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Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

817 related items for PubMed ID: 25340969

  • 21. Estimating energy expenditure using body-worn accelerometers: a comparison of methods, sensors number and positioning.
    Altini M, Penders J, Vullers R, Amft O.
    IEEE J Biomed Health Inform; 2015 Jan; 19(1):219-26. PubMed ID: 24691168
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  • 24. Separating bedtime rest from activity using waist or wrist-worn accelerometers in youth.
    Tracy DJ, Xu Z, Choi L, Acra S, Chen KY, Buchowski MS.
    PLoS One; 2014 Jan; 9(4):e92512. PubMed ID: 24727999
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  • 30. Measuring reliability and validity of the ActiGraph GT3X accelerometer for children with cerebral palsy: a feasibility study.
    O'Neil ME, Fragala-Pinkham MA, Forman JL, Trost SG.
    J Pediatr Rehabil Med; 2014 Jan; 7(3):233-40. PubMed ID: 25260506
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  • 31. Evaluation of the activPAL accelerometer for physical activity and energy expenditure estimation in a semi-structured setting.
    Montoye AHK, Pivarnik JM, Mudd LM, Biswas S, Pfeiffer KA.
    J Sci Med Sport; 2017 Nov; 20(11):1003-1007. PubMed ID: 28483558
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  • 33. Machine learning for activity recognition: hip versus wrist data.
    Trost SG, Zheng Y, Wong WK.
    Physiol Meas; 2014 Nov; 35(11):2183-9. PubMed ID: 25340887
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  • 34. Raw and Count Data Comparability of Hip-Worn ActiGraph GT3X+ and Link Accelerometers.
    Montoye AHK, Nelson MB, Bock JM, Imboden MT, Kaminsky LA, Mackintosh KA, McNarry MA, Pfeiffer KA.
    Med Sci Sports Exerc; 2018 May; 50(5):1103-1112. PubMed ID: 29283934
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  • 35. Validity of hip-mounted uniaxial accelerometry with heart-rate monitoring vs. triaxial accelerometry in the assessment of free-living energy expenditure in young children: the IDEFICS Validation Study.
    Ojiambo R, Konstabel K, Veidebaum T, Reilly J, Verbestel V, Huybrechts I, Sioen I, Casajús JA, Moreno LA, Vicente-Rodriguez G, Bammann K, Tubic BM, Marild S, Westerterp K, Pitsiladis YP, IDEFICS Consortium.
    J Appl Physiol (1985); 2012 Nov; 113(10):1530-6. PubMed ID: 22995396
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  • 36. Intensity classification accuracy of accelerometer-measured physical activities in Chinese children and youth.
    Zhu Z, Chen P, Zhuang J.
    Res Q Exerc Sport; 2013 Dec; 84 Suppl 2():S4-11. PubMed ID: 24527562
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  • 37. Validation of five minimally obstructive methods to estimate physical activity energy expenditure in young adults in semi-standardized settings.
    Schneller MB, Pedersen MT, Gupta N, Aadahl M, Holtermann A.
    Sensors (Basel); 2015 Mar 13; 15(3):6133-51. PubMed ID: 25781506
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  • 38. Investigating optimal accelerometer placement for energy expenditure prediction in children using a machine learning approach.
    Mackintosh KA, Montoye AH, Pfeiffer KA, McNarry MA.
    Physiol Meas; 2016 Oct 13; 37(10):1728-1740. PubMed ID: 27653339
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  • 39. Triaxial accelerometer output predicts oxygen uptake in adults with Down syndrome.
    Allred AT, Choi P, Agiovlasitis S.
    Disabil Rehabil; 2021 Sep 13; 43(18):2602-2609. PubMed ID: 31880164
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  • 40. Estimating activity and sedentary behavior from an accelerometer on the hip or wrist.
    Rosenberger ME, Haskell WL, Albinali F, Mota S, Nawyn J, Intille S.
    Med Sci Sports Exerc; 2013 May 13; 45(5):964-75. PubMed ID: 23247702
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