4.5 Article

Electronic health technology for the assessment of physical activity and eating habits in children and adolescents with overweight and obesity IDA

Journal

APPETITE
Volume 58, Issue 2, Pages 432-437

Publisher

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.appet.2011.11.021

Keywords

Telemedicine; Morbidity; Mortality; Body-mass index; Motivation; DiaTrace

Funding

  1. Verein zur Forderung des Rehabilitationsforschung in Hamburg. Mecklenburg-Vorpommern und Schleswig-Holstein e.V. (vffr) [121, 132]
  2. Institute of Community Medicine, Ernst-Moritz-Arndt-University, Greifswald, Germany

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Objective: It was the goal of the trial to study the impact of electronic healthcare technology into treatment. Methods: One hundred and twenty-four children/adolescents (females 56%, age 13.5 +/- 2.8 years, height 1.64 +/- 0.13 m, weight 85.4 +/- 23.0 kg, body-mass index (BMI) 31.3 +/- 5.2 kg/m(2), BMI-standard deviation score (SDS) 2.50 +/- 0.5) were included. To assess physical activity and eating habits, a mobile motion sensor integrated into a mobile phone with digital camera was used. Results: The children/adolescents had a significant weight reduction of 7.1 +/- 3.0 kg. BMI/BMI-SDS decreased (p < 0.01). Intensity (14.1 +/- 6.4 activity units) and duration of physical activity (290.4 +/- 92.6 min/day) were assessed with sensors. Time walking: median 45.5 (range, 2.5-206.5), running 8.0 (range, 0-39.5), cycling 27.7 (range, 0-72.5), car driving 23.7 (range, 0-83.0) min/day. Comparing self-reported physical activity (walking 292.9 (range, 9.6-496.1), running 84.8 (range, 8.4-130.2) min/day) with assessment with sensors there were significant differences (p < 0.01). Duration of physical activity documented by children/adolescents was higher than the assessment with motion sensors (walking 292.9 vs 45.5 min, p < 0.01. running 84.8 vs 8.0 min, p < 0.01). Sensor derived energy intake was higher than recommended (469.14 +/- 88.75 kcal vs 489.03 +/- 108.25 kcal, p = 0.09). Performing multivariate analysis the following parameters showed associations with weight reduction (R-square = 0.75): body weight ( beta = -0.95, p < 0.01), C-reactive protein (CRP, beta = 0.15, p = 0.07), physical activity, time spent in activities measured with sensors (beta = -0.18, p = 0.04), stress management (beta=0.16, p = 0.06), body fat mass at onset of the trial ( beta = 0.45, p < 0.01) and body shape (beta = -0.25, p = 0.01). Conclusion: The innovative mobile movement detection system is highly accepted by children and adolescents. The system is able to augment existing weight reduction and stabilization strategies. (C) 2011 Elsevier Ltd. All rights reserved.

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