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. 2026 Jun 7;20:100455. doi: 10.1016/j.dadr.2026.100455

Craving mediates the association between affect and substance use in daily life among individuals with substance use disorders

Emmanuelle Baillet a,⁎, Nicholas R Harp a, Hedy Kober a,b,1, Corey Roos b,1
PMCID: PMC13312586  PMID: 42376470

Abstract

Background

Craving, the desire to use substances, is a key diagnostic feature for substance use disorders (SUDs) that predicts substance use. Experiencing affective states, both positive (PA) and negative affect (NA), are also implicated in SUDs. Yet, their dynamic interrelationships with craving and substance use remain unclear, particularly at the within-person level of analysis. This study aimed to use ecological momentary assessment (EMA) in daily life to test whether craving mediates the association between PA/NA and subsequent substance use.

Methods

Adults with SUDs (N = 36), from outpatient addiction treatment centers, were included in an observational study using EMA over 14 days prior to randomization in a clinical trial. They completed EMA surveys three times daily [midday, afternoon, evening], reporting on PA, NA, craving, and substance use. Multilevel mediation models with bootstrapping were used to examine whether craving mediated within-person associations between affect (PA and NA) and subsequent substance use.

Results

Craving significantly mediated the association between affect and subsequent substance use. Higher PA was associated with lower craving at the same timepoint, which in turn led to reduced likelihood of substance use at the next timepoint (β=-0.04, p = 0.02). Conversely, higher NA was associated with higher craving, leading to greater likelihood of substance use at the next timepoint (β=0.08, p = 0.03).

Conclusion

At the within-person level, craving mediates the association between affective states and subsequent substance use in individuals with SUD. Targeting momentary affect and craving in daily life, such as through ecological momentary interventions, is a promising strategy for treating SUDs.

Keywords: Craving, Affect, Substance Use, Addiction, EMA, Mediation analyses

Graphical Abstract

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Highlights

  • •

    Craving mediates the link between affect and substance use in daily life.

  • •

    Higher NA is associated with increased craving and predicts subsequent substance use.

  • •

    Higher PA is associated with reduced craving and predicts lower subsequent substance use.

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    EMA reveals within-person dynamics in individuals with SUDs.

  • •

    Targeting affect and craving may improve SUD interventions.

1. Introduction

Substance Use Disorders (SUDs) are chronic, relapsing conditions characterized by a loss of control over substance use (Koob and Volkow, 2010b, Koob and Volkow, 2016). Craving, a “strong desire or urge to use”, is the most central, discriminating, and prevalent criterion among all DSM−5 criteria (American Psychiatric Association., 2013, Gauld et al., 2023, Kervran et al., 2020), and across SUDs. Further, a recent meta-analysis (N = 51,788) confirmed that craving prospectively predicts substance use and return to substance use (Vafaie and Kober, 2022), making it an important trigger of use, especially during abstinence and after recovery, even in the long-term (Baillet et al., 2024, Fredriksson et al., 2021, Vafaie and Kober, 2022).

Understanding antecedents of craving is critical for informing targeted interventions. A variety of antecedents – both external (e.g., seeing substances) and internal (e.g., affective states) – can elicit craving through conditioning processes (Betts et al., 2021, Carter and Tiffany, 1999, Paulus and Stewart, 2014). External cues are known to trigger cue reactivity, including cue-induced craving (Carter and Tiffany, 1999). One key internal cue is affect, often conceptualized along two dimensions: positive affect (PA; e.g., joy, excitement, etc.) and negative affect (NA; e.g., sadness, anger, etc.). Several studies, summarized in a meta-analysis (N = 2257; Cyr et al., 2023; Pombo et al., 2016; Schlauch et al., 2013), have reported positive associations between NA and craving. Another meta-analyses recently demonstrated that inducing NA in the laboratory leads to alcohol craving (N = 2403; Bresin et al., 2018) and nicotine craving (N = 1412; Heckman et al., 2013). Stress, a specific form of NA, has also been strongly linked to craving and substance use across clinical, laboratory, and neurobiological studies (Sinha, 2011, Sinha, 2024). Exposure to stressors is known to precipitate return to use (Sinha, 2011, Sinha, 2024) and exacerbate withdrawal symptoms and craving, making abstinence more challenging (Breese et al., 2011, Sinha, 2024). This work underscores the critical role of stress and NA in triggering craving and maintaining substance use behaviors, particularly in alcohol and nicotine/tobacco research.

