Motives for Substance Use in Daily Life: A Systematic Review of Studies Using Ecological Momentary Assessment

Abstract

The motivational model of substance use posits that four motive subtypes (coping, enhancement, social, conformity) dynamically interact with contextual factors to affect decisions about substance use. Yet prior studies assessing the motivational model have relied on between-persons, cross-sectional evaluations of trait motives. We systematically reviewed studies using ecological momentary assessment (EMA; 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 that 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.

Keywords

daily diary ecological momentary assessment motivation motive substance use preregistered

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 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, 1995). An increased focus on motives for substance use in daily life may further improve prevention and treatment efforts in that 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 individual’s 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 individual’s 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 individual’s 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, nonpharmacological 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 on the basis of 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 and avoidance tendencies and internal and external motivation. Accordingly, combinations of these drives result in four distinct motives subtypes: coping (internal, avoid), enhancement (internal, approach), conformity (external, avoid), and social (external, approach; Cooper et al., 2015). Given 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 (2015) asserted that motives are the “final common pathway” (pp. 4, 33, 35) to substance use through which distal factors (e.g., traits, situational factors, affective states, substance expectancies) are mediated. 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 finally, social motives (Cooper et al., 2015). This model suggests that distinct processes influence motives not only within a person, across different times and situations, but also between persons (Cooper et al., 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 (for a description, see Table 1).

Table 1.

