One of the more consequential conceptual contributions of the collection is the insistence that digital engagement and problematic digital use are not points on a single continuum of screen time but qualitatively different states that must be distinguished analytically. This distinction has practical implications for how studies are designed and interpreted. A survey that measures only hours of daily use cannot distinguish between an older adult who spends substantial time online maintaining social ties, attending telehealth appointments, and pursuing hobbies, and one whose use has displaced sleep, eroded offline relationships, and become difficult to control. The finding by Hu and colleagues that internet use among rural older adults was associated with better mental health, operating partly through increased social participation, illustrates why this matters. For geographically isolated populations, digital connectivity may be among the few available channels for sustained social contact, and the counterfactual against which internet use should be evaluated is not abstinence but loneliness. Framing the question as technology versus no technology misses the relevant comparison entirely.

The psychosocial embedding of problematic use, emphasized across several contributions, also aligns with broader theoretical developments in behavioral addiction research. Contemporary models increasingly treat addictive behaviors as arising from the interaction between individual vulnerability, environmental affordances, and circumstantial stressors rather than from exposure alone. The study by Chen X.-M. and colleagues on older teachers during the COVID-19 pandemic is instructive here: the association between problematic internet use and psychological distress was entangled with fear, frustrated psychological needs, and occupational burnout. This suggests that during periods of heightened stress, digital behaviors may function as coping strategies that provide short-term relief while contributing to longer-term dysregulation. Such a reading complicates simple causal narratives in which screen time produces distress; it is equally plausible that distress drives escape-oriented use, which then deepens the original difficulties. Disentangling these directions requires longitudinal designs, which remain scarce in this population.

The contribution by Gui and colleagues deserves particular attention for its methodological approach. By applying interpretable machine learning to identify which variables most strongly predict the co-occurrence of internet addiction and maladaptive cognitive emotion regulation among community-dwelling older adults, the study moves beyond the bivariate associations that dominate much of the literature. The emergence of general wellbeing as the strongest predictor, alongside educational attainment and utilization of support services, reinforces the picture of problematic digital use as one manifestation of a broader vulnerability configuration rather than a free-standing pathology. This has implications for screening: a clinician encountering an older patient with signs of problematic internet use may be looking at a signal of depression, social deprivation, or unmet support needs rather than a technology-specific disorder, and treatment planning should reflect that possibility.

The emphasis on exercise in the longitudinal work by Chen B. and colleagues connects the Research Topic to a substantial body of evidence on behavioral substitution. Physical activity is among the most consistently supported protective factors in aging research, associated with preserved cognition, reduced depression risk, and improved sleep. The finding that positive exercise experiences predicted lower subsequent mobile phone addiction tendencies suggests a plausible mechanism: activities that provide reward, structure, and social contact may compete successfully with digital engagement for time and attention. From a prevention standpoint, this reframing is liberating. Rather than prescribing abstinence from screens, which is neither realistic nor desirable in an increasingly digital society, interventions can aim to enrich the offline reward landscape so that digital use occupies a smaller and less compulsive share of daily life.

The shift from studying vulnerable users to studying addictive environments, highlighted by the findings of Li and colleagues on personalized recommendations, interactive incentives, economic rewards, and goal-feedback mechanisms, situates this collection within a larger debate about the ethics of persuasive design. Attention-economy business models are built on maximizing engagement, and the psychological mechanisms they exploit, such as variable reward schedules, social comparison, and endless scroll architectures, do not discriminate by age. If anything, older adults may be differentially susceptible to certain design features: unfamiliarity with how recommendation algorithms operate, reduced capacity to detect manipulative interface patterns, and in some cases age-related changes in impulsivity or decision-making could all modulate vulnerability. The observation that differences in digital literacy affect the ability to recognize and regulate these mechanisms suggests that educational interventions targeting algorithmic awareness could form part of a prevention strategy, though this hypothesis awaits direct testing.

The discussion of generative AI by Vasiliu opens a frontier that the digital addiction literature is only beginning to map. Conversational agents differ from earlier digital technologies in ways that may be psychologically significant: they simulate empathy, remember personal details, respond fluently to emotional disclosure, and are available at any hour. Parasocial attachment to such systems could plausibly serve adaptive functions for isolated older adults, offering cognitive stimulation and a sense of being heard, while simultaneously displacing human contact or fostering dependence on interactions that lack genuine reciprocity. The review’s caution against prematurely codifying intensive AI engagement as a diagnostic category is well taken. The history of behavioral addiction research includes repeated episodes in which enthusiasm for new diagnostic labels outpaced the evidence base, and the field’s credibility depends on resisting that pattern. Loss of control, functional impairment, and distress remain the appropriate anchors for clinical concern, whatever the medium.

The scoping review by Jiang and colleagues exposes perhaps the most striking evidentiary gap in the collection. That 18 of 22 studies of smartphone-related addictive behaviors in adults over 60 were cross-sectional means the field cannot currently answer its most basic causal questions. Cross-sectional designs cannot determine whether problematic use precedes depression or follows it, whether sleep disturbance drives nocturnal phone use or results from it, or whether cognitive changes alter susceptibility to compulsive engagement. The relative strength of evidence for associations with sleep, mood, and loneliness, contrasted with the insufficiency of evidence on cognition, likely reflects measurement history as much as underlying biology: instruments for depression and sleep are well validated across age groups, whereas cognitive outcomes require longer observation windows and more sensitive tools. Prospective cohort studies with repeated cognitive assessment, ideally spanning years, would be needed to determine whether problematic digital behaviors influence cognitive trajectories in aging, and whether any such influence is harmful, protective, or bidirectional.

It is also worth noting what the collection implies about measurement. Most diagnostic criteria and screening instruments for internet and smartphone addiction were developed and validated in younger populations, often students, and their items may presuppose occupational or academic impairment domains that translate poorly to retirement. Questions about neglecting work deadlines or staying up gaming before school have limited purchase for a 72-year-old widow whose problematic use manifests as nocturnal scrolling that worsens insomnia or as withdrawal from community activities. Developing age-sensitive measures, a priority the editors explicitly identify, is therefore not a technical nicety but a prerequisite for accurate prevalence estimates and meaningful cross-cultural comparison. Without such instruments, apparent age differences in prevalence may reflect measurement artifacts rather than genuine epidemiological patterns.

The vision of healthy digital aging that emerges from this Research Topic is ultimately one of calibrated balance rather than prohibition. Digital participation can sustain autonomy, extend social networks into geographies and schedules that would otherwise be inaccessible, and support independent living as physical capacities change. The same environments can, under certain individual and design conditions, become vehicles for compulsive engagement that coexists with depression, poor sleep, and diminished wellbeing. The task for research and policy is to identify the boundary conditions: which patterns of use, in which people, at which points in the aging process, and within which design environments, shift engagement from adaptive to harmful. Answering that question demands longitudinal and cross-cultural evidence, integration of mental, physical, social, and cognitive health domains, and a conceptual framework that treats the older adult, their circumstances, and the digital environment as a single interacting system rather than assigning responsibility to any one element in isolation.

Subject of Research: Editorial: Impact of digital addiction on mental health and comorbidities in older adults

Article Title: Editorial: Impact of digital addiction on mental health and comorbidities in older adults

Article References: Molinaro, S., Feher, G., & Truzoli, R. (2026). Editorial: Impact of digital addiction on mental health and comorbidities in older adults. Frontiers in Behavioral Neuroscience, 20, Article 1988680. https://doi.org/10.3389/fnbeh.2026.1988680

Image Credits: AI Generated

DOI: 10.3389/fnbeh.2026.1988680

Keywords: digital addiction, older adults, healthy aging, mental health, problematic internet use, smartphone use, generative AI, cognitive aging, loneliness, depression, sleep quality, digital inclusion

The research team, led by Jorge de Andrés-Sánchez of the URV’s Department of Business Management together with Ángel Belzunegui-Eraso, Inma Pastor-Gosálbez and Anna Sánchez-Aragón of the Department of History and Art History, all members of the university’s Social and Business Research Laboratory, set out with a deliberately modest goal. Rather than attempting to explain why adolescents turn to pornography, they wanted to determine which personal, family, digital and behavioural variables are most useful for predicting whether an adolescent reports having viewed or used pornography in the previous 30 days. That framing matters, because prediction and explanation are not the same thing, and the authors are careful throughout to keep the distinction in view.

