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. Author manuscript; available in PMC: 2026 Mar 19.
Published in final edited form as: Sleep Med. 2025 Dec 1;138:108689. doi: 10.1016/j.sleep.2025.108689

Insomnia in stimulant use disorders: Prevalence and associations with negative affect

Isabella G Bourtin a, Constanza de Dios b, Jessica Vincent b, Joy M Schmitz b, Scott D Lane b, Heather E Webber b,*
PMCID: PMC12997286  NIHMSID: NIHMS2154119  PMID: 41338083

Abstract

Research suggests that sleep disturbances are common among people who use psychostimulants, including cocaine use and methamphetamine use. These sleep disturbances hinder recovery, heighten relapse risk, and worsen physical and mental health outcomes. This study aimed to estimate the prevalence and severity of insomnia among individuals who use cocaine and methamphetamine and to explore potential psychological factors that are associated with more severe insomnia. The sample included participants who completed in-person screening assessments for ongoing studies at an outpatient research clinic. As a part of this screening, participants completed the Insomnia Severity Index (ISI) and validated assessments of psychological domains related to negative affect, such as distress tolerance, anhedonia, emotion regulation, and posttraumatic stress symptoms. Regularized regression identified and retained the most important psychological variables for predicting the presence of clinically-significant insomnia. Results indicated that the prevalence of clinically significant insomnia was 36.6 % in those who use cocaine and 44.7 % in those who use methamphetamine, and the prevalence of subthreshold insomnia was 35.3 % in individuals who use cocaine and 39.3 % in individuals who use methamphetamine. Regression results indicated that depression symptoms were the strongest predictor of insomnia severity, along with difficulties in emotion regulation and posttraumatic stress symptoms. Similarities and differences between cocaine and methamphetamine subgroups were also identified. These results suggest that insomnia in individuals who use cocaine and methamphetamine may be shaped by a collection of modifiable psychological processes, and highlight the need for tailored, integrated interventions that address sleep, affective functioning, and trauma-related processes within stimulant-focused SUD treatment.

Keywords: Insomnia, Psychostimulant, Cocaine, Methamphetamine, Sleep, Depression, Emotion regulation

1. Introduction

An estimated 46.3 million Americans (16.5 % of the population) age 12 or older had a substance abuse disorder (SUD) in 2021. Of those, 1.4 million had a cocaine use disorder (CUD) and 1.6 million had a methamphetamine use disorder (MUD) [1]. Sleep problems are highly prevalent amongst those with stimulant use disorder (StimUD) during all stages, from acute use to withdrawal [2–6].

Sleep is a fundamental biological process essential for physical and mental health. It plays a crucial role in memory consolidation, cognitive functioning, emotional regulation, and metabolic processes [7]. Poor sleep quality or insufficient sleep has been linked to numerous adverse health outcomes, including cardiovascular diseases, metabolic disorders, mood disorders, and impaired immune function [8]. For individuals with SUDs, sleep disturbances exacerbate these risks, as inadequate or fragmented sleep can worsen both mental health symptoms and the physical toll of substance use [6,9]. Sleep disruption also impairs decision-making and impulse control [10,11], making it harder for individuals with SUDs to maintain abstinence [65,12]. Given the intricate relationship between sleep and brain function, addressing sleep issues in populations with SUDs is vital for improving overall treatment outcomes and reducing the risk of symptom recurrence.

Insomnia is the most prevalent sleep disorder in the general population [13], affecting approximately 10 % (and up to 30 %) of adults globally. Insomnia can present as difficulty falling asleep, staying asleep, or waking too early, and it is often associated with impaired daytime functioning and a reduced quality of life [14]. Having a sleep disorder is a risk factor for worse SUD. Individuals with sleep disorders are more likely to be treated for SUDs and sleep disturbances are associated with SUD severity and higher rates of resumption of use [6,9,15–17]. Bidirectionally, sleep disorders, namely insomnia and sleep apnea are prevalent across the board amongst people with SUDs [18–21] and have a higher prevalence than that of the general population [22,23]. The prevalence of insomnia among CUD and MUD patients ranges from ~30 % to 97 %, depending on factors such as stage of use (current, withdrawal, length of abstinence) [23–29].

