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Impact of different evaluation methods on the dropout rate of objectively detected physical activity on age specific subgroups: Results from the Motorik-Modul-Study (MoMo)

Burchartz, Alexander ORCID iD icon; Anedda Bastian; Oriwol, Doris; Albrecht, Claudia ORCID iD icon; Woll, Alexander ORCID iD icon

Abstract (englisch):

Introduction: In order to determine physical activity in free-living conditions the most common used objective method is accelerometry. This study analyzed the different technical methods and their outcomes evaluating accelerometer data in the large-scale epidemiological MoMo study. MoMo marks the first nationwide objective collection of data on physical activity and sedentary behavior of German children, adolescents and young adults. Method: The accelerometer sample size in MoMo from the latest survey from 2014-2017 is n = 1.971 (4-18 years). Wearing ActiGraph accelerometers GT3X+/wGT3X-BT movement behavior was objectively registered for one week. Effects on the dropout rate were analyzed with different epoch lengths (EL), non-wear-time (NWT) definitions, different valid day/week criteria and intensity-classification-algorithms. Results: Recording is in EL of 1s with the possibility to convert into 5s, 10s, 15s, 30s and 60s for future analysis. For the NWT Calculation the Choi-Algorithm was chosen because of the constancy in detecting NWT whereas Troiano shows variance up to 3% in NWT with different EL. Valid day criteria is 8h of recordings (+13% valid data compared to 10h) on four weekdays and one further weekend day when wearing the device for 7d. ... mehr

DOI: 10.13140/RG.2.2.10780.74883
Zugehörige Institution(en) am KIT Institut für Sport und Sportwissenschaft (IfSS)
Publikationstyp Poster
Publikationsjahr 2018
Sprache Englisch
Identifikator KITopen-ID: 1000091052
Veranstaltung 7th International Society for Physical Activity and Health Congress (ISPAH 2018), London, Vereinigtes Königreich, 15.10.2018 – 17.10.2018
Schlagwörter Accelerometer, Physical Activity, MoMo-Study, Epoch Length, Non-Wear-Time, intensitiy classification algorithms
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