Abstract
The motivational model of substance use posits four motive subtypes (coping, enhancement, social, conformity) dynamically interact with contextual factors to impact decisions about substance use. Yet, prior studies assessing the motivational model have relied on between-person, cross-sectional evaluations of trait motives. We systematically reviewed ecological momentary assessments (EMA) studies (N=64) on motives for substance use to examine: methodological features of EMA studies examining the motivational model, support for the motivational model between and within individuals, and associations between trait motives and daily processes. Results of the reviewed studies provide equivocal support for the motivational model, and suggest that EMA measures and trait measures of motives might not reflect the same construct. The reviewed body of research indicates most studies have not examined the momentary and dynamic nature of the motivational model and more research is needed to inform interventions that address heterogeneous reasons for substance use in daily life.
Introduction
Motives for substance use, commonly defined as reasons for substance use, have been a major focus of research among addiction psychologists since the 1940s (Cooper et al., 2015; Cox & Klinger, 1988; Cutter & O’Farrell, 1984; Glynn et al., 1983; Kuntsche et al., 2005; Riley Jr. et al., 1948). This work has largely focused on the assumption that distinct motives have unique substance use outcomes, including heavier use, consequences of use, and the progression of substance use disorder, and therefore motives might be an optimal target for prevention and treatment. Although motives for substance use were originally proposed as a momentary motivational drive (Cox & Klinger, 1988; Cutter & O’Farrell, 1984), early work in this area has primarily assessed a trait-like tendency to use substances for particular reasons (Cooper et al., 1992; Cooper & Russell, 1995). An increased focus on motives for substance use in daily life may further improve prevention and treatment efforts, where motives could be targeted as momentary processes in daily life.
History of Motivational Models of Substance Use: From a Momentary Drive to a Trait
In an effort to inform prevention and treatment, Cox and Klinger (1988, 1990, 2011) proposed the incentive motivation model of alcohol use. They hypothesized that alcohol use is a goal-directed decision, whether implicit or explicit, such that individuals’ motivational drive to use alcohol is directly related to the expected interoceptive changes produced by alcohol. This motivational drive is the product of complex relationships among context and incentives. Specifically, an individuals’ learning history and traits (e.g., individual differences in alcohol response, affective vulnerabilities, past reinforcement of alcohol use) and current contextual factors (e.g., affect, availability of alcohol, alternative reinforcing activities, coping skills, physical setting) interact to influence the expected effects of alcohol use. An individuals’ reaction to these expected effects, particularly whether the positive consequences of alcohol use are expected to outweigh the negative consequences, then influences the decision to approach or avoid alcohol. An individuals’ motivation to use alcohol is represented by the strength of the paths from these factors, including historical and current factors, traits, and expectancies, to the decision to use. According to this model, motives for alcohol use are hypothesized to fall into four broad categories: decreasing negative affect with the expected pharmacological effects of alcohol, increasing positive affect with the expected pharmacological effects of alcohol, decreasing negative affect with the expected instrumental effects (i.e., external, non-pharmacological effects, such as socialization) of alcohol, and increasing positive affect with the expected instrumental effects of alcohol (Cox & Klinger, 1988).
Drawing from this model, as well as earlier models of motives (Wills & Shiffman, 1985), Cooper and colleagues (Cooper, 1994; Cooper et al., 1992; Cooper & Russell, 1995) characterized distinct motivational subtypes, based on the different needs or functions that alcohol use serves. These subtypes were derived from combinations of two basic human desires that drive decisions and that are relevant for understanding decisions about alcohol use: approach versus avoidance tendencies and internal versus external motivation. Accordingly, combinations of these drives results in four distinct motives subtypes: coping (internal, avoid), enhancement (internal, approach), conformity (external, avoid), social (external, approach) (Cooper et al., 2015). Based on this characterization, coping motives refer to substance use to relieve negative affective states, enhancement motives refer to substance use to increase positive emotions, conformity motives refer to substance use to avoid social rejection, and social motives refer to substance use to increase positive experiences with peers.
Like Cox and Klinger (1988), Cooper and colleagues assert that motives are the “final common pathway” (pages 4, 33, 35) to substance use through which distal factors (e.g., traits, situational factors, affective states, substance expectancies) are mediated (Cooper et al., 2015). In addition, distinct motive subtypes are expected to demonstrate differential consequences, such that coping motives are hypothesized to be associated with the poorest outcomes, followed by enhancement, conformity, and, lastly, social motives (Cooper et al., 2015). This model suggests that distinct processes not only influence motives within a person, across different times and situations, but also between persons (Cooper & Russell, 1995). Indeed, the most widely used measure of alcohol use motives (Cooper et al., 2015) developed by Cooper (1994), the Drinking Motives Questionnaire-Revised (DMQ-R), assesses the general frequency of different motives, resulting in a trait-like measure of reasons for substance use (see Table 1 for description).
Table 1.
Trait and ecological momentary assessment measures of motives utilized by studies included in the present review.
| Measure Title | Overview of Trait Measure | Ecological Momentary Assessment Adaptations |
|---|---|---|
| Alcohol motives measures | ||
| Drinking Motives Questionnaire-Revised (DMQ-R; also known as the Alcohol Use Motivation Scale; Cooper, 1994) |
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| Drinking Motives Questionnaire-Revised Short Form (DMQ-R SF; Kuntsche & Kuntsche, 2009) |
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| Modified Drinking Motives Questionnaire-Revised (Modified DMQ-R; Grant, Stewart, O’Connor, Blackwell, & Conrod, 2007) |
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| Daily Coping Inventory (Stone & Neale, 1984) |
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| Reasons for Drinking Questionnaire negative reinforcement composite (Farber et al., 1980) |
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| Cannabis motives measures | ||
| Marijuana Motives Measure (MMM; also known as the Marijuana Motives Questionnaire; Simons, Correia, Carey, & Borsari, 1998) |
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| Tobacco motives measures | ||
| Wisconsin Inventory of Smoking Dependence Motives (WISDM; Piper et al., 2004) |
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| Teen Smoking Motives Questionnaire (see Otsuki, Tinsley, Chao, & Unger, 2008) |
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| Russell’s Smoking Motivation Questionnaire (Russell et al., 1974) |
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| Wills Tobacco Motives Inventory (Wills et al., 1999) |
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| General measures of motives for substance use | ||
| Use of Drugs and Alcohol subscale of the COPE (Carver et al., 1989) |
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| Untitled measure of reasons for substance use |
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It is important to note that motivational models of substance use were originally developed to explain the initiation and maintenance of alcohol use (Cooper et al., 2015; Cox & Klinger, 1988), but have been extended to explain cannabis, tobacco, prescription drug, and other substance use. Although individuals use other substances for reasons described by Cooper and colleagues’ motivational model, substance-specific motives have also been identified, such as expansion motives for cannabis use, pain relief motives for prescription opioid misuse, and habit motives for tobacco use (Cooper et al., 2015; Jones et al., 2014; Messina et al., 2016; Piper et al., 2004; Simons et al., 1998). Given these differences, substance-specific measures of motives have been developed, including the Marijuana Motives Measure (MMM; also known as the Marijuana Motives Questionnaire) and the Wisconsin Inventory of Smoking Dependence Motives (WISDM; see Table 1 for descriptions). As with the DMQ-R, these measures are trait-like, assessing the general frequency of reasons for substance use.
Integrating Motivational Models of Substance Use: Refocusing on Momentary Processes
Drawing from aspects of the motivational model presented by Cox and Klinger (1988), Cooper and colleagues (Cooper, 1994; Cooper et al., 2015; Cooper & Russell, 1995), and Kuntsche and colleagues (2005), we created Figure 1 to represent the interacting factors that are hypothesized to influence different motives for substance use and, ultimately, substance use outcomes. We also drew from a contextual model of self-regulation change in substance use disorder treatment (Roos & Witkiewitz, 2017) to add additional contextual factors that may be antecedents to motives for substance use in daily life. In Figure 1, we first depicted the interplay between an individual’s broader context, including historical factors and traits, and their immediate context, including internal states, situations, and alternative incentives. Interactions between these contextual factors contribute to an individual’s expectations of how substance use might modify their momentary experience. The desire to modify momentary experiences can be driven by internal and external motivations, as well as approach and avoidance tendencies. When substance use outweighs other incentives in these domains, an individual is motivated to use substances. Distinct motivational processes for substance use are expected to differentially predict substance use outcomes, which might also result in longer-term consequences, such as increased substance reinforcement, negative emotionality, and somatic distress. Although momentary motivational processes are depicted in the figure, repeated momentary processes over time might contribute to individual differences in trait-like motives.
