Search Results (65)

Objectives: Evidence suggests that COVID-19 infection contributes to elevated risks of psychiatric disorders, including depression and anxiety, however, this association remains underexplored. This study aimed to examine the incidence of depression and anxiety in individuals with COVID-19 compared to those without any infection. Method: Using the Utah All Payers Claims Database (2019 to 2021), we examined adult patients with continuous insurance enrollment. Individuals with pre-existing depression or anxiety were excluded. COVID-19 infection in 2020 was identified using diagnostic and procedural codes. The Least Absolute Shrinkage and Selection Operator (LASSO) method was applied to select covariates, followed by entropy balancing to adjust for baseline differences. Weighted logistic regression models were used to estimate the association between COVID-19 infection and incident mental health diagnoses in 2021. Results: Among 356,985 adults included in the final analytic sample for depression analysis, 37.6 percent had a documented COVID-19 infection in 2020. Individuals with prior infection had significantly higher odds of receiving a depression diagnosis in 2021 compared to those without infection (OR = 1.48, p < 0.01). A similar pattern was observed for anxiety: among 371,491 adults, 38.1 percent had a COVID-19 infection, and infected individuals had 46 percent greater odds of receiving an anxiety diagnosis (OR = 1.46, p < 0.01), after adjusting for demographic and clinical characteristics. Conclusions: This study highlights the elevated risk of depression and anxiety among patients who had been infected with COVID-19, emphasizing the importance of addressing the mental health needs of individuals affected by the virus. Full article

Migraine is a common disabling primary headache disorder with significant personal and socio-economic impacts. A combination of medication and non-pharmacological therapies is essential for migraine management. Outpatient multidisciplinary headache therapy has not yet been evaluated in Switzerland. This study evaluates the effectiveness of the headache management program at Inselspital, Bern University Hospital, in improving headache-related disability in migraine patients. This open-label pilot study used prospectively assessed routine data from our headache registry. Participants aged 18 years or older with a diagnosis of migraine, confirmed by a headache specialist, were included. The program consisted of seven weekly sessions, each with a 50 min educational lecture and a 30 min progressive muscle relaxation (PMR) exercise. Primary outcomes were headache-related impact and disability, measured by the Headache Impact Test 6 (HIT-6) and Migraine Disability Assessment (MIDAS). Secondary outcomes included symptoms of anxiety, measured by the Generalized Anxiety Disorder 7-item scale (GAD-7), and symptoms of depression, assessed using the eight-item Patient Health Questionnaire depression scale (PHQ-8). Data were analysed using paired t-test and Wilcoxon signed rank tests. Significant improvements were observed in HIT-6 scores (pre-program: 65.2; post-program: 61.9; p = 0.012) and MIDAS scores (pre-program: 38; post-program: 27; p = 0.011), while PHQ-8 also showed a statistically significant reduction. Although the GAD-7 scores improved numerically, this change was not statistically significant. These findings suggest that the headache management program may reduce headache burden and disability; however, further research with larger samples is needed to confirm these preliminary results. Full article

Background/Objectives: Infection with severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) poses a significant health risk, especially for individuals with chronic medical conditions. Multiple sclerosis (MS) is the most prevalent chronic, immune-mediated neurological disorder, and vaccinations are essential to its management. This study aimed to compare the reported willingness to be vaccinated against SARS-CoV-2 with the actual vaccination status among people with MS (pwMS) and identify factors explaining the discrepancy. Methods: In a longitudinal, two-center study, we analyzed 149 patients aged 18 or older with a diagnosis of clinically isolated syndrome or MS. The participants completed three surveys: a baseline survey (from June 2019 to June 2020), a pre-vaccine follow-up (from May to July 2020), and a post-vaccine follow-up (from October 2021 to January 2022). The data included sociodemographic, clinical, and psychological information. Results: Among the 149 participants, 122 (81.9%) received a SARS-CoV-2 vaccination, while 27 (18.1%) did not. The pwMS who were unwilling to become vaccinated and remained unvaccinated were less likely to live with a partner, had higher smoking rates, took more medications, had a higher number of previously discontinued disease-modifying therapies, and found pandemic policies inappropriate. No significant associations were found between vaccination willingness/status and factors like age, sex, depression, or anxiety. Conclusions: This study highlights the gap between vaccination willingness and actual status in pwMS, revealing factors associated with vaccine hesitancy. The findings of this study offer insights into addressing vaccine uptake. Full article

