Examining Screen Time, Screen Use Experiences, and Well-Being in Adults

Wang, Tracy Liran; Vella-Brodrick, Dianne

doi:10.4236/sn.2018.71003

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…Screen time is explained as time spent using an electronic screen, such as a TV, computer, or a mobile device (Wang, 2018), "anything else that requires watching a screen" (Elizabeth, 2013). Research has emphasized the influential role of parental perceptions of children's literacy development.…”

Section: Introductionmentioning

confidence: 99%

Impact of Excessive Screen Time for Work Entertainment on Eyesight

Muller

2023

IJS

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Purpose: The study sought to investigate impact of excessive screen time for work entertainment on eyesight. Methodology: The study adopted a desktop methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The study found that digital eye strain is non-vision-threatening but discomfort caused due to it can have implications on overall physical, mental, and social well-being. The study highlights the increase in digital screen time during the pandemic and the resultant eye strain. There is need of spreading awareness regarding the adverse effects of digital device use and the preventive measures to safeguard our ocular health. Unique contribution to theory, practice and policy: The theory on planned behaviour was very instrumental in guiding the study, future studies can use the theory especially in shedding light on the impact of screen time on other health issues. No screen usage guidelines are there for adults till date thus our study strongly recommends that guidelines should be formulated and strictly imposed. It is also responsibility of parents to guide their children by becoming role model as well as emphasize digital etiquette. Screen time related problems are non vision‑threatening but discomfort caused due to it is gruesome in majority of population. With joint efforts of parents, policymakers, teachers, and health workers excessive screen time related issues could be superseded.

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Section: Introductionmentioning

confidence: 99%

Impact of Excessive Screen Time for Work Entertainment on Eyesight

Muller

2023

IJS

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“…Looking at the computer or laptop screen in a prolonged period may strain the eyes and make people feel tired. The average adult maximum screen time is two hours [1]. After that, it is advisable to move to distant viewing or to look at green objects.…”

Section: Introductionmentioning

confidence: 99%

Fatigue Detection on Face Image Using FaceNet Algorithm and K-Nearest Neighbor Classifier

Adhinata¹,

Rakhmadani²,

Wijayanto³

2021

JISEBI

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Background: The COVID-19 pandemic has made people spend more time on online meetings more than ever. The prolonged time looking at the monitor may cause fatigue, which can subsequently impact the mental and physical health. A fatigue detection system is needed to monitor the Internet users well-being. Previous research related to the fatigue detection system used a fuzzy system, but the accuracy was below 85%. In this research, machine learning is used to improve accuracy.Objective: This research examines the combination of the FaceNet algorithm with either k-nearest neighbor (K-NN) or multiclass support vector machine (SVM) to improve the accuracy.Methods: In this study, we used the UTA-RLDD dataset. The features used for fatigue detection come from the face, so the dataset is segmented using the Haar Cascades method, which is then resized. The feature extraction process uses FaceNet's pre-trained algorithm. The extracted features are classified into three classes—focused, unfocused, and fatigue—using the K-NN or multiclass SVM method.Results: The combination between the FaceNet algorithm and K-NN, with a value of resulted in a better accuracy than the FaceNet algorithm with multiclass SVM with the polynomial kernel (at 94.68% and 89.87% respectively). The processing speed of both combinations of methods has allowed for real-time data processing.Conclusion: This research provides an overview of methods for early fatigue detection while working at the computer so that we can limit staring at the computer screen too long and switch places to maintain the health of our eyes.

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