Temporal Relationships between Physical Activity and Sleep in Older Women

PURPOSEThe objective of this study is to examine the temporal and bidirectional relationships between accelerometer-derived physical activity estimates and actigraphy-assessed sleep characteristics among older women. METHODSA subgroup of participants (N = 143, mean age = 73 yr) enrolled in the Healt...

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Veröffentlicht in:Medicine and science in sports and exercise 2013-12, Vol.45 (12), p.2362-2368
Hauptverfasser: LAMBIASE, MAYA J, GABRIEL, KELLEY PETTEE, KULLER, LEWIS H, MATTHEWS, KAREN A
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container_end_page 2368
container_issue 12
container_start_page 2362
container_title Medicine and science in sports and exercise
container_volume 45
creator LAMBIASE, MAYA J
GABRIEL, KELLEY PETTEE
KULLER, LEWIS H
MATTHEWS, KAREN A
description PURPOSEThe objective of this study is to examine the temporal and bidirectional relationships between accelerometer-derived physical activity estimates and actigraphy-assessed sleep characteristics among older women. METHODSA subgroup of participants (N = 143, mean age = 73 yr) enrolled in the Healthy Women Study wore an ActiGraph accelerometer on their waist and an Actiwatch sleep monitor on their wrist concurrently for seven consecutive days. Multilevel models examined whether ActiGraph-assessed daily activity counts (ct·min·d) and moderate- to vigorous-intensity physical activity (MVPA; min·d) predicted Actiwatch-assessed sleep onset latency, total sleep time, sleep efficiency, and sleep fragmentation. Similar models were used to determine whether nighttime sleep characteristics predicted physical activity the following day. RESULTSIn unadjusted models, greater daily activity counts (B = −0.05, P = 0.005) and more minutes of MVPA (B = −0.03, P = 0.01) were temporally associated with less total sleep time across the week. Greater sleep efficiency was associated with greater daily activity counts (B = 0.37, P = 0.01) and more minutes of MVPA (B = 0.64, P = 0.009) the following day. Less sleep fragmentation was also associated with greater daily activity counts and more MVPA the following day. Findings were similar after adjustment for age, education, body mass index, depressive symptoms, arthritis, and accelerometer wear time. CONCLUSIONSFew studies have used objective measures to examine the temporal relationships between physical activity and sleep. Notably, these findings suggest that nightly variations in sleep efficiency influence physical activity the following day. Thus, improving overall sleep quality in addition to reducing nightly fluctuations in sleep may be important for encouraging a physically active lifestyle in older women.
doi_str_mv 10.1249/MSS.0b013e31829e4cea
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METHODSA subgroup of participants (N = 143, mean age = 73 yr) enrolled in the Healthy Women Study wore an ActiGraph accelerometer on their waist and an Actiwatch sleep monitor on their wrist concurrently for seven consecutive days. Multilevel models examined whether ActiGraph-assessed daily activity counts (ct·min·d) and moderate- to vigorous-intensity physical activity (MVPA; min·d) predicted Actiwatch-assessed sleep onset latency, total sleep time, sleep efficiency, and sleep fragmentation. Similar models were used to determine whether nighttime sleep characteristics predicted physical activity the following day. RESULTSIn unadjusted models, greater daily activity counts (B = −0.05, P = 0.005) and more minutes of MVPA (B = −0.03, P = 0.01) were temporally associated with less total sleep time across the week. Greater sleep efficiency was associated with greater daily activity counts (B = 0.37, P = 0.01) and more minutes of MVPA (B = 0.64, P = 0.009) the following day. Less sleep fragmentation was also associated with greater daily activity counts and more MVPA the following day. Findings were similar after adjustment for age, education, body mass index, depressive symptoms, arthritis, and accelerometer wear time. CONCLUSIONSFew studies have used objective measures to examine the temporal relationships between physical activity and sleep. Notably, these findings suggest that nightly variations in sleep efficiency influence physical activity the following day. Thus, improving overall sleep quality in addition to reducing nightly fluctuations in sleep may be important for encouraging a physically active lifestyle in older women.</description><identifier>ISSN: 0195-9131</identifier><identifier>EISSN: 1530-0315</identifier><identifier>DOI: 10.1249/MSS.0b013e31829e4cea</identifier><identifier>PMID: 23739529</identifier><identifier>CODEN: MSPEDA</identifier><language>eng</language><publisher>Hagerstown, MD: American College of Sports Medicine</publisher><subject>Actigraphy - instrumentation ; Aged ; Biological and medical sciences ; Cross-Sectional Studies ; Female ; Fundamental and applied biological sciences. Psychology ; Human physiology applied to population studies and life conditions. Human ecophysiology ; Humans ; Life Style ; Medical sciences ; Motor Activity - physiology ; Physical Exertion - physiology ; Sleep ; Vertebrates: body movement. Posture. Locomotion. Flight. Swimming. Physical exercise. Rest. 