While sleep disturbance is a common problem in patients with StimUD, risk factors for insomnia within this population are not well-defined. Research indicates that several psychological and behavioral factors associated with negative affect play a pivotal role in sleep health. Depression and anhedonia, for instance, are well-established contributors to insomnia, with estimates suggesting that around 67 % of individuals with major depressive disorder experience some form of sleep disturbance [30–33]. Stress and posttraumatic stress disorder symptoms, characterized by hyperarousal and nightmares that interfere with both sleep initiation and maintenance, also contribute to sleep disturbances [34,35]. Finally, studies have indicated that difficulties in coping with negative affect, such as emotion regulation difficulties, distress intolerance, and cognitive and behavioral avoidance, are associated with poorer sleep [36,37]. These same psychological and behavioral variables are also highly prevalent among individuals with StimUD [38–40], but it is unknown if these factors contribute to insomnia severity within the StimUD population. Importantly, many of these factors are modifiable and may represent viable treatment targets for addressing co-occurring sleep disturbances in this population.

As demonstrated, there is a substantial body of literature connecting sleep disturbances with psychostimulant use and returning to use, yet sleep is often overlooked when it comes to treatment. Further, psychological factors such as negative affect and relevant coping behaviors are common in those with SUDs and also contribute to poor sleep. Therefore, the goals of the currents study were to 1) confirm the prevalence of insomnia in individuals with current cocaine use and methamphetamine use in an outpatient SUD research clinic sample and 2) to identify key modifiable, psychological and behavioral factors that contribute to sleep disturbances within StimUD.

2. Methods

2.1. Participants and procedures

Participants were recruited through flyers and online ads posted by an outpatient SUD research clinic. Potential participants called the clinic and expressed interest in participating in SUD research, including those not currently seeking treatment. Individuals calling in were screened for both treatment and non-treatment-related CUD or MUD studies. The phone screening inquired about substance use, mental health, and medical history. If an individual appeared to qualify for any study at the clinic, they were asked to attend a more thorough in-person screening, which consisted of self-report assessments and a urine drug screen (UDS). All data were securely recorded and entered into REDcap, a private and secure data collection platform. All participants who attended the in-person screening and completed the self-report measures and whose self-reported use could be biologically verified via the UDS results were included in the current analyses. The in-person intake procedures were approved by the local IRB and written informed consent was obtained prior to participation in accordance with ethical guidelines.

2.2. Measures

Demographic and Medical History Data.

Participants completed a general medical survey, which collected demographic data (age, sex, race/ethnicity, education) and relevant health history, including other physical health conditions.

Insomnia.

The Insomnia Severity Index (ISI), a 7-item self-report questionnaire, was used to assess the nature, severity, and impact of insomnia among participants. Total scores range from 0 to 28, with higher scores indicating more severe insomnia symptoms. The total score is then divided into 4 categories: no clinically significant insomnia (0–7); sub-threshold insomnia (8–14); moderate insomnia (15–21); and severe insomnia (22–28) [41]. For the purposes of the current analyses, we created a binary variable that indicated the presence of clinically significant insomnia (ISI >14) or no clinically significant insomnia (ISI <15). The ISI had acceptable/good/excellent internal reliability (Cronbach’s alpha = 0.892).

Psychological Measures.

