The Effect of Computer Use on the Onset of Primary Headaches

U poslednje vreme upotreba računara se smatra značajnim okidačem za nastanak primarnih glavobolja. Svi rizikofaktori pri radu na računaru značajni za nastanak glavobolja su nedovoljno jasni i ispitani, te su i strategije prevencije nepoznate. Ciljevi ovog istraživanja su utvrđivanje razlika u prevalenciji primarnih glavobolja kod ispitanika koji koriste i koji ne koriste računar, kao i utvrđivanje uticaja vremena provedenog u radu na računaru i ponašanja pri radu na računaru na ispoljavanje određenog tipa primarne glavobolje. U studiji preseka finalni uzorak je činilo 1500 ispitanika. Uzorak je podeljen na dve grupe: 1. ispitanici koji koriste računar (95.7%), 2. ispitanici koji ne koriste računar (4.3%). Obe grupe su nadalje podeljene na: 1. one koji imaju glavobolju i 2. one koji nemaju glavobolju. Kod ispitanika koji koriste računar, glavobolju je imalo 69.6% ispitanika, a kod ispitanika koji ne koriste računar 27.4%. Nadalje, kod ispitanika koji koriste računar od glavobolje tenzionog tipa boluje 30.3%, od migrene 16.7%, od verovatno sekundarne glavobolje 14.0% ispitanika. Kod ispitanika koji ne koriste računar od glavobolje tenzionog tipa boluje 19.4%, od migrene 4.8% ispitanika, i od verovatno sekundarne glavobolje 3.2% ispitanika. Utvrđeno je da su korisnici računara imali značajno veću prevalenciju primarnih glavobolja u odnosu na one koji ne koriste računar. Korisnici računara sa glavoboljom u odnosu na one bez glavobolje duže rade na računaru, češće ne prave pauzu, a kada je i naprave, one traju kratko, češće zauzimaju nepravilan položaj tela. Korisnici računara sa migrenom u odnosu na one sa glavoboljom tenzionog tipa značajno duže vremena provode na računaru kod kuće, ređe prave pauze, a kada ih prave one su kratke, pravilnije sede za računarom, a u pauzi ređe sede i koriste mobilni telefon ili tablet. Prilikom klasifikacije glavobolja izdvojila se grupa od 8.6% ispitanika koji su imali glavobolju koja se nije ispunjavala kriterijume za migrenu, glavobolju tenzionog tipa, ni trigeminalnu autonomnu glavobolju, a nije postojala sumnja da je to sekundarna glavobolja. Obzirom da se ona javljala isključivo kod korisnika računara i da je većina njih izvestila da rad na računaru može biti okidač, ona je svrstana u ostale primarne glavobolje, za koju se pretpostavlja da za njen nastanak upotreba računara ima značajan uticaj. Korisnici računara oboleli od ostalih primarnih glavobolja u odnosu na obolele od migrene više vremena provode u radu na računaru na poslu, ređe zauzimaju pravilan položaj, ali češće prave pauzu, a u odnosu na one sa glavoboljom tenzionog tipa, češće izveštavaju da više vremena provode na računaru i na poslu i kod kuće, da nakon dužeg vremena prave pauzu ili nikada, i da im pauze kraće traju, ali da pravilnije sede pri radu na računaru. Na osnovu rezultata može se zaključiti da primarne glavobolje predstavljaju značajan zdravstveni problem kod korisnika računara. U uzorku su detektovani ispitanici sa glavoboljom, koja je bila prisutna samo kod korisnika računara, koja nije ogovarala postojećim kriterijumima klasifikacije ni za jednu primarnu glavobolju i za koju se sumnja da za njen nastanak rad na računaru imao značajan uticaj. Korisnici računara sa glavoboljom se ne pridržavaju definisanih ergonomskih preporuka pri radu na računaru, te se javlja potreba za sprovođenjem sistematske edukacije korisnika računara o ergonomskim preporukama u cilju prevencije ataka glavobolja.As of recently, the use of computers is considered a significant trigger for the development of primary headaches. The risk factors when working on a computer which are important for the occurrence of headaches are insufficiently clear and unexamined, thus making prevention strategies unknown. The aims of this study are to determine differences in the prevalence of primary headaches in respondents who use and do not use a computer, as well as to determine the impact of time spent working on a computer and computer work related behavior on the manifestation of a particular type of primary headache. In the cross-sectional study, the final sample consisted of 1500 subjects. The sample was divided into two groups: 1. respondents who use a computer (95.7%), 2. respondents who do not use a computer (4.3%). Both groups were further divided into: 1. those who have a headache and 2. those who do not have a headache. Among respondents who use a computer, 69.6% of respondents had a headache, and among respondents who do not use a computer, 27.4%. Furthermore, in respondents who use a computer, 30.3% suffer from tension-type headaches, 16.7% from migraines, and 14.0% from probable secondary headaches. In subjects who do not use a computer, 19.4% suffer from tension-type headaches, 4.8% from migraines, and 3.2% from suspected secondary headaches. Computer users were found to have a significantly higher prevalence of primary headaches compared to those who did not use a computer. Computer users with headaches, compared to those without headaches, work on As of recently, the use of computers is considered a significant trigger for the development of primary headaches. The risk factors when working on a computer which are important for the occurrence of headaches are insufficiently clear and unexamined, thus making prevention strategies unknown. The aims of this study are to determine differences in the prevalence of primary headaches in respondents who use and do not use a computer, as well as to determine the impact of time spent working on a computer and computer work related behavior on the manifestation of a particular type of primary headache. In the cross-sectional study, the final sample consisted of 1500 subjects. The sample was divided into two groups: 1. respondents who use a computer (95.7%), 2. respondents who do not use a computer (4.3%). Both groups were further divided into: 1. those who have a headache and 2. those who do not have a headache. Among respondents who use a computer, 69.6% of respondents had a headache, and among respondents who do not use a computer, 27.4%. Furthermore, in respondents who use a computer, 30.3% suffer from tension-type headaches, 16.7% from migraines, and 14.0% from probable secondary headaches. In subjects who do not use a computer, 19.4% suffer from tension-type headaches, 4.8% from migraines, and 3.2% from suspected secondary headaches. Computer users were found to have a significantly higher prevalence of primary headaches compared to those who did not use a computer. Computer users with headaches, compared to those without headaches, work on influenced by computer use. Computer users with headaches do not adhere to the defined ergonomic recommendations when working on the computer, hence there is a need for systematic education of computer users on ergonomic recommendations in order to prevent headache attacks

