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. Author manuscript; available in PMC: 2025 Jan 1.
Published in final edited form as: Addict Res Theory. 2023 Jul 14;32(3):178–185. doi: 10.1080/16066359.2023.2234289

Other Momentary Substance Behaviors as Predictors of College Students’ Prescription Drug Misuse in Daily Life: An Exploratory Study

Lauren M Papp 1, Chrystyna D Kouros 2
PMCID: PMC11299861  NIHMSID: NIHMS1917883  PMID: 39109167

Abstract

Background:

Limited prior research to examine co-occurrence of prescription drug misuse with other substances among young adults has documented outcomes that are more problematic for those with higher rates of co-ingesting alcohol. There is a need to understand how college students in this period of heightened risk use other salient substances in moments of their prescription misuse in daily life.

Method:

Young-adult college students who engaged in recent prescription misuse (N = 297) completed ecological momentary assessment (EMA) over a 28-day period, resulting in 23,578 reports. Multilevel modeling examined within-person associations between other momentary substance use (including alcohol, nicotine, energy drinks, and marijuana) and prescription misuse in daily life. Analyses accounted for between-person characteristics, having a current focal prescription, and effects of reporting over time. Participant sex was also explored as a moderator.

Results:

In adjusted multilevel models, college students’ momentary nicotine use and energy drink use each were associated with their greater likelihood of prescription misuse in daily life. In contrast, momentary marijuana use was linked with lower likelihood of misuse. Moderation results indicated that males (but not females) were less likely to engage in prescription misuse in moments of their alcohol use.

Conclusions:

Drawing from data obtained using EMA, findings provide novel insights about the real-world associations between prescription drug misuse and other salient substance behaviors during a developmental period that is important for establishing later substance use and health.

Keywords: college, prescription drug misuse, alcohol use, nicotine use, marijuana use

1. Introduction

Young adults aged 18–25 have the highest rates of recent prescription drug misuse, and those attending college experience particular risk factors for this behavior, including heightened access to diverted medications and academic stress (McCabe et al. 2006; Norman and Ford 2018). Health-related behaviors in early adulthood hold unique and lasting consequences for the course of adult development (Brown et al. 2008), and prescription drug misuse in this period confers particular risk for later substance use problems (McCabe et al. 2019).

Prior research suggests that prescription misuse is reliably connected with the use of other substances. In an earlier longitudinal study spanning adolescence into early adulthood, Catalano et al. (2011) documented substantial overlap between opioid misuse and ingestion of other substances; indeed, nearly all participants reporting misuse behavior had used alcohol, tobacco, and marijuana, and the majority had also used other illicit drugs (e.g., cocaine, psychedelics). In a recent secondary analysis of a large, US-based survey of youth, Barnett and colleagues (2019) examined prescription opioid misuse (13.8% of the sample reported lifetime opioid misuse) and showed that the odds of ever misusing were significantly greater among those who had used electronic vapors, smoked cigarettes, used marijuana, or used alcohol. Examining relevant predictors of opioid misuse in young adulthood, Capaldi et al. (2019) tracked men’s misuse of prescription opioids from early to middle adulthood (29% of the sample endorsed misuse) and found that current substance use and criminal behavior were more predictive of misuse during young adulthood than family and adolescent predictors were. In addition, Woolsey et al. (2015) used a retrospective survey design in a large sample of college students to document indicators of more frequent energy drink consumption (e.g., number of days using energy drinks during the past 30, greatest number of energy drinks in the past year on a single occasion) that served as significant predictors of prescription stimulant misuse. The authors noted the need to better understand patterns of energy drink consumption given their wide availability and particular risks when combined with prescription stimulants.

