self experiments analysis of patients’ causal diaries

Self ExperimentsSelf Experiments

Analysis of Patients’ Causal Analysis of Patients’ Causal DiariesDiaries

System Change for System Change for Exercise Maintenance in Exercise Maintenance in Older Cardiac Patients Older Cardiac Patients

National Heart and Blood Institute National Heart and Blood Institute 09/1/2006- 09/1/2009 09/1/2006- 09/1/2009

PI: Shirley Moore RN, PhDPI: Shirley Moore RN, PhDCo-PI: Farrokh Alemi, PhDCo-PI: Farrokh Alemi, PhD

Exercise Causes & Exercise Causes & Constraints 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


“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


“When I bike, I do not exercise. I commute to work. ”

42 year old man


People have different reasonsPeople have different reasons– One solution does not fit allOne solution does not fit all

People have wrong perceptionsPeople have wrong perceptions– I fail because of my environmentI fail because of my environment– I succeed because of myselfI succeed because of myself


People have different reasonsPeople have different reasons– One solution does not fit allOne solution does not fit all

People have wrong perceptionsPeople have wrong perceptions– I fail because of my environmentI fail because of my environment– I succeed because of myselfI succeed because of myself

We cannot succeed, if we do not know why we

have failed

Post-cardiac Exercise Post-cardiac Exercise PatternsPatterns

0

1020

30

4050

60

70

8090

100

Rehab 6-months

% E

xe

rcis

ing

ObjectivesObjectives Health

Sustain exercise post rehab



Understand causes of & constraints for exercise



Understand causes of & constraints for exercise

Conduct self experiments: Maintain diary, analyze data, repeat the process. Gain insight.

Self ExperimentsSelf Experiments

List possible causes/constraintsList possible causes/constraints Trace occurrences Trace occurrences Analyze dataAnalyze data

– Small data sets of 10-14 data pointsSmall data sets of 10-14 data points

ExampleExample

DiaryDiary

Check

if cause

Other causes (please specify):

List Constraints

Consider as constraints factors that prevent you from completing structured exercise. For example, rainy weather may prevent you from going for a run or walk.

List Causes

Consider as possible causes factors that enable you to complete structured exercise. Think through the impact of daily routines (e.g. commuting, sleeping patterns, bathing patterns), medication changes, weather and environmental changes, socialization routines and other physical changes in the environment. Do not list your motivation or your time as causes but list factors that improve your motivation or available time.

Check

if present

Day 1

YYMMDD//

Did you exercise? Yes No Not sure

Steps taken today: ? ? ? , ? ? ? ,? ? ?

Did you talk about exercise with others? Yes, with friends or family members Yes, with project staff No

Start time of exercise:

MMHH:

End time of exercise:

MMHH:

Type of exercise:

Level of exertion: Very, very light Somewhat hard Very light Hard Light Very hard Fairly light Very, very hard

Did you reach your prescribed exercise zone? Yes No Not sure

Any changes in your medications? Yes No Not sure

Any contact with health services? Emergency Room Office visit Hospital

Was the contact heart related? Yes No

Bike to work

Shower at gym

Rain

Sleep early

14-day Diary14-day Diary

Day Rain

Plan to commute with bike

Plan to shower at

gymSleep early

Exercise pattern

kept

1 1 1 0 0 0

2 0 0 1 1 1

3 0 0 1 1 0

4 0 1 1 0 1

5 1 1 1 0 1

6 1 1 0 0 0

7 0 0 0 0 0

8 0 0 1 0 1

9 0 0 0 0 0

10 1 1 0 1 1

11 0 0 0 0 0

12 0 0 1 0 1

13 1 0 1 0 1

14 0 1 0 0 1

14-day Diary14-day Diary

Day Rain


Plan to shower at

gymSleep early

Exercise pattern

kept

1 1 1 0 0 0

2 0 0 1 1 1

3 0 0 1 1 0

4 0 1 1 0 1

5 1 1 1 0 1

6 1 1 0 0 0

7 0 0 0 0 0

8 0 0 1 0 1

9 0 0 0 0 0

10 1 1 0 1 1

11 0 0 0 0 0

12 0 0 1 0 1

13 1 0 1 0 1

14 0 1 0 0 1

Too little data for most statistical methods

of analysis

Obvious LessonsObvious Lessons

No variation in outcomes: No variation in outcomes: – No exercise in the entire 2 weeksNo exercise in the entire 2 weeks– Exercise every dayExercise every day

