Using GIS and Remote Sensing Technologies to Identify Environmental Variables of Malaria Vector Breeding Sites in Western Kenya

This study used Landsat 8 satellite imagery to identify environmental variables of households with malaria vector breeding sites in a malaria endemic rural district in Western Kenya. Understanding the influence of environmental variables on the distribution of malaria has been critical in the strengthening of malaria control programs. Using remote sensing and GIS technologies, this study performed a land classification, NDVI, Tasseled Cap Wetness Index, and derived land surface temperature values of the study area and examined the significance of each variable in predicting the probability of a household with a mosquito breeding site with and without larvae. The findings of this study revealed that households with any potential breeding sites were characterized by higher moisture, higher vegetation density (NDVI) and in urban areas or roads. The results of this study also confirmed that land surface temperature was significant in explaining the presence of active mosquito breeding sites (P< 0.000). The present study showed that freely available Landsat 8 imagery has limited use in deriving environmental characteristics of malaria vector habitats at the scale of the Bungoma East District in Western Kenya.

Social-Cognitive Determinants of Success in Online Communities for Weight Management

Background: Online communities may be an effective, convenient, and relatively inexpensive intervention platform for individuals seeking assistance with weight management. Recent research suggests that these communities may be as effective as in-person treatments for weight management; however, very little is known about the characteristics that predict weight loss amongst those using an online community. Methods: Within a social-cognitive framework, we sought to identify the psychosocial characteristics that are associated with successful weight management for users of MyFitnessPal, a popular online community for weight management. We recruited participants who were new to the online community and asked them to complete 2 surveys (one at baseline and one 3 months later) that assessed various psychosocial constructs as well as self-reported height and weight. Results: Participants in our sample reported losing, on average, 4.55 kg during the 3-month time period. We found that engaging in weight control behaviors (e.g., monitoring food intake, weighing oneself, etc.) fully mediated the relationship between several of our variables of interest (i.e., baseline self-efficacy and perceived social support within the community) and weight loss. We also found that participants who expected to lose more weight at baseline were significantly more likely to have lost more weight at follow-up. Conclusions: On average, participants in our study lost a clinically meaningful amount of weight. Predictors of weight loss within this community included perceived support within the community (mediated by weight control behaviors), baseline self-efficacy (mediated by weight control behaviors), and baseline outcome expectations. Results of this study can ultimately serve to inform the design of future eHealth interventions for weight management.