Sleep Quality as a Mediator of the Relationship Between Attachment Anxiety and Health Outcomes During Romantic Conflict

Attachment anxiety, or a fear of abandonment by those close to you, is an important predictor of many individual and interpersonal outcomes. Individuals high in attachment anxiety are more likely to experience physical illness due to disrupted immune functioning and deregulated stress responses. I was interested in examining potential mechanisms accounting for why individuals high in attachment anxiety are more likely to become ill. One variable that has been demonstrated to mediate the relationship between stress and health is sleep quality. As attachment anxiety is characterized by the experience of stress and worry over abandonment by romantic partners, I predicted sleep quality would mediate the relationship between attachment anxiety and health. Further, I predicted attachment anxiety would interact with romantic threat, in that individuals high in attachment anxiety who perceive threat to their relationships would have poor sleep quality (compared with individuals low in attachment anxiety and individuals high in anxiety who do not perceive threat) which would mediate the most unhealthy outcomes. I tested these hypotheses using three online diary studies. In the first two studies, participants completed a seven-night diary describing their sleep quality, health, and interaction with their partner. In Study 3, I surveyed participants once a week for eight weeks to examine longer-term health outcomes. Sleep quality did indeed mediate the relationship between attachment anxiety and various health outcomes over one week (Study 2), and showed a trend towards mediating effects over two months (Study 3). Interestingly, however, attachment anxiety did not interact with perceived romantic threat to predict health in the mediation analyses. Implications for sleep as a mediating variable are discussed, as well as the lack of attachment anxiety by romantic threat interaction.

The application of PCA as a movement pattern recognition technique: A proof of principle

Quantitative methods can help us understand how underlying attributes contribute to movement patterns. Applying principal components analysis (PCA) to whole-body motion data may provide an objective data-driven method to identify unique and statistically important movement patterns. Therefore, the primary purpose of this study was to determine if athletes’ movement patterns can be differentiated based on skill level or sport played using PCA. Motion capture data from 542 athletes performing three sport-screening movements (i.e. bird-dog, drop jump, T-balance) were analyzed. A PCA-based pattern recognition technique was used to analyze the data. Prior to analyzing the effects of skill level or sport on movement patterns, methodological considerations related to motion analysis reference coordinate system were assessed. All analyses were addressed as case-studies. For the first case study, referencing motion data to a global (lab-based) coordinate system compared to a local (segment-based) coordinate system affected the ability to interpret important movement features. Furthermore, for the second case study, where the interpretability of PCs was assessed when data were referenced to a stationary versus a moving segment-based coordinate system, PCs were more interpretable when data were referenced to a stationary coordinate system for both the bird-dog and T-balance task. As a result of the findings from case study 1 and 2, only stationary segment-based coordinate systems were used in cases 3 and 4. During the bird-dog task, elite athletes had significantly lower scores compared to recreational athletes for principal component (PC) 1. For the T-balance movement, elite athletes had significantly lower scores compared to recreational athletes for PC 2. In both analyses the lower scores in elite athletes represented a greater range of motion. Finally, case study 4 reported differences in athletes’ movement patterns who competed in different sports, and significant differences in technique were detected during the bird-dog task. Through these case studies, this thesis highlights the feasibility of applying PCA as a movement pattern recognition technique in athletes. Future research can build on this proof-of-principle work to develop robust quantitative methods to help us better understand how underlying attributes (e.g. height, sex, ability, injury history, training type) contribute to performance.