The coronavirus disease 2019 pandemic has had an ongoing impact on the public's mental health that requires long-term attention. Exploring the relationship between mental health indicators would aid in identifying solutions to improve public mental health.

To investigate the prevalence of anxiety, depression, and insomnia and explore the relationship among neuroticism, cognitive failures, and mental health.

A cross-sectional survey was conducted in December 2023 using an online platform to recruit adult participants. The neuroticism, subjective cognitive function, and mental health of the participants were assessed using the neuroticism subscale of the Chinese Big Five Personality Inventory Brief Version, cognitive failures questionnaire, generalized anxiety disorder-7, patient health questionnaire-9, and insomnia severity index. Pearson's correlation analysis, independent samples t-tests, one-way analysis of variance, and structural equation model were used to examine the relationship between mental health indicators.

A total of 1011 valid questionnaires were collected, of which 343 were completed by male (33.93%) respondents and 668 were completed by female (66.07%) respondents. The rates of anxiety, depression, and insomnia were 41.3%, 44.6%, and 36.3%, respectively, most cases of which were mild. Among the mental health indicators, there were significant differences by age and between those with siblings and those who were only children. Neuroticism and cognitive failures were significantly positively correlated with mental health indicators. Further moderated mediation analysis showed that cognitive failures mediated the relationship between insomnia and anxiety and between insomnia and depression, with neuroticism moderating the first half of this pathway and the effect being greater in the low-neuroticism group.

Cognitive failures and neuroticism play important roles in mental health. Therefore, enhancing subjective cognitive function and regulating emotional stability may contribute to the improvement of mental health.

Psychometric properties of the Insomnia Severity Index (ISI) were analyzed in U.S. college samples. ISI items and total score with sleep and psychosocial questionnaires were examined in Experiment I. ISI diagnostic accuracy in a clinical sample with and without insomnia was assessed in Experiment II. ISI test-retest validity, confirmatory factor analysis (CFA), and item response theory via graded response model (GRM) were assessed in Experiment III. Results indicated analogous ISI and sleep diary items showed moderate correlations (r1 = .40; r2 = .45). The ISI total had weak to strong correlations with other indicators of sleep-related disturbance (rs = .25-.62). The ISI had weak to moderate correlations with psychosocial measures commonly associated with insomnia (rs = .10-.57). The diagnostic accuracy of the ISI was very high (area under the curve [AUC] = .999). Sensitivity and specificity were maximized at a cutoff score ≥ 8. The ISI demonstrated good test-retest reliability (ICC = .87). CFA revealed a three-factor model for two study samples and GRM indicated better ability of the ISI to assess moderate (Sample III) and moderate to high (Sample I) levels of insomnia severity. The ISI demonstrated good psychometric properties and appears generally valid for screening insomnia disorder and assessing insomnia severity in college students. Overlap with psychological symptoms suggests caution while interpreting these constructs independently.

The study explored the prospective relations between depression and approach-avoidance achievement goals of undergraduate students in China.

2473 full-time undergraduates reported their depression and achievement goals annually from the freshman to the senior year. Data were analysed using descriptive statistics, correlation analysis and cross-lagged models.

Students' achievement goals decreased gradually during the first 3 years but rose in the fourth year, and the avoidance goals appeared to be less prevalent than the approach goals over time. Depression was negatively associated with approach goals, whereas positively correlated with avoidance goals. Depression in the freshman and sophomore years resulted in more avoidance goals 1 year later, and the depressive problems in the junior year predicted the decline of approach goals in the senior year.

The present study highlighted the deleterious effects of depression on the achievement goals of college students.

Cognitive behavioral therapy for insomnia (CBT-I) is the universally recommended treatment of choice for insomnia disorder based on its safety and posttreatment durability of benefit. However, CBT-I does not help all patients achieve remission. The second most evidence-based treatment, hypnotic pharmacotherapy (PCT), does not resolve perpetuating factors of insomnia, resulting in potential waning of benefit and dependence. This article presents a rationale that supports consideration of hypnotic augmentation of CBT-I (COMB), along with a review of select randomized controlled trials relevant to clinical decision-making.

Background: More than 300 million people worldwide suffer from depression, which is a significant contributor to the global burden of disease. This study investigated the factors influencing psychological resilience and cognitive fusion in patients with depression and the relationships of psychological resilience and cognitive fusion with depression. Methods: This study enrolled 172 participants (65.8% of them were female). Psychological resilience, cognitive fusion, and depression were assessed with the psychological resilience scale, the cognitive fusion questionnaire, the Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale (HAMD), and Zung Self-rating Depression Scale (SDS), respectively. Furthermore, the relationships of psychological resilience and cognitive fusion with depression were investigated. Results: The psychological resilience and cognitive fusion scores of patients with depression varied significantly among different education levels, and HAMA, HAMD, and SDS scores were significantly negatively correlated with psychological resilience but positively correlated with cognitive fusion. Conclusions: Depression levels in patients with depression are closely related to psychological resilience and cognitive fusion. Therefore, anxiety and depression could be alleviated by improving the psychological resilience or reducing the cognitive fusion of patients with depression.

