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. Author manuscript; available in PMC: 2026 Apr 7.
Published in final edited form as: Psychol Addict Behav. 2025 Nov 13;40(1):11–22. doi: 10.1037/adb0001107

Quality-of-life improvements associated with reductions in cocaine-positive urine drug screens

Sean D Regnier 1, Kelsey Karnik 2, Thomas P Shellenberg 1,4, David H Cox 1, Christopher J McLouth 2, Lon R Hays 3, Danielle M Anderson 3, Joy M Schmitz 6, Jennifer R Havens 1,5, Joshua A Lile 1,3,4, William W Stoops 1,3,4,5
PMCID: PMC13051296  NIHMSID: NIHMS2147615  PMID: 41231559

Abstract

Objective:

Objectively verified reductions in cocaine use may be a more viable treatment target compared to complete abstinence. However, few studies have examined the associated health benefits of this change. This study assessed how quality-of-life outcomes (psychological functioning, social functioning, sleep) change with reductions in cocaine-positive urine drug screens.

Method:

Participants (n=107) with cocaine use disorder enrolled in a 12-week contingency management trial and were randomly assigned to High-Value, Low-Value, or Non-Contingent Control groups. Quality-of-life was measured at predetermined intervals over the course of the trial. Linear mixed models disaggregated the proportion of cocaine-negative urine screens into between-subject (i.e., a participant’s average use across the trial) and within-subject (i.e., a participant’s deviation from their average) components to separately estimate their associations with quality-of-life outcomes.

Results:

Overall, higher proportions of cocaine-negative urine test results were associated with statistically significant, although modest, between- and within-subject changes in several quality-of-life measures, including psychosocial functioning, mental health, and sleep. Participants who reached at least 75% cocaine negative urine test results during treatment demonstrated improvements in all SIP-C outcomes, excluding Total Score.

Conclusions:

These findings indicate that reducing cocaine use improves quality-of-life outcomes in people with cocaine use disorder. These results also extend prior research on more robust health improvements that emerge when trial participants attain 75% negative urine test results over a trial. Future research should explore the extent to which these beneficial outcomes apply to other CUD samples, including those with more severe comorbid psychosocial challenges at baseline.

Keywords: Cocaine, reduction, contingency management, quality of life, psychosocial health

Over 5 million Americans used cocaine in 2023, making it the most widely used illicit stimulant in the United States (SAMHSA, 2024). Approximately 1.4 million people met criteria for cocaine use disorder (CUD) in 2023 (SAMHSA, 2024). In addition to overdose-related mortality risks (Garnett & Miniño, 2024), CUD is associated with decrements in quality-of-life, a broad construct encompassing psychological and social factors that influence a person’s well-being, such as mental and behavioral health, employment, and social relationships (Jackson et al., 2014). For example, cocaine use is related to disruptions of sleep (Lima et al., 2008; Morgan & Malison, 2007), comorbid mental health conditions (Conner et al., 2008; Rounsaville, 2004), disrupted social relationships (Preller et al., 2014), and other interpersonal and intrapersonal problems, important predictors of treatment retention (Kiluk et al., 2013). In one study, participants who primarily used cocaine had higher Total Scores on the Short Inventory of Problems–Revised (SIP-R), a measure of adverse consequences of drug use, compared to participants who primarily used marijuana, alcohol, or methamphetamine (Kiluk et al., 2013).

Abstinence from cocaine use during treatment has been associated with improvements in quality-of-life measures. For example, one pooled analysis found that participants with CUD across three trials who received prize-based contingency management (CM) had improved outcomes in several domains of the Quality-of-Life Inventory (QOLI; Frisch et al., 1992), including Total Score, Civic Action, Home, Relationships with Relatives, Self-Regard, Standard of Living, and Work (Petry et al., 2007). Similar improvements were found in psychiatric symptoms of the Brief Symptom Inventory (BSI; Petry et al., 2013). In both studies, cocaine abstinence and proportion of negative cocaine samples submitted mediated the effects of CM on QOLI & BSI scores (e.g., CM vs standard care), indicating that changes in these outcomes were due to reductions in cocaine use. Another study of adults who used cocaine and participated in weekly psychotherapy, family therapy, or group therapy found that abstinence 6–12 months after treatment was associated with improvements in psychiatric symptoms and family relationships, as measured by the Addiction Severity Index (ASI; Kang et al., 1991). Taken together, these studies provide important evidence that cocaine abstinence can improve quality-of-life.

