Aims: Seizure detection using heart rate variability, from a detailed analysis by deep learning analysis system, may help patients with epilepsy to manage their symptoms. This exploratory study aims to identify patient and caregiver groups, according to acceptability factors.
Methods: Two versions of the same questionnaire were designed to survey quality of life, self-efficacy, and patients with epilepsy and caregivers on seizure detection acceptability using a patch, after watching a video that described a patch connected to a companion application. Participation was voluntary and anonymous.
Results: Responses from 68 patients with epilepsy and 33 caregivers were collected. Patients with epilepsy were grouped into three clusters: supportive, indeterminate, and reluctant to use the technology. Caregivers were also grouped into three clusters: supportive, reluctant to use the technology, either with sensitivity to their environment, or with hedonic motivation. The clusters enable the distinction between participants in self-efficacy.
Conclusions: Clustering of patients with epilepsy and caregivers should be a prerequisite to the design of a technological device intended to promote self-management of seizure detection. These groupings distinguish those who are favorable, reluctant or undecided to use the technology. These can be based on an assessment of self-efficacy.
Keywords: Acceptability; Caregiver view; Epilepsy; Patient views; Seizure detection.
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