Jane Phillips1, Eleonora Dal Grande2, Christine Ritchie3, Amy P Abernethy4, David C Currow5. 1. Centre for Cardiovascular and Chronic Care, Faculty of Health, University of Technology Sydney, Sydney, New South Wales, Australia. 2. Population Research and Outcomes Studies Unit, Discipline of Medicine, Health Sciences Faculty, The University of Adelaide, South Australia, Australia. 3. Division of Geriatrics, Department of Medicine, University of California, San Francisco, California, USA. 4. Discipline, Palliative and Supportive Services, Flinders University, Bedford Park, South Australia, Australia; Division of Medical Oncology, Department of Medicine, Duke University Medical Centre, Durham, North Carolina, USA. 5. Discipline, Palliative and Supportive Services, Flinders University, Bedford Park, South Australia, Australia; Southern Adelaide Palliative Services, Repatriation General Hospital, Daw Park, South Australia, Australia. Electronic address: david.currow@flinders.edu.au.
Abstract
CONTEXT: Mobility is linked to health status and quality of life. Life-Space Mobility Assessment (LSMA; range 0-120) measures the spatial extent of people's excursion and physical support needs over the preceding month. OBJECTIVES: The aim of this study was to generate normative population data for an LSMA-Composite (LSMA-C) score, irrespective of age or health service contact and explore the LSM of people with diabetes, current asthma, arthritis, and osteoporosis. METHODS: LSMA questions were included in the 2011 South Australian Health Omnibus Survey, a multistage, systematic, and clustered sample of household face-to-face interviews. Sociodemographic and clinical variables were explored in relation to LSMA scores using descriptive, univariable, and multivariable analyses and receiver operator curves. RESULTS: For the 3032 respondents, the mean LSMA score was 98.3 (SD 20.3; median 100; interquartile range 34 [86-120]; range 6-120). Five percent of respondents scored <60, 11% scored between ≥ 60 and 79, 27% scored between ≥ 80 and 99, and the remainder scored between 100 and 120. After 55 years of age, LSMA-C scores declined, more so in females. In multivariable analysis, declining scores were associated with being female, being older, living in rural areas, lower educational attainment, not working, lower household income, and higher numbers of chronic conditions (R(2) = 0.35, P < 0.001). The receiver operator curve demonstrated a highly specific but relatively insensitive measure. CONCLUSION: Having controlled for known confounders, the male/female difference cannot be easily explained. These data will help to contextualize studies in the future that use the LSMA-C score.
CONTEXT: Mobility is linked to health status and quality of life. Life-Space Mobility Assessment (LSMA; range 0-120) measures the spatial extent of people's excursion and physical support needs over the preceding month. OBJECTIVES: The aim of this study was to generate normative population data for an LSMA-Composite (LSMA-C) score, irrespective of age or health service contact and explore the LSM of people with diabetes, current asthma, arthritis, and osteoporosis. METHODS: LSMA questions were included in the 2011 South Australian Health Omnibus Survey, a multistage, systematic, and clustered sample of household face-to-face interviews. Sociodemographic and clinical variables were explored in relation to LSMA scores using descriptive, univariable, and multivariable analyses and receiver operator curves. RESULTS: For the 3032 respondents, the mean LSMA score was 98.3 (SD 20.3; median 100; interquartile range 34 [86-120]; range 6-120). Five percent of respondents scored <60, 11% scored between ≥ 60 and 79, 27% scored between ≥ 80 and 99, and the remainder scored between 100 and 120. After 55 years of age, LSMA-C scores declined, more so in females. In multivariable analysis, declining scores were associated with being female, being older, living in rural areas, lower educational attainment, not working, lower household income, and higher numbers of chronic conditions (R(2) = 0.35, P < 0.001). The receiver operator curve demonstrated a highly specific but relatively insensitive measure. CONCLUSION: Having controlled for known confounders, the male/female difference cannot be easily explained. These data will help to contextualize studies in the future that use the LSMA-C score.
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