Dementia-Friendly Neighborhood and the Built Environment: A Scoping Review

Abstract

Background and Objectives

There has been a proliferation of research on dementia-friendly communities in recent years, particularly on interpersonal and social aspects. Nonetheless, the neighborhood built environment remains a co-constituent of the lived experience of people living with dementia (PLWD) that is amenable to interventions for health and well-being in the community. This scoping review presents a narrative synthesis of empirical research on dementia-friendly neighborhoods, with a focus on the built environment and its associated sociobehavioral aspects. Planning and design principles are distilled to identify research and policy implications.

Research Design and Methods

We reviewed 29 articles identified through a systematic search of AgeLine, PsycINFO, Cumulative Index to Nursing and Allied Health Literature, Global Health, Medical Literature Analysis and Retrieval System Online, and Scopus. Peer-reviewed articles that employed quantitative and/or qualitative methods in community settings were included.

Results

An equal number of studies focused on behavioral/psychosocial aspects of the built environment and assessment of specific environmental features. The former often used qualitative methods, whereas statistical methods were common in studies on discrete features of the neighborhood built environment. Few studies focused on rural contexts. Emerging research areas include interactions between dementia risk factors and neighborhood environments to support primary and secondary prevention.

Discussion and Implications

The body of literature needs expansion into planning and design fields to foster community participation of PLWD by optimizing environmental stimuli, minimizing environmental barriers, and engaging PLWD in dementia-friendly community initiatives. While evidence has accumulated on landmarks and social participation at the individual level, research at the community and policy levels is limited. This requires advanced mixed methods.