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Iccbr 2018 keynote healthy recommendations

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Healthy Recommendations - On the Role of Recommender Systems to Promote Health and Exercise

Invited keynote at the 26th International Conference on Case-Based Reasoning, Stockholm, Sweeden

Published in: Data & Analytics
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Iccbr 2018 keynote healthy recommendations

  1. 1. Healthy Recommendations On the role of recommender systems to promote health and fitness. Barry Smyth
  2. 2. After 25 years of recommending movies, music, books, hotels, gadgets … … is there more to my recommender-life?
  3. 3. Key Message Exciting opportunities exist to take advantage of new sources of real-world data (IoT, sensors, wearables, …) Bringing novel opportunities to have a positive impact on how we all live, work and play. Especially health & well-being ⟹ improved quality of life.
  4. 4. Overview 1. From Small Sensors to Big Data The power of the IoT to transform how we live, work, and play. 2. Why Exercise is the Best Medicine From data-driven healthcare to personalised well-being. 3. A Case-Study in Marathon Running Applications of recommender systems to support recreational marathon/endurance runners.
  5. 5. Small Sensors. Big Data.
  6. 6. Small Sensors. Big Data.
  7. 7. Small Sensors. Big Data.
  8. 8. Small Sensors. Big Data.
  9. 9. how much information? 1 = 10bytes 18 exabyte
  10. 10. how much information? 1 = 10bytes 18 exabyte 20,000 x all of the printed material in the US Library of Congress. Or all of the words spoken by humans. Ever!
  11. 11. how much information? 1 = 10bytes 18 exabyte 6 ! hours But, we now create this much information every
  12. 12. 1960 Census Data Static, demographic Gender, age, income, …
  13. 13. 1960 Census Data Static, demographic Gender, age, income, … 1980 Transaction Data Near realtime events Payments, travel, access
  14. 14. 1960 Census Data Static, demographic Gender, age, income, … 1980 Transaction Data Near realtime events Payments, travel, access 2000 Events/Preferences Data Realtime event, online Searches, impressions, prefs, intent, …
  15. 15. 1960 Census Data Static, demographic Gender, age, income, … 1980 Transaction Data Near realtime events Payments, travel, access 2000 Events/Preferences Data Realtime event, online Searches, impressions, prefs, intent, … 2010 Behavioural Data Real-time/world, micro-events Location, activity, social habits, health, …
  16. 16. TWO-SIDES OF THE SENSOR WEB Connecting atoms & bits.
  17. 17. There’s an app for that …
  18. 18. Exercise & Fitness Runkeeper, Strava, MapMyRun Running, Walking, Biking, ... Age, gender, weight, injuries, … Location, pace, duration, 
 climb, calories, heart rate, 
 cadenc, power,…
  19. 19. Sleep Tracking Motion-based ‘sleep tracking’. Duration vs Movement Sleep Quality (≈ time/move) Sleep Notes / Wakeup Moods Comparative Analytics
  20. 20. Mood & Focus The Melon Headband Uses EEG to track brain 
 activity to assess ‘focus’. Tagging, location, and 
 activity information helps
 users to better assess 
 what impacts their focus.
  21. 21. Food & Diet Meal logging and nutritional
 analysis. Manual vs Semi-Automatic. Calorie goals and diet plans. Integrated weight tracking.
  22. 22. Heart Rate Sensing Using smartphone camera with your finger. No external sensor required. Detecting colour changes due to capillary blood-flow. Tagging, comparative analytics etc
  23. 23. Blood Glucose External blood glucose sensor
 automatically syncs readings with app. Readings tagged with mealtime, exercise etc. Analysis and visualization of trends, logs, stats.
  24. 24. Mobile Spirometry Using a mobile phone 
 microphone to evaluate lung 
 function. FVC, FEV, PEF measures. Audio Features Machine Learning. Mean 5.1% error wrt clinical spirometry suitable for home- based monitoring.
  25. 25. Mobile Spirometry Using a mobile phone 
 microphone to evaluate lung 
 function. FVC, FEV, PEF measures. Audio Features Machine Learning. Mean 5.1% error wrt clinical spirometry suitable for home- based monitoring.