Although the importance of understanding co-use of multiple substances among young adult samples has been established (Haardörfer et al. 2016), prescription misuse remains relatively understudied in this area of research. Of note, Kelly and colleagues (2014) recruited young adults from urban nightlife venues who reported prescription misuse in the past 6 months. Young adults commonly reported misuse in combination with other substances over the same timeframe: 65.9% engaged in misuse in combination with illicit drug use, 59.2% combined with marijuana, and 71.2% combined with alcohol. Indeed, recent nationally representative data in the US indicated that young adults who simultaneously co-ingest prescription medications and alcohol not only consumed more alcohol but also were more likely to have concurrent substance use disorders (Schepis et al. 2022). To date, naturalistic experiences of how prescription drug misuse co-occurs with other salient substance behaviors are not well documented. Of note, a recent college-based study documented students’ motivations for prescription stimulant misuse (including to study better, concentrate, and stay awake) soon after misuse episodes occurred in daily life (Schepis et al. 2021). However, the sample size was relatively small (n=41) and reports of the students’ other substance behaviors were not obtained. Improving current understanding of college students’ other substance behaviors that co-occur in actual moments of their prescription misuse could point to modifiable prevention and intervention targets in this population.

The current study was intended to examine momentary links between college students’ use of other salient substances and engaging in prescription drug misuse in daily life and thus provide important contextual information about students’ experiences at the time of misuse in daily life. Acknowledging that the measures were drawn from a larger funded project, we also conducted person-level analyses to provide initial evidence to identify who is at risk for concurrent prescription drug misuse and other substance use in daily life. These tests were exploratory and drew from available measures linked to other problematic substance use in this population.

Drawing on co-use models of substance behaviors and prior survey-based findings that assessed substance use on more global timeframes, we expected college students’ in-the-moment use of alcohol, nicotine, energy drinks, and marijuana to uniquely predict instances of prescription drug misuse occurrence in daily life. We tested these within-person associations, over and above participants’ relative average levels of substances use in daily life, by statistically controlling for the proportion of their moments across the reporting period that included use of each substance behavior. Models also accounted for having a current prescription for one or more focal medications, given prior evidence that individuals’ own prescriptions are a meaningful source of college-based misuse (Garnier-Dykstra et al. 2012; McCabe et al. 2014), and for any potential effect of reporting on these behaviors over time.

Finally, we explored the moderating role of participant sex to understand whether the direct, within-person associations tested above vary for male versus female college students. Prior survey-based research has been inconsistent in identifying differences in male vs. female college students’ prescription drug misuse (e.g., Galluci et al. 2014; Summit and Noel 2021; Wong et al. 2022). Given limited prior study, we included sex as a potential moderator to guide future efforts aimed at preventing concurrent prescription misuse and other substance use in this population.

2. Materials and methods

2.1. Enrollment procedure and participants

Between September 2017 and September 2019, students at a large, public university in the Midwestern US were continuously enrolled into a NIDA-funded longitudinal study on daily behaviors and health in college life (see Papp et al. 2020). The current study is drawn from the first phase of participation. Participants were recruited via flyers and announcements that stated, “We are particularly interested in how people use prescription medications.” Prospective participants completed an online screening and a telephone call confirmed eligibility. Inclusion criteria were being enrolled as a freshman or sophomore and being 18 to 21 years of age. Based on screening, participants were oversampled for recent prescription misuse. The screening measure assured confidential responses and described, “Sometimes people use prescription drugs in ways that a doctor did not direct them to. Please think back over the past 3 months and consider whether you have used the following types of medications in any way a doctor did not direct you to use them, including using it without a prescription of your own; using it in greater amounts, more often, or longer than you were told to take it; or using it in any way a doctor did not direct you to use it.” This question was asked for 4 prescription medication classes with common examples listed, including pain relievers, tranquilizers, stimulants, and sedatives or barbiturates. Response options were Yes/No and prospective participants could endorse multiple misuse classes.

Of the 355 total participants enrolled, 300 (84.5%) endorsed misuse at screening and served as the current analytic sample, consistent with the focus on predicting the occurrence of prescription drug misuse in daily life. The majority of the sample identified as female (69%) and the M age was 19.5 years (SD = 0.71). In terms of racial and ethnic background, 83.2% of the current sample self-identified as White and 8.1% as Asian; nearly 7% reported Hispanic or Latino/a background. Remaining participants (8.4%) self-identified as American Indian/Alaska Native, Black or African American, Native Hawaiian or Pacific Islander, or reported multiple or other races (individual identities included <3% of respondents); one participant (0.3%) did not respond.

