| Literature DB >> 25935151 |
Felipe Besoain1,2, Antoni Perez-Navarro3, Joan A Caylà4,5, Constanza Jacques Aviñó6, Patricia García de Olalla7,8.
Abstract
BACKGROUND: Advances in the development of information and communication technologies have facilitated social interrelationships, but also sexual contacts without appropriate preventive measures. In this paper, we will focus on situations in which people use applications to meet sexual partners nearby, which could increase their chance of exposure to sexually transmitted infections (STI). How can we encourage users to adopt preventive measures without violating their privacy or infringing on the character of the application?Entities:
Mesh:
Year: 2015 PMID: 25935151 PMCID: PMC4428096 DOI: 10.1186/s12942-015-0010-z
Source DB: PubMed Journal: Int J Health Geogr ISSN: 1476-072X Impact factor: 3.918
General detection of a
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| 1.- This use case starts when the | |
| 2.- The notification service requestsinformation from the server withusers’ geoposition and the | |
| 3.- It determines if users are near a | |
| 4.- It notifies users of the | |
| 5.- Users select thenotification. | |
| 6.- The system opens a windowusing | |
| 7.- Users can select arisk presented in themapview and obtaininformation about it(available when theapplication is usedfor infectious diseasesprevention). | |
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| 3.- The |
This table presents an extension of the General detection of a geographic use case, in a conversational format, which emphasizes the interaction between the actors and the system.
General detection of a monitorized application
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| 1.- This use case startswhen users start arisk-associated software. | |
| 2.- The | |
| 3.- It notifies users of the launch ofthe application through thenotifications bar | |
| 4. - Users select thenotification. | |
| 5. - The system opens a window with mapview in which the nearest medical center to his/her position is shown. | |
| 6. - Users can select thehealth center or pharmacyand obtain informationabout it. | |
| 7.- Users can select a riskpresented in the mapviewand obtain informationabout it. | |
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| 2.- The |
This table presents an extension of the General detection of a monitorized application, in a conversational format, which emphasizes the interaction between the actors and the system.
Figure 1Implemented architecture and essential components and services. The architecture consists of three main layers: the presentation, domain and data layers, representing the interaction between the essential components of the solution. This representation is divided between the Android client (top) and the web service (bottom). The re-used open source components in the proposed architecture are also shown.
Figure 2Web client: The user interface of the web server has been constructed by integrating OpenLayers and OpenStreeMap such that users can visualize the various alerts that have been included in the database. New alerts can be created by georeferencing through the map and mouse interaction, and relevant information can be added to the alert. (Labels translated from Spanish).
Figure 3User Interface, Android client. The interface shows the various nearby hot zones (red circles) and users can select these areas to obtain more information. (Labels translated from Spanish).
Figure 4User Interface, Android client. The software runs in the background and alerts users when certain applications are run. (Labels translated from Spanish).
Demographic characteristics of volunteers who helped to choose the messages ( =17)
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| 21–30 | 17.6% | Spain | 64.7% |
| 31–40 | 47.1% | Andorra | 5.9% |
| 41–50 | 11.8% | Brazil | 5.9% |
| 51–60 | 23.5% | Chile | 5.9% |
| Honduras | 5.9% | ||
| Peru | 5.9% | ||
| Dominican Republic | 5.9% |
This table shows the demographic characteristics of people who helped to choose the messages.
Places where users usually find partner ( =17)
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| Gay sauna | 28.1% |
| Internet | 28.1% |
| Geolocators | 21.9% |
| Discoteques | 21.9% |
| Bars | 21.9% |
| Has partner | 3.1% |
| Gimnasium | 3.1% |
| Beach | 3.1% |
| Park | 3.1% |
| Street | 3.1% |
Percentages does not sum 100% because every volunteer could mark several options.
Target of the application according to potential users’ perception ( =17)
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| Anybody | 43.8% | 15–20 | 16.7% |
| Someone like me | 12.5% | 21–30 | 30.6% |
| Someone different from me | 43.8% | 31–40 | 19.4% |
| 41–50 | 11.1% | ||
| 51–65 | 2.8% | ||
| Any age | 19.4% |
This table describes the volunteers’ perception as the target of the application, from the profile point of view, as well as from the age point of view. Percentages do not sum to 100% because each volunteer could mark several options.
Figure 5Notification service of hot zones. This sequence shows appears when users launch the notification service is running, and the service detects a hot zone near the user’s location of users. The service also notifies users in the message bar. When users select that notification, the software opens a new window with the location of the hot zone and the users. (Labels translated from Spanish).