استفاده ماری جوانا، وسوسه، و انگیزش تحصیلی و عملکرد در میان دانشجویان: مطالعه در لحظه
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|30110||2015||6 صفحه PDF||سفارش دهید||4800 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Addictive Behaviors, Volume 47, August 2015, Pages 42–47
Abstract Introduction Marijuana is the most commonly used illicit substance in the U.S., with high rates among young adults in the state of Colorado. Chronic, heavy marijuana use can impact cognitive functioning, which has the potential to influence academic performance of college students. It is possible that craving for marijuana may further contribute to diminished cognitive and affective functioning, thus leading to poor outcomes for students. Methods College student marijuana users (n = 57) were recruited based on heavy use and completed ecological momentary assessment (EMA) via text-messaging. The association between marijuana use and craving in a college setting was explored, as well as how these variables might relate to academic motivation, effort and success. The participants were sent text messages for two weeks, three times per day at random times.
1. Introduction Marijuana is the most commonly used illicit drug in the U.S., with over 7% of the general population and 19% of 18–25 year olds reporting use of marijuana within the last month (Substance Abuse & Mental Health Services Administration [SAMHSA], 2014). In the state of Colorado, rates of marijuana use are among the highest in the nation, with 25% of 18–25 year olds reporting use within the last month (SAMHSA, 2012). Approximately one-third of college students report use of marijuana annually (Johnston et al., 2014 and Mohler-Kuo et al., 2003) and a significant portion (25%) of past-year cannabis users meet criteria for a cannabis disorder (Caldeira, Arria, O'Grady, Vincent, & Wish, 2008). Chronic marijuana users experience significant consequences as a result of their use, including a range of cognitive deficits. Acute intoxication effects include deficits in psychomotor functioning (e.g., speed, accuracy), attention (including sustained selective, focused and divided attention problems), pre-attentive sensory memory, and short-term/working memory (problems in verbal learning/memory, immediate and delayed free recall; see Solowij & Pesa, 2010 for a review). When examining long-term deficits, studies have consistently shown problems with attention, inhibition, working memory, executive functioning, verbal memory, and time estimation in heavy, chronic users (Solowij & Pesa, 2010). Of important note, such deficits appear to persist even after waiting for intoxication effects to diminish. The degree of such problems appears to depend on frequency and duration of use, dose, and age of onset (Solowij & Pesa, 2010). Many of these cognitive deficits could impact college success, as a number of specific impairments (e.g., attention, inhibition, and executive functioning) are directly connected to self-regulation in a learning environment (Pintrich, 2004, Tangney et al., 2004, Zimmerman, 2008 and Zimmerman et al., 1992). It is possible that academic problems and failure could be impacted not only by the substance use itself, but also other addictive processes. Craving is one such process that is often described as a strong or intense urge or desire to use a particular substance. Tiffany's Cognitive Processing Model offers a way to conceptualize the impact of craving on cognitive and academic skills (Tiffany, 1990 and Tiffany and Conklin, 2000). Tiffany (1990) describes addictive behavior as largely an automatic process, whereby behaviors associated with long-term substance use become regulated outside of consciousness, develop with practice and become difficult to control. Craving, on the other hand, is suspected to function more at the non-automatic level, though in parallel with the more automated behaviors of drug use. Because craving is demanding at the cognitive level and requires substantial effort, it can impede other non-automatic processes. Similar to a self-regulation model for nicotine addiction proposed by Sayette and Griffin (2011), active marijuana users have to maintain some degree of self-control over their use, and at times, must delay using marijuana in circumstances where using is not acceptable (e.g., while at work, when in class). Such delays may lead to increased urge or craving, which has the potential to impact one's attentional control at the non-automatic level (Field, Munafò, & Franken, 2009). Baumeister and colleagues (Baumeister et al., 1994, Baumeister et al., 2007 and Muraven and Baumeister, 2000) have proposed a self-regulatory strength model whereby individuals are believed to have a limited capacity to engage in