Deodorant use and related adverse effects: A cross-sectional survey among Palestinian students.

Background: Deodorants are widely used to mask unpleasant body odors. They are reported to cause some adverse effects depending on the form and ingredients. The purpose of this study was to assess the prevalence of deodorant use and related adverse effects among Palestinian students. Methods: This cross-sectional study was carried out at An-Najah National University from Aug 2018 to Mar 2020. A questionnaire was constructed using a Google survey form. All analyses were done using SPSS 21.0, Fisher test was used for comparative analysis and the P-value < 0.05 was considered as statistically significant. Results: A total of 554 students participated in the study. About 93% of the participants were using deodorants and adverse effects were reported by 25% of the participants. The reported adverse effects were itching (26%), coloration (25%), sneezing (21%), and eye redness (8%). Only 4.1% of the participants visited a physician for counselling regarding various adverse effects. Conclusion: Deodorants were widely used by Palestinian students with reported adverse effects, but these side effects rarely prompted the participants to seek medical advice.

Internet of things-assisted intelligent monitoring model to analyse the physical health condition.

BACKGROUND: Nowadays, smart healthcare minimizes medical facilities costs, ease staff burden, achieve unified control of materials and records, and enhance patients' medical experience. Smart healthcare treatments have critical barriers to improving patient outcomes, reducing the regulatory burden, and promoting the transition from volume to benefit. OBJECTIVE: In this paper, the Internet of Things-assisted Intelligent Monitoring Model (IoT-IMM) has been proposed to improve patient health and maintain health records. METHOD: The advanced IoT sensors can monitor patient health and insert into the patients' bodies. Information collected can be analyzed, aggregated, and mined to predict diseases at an early stage. For that, an enhanced deep learning network using Bayes theorem (EDLN-BT) benefits to obtain and verify various patient health data in a specific aspect, making it easy to supervise the patient's activities. RESULTS: The IoT-IMM-based EDLN-BT results show the smart health care monitoring has undergone substantial growth, improving patient satisfaction for the quality of the healthcare services offered in hospitals and many other healthcare facilities. It helps predict health diseases with increased accuracy, prediction rate with minimal residual error delay, and energy consumption.