Dataset Information

Predictors of social networking service addiction.

ABSTRACT: The surge in social network services (SNS) usage has ignited concerns about potential addictive behaviors stemming from excessive engagement. This research focuses on pinpointing the primary determinants of SNS addiction by introducing a theoretical framework centered on flow, perceived enjoyment, and habit. A sample of 282 SNS users from South Korea was surveyed, and the gathered data was assessed through partial least squares structural equation modeling (PLS-SEM). The evaluation revealed that positive affect closely relates to flow and perceived enjoyment, whereas negative affect amplifies flow but diminishes perceived enjoyment. Additionally, the research underscored that social influence significantly shapes habits and affects perceived enjoyment. Notably, flow demonstrated a strong connection to addiction, and perceived enjoyment influenced both flow and habit significantly. Habit was directly linked to addiction. These insights pave the way for more in-depth studies on SNS addiction patterns and offer a foundation for devising effective strategies to mitigate its adverse effects.

SUBMITTER: Jo H

PROVIDER: S-EPMC10550908 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Predictors of social networking service addiction.

Jo Hyeon H Baek Eun-Mi EM

Scientific reports 20231004 1

The surge in social network services (SNS) usage has ignited concerns about potential addictive behaviors stemming from excessive engagement. This research focuses on pinpointing the primary determinants of SNS addiction by introducing a theoretical framework centered on flow, perceived enjoyment, and habit. A sample of 282 SNS users from South Korea was surveyed, and the gathered data was assessed through partial least squares structural equation modeling (PLS-SEM). The evaluation revealed that ...[more]

PMID: 37794135

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Project description:BackgroundIn most industrialized societies, regulations, inspections, insurance, and legal options are established to support workers who suffer injury, disease, or death in relation to their work; in practice, these resources are imperfect or even unavailable due to workplace or employer obstruction. Thus, limitations exist to identify unmet needs in occupational safety and health information.ObjectiveThe aim of this study was to explore hidden issues related to occupational accidents by examining social network services (SNS) data using topic modeling.MethodsBased on the results of a Google search for the phrases occupational accident, industrial accident and occupational diseases, a total of 145 websites were selected. From among these websites, we collected 15,244 documents on queries related to occupational accidents between 2002 and 2018. To transform unstructured text into structure data, natural language processing of the Korean language was conducted. We performed the latent Dirichlet allocation (LDA) as a topic model using a Python library. A time-series linear regression analysis was also conducted to identify yearly trends for the given documents.ResultsThe results of the LDA model showed 14 topics with 3 themes: workers' compensation benefits (Theme 1), illicit agreements with the employer (Theme 2), and fatal and non-fatal injuries and vulnerable workers (Theme 3). Theme 1 represented the largest cluster (52.2%) of the collected documents and included keywords related to workers' compensation (ie, company, occupational injury, insurance, accident, approval, and compensation) and keywords describing specific compensation benefits such as medical expense benefits, temporary incapacity benefits, and disability benefits. In the yearly trend, Theme 1 gradually decreased; however, other themes showed an overall increasing pattern. Certain queries (ie, musculoskeletal system, critical care, and foreign workers) showed no significant variation in the number of queries.ConclusionsWe conducted LDA analysis of SNS data of occupational accident-related queries and discovered that the primary concerns of workers posting about occupational injuries and diseases were workers' compensation benefits, fatal and non-fatal injuries, vulnerable workers, and illicit agreements with employers. While traditional systems focus mainly on quantitative monitoring of occupational accidents, qualitative aspects formulated by topic modeling from unstructured SNS queries may be valuable to address inequalities and improve occupational health and safety.

Project description:BACKGROUND:Stage 5 chronic kidney disease (CKD) presents a high risk for dialysis initiation and for complications such as uremic encephalopathy, uremic symptoms, gastrointestinal bleeding, and infection. One of the most common barriers to health care for patients with stage 5 CKD is poor continuity of care due to unresolved communication gaps. OBJECTIVE:Our aim was to establish a powerful care model that includes the use of a social networking service (SNS) to improve care quality for patients with CKD and safely delay dialysis initiation. METHODS:We used a retrospective cohort of CKD patients aged 20-85 years who received care between 2007 and 2017 to evaluate the efficacy of incorporating an SNS into the health care system. In 2014, author F-JY, a nephrologist at the National Taiwan University Hospital Yunlin Branch, started to use an SNS app to connect with stage 5 CKD patients and their families. In cases of emergency, patients and families could quickly report any condition to F-JY. Using this app, F-JY helped facilitate productive interactions between these patients and the health care system. The intention was to safely delay the initiation of dialysis therapy. We divided patients into four groups: group 1 (G1) included patients at the study hospital during the 2007-2014 period who had contact only with nephrologists other than F-JY; group 2 (G2) included patients who visited F-JY during the 2007-2014 period before he began using the SNS app; group 3 (G3) included patients who visited nephrologists other than F-JY during the 2014-2017 period and had no interactions using the SNS; and group 4 (G4) included patients who visited F-JY during the 2014-2017 period and interacted with him using the SNS app. RESULTS:We recruited 209 patients with stage 5 CKD who had been enrolled in the study hospital's CKD program between 2007 and 2017. Each of the four groups initiated dialysis at different times. Before adjusting for baseline estimated glomerular filtration rate (eGFR), the G4 patients had a longer time to dialysis (mean 761.7 days, SD 616.2 days) than the other groups (G1: mean 403.6 days, SD 409.4 days, P=.011 for G4 vs G1; G2: 394.8 days, SD 318.8 days, P=.04; G3: 369.1 days, SD 330.8 days, P=.049). After adjusting for baseline eGFR, G4 had a longer duration for each eGFR drop (mean 84.8 days, SD 65.1 days) than the other groups (G1: mean 43.5 days, SD 45.4 days, P=.005; G2: mean 42.5 days, SD 26.5 days, P=.03; G3: mean 3.8.7 days, SD 33.5 days, P=.002). CONCLUSIONS:The use of an SNS app between patients with stage 5 CKD and their physicians can reduce the communication gap between them and create benefits such as prolonging time-to-dialysis initiation. The role of SNSs and associated care models should be further investigated in a larger population.

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Predictors of social networking service addiction.

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Predictors of social networking service addiction.

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