Procedures

All procedures were approved by the Institutional Review Board. Participants attended 2 lab sessions that were scheduled an average of 35 days apart and were trained to complete reporting procedures in daily life between the sessions. During the first session, participants completed informed consent procedures and demographic (e.g., sex, race, ethnicity) and survey measures. Participants also were trained to use an iPod Touch application designed specifically for the present research to administer the ecological momentary assessments (EMA); they chose a private password to access the application and completed a sample report in the lab. Access to all other device features was restricted. The scheduled reporting period started the following day. Although the typical reporting period was scheduled for the 28 days following the first lab session, some participants continued reporting until they returned their device at the second lab session; reports obtained across all days were retained in the current analyses to maximize statistical power of hypothesis tests. During the second lab session, participants returned their devices and completed additional measures. A research assistant then unlocked the device, downloaded the EMA reports to a secure server, and reset the devices. Participants received their choice of electronic or check payments; compensation consisted of $75 for session 1, $84 for reporting in daily life (prorated for partial completion), $55 for session 2, and a $36 bonus incentive for maintaining compliance across the reporting period.

The EMA application administered both signal-contingent and event-based assessments in daily life. Signal-contingent reporting involved responding to a device prompt sent within four time-windows (8:00–11:30 a.m., 11:30 a.m.−3:00 p.m., 3:00–7:00 p.m., and 7:00–11:00 p.m.); prompts were sent at randomly varying times within each window across days. Participants were instructed to respond as soon as possible, as appropriate. Participants were also trained to self-initiate a report any time they intended to take one of four medications in any way a doctor did not direct them to use it (i.e., event-based assessment). There was no limit on the timing or number of event-based reports that could be completed; all reports were retained to maximize statistical power. To reduce burden, a signal-contingent prompt was not sent within 2 hours after a self-initiated, event-based report had been completed. EMA report questions included intentions to misuse (see Measures) and focused on participants’ current location and social context and potential triggers of prescription drug misuse (e.g., mood states, pain, stressors, other substance behaviors). Signal-contingent and event-based assessments were identical and thus were indistinguishable in the resulting data files. If misuse intention of one or more of the medication classes was endorsed in the EMA report, participants were then sent a brief follow-up report 15 minutes later to assess whether misuse behaviors had occurred; participants were instructed to respond to the follow-up within 15 minutes. All timestamps of report and follow-up completion were automatically recorded. Additional method details are provided (Papp et al. 2020).

2.2. Measures

Outcome: Prescription drug misuse

Participants were first asked about intentions to misuse a prescription drug. The EMA report asked, “Are you about to take a medication listed here in any way a doctor did not direct you to use it? Remember, this can include using a medication without a prescription of your own; using it in greater amounts, more often, or longer than you were told to take it; or using it in any other way a doctor did not direct you to use it.” Participants indicated No (0) or Yes (1) for 4 classes of prescription drugs (examples of each were provided): sedatives or sleeping pills, tranquilizers or anxiety medications, stimulants, and pain relievers. If misuse intention of one or more of the medication classes was endorsed, participants were then sent a brief follow-up report 15 minutes later. In the follow-up report, participants were asked, “Have you recently taken a medication listed here, in any way a doctor did not direct you to use it?” The same 4 medication classes and examples were presented, and participants responded No (0) or Yes (1). If any prescription misuse was endorsed across the four classes, this was considered an instance of prescription misuse, the behavior outcome of interest. Misuse was scored 1 when the behavior was endorsed on the follow-up, and 0 when the behavior was not endorsed on the follow-up or not administered due to responses of No to the intention questions. In instances when the intention questions were left blank (on 57 EMA reports), the misuse variable was left as missing data.

