Gender differences on psychological factors in fibromyalgia: a systematic review on the male experience.

OBJECTIVES: Fibromyalgia (FM) is highly prevalent in the female gender. Scarce attention has been given to the exploration and description of this syndrome, from a psychological point of view, when occurring in males. The aim of the present study is to develop further knowledge, and to summarise the literature regarding subjective psychological experience, characteristics of symptoms presentation (both onset and development), and treatment options for FM in male patients, in order to highlight differences with FM in females. METHODS: All studies published between January 1993 and February 2020 using PubMed and PsycInfo were included, provided that they met the following criteria: 1) written in English; 2) original articles on studies with a longitudinal design; 3) prospective or retrospective, observational (analytical or descriptive), experimental or quasi-experimental, controlled or non-controlled studies. Reviews and non-original articles (i.e. editorials, letters to the editor, and book chapters) were not included. We utilised the following keywords: (male), (female), (fibromyalgia), combined with Boolean operators 'AND' and 'NOT'. RESULTS: We found an initial number of 55 papers. Duplicated records were excluded (n=13), as well as papers not focusing on male patients or not fulfilling the inclusion criteria (n=25), thus narrowing the research to 17 papers. CONCLUSIONS: FM male patients consider their masculine identity as inefficiently re-negotiated after the onset of symptoms. FM males tend to endure pain for longer periods of time than females before seeking treatment; bodily symptoms are prevalent with a compromised exploration of feelings about FM. Unfortunately, there is still a paucity of evidence on clinical characterisation and treatment options when FM occurs in males. Moreover, no studies have addressed the issue of the psychopharmacological/non-pharmacological management of males with FM and comorbid psychiatric syndromes.

Catastrophisation, chronic pain and sexuality: a cross-sectional investigation in fibromyalgia and rheumatoid arthritis.

OBJECTIVES: In the present study we investigate the putative differences in pain catastrophising (PC), pain perception (PP), sexual functioning (SF), satisfaction (SS), and overall quality of life between fibromyalgia (FM) and rheumatoid arthritis (RA) patients as compared to healthy controls (HC). METHODS: Fifty-seven native Italian-speaking female individuals suffering either from FM or RA and thirty-eight healthy female controls (FM = 40; RA = 17; HC = 38) were submitted to a semi-structured interview aimed at assessing PP intensity (Visual Analog Scale; VAS), general health conditions (36-items Short-Form Health Survey; SF-36), PC (Pain Catastrophising Scale; PCS), SF and SS (Index of Sexual Satisfaction; ISS/ Female Sexual Function Index; FSFI). RESULTS: FM patients had a significantly higher PP both as compared to RA and HC (p<0.002 for both), and higher PC as compared to HC but not as compared to RA patients (p<0.03 and p<0.64). When compared to RA patients and HC, they showed a lower quality of life (p<0.002 for both comparisons), a compromised SF (p<0.003 and p<0.002, respectively) and a lower index of SS with respect to HC (p<0.002). RA patients had higher PP (VAS; p<0.002), lower quality of life and SF as compared to HC (p<0.002 and p<0.003, respectively). CONCLUSIONS: FM and RA patients showed a significantly lower quality of life, SF and SS as compared to HC. PC was significantly related to PP and low quality of life in FM patients while in RA patients it negatively affected quality of life and especially the sexual sphere both when considering SF and SS.

Machine learning in liver transplantation: a tool for some unsolved questions?

Machine learning has recently been proposed as a useful tool in many fields of Medicine, with the aim of increasing diagnostic and prognostic accuracy. Models based on machine learning have been introduced in the setting of solid organ transplantation too, where prognosis depends on a complex, multidimensional and nonlinear relationship between variables pertaining to the donor, the recipient and the surgical procedure. In the setting of liver transplantation, machine learning models have been developed to predict pretransplant survival in patients with cirrhosis, to assess the best donor-to-recipient match during allocation processes, and to foresee postoperative complications and outcomes. This is a narrative review on the role of machine learning in the field of liver transplantation, highlighting strengths and pitfalls, and future perspectives.

Numerical Activities of Daily Living - Financial: a short version.

