| Agitation in dementia patients is characterized by physical and verbally aggressive and non-aggressive behaviors. Such behaviors affect not only the patients, but also the quality of life of caregivers. The onset of agitated behaviors is unpredictable and can be influenced by environmental factors. This study applied a Living Space Model to develop a preliminary user-centered data framework for data planning, collection, preparation, structuring, and analysis. The processes included in the data framework were validated using data extracted from a cyber-socio-physical systems testbed. In the validation process, multiple forms of quantitative data were obtained through behavioral and environmental sensors. The behavioral patterns of a person with dementia were simulated in a laboratory setup by actors. Data about body motion, ambient light, sound, and temperature were collected from the environment where the behaviors of the person with dementia were simulated. The acquired data was used to model the relationship between the simulated behaviors and environmental factors. A two-phase structural equation modeling approach was implemented to identify the environmental contexts associated with agitation. A model with acceptable fit was obtained for the acquired data, and the environmental context that triggers agitation was identified. The results from this research showed it is possible to identify the covariates and triggering conditions of agitation using causal path analysis in a simulated environment. The ability to identify the triggering environmental conditions can empower caregivers to act proactively to minimize occurrences of agitation in persons with dementia. Such causal relationship models can also serve as input to the design of caregiver notification systems. In addition, the simulation and data analysis procedures implemented in this study are deemed to contribute to the development of processes which could be applicable to future field studies. |
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