الفهرس 
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Abstract
Background:
The use of a smartphone as everyday pleasurable activity transformed its use to a state of addiction. During the COVID-19 pandemic there was unprecedented expansion and utilization of smartphone and other internet related devices. The increasing problematic smartphone use has led to a growing interest in the predictors and consequences related to it. Sleep disturbances and social isolation have a strong relation with smartphone abuse.
Aim of the study:
The study was conducted to determine the prevalence of smartphone addiction among medical students in Minia University during COVID 19 era and its relationship with sleep disturbances and social isolation.
Subjects and methods:
The study was an analytical cross-sectional study that had been conducted in faculty of medicine at Minia University, Minia governorate, Egypt, during the period from October 2021 to March 2022. A total sample of 358 participants was collected randomly from the 2nd and 4th academic years. An ethical permission was taken by the ethical committee of Faculty of Medicine, Minia University. And approval was taken from each participant before filling out the questionnaires. These questionnaires included Socio-demographic data, data about patterns of smartphone use, Arabic version of Smart phone addiction scale – short version (SAS‐SF): Arabic version of Internet addiction test (IAT): Arabic version of Pittsburgh Sleep Quality Index (PSQI) and Arabic version of De Jong Gierveld Scale (Social and emotional loneliness scale).
Results:
The study was conducted among 358 participants, including 138 (38.5%) males and 220 (61.5%) females. The participants were in their 2nd year (n = 232) (median age = 19) and 4th year (n = 126) (median age = 21). About two thirds of fathers (70.8%) and nearly half of mothers (50.7%) were University graduates or postgraduates, while only 8.7% of fathers and 19.8% of mothers were illiterate. More than two thirds of fathers have Professional (55.8%) and semi-Professional work (23.4%), and almost half of mothers (49.4%) don’t work.
Regarding the pattern of smartphone use, about 56% of the participants spent more than 5 hours on smartphones per day, with no statistically significant difference between genders. Males had a significantly longer duration (median = 7 y) of smartphone ownership than females (median = 5 y) (p = 0.001). On the other hand, females spent more time using smartphones for calls (> 5 h) (p < 0.05).
The prevalence of smartphone addiction was About 62%, with no statistically significant difference between males (66.7%) and females (58.6%). The number of hours of smartphone use per day was significantly higher in smartphone addicts (69.2%) than non-addicts (35%) (p<0.001). The majority of smartphone addicts (91.9%) were chatting for more than 1 hour per day, in contrast to 39.8% of non-addicts, with statistically significant differences (p =0.005) between both.
The study revealed a connection between declining academic performance and a rise in smartphone use disorder symptoms. About 51% of addicts received excellent grades, and 5.4% received weak grades; on the other hand, in non-addicts, 69.3% received excellent grades and only 1.5% received weak grades, with a statistically significant difference between smartphone addicts and non-addicts (p = 0.003).
In relation to predictors of smartphone addiction, the current study showed that the higher sleep quality index, loneliness scale score, use of social network sites, smartphone use in studying, chatting hours per day and higher occupation of fathers were significantly related to higher scores of smartphone addiction. (β=0.22 - p<0.001, β=0.197 - p<0.001, β=0.188 - p<0.001, β=0.143 - p= 0.002, β=0.101- p= 0.035 and β= 0.127 - p= 0.031respectively). While lower academic achievement was associated with higher scores of smartphone addiction (β= - 0.128, p= 0.008). This set of predictors represented (28.3%) of the total variance of smartphone addiction scale among the studied participants.
Regarding sleep quality, the prevalence of poor sleep quality among medical students was about 80%. The current results showed that the majority of smartphone addicts (84.6%) were poor sleepers, while in non-addicts, the poor sleepers were less (71.5%), with a statistically significant difference between both (p = 0.003).
Academic achievement in the present study was statistically significantly different between good and poor sleepers (p=0.016), with 74% of good sleepers receiving excellent scores and just 1.4% receiving weak scores, compared to 53.7% of poor sleepers receiving excellent scores and 4.6% receiving weak scores.
The prevalence of loneliness was around 81.3% among all participants. About half of them had moderate loneliness (52.8%), 16.8% had severe loneliness, and only 11.7% had very severe loneliness. The present study found a statistically significant difference in levels of loneliness between smartphone addicts and non-addicts (p = 0.024). Addicts were more likely to be severely and extremely severe lonely (33.9%) than non-addicts (19.7%). Also, there was a significant positive correlation between the SAS score and the sleep loneliness scale score (r = 0.263, p<.001).
Regarding loneliness and sleep quality, 31.5% of good sleepers had no Loneliness, while 84.6% of poor sleepers had different degrees of Loneliness from moderate to very severe, with statistically significant differences between both (p<0.001).
Regarding loneliness and academic achievement, there was a statistically significant difference among levels of loneliness and academic achievement: in not lonely participants, Excellent grade was highest (64.2%) and weak grade was lowest (1.5%), while in severely lonely participants, Excellent grade was lowest (28.5%) and weak grade was highest (21.5%) in relation to other loneliness grades. This means that there was an inverse relationship between academic achievement and levels of loneliness (p = 0.004).
Recommendations:
1. Health education about the negative impact of smartphone addiction on mental health and sleep quality should be raised among medical students.
2. Educational policies should be implemented to encourage them to get rid of their addiction, eliminate its effect on their academic and personal lives, and learn healthier sleep habits and social connections.
3. Mental health professionals should be involved to support students who are experiencing social isolation, poor sleep quality, and other mental or psychological smartphone addiction drawbacks.
4. More attention and efforts should be directed to students’ families to establish a healthy psychological atmosphere and social support for students.
5. Improve medical students’ socialization outside their academic life by playing sports or engaging in hobbies
6. Install mobile phone applications which limit screen time and allow users to know the time spent on different apps and website.