SynthesisJournal of primary care & community health
Machine Learning Applications for In-School Physical Activity Data Using IMUs in Children and Adolescents: A Systematic Review for Health Promotion.
Synthesis in Journal of primary care & community health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Injury Prediction and Risk Modelling in Team Sports Using Artificial Intelligence and Sensor-Based Monitoring: A Scoping Review.Journal of functional morphology and kinesiology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCaring health from childhood is a most important challenge. To date, machine learning (ML) algorithms have been introduced to several fields of knowledge, while in education it is a novel perspective. This review aims to evaluate the effectiveness of ML applications on data registered by inertial measurement units collected from preschool to secondary education children's physical activity during school-hours. Furthermore, the review aims to explore how ML is used to process and interpret this data for outcomes like motor competence, physical activity intensity, sedentary behavior and academic/developmental indicators.
methodsFollowing PRISMA guidelines, we systematically searched PubMed, Web of Sciences, SCOPUS, SPORTDiscus and ProQuest Central databases.
results13 studies met the inclusion criteria, covering preschool to secondary education settings across multiple countries. The methodological quality ranged from moderate to high (11-17/18 MINORS points). ML algorithms, mainly Random Forest, Support Vector Machines, Gradient Boosting and Convolutional Neural Networks, were successfully applied to classify or predict various outcomes such as motor competence, physical activity intensity, sedentary behavior and developmental or academic indicators.
conclusionReported accuracies ranged from approximately 70% to 99%, demonstrating the strong potential of wearable sensor data combined with ML to objectively monitor and assess school-related physical activity.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.