Chapter 1 20 gathered from these sensors can be used to make inferences about peoples’ states of affect” [109]. Although the quality of the data gathered by the ever-increasing diversity of wearable devices is not yet fully meeting the gold standards of state-ofthe-art lab equipment, collecting data in more ecologically valid settings is promising for psychological assessment and intervention [110, 111]. The use of mobile technology in health care has been termed mHealth [112]. Mobile apps are of particular interest because they may have additional benefits beyond accessing websites and text messaging that may make them a particularly valuable platform for dissemination of interventions. Recent years have seen an enormous increase in the number of mHealth apps, claiming to help improving one’s mood, emotional balance, or other aspects of mental health [112-114]. At the start of 2020, there were already nearly 20,000 mobile MHealth apps in the app stores [115]. mHealth broadly includes the use of mobile devices such as smartphones, tablets, personal digital assistants, and more recently, wearable devices. Lui, Marcus and Barry [113] have given an overview of the potential advantages of mHealth that are identified by scholars. These are that mHealth can overcome barriers associated with cost, transportation, lack of therapists, lack of insurance, or a long waitlist for services, and that it can contribute to less stigma and more privacy. Another important potential advantage is that interventions can be delivered in the moment of need in any location and time, such as during high-risk or triggering situations, or times of significant distress. Furthermore, when mHealth is used as an adjunct to traditional therapy, it has the potential to increase homework compliance and generalization of therapeutic skills outside of sessions. Finally, mHealth may also promote early identification and early intervention, as well as offer brief services to those who may have less severe or subthreshold symptoms. The connection capabilities of smartphones and their embedded sensors allows the unobtrusive collection of active information from subjects on their natural environment including ecological momentary assessments using tests or questions, as well as passive objective data from device usage patterns and sensors. These methods avoid the possibility of recall bias, which affects standardised scales and questionnaires applied at a specific time point to assess the presence of symptoms over the last previous weeks or months [116, 117]. 1.8 Gaps and risks of mHealth apps However, the use of mobile apps for clinical purposes is not without risk. Apart from a limited number of decent exceptions (such as [118]), the majority of these apps seem to be developed with little regard to the specific characteristics and actual needs of its target users. Many of these applications also fail to incorporate current
RkJQdWJsaXNoZXIy MjY0ODMw