Reproducibility and day time bias correction of optoelectronic leg volumetry: a prospective cohort study

Background Leg edema is a common manifestation of various underlying pathologies. Reliable measurement tools are required to quantify edema and monitor therapeutic interventions. Aim of the present work was to investigate the reproducibility of optoelectronic leg volumetry over 3 weeks' time period and to eliminate daytime related within-individual variability. Methods Optoelectronic leg volumetry was performed in 63 hairdressers (mean age 45 ± 16 years, 85.7% female) in standing position twice within a minute for each leg and repeated after 3 weeks. Both lower leg (legBD) and whole limb (limbBF) volumetry were analysed. Reproducibility was expressed as analytical and within-individual coefficients of variance (CVA, CVW), and as intra-class correlation coefficients (ICC). Results A total of 492 leg volume measurements were analysed. Both legBD and limbBF volumetry were highly reproducible with CVA of 0.5% and 0.7%, respectively. Within-individual reproducibility of legBD and limbBF volumetry over a three weeks' period was high (CVW 1.3% for both; ICC 0.99 for both). At both visits, the second measurement revealed a significantly higher volume compared to the first measurement with a mean increase of 7.3 ml ± 14.1 (0.33% ± 0.58%) for legBD and 30.1 ml ± 48.5 ml (0.52% ± 0.79%) for limbBF volume. A significant linear correlation between absolute and relative leg volume differences and the difference of exact day time of measurement between the two study visits was found (P < .001). A therefore determined time-correction formula permitted further improvement of CVW. Conclusions Leg volume changes can be reliably assessed by optoelectronic leg volumetry at a single time point and over a 3 weeks' time period. However, volumetry results are biased by orthostatic and daytime-related volume changes. The bias for day-time related volume changes can be minimized by a time-correction formula.

Towards Energy Consumption Measurement in a Cloud Computing Wireless Testbed

The evolution of the Next Generation Networks, especially the wireless broadband access technologies such as Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX), have increased the number of "all-IP" networks across the world. The enhanced capabilities of these access networks has spearheaded the cloud computing paradigm, where the end-users aim at having the services accessible anytime and anywhere. The services availability is also related with the end-user device, where one of the major constraints is the battery lifetime. Therefore, it is necessary to assess and minimize the energy consumed by the end-user devices, given its significance for the user perceived quality of the cloud computing services. In this paper, an empirical methodology to measure network interfaces energy consumption is proposed. By employing this methodology, an experimental evaluation of energy consumption in three different cloud computing access scenarios (including WiMAX) were performed. The empirical results obtained show the impact of accurate network interface states management and application network level design in the energy consumption. Additionally, the achieved outcomes can be used in further software-based models to optimized energy consumption, and increase the Quality of Experience (QoE) perceived by the end-users.

AccessMod 3.0: computing geographic coverage and accessibility to health care services using anisotropic movement of patients

Background Access to health care can be described along four dimensions: geographic accessibility, availability, financial accessibility and acceptability. Geographic accessibility measures how physically accessible resources are for the population, while availability reflects what resources are available and in what amount. Combining these two types of measure into a single index provides a measure of geographic (or spatial) coverage, which is an important measure for assessing the degree of accessibility of a health care network. Results This paper describes the latest version of AccessMod, an extension to the Geographical Information System ArcView 3.×, and provides an example of application of this tool. AccessMod 3 allows one to compute geographic coverage to health care using terrain information and population distribution. Four major types of analysis are available in AccessMod: (1) modeling the coverage of catchment areas linked to an existing health facility network based on travel time, to provide a measure of physical accessibility to health care; (2) modeling geographic coverage according to the availability of services; (3) projecting the coverage of a scaling-up of an existing network; (4) providing information for cost effectiveness analysis when little information about the existing network is available. In addition to integrating travelling time, population distribution and the population coverage capacity specific to each health facility in the network, AccessMod can incorporate the influence of landscape components (e.g. topography, river and road networks, vegetation) that impact travelling time to and from facilities. Topographical constraints can be taken into account through an anisotropic analysis that considers the direction of movement. We provide an example of the application of AccessMod in the southern part of Malawi that shows the influences of the landscape constraints and of the modes of transportation on geographic coverage. Conclusion By incorporating the demand (population) and the supply (capacities of heath care centers), AccessMod provides a unifying tool to efficiently assess the geographic coverage of a network of health care facilities. This tool should be of particular interest to developing countries that have a relatively good geographic information on population distribution, terrain, and health facility locations.