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Identifying lifestyle behaviours (invited talk at Association for the Study of Obesity)
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Identifying lifestyle behaviours (invited talk at Association for the Study of Obesity)

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  • 1. BRITISH HEART FOUNDATION HEALTH PROMOTION RESEARCH GROUPNovel Technologies to CaptureLifestyle BehavioursPaul Kelly, Aiden Doherty, Charlie FosterBritish Heart Foundation Health Promotion Research GroupDepartment of Public HealthUniversity of OxfordApril 08, 2011
  • 2. Physical activity, nutrition and obesity
  • 3. Behavioural epidemiology frameworkEstablish linksbetween physicalactivity & health Translate Identify Test into correlates interventions practiceMeasurephysicalactivity Sallis and Owen (1999)
  • 4. Current tools and technologiesPedometer GPS tracker Accelerometer Self-report diary
  • 5. Percentage of adults from same study meeting physical activity recommendations: NHANES (self report): 50% Accelerometer: 5% (Troiano et al, 2009) Self-report questionnaire: 38% Accelerometer: 5% (HSE, 2009)
  • 6. The gold standardis direct observation
  • 7. SenseCam:Lightweight digital cameraTakes 1 image every 10-15secondsCan store 10,000s of imageswith battery life of 24 hours
  • 8. 3 Pilot studies 1. Quantifying the error on self-report2. Combining images with GPS and accelerometer data 3. Investigating the school food journey
  • 9. 1. Quantifying error on self-report
  • 10. Journey time = 20 minutes
  • 11. How did they compare? Journey time = 12 min 48 sec
  • 12. Car +2 min 08 sec(S.E. 60 sec) Walk +1 min 41 sec (S.E. 45 sec) All journeys Bike +4 min 33 sec +2 min 34 sec (S.E. 64 sec) (S.E. 32 sec) Kelly et. al. 2011. Can we use digital life-log images to investigate active and sedentary travel behaviour? Results from a pilot study. IJBNPA (in press)
  • 13. So what…? 154 sec per journey = 6 min 42 sec per day* = 54 min per week = 36% of recommended amount***3 ‘Active transportation’ journeys per participant per day**Physical activity recommendations; 30 min per day, 5 days per week…or 150 minutes per week (Chief Medical Officer, Department of Health)
  • 14. 2. Combination with GPS and accelerometer
  • 15. (QStarz BT Q1000X) 16:24:0 3 16:01:4 818:33:53 16:25:2 8
  • 16. Page 41
  • 17. Better understanding non-wear vs. sedentary time
  • 18. Better understanding non-wear vs. sedentary time Non wear time
  • 19. Better understanding non-wear vs. sedentary time Non wear time
  • 20. Better understanding non-wear vs. sedentary time
  • 21. Cycling MVPA Classification – traffic lights
  • 22. 3. Investigating the school food journey N = 10 participants 2 schools Data collection ongoing Funded by School Food Trust
  • 23. All these images present a challenge…
  • 24. Daily Browser Overview SenseCam Images of a day (about 3,000) Event Segmentation EVENT SEGMENTATION Using MOTION sensors – very quick & accurate 49
  • 25. BRITISH HEART FOUNDATION HEALTH PROMOTION RESEARCH GROUP
  • 26. Identifying Activities Sitting/Standing = 75% accurateUsing a range of classifiers: Logistic Regression,Naïve Bayes, J48, SVM, Etc.
  • 27. Identifying ActivitiesWalking = 77% Accurate
  • 28. Identifying ActivitiesDriving = 88% Accurate
  • 29. Image Processing Colour Descriptors Edge Descriptorshttp://cns.bu.edu/~gsc/ColorHistograms.html
  • 30. Concept detection process Colour SVM Layout Feature Classifier SVM Fusion Fusion Scalable SVM Colour Lifelog images Labeled examples Visual features Concept probability 55
  • 31. Activity Recognition using Images•27 “activities”•Validated on 95kannotated images Doherty et. al. 2011. Passively Recognising Human 56 Activities through Lifelogging. CHB (in press)
  • 32. Comparison of Lifestyle Within People steeringWheel 3.5 eating insideVehicle vehiclesExternal readingstandard deviations away from sample mean 2.5 holdingPhone steeringWheel vehiclesExternal holdingPhone reading 1.5 eating insideVehicle 0.5 -0.5 user 1 user 2 user 3 user 4 user 5 -1.5 57
  • 33. But let’s use more people (34x)... Participant Group and Median # of Days Median # of Median # Median (#) of SenceCam data Events per Day SenseCam Images SenseCam wear per Day per Day Office Workers (6) 7 19.5 1,599 6h 55m Researchers (15) 8 20 1,640 7h 15m Retired (5) 3 23 1,886 7h 45m Regular lifeloggers 42 18.5 1,517 10h 21m (8) Overall Averages 15.1 20.9 1,712 8h 45m
  • 34. Differences between groups...
  • 35. When do people eat? 30% Eating Patterns During Average DayP 25% lifelogger office researcher retiredrob 20%a Eb a 15%i tl ii n 10%t gy 5%of 0% 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Hour in Day
  • 36. When do people look at screens? "Screen" Patterns During Average Day lifelogger office researcher retired 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Hour in Day
  • 37. 8th April 2011 – ASO New Approaches to Diet and Lifestyle Monitoring at Individual & Population Level Novel Technologies to Capture Lifestyle Behaviours Paul Kelly, Aiden Doherty, Charlie Foster