Trait and Ecological Momentary Assessment (EMA) Measures of Motives Used 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)	• Twenty-item questionnaire designed to assess the frequency with which individuals drink alcohol for four different motives subtypes: coping, enhancement, conformity, social (5 items per subtype) • Participants respond to each item on a 5-point scale from 1 (almost never/never) to 5 (almost always/always) • Example instructions and items: “Decide how frequently your own drinking is motivated by each of the reasons listed: To forget your worries, Because it helps you enjoy a party, Because you like the feeling, To fit in with a group you like”	• All five coping subscale items and the two highest loading items (as reported in Cooper, 1994) for the enhancement subscale; participants respond on a 3-point scale representing the strength of each motive on the previous night (e.g., Armeli et al., 2018) • All five items from both the coping and enhancement subscales; participants responded on a 5-point response scale representing the strength of each motive on the previous night (Arbeau et al., 2011) • Four coping items and two items from other subscales; participants responded on a 4-point scale representing the strength of each motive on the previous night (Dworkin et al., 2018) • Two highest loading items for each subscale (as reported in Cooper, 1994); participants responded on a 6-point scale representing how much they were motivated to drink for each reason in the moment even if they were not currently drinking (O’Donnell et al., 2019) • All five items from the coping subscale; participants responded on a 5-point scale representing the strength of each motive on the previous night (Richardson et al., 2020)
Drinking Motives Questionnaire–Revised Short Form (DMQ-R SF; Kuntsche & Kuntsche, 2009)	• Twelve-item adaptation of the DMQ-R using 3 items from each subscale (i.e., coping, enhancement, conformity, social); chose the 3 items for each subscale both empirically (examining highest loading items from previous analyses) and conceptually (items that measured different aspects of motives subtypes) • Participants respond to each item on a 3-point scale from 1 (never) to 3 (almost always)	• All three items from the coping, enhancement, and social subscales (did not assess conformity motives); participants responded on a visual analogue scale from 0 to 100 representing the strength of each motive in the moment (Joyce et al., 2018)
Modified Drinking Motives Questionnaire–Revised (Modified DMQ-R; Grant et al., 2007)	• Twenty-eight-item adaptation of the DMQ-R that separates coping motives into coping-depression motives and coping-anxiety motives; added one item relevant to coping-anxiety and 6 items relevant to coping-depression to the DMQ-R and split one DMQ-R item into two items to differentially capture coping-anxiety and coping-depression • Participants respond to each item on the same 5-point scale used in the DMQ-R • Coping-depression item example: “to numb my pain”; coping-anxiety item example: “because it helps me when I am feeling nervous”	• Two items from the coping-depression, coping-anxiety, and enhancement subscales; participants responded on a 5-point scale representing how much they wanted to drink for each motive later in the evening (Dvorak et al., 2014) • Two highest loading items (as reported in Cooper, 1994) for the enhancement, conformity, and social subscales and four coping-depression items, two coping-anxiety items, and one coping-anger item (written by the research team); participants respond on a 3-point scale representing the strength of each motive on the previous night (e.g., O’Hara et al., 2015) • Five enhancement items, six coping-anxiety items, and three coping-depression items; participants responded on a 5-point scale representing how much they wanted to drink for each motive later in the evening (Stevenson et al., 2019)
Daily Coping Inventory (Stone & Neale, 1984)	• Use of this trait measure was not identified in the present review	• If a participant reported any negative mood, they were presented a series of eight “yes” or “no” items assessing strategies used to cope with the most severe negative mood (e.g., tried to relax, found something to do), one of which was alcohol use (Todd et al., 2005)
Reasons for Drinking Questionnaire negative reinforcement composite (Farber et al., 1980)	• Five-item questionnaire designed to assess the importance of different reasons for drinking • Participants responded to each item on a 4-point scale from 1 (not at all important) to 4 (very important) • Example instructions and items: “Please indicate how important each of the following statements is to you as a reason for drinking: I drink alcohol to relax or feel less tense and I drink alcohol to cheer me up when I am down”	• An EMA adaptation of this measure was not identified in the present review
Cannabis motives measures
Marijuana Motives Measure (MMM; also known as the Marijuana Motives Questionnaire; Simons et al., 1998)	• Twenty-five-item adaptation of the DMQ-R that changes instructions to refer to cannabis use and adds a five-item expansion motives subscale • Participants respond to each item on the same 5-point scale used in the DMQ-R • Expansion motives item examples: “I use marijuana because it helps me be more creative and original,” “I use marijuana to be more open to experiences”	• Included all 25 MMM items but used a checklist indicating the presence (“yes” or “no”) of each motive for in the moment (Buckner et al., 2015) • The single highest loading item from each subscale (as reported in Simons et al., 1998); participants responded on a 4-point scale representing the strength of each motive on the previous night (Bonar et al., 2017) • Two highest loading items from each subscale (as reported in Simons et al., 1998); participants responded on a visual analog scale from 0 to 100 representing the strength of each motive in the previous 24 hr (Pearson et al., 2020) • Participants chose the main reason they were about to use cannabis and the main reason they just used cannabis (for pleasure, to cope, to conform, to be more social, to expand my mind; Shrier & Scherer, 2014)
Tobacco motives measures
Wisconsin Inventory of Smoking Dependence Motives (WISDM; Piper et al., 2004)	• Sixty-eight-item questionnaire assessing participants’ general level of agreement with different smoking motives • Includes two broad and 13 minor subscales: primary dependence motives (automaticity, craving, loss of control, tolerance) and secondary dependence motives (affiliative attachment, behavioral choice/melioration, cognitive enhancement, cue exposure, negative reinforcement, positive reinforcement, social/environmental goads, taste/sensory processes, weight control) • Participants respond to each item on a 7-point scale from 1 (not true of me at all) to 7 (extremely true of me) • Example instructions and items: “Please rate your level of agreement for each using the following scale: I often smoke without thinking about it, Smoking helps me stay focused, Smoking keeps me from overeating”	• Participants responded to a checklist of motives indicating reasons they were smoking in the moment, including reduce craving, habit/automatic, boredom/to kill time, opportunity to socialize, enhance positive emotion, enhance alcohol/drug effects, break from work/studying, cope with negative emotion, environmental cue/trigger, soon going where cannot smoke, concentrate/think better, curb hunger/curb weight (e.g., Piasecki et al., 2011)
Teen Smoking Motives Questionnaire (see Otsuki et al., 2008)	• Twenty-item adaptation of the DMQ-R that changes instructions to refer to cigarette smoking • Participants respond to each item on the same 5-point scale used in the DMQ-R	• An EMA adaptation of this measure was not identified in the present review
Russell’s Smoking Motivation Questionnaire (Russell et al., 1974)	• Thirty-four-item measure assessing typical motives for cigarette smoking across 6 subtypes (psychosocial/negative affect, indulgent, sensorimotor, stimulation, addictive, and automatic) • Participants respond to each item on a 4-point scale from 0 (not at all) to 3 (very much so) • Example items: “I smoke for the pleasure of offering and accepting cigarettes from other people,” “I smoke automatically without even being aware of it,” “I smoke when I am worried about something”	• An EMA adaptation of this measure was not identified in the present review
Wills Tobacco Motives Inventory (Wills et al., 1999)	• Fifteen-item questionnaire assessing participants’ typical motives for smoking cigarettes across four subscales: social motives (four items), self-enhancement motives (four items), boredom relief motives (two items), and affect regulation motives (five items) • Participants responded to each item on a 5-point scale from 1 (not at all true) to 5 (very true) • Examples items: “Smoking helps you feel more relaxed,” “Smoking helps you to feel more confident,” “Smoking makes social gatherings more fun”	• An EMA adaptation of this measure was not identified in the present review
General measures of motives for substance use
Use of Drugs and Alcohol subscale of the COPE (Carver et al., 1989)	• Four-item subscale of the COPE questionnaire assessing individuals’ tendency to use drugs and alcohol to cope with negative emotions and stressful events • Participants respond to each item on a 4-point scale from 1 (I usually don’t do this at all) to 5 (I usually do this a lot) • Example instructions and items: “Indicate what you generally do and feel when you experience stressful events: I drink alcohol or take drugs in order to think about it less, I use alcohol or drugs to make myself feel better”	• An EMA adaptation of this measure was not identified in the present review
Untitled measure of reasons for substance use	• N.A.	• If participants reported substance use, they were asked what went through their mind before use, including “Had a bad day,” “Want2 relax,” “Want2 feel better,” “Want2 fit in,” “Deserve it,” or “Want2 get buzzed” (Comulada et al., 2016)