Financial capacity is the ability to manage money and finances according to a person's values and self-interests. In Italy, the first instrument specifically designed to assess financial capacity has recently been developed: the Numerical Activities of Daily Living - Financial (NADL-F). The aims of this study are (1) to prepare a shorter version of NADL-F and (2) to examine the relationship between the new short version and a measure of general cognitive functioning of comparable length, the MMSE. After an item analysis performed on NADL-F, the items presenting a higher internal consistency were selected. The resulting NADL-F Short is a reliable, easy to use, and quick to administer tool for assessing financial capacity both in clinical and legal practice. Correlation analysis showed a low positive correlation between four NADL-F Short tasks and the MMSE, for the healthy participants group. For the patient group, instead, a low-moderate correlation was found for all the NADL-F Short tasks, except one. Many participants scoring high in the MMSE (both healthy controls and neurological patients) showed low NADL-F Short scores. These findings suggest that it is not correct to use generic tools like the MMSE to make inferences on a person's financial capacity.

The detection of faked identity using unexpected questions and choice reaction times.

The identification of faked identities, especially within the Internet environment, still remains a challenging issue both for companies and researchers. Recently, however, latency-based lie detection techniques have been developed to evaluate whether the respondent is the real owner of a certain identity. Among the paradigms applied to this purpose, the technique of asking unexpected questions has proved to be useful to differentiate liars from truth-tellers. The aim of the present study was to assess whether a choice reaction times (RT) paradigm, combined with the unexpected question technique, could efficiently detect identity liars. Results demonstrate that the most informative feature in distinguishing liars from truth-tellers is the Inverse Efficiency Score (IES, an index that combines speed and accuracy) to unexpected questions. Moreover, to focus on the predictive power of the technique, machine-learning models were trained and tested, obtaining an out-of-sample classification accuracy of 90%. Overall, these findings indicate that it is possible to detect liars declaring faked identities by asking unexpected questions and measuring RTs and errors, with an accuracy comparable to that of well-established latency-based techniques, such as mouse and keystroke dynamics recording.

Detecting faking-good response style in personality questionnaires with four choice alternatives.

Deliberate attempts to portray oneself in an unrealistic manner are commonly encountered in the administration of personality questionnaires. The main aim of the present study was to explore whether mouse tracking temporal indicators and machine learning models could improve the detection of subjects implementing a faking-good response style when answering personality inventories with four choice alternatives, with and without time pressure. A total of 120 volunteers were randomly assigned to one of four experimental groups and asked to respond to the Virtuous Responding (VR) validity scale of the PPI-R and the Positive Impression Management (PIM) validity scale of the PAI via a computer mouse. A mixed design was implemented, and predictive models were calculated. The results showed that, on the PIM scale, faking-good participants were significantly slower in responding than honest respondents. Relative to VR items, PIM items are shorter in length and feature no negations. Accordingly, the PIM scale was found to be more sensitive in distinguishing between honest and faking-good respondents, demonstrating high classification accuracy (80-83%).

The clinical application of transcranial direct current stimulation in patients with cerebellar ataxia: a systematic review.

AIM: The aim of this review was to investigate the effects of transcranial direct current stimulation (tDCS) on motor function in patients with cerebellar ataxia. MATERIALS AND METHODS: Our systematic review has been performed by searching full-text articles on Pubmed and Scopus. Only studies investigating the motor effects of tDCS in patients with cerebellar ataxias were considered. A qualitative analysis of data was performed, as the methodology of the selected studies was highly heterogeneous. RESULTS: Our search yielded a total of twenty-seven hits. Based on the inclusion criteria, 19 of these were excluded and 89 were retained (number of patients = 81).The results reviewed so far suggest that tDCS over cerebellum combined or not with extra-cerebellar areas might be promising approach to improve motor outcomes, with a greater success in patients less impaired. In particular, it is been shown an improvement in both clinical measures assessing cerebellar deficits (i.e. gait, stance and oculomotor disorders) and performance measures (finger dexterity, upper limb coordination and gait speed). Some of the assessed investigations highlighted a restore effect of cerebellar brain inhibition pathway and resting motor threshold after tDCS. CONCLUSIONS: tDCS could be considered an effective approach to promote plasticity in patient with cerebellar ataxia with significant motor effects. Future studies, with larger sample sizes are needed in order to evaluate the effective tDCS benefits on motor functionality. Due to the limited number of studies available so far, conclusions on the effectiveness of the reported approaches are premature.

