Social media systems have permeated daily life. Information is collected, organized, and disseminated worldwide via informational collection and dissemination, micro-blogging and blogging services. Other social media are mobile and positional in nature and can be referred to as Mobile Positional Social Media (MPSM). As these systems focus on locations, mobile device implementations permeate the space. That said, however, while MPSM implementations are targeted to primarily execute on mobile devices, such as but not limited to smart-phones (e.g., Apple's iPhone, Google's Android), tablets (e.g., Apple's iPad, HP TouchPad), and laptop computers, they often support implementations for non-mobile environments such as but not limited to desktops and workstations, and large scale compute farms and cloud computing servers.
MPSM systems focus on locating users and notifying each other within their community of their respective locations or nearby content of interest. For example, Foursquare's application locates users and informs them of items of interest in their vicinity or the vicinity of their choice. Users are motivated to actively and manually “check-in” at their location with specificity rewarded. Rewards include “badges” and honors such as being named “Mayor.” Additional enticements are group texting facilities as provided by the likes of BrightKite. Other MPSM include but are not limited to Gowalla, Loopt, Jaiku, Plazes, and Fire Eagle.
One limitation of MPSM systems is their reliance on global positioning systems (GPS). The use of GPS devices does typically simplify location tracking implementation; however, this comes at a significant energy cost. Since a significant portion of MPSM systems usage is via mobile devices, reducing energy consumption is critical.
Another limitation of current MPSM systems is their reliance on active users identifying their location and/or their activity at the location. Another limitation of current MPSM systems is the limited modes of informational guidance provided to the user. For example, no reminders or instructional commenting is provided. That is, users are not reminded of activities that fit their given location and context in a push manner; rather, user inquiry of locally available options is needed. Ideally, given location and context users are proactively pushed information that is immediately relevant to them. Additionally, activities that are nearby to their current location or will become available can likewise be identified.
The use of smartphone technology for medical applications is increasing. In August 2014, the Food and Drug Administration (FDA) cleared a smartphone device, AliveCor, that detects atrial fibrillation, a potential warning sign for heart failure and stroke. Additionally, Apple Corporation, in support of medical smartphone technology, recently developed an open-source tool kit to aid in the authoring of medical research applications. Also known in the art is the use of activity and trend detection using smartphone technology for medical applications. Recently, an Android based application called StudentLife was introduced at Dartmouth College. This application monitors smartphone use (e.g., text messaging, e-mail traffic, etc.) as well as additional activity (e.g., mobility and sleep patterns) to infer state of being. Automatic sensed, as well as interactive user probing, data were collected to determine potential stress.
There is thus a continuing need for and interest in improved mobile device-based systems and applications for medical applications.