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  • Ekelund, Ulf,1960-Örebro universitet,Institutionen för hälsovetenskap och medicin,Unit for Preventive Nutrition, Department of Medical Nutrition, Karolinska Institutet, Stockholm, Sweden,Örebro universitet, Institutionen för hälsovetenskap och medicin (author)

Physical activity assessed by activity monitor and doubly labeled water in children

  • Article/chapterEnglish2001

Publisher, publication year, extent ...

  • Ovid Technologies (Wolters Kluwer Health),2001
  • printrdacarrier

Numbers

  • LIBRIS-ID:oai:DiVA.org:oru-30490
  • https://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-30490URI
  • https://doi.org/10.1097/00005768-200102000-00017DOI
  • https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-304877URI
  • http://kipublications.ki.se/Default.aspx?queryparsed=id:1949086URI

Supplementary language notes

  • Language:English
  • Summary in:English

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  • Subject category:ref swepub-contenttype
  • Subject category:art swepub-publicationtype

Notes

  • PURPOSE: To validate the Computer Science and Application's (CSA) activity monitor for assessment of the total amount of physical activity during two school-weeks in 9-yr-old children and to develop equations to predict total energy expenditure (TEE) and activity energy expenditure (AEE) from activity counts and anthropometric variables.METHODS: A total of 26 children (15 boys and 11 girls, mean age 9.1 +/- 0.3 yr) were monitored for 14 consecutive days. TEE was simultaneously measured by the doubly labeled water method. Averaged activity counts (counts.min(-1)) were compared with data on: 1) TEE, 2) AEE = TEE minus basal metabolic rate (BMR; estimated from predictive equations), and 3) daily physical activity level (PAL = TEE/BMR).RESULTS: Physical activity determined by activity counts was significantly related to the data on energy expenditures: TEE (r = 0.39; P < 0.05), AEE (r = 0.54; P < 0.01), and PAL (r = 0.58; P < 0.01). Multiple stepwise regression analysis showed that TEE was significantly influenced by gender, body composition (body weight or fat free mass), and activity counts (R(2) = 0.54--0.60). AEE was significantly influenced by activity counts and gender (R(2) = 0.45). There were no significant differences between activity counts and PAL in discriminating among activity levels with "low" (PAL < 1.56), "moderate" (1.57 < or = PAL > or = 1.81), and "high" (PAL > 1.81) intensity.CONCLUSION: Activity counts from the CSA activity monitor seems to be a useful measure of the total amount of physical activity in 9-yr-old children. Activity counts contributed significantly to the explained variation in TEE and was the best predictor of AEE.

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  • Sjöström, MichaelKarolinska Institutet (author)
  • Yngve, Agneta,1953-Karolinska Institutet(Swepub:uu)agnyn302 (author)
  • Poortvliet, EricKarolinska Institutet (author)
  • Nilsson, Andreas,1973-Örebro universitet,Institutionen för hälsovetenskap och medicin,RISPA,Örebro universitet, Institutionen för hälsovetenskap och medicin(Swepub:oru)asnn (author)
  • Fröberg, KarstenInstitute of Sport Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark (author)
  • Wedderkopp, NielsInstitute of Sport Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark (author)
  • Westerterp, KlaasDepartment of Human Biology, Maastricht University, Maastricht, The Netherlands (author)
  • Örebro universitetInstitutionen för hälsovetenskap och medicin (creator_code:org_t)

Related titles

  • In:Medicine & Science in Sports & Exercise: Ovid Technologies (Wolters Kluwer Health)33:2, s. 275-2810195-91311530-0315

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