I'm alone but not lonely. U-shaped pattern of self-perceived loneliness during the COVID-19 pandemic in the UK and Greece

Objectives: In the past months, many countries have adopted varying degrees of lockdown restrictions to control the spread of the COVID-19 virus. According to the existing literature, some consequences of lockdown restrictions on people's lives are beginning to emerge yet the evolution of such consequences in relation to the time spent in lockdown is understudied. To inform policies involving lockdown restrictions, this study adopted a data-driven Machine Learning approach to uncover the short-term time-related effects of lockdown on people's physical and mental health. // Study design: An online questionnaire was launched on 17 April 2020, distributed through convenience sampling and was self-completed by 2,276 people from 66 different countries. // Methods: Focusing on the UK sample (N = 325), 12 aggregated variables representing the participant's living environment, physical and mental health were used to train a RandomForest model to estimate the week of survey completion. // Results: Using an index of importance, Self-Perceived Loneliness was identified as the most influential variable for estimating the time spent in lockdown. A significant U-shaped curve emerged for loneliness levels, with lower scores reported by participants who took part in the study during the 6th lockdown week (p = 0.009). The same pattern was replicated in the Greek sample (N = 137) for week 4 (p = 0.012) and 6 (p = 0.009) of lockdown. // Conclusions: From the trained Machine Learning model and the subsequent statistical analysis, Self-Perceived Loneliness varied across time in lockdown in the UK and Greek populations, with lower symptoms reported during the 4th and 6th lockdown weeks. This supports the dissociation between social support and loneliness, and suggests that social support strategies could be effective even in times of social isolation

Self-Perceived Loneliness and Depression During the COVID-19 Pandemic: a Two-Wave Replication Study

COVID-19 studies to date have documented some of the initial health consequences of lockdown restrictions adopted by many countries. Combining a data-driven machine learning paradigm and a statistical analysis approach, our previous paper documented a U-shape pattern in levels of self-perceived loneliness in both the UK and Greek populations during the first lockdown (17 April to 17 July 2020). The current paper aimed to test the robustness of these results. Specifically, we tested a) for the dependence of the chosen model by adopting a new one - namely, support vector regressor (SVR). Furthermore, b) whether the patterns of self-perceived loneliness found in data from the first UK national lockdown could be generalizable to the second wave of the UK lockdown (17 October 2020 to 31 January 2021). The first part of the study involved training an SVR model on the 75% of the UK dataset from wave 1 (n total = 435). This SVR model was then tested on the remaining 25% of data (MSE training = 2.04; MSE test = 2.29), which resulted in depressive symptoms to be the most important variable - followed by self-perceived loneliness. Statistical analysis of depressive symptoms by week of lockdown resulted in a significant U-shape pattern between week 3 to 7 of lockdown. In the second part of the study, data from wave 2 of the UK lockdown (n = 263) was used to conduct a graphical and statistical inspection of the week-by-week distribution of scores regarding self-perceived loneliness. Despite a graphical U-shaped pattern between week 3 and 9 of lockdown, levels of loneliness were not between weeks of lockdown. Consistent with past studies, study findings suggest that self-perceived loneliness and depressive symptoms may be two of the most relevant symptoms to address when imposing lockdown restrictions

I’m alone but not lonely. U-shaped pattern of perceived loneliness during the COVID-19 pandemic in the UK and Greece

Many countries have adopted lengthy lockdown measures to mitigate the spreading of the COVID-19 virus. In this study, we train a RandomForest model using 10 variables quantifying individuals’ living environment, physical and mental health statuses to predict how long each of the UK participants (N=382) had been in lockdown. Self-perceived loneliness was found to be the most important variable predicting time in lockdown and, therefore, the aspect most influenced by the time the participant spent in lockdown. Subsequent statistical analysis showed a significant U-shaped curve for the levels of perceived loneliness (p<0.012), specifically decreasing during the 4th and 5th lockdown weeks. The same pattern was found on data from Greek citizens (N=129, p<0.041). These results suggest that lockdown measures may have affected how people evaluated their social support while in lockdown, leading to a decreased sense of loneliness. Implications of this study should be reflected on policies and countermeasures to current and future pandemics. State of relevance This study aims to inform policies for the current and/or future pandemics, particularly those involving lockdown restrictions. It highlights that self-perceived loneliness was the trait most affected by the time spent in lockdown: data show that the very first period of lockdown was characterised by a decrease in levels of perceived loneliness. The machine learning approach adopted and the statistical validation on two different Western European countries ensure that the uncovered pattern is substantial. This result highlights the dissociation between objective social support and perceived loneliness: initially, restrictions may have triggered better social behaviours among communities or increased the level of gratitude for the social support people have always received. The short duration of these desirable effects suggests that measures and campaigns promoting better social support strategies could be potentially effective, even in social isolation, to keep the levels of perceived loneliness low. Competing Interest Statement The authors have declared no competing interest. Funding Statement This research is supported by Nanyang Technological University (Singapore) under the NAP-SUG grant (G.E.). Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethical approval for the COVID-19 Social Study was granted by the UCL Ethics Committee (REC 1331). The study is GDPR compliant. All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Ye