Compared to NA, PA has been relatively understudied, and findings are mixed regarding its association with craving. Some studies suggest that PA reduces craving (Lydon-Staley et al., 2017, Schlauch et al., 2013, Wang et al., 2024), while others suggest no direct effect (Maude-Griffin and Tiffany, 1996, VanderVeen et al., 2016), or an indirect effect via emotion regulation strategies (Khosravani et al., 2017). One study utilized ecological momentary assessment (EMA) and found that momentary PA was positively associated with craving to drink among young adult with heavy alcohol use (Bold et al., 2016). PA may also enhance the capacity for emotion regulation and redirect the drive for rewards, thereby reducing craving (Koob and Volkow, 2010a, Leventhal et al., 2013, Sinha, 2009). It is also possible that the effects of PA on craving depend on the subpopulation: young adult who engage in heavy alcohol consumption are often characterized by reward drinking tendencies, and PA may be a trigger to drink for them (Roos et al., 2021). Alternatively, older individuals with SUDs are often characterized by relief drinking tendencies and PA may be protective (or low PA may be a trigger to use; Roos, Mann, et al., 2017).

Notably, a limitation of the work on affect and craving is that it has mostly examined association at the between-person level, and at a single timepoint, with few exceptions (e.g., Bold et al., 2016). Testing within-person association between affect and craving can reveal whether momentary changes in affect are related to changes in craving, for a given individual. Such work can reveal dynamic momentary processes occurring within individuals across time.

Beyond their effect on craving, PA and NA have been identified as potential mechanisms underlying the maintenance of substance use (Cheetham et al., 2010). Meta-analyses have shown that laboratory-induced NA increases substance use (Bresin et al., 2018, Heckman et al., 2015), and that NA is a significant predictor of relapse (van Lier et al., 2018). Another mega-analysis from 69 daily-diary and EMA studies (N = 12,394) found that high NA did not predict alcohol use, whereas high PA did (Dora et al., 2023). These results suggest that affect may influence substance use through different mechanisms, and potentially in different contexts and across different substance types – and may or may not have a direct influence on substance use.

Given the links between affect, craving, and substance use, one possibility is that craving might mediate the relationship between affect and substance use. Indeed, affect may indirectly impact substance use by triggering craving. Examining this mediational pathway is crucial for understanding how affect drives substance use. To our knowledge, two studies tested such a mediational pathway. Firstly, among patients with chronic pain using prescription opioids, craving mediated the link between NA and opioid misuse (Martel et al., 2014). Secondly, among individuals with SUDs, craving mediated the link between depressive symptoms and substance use (Witkiewitz and Bowen, 2010a). However, participants were not diagnosed with SUD (Martel et al., 2014), and only depressive symptoms were evaluated (not affect; Witkiewitz and Bowen, 2010a). Finally, these studies relied on retrospective questionnaires, limiting ecological validity and introducing recall bias.

Since craving and affect are dynamic fluctuating states influenced by internal and environmental factors (Enkema et al., 2020, Sayette et al., 2000), EMA offers a more sensitive approach by capturing real-time states in the natural environment, through repeated measurements (Shiffman et al., 2008, Stone and Shiffman, 1994). To date, no studies have investigated whether craving mediates the link between affect and substance use in daily life using EMA, in individuals with SUD. The objective of this study was to use EMA to investigate whether affect (PA and NA) assessed at one EMA prompt (T0) predicted substance use at the subsequent EMA prompt (T1), as well as the presence of indirect effects via craving. We hypothesized that (1) PA and NA at T0 predict craving at T0; (2) craving at T0 predicts substance use at T1; and (3) craving at T0 would mediate that the effect of PA and NA at T0 on substance use T1.

