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)

  1. 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. 2. Physical activity, nutrition and obesity
  3. 3. Behavioural epidemiology frameworkEstablish linksbetween physicalactivity & health Translate Identify Test into correlates interventions practiceMeasurephysicalactivity Sallis and Owen (1999)
  4. 4. Current tools and technologiesPedometer GPS tracker Accelerometer Self-report diary
  5. 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. 6. The gold standardis direct observation
  7. 7. SenseCam:Lightweight digital cameraTakes 1 image every 10-15secondsCan store 10,000s of imageswith battery life of 24 hours
  8. 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. 9. 1. Quantifying error on self-report
  10. 10. Journey time = 20 minutes
  11. 11. How did they compare? Journey time = 12 min 48 sec
  12. 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. 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. 14. 2. Combination with GPS and accelerometer
  15. 15. (QStarz BT Q1000X) 16:24:0 3 16:01:4 818:33:53 16:25:2 8
  16. 16. Page 41
  17. 17. Better understanding non-wear vs. sedentary time
  18. 18. Better understanding non-wear vs. sedentary time Non wear time
  19. 19. Better understanding non-wear vs. sedentary time Non wear time
  20. 20. Better understanding non-wear vs. sedentary time
  21. 21. Cycling MVPA Classification – traffic lights
  22. 22. 3. Investigating the school food journey N = 10 participants 2 schools Data collection ongoing Funded by School Food Trust
  23. 23. All these images present a challenge…
  24. 24. Daily Browser Overview SenseCam Images of a day (about 3,000) Event Segmentation EVENT SEGMENTATION Using MOTION sensors – very quick & accurate 49
  25. 25. BRITISH HEART FOUNDATION HEALTH PROMOTION RESEARCH GROUP
  26. 26. Identifying Activities Sitting/Standing = 75% accurateUsing a range of classifiers: Logistic Regression,Naïve Bayes, J48, SVM, Etc.
  27. 27. Identifying ActivitiesWalking = 77% Accurate
  28. 28. Identifying ActivitiesDriving = 88% Accurate
  29. 29. Image Processing Colour Descriptors Edge Descriptorshttp://cns.bu.edu/~gsc/ColorHistograms.html
  30. 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. 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. 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. 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. 34. Differences between groups...
  35. 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. 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. 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

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