Several psychometric tools that measure different aspects of negative emotion were included in the self-report questionnaires. Depression. Depressive symptoms were measured using the Beck’s Depression Inventory (BDI), a 21-item self-report scale which assesses the presence and severity of depressive symptoms. The BDI yields a total score (0–63), with higher scores indicating greater depression severity, and two subscale scores: 1) the Cognitive subscale, which reflects cognitive-affective symptoms such as guilt, pessimism, and self-criticism, and 2) the Non-Cognitive subscale, which reflects somatic-performance symptoms such as changes in sleep, fatigue, and concentration [42] (Cronbach’s alpha = 0.937). Avoidance and Inflexibility. The Avoidance and Inflexibility Scale (AIS) is a 13-item scale, with possible scores from 13 to 65, which measures participants’ avoidance behaviors and cognitive inflexibility, particularly in the context of substance use, capturing the extent to which avoidance interfered with recovery efforts. Items are typically rated on a 1–5 Likert scale [43] (Cronbach’s alpha = 0.937). Anhedonia. The Snaith-Hamilton Pleasure Scale (SHAPS) was used to assess anhedonia, or the diminished ability to experience pleasure. This 14-item scale captures hedonic capacity across various domains of daily life. Responses are often scored in a binary or Likert format, with higher scores indicating greater anhedonia [44]. (Cronbach’s alpha = 0.896). Distress Tolerance. Distress tolerance, or the perceived ability to endure emotional distress, was measured using the Distress Tolerance Scale (DTS) which is a 15-item scale that includes four subscales (tolerance, appraisal, absorption, and regulation) and ranges from 15 to 75 [45]. (Cronbach’s alpha = 0.898). Emotion Regulation. The Difficulties in Emotion Regulation Scale (DERS) was used to evaluate participants’ ability to understand, accept, and modulate emotional responses. It is a 36-item instrument with six subscales assessing different dimensions of emotional dysregulation: non-acceptance, goals, impulse, awareness, strategies, and clarity. Scores range from 36 to 180, with higher scores indicating greater emotion regulation difficulties [46] (Cronbach’s alpha = 0.910). Posttraumatic Stress Disorder. The PTSD Checklist for DSM-5 (PCL-5) is a 20-item self-report tool with four subscales representing the DSM-5 PTSD symptom clusters: re-experiencing, avoidance, negative alterations in cognition and mood (NACM), and alterations in arousal and reactivity (AAR). Scores range from 0 to 80, with higher scores indicating greater PTSD symptom severity [47] (Cronbach’s alpha = 0.961). The Life Events Checklist for DSM-5 (LEC-5) captured participants’ exposure to potentially traumatic life events. It includes a broad range of event types (e.g., accidents, assaults, natural disasters) and asks respondents to indicate the nature and timing of their exposure [48]. The LEC-5 was used for descriptive purposes.

2.3. Statistical analyses

Regularized regression was used to model dichotomous ISI score (low severity: no significant or subthreshold insomnia; high severity: moderate or severe insomnia) among the entire set of 18 baseline measures (which included subscales, if available, for each measure). Regularization shrinks regression coefficients to prevent inflated estimates that may arise from multicollinearity. Regularization via least absolute shrinkage and selection operator (LASSO, i.e., L1 regularization) reduces small coefficients to zero, thus retaining the most important predictors and enabling variable selection. LASSO models provided penalized coefficients that may be interpreted in the same fashion as a traditional regression, indicating magnitude and direction of the association between the retained predictor and outcome variable. Coefficients were exponentiated to produce odds ratios (OR). Cohen’s d was converted from the log-odds coefficient through the formula (coefficient/(π/√3)). Model discrimination performance was evaluated using area under the receiver operating characteristic curve (AUC).

The predictor set of the LASSO regression included the following 18 baseline measures: AIS total score, SHAPS total score, BDI-II Cognitive and Non-Cognitive subscales, PCL-5 Re-experiencing, Avoidance, Negative Alterations in Cognition and Mood (NACM), and AAR subscales, DTS Tolerance, Appraisal, Regulation, and Absorption subscales, and DERS Strategies, Non-acceptance, Impulse, Goals, Awareness, and Clarity subscales. As sensitivity analyses, a separate set of models excluded both the BDI-II Non-Cognitive and PCL-5 AAR subscales, as these subscales included items regarding sleep (BDI-II Non-Cognitive: “Changes in Sleeping Pattern”; PCL-5 AAR: “Trouble falling or staying asleep”). Primary analyses focused on confirming the correlations of these two sleep-related subscales with ISI score, and on measuring the strength of relationships between other predictors and insomnia when these subscales were included. Sensitivity analyses quantified the maximum possible strength of relationships among remaining variables when sleep-relevant predictors were removed.

Analyses were performed in the entire participant sample (N = 185), and, as subgroup analyses, in individuals using cocaine (N = 129) and methamphetamine (N = 56) separately. Missingness occurred in 0.8 % of the data; missing numeric values were imputed. Regression analyses were conducted on the R Statistical Computing Environment via the glmnet package.

