Locomotive syndrome in rheumatoid arthritis patients during the COVID-19 pandemic.

This study aimed to longitudinally evaluate the development of locomotive syndrome (LS) in rheumatoid arthritis (RA) patients during the COVID-19 pandemic using the 25-question Geriatric Locomotive Function Scale (GLFS-25). Subjects were 286 RA patients (female, 70.6%; mean age, 64.2 years) who had GLFS-25 and Clinical Disease Activity Index (CDAI) data available for a 1-year period during the COVID-19 pandemic and who did not have LS at baseline. Associations between subject characteristics and development of LS were determined using logistic regression analysis. Among the 286 patients, 38 (13.3%, LS group) developed LS at 1 year after baseline. In the LS group, scores of the GLFS-25 categories "GLFS-5" and "Social activities" were significantly increased at 1 year relative to baseline. GLFS-5 is a quick 5-item version of the GLFS-25, including questions regarding the difficulty of going up and down stairs, walking briskly, distance able to walk without rest, difficulty carrying objects weighing 2 kg, and ability to carry out load-bearing tasks and housework. A significant correlation was also observed between changes in "Social activities" and that of "GLFS-5." Multivariable logistic regression analysis revealed that the development of LS was significantly associated with BMI (OR: 1.11 [95% confidence interval (CI): 1.00-1.22]) and CDAI (OR: 1.08 [95%CI: 1.00-1.16]) at baseline. Adequate exercise and tight control of RA disease activity are important for preventing the development of LS in view of restrictions on going out imposed during the COVID-19 pandemic. GLFS-5 is useful for evaluating the physical function of RA patients.

LOng COvid Multidisciplinary consortium Optimising Treatments and servIces acrOss the NHS (LOCOMOTION): protocol for a mixed-methods study in the UK.

INTRODUCTION: Long COVID, a new condition whose origins and natural history are not yet fully established, currently affects 1.5 million people in the UK. Most do not have access to specialist long COVID services. We seek to optimise long COVID care both within and outside specialist clinics, including improving access, reducing inequalities, helping self-management and providing guidance and decision support for primary care. We aim to establish a 'gold standard' of care by systematically analysing current practices, iteratively improving pathways and systems of care. METHODS AND ANALYSIS: This mixed-methods, multisite study is informed by the principles of applied health services research, quality improvement, co-design, outcome measurement and learning health systems. It was developed in close partnership with patients (whose stated priorities are prompt clinical assessment; evidence-based advice and treatment and help with returning to work and other roles) and with front-line clinicians. Workstreams and tasks to optimise assessment, treatment and monitoring are based in three contrasting settings: workstream 1 (qualitative research, up to 100 participants), specialist management in 10 long COVID clinics across the UK, via a quality improvement collaborative, experience-based co-design and targeted efforts to reduce inequalities of access, return to work and peer support; workstream 2 (quantitative research, up to 5000 participants), patient self-management at home, technology-supported monitoring and validation of condition-specific outcome measures and workstream 3 (quantitative research, up to 5000 participants), generalist management in primary care, harnessing electronic record data to study population phenotypes and develop evidence-based decision support, referral pathways and analysis of costs. Study governance includes an active patient advisory group. ETHICS AND DISSEMINATION: LOng COvid Multidisciplinary consortium Optimising Treatments and servIces acrOss the NHS study is sponsored by the University of Leeds and approved by Yorkshire & The Humber-Bradford Leeds Research Ethics Committee (ref: 21/YH/0276). Participants will provide informed consent. Dissemination plans include academic and lay publications, and partnerships with national and regional policymakers. TRIAL REGISTRATION NUMBER: NCT05057260, ISRCTN15022307.

Adolescent THC Treatment Does Not Potentiate the Behavioral Effects in Adulthood of Maternal Immune Activation.

Both in utero exposure to maternal immune activation and cannabis use during adolescence have been associated with increased risk for the development of schizophrenia; however, whether these exposures exert synergistic effects on brain function is not known. In the present study, mild maternal immune activation (MIA) was elicited in mice with prenatal exposure to polyinosinic-polycytidylic acid (poly(I:C)), and ∆9-tetrahydrocannabinol (THC) was provided throughout adolescence in cereal (3 mg/kg/day for 5 days). Neither THC nor MIA pretreatments altered activity in assays used to characterize hyperdopaminergic states in adulthood: amphetamine hyperlocomotion and prepulse inhibition of the acoustic startle reflex. Adolescent THC treatment elicited deficits in spatial memory and enhanced spatial reversal learning in adult female mice in the Morris water maze, while exposure to MIA elicited female-specific deficits in fear extinction learning in adulthood. There were no effects in these assays in adult males, nor were there interactions between THC and MIA in adult females. While doses of poly(I:C) and THC were sufficient to elicit behavioral effects, particularly relating to cognitive performance in females, there was no evidence that adolescent THC exposure synergized with the risk imposed by MIA to worsen behavioral outcomes in adult mice of either sex.