Is Interoception Deficit Linking Alexithymia and Eating Spectrum Symptoms? Study on a Non-Clinical Sample of Young Adults.

We investigated if interoceptive deficits could be the link between alexithymic traits and eating spectrum manifestations in a non-clinical sample. One-hundred sixty-one young adults (mean age: 23.2 ± 2.4 years) were evaluated with the Toronto Alexithymia Scale-20 (TAS-20), the Interoceptive Accuracy Scale (IAS), the Interoceptive Confusion Questionnaire (ICQ), and the Eating Attitudes Test-26 (EAT-26). Questionnaires were administered with an online procedure (Microsoft Form, Office 365 A1, Pisa, Italy) (Study Protocol #0012005/2023). We compared ICQ, IAS, and TAS-20 scores in subjects who met the threshold for a potential eating spectrum disorder according to EAT-26 scores ≥ 20 (n = 27) vs. subjects who scored <20 (n = 134), with an ANCOVA corrected for 'age' and 'gender'. Subjects with EAT-26 ≥ 20, scored significantly higher at ICQ (54.4 ± 13.2 vs. 50.2 ± 6.8; p = 0.011), TAS-20 'Total Score' (60.8 ± 11.9 vs. 58.1 ± 9.2; p = 0.006), and TAS-20 'Identifying Feelings' (21.5 ± 7.6 vs. 17.3 ± 5.8; p = 0.0001). A binary logistic regression analysis, with EAT-26 scores < 20 vs. ≥20 as the dependent variable, and ICQ, IAS, TAS-20 total scores and dimensions, age, and gender (categorical) as covariates, showed that the only variable predicting eating spectrum symptomatology was 'ICQ Total Score' (OR = 1.075, 95% CI: 1.016-1.139; p = 0.013). Interoceptive confusion was the dimension linking the occurrence of alexithymic traits and eating spectrum manifestations.

Language models and psychological sciences.

Large language models (LLMs) are demonstrating impressive performance on many reasoning and problem-solving tasks from cognitive psychology. When tested, their accuracy is often on par with average neurotypical adults, challenging long-standing critiques of associative models. Here we analyse recent findings at the intersection of LLMs and cognitive science. Here we discuss how modern LLMs resurrect associationist principles, with abilities like long-distance associations enabling complex reasoning. While limitations remain in areas like causal cognition and planning, phenomena like emergence suggest room for growth. Providing examples and increasing the dimensions of the network are methods that further improve LLM abilities, mirroring facilitation effects in human cognition. Analysis of LLMs errors provides insight into human cognitive biases. Overall, we argue LLMs represent a promising development for cognitive modelling, enabling new explorations of the mechanisms underlying intelligence and reasoning from an associationist point of view. Carefully evaluating LLMs with the tools of cognitive psychology will further understand the building blocks of the human mind.

Human-like problem-solving abilities in large language models using ChatGPT.

Backgrounds: The field of Artificial Intelligence (AI) has seen a major shift in recent years due to the development of new Machine Learning (ML) models such as Generative Pre-trained Transformer (GPT). GPT has achieved previously unheard-of levels of accuracy in most computerized language processing tasks and their chat-based variations. Aim: The aim of this study was to investigate the problem-solving abilities of ChatGPT using two sets of verbal insight problems, with a known performance level established by a sample of human participants. Materials and methods: A total of 30 problems labeled as "practice problems" and "transfer problems" were administered to ChatGPT. ChatGPT's answers received a score of "0" for each incorrectly answered problem and a score of "1" for each correct response. The highest possible score for both the practice and transfer problems was 15 out of 15. The solution rate for each problem (based on a sample of 20 subjects) was used to assess and compare the performance of ChatGPT with that of human subjects. Results: The study highlighted that ChatGPT can be trained in out-of-the-box thinking and demonstrated potential in solving verbal insight problems. The global performance of ChatGPT equalled the most probable outcome for the human sample in both practice problems and transfer problems as well as upon their combination. Additionally, ChatGPT answer combinations were among the 5% of most probable outcomes for the human sample both when considering practice problems and pooled problem sets. These findings demonstrate that ChatGPT performance on both set of problems was in line with the mean rate of success of human subjects, indicating that it performed reasonably well. Conclusions: The use of transformer architecture and self-attention in ChatGPT may have helped to prioritize inputs while predicting, contributing to its potential in verbal insight problem-solving. ChatGPT has shown potential in solving insight problems, thus highlighting the importance of incorporating AI into psychological research. However, it is acknowledged that there are still open challenges. Indeed, further research is required to fully understand AI's capabilities and limitations in verbal problem-solving.

