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@ARTICLE{Jeran:181958,
      author       = {S. Jeran and A. Steinbrecher and V. Haas and A. Mähler and
                      M. Boschmann and K. R. Westerterp and B. A. Brühmann and K.
                      Steindorf$^*$ and T. Pischon},
      title        = {{P}rediction of activity-related energy expenditure under
                      free-living conditions using accelerometer-derived physical
                      activity.},
      journal      = {Scientific reports},
      volume       = {12},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Macmillan Publishers Limited, part of Springer Nature},
      reportid     = {DKFZ-2022-02328},
      pages        = {16578},
      year         = {2022},
      abstract     = {The purpose of the study was to develop prediction models
                      to estimate physical activity (PA)-related energy
                      expenditure (AEE) based on accelerometry and additional
                      variables in free-living adults. In 50 volunteers (20-69
                      years) PA was determined over 2 weeks using the hip-worn
                      Actigraph GT3X + as vector magnitude (VM) counts/minute. AEE
                      was calculated based on total daily EE (measured by
                      doubly-labeled water), resting EE (indirect calorimetry),
                      and diet-induced thermogenesis. Anthropometry, body
                      composition, blood pressure, heart rate, fitness,
                      sociodemographic and lifestyle factors, PA habits and food
                      intake were assessed. Prediction models were developed by
                      context-grouping of 75 variables, and within-group stepwise
                      selection (stage I). All significant variables were jointly
                      offered for second stepwise regression (stage II). Explained
                      AEE variance was estimated based on variables remaining
                      significant. Alternative scenarios with different
                      availability of groups from stage I were simulated. When all
                      11 significant variables (selected in stage I) were jointly
                      offered for stage II stepwise selection, the final model
                      explained $70.7\%$ of AEE variance and included VM-counts
                      $(33.8\%),$ fat-free mass $(26.7\%),$ time in moderate PA +
                      walking $(6.4\%)$ and carbohydrate intake $(3.9\%).$
                      Alternative scenarios explained $53.8-72.4\%$ of AEE. In
                      conclusion, accelerometer counts and fat-free mass explained
                      most of variance in AEE. Prediction was further improved by
                      PA information from questionnaires. These results may be
                      used for AEE prediction in studies using accelerometry.},
      cin          = {C110},
      ddc          = {600},
      cid          = {I:(DE-He78)C110-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:36195647},
      doi          = {10.1038/s41598-022-20639-0},
      url          = {https://inrepo02.dkfz.de/record/181958},
}