Longitudinal changes in Super League match locomotor and event characteristics: A league-wide investigation over three seasons in rugby league.

The 2019 and 2020 Super League (SL) seasons included several competition rule changes. This study aimed to quantify the difference between the 2018, 2019 and 2020 SL seasons for duration, locomotor and event characteristics of matches. Microtechnology and match event data were analysed from 11 SL teams, comprising 124 players, from 416 competitive matches across a three-year data collection period. Due to an enforced suspension of league competition as a consequence of COVID-19 restrictions, and subsequent rule changes upon return to play, season 2020 was divided into season 2020a (i.e. Pre-COVID suspension) and season 2020b (i.e. Post-COVID suspension). Duration, locomotor variables, and match events were analysed per whole-match and ball-in-play (BIP) periods with differences between seasons determined using mixed-effects models. There were significant (ρ ≤ 0.05) reductions in whole-match and BIP durations for adjustables and backs in 2019 when compared to 2018; albeit the magnitude of reduction was less during BIP analyses. Despite reduced duration, adjustables reported an increased average speed suggesting reduced recovery time between bouts. Both forwards and adjustables also experienced an increase in missed tackles between 2018 and 2019 seasons. When comparing 2019 to 2020a, adjustables and backs increased their average speed and distance whilst all positional groups increased average acceleration both for whole-match and BIP analyses. When comparing 2020a to 2020b, all positional groups experienced reduced average speed and average acceleration for both whole-match and BIP analyses. Forwards experienced an increased number of tackles and carries, adjustables experienced an increased number of carries, and backs experienced an increased number of missed tackles when comparing these variables between season 2020a and 2020b. Rule changes have a greater effect on whole-match duration and locomotor characteristics than those reported during BIP periods which suggests the implemented rule changes have removed stagnant time from matches. Amendments to tackle related rules within matches (e.g., introduction of the 'six-again' rule) increases the number of collision related events such as carries and tackles.

Selective Inhibition of the Interaction between SARS-CoV-2 Spike S1 and ACE2 by SPIDAR Peptide Induces Anti-Inflammatory Therapeutic Responses.

Many patients with coronavirus disease 2019 in intensive care units suffer from cytokine storm. Although anti-inflammatory therapies are available to treat the problem, very often, these treatments cause immunosuppression. Because angiotensin-converting enzyme 2 (ACE2) on host cells serves as the receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), to delineate a SARS-CoV-2-specific anti-inflammatory molecule, we designed a hexapeptide corresponding to the spike S1-interacting domain of ACE2 receptor (SPIDAR) that inhibited the expression of proinflammatory molecules in human A549 lung cells induced by pseudotyped SARS-CoV-2, but not vesicular stomatitis virus. Accordingly, wild-type (wt), but not mutated (m), SPIDAR inhibited SARS-CoV-2 spike S1-induced activation of NF-κB and expression of IL-6 and IL-1ß in human lung cells. However, wtSPIDAR remained unable to reduce activation of NF-κB and expression of proinflammatory molecules in lungs cells induced by TNF-α, HIV-1 Tat, and viral dsRNA mimic polyinosinic-polycytidylic acid, indicating the specificity of the effect. The wtSPIDAR, but not mutated SPIDAR, also hindered the association between ACE2 and spike S1 of SARS-CoV-2 and inhibited the entry of pseudotyped SARS-CoV-2, but not vesicular stomatitis virus, into human ACE2-expressing human embryonic kidney 293 cells. Moreover, intranasal treatment with wtSPIDAR, but not mutated SPIDAR, inhibited lung activation of NF-κB, protected lungs, reduced fever, improved heart function, and enhanced locomotor activities in SARS-CoV-2 spike S1-intoxicated mice. Therefore, selective targeting of SARS-CoV-2 spike S1-to-ACE2 interaction by wtSPIDAR may be beneficial for coronavirus disease 2019.

Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study.