Anti-Vaccination Attitude and Vaccination Intentions Against Covid-19: A Retrospective Cross-Sectional Study Investigating the Role of Media Consumption.

Objective: The present study explores, retrospectively, the link between anti-vaccination attitude and vaccination intentions and extends this relationship to the effect of media consumption style on attitude. Method: Generalized linear mixed-models were used to estimate vaccination intentions (related to each of the four available vaccines at the time of the survey) relying on anti-vaccination attitude measured using the Italian translation of the Vaccination Attitude Examination (VAX) scale. Multiple linear regression was used to estimate which media type and which consumption frequency were responsible for variation in the anti-vaccination attitude. Results: Greater anti-vaccination attitude led to lower vaccination intention (b=-8.33, p<.0001) confirming the attitude-intention link. Crucially, consuming vaccination related information via printed press weekly (b=-0.74, p=.0001) or daily (b=-0.96, p<.0001) were the only protective factors against developing anti-vaccination attitudes. On the other hand, discussing vaccination with the family physician weekly (b=0.66, p=.002) or even daily (b=0.52, p=.026), and actively looking for vaccination related information on specialized websites and blogs every day (b=0.64, t=2.78, p=.006) were risk factors related to increased anti-vaccination attitude. No effects of social media on anti-vaccination attitude were found. Conclusions: The results confirm that vaccination intentions can be linked to the underlying anti-vaccination attitude. Moreover, our results suggest that the web and the blog sphere, but not social media, are the most anti-vaccination fuelling media and that health practitioners engage with the most vaccination-hesitant individuals. Further interventions could leverage these insights to tackle the vaccination hesitancy issue.

Reconstructing individual responses to direct questions: a new method for reconstructing malingered responses.

Introduction: The false consensus effect consists of an overestimation of how common a subject opinion is among other people. This research demonstrates that individual endorsement of questions may be predicted by estimating peers' responses to the same question. Moreover, we aim to demonstrate how this prediction can be used to reconstruct the individual's response to a single item as well as the overall response to all of the items, making the technique suitable and effective for malingering detection. Method: We have validated the procedure of reconstructing individual responses from peers' estimation in two separate studies, one addressing anxiety-related questions and the other to the Dark Triad. The questionnaires, adapted to our scopes, were submitted to the groups of participants for a total of 187 subjects across both studies. Machine learning models were used to estimate the results. Results: According to the results, individual responses to a single question requiring a "yes" or "no" response are predicted with 70-80% accuracy. The overall participant-predicted score on all questions (total test score) is predicted with a correlation of 0.7-0.77 with actual results. Discussion: The application of the false consensus effect format is a promising procedure for reconstructing truthful responses in forensic settings when the respondent is highly likely to alter his true (genuine) response and true responses to the tests are missing.

A Machine Learning Approach Unveils the Relationships between Sickness Behavior and Interoception after Vaccination: Suggestions for Psychometric Indices of Higher Vulnerability.