Momentary predictors: Other substance behaviors

Each EMA report included a series of questions about other substance behaviors. Participants were prompted to indicate whether in the past 15 minutes they had consumed alcohol, used nicotine, had an energy drink, used marijuana, or used other illicit drugs such as ecstasy, cocaine, heroin, or mushrooms. Responses for each prompt were No (0) or Yes (1). The dichotomous scores were the within-person predictors. A between-person version of each predictor variable was also created by person-mean centering the summed instances of the behavior divided by the number of reports completed by the individual.

Covariate: Current prescription for a focal medication

During the first lab visit, participants completed a health assessment that asked them about having a current prescription for the study’s four focal prescription medication classes with common examples listed, including pain relievers, tranquilizers, stimulants, and sedatives or barbiturates. Response options were No (0) or Yes (1) and participants could endorse multiple medication classes. For the current covariate, having one or more focal prescriptions was coded as 1 and having no prescription was coded as 0.

Background characteristics

Background characteristics reported at the baseline lab visit include participant sex (−0.5= male, 0.5= female), endorsing Greek status (i.e., sorority or fraternity during the study period [−0.5= no, 0.5= yes]), and family history of problems with alcohol and/or drug use during their childhood, as reported at T1 (−0.5= no, 0.5= yes). The Rutgers Alcohol Problem Index (RAPI; White and Labouvie 1989) consists of 18 items that examine various consequences of alcohol use that could have been experienced recently (e.g., “neglected your responsibilities”). Following Neal et al. (2006), participants responded to each item using options of 0 (did not experience) or 1 (experienced at least one time in the last 3 months). Endorsed consequences were summed, with higher scores indicating greater alcohol use and alcohol-related consequences. In the current sample, α = .80. Participants completed two versions of the 10-item Drug Abuse Screening Test (DAST-10; Skinner 1982), a measure of past-year drug use. The measure’s standard instructions define “drugs” by having respondents consider nonmedical use of prescription medications, along with other drugs such as marijuana, cocaine, crack, meth, hallucinogens, and heroin; participants were explicitly instructed to exclude alcohol and tobacco from consideration. Given the broader project’s recruitment, we modified the DAST-10 by having one version focused only on nonmedical use of prescriptions (excluding alcohol, tobacco, and the other drugs) and one version focused on the other drugs mentioned (excluding alcohol, tobacco, and nonmedical use of prescriptions). One item asked about any involvement with drugs other than those required for medical purposes. One item asked whether the respondent is always able to stop using drugs when they want to (reverse scored). Other items tapped potential consequences of drug use; a sample item is, “Have you engaged in illegal activities to obtain drugs?” (Options are Yes/No). Endorsed items were summed, with higher scores reflecting higher drug use (prescription misuse version α = 0.71; other drugs version α = 0.73).

Depressive symptoms experienced in the past two weeks were assessed using the brief Patient Health Questionnaire (PHQ-9; Kroenke et al. 2001). The instrument asks, “How often have you been bothered by…” and includes items such as feeling down or depressed, appetitive disturbances, and feelings of failure. Participants respond on a scale of 0 (not at all) to 3 (nearly every day). Continuous summed scores were used in the current study (α = 0.86). Externalizing behaviors and tendencies were assessed by the brief version of the Externalizing Spectrum Inventory (ESI-BF; Patrick et al. 2013). Specifically, the general disinhibition scale was used to assess externalizing and impulsive behavior, excluding substance use, mental health diagnoses, and aggression. An example item is, “Things are more fun if a little danger is involved.” Participants evaluated how much each statement reflects themselves using a 4-point Likert-type scale (false, somewhat false, somewhat true, and true). Responses were summed and scored such that higher scale scores indicate greater deviancy (α = 0.85).

2.3. Data screening and analysis

Participants (N = 297) provided an average of 79.63 (SD = 26.03, range = 11–130) momentary reports for this analysis. Device data from 3 participants were not available due to lost devices/password malfunction. The momentary predictor of other illicit drug use caused statistical models to run with errors and thus was dropped from the analytic plan. Examination of this behavior indicated that 34 participants reported illicit drug use in daily life (range = 1–16 instances), and it very rarely (<2% of the reports) occurred along with their prescription drug misuse.

