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For country-specific analyses, weight change was treated as categorical and continuous variable.
In multivariate analyses, weight change (> 5%) was positively associated with an increased risk of both recurrence (RR 2.28; 95% CI: 1.29-4.03) andeathth (RR 2.11; 95% CI: 1.21-3.66).
Tests solicited at the time of diagnosis: spirometry (FEV1 value and spirometry stage, chest X-ray, gasometry (PO2, Sat02, CO2), electrocardiogram, blood analyses, weight and height and body mass index (BMI).
As part of secondary analyses, weight loss will also be examined as 6-month change in BMI percentile, absolute body weight, percent body weight, and total body fat assessed with Dual-Energy X-Ray Absorptiometry (DEXA).
In these analyses, weight (adjusted hazard ratio = 1.02, 95% CI = 1.00 to 1.1) and the absence of probiotic treatment (adjusted hazard ratio = 3.2, 95% CI = 1.1 to 9.1) were found to be independent factors associated with increased risk for P. aeruginosa respiratory infection and or colonization (Table 5).
In completers analyses, weight and waist circumference in the phone group were reduced with 1.6 kg (95% CI -2.2 -1.0, p < 0.001) and 1.9 cm (95% CI -2.7 -1.0, p < 0.001) respectively, fat intake decreased with 1 fatpoint (1 to 4 grams)/day (95% CI -1.7 -0.2, p = 0.01) and physical activity increased with 866 METminutes/week (95% CI 203 1530, p = 0.01), compared with controls.
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Sampling weights were also taken into account in the analyses (weighting variable supplied with the data).
In these analyses, weighting with standardise active playing time was employed.
Saaristo et al. [ 20] analysed weight change and diabetes incidence in their DPS-based study, reporting a stepwise association.
The other nine studies mainly analysed weight handling and strength subtests, most frequently the lifting low task.
Linkov et al. [8] analysed weight-of-evidence evaluations and came up with a classification system based on the amount of use of qualitative and quantitative methods.
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