OBJECTIVE: Prior research has suggested a possible connection between vaccination and manifestations of Sickness Behavior; however, a need remains to first delve deeper into this association and second examine how Interoceptive Awareness and emotional factors may modulate individuals' perceptions of their health status post vaccination. METHOD: An online retrospective cross-sectional survey of 647 individuals who received a COVID-19 vaccination was conducted. Together with vaccination side effects, socio-demographic characteristics, health status, level of concern about vaccination, and Interoceptive Awareness were collected at the baseline level. Mood, sleep, and Sickness Behavior were assessed at baseline and after vaccination. Data were analyzed using inferential statistics and machine learning techniques. RESULTS: After vaccination, there was a significant increase in Sickness Behavior levels (mean (±SD) SicknessQ T0 = 1.57 (±2.72), mean (±SD) SicknessQ T1 = 5.54 (±5.51); p-value = 0.001; ES = 0.77). A Machine Learning analysis revealed specific patterns of individual dispositions (sex and age), baseline emotional characteristics (levels of depression, anxiety, stress, and concern about adverse reactions), as well as some components of Interoceptive Awareness (Noticing, Body Listening, and Attention Regulation), as predictors of high levels of Sickness Behavior, both in terms of overall scores (JRIP: 72.65% accuracy, AUC = 0.692, d = 0.709; F1 = 0.726) and individual items (JRIP: 75.77% accuracy, AUC = 0.694; d = 0.717; F1 = 0.754). CONCLUSIONS: Our results provide new insight into post-immune reactions by highlighting the contribution of Interoceptive Awareness in modulating the severity of Sickness Behavior. This sheds light on the role of awareness of bodily sensations in modulating perceptions of health status, helping to identify the characteristics that make individuals more prone to feeling sick.

COVID-19 Pandemic and Internet Addiction in Young Adults: A Pilot Study on Positive and Negative Psychosocial Correlates.

Objective: During and after the lockdowns of the COVID-19 pandemic period, a strain was put on young adults who have taken refuge in the problematic use of internet, social media, gaming, and gambling. This paper aims to investigate possible correlates of Internet Addiction Disorder (IAD), hypothesizing anxiety, depression and perceived stress would promote addiction, while mindfulness skills, resilience and socialization would, conversely, hamper IAD, and promote, consequently, a more functional internet use. Method: A pilot study was carried out with a sample of 31 young adults, recruited through a snowball sampling using social networks. Participants filled out an online questionnaire including the following measures: Internet Use, Abuse and Addiction (UADI), Beck Depression Inventory (BDI-II); State-Trait Anxiety Inventory (STAI-Y); Perceived Stress Scale (PSS); Five Facet Mindfulness Questionnaire (FFMQ); Mindfulness Attention and Awareness Scale (MAAS); Resilience Scale (RS-14). It was also administered an ad hoc questionnaire for the assessment of socialization behaviour (6 items) and gambling (2 items). Results: All the measures, but socialization, showed adequate reliability. Our sample showed high levels of anxiety, stress, IA and gambling, while presenting low levels of resilience, the mindful skill of no-reactivity and socialization. The first hypothesis was confirmed, finding positive and significant correlations between Internet Addiction on one side and PSS, STAI-Y1, STAY-2, and BDI II on the other side. We also partially confirmed the second hypothesis about UADI negatively correlated with both RS-14 and Mindful Acting. Finally, no correlations were found between Gambling and IA. Conclusions: In conclusion, the more one perceives an emotional overload with less stress-control, high anxiety, and depression, and the less one can leverage on the skills of mindful acting and resilience, the more one uses the internet as a strategy to escape from a threatening reality.

Social Media in Adolescents: A Retrospective Correlational Study on Addiction.

Considering the growing interest in the possible effects of internet's addiction on adoles-cent's mental health, this study aimed at exploring the psychological correlates of social media and internet problematic use during the first year of the covid-19 pandemic. A cross-sectional study was conducted in a sample of secondary school students (N = 258); participants were asked to complete an online survey, investigating social media addiction (BSMAS), self-esteem (RSES), feelings of isolation (CSIQ-A) and anxiety (STAI-Y). Data analysis (descriptive statistics, correlational and regression analyses) was conducted through XLSTAT software ©. An additional ad hoc questionnaire was administrated. Findings showed that the 11% of the participants were significantly addicted to social media, mostly females (59%). Gender represented an exposure factor for the hours spent on social media and the checking activity while performing other daily activities. Significant correlations emerged between the self-report measure of social media addiction and self-esteem and anxiety. Low scores at RSES corresponded to higher checking activity, hours spent on social networks, and playing videogames that were investigated as supplementary indicators of addiction with ad hoc questionnaire. The regression analysis showed just two predictors of social media addiction, gender (female) and trait anxiety. Limitations and implications of the study were argued in order to give some indications for future programs.