Note: N.A. = not applicable.

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; for descriptions, see Table 1). 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., 1995, 2015), 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 a 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 Figure 1, repeated momentary processes over time might contribute to individual differences in trait-like motives.

Fig. 1.

Visual representation of the motivational model. This visual representation of the motivational model draws from aspects of models presented by Cox and Klinger (1988), Cooper and colleagues (Cooper, 1994; Cooper et al., 1995, 2015), and Kuntsche and colleagues (2005), 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). To fully test the motivational model, it is critical to investigate momentary processes in daily life. Microlongitudinal 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 Fig. 1) both between and within persons (Shiffman et al., 2008). These studies, although 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 microlongitudinal 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, associations between affect and substance use). Within the past 2 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 article 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 on the basis of these studies. Finally, 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 research.

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 used 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 articles published in English with human participants. In addition, we manually searched Google Scholar using the authors identified in the initial search and examined references lists of included publications to identify eligible articles. Finally, we used 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 used microlongitudinal designs (e.g., EMA, daily diary) to examine motives for substance use in daily life or to assess the association between trait motives and daily processes (e.g., daily motives, affect, substance-use outcomes, affect–substance use associations). We included only studies that used electronic diaries, as opposed to paper diaries, because the inability to provide time-verified reports in paper diaries potentially results in less accurate data (Tennen et al., 2006). We did not include articles published in a language other than English, publications that were not peer reviewed, case reports, case series, editorials, commentaries, letters to the editor, book chapters, previously published narrative reviews, or theses/dissertations. Studies that evaluated motives for behaviors not related to substance use (e.g., eating, behavioral addictions) were also ineligible for inclusion.

V. R. Votaw conducted the search, determined eligibility, and extracted data from included studies. K. Witkiewitz 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 vs. 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 levels); 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 microlongitudinal research designs, including ecological momentary assessment (EMA), daily diary, experience sampling, intensive longitudinal studies, and ambulatory assessment (Shiffman et al., 2008). Although there are some substantive differences between different microlongitudinal methods (e.g., daily diary referring to data collection occurring once a day; Shiffman et al., 2008), they all refer to the repeated collection of data in participants’ real-world environments; periods between assessments ranged from hours to days. Consistent with a previous review (Wycoff et al., 2018), we refer to all microlongitudinal research designs as EMA studies.

