Sources of variance in daily physical activity levels as measured by an accelerometer
To examine sources of variance in objective measures of physical activity in a group of healthy adults (N = 92) participating in a physical activity measurement study. Physical activity was assessed for up to 21 consecutive days using the Computer Science Applications (CSA) accelerometer. Day-of-the...
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Veröffentlicht in: | Medicine and science in sports and exercise 2002-08, Vol.34 (8), p.1376-1381 |
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creator | MATTHEWS, Charles E AINSWORTH, Barbara E THOMPSON, Raymond W BASSETT, David R |
description | To examine sources of variance in objective measures of physical activity in a group of healthy adults (N = 92) participating in a physical activity measurement study.
Physical activity was assessed for up to 21 consecutive days using the Computer Science Applications (CSA) accelerometer. Day-of-the-week effects were evaluated for activity counts (ct.min(-1).d(-1), ct.d(-1)) and time (min.d(-1)) spent in inactivity (0-499 ct), moderate-1 (500-1951 ct), and moderate-2-vigorous activity (> or =1952 ct). Random effects models were employed to estimate variance components for subject, day of the week, and residual error from which the number of days of assessment required to achieve 80% reliability were estimated.
Physical inactivity was lower on weekend days, and Saturday was the least inactive day of the week for both men and women. Inter-individual variation, or differences between subjects, was proportionally the largest source of variance (55-60% of total) in accelerometer counts and time spent in moderate to vigorous activity. Differences within subjects (intra-individual variation) accounted for 30-45% of the overall variance, and day-of-the-week effects accounted for 1-8%. For activity counts, and time spent in moderate to vigorous activity, at least 3-4 d of monitoring were required to achieve 80% reliability. Reliable measures of physical inactivity required at least 7 d of monitoring.
These findings provide insight for understanding the behavioral variability in the activity patterns of adults and suggest that reliable measures of activity behaviors require at least 7 d of monitoring. |
doi_str_mv | 10.1097/00005768-200208000-00021 |
format | Article |
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Physical activity was assessed for up to 21 consecutive days using the Computer Science Applications (CSA) accelerometer. Day-of-the-week effects were evaluated for activity counts (ct.min(-1).d(-1), ct.d(-1)) and time (min.d(-1)) spent in inactivity (0-499 ct), moderate-1 (500-1951 ct), and moderate-2-vigorous activity (> or =1952 ct). Random effects models were employed to estimate variance components for subject, day of the week, and residual error from which the number of days of assessment required to achieve 80% reliability were estimated.
Physical inactivity was lower on weekend days, and Saturday was the least inactive day of the week for both men and women. Inter-individual variation, or differences between subjects, was proportionally the largest source of variance (55-60% of total) in accelerometer counts and time spent in moderate to vigorous activity. Differences within subjects (intra-individual variation) accounted for 30-45% of the overall variance, and day-of-the-week effects accounted for 1-8%. For activity counts, and time spent in moderate to vigorous activity, at least 3-4 d of monitoring were required to achieve 80% reliability. Reliable measures of physical inactivity required at least 7 d of monitoring.
These findings provide insight for understanding the behavioral variability in the activity patterns of adults and suggest that reliable measures of activity behaviors require at least 7 d of monitoring.</description><identifier>ISSN: 0195-9131</identifier><identifier>EISSN: 1530-0315</identifier><identifier>DOI: 10.1097/00005768-200208000-00021</identifier><identifier>PMID: 12165695</identifier><identifier>CODEN: MSPEDA</identifier><language>eng</language><publisher>Hagerstown, MD: Lippincott Williams & Wilkins</publisher><subject>Acceleration ; Adult ; Age Factors ; Aged ; Analysis of Variance ; Biological and medical sciences ; Body Mass Index ; Cohort Studies ; Energy Intake ; Energy Metabolism - physiology ; Exercise ; Female ; Fundamental and applied biological sciences. Psychology ; Humans ; Leisure Activities ; Life Style ; Male ; Middle Aged ; Physical Fitness - physiology ; Probability ; Sex Factors ; Space life sciences ; Sports Medicine - instrumentation ; Time Factors ; Vertebrates: body movement. Posture. Locomotion. Flight. Swimming. Physical exercise. Rest. Sports</subject><ispartof>Medicine and science in sports and exercise, 2002-08, Vol.34 (8), p.1376-1381</ispartof><rights>2002 INIST-CNRS</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c488t-81764019270d0dbcf0a0eb244e90db5657d140aa8e80323f9dd73bdf99ccf5613</citedby><cites>FETCH-LOGICAL-c488t-81764019270d0dbcf0a0eb244e90db5657d140aa8e80323f9dd73bdf99ccf5613</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=13832150$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/12165695$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>MATTHEWS, Charles E</creatorcontrib><creatorcontrib>AINSWORTH, Barbara E</creatorcontrib><creatorcontrib>THOMPSON, Raymond W</creatorcontrib><creatorcontrib>BASSETT, David R</creatorcontrib><title>Sources of variance in daily physical activity levels as measured by an accelerometer</title><title>Medicine and science in sports and exercise</title><addtitle>Med Sci Sports Exerc</addtitle><description>To examine sources of variance in objective measures of physical activity in a group of healthy adults (N = 92) participating in a physical activity measurement study.
