Depression has become a major concern for educational institutes due to its increasing incidence and detrimental impacts on students’ well-being. However, its global prevalence in the context of tertiary education remains unclear. This umbrella review summarised existing meta-analyses and systematic reviews to examine the worldwide prevalence of depression among university students. Several sociodemographic and methodological factors, including differences in depression prevalence before, during, and after COVID-19, were examined as moderators. Structured and exhaustive searches were conducted in five bibliographic databases and five journals, alongside two sources of grey literature. A total of 61 meta-analyses and systematic reviews were included in the final synthesis. Review-level sample sizes ranged from 823 to 8,238,954 (Mdn = 24,047), covering all inhabited continents and a broad range of countries across high-, middle-, and low-income settings. The global median prevalence of depression across the included reviews was 32.00%, with reported estimates ranging from 11.00% to 63.00%. Analysis of specific populations highlighted greater vulnerability among female students and those studying in Asia compared to Europe. The pandemic was shown to have contributed to the increase in depression prevalence among university students. Given this high prevalence of depression, there is an imperative need for educational institutions to prioritise mental well-being.

We provide a step-by-step guide on conducting a quantitative systematic review (i.e., meta-analysis) using the open-source programming language R, as well as conducting a multilevel meta-analysis, in contexts where effect sizes are non-independent (e.g., multiple effect studies from the same lab). Quantitative systematic reviews offer researchers a method to synthesise large bodies of literature, helping to clarify inconsistent findings, identify research gaps, and refine theoretical models. However, existing tutorials often assume prior knowledge and/or experience, often overlooking foundational concepts. To address this gap, a comprehensive walkthrough of the systematic review process is presented, covering pre-registration, literature search and retrieval, screening, risk of bias assessment, and data extraction following the PRISMA framework. We then present detailed guidance on how to conduct both traditional and multilevel meta-analyses in R. Specifically, the tutorial explains how to estimate overall meta-analytic effect sizes when effect sizes are independent (traditional meta-analysis) and when effect sizes are nested within labs (multilevel meta-analysis). Procedures for assessing heterogeneity, testing for publication bias, and conducting moderation analyses are also covered. To accompany this tutorial, we supplement annotated R scripts and R notebooks to support transparency, reproducibility, and accessibility for researchers of all levels of experience.

This study aims to investigate the effects of different smartphone lock screen wallpapers on weekly perceived well-being, procrastination, and productivity in young adults. Through a pre-registered within-subject experiment, 60 participants were exposed to three smartphone wallpaper conditions: nature, animal, and neutral (control). Each participant experienced each condition over three weeks, with the order of conditions counterbalanced. Using Frequentist and Bayesian analyses, we did not find any differences between conditions across the pre-registered confirmatory outcomes (i.e., life satisfaction, positive affect, negative affect, stress, productivity, and procrastination). Exploratory outcomes related to lock screen engagement, however, revealed some meaningful effects. Animal-themed smartphone wallpapers increased recognition and distractibility, while nature-themed smartphone wallpapers were rated as more pleasing than both animal and control images. Our findings suggest that brief visual exposure to nature or animal-themed lock screens may be insufficient to influence broader well-being or behaviour. Implications for designing strategies to promote psychological health in everyday technology use are discussed.

The current research examined if dispositional optimism buffers against negative consequences of daily stressor exposure. The work also provides a direct test of positive affect as a mediator, in line with the broaden-and-build theory of positive emotions. Study 1 utilised data from a US older-adult sample ( N = 958, M age = 62.57, 45% male) to examine optimism’s buffering role on affective (negative affect) and cognitive (rumination) outcomes. Study 2 extended these findings with a Singapore young adult sample ( N = 995, M age = 21.93, 27% male), incorporating behavioural outcomes (procrastination and impulsivity), introducing additional affective indicators (depressive and anxious mood), and broadening the cognitive domain to include cognitive failure in addition to rumination. Using multilevel regression and multivariate modelling, we found that optimism acted as a buffer against all indicators across both studies, except for anxious mood and procrastination. Trait positive affect mediated the associations between optimism and negative affect (in Study 1), rumination (in Study 1 but not in Study 2), and depressive mood, impulsivity and cognitive failure (in Study 2). These findings shed light on the boundary conditions of the buffering effect of dispositional optimism and the broaden-and-build theory of positive emotions.