Figure 1.
Visual Representation of the Motivational Model
Note: This visual representation of the motivational model draws from aspects of models presented by Cox and Klinger (1988) and Cooper and colleagues (Cooper, 1994; Cooper et al., 2015; Cooper & Russell, 1995), as well as a recently published model of self-regulation change in substance use disorder treatment (Roos & Witkiewitz, 2017). Several key assumptions are presented in this model: people use substances for reasons posited by the motivational model (coping, enhancement, conformity, and social), contextual factors and expectancies influence motivations to use and to not use substances, and motives mediate associations between contextual factors and expectancies and substance use outcomes. We also present direct arrows from substance use outcomes to motivations, expectancies, and contextual factors; these arrows represent processes such as substance use reinforcement, increased negative emotionality, and increased somatic distress, among others. This figure was also created to concisely present the most important aspects of the motivational model, but other processes not presented in the model likely occur (e.g., contextual factors directly influencing motivations and substance use outcomes).
Gaps in the Literature: Investigating the Motivational Model in Daily Life
To date, most studies examining motives for substance use and the motivational model have evaluated trait motives (Cooper et al., 2015). In order to fully test the motivational model, it is critical to investigate momentary processes in daily life. Micro-longitudinal study designs, such as ecological momentary assessment (EMA), allow investigators to collect repeated data from participants in their real-world environments. Thus, these methodologies provide the opportunity to examine assumptions of the motivational model (see Figure 1) both between and within persons (Shiffman et al., 2008). These studies (though, often still retrospective) also collect data in much shorter periods following an event, potentially providing more accurate data about the associations between constructs in near real-time and information on the temporality of relationships. Such micro-longitudinal studies can also aid in evaluating the predictive validity of trait-like measures of motives by examining prospective associations between trait motives (typically measured at an initial study assessment, i.e., baseline assessment) and daily processes in near real time (e.g., daily affect, substance use, affect-substance use associations). Within the past two decades, researchers have begun using EMA methods to assess the predictive validity of trait motives and to evaluate the motivational model with EMA-reported motives.
Aims of the Systematic Review
The overarching aim of this manuscript was to conduct a systematic review of EMA studies that examined motives for substance use in daily life, with a focus on three main goals. First, we provided an overview of methodological features of EMA studies examining motives in daily life. Second, we characterized the degree of support for the motivational model, based on these studies. Lastly, given the widespread use of trait measures of motives (Cooper et al., 2015), we reported on associations between these measures and daily processes measured with EMA. We then synthesized these findings and provided a discussion of methodological challenges of assessing substance use motives in daily life and substantive gaps in the current literature.
Method
Search Strategy and Study Eligibility
We searched PubMed and PsycINFO databases from the earliest date available through March 2020 for peer-reviewed articles that utilized EMA methods to examine motives for substance use in daily life. Search terms included the following combinations: (‘reason’ OR ‘motive’ OR ‘motivation’) AND (‘ecological momentary assessment’ OR ‘experience sampling’ OR ‘electronic diary’ OR ‘daily diary’) AND (‘alcohol’ OR ‘drinking’ OR ‘cannabis’ OR ‘marijuana’ OR ‘tobacco’ OR ‘cigarette’ OR ‘opioid’ OR ‘heroin’ OR ‘cocaine’ OR ‘amphetamine’ OR ‘prescription drug’ OR ‘substance use’ OR ‘drug use’). We applied filters to limit the search to manuscripts published in English with human participants. In addition, we manually searched Google Scholar based on authors identified in the initial search and examined references lists of included publications to identify eligible articles. Lastly, we utilized Google Scholar citation alerts (using the keywords “daily diary motives,” “ecological momentary assessment motives,” “daily diary substance use,” and “ecological momentary assessment substance use”) to identify eligible articles that were recently published and had not yet been indexed in PubMed and PsycINFO. Our search strategy was informed by prior systematic reviews of motives for substance use and EMA studies of craving and negative affect in substance use (Kuntsche et al., 2005; Serre et al., 2015; Wycoff et al., 2018). All methods were carried out in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA; Moher et al., 2015). We registered this systematic review with PROSPERO (#CRD42020150690).
Peer-reviewed publications were eligible for inclusion in the present review if they utilized micro-longitudinal designs (e.g., EMA, daily diary) to examine motives for substance use in daily life or utilized these methodologies to assess the association between trait motives and daily processes (e.g., daily motives, affect, substance use outcomes, affect-substance use associations). We only included studies that utilized electronic diaries, as compared to paper diaries, as the inability to provide time-verified reports in paper diaries potentially results in less accurate data (Tennen et al., 2006). We did not include manuscripts published in a language other than English, non-peer reviewed publications, case reports, case series, editorials, commentaries, letters to the editor, book chapters, previously published narrative reviews, or theses/dissertations. Studies that evaluated motives for non-substance use behaviors (e.g., eating, behavioral addictions) were also ineligible for inclusion.
The first author (V.R.V) conducted the search, determined eligibility, and extracted data from included studies. The senior author (K.W.) reviewed studies to confirm inclusion/exclusion criteria. Data extraction was performed using a Microsoft Excel template to collect the following information (if available): citation (i.e., first author, year, journal); year published; study aims; total sample size and percentage of the sample who identified as female; substance examined (i.e., alcohol, tobacco, cannabis, multiple substances); age range/average age; overview of the population (e.g., student/community/clinical sample, demographics, relevant recruitment methods); methodology (i.e., EMA versus daily diary); duration of EMA data collection period; number of assessments per day; description of event-contingent assessments; compliance rates; description of EMA measures of substance use, affect, and motives; description of trait-like measures of motives; level of analysis for reported results (i.e., momentary-, day-, person-level); additional notes on methodology; overview of results; and notes/limitations.
Characterization of Study Designs: Definitions Used in the Current Review
Many terms fall under the umbrella of micro-longitudinal research designs, including ecological momentary assessment (EMA), daily diary, experience sampling, intensive longitudinal studies, and ambulatory assessment (Shiffman et al., 2008). Though there are some substantive differences between different micro-longitudinal methods (e.g., daily diary referring to once a day data collection) (Shiffman et al., 2008), they all refer to the repeated collection of data in participants’ real-world environments, with periods between assessments ranging from hours to days. Consistent with a previous review (Wycoff et al., 2018), we refer to all micro-longitudinal research designs as EMA studies.
An important characteristic shared by all EMA studies is the ability to measure and analyze between-person and within-person processes. Between-person effects refer to mean-level differences between groups of participants, while within-person effects refer to variability in relationships across time and situations within a single participant. Notably, the frequency and scheduling of data collection in EMA studies influences the interpretation of within-person effects. Three different types of scheduling are commonly utilized (alone or in combination), including: event-contingent scheduling, where participants complete study measures when they engage in a behavior (e.g., substance use); time- or signal-contingent, where participants are prompted to complete study measures at random times throughout the day or in time blocks; and time milestones, where participants complete study measures at specific times (e.g., evening diaries) (Shiffman, 2014). In studies that collect data once per day, conclusions about processes within persons can be made only at the daily level (e.g., on days that individuals had greater than average negative affect, they were more likely to use substances to cope). When data is collected more frequently within a day, conclusions can be made at the momentary level (e.g., greater than average negative affect in one hour is associated with a greater probability of using substances to cope in the next hour).
In this review, we also differentiate between trait measures and mean-level EMA data, though both are utilized to determine between-person effects. Trait measures refer to participants’ cross-sectional reports of how they typically behave. In particular, we discuss trait measures of motives, which are self-reported general frequencies of motives for substance use and are commonly assessed at baseline assessments prior to EMA data collection periods. On the other hand, mean-level EMA data refers to aggregate data on the same measure across all EMA assessments and can be utilized to evaluate between-person associations (e.g., higher average negative affect during an EMA period is associated with higher average coping motives). Taken together, trait measures represent a retrospective report of participants’ perceptions of how they typically behave, while mean-level EMA data is an approximation of how participants do behave, on average, in real-world environments.
Results
Search Results
Results of the search are presented in Figure 2. The search identified 389 publications and, of those, 64 articles were eligible for data extraction and inclusion. Of the 251 full-text articles assessed for eligibility, 187 were excluded for the following reasons: did not report on motives for substance use (n=158), utilized paper diaries (n=13), did not utilize EMA (n=11), review, commentary, dissertation, or irrelevant erratum (n=7), only assessed trait motives at baseline and did not examine associations with daily processes (n=4), and unclear methods (n=1).
Figure 2.
Flow diagram of records identified, screened, and included.