Methodologically, the study is a showcase of how contemporary data science can be layered onto traditional epidemiological survey work. The researchers ran a conventional logistic regression alongside three gradient-boosting machine learning models: XGBoost, LightGBM and CatBoost. These ensemble tree algorithms are widely used in fields ranging from finance to medicine because they excel at detecting complex, non-linear relationships between variables that simpler models can miss. On top of the predictions, the team applied explainable artificial intelligence techniques, a family of methods that reveal how much weight each input variable contributes to a model’s output. Without such tools, machine learning models often function as black boxes, producing accurate forecasts while hiding the reasoning behind them.

The results were notable for their consistency. All of the models, from the simplest logistic regression to the most sophisticated boosting algorithms, produced broadly similar rankings of predictive variables. The machine learning systems did slightly improve some performance indicators, but the differences compared with logistic regression were small. The authors draw a practical lesson from this: more complex models should not automatically replace traditional statistical techniques. In cases like this one, where the underlying relationships appear largely additive and interpretable, a well-specified regression can deliver nearly the same insight with far greater transparency. The added complexity of ensemble methods, they suggest, buys only marginal gains here.

What the models did agree on is the hierarchy of predictors. The sex of the individual stands out by a wide margin as the variable carrying the most weight, with boys considerably more likely to report pornography use. Age is also associated with use, though its predictive importance is lower. After sex, the next most significant factor is problematic internet use, a construct defined by behaviours such as having difficulty staying offline, sleeping less in order to be online, neglecting obligations, or using the internet as an escape from worries or negative emotions. The researchers are careful to note that problematic internet use, as measured in the survey, does not equate to a clinical diagnosis of internet addiction; it is a behavioural profile, not a medical label.

Further down the ranking sit cannabis and alcohol use and family conflict, both of which add meaningful predictive signal. Certain risky sexual behaviours, such as having sex without a condom or experiencing a sexual encounter that the teenager regrets the next day, also contribute slightly to the predictions. Perhaps counterintuitively, parental control shows a much more limited effect once all the other variables are analysed together. In isolation, parental oversight might appear influential, but in a multivariable model its apparent power largely dissolves, suggesting that its observed association overlaps with other family and behavioural factors captured more directly elsewhere in the data.

One of the study’s most interesting contributions is its analysis of differences between boys and girls. When the researchers excluded sex from the analysis and ran the models separately for each group, the internal hierarchy of predictors shifted. Among boys, alcohol consumption emerged as the most important predictor of pornography use, whereas among girls it was problematic internet use. The weight of family conflict also varied between the two groups, while age-related differences proved smaller. This kind of subgroup analysis is exactly where explainable machine learning earns its keep, revealing patterns that a single pooled model would average away and that could easily be missed in a conventional analysis.

The authors are emphatic about the limits of what their models can claim. The study is observational, based on cross-sectional survey data, which means it can identify associations and predictive patterns but cannot establish cause-and-effect relationships. Whether problematic internet use drives pornography use, whether pornography consumption contributes to problematic internet habits, or whether both reflect some shared underlying disposition simply cannot be determined from this design. For the same reason, the researchers insist the models should never be used to diagnose, label or sanction specific adolescents. Their purpose, as de Andrés-Sánchez puts it, was not to single out individuals but to better understand which factors tend to be associated with this behaviour at the population level.

That understanding points towards a particular vision of prevention. The conclusions argue for programmes that combine sex and relationship education, digital literacy, critical reflection on pornographic content, substance misuse prevention, the promotion of safe sexual practices and the strengthening of family communication. The logic is straightforward: if pornography use clusters alongside problematic internet habits, substance use and family conflict, then interventions that treat it in isolation are likely to miss the mark. Effective prevention, on this view, has to address the broader ecosystem in which the behaviour is embedded rather than targeting a single variable.

The study’s authors summarise the message in terms that capture both its scientific and its social significance. Pornography, they argue, should not be addressed as a phenomenon disconnected from the other dimensions of adolescent life; it is part of a broader ecosystem of digital practices, family relationships, substance use and sex education. In an era when artificial intelligence is increasingly applied to sensitive social questions, this research offers a model of restraint as much as discovery: powerful algorithms, transparent explanations, honest acknowledgement of correlational limits, and a refusal to convert population-level patterns into individual judgments. What remains is for educators, families and policymakers to translate those population-level patterns into approaches that treat adolescent digital life as the connected whole the data show it to be.

Subject of Research: Predictors of adolescent pornography use identified through machine learning analysis of a large Spanish youth survey

Article Title: Researchers identify the main factors in pornography use among adolescents

Article References: Researchers identify the main factors in pornography use among adolescents. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: adolescence, pornography use, machine learning, XGBoost, problematic internet use, substance use, family conflict, risky sexual behavior, ESTUDES survey, logistic regression, explainable AI, prevention

The research team, led by Yasuhiro Ogawa of Morinomiya University of Medical Sciences in Osaka, analyzed questionnaire data from 399 health profession students in Japan. Rather than relying on formal clinical diagnoses, the investigators classified participants into four groups based on established screening thresholds: a typical group, a group with ASD traits only, a group with ADHD traits only, and a group with co-occurring ASD and ADHD traits. This trait-based approach allowed the researchers to capture students whose neurodevelopmental characteristics might otherwise go unrecognized in university settings, where many individuals with mild traits never receive a diagnosis.

Three well-validated instruments formed the backbone of the measurement strategy. Sleep quality was assessed with the Pittsburgh Sleep Quality Index, a widely used self-report tool that evaluates dimensions such as sleep duration, sleep latency, disturbances, and daytime dysfunction. Psychological distress and general mental health were measured with the 12-item General Health Questionnaire, a screening instrument sensitive to recent changes in mood, anxiety, and social functioning. Problematic internet use was quantified with the Internet Addiction Test, which probes the degree to which online activity interferes with daily life, relationships, and self-control. Participants also reported their age and sex, which were included as covariates in all analyses.

The statistical design was deliberately conservative. The researchers used multivariate and univariate general linear models, a framework that allows several related outcomes to be tested simultaneously while adjusting for confounding variables. Their central question was whether the trait-defined groups differed in the combined outcome of sleep quality and mental health, and if so, whether those differences survived once the Internet Addiction Test score was added to the model. They also tested for an interaction between group membership and internet use severity, asking whether the relationship between internet problems and poor outcomes varied depending on a student’s neurodevelopmental profile.

The results were striking. The four groups differed significantly in the combined sleep and mental health outcome, and crucially, this multivariate effect remained significant after problematic internet use severity entered the model. No group-by-internet-use interaction was detected, meaning that the burden carried by students with ASD and ADHD traits was not simply an amplified version of the harm associated with excessive internet use. In adjusted comparisons, the co-occurring ASD-ADHD group fared worst of all, exhibiting the poorest sleep quality and the poorest mental health outcomes relative to both the typical group and the single-trait groups.

This pattern carries important theoretical weight. ASD and ADHD frequently co-occur, with shared genetic and neurocognitive underpinnings, and individuals carrying both sets of traits often face compounded challenges: sensory sensitivities and social communication differences from the autism side, alongside impulsivity, inattention, and executive function difficulties from the ADHD side. Sleep disruption is a well-documented feature of both conditions, involving atypical circadian regulation, heightened pre-sleep arousal, and difficulties winding down. The present findings extend this literature by demonstrating that in a high-achieving student population, these trait-related burdens are not reducible to lifestyle factors such as late-night screen time.

Problematic internet use has become a default explanation for student sleep and mental health problems, and device-focused interventions, from digital detox campaigns to app timers, have proliferated accordingly. The Japanese data complicate that narrative. If internet overuse were the primary driver of the observed differences, adjusting for its severity should have substantially attenuated the group effects. Instead, the effects persisted essentially intact, indicating that problematic internet use alone is unlikely to fully account for the poorer sleep and mental health seen in students with ASD and ADHD traits. Internet use may still contribute to the overall burden, but it appears to sit alongside, rather than beneath, neurodevelopmental vulnerability.