Modern treatments often fall short of achieving continuous and complete abstinence from cocaine use (for reviews, see Czoty et al., 2016; Kampman, 2019; Tardelli et al., 2020). As a result, experts in the field have suggested that objectively verified reductions in drug use, rather than abstinence only, are clinically meaningful and may be a more viable treatment target (Donovan et al., 2012; Volkow, 2022). This has important societal implications as harm reduction approaches have become more prominent. For instance, many people with a substance use disorder (SUD) may avoid treatment due to the common goal of abstinence only (Paquette et al., 2022; Volkow, 2020), with potential punishing consequences for those who do not abstain (White et al., 2005). The recently updated FDA guidance for stimulant use disorder medication development now recognizes change in pattern of cocaine use (e.g., reduction in frequency) as an acceptable non-abstinence endpoint (Food and Drug Administration, 2023). This guidance has been supported by several pooled analyses, which have found that reductions in cocaine use have produced improvements in several psychosocial health measures. For example, one analysis of several clinical trials found that achieving a 75% rate of cocaine-negative urine samples was associated with improvements in ASI composite scores (Loya et al., 2024). A second study utilizing a pooled dataset found that decreasing from high frequency cocaine use (i.e., 5+ days per month) to low frequency (1–4 days) or abstinence was associated with improvements in several ASI domains, including psychological, employment, and legal (Roos et al., 2019). This study demonstrated important improvements associated with within-subject changes in cocaine use. However, further replication of between- and within-subject findings and extension to other quality-of-life measures is needed.

To replicate and extend the effects of cocaine use reduction on psychosocial quality-of-life, we conducted a randomized, controlled 12-week trial, plus 24 weeks of follow-up, designed to examine the effects of decreased levels of cocaine use on psychosocial quality-of-life. This project extends prior research in several important ways. First, we incorporated multiple quality-of-life measures, beyond the ASI, to provide a more comprehensive assessment. As discussed by Petry et al. (2007), individuals with SUD often experience heterogenous detriments to quality-of-life, which can make it difficult to detect group-level changes using ASI composite scores alone. We also completed a secondary analysis to determine the benefits imparted by the 75% threshold for the reduction in cocaine use identified previously (Loya et al., 2023). Finally, we include a between- and within-subject analysis which when combined with the 75% reduction threshold analysis, illustrates both immediate, within-person variability and longer-term, clinically significant reductions in cocaine use. The primary hypothesis of this study was that lower levels of cocaine use during treatment would be associated with improvements in quality-of-life measures.

Method

The participants, procedures, and outcomes discussed in this manuscript are part of a randomized controlled trial designed to demonstrate the immune (Aim 1), cardiovascular (Aim 2), and psychosocial (Aim 3) health changes associated with reduced cocaine use. Other results from Aims 1 and 2 of this trial have been reported previously (Stoops et al., 2024; Stoops et al., 2025). This trial is registered on clinicaltrials.gov under NCT03224546 and was approved by the University’s Institutional Review Board. All participants provided written, informed consent. This study was not preregistered.

Participants

See Table 1 for a CONSORT diagram of study recruitment. Participants were recruited between December 2017 and December 2023. A total of 357 people were assessed for inclusion in this single-blind trial. Of these, 127 participants were randomly assigned to one of three intervention groups (High Value CM, Low Value CM, or Control) and completed a baseline visit. Twenty participants did not attend their first day of treatment, leaving a total of 107 people included in this trial for data analysis. To be included in this trial, participants had to 1) be at least 18 years old; 2) self-report cocaine use in the week prior to screening; 3) provide a benzoylecgonine (BE)-positive (the primary metabolite of cocaine) urine sample during the screening process; 4) meet Diagnostic and Statistical Manual 5 (DSM 5) criteria for moderate to severe CUD; and 5) be seeking treatment for their cocaine use. Participants were excluded if they reported a current or past illness that could interfere with study participation (e.g., physical dependence on drugs requiring medically managed detoxification) or had poor venous access precluding blood draws.

Table 1.

CONSORT Diagram

graphic file with name nihms-2147615-t0003.jpg
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Retention differed by group assignment: 71% of participants in the High Value CM group, 58% in the Low Value CM group, and 79% in the Control group remained in the study through Week 12. There were 1,545 missed visits (41.1%) across all participants. Missed visits also differed by group assignment, with 40%, 50%, and 30% of visits being missed from participants in the High Value, Low Value, and Control groups, respectively. See Data Analysis section for how missing data were handled.

Randomization

After completing their baseline visit, participants were randomized 1:1:1 to one of three groups (see Treatment) using stratified block randomization: 1) High Value CM (n=44), 2) Low Value CM (n=41) or 3) non-contingent Control (n=42). Strata were based on sex (male/female), age (>35/≤35 years), and 30-day cocaine use (>15/≤15 days used in the past 30) based on prior research (Walsh et al., 2013). These data were gathered from the ASI-Lite (Cacciola et al., 2007) which was completed during their baseline visit.

Procedure

Participants visited the laboratory three days per week (MWF) across the 12-week treatment period, totaling 36 scheduled clinic visits. Participants were withdrawn from the trial if they expressly asked to be withdrawn or missed eight consecutive clinic visits, excluding medical or personal circumstances (e.g., illness). Thirty-three participants were withdrawn for these reasons (data from these participants were included for analysis up to their withdrawal date). Additionally, participants completed four follow-up appointments over a 24-week period after treatment completion (i.e., 4-, 8-, 12- and 24-weeks post-treatment). Participants provided observed urine samples during each visit. Samples were coded as positive or negative for BE using qualitative urine screens with a 300 ng/ml cutoff, which is commonly used in clinical trials (e.g., Johnson et al., 2020; Levin et al., 2020). This testing schedule was based on the finding that approximately three days of abstinence is required to provide a urine sample negative for recent cocaine use (Preston et al., 2002) .