  26. 26. ASTHMOPOLIS SMART INHALER
  27. 27. Exercise as 
 (the Best)
 Medicine …
  28. 28. The Origins 
 of Exercise
  29. 29. Born to Run Humans evolved to be the world’s best endurance runners. … and quadrupeds cannot gallop long- distances because they cant stay cool. gallop ⟹ no panting ⟹ overheating We can run long distances faster than the trot-gallop transition speed of quadrupeds!
  30. 30. Exercise as Medicine Humans evolved to be adapted for regular, moderate amounts of endurance physical activity into late age. ⟹ significant disease consequences associated with prolonged and chronic inactivity.
  31. 31. Lieberman’s ‘Mismatch Diseases’ Obesity, type-2 diabetes, back pain, sleep disorders, … Diseases primarily caused by lack of adaptation to novel gene- environment contexts. Cultural evolution speeding ahead of natural evolution Diseases that are otherwise, previously rare, largely preventable, and almost entirely absent from (well-adapted) hunter-gatherer populations. The Paradox of the Modern World Reduced mortality (living longer) comes with increased morbidity (more years spent suffering from chronic preventable diseases).
  32. 32. Consequence of Inactivity Type 2 Diabetes Globally, 285m suffers growing by 50%+ by 2030 (>440m) Heart Disease 150 mins exercise/week decreases risk by ~45% Osteoporosis (~30% of elderly women and ~10% of men) Physical activity promotes more peak bone-mass better able to cope with inevitable decline in bone-mass in later life. Some Types of Cancer E.g. >2x reduction in breast cancer risk when comparing low to high activity groups (adjsuted for age, smoking, BMI, alcohol etc.).
  33. 33. Leading Actual Causes of Death (US) Mogdad et al. JAMA(2004)
  34. 34. Benefits of Activity Aerobics Center Longitudinal Study (>80k patients, 1970-2005) All-cause mortality ~3x lower, improvers vs unfit, always-fit vs improvers. Nurses’ Health Study (>111k women x 24 years) Relative mortality (vs lean, active) increases by ~50% due to inactivity or obesity. Stanford Runners Study (538 runners vs 423 control x 25 years) Runners have 20% lower mortality and 50% lower disability scores .
  35. 35. Exercise = Best Medicine Heart Disease Stroke Diabetes High BP Breast Cancer Colon Cancer Alzheimers Depression 0 15 30 45 60 Consensus Reduction in Risk Lower Costs Fewer Side-Effects As effective as drugs/therapy +
  36. 36. Squaring the Geriatric CurveFunctionalCapacity Age High Risk (Inactive) Lifestyle Low Risk (Active) Lifestyle
  37. 37. 0 20 40 60 1990 2000 %NoActivity Males FemalesExercise Paradox Humans evolved to be active … Adapted for regular, moderate amounts 
 of endurance activity into late age. 
 While, prioritising rest/inactivity … Limited food/energy ⟹ avoid unnecessary exertion ⟹ selection ignored effects of chronic inactivity.
  38. 38. A Personalisation Opportunity Promoting exercise means altering our environments Need to nudge people ⟹ relevant, personalised, fun, … Much work to date by CBR/RecSys/UM communities … Personalised/persuasive eHealth, health profiling for chronic disease management, diagnosis, treatment planning, recommender systems for food and diet, personalised exergaming, …
  39. 39. Let’s Talk About … The Marathon
  40. 40. The Marathon 26.2 miles / 42.2 kms Largest mass-participation sporting events in the world. 12-16 weeks of dedicated training (5-8 hrs/week, ~50km/week) WRs 2.02.57 (Denis Kimetto, 2014), 2.15.25 (Paula Radcliffe, 2003) ~40 year old men (4:10 finish) vs ~36 year old women (4:36 finish)
  41. 41. The Marathon as an Ideal Domain for Recommender Systems
  42. 42. Motive, Means, and Opportunity. Motivated, yet often inexperienced, users/runners need for advice/support/guidance. Plentiful supply of rich data training, rest, races, nutrition.. Many recommendation tasks/opportunities deliver real-time recommendations (devices, audio, haptic etc.)
