# Direct Mobile Coaching as a Paradigm for the Creation of Mobile Feedback Systems
## 2 Background
### Related Work  
There are several other frameworks or models that could be used for the creation of mobile feedback systems. While they do not directly propose design principles or their publication does not feature the term \"model\", they are built to allow the creation of systems with a common architecture. Consequently, each of these frameworks also has an underlying model.  
A domain similar to feedback systems for sporting applications is the domain of applications for mobile health (mHealth). In the field of behavioural change interventions, an approach similar to MC is followed by the MobileCoach framework [16]. Its main target audience is creators of digital health interventions that are modelled--using automata theory--as state machines. These machines aggregate user inputs and variables to states, which are used in combination with rules (the actual intervention) to interact with athletes/participants. Data from users/patients are collected in the form of surveys or text messages. Feedback in the form of text messages or additional/other surveys is provided using these state machines.  
Another framework focusing on the scalability and security of mHealth applications is RADAR [17]. Its aim is to collect data from smartphones and wearables in near-real-time. RADAR offers several apps for collecting data from accelerometers or in the form of questionnaires, as well as a web-based platform for managing study participants and viewing data of a study.  
As cycling and running are popular recreational activities, various feedback systems, such as bike computers and heart rate monitors, are offered by different commercial vendors. The manufacturer Garmin offers the possibility to extend the capabilities of some of their devices by using the \"Garmin Connect IQ SDK\" (_GCIS_) [18]. These devices record data from wireless sensors using Ant+ or BLE or sensors built into the device such as a Global Positioning System (GPS) module or accelerometer. Consequently, clients are restricted to certain Garmin devices. The Software Development Kit (SDK) can be used to extend the recording capabilities of the devices by adding additional \"data fields\", to the data recorded by the device. Furthermore, visual feedback can be provided by creating \"views\". However, the framework has only limited capabilities for providing feedback. For example, it does not allow arbitrary computations but only a restricted set of operations and limited access to data. Furthermore, it cannot be used to model systems with multiple athletes as no communication between devices, and the central server (other than uploading recorded data) can be modelled.  
In summary, all frameworks presented above pose certain challenges when used for the creation of mobile feedback systems (see Table 1). For example, their target field is not directly related to general mobile feedback systems but rather specific types such as endurance sports or mHealth. In the case of GCIS, the target field is within the domain of feedback systems but has a focus on specific types of systems. Furthermore, recording arbitrary data is often required when developing systems based on novel sensors that often do not offer data in standardised formats. Additionally, the frameworks can be challenging with regard to offered feedback modalities: for example, not all frameworks offer the ability to provide both concurrent and terminal feedback. A small challenge is posed by GCIS due to the reliance on the non-standard programming language Monkey-C. Especially for novices, the complexity of RADAR and its technology stack can pose a challenge. Furthermore, not all frameworks offer the possibility to modify or extend the capabilities.