Parenting Stress Undermines Mother-Child Brain-to-Brain Synchrony: A Hyperscanning Study

Synchrony refers to the coordinated interplay of behavioural and physiological signals that reflect the bi-directional attunement of one partner to the other's psychophysiological, cognitive, emotional, and behavioral state. In mother-child relationships, a synchronous pattern of interaction indicates parental sensitivity. Parenting stress has been shown to undermine mother-child behavioural synchrony. However, it has yet to be discerned whether parenting stress affects brain-to-brain synchrony during everyday joint activities. Here, we show that greater parenting stress is associated with less brain-to-brain synchrony in the medial left cluster of the prefrontal cortex when mother and child engage in a typical dyadic task of watching animation videos together. This brain region overlaps with the inferior frontal gyrus, the frontal eye field, and the dorsolateral prefrontal cortex, which are implicated in inference of mental states and social cognition. Our result demonstrates the adverse effect of parenting stress on mother-child attunement that is evident at a brain-to-brain level. Mother-child brain-to-brain asynchrony may underlie the robust association between parenting stress and poor dyadic co-regulation. We anticipate our study to form the foundation for future investigations into mechanisms by which parenting stress impairs the mother-child relationship

3D reconstruction of the crural and thoracolumbar fasciae

PURPOSE: To create computerized three-dimensional models of the crural fascia and of the superficial layer of the thoracolumbar fascia. METHODS: Serial sections of these two fasciae, stained with Azan-Mallory, van Gieson and anti-S100 antibody stains, were recorded. The resulting images were merged (Image Zone 5.0 software) and aligned (MatLab Image Processing Toolkit). Color thresholding was applied to identify the structures of interest. 3D models were obtained with Tcl/Tk scripts and Paraview 3.2.1 software. From these models, the morphometric features of these fasciae were evaluated with ImageJ. RESULTS: In the crural fascia, collagen fibers represent less than 20% of the total volume, arranged in three distinct sub-layers (mean thickness, 115 ?m), separated by a layer of loose connective tissue (mean thickness, 43 ?m). Inside a single sub-layer, all the fibers are parallel, whereas the angle between the fibers of adjacent layers is about 78\ub0. Elastic fibers are less than 1%. Nervous fibers are mostly concentrated in the middle layer. The superficial layer of the thoracolumbar fascia is also formed of three thinner sub-layers, but only the superficial one is similar to the crural fascia sub-layers, the intermediate one is similar to a flat tendon, and the deep one is formed of loose connective tissue. Only the superficial sub-layer has rich innervation and a few elastic fibers. DISCUSSION: Computerized three-dimensional models provide a detailed representation of the fascial structure, for better understanding of the interactions among the different components. This is a fundamental step in understanding the mechanical behavior of the fasciae and their role in pathology

Autonomic activity and surgical flow disruptions in healthcare providers during cardiac surgery

Cardiac surgery represents a complex sociotechnical environment relying on a combination of technical and non-technical team-based expertise. Surgical flow disruptions (SFDs) may be influenced by a variety of sources, including social, environmental, and emotional factors affecting healthcare providers (HCPs). Many of these factors can be readily observed, except for emotional factors (i.e. distress), which represents an underappreciated yet critical source of SFDs. The aim of this study was to demonstrate the sensitivity of autonomic activity metrics to detect an SFD during cardiac surgery. We integrated heart rate variability (HRV) analysis with observation-based annotations to allow data triangulation. Following a critical medication administration error by the anesthesiologist in-training, data sources were consulted to identify events precipitating this near- miss event. Using pyphysio, an open-source physiological signal processing package, we analyzed the attending anesthesiologists’ HRV, specifically the low frequency (LF) power, high frequency (HF) power, LF/HF ratio, standard deviation of normal-to-normal (SDNN), and root mean square of the successive differences (RMSSD) as indicators of ANS activity. A heightened SNS response in the attending anesthesiologists’ physiological arousal was observed as elevations in LF power and LF/HF ratio, as well as depressions in HF power, SDNN, and RMSSD prior to the near- miss event. The attending anesthesiologist subjectively confirmed a state of high distress induced by task-irrelevant environmental factors during this time. Qualitative analysis of audio/video recordings objectively revealed that the autonomic nervous system (ANS) activation detected was temporally associated with an argument over operating room management. This study confirms that it is possible to recognize detrimental psychophysiological influences in cardiac surgery procedures via advanced HRV analysis. To our knowledge, ours is the first such case demonstrating ANS activity coinciding with strong self-reported emotion during live surgery using HRV. Despite extensive experience in the cardiac OR, transient but intense emotional changes may have the potential to disrupt attention processes in even the most experienced HCP. A primary implication of this work is the possibility to detect real-time ANS activity, which could enable personalized interventions to proactively mitigate downstream adverse events. Additional studies on our large database of surgical cases are underway and new studies are actively being planned to confirm this preliminary observation.Accepted versio

