Lupus band test for diagnostic evaluation in systemic lupus erythematosus

Background The lupus band test (LBT) using a sample of clinically normal skin was proposed as a useful diagnostic test for systemic lupus erythematosus (SLE). It is mostly performed to help diagnosing SLE in patients with insufficient clinical and serological profiles. However, most published studies on its utility are outdated and the results remain controversial. Objectives To determine the diagnostic performance of LBT on non-lesion sun-protected (NLSP) and sun-exposed (NLSE) skin for SLE. Methods Consecutively presenting patients with clinical and serological suspicion of SLE who had LBT performed on non-lesion skin during January 2012 to August 2021 were retrospectively studied. LBT performed on either NLSE or NLSP skin biopsies were all included. Laboratory characteristics, number, types and patterns of deposited immunoreactants and disease activity were also assessed. Results LBT was performed in 57 patients with suspected SLE. LBT was positive in 18/57, 9/28 and 6/21 patients in overall non-lesion, NLSE and NLSP, respectively. Of all patients, 23 patients were diagnosed with SLE and 34 patients with other diseases. Overall, the sensitivity and specificity of LBT on non-lesion skin was 56.5% and 88.2%, respectively. The ability of the test to discriminate between those with and without SLE, assessed by the area under the Receiver-Operating Characteristic curve, was 0.72 (0.61–0.84). The sensitivity and specificity of LBT on NLSE skin was 58.3% and 87.5% while those of NLSP skin, were 57.1% and 85.7%, respectively. We found no significant correlation between the positivity of LBT and overall disease activity. Types, number and pattern of deposited immunoreactants also showed no correlation with disease activity (all p > 0.05). Conclusions Used as a diagnostic adjunct, non-lesion LBT is still of value for diagnosing SLE in inconclusive cases.

Comparison of Head Impact Exposure Across Common Activities in Youth Soccer

ObjectiveThe objective of this study was to compare head impact exposure across common training activities in soccer.BackgroundSoccer is a popular youth sport in the United States, but repetitive head impacts during training may result in neurocognitive deficits. Current research has identified factors associated with increased head impact exposure in soccer, but research has yet to contextualize head impact exposure across soccer activities. Modifying practice structure may be an avenue for reducing head impact exposure and concussion risk in soccer.Design/MethodsEight U15 soccer players participated in this study for 2 soccer seasons. Players wore a custom instrumented mouthpiece sensor during all practices and games. On-field activities were recorded with a time-synchronized camera. Research personnel recorded the duration of all practice (e.g., technical training, team interaction) and game activities performed by each player, and film review was performed to identify all head contact events during each session. Head impact exposure was quantified in terms of peak kinematics and impacts per player per hour. The amount of time an athlete was exposed to an activity was also evaluated. Mixed effects models were used to compare peak kinematics and generalized linear models were used to compare impact rates across activity types.ResultsActivity types were associated with peak kinematics and impact rate. Technical training activities were associated with higher impact rates and lower mean kinematics compared to other activity types. Team interaction activities and game play were associated with the highest rotational kinematics, but the lowest impact rates. A similar number of player-to-player contact events occurred within technical training, team interaction, and game play activities.ConclusionsInterventions designed to reduce head impact frequency in soccer may benefit from targeting technical training activities; whereas, interventions designed to reduce head impact magnitude may benefit from targeting team interaction and game activities.

HYBRID DISCOURSE AND THE EMERGENCE OF CONTEXT IN BBC’S QUESTION TIME

This paper explores how hybrid discourse, instantiated in talk and interaction, can be shaped not only by a situational context (TV panel show) and cultural context (TV’s increasing democratisation of laity), but also by human volition in pursuit of recognizable to others and allowed within the confi nes of the setting. It does this by examining the emergence of context in light of a non-mainstream hybrid and refl exive activity. Specifically, it examines a non-normative interview format that has arisen in contemporary broadcasting through the analysis of three transcribed segments which were taken from two key episodes of the BBC’s fl agship political program: Question Time. Using a range of analytical concepts from symbolic interactionism, pragmatics, and conversational analysis, such as frames and footings, activity types, discourse types, and turn-taking, the analysis shows how institutional (political) and non-institutional (normative) practices can come together in the pursuit of individual goals and contemporary media’s goal for increasingly partisan journalism and confrontainment. Overall, the paper highlights the importance of a multidimensional approach to context, whereby meaning both emerges from and is constitutive of the forms and functions of an activity’s discourse, whilst further highlighting the role of hybridity in contemporary discourse.

Switching up How You Get in Your Steps: Daily Activity Diversity and Cognitive Functioning

Active lifestyles are related to better cognitive health. More work is needed, however, to examine whether participating in a variety of daily activities (i.e., activity diversity) has unique importance beyond amount of activity. The current study examined associations between daily activity diversity and cognitive functioning among community-dwelling older adults (N = 313, ages 65-90). Participants completed a cognitive battery, then responded to ecological momentary assessments of their participation in 10 common activity types (e.g., exercise, chores, social visits, volunteering) every 3 hours for 5-6 days, and wore accelerometers to track daily step counts and duration of activity. Multiple regression models revealed that greater daily activity diversity related to higher overall cognitive functioning, executive functioning, memory, and crystallized intelligence. These associations remained significant after adjusting for step count and duration of activity. Findings suggest daily activity diversity has unique importance beyond sheer amount of activity for cognitive health in later adulthood.

