Gender and Age Differences in Hourly and Daily Patterns of Sedentary Time in Older Adults Living in Retirement Communities

被引:70
作者
Bellettiere, John [1 ,2 ]
Carlson, Jordan A. [3 ]
Rosenberg, Dori [4 ]
Singhania, Anant [5 ]
Natarajan, Loki [5 ]
Berardi, Vincent [2 ,6 ]
LaCroix, Andrea Z. [5 ]
Sears, Dorothy D. [5 ]
Moran, Kevin [5 ]
Crist, Katie [5 ]
Kerr, Jacqueline [5 ]
机构
[1] Univ Calif San Diego, San Diego Joint Doctoral Program Publ Hlth Epidem, San Diego State Univ, La Jolla, CA 92093 USA
[2] San Diego State Univ, Grad Sch Publ Hlth, Ctr Behav Epidemiol & Community Hlth, San Diego, CA 92182 USA
[3] Childrens Mercy Hosp, Ctr Childrens Hlth Lifestyles & Nutr, Kansas City, MO 64108 USA
[4] Univ Washington, Dept Hlth Serv, Sch Publ Hlth, Seattle, WA 98195 USA
[5] Univ Calif San Diego, Dept Family Med & Publ Hlth, La Jolla, CA 92093 USA
[6] San Diego State Univ, Computat Sci Res Ctr, San Diego, CA 92182 USA
关键词
PHYSICAL-ACTIVITY; CARDIOVASCULAR-DISEASE; BEHAVIOR; ASSOCIATION; BREAKING; HEALTH;
D O I
10.1371/journal.pone.0136161
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Background Total sedentary time varies across population groups with important health consequences. Patterns of sedentary time accumulation may vary and have differential health risks. The purpose of this study is to describe sedentary patterns of older adults living in retirement communities and illustrate gender and age differences in those patterns. Methods Baseline accelerometer data from 307 men and women (mean age = 84 +/- 6 years) who wore ActiGraph GT3X+ accelerometers for >= 4 days as part of a physical activity intervention were classified into bouts of sedentary time (<100 counts per minute). Linear mixed models were used to account for intra-person and site-level clustering. Daily and hourly summaries were examined in mutually non-exclusive bouts of sedentary time that were 1+, 5+, 10+, 20 +, 30+, 40+, 50+, 60+, 90+ and 120+ minutes in duration. Variations by time of day, age and gender were explored. Results Men accumulated more sedentary time than women in 1+, 5+, 10+, 20+, 30+, 40+, 50+ and 60+ minute bouts; the largest gender-differences were observed in 10+ and 20+ minute bouts. Age was positively associated with sedentary time, but only in bouts of 10+, 20+, 30 +, and 40+ minutes. Women had more daily 1+ minute sedentary bouts than men (71.8 vs. 65.2), indicating they break up sedentary time more often. For men and women, a greater proportion of time was spent being sedentary during later hours of the day than earlier. Gender differences in intra-day sedentary time were observed during morning hours with women accumulating less sedentary time overall and having more 1+ minute bouts. Conclusions Patterns identified using bouts of sedentary time revealed gender and age differences in the way in which sedentary time was accumulated by older adults in retirement communities. Awareness of these patterns can help interventionists better target sedentary time and may aid in the identification of health risks associated with sedentary behavior. Future studies should investigate the impact of patterns of sedentary time on healthy aging, disease, and mortality.
引用
收藏
页数:15
相关论文
共 36 条
[1]  
American College of Rheumatology, PRACT MAN
[2]   Methods of Measurement in epidemiology: Sedentary Behaviour [J].
Atkin, Andrew J. ;
Gorely, Trish ;
Clemes, Stacy A. ;
Yates, Thomas ;
Edwardson, Charlotte ;
Brage, Soren ;
Salmon, Jo ;
Marshall, Simon J. ;
Biddle, Stuart J. H. .
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, 2012, 41 (05) :1460-1471
[3]   Sedentary Time and Its Association With Risk for Disease Incidence, Mortality, and Hospitalization in Adults A Systematic Review and Meta-analysis [J].
Biswas, Aviroop ;
Oh, Paul I. ;
Faulkner, Guy E. ;
Bajaj, Ravi R. ;
Silver, Michael A. ;
Mitchell, Marc S. ;
Alter, David A. .
ANNALS OF INTERNAL MEDICINE, 2015, 162 (02) :123-+
[4]   Methods for objective measure, quantification and analysis of sedentary behaviour and inactivity [J].
Chastin, S. F. M. ;
Granat, M. H. .
GAIT & POSTURE, 2010, 31 (01) :82-86
[5]  
Chief Medical Officers of England, 2011, START ACTIVE STAY AC
[6]   Validation of Accelerometer Wear and Nonwear Time Classification Algorithm [J].
Choi, Leena ;
Liu, Zhouwen ;
Matthews, Charles E. ;
Buchowski, Maciej S. .
MEDICINE AND SCIENCE IN SPORTS AND EXERCISE, 2011, 43 (02) :357-364
[7]  
Davis MG, 2014, J AGING PHYS ACTIV, V22, P474, DOI [10.1123/JAPA.2013-0042, 10.1123/japa.2013-0042]
[8]   Context, composition and heterogeneity: Using multilevel models in health research [J].
Duncan, C ;
Jones, K ;
Moon, G .
SOCIAL SCIENCE & MEDICINE, 1998, 46 (01) :97-117
[9]   Breaking Up Prolonged Sitting Reduces Postprandial Glucose and Insulin Responses [J].
Dunstan, David W. ;
Kingwell, Bronwyn A. ;
Larsen, Robyn ;
Healy, Genevieve N. ;
Cerin, Ester ;
Hamilton, Marc T. ;
Shaw, Jonathan E. ;
Bertovic, David A. ;
Zimmet, Paul Z. ;
Salmon, Jo ;
Owen, Neville .
DIABETES CARE, 2012, 35 (05) :976-983
[10]   Identifying active travel behaviors in challenging environments using GPS, accelerometers, and machine learning algorithms [J].
Ellis, Katherine ;
Godbole, Suneeta ;
Marshall, Simon ;
Lanckriet, Gert ;
Staudenmayer, John ;
Kerr, Jacqueline .
FRONTIERS IN PUBLIC HEALTH, 2014, 2