The study, led by Lei Yu of the Third People’s Hospital of Qujing with colleagues including Wenzhi Wu of Beijing Normal University, followed 1,474 adolescents across three annual assessments beginning in October 2021 and ending in October 2023. Participants, whose average age at baseline was about 15.5 years and nearly 60 percent of whom were female, were drawn from 27 classrooms in two public secondary schools in Southwest China. At each wave, the researchers measured three constructs: perceived stress, core self-evaluations, and problematic smartphone use. Perceived stress was assessed with the 10-item Perceived Stress Scale, which captures the appraisal that environmental demands exceed one’s coping resources rather than the mere presence of demands. Core self-evaluations, measured with the 10-item Core Self-Evaluation Scale, represent a higher-order judgment of one’s own worth, competence, sense of control, and emotional stability, integrating self-esteem, generalized self-efficacy, locus of control, and emotional stability into a single psychological resource.

Problematic smartphone use, the third pillar of the study, was measured with a 22-item scale covering withdrawal-like behavior, salience, social comfort, negative effects, and patterns of application use. The construct matters because it is distinct from heavy use. Frequent smartphone use is not inherently maladaptive; the developmental concern arises when use becomes compulsive or poorly controlled and begins to interfere with sleep, learning, emotional functioning, or offline relationships. Reviews have linked problematic smartphone use to poorer mental health and sleep outcomes in young people, but the direction of these associations and the mechanisms behind them have remained unsettled, largely because most studies are cross-sectional snapshots that cannot establish temporal ordering.

The methodological innovation at the heart of the new study is the random-intercept cross-lagged panel model, or RI-CLPM. Conventional cross-lagged panel models, which compare variables across repeated waves, have a well-documented weakness: they can blend stable between-person differences with genuine within-person change. In adolescence this distinction is critical. Some teenagers may be chronically more stressed, lower in self-evaluation, and more prone to problematic phone use because of relatively stable factors such as temperament, family context, academic environment, or peer experiences. Those stable differences are meaningful for identifying risk groups, but they are not the same as dynamic processes unfolding inside an individual. The RI-CLPM solves this by estimating a random intercept for each person on each variable, capturing that adolescent’s typical level, and then asking whether deviations from that own typical level predict later deviations. In other words, it asks whether a teenager who is more stressed than usual this year reports lower self-evaluation next year, and whether that dip forecasts more problematic phone use the year after.

The data showed that the separation was necessary. Intraclass correlations indicated that stable between-person differences accounted for roughly half or more of the total variance in all three constructs, between 52 and 59 percent, with the remainder reflecting within-person fluctuation. At the between-person level, the correlations were strong and unsurprising: adolescents who were generally lower in core self-evaluations tended to report more perceived stress and more problematic smartphone use, and generally more stressed adolescents reported more problematic use. The random intercepts for self-evaluations and stress were correlated at about negative 0.84, an unusually strong trait-level association. But these chronic risk profiles, the researchers emphasize, cannot be treated as evidence of within-person change.

The within-person findings were the study’s core contribution. Core self-evaluations and perceived stress were reciprocally and negatively linked over time. When adolescents reported self-evaluations higher than their own usual level, they subsequently reported lower perceived stress, with a standardized cross-lagged coefficient around negative 0.15. When they reported stress higher than their own usual level, they subsequently reported lower core self-evaluations, with a coefficient around negative 0.12. By the effect-size benchmarks proposed for cross-lagged designs, these associations fall in the large range. The pattern fits appraisal theories of stress, which hold that demands become stressful when coping resources are judged insufficient, and resource-based accounts in which personal resources both protect against stress and are depleted by sustained demands. Higher-than-usual self-belief may buffer the impact of academic competition, peer conflict, and family expectations; repeated experiences of overload, failure, or uncontrollability may in turn erode a teenager’s sense of competence and control.

