Family Functioning and Weight Loss in a Sample of African Americans and Whites

BACKGROUND: Traditionally, weight management behavioral research has focused on individual-level influences, with little attention given to interpersonal factors that relate to the family behavioral context. PURPOSE: This research examines the association between baseline family functioning scores and weight loss success in a sample of African Americans and Whites enrolled in a 20-week weight loss program with a weight loss goal of ≥4 kg. METHODS: Baseline surveys measuring six family functioning constructs were completed by 291 participants in a trial of weight loss maintenance. Analysis was limited to 217 participants in households with at least one other family member, and providing final weight measurements. We evaluated associations of family functioning, family composition, and demographic variables with weight loss success defined as losing ≥5% of initial body weight. Baseline predictors of weight loss success were determined using logistic regression analysis. RESULTS: Participants were on average 61 years of age with BMI of 34 kg/m(2); 57% were female and 75% self-identified as African American. Sixty-two percent lost at least 5% of initial body weight. In bivariate analysis, weight loss success was associated with higher income and education (p<0.01 and p=0.05, respectively), ethnicity (p<0.01), and the presence of a spouse (p=0.01). After adjusting for socio-demographic covariates in a multivariable model, the odds of weight loss success were independently influenced by a significant interaction between ethnicity and family cohesion (p<0.01). CONCLUSIONS: These findings suggest that family context factors influence weight loss behaviors

Modeling and Inversion in Thermal Infrared Remote Sensing over Vegetated Land Surfaces

Thermal Infra Red (TIR) Remote sensing allow spatializing various land surface temperatures: ensemble brightness, radiometric and aerodynamic temperatures, soil and vegetation temperatures optionally sunlit and shaded, and canopy temperature profile. These are of interest for monitoring vegetated land surface processes: heat and mass exchanges, soil respiration and vegetation physiological activity. TIR remote sensors collect information according to spectral, directional, temporal and spatial dimensions. Inferring temperatures from measurements relies on developing and inverting modeling tools. Simple radiative transfer equations directly link measurements and variables of interest, and can be analytically inverted. Simulation models allow linking radiative regime to measurements. They require indirect inversions by minimizing differences between simulations and observations, or by calibrating simple equations and inductive learning methods. In both cases, inversion consists of solving an ill posed problem, with several parameters to be constrained from few information. Brightness and radiometric temperatures have been inferred by inverting simulation models and simple radiative transfer equations, designed for atmosphere and land surfaces. Obtained accuracies suggest refining the use of spectral and temporal information, rather than innovative approaches. Forthcoming challenge is recovering more elaborated temperatures. Soil and vegetation components can replace aerodynamic temperature, which retrieval seems almost impossible. They can be inferred using multiangular measurements, via simple radiative transfer equations previously parameterized from simulation models. Retrieving sunlit and shaded components or canopy temperature profile requires inverting simulation models. Then, additional difficulties are the influence of thermal regime, and the limitations of spaceborne observations which have to be along track due to the temperature fluctuations. Finally, forefront investigations focus on adequately using TIR information with various spatial resolutions and temporal samplings, to monitor the considered processes with adequate spatial and temporal scales. 10.1 Introduction Using TIR remote sensing for environmental issues have been investigated the last three decades. This is motivated by the potential of the spatialized information for documenting the considered processes within and between the Earth system components: cryosphere [1–2], atmosphere [3–6], oceans [7–9], and land surfaces [10]. For the latter, TIR remote sensing is used to monitor forested areas [11–14], urban areas [15–17], and vegetated areas. We focus here on vegetated areas, natural and cultivated. The monitored processes are related to climatology, meteorology, hydrology and agronomy: (1) radiation, heat and water transfers at the soil–vegetation–atmosphere interface [18–24]; (2) interactions between land surface and atmospheric boundary layer [25]; (3) vegetation physiological processes such as transpiration and water consumption, photosynthetic activity and CO2 uptake, vegetation growth an