3. Results

3.1. Sample characteristics

Participant characteristics are shown in Table 1. Participants had a mean age of 46.0 years (SD = 9.9), were composed mostly of men (65.4 %), and were predominantly non-Hispanic (81.1 %) and Black/African American (54.6 %). The majority of participants completed High School or GED equivalent (44.0 %). Subgroups of participants with cocaine and methamphetamine use showed similar relative proportions of age, ethnicity, gender, and education level. The cocaine subgroup had a larger proportion of Black/African American individuals (70.5 %), while the methamphetamine subgroup had a larger proportion of White/Caucasian individuals (75.0 %). The prevalence of clinically significant insomnia in the total sample was 38.9 % (36.5 % in the cocaine subgroup and 44.7 % in the methamphetamine subgroup). A large proportion (36.2 %) of the total sample also met criteria for subthreshold insomnia. The two most commonly experienced traumas, measured by the LEC-5, were natural disasters (74.1 %) and transportation accidents (66.5 %). Other commonly experienced traumas included physical assault (69.2 %) and sexual assault (38.9 %).

Table 1.

Participant sociodemographic characteristics.

Characteristic All N = 185 Cocaine N = 129 Methamphetamine N = 56
Age, M (SD) 46.0 (9.9) 47.9 (9.9) 41.6 (8.4)
Ethnicity, n (%)
Hispanic or Latino 29 (15.7 %) 22 (17.1 %) 7 (12.5 %)
Not Hispanic or Latino 150 (81.1 %) 102 (79.1 %) 48 (85.7 %)
Unknown/Not reported 6 (3.2 %) 5 (3.9 %) 1 (1.8 %)
Race, n (%)
Black/African American 101 (54.6 %) 91 (70.5 %) 10 (17.9 %)
White/Caucasian 68 (36.8 %) 26 (20.2 %) 42 (75.0 %)
Asian 2 (1.1 %) 1 (0.8 %) 1 (1.8 %)
More than one race 11 (5.9 %) 8 (6.2 %) 3 (5.4 %)
Unknown/Not reported 3 (1.6 %) 3 (2.3 %) 0 (0.0 %)
Gender, n (%)
Men 121 (65.4 %) 91 (70.5 %) 30 (53.6 %)
Women 62 (33.5 %) 37 (28.7 %) 25 (44.6 %)
Other 2 (1.1 %) 1 (0.8 %) 1 (1.8 %)
Education level, n (%)
Grade School (<9 years) 4 (2.2 %) 3 (2.3 %) 1 (1.8 %)
Partial Completion of High School (<12 years) 16 (8.7 %) 9 (7.0 %) 7 (12.5 %)
High School or GED Completion (12 years) 81 (44.0 %) 59 (46.1 %) 22 (39.3 %)
Partial Completion of College (13 years) 42 (22.8 %) 27 (21.1 %) 15 (26.8 %)
Associate’s Degree (14 years) 21 (11.4 %) 12 (9.4 %) 9 (16.1 %)
Bachelor’s Degree (16 years) 18 (9.8 %) 16 (12.5 %) 2 (3.6 %)
Master’ s Degree (18 years) 2 (1.1 %) 2 (1.6 %) 0 (0.0 %)
(Missing) 1 1 0
ISI category, n (%)
Not clinically significant 46 (24.9 %) 37 (28.7 %) 9 (16.1 %)
Subthreshold 67 (36.2 %) 45 (34.9 %) 22 (39.3 %)
Clinical - moderate 57 (30.8 %) 34 (26.4 %) 23 (41.1 %)
Clinical - severe 15 (8.1 %) 13 (10.1 %) 2 (3.6 %)
Past 30 days cocaine use (days), M (SD) 11.6 (14.2) 16.2 (14.5) 0.6 (2.7)
(Missing) 76 52 24
Lifetime cocaine use (years), M (SD) 13.5 (13.0) 17.8 (12.5) 3.2 (6.9)
(Missing) 76 52 24
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3.2. Primary analyses

LASSO regression results are shown in Table 2 and summarized as follows.

Table 2.

Primary regularized regression results.

All (N = 185) Cocaine (N = 129) Methamphetamine (N = 56)
Measure OR OR % change d OR OR % change d OR OR % change d
AIS
SHAPS
BDI-II Cognitive
BDI-II Non-Cognitive 3.002 200.2 % 0.61 2.493 149.3 % 0.50 2.438 143.8 % 0.49
PCL-5 Re-experiencing
PCL-5 Avoidance 1.052 5.2 % 0.03
PCL-5 NACM 1.089 8.9 % 0.05 1.170 17.0 % 0.09
PCL-5 AAR 1.288 28.8 % 0.14
DTS Tolerance
DTS Appraisal 0.771 −22.9 % −0.14
DTS Regulation 1.133 13.3 % 0.07 1.349 34.9 % 0.17
DTS Absorption
DERS Strategies
DERS Non-Acceptance 1.085 8.5 % 0.04
DERS Impulse
DERS Goals 1.363 36.3 % 0.17 1.390 39.0 % 0.18 1.021 2.1 % 0.01
DERS Awareness
DERS Clarity
AUC 0.876 0.882 0.902
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d = Cohen’s d. AUC = area under the receiver operating characteristic curve. OR = odds ratio. Blank cells indicate predictors that were not retained by the model.

