Wearable, battery-free, wireless multiplexed printed sensors for heat stroke prevention with mussel-inspired bio-adhesive membranes.

Wearable technologies are becoming pervasive in our society, and their development continues to accelerate the untapped potential of continuous and ubiquitous sensing, coupled with big data analysis and interpretation, has only just begun to unfold. However, existing wearable devices are still bulky (mainly due to batteries and electronics) and have suboptimal skin contact. In this work, we propose a novel approach based on a sensor network produced through inkjet printing of nanofunctional inks onto a semipermeable substrate. This network enables real-time monitoring of critical physiological parameters, including temperature, humidity, and muscle contraction. Remarkably, our system operates under battery-free and wireless near-field communication (NFC) technology for data readout via smartphones. Moreover, two of the three sensors were integrated onto a naturally adhesive bioinspired membrane. This membrane, developed using an eco-friendly, high-throughput process, draws inspiration from the remarkable adhesive properties of mussel-inspired molecules. The resulting ultra-conformable membrane adheres effortlessly to the skin, ensuring reliable and continuous data collection. The urgency of effective monitoring systems cannot be overstated, especially in the context of rising heat stroke incidents attributed to climate change and high-risk occupations. Heat stroke manifests as elevated skin temperature, lack of sweating, and seizures. Swift intervention is crucial to prevent progression to coma or fatality. Therefore, our proposed system holds immense promise for the monitoring of these parameters on the field, benefiting both the general population and high-risk workers, such as firefighters.

Factor Structure and Internal Consistency on a Reduced Version of the Revised Test of Need for Cognitive Closure.

The need for cognitive closure is a construct postulated by Kruglanski that explains the motivational aspects which influence decision-making and its impact on the social environment. Initially, it was assessed through a unidimensional scale, later criticized for its poor satisfactory reliability and validity. Regarding these criticisms, Pierro and Kruglanski developed a new 14-item scale to measure two dimensions, which were not previously evaluated: urgency tendency and permanence tendency. Although the Revised Test of Need for Cognitive Closure is more economical in terms of assessment time, it would be optimal to develop a reduced test that can assess faster while maintaining validity and reliability. The present research aims to reduce the Revised Test of Need for Cognitive Closure scale to the Argentinian context. To this end, we worked on a non-experimental design, assessing this scale within a sample of 690 Argentinian university students (Women = 81.16%, Men = 18.84%), and proceeded to perform reliability, as well as confirmatory factor analysis, convergent validity, and factorial invariance analysis. The results indicate a bi-factorial structure of a Need for Cognitive Closure instrument with eight items and two dimensions: urgency tendency (α = 0.76) and permanence tendency (α = 0.64), suggesting good reliability in both of them. In addition, well convergent validity was checked with other validated instruments, and finally, the factor loadings were shown to be invariant. In conclusion, it was demonstrated the reliability and validity of reducing the Revised Test of Need for Cognitive Closure in our social environment.

Architecture and User-Context Models of CoCare: A Context-Aware Mobile Recommender System for Health Promotion.

Recommender systems (RS) are useful tools for filtering and sorting items and information for users. There is a wide diversity of approaches that help creating personalized recommendations. Context-aware recommender systems (CARS) are a kind of RS which provide adaptation capabilities to the user's environment, e.g., by sensing data through wearable devices or other biomedical sensors. In healthcare and wellbeing, CARS can support health promotion and health education, considering that each individual requires tailored intervention programs. Our research aims at proposing a context-aware mobile recommender system for the promotion of healthy habits. The system is adapted to the user's needs, his/her health information, interests, time, location and lifestyles. In this paper, the CARS computational architecture and the user and context models of health promotion are presented, which were used to implement and test a prototype recommender system.