hildhood undernutrition while being a preventable condition remains a major public health issue because it contributes to the mortality and morbidity of children globally. Intervention to improve the nutritional status of children includes supplementary feeding, fortified foods, cash transfers and nutritional education.
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Erik, HT, Onn, SW, and Montalvo, S. Vertical jump height with artificial intelligence through a cell phone: a validity and reliability report. J Strength Cond Res 38(9): e529-e533, 2024-This study estimated the reliability and validity of an artificial intelligence (AI)-driven model in the My Jump 2 (My Jump Lab ) for estimating vertical jump height compared with the Force Platform (FP). The cross-sectional study involved 88 athletes (33 female and 55 male athletes), performing a total of 264 countermovement jumps with hands on hips. "Jump heights were simultaneously measured using the FP and the My Jump 2 app." The FP estimated jump heights using the impulse-momentum method, whereas My Jump 2 used the flight-time method, with the latter using an AI feature for automated detection of jump take-off and landing. Results indicated high reliability for the AI model (intraclass correlation coefficient [ICC 1,3 ] = 0.980, coefficient of variation [CV] = 4.12) and FP (ICC 1,3 = 0.990, CV = 2.92). Validity assessment showed strong agreement between the AI model and FP (ICC 2,k = 0.973). This was also supported by the Bland-Altman analysis, and the ordinary least products regression revealed no significant systematic or proportional bias. The AI-driven model in My Jump 2 is highly reliable and valid for estimating jump height. Strength and conditioning professionals may use the AI-based mobile app for accurate jump height measurements, offering a practical and efficient alternative to traditional methods.