Linking recorded data with emotive and adaptive computing in an eHealth environment

Telecare, and particularly lifestyle monitoring, currently relies on the ability to detect and respond to changes in individual behaviour using data derived from sensors around the home. This means that a significant aspect of behaviour, that of an individuals emotional state, is not accounted for in reaching a conclusion as to the form of response required. The linked concepts of emotive and adaptive computing offer an opportunity to include information about emotional state and the paper considers how current developments in this area have the potential to be integrated within telecare and other areas of eHealth. In doing so, it looks at the development of and current state of the art of both emotive and adaptive computing, including its conceptual background, and places them into an overall eHealth context for application and development

Technology for Older Adults: Maximising Personal and Social Interaction : Exploring Opportunities for eHealth to Support the Older Rural Population with Chronic Pain

Funding The TOPS project is supported by an award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub, award reference EP/G066051/1.Peer reviewedPublisher PD

Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity

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.BMBF, 13GW0157A, Verbundprojekt: Self-administered Psycho-TherApy-SystemS (SELFPASS) - Teilvorhaben: Data Analytics and Prescription for SELFPASSTU Berlin, Open-Access-Mittel - 201

Getting Change-Space: A Grounded Theory Study of Automated eHealth Therapy

A promising tool for bettering people’s health is eHealth (or “mHealth”) programs: fully automated, web-based health interventions. However, we know surprisingly little about eHealth’s working mechanisms. One possible working mechanism is that program users benefit from a collaborative “relationship”—a “working alliance”—with the program. Although evidence support the existence of a person-to-program alliance it is unclear if and how it influences change. Therefore, we conducted a grounded theory study of how relating to an eHealth program for quitting smoking influenced the participants’ change processes. The ensuing model focuses on how participants got change-space—feeling free from social forcing and able to work constructively on changing—and how the relational processes “making come-alive” and “keeping un-alive” were instrumental in this process. By presenting evidence that relating may influence change in automated therapy, this study supports the person-to-program alliance as a working mechanism in eHealth