) • Enough workout, enough food, & self- awareness Lifestyle habit formation with real-time learning Enjoy Life & Share with Others!
- 35. Future directions Take the solution to the masses • Larger pilot with an academic medical center • Personalized lifestyle-medical informatics • Holistic mind-body health • Motion interval training • Games for relaxation, brain fitness, and social nudging • More meaningful biometric data & feedback
- 36. Thank you! Enjoy life & share withothers Contact: david.kil@healthmantic.com
Notes de l'éditeur
- Case study: Start with a powerful message and demo. 2010 start study We got these results. Background, demo, slides
- Mixed biomarker results highlighting distinct physiological events …6 months is too short to sort these out effectively…but iWell shows that it is possible to document clinically relevant co-occurrences that will eventually allow us to separate out weight loss that is health promoting from weight loss that is not.
- Statistically significant weight loss 3.2% for overall7.8% for those ≥ 65Predictive model showed the importance of social components and key service features in weight loss Social network perturbation and social nudging effective in improving activities The more friends, the more steps Role model and social nudgingCompetitions, wellness games, and goal-related tasks Emerging influencers Peer-to-peer Significant increase in physical activities 2-4K to 8.5K steps per dayGood engagement in service usage stats 20% drop out50% still using after the pilotThe higher the disease burden, the better outcomesGood biomarker results for those at high risk, but mixed results for the rest highlighting distinct physiological events (6 months too short)
- Statistically significant weight loss 3.2% for overall7.8% for those ≥ 65Predictive model showed the importance of social components and key service features in weight loss Social network perturbation and social nudging effective in improving activities The more friends, the more steps Role model and social nudgingCompetitions, wellness games, and goal-related tasks Emerging influencers Peer-to-peer Significant increase in physical activities 2-4K to 8.5K steps per dayGood engagement in service usage stats 20% drop out50% still using after the pilotThe higher the disease burden, the better outcomesGood biomarker results for those at high risk, but mixed results for the rest highlighting distinct physiological events (6 months too short)
- Statistically significant weight loss 3.2% for overall7.8% for those ≥ 65Predictive model showed the importance of social components and key service features in weight loss Social network perturbation and social nudging effective in improving activities The more friends, the more steps Role model and social nudgingCompetitions, wellness games, and goal-related tasks Emerging influencers Peer-to-peer Significant increase in physical activities 2-4K to 8.5K steps per dayGood engagement in service usage stats 20% drop out50% still using after the pilotThe higher the disease burden, the better outcomesGood biomarker results for those at high risk, but mixed results for the rest highlighting distinct physiological events (6 months too short)
- Statistically significant weight loss 3.2% for overall7.8% for those ≥ 65Predictive model showed the importance of social components and key service features in weight loss Social network perturbation and social nudging effective in improving activities The more friends, the more steps Role model and social nudgingCompetitions, wellness games, and goal-related tasks Emerging influencers Peer-to-peer Significant increase in physical activities 2-4K to 8.5K steps per dayGood engagement in service usage stats 20% drop out50% still using after the pilotThe higher the disease burden, the better outcomesGood biomarker results for those at high risk, but mixed results for the rest highlighting distinct physiological events (6 months too short)
- Statistically significant weight loss 3.2% for overall7.8% for those ≥ 65Predictive model showed the importance of social components and key service features in weight loss Social network perturbation and social nudging effective in improving activities The more friends, the more steps Role model and social nudgingCompetitions, wellness games, and goal-related tasks Emerging influencers Peer-to-peer Significant increase in physical activities 2-4K to 8.5K steps per dayGood engagement in service usage stats 20% drop out50% still using after the pilotThe higher the disease burden, the better outcomesGood biomarker results for those at high risk, but mixed results for the rest highlighting distinct physiological events (6 months too short)
- Statistically significant weight loss 3.2% for overall7.8% for those ≥ 65Predictive model showed the importance of social components and key service features in weight loss Social network perturbation and social nudging effective in improving activities The more friends, the more steps Role model and social nudgingCompetitions, wellness games, and goal-related tasks Emerging influencers Peer-to-peer Significant increase in physical activities 2-4K to 8.5K steps per dayGood engagement in service usage stats 20% drop out50% still using after the pilotThe higher the disease burden, the better outcomesGood biomarker results for those at high risk, but mixed results for the rest highlighting distinct physiological events (6 months too short)
- FPG: 64.0% of high-risk people lowered FPG while 47.5% of the rest moved in the right directionWeight Loss: Greater weight loss for the higher-risk subgroup vs. the rest 6.4 vs. 3.7 lbs Seniors lost 7.8% weightCholesterol: Reduction from 223 to 214 mg/dL vs. little change for the rest Triglycerides (more meaningful measure for insulin resistance): Reduction from 183 to 157 mg/dL vs. little change for the rest
- Statistically significant weight loss 3.2% for overall7.8% for those ≥ 65Predictive model showed the importance of social components and key service features in weight loss Social network perturbation and social nudging effective in improving activities The more friends, the more steps Role model and social nudgingCompetitions, wellness games, and goal-related tasks Emerging influencers Peer-to-peer Significant increase in physical activities 2-4K to 8.5K steps per dayGood engagement in service usage stats 20% drop out50% still using after the pilotThe higher the disease burden, the better outcomesGood biomarker results for those at high risk, but mixed results for the rest highlighting distinct physiological events (6 months too short)
- Statistically significant weight loss 3.2% for overall7.8% for those ≥ 65Predictive model showed the importance of social components and key service features in weight loss Social network perturbation and social nudging effective in improving activities The more friends, the more steps Role model and social nudgingCompetitions, wellness games, and goal-related tasks Emerging influencers Peer-to-peer Significant increase in physical activities 2-4K to 8.5K steps per dayGood engagement in service usage stats 20% drop out50% still using after the pilotThe higher the disease burden, the better outcomesGood biomarker results for those at high risk, but mixed results for the rest highlighting distinct physiological events (6 months too short)
- Statistically significant weight loss 3.2% for overall7.8% for those ≥ 65Predictive model showed the importance of social components and key service features in weight loss Social network perturbation and social nudging effective in improving activities The more friends, the more steps Role model and social nudgingCompetitions, wellness games, and goal-related tasks Emerging influencers Peer-to-peer Significant increase in physical activities 2-4K to 8.5K steps per dayGood engagement in service usage stats 20% drop out50% still using after the pilotThe higher the disease burden, the better outcomesGood biomarker results for those at high risk, but mixed results for the rest highlighting distinct physiological events (6 months too short)
- Statistically significant weight loss 3.2% for overall7.8% for those ≥ 65Predictive model showed the importance of social components and key service features in weight loss Social network perturbation and social nudging effective in improving activities The more friends, the more steps Role model and social nudgingCompetitions, wellness games, and goal-related tasks Emerging influencers Peer-to-peer Significant increase in physical activities 2-4K to 8.5K steps per dayGood engagement in service usage stats 20% drop out50% still using after the pilotThe higher the disease burden, the better outcomesGood biomarker results for those at high risk, but mixed results for the rest highlighting distinct physiological events (6 months too short)