The paper discusses the application of machine learning (ML) in the study of physical activity, sedentary behavior, and sleep behaviors. It highlights the complexity and multifaceted nature of these behaviors, which are increasingly monitored using wearable activity monitors. The increasing volume of data generated necessitates new data analysis methods, and ML, with its ability to handle complex data, is proposed as a suitable tool. The paper outlines the principles of ML modeling, including supervised and unsupervised learning, and introduces common algorithms such as decision trees, random forests, and support vector machines (SVM). It also covers feature engineering, model development, and validation techniques. The paper emphasizes the importance of domain knowledge in feature engineering and the need for proper feature selection and validation techniques. It discusses the challenges of overfitting, underfitting, and the curse of dimensionality, and provides strategies to address these issues. The paper concludes with a review of ML applications in three key research areas: activity recognition, posture detection, and profile analysis, highlighting both successes and challenges. The goal is to provide a resource for researchers in physical activity, sedentary, and sleep behavior research, offering guidance on the use of ML techniques.The paper discusses the application of machine learning (ML) in the study of physical activity, sedentary behavior, and sleep behaviors. It highlights the complexity and multifaceted nature of these behaviors, which are increasingly monitored using wearable activity monitors. The increasing volume of data generated necessitates new data analysis methods, and ML, with its ability to handle complex data, is proposed as a suitable tool. The paper outlines the principles of ML modeling, including supervised and unsupervised learning, and introduces common algorithms such as decision trees, random forests, and support vector machines (SVM). It also covers feature engineering, model development, and validation techniques. The paper emphasizes the importance of domain knowledge in feature engineering and the need for proper feature selection and validation techniques. It discusses the challenges of overfitting, underfitting, and the curse of dimensionality, and provides strategies to address these issues. The paper concludes with a review of ML applications in three key research areas: activity recognition, posture detection, and profile analysis, highlighting both successes and challenges. The goal is to provide a resource for researchers in physical activity, sedentary, and sleep behavior research, offering guidance on the use of ML techniques.