An important characteristic shared by all EMA studies is the ability to measure and analyze between-persons and within-persons processes. Between-persons effects refer to mean-level differences between groups of participants, whereas within-persons effects refer to variability in relationships across time and situations within a single participant. Note that the frequency and scheduling of data collection in EMA studies influences the interpretation of within-persons effects. Three different types of scheduling are commonly used (alone or in combination), including event-contingent scheduling, in which participants complete study measures when they engage in a behavior (e.g., substance use); time- or signal-contingent scheduling, in which participants are prompted to complete study measures at random times throughout the day or in time blocks; and time milestones, in which 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 are 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, although both are used to determine between-persons 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 before EMA data collection periods. On the other hand, mean-level EMA data refer to aggregate data on the same measure across all EMA assessments and can be used to evaluate between-persons 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, whereas mean-level EMA data are 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); used paper diaries (n = 13); did not use EMA (n = 11); review, commentary, dissertation, or irrelevant erratum (n = 7); assessed trait motives at baseline only and did not examine associations with daily processes (n = 4); and unclear methods (n = 1).

Fig. 2.

Flow diagram of records identified, screened, and included. Note that 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 Table S1 in the Supplemental Material available online. 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 1 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 used EMA measures of motives were adaptations of the DMQ-R for alcohol motives, the MMM for cannabis motives, and the WISDM for tobacco motives (for details on the measures, see Table 1; for details on motives measures used in each eligible study, see Table S1 in the Supplemental Material). Researchers using 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 on the basis of Likert-type scales or visual analogue scales. In many studies, researchers administered items from only 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 analogue 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 hr. Finally, only one modification of the WISDM was identified, which included 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. Note that none of the reviewed studies reported reliabilities of at both the between- and within-persons levels, and authors of few studies explicitly reported the level of analysis for the reliability estimates (e.g., between levels; Dworkin et al., 2018; O’Hara, Armeli, & Tennen, 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 two items each for the social and enhancement subscales and a seven-item coping subscale (O’Hara, Boynton, et al., 2014). These authors found that a three-factor model provided a good fit to the data and that 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); in some studies, motives were assessed 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 p.m. and 6:00 p.m.; Dvorak et al., 2014; Stevenson et al., 2019). Although most studies assessed motives only if participants had used substances (or were about to use substances), in a few studies (e.g., Goodhines et al., 2019; O’Donnell et al., 2019), motivation was assessed regardless of actual substance use. Accordingly, there was some discrepancy between studies with regard to whether only substance-use days were analyzed, and therefore substance-use quantity and consequences were outcomes, or whether 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 finally, 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 percentage of variation in daily motives that are attributable to differences within a person across time (vs. between persons; Kahn, 2011). Accordingly, higher within-persons ICC values indicate lower stability because a greater percentage 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-persons ICC; 60% to 82% of the variation in social motives was attributed to the within-persons level of analysis (O’Donnell et al., 2019; O’Hara et al., 2015). Other motives for drinking appear to be slightly more stable; the within-persons level of analysis explained 45% to 48% of the variation in conformity motives, 41% to 57% of the variation in enhancement motives, and 35% to 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-persons 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., 2020).

Aspects of the motivational model examined in EMA studies

Broader contextual factors that were assessed in the reviewed studies included only 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); three studies in total assessed 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 whether 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 motivational 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 premises 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 preexisting measures of trait motives to assess motives for substance use in daily life. In most studies, EMA-reported motives were assessed after substance use; few studies examined 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 article was to characterize the degree of support for the motivational model of substance use on the basis of 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: (a) People use substances for reasons posited by the motivational model, (b) motive subtypes demonstrate unique antecedents and consequences, and (c) 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 more than 80% of drinking episodes, social motives on more than 50% of drinking episodes, and coping motives on more than 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 et al., 2011). Studies reporting the average strength (via Likert-type or visual Analogue scales of the highest loading item) of motive endorsement across EMA assessment periods have evinced similar results in college students; social and enhancement motives demonstrated 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, & Tennen, 2014; O’Hara et al., 2015; Stevenson et al., 2019). Two studies enrolling non-college-student samples, including youths 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, although 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; enhancement motives were endorsed on 32% to 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% to 63% of cannabis use episodes (Buckner et al., 2013, 2015; Comulada et al., 2016), and coping was the primary motive on 10% to 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 were reported on 8% to 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). Likewise, 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 finally, conformity motives (Bonar et al., 2017; Pearson et al., 2020).