Treatment (CM)

CM was selected to promote reductions in cocaine use necessary to measure associated changes in quality-of-life. CM is a type of consequence-based intervention in which alternative reinforcers are provided contingent on the occurrence or nonoccurrence of a target behavior of interest, typically abstinence from drug use. Overall, CM interventions are the most effective psychosocial interventions for SUD (Bentzley et al., 2021; Dutra, 2008), demonstrating efficacy in many domains including decreasing cocaine use (DeFulio et al., 2009; Miguel et al., 2016; Miguel et al., 2021; Regnier et al., 2022). Although CM is designed to promote abstinence, it is not completely effective in all patients (Bentzley et al., 2021) and typically promotes reductions in frequency of cocaine use rather than complete abstinence. We therefore used a CM approach to promote reductions in cocaine use and measure associated changes in psychosocial quality-of-life.

CM group assignment was associated with one of three incentive schedules. The first 17 participants received an escalating, resetting schedule with increased value incentives for consecutive BE-negative urine samples. However, due to low rates of abstinence and retention, the CM schedule was revised to fixed-value incentives with the same maximum earning potential. Remaining participants assigned to High Value CM received $55 for providing a BE-negative urine sample, with a maximum possible total earning of $1980. Remaining participants assigned to the Low Value CM received $13 for providing a BE-negative urine sample and remaining participants assigned to the non-contingent Control group received $13 per urine sample regardless of urine screen results; participants in both of those groups could earn a maximum of $468.

Participants also received manual-guided, weekly Matrix Intensive Outpatient Treatment (SAMHSA, 2013) offered by a graduate-level counselor who was blinded to all assigned study conditions. In addition to urine test result payments, participants could receive $10 for travel and between $15 and $60 for visits, which ranged in duration from thirty minutes to four hours, depending on the outcome variables being measured. These payments were the same across all groups.

Measures

Demographics.

Demographic information (e.g., age, race, sex) was collected using the Addiction Severity Index-Lite (Cacciola et al., 2007) and a questionnaire assessing the number of DSM-5 CUD criteria met (American Psychiatric Association, 2013).

Cocaine Use.

Cocaine use was monitored using qualitative urine drug screening at every visit, supplemented by self-reported cocaine-use frequency via the Timeline Follow-Back (Robinson et al., 2014) once per week. Urine drug screens were utilized due to the biological verification required for the CM incentive. Missed visits were considered missing data and excluded from the denominator when calculating the weekly proportion of negative tests.

Quality-of-life.

Quality-of-life measures were selected a priori from a larger pool of psychosocial health assessments. These measures were chosen to replicate and expand upon the prior literature, thereby providing a comprehensive overview of both physical and mental health, as well as other important psychosocial outcomes affected by cocaine use (e.g., sleep, relationships with family). Data were collected at intervals described below using standardized questionnaires, which were administered at the laboratory either via paper-and-pencil or electronically through Qualtrics and independently double-entered by two study team members.

The Hamilton Rating Scale for Depression (HAMD; Hamilton, 1960) is a 17-item, clinician-administered measure of depression over the past week, with each item rated on a 0–2 or 0–4 scale. Scores range from 0 to 54, indicating the following levels of depression: mild (10–13); mild to moderate (14–17); and severe (>24). The HAMD was measured at baseline, once per week for all participants during treatment, and during each of the four follow-up visits. The HAMD demonstrated good internal consistency (Cronbach’s alpha = 0.80).

The Patient-Reported Outcomes Measurement Information System (PROMIS; Cella et al., 2010; PROMIS, 2021) is a validated set of self-report measures evaluating physical, mental, and social health domains. The Global Physical and Global Mental Health domains of the PROMIS were used in this study, with increases in these scores indicating improvements in physical and mental health, respectively. Each domain includes four items that are answered on a 1–5 Likert Scale, except for pain (Global Physical; 1–10 scale). The PROMIS was measured at baseline, at weeks 4, 8, and 12, and during each of the four follow-up visits. PROMIS outcomes were analyzed by following guidance provided in the PROMIS Adult Profile Instruments Scoring Manual (PROMIS, 2021) to convert raw scale scores to standardized T-scores. Standardized T-scores are metrics that indicate the amount of a construct being measured. Therefore, a higher or lower T-score may be considered better or worse depending on the metric (e.g., lower Anxiety may be better, lower Cognitive Function may be worse). T-scores allow for comparisons to the general United States population, which has an average T-score of 50 and a standard deviation of 10 on all PROMIS domains. PROMIS Physical Health scale showed acceptable reliability (Cronbach’s alpha = 0.59), and the Mental Health scale demonstrated acceptable to good internal consistency (Cronbach’s alpha = 0.78).

