Background and objectives Inappropriate medication use is common in the care of patients with CKD. The feasibility of a simple mobile health tool designed to advise patients on safe medication usage in CKD was examined.Design, setting, participants, & measurements Participants with predialysis CKD (defined as eGFR,60 ml/min per 1.73 m 2 ) in the Safe Kidney Care Cohort Study were recruited for home usability testing of a novel medication inquiry system between January and September of 2013. Testing was through two mobile platforms: (1) short messaging service text or (2) personal digital assistant (e.g., iPod Touch). Twenty participants (one half assigned to one device and one half assigned to the other device) were enrolled and received an in-center tutorial on device usage before the end of the study visit. Participants were subsequently mailed three sample pill bottles with the name of randomly selected medications and asked to input these medications into the medication inquiry system. The medication inquiry system response options were as follows: (1) safe in CKD, (2) not safe in CKD, (3) use with caution/speak with your health care provider, or (4) error message (for an incorrectly inputted medication). Participants were asked to record the response issued by the medication inquiry system for each medication sent for usability testing. A user satisfaction survey was administered after completion of the protocol.Results All participants owned a mobile telephone, but few owned a smartphone. Of 60 total medication queries, there were only three recorded errors, two of which occurred in the short messaging service texting group. Overall satisfaction with the application was high, with slightly higher satisfaction noted in the personal digital assistant group compared with the short messaging service group.
ConclusionsThe mobile health medication inquiry system application had general ease of use and high acceptance across two platforms among individuals representative of the CKD population. Tailored mobile health technology may improve medication safety in CKD.
Social networks are flourishing because of fast growing Internet and the World Wide Web, and more research efforts have been put on Social Network Analysis (SNA). A social network can be modeled like a graph, where the nodes represent persons, and an edge between them represent direct relationship between the persons. One of the issues in SNA is to identifying criminals from groups of individuals. In a real social network, there must have various relationships between individuals, like friendships, business relationships, and common interest relationships etc. The internet itself is a huge social network. To model such a network, link analysis need to be proposed. A page in web may treat as a node, and hyperlink between them can be represented as relationships. After social network graph is constructed, link analysis and graph partitioning algorithms may be applied to identify the hidden links in that network. Most of the existing algorithms related to social network analysis assume that their existing only one single social network, with relatively multiple relationship like Web page linkage. In typical social networks, there always have various kinds of relations. Every relation can be identified as a relation network. These different types of relations play different tasks in different roles.The work here attempts to find the problem of mining hidden relationships on social networks. Social network analysis (SNA) is a set of powerful techniques that can be used to identify clusters, patterns and hidden structures within social networks. Here the problem is identified with the following steps.1. Analyzing information flow through the network using affected dataset, 2. Discovering non-obvious relations between actors, and 3. Identifying nodes that are directly or indirectly connected to most other nodes in the social network. This is done with the help of mining algorithms like Min-cut and Regression.
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