The choice of population matters as well. Health profession students face demanding curricula, clinical placements, and high-stakes examinations, conditions that strain anyone’s sleep and coping resources. For students with ASD or ADHD traits, these pressures arrive on top of neurodevelopmental differences in sensory processing, organization, and emotional regulation. The fact that the co-occurring group showed the worst outcomes suggests a dose-like relationship between trait burden and impairment, consistent with the idea that overlapping traits interact rather than merely add together. For faculties of medicine, nursing, rehabilitation, and allied health, this points to a student population whose needs may be systematically underestimated.

The authors are careful about the study’s limits. The design is cross-sectional, so it cannot establish causal direction; it is possible, for example, that poor sleep exacerbates trait-related difficulties, or that distress drives more time online, or both. The data come from self-report questionnaires collected in an anonymous survey, and the sample was drawn from health profession programs in Japan, which may limit generalizability to other student populations and cultural contexts. Classification relied on screening thresholds rather than clinical diagnoses, so the groups represent trait profiles rather than confirmed diagnostic categories. The study was approved by the Ethics Committee of Morinomiya University of Medical Sciences and conducted in accordance with the Declaration of Helsinki, with electronic informed consent obtained from all participants.

Even with those caveats, the practical implications are clear. The researchers argue that student support should extend beyond device-focused interventions to include neurodevelopmentally informed accommodations and targeted sleep and mental health care. In concrete terms, that could mean flexible scheduling and quiet study environments for students with sensory sensitivities, executive function coaching for those with attentional difficulties, and sleep interventions tailored to the circadian and arousal profiles associated with ASD and ADHD. For the growing number of universities screening for problematic internet use, the message is that a high internet addiction score may be a signal worth investigating, but it is not necessarily the root cause. Understanding who the student is, not just what the student does online, may be the key to protecting both their sleep and their mental health during some of the most demanding years of their training.

Subject of Research: Associations between co-occurring autism spectrum disorder and ADHD traits and sleep quality and mental health among health profession students

Article Title: Co-occurring autism spectrum disorder and attention deficit hyperactivity disorder traits are associated with poorer sleep and mental health beyond problematic internet use among health profession students: a cross-sectional analysis

Article References: Ogawa, Y., Tanaka, H., Haga, D., Higashi, Y., Yokota, S., & Tano, K. (2026). Co-occurring autism spectrum disorder and attention deficit hyperactivity disorder traits are associated with poorer sleep and mental health beyond problematic internet use among health profession students: a cross-sectional analysis. BMC Psychology. https://doi.org/10.1186/s40359-026-05694-w

Image Credits: AI Generated

DOI: 10.1186/s40359-026-05694-w

Keywords: autism spectrum disorder, ADHD, sleep quality, mental health, problematic internet use, health profession students, psychological distress, cross-sectional study, neurodevelopmental traits, Pittsburgh Sleep Quality Index, Internet Addiction Test, student support

The study, led by Manish Rajbanshi and colleagues, was designed to fill a conspicuous evidence gap. While problematic internet use, often abbreviated as PIU, has been extensively studied in East Asia, Europe, and North America, researchers have had comparatively little data from South Asia, and almost none focused specifically on Nepali adolescents in the critical final years of secondary school. The authors note that access to electronic devices and internet connectivity has expanded rapidly in Nepal, making adolescents in developing countries an increasingly vulnerable population. Excessive use has been linked in prior literature to academic difficulties, inattention, and social isolation, outcomes with obvious relevance for students preparing for the high-stakes examinations that shape university admission in the country.

Methodologically, the team employed a cross-sectional design with multistage random sampling, a technique that first selects schools and then randomly samples students within them, reducing the risk that the sample reflects only a handful of particularly wired institutions. Data collection relied on a self-administered questionnaire built around the Internet Addiction Test, or IAT, one of the most widely used screening instruments in the field. The IAT asks respondents to rate the frequency of behaviors such as failing to control time online, concealing use from family members, and feeling restless when disconnected, producing a score that researchers can threshold to classify respondents as having problematic use. A total of 406 students participated, with a mean age of 17.22 years and a standard deviation of 1.03, placing the cohort squarely in late adolescence.

The statistical approach followed a conventional two-stage logic. Bivariate analyses using the Chi-square test and bivariate binary logistic regression identified variables associated with PIU at a liberal significance threshold of p less than 0.1. Those candidate variables were then entered into a multivariate logistic regression model, which estimates the independent effect of each factor while holding the others constant. Results were reported as adjusted odds ratios, or AORs, with 95 percent confidence intervals. An AOR above 1 indicates elevated odds of problematic use compared with the reference group, and a confidence interval that excludes 1 signals a statistically reliable association. This layered approach matters because crude associations in bivariate analysis can dissolve once confounders such as age, family income, and usage patterns are accounted for.

What emerged from the multivariate model was a coherent portrait of who is most at risk. Students aged 19 and above had roughly double the odds of PIU compared with younger classmates, with an adjusted odds ratio of 2.16 and a confidence interval spanning 1.81 to 5.24. Family economic context also mattered: adolescents from households with non-agricultural income sources showed higher odds, at an AOR of 1.27 (95 percent CI: 1.07 to 2.25), a finding the authors interpret in light of greater device ownership and disposable income in urban, non-farming households. Platform preference proved telling as well. Students who favored YouTube had an AOR of 1.70 (95 percent CI: 1.07 to 2.72), and those who gravitated toward Facebook had an AOR of 1.65 (95 percent CI: 1.40 to 2.06), suggesting that algorithmically curated and socially driven feeds may be particularly effective at capturing and holding adolescent attention.

Timing and duration of use were among the strongest predictors in the model. Students who primarily used the internet in the evening had an AOR of 2.01 (95 percent CI: 1.09 to 3.69), while those whose use extended into the night had an AOR of 2.03 (95 percent CI: 1.21 to 3.38). The most striking figure belonged to heavy users: adolescents spending more than eight hours per day online had an AOR of 2.69 (95 percent CI: 1.48 to 4.91), meaning their odds of problematic use were nearly triple those of lighter users. The evening and night findings align with a broader body of research linking late-night screen exposure to sleep displacement, circadian disruption, and next-day fatigue, all of which can feed back into the cycle of compulsive use by degrading the self-regulation needed to log off.

The prevalence figure itself deserves scrutiny. At 42.6 percent, with a 95 percent confidence interval of 37.0 to 47.0 percent, the estimate suggests that problematic internet use is not a marginal phenomenon among Kathmandu’s older students but a mainstream behavioral health concern. The confidence interval is wide enough to encompass a range from just over a third to nearly half of all students, reflecting the sample size of 406, yet even the lower bound would represent a substantial burden. Cross-sectional studies of this kind cannot establish causation; they capture a snapshot in which, for example, depression-like withdrawal and heavy internet use may reinforce each other. Still, the consistency of the associations with duration, timing, and platform choice gives the findings practical traction for intervention design.

The authors argue that their results point toward targeted rather than blanket interventions. Awareness programs for students and parents, school-based preventive strategies embedded in the academic calendar, and regulation of the time adolescents spend online are the three pillars they highlight for promoting healthier internet behavior. The platform-specific associations offer a concrete lever: if YouTube and Facebook users carry elevated risk, digital literacy curricula could focus on how recommendation algorithms and social feedback loops are engineered to maximize engagement, teaching students to recognize the mechanics of the feeds they scroll. Similarly, the evening and night associations suggest that simple structural changes, such as device-free hours before bedtime or school counseling that addresses sleep hygiene alongside screen time, could yield outsized benefits.

The study also carries implications for how Nepal’s education and health authorities monitor the digital transition. Because risk rose with age within the school cohort, and because non-agricultural household income marked greater vulnerability, screening efforts might be concentrated in urban schools serving older students from economically diversified households. The researchers emphasize that the evidence base for Nepal remains thin, and this study of one metropolitan area cannot be generalized to rural districts where connectivity, device access, and family livelihoods differ substantially. Repeated surveys using the same IAT instrument would allow trend tracking as internet penetration continues to climb, turning a one-time snapshot into a surveillance tool.