The Interpersonal, Intrapersonal, Social Responsibility, Physical, and Impulse Control domains of the Short Inventory of Problems-Alcohol and Drugs, specific to Cocaine (SIP-C; Kiluk et al., 2013) were measured at baseline and at weeks 4, 8, and 12, and during the first, third, and fourth follow-up visits. The SIP-C is a 15-item Likert Scale questionnaire that evaluates the adverse events related to a participant’s drug use during the past three months, and was phrased to reference cocaine, specifically. Overall, SIP-C domains showed good reliability (Cronbach’s alphas ranging from 0.64–0.89).

The Addiction Severity Index (ASI) is an assessment interview designed to measure several domains that may be impacted by substance use (i.e., Medical, Employment/Support Status, Alcohol, Drug, Legal, Family/Social, and Psychiatric). Family and Psychiatric domains were selected for analysis in this study to account for important quality-of-life measures not captured by the other assessments (e.g., PROMIS, SIP-C). Psychiatric and Family composite scores from the ASI were calculated using guidance from the ASI Composite Scores Manual (McLellan et al., 1986), with final values ranging from 0 to 1. The ASI was measured at baseline and at weeks 4, 8, and 12 of treatment, and during each follow-up visit. ASI measures typically reference the past 30 days and/or lifetime. The ASI Family domain demonstrated poor internal consistency, (Cronbach’s alpha = 0.39) whereas the Psychological domain showed moderate internal consistency (Cronbach’s alpha = 0.43).

Finally, the Saint Mary’s Hospital Sleep Questionnaire (SMHSQ; Ellis et al., 1981) was administered at every clinic visit, including follow-ups. The SMHSQ is a validated 14-item self-report measure of previous-night sleep duration and quality (e.g., How many times did you wake up? How clear-headed did you feel after getting up this morning?). Items 5 – 14 were included in this study for analysis because they addressed both sleep duration and quality. Items 1 – 4, which asked participants about specific times they settled down, fell asleep, etc., were excluded due to redundancy with items 5 – 14. To generate weekly values for analysis, measurements from multiple clinic visits during treatment weeks were averaged into a single value.

The timing of scheduled assessments varied by outcome. For the PROMIS & ASI measures, this included data from baseline, treatment weeks 4, 8, and 12, and each of the four post-treatment follow-up visits. Other outcomes such as the HAM-D were assessed weekly, so all 17 available time points (baseline, 12 weekly treatment assessments, and four weekly follow-up assessments) were included.

Analytic Strategy

Overall demographic characteristics were summarized and compared between groups. A chi-square test compared categorical demographic variables across groups, while analysis of variance (ANOVA) assessed mean differences for continuous variables.

The effects of CM on cocaine use in this study, analyzed using a generalized linear mixed model, have been reported in other publications (Stoops et al., 2024). Descriptive statistics (i.e., mean, standard deviations) are provided in the present manuscript to demonstrate changes in cocaine use across the duration of the study. Self-reported past 30-day cocaine use was also analyzed using descriptive statistics. See Supplemental Figure 1 for urine test results for all participants across all study visits.

Linear mixed models were utilized to assess relationships between quality-of-life outcomes and the proportion of cocaine-negative urines (primary predictor) while controlling for week (i.e., time), group assignment (i.e., High Value, Low Value, Control), and the interaction between time and group. During the intervention weeks, the weekly proportion of cocaine-negative urine tests was calculated by dividing the number of negative coded tests by the total number of tests conducted each week. For baseline and post-treatment weeks, the “weekly” test proportion is based on the participant’s single test outcome for that study visit (e.g., a 1 for a negative test or 0 for a positive). In mixed effects models, time-varying covariates typically reflect a combination of between-person differences and within-person variation over time (Fitzmaurice et al., 2012). To distinguish these effects, we disaggregated the time-varying predictor—proportion of cocaine-negative urine drug screens—into two components: (1) each participant’s overall average proportion of negative tests (capturing between-person differences) and (2) the deviation from this average at each time point (capturing within-person variability). Including both components in the model enabled separate estimation of the association between average cocaine use (reflecting between-person differences) and quality-of-life, and the association between fluctuations from an individual’s average use at each time point (reflecting within-person variation) and their quality-of-life outcomes. All responses were assumed to be normally distributed, and residual plots were examined to verify this assumption. No violations of this assumption were found. To account for the hierarchical structure of the data and repeated measures, two random effects were included in the model. A random intercept was specified for each participant within group assignments to account for individual-level variability (i.e., between-subject differences). An autoregressive heterogeneous covariance structure [ARH (1)] was applied to the residuals to model correlations among repeated measures for the same participant over time. Linear mixed-effects models inherently account for missing data by incorporating all available data at each time point and adjusting the variability of the parameter estimates accordingly. The data collection timepoint for each quality-of-life measure was matched with the average urine test results for that week to ensure temporal alignment between both variables. For frequent measures like the SMSHQ, we averaged scores across the number of assessments completed each week. For monthly measures such as the Addiction Severity Index (ASI) and PROMIS subscales, we used the weekly percentage of negative urine drug screens from the specific weeks in which those assessments occurred (e.g., weeks 4, 8, and 12).