Total sample (N=185).

The model retained 6 of 18 total measures in the whole sample with acceptable discrimination (AUC = 0.876). The BDI-II Non-Cognitive subscale had the largest positive coefficient (OR = 3.00), indicating that a one-unit increase in score within this subscale was associated with a 200.2 % increase in odds of high severity insomnia on the ISI. This was followed by DERS Goals (OR = 1.37), DTS Regulation (OR = 1.13), PCL-5 NACM (OR = 1.09), DERS Non- Acceptance (OR = 1.09), and PCL-5 Avoidance (OR = 1.05).

Cocaine (N=129).

The LASSO model retained 5 measures in the cocaine subgroup (AUC = 0.882) (Table 2). BDI-II Non-Cognitive showed the largest positive association with ISI score (OR = 2.49), followed by DERS Goals (OR = 1.39), DTS Regulation (OR = 1.35), PCL-5 AAR (OR = 1.29), and PCL-5 NACM (OR = 1.17).

Methamphetamine (N=56).

Three measures were retained in the methamphetamine subgroup (AUC = 0.902) (Table 2). BDI-II Non-Cognitive (OR = 2.44) and DERS Goals (OR = 1.02) were positively associated with odds of high-severity ISI, and DTS Appraisal (OR = 0.77) was negatively associated.

3.3. Sensitivity analyses

Results from analyses excluding BDI-II Non-Cognitive and PCL-5 AAR subscales are shown in Table 3.

Table 3.

Sensitivity regularized regression results.

All (N = 185) Cocaine (N = 129) Methamphetamine (N = 56)
Measure OR OR % change d OR OR % change d OR OR % change d
AIS
SHAPS 1.508 50.8 % 0.23 1.268 26.8 % 0.13 1.408 40.8 % 0.19
BDI-II Cognitive 1.006 0.6 % 0.00 1.003 0.3 % 0.00
(Excluded: BDI-II Non-Cognitive)
PCL-5 Re-experiencing
PCL-5 Avoidance 1.167 16.7 % 0.09
PCL-5 NACM 1.591 59.1 % 0.26 1.858 85.8 % 0.34
(Excluded: PCL-5 AAR)
DTS Tolerance 0.865 −13.5 % −0.08 0.786 −21.4 % −0.13
DTS Appraisal 0.730 −27.0 % −0.17
DTS Regulation 1.366 36.6 % 0.17 1.419 41.9 % 0.19
DTS Absorption
DERS Strategies
DERS Non-Acceptance 1.179 17.9 % 0.09 1.029 2.9 % 0.02 1.131 13.1 % 0.07
DERS Impulse
DERS Goals 1.839 83.9 % 0.34 1.962 96.2 % 0.37 1.375 37.5 % 0.18
DERS Awareness
DERS Clarity 1.014 1.4 % 0.01 1.131 13.1 % 0.07
AUC 0.841 0.850 0.877
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d = Cohen’s d. AUC = area under the receiver operating characteristic curve. OR = odds ratio. Analyses were performed after exclusion of sleep-related subscales (noted in table as “Excluded”). Blank cells indicate predictors that were not retained by the model.

Total sample (N=185).

The model retained more measures (8 of 16) and had lower discrimination performance compared to the primary model (AUC = 0.841). One-unit changes in DERS Goals (OR = 1.84), PCL-5 NACM (OR = 1.59), SHAPS (OR = 1.51), DTS Regulation (OR = 1.37), DERS Non-Acceptance (OR = 1.18), DERS Clarity (OR = 1.01), and BDI-II Cognitive (OR = 1.01) were positively associated with odds of high-severity ISI, and DTS Tolerance (OR = 0.87) was negatively associated with ISI score.

Cocaine (N=129).

