Background: TikTok use is constantly increasing, especially among young adults. Although there is a negative association between problematic TikTok use and users’ health, no study until recently has investigated the association between TikTok use and poor sleep. Thus, this study aimed to evaluate TikTok use among young adults and its association with poor sleep. Methods: A cross-sectional study was conducted in Greece. The study sample included 361 adults aged 18–35. A convenience sample through social media was employed. Levels of TikTok use among participants were measured with the TikTok Addiction Scale (TTAS). Results: The mean time of TikTok use was 2.4 h (SD; 1.6), while the mean TTAS score was 2.3 (SD; 0.7). The 11.6% of the participants are problematic TikTok users. A negative correlation between the duration of night sleep and levels of TikTok addiction was found. Also, a positive correlation between sleepiness in work or class and levels of TikTok addiction was found. Furthermore, younger ages were correlated to increased levels of TikTok addiction and time on TikTok. The levels of TikTok addiction were higher among females. Conclusions: This study suggested that higher levels of TikTok addiction and TikTok use were correlated with reduced duration of night sleep and increased sleepiness in work/class.

Background/Objectives: Although it is known that widespread online misinformation has negative consequences, there is no scale to measure susceptibility to online misinformation. Thus, our aim was to develop and validate a tool for measuring susceptibility to online misinformation: the Online Misinformation Susceptibility Scale (OMISS). Methods: A comprehensive literature review was conducted to generate a preliminary pool of items. Subsequently, a multidisciplinary panel of experts assessed the content validity of these items. To establish face validity, cognitive interviews were performed. Both exploratory and confirmatory factor analyses were performed to verify the underlying construct structure of the OMISS. We examined the concurrent validity of the OMISS by using a fake news detection scale, the Trust in Scientists Scale, a single-item trust in scientists scale, the Conspiracy Mentality Questionnaire, and a single-item conspiracy belief scale. Reliability was rigorously examined using multiple indices, including Cronbach’s alpha, McDonald’s Omega, Cohen’s kappa, and intraclass correlation coefficient. Results: The psychometric evaluation using both exploratory and confirmatory factor analyses supported a one-factor nine-item model for the OMISS. The OMISS demonstrated strong concurrent validity, evidenced by statistically significant correlations with the five scales mentioned above. Cronbach’s alpha and McDonald’s Omega were 0.920 and 0.921, respectively. The intraclass correlation coefficient for the OMISS was 0.994. Conclusions: Our comprehensive psychometric evaluations confirmed the OMISS as a valid tool for measuring online misinformation susceptibility. The OMISS holds promise as an effective tool for identifying susceptibility to misinformation and could support policymakers, health educators, healthcare professionals, and stakeholders in pinpointing high-risk groups.

Background/Objectives: The present study seeks to address an important empirical gap by examining the associations of workplace gaslighting with symptoms of anxiety and depression, quiet quitting, and work engagement among a sample of Greek employees. Methods: An online cross-sectional study was conducted in Greece in December 2024, with 291 employees, aged 18 years or older, who reported at least one year of work experience. The validated Greek versions of already published tools were used to measure workplace gaslighting (GWS), anxiety and depression (PHQ-4), quiet quitting (QQS) and work Engagement (UWES-3). Associations between gaslighting and mental health and occupational outcomes were tested using multivariable linear regression, adjusting for demographic and occupational covariates. Results: Higher workplace gaslighting scores were significantly predictive of anxiety (b = 0.565, p < 0.001) and depression (b = 0.571, p < 0.001). Gaslighting was also a significant predictor of both quiet quitting (b = 0.368, p < 0.001) and work engagement (b = −0.373, p < 0.001). Conclusions: These results highlight the negative consequences of gaslighting on the mental health and work engagement of employees. Employees should be encouraged to report instances of supervisory gaslighting, while senior leadership and organizational governance structures ought to implement and enforce a zero-tolerance policy toward such behaviors.

