| Literature DB >> 30453936 |
Jayne Lucke1,2, Charmaine Jensen3, Matthew Dunn4,5, Gary Chan3, Cynthia Forlini6, Sharlene Kaye5, Bradley Partridge7,8, Michael Farrell5, Eric Racine9, Wayne Hall3.
Abstract
BACKGROUND: Some university students consume pharmaceutical stimulants without a medical prescription with the goal of improving their academic performance. The prevalence of this practice has been well documented in the US, but less so in other countries. The potential harms of using prescription stimulants require a better understanding of the prevalence of this practice within Australian universities.Entities:
Keywords: Australia; Caffeine; Cognitive enhancement, academic performance; Correlates; Prescription stimulants; Prevalence; University students
Mesh:
Substances:
Year: 2018 PMID: 30453936 PMCID: PMC6245847 DOI: 10.1186/s12889-018-6212-0
Source DB: PubMed Journal: BMC Public Health ISSN: 1471-2458 Impact factor: 3.295
Sample characteristics and their association with prescription stimulant use (N = 1136)
| Life time Prescription stimulant use (Weighted mean/ prevalence) | ||||
|---|---|---|---|---|
| Overall sample (Unweighted descriptive) | No | Yes | ||
| Characteristics | M (SD) | M | M | |
| Age | 21.29 (2.78) | 21.26 | 22.14 | .005 |
| % | % | |||
| Sex | ||||
| Female | 706 (62.2) | 41.3 | 69.4 | < .001 |
| Male | 430 (37.8) | 58.7 | 30.6 | |
| Residency | ||||
| Australian citizen/resident | 969 (85.6) | 85.5 | 84.1 | .753 |
| Other | 163 (14.4) | 14.5 | 15.9 | |
| International student | ||||
| Yes | 158 (14.1) | 14.0 | 15.9 | .917 |
| No | 964 (85.9) | 86.0 | 84.1 | |
| Study level | ||||
| Undergraduate | 984 (87.5) | 87.7 | 82.9 | .480 |
| Graduate/ Post-graduate | 140 (12.4) | 12.3 | 17.1 | |
| Other | 1 (0.1) | 0.0 | 0.0 | |
| Highest level of parents education | ||||
| High school or below | 253 (22.4) | 21.7 | 25.2 | .588 |
| Post-secondary school | 869 (76.8) | 77.4 | 74.8 | |
| Other | 10 (0.8) | 0.9 | 0.0 | |
| Hours of paid work during semester | ||||
| Zero hours | 240 (21.1) | 22.1 | 17.9 | .865 |
| 1–25 h | 636 (56.0) | 55.2 | 56.5 | |
| > 25 h | 70 (6.2) | 6.1 | 7.9 | |
| Hours vary weekly | 73 (6.4) | 6.5 | 5.3 | |
| Do not work during semester | 117 (10.3) | 10.1 | 12.4 | |
| Years studying at university | ||||
| Less than 1 year | 232 (20.6) | 20.9 | 14.3 | .207 |
| 1–3 years | 455 (40.5) | 40.6 | 34.5 | |
| 3–5 years | 329 (29.3) | 29.2 | 37.8 | |
| More than 5 years | 108 (9.6) | 9.3 | 13.4 | |
| Current study load | ||||
| Full-time | 1033 (92.0) | 92.0 | 91.9 | .943 |
| Part-time | 81 (7.2) | 7.2 | 7.0 | |
| Withdrawn/ Deferred | 9 (0.8) | 0.8 | 1.1 | |
| Mode of study | ||||
| On-campus | 1054 (94.4) | 94.6 | 93.0 | .586 |
| Off-campus/online student | 63 (5.6) | 5.4 | 7.0 | |
| Living on campus | ||||
| Yes | 102 (10.7) | 10.6 | 9.8 | .858 |
| No | 849 (89.3) | 89.4 | 90.2 | |
| Current Grade Point Average (GPA) | ||||
| Fail | 16 (1.4) | 1.4 | 4.3 | .215 |
| Pass (4.0–4.9) | 168 (15.0) | 15.2 | 18.7 | |
| Credit (5.0–5.9) | 416 (37.0) | 37.2 | 31.1 | |
| Distinction (6.0–6.9) | 376 (33.4) | 32.7 | 38.9 | |
| High Distinction (7.0) | 92 (8.2) | 8.4 | 4.0 | |
| Other | 56 (5.0) | 5.1 | 3.0 | |
| Physical health problem diagnosis | ||||
| Yes | 198 (17.4) | 16.9 | 17.8 | .428 |
| No | 938 (82.6) | 83.1 | 82.2 | |
| Mental health problem diagnosis | ||||
| Yes | 227 (20.4) | 19.0 | 32.1 | .064 |
| No | 887 (79.6) | 81.0 | 67.9 | |
| Prescription for pharmaceutical stimulants | ||||
| Yes | 16 (1.4) | 0.8 | 11.8 | < .001 |
| No | 1094 (98.6) | 99.2 | 88.2 | |
| Previous illicit drug use | ||||
| Yes | 286 (25.9) | 22.9 | 77.5 | < .001 |
| No | 818 (74.1) | 77.1 | 22.5 | |
| Frequency of alcohol use | ||||
| Never/ Less than yearly | 237 (21.5) | 22.4 | 7.7 | < .001 |
| Yearly | 228 (20.6) | 20.9 | 10.9 | |
| Monthly | 365 (33.0) | 32.7 | 39.0 | |
| Weekly or more frequent | 275 (24.9) | 24.0 | 42.4 | |
| Peer use of prescription stimulants to improve academic performance | ||||
| Yes | 369 (34.8) | 32.1 | 81.8 | < .001 |
| No | 691 (65.2) | 67.9 | 18.2 | |
*Weighted analyses were performed to examine the association between life time prescription stimulant use and sample characteristics. P-values were calculated based on designed based F-statistics
Weighted prevalence of prescription stimulant use to improve academic performance
| Prescription stimulant | LifetimeUse n (%) | Past Year Use n (%) | Past 6 months n (%) | Past 3 months n (%) | Past Month n (%) | Past Week n (%) |
|---|---|---|---|---|---|---|
| Modafinil | 30 (2.7%) | 27 (2.3%) | 23 (2.0%) | 21 (1.8%) | 17 (1.5%) | 12 (1.0%) |
| Adderall | 33 (2.9%) | 19 (1.6%) | 16 (1.4%) | 13 (1.2%) | 11 (1.0%) | 5 (0.4%) |
| Concerta/Ritalin | 29 (2.6%) | 16 (1.5%) | 12 (1.1%) | 11 (1.0%) | 10 (0.9%) | 9 (0.8%) |
| Racetams | 14 (1.2%) | 9 (0.8%) | 7 (0.6%) | 7 (0.6%) | 6 (0.5%) | 3 (0.3%) |
| Atomoxetine | 1 (0.1%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) |
| Phentermine | 1 (0.1%) | 1 (0.1%) | 1 (0.1%) | 1 (0.1%) | 1 (0.01%) | 1 (0.1%) |