The pathway to the phone ran through this resource, not around it. Higher-than-usual core self-evaluations predicted lower subsequent problematic smartphone use, a significant prospective association with a coefficient around negative 0.13. Perceived stress, by contrast, showed no direct predictive path to later problematic use once stable differences and self-evaluations were modeled. Yet a longitudinal indirect association from stress to problematic smartphone use through core self-evaluations was statistically different from zero, estimated with 5,000 bootstrap samples and bias-corrected confidence intervals. The reverse sequence, in which problematic use would erode self-evaluations and later raise stress, was not supported. This asymmetry gives the model a sharper developmental interpretation: at the annual time scale examined here, the road from stress to the smartphone passes through the self. The finding is consistent with compensatory internet-use theory and the I-PACE model, both of which frame problematic digital behavior as emerging from interactions among affective states, person-level vulnerabilities, cognitive appraisals, and executive control, rather than from stress alone.

The authors are careful about what the design can and cannot claim. Because the study is observational, the temporally ordered indirect associations cannot be read as causal mediation, and the hypotheses and analytic plan were not preregistered. The researchers also stress-tested their conclusions. The results held under 200 multiple imputations of missing data, under classroom-cluster-robust standard errors accounting for nesting within 27 classrooms, and under partial scalar measurement-invariance specifications. The within-person pathways did not differ significantly between boys and girls, though the authors caution that this invariance concerns the path coefficients only, not mean levels, stress exposure, or patterns of use. One measurement caveat deserves attention: the Perceived Stress Scale showed only modest internal consistency in this sample, with reliability coefficients between roughly 0.66 and 0.72, which may have attenuated associations involving stress and warrants replication with alternative measures.

The practical implications are framed as hypotheses for future work rather than prescriptions. The prospective association between core self-evaluations and later problematic smartphone use suggests that self-evaluative resources may be worth examining as a candidate mechanism in experimental and intervention research, for example by testing whether programs that strengthen perceived competence, control, coping confidence, or emotion regulation produce subsequent changes in smartphone behavior. But until such evidence exists, core self-evaluations should be viewed as a potentially relevant psychological process, not an established intervention target. The authors also note limitations of scale and scope: all constructs were self-reported, the one-year lag may not match the faster rhythms at which stress and phone use interact day to day, and the sample came from only two public secondary schools in Southwest China, limiting generalizability across regions and school types. Future studies combining self-reports with device-recorded behavior, sleep indicators, and daily diary or ecological momentary assessment could capture shorter-term dynamics that annual waves cannot.

What the study ultimately offers is a more conditional account of why some adolescents lose control of their smartphone habits. Stressful periods appear most relevant to problematic use when they are accompanied by declines in self-evaluative resources; a generalized stress-to-phone link was not supported. Core self-evaluations, often treated as a fixed background trait, emerge instead as a dynamic resource that fluctuates meaningfully around each adolescent’s typical level and carries developmental information across years. For a generation growing up with smartphones woven into school coordination, peer contact, and family communication, the message from this three-year dataset is subtle but consequential: the psychological resource that matters may not be how much stress a teenager faces, but how capable, in control, and emotionally stable that teenager feels while facing it.

Subject of Research: Within-person longitudinal relations among perceived stress, core self-evaluations, and problematic smartphone use in Chinese adolescents

Article Title: Within-person reciprocal relations among perceived stress, core self-evaluations, and problematic smartphone use in Chinese adolescents

Article References: Yu, L., Wu, W., Jia, Y., Chang, L., & Ji, J. (2026). Within-person reciprocal relations among perceived stress, core self-evaluations, and problematic smartphone use in Chinese adolescents. Current Psychology, 45(20), Article 1587. https://doi.org/10.1007/s12144-026-10123-7

Image Credits: AI Generated

DOI: 10.1007/s12144-026-10123-7

Keywords: adolescents, perceived stress, core self-evaluations, problematic smartphone use, random-intercept cross-lagged panel model, longitudinal study, self-esteem, compensatory internet-use theory, I-PACE model, China, mental health, self-regulation

