Three-month study of college student disordered gambling using the Transtheoretical Model: findings and lessons learned.
Activities are considered gambling when something of value is risked on the outcome of an event when the probability of winning or losing is less than certain (Korn & Shaffer, 1999). The American Psychiatric Association classifies pathological gambling as an impulse control disorder and defines it as the "persistent and recurrent maladaptive gambling behavior that disrupts personal, family or vocational pursuits" (1994; p. 615). Gambling is considered problem (i.e., sub-clinical) gambling when it does not meet the criteria for PG but results in harmful effects to gamblers, their families, significant others, friends, co-workers, and others (National Research Council, 1999). The term "disordered gambling" has been used to describe the full spectrum of gambling problems, which includes pathological and sub-clinical gambling (Shaffer, Hall, & Vander Bilt, 1997).
The findings of gambling-related epidemiological prevalence studies of adults in the general population are well established and have remained relatively consistent over time (Shaffer & Korn, 2002). For instance, recent studies among nationally representative samples of adults found rates of pathological gambling (0.4-0.6%; Kessler et al., 2008; Petry, Stinson, & Grant, 2005) that are consistent with findings among a representative samples from over 30+ years ago (0.7%; Commission on the Review of the National Policy Toward Gambling, 1976; Kallick, Suits, Dielman, & Hybels, 1979). Consequently, researchers suggest that more gambling-related research examine vulnerable population segments (Shaffer & Korn, 2002). Additionally, researchers have stated that the time has come for further examination of the determinants of gambling disorders among both resilient and vulnerable segments of the population; in particular, investigations that focus on the determinants of resilience and vulnerability are very desirable (Shaffer, LaBrie, LaPlante, Nelson, & Stanton, 2004). As we will describe in the proceeding section, college students are one population that has shown increased vulnerability to disordered gambling in the research literature.
College Student Gambling
Research indicates that college students are particularly vulnerable to disordered gambling. Specifically, although the prevalence of college students that participate in gambling varies across studies (e.g., LaBrie, Shaffer, LaPlante, & Wechsler, 2003; Slutske, Jackson, & Sher, 2003), research indicates that college students who gamble are more likely than other population segments to do so at a disordered level (e.g., Barnes, Welte, Hoffman, & Tidwell, 2010; Blanco et al., 2008; Blinn-Pike, Lokken Worthy, & Jonkman, 2007). Further, studies examining college student gambling indicate that gambling participation and disordered gambling are correlated with other risky behaviors, including driving under the influence, binge drinking, illicit drug use, depression, stress, and considering and attempting suicide (e.g., Engwall, Hunter, & Steinberg, 2004; LaBrie et al., 2003; Stuhldreher, Stuhldreher, & Forrest, 2007).
Collectively, these findings suggest that college students might be at greater risk for gambling-related harm than other segments of the population. Despite these epidemiological findings, there is a paucity of theory-driven college gambling research. One potential method to better understand college student disordered gambling is via health behavior theories/models, such as the Transtheoretical Model (TTM).
Transtheoretical Model (TIM)
The TTM was originally developed by James Prochaska and C.C. DiClemente and was used in regards to smoking cessation and psychotherapy interventions (DiClemente & Prochaska, 1982; Prochaska & DiClemente, 1982, 1983, 1986). The model evolved over time and is currently used to predict behavior change and intervene with various health behaviors (e.g., Callaghan et al., 2005; Fernandez, Davidson, Griffiths, Juergens, & Salamonson, 2009; Nochajski & Stasiewicz, 2005; Pruitt et al., 2010). In general, the TTM explains the way people move through phases of behavior change via four main constructs (i.e., the stages of change, processes of change, decisional balance, and situational self-efficacy). The present study uses the stages of change construct (i.e., five stages of change that indicate an individual's readiness to change their behavior: Precontemplation, contemplation, decision/preparation, action, and maintenance) and situational self-efficacy (i.e., a person's rating of how tempted they would be to not perform the new behavior in different settings) (Prochaska, Redding, & Evers, 2002).