  43. 43. Recommender Systems Personalised Training Programmes
  44. 44. Recommender Systems Rest & Recovery When/how to rest Training load Cross-training
  45. 45. Recommender Systems Injury Prevention & Rehabilitation Injury prediction Recovery time Rehab. exercises/drills Maintenance
  46. 46. Recommender Systems Race Planning Goal-time prediction Pace planning
  47. 47. Recommender Systems Nutrition & Hydration Training diet Pre/post session fueling In-race fueling
  48. 48. Recommender Systems Race/Route
 Recommendations Race suggestions (5k, 10k, …) Fast/slow courses; BQ races New-city, route suggestions
  49. 49. Recommender Systems Motivational Media Persuasive content Instructional media Training (music, podcasts) Personalised race reports
  50. 50. Recommender Systems Gear & Equipment Personalised footwear
 (road/trails, neutral/structured,…) Clothing (warm, cod, wet, dry) Devices, sensors, apps
  51. 51. Recommender Systems Personalised Training Programmes Types of Session Distance/Duration/Pace/Effort Terrain/Elevation Rest Days
  52. 52. Personalised Training
  53. 53. Training Base Building Taper Recovery 6+ weeks 4x4 weeks 2-3 weeks 6+ weeks
  54. 54. 12x400m Intervals 6x800m Intervals 3x1mi Intervals Long Run (26km) Long Run (28km) Long Run (30km) Long Run (20km) Tempo Hills Marathon Pace Recovery Run Rest Rest Rest Rest Recovery Run Recovery Run Easy Run Rest Rest Rest Rest Tempo Recovery Run Easy Run Easy Run Easy Run Easy Run Week 9 Week 10 Week 11 Week 12 When and where to run and rest? How far, long, fast? Type of session (easy, intervals, hills, long, tempo).
  55. 55. Case-Study
  56. 56. Recommender Systems Race Planning Goal-time prediction Pace planning Fueling strategy
  57. 57. How to Run a Personal-Best? Dual prediction/recommendation tasks: 1. Predicting challenging but achievable personal-best times. 2. Recommending a race-plan (pacing plan) to achieve this PB.
  58. 58. Race Records & Pacing PlansFasterSlower RelativePace 10k5k 15k 20k 25k 30k 35k 40k Finish Average Pace 6 mins/km Finish Time 4 hours 13 mins Getting goal-time predictions or pacing wrong can have disastrous consequences even for experienced runners …
  59. 59. A CBR Approach (ICCBR/RecSys 2017, IJCAI 2018)
  60. 60. CBR systems solve novel problems by reusing the solutions to similar problems.
  61. 61. Predict a goal-time for a new runner by reusing the PB races of runners with similar non-PB races…
  62. 62. From Races to Cases 239 mins (PB) 253 mins 268 mins 254 mins
  63. 63. Simple Paired Cases Case 1 Case 2 Case 3 nPB (problem) PB (solution)
  64. 64. Basic Case Representation Runner Recent non-PB PB {finish-time, %pace_5k, %pace_10k, …}
  65. 65. non-PB PB non- P no P Case Base non-PB PB Each case encodes a specific PB marathon progression for a given runner. Large and varied case base captures progressions for runners of all abilities.
  66. 66. non-PB PB non- P no P A Case-Based Approach Target Runner Recent Race Case Base
  67. 67. Similarity ( , ) non-PB PB non-PB PB non-PB PB Retrieve Similar Cases Target Runner Recent Race non-PB PB SimilarCases(nPBs) target non-pb case non-pb
  68. 68. no no no Adapt PBs (Solutions) Target Runner Recent Race no CorrespondingPBs case solutions
  69. 69. Mean PB/Pacing Target Runner Recent Race Predicted Time Recommended Pacing
  70. 70. Why limit cases 
 to pairs of races?
  71. 71. Baseline Representation 239 mins 253 mins 268 mins 254 mins
  72. 72. Simple Paired Cases Case 1 Case 2 Case 3 nPB (problem) PB (solution)
  73. 73. Extended Cases nPB (problem) PB (solution) Now each case is reflects a complete runner history with a set of n-1 nPB races paired with a single PB race. But … How to match/compare runners with different numbers of races? How should races be ordered in a case for matching?
  74. 74. Landmark Races Certain types of races standout in a runner’s history as examples of good/bad races: MR(r)/LR(r) – Most/least recent race in a runner’s history. MV(r)/LV(r) – Most/least varied (pacing) race. PPB(r)– Previous PB race. PW(r)– Personal worst race (slowest). MnPB(r)– a pseudo-race based on the mean of the runner’s non-PB.