Smartphone addiction has become one of the most intensively studied behavioral phenomena of the past decade, and for good reason. Systematic reviews have linked problematic smartphone use to a range of negative health outcomes in adult populations, including disturbed sleep, sedentary behavior, and psychological distress. Early research concentrated heavily on personality traits as the driving determinants, asking which dispositional characteristics make some people more vulnerable than others. More recently, however, the field has shifted toward relational variables — the quality of people’s attachments and their fear of missing out — as more proximate and more modifiable predictors. What had remained surprisingly neglected, the Italian team argues, is the role of social identity: the part of a person’s self-concept that derives from membership in social groups.

Social identity theory, rooted in the classic work of Henri Tajfel, holds that people do not merely belong to groups; they internalize group membership as part of who they are. In the digital era, this process has migrated substantially online. Young adults increasingly derive their sense of belonging, status, and self-worth from the communities they inhabit on social media platforms, from fandoms and friend groups to professional networks. Previous studies have shown that a strong online social identity predicts engagement with social networking sites and even vulnerability to other addictive behaviors, such as alcohol use when adolescents identify with peer crowds portrayed drinking online. But the mechanism connecting that identity to compulsive smartphone behavior had never been tested within a single comprehensive statistical framework alongside attachment and FoMO.

To build that framework, the researchers surveyed 760 Italian young adults with an average age of approximately 24 years. Participants completed validated measures of smartphone addiction, fear of missing out, online social identity, and attachment to both parents and peers, the latter assessed with instruments descended from the widely used Inventory of Parent and Peer Attachment developed by Armsden and Greenberg. The analytical approach was a moderated mediation model, a regression-based technique associated with the work of Andrew Hayes that allows researchers to test simultaneously whether one variable transmits the effect of another (mediation) and whether a third variable changes the strength of a given pathway (moderation). Before analysis, the team checked that the distributions of the key variables met statistical assumptions, following standard practice for interpreting correlation coefficients and model estimates.

The results traced a clear causal-looking pathway, though the cross-sectional design means the direction of effects is inferred rather than proven. Young adults whose self-concept was more strongly anchored in their online social groups reported higher levels of smartphone addiction — but that association ran almost entirely through fear of missing out. In other words, identifying strongly with online communities did not directly translate into compulsive phone use; rather, it amplified the anxiety that something important is happening elsewhere, that friends are sharing experiences from which one is absent, and that anxiety drove the compulsive checking, scrolling, and connectivity that define smartphone addiction. FoMO, first systematically characterized by Przybylski and colleagues in 2013 as a pervasive apprehension that others might be having rewarding experiences from which one is absent, thus emerged as the psychological engine converting social identity into behavior.

The moderation findings added a layer of nuance that the authors describe as the protective role of attachment. Peer attachment — the security, trust, and communication young adults experience in their close friendships — weakened the association between online social identity and both fear of missing out and smartphone addiction itself. When friendships are solid and satisfying in the offline world, the pull of the online group loses much of its urgency; being logged off no longer feels like being left out. Parent attachment operated at a different point in the model: it moderated the relationship between fear of missing out and smartphone addiction. Even among young adults with high FoMO, a secure bond with parents was associated with less compulsive smartphone use, as though a reliable family base reduced the need to regulate that anxiety through the device.

These findings fit within broader theoretical accounts of addictive behaviors, notably the Interaction of Person-Affect-Cognition-Execution model, which frames internet-use disorders as the product of interacting personal vulnerabilities, affective responses, and cognitive processes rather than any single cause. They also resonate with a long line of attachment research going back to John Bowlby, and with the proposition, articulated by psychologist P. J. Flores, that addiction can be understood as an attachment disorder. Meta-analytic evidence has already shown that insecure attachment is reliably linked to mobile phone addiction, and studies of adolescents have found that family cohesion and strong peer relationships reduce problematic use. The Italian study’s contribution is to specify where in the psychological chain those relationships exert their influence — a level of precision that matters for designing interventions.