Five measures were retained in the cocaine subgroup (AUC = 0.850) (Table 3). DERS Goals (OR = 1.96), PCL-5 NACM (OR = 1.86), DTS Regulation (OR = 1.42), SHAPS (OR = 1.27), and DERS Non-Acceptance (OR = 1.03) were associated with higher odds of high-severity ISI.

Methamphetamine (N=56).

Eight measures were retained in the methamphetamine subgroup (AUC = 0.877) (Table 3). SHAPS (OR = 1.41), DERS Goals (OR = 1.38), PCL-5 Avoidance (OR = 1.17), DERS Clarity (OR = 1.13), DERS Non-Acceptance (OR = 1.13), and BDI-II Cognitive (OR = 1.00) were associated with higher odds of high-severity ISI score. DTS Appraisal (OR = 0.73) and DTS Tolerance (OR = 0.79) were associated with lower odds of high-severity ISI score.

4. Discussion

This study investigated the prevalence and severity of insomnia in individuals with StimUD, specifically those who use cocaine and those who use methamphetamine, and identified key psychological factors associated with clinically significant insomnia using regularized regression. The results affirm prior research linking stimulant use with sleep disturbances and further reveal a nuanced set of psychological contributors that may serve as actionable targets for treatment interventions.

As expected, the rates of moderate to severe insomnia were high in this outpatient sample, nearly 40 %, which is consistent with previous studies reporting elevated insomnia prevalence among individuals with psychostimulant use. These rates are markedly higher than those observed in the general population, where insomnia disorder affects approximately 10 % (and up to 30 %) of adults [14]. These findings reinforce the critical need to integrate sleep assessments and treatment into SUD care, especially given that disrupted sleep may not only reflect withdrawal symptoms but also play a role in risk for resumption of use and overall functional impairment [9,12]; HHS, Office of the Surgeon General, 2016).

Across the total sample, depressive symptoms (particularly those indexed by the BDI-II Non-Cognitive subscale) emerged as the most potent predictor of insomnia severity, which is a finding that held true in both the cocaine and methamphetamine subgroups; however, this subscale includes somatic symptoms such as fatigue and sleep disruption, which potentially overlaps with the sleep measurement. Nonetheless, the strong association underscores the pervasive impact of somatic depressive symptoms on sleep health in this population. Importantly, in a sensitivity analysis that excluded BDI-II sleep-related subscale items, the SHAPS scale emerged as a key predictor, which could indicate a potentially robust role of anhedonia in insomnia among individuals with StimUD; difficulties in experiencing pleasure may contribute to both the initiation and maintenance of sleep problems, and could be an important affective pathway linking stimulant use and insomnia.

In addition to depression symptoms, difficulties with emotional regulation, distress tolerance regulation, and PTSD symptoms were robust predictors across the main model and sensitivity analysis in the total sample, with generally medium effect sizes. Specifically, the DERS goals subscale results indicated that experiencing difficulties engaging in and accomplishing tasks while experiencing negative emotions may impair the ability to manage arousal and negative emotions at bedtime, which then perpetuates hyperarousal and sleep fragmentation [36,37]. The DTS regulation subscale was also retained, such that higher reported ability to regulate distress was paradoxically associated with worse sleep quality.

Regarding PTSD symptoms, the negative alterations in cognition and mood (NACM) cluster emerged as a consistent predictor of insomnia severity across the total sample and was especially prominent in the cocaine subgroup. NACM symptoms, including pervasive negative beliefs, self-blame, and trauma-related rumination, have been previously shown to be associated with difficulties with sleep initiation and increased nocturnal cognitive arousal [49,50]. These cognitive processes may amplify presleep worry and disrupt the downregulation of arousal required for sleep onset. In contrast, in the methamphetamine subgroup, avoidance symptoms demonstrated stronger relevance for sleep disturbance. Avoidance of trauma-related thoughts or internal cues can maintain hyperarousal by preventing emotional processing and perpetuating fear-based physiological activation during nighttime, consistent with established models of PTSD-related insomnia [51,52]. The cocaine subgroup also showed a unique association between arousal/reactivity and insomnia, which aligns with previous work that indicates that physiological hyperactivation, including exaggerated startle response, irritability, and heightened vigilance, directly interferes with sleep initiation and continuity [53,54].

