Predictive roles of cognitive biases in health anxiety: A machine learning approach.

Prior work suggests that cognitive biases may contribute to health anxiety. Yet there is little research investigating how biased attention, interpretation, and memory for health threats are collectively associated with health anxiety, as well as the relative importance of these cognitive processes in predicting health anxiety. This study aimed to build a prediction model for health anxiety with multiple cognitive biases as potential predictors and to identify the biased cognitive processes that best predict individual differences in health anxiety.

Efficacy of internet-based psychological interventions for pathological health anxiety: A three-level meta-analysis of randomized controlled trials.

Objective: This study aimed to provide a comprehensive summary and synthesis of available evidence on the efficacy of internet-based psychological interventions for pathological health anxiety, as well as to examine the variables that possibly moderate intervention effects.

Method: Four databases were searched for the literature up to October 2023. A three-level random-effects model was used to estimate the pooled effect size, with Hedge's g as the measure.

Effects of digital psychological interventions on physical symptoms in cancer patients: A systematic review and meta-analysis.

Purpose: This meta-analysis was to assess the efficacy of digital psychological interventions to improve physical symptoms (i.e., fatigue, pain, disturbed sleep, and physical well-being) among cancer patients, as well as to evaluate the variables that possibly moderate intervention effects.

Interpretation bias in health anxiety: a systematic review and meta-analysis.

Interpretation bias (i.e. the selective negative interpretation of ambiguous stimuli) may contribute to the development and maintenance of health anxiety.

Attentional bias toward health-threat in health anxiety: a systematic review and three-level meta-analysis.

Attentional bias toward health-threat may theoretically contribute to the development and maintenance of health anxiety, but the empirical findings have been controversial. This study aimed to synthesize and explore the heterogeneity in a health-threat related attentional bias of health-anxious individuals, and to determine the theoretical model that better represents the pattern of attentional bias in health anxiety. Four databases (Web of Science, PubMed, PsycINFO, and Scopus) were searched for relevant studies, with 17 articles (N = 1546) included for a qualitative review and 16 articles (18 studies) for a three-level meta-analysis (N = 1490).

Prevalence of depressive and anxiety symptoms among Chinese older adults during the COVID-19 pandemic: A systematic review and meta-analysis.

Estimating the prevalence of depressive and anxiety symptoms among older adults with different health conditions can inform mental health services for this population during the corona virus disease-2019 (COVID-19) pandemic. A search of 12 scientific databases identified 17 studies with 11,237 Chinese older adults who were infected by COVID-19, were generally healthy, or had chronic illnesses. Meta-analysis was used to estimate the overall prevalence of depressive and anxiety symptoms in these three groups.

Non-destructive analysis the dating of paper based on convolutional neural network.

A non-destructive method based on Fourier Transformed Infrared Spectroscopy (FT-IR) was proposed to estimate the date of paper from different years in this article. For the paper samples, dated from 1940 to 1980, naturally aged and conserved in library. Partial least squares-discriminate analysis (PLS-DA), Logistic regression and convolutional neural network (CNN), were employed to evaluate the date of paper, with the accuracy 60.