2. Methods

2.1. Participants

Participants were 36 individuals enrolled in a pilot randomized controlled trial (described below) evaluating an app-delivered mindfulness-based intervention for SUDs (Roos, Bricker, et al., 2024). Inclusion criteria were: 18 + years of age; English-speaking; SUD diagnosis; in early phase of SUD treatment, as demonstrated by completing at least one month of SUD treatment in the past 4 months, and using their primary substance of choice in the past 6 months; not currently enrolled in residential treatment; willingness to be randomized and to participate in the 18-week study period. Exclusion criteria were: current psychotic symptoms; high suicide risk characterized by self-reported suicidal ideation with intent; homicidal ideation posing imminent danger to others; pending legal case, imminent incarceration, or inability to commit to the entire study period (Roos, Kiluk, et al., 2024). The protocol was approved by Yale University’s Institutional Review Board, and all participants provided informed consent.

2.2. Study design

Following a baseline assessment visit, participants completed 2 weeks of EMA prior to being randomized (Roos, Bricker, et al., 2024). EMA consisted of 4 brief surveys per day over 2-weeks: a survey delivered in the morning, during which participants reported their past-day substance use (which is not used in the current analyses), and 3 additional momentary surveys delivered randomly every ≈ 4 h: midday, afternoon and evening report. Each EMA survey was administered via the Catalyst app (Metric Wire, Inc.), which delivered smartphone notifications to complete the surveys. For the momentary reports, participants had 1 h to complete them with reminders every 15 min. Participants received financial compensation up to $81 dollars for EMA ($1 per survey and a bonus of $25 for completing 75% or more of EMA surveys) (see Roos, Bricker, et al. 2024 for further details). Analyses included participants who completed the baseline pre-randomization EMA period.

2.3. Measurements

At baseline, SUD diagnosis was assessed using the Mini International Neuropsychiatric Interview (MINI; Sheehan et al., 1998). For each EMA survey, participants rated their PA and NA. For example, they were asked to rate “right now (or in the past 15 min), I feel happy.” Participants also rated their craving (“Right now (or in the past 15 min), I crave, want, or desire alcohol or drugs”). Items were rated on a 10-point scale (1 not at all to 10 extremely). The PA items were: excited, grateful, sense of connection, happy, relaxed, sense of meaning/purpose; the NA items were: sad, angry, lonely, ashamed, anxious, bored. The PA/NA items were chosen based on the circumplex model of affect (Posner et al., 2005). We averaged responses within each category to calculate continuous PA and NA scores for each random-prompt entry (i.e., average NA/PA score for the midday, afternoon, and evening surveys), with higher scores reflecting greater momentary positive or negative affect intensity, respectively. At each random prompt, participants also reported substance use via two items assessing alcohol (“Did you drink any alcohol?”) or other drug use (“Have you used any other drugs, such as marijuana, cocaine, heroin, opioids, benzos, amphetamines, hallucinogens, or dissociative drugs?”). A composite binary variable indicated whether any substance use occurred. Time windows varied by survey: for midday (random prompt between noon and 1:30 pm), “Since waking up today until now”; afternoon (4:00 and 5:30 pm), “Since about 2 pm”; and evening (8:00 and 9:30 pm), “Since about 6 pm”. Given the nature of time-lagged analyses and the structure of the EMA surveys, we only used prompts from midday, afternoon, and evening reports in the current analyses.

2.4. Analyses

Our analytic plan was guided by our conceptualization of affect and craving as momentary processes occurring at the same time, and that may confer risk for subsequent substance use at the next timepoint. Accordingly, we examined: (1) the associations between PA/NA and craving; (2) the associations between PA/NA and substance use; (3) the associations between craving and substance use; and (3) whether craving mediated the link between PA/NA and subsequent substance use.

2.4.1. Associations between affect, craving, and substance use

We explored how within-person fluctuations in affect intensity (PA and NA), relative to an individual’s mean level of PA and NA at each random prompt, were associated with craving intensity and substance use at the same random-prompt timepoint (T0; concurrent effects). This approach isolates the impact of momentary deviations in affect from each participant’s average emotional experience. On an exploratory basis, we then examined whether the intensity of PA and NA at T0 influenced craving and substance use at the subsequent timepoint (T1; cross-lagged effects). Similarly, we examined whether craving at T0 predicted substance use at T1, as well as concurrent substance use at T0. These analyses allowed us to investigate both concurrent and temporal relationships between PA/NA, craving and substance use, while accounting for within-person fluctuations over time.