Integrating deep and radiomics features in cancer bioimaging

Almost every clinical specialty will use artificial intelligence in the future. The first area of practical impact is expected to be the rapid and accurate interpretation of image streams such as radiology scans, histo-pathology slides, ophthalmic imaging, and any other bioimaging diagnostic systems, enriched by clinical phenotypes used as outcome labels or additional descriptors. In this study, we introduce a machine learning framework for automatic image interpretation that combines the current pattern recognition approach (“radiomics”) with Deep Learning (DL). As a first application in cancer bioimaging, we apply the framework for prognosis of locoregional recurrence in head and neck squamous cell carcinoma (N=298)from Computed Tomography (CT)and Positron Emission Tomography (PET)imaging. The DL architecture is composed of two parallel cascades of Convolutional Neural Network (CNN)layers merging in a softmax classification layer. The network is first pretrained on head and neck tumor stage diagnosis, then fine-tuned on the prognostic task by internal transfer learning. In parallel, radiomics features (e.g., shape of the tumor mass, texture and pixels intensity statistics)are derived by predefined feature extractors on the CT/PET pairs. We compare and mix deep learning and radiomics features into a unifying classification pipeline (RADLER), where model selection and evaluation are based on a data analysis plan developed in the MAQC initiative for reproducible biomarkers. On the multimodal CT/PET cancer dataset, the mixed deep learning/radiomics approach is more accurate than using only one feature type, or image mode. Further, RADLER significantly improves over published results on the same data

Feasibility of healthcare providers' autonomic activation recognition in real-life cardiac surgery using noninvasive sensors

Cardiac surgery is one of the most complex specialties in medicine, akin to a complex sociotechnical system. Patient outcomes are vulnerable to surgical flow disruptions (SFDs), a source of preventable harm. Healthcare providers’ (HCPs) sympathetic activation secondary to emotional states represent an underappreciated source of SFDs. This study’s objective was to demonstrate the feasibility of detecting elevated sympathetic nervous system (SNS) activity as a proxy for emotional distress associated with a medication error using heart rate variability (HRV) analysis. After obtaining informed consent, audio/video and HRV data were captured intraoperatively during cardiac surgery from multiple HCPs. Following a critical medication administration error by the anesthesiologist in-training, the attending anesthesiologists’ recorded HRV data was analyzed using pyphysio, an open-source signal analysis package, to identify events precipitating this near-miss event. We considered elevated low-frequency/high-frequency (LF/HF) HRV ratio (normal value <2) as a primary indicator of SNS activity and emotional distress. A heightened SNS response by the attending anesthesiologist, observed as an LF/HF ratio value of 3.39, was detected prior to the near-miss event. The attending anesthesiologist confirmed a state of significant SNS activity/distress induced by task-irrelevant environmental factors, which led to a temporarily ineffective mental model. Qualitative analysis of audio/video recordings revealed that SNS activation coincided with an argument over operating room management causing SFD. This preliminary study confirms the feasibility of recognizing potentially detrimental psychophysiological states during cardiac surgery in the wild using HRV analysis. To our knowledge, this is the first case demonstrating SNS activation coinciding with self-reported and observable emotional distress during live surgery using HRV. Irrespective of the HCP’s expertise, transient but intense emotional changes may disrupt attention processes leading to SFDs and preventable errors. This work supports the possibility to detect real-time SNS activation, which could enable interventions to proactively mitigate errors. Additional studies on our large database of surgical cases are underway to confirm this observation.Accepted versio

Computational methods for the assessment of empathic synchrony

The synchronization of physiological signals between persons is a well-known proxy of empathy. However, it is also influenced by physiological and environmental factors that should be discriminated to correctly characterize the empathy component. We discuss a framework to compute synchrony and introduce physynch, an open-source package developed to easily replicate its computational procedures. We adopted physynch to study the synchrony of the electrodermal activity in 61 male-female dyads with different types of relationship: strangers (18 dyads), friends (23 dyads) and lovers (20 dyads). The findings confirm previous results on Heart Rate Variability synchrony and suggest that synchrony is influenced by the type of relationship and is stronger in dyads of strangers. physynch is made available for download and use for researchers interested in measuring physiological synchrony.Accepted versio