Early and self-identification of locomotive degradation facilitates us with awareness and motivation to prevent further deterioration. We propose the usage of nine squat and four one-leg standing exercise features as input parameters to Machine Learning (ML) classifiers in order to perform lower limb skill assessment. The significance of this approach is that it does not demand manpower and infrastructure, unlike traditional methods. We base the output layer of the classifiers on the Short Test Battery Locomotive Syndrome (STBLS) test used to detect Locomotive Syndrome (LS) approved by the Japanese Orthopedic Association (JOA). We obtained three assessment scores by using this test, namely sit-stand, 2-stride, and Geriatric Locomotive Function Scale (GLFS-25). We tested two ML methods, namely an Artificial Neural Network (ANN) comprised of two hidden layers with six nodes per layer configured with Rectified-Linear-Unit (ReLU) activation function and a Random Forest (RF) regressor with number of estimators varied from 5 to 100. We could predict the stand-up and 2-stride scores of the STBLS test with correlation of 0.59 and 0.76 between the real and predicted data, respectively, by using the ANN. The best accuracies (R-squared values) obtained through the RF regressor were 0.86, 0.79, and 0.73 for stand-up, 2-stride, and GLFS-25 scores, respectively.

Effects of long-term social isolation on central, behavioural and metabolic parameters in middle-aged mice.

Social isolation gained discussion momentum due to the COVID-19 pandemic. Whereas many studies address the effects of long-term social isolation in post-weaning and adolescence and for periods ranging from 4 to 12 weeks, little is known about the repercussions of adult long-term social isolation in middle age. Thus, our aim was to investigate how long-term social isolation can influence metabolic, behavioural, and central nervous system-related areas in middle-aged mice. Adult male C57Bl/6 mice (4 months-old) were randomly divided into Social (2 cages, n = 5/cage) and Isolated (10 cages, n = 1/cage) housing groups, totalizing 30 weeks of social isolation, which ended concomitantly with the onset of middle age of mice. At the end of the trial, metabolic parameters, short-term memory, anxiety-like behaviour, and physical activity were assessed. Immunohistochemistry in the hippocampus (ΔFosB, BDNF, and 8OHDG) and hypothalamus (ΔFosB) was also performed. The Isolated group showed impaired memory along with a decrease in hippocampal ΔFosB at dentate gyrus and in BDNF at CA3. Food intake was also affected, but the direction depended on how it was measured in the Social group (individually or in the group) with no alteration in ΔFosB at the hypothalamus. Physical activity parameters increased with chronic isolation, but in the light cycle (inactive phase), with some evidence of anxiety-like behaviour. Future studies should better explore the timepoint at which the alterations found begin. In conclusion, long-term social isolation in adult mice contributes to alterations in feeding, physical activity pattern, and anxiety-like behaviour. Moreover, short-term memory deficit was associated with lower levels of hippocampal ΔFosB and BDNF in middle age.

Thinking clearly about social aspects of infectious disease transmission.

Social and cultural forces shape almost every aspect of infectious disease transmission in human populations, as well as our ability to measure, understand, and respond to epidemics. For directly transmitted infections, pathogen transmission relies on human-to-human contact, with kinship, household, and societal structures shaping contact patterns that in turn determine epidemic dynamics. Social, economic, and cultural forces also shape patterns of exposure, health-seeking behaviour, infection outcomes, the likelihood of diagnosis and reporting of cases, and the uptake of interventions. Although these social aspects of epidemiology are hard to quantify and have limited the generalizability of modelling frameworks in a policy context, new sources of data on relevant aspects of human behaviour are increasingly available. Researchers have begun to embrace data from mobile devices and other technologies as useful proxies for behavioural drivers of disease transmission, but there is much work to be done to measure and validate these approaches, particularly for policy-making. Here we discuss how integrating local knowledge in the design of model frameworks and the interpretation of new data streams offers the possibility of policy-relevant models for public health decision-making as well as the development of robust, generalizable theories about human behaviour in relation to infectious diseases.

Untangling introductions and persistence in COVID-19 resurgence in Europe.

After the first wave of SARS-CoV-2 infections in spring 2020, Europe experienced a resurgence of the virus starting in late summer 2020 that was deadlier and more difficult to contain1. Relaxed intervention measures and summer travel have been implicated as drivers of the second wave2. Here we build a phylogeographical model to evaluate how newly introduced lineages, as opposed to the rekindling of persistent lineages, contributed to the resurgence of COVID-19 in Europe. We inform this model using genomic, mobility and epidemiological data from 10 European countries and estimate that in many countries more than half of the lineages circulating in late summer resulted from new introductions since 15 June 2020. The success in onward transmission of newly introduced lineages was negatively associated with the local incidence of COVID-19 during this period. The pervasive spread of variants in summer 2020 highlights the threat of viral dissemination when restrictions are lifted, and this needs to be carefully considered in strategies to control the current spread of variants that are more transmissible and/or evade immunity. Our findings indicate that more effective and coordinated measures are required to contain the spread through cross-border travel even as vaccination is reducing disease burden.