Physical activity was assessed for up to 21 consecutive days using the Computer Science Applications (CSA) accelerometer. Day-of-the-week effects were evaluated for activity counts (ct.min(-1).d(-1), ct.d(-1)) and time (min.d(-1)) spent in inactivity (0-499 ct), moderate-1 (500-1951 ct), and moderate-2-vigorous activity (> or =1952 ct). Random effects models were employed to estimate variance components for subject, day of the week, and residual error from which the number of days of assessment required to achieve 80% reliability were estimated.
Physical inactivity was lower on weekend days, and Saturday was the least inactive day of the week for both men and women. Inter-individual variation, or differences between subjects, was proportionally the largest source of variance (55-60% of total) in accelerometer counts and time spent in moderate to vigorous activity. Differences within subjects (intra-individual variation) accounted for 30-45% of the overall variance, and day-of-the-week effects accounted for 1-8%. For activity counts, and time spent in moderate to vigorous activity, at least 3-4 d of monitoring were required to achieve 80% reliability. Reliable measures of physical inactivity required at least 7 d of monitoring.
These findings provide insight for understanding the behavioral variability in the activity patterns of adults and suggest that reliable measures of activity behaviors require at least 7 d of monitoring.</description><subject>Acceleration</subject><subject>Adult</subject><subject>Age Factors</subject><subject>Aged</subject><subject>Analysis of Variance</subject><subject>Biological and medical sciences</subject><subject>Body Mass Index</subject><subject>Cohort Studies</subject><subject>Energy Intake</subject><subject>Energy Metabolism - physiology</subject><subject>Exercise</subject><subject>Female</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Humans</subject><subject>Leisure Activities</subject><subject>Life Style</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Physical Fitness - physiology</subject><subject>Probability</subject><subject>Sex Factors</subject><subject>Space life sciences</subject><subject>Sports Medicine - instrumentation</subject><subject>Time Factors</subject><subject>Vertebrates: body movement. Posture. Locomotion. Flight. Swimming. Physical exercise. Rest. Sports</subject><issn>0195-9131</issn><issn>1530-0315</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2002</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkMtKxDAUhoMoOo6-gmSju2pO0rTJUgZvMOBCXZc0OcVIOx2TdqBvb9SqSwOH8MN3LnyEUGCXwHR5xdKTZaEyzhhnKqUsFYc9sgApUhAg98mCgZaZBgFH5DjGt4SUQsAhOQIOhSy0XJCXp34MFiPtG7ozwZuNReo31BnfTnT7OkVvTUuNHfzODxNtcYdtpCbSDk0cAzpaT9RsEmGxxdB3OGA4IQeNaSOezv-SvNzePK_us_Xj3cPqep3ZXKkhU1AWeTqSl8wxV9uGGYY1z3PUKcpClg5yZoxCxQQXjXauFLVrtLa2kQWIJbn4nrsN_fuIcag6H9MdrdlgP8aqBK2UZPm_IKhcFZJ_guobtKGPMWBTbYPvTJgqYNWn--rHffXrvvpyn1rP5h1j3aH7a5xlJ-B8BkxMUpuQZPv4xwklOEgmPgBX4YvC</recordid><startdate>20020801</startdate><enddate>20020801</enddate><creator>MATTHEWS, Charles E</creator><creator>AINSWORTH, Barbara E</creator><creator>THOMPSON, Raymond W</creator><creator>BASSETT, David R</creator><general>Lippincott Williams & Wilkins</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TS</scope><scope>7X8</scope></search><sort><creationdate>20020801</creationdate><title>Sources of variance in daily physical activity levels as measured by an accelerometer</title><author>MATTHEWS, Charles E ; AINSWORTH, Barbara E ; THOMPSON, Raymond W ; BASSETT, David R</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c488t-81764019270d0dbcf0a0eb244e90db5657d140aa8e80323f9dd73bdf99ccf5613</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2002</creationdate><topic>Acceleration</topic><topic>Adult</topic><topic>Age Factors</topic><topic>Aged</topic><topic>Analysis of Variance</topic><topic>Biological and medical sciences</topic><topic>Body Mass Index</topic><topic>Cohort Studies</topic><topic>Energy Intake</topic><topic>Energy