Detecting distress in cognitive impaired people to prevent suffering: An observational feasibility study of a radar-based technology augmented with photopletysmographic sensors and audio signals (SURREAL)

Background

Due to well-known sociodemographic changes and the rising prevalence of multimorbidity, the demand for palliative and end-of-life care is expected to increase significantly in the coming years. This is particularly driven by the growing incidence of frailty, dementia, and chronic, progressive, and life-limiting conditions such as heart failure, lung diseases (e.g., chronic obstructive pulmonary disease [COPD]), and multimorbidity. Digital health technologies have the potential to facilitate automated detection of distress in palliative care, including symptoms like pain, breathlessness (dyspnea), panic, agitation, and delirium. Such advancements could alleviate the suffering of cognitively impaired patients who are unable to call for help, for instance, by pressing an alarm button, yet urgently require professional intervention (e.g., administration of medication for pain or breathlessness).

Aim

This observational feasibility study aims to assess if data output from a sensor system consisting of a 3-D-radar, photoplethysmographic sensors (wearables), and audio detection (microphones) is associated with patient’s distress events as identified by medical professionals during standard care.

Research Plan

We will utilize well-established sensor systems, including Qumea® and wearables, to monitor heart rate, respiratory rate, heart rate variability, body movements, postures, and audio signals, such as changes in sound intensity or voice frequency. These data will be compared against the current gold standard for detecting distress in cognitively impaired patients: assessments conducted by trained healthcare professionals, specifically specialist palliative care nurses and physicians. Identifying correlations between sensor data and professional distress assessments could facilitate future validation studies, potentially leading to the development of automated distress detection systems using sensor arrays. This advancement would significantly improve distress detection rates and reduce patient suffering.