{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T18:07:46Z","timestamp":1778782066391,"version":"3.51.4"},"reference-count":29,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T00:00:00Z","timestamp":1712793600000},"content-version":"vor","delay-in-days":10,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T00:00:00Z","timestamp":1712793600000},"content-version":"tdm","delay-in-days":10,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Physiol. Meas."],"published-print":{"date-parts":[[2024,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Increasing interest in measuring key components of the 24 h activity cycle (24-HAC) [sleep, sedentary behavior (SED), light physical activity (LPA), and moderate to vigorous physical activity (MVPA)] has led to a need for better methods. Single wrist-worn accelerometers and different self-report instruments can assess the 24-HAC but may not accurately classify time spent in the different components or be subject to recall errors.\n                    <jats:italic>Objective<\/jats:italic>\n                    . To overcome these limitations, the current study harmonized output from multiple complimentary research grade accelerometers and assessed the feasibility and logistical challenges of this approach.\n                    <jats:italic>Approach<\/jats:italic>\n                    . Participants (\n                    <jats:italic>n<\/jats:italic>\n                    = 108) wore an: (a) ActiGraph GT9X on the wrist, (b) activPAL3 on the thigh, and (c) ActiGraph GT3X+ on the hip for 7\u201310 d to capture the 24-HAC. Participant compliance with the measurement protocol was compared across devices and an algorithm was developed to harmonize data from the accelerometers. The resulting 24-HAC estimates were described within and across days.\n                    <jats:italic>Main results<\/jats:italic>\n                    . Usable data for each device was obtained from 94.3% to 96.7% of participants and 89.4% provided usable data from all three devices. Compliance with wear instructions ranged from 70.7% of days for the GT3X+ to 93.2% of days for the activPAL3. Harmonized estimates indicated that, on average, university students spent 34% of the 24 h day sleeping, 41% sedentary, 21% in LPA, and 4% in MVPA. These behaviors varied substantially by time of day and day of the week.\n                    <jats:italic>Significance<\/jats:italic>\n                    . It is feasible to use three accelerometers in combination to derive a harmonized estimate the 24-HAC. The use of multiple accelerometers can minimize gaps in 24-HAC data however, factors such as additional research costs, and higher participant and investigator burden, should also be considered.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/ad37ed","type":"journal-article","created":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T11:36:48Z","timestamp":1711453008000},"page":"045003","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Harmonization of three different accelerometers to classify the 24 h activity cycle"],"prefix":"10.1088","volume":"45","author":[{"given":"Benjamin D","family":"Boudreaux","sequence":"first","affiliation":[{"name":"Columbia University Irving Medical Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ginny M","family":"Frederick","sequence":"additional","affiliation":[{"name":"University of Georgia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick J","family":"O\u2019Connor","sequence":"additional","affiliation":[{"name":"University of Georgia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ellen M","family":"Evans","sequence":"additional","affiliation":[{"name":"Indiana University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael D","family":"Schmidt","sequence":"additional","affiliation":[{"name":"University of Georgia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2024,4,11]]},"reference":[{"key":"pmeaad37edbib1","doi-asserted-by":"publisher","first-page":"S4\u2013s38","DOI":"10.1080\/15402002.2015.1046356","type":"journal-article","article-title":"The SBSM guide to actigraphy monitoring: clinical and research applications","volume":"13","author":"Ancoli-Israel","year":"2015","journal-title":"Behav. 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