Facial expressions reflect psychological states and are crucial for understanding human emotions. Traditional facial expression recognition methods face challenges in real-world healthcare applications due to variations in facial structure, lighting conditions and occlusion. We present a methodology based on transfer learning with the pre-trained models VGG-19 and ResNet-152, and we highlight dataset-specific preprocessing techniques that include resizing images to 124 × 124 pixels, augmenting the data and selectively freezing layers to enhance the robustness of the model. This study explores the application of deep learning-based facial expression recognition in healthcare, particularly for remote patient monitoring and telemedicine, where accurate facial expression recognition can enhance patient assessment and early diagnosis of psychological conditions such as depression and anxiety. The proposed method achieved an average accuracy of 0.98 on the CK+ dataset, demonstrating its effectiveness in controlled environments. However performance varied across datasets, with accuracy rates of 0.44 on FER2013 and 0.89 on JAFFE, reflecting the challenges posed by noisy and diverse data. Our findings emphasize the potential of deep learning-based facial expression recognition in healthcare applications while underscoring the importance of dataset-specific model optimization to improve generalization across different data distributions. This research contributes to the advancement of automated facial expression recognition in telemedicine, supporting enhanced doctor–patient communication and improving patient care. Full article

Background/Objectives: Treatment with aromatase inhibitors can worsen frailty syndrome and psychological symptoms in women diagnosed with breast cancer (BC) receiving these drugs to prevent cancer recurrence. We analyze whether postmenopausal women with localized BC receiving aromatase inhibitors (AIs) treatment can achieve improvements in their mental health and their level of frailty through a multimodal program that includes supervised physical exercise and health education workshops. Methods: A total of 52 postmenopausal women with a prior diagnosis of BC and receiving hormonal treatment with AIs were included in the multimodal physical exercise and health education program and evaluated before and after it. The assessment included the following five frailty syndrome (FS) criteria: involuntary weight loss, weakness, low physical activity, slow gait speed, and low muscle strength. Mental health was assessed using the Goldberg scale, with its subscales for anxiety and depressive symptoms. The Athens scale was used to assess subjective sleep quality. Results: There was a significant difference in the number of robust, pre-frail and frail women after the program compared to the baseline. Six women did not fulfill any criteria for (robust) FS before the program (11.5%), and thirty-three women (63.5%) after the program did not fulfill any criteria for FS. A total of 33 (63.5%) women met one or two FS criteria (pre-frail) before the program, and 18 (34.6%) met one or two FS criteria after the program; thirteen (25%) women met three or more FS criteria (frail) before the program and one (1.9%) after it (p < 0.001). A statistically significant improvement on the Goldberg scale was observed (on both the subscales for anxiety and depressive symptoms) (p < 0.001). A statistically significant improvement was also noted on the Athens insomnia scale (p < 0.001). A multivariate regression model analysis identified marital status (being married) (p = 0.047, beta coefficient= −0.249, 95% CI −1.4844–−0.14) and the percentage of attendance at training sessions (p = 0.041, beta coefficient = −0.290, 95% CI 0.104–0.002) as associated variables, with a lower score on the Goldberg depression subscale. Conclusions: Mental health and frailty, common in postmenopausal women diagnosed with BC on hormonal treatment with AI, can be improved with multimodal programs of supervised physical exercise and health education. Full article