Impacts of social distancing policies on mobility and COVID-19 case growth in the US.

Social distancing remains an important strategy to combat the COVID-19 pandemic in the United States. However, the impacts of specific state-level policies on mobility and subsequent COVID-19 case trajectories have not been completely quantified. Using anonymized and aggregated mobility data from opted-in Google users, we found that state-level emergency declarations resulted in a 9.9% reduction in time spent away from places of residence. Implementation of one or more social distancing policies resulted in an additional 24.5% reduction in mobility the following week, and subsequent shelter-in-place mandates yielded an additional 29.0% reduction. Decreases in mobility were associated with substantial reductions in case growth two to four weeks later. For example, a 10% reduction in mobility was associated with a 17.5% reduction in case growth two weeks later. Given the continued reliance on social distancing policies to limit the spread of COVID-19, these results may be helpful to public health officials trying to balance infection control with the economic and social consequences of these policies.

Mental Health and Behavior of College Students During the COVID-19 Pandemic: Longitudinal Mobile Smartphone and Ecological Momentary Assessment Study, Part II.

BACKGROUND: Since late 2019, the lives of people across the globe have been disrupted by COVID-19. Millions of people have become infected with the disease, while billions of people have been continually asked or required by local and national governments to change their behavioral patterns. Previous research on the COVID-19 pandemic suggests that it is associated with large-scale behavioral and mental health changes; however, few studies have been able to track these changes with frequent, near real-time sampling or compare these changes to previous years of data for the same individuals. OBJECTIVE: By combining mobile phone sensing and self-reported mental health data in a cohort of college-aged students enrolled in a longitudinal study, we seek to understand the behavioral and mental health impacts associated with the COVID-19 pandemic, measured by interest across the United States in the search terms coronavirus and COVID fatigue. METHODS: Behaviors such as the number of locations visited, distance traveled, duration of phone use, number of phone unlocks, sleep duration, and sedentary time were measured using the StudentLife mobile smartphone sensing app. Depression and anxiety were assessed using weekly self-reported ecological momentary assessments, including the Patient Health Questionnaire-4. The participants were 217 undergraduate students. Differences in behaviors and self-reported mental health collected during the Spring 2020 term, as compared to previous terms in the same cohort, were modeled using mixed linear models. RESULTS: Linear mixed models demonstrated differences in phone use, sleep, sedentary time and number of locations visited associated with the COVID-19 pandemic. In further models, these behaviors were strongly associated with increased interest in COVID fatigue. When mental health metrics (eg, depression and anxiety) were added to the previous measures (week of term, number of locations visited, phone use, sedentary time), both anxiety and depression (P<.001) were significantly associated with interest in COVID fatigue. Notably, these behavioral and mental health changes are consistent with those observed around the initial implementation of COVID-19 lockdowns in the spring of 2020. CONCLUSIONS: In the initial lockdown phase of the COVID-19 pandemic, people spent more time on their phones, were more sedentary, visited fewer locations, and exhibited increased symptoms of anxiety and depression. As the pandemic persisted through the spring, people continued to exhibit very similar changes in both mental health and behaviors. Although these large-scale shifts in mental health and behaviors are unsurprising, understanding them is critical in disrupting the negative consequences to mental health during the ongoing pandemic.

Social isolation is closely linked to a marked reduction in physical activity in male mice.

The effects of social isolation on an individual's behavior is an important field of research, especially as public health officials encourage social distancing to prevent the spread of pandemic disease. In this study we evaluate the effects of social isolation on physical activity in mice. Utilizing a pixel-based tracking system, we continuously monitored the movement of isolated mice compared with paired cage mates in the home cage environment. We demonstrate that mice that are socially isolated dramatically decrease their movement when separated from their cage mate, and especially in the dark cycle, when mice are normally most active. When isolated mice are re-paired with their original cage mate, this effect is reversed, and mice return to their prior levels of activity. These findings suggest a close link between social isolation and physical activity, and are of particular interest in the wake of coronavirus disease 2019, when many are forced into isolation. Social isolation may affect an individual's overall activity levels in humans too, which may have unintended effects on health that deserve further consideration.

Risk attitudes and human mobility during the COVID-19 pandemic.