In summary, between-subjects effects reflect how a participant’s average proportion of negative urine drug screens over the study relates to their average quality-of-life score. A non-statistically significant effect here suggests that participants who generally maintain higher abstinence do not necessarily differ in the respective quality-of-life score from those who do not. This indicates that there isn’t a strong relationship between cocaine-use and quality-of-life across participants. The within-subjects effects reflect the longitudinal nature of the data. This term captures how deviations from a participant’s own average drug use are associated with their quality-of-life outcomes. A statistically significant within-subjects effect suggests that during weeks when a participant has a higher proportion of negative urine drug screens, their quality-of-life tends to change, regardless of what their average is overall.

To replicate the 75% cocaine-negative urine screen threshold proposed by Loya et al. (2023) we first calculated whether each participant attained 75% or more negative test results during treatment and then used linear mixed-effects models with the quality-of-life outcomes during follow-up (i.e., post-treatment weeks 4, 8, 12, 24; visits 37–40) as the dependent variables. Predictors included the time of quality-of-life measurement, whether a participant achieved 75% of more negative tests during the treatment period, and the interaction between these two variables. Treatment group assignment was also included as a covariate to account for group effects in the models. Random effects were included to account for participant-level variability, along with a repeated measures statement to model the correlation of outcomes within-participant over the four post-treatment time points. Data were analyzed using SAS version 9.4 (SAS Institute, INC, Cary, NC). A p-value of 0.05 was used as the cutoff for statistical significance. All data & analysis code are available from the corresponding author upon request.

Results

Demographics

The proportion of Black participants was statistically significantly higher in the Low Value group compared to the High Value and Control groups (Table 2; p=0.035). However, the difference in proportion was small (i.e., 12%). Therefore, race was not included in the overall analyses as a covariate. No other statistically significant group differences in demographic variables were observed.

Table 2.

Demographics

Overall (n = 107) High Value n = 41 Low Value n = 33 Control n = 33 P-value
n % n % n % n %
Sex 0.999
Male 68 63.6 26 63.4 21 63.6 21 63.6
Female 39 36.4 15 36.6 12 36.4 12 36.4
Race 0.035 *
Black 88 82.2 32 78.0 30 90.9 26 78.8
White 11 10.3 3 7.3 1 3.0 7 21.2
Hispanic 3 2.8 1 2.4 2 6.0 0 0.0
Multiracial 4 3.7 4 9.8 0 0.0 0 0.0
American Indian/Alaska Native 1 0.9 1 2.4 0 0.0 0 0.0
Age in Years, mean (SD) 51.7 (9.0) 53.7 (9.0) 50.4 (7.8) 50.5 (10.0) 0.196
Education in Years, mean (SD) 12.7 (1.7) 12.9 (1.9) 12.6 (1.6) 12.5 (1.5) 0.568
DSM-5 Cocaine Use Disorder 0.167
Moderate 11 8.4 2 4.9 6 18.2 3 9.1
Severe 96 89.7 39 95.1 27 81.8 30 90.9
Days of Use in Past 30, mean (SD)
Cocaine 11.5 (8.0) 11.5 (8.7) 11.2 (7.5) 11.9 (7.6) 0.936
Alcohol 7.4 (8.7) 7.8 (9.2) 8.3 (8.3) 6.0 (8.4) 0.604
Number of Cigarettes Smoked in Past 30 Days, mean (SD) 239.6 (249.5) 291.7 (285.7) 165.5 (185.4) 248.4 (246.8) 0.142
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Note. N=107.

*

The proportion of Black participants was statistically significantly greater in the Low Value group compared to the High Value and Control groups.

Changes in Cocaine Use

Figure 1 includes group-level urine test results and self-reported cocaine use across the duration of the study. See Supplemental Figure 1 for individual-level data across all study visits. Baseline proportion of negative urine tests was 22.7% (SD = 12.0) across all participants, peaking during week 8 (31.7%, SD = 11.4). However, this varied by group assignment. The proportion of negative urine tests at baseline was 31.5% in the High Value group, peaking at week 4 (51.9%, SD = 44.4). In the Low Value group, 9.1% of baseline tests were negative, with a peak at week 8 (24.6%, SD = 36.7). The Control group had a baseline of 27.3%, with a peak at week 6 (32.7%, SD = 44.0). Eighteen participants (17%) produced cocaine-negative urine test results at least 75% of the time during the trial, with 13 of those belonging to the High Value group.

Figure 1.

Figure 1

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Cocaine Use Outcomes Sorted by Group Assignment

Week 0 indicates the baseline visit.

Weeks 16–36 were post-treatment follow-up appointments.

