Measurement & Analysis Considerations:

1. Measurement & Analysis Considerations: Sean Collins

2. General Introduction - 1 Stress Disequilibrium Theory Biological hypothesis that there is such a thing as ordering capacity; that it is finite, periodic, requires refreshing -> can be exhausted Low Social Control Exhausted Ordering Capacity Deregulation of physiological systems Chronic disease

3. General Introduction - 2 Three Presentations with a practical focus I. Getting & Testing Data II. Measuring Exhaustion Response III. Complexity of Modeling Contrasts

4. Getting & Testing the Data First pass processing for A: Job Strain and B: multi – level S-D Testing

5. Objectives & Outline • Impact of S-D hypotheses on measurement & analysis • Getting response data (ECG) • Standardization of exposure • Temporal aggregates • Nested Hierarchies

6. Impact of S-D hypotheses on measurement & analysis - 1 • S-D proposes high level mechanism of chronic disease development in “high level” organisms (Humans) in a natural ecological framework (work environment) • Despite rather basic biological hypotheses, it requires field study • Therefore – measurement ends up looking like several rather sophisticated “pseudo experimental” biological studies nested within a larger observational ecological study • ECG – HRV is an ideal measurement because it provides easy to obtain data across a range of spatial and temporal scales

7. Impact of S-D hypotheses on measurement & analysis - 2 • Analytic impact – • Standardized exposure (as best as possible for the pseudo experimental biological tests) embedded into the macro observational study; (standardized response – covered later) • Multiple Comparisons need to be tested • within and between subjects • 2 dimensions – nested hierarchies and temporal aggregates • Requires sufficient within and between subject variance • Measurement consequences – • Standardized exposure • Multiple comparisons need to be embedded • Careful sample and recruitment considerations • Data collection at a variety of temporal and hierarchies

8. Getting Response Data - 1 • ECG data collected over a minimum of 24 (and preferably 48 hours) for maximizing the number of comparisons • Temporal aggregates – several time scales for comparison • Nested hierarchies – RR interval vs. Frequency spectrum vs. Response of ANS components; Controller – Controlled relationships • ECG Data collection • 256 Hz or higher is preferred for frequency measures • Once data is collected as an ECG signal have several first pass processing options

9. Getting Response Data - 2 • ECG as Response Data (what is responding) • Easiest wave to identify = R wave • HRV is based on variations of the RR wave intervals • Responses: • Heart rate responds • HRV responds • HFP responds • Non – linear components respond • Looking at different temporal and spatial hierarchies allows different questions to be answered with this one form of data collection

10. Getting Response Data - 3 • First pass processing of ECG • RR intervals across entire period • RR intervals into 1.25; 5; 15; 30; 60; etc; Minute Epochs • Epochs can be analyzed for: • Time domain (HR, SDNN (HRV)) • Frequency domain (HFP, LFP, Lo/Hi) • Non linear (APEN, Sample Entropy, Multi scale entropy, Correlation dimension, etc)

11. Getting Response Data - 4 Vagal Variance and/or Patterns HRV HFP – RSA (Vagal) Non -Linear RR intervals ECG

12. Regulatory System Variance System Regulation HFP Time Series (Vagal Control) 4 Hours (= 48 Epochs) Beat-to-Beat Variability RR Interval Series 5 – Minutes Frequency Analysis – HFP (.15-.4 Hz) 1 Epoch

13. Standardization of exposure - 1 Accuracy of Measurement • JCQ • Individual self report • Diary assessment for multiple individual self report and within day / between day / between sociological period assessment • National occupational code imputation • Combination of three approaches

14. Occupational Code .641 Decision Latitude .475 Job Content Questionnaire Work Day Diary .681 Occupational Code .234 Psychological Demands -.051 Job Content Questionnaire Work Day Diary .562 Exposure Triangulation Collins, Karasek & Costas, AJIM, 2005

15. Standardization of exposure - 2 A second important component of standardization of exposure is related to the next topic – Multiple comparisons Standardized control measurements allow comparisons to extend beyond relative differences of a sample

16. Multiple comparisons - 1 • Multiple comparisons of exposure • Between subjects • Standardization of exposure • Careful sample selection and recruitment • Honest allocation of “low control” status as opposed to relative assignment based on the study sample • Within subject • Several measurements of JCQ control questions (Diary) • Measurements are aggregated during sociological periods and/or Diary Periods

17. Multiple comparisons - 2 • Obvious comparisons • Work day vs. rest day • Awake vs. asleep • At work vs. not at work • Sociological Periods • Morning at work • Lunch • Afternoon at work • Etc… • Diary Periods

18. Collins, Karasek & Costas, AJIM, 2005

19. Multiple Comparisons - 3 Multiple comparisons of exposure then extend to comparing response between exposure • First – need to get all the data • JCQ – Occupational Code – Diary • 24-48 Hour Holter ECG • Process the ECG data as needed for analysis • Second – need to link the exposure and response data • Linkage of time periods between Diary and / or Sociological Periods with ECG sequence (more to come with Nested Hierarchies and Temporal Aggregates)

20. Example: HRV, Control & Sociological Period(Between subjects across sociological linked time)

21. Diary – ECG Linkage • Times entered for the diary were utilized to match the 20-minute period prior to each diary entry to the same 20-minute period for the ECG recording • ECG data during this 20-minute period was aggregated (average of the four 5 minute epochs) and linked to the appropriate diary data. HFP Data ECG Data Diary Data

22. Timing Accuracy • It is assumed that the majority of the ECG data classified as having occurred in the 20 minutes prior to the diary recording are correct.    • Utilizing a sliding time boundary window, we tested the models at 20-minute periods surrounding or following the diary period.

23. Temporal Aggregates • Temporal Aggregates – referring to the vast array of time scales for comparison with ECG and RR interval data • ECG – greatly untapped thus far is the possible explanatory framework of within and between RR vs. QT interval relations • RR intervals – multi scale entropy; fractal and correlation dimensions consider the variety of temporal scales within a RR interval signal • However – still have not considered Connections with sociological activity across those temporal scales • Take home message: get the ECG data – there is LOTS of data embedded in the ECG signal across time that is useful for testing S-D hypotheses

24. 6 Hours 1 Hour 5 Minutes

25. Nested Hierarchies • Exposure • Segments of day within whole day • Whole day within occupation • Response • RR interval -> HFP -> Vagal variance • Controller – Controlled relationships • Exposure – Response relationships • HFP within vs. between subject

26. Physical / Mental Activity? 2 Hour vagal activity series Power Spectrum Extract variance for vagal activity 5 Minute RR Series

27. How do Temporal Aggregates and Nested Hierarchies interact & influence what we can measure with HRV? HR - ANS – Complexity – Exhaustion? Space >1-1 - 2 - - 5 ----- 30 ------- 480 ------- 1440 --- 2880 Time (min)

28. End of Getting the Data….