The practical implications are potentially significant for both prevention and clinical practice. If online social identity and FoMO are risk factors, and attachment relationships are protective ones, then interventions need not target the smartphone directly. Programs that strengthen young people’s offline friendships and family communication could, according to this model, dampen the FoMO that fuels compulsive use, even for those deeply embedded in online communities. For clinicians, the results suggest that assessment of problematic smartphone use should include questions about attachment security and fear of missing out, and that therapeutic work on relational security — a staple of attachment-informed therapy — may have downstream benefits for digital behavior. The finding that peer and parent attachment protect at different points in the model also implies that family-based and peer-based interventions may be complementary rather than interchangeable.

The authors are careful about the study’s limits. The data are cross-sectional, so the mediation pathway — online social identity raising FoMO, which raises smartphone addiction — represents a theoretically motivated model fitted to a single snapshot rather than a demonstration of temporal causation. Longitudinal designs would be needed to confirm the direction of the arrows. The sample consists of Italian young adults, and cultural context may shape both online identity and family relationships, so generalization to other populations requires caution. All measures were self-reported, which introduces the possibility of bias, although validated scales mitigate this concern. The researchers also note that the study received no specific external funding and that the authors declared no conflicts of interest, with ethical approval obtained from the Ethics Committee for Scientific Research of LUMSA University of Rome in accordance with the Declaration of Helsinki.

Even with those caveats, the study offers one of the most complete pictures to date of how digital life, social psychology, and attachment theory intersect in the smartphone era. It reframes smartphone addiction not as a simple failure of self-control or an inevitable consequence of persuasive technology, but as the endpoint of a relational process: people who build their identity in online groups become anxious about absence from those groups, and that anxiety — unless buffered by secure bonds with friends and parents — finds its outlet in the device that keeps them perpetually connected. In a world where online and offline identities are increasingly intertwined, the research suggests that the strongest defense against the compulsive pull of the screen may be the quality of the relationships waiting on the other side of it. The data supporting the findings are available from the corresponding author upon reasonable request, and the full model awaits replication across cultures and age groups.

Subject of Research: Psychological determinants of smartphone addiction in young adults, focusing on online social identity, fear of missing out, and attachment relationships

Article Title: Smartphone addiction, FoMO, and online social identity: the protective role of peer and parent attachment

Article References: Topino, E., Rinallo, E., Gori, A., Scopelliti, M., & Cacioppo, M. (2026). Smartphone addiction, FoMO, and online social identity: the protective role of peer and parent attachment. Current Psychology, 45(19), Article 1564. https://doi.org/10.1007/s12144-026-09798-9

Image Credits: AI Generated

DOI: 10.1007/s12144-026-09798-9

Keywords: smartphone addiction, fear of missing out, FoMO, online social identity, attachment, peer attachment, parent attachment, young adults, moderated mediation, social media, Current Psychology, behavioral addiction

Since Andrew Przybylski and colleagues first formalized the construct in 2013, fear of missing out has been defined by two features: a pervasive apprehension that others might be having rewarding experiences from which one is absent, and the desire to stay continually connected with what others are doing. The first feature is largely cognitive and emotional in character, resembling worry or repetitive negative thinking about social exclusion. The second is more behavioral, functioning much like a safety behavior—frequently checking social media and messaging apps to reduce anxiety about missing rewarding experiences. Researchers have labeled these dimensions “apprehension of missing out” and “constant connection desire,” and while they are conceptually and statistically related, they are increasingly understood as distinct constructs.

The distinction matters because the two dimensions, if truly separable, might respond to different interventions and predict different downstream behaviors. A purely emotional apprehension about exclusion might be expected to travel with mood disturbance, while a behavioral drive to remain connected might be expected to manifest in compulsive device checking. Whether the empirical data support this tidy division has, until recently, been unclear, since most studies have measured FOMO as a single total score and correlated it with outcomes in aggregate.