Participants reported using cocaine 11.5 days in the month prior to baseline, which was similar across all study groups due to stratified block randomization. Participants in the high value group had the most significant reductions in self-reported cocaine use days, which reached its lowest during week 8 (5.8 days, 50% reduction, SD = 7.2).

Quality-of-Life

Table 3 presents between- and within-subject fixed effect parameter estimates for all quality-of-life measures, along with corresponding p-values. Also reported are the coefficients for the proportion of negative test values, along with their standard errors. These coefficients describe the direction of the relationship between the proportion of negative tests and the outcome variable of interest. Coefficients are interpreted as the expected change in the outcome variable for every one-unit increase in the proportion of cocaine-negative urine test results. Coefficients were scaled to reflect the change in quality-of-life outcomes associated with a 25% increase in negative urine tests. For differences in psychosocial quality-of-life outcomes based on group assignment and time, see Supplemental Tables 1 and 2.

Table 3.

Fixed effects outputs for all quality-of-life measures

Variable Between-Subject Fixed Effects Within-Subject Fixed Effects
Coefficient (SE) P-Value Coefficient (SE) P-Value
HAMD (Depression) 0.061 (0.300) 0.839 −0.286 (0.086) 0.001
PROMIS
Physical Health 0.403 (0.564) 0.476 0.265 (0.185) 0.153
Mental Health 0.417 (0.604) 0.490 0.414 (0.201) 0.041
SIP-C
Interpersonal −0.581 (0.245) 0.019 −0.062 (0.081) 0.446
Intrapersonal −0.564 (0.202) 0.006 −0.213 (0.079) 0.008
Social Responsibility −0.627 (0.205) 0.003 −0.103 (0.081) 0.205
Physical −0.611 (0.172) 0.001 −0.216 (0.073) 0.004
Impulse Control −0.289 (0.143) 0.045 −0.142 (0.056) 0.012
Total Score 0.007 (0.123) 0.955 0.136 (0.080) 0.092
ASI
Psychiatric −0.004 (0.010) 0.687 −0.003 (0.004) 0.567
Family 0.012 (0.007) 0.064 −0.005 (0.004) 0.182
SMHSQ (Sleep)
Item 5: Deepness of Sleep −0.026 (0.091) 0.779 −0.007 (0.025) 0.779
Item 6: Times Woken Up 0.015 (0.069) 0.826 0.034 (0.023) 0.143
Item 7: How Much Sleep (minutes) −0.131 (0.100) 0.189 0.049 (0.043) 0.248
Item 8: Sleep during the day −0.197 (0.122) 0.106 0.009 (0.004) 0.782
Item 9: Sleep Quality −0.073 (0.052) 0.158 0.019 (0.018) 0.298
Item 10: Clear Headed −0.021 (0.061) 0.732 0.067 (0.019) <0.001
Item 11: Satisfaction −0.025 (0.051) 0.624 0.031 (0.019) 0.109
Item 12: Waking up too Early 0.020 (0.009) 0.024 0.010 (0.006) 0.080
Item 13: Difficulty Falling Asleep 0.011 (0.031) 0.734 −0.021 (0.013) 0.089
Item 14: Time to Fall Asleep 0.091 (0.185) 0.050 −0.011 (0.069) 0.519
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Original coefficients have been multiplied by 0.25 to reflect QoL outcomes associated with 25% negative urine samples. Items in bold were statistically significant at the 0.05 level.

HAMD

Higher proportions of within-subject cocaine-negative urine tests were associated with a statistically significant lower HAMD score after accounting for all the other terms in the overall model (F (1,920) = 11; p = 0.001; coefficient = −0.286), with a 25% reduction compared to average in use corresponding to a 0.286-point reduction in HAMD score.

PROMIS

No significant estimated between- or within-subject changes in Global Physical Health scores were found based on the proportion of cocaine-negative urines over time (p > 0.40). However, higher within-subject proportions of cocaine-negative urine tests were associated with significantly improved estimated Global Mental Health scores (F (1,347) = 4.22; p = 0.041; coefficient = 0.414).

SIP-C

Both between- and within-subjects, higher proportions of cocaine-negative urines were associated with lower scores on SIP-C scales. Between-subject improvements include all subscales except Total Score (F’s (1,150) >4.0; p values < 0.05; coefficients ranging from −0.289 to 0.627). Within-subject associations include Intrapersonal, Physical, and Impulse Control subscales (F’s (1,150) >6.0; p values < 0.05; coefficients ranging from −0.142 to 0.216).

ASI

No significant changes in estimated scores were found based on the proportion of negative urine test results (p > 0.60) during treatment.

SMHSQ

Higher proportions of cocaine-negative urine test results were associated with between-subject increases in the average participant response regarding “waking up too early” (Q12; F (1, 1083) = 5.11; p = 0.024; coefficient = 0.020) and in time to fall asleep (Q14; F (1, 1083) = 3.86; p = 0.050; coefficient = 0.091). Within-subjects, higher proportions of cocaine-negative urines were associated with improvements in clear headedness (Q10; F (1, 1083) = 12.65, p < 0.001, coefficient = 0.067).

