Prevalence of Nomophobia and an Analysis of Its Contributing Factors in the Undergraduate Students of Pakistan.
Minaam FarooqMusa Ali RizviWaaiz Ali WajidMohammed AshrafMukarram FarooqHaseeba JavedMuhammad Ahmad SadiqHamza Muhammad JafarFarooq HameedMehdi Ali RizviAalia TayybaPublished in: Cyberpsychology, behavior and social networking (2022)
Nomophobia (no-mobile-phone phobia) is a relatively new term that describes the growing fear and anxiety associated with being without a mobile phone. Our study aims to determine the prevalence of nomophobia among the undergraduate students of Pakistan, and to determine its correlation with age and gender. It also aims to determine the contributory factors of nomophobia. A cross-sectional study was conducted through an online survey from March 25 to April 25, 2021. The snowball sampling technique was used for data collection. The Nomophobia Questionnaire (NMP-Q) developed by Yildirim and Correia was circulated among the target population. It was a 7-point Likert Scale that was analyzed on the basis of age and gender using IBM SPSS version 22 and MS Excel 2007. The contributing factors were also analyzed. Of the 483 responses we received, 28 were discarded due to incompleteness and respondents being out of age under study that is , 15-25 years. Most of the respondents were women ( n = 314, 69.01 percent). Men were less in number than women ( n = 141, 31 percent). The ages of most of the respondents lied between 15 and 25 years. Twenty was the mode age. One hundred eighty-six (40.88 percent) had severe, 221 (48.57 percent) had moderate, and 48 (10.55 percent) had mild nomophobia. Average factor-wise scores and individual item scores were also added. Our findings reached a conclusion that the majority of the undergraduate students in Pakistan suffer from nomophobia ranging from its mild to severe form. Nomophobia can possibly be included as a recognized phobia in the DSM. Wider research on the subject to investigate it further and evaluate the clinical significance should be done.
Keyphrases
- risk factors
- polycystic ovary syndrome
- nursing students
- psychometric properties
- medical education
- high school
- early onset
- multiple sclerosis
- medical students
- preterm infants
- metabolic syndrome
- type diabetes
- machine learning
- big data
- insulin resistance
- deep learning
- depressive symptoms
- high intensity
- middle aged
- gestational age
- preterm birth
- patient reported