Alberto Raggi1, Barbara Corso2, Laura De Torres3, Rui Quintas4, Somnath Chatterji5, Päivi Sainio6, Andrea Martinuzzi7, Katarzyna Zawisza8, Josep Maria Haro9, Nadia Minicuci10, Matilde Leonardi11. 1. Neurological Institute C. Besta IRCCS Foundation, Neurology, Public Health and Disability Unit, Milan, Italy. Electronic address: alberto.raggi@istituto-besta.it. 2. National Research Council, Neuroscience Institute, Padova, Italy. Electronic address: barbara.corso@in.cnr.it. 3. Neurological Institute C. Besta IRCCS Foundation, Neurology, Public Health and Disability Unit, Milan, Italy. Electronic address: laura.vicentedetorres@istituto-besta.it. 4. Neurological Institute C. Besta IRCCS Foundation, Neurology, Public Health and Disability Unit, Milan, Italy. Electronic address: rui.quintas@istituto-besta.it. 5. World Health Organization, Information, Evidence and Research Unit, Geneva, Switzerland. Electronic address: chatterjis@who.int. 6. National Institute for Health and Welfare, Ageing, Disability and Functioning Unit, Helsinki, Finland. Electronic address: paivi.sainio@thl.fi. 7. E. Medea Scientific Institute, Conegliano-Pieve di Soligo Research Centre, Conegliano Veneto, Italy. Electronic address: andrea.martinuzzi@lanostrafamiglia.it. 8. Department of Medical Sociology, Chair of Epidemiology and Preventive Medicine, Jagiellonian University Medical College. Krakow, Poland. Electronic address: katarzyna.zawisza@uj.edu.pl. 9. Parc Sanitari Sant Joan de Déu, University of Barcelona, CIBERSAM, Barcelona, Spain; Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Spain. Electronic address: jmharo@pssjd.org. 10. National Research Council, Neuroscience Institute, Padova, Italy. Electronic address: nadia.minicuci@unipd.it. 11. Neurological Institute C. Besta IRCCS Foundation, Neurology, Public Health and Disability Unit, Milan, Italy. Electronic address: matilde.leonardi@istituto-besta.it.
Abstract
OBJECTIVE: To identify the determinants of mobility among people aged 50+ from Finland, Spain and Poland. STUDY DESIGN: Observational cross-sectional population study. MAIN OUTCOME MEASURES: A mobility score was based on responses to items referring to body movements, walking, moving around and using transportation. Determinants of mobility were entered in hierarchical regression models in the following order: sociodemographic characteristics, health habits, chronic conditions, description of general state of health, vision and hearing, social networks, built environment. RESULTS: Complete data were available for 3902 participants (mean age 65.1, SD 9.8). The final model explained 64.7% of the variation in mobility. The most relevant predictors were: pain, age and living in Finland, presence of arthritis, stroke and diabetes, high-risk waist circumference, physical inactivity, and perceiving the neighborhood environment as more exploitable. CONCLUSIONS: Our results provide public health indications that could support concrete actions to address the modifiable determinants of mobility. These include the identification and treatment of pain-related problems, increasing the level of physical activity and the improvement of neighborhood features in terms of presence of general utility places or means of transportation. These factors can be modified with short- to medium-term interventions and such a change could improve the mobility of ageing population, with evident benefits for health.
OBJECTIVE: To identify the determinants of mobility among people aged 50+ from Finland, Spain and Poland. STUDY DESIGN: Observational cross-sectional population study. MAIN OUTCOME MEASURES: A mobility score was based on responses to items referring to body movements, walking, moving around and using transportation. Determinants of mobility were entered in hierarchical regression models in the following order: sociodemographic characteristics, health habits, chronic conditions, description of general state of health, vision and hearing, social networks, built environment. RESULTS: Complete data were available for 3902 participants (mean age 65.1, SD 9.8). The final model explained 64.7% of the variation in mobility. The most relevant predictors were: pain, age and living in Finland, presence of arthritis, stroke and diabetes, high-risk waist circumference, physical inactivity, and perceiving the neighborhood environment as more exploitable. CONCLUSIONS: Our results provide public health indications that could support concrete actions to address the modifiable determinants of mobility. These include the identification and treatment of pain-related problems, increasing the level of physical activity and the improvement of neighborhood features in terms of presence of general utility places or means of transportation. These factors can be modified with short- to medium-term interventions and such a change could improve the mobility of ageing population, with evident benefits for health.
Authors: Erika Guastafierro; Claudia Toppo; Barbara Corso; Rosa Romano; Rino Campioni; Ersilia Brambilla; Carla Facchini; Sara Bordoni; Matilde Leonardi Journal: Front Med (Lausanne) Date: 2022-05-23