75% Cocaine-Negative Threshold

Participants who provided at least 75% cocaine-negative urine test results during treatment demonstrated statistically significant improvements in all SIP-C subscales except Total Score (F’s (1, 114) ≥ 4.94, p-values ≤ 0.045; see Figure 2 for all SIP-C outcomes) and SMHSQ deepness of sleep (Q5; F (1, 116) = 5.32; p = 0.02). No other statistically significant differences were found (see Supplemental Table 2 for all fixed effects for the 75% threshold).

Figure 2.

Figure 2

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SIP-C Outcomes Based on 75% Cocaine-Negative Threshold

*Indicates statistically significant differences at the 0.05 level during post-treatment follow-up (i.e., post-treatment weeks 4, 8, 12, 24; visits 37–40).

Discussion

The purpose of this study was to demonstrate changes in quality-of-life outcomes as a function of lower levels of cocaine use resulting from CM treatment. Overall, higher proportions of cocaine-negative urine test results were associated with statistically significant, although modest, changes in several quality-of-life measures, including psychosocial functioning (e.g., SIP-C), mental health (e.g., PROMIS, HAMD), and sleep (e.g., SMHSQ). Between-subject effects found in this study demonstrate differences in quality-of-life for participants who maintain higher abstinence. Within-subject findings indicate that as participants change their own drug use during the study, they experience changes to their quality-of-life.

The improvements in all subscales of the SIP-C, except Total Score, were particularly compelling, especially considering that this effect was observed for between- and within-subject fixed effects in the mixed model and the mixed model for the 75% cutoff, extending the results of Loya et al. (2023). Between-subject effects found here demonstrate lower SIP-C scores for participants who maintain higher abstinence. Within-subject findings indicate that as participants change their own drug use during the study, they experience changes to their SIP-C scores. The within-subject findings are especially clinically meaningful because they indicate a treatment response beyond reduced drug use alone. Although there is little research that assessed whether SIP-C outcomes differ based on cocaine use frequency, one study found that the SIP-C did not predict substance use frequency during or after treatment in a combined sample of participants with alcohol and SUD (Kiluk et al., 2013). However, that study did not administer the SIP-C posttreatment, precluding an evaluation of the change in scores over time.

Results from the ASI and PROMIS measures partially align with the literature on the effects of changes in cocaine use on psychosocial health. A secondary analysis of five clinical trials for CUD reported statistically significant improvements from baseline in ASI Psychiatric composite scores among participants who decreased their use (Kiluk et al., 2021), a finding that contrasts with the present study where no such changes were observed. However, in that study, the authors also reported low effect sizes and modest clinically meaningful improvements in ASI outcomes, consistent with other retrospective analyses comparing different treatment types and modalities (Coviello et al., 2001; Crits-Christoph et al., 2001). Authors have generally attributed these modest changes to a potential floor effect, with over half of the participants in Kiluk et al., for example, reporting an ASI Psychiatric score of zero. Although research on changes in PROMIS domains as a function of cocaine abstinence is limited, the observed within-subject improvements in the Mental Health domain align with the Psychiatric composite improvements of the ASI found in the prior research discussed above.

Lower levels of cocaine use were also associated with mixed effects on sleep outcomes. Participants with higher proportions of cocaine-negative urine tests reported waking up clearer headed (SMHSQ, Q10) but more likely to be troubled by waking early and taking longer to fall asleep (SMHSQ, Q12 & Q14). These findings provide partial support for previous research on the relationship between subjective measures of sleep quality and cocaine abstinence. In a 23-day inpatient study, improvements in self-reported sleep quality, depth of sleep, feeling-well rested, and mental alertness were positively and significantly correlated with days of abstinence. However, cocaine administration was carefully controlled due to the inpatient nature of the study. Additionally, in that same study, decreased total sleep time and sleep latency were found using electrophysiological measures of sleep, which peaked at 14–17 days of abstinence (Morgan et al., 2006). These findings indicate an improvement in some sleep measures and a potential “rebounding” effect on others (i.e., latency) that may occur with abstinence or lower use following chronic cocaine use.

Taken together, these findings suggest that modest reductions in cocaine use are not always sufficient to drive observable changes in quality-of-life indicators. Significant decreases in cocaine use may be required to produce meaningful improvements in quality-of-life outcomes. Collectively, these results suggest that certain measures, like the ASI, have limited sensitivity to change when repeatedly assessed over time, especially among patients with lower baseline severity (Petry et al., 2013). These measures may be more effective at detecting improvements in participants who present with greater physical and mental health problems at baseline, as suggested by our results. An additional explanation could be that changes in some outcomes could take extended periods of time to appear. For example, the results of Petry et al. (2013) suggest that more clinically meaningful improvements in BSI scores may take over a year to emerge. Therefore, the 12-week treatment plus 24-week follow-up may have been insufficient to detect changes in some outcomes in the present study.

