Physical Activity Change in an RCT: Comparison of Measurement Methods.
Objectives: We aimed to quantify the agreement between self-report, standard cut-point accelerometer, and machine learning accelerometer estimates of physical activity (PA), and examine how agreement changes over time among older adults in an intervention setting. Methods: Data were from a randomize...
| Publicado en: | American Journal of Health Behavior Vol. 43; no. 3; pp. 543 - 556 |
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| Autores principales: | , , , , , , , , |
| Formato: | research tables/charts randomized controlled trial Journal Article |
| Publicado: |
PNG Publications
May/Jun2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=136145979&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136145979 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10873244 8GA jtl: American Journal of Health Behavior issn: 10873244 maglogo: N pubinfo: dt: May/Jun2019 vid: 43 iid: 3 pid: 8968 pub: PNG Publications place: Oak Ridge, North Carolina artinfo: ui: 136145979 136145979 136145979 10.5993/AJHB.43.3.9 136145979 ppf: 543 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Physical Activity Change in an RCT: Comparison of Measurement Methods. aug: au: Nelson, Sandahl H. Natarajan, Loki Patterson, Ruth E. Hartman, Sheri J. Thompson, Caroline A. Godbole, Suneeta V. Johnson, Eileen Marinac, Catherine R. Kerr, Jacqueline affil: Department of Family Medicine and Public Health, University of California-San Diego, La Jolla, CA sug: subj: Postmenopause Breast Neoplasms Cancer Survivors Self Report Physical Activity Accelerometry Evaluation Human Middle Age Machine Learning Algorithms Randomized Controlled Trials Obesity Descriptive Statistics Body Mass Index Questionnaires Funding Source Middle Aged: 45-64 years ab: Objectives: We aimed to quantify the agreement between self-report, standard cut-point accelerometer, and machine learning accelerometer estimates of physical activity (PA), and examine how agreement changes over time among older adults in an intervention setting. Methods: Data were from a randomized weight loss trial that encouraged increased PA among 333 postmenopausal breast cancer survivors. PA was estimated using accelerometry and a validated questionnaire at baseline and 6-months. Accelerometer data were processed using standard cut-points and a validated machine learning algorithm. Agreement of PA at each time-point and change was assessed using mixed effects regression models and concordance correlation. Results: At baseline, self-report and machine learning provided similar PA estimates (mean difference = 11.5 min/day) unlike self-report and standard cut-points (mean difference = 36.3 min/ day). Cut-point and machine learning methods assessed PA change over time more similarly than other comparisons. Specifically, the mean difference of PA change for the cut-point versus machine learning methods was 5.1 min/day for intervention group and 2.9 in controls, whereas it was ⩾ 24.7 min/day for other comparisons. Conclusions: Intervention researchers are facing the issue of self-report measures introducing bias and accelerometer cut-points being insensitive. Machine learning approaches may bridge this gap. pubtype: Academic Journal doctype: research tables/charts randomized controlled trial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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