Therapeutic Interventions (TIs) play an important role in modern medical and psychological treatments, but their integration into the digital world still shows deficits, e.g., in the integration of the auditory interface. Initiatives to integrate this interface into existing Internet-and Mobile-Based Interventions (IMIs) are largely focused on a small group of Voice Assistants (VAs) and their specific capabilities. To mitigate these drawbacks, the presented concept seamlessly integrates arbitrary VAs into the treatment process of TIs. To this end, an architecture -including a discussion of relevant requirements -is presented that, on the one hand, uses VAs as the only point of contact with patients and, on the other hand, provides a comprehensive webbased backend for Healthcare Providers (HCPs). Based on the architecture, a proof-of-concept implementation using Amazon Alexa is presented. Finally, it is discussed that the scenario addressed and the solution presented have great potential, but still need a lot of work and technical considerations.

The technological capabilities and ubiquity of smart mobile devices favor the combined utilization of Ecological Momentary Assessments (EMA) and Mobile Crowdsensing (MCS). In the healthcare domain, this combination particularly enables the collection of ecologically valid and longitudinal data. Furthermore, the context in which these data are collected can be captured through the use of smartphone sensors as well as externally connected sensors. The TrackYourTinnitus (TYT) mobile platform uses these concepts to collect the user's individual subjective perception of tinnitus as well as an objective environmental sound level. However, the sound level data in the TYT database are subject to several possible sensor errors and therefore do not allow a meaningful interpretation in terms of correlation with tinnitus symptoms. To this end, a datacentric approach based on Principal Component Analysis (PCA) is proposed in this paper to cleanse MCS mHealth data sets from erroneous sensor data. To further improve the approach, additional information (i.e., responses to the EMA questionnaire) is considered in the PCA and a prior check for constant values is performed. To demonstrate the practical feasibility of the approach, in addition to TYT data, where it is generally unknown which sensor measurements are actually erroneous, a simulation with generated data was designed and performed to evaluate the performance of the approach with different parameters based on different quality metrics. The results obtained show that the approach is able to detect an average of 29.02% of the errors, with an average false-positive rate of 14.11%, yielding an overall error reduction of 22.74%.