Although several psychosocial quality-of-life outcomes reached statistical significance, the clinical importance of these findings may be limited. Further research is needed to determine how these effects generalize to certain samples with greater clinical need. For example, participants with major depression were excluded from this study, which may result in an average baseline HAMD score of 5.4, substantially below the mild depression range (10 – 13). However, the relationship between cocaine use and depression may also be modest, and not a strong indicator of future cocaine use (Conner et al., 2008), which may explain the observed baseline HAMD scores. Similarly, participants in our sample reported being “fairly clear-headed” on average at baseline (SMHSQ) and having PROMIS composite scores within half a standard deviation of the United States average. This trend aligns with several other clinical trials (Crits-Christoph et al., 2001; Kiluk et al., 2021) and may extend to other treatment outcomes, highlighting a research gap on the benefits of reduced cocaine use for individuals with more severe psychosocial quality-of-life issues at baseline. Future research that improves our understanding the quality-of-life benefits of reduced cocaine use in these samples would be especially clinically meaningful. However, the present results support the hypothesis that lower cocaine use can improve quality-of-life, even in patients with less severe problems at baseline. This was especially apparent for the PROMIS (Mental) T-Score, which approached the United States average as the proportion of cocaine-negative urine tests increased.

There are some important limitations to consider that might guide the development of future studies. First, about three days of cocaine abstinence was required to provide a negative urine test result, given the qualitative nature of testing used. Therefore, the results of this study cannot easily be generalized to more subtle changes in cocaine use (e.g., 20 milligrams per day compared to 80 milligrams per day). Quantitative urinalyses are needed to understand how differences in the amount of daily cocaine use might affect psychosocial function. Second, prolonged decreases in cocaine use may be required to produce substantial improvements in quality of life, which does not match the weekly fluctuations in quality-of-life analyzed in this study. Our analysis was selected to maximize sample size and leverage all available data. Combined with the 75% reduction threshold analysis, these approaches complement each other by illustrating both within-subject changes and longer-term, reductions in cocaine use through the follow-up phase of the study. Additionally, although the inclusion/exclusion criteria used in this study were broad, there may be a subset of people with CUD who experience significant disruptions to quality-of-life who are not represented by the present results. Finally, the ASI had poor internal consistency in this study. Although this is a limitation, it also provides evidence of substantially skewed results in the ASI values, with many participants not experiencing what the questions were asking in the time frames of interest. This trend was found in several studies discussed above and suggests that the ASI has limited use when used in a longitudinal trial.

Conclusion

In conclusion, the results of this study provide preliminary evidence that lower levels of cocaine use can improve psychosocial quality-of-life outcomes, including mental health, problems related to drug use, and sleep. Future studies should examine how reduced cocaine use impacts quality-of-life outcomes in people with clinically significant baseline difficulties, such as depression or severe psychiatric, familial, or social challenges.

Supplementary Material

S Table 2
S Table 1
S Figure 1

Public Health Significance:

The results of this study provide evidence that lower levels of cocaine use can improve psychosocial quality-of-life outcomes, including mental health, problems related to drug use, and sleep for adults with cocaine use disorder.

Acknowledgment:

This research was supported by grants from the National Institute on Drug Abuse (R01DA043938; T32DA035200; K99DA060267). The funding agencies had no role in study design, data collection or analysis, or preparation and submission of the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors have no relevant conflicts of interest to declare.

Footnotes

Clinical Trial Registration: This trial is registered on clinicaltrials.gov under NCT03224546.

Author Contributions Statement (CRediT):

Regnier (Validation, Formal Analysis, Investigation, Data Curation, Writing-Original Draft, Writing-Review & Editing, Visualization, Project Administration), Karnick (Validation, Formal Analysis, Writing-Review & Editing), Shellenberg (Validation, Formal Analysis, Investigation, Data Curation, Writing-Review & Editing), Cox (Software, Validation, Investigation, Data Curation, Writing-Review & Editing, Project Administration), McLouth (Validation, Formal Analysis, Writing-Review & Editing), Hays (Investigation, Writing-Review & Editing), Anderson (Investigation, Writing-Review & Editing), Schmitz (Conceptualization, Writing-Review & Editing), Havens (Conceptualization, Methodology, Validation, Writing-Review & Editing), Lile (Conceptualization, Methodology, Investigation, Writing-Review & Editing, Supervision, Project Administration), Stoops (Conceptualization, Methodology, Investigation, Resources, Writing-Original Draft, Writing-Review & Editing, Visualization, Supervision, Project Administration, Funding Acquisition),

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Associated Data

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

Supplementary Materials

S Table 2
NIHMS2147615-supplement-S_Table_2.docx (31.9KB, docx)
S Table 1
NIHMS2147615-supplement-S_Table_1.docx (38.1KB, docx)
S Figure 1
NIHMS2147615-supplement-S_Figure_1.docx (1.1MB, docx)

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