Challenges in Collecting Big Data in A Clinical Environment with Vulnerable Population: Lessons Learned from A Study...

Area of Science:

• Gerontology and Dementia Care
• Biomedical Engineering and Sensor Technology
• Behavioral Health and Psychology

Background:

• Agitation is a prevalent behavioral symptom in people living with dementia (PLwD), causing significant distress to caregivers and healthcare providers.
• Current methods for measuring agitation, such as direct observation, are subjective, prone to bias, time-consuming, and lack predictive capabilities.
• A need exists for continuous monitoring systems to detect and predict agitation onset in PLwD.

Purpose of the Study:

• To develop a predictive system for identifying the onset of agitation in people living with dementia (PLwD).
• To implement a multi-modal sensor platform within a hospital-based dementia care unit for continuous monitoring.
• To address and discuss the challenges encountered during the study's development and initiation.

Main Methods:

• Deployment of a multi-modal sensor platform including video cameras, motion sensors, door sensors, wristbands, and pressure mats.
• Installation within a hospital-based dementia behavioural care unit.
• Focus on developing a predictive system for agitation onset.

Main Results:

• The study successfully set up a multi-modal sensor platform in a clinical dementia care setting.
• Identified and documented key barriers encountered during the study's development and initiation phases.
• Strategies and methodologies were implemented to overcome these challenges.

Conclusions:

• The development of a continuous monitoring system is crucial for predicting agitation in people living with dementia (PLwD).
• Addressing ethical, logistical, cost, device design, and hospital-use limitations is vital for implementing such systems.
• The discussed strategies offer valuable insights for future researchers conducting similar studies in hospital environments.