Literature DB >> 31051183

Intensity and temporal patterns of physical activity and cardiovascular disease risk in midlife.

Maisa Niemelä1, Maarit Kangas2, Vahid Farrahi3, Antti Kiviniemi4, Anna-Maiju Leinonen5, Riikka Ahola6, Katri Puukka7, Juha Auvinen8, Raija Korpelainen9, Timo Jämsä10.   

Abstract

Physical activity (PA) and sedentary time (SED) are associated with the risk of cardiovascular disease (CVD), but the temporal patterns of these behaviors most beneficial for cardiovascular health remain unknown. We aimed to identify the intensity and temporal patterns of PA and SED measured continuously by an accelerometer and their relationship with CVD risk. At the age of 46 years, 4582 members (1916 men; 2666 women) of the Northern Finland Birth Cohort 1966 study underwent continuous measurement of PA with Polar Active (Polar Electro, Finland) accelerometers for one week. X-means clustering was applied based on 10 min average MET (metabolic equivalent) values during the measurement period. Ten-year risk of CVD was estimated using the Framingham risk model. Most of the participants had low risk for CVD. Four distinct PA clusters were identified that were well differentiable by the intensity and temporal patterns of activity (inactive, evening active, moderately active, very active). A significant difference in 10-year CVD risk across the clusters was found in men (p = 0.028) and women (p < 0.001). Higher levels of HDL cholesterol were found in more active clusters compared to less active clusters (p < 0.001) in both genders. In women total cholesterol was lower in the moderately active cluster compared to the inactive and evening active clusters (p = 0.001). Four distinct PA clusters were recognized based on accelerometer data and X-means clustering. A significant difference in CVD risk across the clusters was found in both genders. These results can be used in developing and promoting CVD prevention strategies.
Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Accelerometry; Cluster analysis; Middle aged; Physical exercise

Mesh:

Substances:

Year:  2019        PMID: 31051183     DOI: 10.1016/j.ypmed.2019.04.023

Source DB:  PubMed          Journal:  Prev Med        ISSN: 0091-7435            Impact factor:   4.018


  8 in total

1.  Joint temporal dietary and physical activity patterns: associations with health status indicators and chronic diseases.

Authors:  Luotao Lin; Jiaqi Guo; Marah M Aqeel; Saul B Gelfand; Edward J Delp; Anindya Bhadra; Elizabeth A Richards; Erin Hennessy; Heather A Eicher-Miller
Journal:  Am J Clin Nutr       Date:  2022-02-09       Impact factor: 8.472

2.  Examining reactivity to the measurement of physical activity and sedentary behavior among women in midlife with elevated risk for cardiovascular disease.

Authors:  Danielle Arigo; Laura M König
Journal:  Psychol Health       Date:  2022-04-12

3.  Temporal physical activity patterns are associated with obesity in U.S. adults.

Authors:  Marah Aqeel; Jiaqi Guo; Luotao Lin; Saul Gelfand; Edward Delp; Anindya Bhadra; Elizabeth A Richards; Erin Hennessy; Heather A Eicher-Miller
Journal:  Prev Med       Date:  2021-03-30       Impact factor: 4.637

4.  Diurnal patterns of sedentary behavior and changes in physical function over time among older women: a prospective cohort study.

Authors:  Chase Reuter; John Bellettiere; Sandy Liles; Chongzhi Di; Dorothy D Sears; Michael J LaMonte; Marcia L Stefanick; Andrea Z LaCroix; Loki Natarajan
Journal:  Int J Behav Nutr Phys Act       Date:  2020-07-09       Impact factor: 6.457

5.  Cohort Profile: 46 years of follow-up of the Northern Finland Birth Cohort 1966 (NFBC1966).

Authors:  Tanja Nordström; Jouko Miettunen; Juha Auvinen; Leena Ala-Mursula; Sirkka Keinänen-Kiukaanniemi; Juha Veijola; Marjo-Riitta Järvelin; Sylvain Sebert; Minna Männikkö
Journal:  Int J Epidemiol       Date:  2021-08-29       Impact factor: 7.196

Review 6.  Conceptualizing and Measuring Appetite Self-Regulation Phenotypes and Trajectories in Childhood: A Review of Person-Centered Strategies.

Authors:  Alan Russell; Rebecca M Leech; Catherine G Russell
Journal:  Front Nutr       Date:  2021-12-22

7.  High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study.

Authors:  Weizhuang Zhou; Yu En Chan; Chuan Sheng Foo; Jingxian Zhang; Jing Xian Teo; Sonia Davila; Weiting Huang; Jonathan Yap; Stuart Cook; Patrick Tan; Calvin Woon-Loong Chin; Khung Keong Yeo; Weng Khong Lim; Pavitra Krishnaswamy
Journal:  J Med Internet Res       Date:  2022-07-29       Impact factor: 7.076

8.  Cardiovascular disease risk and all-cause mortality associated with accelerometer-measured physical activity and sedentary time ‒ a prospective population-based study in older adults.

Authors:  Miia Länsitie; Maarit Kangas; Jari Jokelainen; Mika Venojärvi; Markku Timonen; Sirkka Keinänen-Kiukaanniemi; Raija Korpelainen
Journal:  BMC Geriatr       Date:  2022-09-05       Impact factor: 4.070

  8 in total

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