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The risk of undernutrition in older community-dwelling adults increases when they are no longer able to shop or cook themselves. Home-delivered products could then possibly prevent them from becoming undernourished. This single-blind randomised trial tested the effectiveness of home-delivered protein-rich ready-made meals and dairy products in reaching the recommended intake of 1·2 g protein/kg body weight (BW) per d and ≥25 g of protein per meal. Community-dwelling older adults (n 98; mean age 80·4 (sd 6·8) years) switched from self-prepared to home-delivered hot meals and dairy products for 28 d. The intervention group received ready-made meals and dairy products high in protein; the control group received products lower in protein. Dietary intake was measured at baseline, after 2 weeks (T1), and after 4 weeks (T2). Multilevel analyses (providing one combined outcome for T1 and T2) and logistic regressions were performed. Average baseline protein intake was 1·09 (se 0·05) g protein/kg BW per d in the intervention group and 0·99 (se 0·05) g protein/kg BW per d in the control group. During the trial, protein intake of the intervention group was 1·12 (se 0·05) g protein/kg BW per d compared with 0·87 (se 0·03) g protein/kg BW per d in the control group (between-group differences P < 0·05). More participants of the intervention group reached the threshold of ≥25 g protein at dinner compared with the control group (intervention T1: 84·8 %, T2: 88·4 % v. control T1: 42·9 %, T2: 40·5 %; P < 0·05), but not at breakfast and lunch. Our findings suggest that switching from self-prepared meals to ready-made meals carries the risk of a decreasing protein intake, unless extra attention is given to protein-rich choices.
This practical book is designed for applied researchers who want to use mixed models with their data. It discusses the basic principles of mixed model analysis, including two-level and three-level structures, and covers continuous outcome variables, dichotomous outcome variables, and categorical and survival outcome variables. Emphasizing interpretation of results, the book develops the most important applications of mixed models, such as the study of group differences, longitudinal data analysis, multivariate mixed model analysis, IPD meta-analysis, and mixed model predictions. All examples are analyzed with STATA, and an extensive overview and comparison of alternative software packages is provided. All datasets used in the book are available for download, so readers can re-analyze the examples to gain a strong understanding of the methods. Although most examples are taken from epidemiological and clinical studies, this book is also highly recommended for researchers working in other fields.