Chao-Yi Wu1,2, Hiroko H Dodge1,2, Sarah Gothard1,2, Nora Mattek1,2, Kirsten Wright1,2, Lisa L Barnes3,4, Lisa C Silbert1,2,5, Miranda M Lim1,5, Jeffrey A Kaye1,2, Zachary Beattie1,2. 1. Department of Neurology, Oregon Health & Science University (OHSU), School of Medicine, Portland, Oregon, USA. 2. Oregon Center for Aging & Technology (ORCATECH), OHSU, Portland, Oregon, USA. 3. Department of Neurological Sciences, Rush Medical College, Chicago, Illinois, USA. 4. Rush Alzheimer's Disease Center, Rush Medical College, Chicago, Illinois, USA. 5. Department of Neurology, Veterans Affairs Portland Health Care System, Portland, Oregon, USA.
Abstract
BACKGROUND: The ability to capture people's movement throughout their home is a powerful approach to inform spatiotemporal patterns of routines associated with cognitive impairment. The study estimated indoor room activities over 24 hours and investigated relationships between diurnal activity patterns and mild cognitive impairment (MCI). METHODS: One hundred and sixty-one older adults (26 with MCI) living alone (age = 78.9 ± 9.2) were included from 2 study cohorts-the Oregon Center for Aging & Technology and the Minority Aging Research Study. Indoor room activities were measured by the number of trips made to rooms (bathroom, bedroom, kitchen, living room). Trips made to rooms (transitions) were detected using passive infrared motion sensors fixed on the walls for a month. Latent trajectory models were used to identify distinct diurnal patterns of room activities and characteristics associated with each trajectory. RESULTS: Latent trajectory models identified 2 diurnal patterns of bathroom usage (high and low usage). Participants with MCI were more likely to be in the high bathroom usage group that exhibited more trips to the bathroom than the low-usage group (odds ratio [OR] = 4.1, 95% CI [1.3-13.5], p = .02). For kitchen activity, 2 diurnal patterns were identified (high and low activity). Participants with MCI were more likely to be in the high kitchen activity group that exhibited more transitions to the kitchen throughout the day and night than the low kitchen activity group (OR = 3.2, 95% CI [1.1-9.1], p = .03). CONCLUSIONS: The linkage between bathroom and kitchen activities with MCI may be the result of biological, health, and environmental factors in play. In-home, real-time unobtrusive-sensing offers a novel way of delineating cognitive health with chronologically-ordered movement across indoor locations.
BACKGROUND: The ability to capture people's movement throughout their home is a powerful approach to inform spatiotemporal patterns of routines associated with cognitive impairment. The study estimated indoor room activities over 24 hours and investigated relationships between diurnal activity patterns and mild cognitive impairment (MCI). METHODS: One hundred and sixty-one older adults (26 with MCI) living alone (age = 78.9 ± 9.2) were included from 2 study cohorts-the Oregon Center for Aging & Technology and the Minority Aging Research Study. Indoor room activities were measured by the number of trips made to rooms (bathroom, bedroom, kitchen, living room). Trips made to rooms (transitions) were detected using passive infrared motion sensors fixed on the walls for a month. Latent trajectory models were used to identify distinct diurnal patterns of room activities and characteristics associated with each trajectory. RESULTS: Latent trajectory models identified 2 diurnal patterns of bathroom usage (high and low usage). Participants with MCI were more likely to be in the high bathroom usage group that exhibited more trips to the bathroom than the low-usage group (odds ratio [OR] = 4.1, 95% CI [1.3-13.5], p = .02). For kitchen activity, 2 diurnal patterns were identified (high and low activity). Participants with MCI were more likely to be in the high kitchen activity group that exhibited more transitions to the kitchen throughout the day and night than the low kitchen activity group (OR = 3.2, 95% CI [1.1-9.1], p = .03). CONCLUSIONS: The linkage between bathroom and kitchen activities with MCI may be the result of biological, health, and environmental factors in play. In-home, real-time unobtrusive-sensing offers a novel way of delineating cognitive health with chronologically-ordered movement across indoor locations.
Authors: Erik S Musiek; Meghana Bhimasani; Margaret A Zangrilli; John C Morris; David M Holtzman; Yo-El S Ju Journal: JAMA Neurol Date: 2018-05-01 Impact factor: 18.302
Authors: Hiroko H Dodge; Jian Zhu; Nora C Mattek; Daniel Austin; Judith Kornfeld; Jeffrey A Kaye Journal: PLoS One Date: 2015-09-17 Impact factor: 3.240
Authors: Lisa C Silbert; Hiroko H Dodge; David Lahna; Nutta-On Promjunyakul; Daniel Austin; Nora Mattek; Deniz Erten-Lyons; Jeffrey A Kaye Journal: J Alzheimers Dis Date: 2016 Impact factor: 4.472
Authors: Jonathan E Elliott; Carolyn E Tinsley; Christina Reynolds; Randall J Olson; Kristianna B Weymann; Wan-Tai M Au-Yeung; Andrea Wilkerson; Jeffrey A Kaye; Miranda M Lim Journal: Sensors (Basel) Date: 2022-07-19 Impact factor: 3.847