| Literature DB >> 36163012 |
Moustafa Sayed1,2, Christina Medhat Naiim3, Marina Aboelsaad3, Michael Kamal Ibrahim4.
Abstract
BACKGROUND AND AIMS: Pharmacy students represent the future of healthcare professionals and with daily use of the internet for different activities has made internet addiction (IA) of a growing concern. The main objectives of this study were to 1) assess internet addiction among pharmacy undergraduate students as well as factors associated with it; 2) assess the relationships between internet addiction and common mental disorders (depression, anxiety, and stress), in addition to academic performance and body mass index factors.Entities:
Keywords: Academic performance; Anxiety; Depression; Internet addiction
Mesh:
Year: 2022 PMID: 36163012 PMCID: PMC9513952 DOI: 10.1186/s12889-022-14140-6
Source DB: PubMed Journal: BMC Public Health ISSN: 1471-2458 Impact factor: 4.135
Cronbach’s-Alpha values for the DASS-21 subscales and IAT
| Scale | Number of items | Cronbach’s-Alpha |
|---|---|---|
| Depression | 7 | 0.85 |
| Anxiety | 7 | 0.8 |
| Stress | 7 | 0.83 |
| IAT | 20 | 0.89 |
Cronbach’s-Alpha values for all scales indicates the high internal consistency and validity of the questionnaires
Demographic characteristics of sample population (N = 808)
| Variable | Number (%) |
|---|---|
| Male | 214 (26.5) |
| Female | 594 (73.5) |
| 18–19 | 132 (16.3) |
| 19–20 | 183 (22.6) |
| 20–21 | 143 (17.7) |
| 21–22 | 171 (21.2) |
| 22–24 | 179 (22.2) |
| Mean age (SD) | 21.16 (1.64) |
| A | 15 (1.9) |
| B+ | 411 (50.9) |
| B | 246 (30.4) |
| C+ | 76 (9.4) |
| C | 52 (6.4) |
| D | 8 (1.0) |
| Thin | 25 (3.1) |
| Normal | 509 (63) |
| Overweight | 212 (26.2) |
| Obesity I | 51 (6.3) |
| Obesity II | 7 (0.9) |
| Obesity III | 4 (0.5) |
Number and percentage of students in each category of the applied questioners: DASS and IAT (N = 808)
| Item | N (%) | Cut-off Point |
|---|---|---|
| Normal | 191 (23.6) | 0–4 |
| Mild | 113 (14) | 5–6 |
| Moderate | 210 (26) | 7–10 |
| Severe | 127 (15.7) | 11–13 |
| Extremely severe | 167 (20.7) | 14 + |
| Mean score (SD) | 8.85 (5.26) | |
| Normal | 282 (34.9) | 0–3 |
| Mild | 139 (17.2) | 4–5 |
| Moderate | 123 (15.2) | 6–7 |
| Severe | 98 (12.1) | 8–9 |
| Extremely severe | 166 (20.5) | 10 + |
| Mean score (SD) | 5.97 (4.5) | |
| Normal | 356 (44.1) | 0–7 |
| Mild | 114 (14.1) | 8–9 |
| Moderate | 159 (19.7) | 10–12 |
| Severe | 114 (14.1) | 13–16 |
| Extremely severe | 65 (8.044) | 17 + |
| Mean score (SD) | 8.72 (4.92) | |
| Normal internet use | 497 (61.5) | 0–30 |
| Potential internet addiction | 311 (38.5) | 50–100 |
| Mean score (SD) | 44.75 (19.72) | |
D Depression, A Anxiety, S Stress, IAT Internet Addiction Test
Univariate analysis of the relationships between potential internet addiction and participants’ characteristics (N = 808)
| 0.297 $ | ||||
| Male | 138 (64.5%) | 76 (35.5%) | ||
| Female | 359 (60.4%) | 235 (39.6%) | ||
| 0.135 $ | ||||
| 18 | 77 (15.5%) | 55 (17.7%) | ||
| 19 | 106 (21.3%) | 77 (24.8%) | ||
| 20 | 86 (17.3%) | 57 (18.3%) | ||
| 21 | 103 (20.7%) | 68 (21.9%) | ||
| 22–24 | 125 (25.2%) | 54 (17.3%) | ||
| A | 9 (1.8%) | 6 (1.9%) | ||
| B+ | 265 (53.3%) | 146 (47%) | ||
| B | 156 (31.4%) | 90 (29%) | ||
| C+ | 34 (6.9%) | 42 (13.5%) | ||
| C | 28 (5.6%) | 24 (7.7%) | ||
| D | 5 1%) | 3 (0.9%) | ||
| 0.592 # | ||||
| Thin | 13 (2.6%) | 12 (3.9%) | ||
| Normal | 318 (64%) | 191 (61.4%) | ||
