AbstractBackgroundThe protocol for ‘Brain Boosters’, a novel combined compensatory training and lifestyle modification intervention aimed at Alzheimer’s disease (AD) risk reduction is presented. Training in compensation will focus on a calendar and tracking system, organizational strategies, and goal setting, all aimed at supporting independent function. These same compensatory tools will be employed to facilitate adoption of lifestyle changes that support brain health (e.g., physical exercise, cognitive exercise, wellbeing exercise) through behavioral monitoring and feedback. A comprehensive suite of digital tools encapsulated in the Electronic Memory and Management Aid (EMMA), an easy to use, interactive application, will be used to facilitate behavioral change and enhance participant motivation. EMMA also allows collection of real‐time data to track intervention adherence and factors that influence adherence.MethodsTo capitalize on a critical window of opportunity to intervene, we will target cognitively normal older adults with subjective cognitive concerns (SCC), an established risk for AD and cognitive impairment. The trial will enroll 200 older adults with SCC who will be randomized to our digital application‐supported compensation training and lifestyle modification intervention or to an education only control group that will not use the EMMA app or be taught how to implement the educational material into their daily lives. Both intervention arms will be delivered in a group setting over 6 months, followed by 12 months of unsupervised follow‐up.ResultsSpecific aims of the project include to: 1) evaluate intervention efficacy on primary outcomes (global cognition and everyday function); secondary outcomes focus on indicators of well‐being, cognitive domains (memory and executive function), physical function, compensation, and health behaviors; 2) evaluate characteristics of treatment responders; 3) evaluate adherence and identify the effective components of the target intervention using a mixed‐method approach; and 4) design machine learning algorithms that use patterns of change in real‐time data metrics to identify incipient declines in treatment adherence and changes in health status.ConclusionsThe project is expected to expand understanding of factors that may impact adherence to and outcomes of a preventative intervention leading to optimization of a scalable intervention to reduce dementia risk applicable to diverse populations.
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