TTM constructs have been used in gambling-related research previously. For instance, researchers have used the TTM's stages of change to predict readiness to change among treatment-seeking pathological gamblers (Petry, 2005) and to discuss the relationship to initiation and cessation of problem gambling among adolescents (DiClemente, Story, & Murray, 2000). In addition, as we will describe more thoroughly in the subsequent methods section, researchers have constructed and psychometrically-tested gambling assessment instruments to assess TTM gambling-related constructs, including readiness to change (based on their stage of change) (Neighbors, Lostutter, Larimer, & Takushi, 2002) and situational self-efficacy (May, Whelan, Steenbergh, & Meyers, 2003).
This study used existing psychometrically-tested gambling-related instruments to examine gambling behavior (e.g., gambling frequency and gambling problems) and TTM gambling-related constructs (i.e., readiness to change and situational self-efficacy) at 3 times over a 3-month period among a sample of college students (n = 20) who indicated that they have experienced disordered gambling in their lifetime. In addition to reporting the findings of this study, we will also discuss considerations of examining a low base rate disorder (e.g., disordered gambling) longitudinally.
Our target population for the present study was college students who indicated that they have experienced disordered gambling in their lifetime. To conduct this examination, we used a classroom-based survey to screen for disordered gambling (i.e., the South Oaks Gambling Screen; SOGS) (Lesieur & Blume, 1987) among a sample of undergraduate students (n = 771) enrolled in 17 general education classes at a university located in the southeastern United States (for the results of our screening see Martin et al., 2010). Of those participants, 4.5% (n = 35) indicated that they had experienced disordered gambling in their lifetime. Over half (n = 20; 57.1%) of the participants who screened for disordered gambling consented to participate in the present study. All 20 participants (100%) completed an assessment battery at three time points over a 3-month period. The majority of participants were male (n = 17; 85.0%), and Caucasian (n = 17; 85.0%) (see Table 1 for participant demographics).
As mentioned above, we used the SOGS (Lesieur & Blume, 1987) to screen for disordered gambling. The 20-item SOGS is a widely used gambling screen based on the DSM-III (American Psychiatric Association, 1980) and DSM-III-R (American Psychiatric Association, 1987) criteria for pathological gambling. SOGS scores determined our classification of disordered gambling (i.e., respondents who endorsed 3 or more items were eligible for the present study).
In the present study, we used DSM-IV 10-item pathological gambling (DSM-IV PG) (American Psychiatric Association, 2000) criteria and the 20-item gambling problem index (GPI) (Neighbors et al., 2002) to assess the number of gambling-related problems that participants experienced in the previous 30 days. Further, we assessed gambling frequency in the past 30 days by asking respondents how often they gambled (i.e., never, once a month, 2 to 3 times a month, weekly, more than once a week, every other day, and every day) on the eight gambling activities assessed in the Harvard School of Public Health College Alcohol Survey by LaBrie et al. (2003): (1) lottery/numbers; (2) casino gambling; (3) cards, dice or other games of chance; (4) professional sports; (5) college sports; (6) horse/dog races; (7) internet gambling; and (8) betting with a bookie. We also assessed whether or not the participant gambled in the past 30 days.
In addition to assessing gambling behavior in the past 30 days, we used TTM gambling-related instruments to examine gambling-related self-efficacy in the past 30 days and current readiness to change. Specifically, we assessed readiness to change via the Gambling Readiness to Change Scale (GRTC) (Neighbors et al., 2002) and perceived behavioral control via the 16-item Gambling Self-Efficacy Questionnaire (GSEQ) (May et al., 2003). We also collected information about participants' sociodemographics, including gender and race/ethnicity.
The GRTC was developed and psychometrically tested by Neighbors et al. (2002), who modeled the questionnaire after the Alcohol Readiness to Change Questionnaire (Rollnick, Heather, Gold, & Hall, 1992) which was based on TTM's stages of change (Prochaska & DiClemente, 1986). The GRTC consists of nine items measuring three stages (i.e., pre-contemplation, contemplation and action) and can be scored compositely or separately for each stage. Psychometric testing found that GRTC scores positively correlate with a participant's readiness to change their behavior; thus, indicating that the GRTC is useful as an outcome measure and as a means of staging participant's readiness to change gambling behavior (Neighbors et al., 2002).