Cognitive Failures Questionnaire (CFQ) is a widely utilized tool to measure the frequency of everyday cognitive lapses. Here we present a validation study of the Hungarian translation of CFQ. A subsample (n = 157) filled out the questionnaire twice within a 7-21 days interval to determine test-retest reliability. Exploratory structural equation modeling (ESEM) method was run on a larger sample (n = 382) for testing a different number of potential factors. Although the multiple-factor solutions showed good fit, the one-factor solution described the data more adequately. The composite reliability of the final model (CR = .822) as well as test-retest reliability (intraclass correlation coefficient = .900) and the internal consistency (Cronbach's alpha = .920) of the CFQ were high. Higher CFQ scores (i.e., more cognitive slips) correlated positively with anxiety and depression while a negative relationship was present with well-being. Furthermore, women were characterized with higher CFQ scores compared to men. Our results are in line with previous studies, and the excellent psychometric properties make the Hungarian version of CFQ an appropriate measure of cognitive failures.

Objective: Although behavioral treatments are recommended for treating insomnia disorder, these treatments are not the most commonly provided treatments due to numerous barriers (e.g., treatment length, time limitations). Brief Behavioral Treatment for Insomnia (BBTI) was developed, in part, to help overcome these barriers. The purpose of the current study was to qualitatively examine the treatment experiences of veterans with insomnia disorder participating in BBTI.Methods: All veterans (n=46) who were randomized to receive BBTI as part of a randomized clinical trial participated in 10-20 minute semi-structured interviews one week after completing treatment. Rapid analysis procedures were used for qualitative analysis.Results: Thirteen qualitative themes were identified: BBTI provided veterans with the skills they believed they needed to continue improving independently post-treatment; beginning BBTI with in-person sessions was valued; phone sessions helped participation; veterans did not perceive that they could cover the same content during phone and in-person sessionsl; materials could be more portable; BBTI created accountability; BBTI required discipline and willingness; BBTI facilitated buy-in; BBTI was aligned with military culture; loved ones could provide important support; BBTI could be improved with more personalization; BBTI challenged expectations of mental health; and BBTI improved awareness of health behaviors beyond sleep.Conclusions: BBTI was successful in overcoming barriers to behavioral insomnia treatment and interviews identified critical treatment aspects that should be maintained to preserve acceptability (e.g., in-person session first). Areas in which BBTI did not fully meet the needs of veterans and targets for improvement (e.g., ameliorating understanding and expectations of phone sessions) were also identified.

Acute and chronic insomnia have different causes and may require different treatments. They are investigated with multi-night nocturnal actigraphy data from two sleep studies. Two different wrist-worn actigraphy devices were used to measure physical activities. This required data pre-processing and transformations to smooth the differences between devices. Statistical, power spectrum, fractal and entropy analyses were used to derive features from the actigraphy data. Sleep parameters were also extracted from the signals. The features were then submitted to four machine learning algorithms. The best performing model was able to distinguish acute from chronic insomnia with an accuracy of 81%. The algorithms were then used to evaluate the acute and chronic groups compared to healthy sleepers. The differences between acute insomnia and healthy sleep were more prominent than between chronic insomnia and healthy sleep. This may be associated with the adaptation of the physiology to prolonged periods of disturbed sleep for individuals with chronic insomnia. The new model is a powerful addition to our suite of machine learning models aiming to pre-screen insomnia at home with wearable devices.

University students experience high prevalence of mental health problems and exacerbation of mental health difficulties, including sleep disturbances and stress during their studies. Stress and poor sleep quality and/or insomnia are interlinked outcomes for this population. The aim was to conduct a systematic review, and meta-analyses, of the relationships between sleep quality and/or insomnia with stress in students. Full-text articles of studies exploring the associations of stress with poor sleep quality and/or insomnia in undergraduate students using validated tools and published in peer-reviewed journals were eligible for inclusion. Thirty-four studies, resulting in 37 effect sizes, included and all were suitable for meta-analysis. The weighted pooled effect size between sleep quality and stress was for 0.39 (25 studies, n = 10,065), whereas a slightly higher pooled association of 0.41 was demonstrated for insomnia and stress (12 studies, n = 5564.5). Pooled associations show moderate effects for associations between sleep quality, insomnia and stress in undergraduate students. High heterogeneity in meta-analyses was found, suggesting the findings should be considered cautiously. Future research should focus on longitudinal studies exploring sleep difficulties across the academic year, whilst university services should consider psychoeducation for stress and sleep in university students, especially during transition to university.

