Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 3 de 3
Filter
Add more filters










Database
Language
Publication year range
1.
Sensors (Basel) ; 23(3)2023 Feb 02.
Article in English | MEDLINE | ID: mdl-36772696

ABSTRACT

Game playing is an accessible leisure activity. Recently, the World Health Organization officially included gaming disorder in the ICD-11, and studies using several bio-signals were conducted to quantitatively determine this. However, most EEG studies regarding internet gaming disorder (IGD) were conducted in the resting state, and the outcomes appeared to be too inconsistent to identify a general trend. Therefore, this study aimed to use a series of statistical processes with all the existing EEG parameters until the most effective ones to identify the difference between IGD subjects IGD and healthy subjects was determined. Thirty subjects were grouped into IGD (n = 15) and healthy (n = 15) subjects by using the Young's internet addition test (IAT) and the compulsive internet use scale (CIUS). EEG data for 16 channels were collected while the subjects played League of Legends. For the exhaustive search of parameters, 240 parameters were tested in terms of t-test, factor analysis, Pearson correlation, and finally logistic regression analysis. After a series of statistical processes, the parameters from Alpha, sensory motor rhythm (SMR), and MidBeta ranging from the Fp1, C3, C4, and O1 channels were found to be best indicators of IGD symptoms. The accuracy of diagnosis was computed as 63.5-73.1% before cross-validation. The most interesting finding of the study was the dynamics of EEG relative power in the 10-20 Hz band. This EEG crossing phenomenon between IGD and healthy subjects may explain why previous research showed inconsistent outcomes. The outcome of this study could be the referential guide for further investigation to quantitatively assess IGD symptoms.


Subject(s)
Behavior, Addictive , Video Games , Humans , Internet Addiction Disorder , Behavior, Addictive/diagnosis , Electroencephalography , Factor Analysis, Statistical , Internet
2.
Sensors (Basel) ; 21(14)2021 Jul 08.
Article in English | MEDLINE | ID: mdl-34300423

ABSTRACT

The purpose of this study is to determine heart rate variability (HRV) parameters that can quantitatively characterize game addiction by using electrocardiograms (ECGs). 23 subjects were classified into two groups prior to the experiment, 11 game-addicted subjects, and 12 non-addicted subjects, using questionnaires (CIUS and IAT). Various HRV parameters were tested to identify the addicted subject. The subjects played the League of Legends game for 30-40 min. The experimenter measured ECG during the game at various window sizes and specific events. Moreover, correlation and factor analyses were used to find the most effective parameters. A logistic regression equation was formed to calculate the accuracy in diagnosing addicted and non-addicted subjects. The most accurate set of parameters was found to be pNNI20, RMSSD, and LF in the 30 s after the "being killed" event. The logistic regression analysis provided an accuracy of 69.3% to 70.3%. AUC values in this study ranged from 0.654 to 0.677. This study can be noted as an exploratory step in the quantification of game addiction based on the stress response that could be used as an objective diagnostic method in the future.


Subject(s)
Behavior, Addictive , Video Games , Behavior, Addictive/diagnosis , Electrocardiography , Heart Rate , Humans , Surveys and Questionnaires
3.
Appl Ergon ; 91: 103280, 2021 Feb.
Article in English | MEDLINE | ID: mdl-33166914

ABSTRACT

For shoulder muscle prevention, we investigated individual shoulder muscle performance and fatigue patterns in various external conditions, including three different % maximum voluntary contractions, six shoulder angles and 60-s durations of exertion. The rating of perceived exertion was also measured for comparison. The upper trapezius (UT), middle deltoid (MD), pectoralis major (PM), latissimus dorsi (LD) and serratus anterior (SA) were selected for assessment. Normalized median power frequency electromyograms were calculated for quantitative fatigue evaluation in ten participants. UT muscle was severely fatigued by extreme flexion angle rather than weight. MD muscle was the most rapidly fatigued after 15 s duration. SA muscle was more fatigued at 0° than 30° adduction. LD and PM muscle fatigue were mostly due to external workload. This muscle specific outcome could help practitioners to design an intervention program targeting particular shoulder injury.


Subject(s)
Shoulder , Superficial Back Muscles , Electromyography , Humans , Muscle Fatigue , Muscle, Skeletal , Pectoralis Muscles , Range of Motion, Articular
SELECTION OF CITATIONS
SEARCH DETAIL
...