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Digital and Substance Dependence in the Post-Digital Era: Comparison
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Please note this is a comparison between Version 2 by Abigail Zou and Version 1 by Vincenzo Maria Romeo.

Digital dependence and substance use may co-occur in post-digital cohorts when platform design, peer norms, stress exposure, and individual vulnerabilities converge to reinforce dysregulated patterns of reward seeking, self-regulation failure, and affective coping. This Entry defines their co-occurrence as a multidetermined phenomenon in which digital environments, including algorithmic feeds, notifications, social comparison, and variable rewards, interact with developmental vulnerabilities such as identity formation, impulsivity, reward sensitivity, and emotional dysregulation. Psychiatric comorbidities, particularly depression, anxiety, Attention-Deficit/Hyperactivity Disorder, and personality pathology, may increase susceptibility, while socioeconomic disadvantage and unequal access to care can intensify harm. Current evidence suggests that problematic digital use and substance use are more strongly related to functional impairment, coping motives, peer norms, and contextual stressors than to screen time alone. This Entry therefore organizes the available evidence around structural determinants, individual mechanisms, mental-health mediators, and prevention strategies, with emphasis on proportionate regulation, digital literacy, culturally adapted interventions, and integrated clinical pathways.

  • digital dependence
  • substance use
  • adolescents
  • attention economy
  • social media
  • behavioral addiction
  • peer influence
  • mental health
  • socioeconomic inequalities
  • digital literacy
Since the early 2000s, young people born and socialized in the post-digital era have experienced a rapid expansion of screen-based activities, including social networking, short-form video, livestreaming, and algorithmic newsfeeds. These changes have occurred alongside evolving patterns of psychoactive substance use. This overlap has renewed scientific debate on whether contemporary digital environments only correlate with risk, or whether they also contribute to shaping risk pathways in adolescents and emerging adults. Developmental neuroscience and population studies emphasize that adolescents’ sensitivity to social reward, identity exploration, and peer evaluation may amplify the impact of platform design features during critical windows of socio-cognitive maturation [1]. In parallel, psychiatric classification systems now include at least one diagnosis related to digital behavior: Gaming Disorder. Its inclusion in ICD-11, and its conceptual proximity to the DSM-5 framework, supports clinical and epidemiological research on addiction-like online behaviors [2].
Several psychological and neuroscientific models help explain why digital dependence and substance use may cluster. The I-PACE model (Interaction of Person–Affect–Cognition–Execution) conceptualizes problematic online behaviors as emerging from interactions among person-level vulnerabilities, affective responses, cognitive biases, cue reactivity, learning processes, and reduced executive control [3]. In substance use disorders, a complementary neurobiological synthesis highlights adaptations within reward, stress, and executive circuits that bias salience attribution, habit formation, and negative reinforcement cycles [4]. The incentive-sensitization framework explains how dopamine-related processes may separate craving from pleasure. This means that repeated cues can increase “wanting” even when pleasure decreases, a mechanism that may apply both to psychoactive drugs and to some digital rewards, such as notifications and intermittent feedback [5]. Together, these accounts predict partially overlapping vulnerabilities and cross-sensitization between digital and substance-related reinforcement environments.
At the social and platform level, the contemporary attention economy can magnify these vulnerabilities through interactions between users and algorithms. Recommender systems often prioritize content that captures attention because it is emotionally charged, identity-relevant, or novel. This can create feedback loops between user attention and algorithm-driven amplification [6]. Large-scale field experiments and platform-scale studies, while methodologically heterogeneous, show that feed algorithms can measurably alter exposure patterns and downstream attitudes/behaviors, including the diffusion of low-credibility content, though effects vary by context and outcome [7]. For adolescents and young adults, such curation may increase social comparison, fear of missing out, and exposure to repeated cues. These processes may strengthen triggers for compulsive checking and for substance-use norms within peer networks.
Empirically, meta-analytic evidence indicates that problematic Internet/social-media use (PIU/PSMU)—as opposed to mere time online—is moderately associated with internalizing symptoms (depression, anxiety), stress, and reduced well-being in student and youth samples [8,9][8][9]. Moving beyond cross-sectional associations, stronger designs are emerging: a recent longitudinal cohort analysis found that addictive patterns of digital use predicted subsequent suicidal ideation, net of confounders, underscoring clinical salience even when effect sizes are modest at the population level [10]. These data situate problematic digital use as a plausible mediator/moderator within broader pathways to psychopathology and health-risk behaviors.
Direct links to substance use are increasingly documented. A systematic review and meta-analysis reported significant associations between Internet addiction and drinking and smoking among adolescents and young adults, with convergent results across correlational and logistic models [11]. Complementing this, a 2024 meta-analysis of self-posting alcohol-related content on social media found small-to-moderate relationships with youth drinking behaviors, suggesting bidirectional processes of social learning, identity signaling, and reinforcement (both offline and online) [12]. In early adolescents from the ABCD Study, problematic social-media use (but not time on social media) was associated with stronger alcohol expectancies, a known cognitive antecedent of initiation and escalation [13]. Collectively, these findings support a clustering model in which digital compulsivity and substance-related cognitions/behaviors co-occur through shared reinforcement contingencies, peer-norm transmission, and stress-coping motives.
Crucially, these processes unfold within structural inequities. The pandemic foregrounded a digital access gradient, where constrained device/connection access and low digital literacy predicted worse mental-health outcomes in youth, likely compounding other adversities [14]. Socioeconomic position also shapes substance-use risk through multiple pathways (stress exposure, neighborhood norms, alternative reinforcement, and opportunity structures), with systematic reviews detailing heterogeneous but meaningful SES–substance links across adolescence [15]. Any explanatory model of co-occurring digital dependence and substance use must therefore integrate contextual determinants—income, schooling, platform governance, and public policy—alongside individual differences. From a prevention standpoint, evidence-informed digital–mental-health interventions tailored to socioeconomically and digitally marginalized youth offer promise but require careful adaptation, co-design, and equitable implementation to avoid widening the very divides they aim to bridge [16].
In sum, evidence from clinical psychology, developmental science, behavioral economics, and policy analysis suggests that post-digital environments can increase exposure to powerful rewards, repeated cues, and peer signals. These environments may interact with pre-existing vulnerabilities and socioeconomic constraints, increasing the likelihood that digital dependence and substance use co-occur. The present Entry synthesizes the existing literature, clarifies mechanisms and boundary conditions, and outlines implications for targeted public policies, digital-literacy education, and culturally adapted prevention capable of mitigating intertwined risks without pathologizing normative digital participation.
To improve accessibility for non-specialist readers, the main explanatory models used in this Entry are summarized in Table 1. These models provide the conceptual bridge between individual vulnerabilities, platform-level reinforcement, affect regulation, adolescent development, and structural determinants of health.
Table 1. Core explanatory models used in this Entry.
Model/Framework Concise Definition Relevance to This Entry
I-PACE model