OBJECTIVE:While intellectual humility has gained increasing prominence as a cognitive construct, its implications for affective well-being remain less understood. METHOD:The present paper draws on 7-day daily diary datasets (Study 1: Nparticipants = 253, Nobservations = 1721; Study 2: Nparticipants = 485, Nobservations = 3218) to examine how trait intellectual humility relates to daily positive and negative affect during uplifting and stressful events. We also tested potential mechanisms underlying the link between intellectual humility and affective well-being by examining whether intellectual humility moderates affective reactions to daily events or whether it indirectly enhances affective well-being by influencing the frequency of daily uplift and stressor exposure. RESULTS:Across both studies, intellectual humility was associated with less daily negative affect. Associations with daily positive affect remained significant until Big Five personality was included. There was no evidence that intellectual humility moderated affective reactivity to daily uplifts or stressors. Instead, mediation analyses supported a pathway where intellectual humility indicated more daily uplift exposure, which predicted less daily negative affect. Daily stressor exposure showed limited indirect effects. CONCLUSIONS:These findings suggest that intellectual humility supports affective well-being by influencing the frequency and nature of daily experiences. This research demonstrates how intellectual humility contributes to emotional well-being in everyday life.

The random-intercept cross-lagged panel model (RI-CLPM) and the panel graphical vector-autoregression model (panelGVAR) are two popular frameworks for multivariate panel data. These models differ along two dimensions, and we contribute new methodology on both. The first dimension is stationarity: the panelGVAR constrains all parameters to be equal over time and treats the first wave as endogenous, whereas the RI-CLPM, as commonly specified, leaves every parameter wave-specific. Our first contribution is a pipeline of nested models: a guarded decision tree that tests one stationarity constraint at a time and releases those that fail. The second dimension is the parameterization: our second contribution integrates the GGMs into the RI-CLPM, yielding the random-intercept cross-lagged panel network model (RI-CLPN), which standard SEM software cannot specify. Both are implemented in the R package psychonetrics. Two simulation studies evaluate the per-constraint verdicts under likelihood-ratio, AIC, and BIC criteria and network recovery under mean and variance non-stationarity. They show which violations the pipeline detects, and what ignoring them costs when a panelGVAR is fitted directly. A tutorial and two empirical examples illustrate the pipeline.

As generative Artificial Intelligence (AI) becomes increasingly integrated into daily life, concerns have emerged about growing dependency on AI and its potential psychological and behavioral consequences. The present research develops and validates the Generative AI Dependency Scale, a multidimensional tool developed to assess individual differences in dependency on generative AI systems. Across six studies involving 1333 participants from the United States and Singapore, the Generative AI Dependency Scale demonstrated strong psychometric properties, including a stable three-factor structure (cognitive preoccupation, negative consequences, withdrawal), good internal consistency (α = .92–.93), and good test-retest reliability (ICC = .87). Confirmatory factor analysis supported a higher-order dependency construct, and scalar measurement invariance was established across sex and cultures. Convergent and discriminant validity were demonstrated through associations with an existing AI addiction scale and the Big Five personality traits respectively. Generative AI dependency was also significantly associated with a range of motivational (e.g., lower basic psychological need satisfaction, greater fear of missing out), behavioral (e.g., increased procrastination and cognitive failures, lower task performance and critical thinking), and psychological (e.g., reduced self-concept clarity, greater loneliness) outcomes. Framed within Goodman's behavioral dependency framework and self-determination theory, these findings suggest that generative AI dependency reflects not merely excessive technology use, but a deeper misalignment between psychological needs and the strategies employed to meet them. The Generative AI Dependency Scale offers a psychometrically robust foundation for future research into the impacts of generative AI, with implications for responsible AI design and use.