A study on the impact of the users’ characteristics on the performance of wearable fall detection systems

Wearable Fall Detection Systems (FDSs) have gained much research interest during last decade. In this regard, Machine Learning (ML) classifiers have shown great efficiency in discriminating falls and conventional movements or Activities of Daily Living (ADLs) based on the analysis of the signals captured by transportable inertial sensors. Due to the intrinsic difficulties of training and testing this type of detectors in realistic scenarios and with their target audience (older adults), FDSs are normally benchmarked against a predefined set of ADLs and emulated falls executed by volunteers in a controlled environment. In most studies, however, samples from the same experimental subjects are used to both train and evaluate the FDSs. In this work, we investigate the performance of ML-based FDS systems when the test subjects have physical characteristics (weight, height, body mass index, age, gender) different from those of the users considered for the test phase. The results seem to point out that certain divergences (weight, height) of the users of both subsets (training ad test) may hamper the effectiveness of the classifiers (a reduction of up 20% in sensitivity and of up to 5% in specificity is reported). However, it is shown that the typology of the activities included in these subgroups has much greater relevance for the discrimination capability of the classifiers (with specificity losses of up to 95% if the activity types for training and testing strongly diverge).

Activity-based epidemic propagation and contact network scaling in auto-dependent metropolitan areas

We build on recent work to develop a fully mechanistic, activity-based and highly spatio-temporally resolved epidemiological model which leverages person-trajectories obtained from an activity-based model calibrated for two full-scale prototype cities, consisting of representative synthetic populations and mobility networks for two contrasting auto-dependent city typologies. We simulate the propagation of the COVID-19 epidemic in both cities to analyze spreading patterns in urban networks across various activity types. Investigating the impact of the transit network, we find that its removal dampens disease propagation significantly, suggesting that transit restriction is more critical for mitigating post-peak disease spreading in transit dense cities. In the latter stages of disease spread, we find that the greatest share of infections occur at work locations. A statistical analysis of the resulting activity-based contact networks indicates that transit contacts are scale-free, work contacts are Weibull distributed, and shopping or leisure contacts are exponentially distributed. We validate our simulation results against existing case and mortality data across multiple cities in their respective typologies. Our framework demonstrates the potential for tracking epidemic propagation in urban networks, analyzing socio-demographic impacts and assessing activity- and mobility-specific implications of both non-pharmaceutical and pharmaceutical intervention strategies.

Benefits of Using Activity Recommender Technology for Self-management of Depressive Symptoms

Behavioral Activation (BA)therapy has shown to be effective in treating depression. Recommending healthy activities is a core principle in Behavioral Activation (BA), which is typically done by the therapist. However, most BA smartphone applications do not recommend specific activities. This article reports quantitative results from an 8-week feasibility study of a previously presented smartphone-based BA recommender system. The system supports the planning and enacting of pleasurable activities and promotes activation of diverse activity types. Enrollment included 43 clinically depressed patients who installed the system on their phone and initiated activity scheduling. Twenty-nine patients used the system daily for more than a week.These patients presented a significant reduction in depressive symptoms during the study period. They displayed a more personalized usage approach and created recurring health goals comprising of their own customized activities. Furthermore, they took inspiration within various types of activities, thereby displaying more activity diversity. This study suggests that enacting a diverse mixture of activities that promote good sleep, personal hygiene, exercise, social contact, and leisure time can be essential in managing depressive symptoms. A smartphone-based activity recommender system can help patients achieve this.

A systematic review of smartphone-based human activity recognition methods for health research

Smartphones are now nearly ubiquitous; their numerous built-in sensors enable continuous measurement of activities of daily living, making them especially well-suited for health research. Researchers have proposed various human activity recognition (HAR) systems aimed at translating measurements from smartphones into various types of physical activity. In this review, we summarized the existing approaches to smartphone-based HAR. For this purpose, we systematically searched Scopus, PubMed, and Web of Science for peer-reviewed articles published up to December 2020 on the use of smartphones for HAR. We extracted information on smartphone body location, sensors, and physical activity types studied and the data transformation techniques and classification schemes used for activity recognition. Consequently, we identified 108 articles and described the various approaches used for data acquisition, data preprocessing, feature extraction, and activity classification, identifying the most common practices, and their alternatives. We conclude that smartphones are well-suited for HAR research in the health sciences. For population-level impact, future studies should focus on improving the quality of collected data, address missing data, incorporate more diverse participants and activities, relax requirements about phone placement, provide more complete documentation on study participants, and share the source code of the implemented methods and algorithms.