Machine learning item selection for short scale construction: A proof-of-concept using the SIMS.

ObjectiveThis proof-of-concept paper provides evidence to support machine learning (ML) as a valid alternative to traditional psychometric techniques in the development of short forms of longer parent psychological tests. ML comprises a variety of feature selection techniques that can be efficiently applied to identify the set of items that best replicates the characteristics of the original test. MethodsIn the present study, we integrated a dataset of 329 participants from published and unpublished datasets used in previous research on the Structured Inventory of Malingered Symptomatology (SIMS) to develop a short version of the scale. The SIMS is a multi-axial self-report questionnaire and a highly efficient psychometric measure of symptom validity, which is frequently applied in forensic settings. Results State-of-the-art ML item selection techniques achieved a 72% reduction in length while capturing 92% of the variance of the original SIMS. The new SIMS short form now consists of 21 items. ConclusionsThe results suggest that the proposed ML-based item selection technique represents a promising alternative to standard psychometric correlation-based methods (i.e. item selection, item response theory), especially when selection techniques (e.g. wrapper) are employed that evaluate global, rather than local, item value.

Cognitive Fusion, Ruminative Response Style and Depressive Spectrum Symptoms in a Sample of University Students.

Psychological inflexibility is related to depressive symptoms through the 'ruminative response style' (RR) and 'cognitive fusion' (CF). We aimed at exploring whether university students were more exposed to CF, RR and depressive symptoms because of their intellectual performance than non-university students of the same age. We compared university students (US) (n = 105) vs. non-university students (NUS) (n = 76) through online administration of the 'Cognitive Fusion Questionnaire' (CFQ-7), the 'Depression-Zung Self-Assessment Scale' (ZSDS) and the 'Perseverative Thinking Questionnaire' (PTQ) (study protocol #0077818/2022, approved by the Ethical Committee of the University of Pisa, Italy). University students scored significantly higher than non-university students in the CFQ-7 Total Score (27.5 ± 9.4 vs. 24.4 ± 9.5; p = 0.040), ZSDS Total Score (41.1 ± 7.7 vs. 39.0 ± 7.3; p = 0.031), PTQ Total Score (26.1 ± 13.1 vs. 21.8 ± 13.9; p = 0.029), PTQ 'Repetitiveness' (5.3 ± 2.8 vs. 4.5 ± 2.9; p = 0.034), 'Intrusiveness' (5.8 ± 3.0 vs. 4.8 ± 3.1; p = 0.046) and 'Repetitive Negative Thinking capturing mental resources' (5.0 ± 3.1 vs. 4.0 ± 3.0; p = 0.013) (MANOVA analysis). In a binary logistic regression analysis of US (with ZSDS scores < 44 vs. ≥44 as the dependent variable, and PTQ Total Score and dimensions, CFQ-7 Total Score, age and gender as the covariates), PTQ Total Score predicted the more severe depressive symptomatology (OR = 1.44, 95% CI: 1.017-2.039; p = 0.040). We believe that RR and CF should be specifically targeted through psychoeducational/psychotherapeutic interventions in university students.

Patients with Chronic Pain: Are Mindfulness Traits Protective Against Distress, Anxiety and Depression?

Objective: To investigate mindfulness traits/attitudes as protective factors against chronic pain related distress, depression and anxiety. Method: Fifty patients (25 with chronic non-oncologic pain-NOP; 25 with chronic oncologic pain-COP) were administered with the following scales: Visual Analogue Scale (VAS), Pain Disability Index (PDI), Italian Questionnaire for Pain (QUID), Perceived Stress Scale (PSS), State and Trait Anxiety Scale (STAY-y1 module), Beck Depression Inventory II (BDI-II), Pittsburgh Sleep Quality Index (PSQI), Psychological General Well Being Index (PGWBI), Mindful Attention Awareness Scale (MAAS). Results: MAAS value ≥ 4.38 was adopted as cut-off to compare 'high level of mindfulness' (HM) vs. 'normal level of mindfulness' (NM) attitudes. Twenty-six patients (52%) scored ≥4.38, with no different distribution between NOP and COP. HM patients scored significantly lower than NM patients on PDI 'family/home responsibilities' domain (4.5±3.2 vs. 6.4±2.8; p<.037), and on PSS (17.8±2.6 vs. 20.9±2.5; p<.0001), STAY-y1 (9.4±1.8 vs. 10.3±2.1; p<.0001), BDI-II (7.8±5.0 vs. 17.6±8.6; p<.0001) total scores. HM scored significantly higher than NM patients in all PGWBI domains. A multiple regression analysis was carried out to analyze the predictor variables of PGWB. The most complete model considered the variables MAAS, STAIy and VAS (F=42.21; p<.0001), that accounted for the 71.6% of PGWB variance. MAAS score was the only variable positively predicting for PGWB; STAIy and VAS scores predicted negatively. Conclusions: Chronic pain patients with high levels of mindfulness attitudes experienced less distress, anxiety, depressive symptoms, and more physical and general wellbeing than patients with low levels of mindfulness attitudes.