For a country where smartphone ownership has become nearly universal among urban youth in less than a decade, the Kathmandu findings read as an early warning from the front lines of the global digital shift. The study’s core message is measured but urgent: problematic internet use among Nepali adolescents is common, patterned, and identifiable with standard screening tools, which means it is also addressable. The authors call for coordinated action across schools, families, and policymakers to promote healthy internet behavior before the cohort currently in grade 11 and 12, already showing a 42.6 percent prevalence, ages into adulthood with habits that are harder to change. As low- and middle-income countries absorb the same technologies that transformed wealthier nations, studies like this one provide the local evidence needed to craft responses grounded in data rather than alarm.

Subject of Research: Prevalence and risk factors of problematic internet use among higher secondary school students in Kathmandu, Nepal

Article Title: Problematic internet use and its associated factors among the students of higher secondary schools of Kathmandu Metropolitan City, Nepal

Article References: Rajbanshi, M., Thapa, S., Aryal, R., Joshi, J., Bishwokarma, B., Neupane, D. R., Shrestha, S. M., & Pahari, D. P. (2026). Problematic internet use and its associated factors among the students of higher secondary schools of Kathmandu Metropolitan City, Nepal. PLOS Global Public Health, 6(10), e0006511. https://doi.org/10.1371/journal.pgph.0006511

Image Credits: AI Generated

DOI: 10.1371/journal.pgph.0006511

Keywords: problematic internet use, internet addiction, adolescents, Nepal, Kathmandu, Internet Addiction Test, cross-sectional study, screen time, public health, school students, digital behavior, logistic regression

The study, led by Boyu Li and Vivian Yawei Guo of Sun Yat-sen University’s School of Public Health in Guangzhou, together with colleagues from the Guangzhou Huangpu District Center for Disease Control and Prevention and the Affiliated Brain Hospital of Guangzhou Medical University, surveyed 6,743 adolescents. Participants completed validated questionnaires assessing their exposure to adverse and positive experiences during childhood. Adverse experiences in this framework typically encompass categories such as abuse, neglect, and household dysfunction, while positive experiences capture supportive relationships, feelings of belonging, and a sense of being cared for at home, at school, and in the community. The researchers then evaluated Internet use using the Young Diagnostic Questionnaire, a screening instrument originally developed in 1998 that adapts diagnostic criteria for pathological gambling to online behavior.

One of the study’s methodological strengths lies in how it categorized Internet use. Rather than treating all heavy use as pathological, the researchers distinguished between adaptive Internet use, maladaptive Internet use, and problematic Internet use. Adaptive use reflects engagement with the online world that remains functional and integrated with daily life. Maladaptive use signals emerging difficulties, while problematic use corresponds to patterns that meet more of the diagnostic thresholds on the Young Diagnostic Questionnaire, resembling the kind of compulsive engagement associated with behavioral addiction. This three-tier approach acknowledges a reality that clinicians have long recognized: adolescents go online for schoolwork, socializing, and entertainment, and the mere accumulation of screen hours says far less about wellbeing than the quality and controllability of that engagement.

Statistically, the team deployed two complementary modeling strategies. Multinomial logistic regression allowed them to estimate relative risk ratios, comparing the odds of falling into the maladaptive or problematic categories against the adaptive category as levels of childhood experience varied. Negative binomial regression, a model suited to count data with skewed distributions, was used to examine associations with continuous scores on the Young Diagnostic Questionnaire. This dual approach means the findings are not artifacts of a single modeling choice, and the count-based analysis captures the graded severity of symptoms rather than collapsing adolescents into coarse categories.

The results were strikingly symmetric. Higher levels of positive childhood experiences were associated with lower relative risk ratios for both maladaptive and problematic Internet use, as well as lower scores on the diagnostic questionnaire. Higher levels of adverse childhood experiences showed precisely the opposite pattern, elevating the risk of maladaptive and problematic use and pushing diagnostic scores upward. In other words, the two dimensions of early experience operate in opposing directions, and each contributes information that the other does not fully capture. A childhood rich in supportive relationships and safe environments appears to steer adolescents toward healthier digital habits, while a childhood marked by adversity tilts them toward patterns of use that resemble self-medication or escape.

Perhaps the most consequential finding concerns the joint analyses. When the researchers crossed the two dimensions, examining positive experience levels within each category of adversity, they found that higher levels of positive childhood experiences were associated with lower relative risk ratios for maladaptive and problematic Internet use within every adversity group. This suggests that positive experiences are not simply the mirror image of adversity, a benign default that matters only when hardship is absent. Instead, they appear to exert a protective influence that persists even among adolescents who have endured significant hardship. The result aligns with a growing body of international research suggesting that benevolent childhood experiences, as some researchers term them, predict better mental health outcomes independently of adverse ones, and may partially buffer their effects.

Why would early experience shape Internet behavior years later? Several theoretical frameworks offer plausible mechanisms. The compensatory Internet use model, articulated by researcher Daniel Kardefelt-Winther, proposes that people turn to the online world to escape from real-life problems and negative mood states. For adolescents whose home or school environments are hostile or neglectful, the Internet can offer connection, achievement, and validation that are scarce offline, and the line between meeting needs and depending on that meeting becomes dangerously thin. The interaction of person-affect-cognition-execution model, a broader account of addictive behaviors, adds that early adversity shapes the affective and cognitive predispositions, such as poor emotion regulation and diminished self-efficacy, that make compulsive engagement with rewarding digital environments more likely. Positive experiences, conversely, build the social connectedness, hope, and self-regulation that keep online engagement anchored to real-world goals.

The Chinese context gives these findings particular weight. China has one of the world’s largest populations of young Internet users, and concerns about Internet addiction among students have prompted national attention since the early 2010s, when nationally representative studies documented substantial prevalence among elementary and middle school students. Prior meta-analyses, including work published in 2024 pooling studies of childhood adversity and Internet addiction, have confirmed a robust association between childhood trauma and problematic use among Chinese students. Yet most of this literature has focused on risk factors alone. By measuring positive experiences alongside adverse ones, the new study shifts the framing from a deficit model to a dual-axis model, in which building strengths is as important as reducing harm.

The authors suggest that their findings may inform future school- and family-based strategies to promote healthier Internet use among adolescents. The practical implication is that interventions should not stop at screening for adversity or restricting access to devices. Programs that cultivate positive experiences, whether through strengthening parent-child relationships, fostering a supportive school climate, or connecting adolescents with caring adults, may reduce the pull of maladaptive online engagement even for young people who cannot be shielded from adversity. This resonates with earlier research showing that positive childhood experiences moderate the impact of adversity on adolescent depression and anxiety, and that family factors, including parental control styles and the quality of the parent-child relationship, are consistently linked to problematic Internet use in systematic reviews.

As with all cross-sectional research, caution is warranted in interpreting the direction of these associations. The study captured childhood experiences and Internet use at a single point in time, so it cannot definitively establish that early experiences cause later patterns of use, and reverse or bidirectional pathways remain possible. The data, held under confidentiality agreements, are not publicly available, and the findings pertain specifically to Chinese adolescents, whose digital environments and family structures may differ from those elsewhere. Nevertheless, the scale of the sample, the validated instruments, the careful separation of adaptive from problematic use, and the dual statistical approach lend the conclusions considerable credibility. What emerges is a clear and actionable picture: the architecture of childhood, both its hardships and its kindnesses, is written into the way teenagers inhabit the digital world, and the kindnesses matter even when the hardships are severe.