The GSEQ was developed and psychometrically tested by May et al. (2003) based on the premise that self-efficacy assessment instruments for other addictive behaviors have been beneficial in monitoring behavior change, predicting maintenance of treatment gains and identifying potential relapses (e.g., Allsop, Saunders, & Phillips, 2000; Condiotte & Lichtenstein, 1981). The instrument was modeled after the Situational Confidence Questionairre-39 (Annis & Graham, 1988) and the results from psychometric testing indicated that GSEQ scores were negatively correlated with a perceived inability to control one's problematic gambling behavior (May et al., 2003). Thus, GSEQ composite scores (1-1600) positively correlate with participants' perceived self-efficacy to control their gambling in various situations.
This study received approval from the institutional review board (IRB) of the university at which we conducted the research. The screening for disordered gambling occurred during October and November of the 2007 fall semester and the present study occurred in Winter/Spring 2008 (January-April). The screenings (n = 771) were conducted in 17 undergraduate classes via in-class paper-and-pencil surveys and screening participants received no incentives. The screening instruments included an ID number. Attached to the instrument was a small slip of paper that also listed the ID number and requested respondent contact information (i.e., name, phone number and e-mail) if they were interested in participating in follow-up study.
Participants of the present study included students who indicated disordered gambling on the screening, provided their contact information and consented to participate (n = 20). Participants completed a paper-and-pencil assessment battery assessing gambling behavior (gambling frequency and number of gambling problems experienced in the past 30 days) and TTM gambling-related constructs (readiness to change and self-efficacy) three times (approximately every 30 days) over a 3-month period. Participants completed the assessment in a campus office specifically reserved for the present study. Participants of the present study received a $20.00 gift card for completing each of the three assessments ($60.00 total).
Participants (n = 20) completed the assessment battery 3 times (approximately every 30 days) over a 3-month period. Survey responses were entered into Excel.
To assess gambling-related readiness to change via the GRTC, we used composite scoring, which calculates an overall composite of readiness to change via weighing the precontemplation items (-2), contemplation items (1) and action items (2), and taking the mean of all weighted items. GRTC scores positively correlate with an individual's readiness to change. To assess gambling-related self-efficacy via the GSEQ we also used composite scoring and scores (0-1600) positively correlate with participants' perceived self-efficacy to control their gambling in various situations. GSEQ scores positively correlate with one's perceived confidence to control their gambling behavior in various situations.
Concerning gambling behavior in the past 30 days, we assessed (1) whether participants gambled (2) gambling frequency and (3) gambling-related problems. To assess the number of gambling-related problems that participants experienced in the past 30 days, we summed two measures separately: the DSM-IV 10-item pathological gambling (DSM-IV PG) (American Psychiatric Association, 2000) criteria and the 20-item gambling problem index (GPI) (Neighbors et al., 2002). To assess gambling frequency in the past 30 days, we asked respondents how often they gambled (i.e., never, once a month, 2 to 3 times a month, weekly, more than once a week, every other day, and every day) on the eight gambling activities: (1) lottery/numbers; (2) casino gambling; (3) cards, dice or other games of chance; (4) professional sports; (5) college sports; (6) horse/dog races; (7) internet gambling; and (8) betting with a bookie. For each gambling activity, participants indicated how often they gambled and a point was assigned for each of the potential responses (i.e., never = 1; once a month = 2; two to three times per month = 3; weekly = 4; more than once per week = 5; every other day = 6; every day = 7). Thus, potential gambling frequency scores for each month range from 8-56 and positively correlate with increased gambling frequency.
Table 2 indicates the gambling behavior of our sample at three time points over a 3 month period of time (approximately every 30 days). Specifically, Table 2 lists (1) whether the participant gambled in the past 30 days; (2) gambling frequency scores for the past 30 days and a gambling frequency sum score; (3) gambling problems in the past 30 days (DSM-IV); (4) gambling problems in the past 30 days (Gambling Problem Index); (5) current gambling-related readiness to change scores; and (6) gambling-related self-efficacy scores in the past 30 days. We listed the participants in Table 2 in the descending order based on their gambling frequency sum scores.
