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. RESULTS: We extracted 28 effect sizes from nine eligible randomized controlled trials with 1079 participants. The results showed that internet-based psychological interventions had a moderate to large between-group effect on health anxiety (g = 0.70) that was significant both at post-intervention (g = 0.74) and follow-up (g = 0.64). Furthermore, these interventions were significantly more effective than passive control conditions including waitlist, usual care, and placebo at post-treatment (g = 1.07), but had effects comparable to active control groups at both post-intervention and follow-up. CONCLUSIONS: Internet-based psychological interventions are an effective way to alleviate pathological health anxiety. We recommend that these interventions be more widely implemented in routine care settings to ensure easy accessibility for patients with health anxiety.

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. METHODS: Nine databases were searched for the literature up to February 2023. Two reviewers independently conducted a quality assessment. Effect sizes were reported as the standardized mean difference (Hedge's g) and estimated using a random-effects model. RESULTS: The meta-analysis included 44 randomized clinical trials comprising 7200 adults with cancer. Digital psychological interventions were associated with significant improvements in short-term fatigue (g = -0.33; 95% CI, -0.58 to -0.07) and disturbed sleep (g = -0.36; 95% CI, -0.57 to -0.15), but with non-significant changes in pain (g = -0.23; 95% CI, -0.68 to 0.21) and physical well-being (g = 0.31; 95% CI, -0.18 to 0.80). Additionally, no alleviation in long-term physical symptoms was observed. In subgroup analysis, results suggest that the country significantly moderated the effectiveness of digital psychological interventions in alleviating fatigue. CONCLUSIONS: Digital psychological interventions can be effective for improving short-term fatigue and disturbed sleep in patients with cancer. Clinicians could consider digital psychological interventions as a possible and efficient addition to better manage some of the physical symptoms during and after cancer treatment.

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. However, the strength of the empirical evidence for this association remains a topic of debate. This study aimed to estimate the association between health anxiety and interpretation bias and to identify potential moderators of this association. Chinese-language databases (CNKI, VIP, and Wanfang), English-language databases (Web of Science, PubMed, PsycINFO, and Scopus), and German-language databases (Psyndex and PubPsych) were searched for relevant studies. There were 36 articles (39 studies) identified by this search (N = 8984), of which 32 articles (34 studies) were included in the meta-analysis (N = 8602). Results revealed a medium overall effect size (g = 0.67). Statistically equivalent effect sizes were observed for patients diagnosed with clinical health anxiety (g = 0.58) and subclinical health anxiety (g = 0.72). The effect sizes for threat stimuli that were health related (g = 0.68) and not health related (g = 0.63) did not differ significantly. The effect size for studies using an offline paradigm (g = 0.75) was significantly higher than that for studies using an online paradigm (g = 0.50). It is concluded that health anxiety is significantly and robustly associated with interpretation bias. These findings are of central importance for the advancement of models and treatment 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). The meta-analytic results indicated that the health anxiety group, compared to the control group, showed significantly greater attentional bias toward health-threat (g = 0.256). Further analyses revealed that attentional bias type, paradigm, and stimuli type were significant moderators. Additionally, compared to the controls, health-anxious individuals displayed significantly greater attention maintenance (g = 0.327) but nonsignificant attention vigilance to health-threat (g = -0.116). Our results provide evidence for the attention maintenance model in health-anxious individuals. The implications for further research and treatment of elevated health anxiety in the context of coronavirus disease-2019 (COVID-19) were also discussed.

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

Background: 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. Method: 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. Assessment criteria, region, and time phase of the pandemic were tested as sources of heterogeneity. Results: With an average risk of bias score of 6.71 (range = 6-8), the majority of included studies employed appropriate statistical methods, used validated measurement tools, and had adequate response rates; however, they might have deficiencies in sample frame, sampling method, and sample size. Within the COVID-19, general, and chronic illness groups, the prevalence of depressive symptoms was 27%, 26%, and 61%, respectively, and the prevalence of anxiety symptoms was 14%, 23%, and 85%, respectively. Among generally healthy older adults, anxiety was more prevalent during the Phase 2 (March-April 2020) of the pandemic compared with other time phases. Conclusions: The results have implications for addressing the mental health problems of Chinese older adults, especially those with chronic illnesses, during the COVID-19 pandemic.

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 60.74%, 95.31% and 98.77%, respectively. Based on the characteristics of CNN model and with the help of network localization, active variables could be recognized in the whole spectrum. Although the localization of active variables showed a discriminative pattern, the selected spectral regions were similar. Most important variables focused on the 1700-1400 cm-1, were corresponding to cellulose crystallinity, which was consisted with the ageing processing. The present work gave the potential of FT-IR combined with chemometric techniques could estimate the dating of unknown paper. Meanwhile, the analysis of active variables obtained further indicated the worthy of CNN model for document dating.