In addition to these overlapping findings in the whole sample, the methamphetamine and cocaine subgroups also showed distinct patterns that were evident in both the main model and sensitivity analysis. While both groups shared the depression and DERS goals predictors, the methamphetamine subgroup uniquely exhibited strong associations between appraisal-based distress tolerance (DT) and insomnia. Specifically, having a more positive appraisal of distress and observing distress as acceptable was a protective factor for good sleep. In turn, previous research has shown that better sleep can also lead to improved appraisal-based DT and appraisal-based DT explains the relationship between sleep and mental health problems [55]. Strategies that aim to improve DT in the population who uses methamphetamine might improve sleep and vice versa. In the cocaine subgroup, higher DTS-Regulation scores (which reflect greater efforts to reduce or modulate distress) were unexpectedly associated with more severe insomnia. Although counterintuitive, this pattern is consistent with existing literature suggesting that people who use stimulants may engage in maladaptive distress-regulation attempts, including substance-mediated coping, suppression, or behavioral avoidance [56,57]. Such strategies may temporarily reduce negative affect but ultimately increase physiological arousal or disrupt circadian rhythms, thus worsening sleep disturbances. As existing evidence on this mechanism is limited, future research using ecological momentary assessment or laboratory paradigms could directly test whether distress-regulation attempts among individuals who use cocaine are more likely to involve substance use or other maladaptive strategies that interfere with sleep. These studies would help to clarify whether the association observed here reflects a causal mechanism or a marker of broader dysregulation.

These findings contribute to a growing recognition of sleep as a modifiable and multidimensional factor in recovery from StimUDs. Importantly, many of the retained psychological and behavioral variables – such as emotion regulation, distress tolerance, PTSD symptoms, and depressive symptoms – are modifiable through existing therapeutic modalities, including cognitive-behavioral therapy for insomnia (CBT-I), mindfulness-based interventions, and trauma-informed care approaches. Integrating sleep-focused modules into standard SUD treatment programs could offer a dual benefit: reducing insomnia-related distress and enhancing emotional resilience during recovery. Pharmacological approaches to improving sleep are also an important area of ongoing research in StimUD [58,59].

4.1. Implications for treatment adaptation in stimulant use disorders

The current findings highlight several modifiable psychological processes that could be targeted within sleep-focused interventions for individuals with StimUD. Although cognitive behavioral therapy for insomnia (CBT-I), mindfulness-based interventions, and trauma-informed approaches each independently improve sleep, few treatments integrate these modalities for stimulant-using populations. Our results suggest several pathways for adaptation.

The subgroup-specific PTSD patterns suggest that trauma-focused sleep interventions may benefit from tailoring to the dominant PTSD symptom cluster. For example, NACM-related cognitive themes may respond well to cognitive restructuring within CBT-I or trauma-informed CBT, whereas hyperarousal symptoms (cluster E) may benefit from incorporating grounding, paced breathing, or other autonomic regulation techniques [51,52]. Additionally, individuals with prominent avoidance symptoms could benefit from gradual exposure-based strategies that reduce avoidance-driven nighttime arousal [51]. These trauma-related mechanisms can also be incorporated within trauma-informed CBT-I or mindfulness-oriented recovery enhancement (MORE), which integrates nighttime grounding, cognitive reframing, autonomic regulation (e.g., paced breathing), and trauma-sensitive relaxation strategies, and may be especially beneficial for individuals with prominent cluster E symptoms [60].

In addition to potential tailoring of sleep intervention strategies based on PTSD symptom clusters, as described above, other identified predictors, such as anhedonia, could be targeted through behavioral activation and reward-enhancement strategies embedded within CBT-I. These modifications may increase engagement in daytime rewarding behaviors, subsequently improving sleep pressure and circadian stability [61]. Distress-tolerance deficits and avoidance patterns, particularly relevant in the methamphetamine subgroup, could be targeted via acceptance-based skills (e.g., willingness, cognitive defusion) or dialectical behavior therapy distress-tolerance techniques integrated into sleep therapy sessions [62].