Although our sample was too small to draw statistical conclusions, the findings indicate evidence that the gambling behavior (i.e., gambling frequency and gambling-related problems) of disordered college student gamblers fluctuates and often becomes more or less serious from month to month (see Table 2). In addition, we found that several students (n = 5) who screened as experiencing disordered gambling in their lifetimes were no longer experiencing gambling-related problems when this study was conducted. Another interesting finding was that some participants (n = 6) did not gamble in at least one of the 30-day periods we assessed; however, none of the participants indicated that they abstained from gambling throughout the 3 month period we assessed.
Again, because of our small sample, we could not draw statistical conclusions regarding the utility of TTM constructs to examine and/or predict gambling behavior among participants who have experienced gambling problems in their lifetimes. However, based on our limited results, it appears that the TTM constructs used in this research (i.e., readiness to change and self-efficacy) might be useful in examining gambling behavior (and changes in gambling behavior) over time. For instance, we found that participants with higher gambling frequency scores tended to indicate experiencing a higher number of gambling-related problems than participants with lower scores. In turn, participants who indicated experiencing a higher number of problems, tended to indicate lower readiness to change scores and lower perceived self-efficacy scores than participants who indicated a lesser number of problems. A negative correlation between the number of gambling-related problems and self-efficacy and readiness to change scores is consistent with the relationship hypothesized by the TTM.
In addition, participants who indicated that they gambled infrequently and were experiencing few, if any, gambling-related problems tended to also specify that they had no intention of changing their behavior. Thus, we found that those not experiencing gambling-related problems were not likely to be contemplating or planning to change their non-problematic behavior.
We conducted this study in response to researchers suggestions that more gambling-related research examine determinants of gambling disorders among vulnerable segments of the population (Shaffer et al., 2004). Because college students are a population vulnerable to experiencing gambling problems (e.g., Barnes et al., 2010; Blanco et al., 2008; Blinn-Pike et al., 2007; Shaffer & Hall, 2001), we examined gambling behavior over a 3 month period among college students who had indicated disordered gambling in their lifetime.
As mentioned in the results section, although our sample was too small to draw statistical conclusions, we found that gambling behavior fluctuated in this population from month to month and some study participants were not currently experiencing gambling-related problems. Collectively, these findings are consistent with research indicating that disordered gamblers move in and out of disordered states (LaPlante, Nelson, LaBrie, & Shaffer, 2008). Further, it appears that consistent with previous studies (May et al., 2003; Neighbors et al., 2002), the TTM constructs used in this research (i.e., readiness to change and self-efficacy) have some utility in examining gambling behavior.
There are several limitations in this research. As mentioned previously, one significant limitation was the small sample (i.e., n = 20). Consequently, we did not have enough power to establish statistically significant differences among groups. Future research examining disordered gambling (cross-sectional or over time) should attempt to recruit from as large of a participant screening pool as possible; thus, increasing the likelihood of an adequate sample and increasing the researchers ability to detect statistical significance.
Another limitation was using the SOGS screening instrument that assessed disordered gambling over an individual's lifetime as opposed to specifically assessing problems in the past year. Thus, some of the participants classified as disordered gamblers were not experiencing problems currently or recently. This finding is consistent with other research that indicates that disordered gambling is not necessarily a progressive disorder and individuals move in and out of PG problems (LaPlante et al., 2008). As a result, we recommend that future researchers interested in screening for disordered gambling, screen for recent (e.g., past year) problems as opposed to lifetime problems.
Further, another limitation is the use of a convenience sample and the lack of generalizability and the selection bias associated with using such samples. Consequently, our findings might not be generalizable to the general college student population. Further, because this study examined college students at one large, public university in the Southeast, findings might not be generalizable to students attending colleges/universities of other sizes and in other locations.
