MARC보기
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020 ▼a 9780438064898
035 ▼a (MiAaPQ)AAI10790311
035 ▼a (MiAaPQ)unc:17812
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 574
1001 ▼a Wilson, Rebbecca P.
24510 ▼a Using the Multilevel Generalized Mixed Model to Impute Missing Accelermometry.
260 ▼a [S.l.] : ▼b The University of North Carolina at Chapel Hill., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 111 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 ▼a Advisers: Shrikant Bangdiwala
5021 ▼a Thesis (Dr.P.H.)--The University of North Carolina at Chapel Hill, 2018.
520 ▼a Accelerometers provide objective measures of physical activity and sedentary behavior. Typically, the device is worn for one week during all waking hours to measure physical activity counts for a period time (e.g., minute). A challenge is accoun
520 ▼a We proposed imputing counts/min for nonwear using a multilevel generalized mixed model (MGMM) and account for multivariate counts under a complex survey design. Using data from the Hispanic Community Health Study/ Study of Latinos (2008 -- 2011)
520 ▼a Our results showed that (1) accelerometer average counts/min were higher for wear versus nonwear segments in an interval, thus, we concluded that the MCAR assumption of the ad hoc approach was not tenable. (2) The MGMM indicated a clear associat
520 ▼a Further research in this area will greatly improve physical activity guidelines established using accelerometer data that better accounts for nonwear time.
590 ▼a School code: 0153.
650 4 ▼a Biostatistics.
690 ▼a 0308
71020 ▼a The University of North Carolina at Chapel Hill. ▼b Biostatistics.
7730 ▼t Dissertation Abstracts International ▼g 79-10B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0153
791 ▼a Dr.P.H.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997559 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자