2.4.2. Mediation analyses

To explore our hypothesis that craving would mediate the relationship between PA/NA and substance use, we conducted multilevel mediation modeling (Zhao et al., 2010). Mediation analysis is used to examine the interrelations among a predictor (X; i.e., PA or NA), a mediating variable (M, i.e., craving), and an outcome variable (Y; i.e., substance use). Accordingly, we evaluated: the effect of PA or NA at T0 on craving at T0 (path a, Model X → M); the effects of craving at T0 on substance use at T1 (Path b, Model M → Y) and then, the effect of PA or NA at T0 on substance use at T1 (Model X → Y), both as a total effect (Path c, without adjusting for craving) and as direct effect (Path c’, adjusting for craving to account for the mediating pathway). From these models, we derived the total, direct, and indirect effects. The indirect effect was computed as the product of the indirect paths (a × b). The direct effect represents the portion of PA/NA’s influence on substance use that is not mediated by craving. The indirect effect represents the portion of PA/NA’s effect on substance use that is mediated by craving. The total effect represents the overall influence of PA/NA on substance use, combining both the direct (unmediated) and indirect (mediated through craving) effects. Finally, to test the robustness of these effects, we applied bootstrapping with 1000 iterations (Efron and Tibshirani, 1994), recommended for estimating the precision of effects (Muthén et al., 2017). We obtained 95% confidence intervals (CI) for the direct, indirect, and total effects from the bootstrap results. To determine whether the effects were statistically significant, we computed a two-tailed p-value from the bootstrap estimates. This mediation analysis was conducted and reported in accordance with the AGReMA Statement (Lee et al., 2021).

Given the repeated and nested nature of EMA data, we used linear mixed-effects models. Intraclass correlation coefficients (ICCs) were calculated to estimate the proportion of variance attributable to between-person versus within-person fluctuations across EMA observations and to inform the appropriateness of multilevel modeling. For all models, we included random intercepts to account for individual differences in baseline outcome and random slopes to capture variability in the relationships between affect, craving or substance use across participants. When included as predictors, PA, NA, and craving were person-mean centered; variables serving as outcomes were not (Curran and Bauer, 2011, Enders and Tofighi, 2007). Fixed effects were estimated to assess the associations between predictors (PA, NA, or craving) and the outcomes (craving or substance use). Models were estimated using maximum likelihood estimation with robust standard errors, and missing data were handled using full information maximum likelihood (FIML). A linear mixed model was used for PA, NA, and craving due to their ordinal nature and generalized linear mixed effects model with logit link function for substance use due to its binary nature (Bolker et al., 2009, Gelman and Hill, 2007, Raudenbush and Bryk, 2002). Random intercept models were controlled for age and sex. If a significant effect was found, it was included in the subsequent analyses. Lagged analyses examined associations across consecutive EMA prompts, with T1 typically occurring approximately 3–4 h after T0 and controlled for prior craving or substance use, depending on the outcome. Statistical analyses were conducted in R (version 4.1.1) using the lme4 and glmer package; code is available upon request.

3. Results

3.1. Sample characteristics

On average, participants (N = 36) were 39.5 years old (SD=9.89), 66.7% identified as men (n = 24), 30.6% as women (n = 11), and 2.8% as transgender (n = 1; Table A). The main primary substances were cocaine (33.3%, n = 12) and alcohol (27.8%, n = 10; Table A). Overall, 32 of 36 participants (88.9%) reported a secondary substance. Marijuana was the most frequently reported secondary substance, particularly among participants reporting cocaine (66.7%, n = 8), alcohol (50.0%, n = 5), or dissociative drugs (50.0%, n = 3) as their primary substance.

Table A.

Descriptive characteristics about the study sample (N = 36).

