Hiroyuki Kikuchi1, Shigeru Inoue2, Takemi Sugiyama3, Neville Owen4, Koichiro Oka5, Tomoki Nakaya6, Teruichi Shimomitsu7. 1. Department of Preventive Medicine and Public Health, Tokyo Medical University, 6-1-1 Shinjuku Shinjuku-ku Tokyo 160-8402, Japan. Electronic address: kikuchih@tokyo-med.ac.jp. 2. Department of Preventive Medicine and Public Health, Tokyo Medical University, 6-1-1 Shinjuku Shinjuku-ku Tokyo 160-8402, Japan. Electronic address: inoue@tokyo-med.ac.jp. 3. Sansom Institute for Health Research & School of Population Health, University of South Australia, North Terrace, Adelaide SA 5000, Australia. Electronic address: takemi.sugiyama@unisa.edu.au. 4. Behavioural Epidemiology Laboratory, Baker IDI Heart and Diabetes Institute, Level 4, 99 Commercial Rd, Melbourne, Victoria, 3004, Australia; School of Population Health, The University of Queensland, Level 2, Public Health Building,School of Population Health, University of Queensland, Herston Road Herston QLD, Brisbane, 4006, Australia; Melbourne School of Population Health, the University of Melbourne, Level 5, 207 Bouverie Street, The University of Melbourne, Victoria 3010, Australia; School of Medicine, Monash University, Faculty of Medicine, Nursing and Health Sciences, Monash University, Victoria 3800, Australia. Electronic address: neville.owen@bakeridi.edu.au. 5. Faculty of Sport Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa, Saitama, 359-1192, Japan. Electronic address: koka@waseda.jp. 6. Department of Geography and Institute of Disaster Mitigation of Urban Cultural Heritage, Ritsumeikan University, 58 Komatsubara Kitamachi, Kita-ku, Kyoto, Kyoto 603-8341, Japan. Electronic address: nakaya@lt.ritsumei.ac.jp. 7. Department of Preventive Medicine and Public Health, Tokyo Medical University, 6-1-1 Shinjuku Shinjuku-ku Tokyo 160-8402, Japan; Japan Health Promotion and Fitness Foundation, 2-6-10 Dai-Tokyo-Building7F, Highashi-Shinbashi, Minato-ku, Tokyo, 105-0021, Japan. Electronic address: tshimo@tokyo-med.ac.jp.
Abstract
OBJECTIVE: Leisure-time sedentary behaviors (LTSBs) have been associated adversely with health outcomes. However, limited research has focused on different categories of LTSB. We aimed at identifying categories of LTSBs and examining their separate associations with indices of health among Japanese older adults. METHODS: A postal survey collected data on self-reported health, psychological distress, body mass index, moderate-to-vigorous physical activity (MVPA), LTSBs (five behaviors) and socio-demographic characteristics from 1,580 Japanese older adults (67% response rate; 65-74 years) in 2010. Exploratory factor analysis was used to classify LTSBs. Odds ratios (ORs) were calculated for associations of LTSB categories with self-reported health, psychological distress, overweight, and lower MVPA. Data were analyzed in 2013. RESULTS: Two categories of LTSB: passive sedentary time (consisting of TV time, listening or talking while sitting, and sitting around) and mentally-active sedentary time (consisting of computer-use and reading books or newspapers) were identified. Higher passive sedentary time was associated with a higher odds of being overweight (OR: 1.39, [95% CI: 1.08-1.80]), and lower MVPA (1.26, [1.02-1.54]). Higher mentally-active sedentary time was associated with lower odds of lower MVPA (0.70, [0.57-0.86]). CONCLUSIONS: Two types of sedentary time-passive and mentally-active-may play different roles in older adults' well-being.
OBJECTIVE: Leisure-time sedentary behaviors (LTSBs) have been associated adversely with health outcomes. However, limited research has focused on different categories of LTSB. We aimed at identifying categories of LTSBs and examining their separate associations with indices of health among Japanese older adults. METHODS: A postal survey collected data on self-reported health, psychological distress, body mass index, moderate-to-vigorous physical activity (MVPA), LTSBs (five behaviors) and socio-demographic characteristics from 1,580 Japanese older adults (67% response rate; 65-74 years) in 2010. Exploratory factor analysis was used to classify LTSBs. Odds ratios (ORs) were calculated for associations of LTSB categories with self-reported health, psychological distress, overweight, and lower MVPA. Data were analyzed in 2013. RESULTS: Two categories of LTSB: passive sedentary time (consisting of TV time, listening or talking while sitting, and sitting around) and mentally-active sedentary time (consisting of computer-use and reading books or newspapers) were identified. Higher passive sedentary time was associated with a higher odds of being overweight (OR: 1.39, [95% CI: 1.08-1.80]), and lower MVPA (1.26, [1.02-1.54]). Higher mentally-active sedentary time was associated with lower odds of lower MVPA (0.70, [0.57-0.86]). CONCLUSIONS: Two types of sedentary time-passive and mentally-active-may play different roles in older adults' well-being.
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