Trait mindfulness has been linked to various adaptive outcomes, including attenuated affective and cognitive responses to laboratory-induced stress. However, the role of trait mindfulness as a resilience factor against daily stressors exposures is less established. Across 2 studies, multilevel analysis was used to examine the relationships between trait mindfulness and daily affect and cognition, as well as affective and cognitive reactivity to and recovery from everyday stressor exposure. Trait mindfulness was significantly associated with higher daily positive affect in both studies, lower negative affect and cognitive failure, and lower cognitive reactivity to daily stressor exposure in Study 2. However, trait mindfulness did not attenuate cognitive reactivity in Study 1, nor affective reactivity to daily stressor exposure and affective and cognitive recovery from previous-day stressor exposure in both studies. Overall, results suggest that the mechanisms underlying the affective and cognitive buffering effect of trait mindfulness are not stress specific.

Numerous studies have demonstrated that positive psychology interventions, including brief interventions, can significantly improve well-being outcomes. These findings are particularly important given that many of these interventions are brief and self-administered, making them both accessible and scalable for large populations. However, the efficacy of positive psychology interventions is often constrained by small effect sizes. In light of advancements in generative Artificial Intelligence (AI), this study explored whether integrating AI chatbots into positive psychology interventions could enhance their efficacy compared to traditional self-administered approaches. Study 1 examined the efficacy of a gratitude intervention delivered through Snapchat's My AI, while Study 2 evaluated a self-affirmation intervention integrated with a customized ChatGPT. Both studies employed within-subject experimental designs. Contrary to our hypotheses, the integration of AI did not yield incremental improvements in gratitude outcomes (Study 1), or self-view outcomes (Study 2) compared to existing non-AI interventions. However, exploratory analyses revealed that the AI-integrated self-affirmation intervention significantly enhanced life satisfaction and medium-arousal positive affect, suggesting potential benefits for selected well-being outcomes. These findings indicate that while AI integration in brief, self-administered positive psychology interventions may enhance certain outcomes, its suitability varies across intervention types. Further research is needed to better understand the contexts in which AI can effectively augment positive psychology interventions.

Background: Cognitive and behavioral interventions have risen in popularity both as an adjunctive treatment to antipsychotic medication and as an alternative treatment for schizophrenia. With the growing number of such interventions, we performed an umbrella review to provide a comprehensive summary comparing the effectiveness of the different interventions among populations with schizophrenia. Methods: This umbrella review included meta-analyses evaluating cognitive and behavioral interventions for schizophrenia. Following PRISMA guidelines, the initial search yielded 4888 records, and after a three-stage screening procedure, 33 meta-analyses met the inclusion criteria for the final analysis. Results: Our findings from the 33 meta-analyses support the efficacy of cognitive and behavioral interventions in reducing total symptoms (Median g = -0.38; Range g = -1.56 to -0.08), positive symptoms (Median g = -0.30; Range g = -0.84 to 0.00), and negative symptoms (Median g = -0.39; Range g = -0.66 to -0.09) of schizophrenia. Cognitive Behavioral Therapy, being the most common intervention studied, exhibited small to medium effects on total and positive symptom alleviation. In addition, there is evidence supporting the effectiveness of family psychoeducation combined with patient behavioral and skills training, exercise therapy, horticultural therapy, and music therapy. Conclusions: While our umbrella review solidifies the current evidence supporting cognitive and behavioral interventions as effective treatments for schizophrenia, it also reveals that treatment efficacy is highly dependent on the type of intervention used.

