Abstract

Question. Different mobile EEG devices were shown to be technical usable and effective and therefore may also be used for EEG home-monitoring of neurological outpatients within their home environment. The HOME project intends to provide evidence of diagnostic and therapeutic yield of such an EEG home-monitoring. Besides the technical usability and feasibility, patients“ acceptability is a crucial aspect in design and implementation of EEG home-monitoring. The present study aims to examine factors influencing the acceptance in terms of behavioral intention to use a mobile dry-electrode EEG device for patient-controlled EEG home-monitoring. Methods. To measure (potential) patients“ acceptance of the EEG home-monitoring, we compiled a questionnaire based on the UTAUT model and additionally used the SF-36 to assess the participants” health status. The current routine care (e.g., monitoring in a hospital) serves as a comparison scenario and enables analyzes to be made as to which types of care are preferred by (potential) patients. We surveyed a sample of nearly 400 potential patients and 40 patients in Germany. For our analyses, we used partial least squares structural equation modeling to identify relevant factors determining the participants“ behavioral intention to use. Subgroup analyses allow comparisons of effects of different information provision formats (e.g., textual and/or visual information) and previous experiences. Results. The HOMETA study revealed the importance of information provision, in particular information regarding (1) the interaction possibilities between patient and physician, (2) the EEG device (e.g., technical properties, data transmission and security) used, and (3) the correct use of the EEG device. We found that the acceptance of the EEG home-monitoring is significantly influenced if it is also explained visually. Conclusions. In line with other studies, we confirm the applicability of the UTAUT model in the context of mobile health services. The results of our HOMETA study support previous findings that specific mobile EEG devices are suitable to establish a patient-controlled EEG home-monitoring in the routine care of neurological outpatients. Moreover, we show that patients accept (in terms of behavioral intention to use) such EEG home-monitoring if it has the necessary properties and characteristics (e.g., secure data transmission, easy handling). Based on these, we are able to derive implications for the design and implementation of patient-controlled EEG home-monitoring.

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