VARIABLES N (%) M (SD)
Sex
Male 23 (63.9)
Female 13 (36.1)
Gender
Men 24 (66.7)
Women 11 (30.6)
Transgender 1 (2.8)
Ethnicity
Hispanic 12 (33.3)
No Hispanic 24 (66.7)
Race
African American/Black 13 (36.1)
White 13 (36.1)
Other 8 (22.2)
Prefer not to say 2 (5.6)
Age (years) 39.5 (9.89)
Education
Graduate degree 1 (2.8)
Undergraduate degree 8 (22.2)
Lower than secondary education 4 (11.1)
High school diploma 16 (44.4)
Some post-secondary education 5 (13.9)
Other 2 (5.6)
Occupation
Full time employment 12 (33.3)
Part time employment 5 (13.9)
Unemployed 15 (41.7)
Unable to work 3 (8.2)
Student 1 (2.8)
Primary substance
Cocaine 12 (33.3)
Alcohol 10 (27.8)
Dissociative drug (PCP, ketamine) 6 (16.7)
Marijuana 4 (11.1)
Heroin 2 (5.6)
Hallucinogens (MDMA, LSD) 1 (2.8)
Opioid pills (not prescribed or misused) 1 (2.8)
Polysubstance use 32 (88.9)
Secondary substance
Marijuana 18 (50.0)
Alcohol 10 (27.8)
Heroin 2 (5.6)
Cocaine 1 (2.8)
Hallucinogens (MDMA, LSD) 1 (2.8)
None 4 (11.1)
DSM−5 SUD
Sum of criteria (0−11) 8.5 (2.9)
Mild (2−3) 4 (11.1)
Moderate (4−5) 4 (11.1)
Severe (6 +) 28 (77.8)
Treatment & Medication
SUD medication 11 (30.5)
Previous inpatient/residential treatment episodes for SUD 2.85 (4.05)
Previous outpatient treatment episodes for SUD 3.86 (3.89)
Referred by the criminal justice system 27 (75%)
EMA features
Completed surveys (overall) 1331 (66.1)
Completed momentary surveys 900 (60.5%)
Craving (1−10) 1.91 (2.26, range 1–10)
Positive Affect (1−10) 6.0 (2.35, range 0–10)
Negative Affect (1−10) 2.5 (1.77, range 0–8.6)
Substance use 193/900 (21.44)1
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Note. M =  mean; SD =  standard deviation.

¹Percentage is calculated based on the number of EMA assessments with available data for variable.

3.2. EMA completion

With four EMAs per day over fourteen days, each participant received 56 prompts, resulting in a potential maximum of 2016 completed EMAs. There were 665 missed EMAs (32.9%) and 1331 completed EMAs (67.1%), which is an acceptable threshold (Jones et al., 2019). Of the four EMAs per day, percent completion ranged from 85.5% for the first EMA of the day to 57.9% for the last EMA of the day.

3.3. Affect, craving, and substance use

Overall, the average score was 2.16 (SD=1.42, range=0–10) for craving, 6.03 (SD=1.19, range=0–10) for PA, and 2.47 (SD=0.98, range=0–8.6) for NA. Substance use was reported at 20.5% (SD=28.5) of the midday EMA surveys, 20.9% (SD=28.5) of the afternoon surveys, and 25.7% (SD=30.7) of the evening surveys. Exploratory analyses did not reveal statistically significant differences by biological sex or race in mean PA, NA, craving, or substance use (all ns >.10). Similarly, exploratory comparisons between participants reporting alcohol versus cocaine as their primary substance did not reveal statistically significant differences in PA, NA, craving, or substance use (all ns >.26).

