ABSTRACT
BACKGROUND: Iron deficiency is known to be the most common nutritional disorder. About 30% of the world's population is iron deficient (ID). Women are more likely to be exposed to anemia than men, which is an epidemic public health problem. OBJECTIVE: A cross-sectional study was carried out to investigate the prevalence of anemia and associated risk factors among non-pregnant women in Riyadh, Saudi Arabia. METHODS: Non-pregnant women (n = 250) aged 20-65 years were involved in this study. Sociodemographic, nutritional status, menstrual history, anthropometric and haematological properties were calculated. Anaemia proxies including haemoglobin (HB), serum ferritin (IDA), Haematocrit (Hct), mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH) and mean corpuscular haemoglobin concentration (MCHC) were determined as well as BMI. RESULTS: The respondents were varied according to demographic factors and according to anemia proxies, the majority of them suffered from anemia. The Student's t-test analysis showed that the average daily food intake was lower than that of the dietary requirement intake (DRI). Correlation and logistic regression analysis between sociodemographic factors and anemia proxies revealed that most of such factors significantly and negatively affected anemia proxies. Moreover, the correlation of daily food intake and anemia proxies showed that the nutrients responsible for the improvement of anemia proxies were not taken in sufficient amount as indicated by a significant and positive correlation. CONCLUSION: In conclusion, various factors including demographic factors, daily food intake appeared to be associated with anemia proxies, which are the most important risk factor for anemia among non-pregnant women in Riyadh, Saudi Arabia.
ABSTRACT
Brain computer interfaces are currently considered to greatly enhance assistive technologies and improve the experiences of people with special needs in the workplace. The proposed adaptive control model for smart offices provides a complete prototype that senses an environment's temperature and lighting and responds to users' feelings in terms of their comfort and engagement levels. The model comprises the following components: (a) sensors to sense the environment, including temperature and brightness sensors, and a headset that collects electroencephalogram (EEG) signals, which represent workers' comfort levels; (b) an application that analyzes workers' feelings regarding their willingness to adjust to a space based on an analysis of collected data and that determines workers' attention levels and, thus, engagement; and (c) actuators to adjust the temperature and/or lighting. This research implemented independent component analysis to remove eye movement artifacts from the EEG signals and used an engagement index to calculate engagement levels. This research is expected to add value to research on smart city infrastructures and on assistive technologies to increase productivity in smart offices.