Beyond trauma-related processes, the prominence of the DERS Goals subscale across models suggests that difficulty staying on track with goal-directed behavior during emotional distress may conflict with behavioral components of insomnia treatment. Reviews of insomnia and emotion regulation describe a bidirectional relationship whereby poor sleep impairs the capacity to flexibly regulate emotions, whereas rigid, maladaptive strategies (e.g., rumination, suppression, and avoidance) sustain presleep arousal and sleep disruption [63,64]). Incorporating structured emotion-regulation skills training into CBT-I and SUD treatment, such as teaching alternative strategies (cognitive reappraisal, acceptance, and problem-solving), enhancing awareness of emotion-sleep links, and rehearsing “good-enough” sleep routines that can be implemented even when mood is low or craving is high, may be particularly valuable for individuals with elevated DERS Goals scores.

Table 4 provides a brief summary of (1) the negative affect measures retained in our analyses, (2) their suggested associations with sleep disturbance, and (3) potential treatment implications derived from these patterns.

Table 4.

Aspects of negative affect, sleep mechanisms, and suggested intervention targets by subgroup.

Negative Affect Measure Subgroup(s) How It Disrupts Sleep Potential Treatment Target
Negative Alterations in Cognition and Mood (NACM) Total sample, Cocaine Rumination, negative beliefs, presleep worry → delayed sleep onset Cognitive restructuring; trauma-focused CBT-I modules
Arousal/Reactivity Cocaine Physiological hyperarousal, hypervigilance → insomnia, fragmented sleep Mindfulness, Grounding, relaxation training, autonomic regulation
Avoidance Methamphetamine Avoidance of internal cues perpetuates hyperarousal → light, unstable sleep Exposure-based strategies; acceptance-based approaches
Anhedonia Total sample Reduced engagement in rewarding daytime activity → weakened sleep pressure, inconsistent circadian cues Behavioral activation; reward-enhancement strategies embedded within CBT-I
Distress Tolerance Defecits/Avoidance Total Sample, Methamphetamine Heightened emotional avoidance and difficulty managing arousal → nighttime distress, restlessness, unstable sleep continuity Acceptance-based skills (willingness, cognitive defusion); DBT distress-tolerance techniques integrated into sleep therapy
Emotion Regulation Difficulties Total Sample Difficulty engaging in goal-directed behavior when distressed → inconsistent sleep routines, greater presleep worry/rumination, which maintain hyperarousal and fragmented sleep Integrate emotion-regulation skills training into CBT-I and SUD treatment; emphasize flexible goal-setting and “distress-compatible” sleep routines
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Given the bidirectional relationship between sleep disruption and stimulant craving, interventions that coordinate insomnia treatment with SUD treatment protocols may offer synergistic benefits. Our findings support the development of an integrated, multi-component intervention that addresses sleep, emotional regulation, trauma symptoms, and substance-use patterns simultaneously.

4.2. Limitations and future directions

Several limitations warrant consideration when interpreting the results of this study. First, the cross-sectional nature of the data limits causal inference; while insomnia may be influenced by psychological dysfunction, it may also exacerbate these symptoms, suggesting potential bidirectional relationships. Second, the reliance on self-report data, though validated, may introduce recall or reporting bias, particularly for sleep-related symptoms. Common method variance arising from all measures using self-report sources may also result in inflated associations among variables. Third, although LASSO regression provides a powerful tool for variable selection, it is sensitive to sample size and inter-variable correlations. While LASSO can perform satisfactorily in larger samples, in small samples (i.e., methamphetamine subgroup) LASSO may not select the most relevant predictors if multicollinearity is present, thus estimates may be more unstable. Findings should be replicated in larger and more diverse cohorts, particularly to confirm subgroup-specific predictors.

Future research should explore longitudinal trajectories of insomnia in StimUD populations and test whether improvements in the identified psychological domains yield reductions in sleep disturbances. Additionally, mechanistic studies using objective sleep metrics (e.g., actigraphy or polysomnography) may help clarify how psychological traits translate into physiological sleep disruption.

Funding

This work was supported by the National Institute on Drug Abuse K01 DA058765, R01 DA039125, and R01 DA048026.

Footnotes

CRediT authorship contribution statement

Isabella G. Bourtin: Writing – original draft, Conceptualization. Constanza de Dios: Formal analysis. Jessica Vincent: Project administration, Data curation. Joy M. Schmitz: Writing – review & editing. Scott D. Lane: Writing – review & editing. Heather E. Webber: Writing – review & editing, Supervision, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data statement

Data will be made available upon reasonable request.

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Data Availability Statement

Data will be made available upon reasonable request.

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