self-control, which could influence operations controlled by the cognitive executive system. This leads to a competition for resources and poor performance on subsequent self-regulatory tasks (e.g., Baumeister et al., 1998 and Muraven et al., 1998). As an example of how this may relate to substance use, Muraven, Collins, Shiffman, and Paty (2005) used ecological momentary assessment (EMA) to examine whether daily fluctuations in self-control influenced alcohol consumption with underage drinkers. They found that when participants had greater demands on their self-control, they were more likely to violate their personal alcohol limits. When considering the academic environment, it is possible that heavy users will struggle to perform at their peak academically if craving impedes their attention and competition for cognitive resources exists. Increased cognitive effort associated with craving may interfere with other cognitively demanding tasks, such as focusing in class, reading comprehension, and managing academic goals. Craving may also lead to greater marijuana use, which could impact the academic performance of college students and interfere with their ability to fully benefit from their academic studies. The association between craving and subsequent marijuana use has not been widely studied. As noted by Tiffany and Wray (2009), studies examining the association between craving and substance use have not always found the two to be related, or if they are, often the association is not particularly strong. Only one study (Buckner, Crosby, Silgado, Wonderlich, & Schmidt, 2012) has examined marijuana use and craving in college students. Though academic variables were not examined, Buckner et al. (2012) assessed 49 college student marijuana users with a 2-week EMA protocol using personal digital assistants (PDAs). When examined temporally, craving tended to increase in the hours before using marijuana and decreased after use. Craving ratings were higher on days when marijuana was used compared to days participants did not use. Further research is needed to explore whether marijuana craving and use are related and how. No studies have examined the contributions of craving and marijuana use on specific academic factors that lead to college success. Furthermore, although some studies have found associations between marijuana use, academic performance, college completion, and hours spent studying (Arria et al., 2013a, Arria et al., 2013b, Bell et al., 1997, Buckner et al., 2010, Fergusson et al., 2003 and Horwood et al., 2010), none have assessed a range of other academic components that might influence completion of one's college degree among marijuana users, such as academic motivation and self-efficacy. In the general college student population, these factors are well-known to influence academic performance and retention (see review by Robbins et al., 2004). The primary aim of this study was to examine the association between marijuana use and craving and how these variables might relate to academic motivation and academic effort when assessed in the moment with college students. A secondary aim focused on exploring associations between academic performance (GPA) and time spent smoking marijuana, time spent studying, academic self-efficacy, and consequences related to marijuana use. It was hypothesized that craving at one instance would predict marijuana use and time spent studying at the next time point and that higher craving would be associated with lower academic motivation in the moment. Finally, it was believed that academic self-efficacy, problems related to marijuana use, time spent studying, and time spent smoking marijuana would predict academic performance (GPA).
نتیجه گیری انگلیسی
3.1. Sample characteristics and patterns of marijuana use Table 2 includes participant marijuana use history, RMPI total score, and means and SDs for EMA marijuana-related items. Though the initial aim of this study was to recruit individuals who smoked weekly or greater, participants reported smoking on average 25 days out of the last 30 (range = 6–30 days) at the baseline interview. Table 2. Marijuana use and history. Measure/variable Mean (SD) RMPI total score 13.95 (9.32) Marijuana frequency in the last 30 days 24.95 (5.89) Age of first marijuana use 15.62 (2.43) Age of regular marijuana use 17.78 (2.25) EMA minutes smoked (number of minutes smoked/day)a 14.79 (29.11) EMA frequency of marijuana use (number of times smoked/day)a 1.07 (1.48) EMA marijuana craving (1–10 scale)a 3.16 (2.65) Note: RMPI = Rutgers Marijuana Problem Index (RMPI). a All EMA variables averaged over entire two-week assessment. Table options 3.2. Does craving predict marijuana use? Two time-lagged hierarchical (mixed-effects) models (Lee