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Hinke Haisma

Publications and source records attributed to Hinke Haisma.

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The Multidimensional Index of Child Growth (MICG) of the Task Force "Towards a Multidimensional Approach for Child Growth" of the International Union for Nutrition Sciences

Children's growth extends beyond height and weight. This paper introduces the Multidimensional Index of Child Growth (MICG), developed by the IUNS Task Force "Towards a Multidimensional Approach for Child Growth." The IUNS-MICG applies a capability- and rights-based framework covering 14 dimensions of child wellbeing, including health, care, mental wellbeing, participation, autonomy, mobility, and safety. Using data from the Young Lives Study in Ethiopia, India, Peru, and Vietnam, we tested the framework with 29 indicators. Comparisons of different weighting methods show that equal weights provide robust and policy-relevant results. MICG uncovers deprivations hidden by physical measures alone; for instance, rural girls in Peru face educational and mental wellbeing disadvantages despite similar physical growth. Further analyses show that community participation in WASH programs is linked to higher multidimensional outcomes, especially for the most deprived. We also extend MICG with a Bayesian approach to estimate children's unrealized opportunities and propose a spiderweb growth chart for visualizing multidimensional progress. MICG offers a practical, equity-focused tool to monitor, evaluate, and strengthen interventions that support the Sustainable Development Goals and ensure no child is left behind.

stat.AP

MICG-AI: A multidimensional index of child growth based on digital phenotyping with Bayesian artificial intelligence

This document proposes an algorithm for a mobile application designed to monitor multidimensional child growth through digital phenotyping. Digital phenotyping offers a unique opportunity to collect and analyze high-frequency data in real time, capturing behavioral, psychological, and physiological states of children in naturalistic settings. Traditional models of child growth primarily focus on physical metrics, often overlooking multidimensional aspects such as emotional, social, and cognitive development. In this paper, we introduce a Bayesian artificial intelligence (AI) algorithm that leverages digital phenotyping to create a Multidimensional Index of Child Growth (MICG). This index integrates data from various dimensions of child development, including physical, emotional, cognitive, and environmental factors. By incorporating probabilistic modeling, the proposed algorithm dynamically updates its learning based on data collected by the mobile app used by mothers and children. The app also infers uncertainty from response times, adjusting the importance of each dimension of child growth accordingly. Our contribution applies state-of-the-art technology to track multidimensional child development, enabling families and healthcare providers to make more informed decisions in real time.

stat.AP