Note: Records could be excluded for multiple reasons.
Characteristics of studies included in the present review, including an overview of the population and sample sizes, duration of the EMA period, compliance rates, and other methodological features, are presented in Supplementary Table 1. Overall, 58% (n=37) of studies focused on motives for alcohol use, 9% (n=6) on cannabis use, 25% (n=16) on tobacco use, and 8% (n=5) on the use of multiple substances. Nearly half (47%; n=30) of the included studies had college student samples, 38% (n=24) recruited from the community, 13% (n=8) enrolled clinical samples, and 3% (n=2) recruited high school students. The duration of EMA assessment periods ranged from one day to 56 days, frequency of time-contingent assessments ranged from once per day to 10 times per day (in addition to one study requiring participants to respond hourly until their bedtime), and 36% (n=23) of studies also included event-contingent assessments (i.e., during drinking occasions).
Aim 1: Methodological Features of EMA Studies Examining Motives in Daily Life
The first aim of the present review was to provide an overview of methodological features of EMA studies examining motives for substance use in daily life.
EMA Measures of Motives
The most commonly utilized EMA measures of motives were adaptations of the DMQ-R for alcohol motives, the MMM for cannabis motives, and the WISDM for tobacco motives (see Table 1 for details on the measures and Supplementary Table 1 for details on motives measures utilized in each eligible study). Adaptations of the DMQ-R commonly administered all items from specific subscales (mostly coping and enhancement) and/or the two highest loading items from subscales, as reported in the original development and validation studies (Cooper, 1994; Grant et al., 2007), with instructions modified to assess each motive in the moment, later in the evening, or for the evening before, depending on the frequency of motive assessment. Almost all adaptations of the DMQ-R evaluated the strength of participants’ motivations, based on Likert-type scales or visual analog scales. Interestingly, many studies only administered items from one or two subscales of the DMQ-R in daily life, most commonly coping and enhancement, as opposed to all four subscales. Conversely, adaptations of the MMM included administering all items as a checklist, assessing the strength (via Likert-type or visual analog scales) of the highest loading items from each subscale, or broadly assessing the main motive (i.e., for pleasure, to cope, to conform, to be more social, to expand), with instructions modified to assess these motives in the moment or for the previous 24 hours. Lastly, only one modification of the WISDM was identified, which comprised a checklist of a reduced number of WISDM motives, with instructions for assessing momentary reasons for smoking (e.g., Piasecki et al., 2007).
Psychometric Properties of EMA-Reported Motives.
Several studies reported internal consistency reliability estimates for EMA measures of drinking motives. These studies generally indicated good reliability for social (α reported in one study = .79), enhancement (αs across studies = .57–.97), conformity (α reported in one study = .79), and coping (αs across studies = .80–.93) motives. It is important to note that none of the reviewed studies reported reliabilities of at both the between and within person level, and authors of few studies explicitly reported the level of analysis for the reliability estimates (e.g., between-level; Dworkin et al., 2018; O’Hara, Armeli, et al., 2014). One study also examined the factor structure of an EMA adaptation of the DMQ-R that assessed reasons for the previous night’s drinking, which included 2-items each for the social and enhancement subscales and a 7-item coping subscale (O’Hara, Boynton, et al., 2014). Based on this analysis, a 3-factor model provided a good fit to the data and items demonstrated high factor loadings (social = .92–.94, enhancement = .57–.92, coping = .81–.90). To our knowledge, no studies examining cannabis or tobacco use motives in daily life with EMA reported internal consistency reliability estimates or the factor structure of these measures.
Timing of EMA-Reported Motives.
Among studies with EMA measures of motives (n=28), most assessed motives after substance use (68%; n=19), as opposed to before substance use (36%; n=10) or concurrently with substance use (7%; n=2); some studies assessed motives both before and after substance use. Of the studies assessing motives before substance use, not all assessed motives immediately before use. In particular, two studies assessed motives for substance use in the early evening (e.g., between 4:00 to 6:00pm; Dvorak et al., 2014; Stevenson et al., 2019). Although most studies only assessed motives if participants had used substances (or were about to use substances), a few studies (e.g., Goodhines et al., 2019; O’Donnell et al., 2019) assessed motivation regardless of actual substance use. Accordingly, there was some discrepancy between studies with regards to whether only substance use days were analyzed, and therefore substance use quantity and consequences were outcomes, or if all days during the EMA assessment period were analyzed, and therefore any substance use was an outcome.
Stability of EMA-Reported Motives.
One study examined motives for cannabis use among young adults immediately before (prospectively) and immediately after (retrospectively) cannabis use episodes to evaluate how often participants changed their originally stated reasons (Shrier & Scherer, 2014). Participants in this study changed their primary motive for cannabis use in 20% of use episodes; they were most likely to change their motive when they initially reported using cannabis to conform, followed by initially reporting expansion, coping, social, and, lastly, enhancement motives. Daily motives for alcohol use have also varied across women’s menstrual cycle (Joyce et al., 2018). An indirect way of evaluating the stability of daily motives is by examining the intraclass correlation (ICC), which determines the percent of variation in daily motives that are attributable to differences within a person across time (versus between persons) (Kahn, 2011). Accordingly, higher within-person ICC values indicate lower stability, given that a greater percent variation in motives is attributable to fluctuations within a person. Findings from studies included in the present review indicate that social motives for drinking demonstrate the highest within-person ICC, with 60–82% of the variation in social motives attributed to the within-person level of analysis (O’Donnell et al., 2019; O’Hara et al., 2015). Other motives for drinking appear to be slightly more stable, with the within-person level of analysis explaining 45–48% of the variation in conformity motives, 41–57% of the variation in enhancement motives, and 35–45% of the variation in coping motives (Dvorak et al., 2014; O’Donnell et al., 2019; O’Hara et al., 2015; Richardson et al., 2020; Stevenson et al., 2019). Only one study in the present review reported ICC estimates for cannabis use. In this analysis, the within-person level of analysis accounted for 86% of the variance in conformity motives, 43% of the variance in social motives, and approximately 25% of the variance in enhancement, coping, and expansion motives (Pearson et al., 2019).
Aspects of the Motivational Model Examined in EMA Studies
Broader contextual factors that were assessed in the reviewed studies only included traits, such as sensation-seeking, contentiousness, self-critical perfectionism, and tension reduction alcohol outcome expectancies (measured as a trait-like, rather than state-like, construct), with three studies in total assessing the relationship between traits and daily motives (Arbeau et al., 2011; Ehrenberg et al., 2016; Richardson et al., 2020). Several important traits, such as individual differences in substance response and reactivity to negative affect (e.g., distress intolerance, anxiety sensitivity), were not assessed, nor were any historical factors (e.g., past substance reinforcement, sociocultural factors). Momentary contextual factors that were assessed as antecedents to motives in reviewed studies included affect (n=8), withdrawal (n=1), and situational antecedents (e.g., day of the week, number of peers, sexuality of peers, perceptions of peers’ substance use; n=8). Four studies also examined affect as a consequence of motives (Armeli et al., 2014, 2016, 2018; Ross et al., 2018). Several notable momentary contextual factors hypothesized to precede motives were not assessed, including alternative incentives, substance availability, and somatic states, such as pain. Surprisingly, no studies evaluated state-like substance expectancies, even though the expected interoceptive effects of substances are theorized to be a key antecedent of motivations to use substances. Although nine studies examined associations between motive subtypes and substance use outcomes in daily life, only two of these studies examined if motives mediated associations between more distal antecedents and substance use (Dvorak et al., 2014; Stevenson et al., 2019).
Many of the studies identified in our review were not explicitly designed to test the motivational model, but do provide relevant data to the question of how the motivational model could be examined using EMA. In particular, many studies enrolling cigarette smokers examined associations between the trait version of the WISDM (assessed at baseline) and daily processes (assessed via EMA), but primarily conceptualized the WISDM as a measure of dependence, as opposed to a measure of motives. These studies often sought to examine trait and state predictors of smoking or symptoms during smoking abstinence (e.g., craving, negative affect) in daily life and examined WISDM scores as a predictor of these processes. Studies that did evaluate smoking motives in daily life (i.e., via state measures of the WISDM) primarily aimed to evaluate the validity of the WISDM, as opposed to evaluating the motivation model. Studies of alcohol and cannabis use motives that were not necessarily designed to assess the motivational model examined predictors of substance use in daily life, including trait and state motives, without regard to the theoretical premise of the motivational model.
