The Experiences and Determinants of Sleep Problems of Immigrant and Canadian-Born Older Adults-Analysis of Canadian Longitudinal Study on Aging Baseline Data.

BACKGROUND: Although prevalent, limited knowledge is available on the experience of sleep problems (i.e., disturbance in sleep latency and in sleep maintenance) and their determinants in immigrant older adults. PURPOSE: To compare immigrant and Canadian-born older adults' experiences of: 1) sleep problems, 2) determinants of sleep problems, categorized into precipitating and perpetuating factors, and 3) determinants most significantly contributing to each sleep problem. METHODS: Baseline data obtained by the comprehensive cohort of the Canadian Longitudinal Study on Aging were analyzed. Participants 55+ years of age and with complete data on their country of birth comprised the sample, with 18,245 Canadian-born and 4,257 immigrant older adults. Single or multiple items were used to assess the precipitating (chronic condition, sleep disorders, pain, depressive symptoms, psychological distress, education, marital and socio-economic status) and perpetuating (smoking, alcohol consumption, physical activity) factors. Chi-square test and independent sample t-test were used in the comparison and multiple regression was applied to determine the most significant determinant of each sleep problem in each group of older adults. RESULTS: Despite differences in a few determinants of sleep problems, the set of factors contributing to disturbance in sleep latency and maintenance was comparable for Canadian-born and immigrant older adults, and included: having a sleep disorder and high level of depressive symptoms and psychological distress. CONCLUSION: The findings highlight the importance of public health campaigns to increase older adults' awareness of sleep problems, the factors that may contribute to disturbance in sleep, and strategies to prevent and/or manage sleep problems.

Energy-efficient cognitive radio sensor networks: parametric and convex transformations.

Designing energy-efficient cognitive radio sensor networks is important to intelligently use battery energy and to maximize the sensor network life. In this paper, the problem of determining the power allocation that maximizes the energy-efficiency of cognitive radio-based wireless sensor networks is formed as a constrained optimization problem, where the objective function is the ratio of network throughput and the network power. The proposed constrained optimization problem belongs to a class of nonlinear fractional programming problems. Charnes-Cooper Transformation is used to transform the nonlinear fractional problem into an equivalent concave optimization problem. The structure of the power allocation policy for the transformed concave problem is found to be of a water-filling type. The problem is also transformed into a parametric form for which a ε-optimal iterative solution exists. The convergence of the iterative algorithms is proven, and numerical solutions are presented. The iterative solutions are compared with the optimal solution obtained from the transformed concave problem, and the effects of different system parameters (interference threshold level, the number of primary users and secondary sensor nodes) on the performance of the proposed algorithms are investigated.