3.4. Link between affect and craving

Craving had an ICC value of 0.62, indicating that 61.7% of craving variance was attributed to between-person differences and 38.3% to within-person differences across EMA observations, which supported the use of multilevel models to account for both between- and within-person variability. Sex and age did not predict craving (Sex: β=−0.09; SE=0.85; t = −0.11; p = 0.91; Age: β=−0.01; SE=0.04; t = −0.26; p = 0.80) and thus, were not included in the following analyses. Multilevel models revealed that both PA and NA at T0 predicted craving at T0. However, their effects differed: higher levels of PA were associated with decreased craving intensity (β=−0.23; SE=0.05; t = −4.32; p < 0.001), whereas higher levels of NA were associated with increased craving intensity (β=0.55; SE=0.05; t = 10.21; p < 0.001; Fig. A). Exploratory analyses revealed that NA at T0 predicted craving at T1 (β=0.29; SE=0.07; t = 3.62, p < 0.001; Figure B), but this was inconclusive for PA (β=−0.01; SE=0.06; t = −0.57, p = 0.81). Importantly, we tested the reverse models, where craving predicted PA or NA at T0, and found no significant effects (PA: β=5.65e−16, p = 1.00; NA: β=−1.88e−16, p = 1.00).

Fig. A.

Fig. A

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Predicted effects of positive (green) and negative (orange) affect at one EMA timepoint (T0) on craving intensity at the same one (T0). Shaded areas indicate 95% confidence intervals (fixed effects, centered affect variables).

Fig. B.

Fig. B

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Predicted effects of positive (green) and negative (orange) affect at one EMA timepoint (T0) on craving intensity at the next timepoint (T1). Shaded areas indicate 95% confidence intervals (fixed effects, centered affect variables, controlled for craving at T0).

3.5. Link between affect and use

Substance use had an ICC value of 0.53, showing between-person variation accounted for 53.0% of the variance, while within-person variation accounted for 47.0%. Sex and age did not predict substance use (Sex: β=−1.06; SE=0.74; z = −1.44; p = 0.15; Age: β=−0.01; SE=0.04; z = −0.31; p = 0.76). There were inconclusive effects of PA and NA at T0 on substance use at T1 (PA: β=−0.01; SE=0.09; z = −0.07, p = 0.96; NA: β=0.17; SE=0.12; z = 1.43, p = 0.15). Similarly, exploratory analyses showed inconclusive effect of either PA or NA at T0 on substance use at T0 (PA: β=−0.02; SE=0.07; z = −0.306, p = 0.76; NA: β=0.17; SE=0.09; z = 1.81, p = 0.07).

3.6. Link between craving and use

Craving at T0 predicted substance use at T1, after controlling for substance use at T0 (β=0.14; SE=0.07; z = 2.06; p = 0.04; Fig. C). Exploratory analyses showed that craving also predicted substance use at T0 (β=0.15; SE=0.05; z = 2.69; p = 0.01; Fig. D).

Fig. C.

Fig. C

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Predicted effects of craving at one EMA timepoint (T0) on the substance use at the next timepoint (T1). Shaded area indicates 95% confidence interval (fixed effect, centered craving variable, controlled for substance use at T0).

Fig. D.

Fig. D

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Predicted effects of craving at one EMA timepoint (T0) on the substance use at the same one (T0). Shaded area indicates 95% confidence interval (fixed effect, centered craving variable).

3.7. Relationship between affect, craving, and use

The total effect of PA and NA at T0 on substance use at T1 was not significant (PA: β=−0.01, SE=0.11, 95% CI [−0.21, 0.22], p = 0.96; NA: β=0.18, SE=0.12, 95% CI [−0.08, 0.43], p = 0.15; Fig. D; Supplementary Materials). Controlling for craving did not change this result (direct effect/PA: β=0.03, SE=0.11, 95% CI [−0.19, 0.26], p = 0.82; NA: β=0.10, SE=0.04, 95% CI [−0.14, 0.34], p = 0.42; Fig. E; Supplementary Materials). This indicates that neither PA nor NA directly predicted substance use over time. However, our results revealed significant indirect effects between PA/NA at T0 and substance use at T1, such that the effect of PA or NA at T0 on substance use at T1 was mediated by craving at T0 (indirect effect/ PA: β=−0.04, SE=0.02, 95% CI [−0.09, −0.01], p = 0.02; NA: β=0.08, SE=0.04, 95% CI [0.01, 0.16], p = 0.03; Fig. E).

Fig. E.