Subject of Research: Associations between positive and adverse childhood experiences and patterns of Internet use among Chinese adolescents

Article Title: Independent and Joint Associations of Positive and Adverse Childhood Experiences with Internet Use among Chinese Adolescents

Article References: Li, B., Luo, S., Feng, X., Xu, D., Xu, Z., Chen, D., Wang, H. H., Chen, W., & Guo, V. Y. (2026). Independent and Joint Associations of Positive and Adverse Childhood Experiences with Internet Use among Chinese Adolescents. International Journal of Mental Health and Addiction. https://doi.org/10.1007/s11469-026-01739-8

Image Credits: AI Generated

DOI: 10.1007/s11469-026-01739-8

Keywords: positive childhood experiences, adverse childhood experiences, Internet use, adolescents, problematic Internet use, Young Diagnostic Questionnaire, China, mental health, behavioral addiction, family environment, public health, cross-sectional study

The study, conducted by Zhang Hao Goh of the Centre for Information Integrity and the Internet at Nanyang Technological University’s Wee Kim Wee School of Communication and Information, and Gulizar Haciyakupoglu of the university’s Centre of Excellence for National Security, set out to test whether experiences of online harms moderate the relationship between internet use and informational gratification. Their starting point was a conceptual gap. Uses and gratifications theory, articulated most influentially by Elihu Katz and colleagues in the 1970s, holds that individuals’ social conditions shape how they use media to satisfy needs, and that this use is typically active, adaptive, and conscious. What the framework did not anticipate is a contemporary internet saturated with harms: harassment, scams, misinformation, privacy violations, and the growing catalogue of abuses that researchers now document across platforms.

The authors argue that exposure to such harms may push users into compensatory use, a mode of engagement driven less by curiosity or information-seeking than by a need to escape or offset distress. Compensatory use, in turn, is closely associated with problematic internet use, a pattern of engagement that can undermine the very benefits users hope to derive from being online. In other words, the same hours spent browsing can represent two very different psychological phenomena depending on what the user has endured along the way.

To capture this heterogeneity, the researchers turned to latent profile analysis, a statistical technique that sorts survey respondents into unobserved subgroups based on patterns in their answers. Rather than treating online harm exposure as a simple continuum, the method allowed the team to identify distinct user profiles defined by how frequently respondents encountered online harms. Three profiles emerged from the data: a group with high frequencies of experiencing online harms, a group with average frequencies, and a group with low frequencies. This person-centered approach acknowledges a reality that variable-centered statistics often flatten: the internet experience of a user who is constantly targeted looks qualitatively different from that of one who rarely is, even if their total screen time is identical.

With the three profiles established, the researchers applied multi-group structural equation modeling to examine whether the pathway from social conditions to gratification through internet use operated differently across the groups. The model they tested was a classic uses and gratifications mediation chain: social conditions influence how people use the internet, and that use, in turn, shapes the gratifications they obtain, particularly informational gratification, the sense of being informed and having one’s informational needs met.

The results were strikingly asymmetric. For the average-exposure group, the mediation held exactly as the traditional theory predicts. Internet usage significantly mediated the influence of users’ social conditions on their gratifications, reflecting what the authors describe as moderate, needs-driven use that aligns with the core uses and gratifications framework. For these users, the internet behaves as the theory always assumed it would: a tool that active individuals bend to their purposes, converting their social circumstances into satisfying informational outcomes.

For the high-exposure group, however, the story fell apart. The indirect effect from social conditions through internet use to gratification was statistically insignificant, even though the direct impact of internet use on gratification was the most pronounced of any of the three profiles. That combination is the study’s most intriguing finding. Highly harmed users still derive gratification from their internet use, arguably more intensely than anyone else, but that gratification no longer follows the orderly path the theory describes. The authors interpret this as evidence that under highly adverse online conditions, internet use may become highly needs-driven and compensatory rather than adaptive, active, and conscious. The user is not choosing the internet to meet a need; the user is being driven to it, and the reward that follows is entangled with the distress that preceded it.

This distinction matters because compensatory and adaptive use are not merely different flavors of the same behavior. Research on problematic internet use has repeatedly linked compensatory engagement with poorer well-being outcomes, including loneliness and diminished quality of life. A meta-analytic review of gratification and problematic internet use published in Addictive Behaviors in 2024 underscored the association between gratification-seeking patterns and problematic engagement, and recent work in the Journal of Behavioral Addictions found that problematic users report heightened experiences of both gratification and compensation simultaneously. The Singapore study adds a crucial structural insight to this literature: the very mechanism that makes internet use rewarding for most users may be absent precisely among those who use it most compulsively.

The findings also carry implications for how scholars and policymakers think about digital safety. Online harms have typically been studied as a discrete problem, the domain of cybersecurity researchers, platform trust-and-safety teams, and anti-cyberbullying campaigns. This study suggests that harm exposure is not an add-on to media psychology but a moderator of it, capable of rewiring the fundamental relationship between use and reward. Survey research from the United Kingdom’s Victims’ Commissioner on the impact of online abuse, and work by Stevens and colleagues documenting gender differences in experiences of online harms, have already shown that victimization shapes how comfortable people feel participating online. The new study extends that logic from participation to gratification itself, implying that a user who is chronically harmed may be psychologically cut off from the benefits that justify their time online, even as their usage intensifies.

There are, of course, limits to what a single survey-based study can establish. The design is cross-sectional, so the direction of causality between harm exposure, use, and gratification cannot be definitively pinned down; it is plausible that people who already use the internet heavily for compensatory reasons also place themselves in harm’s way more often. The profiles were derived from self-reported frequencies of harm experiences, which are subject to recall and interpretation biases. And the mediation held only for the average-exposure group, leaving open questions about users at the extremes of both distributions. The authors note that the data supporting the findings are available from the first author upon reasonable request, and the research was approved by the Institutional Review Board of Nanyang Technological University under reference IRB-2023-317.

Even with those caveats, the study lands at a consequential moment. As AI-powered platforms personalize feeds and conversational agents mediate more of the information environment, the uses and gratifications tradition is being asked to explain behaviors its originators never imagined. The theory has proven adaptable before, absorbing considerations of habit, social cognition, and algorithmic curation. What this study proposes is perhaps its most demanding update yet: that any account of why people use the internet, and what they get from it, must reckon with what the internet does to them. For the majority of users who experience harms at moderate rates, the internet remains what the theory promised, a responsive instrument of need satisfaction. For those who live with frequent online harms, the instrument may be playing a different tune entirely, one driven by compensation rather than choice, and one that the next generation of media theory will need to name, measure, and address.

Subject of Research: How experiences of online harms moderate the relationship between internet use and informational gratification within uses and gratifications theory

Article Title: Do experiences of online harms moderate the relationship between Internet use and informational gratification? insights from the Uses and Gratifications perspective

Article References: Goh, Z. H., & Haciyakupoglu, G. (2026). Do experiences of online harms moderate the relationship between Internet use and informational gratification? insights from the Uses and Gratifications perspective. Current Psychology, 45(19), Article 1568. https://doi.org/10.1007/s12144-026-10043-6

Image Credits: AI Generated

DOI: 10.1007/s12144-026-10043-6

Keywords: uses and gratifications theory, online harms, internet use, informational gratification, latent profile analysis, structural equation modeling, problematic internet use, compensatory internet use, cyberpsychology, media psychology, digital safety, survey research

The scale of the study alone makes it noteworthy. Among the nearly twenty-five thousand participants, 4,350 adolescents—17.8 percent of the sample—met the operational criterion for problematic internet use, a proportion that underscores how common the phenomenon is in secondary school populations. The researchers measured three psychological domains using validated Chinese-language instruments: the Internet Addiction Test for problematic use, the Multidimensional Anxiety Scale for Children for anxiety symptoms, and the Ruminative Response Scale for rumination, the repetitive, self-focused pattern of thinking in which individuals dwell on their distress and its possible causes and consequences. Adolescents with problematic internet use reported significantly higher levels of both anxiety and rumination than their peers without problematic use, confirming the close association between maladaptive online behavior and dysregulated emotional and cognitive processing that has been described in the cognitive-behavioral model of the condition.