The perinatal period, due to the many physical, psychological, and social changes in future mothers, may represent a critical phase with an increased risk for mental health. Postpartum depression (PPD) is one of the main syndromes that affect around 17 percent of women after pregnancy and in the first months of motherhood. This systematic review, following PRISMA guidelines, aimed to identify the main pre-partum psychiatric risk factors that may influence the occurrence and diagnosis of PPD with a focus on the antenatal and clinical history of depression, bipolar disorders, obsessive–compulsive disorder, and psychosis. From the search in main scientific databases (Web of Science, Pubmed, Psychinfo, and Scopus), 37 articles were included for the critical evaluation. The studies showed that antenatal depression and depressive episodes during pregnancy represent higher risk factors for PPD. Also, a clinical history of major depression, especially if associated with other risk factors (such as poor demographic or social conditions) increases the risk for PPD. From the systematic analysis emerged a paucity of studies considering the other psychiatric syndromes that should be overcome. PPD represents a multisystemic syndrome involving all the aspects of a mother’s life as well as affecting children’s development; for this reason, exploring the role of mental health risk factors for PPD onset, progression, and prognosis is relevant, from a clinical point of view, to find the best way to promote the mother’s psychological well-being from the antenatal period. Full article

Background: Mental disorders are disturbances of brain functions that cause cognitive, affective, volitional, and behavioral functions to be disrupted to varying degrees. One of these disorders is depression, a significant factor contributing to the increase in suicide cases worldwide. Consequently, depression has become a significant public health issue globally. Electroencephalogram (EEG) data can be utilized to diagnose mild depression disorder (MDD), offering valuable insights into the pathophysiological mechanisms underlying mental disorders and enhancing the understanding of MDD. Methods: This survey emphasizes the critical role of EEG in advancing artificial intelligence (AI)-driven approaches for depression diagnosis. By focusing on studies that integrate EEG with machine learning (ML) and deep learning (DL) techniques, we systematically analyze methods utilizing EEG signals to identify depression biomarkers. The survey highlights advancements in EEG preprocessing, feature extraction, and model development, showcasing how these approaches enhance the diagnostic precision, scalability, and automation of depression detection. Results: This survey is distinguished from prior reviews by addressing their limitations and providing researchers with valuable insights for future studies. It offers a comprehensive comparison of ML and DL approaches utilizing EEG and an overview of the five key steps in depression detection. The survey also presents existing datasets for depression diagnosis and critically analyzes their limitations. Furthermore, it explores future directions and challenges, such as enhancing diagnostic robustness with data augmentation techniques and optimizing EEG channel selection for improved accuracy. The potential of transfer learning and encoder-decoder architectures to leverage pre-trained models and enhance diagnostic performance is also discussed. Advancements in feature extraction methods for automated depression diagnosis are highlighted as avenues for improving ML and DL model performance. Additionally, integrating Internet of Things (IoT) devices with EEG for continuous mental health monitoring and distinguishing between different types of depression are identified as critical research areas. Finally, the review emphasizes improving the reliability and predictability of computational intelligence-based models to advance depression diagnosis. Conclusions: This study will serve as a well-organized and helpful reference for researchers working on detecting depression using EEG signals and provide insights into the future directions outlined above, guiding further advancements in the field. Full article

Ischemic stroke is a major cause of mortality and disability and has become a significant public health concern among women. Overall, women have more ischemic stroke events than men, in part due to their longer life span, and also suffer from more severe stroke-related disabilities compared to men. Women are also more likely than men to present with atypical non-focal neurological symptoms, potentially leading to delayed diagnosis and treatment. Female-specific risk factors, especially those related to pregnancy, are often under-recognized. A woman’s risk for ischemic stroke evolves throughout her lifespan, influenced by various factors including the age of menarche, pregnancy and its complications (such as parity, pre-eclampsia/eclampsia, and preterm delivery), postpartum challenges, oral contraceptive use, and menopause. Additionally, vascular risk factors like hypertension, diabetes, and atrial fibrillation are more prevalent among older women. Despite comparable treatment efficacies, women generally experience poorer outcomes after stroke. They also face higher rates of post-stroke depression, further complicating recovery. Although significant strides have been made in reducing the incidence of ischemic stroke, our understanding of the unique risks, underlying causes, and long-term consequences for women remains limited. While sex hormones may explain some differences, a lack of awareness regarding sex-related disparities can result in suboptimal care. This review aims to illuminate the unique risks and burdens of ischemic stroke faced by women, advocating for a more nuanced understanding to enhance prevention and treatment strategies. Full article