No variation in causes:No variation in causes:– Always present causeAlways present cause– Always absent causeAlways absent cause

Causal AnalysisCausal Analysis

1.1. SequenceSequence Cause precedes exerciseCause precedes exercise

2.2. AssociationAssociation When the cause is present, exercise When the cause is present, exercise

should be likely should be likely Counter-factualCounter-factual

If the cause is absent, and no other If the cause is absent, and no other causes are present, exercise should be causes are present, exercise should be unlikelyunlikely

Methods of AnalysisMethods of Analysis

1.1. Logistic regressionLogistic regression

2.2. Bayesian networksBayesian networks

3.3. Causal analysisCausal analysis

Method 1: Logistic Method 1: Logistic RegressionRegression

Log [pi /(1-pi )] = α 0 + α1xi + εi

Log [pi /(1-pi )] = α 0 + α2yi + εi

Log [pi /(1-pi )] = α 0 + α3zi + εi

Using the coefficients estimated in logistic regression, the various conditional probabilities of success, the main effect of the cause on success, can be calculated as:

P(S | xi=1) Max

= e α 0+ α1 / (1+ e α 0+ α1 ) P(S | xi=1)

Min = e0+1 / (1+ e0+1 )

P(S | yi=1) Max

= e α 0+ α2 / (1+ e α 0+ α2 ) P(S | yi=1) Min = e0+2 / (1+ e0+2 ) P(S | zi=1)

Max = e α 0+ α3 / (1+ e α 0+ α3 )

P(S | zi=1) Min = e0+3 / (1+ e0+3 )

Log [pi /(1-pi )] = β0 + β1xi+β2yi+β3zi + ...+ εi

Method 2: Bayesian Method 2: Bayesian NetworkNetwork

Markov blanketMarkov blanket Use of conditional probabilitiesUse of conditional probabilities

– Serial conditional independenceSerial conditional independence– Common causeCommon cause– Common effectCommon effect

Method 3: Causal AnalysisMethod 3: Causal Analysis

Xined cause unconstracases withNumber of

e Xained caush unconstr cases witsuccessfulNumber of MaxX)|p(S

causeotherno Xined cause unconstracases withNumber of

ther causee X & no oained caush unconstr cases witsuccessfulNumber of

&X)|p(S Min

1-Cou

nter

fact

ual

Conditional

14-day Diary with No 14-day Diary with No ConstraintsConstraints

Day


Plan to shower at

gym Sleep earlyExercise

pattern kept

1 0 0 0 0

2 0 1 1 1

3 0 1 1 0

4 1 1 0 1

5 0 1 0 1

6 0 0 0 0

7 0 0 0 0

8 0 1 0 1

9 0 0 0 0

10 0 0 1 1

11 0 0 0 0

12 0 1 0 1

13 0 1 0 1

14 1 0 0 1


Day


Plan to shower at

gym Sleep early

Exercise pattern

kept

4 1 1 0 1

14 1 0 0 1

Exercise Pattern on Days in which Client was Ready to Bike

Probability of Success Given the Cause

1-Counterfactual Conditional

Plan to commute with bike 1 1


Day


Plan to shower at

gym Sleep earlyExercise

pattern kept

2 0 1 1 1

3 0 1 1 0

4 1 1 0 1

5 0 1 0 1

8 0 1 0 1

12 0 1 0 1

13 0 1 0 1

Exercise on Days in which the Client was Ready to Take Shower at Gym



Plan to shower at gym 0.80 0.86




Plan to commute with bike 1 1

Plan to shower at gym 0.80 0.86

Sleep early 0.50 0.67

Probability of Success Associated with Different Causes

Study Phase IStudy Phase I

Which of the methods is most Which of the methods is most accurate?accurate?

Which method is easier to Which method is easier to understand?understand?

Which method is easier to use?Which method is easier to use?

Questions & Questions & CommentsComments

self experiments analysis of patients’ causal diaries

Documents

exercise slide

exercise maintenance

exercise sho

exercise post rehab

phd slide

absent cause slide

cardiac exercise patterns

causes of constraints