Aydin Muhammet Ali, Yıldız Metin, Kiyici Mübin, Yildiz Mehmet, Kirksekiz Ali, Kızıloğlu Şeyda
Faculty of Health Sciences, Department of Nursing, Erzurum Technical University, Erzurum, Turkey.
Department of Nursing, Sakarya University, Sakarya, Turkey.
BMC Psychol. 2024 Dec 18;12(1):754. doi: 10.1186/s40359-024-02256-w.
In this study, the effects of individuals' digital obesity and phubbing behaviors on their life satisfaction were investigated by latent profile analysis (LPA) method. LPA is a statistical technique used to identify unobserved subgroups within a population based on individuals' responses to various observed variables.
The present study was conducted in a correlational cross-sectional descriptive design between November 2023- January 2024. Digital obesity scale, phubbing scale, life satisfaction scale were used in this study.
As a result of LPA, Class 1 (Low Digital Addicts) has the lowest arithmetic mean in all indicators. When life satisfaction was analyzed on the basis of the classes, it was found out that Class 2 (High Digital Addicts), which was collected in the group with high levels of digital obesity and phubbing, had lower life satisfaction. Considering the demographic levels of individuals according to class level, it was concluded that high digital addiction was more common among individuals with secondary and postgraduate education.
In the present study, two classes were found as a result of LPA. In the analysis, Class 1(Low Digital Addicts) was found to have the lowest arithmetic mean in all indicators. On the other hand, when life satisfaction was analyzed according to the classes, it was detected that Class 2 (High Digital Addicts) life satisfaction was lower in the group with high levels of digital obesity and phubbing. In the study, the life satisfaction of class 1, which is characterized as low digital addicts, was found to be higher. Longitudinal studies on digital addictions affecting life satisfaction are recommended.
在本研究中，通过潜在剖面分析（LPA）方法调查了个体的数字肥胖和低头族行为对其生活满意度的影响。LPA是一种统计技术，用于根据个体对各种观察变量的反应在人群中识别未观察到的亚组。
本研究采用相关横断面描述性设计，于2023年11月至2024年1月进行。本研究使用了数字肥胖量表、低头族量表和生活满意度量表。
LPA结果显示，第1类（低数字成瘾者）在所有指标中的算术平均值最低。在根据类别分析生活满意度时，发现聚集在数字肥胖和低头族水平较高组中的第2类（高数字成瘾者）生活满意度较低。根据类别水平考虑个体的人口统计学水平，得出结论：高中数字成瘾在中等教育和研究生教育个体中更为常见。
在本研究中，LPA结果发现了两类。分析发现，第1类（低数字成瘾者）在所有指标中的算术平均值最低。另一方面，在根据类别分析生活满意度时，发现在数字肥胖和低头族水平较高的组中，第2类（高数字成瘾者）的生活满意度较低。在该研究中，被表征为低数字成瘾者的第1类的生活满意度较高。建议对影响生活满意度的数字成瘾进行纵向研究。
