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Thursday, October 16, 2017
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First United Methodist Church - Midlothian

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 Image goes here.So why are so many sites broadcasting your confidential, potentially embarrassing, and possibly damaging health information to corporations? So what you have is a government web developer passing the cost to users who have no idea their data is being traded away without their consent. But second place is fairly distant: 38 percent of the 80,000 pages analyzed sent third party requests to comScore, another internet analytics company. Meanwhile, 31 percent of sites funneled data to Facebook, 22 percent to AppNexus, 18 percent to AddThis a web tracking company , 18 percent to Twitter, 16 percent to Amazon, and 12 percent to Yahoo. A number sent requests to a combination of many of the separate companies listed above, and more. And it is notable that Google receives so much more data than any other company. Libert found that Analytics was spurring such requests on a full 45 percent of the 80,000 health pages he analyzed viagra without a doctor prescription usa. He had previously used the search engine to learn about the condition and to search for similar devices, but had never volunteered consent. The Google case is an example of the first—if Google wants to, it has the data to discern who you are and what ails you. This is creepy, certainly, but it also has real-world ramifications beyond the fact that your search provider knows that you have IBS. Users have no control over how that data is stored or secured, for instance, and it may be vulnerable to hackers. We have strict policies prohibiting such websites from passing any personally identifiable data. We don't want and don't use that kind of sensitive data. And to be clear: we absolutely don't allow our ad systems to be used to form profiles, or to target ads, based on health or medical information. And yes, that includes private health data.
Mobile phones are ubiquitous and have an increasingly large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms. Objective: The objective of this study was to explore the detection of daily-life behavioral markers using mobile phone global positioning systems GPS and usage sensors, and their use in identifying depressive symptom severity. Methods: A total of 40 adult participants were recruited from the general community to carry a mobile phone with a sensor data acquisition app Purple Robot for 2 weeks. Of these participants, 28 had sufficient sensor data received to conduct analysis. At the beginning of the 2-week period, participants completed a self-reported depression survey PHQ-9. Behavioral features were developed and extracted from GPS location and phone usage data. While these findings must be replicated in a larger study among participants with confirmed clinical symptoms, they suggest that phone sensors offer numerous clinical opportunities, including continuous monitoring of at-risk populations with little patient burden and interventions that can provide just-in-time outreach. IntroductionDepression is a common mental disorder. Estimates of the 12-month prevalence rate for major depressive disorder range from 6. Thus, more efficient methods of monitoring could significantly improve the delivery of services to those in need.
joinThe aim of this study was to extend previous work by focusing specifically on behavioral markers related to movement through geographic space, which we hypothesized would be related to depressive symptom severity viagra online prescription free, given depression results in decreased motivation, withdrawal, and activity. Thus, we also explored the relationship of depression symptom severity to the use of the phone that was used to collect the sensor data. We defined a number of behavioral features based on these data and built classification and regression models to examine their relationship to depression symptom severity. We recruited 40 adult participants from April-July 2013 using craigslist advertisements. Participants were eligible if they had an email account, computer, and broadband access to the Internet, were within a cellular network range the majority of the day, were able to speak and read English, were at least 19 years of age, and lived within the United States of America. Participants signed an online consent form, and research staff reviewed the consent over the phone. The study was approved by the Northwestern University Institutional Review Board. If participants owned and used an Android device with operating system 2.
The app implements a store-and-forward architecture wherein the sensor data are gathered, stored on the device, and transmitted as network connectivity becomes available. This allows us to collect data in a variety of wireless connectivity scenarios with the confidence that intermittent network access did not affect the nature, quality, or quantity of the collected data. Once the data are anonymized, they are stored and later transmitted to the data collection server before being deleted from the device. Sensor data residing on the server can be linked with other information gathered during the study only if the unique identifiers used by the participants and the study-specific keys used to encrypt the data are known. It also enables us to craft a complete data collection strategy configured for analyzing the relationship between depression and behavior data features of daily life. In this study, we configured Purple Robot to collect the GPS location and phone usage data, as the aim of this study was to focus on behavioral markers related to movement through space and the phone usage behavior. In our next study, we plan to use Purple Robot to collect data from a variety of phone sensors.
Example GPS location data, overlaid on satellite image. Each small circle represents a histogram bin, which has a size of 500 by 500 meters. The colors indicate the number of samples captured by each bin brighter means more samples. The bigger blue circles show the center of the clusters detected by the clustering algorithm. Example phone usage data from a participant. Each row is a day, and the black bars show the extent of time during which the phone has been is use. The bars on the right side show the overall phone usage duration for each day. To calculate location variance, we used only the location data of stationary states see Data Preprocessing. Number of clusters represented the number of location clusters found by the K-means algorithm in the preprocessing stage. We defined entropy to measure the variability of the time the participant spent at the location clusters. High entropy indicated that the participant spent time more uniformly across different location clusters, while lower entropy indicated greater inequality in the time spent across the clusters. We defined normalized entropy by dividing the entropy by its maximum value non prescription viagra, which is the logarithm of the total number of clusters:Unlike entropy, normalized entropy is invariant to the number of clusters and thus depends solely on the distribution of the visited location clusters. The value of normalized entropy ranges from 0-1, where 0 indicates that all location data points belong to the same cluster, and 1 implies that they are uniformly distributed across all the clusters. Home stay measured the percentage of time a participant spent at home relative to other location clusters. We identified the home cluster based on two heuristics: 1 the home cluster is among the first to the third most visited clusters, and 2 the home cluster is the cluster most visited during the time period between 12 a. In our dataset, which did not contain participants having night shift work, these two heuristics led to one and only one location cluster for every participant. We defined circadian movement to capture the temporal information of the location data. For example, if a participant left home for work and returned home from work around the same time each day, the circadian movement was high. On the contrary, a participant with a more irregular pattern of moving between locations had a lower circadian movement. We calculated E separately for longitude and latitude and obtained the total circadian movement as:Transition time represented the percentage of time during which a participant was in a non-stationary state see Data Preprocessing. This was calculated by dividing the number of GPS location samples in transition states by the total number of samples. Total distance measured the total distance in kilometers taken by a participant. It was calculated by accumulating the distances between the location samples.


 
 

 

 
 
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