Preliminary analyses calculated descriptive statistics and tested correlations between person-level study variables. Central tests relied on multilevel modeling to estimate within-person associations between momentary other substance use behaviors (i.e., alcohol use, nicotine use, energy drink use, and marijuana use) and prescription misuse in daily life. Models were run as a 2-level hierarchical generalized linear model (HGLM) with Bernoulli distribution to account for the nested data (i.e., moments across the reporting period, moments nested within people) and the binary dependent variable (Guo and Zhao 2000). Of note, 3-level models that accounted for momentary reports nested within days did not successfully converge, likely because participants rarely engaged in prescription misuse along with other substance use behaviors more than once per day. Models were conducted using HLM v.8 (Raudenbush et al. 2019). Centering guidelines for separating within-person and between-person effects were followed (Bolger and Laurenceau 2013). The dichotomous other substance use scores were the within-person predictors at Level 1. The Level 1 model also included a variable for the momentary report number (group-centered at reporting midpoint) to account for any systematic effects of reporting over time on prescription medication misuse. Person-mean variables calculated the summed instances of the respective substance behavior divided by the number of EMA reports completed by the individual and were included at Level 2 (grand-mean centered). An indicator reflecting whether the participant had a current prescription for a focal medication (0 = no, 1 = yes) was also included as an intercept control variable. Only the random effect for the intercept term was retained. Odds ratios were converted to Cohen’s d values to facilitate interpretation of effect sizes (Chinn 2000).

To test whether participant sex moderated the four within-person links between other substance use behaviors and prescription misuse, sex (−0.5 = male, 0.5 = female) was entered as a Level 2 cross-level moderator of the slopes for the Level 1 other substance behavior predictors and as a Level 2 intercept control, along with the covariates described above.

3. Results

3.1. Preliminary results

Nearly all participants (97% of the sample) indicated using one or more substances in their daily life reporting. Among the predictor substances examined, alcohol use was the most common behavior (reported by 82.2% of the sample), followed by nicotine use (59.9%), marijuana use (58.6%), and energy drink consumption (44.4%). Additional descriptive information is shown in Table 1. Examining the person-mean indicators (i.e., proportion of momentary reports on which students engaged in the respective substance behavior) indicates that nicotine was the most frequently used substance in daily life. The outcome variable of prescription drug misuse was both the least commonly used substance behavior (reported by 35.4% of the sample) and the least frequently used substance in daily life among those who reported it.

Table 1.

Descriptive Statistics and Correlations for Person-level Variables from Momentary Reports

M
or n, %		 SD Observed
range 		1 2 3 4 5 6 7
1. Female 205, 69% 0–1 -
2. Person-mean alcohol use 0.05 0.06 0–0.53 −.10 -
3. Person-mean nicotine use 0.15 0.22 0–0.97 −.11 .22** -
4. Person-mean energy drink use 0.03 0.05 0–0.48 −.05 .15* .15* -
5. Person-mean marijuana use 0.07 0.11 0–0.76 −.16** .29** .03 .03 -
6. Person-mean prescription misuse 0.01 0.03 0–0.18 −.11 .08 .22** .11+ .10 -
7. Current focal prescription 69, 23.2% 0–1 .01 −.04 −.05 −.01 −.01 .09 -
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Note. N = 297 participants; N = 23,578 momentary reports. Person-mean variables were calculated as the sum of the instances of the respective substance use behavior / n momentary reports for each participant (i.e., proportion of reports that included an instance of the specific substance use behavior).

+

p ≤ .05.

*

p < .05.

**

p < .01.

Most participants who endorsed prescription drug misuse in daily life (n = 90, 68.7%) reported misuse of one medication class, 13 of 2 classes, and 2 of 3 classes. Examining prescription misuse by medication class indicated that misuse of sedatives was endorsed by 4 participants, tranquilizers by 17 participants, stimulants by 91 participants, and pain medications by 10 participants.

