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Complexity of Modeling Contrasts

Complexity of Modeling Contrasts. Allocating the Variance – A variety of analytic examples. Brief outline. Two examples Within and Between Subject – Multi Level modeling Complexity from Nested Hierarchies of Exposure Coordination of Response

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Complexity of Modeling Contrasts

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  1. Complexity of Modeling Contrasts Allocating the Variance – A variety of analytic examples

  2. Brief outline Two examples • Within and Between Subject – Multi Level modeling • Complexity from Nested Hierarchies of Exposure • Coordination of Response • Complexity from Nested Hierarchies and Temporal Aggregates of Response as well as Exposure

  3. Example 1 – Within and Between Subject – Multi Level modeling Complexity from Nested Hierarchies of Exposure

  4. Required • Exposure • JCQ decision latitude, Imputed Occupational Code, Diary data about demands / control • Decision Latitude; Diary Strain; Physical Exertion; Diary Social Support • Response • ECG -> RR intervals -> Frequency Analysis -> HFP / Total Power -> HFP nu

  5. Low Control High Demands High Control Low Demands Age, Gender, Condition, etc. ID=1 ID=2 ID=3 ID=4 ID=N S S S S S S S S S S W W W W W R R R R R General Statistical Modeling of Contrasts: Multilevel/Hierarchical Data Structure Job Stress, Chronic Disease and Heart Rate Variability Copenhagen, June 2006

  6. SPSS Mixed Models for Multilevel Modeling • Fixed & Random Effects; or just fixed, just random effects • Between & Within Subject contrasts • Simplest explanation is a two step model

  7. Two Step Model • Step 1: • Determine βcoefficients at level 1 (i.e. Y = vagal tone; X = position within each subject) • Step 2: • Save these coefficients and enter them as Y in the level two model (i.e. Y = βcoefficients of the relationship between vagal tone and position within each subject; X = job strain)

  8. Advantages of using the Mixed Model • Allow intercept and slope coefficients to be simultaneously handled • Allow random effects to be properly specified and computed • Allow correlation of errors, therefore has more flexibility in modeling the error covariance structure • Allows the error terms to exhibit nonconstant variability, allowing more flexibility in modeling the dependent variable • Can handle missing data

  9. Advantages of using the Mixed Model – specific to HRV research • Allow between subject contrast with methodological control of activity/circadian variation • Allow within subject contrasts that take into consideration the individuals baseline

  10. Small sample – large baseline variation

  11. Disadvantages of using the Mixed Model – specific to HRV research • Many steps in complex data set management • Care required to make sure you know what you have obtained from the “black box” analysis • Difficult to explain

  12. Other views / methods for contrast in light of baseline variations • Compare means of large aggregations of data • Residual analysis with smoothing, moving average models • Subject or group based standardization

  13. Within and Between Subject Cardiac Vagal Responses to Work & Rest Day Strain Purpose • Test the hypothesis that cardiac vagal control throughout the day, when controlling for relevant covariates, will be affected by job strain as reported on an activity diary • It was also hypothesized that cardiac vagal activity will vary between subjects based on macro level job characteristics as measured by a standard questionnaire assessment

  14. 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.

  15. Job Stress, Chronic Disease and Heart Rate Variability Copenhagen, June 2006

  16. Job Stress, Chronic Disease and Heart Rate Variability Copenhagen, June 2006

  17. Estimates for Measure of Effect Based on Coefficients for Normalized High Frequency Power Estimates based on Significant Predictors in Model IV

  18. Within Subject Cardiac Vagal Responses to Work and Rest Day Stressors Discussion • Random Slope Coefficients were not significant • Posture, but no Exertion effect • No Smoking or Caffeine Effect Identified • Cardiac vagal regulatory reductions

  19. Within Subject Cardiac Vagal Responses to Work and Rest Day Stressors Limitations • Timing accuracy • Other occupational exposures • Other Level 2 effects • Physical fitness

  20. Conclusions • Daily fluctuations of psychosocial strain reduces cardiac vagal control. • Social Support may be activating physiologically, however with high strain it is beneficial for cardiac vagal control. • JCQ (macro) assessed decision latitude is associated with cardiac vagal activity with control for within subject reports of strain, exertion, position, and between subject effects of age. • Reduced cardiac vagal activity identified within subjects may provide a physiological marker for exposure monitoring in future studies.

  21. Example 2 – Coordination of Response Complexity from Nested Hierarchies and Temporal Aggregates of Response as well as Exposure

  22. Required • Exposure • JCQ, Occupational Code imputed demand / control; Diary (for sociological periods) • Response • ECG -> RR intervals -> HR & HFP • HR & HFP -> Residual HR (indicator of sympathetic activity) • HR, HFP & Residual HR -> Coordination Patterns

  23. Reciprocal: Sympathetic Activity Parasympathetic Activity Non-Reciprocal: Co – Activation & Co – Inhibition UnCoupled – Sympathetic Activation Berntson’s Doctrine of Autonomic Space

  24. Bernston’s Doctrine of Autonomic Space Subjects HR > Mean Subjects HR < Mean If: HFPnu > .1 SD & Res HR < -.1 SD OR If: HFPnu < -.1 SD & Res HR > .1 SD If: HFPnu and ResHR > .1 SD If: HFPnu and ResHR < -.1 SD If: HFPnu < .1 and > -.1 SD and ResHR > .25 or < -.25 SD If: If: ResHR < .1 and > -.1 SD and HFPnu > .25 or < -.25 SD

  25. Bernston’s Doctrine of Autonomic SpaceReciprocal Sympathetic Control

  26. Bernston’s Doctrine of Autonomic SpaceNon Reciprocal Parasympathetic Control

  27. Discussion • Coordination Pattern algorithm requires further testing / validation in laboratory studies • Followed by – studies to connect to other physiological / health outcomes

  28. Final Thoughts: Toward a Biological Approach to the S-D Theory • In many ways the S-D Theory of Karasek calls for a reinterpretation of basic biological analysis – the concept of “Control Capacity” has not been adequately conceptualized in the biological sciences for considering organism development and health • There is a need – and it is recognized (i.e. USA – NSF “Grand Challenges in Organismal Biology” – these challenges are tightly aligned (although not recognized to date by most) to the S-D Theory • Collaborators from areas of: Evo-Devo; Eco-Devo; Evolution of complexity; and Modularity of Evolution – same concepts but slightly different language and different departments / meetings

  29. Thank you! Thank you for allowing me the opportunity to present at this important workshop despite my inability to attend. Contact information: Sean Collins, PT, ScD Chair of Physical Therapy University of Massachusetts Lowell 3 Solomont Way, Suite 5 Lowell, MA 01854 USA 1-978-934-4375 Sean_Collins@uml.edu http://faculty.uml.edu/scollins

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