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Self Experiments

Self Experiments. Analysis of Patients’ Causal Diaries. System Change for Exercise Maintenance in Older Cardiac Patients. National Heart and Blood Institute 09/1/2006- 09/1/2009 PI: Shirley Moore RN, PhD Co-PI: Farrokh Alemi, PhD. Exercise Causes & Constraints.

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Self Experiments

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  1. Self Experiments Analysis of Patients’ Causal Diaries

  2. System Change for Exercise Maintenance in Older Cardiac Patients National Heart and Blood Institute 09/1/2006- 09/1/2009 PI: Shirley Moore RN, PhDCo-PI: Farrokh Alemi, PhD

  3. Exercise Causes & Constraints “… I realized how many great reasons I had to skip my workout today. First: rain… Second: low quality sleep after a night spent with my 17-pound cat getting tangled in the blinds. How could anyone exercise after a night like that?” Paige Waehner

  4. Exercise Causes & Constraints “What makes me exercise is that I have to take a shower and the only place I can take a fun shower, with lost of water, is at the gym. My shower at home does not have much water pressure. I have no choice but to go to the gym.” 68 years old woman

  5. Exercise Causes & Constraints “When I bike, I do not exercise. I commute to work. ” 42 year old man

  6. Exercise Causes & Constraints • People have different reasons • One solution does not fit all • People have wrong perceptions • I fail because of my environment • I succeed because of myself

  7. Exercise Causes & Constraints • People have different reasons • One solution does not fit all • People have wrong perceptions • I fail because of my environment • I succeed because of myself We cannot succeed, if we do not know why we have failed

  8. Post-cardiac Exercise Patterns

  9. Health Sustain exercise post rehab Objectives

  10. Health Sustain exercise post rehab Understand causes of & constraints for exercise Objectives

  11. Health Sustain exercise post rehab Understand causes of & constraints for exercise Conduct self experiments: Maintain diary, analyze data, repeat the process. Gain insight. Objectives

  12. Self Experiments • List possible causes/constraints • Trace occurrences • Analyze data • Small data sets of 10-14 data points

  13. Example

  14. Bike to work Shower at gym Sleep early Rain Diary

  15. 14-day Diary

  16. 14-day Diary Too little data for most statistical methods of analysis

  17. Obvious Lessons • No variation in outcomes: • No exercise in the entire 2 weeks • Exercise every day • No variation in causes: • Always present cause • Always absent cause

  18. Causal Analysis • Sequence • Cause precedes exercise • Association • When the cause is present, exercise should be likely • Counter-factual • If the cause is absent, and no other causes are present, exercise should be unlikely

  19. Methods of Analysis • Logistic regression • Bayesian networks • Causal analysis

  20. Method 1: Logistic Regression

  21. Method 2: Bayesian Network • Markov blanket • Use of conditional probabilities • Serial conditional independence • Common cause • Common effect

  22. Method 3: Causal Analysis Conditional 1-Counterfactual

  23. 14-day Diary with No Constraints

  24. Method 3: Causal Analysis

  25. Method 3: Causal Analysis

  26. Method 3: Causal Analysis

  27. Method 3: Causal Analysis

  28. Study Phase I • Which of the methods is most accurate? • Which method is easier to understand? • Which method is easier to use?

  29. Questions & Comments

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