Background/Objectives: The impact of exercise on affective disorders has been demonstrated in various studies. However, almost no data are available on performance effects. Therefore, this study investigated exercise performance related to the severity of depression symptoms in a pre–post within-subjects design in a 6-week standard inpatient psychiatric rehabilitation program. Methods: A total of 53 individuals (20 female; mean age, 40.98 ± 11.33) with a primary diagnosis of depression performed a cardiopulmonary exercise test (CPX) to obtain maximal oxygen uptake (VO_2max), maximal power output (P_max), and the first and second ventilatory threshold (VT₁, VT₂) at the start and the end of the rehabilitation. Degree of depression was assessed by Becks Depression Inventory (BDI) and the Brief Symptom Inventory test (BSI). Overall activity was measured by accelerometer step-counts. Results: Mean total step-count per day during rehabilitation was high (12,586 ± 2819 steps/day). Patients’ BDI and BSI at entry were 21.6 ± 8.83 and 65.1 ± 6.8, respectively, and decreased significantly (p < 0.001) following rehabilitation, to 10.1 ± 9.5 and 54.5 ± 11.3, respectively. P_max and VO_2max increased significantly (p < 0.001) from entry values (182.6 ± 58.7 W, 29.74 ± 5.92 mL·kg⁻¹·min⁻¹) following rehabilitation: by 11.91 ± 12.09 W and 1.35 ± 2.78 mL·kg⁻¹·min⁻¹, respectively. VT₁ and VT₂ showed a similar behavior. An increase in physical performance could predict improvement in BDI (R² = 0.104, F(1,48) = 5.582, p = 0.022) but not in BSI. Conclusions: The program was associated with improved mental health status in all patients and increased performance in the majority of patients, although increases were small. Since improvements in exercise performance may be positively related to depression symptoms and comorbidities, it is recommended to individualize and tailor exercise programs, which could yield larger effects. Full article

Background/Objectives: Psychological distress is a significant concern among cancer patients, negatively affecting their quality of life and adherence to treatment. The Cancer Patient Empowerment Program (CancerPEP) was developed as a comprehensive, home-based intervention aimed at reducing psychological distress by incorporating physical activity, dietary guidance, and social support. This study aimed to evaluate the feasibility, accrual and attrition rates, safety, and effectiveness of the CancerPEP intervention, with and without the biofeedback device, on psychological distress from baseline to 6 months, specifically focusing on the effects of group randomization and the difference between pre- and post-intervention results. Methods: This single-site, crossover randomized clinical trial included 104 cancer patients who were randomized to receive the CancerPEP intervention, with or without a Heart Rate Variability (HRV) biofeedback monitor. At 6 months, participants who did not receive the device were allowed to use one until the end of the year, while those who did receive the device were followed up to 12 months. Randomization was stratified by the presence or absence of clinically significant psychological distress and metastatic status. Psychological distress was assessed using the Kessler Psychological Distress Scale (K10) at baseline, 6 months, and 12 months. The primary endpoint was the presence of nonspecific psychological distress, as measured by the K10 scale at 6 months from the trial start, based on group randomization. A secondary exploratory analysis assessed psychological distress at baseline, 6 months, and 12 months for both groups, while controlling for group randomization and prognostic covariates. Prognostic covariates included age; comorbidities; time between diagnosis and randomization; treatment modality; relationship status; and use of prescribed medications for anxiety, depression, or both. An exploratory sub-analysis was conducted for the breast cancer subgroup, based on the sample size available after recruitment. The trial is registered at ClinicalTrials.gov (NCT05508412). Results: The provision of the HRV biofeedback monitor in conjunction with the CancerPEP intervention did not significantly affect the primary outcome in either the full sample or the breast cancer subgroup, indicating that the HRV biofeedback provision was not beneficial in this trial. No self-reported or otherwise discovered adverse events at the 6-month mark were observed. About 10% of participants were lost to follow-up in both the early and late HRV monitor provision groups. Participation in the CancerPEP program led to a significant reduction in psychological distress over time. The odds of psychological distress were significantly higher at the start of the trial than at the end of the intervention (aOR = 2.64, 95% CI: 1.53–4.56) or 6 months after the intervention (aOR = 2.94, 95% CI: 1.62–5.30). Similarly, in the breast cancer subgroup, distress was higher at the trial’s start than at 6 months, i.e., after the intervention (aOR = 2.25, 95% CI: 1.24–4.08), or at the end of the trial at 12 months (aOR = 2.73, 95% CI: 1.35–5.52). Conclusions: CancerPEP significantly reduces psychological distress in cancer patients, with consistent improvements noted across various cancer types and stages, including benefits specifically for breast cancer patients. These findings build upon the success of the Prostate Cancer Patient Empowerment Program (PC-PEP), indicating that a similar comprehensive intervention can be advantageous for all cancer patients and may be further tailored to address specific needs. With its holistic approach—encompassing physical, dietary, and psychosocial support—CancerPEP shows promise as a vital component of survivorship care. Ongoing 24-month evaluations will yield critical data on its long-term benefits. Additionally, a randomized trial with a control group (usual care without intervention) for breast cancer patients is currently under way and could potentially guide the integration of CancerPEP into standard oncology care to enhance patient outcomes and quality of life. Full article