We propose a novel machine learning-based method for analysing multi-night actigraphy signals to objectively classify and differentiate nocturnal awakenings in individuals with chronic insomnia (CI) and their cohabiting healthy partners. We analysed nocturnal actigraphy signals from 40 cohabiting couples with one partner seeking treatment for insomnia. We extracted 12 time-domain dynamic and nonlinear features from the actigraphy signals to classify nocturnal awakenings in healthy individuals and those with CI. These features were then used to train two machine learning classifiers, random forest (RF) and support vector machine (SVM). An optimization algorithm that incorporated the predicted quality of each night for each individual was used to classify individuals into CI or healthy sleepers. Using the proposed actigraphic signal analysis technique, coupled with a rigorous leave-one-out validation approach, we achieved a classification accuracy of 80% (sensitivity: 76%, specificity: 82%) in classifying CI individuals and their healthy bed partners. The RF classifier (accuracy: 80%) showed a better performance than SVM (accuracy: 75%). Our approach to analysing the multi-night nocturnal actigraphy recordings provides a new method for screening individuals with CI, using wrist-actigraphy devices, facilitating home monitoring.

Previous studies have reported well-documented findings that mobile phone addiction (MPA) is associated with negative emotion-related consequences; however, sporadic research has investigated the associations between MPA and cognitive outcomes related to daily cognitive functioning. Sleep duration, sleep quality, and trait self-regulation are thought to be linked to this association. The present study aimed to examine the mediating roles of sleep duration and quality and the moderating role of trait self-regulation between MPA and daily cognitive failures. A total of 1721 secondary school students were recruited to complete four self-reported questionnaires. The model results indicated that sleep quality (but not sleep duration) partially mediated the association between MPA and daily cognitive failures, and high levels of trait self-regulation could attenuate the potential impact of MPA on daily cognitive failures through sleep quality. Overall, these findings address the issue of how and when MPA is linked with cognitive performance in daily life, which can advance a better understanding of the negative consequences induced by MPA. Limitations and implications are discussed.

Objective/Background: Actigraphy is an inexpensive and objective wrist-worn activity sensor that has been validated for the measurement of sleep onset latency (SOL), number of awakenings (NWAK), wake after sleep onset (WASO), total sleep time (TST), and sleep efficiency (SE) in both middle-aged and older adults with insomnia. However, actigraphy has not been evaluated in young adults. In addition, most previous studies compared actigraphy to in-lab polysomnography (PSG), but none have compared actigraphy to more ecologically valid ambulatory polysomnography.Participants: 21 young adults (mean age = 19.90 ± 2.19 years; n = 13 women) determined to have chronic primary insomnia through structured clinical interviews.Methods: Sleep diaries, actigraphy, and ambulatory PSG data were obtained over a single night to obtain measures of SOL, NWAK, WASO, time spent in bed after final awakening in the morning (TWAK), TST, and SE.Results: Actigraphy was a valid estimate of SOL, WASO, TST, and SE, based on significant correlations (r = 0.45 to 0.87), nonsignificant mean differences between actigraphy and PSG, and inspection of actigraphy bias from Bland Altman plots (SOL α = 1.52, WASO α = 7.95, TST α = -8.60, SE α = -1.38).Conclusions: Actigraphy was a valid objective measure of SOL, WASO, TST, and SE in a young adult insomnia sample, as compared to ambulatory PSG. Actigraphy may be a valid alternative for assessing sleep in young adults with insomnia when more costly PSG measures are not feasible.

Depression is a highly prevalent psychological disorder experienced disproportionately by college student military veterans with many deleterious effects including risk for suicide. Treatment can help, but the debilitating nature of depression often makes seeking in-person treatment difficult and many are deterred by stigma, inconvenience, concerns about privacy, or a preference to manage problems themselves. The current study examines the efficacy of a computer-guided Problem-Solving Treatment (ePST^®) for reducing symptoms of depression, posttraumatic stress disorder (PTSD), and insomnia in student military veterans. Twenty-four student veterans (Mean_age = 32.7) with symptoms of depression were randomly assigned either to a treatment group receiving six weekly sessions of ePST or to a minimal contact control group (MCC). Participants completed the Patient Health Questionnaire-9 (PHQ-9) depression scale at baseline and then weekly through post-ePST or post-MCC. PTSD and insomnia questionnaires were also completed at baseline and posttreatment. A linear mixed model regression showed a statistically significant Group (ePST vs. MCC) × Time (pretreatment through posttreatment) interaction for depression, with the ePST showing substantial improvements in depressive symptoms over the 6-week period. Significant improvements were also seen in PTSD and insomnia symptoms. Results suggest that ePST can effectively treat depression, PTSD, and insomnia symptoms in student military veterans and may be a viable alternative for those who are not able to access live therapy. Future work should examine the durability of treatment effects and utility for more severe depression and suicide prevention.