Testing the Feasibility of Tracking Behavior and Physiology Patterns using Wearable Technology to Detect Potential Cognitive Risk among Rural, Multicultural Older Adults

Abstract

AbstractBackgroundRural residents often experience multiple factors that increase their risk of cognitive impairment, e.g. less access to formal education and specialized healthcare (Livingston et al., 2020). Early, accurate diagnosis in the mild cognitive impairment stage could save up to 7.9 trillion dollars in health and long‐term care costs by 2050 (Alzheimer’s Association, 2021). First year results from this federally‐funded new investigator mentoring program between computer science researchers and a nurse investigator are reported. The purpose was to test the feasibility of a novel approach to detecting early cognitive decline using smartwatch technology among a 92% racially and ethnically diverse rural community challenged by low digital access (51%) and digital literacy (48%).MethodWe tested smartwatches programmed with the novel “n‐back” shape test for cognitive assessment, ecological momentary assessment, and movement tracking. Paper‐and pencil measures to assess cognitive risk were the PROMIS ® Applied Cognition, Mini‐Cog (Borson, 2003), and Mini‐MoCA (Nasreddine, 2019). Another novel aspect of the study was hiring the local high school students to mentor older adults in use of smartwatches during home visits. Student engagement with their mentees was monitored by their high school advisor, with oversight by well‐known faith‐based research assistants. Six additional pre‐post intervention surveys included “Technology Activities of Daily Living (T‐ADL) (Muñoz‐Neira, 2012) and “Relating to Older People” (Cherry and Palmore, 2008).ResultMean years lived rural of the n = 28 enrolled was 60.3 (SD = 18.2), with an average age of 71.6 (SD = 8.1). Results regarding cognitive risk using the n‐back Shape test included correlation with Ecological Momentary Assessment Question 7 (“Right now, my environment is distracting”; r = ‐.35, p = .046) and approached significance with Question 1 (“In the past 2 hours, do you think your mind has been as sharp as usual?”; r = .33, p = .058). Pre‐post correlations in T‐ADL (r = .87) and Applied Cognition (r = .38) were significant (p < .001, p < .04).ConclusionTesting this novel two‐pronged approach of detecting cognitive risk through smartwatch technology, and hiring high school students to mentor older adults in the home setting yielded implications for ADRD‐related research among larger samples of underrepresented groups.