Alexithymia and Interoceptive Confusion in Covid-19 Pandemic Distress.

Objective: Alexithymic traits are associated with the difficulty of perceiving 'non-affective interoceptive signals', and are related to a problematic management of stressful life events (SLEs). The main purpose of this study was to quantify the psychological response of the general population to COVID-19 pandemic stress and to evaluate potential correlations with the presence of 'alexithymic traits' and 'interoceptive confusion' (study protocol # 0077794/2022). Method: 175 subjects from general population were assessed with the Toronto-Alexithymia Scale (TAS-20), the Event-Revised Impact Scale (IES-R), the Interoceptive Accuracy Scale (IAS), and the Interoceptive Confusion Questionnaire (ICQ). Results: a significant relationship emerged between alexithymic traits (assessed with the TAS-20), the ICQ 'interoceptive confusion', and the domain of 'hyper-arousal' as assessed with IES-R. Logistic regression model showed that ICQ-Total Score and IES-R 'hyper-arousal' domain were significantly correlated with TAS-20 total score, with DR value (R2 corrected) explaining the 36.8% of the variability (standard error: 10.7). Conclusions: This study, albeit with the limitations of a cross-sectional experimental design with self-evaluation tools in a general population sample, showed a vulnerability to COVID-19 pandemic stress due to high levels of hyper-arousal in subjects with alexithymic traits and interoceptive confusion.

The Hidden Impact of Covid-19 on Memory: Disclosing Subjective Complaints.

Objective: A significant body of research has suggested that the contraction of SARS-CoV-2 may cause memory impairment, even in the months following recovery. In this regard, studies suggest that COVID-19 predominantly targets structures and cortices within the temporal lobe, and the hippocampus, a critical brain structure for memory and spatial navigation.The purpose of this study was to investigate the potential impact of the COVID-19 pandemic on subjective memory complaints, which represent an individual's perception of subtle changes in memory in the absence of an objective memory impairment. Method: to explore how the COVID-19 pandemic may affect subjective memory complaints, we incorporated ad hoc self-reported measures of subjective memory complaints, the "Subjective Memory Complaints Questionnaire" (SMCQ) and the "Prospective and Retrospective Memory Questionnaire" (PRMQ), in our cross-sectional study. Both measures referred to two periods: the pre-pandemic period (T0) and the moment of survey administration (T1) (December 28th, 2021, to February 6th, 2022). Results: 207 Italian participants accessed the survey, out of which 189 participants were included in the final sample. The majority of the participants were females, and their age ranged from 55 to 65 years. The study revealed a significant increase in the total PRMQ score at T1 compared to T0 (p = 0.02). However, no significant differences were found between PRMQ and SMCQ scores of COVID-19-negative individuals and those who tested positive for COVID-19 in the last 12 months from the date of completing the survey. McNemar's test showed a statistically significant increase in the score of item 1 ("Do you think that you have a memory problem?" (p = 0.016) and item 10 ("Do you lose objects more often than you did previously") (0.019) of the SMCQ, while for the PRMQ, significant increases were found in several individual items. Conclusions: our study suggests that subjective memory complaints increased during the pandemic, potentially due to the compound effects of stress and social isolation, rather than solely due to COVID-19 infection. Although a marginal association between COVID-19 and reported prospective memory issues was detected, further investigation is warranted to understand its persistent effects.