The study was led by Jon D. Elhai of the University of Toledo, working with Tali Gazit of Bar-Ilan University, Silvia Casale of the University of Florence, and Christian Montag of the University of Macau. Between January and December 2025, the team recruited 388 undergraduate students from Introductory Psychology courses at a medium-sized Midwestern US university. Participants completed a cross-sectional web survey hosted on Psychdata.com and received course research points for taking part. The university’s Institutional Review Board approved the study before data collection began, and all participants provided informed consent.

The researchers applied careful data-cleaning procedures to ensure the quality of their sample. From an initial 422 rows of data, they excluded 14 participants who provided substantial missing data, six duplicate responses, four overly fast completers who finished in under 215 seconds, six participants younger than 18, one participant flagged by longstring detection for giving 23 consecutive identical answers, and three participants who reported not using social media at all. The final sample averaged 20.06 years of age (SD = 4.18), was roughly two-thirds female (65.7%), and was predominantly Caucasian (69.8%), with Black, Asian, and Hispanic/Latino participants also represented. Most participants were freshmen, and most worked part-time or not at all.

Participants completed a battery of validated self-report measures. FOMO was assessed with the ten-item FOMO Scale, whose items the researchers mapped onto seven apprehension items and three connection desire items following procedures established in prior work. Problematic smartphone use was measured with the Smartphone Addiction Scale–Short Version, and problematic social media use with the Bergen Social Media Addiction Scale. Negative affectivity was captured through the depression and anxiety subscales of the Depression Anxiety Stress Scale–21, the brooding subscale of the Ruminative Responses Scale–10, and the Distress Tolerance Scale—which the team rescored so that higher values indicated worse distress tolerance, aligning the direction of all measures toward pathological responding. Internal consistency was strong across most scales, ranging from coefficient alpha 0.82 to 0.92, with one notable exception: the three-item connection desire subscale showed relatively low reliability (alpha = 0.60, omega = 0.62), a limitation the authors attribute to its brevity.

Analysis proceeded in two stages. After imputing small amounts of missing item-level data—only 0.63% of item values were missing, and the missingness was statistically consistent with data missing completely at random—the team ran confirmatory factor analyses in Mplus, treating questionnaire items as ordinal given their limited response options. They first tested a single-factor FOMO model against the two-factor apprehension/connection desire model, using chi-square difference testing to compare fit. They then built combined models pairing the two FOMO factors with each other construct, using Wald chi-square tests with parameter constraints to determine whether the two FOMO dimensions correlated differentially with problematic smartphone use, problematic social media use, depression, anxiety, brooding rumination, or distress tolerance. This two-step logic—establishing the dimensional structure before testing differential correlates—guards against drawing conclusions about distinct facets from a measure that may not in fact be multidimensional.

The psychometric results supported treating FOMO as two-dimensional, though the advantage was modest. The two-factor model fit significantly better than the single-factor model (χ² difference test p = 0.0007), but the improvement in fit was small—for example, a difference of only 0.002 in the comparative fit index. This echoes earlier work by Elhai and colleagues in 2025, which had found more robust incremental fit for the two-factor structure across American and Italian samples.

The central finding concerned problematic smartphone use. Using Wald chi-square tests, the researchers found that PSU severity correlated more strongly with FOMO’s connection desire factor (r = 0.600) than with its apprehension factor (r = 0.493), a statistically significant difference (p = 0.009). This replicated the earlier 2025 findings and supports the theoretical prediction that the behavioral, safety-behavior-like dimension of FOMO is the one most tightly bound to compulsive phone use. Problematic social media use, however, told a different story: it was equally related to both dimensions (r = 0.552 for apprehension; r = 0.600 for connection desire; p = 0.122), a departure from the earlier work that had found connection desire more strongly tied to PSMU as well.