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% to 63% of smoking episodes, and smoking out of habit was reported on 42% to 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 more 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 less than 15% of smoking occasions; Cerrada et al., 2016; Piasecki et al., 2007, 2011).

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 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 compared with weekdays (Ehrenberg et al., 2016; O’Hara, Armeli, & Tennen, 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, given that relationships varied 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 research.

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., 2020). Furthermore, social motives were more commonly reported on April 20—also known as 4/20, a “cannabis holiday”—and coping motives were less commonly reported on this date.

Although there are few studies have examined 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-persons 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; few examined 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

Reference	Sample	Substance assessed	Affect assessed before or after motives	Within-persons level of analysis	Within-persona level of analysis		Between-persona level of analysis
Reference	Sample	Substance assessed	Affect assessed before or after motives	Within-persons level of analysis	NA	PA	NA	PA
Enhancement motives
Arbeau et al. (2011)	College students; drank at least once in past 2 weeks	Alcohol	Before	Daily	Null (general NA)	+ (general PA)	Null (general NA)^a	+ (general PA)^a
Dvorak et al. (2014)	College students; drank at least 2 times/month	Alcohol	Before	Daily		Null (general PA; women)+ (general PA; men)+ (general PA)	Null (general NA)^a	+ (general PA)
Stevenson et al. (2019)	College students; drank at least once in past 2 weeks	Alcohol	Before	Daily			Null (anxious)^a Null (depressed)^a	Null (general PA)
Buckner et al. (2015)	Community adults; used cannabis at least once in past month	Cannabis	Concurrent	Momentary	Null (withdrawal) Null (general NA)
Social motives
Arbeau et al. (2011)	College students; drank at least once in past 2 weeks	Alcohol	Before	Daily			Null (general NA)^a	Null (general PA)^a
Buckner et al. (2015)	Community adults; used cannabis at least once in past month	Cannabis	Concurrent	Momentary	+ (withdrawal)+ (general NA)
Coping motives
Arbeau et al. (2011)	College students; drank at least once in past 2 weeks	Alcohol	Before	Daily	+ (general NA)	− (general PA)	+ (general NA)^a	Null (general PA)^a
Dvorak et al. (2014)	College students; drank at least 2 times/month	Alcohol	Before	Daily	+ (general NA)		+ (general NA)	Null (general PA)^a
Ehrenberg et al. (2016)	College students; drank at least twice in past month	Alcohol	Before	Daily	+ (general NA)	+ (general PA)	+ (general NA)	− (general NA)
O’Hara, Armeli, & Tennen (2014)	College students; drank at least twice in past month	Alcohol	Before	Daily	+ (sadness)Null (anxiety)null (anger)	+ (general PA)	+ (sadness)^a+ (anxiety)^a+ (anger)^a	− (general PA)^a
Richardson et al. (2020)	College students; drank at least once during EMA period	Alcohol	Before	Daily	+ (general NA)	Null (general PA)	+ (general NA)*	− (general PA)^a
Stevenson et al. (2019)	College students; drank at least once in past 2 weeks	Alcohol	Before and after (mean of all random mood assessments)		+ (anxious)+ (depressed)		+ (anxious)+ (depressed)	Null (general PA)^a
Buckner et al. (2015)	Community adults; used cannabis at least once in past month	Cannabis	Concurrent	Momentary	+ (withdrawal)+ (general NA)
Conformity motives
Arbeau et al. (2011)	College students; drank at least once in past 2 weeks	Alcohol	Before	Daily			+ (general NA)^a	Null (general PA)^a
Buckner et al. (2015)	Community adults; used cannabis at least once in past month	Cannabis	Concurrent	Momentary	Null (withdrawal) Null (general NA)
Expansion motives
Buckner et al. (2015)	Community adults; used cannabis at least once in past month	Cannabis	Concurrent	Momentary	Null (withdrawal) Null (general NA)

Note: + = positive statistically significant association; − = negative statistically significant association; null = no statistically significant association; blank = study did not report findings for that cell; EMA = ecological momentary assessment; NA = negative affect; PA = positive affect.

Reported bivariate correlations only 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 because 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 that 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-persons associations between motives and alcohol and cannabis use patterns is presented in Table 3. All identified studies examining within-persons 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-persons level of analysis.

Table 3.

Overview of Findings on Associations Between EMA-Reported Motives and Substance Use Outcomes

Reference	Sample	Within-persons level of analysis					Between-persons 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 once in past month	+	Null	Null	Null
Drinking quantity (number of drinks)
Dvorak et al. (2014)	College students; drank at least twice/month	Null		+			+		Null
O’Hara, Boynton, et al. (2014)	HBCU students; drank at least once in past month	+ (any) + (social) Null (nonsocial)	+ (any) + (social) Null (nonsocial)	+ (any)^a Null (social)^a +(nonsocial)			Null (any) Null (social)Null (nonsocial)	+ (any) + (social) Null (nonsocial)	+ (any) Null (social) + (nonsocial)
Stevenson et al. (2019)	College students; drank at least once in past 2 weeks	+		Null			+		Null
Dworkin et al. (2018)	Young adult lesbian and bisexual women; drank ≥ 2 drinks ≥ 7 times in past month	+	+	+	Null		Null	Null	+	Null
O’Donnell et al. (2019)	Young adults; drank at least once 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 once in past month	+ (any) + (social) Null (nonsocial)	+ (any) + (social) Null (nonsocial)	Null (any)^a Null (social)^a + (nonsocial)			Null (any) Null (social) Null (nonsocial)	+ (any) + (social) Null (nonsocial)	Null (any) Null (social) + (nonsocial)
Acute alcohol use disorder symptoms
Dvorak et al. (2014)	College students; drank at least twice/month	+		Null			Null		Null
Alcohol-related consequences
Goodhines et al. (2019)	College students; drank and used cannabis at least once in past month			Null (sleep motives)					Null (sleep motives)
(Stevenson et al. (2019)	College students; drank at least once 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 once in the past week and reported heavy drinking at least twice in past month	−	Null	Null	+
Number of cannabis use sessions
Pearson et al. (2020)	College students; used cannabis at least once in past month	+	Null	Null	+	Null	Null	Null	Null	Null	+
Quantity of cannabis use (grams/n joints)
Pearson et al. (2020)	College students; used cannabis at least once 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. (2020)	College students; used cannabis at least once 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 once in past month			Null (sleep motives)					+ (sleep motives)
Pearson et al. (2020)	College students; used cannabis at least once in past month	+	+	+	+	Null	Null	Null	Null	Null	Null

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.

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 Fig. 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-persons 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, whereas coping motives mediated associations between negative affect and alcohol-related consequences, but not consumption, between persons but not within persons. It is important to note that the analyses of Stevenson and colleagues included all EMA days, including nondrinking days, whereas the analyses by Dvorak and colleagues assessed associations only 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 Fig. 1). Reasons for substance use in daily life were consistent with the motivational model for alcohol and cannabis use; most individuals reported social, enhancement, and coping motives. Motives for tobacco use in daily life were less consistent with the motivational model given that participants most commonly reported that their smoking was 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-persons levels 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, & Tennen, 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-reported 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 goals).

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 Table S2 in the Supplemental Material. 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 motive subtypes compared with examining only one or two subtypes in statistical models. Compared with 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 Table S2 in the Supplemental Material because of different motive subtypes. Several studies reporting 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 studies 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 found associations between trait coping motives and subsequent negative affect (Armeli et al., 2015; Heggeness et al., 2019), whereas one did not (Gorka et al., 2017).

Similar research designs among cigarette smokers have also been used, 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 people 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

To assess the validity of trait measures of motives, determining whether trait motives moderate within-persons relationships has been examined in a relatively large body of research. Of particular interest has been the moderating effect of trait coping motives on within-persons 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 were examined (i.e., assessing varying measures of affect and substance use within one study). Specifically, findings were mostly consistent with hypotheses (i.e., people 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), whereas results did support the validity of trait motives in 10 studies (Armeli et al., 2010; Gautreau et al., 2015; Littlefield et al., 2012; Mohr et al., 2001; O’Hara, Armeli, & Tennen, 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 were 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-persons effects of pregaming 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. 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 to inform future research in this area. Second, we characterized the degree of support for the motivational model of substance use according to EMA studies. Finally, 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 reported use of preexisting 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 analogue scales to assess motive strength and 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 p.m. and 6:00 p.m.; Dvorak et al., 2014), which might occur several hours before 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 contexts (see Fig. 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 Fig. 1). Reasons for alcohol and cannabis use reported in daily life were consistent with those suggested by the motivational model; participants reported 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; social motives and positive reinforcement played 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. Finally, 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 used EMA methodology to determine whether 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 using 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 toward a particular motive subtype. Perhaps most strikingly, trait motives did not consistently moderate within-persons associations between affect and substance use. Cooper and colleagues (2015) 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 people 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 regardless of whether 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 . . .”; Stevenson et al., 2019, p. 815). It is possible that a combination of EMA scheduling types (e.g., event contingent during 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).

Additional research is needed on the psychometric properties of these measures to better understand the utility and validity of the EMA measures of motives described in the present review. 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 was conducted in only one prior study; O’Hara, Boynton, et al., 2014) and assessing additional motives that may be identifiable in daily life only 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-persons levels 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-levels 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 (for instruction and code, see Geldhof et al., 2014).

Several studies in the present review evaluated only one motive subtype in daily life (most commonly coping motives) or included only one motive subtype in analyses. Without adjusting for other motive subtypes in analyses, associations might reflect a general overall motivation to use substances, as opposed to with 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 (for discussion of multicollinearity in multilevel models, see Shieh & Fouladi, 2003). 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 with the relative strength of alternative incentives likely influences prolonged and problematic substance use. Therefore, we recommend that investigators use Likert-type or visual analogue 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, in a recent EMA study, researchers 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 Fig. 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. Also 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 Fig. 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 Table S1 in the Supplemental Material). Perhaps people 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 whether 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, and assessment length ranged from 1 to 56 days (see Table S1 in the Supplemental Material). 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; most studies assessed 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 Table S1 in the Supplemental Material; 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 use EMA to determine how sociocultural factors (a broader contextual factor), 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 used 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 preregistered time frame for the present systematic review) are beginning to answer important questions about motives for polysubstance use in daily life (Arterberry et al., 2021).

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 because such technologies are designed to deliver relevant treatment components in the client’s natural environment and according to the client’s affect and context (Nahum-Shani et al., 2018). For example, individuals with a strong within-persons 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 whether daily motives for substance use change during treatment and whether 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 clients 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, p. 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 according to studies included in the present review. Note that 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 gray 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. In addition, 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 using 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 analogue scales) instead of the mere presence of motives. The motivational model should also be assessed in more diverse samples, including people with greater substance-use severity, people who use multiple substances, and samples that 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.

Supplemental Material

sj-pdf-1-cpx-10.1177_2167702620978614 – Supplemental material for Motives for Substance Use in Daily Life: A Systematic Review of Studies Using Ecological Momentary Assessment

Supplemental material, sj-pdf-1-cpx-10.1177_2167702620978614 for Motives for Substance Use in Daily Life: A Systematic Review of Studies Using Ecological Momentary Assessment by Victoria R. Votaw and Katie Witkiewitz in Clinical Psychological Science

Footnotes

Acknowledgements

We thank Elizabeth Yeater and Kamilla Venner for their feedback on this article.

Transparency

Action Editor: John Curtin

Editor: Kenneth J. Sher

Author Contributions

Both authors developed the study concept. V. R. Votaw conducted the systematic search, extracted data from eligible studies, and drafted the manuscript. K. Witkiewitz reviewed study eligibility and provided critical revisions. Both authors approved the final manuscript for submission.

ORCID iD

Victoria R. Votaw

Supplemental Material

Additional supporting information can be found at

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