Online activity could serve in the future as behavioral markers of emotional states for computer systems (i.e., affective computing). Hence, this study considered relationships between self-reported stimulant use and online study patterns. Sixty-two undergraduate psychology students estimated their daily caffeine use, and this was related to study patterns as tracked by their use of a Learning Management System (Blackboard). Caffeine dependency was associated with less time spent online, lower rates of file access, and fewer online activities completed. Reduced breadth or depth of processing during work/study could be used as a behavioral marker of stimulant use.
Get full access to this article
View all access options for this article.
References
1.
TaoJ, TanT. Affective computing: a review. Lecture Notes in Computer Science, 2005; 3784:981–995.
2.
WiederholdBK. mHealth sensors can promote behavior change and reduce healthcare costs. Cyberpsychology, Behavior & Social Networking, 2015; 18:559–560.
ChenJ, StallaertJ. An economic analysis of online advertising using behavioural targeting. MIS Quarterly, 2014; 38:429–449.
5.
CranwellJ, BenfordS, HoughtonRJ, et al.Increasing self-regulatory energy using an internet-based training application delivered by smartphone technology. Cyberpsychology, Behavior, & Social Networking, 2014; 17:181–186.
6.
HeronKE, SmythJM. Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments. British Journal of Health Psychology, 2010; 15:1–39.
7.
SekoY, KiddS, WiljerD, et al.Youth mental health interventions via mobile phones: a scoping review. Cyberpsychology, Behavior, & Social Networking, 2014; 17:591–602.
8.
GladeMM. Caffeine—not just a stimulant. Nutrition, 2010; 26:932–938.
9.
SahakianB, Morein-ZamirS. Professor's little helper. Nature, 2007; 450:1157–1159.
10.
CappellettiS, DariaP, SaniG, et al.Caffeine: cognitive and physical performance enhancer or psychoactive drug?. Current Neuropharmacology, 2015; 13:71–88.
11.
FreedmanND, ParkY, AbnetCC, et al.Association of coffee drinking with total and cause-specific mortality. New England Journal of Medicine, 2012; 366:1891–1904.
12.
JulianoLM, EvattDP, RichardsBD, et al.Characterization of individuals seeking treatment for caffeine dependence. Psychology of Addictive Behaviors, 2012; 26:948–954.
13.
MeredithSE, JulianoLM, HughesJR, et al.Caffeine use disorder: a comprehensive review and research agenda. Journal of Caffeine Research, 2013; 3:114–130.
14.
TempleJL. Caffeine use in children: what we know, what we have left to learn, and why we should worry. Neuroscience & Biobehavioral Reviews, 2009; 33:793–806.
15.
YoungKS. Internet addiction: symptoms, evaluation, and treatment. In VandeCreekL, JacksonTL, eds.) Clinical practice (Vol. 17). Sarasota, FL: Professional Resource Press.
16.
ReamGL, ElliottLC, DunlapE. Patterns of and motivations for concurrent use of video games and substances. International Journal of Environmental Research and Public Health, 2011; 8:3999–4012.
17.
GreenbergJL, LewisSE, DoddDK. Overlapping addictions and selfesteem among college men and women. Addictive Behaviors, 1999; 24:565–571.
18.
PallantiS, BernardiS, QuercioliL. The shorter PROMIS questionnaire and the Internet Addiction Scale in the assessment of multiple addictions in a high-school population: prevalence and related disability. CNS Spectrums, 2006; 11:966–974.
19.
YauYHC, PilverCE, SteinbergMA, et al.Relationships between problematic Internet use and problem-gambling severity: findings from a high-school survey. Addictive Behaviors, 2014; 39:13–21.
20.
PorterG, StarcevicV, BerleD, et al.Recognizing problem video game use. Australian and New Zealand Journal of Psychiatry, 2010; 44:120–128.
21.
ChristopherG, SutherlandD, SmithA. Effects of caffeine in non-withdrawn volunteers. Human Psychopharmacology, 2005; 20:47–53.
22.
HaskellCF, KennedyDO, WesnesKA, et al.Cognitive and mood improvements of caffeine in habitual consumers and habitual non-consumers of caffeine. Psychopharmacology, 2005; 179:813–825.
23.
JamesJE, KeaneMA. Caffeine, sleep and wakefulness: implications of new understanding about withdrawal reversal. Human Psychopharmacology, 2007; 22:549–558.
24.
JamesJE, RogersPJ. Effects of caffeine on performance and mood: withdrawal reversal is the most plausible explanation. Psychopharmacology, 2005; 182:1–8.
25.
RogersPJ, HeatherleySV, MullingsEL, et al.Faster but not smarter: effects of caffeine and caffeine withdrawal on alertness and performance. Psychopharmacology, 2013; 226:229–240.
26.
SmithAP. Caffeine at work. Human Psychopharmacology, 2005; 20:441–445.
27.
SmithAP. Caffeine, cognitive failures and health in a non-working community sample. Human Psychopharmacology, 2009; 24:29–34.
28.
StreufertS, PogashR, MillerJ, et al.Effects of caffeine deprivation on complex human functioning. Psychopharmacology, 1995; 118:377–384.
29.
StreufertS, SatishU, PogashR, et al.Excess coffee consumption in simulated complex work settings: detriment or facilitation of performance?. Journal of Applied Psychology, 1997; 82:774–782.
30.
OgeilRP, RajaratnamSMW, RedmanJR, et al.Acute MDMA administration alters the distribution and circadian rhythm of wheel running activity in the rat. Brain Research, 2010; 1359:128–136.