Background/Objectives: Artificial intelligence (AI)-based chatbots present a viable approach to overcoming several challenges associated with conventional psychotherapy, such as high financial costs, limited access to mental health professionals, and geographical or logistical barriers. Thus, these chatbots are increasingly employed as complementary tools to traditional therapeutic practices in mental health care. Our aim was to develop and validate a scale to measure attitudes toward the use of AI-based chatbots for mental health support, i.e., the Artificial Intelligence in Mental Health Scale (AIMHS). Methods: A multidisciplinary panel of experts assessed the content validity. To confirm face validity, we carried out cognitive interviews and calculated the item-level face validity index. We applied factor analysis to verify the construct structure. We assessed measurement invariance across demographic subgroups. Concurrent validity was evaluated using three valid instruments. Reliability was tested through Cronbach’s alpha, Cohen’s kappa, and the intraclass correlation coefficient. Results: Factor analysis supported a two-factor five-item model. The two factors were technical and personal advantages, and explained 81.28% of the variance. The AIMHS demonstrated adequate concurrent validity, evidenced by statistically significant correlations with Artificial Intelligence Attitude Scale (r = 0.405, p-value < 0.001), Attitudes Towards Artificial Intelligence Scale (acceptance subscale; r = 0.401, p-value < 0.001, fear subscale; r = −0.151, p-value = 0.002), and Short Trust in Automation Scale (r = 0.450, p-value < 0.001). Configural, metric and scalar invariance were supported by our findings. Cronbach’s alpha was 0.798, and intraclass correlation coefficient was 0.938. Cohen’s kappa for the five items ranged from 0.760 to 0.848. Conclusions: The AIMHS is a five-item psychometrically sound and user-friendly instrument capturing two dimensions; technical and personal advantages. Future research should be undertaken to further evaluate the psychometric properties of the AIMHS across diverse populations and contexts.

Background: Health-related misinformation is a pervasive phenomenon that expanded substantially during the COVID-19 pandemic, particularly regarding concerns about vaccine safety and effectiveness. Objective: The aim of this study was to investigate the impact of online health misinformation susceptibility on health behaviors and vaccine hesitancy. Methods: A cross-sectional study with a convenience sample of 402 individuals was conducted in Greece, with data collected via an online survey during September 2025. We used the Health-Related Online Misinformation Susceptibility Scale to measure online health misinformation susceptibility. The Health Behavior Inventory–Short Form was used to measure health behaviors, while the Vaccine Hesitancy Scale (VHS) was used to measure participants’ hesitancy towards vaccination. We performed multivariable analysis to identify the independent effect of health misinformation after adjustment for several confounders. Results: Multivariable linear regression analysis showed a positive association between online health misinformation susceptibility and diet score (adjusted coefficient beta = 0.026; 95% confidence interval [CI] = 0.006 to 0.046; p = 0.010) and anger and stress score (adjusted coefficient beta = 0.033; 95% CI = 0.013 to 0.052; p = 0.001). After adjustment for confounders, we found a positive association between online health misinformation susceptibility and score on the factors “lack of confidence” (adjusted coefficient beta = 0.016; 95% CI = 0.005 to 0.028; p = 0.006) and “risk perception” (adjusted coefficient beta = 0.023; 95% CI = 0.010 to 0.036; p = 0.001). Conclusions: Our findings suggest that higher susceptibility to online health misinformation is associated with poorer health behaviors and greater vaccine hesitancy.

Introduction: Despite the growing literature on bullying, mobbing, harassment and abuse at work, the measurement of workplace gaslighting has received scarce attention. There is an absence of valid tools to measure workplace gaslighting. Given that workplace gaslighting is an alarming issue, there is a need for valid tools to measure this construct. Aim: To develop and validate a tool to measure workplace gaslighting. Methods: First, we performed a complete and thorough literature review to create a pool of items that could measure workplace gaslighting. Second, we employed an expert panel to assess the content validity of the initial set of our items. Third, we examined the face validity of our items by performing cognitive interviews with employees, and calculating the item-level face validity index. Fourth, we performed an item analysis for the items that were produced after the initial development phase of the Gaslighting at Work Scale (GWS). Fifth, we examined the construct validity of the GWS by performing exploratory and confirmatory factor analysis. Sixth, we examined the concurrent validity of the GWS by using the Patient Health Questionnaire-4 (PHQ-4), the Quiet Quitting Scale (QQS), the Utrecht Work Engagement Scale (UWES-3), and the Single Item Burnout (SIB) measure. Finally, we examined the reliability of the GWS by calculating Cronbach’s alpha, McDonald’s Omega, Cohen’s kappa, and intraclass correlation coefficient. Results: Initially, we developed a pool of 32 items and, after expert panel review and item analysis, we deleted 15 items. Then, we conducted exploratory factor analysis, and we removed six items. Confirmatory factor analysis verified the results from the exploratory factor analysis. Thus, we found that the final version of the GWS includes 11 items. Moreover, we identified two factors; loss of trust (five items), and abuse of power (six items). Concurrent validity of the GWS was excellent since we found statistically significant correlations between GWS, PHQ-4, QQS, UWES-3 and SIB. Cronbach’s alpha and McDonald’s Omega for the GWS was 0.939 and 0.940, respectively. Cohen’s kappa for the 11 items ranged from 0.840 to 0.914. Intraclass correlation coefficient for the GWS was 0.997. Therefore, the reliability of the GWS was excellent. Conclusions: The GWS is a two-factor 11-item scale with robust psychometric properties. The GWS is a short and easy-to-use tool that measures gaslighting behaviors of supervisors in a valid way. Valid identification of harmful supervisor behaviors is crucial to promote subordinates’ mental health and work productivity.