Effects of retirement on cognitive function: A gender and international perspecive

Background: As the global population is ageing, studying cognitive impairments including dementia, one of the leading causes of disability in old age worldwide, is of fundamental importance to public health. As a major transition in older age, a focus on the complex impacts of the duration, timing, and voluntariness of retirement on health is important for policy changes in the future. Longer retirement periods, as well as leaving the workforce early, have been associated with poorer health, including reduced cognitive functioning. These associations are hypothesized to differ based on gender, as well as on pre-retirement educational and occupational experiences, and on post-retirement social factors and health conditions. Methods: A cross-sectional study is conducted to determine the relationship between duration and timing of retirement and cognitive function, using data from the five sites of International Mobility in Aging Study (IMIAS). Cognitive function is assessed using the Leganes Cognitive Test (LCT) scores in 2012. Data are analyzed using multiple linear regressions. Analyses are also done by site/region separately (Canada, Latin America, and Albania). Robustness checks are done with an analysis of cognitive change from 2012 to 2014, the effect of voluntariness of retirement on cognitive function. An instrumental variable (IV) approach is also applied to the cross-sectional and longitudinal analyses as a robustness check to address the potential endogeneity of the retirement variable. Results: Descriptive statistics highlight differences between men and women, as well as between sites. In linear regression analysis, there was no relationship between timing or duration of retirement and cognitive function in 2012, when adjusting for site/region. There was no association between retirement characteristics and cognitive function in site/region/stratified analyses. In IV analysis, longer retirement and on time or late retirement was associated with lower cognitive function among men. In IV analysis, there is no relationship between retirement characteristics and cognitive function among women. Conclusions: While results of the thesis suggest a negative effect of retirement on cognitive function, especially among men, the relationship remains uncertain. A lack of power results in the inability to draw conclusions for site/region-specific analysis and site-adjusted analysis in both linear and IV regressions.

Characterization of New Carbosilane Liquid Crystalline Monomers and Dimers

‘De Vries-like’ smectic liquid crystals exhibit low layer contraction of approximately 1% on transitions from the SmA to the SmC phase. These materials have received considerable attention as potential solutions for problems affecting liquid crystal displays using surface-stabilized ferroelectric liquid crystals (SSFLC). In SSFLCs, layer contraction of 710% is normally observed during the SmA to SmC phase transition. A study by the Lemieux group has shown that liquid crystals with nanosegregating carbosilane segments exhibit enhanced ‘de Vries-like’ properties through the formation of smectic layers and by lengthening the nanosegregating carbosilane end-groups from monocarbosilane to tricarbosilane. This observed enhancement is assumed to be due to an increase in the cross-section of the free volume in the hydrocarbon sub-layer. To test this hypothesis, it is assumed that dimers with a tricarbosilane linking group have smaller cross-sections on time average. In his thesis, this hypothesis is tested through the characterization of new liquid crystalline monomers (QL39-n) and dimers (QL40-n) with 2-phenylpyrimidine cores and tricarbosilane end-groups and spacers, respectively. The thesis describes the synthesis of two homologous series of liquid crystals and their characterization using a variety of techniques, including polarized optical microscopy, differential scanning calorimetry and X-ray diffraction. The results show that the monomers QL39-n form a tilted SmC phase only, whereas the dimers QL40-n form an orthogonal SmA phase. These results are discussed in the context of our hypothesis.

Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.