Fig. E

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Mediation models for the association between affect (PA or NA) at one EMA timepoint (T0), craving at the same timepoint (T0), and substance use at the next timepoint (T1), whereby craving is a complete mediator of the affect–use relationship. Path coefficients are presented alongside arrows representing each analyzed link, with standard errors (SEs) in parentheses. Path a denotes the effect of affect on craving, while path b represents the effect of craving on substance use. Path c’ illustrates the relationship between affect and substance use while accounting for the mediator (craving), whereas path c represents the total association between affect and substance use without considering the mediator. Significance levels are indicated as follows: *p < 0.05, **p < 0.01, ***p < 0.001.

4. Discussion

This EMA study examined how momentary affect, craving, and substance use interact in daily life among individuals with SUDs. Results confirm that craving mediates the association between affect and substance use: PA predicted lower craving, which in turn predicted lower likelihood of substance use. While NA predicted higher craving, which in turn predicted greater likelihood of substance use. These findings highlight the importance of craving, and its associations with affective states, in the momentary dynamics of substance use in SUDs.

Our findings are consistent with prior research showing that NA predicts craving; however, here we show that this association also occurs at the within-person level. The findings are also consistent with neurobiological models of SUDs that have highlighted the role of stress and NA in craving and SUDs (Koob and Volkow, 2016). Prior work on the association between PA and craving has primarily examined effects at the between-person level, and results have been mixed. Here, we show that PA was associated with lower craving and may thus protect against craving at the within-person level, in line with some prior work (Huhn et al., 2016, Veilleux et al., 2013). High PA may correspond with non-substance-related rewarding experiences in daily life. Hence, individuals may be less tempted to use substances because they are experiencing alternative rewards (Garland, 2020, Stone, 2022).

Importantly, we found that affect did not predict substance use at T0 or at T1. Rather, our results highlight an indirect pathway: PA was associated with reduced craving, which in turn predicted lower subsequent substance use, while NA was associated with increased craving and subsequent substance use. For PA, the direct and indirect effects were in opposite directions (direct: 0.03; indirect: −0.04), resulting in a near-zero total effect. This pattern reflects indirect-only mediation (Bauer et al., 2006, Hayes, 2009, Imai et al., 2010, MacKinnon, 2012, Preacher and Hayes, 2008, Zhao et al., 2010). It occurs when opposing pathways cancel each other out or when indirect effects are more sensitive to subtle influences than the total effect. This suggest that affect may be indirectly associated with substance use through their associations with craving. Results conflict with prior reports of a positive link between PA and alcohol use (Dora et al., 2023), possibly due to differences in sample (multi-substances vs. alcohol only), temporal resolution (momentary vs. daily-level data), and modeling (craving modeled vs. not modeled in analyses). Additional research is needed to elucidate the conditions under which PA may affect craving and use across time resolutions, samples, etc.

Exploratory analyses showed NA increases craving at both at T0 and T1, while PA reduces craving only at T0. These aligns with previous studies demonstrating that momentary affect can trigger craving (Shiffman et al., 2002, Waddell et al., 2023, Waddell et al., 2024, Witkiewitz and Bowen, 2010b). The novel temporal associations suggest that NA may have a more persistent impact on craving, beyond the moment when it is experienced. Conversely, PA could be more short-lived, offering only a temporary distraction from craving. Notably, these findings further highlight the temporal nature of these relationships in daily life, while highlighting the critical role of craving in substance use.

Importantly, the mediation analysis suggests that associations between affect and subsequent substance use are indirect, operating through craving. Neither PA nor NA directly predicted substance use. However, both were indirectly associated with subsequent substance use through their momentary association with craving. It is worth noting that the presence of significant indirect effects in the absence of total or direct effects may appear counterintuitive. However, such a pattern has been well documented in the literature (Hayes, 2009, Hayes, 2017), and may occur when opposing pathways cancel each other out, or when indirect paths are more sensitive to detecting subtle effects than total effects. Importantly, the absence of direct effects does not imply that affect is unrelated to substance use. Rather, the present findings suggest that momentary affective states may contribute to changes in the likelihood of subsequent substance use primarily through their influence on craving. This pattern of indirect-only mediation highlights craving as a potentially more proximal and clinically actionable target than affect alone, as interventions may reduce substance use indirectly by modifying emotional responses that influence craving. Overall, these findings emphasize craving as a key mechanism linking momentary affective states with substance use in everyday life. It also supports influential addiction theories suggesting that craving is a proximal predictor of use shaped by emotion states (Kober et al., 2010, Koob and Volkow, 2010b, Sinha, 2011, Sinha, 2024, Vafaie and Kober, 2022).

