Jacki Liddle1, David Ireland2, Karolina Krysinska3,4, Fleur Harrison5, Robyn Lamont6, Mohan Karunanithi2, Kristan Kang5, Simone Reppermund7, Perminder S Sachdev5, Louise Gustafsson8, Sandra Brauer6, Nancy A Pachana9, Henry Brodaty5. 1. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Qld, Australia. 2. Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Qld, Australia. 3. School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia. 4. Centre for Mental Health, School of Population and Global Health, University of Melbourne, Melbourne, Vic, Australia. 5. Centre for Healthy Brain Ageing, University of New South Wales, Sydney, NSW, Australia. 6. School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Qld, Australia. 7. School of Psychiatry, University of New South Wales, Sydney, NSW, Australia. 8. School of Allied Health Sciences, Griffith University, Brisbane, Qld, Australia. 9. School of Psychology, University of Queensland, Brisbane, Qld, Australia.
Abstract
OBJECTIVE: Lifespace, the physical area in which someone conducts life activities, indicates lived community mobility. This study explored the feasibility of technology-based lifespace measurement for older people with dementia and mild cognitive impairment (MCI), including the generation of a range of lifespace metrics, and investigation of relationships with health and mobility status. METHODS: An exploratory study was conducted within a longitudinal observational study. Eighteen older adults (mean age 86.7 years (SD: 3.2); 8 men; 15 MCI), participated. Lifespace metrics were generated from geolocation data (GPS and Bluetooth beacon) collected through a smartphone application for one week (2015-2016). Cognitive and mobility-related outcomes were compared from study data sets at baseline (2005-2007) and 6-year follow-up (2011-2014). RESULTS: Lifespace data could be collected from all participants, and metrics were generated including percentage of time at home, maximum distance from home, episodes of travel in a week, days in a week participants left home, lifespace area (daily, weekly and total), indoor lifespace (regions in the home/hour), and a developed lifespace score that combined time, frequency of travel, distance and area. Results indicated a large range of lifespace areas (0.1 - 97.88 km2 ; median 6.77 km2 ) with similar patterns across lifespace metrics. Significant relationships were found between lifespace metrics and concurrent driving status and anteceding scores on the sit-to-stand test (at baseline and follow-up). CONCLUSIONS: Further longitudinal exploration of lifespace is required to develop an understanding of the nature of lifespace of older community-dwelling people, and its relationship with health, mobility and well-being outcomes.
OBJECTIVE: Lifespace, the physical area in which someone conducts life activities, indicates lived community mobility. This study explored the feasibility of technology-based lifespace measurement for older people with dementia and mild cognitive impairment (MCI), including the generation of a range of lifespace metrics, and investigation of relationships with health and mobility status. METHODS: An exploratory study was conducted within a longitudinal observational study. Eighteen older adults (mean age 86.7 years (SD: 3.2); 8 men; 15 MCI), participated. Lifespace metrics were generated from geolocation data (GPS and Bluetooth beacon) collected through a smartphone application for one week (2015-2016). Cognitive and mobility-related outcomes were compared from study data sets at baseline (2005-2007) and 6-year follow-up (2011-2014). RESULTS: Lifespace data could be collected from all participants, and metrics were generated including percentage of time at home, maximum distance from home, episodes of travel in a week, days in a week participants left home, lifespace area (daily, weekly and total), indoor lifespace (regions in the home/hour), and a developed lifespace score that combined time, frequency of travel, distance and area. Results indicated a large range of lifespace areas (0.1 - 97.88 km2 ; median 6.77 km2 ) with similar patterns across lifespace metrics. Significant relationships were found between lifespace metrics and concurrent driving status and anteceding scores on the sit-to-stand test (at baseline and follow-up). CONCLUSIONS: Further longitudinal exploration of lifespace is required to develop an understanding of the nature of lifespace of older community-dwelling people, and its relationship with health, mobility and well-being outcomes.
Authors: Neda Firouraghi; Behzad Kiani; Hossein Tabatabaei Jafari; Vincent Learnihan; Jose A Salinas-Perez; Ahmad Raeesi; MaryAnne Furst; Luis Salvador-Carulla; Nasser Bagheri Journal: Int J Health Geogr Date: 2022-08-04 Impact factor: 5.310