Summary of Aim 1
In Aim 1 of the present review, we characterized methodological features of EMA studies examining motives for substance use in daily life. Investigators most commonly adapted pre-existing measures of trait motives to assess motives for substance use in daily life. In most studies, EMA-reported motives were assessed after substance use, with few studies examining motives before substance use. EMA measures of drinking motives demonstrated good internal consistently reliability in a handful of studies, but few studies examined the facture structure of these measures and it was often unclear whether reliability was assessed at the within or the between level of analysis. No studies examined the psychometric properties of cannabis or tobacco use motives in daily life. EMA-reported motives were relatively unstable, indicating that motives for substance use vary within persons, across time and situations. Several important aspects of the motivational model have not yet been assessed in EMA studies, such as the assumption that expectancies are an antecedent to motives, and many studies identified in the review were not explicitly designed to test the motivational model. Overall, most of the reviewed studies did not display methodological features optimal for evaluating the motivational model.
Aim 2: Support for the Motivational Model from EMA Studies
The second aim of this manuscript was to characterize the degree of support for the motivational model of substance use, based on EMA studies. The main premises of this model are described in Figure 1. We focused on three premises that have been examined in EMA studies: 1) people use substances for reasons posited by the motivational model; 2) motive subtypes demonstrate unique antecedents and consequences; 3) motives mediate associations between these theoretically more distal antecedents and substance use.
Common Subtypes of EMA-Reported Motives
College student drinkers reported enhancement motivation on over 80% of drinking episodes, social motives on over 50% of drinking episodes, and coping motives on over 30% of drinking episodes in daily life (Arbeau et al., 2011; Armeli et al., 2014, 2018; O’Hara, Boynton, et al., 2014). Conformity motives were infrequently assessed in the included studies and were rare when reported (i.e., endorsed on less than 4% of drinking days; Arbeau, Kuiken, & Wild, 2011b). Studies reporting the average strength (via Likert-type or visual analog scales of the highest loading item) of motive endorsement across EMA assessment periods have evinced similar results in college students, with social and enhancement motives demonstrating the highest mean strengths, followed by coping motives, then conformity motives (Armeli et al., 2014; Dvorak et al., 2014; Linden-Carmichael & Lau-Barraco, 2018; O’Hara, Armeli, et al., 2014; O’Hara et al., 2015; Stevenson et al., 2019). Two studies enrolling non-college student samples, including youth recruited from outpatient substance use disorder treatment and lesbian and bisexual women recruited from the community, reported higher rates of coping motives (Comulada et al., 2016; Dworkin et al., 2018).
Results on motive prevalence are somewhat similar for cannabis use motives, though these studies have primarily been conducted among community and clinical samples with heavy cannabis use. Enhancement motives were also commonly reported in daily life among those with cannabis use, with enhancement motives endorsed on 32–78% of cannabis use episodes (Buckner et al., 2013, 2015; Ross et al., 2018; Shrier & Scherer, 2014). Conversely, coping motives for cannabis use were more common than coping motives for alcohol use. Participants endorsed any level of coping motives on approximately 47–63% of cannabis use episodes (Buckner et al., 2013, 2015; Comulada et al., 2016) and coping has been the primary motive on 10–16% of cannabis use episodes (Ross et al., 2018; Shrier & Scherer, 2014). Other commonly endorsed motives for cannabis use include expansion motives and social motives, both of which have been reported on 8–32% of cannabis use episodes (Buckner et al., 2013, 2015; Shrier & Scherer, 2014). As with alcohol use, conformity motives appear to be uncommon for cannabis use (Buckner et al., 2015; Shrier & Scherer, 2014). Similarly, two studies examining mean motive strength across cannabis use days found that enhancement motives had the highest average strength, followed by coping and expansion motives, social motives, and, lastly, conformity motives (Bonar et al., 2017; Pearson et al., 2019).
Findings among young adult cigarette smokers indicate that they smoke to reduce craving and out of habit, also known as primary dependence motives, on a majority of smoking episodes in daily life. Specifically, smoking to reduce craving was reported on 39–63% of smoking episodes and smoking out of habit was reported on 42–54% of smoking episodes (Cerrada et al., 2016; Piasecki et al., 2007, 2011). Secondary dependence motives, or reasons for smoking other than craving and automaticity, were less commonly reported among young adult smokers. Secondary dependence motives reported on greater than 15% of smoking occasions include taking a break/killing time, socializing, boredom, and soon going where smoking is not allowed (Cerrada et al., 2016; Piasecki et al., 2007, 2011). Contrary to motives for alcohol and cannabis use, smoking to cope with negative emotions and to enhance positive emotions were not commonly endorsed (consistently endorsed on <15% of smoking occasions; Cerrada, Ra, Shin, Dzubur, & Huh, 2016; Piasecki, Piper, Baker, & Hunt-Carter, 2011; Piasecki, Richardson, & Smith, 2007).
Antecedents and Consequences of EMA-Reported Motives
Dispositional Traits and EMA-Reported Motives.
Few studies have examined dispositional traits and EMA-reported motives. Extant studies have focused on sensation-seeking, tension reduction alcohol outcome expectancies, self-critical perfectionism, and conscientiousness (Arbeau et al., 2011; Ehrenberg et al., 2016; Richardson et al., 2020), and findings have been largely consistent with hypotheses (e.g., positive associations between sensation-seeking and daily enhancement motives). Three identified studies also examined complex interactions between dispositional traits/genotypes thought to influence emotion regulation traits, EMA-reported affect, EMA-reported situational context, and EMA-reported motives (Arbeau et al., 2011; Covault et al., 2020; Richardson et al., 2020). Results from these studies were both consistent (e.g., high sensation seekers were more likely to endorse daily enhancement motives on days with high levels of task accomplishment) and inconsistent (e.g., self-critical perfectionism did not moderate the association between daily negative affect and drinking to cope) with the motivational model. No identified studies have examined the associations between dispositional traits and EMA-reported motives for cannabis or tobacco use.
EMA-Reported Situational Context and Motives.
As with dispositional traits, very few studies have examined associations between EMA-reported situational contexts and motives. Situational factors that have been assessed in prior studies include weekends versus weekdays (Ehrenberg et al., 2016; O’Hara, Armeli, et al., 2014), social factors (Dworkin et al., 2018; O’Hara et al., 2015), and daily task accomplishment (Arbeau et al., 2011). These studies evidenced interesting findings on the impact of social factors on motives for substance use, with relationships varying across levels of analysis and demographic factors (i.e., sexual identity) (Dworkin et al., 2018; O’Hara et al., 2015). Taken together with the small number of studies that have been conducted on social context and motives, this level of nuance makes it difficult to draw strong conclusions about this body of literature.
The only study, to our knowledge, examining daily associations between situations and motives for cannabis use found that weekends were associated with a greater likelihood of reporting social motives but were not associated with endorsement of any other motive subtype (Pearson et al., 2019). Furthermore, social motives were more commonly reported on April 20th—also known as 4/20, a “cannabis holiday”—and coping motives were less commonly reported on this date.
Although there are few studies examining associations between situational factors and smoking motives, results of the two available studies are consistent with hypothesized relationships (Cerrada et al., 2016; Piasecki et al., 2007). For example, socializing, being around friends who are smoking, and being at a bar or restaurant were associated with social smoking motives and endorsing the motive “to take a break from work or studying” was most likely to occur when studying, reading, or working, among other hypothesized associations.
EMA-Reported Affect and Motives.
An overview of findings on within- and between-person associations between affect and motives for alcohol and cannabis use is presented in Table 2. These studies were all designed to assess prospective associations between affect and subsequent motives. Most studies examining associations between affect and motives for alcohol use focused on enhancement and coping motives, with few studies examining these relationships for social and conformity motives (see Table 2). Findings on emotions and motive subtypes largely support premises of the motivational model. Specifically, higher than average positive affect has been associated with greater enhancement motivation for alcohol use at the daily level, and greater mean positive affect has been associated with greater average enhancement motives. Higher than average negative affect has been associated with higher coping motives at the daily level of analysis. Between persons, average negative affect and mean EMA-reported coping motives have been positively related and average positive affect and coping motives have been either negatively associated or not significantly associated.
Table 2.