Background: Bipolar disorder (BD) involves significant mood and energy shifts reflected in speech patterns. Detecting these patterns is crucial for diagnosis and monitoring, currently assessed subjectively. Advances in natural language processing offer opportunities to objectively analyze them. Aims: To (i) correlate speech features with manic-depressive symptom severity in BD, (ii) develop predictive models for diagnostic and treatment outcomes, and (iii) determine the most relevant speech features and tasks for these analyses. Methods: This naturalistic, observational study involved longitudinal audio recordings of BD patients at euthymia, during acute manic/depressive phases, and after-response. Patients participated in clinical evaluations, cognitive tasks, standard text readings, and storytelling. After automatic diarization and transcription, speech features, including acoustics, content, formal aspects, and emotionality, will be extracted. Statistical analyses will (i) correlate speech features with clinical scales, (ii) use lasso logistic regression to develop predictive models, and (iii) identify relevant speech features. Results: Audio recordings from 76 patients (24 manic, 21 depressed, 31 euthymic) were collected. The mean age was 46.0 ± 14.4 years, with 63.2% female. The mean YMRS score for manic patients was 22.9 ± 7.1, reducing to 5.3 ± 5.3 post-response. Depressed patients had a mean HDRS-17 score of 17.1 ± 4.4, decreasing to 3.3 ± 2.8 post-response. Euthymic patients had mean YMRS and HDRS-17 scores of 0.97 ± 1.4 and 3.9 ± 2.9, respectively. Following data pre-processing, including noise reduction and feature extraction, comprehensive statistical analyses will be conducted to explore correlations and develop predictive models. Conclusions: Automated speech analysis in BD could provide objective markers for psychopathological alterations, improving diagnosis, monitoring, and response prediction. This technology could identify subtle alterations, signaling early signs of relapse. Establishing standardized protocols is crucial for creating a global speech cohort, fostering collaboration, and advancing BD understanding. Full article

Background: During pregnancy, women can experience mental alterations, particularly anxiety and depression, which mark an important transition period in their lives. Social support appears to be a crucial alleviating factor for these disorders. The aim of this study is to assess the extent of psychological disturbances and their relieving factors by investigating correlations between mental status and different sociodemographic and clinical characteristics during the third trimester of pregnancy. Methods: A cross-sectional study including 160 pregnant women in their last trimester was carried out in Morocco, notably at the Ibn Sina University Hospital and in two health centers. A pre-structured questionnaire, including sociodemographic and clinical variables and internationally recognized scales such as the Multidimensional Scale of Perceived Social Support (MSPSS), the Perceived Stress Scale (PSS), the Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS), the Bergen Insomnia Scale (BIS), and the Hospital Anxiety and Depression Scale (HADS), was mobilized. Results: The prevalence of depression and anxiety was 18.75% and 12.5%, respectively. A correlation between these two mental disorders and the level of education, pregnancy planning, monthly income, and provision of health coverage was found (p-value < 0.05). The main determinants of anxiety were stress (p-value = 0.047) and social support (p-value < 0.001), while depression was limited to social support (p-value < 0.001) and sleep quality (p-value = 0.015). Conclusions: It is essential to take action against these disorders and their predictive factors by raising awareness and implementing a diagnosis and care protocol with healthcare professionals to guide and orient distressed women. Full article

