Anxiety sensitivity and intolerance of uncertainty are unique and interactive risk factors for COVID-19 safety behaviors and worries.

Intolerance of uncertainty (IU) and anxiety sensitivity (AS) predict distress during the COVID-19 pandemic. However, little is known about how AS and IU jointly predict COVID-19 worries and behaviors. Structural equation modeling was used to examine the direct and interactive effects of AS and IU as related to COVID-19 worries and behaviors in two samples of community adults recruited in April 2020 (N = 642; M age = 38.50 years, SD = 10.00) and May 2020 (N = 435; M age = 34.92 years, SD = 14.98). In sample 1, there was a significant AS by IU interaction predicting COVID-19-related catastrophizing worries, stockpiling, and cleaning behaviors such that the relationship of AS to COVID-19-related behaviors was stronger at high levels of IU. However, although AS predicted some COVID-19 related worries and behaviors, AS and IU did not interact in sample 2. Across samples, AS was directly related to financial and health worries, whereas IU was not a significant predictor after controlling for AS. These findings suggest AS consistently impacts COVID-19 worries and behaviors, particularly among individuals with high IU. Brief interventions targeting AS and IU may help people cope during the COVID-19 pandemic and reintegrate following the pandemic.

Are Cyberchondria and Intolerance of Uncertainty Related to Smartphone Addiction?

Smartphones are a medium for performing online activities, and one such activity could be the compulsive online health information search - cyberchondria. This study aimed to test whether cyberchondria and intolerance of uncertainty (IU) positively predict smartphone addiction (SA), adjusted for age, gender, daily use duration, the reason for using smartphones, and symptoms of anxiety and depression. The sample consisted of 471 adults (55.2% women) from the general population without chronic diseases (Mage = 38.67). Regression analysis showed that IU was a positive predictor of SA (ß = .17, p < .001), as well as cyberchondria (ß = .14, p < .001), which had a unique contribution to the explanation of SA, relative to IU. Other significant predictors were average daily smartphone use and entertainment use, the latter being the strongest predictor in the model. These results revealed cyberchondria as a unique predictor of SA.

Risk factors of suicidal behaviors in a high-risk longitudinal veteran sample: A network analysis.

INTRODUCTION: Suicide is a substantial public health burden, particularly among veterans. Risk factors have been delineated for suicide; however, the dynamic interrelations between risk factors have not been fully examined. Such research has the potential to elucidate processes that contribute to suicide risk between individuals with a past suicide attempt (attempters) and those without a past suicide attempt (nonattempters). METHODS: In the current study, network analysis was used to compare networks between attempters and nonattempters in a high-risk veteran sample (N = 770; Mage  = 32.3 years, SD = 6.8; 326 with a past suicide attempt) who were followed over 1 year. Networks were estimated to examine (1) concurrent relations of suicide risk factors at baseline and (2) predictability of prospective suicidal behavior (SB). RESULTS: There were no differences in the overall connectivity of attempter and nonattempter networks. Perceived burdensomeness and posttraumatic stress disorder (PTSD) symptoms were most central in the attempters' network, whereas PTSD symptoms and insomnia were most central in the nonattempters' network. The risk factors prospective SB in either network. However, attempters were more likely to engage in SB over the course of the study. CONCLUSION: These findings highlight the difficulty in predicting who will attempt suicide.

Are we still uncertain about the latent structure of intolerance of uncertainty: Results from factor mixture modeling in a Serbian sample.

BACKGROUND: Intolerance of uncertainty (IU) is a transdiagnostic risk factor for emotional disorders. Exploring the nature of IU might allow us to understand better a difference between pathological and normal worry processes. For that purpose, factor mixture modeling (FMM) can be used to test the latent structure of IU across a categorical-dimensional spectrum. METHOD: In the current study, FMM was used to examine the structure of IU in a sample of 1440 (Mage = 19.71, SD = 1.14) university students in Serbia. A Serbian Intolerance of Uncertainty Scale-11 was used. RESULTS: Results suggest that IU has four latent classes, named as Low IU, Moderate-Low IU, Moderate-High IU and High IU. The Low IU and High IU classes significantly differed in levels of positive and negative affect (NA), symptoms of depression (D), anxiety (A), stress (A), depressive cognitive bias and anxiety sensitivity (AS). Moderate-Low IU and Moderate-High classes had higher symptoms than Low IU class, but lower than High IU class. The Moderate-Low IU and Moderate-High IU classes differed in symptoms of D, A, S, and in levels of AS and NA. LIMITATIONS: This study was cross-sectional and included only non-clinical sample. CONCLUSION: The latent structure of IU tested on a Serbian sample can be described in terms of hybrid latent structure The cut-off scores of ≤ 19 and ≥ 26 can be used to detect those who are non-vulnerable or at risk for IU-related psychopathology.

Lonely, anxious, and uncertain: Critical risk factors for suicidal desire during the COVID-19 pandemic.

Public health measures enacted early in response to the COVID-19 pandemic resulted in unprecedented physical isolation. Social isolation, or the objective experience of being alone, and loneliness, the subjective feeling of being lonely, are both implicated in suicidal ideation. Anxiety sensitivity (i.e., fear of somatic anxiety) and intolerance of uncertainty (distress due to uncertainty), may also be heightened in response to the pandemic increasing risk for suicidal ideation in response to social isolation and loneliness. The direct and interactive relations loneliness, anxiety sensitivity, and intolerance of uncertainty shared with suicidal ideation were examined using structural equation modeling across two samples. Sample 1 comprised 635 people (M age = 38.52, SD = 10.00; 49.0% female) recruited using Mechanical Turk in May 2020. Sample 2 comprised 435 people (M age = 34.92, SD = 14.98; 76.2% female) recruited from faculty, staff, and students at a midwestern university in June 2020. Loneliness and anxiety sensitivity were positively, uniquely associated with suicidal ideation across samples. Results of this study were cross-sectional and included only self-report measures. These findings highlight loneliness and anxiety sensitivity as important correlates of suicidal ideation. Modular treatments should be employed to target these mechanisms to reduce COVID-19-related suicidal ideation.

Factor mixture modeling of anxiety sensitivity: Support for the three-class solution in a Serbian sample.

Anxiety sensitivity (AS) is an established transdiagnostic risk factor for emotional disorders. It is defined as the fear of anxiety sensations that arises from beliefs that those sensations can have harmful consequences on cognitive, physical, and social functioning. Thus, AS is usually considered a multidimensional construct comprised of the following dimensions: Cognitive, Physical, and Social concerns. Some studies have questioned its continuous latent structure providing taxometric evidence for its categorical nature. However, more advanced factor mixture modeling (FMM) has offered support for a hybrid, dimensional-categorial latent structure of AS. In other words, 3 qualitatively different classes were identified, with a 3-factor model within each class. In the current study, FMM was used in 2 independent Serbian samples: an adult convenient community (Facebook) sample (N = 359) and a young treatment-seeking sample (N = 342). The obtained results are fairly in line with the most recent work targeting the nature of the AS construct, using the Anxiety Sensitivity Index-3 (ASI-3). By model fit, especially comparing the 2- and 3-class solution in Sample 1, and testing external validity in Sample 2, the empirical evidence supports the 3-class solution, with classes labeled as Normative AS, Moderate AS, and High AS. Important theoretical and practical questions were raised and discussed in the article. (PsycInfo Database Record (c) 2020 APA, all rights reserved).