and Nelder, 1996, Lee and Nelder, 2001 and Lee et al., 2006) examined whether craving at one assessment point predicted marijuana use at the next instance, controlling for day of the week. Marijuana use was operationalized by the amount of time (in minutes) participants spent smoking (Model 1) and the number of times participants smoked (Model 2), both at the next assessment point. Estimates of the effects for each day of the week are omitted for simplicity of presentation. Model 1 showed that craving significantly predicted the amount of time participants spent smoking at the next time assessment (F[1,1771] = 1869.35, p < .001), with a positive association (β = .13). Similarly, Model 2 showed that craving also positively predicted the number of times participants smoked at the next time assessment (F[1,1704] = 136.74, p < .001; β = .11). To account for excessive zero counts ( Kassahun, Neyens, Molenberghs, Faes, & Verbeke, 2014) with the number of times smoked variable in Model 2, data were re-fit using a mixed Zero-Inflated Poisson (ZIP) distribution model ( Cameron and Trivedi, 1998 and Hu et al., 2011) and similar results were observed. Thus as hypothesized, when craving increased, the number of minutes spent smoking and the frequency of participants' use at the next reported assessment also increased. 3.3. Is craving associated with academic effort and motivation? Similar to the previous time-lagged models, Model 3 examined whether craving at one assessment point would predict the amount of effort (measured as time in minutes participants spent studying) at the next assessment point. Day of the week was controlled for and cumulative GPA was added as a control variable due to the relevance of examining the academic variable time spent studying. Consistent with what would be expected, craving negatively predicted the amount of time in minutes spent studying at the next time point (F[1,1701] = 230.96, p < .001; β = − .03). Thus, as craving levels increased, the number of minutes spent studying decreased at the next time point. In this model, cumulative GPA was not a significant factor (F[1,1701] = .48, p = .48; β = − .11) and was dropped from the model. Data were re-fit using a mixed ZIP model to account for excessive zero counts and similar results were observed. A hierarchical (mixed-effects) linear model (Fitzmaurice et al., 2011, Laird and Ware, 1982 and Robinson, 1991) was applied to evaluate the significance of the association between momentary craving (criterion variable) and academic motivation (outcome variable) while in the same moment (Model 4), controlling for day of week, and adjusting for the minutes spent studying (p < .001; β = .009) and minutes spent smoking (p = .09; β = − .0004). Craving was negatively associated with academic motivation at the momentary level (F[1,1969] = 5.06, p = .025; β = − .06). Thus, when craving at a particular moment was higher, the level of academic motivation at that same moment was lower and vice versa (see Fig. 1). Full-size image (35 K) Fig. 1. Average EMA values across participants for academic motivation and marijuana craving over the two-week assessment. Each time point on the x-axis refers to the day of the study (D) and the specific time point when the data was collected (out of three random times each day). Figure options 3.4. How does marijuana use relate to academic performance? For the final analysis (Model 5), a sequential multiple regression model was used to examine whether race/ethnicity, gender, academic self-efficacy, time spent studying (averaged EMA variable), problem marijuana use, and time spent using marijuana (averaged EMA variable) predict academic performance (cumulative GPA). At the first level, race/ethnicity (β = − .32, p = .03) and gender (β = .25, p = .08) together predicted cumulative GPA (F[2,43] = 4.04, p = .03) and accounted for approximately 15.80% of the variation in GPA. At the second level, the linear combination of academic self-efficacy (MSLQ-SE; β = .31, p = .02) and problem marijuana use (RMPI; β = .12, p = .38) together were significant (F[4,41] = 3.80, p = .01) and accounted for approximately 11.20% of additional variation in cumulative GPA. While accounting for these four predictor variables, average EMA minutes spent studying (β = − .10, p = .48) at the third level was not a significant predictor of cumulative GPA (F[5, 40] = 3.10, p = .02), accounting for only 1% of additional variation in cumulative GPA. Finally, accounting for all five of the previous predictor variables, the average EMA minutes spent smoking (β = − .29, p = .04) was a significant negative predictor of cumulative GPA (F[6, 39] = 3.54, p < .01; R2 = .35), accounting for approximately 7.30% of additional variation in cumulative GPA.