Overview of findings on associations between EMA-reported affect and motives for substance use.
| Within-person level of analysis | Between-person level of analysis | |||||||
|---|---|---|---|---|---|---|---|---|
| Reference | Sample | Substance assessed | Affect assessed before/after motives | Within-person level of analysis | Negative affect | Positive affect | Negative affect | Positive affect |
| Enhancement motives | ||||||||
| (Arbeau et al., 2011) | College students; drank at least 1x in past 2 weeks | Alcohol | Before | Daily | null (general NA) | + (general PA) | null (general NA)* | + (general PA)* |
| (Dvorak et al., 2014) | College students; drank at least 2x/month | Alcohol | Before | Daily | null (general PA; women) + (general PA; men) |
null (general NA)* | + (general PA) | |
| (Stevenson et al., 2019) | College students; drank at least 1x in past 2 weeks | Alcohol | Before | Daily | + (general PA) | null (anxious)* null (depressed)* |
null (general PA) | |
| (Buckner et al., 2015) | Community adults; used cannabis at least 1x in past month | Cannabis | Concurrent | Momentary | null (withdrawal) null (general NA) |
|||
| Social motives | ||||||||
| (Arbeau et al., 2011) | College students; drank at least 1x in past 2 weeks | Alcohol | Before | Daily | null (general NA)* | null (general PA)* | ||
| (Buckner et al., 2015) | Community adults; used cannabis at least 1x in past month | Cannabis | Concurrent | Momentary | + (withdrawal) + (general NA) |
|||
| Coping motives | ||||||||
| (Arbeau et al., 2011) | College students; drank at least 1x in past 2 weeks | Alcohol | Before | Daily | + (general NA) | − (general PA) | + (general NA)* | null (general PA)* |
| (Dvorak et al., 2014) | College students; drank at least 2x/month | Alcohol | Before | Daily | + (general NA) | + (general NA) | null (general PA)* | |
| (Ehrenberg et al., 2016) | College students; drank at least 2x in past month | Alcohol | Before | Daily | + (general NA) | + (general PA) | + (general NA) | − (general NA) |
| (O’Hara, Armeli, et al., 2014) | College students; drank at least 2x in past month | Alcohol | Before | Daily | + (sadness) null (anxiety) null (anger) | + (general PA) | + (sadness)* + (anxiety)* + (anger)* |
− (general PA)* |
| (Richardson et al., 2020) | College students; drank at least 1x during EMA period | Alcohol | Before | Daily | + (general NA) | null (general PA) | + (general NA)* | − (general PA)* |
| (Stevenson et al., 2019) | College students; drank at least 1x in past 2 weeks | Alcohol | Before and after (mean of all random mood assessments) | + (anxious) + (depressed) |
+ (anxious) + (depressed) |
null (general PA)* | ||
| (Buckner et al., 2015) | Community adults; used cannabis at least 1x in past month | Cannabis | Concurrent | Momentary | + (withdrawal) + (general NA) |
|||
| Conformity motives | ||||||||
| (Arbeau et al., 2011) | College students; drank at least 1x in past 2 weeks | Alcohol | Before | Daily | + (general NA)* | null (general PA)* | ||
| (Buckner et al., 2015) | Community adults; used cannabis at least 1x in past month | Cannabis | Concurrent | Momentary | null (withdrawal) null (general NA) |
|||
| Expansion motives | ||||||||
| (Buckner et al., 2015) | Community adults; used cannabis at least 1x in past month | Cannabis | Concurrent | Momentary | null (withdrawal) null (general NA) |
|||
Note: + = positive statistically significant association, − = negative statistically significant association, null=non-statistically significant association, blank=study did not report findings for that cell.
only reported bivariate correlations, and not results from multilevel models with covariates.
Several studies were not presented in Table 2, including three that examined relationships between alcohol use motives and next-day negative affect (Armeli et al., 2014, 2016, 2018) and two that assessed the relationship between cannabis use and subsequent negative affect and fatigue (Goodhines et al., 2019; Ross et al., 2018). These findings generally indicate that EMA-reported coping motives for alcohol and cannabis use are associated with subsequent negative affect.
Findings on smoking motives and affect are not presented in Table 2, given that motive subtypes for smoking differ substantially from alcohol and cannabis use motives. A study conducted by Piasecki and colleagues (2007) is the only analysis in the present review to examine relationships between EMA-reported affect and smoking motives. These findings were consistent with hypothesized relationships, such that smoking to reduce negative emotions in daily life was associated with concurrent negative affect and a greater number of stressors and smokers reported greater momentary positive affect when they smoked for the purpose of enhancing positive emotions.
EMA-Reported Motives and Substance Use.
An overview of findings on within- and between-person associations between motives and alcohol and cannabis use patterns is presented in Table 3. All identified studies examining within-person relationships between alcohol and cannabis use motives and substance use outcomes did so at the daily level of analysis. Findings on the associations between daily motives and substance use outcomes were largely inconsistent across motive subtypes (see Table 3), and it is therefore difficult to draw strong conclusions about these relationships. More often than not, daily enhancement motives were associated with poorer daily drinking outcomes, including higher odds of initiating drinking, consuming a greater amount of alcohol, and experiencing more alcohol-related consequences. Higher than average daily coping motives were largely unrelated to alcohol use outcomes, with a couple of exceptions (Dworkin et al., 2018; O’Hara, Boynton, et al., 2014). Daily social motives demonstrated positive associations with alcohol use outcomes as often as they demonstrated null associations. Consistent with studies of trait motives (Cooper et al., 2015), conformity motives were primarily unrelated to alcohol use outcomes and were infrequently assessed. Across all motives subtypes and alcohol use outcomes, null associations were most commonly identified at the between-person level of analysis.
Table 3.
Overview of findings on associations between EMA-reported motives and substance use outcomes.
| Within-person level of analysis | Between-person level of analysis | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Reference | Sample | Enhance | Social | Coping | Conform | Expand | Enhance | Social | Coping | Conform | Expand |
| Any alcohol use | |||||||||||
| (O’Donnell et al., 2019) | Young adults; drank at least 1x in past month | + | null | null | null | ||||||
| Drinking quantity (number of drinks) | |||||||||||
| (Dvorak et al., 2014) | College students; drank at least 2x/month | null | + | + | null | ||||||
| (O’Hara, Boynton, et al., 2014) | HBCU students; drank at least 1x in past month | + (any) + (social) null (non-social) |
+ (any) + (social) null (non-social) |
+ (any)* null (social)* + (non-social) |
null (any) null (social) null (non-social) |
+ (any) + (social) null (non-social) |
+ (any) null (social drinks) + (non-social) |
||||
| (Stevenson et al., 2019) | College students; drank at least 1x in past 2 weeks | + | null | + | null | ||||||
| (Dworkin et al., 2018) | Young adult lesbian and bisexual women; drank at least 2+ drinks on 7+ times in past month | + | + | + | null | null | null | + | null | ||
| (O’Donnell et al., 2019) | Young adults; drank at least 1x in past month | null | null | null | null | ||||||
| Heavy drinking episode (4+/5+ drinks for men/women) | |||||||||||
| (O’Hara, Boynton, et al., 2014) | HBCU students; drank at least 1x in past month | + (any) + (social) null (non-social) |
+ (any) + (social) null (non-social) |
null (any)* null (social)* + (non-social) |
null (any) null (social) null (non-social) |
+ (any) + (social) null (non-social) |
null (any) null (social) + (non-social) |
||||
| Acute alcohol use disorder symptoms | |||||||||||
| (Dvorak et al., 2014) | College students; drank at least 2x/month | + | null | null | null | ||||||
| Alcohol-related consequences | |||||||||||
| (Goodhines et al., 2019) | College students; drank and used cannabis at least 1x in past month | null (sleep motives) | null (sleep motives) | ||||||||
| (Stevenson et al., 2019) | College students; drank at least 1x in past 2 weeks | + | null | + | + | ||||||
| Alcohol and energy drink co-use | |||||||||||
| (Linden-Carmichael & Lau-Barraco, 2018) | College students; co-used caffeine and alcohol at least 1x in the past week and reported heavy drinking at least 2x in past month | negative | null | null | + | ||||||
| Number of cannabis use sessions | |||||||||||
| (Pearson et al., 2019) | College students; used cannabis at least 1x in past month | + | null | null | + | null | null | null | null | null | + |
| Quantity of cannabis use (grams/# of joints) | |||||||||||
| (Pearson et al., 2019) | College students; used cannabis at least 1x in past month | null | null | null | null | null | null | null | null | null | null |
| (Bonar et al., 2017) | Young adults; reported past-month illicit substance use | + | + | + | null | null | |||||
| Cannabis subjective high | |||||||||||
| (Pearson et al., 2019) | College students; used cannabis at least 1x in past month | null | null | null | null | + | null | null | null | − | null |
| Cannabis-related consequences | |||||||||||
| (Goodhines et al., 2019) | College students; drank and used cannabis at least 1x in past month | null (sleep motives) | + (sleep motives) | ||||||||
| (Pearson et al., 2019) | College students; used cannabis at least 1x in past month | + | + | + | + | null | null | null | null | null | null |
Note: + = positive statistically significant association, − = negative statistically significant association, null=non-statistically significant association, blank=study did not report findings for that cell.
effects qualified by an interaction with gender, whereby men consumed more alcohol on days with higher than average coping motives and women consumed less alcohol and were less likely to engage in heavy drinking on days with higher than average coping motives. HBCU = Historically Black College or University.
