Prevalence of Restless Legs Syndrome and Associated Factors Among University IT Students in Mirpurkhas, Sindh

Background: Restless Legs Syndrome (RLS) is a chronic neurological disorder characterized by uncomfortable sensations and an irresistible urge to move the legs, particularly during rest. The prevalence of RLS in Pakistan, especially among students, remains underexplored.Objective: This study aimed to determine the prevalence and associated factors of RLS among information technology students in Mirpurkhas, Sindh.Methods: A cross-sectional study was conducted from July 21 to August 4, involving 200 IT students (115 males, 85 females) aged 18-28 years. Data were collected using a structured questionnaire based on the International Restless Legs Syndrome Study Group (IRLSSG) criteria. Participants were categorized into mild, moderate, severe, and very severe RLS. Data were analyzed using SPSS version 25, with statistical significance set at p < 0.05.Results: The prevalence of RLS was 78.5%, with 57.5% of males and 42.5% of females affected. Severe discomfort was reported by 17.8% of students aged 18-21 years. The need to move legs due to RLS symptoms was significant (p = 0.030).Conclusion: The study highlights a high prevalence of RLS among IT students, emphasizing the need for targeted interventions to manage this condition in academic settings.

Authors

Institutions

Publication Details

Journal
Journal of Health and Rehabilitation Research
Published
2024-08-30
DOI
https://doi.org/10.61919/jhrr.v4i3.1382
Primary Topic
Restless Legs Syndrome Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Prevalence of Restless Legs Syndrome and Associated Factors Among University IT Students in Mirpurkhas, Sindh

Ahsan Naveed, Noor Jahan Shaikh, Faria Khan, Atif Mahmood
Journal of Health and Rehabilitation Research
Restless Legs Syndrome Research
article

Prevalence of Restless Legs Syndrome and Associated Factors Among University IT Students in Mirpurkhas, Sindh

Ahsan Naveed, Noor Jahan Shaikh, Faria Khan, Atif Mahmood
article en

Abstract

Background: Restless Legs Syndrome (RLS) is a chronic neurological disorder characterized by uncomfortable sensations and an irresistible urge to move the legs, particularly during rest. The prevalence of RLS in Pakistan, especially among students, remains underexplored.Objective: This study aimed to determine the prevalence and associated factors of RLS among information technology students in Mirpurkhas, Sindh.Methods: A cross-sectional study was conducted from July 21 to August 4, involving 200 IT students (115 males, 85 females) aged 18-28 years. Data were collected using a structured questionnaire based on the International Restless Legs Syndrome Study Group (IRLSSG) criteria. Participants were categorized into mild, moderate, severe, and very severe RLS. Data were analyzed using SPSS version 25, with statistical significance set at p < 0.05.Results: The prevalence of RLS was 78.5%, with 57.5% of males and 42.5% of females affected. Severe discomfort was reported by 17.8% of students aged 18-21 years. The need to move legs due to RLS symptoms was significant (p = 0.030).Conclusion: The study highlights a high prevalence of RLS among IT students, emphasizing the need for targeted interventions to manage this condition in academic settings.

Journal of Health and Rehabilitation ResearchVol. 4(3)
Karachi Medical and Dental College (PK), College of Medical Sciences (NP), Jinnah Medical & Dental College (PK)
Quality Education
Openalex Percentile: Top 77%
Restless Legs Syndrome Research
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.