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Purpose This study aimed to identify clusters of lifestyle behaviours in toddlers and assess associations with socio-demographic characteristics. Methods We used data from the Dutch National Food Consumption Survey 2012–2016 and included 646 children aged 1–3 years. Based on 24-h dietary recalls and a questionnaire, a two-step cluster analysis was conducted to identify clusters in the intake of fruit, vegetables, sugar-sweetened beverages and unhealthy snacks, physical activity and screen time. Logistic regression models assessed associations between socio-demographic characteristics and cluster allocation. Results Three clusters emerged from the data. The ‘relatively healthy cluster’ demonstrated a high intake of fruit and vegetables, low sugar-sweetened beverage and unhealthy snack intake and low screen time. The ‘active snacking cluster’ was characterised by high unhealthy snack intake and high physical activity, and the ‘sedentary sweet beverage cluster’ by high intake of sugar-sweetened beverages and high screen time. Children aged 1 year were most likely to be allocated to the ‘relatively healthy cluster’. Compared to children of parents with a high education level, children of parents with a low or middle education level were less likely to be in the ‘relatively healthy cluster’, but more likely to be in the ‘sedentary sweet beverage cluster’. Conclusion Clusters of lifestyle behaviours can be distinguished already in children aged 1–3 years. To promote healthy lifestyle behaviour, efforts may focus on maintaining healthy behaviour in 1-year-olds and more on switching towards healthy behaviour in 2- and 3-year-olds.

Screening of children’s lifestyle, including nutrition, may contribute to the prevention of lifestyle-related conditions in childhood and later in life. Screening tools can evaluate a wide variety of lifestyle factors, resulting in different (risk) scores and prospects of action. This systematic review aimed to summarise the design, psychometric properties and implementation of lifestyle screening tools for children in community settings. We searched the electronic databases of Embase, Medline (PubMed) and CINAHL to identify articles published between 2004 and July 2020 addressing lifestyle screening tools for children aged 0–18 years in the community setting. Independent screening and selection by two reviewers was followed by data extraction and the qualitative analysis of findings. We identified 41 unique lifestyle screening tools, with the majority addressing dietary and/or lifestyle behaviours and habits related to overweight and obesity. The domains mostly covered were nutrition, physical activity and sedentary behaviour/screen time. Tool validation was limited, and deliberate implementation features, such as the availability of clear prospects of actions following tool outcomes, were lacking. Despite the multitude of existing lifestyle screening tools for children in the community setting, there is a need for a validated easy-to-administer tool that enables risk classification and offers specific prospects of action to prevent children from adverse health outcomes.