Few studies examined associations between EMA-reported cannabis use motives and patterns of cannabis use (see Table 3). These studies also enrolled different populations (i.e., college students vs. young adults recruited from the emergency department) and assessed varied cannabis use outcomes (e.g., number of cannabis use sessions, quantity of cannabis consumed in grams, subjective high, cannabis-related consequences), rendering comparisons across studies difficult. To our knowledge, no studies have examined associations between EMA-assessed motives for smoking and smoking patterns in daily life.
Mediational Role of EMA-Reported Motives
Only two studies (Dvorak et al., 2014; Stevenson et al., 2019) identified in the present review evaluated the assumption that motives mediate associations between other antecedents and substance use—a key postulation of the motivational model (see Figure 1). The first of these found that coping motives mediated daily associations between higher negative mood and greater alcohol consumption and acute alcohol use disorder symptoms within persons, but limited support was found for a mediational role of enhancement motives at the daily level or for either motive subtype at the between-person level (Dvorak et al., 2014). Conversely, Stevenson and colleagues (2019) found that enhancement motives mediated associations between positive mood and alcohol use and consequences within persons but not between persons, while coping motives mediated associations between negative affect and alcohol-related consequences (but not consumption) between persons but not within persons. Importantly, the analyses of Stevenson and colleagues (2019) included all EMA days, including non-drinking days, whereas the analyses by Dvorak and colleagues (2014) only assessed associations on days when drinking occurred.
Summary of Aim 2
The second aim of this study was to characterize the degree of support for the motivational model of substance use (see Figure 1). Reasons for substance use in daily life were consistent with the motivational model for alcohol and cannabis use, with most individuals reporting social, enhancement, and coping motives. Motives for tobacco use in daily life were less consistent with the motivational model, as participants most commonly reported smoking due to craving and out of habit. With respect to antecedents of EMA-reported motives, dispositional traits and situational contexts were not commonly assessed, thus limiting strong conclusions about these antecedents. With respect to affective antecedents, affect was consistently associated with coping and enhancement motives at the within- and between-person level of analysis. Unexpectedly, and contrary to findings on trait motives (Cooper et al., 2015), associations between EMA-reported motives and substance use consequences were relatively inconsistent. Only two studies examined a key assumption of the motivational model—that motives mediate the associations between more distal antecedents and substance use outcomes—and findings were inconsistent across these studies. Taken together, these findings provide equivocal support for the motivational model of substance use in daily life.
Aim 3: Associations Between Trait Motives and Daily Processes
The final aim of this systematic review was to report associations between trait motives and daily processes assessed with EMA.
Trait Motives and EMA-Reported Motives
Evaluating relationships between trait motives and EMA-reported motives can help identify the extent to which motives in daily life are influenced by a dispositional motivational trait. Previous studies have found only modest associations between trait drinking motives and EMA-reported motives (Arbeau et al., 2011; Dvorak et al., 2014; O’Hara, Armeli, et al., 2014; O’Hara, Boynton, et al., 2014; Todd et al., 2005).
Two studies have also examined associations between trait smoking motives, assessed via the WISDM, and EMA adaptations of this measure (Piasecki et al., 2007, 2011). Results suggest that trait motives were largely associated with EMA-report motives for major subscales (i.e., primary dependence motives, secondary dependence motives), but trait motives and EMA-reported motives were inconsistently associated for minor subscales (e.g., negative reinforcement, positive reinforcement, cognitive enhancement, social-environmental goads).
Trait Motives and EMA-Reported Substance Use
An overview of associations between trait motives and EMA-assessed alcohol and cannabis use outcomes is presented in Supplementary Table 2. In general, trait motives were unrelated to substance use outcomes and identified effects were inconsistent. Several differences in study designs might have influenced these discrepancies, such as combining motives (i.e., social and enhancement), including covariates in statistical models (e.g., controlling for alcohol problems might mitigate effects), and simultaneously examining all four motives subtypes vs. only examining one or two subtypes in statistical models. As compared to other motive subtypes, trait enhancement motives demonstrated the most consistent associations with poorer drinking outcomes in daily life, particularly when combined with trait social motives. Trait coping motives demonstrated the most conflicting findings. Both social and conformity motives were largely unrelated to EMA-assessed drinking outcomes.
Findings on trait smoking motives and outcomes are not presented in Supplementary Table 2 due to different motive subtypes. Several studies examining these research questions have found that a greater dispositional motivation to smoke (i.e., WISDM total scores; Bold et al., 2016; Japuntich et al., 2009), higher WISDM primary and secondary dependence motives (Shiffman et al., 2012, 2015; Tarantola et al., 2017), the WISDM craving subscale (Japuntich et al., 2009; Yeh et al., 2012), and coping motives (assessed with an adaptation of the DMQ-R; Otsuki et al., 2008) are associated with poorer smoking outcomes in daily life, including smoking frequency, durations of abstinence, and craving.
Trait Motives and EMA-Reported Affect
Several previous analyses have investigated the hypothesis that coping motives might contribute to prolonged emotion dysregulation by examining associations between trait motives and daily/momentary negative affect during a subsequent EMA period. Two studies have found associations between trait coping motives and subsequent negative affect (Armeli et al., 2015; Heggeness et al., 2019), while one has not (Gorka et al., 2017).
Similar research designs among cigarette smokers have also been utilized, but these studies have primarily focused on negative affect during periods of tobacco abstinence and have included measures of more varied affective experiences, such as anhedonia (i.e., the inability to feel pleasure) and emotion differentiation (i.e., the ability to label and recognize emotions). In general, these findings indicate that smokers with more overall motivation to smoke and those who smoke for dependence and negative reinforcement motives have more emotion dysregulation, particularly during tobacco abstinence (Cook et al., 2015; Japuntich et al., 2009; Piper et al., 2017; Sheets et al., 2015; Weinstein & Mermelstein, 2013).
Moderation of EMA-Reported Processes by Trait Motives
In order to assess the validity of trait measures of motives, a relatively large body of literature has aimed to determine if trait motives moderate within-person relationships. Of particular interest has been the moderating effect of trait coping motives on within-person relationships between emotions or stressors (e.g., anxiety, anger, depressive affect, sadness, positive affect, low-arousal and high-around moods, negative interpersonal exchanges, coping strategy) and alcohol use (e.g., drinking quantity, drinking initiation, heavy drinking, weekly time-to-first drink, daily coping motives, drinking consumption context) in daily life. Results have been in conflict across these studies and within studies when multiple interaction effects are examined (i.e., assessing varying measures of affect and substance use within one study). Specifically, findings have been mostly consistent with hypotheses (i.e., those high in coping motives are more likely to drink on days characterized by high negative emotions/stressors or low positive emotions) in four studies (Arbeau et al., 2011; Armeli et al., 2008; Grant et al., 2009; Mohr et al., 2005, 2013), while results have not supported the validity of trait motives in 11 studies (Armeli et al., 2010; Gautreau et al., 2015; Littlefield et al., 2012; Mohr et al., 2001; O’Hara, Armeli, et al., 2014; Park et al., 2004; Sacco et al., 2015; Todd et al., 2003, 2005, 2009). Several studies have also examined trait motives beyond coping, as well as additional daily processes. Findings on the moderating effect of trait social and enhancement motives (alone or in combination) on daily mood-drinking relationships are also inconsistent (Armeli et al., 2010; Gautreau et al., 2015; Littlefield et al., 2012; Mohr et al., 2005, 2013; Sacco et al., 2015). With respect to other daily relationships, trait motives have moderated the within-person effects of pre-gaming and social contact on drinking outcomes, but the directions of these moderation effects have varied and have also differed by gender (Kuntsche & Labhart, 2013; Smit et al., 2015; Thrul & Kuntsche, 2016).
No studies in the present review evaluated the impact of trait cannabis motives on daily processes and only two studies investigated these research questions in smokers. As with findings for alcohol use, trait smoking motives inconsistently predicted relationships between social context, affect, and smoking outcomes (Otsuki et al., 2008; Shiffman et al., 2007).