Muscular temporomandibular joint disorders (M-TMDs) encompass a wide range of painful muscular conditions, which can provoke functional limitation and severely affect quality of life. The aim of the present study was to assess the treatment outcomes in patients affected by M-TMDs in terms of pain scores assessed with pressure pain threshold (PPT). The levels of depression, anxiety, and the Oral Health Impact Profile were also assessed and compared to healthy controls. Patients with a clinical diagnosis of M-TMDs and a control group of healthy subjects were enrolled. At baseline, OHIP-14, PHQ-9, and GAD-7 were administered. PPT was registered at the level of masseter and temporalis muscles. The patients affected by M-TMDs were then treated with oral splints and physio-kinesiotherapy following a standardized treatment protocol. At the 6-month follow-up of M-TMD-affected patients, PPT was registered, and the questionnaires were re-administered to evaluate treatment outcomes. In total, sixty patients and sixty controls were enrolled. The treatment of M-TMDs produced a significant improvement in PPT at the level of the masseter muscle. OHIP-14 at baseline in the M-TMD group was significantly higher compared to the control group (p < 0.05). At the 6-month follow-up, a significant reduction in OHIP-14 scores was registered, although with higher scores compared to the control group (p < 0.05). PHQ-9 was significantly higher at baseline in the M-TMD group (p < 0.05) and decreased to values comparable to the control group after treatment. GAD-7 presented statistically significant differences between the control group and M-TMD patients at baseline (p < 0.05) and between pre- and post-treatment in the M-TMD group. Following treatment, the GAD-7 scores were comparable to the control group. The symptom burden associated with M-TMDs negatively affects quality of life, with higher oral health impairment and a tendency towards depression and anxiety compared to healthy subjects. Following treatment, an improvement in both PPT and quality of life was observed. Full article

Despite recent efforts to search for biomarkers for the pre-symptomatic diagnosis of Parkinson’s disease (PD), the presence of risk factors, prodromal signs, and family history still support the classification of individuals at risk for this disease. Human epidemiological studies are useful in this search but fail to provide causality. The study of well-known risk factors for PD in animal models can help elucidate mechanisms related to the disease’s etiology and contribute to future prevention or treatment approaches. This narrative review aims to discuss animal studies that investigated four of the main risk factors and/or prodromal signs related to PD: advanced age, male sex, sleep alterations, and depression. Different databases were used to search the studies, which were included based on their relevance to the topic. Although still in a reduced number, such studies are of great relevance in the search for evidence that leads to a possible early diagnosis and improvements in methods of prevention and treatment. Full article

(1) Background: A pre-existing psychiatric condition may impact decision making by patients and/or physicians following a thyroid cancer diagnosis, such as potentially electing surgery over active surveillance, thus shortening the time to cancer removal. This is the first study to investigate the association between pre-existing anxiety and/or depression and time to receive surgical treatment for thyroid cancer. (2) Methods: Retrospective data were collected from 652 surgical thyroid cancer patients at our institution from 2018 to 2020. We investigated the time between thyroid cancer diagnosis and surgery, comparing patients with pre-existing anxiety and/or depression to those without. (3) Results: Patients with anxiety, depression, and both anxiety and depression had a significantly shorter time between diagnosis and surgery (51.6, 57, and 57.4 days, respectively) compared to patients without (111.9 days) (p = 0.002, p = 0.004, p = 0.003, respectively). (4) Conclusions: Although little is known about the impact of pre-existing psychiatric conditions in the decision-making process for thyroid cancer surgery, this present study showed that anxiety and/or depression may lead to more immediate surgical interventions. Thus, psychiatric history may be an important factor for physicians to consider when counseling patients with thyroid cancer. Full article