Gambling behavior fluctuated in this population over time and some participants were not currently experiencing gambling-related problems, which is consistent with other research examining gambling longitudinally (LaPlante et al., 2008). Further, it appears that consistent with previous studies (May et al., 2003; Neighbors et al., 2002), the TTM constructs readiness to change and self-efficacy have some utility in examining gambling behavior. Finally, regarding examinations of disordered gambling (and other low base rate disorders), findings from this study suggest: (1) screening from as large a participant pool as possible to assure an adequate sample; and (2) carefully considering the appropriate timeframe of screens (e.g., lifetime, past year, past month).
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RYAN J. MARTIN, Ph.D.
Department of Health Education and Promotion
East Carolina University
STUART USDAN, PH.D.
Department of Health Science, University of Alabama
LORI TURNER, Ph.D.
Department of Health Science, University of Alabama
Table 1 Demographics in a Sample of College Students who Indicated Disordered Gambling in their Lifetimes (n = 20) N (%) Gender Male 17 (85.0) Female 3 (15.0) Race/ethnicity White 17 (85.0) African American 2 (10.0) Hispanic/Latino 1 (5.0) Other 1 (5.0) Class status Underclassman 9 (55.0) Upperclassman 11 (45.0) Greek affiliation Yes 7 (35.0) No 13 (65.0) Mean (SD) Age 20.7 (2.1) Table 2 Gambling measures over 3 months among a sample of college students who indicated experiencing disordered gambling in their lifetimes (n = 20) Gambled in the Gambling frequency past 30 days score in the past (yes or no) 30 days MO MO MO MO MO MO Summed 1 2 3 1 2 3 Y Y Y 25 24 18 67 Y Y Y 22 21 19 62 Y Y Y 14 31 15 60 Y Y Y 24 19 13 56 Y Y Y 20 15 20 55 Y Y Y 17 21 17 55 Y Y Y 22 17 12 51 Y Y Y 16 15 19 50 Y Y Y 13 16 13 42 Y Y Y 9 14 14 37 Y Y Y 18 9 10 37 Y Y Y 15 11 10 36 Y Y Y 14 9 13 36 N Y N 8 15 8 31 Y N N 14 8 8 30 Y Y Y 9 10 9 28 Y N Y 9 8 10 27 N Y Y 8 9 10 27 Y Y N 9 9 8 26 N N Y 9 8 9 25 Gambling Gambling problems in the problems in the past 30 days past 30 days (DSM-IV (Gambling criteria) Problem Index) MO MO MO MO MO MO 1 1 3 1 2 3 4 7 3 10 15 11 2 2 2 11 7 5 0 3 1 6 12 1 0 1 0 2 1 0 1 t 0 4 1 1 3 6 4 4 15 6 2 1 1 7 6 0 1 1 2 7 8 5 0 0 0 10 6 4 1 0 0 0 2 2 4 0 0 8 3 0 3 1 0 8 2 0 1 1 1 3 1 2 0 1 0 0 3 0 2 4 2 2 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2 0 0 0 0 0 0 0 0 0 0 0 0 Gambling-related Gambling-related readiness to change self-efficacy MO MO MO MO MO MO 1 2 3 1 2 3 0.56 0.89 1.89 400 400 740 0.67 1.67 1.78 740 1220 1040 -1.11 0.11 0.78 1180 860 1520 -1.11 -0.78 1 1340 1400 1120 0 -0.33 -0.89 1480 1420 1460 1 1.56 0.89 1300 1080 640 3 2.89 2.89 1060 920 1140 1.22 0.44 -0.22 700 700 920 -0.11 0.78 0.33 1260 1220 1220 0 0 -0.11 1380 1340 1420 3.44 2.89 2.44 740 1200 1260 -0.22 -0.56 -0.56 1000 1180 1300 0.78 -0.11 0.33 1040 900 1100 2.78 1.56 .. 1380 1360 1540 -0.11 2.89 1.44 1020 580 740 1.56 1.11 0.89 1440 1520 1520 -0.44 -1 -1.33 1400 1500 1480 -0.33 0.67 0.67 1320 860 1320 -0.56 0.56 0.33 1420 1540 1520 -0.44 -0.33 -1.67 1160 1600 1600
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|Author:||Martin, Ryan J.; Usdan, Stuart; Turner, Lori|
|Publication:||College Student Journal|
|Date:||Dec 1, 2012|
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