| Over wt | 128 (25.8%) | 84 (27%) | ||
| Obesity I | 30 (6%) | 21 (6.7%) | ||
| Obesity II | 4 (0.8%) | 3 (1%) | ||
| Obesity III | 4(0.8%) | 0 (0%) | ||
| Normal | 158 (31.8%) | 33 (10.6%) | ||
| Mild | 76 (15.3%) | 37 (11.9%) | ||
| Moderate | 133 (26.7%) | 77 (24.8%) | ||
| Severe | 66 (13.3%) | 61 (19.6%) | ||
| Extremely severe | 64 (12.9%) | 103 (33.1%) | ||
| Normal | 214 (43%) | 68 (21.8%) | ||
| Mild | 90 (18.1%) | 49 (15.8%) | ||
| Moderate | 81 (16.3%) | 42 (13.5%) | ||
| Severe | 49 (9.9%) | 49 15.8%) | ||
| Extremely severe | 63 (12.7%) | 103 (33.1%) | ||
| Normal | 274 (55.1%) | 82 (26.3%) | ||
| Mild | 73 14.7%) | 41 (13.2%) | ||
| Moderate | 78 (15.8%) | 81 (26%) | ||
| Severe | 50 (10%) | 64 (20.7%) | ||
| Extremely severe | 22 (4.4%) | 43 (13.8%) | ||
$Chi square test of association
#Fisher’s exact test
Correlation analysis between population characteristics and IAT score
| Gender | Age | GPA | BMI | DASS D | DASS A | DASS S | IAT score | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Gender | 1.000 | -.063- | .059 | -.089-* | .084* | .089* | .037 | .041 | ||||||||
| .072 | .092 | .012 | .017 | .011 | .298 | .244 | ||||||||||
| 808 | 808 | 808 | 808 | 808 | 808 | 808 | 808 | |||||||||
| Age | -.063- | 1.000 | .152** | .007 | .005 | -.023- | .006 | -.040- | ||||||||
| .072 | .000 | .836 | .881 | .514 | .862 | .256 | ||||||||||
| 808 | 808 | 808 | 808 | 808 | 808 | 808 | 808 | |||||||||
| GPA | .059 | .152** | 1.000 | .026 | -.048- | -.041- | -.030- | -.091-** | ||||||||
| .092 | .000 | .453 | .177 | .249 | .397 | .009 | ||||||||||
| 808 | 808 | 808 | 808 | 808 | 808 | 808 | 808 | |||||||||
| BMI | -.089-* | .007 | .026 | 1.000 | .044 | -.005- | .058 | -.006- | ||||||||
| .012 | .836 | .453 | .209 | .898 | .102 | .874 | ||||||||||
| 808 | 808 | 808 | 808 | 808 | 808 | 808 | 808 | |||||||||
| DASS D | .084* | .005 | -.048- | .044 | 1.000 | .608** | .684** | .362** | ||||||||
| .017 | .881 | .177 | .209 | .000 | .000 | .000 | ||||||||||
| 808 | 808 | 808 | 808 | 808 | 808 | 808 | 808 | |||||||||
| DASS A | .089* | -.023- | -.041- | -.005- | .608** | 1.000 | .642** | .350** | ||||||||
| .011 | .514 | .249 | .898 | .000 | .000 | .000 | ||||||||||
| 808 | 808 | 808 | 808 | 808 | 808 | 808 | 808 | |||||||||
| DASS S | .037 | .006 | -.030- | .058 | .684** | .642** | 1.000 | .358** | ||||||||
| .298 | .862 | .397 | .102 | .000 | .000 | .000 | ||||||||||
| 808 | 808 | 808 | 808 | 808 | 808 | 808 | 808 | |||||||||
| IAT Score | .041 | -.040- | -.091-** | -.006- | .362** | .350** | .358** | 1.000 | ||||||||
| .244 | .256 | .009 | .874 | .000 | .000 | .000 | ||||||||||
| 808 | 808 | 808 | 808 | 808 | 808 | 808 | 808 | |||||||||
**Correlation is significant at the 0.01 level (2-tailed)
*Correlation is significant at the 0.05 level (2-tailed)
Multiple logistic regression for the predictors of internet addiction in pharmacy students
| Predictor | B | df | OR | 95% CI for OR | Co-linearity statistics | |||
|---|---|---|---|---|---|---|---|---|
| Age | -.129- | 1 | .008 | .879 | .799 | .966 | .982 | 1.018 |
| GPA | -.250- | 1 | .101 | .779 | .577 | 1.050 | .974 | 1.027 |
| Depression | .057 | 1 | .011 | 1.059 | 1.013 | 1.107 | .404 | 2.477 |
| Anxiety | .043 | 1 | .078 | 1.044 | .995 | 1.095 | .475 | 2.104 |
| Stress | .082 | 1 | .002 | 1.086 | 1.030 | 1.144 | .335 | 2.984 |
| Constant | 1.550 | 1 | .161 | 4.711 | ||||