Clinically, this study suggests that interventions targeting momentary affective processes may have the potential to indirectly reduce the likelihood of substance use though reductions in craving. Interventions focused on improving emotion regulation, reducing NA, and maintaining or enhancing PA may therefore be particularly beneficial. For example, mindfulness- and cognitive behavioral therapy (CBT)-based strategies may help individuals better regulate distress, tolerate craving-related experiences, and sustain adaptive positive emotional states (Blanke et al., 2018, Garland et al., 2019, Suzuki et al., 2020, Westbrook et al., 2013, Whelen and Strunk, 2021). In addition, these results support the potential utility of ecological momentary interventions (EMIs) and just-in-time adaptive interventions (JITAIs), which could identify periods of heightened affective vulnerability in real time and deliver timely support before craving escalates into substance use (Roos et al., 2023, Vafaie and Kober, 2022). Together, these findings reinforce the clinical relevance of craving as a dynamic and actionable intervention target in daily life, both directly and through affect regulation processes (Kober, 2014).

This study has strengths including real-time EMA data capturing within-day dynamics and identifying the mediating role of craving. However, these implications should be interpreted cautiously given the observational nature of the data, the binary measurement of substance use, and the indirect-only mediation pattern observed in the present study. Limitations include possible unmeasured fluctuations, moderate EMA completion rates, and a relatively small and clinically severe sample; future investigations should test whether these findings generalize to different populations in terms of severity and type of substance, as craving, affect and substance use may differ across substance use profiles and potentially influence the nature and strength of affect–craving–substance use associations (Roos et al., 2021, Roos et al., 2017, Sinha, 2024). Lower EMA completion rates may have limited the ability to fully capture momentary fluctuations in affect, craving, and substance use in daily life. Future research should also consider a broader range of contextual and individual factors that may dynamically influence affect, craving, and substance use in daily life, including stress exposure, substance-related cues, interpersonal context, and sex- and gender-related influences (Becker and Koob, 2016, Becker et al., 2017, Peltier et al., 2019, Sinha, 2011, Sinha, 2024). Examining how these factors interact across time may help refine more personalized and context-sensitive prevention and intervention approaches. Finally, the mediation model was based on data from an observational design which limits causal interpretation (Narita et al., 2025). Nevertheless, we believe that these results are novel, interesting, and informative about the dynamics of affect, craving, and substance use in daily life.

Author contribution

HK and CR developed the study design and methods. EB and HK conceptualized and conducted the analyses, interpreted the data, and wrote the manuscript. NH helped in analysis. All authors undertook the critical revision of the manuscript for important intellectual content and all authors significantly contributed to the manuscript and approved the final version.

CRediT authorship contribution statement

Hedy Kober: Writing – review & editing, Visualization, Validation, Supervision, Investigation, Funding acquisition, Conceptualization. Nicholas R. Harp: Formal analysis. Emmanuelle Baillet: Writing – review & editing, Writing – original draft, Visualization, Formal analysis, Conceptualization. Corey Roos: Writing – review & editing, Validation, Supervision, Investigation, Funding acquisition.

Primary funding

This project was supported by K23AT011342 to CR; Drs. Baillet, Harp, Kober, and Roos were also supported by R01AA029137 to HK.

Clinical trial registration details

NCT05852015

Declaration of Competing Interest

None.

Acknowledgements

We thank all the participants who took part in this study and the research staff who contributed to data collection and management, with special thanks to Maya John for her help with data collection. We are grateful to the Yale University School of Medicine and the University of California, Berkeley, for institutional support.

Footnotes

Appendix A

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.dadr.2026.100455.

Appendix A. Supplementary material

Supplementary material

mmc1.docx (407.9KB, docx)

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