Examining Trauma Survivors’ Structural Integrity during Study Tasks

• Speaker: Alexander Stover, Lyda Hill Institute for Human Resilience at University of Colorado, Colorado Springs
• Abstract: Systems self-organize from complex interactions between their components. Reciprocal feedback from the system-asa-whole to its components maintains this organization. Selforganization in living systems is adaptive as it enables them to alter structural organization in response to environmental demands. This study (N = 68) examined structural integrity' the degree of fit ($R^{2}$) between an observed distribution of heart rate variability (HRV) and an inverse power law (IPL)' as an indicator of self-organization. These data were collected from trauma survivors using an e-health trauma intervention, My Trauma Recovery (MTR). Data were gathered across five segments in a session that required participants to sit quietly, complete a cold-pressor task, watch a nature video, complete three modules from MTR, and discuss their index trauma. HRV is a continuous measure of individuals' physiological response to stress and reflects autonomic functioning. Thus, calculated degrees of fit indicate how each task affected participants’ structural integrity. The results demonstrated a significant decrease in structural integrity during MTR module completion ($R^{2}$ = .89) compared to segments in which participants completed the cold pressor task ($R^{2}$ = .97) and sat quietly ($R^{2}$ = .98), p < .05. This is the first study to utilize HRV and IPL to investigate short-term changes in structural integrity to our knowledge. The findings indicate that this approach provides a new tool for analyzing self-organization. Joint work with Bernard Ricca, Margaret Morison, Abbey Westphal, Michael Dolezal, and Charles Benight, Lyda Hill Institute for Human Resilience at University of Colorado, Colorado Springs.

Using Dynamic Systems Analysis to Gain Insight into Post-Trauma Distress

• Speaker: Charles Benight, Lyda Hill Institute for Human Resilience at University of Colorado, Colorado Springs
• Abstract: Relationships between posttraumatic growth, perceived selfefficacy, and distress in trauma survivors have been studied previously. However, those studies have modeled only the reported levels of those quantities and not their speed (the rate at which they change) or acceleration (the rate at which the speed changes) of those quantities. The failure to include speed and acceleration in the modeling has limited the insight into the dynamics of post-trauma distress and resulted in inconsistent empirical results. This investigation used data collected from wildfire survivors daily over a period of 30 days and a model including speed and acceleration to gain new insights into post-trauma dynamics and to generate hypotheses about the origin of some previous inconsistent results in the field. We found that the speed at which selfefficacy increases (“mastery”) decreases distress. Additionally, the inter-action between the level or speed of self-efficacy and the acceleration of post-traumatic growth significantly improved the prediction of change in distress and provided insight into notions of “real” and “illusory” posttraumatic growth (and subsequent unsustainable decrease in distress). Joint work with Bernard Ricca and Alex Stover, Lyda Hill Institute for Human Resilience at University of Colorado, Colorado Springs, and Pascal Deboeck, Psychology Department, University of Utah.

Good days and bad days: using time-series analysis to understand mood regulation

• Speaker: Abigail Ortiz, University of Toronto
• Abstract: Mood regulation is a complex and poorly understood process. In this paper, I will give an overview of our theoretical model to understand mood regulation using time-series analysis. This is a new perspective that is clinically relevant to understand how mood regulation differs in people without any mood disorder, in patients with mood disorders (bipolar disorders) and their unaffected first-degree relatives. I will discuss our findings in relation to two different studies in clinical populations, which showed that mood regulation in unaffected first-degree relatives is indistinguishable from the one seen patients with mood disorders. We will discuss potential future applications of these findings, in particular to forecast episodes of illness. Joint work with Garmham Julie, Slaney Claire, Nova Scotia Health, and Alda Martin, Dalhousie University.

Predicting persistence of effect in an acupuncture Randomized Controlled Trial

• Speaker: Lisa Conboy, BIDMC at Harvard Medical School, Complexity in Medicine
• Abstract: Purpose: Gulf War Illness (GWI), or chronic multi-symptom illness (CMI), is characterized by multiple symptoms. In 2013 our study team completed a Department of Defense funded study The Effectiveness of Acupuncture in the Treatment of Gulf War Illness. With our robust sample (n=104) we found a clinically and statistically significant improvement in physical function and pain following 6-months of treatment. We are currently conducting multiple secondary data analyses to better describe this complex disease and how healing may happen using acupuncture. Herein we report on our use of Structural Equation Modeling to model the mechanisms of change and improvement across the entire sample on the biological, psychosocial, and clinical levels. Methods: We used the data reduction technique of factor analysis to better understand the differences in clinical, psycho-social, and experiential variables by treatment group (weekly versus biweekly treatment). We next completed a longitudinal analysis of outcome differences by dose at 2, 4, and 6 months, and long term follow up (3-5 years). Results: We found 5 independent factors at baseline: belief/expectation, physical/psychological health, social network, social support, relationship with treatment practitioner. These are used to predict different types of responders at endpoint. Conclusion: Studying responses to acupuncture may elucidate mechanisms, subgroups of the disease, and inform the study of other CMI. Results can be applied clinically to design effective protocols and better understand how different doses of treatment work differently. Understanding associations with the persis-tence of treatment effect can help us better understand how healing happens. Joint work with Claire Cassidy, John Hopkins, Complexity in Medicine, and Tanuja Prasad, Complexity in Medicine.