Nearly a quarter of the sample (23.2%) had a current prescription for one or more of the study’s focal medications. Individuals with a current prescription (vs. those without a current prescription) were not significantly more likely to endorse prescription misuse at any point during the reporting period, χ2(1) = 1.53, p = .22. Person-level analyses of the four focal medication classes indicated only that having a current prescription for tranquilizer medications was associated with greater likelihood of tranquilizer misuse during the reporting period, χ2(1) = 21.35, p < .001. Having a focal prescription for the other three medication classes was not reliably related to likelihood of misusing the respective medication in daily life (p-values > .17).

Exploratory tests to identify who is at risk for concurrent prescription drug misuse and other substance use in daily life indicated that background factors of sex, Greek membership, and family history of substance use were not reliably associated with concurrent prescription misuse and other substance use in daily life (see Table 2). Additional person-level analyses indicated that individuals with higher levels of alcohol problems, problems associated with prescription misuse and other drug use, and mental health symptoms (depression, externalizing) reported higher levels of concurrent prescription misuse and other substance use in daily life (Table 2). Correlations among the person-mean variables (i.e., proportion of momentary reports that included substance misuse) show the between-person associations among the substance use variables. Participants’ greater proportion of alcohol use across the reporting period was positively related to their nicotine use, energy drink consumption, and marijuana use in daily life. Nicotine use was positively related to energy drink use and prescription drug misuse. Females reported fewer instances of marijuana use in daily life. Having a current prescription for a focal medication was not related to these other person-level substance use variables (see Table 2).

Table 2.

Associations between Background Characteristics and Concurrent Prescription Drug Misuse and Other Substance Use in Daily Life

Background characteristics M (or n, %) SD Test statistic
 Female 205, 69% t(295) = −0.37, p = .71
 Greek membership 107, 36% t(295) = 1.06, p = .29
 Family history of substance use 104, 35% t(295) = −1.00, p = .28
 RAPI 3.95 3.31 r = .14, p = .013
 DAST-10 prescription drug misuse 1.90 1.70 r = .26, p <.001
 DAST-10 other drugs 2.38 1.96 r = .23, p <.001
 Depression 8.41 5.45 r = .12, p = .04
 Externalizing 15.62 8.74 r = .16, p = .007
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Note. N = 297. The outcome variable of concurrent prescription drug misuse and other substance use in daily life was calculated as the sum of moments that included prescription drug misuse and at least one other substance use behavior (alcohol, nicotine, energy drink, and marijuana) / n momentary reports for each participant. RAPI = Rutgers Alcohol Problem Index; DAST-10 = 10-item Drug Abuse Screening Test.

Table 3 shows the within-person, momentary associations between the other substance use behaviors, based on multilevel modeling. At the momentary level, alcohol use in the moment was associated with nicotine use, energy drink consumption, and marijuana use. Nicotine use in daily life was reliably associated with energy drink use and marijuana use.

Table 3.

Within-person Momentary Associations Among the Other Substance Use Behaviors

Dependent Variable
Alcohol Nicotine Energy drink Marijuana
Predictor
Alcohol -
Nicotine 6.29**
[5.16, 7.67]		 -
Energy drink 1.10
[0.74, 1.61]		 1.69**
[1.33, 2.16]		 -
Marijuana 4.09**
[3.29, 5.10]		 2.57**
[2.11, 3.14]		 0.92
[0.55, 1.53]		 -
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Note. Results from 2-level HGLM population-average models with robust standard errors. Model tests account for the person-level effect of the substance use predictor and for reporting over time. Table presents AOR [95% CI]. AOR = adjusted odds ratio. CI = confidence interval.

**

p < .01.

3.2. Central results

3.2.1. Tests of direct associations

Results from the HGLM testing associations between other substance use and prescription drug misuse in daily life are presented in Table 4. Several between-person associations were found. Prescription drug misuse was more likely to occur earlier in the reporting period (d = −0.002). The average likelihood of prescription misuse in the moment was also significantly higher among college students who used more marijuana across the study period (d = 0.72) and among those with a current focal prescription (d = 0.27).

Table 4.