31.
PhillipsJG, CurrieJ, OgeilRP. Consumption and foraging behaviors for common stimulants (nicotine, caffeine). Journal of Addictive Diseases, 2016; 35:15–21.
32.
GossopM, DarkeS, GriffithsP, et al.The Severity of Dependence Scale (SDS): psychometric properties of the SDS in English and Australian samples of heroin, cocaine and amphetamine users. Addiction, 1995; 90:605–614.
33.
OgeilRP, PhillipsJG. Commonly used stimulants: sleep problems, dependence and psychological distress. Drug and Alcohol Dependence, 2015; 153:145–151.
34.
MartinG, CopelandJ, GatesP, et al.The Severity of Dependence Scale (SDS) in an adolescent population of cannabis users: reliability, validity and diagnostic cut-off. Drug and Alcohol Dependence, 2006; 83:90–93.
35.
ToppL, MattickRP. Choosing a cut-off on the Severity of Dependence Scale (SDS) for amphetamine users. Addiction, 1997; 92:839–845.
36.
PhillipsJG, JoryM, MogfordN. (2007) Decisional style and eParticipation. In Proceedings of OZCHI2007, pp. 139–141. http://dl.acm.org/citation.cfm?id=1324892.1324917&coll=DL&dl=ACM&CFID=460532423&CFTOKEN=19927600 (accessed December3, 2014).
37.
MannL, BallC. The relationship between search strategy and risky choice. Australian Journal of Psychology, 1994; 46:131–136.
38.
BallC, MannL, StammC. Decision making abilities of intellectually gifted and non-gifted children. Australian Journal of Psychology, 1999; 46:13–20.
39.
HerekGM, JanisI, HuthP. Decision making during international crisis: is quality of process related to outcome?. Journal of Conflict Resolution, 1987; 31:203–206.
40.
BeswickG, RothblumE, MannL. Psychological antecedents of student procrastination. Australian Psychologist, 1988; 23:207–217.
41.
PhillipsJG, OgeilRP. Decision‐making style, nicotine and caffeine use and dependence. Human Psychopharmacology, 2015; 30:442–450.
42.
StrainEC, MumfordGK, SilvermanK, et al.Caffeine dependence syndrome: evidence from case histories and experimental evaluations. JAMA, 1994; 272:1043–1048.
43.
LaneJD. Effects of brief caffeinated-beverage deprivation on mood, symptoms, and psychomotor performance. Pharmacology, Biochemistry and Behavior, 1997; 58:203–208.
44.
LiebermanHR, TharionWJ, Shukitt-HaleB, et al.Effects of caffeine, sleep loss, and stress on cognitive performance and mood during U.S. Navy SEAL training. Psychopharmacology, 2002; 164:250–261.
45.
AttwoodAS, HiggsS, TerryP. Differential responsiveness to caffeine and perceived effects of caffeine in moderate and high regular caffeine consumers. Psychopharmacology, 2007; 190:469–477.
46.
YeomansMR, RipleyT, DaviesLH, et al.Effects of caffeine on performance and mood depend on the level of caffeine abstinence. Psychopharmacology, 2002; 164:241–249.
47.
BentleyRA, O'BrienMJ, BrockWA. Mapping collective behavior in the big-data era. Behavioral & Brain Sciences, 2014; 37:63–119.
48.
HöferI, BättigK. Cardiovascular, behavioural, and subjective effects of caffeine under field conditions. Pharmacology, Biochemistry and Behavior, 1996; 48:899–908.
49.
HuY-H, LoC-L, ShihS-P. Developing early warning systems to predict students' online learning performance. Computers in Human Behavior, 2014; 36:469–478.
50.
KrummAE, WaddingtonRJ, TeasleySD, et al. (2014) A learning management system-based early warning system for academic advising in undergraduate engineering. In LarussonJA, WhiteB, eds. Learning analytics: from research to practice. New York: Springer Science+Business Media, pp. 103–119.
51.
KimYS, YumB-J. Recommender system based on click stream data using association rule mining. Expert Systems with Applications, 2011; 38:13320–13327.
52.
KimYS, YumB-J, SongJ, et al.Development of a recommender system based on navigational and behavioral patterns of customers in e-commerce sites. Expert Systems with Applications, 2005; 28:381–393.
53.
MoeWW. Buying, searching, or browsing: differentiating between online shoppers using in-store navigational clickstream. Journal of Consumer Psychology, 2003; 13:29–40.
54.
StrebeR. Aesthetics on the web: effects on approach and avoidance behaviour. Behaviour & Information Technology, 2015; 35:1–17.
55.
NahinAFMNH, AlamJM, MahmudH, et al.Identifying emotion by keystroke dynamics and text pattern analysis. Behaviour & Information Technology, 2014; 33:987–996.
56.
MacaulayM. The speed of mouse-click as a measure of anxiety during human-computer interaction. Behaviour & Information Technology, 2004; 23:427–433.
57.
QuiltyLC, Avila MuratiD, BagbyRM. Identifying indicators of harmful and problem gambling in a Canadian sample through receiver operating characteristic analysis. Psychology of Addictive Behaviors, 2014; 28:229–237.
58.
SchellinckT, SchransT. Identifying problem gamblers at the gaming venue: finding combinations of high confidence indicators. Gambling Research, 2004; 16:8–24.
59.
DelfabbroP, KingDL, GriffithsM. Behavioural profiling of problem gamblers: a summary and review. International Gambling Studies, 2012; 12. DOI:10.1080/14459795.2012.678274