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METHODSA subgroup of participants (N = 143, mean age = 73 yr) enrolled in the Healthy Women Study wore an ActiGraph accelerometer on their waist and an Actiwatch sleep monitor on their wrist concurrently for seven consecutive days. Multilevel models examined whether ActiGraph-assessed daily activity counts (ct·min·d) and moderate- to vigorous-intensity physical activity (MVPA; min·d) predicted Actiwatch-assessed sleep onset latency, total sleep time, sleep efficiency, and sleep fragmentation. Similar models were used to determine whether nighttime sleep characteristics predicted physical activity the following day. RESULTSIn unadjusted models, greater daily activity counts (B = −0.05, P = 0.005) and more minutes of MVPA (B = −0.03, P = 0.01) were temporally associated with less total sleep time across the week. Greater sleep efficiency was associated with greater daily activity counts (B = 0.37, P = 0.01) and more minutes of MVPA (B = 0.64, P = 0.009) the following day. Less sleep fragmentation was also associated with greater daily activity counts and more MVPA the following day. Findings were similar after adjustment for age, education, body mass index, depressive symptoms, arthritis, and accelerometer wear time. CONCLUSIONSFew studies have used objective measures to examine the temporal relationships between physical activity and sleep. Notably, these findings suggest that nightly variations in sleep efficiency influence physical activity the following day. Thus, improving overall sleep quality in addition to reducing nightly fluctuations in sleep may be important for encouraging a physically active lifestyle in older women.</description><subject>Actigraphy - instrumentation</subject><subject>Aged</subject><subject>Biological and medical sciences</subject><subject>Cross-Sectional Studies</subject><subject>Female</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Human physiology applied to population studies and life conditions. Human ecophysiology</subject><subject>Humans</subject><subject>Life Style</subject><subject>Medical sciences</subject><subject>Motor Activity - physiology</subject><subject>Physical Exertion - physiology</subject><subject>Sleep</subject><subject>Vertebrates: body movement. Posture. Locomotion. Flight. Swimming. Physical exercise. Rest. 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Psychology</topic><topic>Human physiology applied to population studies and life conditions. Human ecophysiology</topic><topic>Humans</topic><topic>Life Style</topic><topic>Medical sciences</topic><topic>Motor Activity - physiology</topic><topic>Physical Exertion - physiology</topic><topic>Sleep</topic><topic>Vertebrates: body movement. Posture. Locomotion. Flight. Swimming. Physical exercise. Rest. Sports</topic><topic>Women's Health</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>LAMBIASE, MAYA J</creatorcontrib><creatorcontrib>GABRIEL, KELLEY PETTEE</creatorcontrib><creatorcontrib>KULLER, LEWIS H</creatorcontrib><creatorcontrib>MATTHEWS, KAREN A</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Physical Education Index</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Medicine and science in sports and exercise</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>LAMBIASE, MAYA J</au><au>GABRIEL, KELLEY PETTEE</au><au>KULLER, LEWIS H</au><au>MATTHEWS, KAREN A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Temporal Relationships between Physical Activity and Sleep in Older Women</atitle><jtitle>Medicine and science in sports and exercise</jtitle><addtitle>Med Sci Sports Exerc</addtitle><date>2013-12</date><risdate>2013</risdate><volume>45</volume><issue>12</issue><spage>2362</spage><epage>2368</epage><pages>2362-2368</pages><issn>0195-9131</issn><eissn>1530-0315</eissn><coden>MSPEDA</coden><abstract>PURPOSEThe objective of this study is to examine the temporal and bidirectional relationships between accelerometer-derived physical activity estimates and actigraphy-assessed sleep characteristics among older women. METHODSA subgroup of participants (N = 143, mean age = 73 yr) enrolled in the Healthy Women Study wore an ActiGraph accelerometer on their waist and an Actiwatch sleep monitor on their wrist concurrently for seven consecutive days. Multilevel models examined whether ActiGraph-assessed daily activity counts (ct·min·d) and moderate- to vigorous-intensity physical activity (MVPA; min·d) predicted Actiwatch-assessed sleep onset latency, total sleep time, sleep efficiency, and sleep fragmentation. Similar models were used to determine whether nighttime sleep characteristics predicted physical activity the following day. RESULTSIn unadjusted models, greater daily activity counts (B = −0.05, P = 0.005) and more minutes of MVPA (B = −0.03, P = 0.01) were temporally associated with less total sleep time across the week. Greater sleep efficiency was associated with greater daily activity counts (B = 0.37, P = 0.01) and more minutes of MVPA (B = 0.64, P = 0.009) the following day. Less sleep fragmentation was also associated with greater daily activity counts and more MVPA the following day. Findings were similar after adjustment for age, education, body mass index, depressive symptoms, arthritis, and accelerometer wear time. CONCLUSIONSFew studies have used objective measures to examine the temporal relationships between physical activity and sleep. Notably, these findings suggest that nightly variations in sleep efficiency influence physical activity the following day. Thus, improving overall sleep quality in addition to reducing nightly fluctuations in sleep may be important for encouraging a physically active lifestyle in older women.</abstract><cop>Hagerstown, MD</cop><pub>American College of Sports Medicine</pub><pmid>23739529</pmid><doi>10.1249/MSS.0b013e31829e4cea</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record>
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ispartof Medicine and science in sports and exercise, 2013-12, Vol.45 (12), p.2362-2368
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source MEDLINE; Journals@Ovid LWW Legacy Archive; Journals@Ovid Complete
subjects Actigraphy - instrumentation
Aged
Biological and medical sciences
Cross-Sectional Studies
Female
Fundamental and applied biological sciences. Psychology
Human physiology applied to population studies and life conditions. Human ecophysiology
Humans
Life Style
Medical sciences
Motor Activity - physiology
Physical Exertion - physiology
Sleep
Vertebrates: body movement. Posture. Locomotion. Flight. Swimming. Physical exercise. Rest. Sports
Women's Health
title Temporal Relationships between Physical Activity and Sleep in Older Women
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