Summary of Aim 3
Our final aim was to report on associations between trait motives and daily processes assessed with EMA. Trait motives have been only modestly associated with EMA-reported motives and inconsistently associated with substance use outcomes in daily life. However, trait coping motives have been relatively consistently associated with greater negative affect and emotion dysregulation in daily life. Surprisingly, trait motives did not consistently moderate associations between various antecedents (e.g., affect, situational contexts) and substance use outcomes. Taken together, these findings indicate that trait motives and EMA-reported motives likely reflect different constructs.
Discussion
Overview of Findings
The overarching aim of the present review was to characterize EMA studies of motives for substance use, with three specific goals. First, we evaluated how the motivational model has been examined in previous EMA studies in order to inform future research in this area. Second, we characterized the degree of support for the motivational model of substance use, based on EMA studies. Lastly, we reported on associations between trait motives and daily processes. Key findings are summarized below.
We identified several methodological challenges and limitations in assessing how the motivational model has previously been evaluated in EMA studies. Studies included in the present review primarily adapted pre-existing measures of motives for EMA measures (e.g., DMQ-R, MMM, WISDM), with some variability in the form of these adaptations, such as using Likert-type or visual analog scales to assess motive strength versus using checklists to assess any presence of a motive. In addition, most studies assessed motives after substance use, and therefore might be biased by retrospective recall (Shrier & Scherer, 2014). On the other hand, several studies assessed motives for substance use in the early evening (e.g., always assessing daily motives between 4:00 to 6:00pm; Dvorak et al., 2014), which might occur several hours prior to substance use or in the absence of substance use. In addition, several key aspects of the motivational model have not been systematically evaluated in EMA studies or have very limited evidence, including assumptions that substance expectancies, availability of alternative incentives, dispositional traits, and situations influence motives to use substances and that motives mediate associations between more distal antecedents and substance use outcomes (Cooper et al., 2015). It was also extremely uncommon for studies to evaluate interactions among antecedents in predicting motives, despite assumptions that motivations to use substances rely on complex interactions between the broader and momentary context (see Figure 1). Taken together, the majority of the reviewed studies did not display methodological features optimal for evaluating the motivational model. The following discussion of support for the motivational model and associations between trait motives and daily processes should be taken in light of these significant methodological limitations.
Next, we focused on evaluating support for assumptions for the motivational model (see Figure 1). Reasons for alcohol and cannabis use reported in daily life were consistent with those suggested by the motivational model, with participants reporting enhancement, social, and coping motives to use alcohol and cannabis on most occasions. The most commonly reported motives for tobacco use (i.e., habit, craving), however, indicate that negative reinforcement and automatic processes primarily underlie tobacco use, with social motives and positive reinforcement playing a much smaller role. Thus, the motivational model for substance use does not appear to adequately explain reasons for tobacco use in daily life. Findings on antecedents and consequences of motives in daily life provided inconclusive support for the motivational model. We found relatively strong support for the assumption that motives demonstrate distinctive affective antecedents. Yet, very few EMA studies examined the relationships between traits and situational factors and motives, several important antecedents to motives posited by the motivational model were not studied (e.g., state expectancies, alternative incentives), and motives for substance use in daily life did not demonstrate reliable substance use outcomes. Lastly, too little information on the mediational role of motives was available to make strong conclusions about this aspect of the motivational model. Only two studies utilized EMA methodology to determine if motives mediated associations between more distal antecedents and alcohol use, and the results of these studies were conflicting (Dvorak et al., 2014; Stevenson et al., 2019). The conflicting findings from these two studies are possibly due to methodological differences (e.g., different alcohol outcomes, analyzing data from only drinking days vs. all EMA days), thus highlighting the importance of utilizing similar methodologies across studies to allow strong conclusions about support for the motivational model.
Finally, we found that trait motives and EMA-reported motives are likely different constructs. Trait motives demonstrated only modest associations with motives reported in daily life, indicating that state motives are not purely influenced by a dispositional propensity towards a particular motive subtype. Perhaps most strikingly, trait motives did not consistently moderate within-person associations between affect and substance use. Cooper and colleagues (2015) have suggested that these findings might be partly attributable to EMA assessment time frames that are too short to identify reliable associations between affect and substance use. Others (e.g., Armeli et al., 2010) have suggested that findings in contrast to hypotheses might be explained by complex interactions between situations and traits. For example, although those higher in trait coping motives perceive themselves as drinking primarily in response to negative emotions, they also tend to be higher in social anxiety and avoidance, which might limit drinking opportunities. It is also possible that trait motives, assessed retrospectively, do not represent individuals’ actual motivation, but instead represent the actual interoceptive effects of substances or attributional biases (e.g., egocentric bias, greater attention to negative emotions/coping motives, etc.).
Gaps in the Literature and Recommendations
As previously discussed, the studies included in the present review demonstrated less than optimal assessment of the motivational model. Below are recommendations for assessing the motives for substance use in ways that are consistent with the momentary and dynamic nature of this model. First, we recommend that investigators more carefully consider the frequency and scheduling of motive assessment, given that motives for substance use are thought to be the most proximal influence in the decision to use substances. Researchers will need to consider both participant burden and the specific research question when making decisions about EMA scheduling (Trull & Ebner-Priemer, 2020). For example, a study examining motives as a mediator of the association between affect and substance use outcomes will likely need to use random time-contingent and event-contingent (e.g., before substance use) assessments to ensure that the report of motives precedes substance use. Random time- and event-contingent assessment of motives will require participants to report motives whether or not substance use has occurred; several of the reviewed studies examining motives in the absence of substance use provide potential phrasing for such measures (e.g., “Regardless of whether or not I plan to use (SUBSTANCE), if I do use (SUBSTANCE), it will be to….”; p. 815, Stevenson et al., 2019). It is possible that a combination of EMA scheduling types (e.g., event-contingent, such as craving and substance use events, time-contingent, and time milestones) may provide the most accurate information about the motivational model. Although investigators will need to consider feasibility of EMA designs combining scheduling types, recent research indicates that frequent short assessments are less burdensome to participants than long questionnaires (Eisele et al., 2020).
To better understand the utility and validity of the EMA measures of motives described in the present review, additional research is needed on the psychometric properties of these measures. Future studies should examine the construct validity of motive subtypes assessed in daily life, including using factor analytic techniques to confirm the factor structure of EMA measures (which has only been conducted in one prior study; O’Hara, Boynton, et al., 2014) and assessing additional motives that may only be identifiable in daily life with open-ended prompts. In the reviewed studies, it was largely unclear whether reported reliability estimates for EMA measures of motives reflected the between or within-person level of analysis, and no studies reported reliability at both levels. Prior simulation studies indicate that single-level reliability estimates are biased unless reliability is identical at both the between- and within-level of analysis (Geldhof et al., 2014). Accordingly, investigators of future studies using EMA measures of motives should examine and clearly report reliability at both levels of analysis (see Geldhof et al., 2014 for instruction and code).
Several studies in the present review only evaluated one motive subtype in daily life (most commonly coping motives) or only included one motive subtype in analyses. Without adjusting for other motive subtypes in analyses, associations might reflect a general overall motivation to use substances, as compared to the unique effect of one motive subtype. Accordingly, we recommend that researchers assess all relevant motive subtypes during EMA periods and simultaneously include all motive subtypes in analyses examining associations between motive subtypes and outcomes. Given that motive subtypes may be highly correlated, assessing multicollinearity and partial correlation coefficients will be important when all subtypes are included in the same model (see Shieh & Fouladi, 2003 for discussion of multicollinearity in multilevel models). A similar consideration is whether to measure the strength of each motive (Likert-type scale) or the presence of each motive (checklist) during EMA assessments. Cox and Klinger’s (1988) motivational model posits that the relative strength of motives in comparison to the relative strength of alternative incentives likely influences prolonged and problematic substance use. Therefore, we recommend that investigators utilize Likert-type or visual analog scales to assess motive strength, rather than checklists of any motives.
As previously mentioned, several key aspects of the motivational model have not been thoroughly investigated in EMA studies. In particular, future research is needed on the role of personality traits, situational factors, substance expectancies, and the mediational role of motives. Distress intolerance and anxiety sensitivity, trait-like constructs representing high reactivity to negative affect, might be particularly important to assess as an antecedent to motives in daily life, given that they have been strongly associated with trait coping and conformity motives for substance use (e.g., DeMartini & Carey, 2011; Zvolensky et al., 2009). Although these constructs are generally described as traits, a recent EMA study found that intolerance of distress varied within individuals, across time and situations (Veilleux et al., 2018). Integrating trait and momentary measurements of reactivity to negative affect in EMA studies of motives might also clarify inconsistent findings regarding the moderating role of trait coping motives on negative affect-substance use relationships. Only one study assessed substance use expectancies and, in this analysis, expectancies were measured as a trait rather than a state (Ehrenberg et al., 2016). According to the motivational model (see Figure 1), substance use expectancies are state-like and are hypothesized to be a vital antecedent of motives for substance use. Thus, future research is needed to develop EMA measures of substance use expectancies and to determine the role of expectancies in influencing motives and, ultimately, substance use outcomes. It is also important to note that very few studies examined interactions among contextual factors in predicting motives, even though the motives are hypothesized to be influenced by complex interactions between historical and current contextual factors (see Figure 1).