What distinguishes this study is its methodological ambition. Rather than relying on a single analytical technique, the team integrated three complementary approaches: network analysis, latent profile analysis, and a computational algorithm called NodeIdentifyR. Network analysis treats psychological symptoms not as isolated scores on a questionnaire but as nodes in an interconnected system, where each node can activate or reinforce its neighbors. In this framework, the edges between nodes represent statistical associations, and the overall architecture of the network—its global strength and its structure—can be compared across groups. Latent profile analysis, by contrast, is a person-centered statistical method that sorts individuals into unobserved subgroups based on their response patterns, allowing researchers to ask whether the population is genuinely heterogeneous rather than assuming it is. NodeIdentifyR then uses network simulations to nominate specific nodes as candidate intervention targets, essentially asking which symptom, if successfully modified, would most efficiently disrupt the maladaptive dynamics of the whole system.

The person-centered analysis delivered the study’s central insight. Latent profile analysis identified two distinct subgroups among the adolescents with problematic internet use, differentiated by the degree to which anxiety and rumination co-occurred: a low anxiety-rumination co-occurrence subgroup and a high co-occurrence subgroup. In other words, some teens with problematic internet use carry a heavy combined burden of anxious and ruminative symptoms, while others show comparatively little of this emotional-cognitive entanglement. The two subgroups differed significantly not only in their overall symptom levels but also in the global strength of their symptom networks and in the structure of those networks—the pattern of connections linking individual anxiety and rumination dimensions to one another.

This structural difference mattered enormously when the researchers ran their network simulations to identify intervention targets. For adolescents in the low co-occurrence subgroup, the simulations pointed to Harm Avoidance—a dimension of anxiety reflecting the tendency to worry about potential future threats and to avoid situations perceived as dangerous—as the most promising candidate target. For the high co-occurrence subgroup, the simulations instead nominated Symptom Rumination, the component of rumination focused on dwelling on one’s distressing feelings and their implications. The implication is striking: the single most efficient point of therapeutic leverage differs depending on which psychological profile an adolescent occupies, and a one-size-fits-all intervention strategy may therefore miss the mark for a substantial fraction of affected teens.

The technical logic behind these simulations is worth unpacking. In network approaches to psychopathology, symptoms are conceptualized as self-reinforcing systems in which activation spreads along the edges of the network. A node with high centrality—many strong connections to other nodes—can, in principle, sustain the entire dysfunctional system, so temporarily activating or perturbing that node in silico and observing the cascade of effects provides a way to estimate where an intervention would produce the greatest downstream benefit. The NodeIdentifyR algorithm formalizes this perturbation logic, and by applying it separately to each subgroup’s network, the researchers could test whether the same node would emerge as the optimal target in both groups. It did not, which is precisely the finding that gives the study its practical significance.

For clinicians and school mental health programs, the results suggest a practical triage strategy. Before selecting an intervention focus, practitioners might first assess whether an adolescent with problematic internet use falls into the high or low anxiety-rumination co-occurrence profile. A teen whose network is dominated by anticipatory worry and threat avoidance may benefit most from interventions that directly target harm avoidance, such as exposure-based cognitive-behavioral techniques that reduce avoidance behavior and catastrophic forecasting. A teen whose symptoms are organized around repetitive negative thinking may respond better to approaches aimed at disengaging from ruminative loops, such as rumination-focused cognitive behavioral therapy or mindfulness-based strategies. The study’s framing within the cognitive-behavioral model supports this logic, since that model explicitly links problematic internet use to maladaptive cognitive processing and dysregulated emotional responses that can be addressed at specific points in the system.

The authors are careful to frame their conclusions appropriately. The findings are model-based, generated from cross-sectional data, and the network simulations identify candidate targets rather than proven ones. Whether intervening on Harm Avoidance in low co-occurrence adolescents or on Symptom Rumination in high co-occurrence adolescents actually reduces problematic internet use over time is an empirical question that can only be answered with longitudinal designs and, ultimately, randomized experimental studies. The researchers state explicitly that these model-based findings require validation in longitudinal and experimental work before they can be translated into clinical guidelines. Cross-sectional networks also cannot establish the direction of causality: anxiety and rumination may drive excessive internet use, excessive use may amplify anxiety and rumination, or both may arise from shared underlying vulnerabilities.

Even with those caveats, the study represents a meaningful advance in how researchers think about problematic internet use in adolescents. By demonstrating that the population is psychologically heterogeneous—that two subgroups with measurably different network architectures exist within the same diagnostic label—it challenges the assumption that a single intervention target will serve everyone. The work also showcases the value of integrating multiple analytical methods, since neither the latent profile analysis alone nor the network analysis alone could have revealed both the existence of the subgroups and the divergence of their optimal intervention points. Funded by the Shandong Excellent Young Scientists Fund Program and approved by the Ethics Committee of Shandong University, with written informed consent obtained from all participants and their legal guardians, the study offers a template for large-scale, methodologically rigorous research on adolescent digital behavior. As screen time continues to climb worldwide, the message for the field is clear: effective intervention may depend less on finding the one right target for problematic internet use and more on matching the target to the individual adolescent’s psychological profile.

Subject of Research: Heterogeneous anxiety-rumination profiles and network-based intervention targets in adolescents with problematic internet use

Article Title: Are candidate intervention targets consistent across adolescents with problematic internet use: an empirical study integrating multiple methods

Article References: Hu, Z., Wang, M., Wu, J., Fang, T., Zheng, D., Yang, Y., & Kong, L. (2026). Are candidate intervention targets consistent across adolescents with problematic internet use: an empirical study integrating multiple methods. BMC Psychology. https://doi.org/10.1186/s40359-026-05727-4

Image Credits: AI Generated

DOI: 10.1186/s40359-026-05727-4

Keywords: problematic internet use, adolescents, anxiety, rumination, network analysis, latent profile analysis, intervention targets, cognitive-behavioral model, mental health, internet addiction, NodeIdentifyR, school psychology

Frontotemporal dementia, or FTD, belongs to a spectrum of disorders called frontotemporal lobar degeneration, which primarily strikes the frontal and temporal lobes of the brain. Unlike Alzheimer’s disease, which typically announces itself through memory lapses, FTD often begins with changes in behavior, personality, and social cognition. The behavioral variant of the disease, known as bvFTD, is particularly notorious for producing stereotyped, compulsive, and ritualistic behaviors, along with a striking vulnerability to addiction-related conduct such as excessive alcohol and tobacco use. Primary progressive aphasia, which includes the nonfluent and semantic variants, erodes language abilities while leaving other functions relatively intact for longer periods.

Researchers at the Center for Neurodegenerative Diseases and the Aging Brain at the University of Bari Aldo Moro, working with the Pia Fondazione Card. Panico Hospital in Tricase, Italy, set out to determine whether patients with FTD are prone to problematic internet and social media use. Between January 2023 and December 2024, they retrospectively screened consecutive patients diagnosed with mild cognitive impairment or Alzheimer’s disease, behavioral variant FTD, and primary progressive aphasia. Diagnoses followed established consensus criteria, and patients with documented histories of overuse, compulsivity, or dependency on digital technologies were identified through clinical records and caregiver interviews.

To characterize these behaviors rigorously, the team administered a semi-structured interview grounded in the biopsychosocial model of addiction developed by Griffiths, probing six core components of behavioral addiction: salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse. Overuse was operationally defined as quantitatively excessive time online, often exceeding twelve hours per day, that disrupted daily routines. Compulsivity captured repetitive, ritualistic digital behaviors lacking any functional goal, such as constant nonfunctional scrolling or repetitive video streaming. Dependency required the presence of the six-component addiction profile. Because patients with FTD frequently lack insight into their own behavior, interviews were conducted separately with patients and caregivers.

The results were striking. Of 61 patients evaluated in the FTD group, 14, or 22.9 percent, exhibited problematic internet or social media use. Ten of these had behavioral variant FTD and four had primary progressive aphasia, with three carrying pathogenic genetic variants, two in C9orf72 and one in GRN. By contrast, in a control cohort of 354 patients with Alzheimer’s disease or mild cognitive impairment, only three patients, or 0.8 percent, showed comparable behaviors. The difference between the groups was highly significant, and the contrast with estimated general-population prevalence figures of roughly six to seven percent for problematic internet use reinforces the notion that this is a specific behavioral feature of FTD rather than a general consequence of cognitive decline.

