Self Experiments Analysis of Patients’ Causal Diaries

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

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

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

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

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

Exercise Causes & Constraints People have different reasons People have different reasons –One solution does not fit all People have wrong perceptions 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

Post-cardiac Exercise Patterns

Objectives Healt h Sustain exercise post rehab

Objectives Healt h Sustain exercise post rehab Understand causes of & constraints for exercise

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

Self Experiments List possible causes/constraints List possible causes/constraints Trace occurrences Trace occurrences Analyze data Analyze data –Small data sets of data points

Example

Diary Bike to work Shower at gym Rain Sleep early

14-day Diary DayRain Plan to commute with bike Plan to shower at gym Sleep early Exercise pattern kept

14-day Diary DayRain Plan to commute with bike Plan to shower at gym Sleep early Exercise pattern kept Too little data for most statistical methods of analysis

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

Causal Analysis 1. Sequence Cause precedes exercise Cause precedes exercise 2. Association When the cause is present, exercise should be likely When the cause is present, exercise should be likely Counter-factual Counter-factual If the cause is absent, and no other causes are present, exercise should be unlikely If the cause is absent, and no other causes are present, exercise should be unlikely

Methods of Analysis 1. Logistic regression 2. Bayesian networks 3. Causal analysis

Method 1: Logistic Regression

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

Method 3: Causal Analysis 1-Counterfactual Conditional

14-day Diary with No Constraints Day Plan to commute with bike Plan to shower at gymSleep early Exercise pattern kept

Method 3: Causal Analysis Day Plan to commute with bike Plan to shower at gymSleep early Exercise pattern kept Exercise Pattern on Days in which Client was Ready to Bike Probability of Success Given the Cause 1-CounterfactualConditional Plan to commute with bike11

Method 3: Causal Analysis Day Plan to commute with bike Plan to shower at gymSleep early Exercise pattern kept Exercise on Days in which the Client was Ready to Take Shower at Gym Probability of Success Given the Cause 1-CounterfactualConditional Plan to shower at gym

Method 3: Causal Analysis Probability of Success Given the Cause 1-CounterfactualConditional Plan to commute with bike 11 Plan to shower at gym Sleep early Probability of Success Associated with Different Causes

Method 3: Causal Analysis

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

Questions & Comments