The reviewed body of literature indicates homogeneity in the substance use severity of study samples. No studies in the present review had alcohol use disorder as an explicit inclusion criterion or examined the extent to which alcohol use disorder diagnosis influenced motivational processes (see Supplementary Table 1). Perhaps those with a substance use disorder would demonstrate more consistent associations between motive subtypes and substance use outcomes, given their greater variability in substance use outcomes, greater frequency of substance use occasions, and stronger negative and positive reinforcement pathways (Koob, 2013). EMA studies aiming to evaluate antecedents and consequences of motives, particularly coping motives, should carefully consider if their target population is appropriate to make conclusions about the role of motives in substance use development and maintenance. A related consideration is the length of EMA assessment periods, with assessment length ranging from one to 56 days (see Supplementary Table 1). If behaviors and events are infrequent, as is the case with substance use and coping motives in college students, longer assessment periods are needed to adequately capture variability in these behaviors.
There was also limited demographic diversity, with most studies assessing non-Hispanic white college students. Despite previous findings that trait motives differentially predict substance use trajectories in Black and white adolescents (Cooper et al., 2008), only a handful of studies included in the present review recruited demographically diverse samples (see Supplementary Table 1) (Armeli et al., 2016; Cerrada et al., 2016; Dworkin et al., 2018; O’Hara, Boynton, et al., 2014; Otsuki et al., 2008). Future studies should continue to utilize EMA to determine how sociocultural factors (a broader contextual factor) and minority stress and culturally relevant stressors (a momentary contextual factor) contribute to motives and substance use. In addition, research on daily and momentary motives for substances beyond alcohol, cannabis, and tobacco, and motives for polysubstance use is needed. Emerging cross-sectional research suggests that motives subtypes can be utilized to characterize use of other substances, such as prescription drugs (Drazdowski, 2016; Messina et al., 2016), and that specific motive subtypes (i.e., coping and enhancement) are associated with polysubstance use (Nattala et al., 2012; Patrick et al., 2018). EMA might be particularly well-suited for examining motives for polysubstance use, given the ability to assess multiple combinations of substances in real-time. The decision to evaluate only one substance during an EMA period might also be obscuring important associations between affect, motives, and substance use outcomes in daily life. For example, weak associations between daily negative affect and coping motives for alcohol use might occur if participants are using another substance (e.g., cannabis) to cope with negative affect. Recent studies (published following the pre-registered timeframe for the present systematic review) are beginning to answer important questions about motives for polysubstance use in daily life (Arterberry et al., 2020).
Clinical Implications and Future Directions
The motivational model of substance use raises the possibility that treatments can be adapted to target heterogeneous motivational processes (Cooper et al., 2015). Several findings reported in the present review indicate clinical utility of addressing motives for substance use in daily life, including consistent associations between negative affect and coping motives and enhancement motives and poorer drinking outcomes. However, the hypothesized momentary and dynamic nature of motives for substance use in daily life complicates both the development and large-scale dissemination of interventions targeting motives. Just-in-time adaptive interventions have the most promise for targeting motives for substance use, given that such technologies are designed to deliver relevant treatment components in the client’s natural environment, and based on their affect and context (Nahum-Shani et al., 2018). For example, individuals with a strong within-person association between negative affect and coping motives could be provided with alternative coping skills at times with higher than average negative affect. The development of such treatments is an important line of future research.
Investigators have also raised the possibility that changes in motives for substance use might function as a mechanism of behavior change in treatment (Cooper et al., 2015). However, current support for trait motives as a mechanism of behavior change is equivocal (Olthuis et al., 2015; Wolitzky-Taylor et al., 2018). Future studies using EMA methodology can examine if daily motives for substance use change during treatment and if changes in daily motives are associated with treatment outcomes. This future direction is somewhat complicated by the fact that motives are not typically assessed in the absence of substance use. Therefore, this mechanism of behavior change may be most relevant to those who have goals to moderate their substance use, as opposed to clients who wish to achieve abstinence. Several studies in the present review did assess motives regardless of substance use, using wording such as the following: “Regardless of whether or not I plan to drink tonight, if I do drink tonight, it will be…. (e.g., “to reduce my anxiety”)” (Stevenson et al., 2019, page 815). Such measures of motives may be more appropriate to assess change among individuals with abstinence goals, but it is also possible that these measures reflect craving, as opposed to motivational drives. Taken together, additional research is needed to understand the construct validity of EMA measures of motives in the absence of substance use, as well as the utility of these measures for assessing change in substance use treatment.
Our conclusion that EMA measures and trait measures of motives might not reflect the same construct has important clinical implications. For example, treatments for substance use disorders often delineate motives for substance use (e.g., via functional analysis in cognitive-behavioral therapy; Carroll & Kiluk, 2017), followed by the introduction of skills to target these motives. If participants do not reliably use substances in daily life for reasons reported retrospectively, such treatments might not produce optimal results. Indeed, trait motives did not reliably predict EMA-assessed substance use, based on studies included in the present review. Notably, trait motives were found to predict negative affect in the present review and also likely have value for predicting future risk of problematic substance use over a longer period (see Cooper et al., 2015), indicating that trait motives may be useful for informing certain aspects of therapy (e.g., tools to decrease negative affect) and prevention efforts.
Limitations of the Present Review
Results of the present review should be taken in light of several limitations. First, our search strategy was limited to the databases and search terms used, and therefore it is possible that we overlooked relevant studies that did not include these keywords or were not indexed in searched databases. We also chose to exclude grey literature (e.g., dissertations, preprint depositories) from our systematic review for feasibility purposes (Mahood et al., 2014), which may have also limited the available literature for review. However, we have captured a large body of literature (N=64 peer reviewed studies), including studies both consistent with and in contrast to the motivational model, to draw broad conclusions relating to the main systematic review aims. In addition, we chose to include EMA studies that assessed motives for substance use, even if they were not designed to assess the motivational model. We also decided to include studies of all substances in the review, even though the motivational model has been primarily used to explain alcohol use and motivational processes might differ between substances. These latter two methodological features might have magnified limitations of the reviewed studies and the prevalence of findings in contrast to the motivational model.
Conclusions
Results of the reviewed EMA studies provide equivocal support for the motivational model of substance use, as shown in Figure 1. Emotional states appear to be a reliable antecedent of motive subtypes, but findings were less clear for other antecedents (e.g., traits, situations) and consequences (e.g., substance use and related consequences) of motive subtypes and for the assumption that motives mediate associations between other antecedents and substance use. Additionally, several intersecting lines of inquiry indicate that trait motives and EMA-reported motives might not reflect the same construct. However, the reviewed body of research did not assess motives for substance use in a fashion consistent with the momentary and dynamic nature of the motivational model, which might explain the lack of support for the model and for the ability of trait motives to predict daily processes.
We suggest that future EMA studies of motives for substance use evaluate motives frequently and with both time- and event-contingent assessments, further assess the psychometric properties of EMA measures of motives, include all motive subtypes in EMA assessments and analyses, and measure motive strength (via Likert-type or visual analog scales) as opposed to the mere presence of motives. The motivational model should also be assessed in more diverse samples, including those with greater substance use severity, those who use multiple substances, and those who are demographically diverse. Many aspects of the motivational model, including broader contextual factors, such as dispositional traits, sociocultural factors, and prior substance use reinforcement, and momentary contextual factors, such as substance use expectancies, alternative incentives, and somatic states, have not been assessed. Researchers should continue to assess these factors and interactions among these factors in predicting motivations to use substances and, ultimately, acute and prolonged consequences of substance use. Continued evaluation and validation of the motivational model in daily life will help inform interventions that address heterogeneous reasons for substance use.
Supplementary Material
Funding and Acknowledgements:
This work was supported by the National Institute on Alcohol Abuse and Alcoholism, award number T32AA018108. The content is the sole responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors have no conflicts of interest. We would like to thank Drs. Elizabeth Yeater and Kamilla Venner for their feedback on this manuscript.
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