[3]		 Problematic online behaviors emerge from interactions among person-level vulnerabilities, affective responses, cognitive biases, cue reactivity, and reduced executive control. This model explains how individual vulnerability, emotional states, cognitive appraisal, and control processes may transform high digital engagement into dysregulated or addiction-like use.
Dual-systems model

[17,18][17][18]		 Adolescent risk-taking is partly explained by heightened reward sensitivity combined with still-maturing cognitive-control capacities. This model helps clarify why adolescents and emerging adults may be especially sensitive to immediate digital rewards, peer feedback, and substance-related cues.
Incentive-sensitization theory [5] Repeated exposure to rewarding cues can increase cue-triggered “wanting” even when pleasure or “liking” decreases. This theory supports the comparison between substance-related craving and some digital reward loops, such as notifications, likes, feed refreshes, and variable reinforcement.
Compensatory Internet Use model

[19]		 Digital engagement may become problematic when it is used primarily to regulate distress, loneliness, rejection, boredom, or dysphoria. This model explains how digital overuse and substance use may both function as short-term coping strategies maintained by negative reinforcement.
Structural determinants framework

[14,15,16,20,[21,22,1423,24]][15][16][20][21][22][23][24]		 Individual risk is shaped by broader social, economic, technological, and policy conditions, including platform design, inequality, access, labor conditions, education, and regulation. This framework prevents an exclusively individualistic interpretation of risk and supports population-level prevention, safer defaults, equitable access to care, and culturally adapted interventions.
These models are complementary rather than mutually exclusive. Together, they indicate that co-occurring digital dependence and substance use should be interpreted as the outcome of interactions between developmental sensitivity, reward learning, affective coping, psychiatric vulnerability, platform architecture, and structural inequalities.

References

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