The authors offer a possible explanation for this divergence that hinges on the device itself. Problematic smartphone use, by definition, involves the smartphone, a device uniquely characterized by instant push notifications—often social in nature—that interrupt daily activities and lure users back to their screens. Recent findings suggest FOMO may serve as a mechanism linking notification receipt to smartphone addiction, potentially creating a feedback loop in which connection desire drives smartphone use, which in turn intensifies FOMO. Consistent with this, participants in the current study reported especially high use of mobile-first platforms built around frequent social updates and push notifications: 93% used Instagram, 83% Snapchat, and 74% TikTok. The authors caution, however, that this platform interpretation is descriptive and exploratory, and that the data cannot establish causality.

Equally striking is what the study did not find. Despite the apprehension dimension’s explicitly cognitive and emotional character—which might suggest a special affinity with negative affect—both FOMO dimensions correlated equally with depression (r = 0.455 vs. 0.408), anxiety (r = 0.527 vs. 0.517), brooding rumination (r = 0.503 vs. 0.453), and distress intolerance (r = 0.497 vs. 0.537), with all Wald tests far from significance. By extending the equal-relationship pattern beyond depression and anxiety to rumination and distress tolerance, the study corroborates and broadens the earlier work of Elhai, Casale, and Bond. The authors suggest that apprehension and connection desire may function as cognitive/emotional and behavioral markers, respectively, of a broader negative affectivity dimension—a view consistent with prior research showing that people high in FOMO experienced greater stress and negative emotion during a real-world social media outage.

That outage finding is worth underscoring, because it offers rare real-world evidence rather than laboratory simulation. When platforms go dark, individuals high in FOMO cannot simply check more often to soothe their worry, and their distress spikes accordingly. Such results imply that the emotional core of FOMO is not incidental to its behavioral expression but inseparable from it—precisely the pattern the equal correlations with negative affectivity suggest.

The findings are framed within two theoretical traditions. Self-determination theory conceptualizes FOMO as arising from unmet social relatedness needs, with problematic internet use serving as a maladaptive attempt to satisfy belonging. The Interaction of Person-Affect-Cognition-Execution model, meanwhile, positions internet-related cognitions like FOMO as risk factors for problematic use, and predicts that the specific technology overused will reflect the nature of the underlying cognition—here, the socially driven connection desire aligning with the notification-saturated smartphone. The authors also note that severe negative affectivity is probably a risk factor for FOMO rather than the reverse, though bidirectional relations are likely, and short-term studies have shown FOMO prospectively driving negative mood.

The study carries important limitations. Its cross-sectional design precludes causal inference, and the authors call for longitudinal and experience-sampling research. The sample—Midwestern US undergraduates in introductory psychology—limits generalizability to other ages, cultures, and settings. All measures were self-report, with no structured diagnostic interviews and no objective smartphone or social media usage data. The two-factor FOMO model showed only minimal fit improvement over the single-factor model, and the connection desire subscale’s low internal consistency complicates interpretation. The team also modified the time-frames of several instruments—the BSMAS and DASS-21 among them—to maintain consistency across measures. Still, the data, survey protocol, and analysis code are publicly available in a Mendeley repository, supporting transparency and replication.

The broader implications are twofold. Clinically and conceptually, the results suggest that experiencing FOMO may have less to do with genuine social deficits and more to do with an underlying emotional vulnerability that saturates both of its dimensions. Practically, they point toward the design of interventions: if constant connection desire is the facet most implicated in problematic smartphone use, efforts to reduce compulsive phone behavior—such as managing notification exposure—may need to target the behavioral checking loop specifically, rather than the anxious apprehension that accompanies it. As smartphone notifications continue to fragment attention across mobile-first platforms, understanding which face of FOMO drives which behavior may prove essential to addressing problematic technology use at its roots.