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The aim of this study is to examine the effects of digital game addiction on the musculoskeletal system and the reflections of these effects on the quality of life of adults.
This study will be conducted with a cross-sectional design in order to examine the relationship between digital game addiction and musculoskeletal disorders. The methods to be used in the research are as follows: Participant Information: The study will include adults aged 18-39 who play digital games in internet cafes for at least 3 hours a day, 4-5 days a week. Participants will be asked to fill out a form to collect demographic information. This form will include information such as age, gender, weekly game playing frequency (by hour and day), and physical activity levels. Scales to be Used: The following measurement tools will be used in the study to evaluate the effects of digital game addiction on the musculoskeletal system. Digital Game Addiction Scale, Arm, Shoulder and Hand Problems Questionnaire, Pittsburgh Sleep Quality, Cornell Musculoskeletal Disorder Questionnaire.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| control | Active Comparator | People who do not have any exercise program |
|
| exercise | Experimental | People who will have an exercise program |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| ergonomic training | Other | will receive ergonomic training. |
| |
| Measure | Description | Time Frame |
|---|---|---|
| Cornell Musculoskeletal Disorder Questionnaire | The questionnaire was conducted in 11 body regions (neck, right and left shoulder, upper arm, forearm and wrist, back and waist region) includes questions about the frequency of musculoskeletal disorders (pain/pain/discomfort), the severity of these disorders, and whether they interfere with work. | 14 weeks |
| The Pittsburgh Sleep Quality Index | Pittsburgh Sleep Quality Index; It evaluates sleep duration, sleep disturbance, sleep efficiency, subjective sleep quality, sleep medication use, daytime dysfunction, and sleep delay and consists of a total of 24 questions. 19 of them are self-report scales and 5 of them consist of questions to be answered by a friend or spouse. There are 7 components with 18 questions scored in the scale, and each component is evaluated between 0 and 3 points. The total score ranges from 0 to 21, and 5 or more is an indicator of "poor sleep quality". | 14 weeks |
| Disabilities of the Arm, Shoulder and Hand Questionnaire | It was developed to evaluate functional status and symptoms by focusing on physical function in upper extremity injuries. According to the results of the survey; a result from 0-100 is obtained from each part; 0-no apology 100-maximum apology. | 14 weeks |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Lobaba Chiah | Uskudar University | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Uskudar university | Istanbul | Turkey (Türkiye) |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 39186761 | Background | Ahmed S, Samuel AJ, Mishra A, Rahman MS, Islam MA, Rashaduzzaman M, Roy SK, Akter R, Islam MJ. Mobile game addiction and its association with musculoskeletal pain among students: A cross-sectional study. PLoS One. 2024 Aug 26;19(8):e0308674. doi: 10.1371/journal.pone.0308674. eCollection 2024. | |
| 32795945 | Background |
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| ID | Term |
|---|---|
| D000088942 | Technology Addiction |
| ID | Term |
|---|---|
| D016739 | Behavior, Addictive |
| D003192 | Compulsive Behavior |
| D007175 | Impulsive Behavior |
| D001519 | Behavior |
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| ID | Term |
|---|---|
| D015444 | Exercise |
| ID | Term |
|---|---|
| D009043 | Motor Activity |
| D009068 | Movement |
| D009142 | Musculoskeletal Physiological Phenomena |
| D055687 | Musculoskeletal and Neural Physiological Phenomena |
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| exercise |
| Other |
People who will receive an exercise program. Participants will receive ergonomic training along with the exercise program. |
|
| Brevers D, King DL, Billieux J. Delineating adaptive esports involvement from maladaptive gaming: a self-regulation perspective. Curr Opin Psychol. 2020 Dec;36:141-146. doi: 10.1016/j.copsyc.2020.07.025. Epub 2020 Aug 3. |
| 36936011 | Background | Gulu M, Yagin FH, Gocer I, Yapici H, Ayyildiz E, Clemente FM, Ardigo LP, Zadeh AK, Prieto-Gonzalez P, Nobari H. Exploring obesity, physical activity, and digital game addiction levels among adolescents: A study on machine learning-based prediction of digital game addiction. Front Psychol. 2023 Mar 3;14:1097145. doi: 10.3389/fpsyg.2023.1097145. eCollection 2023. |