Behavioural responses to pandemics are less shaped by actual mortality or hospitalisation risks than they are by risk attitudes. We explore human mobility patterns as a measure of behavioural responses during the COVID-19 pandemic. Our results indicate that risk-taking attitudes are a critical factor in predicting reductions in human mobility and social confinement around the globe. We find that the sharp decline in mobility after the WHO (World Health Organization) declared COVID-19 to be a pandemic can be attributed to risk attitudes. Our results suggest that regions with risk-averse attitudes are more likely to adjust their behavioural activity in response to the declaration of a pandemic even before official government lockdowns. Further understanding of the basis of responses to epidemics, e.g., precautionary behaviour, will help improve the containment of the spread of the virus.

Mobility network models of COVID-19 explain inequities and inform reopening.

The coronavirus disease 2019 (COVID-19) pandemic markedly changed human mobility patterns, necessitating epidemiological models that can capture the effects of these changes in mobility on the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)1. Here we introduce a metapopulation susceptible-exposed-infectious-removed (SEIR) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas. Our mobility networks are derived from mobile phone data and map the hourly movements of 98 million people from neighbourhoods (or census block groups) to points of interest such as restaurants and religious establishments, connecting 56,945 census block groups to 552,758 points of interest with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in the behaviour of the population over time. Our model predicts that a small minority of 'superspreader' points of interest account for a large majority of the infections, and that restricting the maximum occupancy at each point of interest is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2-8 solely as the result of differences in mobility: we find that disadvantaged groups have not been able to reduce their mobility as sharply, and that the points of interest that they visit are more crowded and are therefore associated with higher risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more-effective and equitable policy responses to COVID-19.

Kinetic Monte Carlo model for the COVID-19 epidemic: Impact of mobility restriction on a COVID-19 outbreak.

As the coronavirus disease 2019 (COVID-19) spreads worldwide, epidemiological models have been employed to evaluate possible scenarios and gauge the efficacy of proposed interventions. Considering the complexity of disease transmission dynamics in cities, stochastic epidemic models include uncertainty in their treatment of the problem, allowing the estimation of the probability of an outbreak, the distribution of epidemic magnitudes, and their expected duration. In this sense, we propose a kinetic Monte Carlo epidemic model that focuses on demography and on age-structured mobility data to simulate the evolution of the COVID-19 outbreak in the capital of Brazil, Brasilia, under several scenarios of mobility restriction. We show that the distribution of epidemic outcomes can be divided into short-lived mild outbreaks and longer severe ones. We demonstrate that quarantines have the effect of reducing the probability of a severe outbreak taking place but are unable to mitigate the magnitude of these outbreaks once they happen. Finally, we present the probability of a particular trajectory in the epidemic progression resulting in a massive outbreak as a function of the cumulative number of cases at the end of each quarantine period, allowing for the estimation of the risk associated with relaxing mobility restrictions at a given time.

Wearable Activity Trackers for Monitoring Adherence to Home Confinement During the COVID-19 Pandemic Worldwide: Data Aggregation and Analysis.

BACKGROUND: In the context of home confinement during the coronavirus disease (COVID-19) pandemic, objective, real-time data are needed to assess populations' adherence to home confinement to adapt policies and control measures accordingly. OBJECTIVE: The aim of this study was to determine whether wearable activity trackers could provide information regarding users' adherence to home confinement policies because of their capacity for seamless and continuous monitoring of individuals' natural activity patterns regardless of their location. METHODS: We analyzed big data from individuals using activity trackers (Withings) that count the wearer's average daily number of steps in a number of representative nations that adopted different modalities of restriction of citizens' activities. RESULTS: Data on the number of steps per day from over 740,000 individuals around the world were analyzed. We demonstrate the physical activity patterns in several representative countries with total, partial, or no home confinement. The decrease in steps per day in regions with strict total home confinement ranged from 25% to 54%. Partial lockdown (characterized by social distancing measures such as school closures, bar and restaurant closures, and cancellation of public meetings but without strict home confinement) does not appear to have a significant impact on people's activity compared to the pre-pandemic period. The absolute level of physical activity under total home confinement in European countries is around twofold that in China. In some countries, such as France and Spain, physical activity started to gradually decrease even before official commitment to lockdown as a result of initial less stringent restriction orders or self-quarantine. However, physical activity began to increase again in the last 2 weeks, suggesting a decrease in compliance with confinement orders. CONCLUSIONS: Aggregate analysis of activity tracker data with the potential for daily updates can provide information regarding adherence to home confinement policies.