With social media deeply embedded in our daily lives, there is an ongoing debate about its potential negative impact on well-being outcomes. Several lines of correlational research suggest that social media use is associated with reduced well-being. However, these findings are preliminary, heavily relying on cross-sectional data. To address this limitation, our study implemented an experimental paradigm that manipulated varying degrees of social media abstinence and assessed fear of missing out, as well as multiple well-being outcomes, including anxiety, depressive symptoms, life satisfaction, perceived stress, affective states, loneliness, sleep quality, and cognitive failures. A total of 280 regular social media users were randomly assigned to either a full social media abstinence condition, a partial social media abstinence condition, or a control condition for 7 days. During the experimental period, participants were required to complete a daily survey for 7 days to measure their well-being. Participants in the full abstinence condition reported higher levels of fear of missing out. However, our multilevel modeling revealed no significant impact of either full or partial social media abstinence on the well-being outcomes. More importantly, this null effect was consistent across various personality traits, perceived social supports, cyberbullying experience, and individual differences in social media usage, motives, and network. Our findings challenge the widely held assumption that digital detoxes or social media unplugging interventions are effective strategies for enhancing well-being.

Purpose The links between autonomy, social contacts, sensory function, and depressive symptoms in older adults remains underexplored, particularly cross-culturally. This study examined their longitudinal associations in England and South Korea. Method Data were obtained from 4590 participants in the English Longitudinal Study of Ageing (ELSA) and 3803 participants in the Korean Longitudinal Study of Ageing (KLoSA), aged 65 and older, across three waves (2014/2015 to 2018/2019). Depressive symptoms were measured using eight CES-D items. Autonomy, social contacts, and sensory function were assessed through instrumental activities of daily living (IADL), frequency of social contact, and subjective vision and hearing. Temporal and contemporaneous network structures were analyzed using graphical vector autoregression modelling by gender and country. Results Impaired IADLs were stronger predictors of depressive symptoms in English men than Korean men (0.20 vs. 0.05). For Koreans, particularly women, social contact and depressive symptoms were mutually predictive (social contact→depressive symptoms: 0.12; depressive symptoms→social contact: 0.07). In English men, vision impairment predicted impaired IADLs (0.07), which then predicted depressive symptoms. In Koreans, hearing impairment predicted low social contacts (0.08), further influencing depressive symptoms. Out-strength centrality showed that IADLs were most influential for English men, while social contact was most influential for Korean men and for women in both countries. Sensory function showed cultural differences, with hearing more influential in Korea, vision in England. Conclusions Depression prevention strategies should account for cultural and gender differences, with autonomy more central in individualist contexts and social contact in collectivist ones.

Research on Fear of Missing Out (FoMO) has gained prominence alongside the rise of digital connectivity and social media, which offer individuals continuous access to others’ experiences in real time. Importantly, it has been increasingly recognised as an important psychological construct, closely associated with various problematic digital behaviours. As the fields of media psychology and human-computer interaction increasingly adopt time-intensive methodologies (e.g., daily and weekly studies) to capture dynamic experiences of technology use, there is a growing need for brief yet psychometrically-sound measures of FoMO suitable for such designs. The present work reports the development of a 3-item FoMO scale (FoMO-3) and evaluates its psychometric properties across trait and time-intensive methodologies such as daily diary studies and weekly diary studies. Across five independent samples (total N=1,258), we used structural equation modelling to evaluate the model fit and psychometric properties of trait-like and state-like versions of the FoMO-3. We found that across all assessments, the FoMO-3 consistently demonstrated high internal consistency. In Study 1, the FoMO-3 demonstrated strict measurement invariance and homogeneity of latent means and variances across sex. In Studies 2 and 3, strong time invariance was established across 13 consecutive weeks and 7 consecutive days respectively. Consistent with prior research, FoMO-3 scores were also positively associated with problematic smartphone use. These findings demonstrate that the concise, three-item FoMO-3 can capture the FoMO construct with strong reliability and validity, without compromising psychometric rigour.