Real-time Other Substance Use Behaviors Predicting Prescription Drug Misuse in Daily Life: Results from Generalized Multilevel Modeling

Predictors – Fixed Effects AOR p value 95% CI d value
Intercept 0.01 <.001 0.010, 0.014 −2.43
Level 2: Person-level (N = 297)
 Alcohol usebp 2.18 0.567 0.15, 31.69 0.43
 Nicotine usebp 2.00 0.086 0.91, 4.41 0.38
 Energy drink usebp 5.71 0.137 0.57, 56.85 0.96
 Marijuana usebp 3.67 0.049 1.01, 13.43 0.72
 Current focal prescription 1.63 0.038 1.03, 2.59 0.27
Level 1: Moment-level (N = 23,578)
 Alcohol usewp 1.07 0.727 0.72, 1.60 0.04
 Nicotine usewp 1.96 <.001 1.49, 2.59 0.38
 Energy drink usewp 1.84 0.005 1.20, 2.82 0.34
 Marijuana usewp 0.65 0.008 0.47, 0.89 −0.24
 Report number (time) 0.996 0.019 0.992, 0.999 −0.002
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Note. Results from 2-level HGLM population-average model with robust standard errors. AOR = adjusted odds ratio. CI = confidence interval. bp = between-person association, wp = within-person association.

Accounting for the person-level predictors and the effect of reporting over time, there were positive within-person associations between college students’ momentary use of nicotine and prescription drug misuse (d = 0.38) and between their energy drink consumption and prescription misuse (d = 0.34). These findings were consistent with hypotheses. In contrast, college students were less likely to engage in prescription misuse in moments of using marijuana (d = −0.24). College students’ use of alcohol in the moment was not directly associated with engaging in prescription misuse in daily life (see Table 4).1

3.2.2. Tests of moderation

Participant sex was tested as a moderator of the four direct within-person associations. One significant finding emerged: the within-person association between momentary alcohol use and prescription misuse differed for males and females (AOR = 2.92, p = .013, 95% CI = 1.26, 6.77). Simple slope analyses indicated that momentary alcohol use was associated with lower likelihood of prescription misuse in daily life among males (AOR = 0.44, p = .02, 95% CI = 0.22, 0.88, d = −0.46), whereas using alcohol in the moment was not related to misuse among females (AOR = 1.27, p = .32, 95% CI = 0.80, 2.02).

4. Discussion

This study implemented EMA to understand college students’ other salient substance use behaviors as contextual correlates of their real-time prescription drug misuse. We also considered person-level characteristics to investigate who is at greater risk for concurrent prescription misuse and other substance use. Of note, students currently prescribed tranquilizer medications were more likely to misuse them; motivations for misuse of tranquilizers among this age group remain relatively unclear (Messina et al. 2016). More broadly, however, the role of having a current prescription medication on misuse was more nuanced. When analyzing bivariate associations between having a current prescription (or not) and any misuse reported during the 28 days (scored as yes or no), there was no reliable association. However, in multilevel models which included other predictors and covariates, having a current prescription was associated with an increased average likelihood of misuse in the moment. The findings highlight the need for continued attention to monitoring prescription medications in this population (Garnier et al. 2010; McCabe et al. 2014). Additional person-level analyses indicated that individuals with higher levels of alcohol problems, problems associated with prescription misuse and other drug use, and mental health symptoms (depression, externalizing) reported higher levels of concurrent prescription misuse and other substance use in daily life.

Central analyses were based on data from a relatively large sample with high engagement in daily-life reporting procedures. This study, based on naturalistic experiences, indicated that college students’ real-time use of nicotine and energy drinks each significantly predicted their increased likelihood of prescription drug misuse in the same moment in daily life. Given that an earlier review of the literature (Drazdowski 2016) found that young adults (retrospectively) report engaging in prescription stimulant misuse to improve their focus and concentration, the use of other momentary substances could have been intended to enhance the clinical effects of particular medication classes. In contrast, moments of marijuana use and alcohol use (among males only) were negatively associated with misuse occurrence. Interestingly, the between-person test for marijuana revealed that college students who used marijuana relatively more across their reporting period also had elevated likelihood of misuse in daily life. Future research should examine the longer-term connections between prescription misuse and marijuana to better understand their time-ordered dynamics, as well as to identify potential mechanisms (such as elevated depression or externalizing symptoms) that might explain why marijuana use was more common among those also endorsing prescription misuse.

