automated detection of non-wear time in comparison to diary information

Estimation of physical activity using 24 h-accelerometry requires detection of accelerometer non-wear time (NWT). It is common practice to define NWT as periods >60 minutes of consecutive zero-accelerations, but this algorithm was originally developed for waking hours only and its applicability to 24 h-accelerometry is unclear. We investigated sensitivity and specificity of different algorithms to detect NWT in 24 h-accelerometry compared to diary in 47 ActivE and 559 KORA participants. NWT was determined with algorithms >60, >90, >120, >150, or >180 minutes of consecutive zero-counts. Overall, 9.1% (ActivE) and 15.4% (KORA) of reported NWT was >60 minutes. Sensitivity and specificity were lowest for the 60-min algorithm in ActivE (0.72 and 0.00) and KORA (0.64 and 0.08), and highest for the 180-min algorithm in ActivE (0.88 and 0.92) and for the 120-min algorithm in KORA (0.76 and 0.74). Nevertheless, when applying these last two algorithms, the overlap of accelerometry with any diary based NWT minutes was around 20% only. In conclusion, only a small proportion of NWT is >60 minutes. The 60-min algorithm is less suitable for NWT detection in 24 h-accelerometry because of low sensitivity, specificity, and small overlap with reported NWT minutes. Longer algorithms perform better but detect lower proportions of reported NWT

Lungenfunktion in der NAKO Gesundheitsstudie: Methoden und erste Ergebnisse

Background A nationwide assessment of the respiratory status on the basis of standardized lung function measurements has so far not been available in Germany. The present work describes the lung function tests in the German National Cohort (GNC) and presents initial results based on the GNC Midterm Baseline Dataset. Material and Methods The assessment of lung function in the GNC comprised spirometry (level 1) and the determination of exhaled nitric oxide (FeNO, level 2). Our quality assurance concept included regular training of lung function test procedures at various GNC sites, interim evaluations of test quality, as well as regular calibration/measurement checks of test equipment. For spirometry, we established a stepwise procedure for offline quality control based on raw flow volume curves. Results In the present dataset (n& x202f;= 101,734), spirometry was available for 86,893 study participants and FeNO was available for 15,228 participants. The average (+/- SD) FEV1 Z score (according to GLI 2012) was -0.321& x202f;+/- 1.047, the FVC Z score was -0.153& x202f;+/- 0.941, and the FEV1/FVC Z score was -0.337& x202f;+/- 0.901. The difference in FEV1/FVC between current smokers and never-smokers increased with age. The average FeNO was 14.2& x202f;divided by 2.0& x202f;ppb. Current smoking reduced FeNO levels by 43%, whereas respiratory allergy increased FeNO levels by 16% in nonsmokers. Discussion The results of spirometry and the FeNO measurements are in the expected range with regard to their distributions and correlates. The GNC provides a valuable basis for future investigations of respiratory health and its determinants as well as research into the prevention of respiratory diseases in Germany

[Lung function in the German National Cohort: methods and initial results].

In the present dataset (n = 101,734), spirometry was available for 86,893 study participants and FeNO was available for 15,228 participants. The average (±SD) FEV1 Z score (according to GLI 2012) was -0.321 ± 1.047, the FVC Z score was -0.153 ± 0.941, and the FEV1/FVC Z score was -0.337 ± 0.901. The difference in FEV1/FVC between current smokers and never-smokers increased with age. The average FeNO was 14.2 ÷ 2.0 ppb. Current smoking reduced FeNO levels by 43%, whereas respiratory allergy increased FeNO levels by 16% in nonsmokers