Analyzing the Impact of Stress: A Comparison Between a Factor Analytic and a Composite Measurement of Allostatic Load (bibtex)
@inproceedings{buckwalter_analyzing_2011,
	address = {Orlando, FL},
	title = {Analyzing the {Impact} of {Stress}: {A} {Comparison} {Between} a {Factor} {Analytic} and a {Composite} {Measurement} of {Allostatic} {Load}},
	url = {http://ict.usc.edu/pubs/Analyzing%20the%20Impact%20of%20Stress-%20A%20Comparison%20Between%20a%20Factor%20Analytic%20and%20a%20Composite%20Measurement%20of%20Allostatic%20Load.pdf},
	abstract = {Stress is possibly the hallmark characteristic of the current conflicts confronting the United States. Extended and repeated deployments require the ability on the part of war-fighters to effectively process stress in ways never before routinely encountered. Stress is well defined as a series of psychological and physiological processes that occur in response to a stressor, or the perception of stress. The physiological response to stress follows an identified path, a robust neuroendocrine response leads to responses in the cardiovascular, metabolic, renal, inflammatory and immune systems. After a stress response, the body's natural tendency is to return to a steady state, a process called allostasis. If the body is not effective in returning to homeostasis, or if the environment is such that stress is repeated, markers of dysfunction may be apparent in the physiological systems that respond to stress. A method of measuring multiple biomarkers of stress responsive systems and determining who shows consistent evidence of dysfunction was developed by Bruce McEwen and labeled allostatic load (AL). AL is most frequently measured by developing a level of risk for each biomarker and obtaining an AL score for the number of biomarkers the criterion for risk is met. This provides a single, equal-weighted measure of AL and does not allow for the identification of multi-systems. We employed a principal component factor analysis on a set of biomarkers and scored each factor using unit weighting. We compared the predictive power of 7 obliquely rotated factors to that of a composite AL marker. The set of factors predicted more of the variance in measures of depression, anxiety, and medical outcomes, it also provided evidence of the systems most involved in the development of pathology. The results confirm that AL is best analyzed as a multi-system construct. Not only does this predict more variance, it also provides suggestions as to the mechanisms underlying stress related disorders.},
	booktitle = {Interservice/{Industry} {Training}, {Simulation} and {Education} {Conference} ({I}/{ITSEC})},
	author = {Buckwalter, John Galen and Rizzo, Albert and John, Bruce Sheffield and Finlay, Lisa and Wong, Andrew and Chin, Ester and Wellman, John and Smolinski, Stephanie},
	year = {2011},
	keywords = {Learning Sciences, MedVR}
}
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