  75. 75. PB MR LV LR MV PW PPB
  76. 76. Landmark Races as Abstract Features PBMRLV LRMVPW PPB nPB (problem) Each case has a fixed landmark-based representation and the same race can be represented multiple times under different landmark labels. Facilitates matching, normalises for different length race histories.
  77. 77. Richer Representations Runner Least Recent PB (Solution) Most Recent Previous PB Personal Worst Mean nPB Many possible combinations of landmark races Multitude of representations. Problem Description
  78. 78. Key Questions Do richer representations lead to better predictions/ recommendations? Are some landmark races better than others? What is the impact of richer representations on runners of differing abilities?
  79. 79. Evaluation
  80. 80. Datasets & Methodology Berlin (34k), London (36k), New York (44k) 170k runners, >3 races, & 5km pacing (5k, 10k, … 40k, Finish) Representations 64 x unique case representations (MR + [landmark races]* ) leading to 64 different test systems (prediction & recommendation) Methodology 10-fold cross-validation (90/10 training/test x 10) using nPB’s as queries and PBs as ground-truths. ICCBR/RecSys 2017 Baseline
  81. 81. Evaluation Metrics % Prediction Error Based on the difference between the predicted PB times and the actual/ground-truth PB times of the test-cases (that is, the actual PB times the runners achieved). Pace Profile Similarity Relative difference between the recommended pacing-plan and the actual pacing-plan (that is the actual/ground-truth pacing profiles the runner achieved in the PB).
  82. 82. Do Richer Representations Help?
  83. 83. The Benefit of Richer Representations In general, richer representations are associated with reduced prediction error, but … ICCBR/RecSys 2017 Baseline (Grouping representations by # landmark races)
  84. 84. … sometimes simpler representations > richer representations. ICCBR/RecSys 2017 Baseline ‘Best’ Representation
  85. 85. Which Landmark Races Help the Most? Compare the average error of representations with/without a given landmark race to compute relative benefit score for that race.
  86. 86. Which Landmark Races Help Most? Including LV/PW races disapproves prediction error. (Benefit < 0) Including PPB greatly improves prediction error (Benefit ~ 30-40%).
  87. 87. Impact for Runners?
  88. 88. Previously … ICCBR/RecSys 2017, IJCAI 2018 Single-race representations (MR) Prediction error disapproved with decreasing runner ability… E.g. ~6% error @ 4hr time, means the PB might be off by almost 15 minutes.
  89. 89. Comparing Baseline (MR) & Best (MR_LR_PPB) Representations
  90. 90. Prediction Error x Finish-Time Significant improvements in prediction error across all levels of ability (~3% vs 6-10%)… Baseline Best
  91. 91. Recommendation Quality x Finish-Time … without any additional cost in pacing-plan recommendation quality.
  92. 92. PB Improvement x Finish-Time Baseline Best Similarly, prediction accuracy is improved across all degrees of PBs.
  93. 93. Conclusions (1) Richer representations better goal-times & pacing-plans. But not all landmark races are created equally! The need for >3 races limits applicability to more experienced marathon runners consider other race types (5k, 10k, …) Same-city cases mixed-city cases (transfer learning) 5km pacing 1km pacing, cadence, heart rate, power, etc.
  94. 94. Conclusions (2)
  95. 95. Conclusions (2) Unique opportunity for novel application domains, enabled by the proliferation of data and devices.
  96. 96. Conclusions (2) Unique opportunity for novel application domains, enabled by the proliferation of data and devices. Health-related applications offer huge potential because recommender systems can help to nudge and guide users towards healthier habits.
  97. 97. Conclusions (2) Unique opportunity for novel application domains, enabled by the proliferation of data and devices. Health-related applications offer huge potential because recommender systems can help to nudge and guide users towards healthier habits. Low-hanging fruit exists in a variety of early-adopter niches (running, cycling, triathalons etc.)
  98. 98. More Information An Analysis of Case Representations for Marathon Race Prediction & Planning, ICCBR 2018 Marathon Race Planning: A Case-Based Reasoning Approach. IJCAI 2018 Fast Starts and Slow Finishes. Journal of Sports Analytics, 2018 Running with Cases: A CBR Approach to Running Your Best Marathon. ICCBR 2017: 360-374 A Novel Recommender System for Helping Marathoners to Achieve a New Personal-Best. ACM Recommender Systems 2017: 116-120 Running with Data (medium.com/running-with-data)

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