Intelligent Data Engineering and Automated Learning – IDEAL 2019 [electronic resource] : 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings, Part II /

This two-volume set of LNCS 11871 and 11872 constitutes the thoroughly refereed conference proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019, held in Manchester, UK, in November 2019. The 94 full papers presented were carefully reviewed and selected from 149 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2019 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models (including neural networks, evolutionary computation and swarm intelligence), agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI.Special Session on Fuzzy Systems and Intelligent Data Analysis -- Computational Generalization in Taxonomies Applied to: (1) Analyze Tendencies of Research and (2) Extend User Audiences -- Unsupervised Initialization of Archetypal Analysis and Proportional Membership Fuzzy Clustering -- Special Session on Machine Learning towards Smarter Multimodal Systems -- Multimodal Web Based Video Annotator with Real-Time Human Pose Estimation -- New Interfaces for Classifying Performance Gestures in Music -- Special Session on Data Selection in Machine Learning -- Classifying Ransomware Using Machine Learning Algorithms -- Artificial Neural Networks in Mathematical Mini-Games for Automatic Students Learning Styles Identification: A First Approach -- The Use of Unified Activity Records to Predict Requests Made by Applications for External Services -- Fuzzy Clustering Approach to Data Selection for Computer Usage in Headache Disorders -- Multitemporal Aerial Image Registration Using Semantic Features -- Special Session on Machine Learning in Healthcare -- Brain Tumor Classification Using Principal Component Analysis and Kernel Support Vector Machine -- Modelling survival by machine learning methods in liver transplantation: application to the UNOS dataset -- Design and Development of an Automatic Blood Detection System for Capsule Endoscopy Images -- Comparative Analysis for Computer-Based Decision Support: Case Study of Knee Osteoarthritis -- A Clustering-Based Patient Grouper for Burn Care -- A comparative assessment of Feed-Forward and Convolutional Neural Networks for the classification of prostate lesions -- Special Session on Machine Learning in Automatic Control -- A Method based on Filter Bank Common Spatial Pattern for Multiclass Motor Imagery BCI -- Safe Deep Neural Network-driven Autonomous Vehicles Using Software Safety Cages -- Wave and viscous resistance estimation by NN -- Neural controller of UAVs with inertia variations -- Special Session on Finance and Data Mining -- A Metric Framework for quantifying Data Concentration -- Adaptive Machine Learning-Based Stock Prediction using Financial Time Series Technical Indicators -- Special Session on Knowledge Discovery from Data -- Exploiting Online Newspaper Articles Metadata for Profiling City Areas -- Modelling the Social Interactions in Ant Colony Optimization -- An Innovative Deep-Learning Algorithm for Supporting the Approximate Classication of Workloads in Big Data Environments -- Control-flow Business Process Summarization via Activity Contraction -- Classifying Flies Based on Reconstructed Audio Signals -- Studying the Evolution of the ‘Circular Economy’ Concept using Topic Modelling -- Mining Frequent Distributions in Time Series -- Time Series Display for Knowledge Discovery on Selective Laser Melting Machines -- Special Session on Machine Learning Algorithms for Hard Problems -- Using Prior Knowledge to Facilitate Computational Reading of Arabic Calligraphy -- SMOTE Algorithm Variations in Balancing Data Streams -- Multi-Class Text Complexity Evaluation via Deep Neural Networks -- Imbalance reduction techniques applied to ECG classification problem -- Machine Learning Methods for Fake News Classification -- A genetic-based ensemble learning applied to imbalanced data classification -- The feasibility of deep learning use for adversarial model extraction in the cybersecurity domain.This two-volume set of LNCS 11871 and 11872 constitutes the thoroughly refereed conference proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019, held in Manchester, UK, in November 2019. The 94 full papers presented were carefully reviewed and selected from 149 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2019 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models (including neural networks, evolutionary computation and swarm intelligence), agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI