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. Author manuscript; available in PMC: 2023 Sep 21.
Published in final edited form as: Subst Use Misuse. 2022 Sep 21;57(13):1961–1972. doi: 10.1080/10826084.2022.2125269

Validation of the Motivational Inventory Underlying Substance Use Engagement (MI-USE)

Rickie Miglin 1, Leah Church 1, Nadia Bounoua 1, Naomi Sadeh 1
PMCID: PMC9733715  NIHMSID: NIHMS1846323  PMID: 36129001

Abstract

Given the growing number of fatalities associated with the use of multiple types of drugs, there is an urgent need for a tool that allows clinicians and researchers to quickly assess diverse reasons for substance use. Here, we sought to validate the Motivational Inventory Underlying Substance Engagement (MI-USE), a new measure that assesses motivations for use across different types of substances. Participants were 538 adults ages 18–60 (48% women) who reported substance use problems and past-year drug or alcohol use. Analyses were conducted to discover and validate the factor structure of the MI-USE and evaluate its construct validity. A 30-item model best fit the MI-USE, with one General Factor capturing overall motivation to engage in substance use and eight motive-specific factors that indexed unique motivations for substance use: Emotional Coping (relief from unpleasant emotions), Pleasure-Seeking (feel pleasurable or exciting emotions and sensations), Dependence Severity (avoid withdrawal and cravings), Expansion (enhance self-insight and spirituality), Social Coping (increase confidence and attractiveness), Advantage (gain a physical or mental advantage), Physical Coping (relief from unpleasant bodily sensations), and Sleep (mitigate sleep problems). Evaluation of the measure’s construct validity and internal consistency support the chosen model and interpretation of the motive-specific factors. Results provide initial validation of the MI-USE as a reliable and valid tool for assessing diverse substance use motivations. It improves upon existing measures by allowing clinicians and researchers to simultaneously evaluate motivations for multiple forms of substance use, which facilitates personalized treatment planning and research on polysubstance use.

Keywords: motivation, drugs, alcohol, polysubstance, coping

Introduction

The United States is facing an epidemic of fatalities associated with the use of multiple types of substances, including opioids, stimulants, and polydrug use (CDC, 2022). Given this public health crisis, there is an urgent need to clarify our understanding of the motivations that drive an individual to use substances. It is well-established that individuals use substances for a variety of reasons (Cooper et al., 1992; Cox & Klinger, 1988; Han et al., 2018; McCabe et al., 2009; Simons et al., 2000), although few assessments have been validated that index diverse motivations for substance use across multiple drug types. Development of such a tool has the potential to facilitate research on the psychological causes of problematic use in samples that use a range of substances, especially among polysubstance users who are most at risk of serious long-term consequences (Cicero et al., 2019; Connor et al., 2014; Hassan & Le Foll, 2019). Further, a better understanding of substance use motivations holds promise for improving the efficacy of intervention strategies (Adams et al., 2003) by allowing clinicians to develop treatment plans that address each reason for substance use without having to administer multiple measures. Interest in this topic is clear from recent publications introducing new measures of substance use motivations (Biolcati & Passini, 2019; Kettner et al., 2019). However, no published studies have validated a broad assessment tool that assesses motives for use across more than one substance. The goal of the study was to address this gap by creating the Motivational Inventory Underlying Substance Use Engagement (MI-USE), a self-report measure that captures a range of commonly endorsed reasons for engaging in different forms of substance use.

Motivational Models of Substance Use

To develop the MI-USE, we reviewed the literature on prominent models of substance use and emerging findings on motivations for drug and alcohol use to identify commonly reported reasons for using substances. We began with the widely-studied and well-validated Incentive Motivation Model of alcohol use (Cox & Klinger, 1988, 1990; Cox & Klinger, 2004), which underscores the importance of self-focused affective approach (using to enhance pleasure/ excitement), self-focused affective avoidance (using to cope with negative emotions), social approach (using to improve social situations), and social avoidance (using to avoid condemnation or gain approval, also known as conformity) motivations (Cooper et al., 2016). However, Cox and Klinger’s model was originally developed to specifically address alcohol use motivation, and research on how well the Incentive Motivation Model translates to other forms of substance use has received considerably less attention.

Expanding on this influential model, we also included physical approach and avoidance motivations, or using substances to feel pleasurable sensations or cope with unpleasant ones (Hunt & Evans, 2008; Price, 2000; Semple, Patterson, & Grant, 2002), which may be distinct from emotional or social motivations. For example, the use of drugs to cope with physical sensations is evident in research documenting the frequent use of substances like opioids and marijuana to numb physical pain (Drazdowski, 2016; Han et al., 2018; Hill et al., 2017; McCabe et al., 2009) and illicit use of prescription opioids and benzodiazepines to abate withdrawal symptoms or self-initiate detoxification (Allen & Harocopos, 2016; Stein et al., 2016). Thus, although conceptually related to using substances to cope with negative affect, emerging evidence suggests that the desire to alleviate physical pain is an important reason individuals engage in substance use that may be distinct from use for emotional coping alone (Price, 2000). In the same vein, it is possible that physical approach motivations, or using substances to either feel pleasurable physical sensations (Hunt & Evans, 2008; Semple et al., 2002) or to relax muscles (Clem, Bigand, & Wilson, 2020; Drumright, Patterson, & Strathdee, 2006), may be distinct from using substances to simply feel pleasurable emotions such as joy or euphoria. However, little research has examined this possibility, as existing substance use motivation measures rarely distinguish between the desire to experience physical sensations and emotional states.

Motivations have also emerged in the literature that are characterized by the use of substances to enhance oneself in some manner. For example, Simons and colleagues (1998, 2000) expanded the Drinking Motives Questionnaire (DMQ, 1994; Cooper, 1994) and created the Marijuana Motives Measure to include an additional “expansion” motivational dimension, meant to index motivation for experiencing the enhancement of perceptual and cognitive experiences due to marijuana’s psychedelic properties (Lee, Neighbors, & Woods, 2007). Although first examined in relation to marijuana, research has found that the desire to use substances to broaden one’s consciousness, induce a spiritual experience, and enhance sensory experiences is also linked to the use of psychedelic drugs and stimulants (Kettner et al., 2019; Lyvers & Meester, 2012). Yet, the extent to which this motivational domain remains applicable across other commonly used drugs (e.g., stimulants) remains to be empirically established. It is also common to use substances to enhance one’s abilities and gain a physical, mental, or social advantage (Hildt et al., 2014; Iversen, 2008; Momaya et al., 2015), such as using stimulants and opioids to increase motivation to work or study (Ilieva & Farah, 2013), improve alertness and concentration (Boys et al., 2001), and enhance athletic performance (Veliz et al., 2013). One limitation of previous research is the lack of attention towards investigating whether or not this motivation falls on a separate dimension from other affective, social, or physical motivations for drug use. Thus, an understanding of the relationship between advantage motivations and other dimensions of substance use requires further study.

Current Study: Development of a Multi-Substance Motivational Measure

The majority of research focuses on examining motivations associated with specific types of substance use (e.g., alcohol, marijuana) in isolation, and almost no research has simultaneously examined motivations across multiple forms of substance use in a single study. Specifically, no published studies have validated a broad measure of substance use motivation for use in i) diverse samples of adults and ii) adults who report varied types and amounts of commonly used licit and illicit substances. The dearth of research on this topic may be driven by a lack of available scales for assessing motivations across substance use types, highlighting the need for a new measurement tool that captures a spectrum of motivations for drug and alcohol use. Thus, the goal of the study was to address these limitations by creating and initially validating a self-report measure, the Motivational Inventory Underlying Substance Use Engagement (MI-USE), that captures a range of commonly endorsed reasons for engaging in substance use.

In the development of the MI-USE, we reviewed the extant literature on prominent motivational models of substance use and integrated these findings to generate items that represent a broad array of motives for engaging in substance use. Based on these findings, we conceptualized a bifactor model of motivations for substance use, with a General Factor representing a desire to alter one’s internal state, hypothesized to be a common element across motivations for substance use, as well as distinct motivational domains not captured by the general bifactor. We chose to evaluate a bifactor model because, in general, individuals use substances to alter affective and internal bodily states (Cooper et al., 2016), and recent work by Lac & Donaldson (2017) shows that a general-specific bifactor model achieves the best fit of all factor models of the Drinking Motives Questionnaire (DMQ; Cooper, 1994), the most widely-cited measure of substance use motivation.

In addition to a General Factor indexing the desire to alter one’s internal state, we also hypothesized eight content specific factors representing the motivations derived from our literature review, specifically affective approach (emotional pleasure-seeking), affective avoidance (emotional coping), social approach (social affiliation), social avoidance (conformity/gaining approval), physical approach (physical pleasure-seeking), physical avoidance (physical pain-coping), expansion (perceptual/cognitive enhancement), and advantage (performance enhancement) motivations. To establish construct validity, we evaluated the psychological correlates of the General Factor as well as the specific motivational factors.

Methods

Participants

To ensure we recruited a sample that was well-suited to answer the research questions, we restricted participation to adults who reported a history of substance use problems and substance use in the past 12 months. To collect data, we used Prolific Academic (www.prolific.co), which is an online crowdsourcing platform geared towards researchers. Prolific Academic provides a range of demographic detail about its participant pool on its website, which researchers can use to pre-screen participants based on target criteria. For the current study, we recruited participants that specifically endorsed a “history of substance use problems”. Research has shown Prolific participants produce higher data quality and are more internationally diverse compared to other commonly used platforms (such as Amazon Mechanical Turk) (Peer et al., 2017). Participants were compensated $7 per hour for their time.

918 adults consented and began taking the survey, and in total, 854 adults (aged 18–60) completed the study (survey response rate = 93%). To minimize recall bias about motivations, the final sample was limited to individuals with past-year substance use, which eliminated 243 participants. We also took precautions to reduce the impact of inattentive, inconsistent, or overly virtuous responding by removing participants who failed validity check items (e.g., “I lied a lot on this survey.”) or consistently claimed uncommon positive attributes (e.g., “My opinions are always completely reasonable.”), which removed 73 participants.

The final sample consisted of 538 adults (M/SDage= 32.1/8.5; 48% women, 2.6% “other gender”) who predominantly identified as white (76.8%), followed by Black/African-American (6.9%), “Other” race (5.6%), Asian (4.8%), American Indian/Alaskan Native (2.8%), and/or Native Hawaiian or Pacific Islander (0.4%). 12.5% were Hispanic or Latino. Educational attainment varied: less than high school (1%), high school diploma or equivalent (14.9%), some college (40.3%), Associate’s Degree (11.3%), Bachelor’s degree (22.9%), and Graduate Degree (7.8%).

In order to determine how well the sample represented the general population of the United States (only 14 participants reported living in countries other than the United States), one-sample chi-square tests were conducted. Results indicated that overall the sample was not representative of the general population of the United States in terms of race [χ2 (df = 5) = 88.75, p < 0.001] ethnicity [χ2 (df = 1) = 9.01, p = 0.003], or educational attainment [χ2 (df = 5) = 256.04, p < 0.001]. The sample was representative in terms of sex [χ2 (df = 1) = 0.21, p = 0.65].

Procedures

Participants completed the measures and informed consent via REDCap (Harris et al., 2019). Approval for the study was obtained from the relevant Institutional Review Board (IRBNet #1465266–1).

Measures

Motivational Inventory Underlying Substance Use Engagement (MI-USE).

A copy of the MI-USE is available in Appendix A. The MI-USE starts with a brief assessment of past-year substance use (“In the past year, how often did you use this type of drug?”) across seven different categories (alcohol, cannabis, opioids, stimulants, hallucinogens, sedatives, and “other”) on a 6-point scale from “Every day” to “Never.” Because motivations for use may vary across substances, even within an individual, the MI-USE is designed to be completed separately for each substance category that is endorsed. For the 40 motivation items, respondents were asked to rate how true each statement was for them on a 3-point scale from “0 = Not at all true of me” to “2 = Very true of me” (e.g., “I use these drugs… to stop feeling numb or sad.”). For the purposes of this validation study, we asked the sample of 538 participants to complete the motivation items for two substances they used most frequently or had the most problems within the past year, if they had two substances to report. This resulted in 376 participants reporting on two substances (i.e., 752 cases) and 162 participants reporting on one substance for a total of 914 cases.

Construct Validity.

To a subset of participants (N = 301), we administered the following questionnaires to examine the correlates of the MI-USE motivation factors: (i) The Risky, Impulsive, and Self-Destructive Behavior Questionnaire (RISQ) (Sadeh & Baskin-Sommers, 2017) was used to assess affective motivations for substance use. The motivation items for six different types of substances (binge-drinking, cannabis, heroin, cocaine, prescription drug, and hallucinogen use) were combined with higher scores on the Approach and Avoidance RISQ scales reflecting a greater tendency to use substances for pleasure-seeking (Cronbach’s alpha = 0.82; M/SD = 2.73/0.94) and emotional coping (Cronbach’s alpha = 0.80; M/SD = 2.27/1.04), respectively. (ii) Because we could not find a validated measure of broad substance use motivations, we altered the Marijuana Motives Questionnaire (MMQ) (Simons et al., 1998) to assess motivations beyond marijuana (“…decide how frequently your own drug and alcohol use is motivated by each of the reasons listed.”). The MMQ has five subscales that index: Emotional Coping (Cronbach’s alpha = 0.87; M/SD = 11.51/3.43), Enhancement of Positive Affect (Cronbach’s alpha = 0.83; M/SD = 15.22/3.58), Social Enhancement (Cronbach’s alpha = 0.91; M/SD = 12.22/4.31), Social Conformity (Cronbach’s alpha = 0.83; M/SD = 6.74/2.52), and Expansion (Cronbach’s alpha = 0.91; M/SD = 9.70/4.00). (iii) To assess the severity of drug use and related problems, we used the Severity of Dependence Scale (SDS) (Gossop et al., 1995) (Cronbach’s alpha = 0.91; M/SD = 6.39/4.61) and Drug Use Disorders Identification Test (DUDIT) (Berman et al., 2005) (Cronbach’s alpha = 0.92; M/SD = 14.23/11.17). (v) The Brief Pain Inventory (BPI) (Cleeland & Ryan, 1994) was given to assess the severity of physical pain and its impact on functioning (Cronbach’s alpha = 0.95; M/SD = 25.04/20.29). (vi) The Multidimensional Personality Questionnaire-Short Form (MPS) (Patrick et al., 2002) Achievement scale was used to index drive to succeed and ambitiousness (Cronbach’s alpha = 0.86; M/SD = 6.13/3.60). (vii) The Pittsburgh Sleep Quality Index (PSQI) (Buysse et al., 1989) was shortened to three items and administered to assess sleep quality, with higher scores indicating worse overall sleep quality (Cronbach’s alpha = 0.74; M/SD = 9.89/3.54).

Data Analyses

The 914 responses to the MI-USE motivation items were randomly divided into two split-half samples of 457 cases each. We estimated a series of 40-item bifactor rotated EFAs in the first split-half sample and dropped items from further consideration if they: loaded on two or more motive-specific factors at a threshold of |0.50| or greater, had low loadings on all factors (< 0.3), or did not load on their theorized factor. Items that loaded at 0.50 or higher on the general bifactor and a motive-specific factor were not dropped.

To assess the reliability of the EFA-derived factor structure, we conducted CFA on the second split-half sample. Factor analyses were performed in Mplus 8.5 (Muthén & Muthén, 2020) using the robust weighted least squares means and variance adjusted (WLSMV) estimator, which is appropriate for ordinal variables and non-normally distributed data. Models with root mean square error of approximation (RMSEA) ≤0.05, standardized root mean square residual (SRMR) ≤0.08, confirmatory fit index (CFI) and Tucker-Lewis index (TLI) ≥0.95 were considered to fit the data well (Hu & Bentler, 1999). Models were also evaluated based on the pattern of item loadings, interpretability of each solution, and convergence with the hypothesized factor structure. To empirically test whether the bifactor model (1 general, 8 specific factors) showed superior fit to a non-bifactor 8-factor model, we calculated the likelihood ratio test using the Mplus DIFFTEST function to directly compare models.

Finally, we examined associations between the substance use motivations and criterion measures to assess construct validity using bivariate correlations. For the construct validity analyses, we only included scores from one substance each participant endorsed. The number of cases available for analysis was 301 for the construct validity data. Given the large sample size, we only interpreted correlation magnitudes greater than p < 0.001 as significant. These analyses were conducted using SPSS v28. This study was not preregistered. The data from this study will be made available upon request.

Results

Substance Use Types

The MI-USE motivation items were completed in reference to a range of different types of past-year substance use. Alcohol (356 cases/38.9%) and cannabis (242 cases/26.4%) were the most common substances used to complete the motivation items. Slightly over a third of the responses for the motivation items were completed for stimulant (153 cases/16.7%), opioid (96 cases/10.5%), sedative (49 cases/5.4%) or hallucinogen (18 cases/2.0%) use.

Substance Use Motivations

Factor Structure.

We submitted the initial pool of 40 items to a bifactor EFA and removed 10 poor performing items from further analysis. Specifically, we removed items that were endorsed at low levels (< 15% of the sample), cross-loaded on multiple specific factors, or did not load significantly with any hypothesized factors (see Supplemental Materials for details).

Model fit for EFAs with the remaining 30 items are summarized in Table 1 [Table 1 near here]. The 9-factor solution was selected as the best fitting model from an empirical and theoretical standpoint (see Table 2 for item loadings)1 [Table 2 near here]. All items loaded significantly on the General Factor and eight motive-specific factors emerged: Emotional Coping (to get relief from unpleasant emotions), Pleasure-Seeking (to feel pleasurable or exciting emotions and sensations), Dependence Severity (to avoid withdrawal and cravings), Expansion (to enhance self-insight and spirituality), Social Coping (to increase confidence and attractiveness), Advantage (to gain a physical or mental advantage), Physical Coping (to get relief from unpleasant bodily sensations), and Sleep (to mitigate sleep problems).

Table 1.

Fit Statistics for Factor Analysis of the MI-USE Motivations

Model Χ 2 df RMSEA SRMR CFI TLI Models Compared ᐃX2(df) , p-value
Bifactor Exploratory Factor Analysis
2 Factor 2520* 376 .11 .12 .73 .69
3 Factor 1708* 348 .09 .09 .83 .79 3 vs. 2 Factor: 579.3(28), p <.001
4 Factor 1208* 321 .08 .07 .89 .85 4 vs. 3 Factor: 375.5(26), p <.001
5 Factor 776* 295 .06 .05 .94 .91 5 vs. 4 Factor: 287.6(25), p <.001
6 Factor 565* 270 .05 .04 .96 .94 6 vs. 5 Factor: 178.7(24), p <.001
7 Factor 450* 246 .04 .04 .97 .96 7 vs. 6 Factor: 103.8(23), p <.001
8 Factor 363* 223 .04 .03 .98 .97 8 vs. 7 Factor: 90.2(22), p <.001
9 Factor 286* 201 .03 .03 .99 .98 9 vs. 8 Factor: 71.5(21), p <.001
10 Factor 224 180 .02 .02 .99 .99 10 vs. 9 Factor: 54.9(20), p <.001
Confirmatory Factor Analysis Bifactor vs. 8-Factor:
Bifactor 894* 322 .06 .09 .94 .93 270.8(24), p < 0.001
8-Factor 1287* 377 .07 .10 .91 .89
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Note. df = degrees of freedom. RMSEA = root mean square error of approximation. SRMR = standardized root mean square residual. CFI = confirmatory fit index. TLI= Tucker-Lewis index. All the listed models only include the final 30 items remaining after removal of poor performing items.

*

p < .001.

Table 2.

Best-Fitting Exploratory Factor Analysis Model of the MI-USE Motivations

MI-USE Items 1 2 3 4 5 6 7 8 9
to stop feeling numb or sad 0.75* 0.30* −0.15* 0.03 −0.14* −0.23* −0.14* 0.03 −0.09
to feel less restless or on-edge 0.49* 0.56* 0.10* 0.01 0.08 0.11* 0.06 0.08 −0.03
to avoid scary or unpleasant thoughts 0.58* 0.27* −0.24* 0.15* 0.00 0.11* −0.02 0.23* −0.09
to stop my anxiety or panic 0.50* 0.38* −0.04 0.09 −0.04 0.16* 0.15* 0.28* 0.00
to stop feeling upset or overwhelmed 0.64* 0.62* −0.08 −0.05 −0.03 −0.03 −0.01 −0.04 −0.06
to feel pleasurable sensations 0.65* −0.10 0.60* 0.09* 0.04 0.05 −0.02 −0.08 −0.04
to feel pleasure or happiness 0.59* 0.10 0.54* 0.04 −0.06 0.01 −0.14* 0.05 −0.03
to feel intense joy or euphoria 0.74* −0.21* 0.25* −0.06 −0.07 −0.16* 0.04 −0.07 −0.07
to feel excitement or get a thrill 0.75* −0.25* 0.17* −0.25* −0.18* −0.03 0.02 −0.21* −0.08
to avoid feeling bored 0.68* −0.05 0.12 0.02 −0.09 −0.09 −0.21* 0.01 −0.10
to stop my drug cravings 0.46* 0.01 −0.04 0.70* −0.02 −0.12* 0.03 −0.11* 0.07
to avoid withdrawal or being hungover 0.38* −0.01 0.10* 0.76* −0.05 0.00 0.01 0.05 −0.02
to gain insight into myself 0.41* 0.14* 0.12* −0.23* 0.56* −0.16* −0.02 0.05 0.23*
to increase feelings of spirituality 0.40* −0.05 −0.07 −0.02 0.73* −0.01 0.08 −0.09 −0.11*
to achieve a dream-like state 0.59* −0.07 0.18* 0.07 0.33* −0.12* 0.02 0.10 −0.13*
to detach from reality or life 0.71* 0.02 0.03 0.07 −0.04 −0.10* −0.13* 0.07 −0.39*
to feel more connected to other people 0.51* 0.05 0.08 −0.17* 0.20* 0.43* −0.05 −0.08 −0.04
to be less shy in social situations 0.51* 0.07* −0.01 −0.06 −0.07 0.68* −0.01 −0.08 −0.05
to increase my confidence 0.63* −0.02 0.03 −0.06 −0.13* 0.59* −0.05 −0.02 0.12*
to appear more attractive 0.64* −0.32* −0.14 0.03 0.04 0.38* −0.11 −0.02 0.09
to avoid rejection or abuse 0.60* −0.19 −0.31* −0.06 0.02 0.07 0.00 0.15 −0.16*
to gain a physical or mental advantage 0.50* −0.12* −0.07 0.06 0.11* 0.06 0.58* 0.03 −0.02
to feel more alert or energized 0.42* 0.01 0.01 0.02 −0.14* −0.09 0.65* 0.07 −0.25*
to make it easier to pay attention 0.36* 0.01 0.01 −0.01 −0.03 0.01 0.88* 0.00 0.08*
to make money or as part of my work 0.44* −0.12 −0.10 0.14 0.22* 0.14 0.51* −0.09 −0.01
to stop physical suffering 0.40* 0.10* −0.03 0.14* 0.00 −0.01 0.06 0.62* 0.08
to make my muscles feel more relaxed 0.40* 0.07 0.12 0.02 0.16* 0.01 −0.07 0.41* 0.33*
to get relief from chronic pain 0.24* −0.02 −0.02 −0.02 0.00 −0.02 0.01 1.01* −0.01
to avoid nightmares or sleep problems 0.33* 0.04 −0.11* 0.04 0.01 −0.08* 0.06* −0.02 0.80*
to fall or stay asleep 0.21* 0.00 0.06 −0.05 −0.06 −0.01 −0.08 0.11* 0.83*
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Note. N = 457. All bolded indicators loaded positively on their theorized factor at p < 0.05. The Factor numbers stand for the following: 1 – General, 2 – Emotional Coping, 3 – Pleasure-Seeking, 4 – Dependence Severity, 5 – Expansion, 6 – Social Coping, 7 – Advantage, 8 – Physical Coping, 9 – Sleep.

To validate the factor structure, we tested a confirmatory bifactor model consisting of one General Factor and eight motive-specific factors in the second sample. As displayed in Tables 1 and 3 [Table 3 near here], fit statistics confirmed that the model fit the data well, with all of the items loading significantly on the General Factor and their hypothesized motive-specific factor. To test whether the CFA model demonstrated adequate fit across genders, we split the total sample by gender and ran the CFA model separately for men and women. We found that the fit statistics for men (RMSEA = .06, CFI = 0.94, TLI = 0.93, SRMR = 0.07) and women (RMSEA = .05, CFI = 0.95, TLI = 0.94, SRMR = 0.07) indicated the model fit the data well across gender.

Table 3.

Bifactor Confirmatory Factor Analysis Model of the MI-USE Motivations

MI-USE Items 1 2 3 4 5 6 7 8 9
to stop feeling numb or sad 0.76 0.19
to feel less restless or on-edge 0.67 0.36
to avoid scary or unpleasant thoughts 0.74 0.39
to stop my anxiety or panic 0.70 0.44
to stop feeling upset or overwhelmed 0.77 0.30
to feel pleasurable sensations 0.64 0.52
to feel pleasure or happiness 0.62 0.50
to feel intense joy or euphoria 0.56 0.65
to feel excitement or get a thrill 0.42 0.71
to avoid feeling bored 0.64 0.35
to stop my drug cravings 0.56 0.68
to avoid withdrawal or being hungover 0.62 0.61
to gain insight into myself 0.38 0.82
to increase feelings of spirituality 0.39 0.75
to achieve a dream-like state 0.55 0.31
to detach from reality or life 0.74
to feel more connected to other people 0.28 0.66
to be less shy in social situations 0.36 0.68
to increase my confidence 0.49 0.77
to appear more attractive 0.43 0.68
to avoid rejection or abuse 0.66
to gain a physical or mental advantage 0.30 0.83
to feel more alert or energized 0.16 0.89
to make it easier to pay attention 0.30 0.86
to make money or as part of my work 0.29 0.71
to stop physical suffering 0.42 0.77
to make my muscles feel more relaxed 0.50 0.61
to get relief from chronic pain 0.28 0.90
to avoid nightmares or sleep problems 0.59 0.68
to fall or stay asleep 0.57 0.75
1 General --
2 Emotional Coping .00 --
3 Pleasure-Seeking .00 −.73* --
4 Dependence Severity .00 −.03 .00 --
5 Expansion .00 .00 .11 −.09 --
6 Social Coping .00 −.02 .14* −.10 .06 --
7 Advantage .00 .00 .18* .20* .30* .19* --
8 Physical Coping .00 .08 −.08 .14* .20* −.12 .02 --
9 Sleep .00 .07 −.22* .00 .06 −.16* −.12 .34* --
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Note. N = 457. All indicators loaded positively on one specific factor at p < 0.05.

*

Correlations significant at the p < 0.001 level.

CFA Model Comparison.

We tested whether a bifactor (with 1 general and 8 specific factors) showed superior fit to a non-bifactor 8-factor model. A likelihood ratio test indicated that the bifactor model achieved significantly better fit than the 8-factor CFA [χ2 (df = 24) = 270.8, p < 0.001]. This was confirmed by examining the fit statistics, which demonstrated that the bifactor CFA achieved better fit across all metrics (RMSEA = .06, CFI = 0.94, TLI = 0.93, SRMR = 0.09) than the 8-Factor CFA (RMSEA = .07, CFI = 0.91, TLI = 0.89, SRMR = 0.10).

Internal Consistency.

The General Factor (30 items) evidenced good reliability (Cronbach’s alpha = 0.89), as did the majority of the motive-specific factors [Emotional Coping = 0.83 (5 items), Pleasure-Seeking = 0.83 (4 items), Dependence Severity = 0.75 (2 items), Expansion = 0.65 (3 items), Social Coping = 0.77 (4 items), Advantage = 0.80 (4 items), Physical Coping = 0.80 (3 items), Sleep = 0.81 (2 items)]. Because individuals reported their motivations in relation to different substances, we investigated whether reliability of the motivation factors differed across substances and found that it did in some cases. Expansion, for example, demonstrated stronger reliability when these motivation items were completed for hallucinogens (Cronbach’s alpha = 0.81), and Dependence Severity items showed stronger reliability when the items were completed in reference to Opioids, Stimulants, or Sedatives (Cronbach’s alphas = 0.81–0.88). Notably, the reliability of the General Factor was strong regardless of the substance used (Cronbach’s alphas = 0.87–0.92).

Construct Validity.

To create the motive-specific scores, each item in a motivation factor was simply summed separately for each substance a participant endorsed. The General Factor (total) score reflected the total item score for each substance endorsed by an individual participant. If a participant endorsed two substances, each of these substances would have a separate set of associated total (General Factor) and motive-specific scores per participant. None of the items are weighted in scoring the MI-USE.

Correlations between the MI-USE factors and external correlates are presented in Table 4 [Table 4 near here]. As expected, Emotional Coping was positively associated with the tendency to use substances to relieve negative emotions, whereas Pleasure-Seeking was positively associated with the tendency to use substances to attain pleasurable or exciting mood states. Dependence Severity showed the strongest associations with measures of drug-related problems and symptoms of dependence, Expansion correlated positively with using drugs to enhance self-insight and spirituality, and Social Coping correlated positively with using substances to conform to social norms and enhance sociability. Finally, Physical Coping and Sleep correlated most strongly with measures of physical pain and poor sleep quality, respectively. Advantage did not relate to its hypothesized external correlate, a trait measure of achievement orientation.

Table 4.

Construct Validity of MI-USE Motivational Factors

MI-USE Motivations
Correlates General Emotional Coping Pleasure-Seeking Dependence Severity Expansion Social Coping Advantage Physical Coping Sleep
Substance Use Coping (MMQ) .50* .49 * .40* .31* .22* .24* .15 .10 .28*
Substance Use Avoidance (RISQ) .48* .47 * .33* .20* .22* .22* .18 .22* .27*
Substance Use Enhancement (MMQ) .34* .14 .54 * .26* .17 .25* .11 −.04 .07
Substance Use Approach (RISQ) .26* .11 .39 * .20* .15 .15 .08 −.02 .09
Severity of Dependence (SDS) .32* .19* .30* .48 * .09 .15 .26* .03 .05
Drug-Related Problems (DUDIT) .37* .21* .33* .52 * .07 .15 .34* .11 .07
Substance Use Expansion (MMQ) .43* .20* .32* .09 .50 * .33* .30* .23* .16
Social Motives (MMQ) .37* .19* .41* .06 .24* .51 * .17 −.02 .10
Conformity Motives (MMQ) .29* .16 .23* .01 .26* .33 * .19* .04 .06
Achievement-Driven (MPS) .05 .02 −.06 −.01 .06 −.04 .13 .18 .08
Physical Pain (BPI) .27* .24* .06 .17 .14 −.05 .17 .49 * .22*
Sleep Quality (PSQI) .36* .36* .16 .18 .08 .15 .16 .25* .31 *
Total Substance Use (MI-USE) .32 * .23* .21* .33* .19* .08 .30* .17 .15
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Note. N = 301. Bolded correlations reflect hypothesized associations.

*

p < 0.001.

To evaluate potential overlap between substance use motivations, type, and frequency, we conducted supplemental analyses to evaluate their interrelations. As illustrated in Figure 1, the motive-specific factors were endorsed across all types of substances. In terms of frequency of use, amount of past-year substance use was only weakly correlated with the General Factor and motive-specific factors (Table 4), indicating the motivation factors index unique variance in substance use not captured by amount of use alone.

Figure 1.

Figure 1.

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MI-USE Motivation Factors as a Function of the Substance Being Used

Note. N = 914. Graph displays the mean total score for each motive-specific factor separately by the type of substance used.

Within-Individual Motives.

We conducted an additional supplemental analysis to examine whether individuals who reported on two different types of substances (n = 376) evidenced significant differences in their motivation ratings for different types of substance use. This within-person mean difference analysis (conducted with paired-sample t-tests) was used to empirically test the hypothesis that individuals who use multiple substances can do so for different reasons, and, therefore, the use of multiple cases from one individual for different types of substances is not redundant. Given that it is the factor that contains all of the items, and thus represents the most conservative test of this hypothesis, we compared an individual’s mean scores on the General Factor for two different substance types. Results of this analysis produced a significant difference in the mean General Factor score between the first (M = 22.7, SD = 10.8) and second (M = 21.5, SD = 11.0) substances reported by the same individual; t(375) = 2.25, p = 0.03.

Discussion

The goal of this study was to create and initially validate a new measure of substance use motivation, the Motivational Inventory Underlying Substance Use Engagement (MI-USE). The MI-USE is unique from existing questionnaires because it was developed to measure a range of affective, physical, and social substance use motivations across different types of substances. The best-fitting model across exploratory and confirmatory factor analyses conformed to a bifactor structure with a General Factor and eight distinct motives for substance use that fell along the dimensions of approach motivations (e.g., using substances to seek pleasure, gain an advantage, or expand cognitive and perceptual experiences), and avoidance motivations (e.g., coping with unpleasant emotional, interpersonal, or physical experiences, problematic sleep, or dependence symptoms). Notably, the motivation factors were endorsed by individuals using a variety of substances, evidence that the MI-USE assesses motivational triggers for a spectrum of drug and alcohol use. Overall, findings indicate the MI-USE has a reliable factor structure, strong psychometric properties, and good construct validity when used to assess motivations for substance use in mixed substance-using samples of adults.

MI-USE General Factor

Because all substance use motivations reflect the desire to alter one’s current state, we hypothesized the MI-USE items would fit a bifactor structure well, with this commonality among the motivation items reflected in a General Factor. As expected, this factor structure fit the data well across two samples of cases representing motivations for engaging in a range of different types of drug and alcohol use. Conceptually, the bifactor model of the MI-USE converges with recent research demonstrating a general-specific bifactor structure best fits models of drinking motivation (Lac & Donaldson, 2017) and suggests that many motivations for substance use share common features. The items with the strongest loadings on the General Factor described particularly intense or arousing states either to be achieved (e.g., “to feel intense joy or euphoria”) or avoided (e.g., “to stop feeling numb or sad”). Based on these observations, the General Factor appears to represent the overall intensity or the strength of an individual’s motivation for substance use (i.e., the desire to alter one’s internal state with substances), suggesting scores on this factor represent the overall intensity or strength of an individual’s motivation to use substances. Two items loaded exclusively on the General Factor in the bifactor EFA: “to detach from reality or life”, and “to avoid rejection or abuse”. Consistent with prior validation studies of bifactor models (Lac & Donaldson, 2017; Sadeh & Baskin-Sommers, 2017), these items were retained in the measure on the General Factor, because they have potential clinical utility for explaining substance use motivation.

Although we did not have an external measure of the intensity or strength of motivation for substance use, because one does not exist aside from the current measure, we examined convergence of the General Factor with scales that measure concepts potentially related to this construct. Given the pattern of item loadings on the General Factor, we expected it to correlate positively with phenotypes that likely co-occur with a strong motivation for use, such as, past-year amount of substance use and drug-related problems. As expected, the General Factor evidenced moderate positive correlations with these constructs, findings that are consistent with our interpretation of the General Factor as reflective of higher overall motivation to engage in substance use. Interestingly, scores on the General Factor did not correlate particularly highly with the frequency of past-year substance use or measures of severity of dependence, indicating it assesses a unique dimension related to substance use that has yet to be investigated in the addiction literature. An important next step will be to research the predictive and clinical utility of this new dimension of overall motivation to use substances for understanding and predicting problematic trajectories of drug and alcohol use, given that the strength of one’s motivation to use substances has not historically been examined in research on addiction.

Motive-Specific Factors

In addition to the General Factor, eight motive-specific factors emerged that captured unique reasons for engaging in substance use that spanned the domains of Emotional Coping (using to cope with unpleasant emotions), Pleasure-Seeking (using to attain pleasurable emotions and sensations), Dependence Severity (using to avoid withdrawal and cravings), Expansion (using to expand consciousness and sensory perception), Social Coping (using to increase confidence or bond), Advantage (using to gain a mental or physical advantage), Physical Coping (using to decrease physical discomfort), and Sleep (using to alleviate sleep problems). Overall, this factor structure converged with the hypothesized motivational domains with some key differences. As expected, an emotional avoidance (Emotional Coping) factor emerged, as did separate physical avoidance (Physical Coping), Advantage, and Expansion motivations. Unexpectedly, the emotional and physical approach items loaded on the same factor (Pleasure-Seeking), and the social items converged on a single Social Coping factor. Distinct factors also emerged for substance use motivated by the desire to alleviate dependence symptoms (Dependence Severity) and sleep problems (Sleep), which was unanticipated.

Examination of the construct validity of the specific motivations showed convergence between the MI-USE factors and measures of related constructs. Pleasure-Seeking was positively correlated with using substances for pleasure enhancement, whereas Emotional Coping was positively correlated with using substances to dampen negative emotions and endorsed most highly in relation to sedative use. As hypothesized, physical avoidance emerged as a factor separate from general emotional coping, which extends prior models by showing empirical differentiation between these motivations. Physical Coping correlated most strongly with past-year physical pain and mean scores were highest in relation to opioid use, unsurprising given the strong relationship between the use of opioids for pain management purposes (Rogers et al., 2019; Trafton et al., 2004). Interestingly, physical approach motivations were not distinct from the desire to seek pleasurable mood states, suggesting less differentiation between affect and bodily sensations for approach than avoidance motivations. A similar pattern emerged for the social motivation items, which may be due to relatively low endorsement of social motivations compared to other triggers. As the majority of research has been conducted with adolescents or college-students (Cooper et al., 2016), our findings may differ from past models that centralize the influence of peers and social pressure on substance use due to changes in the relative importance of social motivations for substance use later in adulthood (Charles & Carstensen, 2010). The items that survived in the Social Coping factor correlated moderately with other measures of social enhancement and conformity, appearing to capture a combination of social approach and avoidance motivations.

Although not hypothesized, the emergence of a Dependence Severity factor composed of items that index substance use to stop drug cravings, avoid withdrawal, or feeling hungover is consistent with prior research highlighting these experiences as potent triggers for drug and alcohol use (de Bruijn et al., 2004). Given the range of substances indexed by the MI-USE, Dependence Severity may be particularly useful for identifying individuals who require further assessment for substance use disorders and identifying the types of substances people are using to cope with symptoms of withdrawal and dependence. Additionally, the items indexing sleep-related disturbances unexpectedly formed a separate factor, indicating that this motivational factor is distinct from coping with unpleasant emotions and physical sensations. The identification of the Sleep factor is noteworthy in that it suggests the desire to regulate sleep is an important and highly reported trigger for substance use.

Finally, the expected Expansion and Advantage motivations emerged as specific factors. The Expansion items were generated based on past research showing that individuals often use marijuana in order to experience enhanced consciousness and awareness (Simons et al., 1998, 2000), and was strongly associated with the expansion measure originally developed by Simons and colleagues. Advantage captured motivations related to gaining a physical or mental advantage and evidenced the highest mean scores for individuals reporting on their motivations for stimulant use, which likely reflects the use of stimulant drugs to improve performance at school or work (Hall et al., 2005). However, this factor did not significantly correlate as expected with an achievement-driven personality trait, possibly because the use of substances to get ahead is distinct from the drive to succeed more broadly. Further investigation of the external correlates of this factor is necessary to understand the construct it represents.

Together, these findings suggest that the different motivational dimensions of the MI-USE correlate in meaningful ways with extant well-validated measures but are not necessarily substance-type-specific. Many of these motivational domains have been previously identified in the literature as related to separate forms of substance use (Cooper et al., 2016; Simons et al., 2000; Rigg & Ibañez, 2010), but, notably, were found in this study to be related to multiple types of substance use. The finding suggests that many of these motivations are not unique to specific types of substance use and highlight the need for future research to emphasize examinations of substance use motivation across substance types (Sadeh et al., 2021). Future research should continue to evaluate the unique convergent validity of the content-specific MI-USE domains with additional self-report measures as well as behavioral tasks and potential relationships with psychiatric diagnoses (Miglin, Bounoua, Spielberg, & Sadeh, 2020).

Strengths and Limitations

This study had several noteworthy strengths, including the development and initial validation of a scale through the use of a relatively large sample of substance-using adults, and a reliable factor structure that was tested and confirmed using exploratory and confirmatory factor analysis in independent samples. The sample also represented adults of varying age groups, in contrast to the majority of substance use motivation research, which typically utilizes college student samples (Cooper et al., 2016). However, our findings ought to be interpreted with consideration to the limitations of the study design. First, recruitment of an online sample may limit the generalizability of the findings to adults with reliable internet access. Second, our sample was not representative of the general population of the United States in terms of race, ethnicity, or educational attainment. Only 23% of the sample was non-white and thus further validation in diverse samples is needed before results can be confidently generalized to minority groups. Third, the types of substances used to complete the motivation items were not equally represented, with motivations for alcohol and cannabis use represented at higher rates than other drug types. Fourth, we did not include motivations for nicotine use as part of the MI-USE. Future studies of the MI-USE should include nicotine use motivations, particularly in light of the growing prevalence of nicotine use in younger populations (Fadus, Smith, & Squeglia, 2019). Future research on how nicotine use motivations relate to other substance use motivations may help shed light on why nicotine co-use in substance use disorders is so common (John et al., 2018). Fifth, we did not examine test-retest reliability or the predictive validity of the scale, limiting knowledge of the temporal stability and predictive utility of the scale. Finally, due to sample size restrictions and limited diversity in the demographic characteristics, we were unable to conduct formal tests of measurement invariance. Future studies should attempt to examine measurement invariance formally across gender, race, ethnicity, education level, and substance type.

Conclusions

In conclusion, findings demonstrate that the MI-USE may be used by researchers and clinicians interested in assessing individuals’ overall degree of motivation to engage in substance use (total score) as well as more specific motivations that may be driving engagement in problematic or frequent substance use. Although established measures that assess motivations within specific drug types exist, a significant advantage of the MI-USE is its flexibility in assessing motivations across multiple forms of substance use within the same individual. Research on how motivations vary across different substances and within individuals may provide new insight into substance use disorder etiology. This measure also offers clinicians a tool for quickly assessing common motivations for substance use and using this information to select interventions that address the unique motivations for different forms of substance use within the same individual. For example, an individual may use stimulants to enhance performance at work and alcohol to feel more confident in social situations, while simultaneously using sedatives to fall asleep. In such a case, the MI-USE could facilitate personalized treatment-planning by allowing clinicians to link different forms of substance misuse to their specific functions for a particular client. The present findings support the validation of this measure and provide a strong basis for future research exploring novel research and clinical applications of the MI-USE.

Supplementary Material

Supp 1

APPENDIX

Appendix A. The Motivational Inventory Underlying Substance Use Engagement (MI-USE)

Directions: Circle the drugs you have used in the past 12 months and mark how frequently you used the drug. If the drug was prescribed, only include times when you used it in ways that were not prescribed (e.g., used more than prescribed).

Circle the drugs you have used in the past year (past 12 months): In the past year, how often did you use this type of drug? Check below if used in the past 30 days:
Cannabis Marijuana (weed, pot, joint, blunt) Hashish (hash, hemp, boom) • Every day
• Every week
• Every month		 • Every few months
• A couple of times
• Never		 graphic file with name nihms-1846323-t0002.jpg
Opioids Heroin, Fentanyl, Opium, Opioid Pain Relievers (Vicodin, Morphine, OxyContin), Methadone • Every day
• Every week
• Every month		 • Every few months
• A couple of times
• Never		 graphic file with name nihms-1846323-t0003.jpg
Stimulants Cocaine (coke, crack), Amphetamine (black beauties), Methamphetamine (meth, ice, speed), Non-prescribed use of stimulants (Adderall, Vyvanse, Ritalin) • Every day
• Every week
• Every month		 • Every few months
• A couple of times
• Never		 graphic file with name nihms-1846323-t0004.jpg
Hallucinogens MDMA (Ecstasy, Molly), LSD (acid), Psilocybin (magic mushrooms), Mescaline (peyote), Ketamine (Special K), PCP (angel dust), GHB (liquid Ecstasy) • Every day
• Every week
• Every month		 • Every few months
• A couple of times
• Never		 graphic file with name nihms-1846323-t0005.jpg
Sedatives Barbiturates, downers, Non-prescribed use of Xanax, Valium, Ativan, sleep medications (Ambien, Lunesta), Serepax • Every day
• Every week
• Every month		 • Every few months
• A couple of times
• Never		 graphic file with name nihms-1846323-t0006.jpg
Alcohol Beer, wine, liquor • Every day
• Every week
• Every month		 • Every few months
• A couple of times
• Never		 graphic file with name nihms-1846323-t0007.jpg
List any other drugs used: • Every day
• Every week
• Every month		 • Every few months
• A couple of times
• Never		 graphic file with name nihms-1846323-t0008.jpg
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Directions: For each type of drug that you used in the past year, rate how true the statement is of you:

0 = Not at all true of me 1 = Somewhat true of me 2 = Very true of me
Cannabis Opioids Stimulants Hallucinogens Sedatives Alcohol
I use these drugs….
1. To avoid nightmares or sleep problems. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
2. To stop feeling numb or sad. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
3. To avoid feeling bored. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
4. To detach from reality or life. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
5. To gain insight into myself. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
6. To stop feeling upset or overwhelmed. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
7. To feel less restless or on-edge. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
8. To fall or stay asleep. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
9. To avoid scary or unpleasant thoughts. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
10. To appear more attractive  0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
11. To stop physical suffering. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
12. To stop my drug cravings. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
13. To gain a physical or mental advantage. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
14. To feel pleasure or happiness. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
15. To stop my anxiety or panic. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
16. To increase feelings of spirituality. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
17. To feel more alert or energized.   0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
18. To be less shy in social situations 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
19. To feel intense joy or euphoria. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
20. To make my muscles feel more relaxed. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
21. To get relief from chronic pain. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
22. To avoid rejection or abuse. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
23. To make it easier to pay attention. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
24. To feel excitement or get a thrill. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
25. To increase my confidence. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
26. To feel pleasurable sensations.  0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
27. To avoid withdrawal or being hungover. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
28. To achieve a dream-like state. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
29. To make money or as part of my work. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
30. To feel more connected to other people. 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
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Data Availability Statement

Data available on request from the authors.

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Associated Data

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

Supplementary Materials

Supp 1
NIHMS1846323-supplement-Supp_1.docx (133.2KB, docx)

Data Availability Statement

Data available on request from the authors.

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