The clinical pictures described in the series are vivid and unsettling. Excessive social media use affected six patients, producing sleep deprivation, religious obsessions, extensive dating app use that led to extramarital affairs and divorce, and hours spent browsing job portals without any genuine intention of seeking employment. Four patients engaged in problematic gaming, with sessions exceeding twelve hours a day and accompanied by social withdrawal and severely disrupted daily routines. Two patients became trapped in binge-watching cycles of repetitive video streaming, and two displayed impulsive online buying that drew them into scams and unnecessary purchases. Half of the problematic-use group showed overlapping behaviors, and social isolation emerged as a pervasive consequence across nearly every case.

Perhaps most telling were the relational dynamics that surrounded these behaviors. Nine of the fourteen patients reported preferring online interactions to face-to-face communication, deepening their withdrawal from family and social life. Six became frustrated and angry when caregivers attempted to limit smartphone access, echoing the agitation commonly seen when substance-dependent individuals are denied their drug of choice. Within the FTD group, patients exhibiting problematic internet use were significantly younger and had an earlier disease onset than those without such behaviors, though education, sex distribution, global cognition as measured by the Mini-Mental State Examination, and overall disease severity did not differ between the groups.

Subgroup analyses hinted at divergent mechanisms driving the behavior across the FTD spectrum. Among patients with behavioral variant FTD, problematic internet use was associated with a younger, early-onset phenotype, potentially reflecting a generational familiarity with digital technology or a specific behavioral trait independent of cognitive decline. Among patients with primary progressive aphasia, by contrast, problematic use correlated with greater disease severity, suggesting that in the language variants these behaviors may emerge as a nonverbal outlet, a way of compensating for the progressive loss of linguistic and social communication abilities as conversation becomes increasingly difficult.

The neurobiological plausibility of these findings is considerable. Previous research indicates that the neural circuits implicated in substance addictions, involving reward processing, impulse control, and decision-making, may also underlie problematic internet-related behaviors. These are precisely the circuits that deteriorate in FTD, particularly in the behavioral variant, where impulsivity, disinhibition, and compulsivity are hallmark features. The parallels between online addiction and more traditional forms of addiction support the hypothesis that both stem from shared neurobiological mechanisms, and the new series adds ecological validity to that framework by documenting real-world cases in a neurodegenerative population.

The authors acknowledge limitations, including the retrospective design, reliance on clinical records and caregiver report, inconsistent recording of specific applications and platforms, and the need for longitudinal research to track how these behaviors evolve as disease progresses. Even so, the societal implications are difficult to ignore. As internet use becomes ever more pervasive, patients with FTD may be especially vulnerable to maladaptive online behaviors that complicate clinical management, deepen social isolation, and increase caregiver burden. The researchers argue that as society adapts to new technologies, clinical frameworks for dementia must evolve in parallel, ensuring that emerging digital-age behavioral patterns are recognized in diagnosis and addressed in care.

Subject of Research: Problematic internet use as an emerging behavioral feature of frontotemporal dementia

Article Title: Problematic Internet Use in Frontotemporal Dementia: A Case Series

Article References: Urso, D., Volpe, G., Valguarnera, A., Vilella, D., Vitulli, A., Gnoni, V., Giugno, A., Rollo, E., Griffiths, M. D., & Logroscino, G. (2026). Problematic Internet Use in Frontotemporal Dementia: A Case Series. Annals of Clinical and Translational Neurology, 13(9), 1947-1950. https://doi.org/10.1002/acn3.70417

Image Credits: AI Generated

DOI: 10.1002/acn3.70417

Keywords: frontotemporal dementia, behavioral variant FTD, problematic internet use, behavioral addiction, primary progressive aphasia, social media, neurodegenerative disease, caregiver burden, compulsivity, digital behavior, Problematic, Internet

The research, published as an open-access article in the Journal of Eating Disorders, addresses a persistent gap in the literature. Most of what scientists know about the psychological effects of image- and video-based platforms has come from English-speaking, non-clinical samples, leaving open the question of whether the same mechanisms operate in other languages and in people who actually meet diagnostic criteria for eating disorders. By systematically translating the Social Media Appearance Preoccupation Scale through a rigorous translation and back-translation procedure and then subjecting it to statistical validation, the team created a tool that German-speaking clinicians and researchers can use to probe these behaviors in their own populations. The work matters because eating disorders carry among the highest mortality rates of any psychiatric illness, and understanding which digital behaviors track with pathology could sharpen both screening and prevention.

The scale itself is built around a three-factor model. The first dimension, Online Self-Presentation, captures the strategic effort people invest in curating how they look on their profiles: selecting, editing, and posting images designed to convey a particular appearance. The second, Appearance-Related Activity, reflects engagement with appearance-focused content more broadly, such as following fitness influencers, viewing beauty tutorials, or spending time on content organized around bodies and looks. The third, Appearance Comparison, measures how frequently users evaluate their own appearance against the appearances they encounter in their feeds. Earlier work with the English version of the scale had linked higher scores on all three dimensions to more intensive social media use, greater anxiety, more depressive symptoms, and more disordered eating, but the relative clinical weight of each dimension had remained uncertain.

In the first study, the researchers administered the German scale to 363 young adults alongside well-established measures of eating disorder pathology, general psychological distress, and problematic internet use. Eating pathology was assessed with the Eating Disorder Examination-Questionnaire 8, a widely used self-report instrument, while psychological distress was measured with the Brief Symptom Inventory-18, whose Global Severity Index summarizes overall symptom burden. Problematic internet use was captured with the Generalized Problematic Internet Use Scale 2, which covers constructs such as preference for online social interaction, mood regulation through the internet, cognitive preoccupation, compulsive use, and negative outcomes. Confirmatory factor analysis, using the weighted least squares mean and variance adjusted estimator appropriate for ordinal questionnaire data, showed that the three-factor structure of the German version fit the data acceptably well, mirroring the architecture of the original instrument.

The psychometric performance of the translated scale was strong. All three subscales exhibited good internal consistency, with omega coefficients ranging from 0.83 to 0.88, indicating that the items within each dimension reliably measured a common underlying construct. Fit indices commonly used in structural equation modeling, including the root mean square error of approximation, the standardized root mean square residual, and the comparative and Tucker-Lewis fit indices, fell within acceptable ranges once a small number of residual covariances were modeled. In practical terms, this means German-speaking researchers now have a validated shortcut to a construct that previously had to be assessed with instruments developed for entirely different linguistic and cultural contexts, a step that should accelerate appearance-focused social media research in the German-speaking world.

The pattern of associations across the validation battery was especially informative. While all three dimensions correlated with eating disorder pathology, general distress, and problematic internet use to some degree, Appearance Comparison showed the most robust and most consistent relationships. People who reported frequently comparing their looks to others online tended to score higher on disordered eating symptoms, reported greater overall psychological distress, and displayed more problematic patterns of internet use, including using the internet to regulate mood and experiencing negative consequences from their online habits. Online Self-Presentation and Appearance-Related Activity also related to these outcomes, but their associations were weaker and less uniform, suggesting that these behaviors, although common, may be less specifically implicated in the chain linking social media to body dissatisfaction and eating pathology.

The second study moved the question into the clinic. The researchers compared 21 outpatients with diagnosed eating disorders to 37 healthy controls using multivariate analysis of variance, examining whether the groups differed across the three SMAPS dimensions. The clinical group scored higher on appearance comparison, and this difference was statistically significant, with p equal to .004 and a partial eta squared of 0.14, a medium-to-large effect size with a 90 percent confidence interval of 0.03 to 0.29. In plain terms, the tendency to compare one’s appearance to others online was markedly elevated in people with eating disorders relative to people without them, even in a modestly sized sample. No other demographic or usage variable in the analysis achieved the same discriminatory clarity, which strengthens the argument that appearance comparison is not merely a byproduct of heavy platform use but a behavior with genuine clinical relevance.