Metabolism - physiology</topic><topic>Exercise</topic><topic>Female</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Humans</topic><topic>Leisure Activities</topic><topic>Life Style</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Physical Fitness - physiology</topic><topic>Probability</topic><topic>Sex Factors</topic><topic>Space life sciences</topic><topic>Sports Medicine - instrumentation</topic><topic>Time Factors</topic><topic>Vertebrates: body movement. Posture. Locomotion. Flight. Swimming. Physical exercise. Rest. Sports</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>MATTHEWS, Charles E</creatorcontrib><creatorcontrib>AINSWORTH, Barbara E</creatorcontrib><creatorcontrib>THOMPSON, Raymond W</creatorcontrib><creatorcontrib>BASSETT, David R</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>MEDLINE - Academic</collection><jtitle>Medicine and science in sports and exercise</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>MATTHEWS, Charles E</au><au>AINSWORTH, Barbara E</au><au>THOMPSON, Raymond W</au><au>BASSETT, David R</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sources of variance in daily physical activity levels as measured by an accelerometer</atitle><jtitle>Medicine and science in sports and exercise</jtitle><addtitle>Med Sci Sports Exerc</addtitle><date>2002-08-01</date><risdate>2002</risdate><volume>34</volume><issue>8</issue><spage>1376</spage><epage>1381</epage><pages>1376-1381</pages><issn>0195-9131</issn><eissn>1530-0315</eissn><coden>MSPEDA</coden><abstract>To examine sources of variance in objective measures of physical activity in a group of healthy adults (N = 92) participating in a physical activity measurement study.
Physical activity was assessed for up to 21 consecutive days using the Computer Science Applications (CSA) accelerometer. Day-of-the-week effects were evaluated for activity counts (ct.min(-1).d(-1), ct.d(-1)) and time (min.d(-1)) spent in inactivity (0-499 ct), moderate-1 (500-1951 ct), and moderate-2-vigorous activity (> or =1952 ct). Random effects models were employed to estimate variance components for subject, day of the week, and residual error from which the number of days of assessment required to achieve 80% reliability were estimated.
Physical inactivity was lower on weekend days, and Saturday was the least inactive day of the week for both men and women. Inter-individual variation, or differences between subjects, was proportionally the largest source of variance (55-60% of total) in accelerometer counts and time spent in moderate to vigorous activity. Differences within subjects (intra-individual variation) accounted for 30-45% of the overall variance, and day-of-the-week effects accounted for 1-8%. For activity counts, and time spent in moderate to vigorous activity, at least 3-4 d of monitoring were required to achieve 80% reliability. Reliable measures of physical inactivity required at least 7 d of monitoring.
These findings provide insight for understanding the behavioral variability in the activity patterns of adults and suggest that reliable measures of activity behaviors require at least 7 d of monitoring.</abstract><cop>Hagerstown, MD</cop><pub>Lippincott Williams & Wilkins</pub><pmid>12165695</pmid><doi>10.1097/00005768-200208000-00021</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Acceleration Adult Age Factors Aged Analysis of Variance Biological and medical sciences Body Mass Index Cohort Studies Energy Intake Energy Metabolism - physiology Exercise Female Fundamental and applied biological sciences. Psychology Humans Leisure Activities Life Style Male Middle Aged Physical Fitness - physiology Probability Sex Factors Space life sciences Sports Medicine - instrumentation Time Factors Vertebrates: body movement. Posture. Locomotion. Flight. Swimming. Physical exercise. Rest. Sports |
title | Sources of variance in daily physical activity levels as measured by an accelerometer |
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