Fluid intake patterns of children and adolescents: results of six Liq.In7 national cross-sectional surveys

• Published in: European journal of nutrition 2018-06, Vol.57 (Suppl 3), p.113-123
• Main Authors: Morin, C., Gandy, J., Brazeilles, R., Moreno, L. A., Kavouras, S. A., Martinez, H., Salas-Salvadó, J., Bottin, J., Guelinckx, Isabelle
• Format: Article
• Language: English
• Subjects: Adolescents; Alcoholic beverages; algorithms; Argentina; Beverages; Brazil; Chemistry; Chemistry and Materials Science; Children; Childrens health; China; Cluster analysis; Cross-sectional studies; Demography; Drinking water; Fluid intake; Fruit juices; Health behavior; Hydration; Indonesia; lifestyle; Mexico; milk; Nutrition; Original Contribution; Public health; sociodemographic characteristics; socioeconomic status; Sugar; Teenagers; Uruguay
• ISSN: 1436-6207; 1436-6215
• DOI: 10.1007/s00394-018-1725-y

Purpose This study aimed to identify and characterize patterns of fluid intake in children and adolescents from six countries: Argentina, Brazil, China, Indonesia, Mexico and Uruguay. Methods Data on fluid intake volume and type amongst children (4–9 years; N  = 1400) and adolescents (10–17 years; N  = 1781) were collected using the validated 7-day fluid-specific record (Liq.In 7 record). To identify relatively distinct clusters of subjects based on eight fluid types (water, milk and its derivatives, hot beverages, sugar-sweetened beverages (SSB), 100% fruit juices, artificial/non-nutritive sweetened beverages, alcoholic beverages, other beverages), a cluster analysis (partitioning around k-medoids algorithm) was used. Clusters were then characterized according to their socio-demographics and lifestyle indicators. Results The six interpretable clusters identified were: low drinkers–SSB ( n 523), low drinkers–water and milk ( n 615), medium mixed drinkers ( n 914), high drinkers–SSB ( n 513), high drinkers–water ( n 352) and very high drinkers–water ( n 264). Country of residence was the dominant characteristic, followed by socioeconomic level, in all six patterns. Conclusions This analysis showed that consumption of water and SSB were the primary drivers of the clusters. In addition to country, socio-demographic and lifestyle factors played a role in determining the characteristics of each cluster. This information highlights the need to target interventions in particular populations aimed at changing fluid intake behavior and improving health in children and adolescents.