We provide a step-by-step guide on conducting a quantitative systematic review (i.e., meta-analysis) using the open-source programming language R, as well as conducting a multilevel meta-analysis, in contexts where effect sizes are non-independent (e.g., multiple effect studies from the same lab). Quantitative systematic reviews offer researchers a method to synthesise large bodies of literature, helping to clarify inconsistent findings, identify research gaps, and refine theoretical models. However, existing tutorials often assume prior knowledge and/or experience, often overlooking foundational concepts. To address this gap, a comprehensive walkthrough of the systematic review process is presented, covering pre-registration, literature search and retrieval, screening, risk of bias assessment, and data extraction following the PRISMA framework. We then present detailed guidance on how to conduct both traditional and multilevel meta-analyses in R. Specifically, the tutorial explains how to estimate overall meta-analytic effect sizes when effect sizes are independent (traditional meta-analysis) and when effect sizes are nested within labs (multilevel meta-analysis). Procedures for assessing heterogeneity, testing for publication bias, and conducting moderation analyses are also covered. To accompany this tutorial, we supplement annotated R scripts and R notebooks to support transparency, reproducibility, and accessibility for researchers of all levels of experience.

With the abundance of social media content that promotes unrealistic beauty standards, there are growing concerns about the potential negative impact of social media use on body image satisfaction. While some studies highlight negative associations, others present null effects, pointing to methodological limitations like biased and unreliable self-reported screen time measures and a focus on singular platforms. Addressing these gaps, our study employed a daily diary method to objectively measure social media screen time across six major platforms ( Twitter , Reddit , TikTok , YouTube , Instagram , and Facebook ), alongside daily body image dissatisfaction among 252 young adults ( M age = 21.67 years, 67.77% female) over 7 days. Through multilevel modeling, our analysis revealed no significant within- or between-person associations between social media screen time and body image dissatisfaction, a finding consistent across all platforms. In addition, the lack of association between social media screen time and body image dissatisfaction was consistent across several exploratory moderators such as sex, self-esteem, and perfectionistic self-presentation. The current study did not find strong evidence supporting the concerns surrounding the potential detrimental link between social media screen time and body image dissatisfaction.

Informal caregiving is a physically and emotionally taxing role that has a profound impact on caregivers’ emotional and mental well-being. Previous research has examined the mental health of caregivers and found elevated levels of depression, anxiety, burden, burnout, or stress. However, the global rates of the five mental health factors and/or outcomes (i.e., depression, anxiety, burden, burnout, and stress) among caregivers remain largely unclear. Therefore, this umbrella review examines the prevalence of these factors and/or outcomes, including various subgroup factors, such as gender, medical and/or psychological condition of the care recipient, region, and assessment tools. A systematic search was conducted in five databases and two sources, and a total of 18 meta-analyses were included for final analysis. The overall median prevalence was 33.35% for depression, 35.25% for anxiety, and 49.26% for burden. However, we could not examine the rates of stress and burnout due to insufficient meta-analysis. Subgroup analyses were comparable across gender, medical and/or psychological condition of the care recipient, and region, suggesting that caregivers face comparable mental health risks across these diverse groups. These findings highlight the need for greater mental health awareness and for governmental and healthcare institutions to introduce effective interventions and stronger support systems.

Attention-Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that significantly impacts various facets of life. While traditional treatments such as medication and behavioural therapy are effective, they often fall short due to limited access and undesirable side effects. In response to these limitations, digital mental health interventions have emerged as a transformative approach in alleviating ADHD symptoms. The current meta-analysis investigates the effectiveness of digital mental health interventions for ADHD synthesising data across 23 randomised controlled trials, with 99 effect sizes across 1,472 participants. Results showed small, significant effects of digital mental health interventions in reducing overall ADHD symptoms (g = −0.32, SE = 0.11, 95 % CI = [-0.53, −0.11], p = .003) and inattentive symptoms (g = −0.25, SE = 0.11, 95 % CI = [-0.47, −0.04], p = .020). No significant effect was found for hyperactivity/impulsivity. Intervention type significantly moderated the effects of digital interventions, with neurofeedback interventions showing lower effectiveness compared to combination-based interventions, while telehealth and mHealth interventions showed no significant differences. Additionally, no significant differences were found between therapist-guided and self-guided interventions, suggesting that both delivery formats may be viable. Other methodological factors (e.g., intervention duration, informant assessed, measures used to assess ADHD, and control type) and sample characteristics (e.g., age, gender, region) did not significantly moderate intervention effects. These findings highlight the potential of digital interventions for ADHD management while suggesting that neurofeedback interventions may require modifications to enhance their effectiveness.