Participant sex was examined as a moderator of the study’s key associations and most tests indicated no differences for males versus females in the links between their other momentary substance use and prescription misuse. The lack of meaningful moderation could be a function of the uneven distribution of participants by sex, which potentially limited statistical power. Of note, the current positive ratio of female to male participants is similar to college-based studies of prescription misuse that were conducted concurrently in other U.S. geographical locations (e.g., Schepis et al. 2021). Additional efforts are needed to understand the sources of these sampling differences.

4.1. Limitations

Given our persistent efforts to recruit a sample with elevated risks for prescription misuse, our EMA approach could have captured more instances of the behavior. Most participants did not report misuse in daily life and the behavior was endorsed less than once per week on average. Our EMA approach relied on participants carrying a separate study-owned device, which was valuable for providing students with strong privacy assurances but likely led to misuse instances not being reported, particularly during late evening hours. We retained follow-up reports that were completed outside of the instructed time limit and combined misuse instances in daily life across distinct medication classes to increase the power of statistical tests; given our sample, it is likely that misuse of stimulant medications drives the overall pattern of results (Sepúlveda et al. 2011). Emerging evidence based on a large sample of college students’ past-year misuse reports suggests that the effects of prescription drug misuse on outcomes are both global (sharing negative risks consistent with illicit drug use) and specific (with variations based on misuse of specific medication classes) (Kerr et al. 2021). Future research could focus on understanding the less normative types of prescription misuse in the college setting (and beyond) to inform broader preventive interventions (Drazdowski 2016).

In addition, all substance-related information examined here relies on self-report methodology and thus is subject to the limitations of this approach. Additional research is needed to understand the predictors and consequences of prescription misuse behaviors among more racially and ethnically diverse samples (Cabriales et al. 2013; Sumstine et al. 2018).

4.2. Implications

The study documented that college students’ nicotine use and energy drink consumption are directly associated with their engaging in prescription drug misuse in the same moment and thus offers implications for promoting students’ health. Similarly, students who used relatively more marijuana during daily life were also likely to engage in misuse (but not in the same moment). College-based education and prevention efforts could highlight that although these substances may be used for similar effects (e.g., increase arousal, reduce negative mood), their combined use can have negatively reinforcing outcomes and implications (Ali et al. 2015; Crummy et al. 2020). Additionally, when prescribing these focal medications in this population, more education about the comprehensive definition of misuse (i.e., not only sharing your medication with others, but also taking your own prescription in a way a doctor did not intend), risks of misuse itself, as well as potential harmful consequences of misusing medications in the context of other substance use behaviors is needed.

Acknowledgments

We have no known conflict of interest to disclose. Research reported in this publication was supported by the National Institute on Drug Abuse of the National Institutes of Health under Award Number R01DA042093. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

1

The generalized multilevel model was estimated again using only the Level 1 EMA reports and associated follow-ups that were completed within 15 minutes of being sent. Results of the focal substance use predictors of misuse in daily life were consistent (in terms of directionality of associations and meeting statistical significance levels) across the full and reduced EMA datasets. Results based on the full EMA dataset were reported to maximize statistical power.

Ethics Statement

We have followed ethical guidelines for conducting research with human participants. Participants completed informed consent procedures prior to data collection. Prior to the study, we obtained a Certificate of Confidentiality from the NIH and University IRB approval.

Disclosure Statement

The authors report there are no competing interests to declare.

Contributor Information

Lauren M. Papp, University of Wisconsin-Madison

Chrystyna D. Kouros, Southern Methodist University

Data Availability Statement

The datasets generated during the current study are not publicly available due to the funded grant’s data-sharing process but are available from the corresponding author on reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets generated during the current study are not publicly available due to the funded grant’s data-sharing process but are available from the corresponding author on reasonable request.

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