These findings dovetail with influential theoretical models of body image disturbance, which posit that appearance-based social comparison and self-focused attention act as conduits through which media exposure translates into body dissatisfaction and, ultimately, disordered eating. Image- and video-based platforms supply an effectively endless stream of curated, filtered, and algorithmically optimized faces and bodies, creating comparison opportunities at a scale and intensity that earlier media environments never approached. The new data suggest that measuring total screen time misses the point: two users can spend identical hours on the same platforms, yet only the one who repeatedly asks how her body measures up against what she sees may be traveling down the pathway toward pathology. Screening tools that capture this specific behavior could therefore identify at-risk individuals that usage-based metrics overlook.

The authors caution that the studies are cross-sectional, so they cannot establish whether appearance comparison drives eating disorder symptoms, results from them, or, perhaps most plausibly, participates in a self-reinforcing loop in which distress fuels comparison and comparison deepens distress. The clinical sample in Study 2 was small, and the outpatient group was diagnostically heterogeneous, so larger and more diagnostically specific samples will be needed to confirm the group differences and test whether the pattern holds across anorexia nervosa, bulimia nervosa, and related conditions. Longitudinal designs and intervention studies, for example those that reduce comparison opportunities or teach users to recognize and resist comparison impulses, represent the logical next steps. Still, the message for clinicians, educators, and parents is concrete: the question is not only how long young people spend on social media, but what they do while they are there. When the time online is organized around comparing bodies, the psychological stakes appear to rise considerably, and that behavior is now measurable in German with a reliable, validated instrument.

Subject of Research: Validation of the German Social Media Appearance Preoccupation Scale and its links between appearance-related social media behaviors and eating disorder pathology.

Article Title: Appearance-related social media behavior and eating disorder pathology: evidence from the german social media appearance preoccupation scale in nonclinical and clinical samples

Article References: Brand, S., Kappelt, I., Kloberdanz, K., Zimmer-Gembeck, M. J., & Jungmann, S. M. (2026). Appearance-related social media behavior and eating disorder pathology: evidence from the german social media appearance preoccupation scale in nonclinical and clinical samples. Journal of Eating Disorders, 14(1), Article 211. https://doi.org/10.1186/s40337-026-01762-z

Image Credits: AI Generated

DOI: 10.1186/s40337-026-01762-z

Keywords: social media, body image, eating disorders, appearance comparison, scale validation, online self-presentation, disordered eating, psychological distress, problematic internet use, German validation, Appearance-related, social

The study, conducted by Paweł Holas, Maria Anna Wasylkowska, Piotr Grajewski, and Patryk Roczon at the University of Warsaw, surveyed 763 adolescents aged 11 to 18, with a mean age of 15.27 years. Roughly half of the participants were female. Using validated self-report instruments administered in classrooms during the school day, the team estimated how common problematic internet use had become among Polish youth and mapped the psychological terrain that surrounds it. Roughly 5.9 percent of the sample, 45 adolescents, met the criteria for problematic internet use, a figure that closely mirrors global prevalence estimates of around six percent.

Problematic internet use is not yet a formal psychiatric diagnosis, but it draws heavily on the logic of behavioral addiction. Clinicians and researchers describe it as excessive, poorly controlled online behavior that damages personal, social, or academic functioning. It shares hallmark features with substance and behavioral addictions, including mood modification, tolerance, salience, and withdrawal-like distress when access is cut off. Adolescents are considered especially vulnerable because the neural circuitry that governs self-regulation and impulse control is still maturing, making compulsive patterns of use easier to establish and harder to break.

The health stakes are considerable. Prior research links problematic internet use to disturbed sleep, poor posture, obesity, and visual strain, as well as to depression, anxiety, stress, and attention-deficit/hyperactivity disorder. It also co-occurs with substance use, including cannabis, nicotine, and alcohol, and with behavioral addictions such as gaming disorder. Among teenagers specifically, it is associated with deteriorating peer relationships and declining school performance. Because these harms span so many domains, identifying modifiable risk and protective factors has become a central goal of prevention science.

The Polish team measured a broad battery of psychological variables: problematic internet use severity with a short form of Young’s Internet Addiction Test, generalized anxiety with the GAD-7, depressive symptoms with the PHQ-2D, perceived stress with the PSS-4, psychological inflexibility with the Avoidance and Fusion Questionnaire for Youth, emotional dysregulation with the short form of the Difficulties in Emotion Regulation Scale, and trait mindfulness with the Child and Adolescent Mindfulness Measure, recently validated in Polish adolescent samples. All scales showed acceptable to excellent internal consistency, with reliabilities ranging from 0.75 to 0.91, with the exception of the two-item depression screener, which showed only low-to-moderate reliability.

The statistical results formed a coherent picture. In hierarchical regression models, greater emotional dysregulation predicted higher problematic internet use severity, while higher trait mindfulness and the presence of siblings each predicted lower severity. Higher psychological inflexibility, anxiety, depression, and stress were each positively associated with problematic use in bivariate correlations. The final regression model accounted for 21.6 percent of the variance in problematic internet use severity, a substantial share for psychological predictors in this field.

The most technically interesting part of the study is its mediation analysis, estimated with structural equation modeling and 10,000 bootstrap resamples. The researchers positioned trait mindfulness as an upstream correlate and tested whether depressive symptoms and generalized anxiety symptoms carried its association with problematic internet use. Depressive symptoms emerged as a significant partial mediator: lower mindfulness was associated with higher depressive symptoms, which in turn were associated with greater problematic use severity, producing a significant indirect effect with a bootstrap confidence interval that excluded zero. Meanwhile, the direct negative association between mindfulness and problematic use remained significant, indicating that depression explains only part of the pathway. Generalized anxiety symptoms, by contrast, did not significantly mediate the relationship, even though mindfulness was strongly inversely associated with anxiety itself.

Group comparisons reinforced these findings. Adolescents classified as having problematic internet use scored significantly worse than their peers on anxiety, psychological inflexibility, emotional dysregulation, and perceived stress, and significantly lower on mindfulness. This pattern fits theoretical models, including acceptance and commitment therapy frameworks and the integrative I-PACE model of addictive behaviors, in which problematic online behavior functions as a maladaptive coping strategy aimed at escaping or numbing negative affect. Within that logic, mindfulness operates as a foundational capacity: adolescents who can observe difficult emotions without immediately reacting may have less need to retreat into the screen.

One of the more unexpected findings concerns family structure. The presence of siblings was associated with lower problematic internet use severity, a novel observation in a Polish sample that echoes earlier longitudinal work suggesting sibling relationships provide social support and help children regulate emotions. The authors suggest that fostering positive family dynamics and social connectedness could complement mindfulness-based strategies in prevention programs, although this correlational finding requires replication before strong claims are warranted.

The researchers are careful about what the data can and cannot show. Because the design is cross-sectional, the associations are correlational and cannot establish that mindfulness causally reduces problematic internet use; alternative directional models, in which distress erodes mindful awareness or in which excessive use itself dampens present-moment attention, remain plausible and would require longitudinal or experimental designs to test. Self-report measures are also vulnerable to reporting bias, and the low reliability of the two-item depression measure may have added noise to the mediation estimates. Still, the study aligns with a growing evidence base: meta-analyses show that mindfulness-based programs significantly reduce problematic internet use symptoms, and longitudinal studies link increases in trait mindfulness to declines in problematic use over time. Taken together, the findings position dispositional mindfulness as a promising candidate protective factor, and depressive symptoms as a key mechanism worth targeting, in the continuing effort to keep adolescents grounded in a hyperconnected world.

Subject of Research: Trait mindfulness as a protective factor against adolescent problematic internet use, with depressive symptoms as a mediator

Article Title: Adolescent problematic internet use: protective associations of trait mindfulness and the mediating role of depressive symptoms

Article References: Holas, P., Wasylkowska, M. A., Grajewski, P., & Roczon, P. (2026). Adolescent problematic internet use: protective associations of trait mindfulness and the mediating role of depressive symptoms. Current Psychology, 45(17), Article 1491. https://doi.org/10.1007/s12144-026-10048-1

Image Credits: AI Generated

DOI: 10.1007/s12144-026-10048-1

Keywords: problematic internet use, mindfulness, adolescents, depressive symptoms, anxiety, emotional dysregulation, psychological inflexibility, mediation analysis, behavioral addiction, mental health, protective factors, Current Psychology