Physical inactivity among health staff: what influences the behaviour?

Introduction: Physical inactivity has been recognised as the fourth leading risk factor for mortality worldwide. Individuals who are physically inactive have an increased risk of 20% to 30% of dying prematurely. Individuals who fulfil the minimum recommendations of physical activity can reduce the development of Non-Communicable Diseases. In 2015, 33.5% of Malaysian adults were reported to be physically inactive. Various factors were found to be associated with physical activity participation and these factors need to be explored. Methods: A cross-sectional study using proportionate simple random sampling was conducted. A total of 310 health staff were sampled according to the proportion from five divisions and data were collected using a self-administered questionnaire. IBM SPSS version 22.0 were used to analyse the data. Predictors for physical activity were also determined. Results: The response rate was 97.7% (303 out of 310). The prevalence of physical inactivity among respondents was 37.6%. The predictors for physical inactivity were smoker/ex-smoker (aOR=2.308, p=0.027), certificate/diploma education (aOR=2.135, p=0.008), personal barrier (aOR=1.055, p=0.017) and social environment barrier (aOR=1.106, p =0.025). Conclusion: People that have a higher possibility of being physically inactive were those with certificate or diploma education and smokers or ex-smokers. Those with personal barriers and social environment barriers likewise have higher probability of being physically inactive. Thus, appropriate health interventions should be developed by taking these factors into consideration to promote physical activity among the health staff

Monitoring vegetation dynamics using multi-temporal Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images of Tamil Nadu

Vegetation indices serve as an essential tool in monitoring variations in vegetation. The vegetation indices used often, viz., normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were computed from MODIS vegetation index products. The present study aimed to monitor vegetation's seasonal dynamics by using time series NDVI and EVI indices in Tamil Nadu from 2011 to 2021. Two products characterize the global range of vegetation states and processes more effectively. The data sources were processed and the values of NDVI and EVI were extracted using ArcGIS software. There was a significant difference in vegetation intensity and status of vegetation over time, with NDVI having a larger value than EVI, indicating that biomass intensity varies over time in Tamil Nadu. Among the land cover classes, the deciduous forest showed the highest mean values for NDVI (0.83) and EVI (0.38), followed by cropland mean values of NDVI (0.71) and EVI (0.31) and the lowest NDVI (0.68) and EVI (0.29) was recorded in the scrubland. The study demonstrated that vegetation indices extracted from MODIS offered valuable information on vegetation status and condition at a short temporal time period