Early childhood—specifically, the period from 0 to 6 years of age—is a critical time in children’s lives with rapid growth in their cognitive, social and emotional development. This period has also been shown to be the most effective time for early interventions. The use of artificial Intelligence (AI) for supporting early child development is increasing alongside the rapid advancement of technology. AI can be used directly by children (eg, for implementing adaptive technologies), by individuals who interact with children (eg, educators, parents, nurses), and by individuals indirectly supporting early child development (eg, early childhood researchers or policy analysts). This scoping review will provide a roadmap for relevant stakeholders on how AI has been applied within and across different contexts to support infants and young children’s development, as well as the most predominant AI technologies used across various contexts.

The current study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review The search syntax will be applied in PsycINFO, ERIC, Education Source, CINHAL, MEDLINE, Embase and IEEE Xplore. The purpose of this study is to curate and synthesise academic papers to examine the application of AI for supporting the development of children between birth and age 6 years of age. Studies with children or individuals who work directly or indirectly with children will be included. Part of the abstract and full-text screening will be conducted by two researchers, with discrepancies being resolved by the lead authors. In addition, AI will be used to help with study screening and data extraction once confirmed to be reliable (Cohen’s kappa >0.80). Thematic and content analyses will be conducted to identify the types of AI products used and their applications in different contexts, the most predominant AI products used within and across each context, as well as how children’s developmental outcomes are impacted by the use of these AI products. Where applicable, visualisations such as tables, graphs and figures will be used to synthesise the data across contexts and AI products used to support early development of young children.

Certain leading medical organisations are considering alternatives to the Body Mass Index (BMI) as a predictor of the risk for non-communicable chronic disease (NCD) or death. Our objective was to evaluate the associations between various measures of body fat and the risk of incident NCDs or mortality, independent of inflammation.

Population-based prospective cohort study (the UK Biobank cohort).

The UK.

Adults (aged between 40 and 69 years) were accrued between March 2006 and October 2010 and followed until December 2022. There were 500 107 participants: the median age was 58 years (IQR 50–63) at baseline, 45.6% were male and 94.7% were white.

BMI, waist-to-hip ratio (WHR), body fat percentage measured by bioimpedance analysis (BIA; fat_BIA), C-reactive protein (CRP) and various other measures of body fat obtained by dual-energy X-ray absorptiometry (DXA; including visceral adipose tissue (VAT)) and magnetic resonance imaging (MRI).

All-cause death, cardiovascular disease (heart failure, hypertension, myocardial infarction, pulmonary embolism and stroke), cancers (breast, colorectal, endometrial, oesophageal, kidney, ovarian, pancreatic and prostate), diabetes, asthma, gallbladder disease, chronic back pain and osteoarthritis.

The 5th and 95th percentiles for measures of body fat were BMI 20.5 (considered ‘healthy’) and 37.0 kg/m² (considered ‘unhealthy’), WHR 0.71 and 0.94 and BIA 24.8% and 47.6% in females, and BMI 22.0 (considered ‘healthy’) and 35.4 kg/m² (considered ‘unhealthy’), WHR 0.83 and 1.05 and BIA 15.5% and 34.7% in males. BMI was strongly correlated to fat_BIA (0.85 in females and 0.80 in males) but less so with WHR (0.46 in females and 0.59 in males). All measures of body fat were positively associated with the incidence of NCDs, but only WHR remained positively associated with death after full adjustment (HR 95th percentile vs 5th percentile (95% CI): BMI 0.80 (0.76 to 0.84), WHR 1.21 (1.16 to 1.28) and BIA 0.80 (0.76 to 0.84) in females; BMI 0.89 (0.85 to 0.93), WHR 1.19 (1.14 to 1.24) and BIA 0.89 (0.85 to 0.92) in males). Simpler models that adjusted for age, sex, CRP, WHR and either BMI or fat_BIA gave similar results. Associations between body fat and the incidence of NCDs after accounting for the competing risk of death were also similar.

BMI was strongly correlated with fat_BIA, but WHR and visceral adipose tissue percentage were less so. All measures of body fat were associated with the incidence of NCDs, but only WHR was independently associated with mortality. These findings support the hypothesis that body fat may be protective against death and that the excess risk associated with higher WHR may be mediated by something other than body fat.