From Pixels to Pain: The Silent Health Risk Associated with Electronic Device Use

In the field of artificial intelligence (AI), machine learning is a subset that focuses on using data to learn and create prediction algorithms. Electronics controls the flow of electricity for processing, and health entails more than just being free from disease. On the other hand, overusing devices can increase health hazards. The inquiry consists of an empirical study that was carried out in Istanbul, Turkey, with 1147 students, as well as a systematic evaluation of ML applications in mental health within the human-computer interface (HCI) literature. The condition of emotional, psychological, and social well-being that affects a person's thoughts, feelings, and behaviour in day-to-day living is known as mental health. The study examines how teens' mental and physical health are affected by their excessive use of digital gadgets, especially cellphones. In this artical the integration of digital technologies and machine learning (ML) in relation to teenage wellbeing and mental health treatment is examined. The assessment also emphasises the necessity of humancentered and morally sound machine learning applications in mental health services. It also discusses how machine learning (ML) might improve mental health services by improving the identification, diagnosis, and treatment of mental health issues. The results show that teens' psychiatric problems, musculoskeletal discomfort, and sleep disruptions are linked to excessive digital device useThe study comes to the conclusion that while ML and digital technologies have a great deal of promise for enhancing mental health outcomes, there are also important socio-technical issues that require attention. These include worries about model bias, data privacy, and the generalisation of machine learning algorithms. Future studies should concentrate on creating rigorous assessment frameworks and transdisciplinary methods to guarantee the ethical and successful application of ML in mental health services.

Authors

Institutions

Publication Details

Published
2024-11-07
DOI
https://doi.org/10.1109/csitss64042.2024.10816898
Primary Topic
Technology Use by Older Adults
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

From Pixels to Pain: The Silent Health Risk Associated with Electronic Device Use

B H Alamma, G V Smitha, Samitha Khaiyum, Sanchita
Technology Use by Older Adults
article

From Pixels to Pain: The Silent Health Risk Associated with Electronic Device Use

B H Alamma, G V Smitha, Samitha Khaiyum, Sanchita
article en

Abstract

In the field of artificial intelligence (AI), machine learning is a subset that focuses on using data to learn and create prediction algorithms. Electronics controls the flow of electricity for processing, and health entails more than just being free from disease. On the other hand, overusing devices can increase health hazards. The inquiry consists of an empirical study that was carried out in Istanbul, Turkey, with 1147 students, as well as a systematic evaluation of ML applications in mental health within the human-computer interface (HCI) literature. The condition of emotional, psychological, and social well-being that affects a person's thoughts, feelings, and behaviour in day-to-day living is known as mental health. The study examines how teens' mental and physical health are affected by their excessive use of digital gadgets, especially cellphones. In this artical the integration of digital technologies and machine learning (ML) in relation to teenage wellbeing and mental health treatment is examined. The assessment also emphasises the necessity of humancentered and morally sound machine learning applications in mental health services. It also discusses how machine learning (ML) might improve mental health services by improving the identification, diagnosis, and treatment of mental health issues. The results show that teens' psychiatric problems, musculoskeletal discomfort, and sleep disruptions are linked to excessive digital device useThe study comes to the conclusion that while ML and digital technologies have a great deal of promise for enhancing mental health outcomes, there are also important socio-technical issues that require attention. These include worries about model bias, data privacy, and the generalisation of machine learning algorithms. Future studies should concentrate on creating rigorous assessment frameworks and transdisciplinary methods to guarantee the ethical and successful application of ML in mental health services.

Dr. Hari Singh Gour University (IN)
Openalex Percentile: Top 52%
Technology Use by Older Adults
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.