Abstract
Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment. We find that the reported receptivity is not sequentially predictable on its own, the mean error and standard deviation are only slightly below by-chance comparison. Nevertheless, predicting the upcoming activity shows to cover about 39% of the day (up to 58% in the best case) and can be linked to user individual intervention preferences to indirectly find an opportune moment of receptivity. Therefore, we introduce an application comprising the influences of sensor data on activities and intervention thresholds, as well as allowing for preferred events on a weekly basis. As a result of combining those multiple approaches, promising avenues for innovative behavioral assessments are possible. Identifying and segmenting the appropriate set of activities is key. Consequently, deliberate and thoughtful design lays the foundation for further development within research projects by extending the activity weighting process or introducing a model reinforcement.
Highlights
Treating depression is of increasing importance to medicine and society
The associate editor coordinating the review of this manuscript and approving it for publication was Yonghong Peng
Evaluation, and presentation of the prototype, the design science research (DSR) approach proposed by [7] was utilized by first learning about the problem and designing a draft, which was concurrently and conclusively evaluated
Summary
Treating depression is of increasing importance to medicine and society. In a hypercompetitive global economy, companies can become high-stress incubators for mental health problems. A mobile application is introduced that serves users as pre-hospital treatment and for preventing depressive moods. Users are able to quantify the severity of their mental stress by self-assessment and thereby are able to assess their emotional burden in everyday life. Patients with diagnosed depression might have little motivation to answer automatic questions every day. Mobile health (mHealth) applications can prompt users to input information about their situation or internal states and provide in-the-moment responses personalized to a user’s immediate needs. A typical week is modeled by reading environmental sensor data and the application will find favorable times for therapy questions. We investigate at what time users are motivated to answer selfassessment questions in their everyday lives and how an intelligent mobile application can take advantage of time windows, where motivation is increased.
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