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All Journal Journal of World Future Medicine, Health and Nursing
Hermansyah Hermansyah
Politeknik Kesehatan Kemenkes Aceh

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Analysis of Physical Activity Patterns using Wearable Sensors in the Management of Heart Disease Belinda Arbitya Dewi; Agus Mukholid; Rini Ambarwati; Hermansyah Hermansyah; Arnes Yuli Vandika
Journal of World Future Medicine, Health and Nursing Vol. 2 No. 2 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v2i2.751

Abstract

Heart disease is one of the leading causes of death worldwide. Adequate physical activity is an important factor in the management of heart disease, but accurately monitoring physical activity can be challenging. The use of wearable sensors offers a potential solution to measure and analyze physical activity patterns more precisely. This study aims to analyze physical activity patterns using wearable sensors in the management of heart disease. As well as to find out whether physical activity using wearable sensors can overcome heart disease. This research method uses a method with a quantitative approach. This study involves participants who suffer from heart disease and use wearable sensors to record their physical activity over a period of time. The physical activity data, including the number of steps, activity intensity, and activity duration, were analyzed using statistical methods and signal processing algorithms to identify activity patterns related to heart conditions. The results of the analysis showed different physical activity patterns between individuals with heart disease and healthy individuals. Individuals with heart disease tend to have lower levels of physical activity and less regular activity patterns. In addition, certain activity patterns, such as long periods of low-intensity activity, are associated with a higher risk for cardiac complications. The implementation of this research is that wearable sensors can be an effective tool in the management of heart disease by enabling accurate monitoring of an individual's physical activity. By analyzing physical activity patterns, we can better understand the relationship between physical activity and heart conditions, allowing for the development of more precise and personalized interventions in the management of heart disease.  
The Role of Big Data Technology in Predicting and Managing the Spread of Infectious Diseases Loso Judijanto; Hermansyah Hermansyah; Kori Puspita Ningsih; Dito Anurogo; Mohamad Firdaus
Journal of World Future Medicine, Health and Nursing Vol. 2 No. 2 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v2i2.757

Abstract

The spread of infectious diseases is a global problem that requires effective approaches for prediction and management. In recent years, Big Data technology has become a major concern in the healthcare field due to its ability to quickly collect, store and analyze large and diverse volumes of data. This opens up new opportunities to improve prediction and management of the spread of infectious diseases. This research aims to investigate the role of Big Data technology in predicting and managing the spread of infectious diseases. We want to identify effective methods for using big data to predict disease spread patterns and manage responses to them. The research method used in this research is a qualitative method in the form of literature analysis about the use of Big Data technology in the health sector, case studies of the implementation of Big Data systems to predict the spread of disease. The research results show that Big Data technology can improve predictions of the spread of infectious diseases by integrating data from various sources, including clinical, geographic, demographic and social data. Integrated Big Data systems can provide a better understanding of the factors that influence the spread of disease and enable faster and more effective decision making in responding to outbreaks. The conclusion of this research is that it confirms that Big Data technology has great potential in improving the prediction and management of the spread of infectious diseases. By effectively leveraging big data, we can improve our understanding of the dynamics of disease spread and implement more timely and efficient intervention strategies. Therefore, further investment and development in Big Data technology in the health sector is essential to strengthen capacity to face global health challenges.
Co-Authors Agus Mukholid Arnes Yuli Vandika Belinda Arbitya Dewi Dito Anurogo Loso Judijanto Mohamad Firdaus Rini Ambarwati