The growing adoption of Artificial Intelligence (AI) in various sectors has introduced significant benefits, but also raised concerns over biases, particularly in relation to gender. Despite AI's potential to enhance sectors like healthcare, education, and business, it often mirrors reality and its societal prejudices and can manifest itself through unequal treatment in hiring decisions, academic recommendations, or healthcare diagnostics, systematically disadvantaging women. This paper explores how AI systems and chatbots, notably ChatGPT, can perpetuate gender biases due to inherent flaws in training data, algorithms, and user feedback loops. This problem stems from several sources, including biased training datasets, algorithmic design choices, and human biases. To mitigate these issues, various interventions are discussed, including improving data quality, diversifying datasets and annotator pools, integrating fairness-centric algorithmic approaches, and establishing robust policy frameworks at corporate, national, and international levels. Ultimately, addressing AI bias requires a multi-faceted approach involving researchers, developers, and policymakers to ensure AI systems operate fairly and equitably.

As smartphones have become portable and immersive devices that afford social, informational, and recreational conveniences unbounded by physical restrictions, most daily activities have become closely intertwined with the presence of smartphones. This constant presence of smartphones in daily activities, however, may be concerning as some studies have suggested that smartphones-even their mere presence-can be distracting and can impair cognitive outcomes. However, such findings have not been consistently observed. To reconcile mixed findings, the current meta-analysis synthesized 166 effect sizes drawn from 53 samples and 33 studies including 4,368 participants on the effect of mere presence of smartphone on cognitive functions. It was found that the mere presence of smartphone had no significant effect on cognitive outcomes (d = -0.02, SE = 0.02, 95% CI [-0.06, 0.01], p = .246). Further, the effect of mere presence of smartphone was not moderated by demographics, trait smartphone dependency, or various methods for manipulating smartphone presence and assessing cognitive outcomes. These findings indicate that there is little reason at present to think that complete isolation from smartphones in a work environment would improve productivity and performance.

BackgroundThe present systematic review aimed to synthesize the results of meta-analyses which examine the effects of digital mental health interventions (DMHIs) on post-traumatic stress disorder (PTSD) symptoms, and investigate whether intervention characteristics (i.e., technique, timeframe, and therapeutic guidance) and methodological characteristics including outcome measures and sample inclusion criteria (age, gender, socioeconomic status, country, comorbidity) moderate the efficacy of digital interventions.MethodsA systematic search of various sources (ECSCOhost PsycInfo, PubMed, Web of Science, Scopus, EBSCOhost ERIC, Google Scholar, ProQuest Dissertations & Theses) including five peer-reviewed journals was conducted to identify relevant meta-analyses up to December 2023, and 11 meta-analyses were included in the final review.ResultsOverall, our review elucidates that DMHIs are appropriate for alleviating PTSD symptoms in adults, with more consistent evidence supporting the efficacy of cognitive behavioral therapy (CBT)-based, compared to non-CBT-based, interventions when compared to control conditions. However, we found inconclusive evidence that the efficacy of DMHIs varied according to intervention timeframe, therapeutic guidance, or sample characteristics.LimitationsA relatively limited number of different populations was sampled across meta-analyses. Further, while our review focused on PTSD symptoms to indicate the efficacy of digital interventions, other indices of effectiveness were not examined.ConclusionOur findings indicate the clinical utility of DMHIs for managing PTSD symptoms particularly when CBT-based intervention techniques are employed.