Search

BackgroundDeveloping effective, widely useful, weight management programs is a priority in health care because obesity is a major health problem. ObjectiveThis study developed and investigated a new, comprehensive, multifactorial, daily, intensive, psychologist coaching program based on cognitive behavioral therapy (CBT) modules. The program was delivered via the digital health care mobile services Noom Coach and InBody. MethodsThis was an open-label, active-comparator, randomized controlled trial. A total of 70 female participants with BMI scores above 24 kg/m2 and no clinical problems besides obesity were randomized into experimental and control groups. The experimental (ie, digital CBT) group (n=45) was connected with a therapist intervention using a digital health care service that provided daily feedback and assignments for 8 weeks. The control group (n=25) also used the digital health care service, but practiced self-care without therapist intervention. The main outcomes of this study were measured objectively at baseline, 8 weeks, and 24 weeks and included weight (kg) as well as other body compositions. Differences between groups were evaluated using independent t tests and a per-protocol framework. ResultsMean weight loss at 8 weeks in the digital CBT group was significantly higher than in the control group (–3.1%, SD 4.5, vs –0.7%, SD 3.4, P=.04). Additionally, the proportion of subjects who attained conventional 5% weight loss from baseline in the digital CBT group was significantly higher than in the control group at 8 weeks (32% [12/38] vs 4% [1/21], P=.02) but not at 24 weeks. Mean fat mass reduction in the digital CBT group at 8 weeks was also significantly greater than in the control group (–6.3%, SD 8.8, vs –0.8%, SD 8.1, P=.02). Mean leptin and insulin resistance in the digital CBT group at 8 weeks was significantly reduced compared to the control group (–15.8%, SD 29.9, vs 7.2%, SD 35.9, P=.01; and –7.1%, SD 35.1, vs 14.4%, SD 41.2, P=.04). Emotional eating behavior (ie, mean score) measured by questionnaire (ie, the Dutch Eating Behavior Questionnaire) at 8 weeks was significantly improved compared to the control group (–2.8%, SD 34.4, vs 21.6%, SD 56.9, P=.048). Mean snack calorie intake in the digital CBT group during the intervention period was significantly lower than in the control group (135.9 kcal, SD 86.4, vs 208.2 kcal, SD 166.3, P=.02). Lastly, baseline depression, anxiety, and self-esteem levels significantly predicted long-term clinical outcomes (24 weeks), while baseline motivation significantly predicted both short-term (8 weeks) and long-term clinical outcomes. ConclusionsThese findings confirm that technology-based interventions should be multidimensional and are most effective with human feedback and support. This study is innovative in successfully developing and verifying the effects of a new CBT approach with a multidisciplinary team based on digital technologies rather than standalone technology-based interventions. Trial RegistrationClinicalTrials.gov NCT03465306; https://clinicaltrials.gov/ct2/show/NCT03465306

We present techniques for enhancing the look-up table (LUT) method, using the Second Simulation of a Satellite Signal in the Solar Spectrum Vector (6 SV) model for atmospheric correction (AC) and accurate surface reflectance (SR) computation across the multispectral (MS) KOMPSAT-3A channels. We improved the LUT-based SR accuracy using three interpolation and prediction methods for AC coefficients: minimum curvature surface (MCS), six-dimensional linear interpolation (6D), and a deep neural network (DNN). When assessed based on the solar zenith angle (SZA) and aerosol optical depth (AOD), MCS had limitations interpolating atmospheric effects, particularly at shorter wavelengths. The DNN method had high predictive ability for atmospheric effect variability with an increased SZA, but struggled to predict minor changes at low SZAs. In comparison, the 6D method, operating in real-time and considering all AC input variables, consistently retrieved high-quality SR across all MS channels. Our findings offer insights into each method's strengths and limitations, guiding future remote sensing research and applications. [ABSTRACT FROM AUTHOR]

In remote sensing applications, enhancing the precision of level 2 (L2) algorithms relies heavily on the accurate estimation of the surface reflectance across the ultraviolet (UV) to visible (VIS) spectrum. However, the mutual dependence between the L2 algorithms and surface reflectance retrieval poses challenges, necessitating an alternative approach. To address this issue, many satellite algorithms generate Lambert Equivalent Reflectance (LER) products as a priori surface reflectance data; however, this often results in an underestimation of these data. This study introduces a novel approach to surface reflectance retrieval, termed background surface reflectance (BSR), which leverages a semi-empirical Bidirectional Reflectance Distribution Function (BRDF) model to simulate surface reflectance based on BRDF components. This study pioneered the application of the BRDF model to hyperspectral satellite data in the UV-VIS region, aiming to provide more realistic preliminary surface reflectance data. In this study, the Geostationary Environment Monitoring Spectrometer (GEMS) data was used, and a comparative analysis of the GEMS BSR and GEMS LER revealed an improvement in the relative Root Mean Squared Error (rRMSE) accuracy of 3 %. Additionally, a time-series analysis across diverse land types indicated a greater stability exhibited by the BSR than by the LER. For further validation, the BSR was compared with other LER databases using ground-truth data, yielding superior simulation performance. These findings present a promising avenue for enhancing the accuracy of surface reflectance retrieval from hyperspectral satellite data, thereby advancing the practical applications of remote sensing algorithms. [ABSTRACT FROM AUTHOR]

Specific humidity (SH) which means the amount of water vapor in 1 kg of air, is used as an indicator of energy exchange between the atmosphere and the Earth's surface. SH is typically computed using microwave satellites. However, the spatial resolution of data for microwave satellite is too low. To overcome this disadvantage, we introduced new methods that applied data collected by the Landsat-8 satellite with high spatial resolution (30 m), a meteorological model, and observation data for South Korea in 2016–2017 to 4 machine learning techniques to develop an optimized technique for computing SH. Among the 4 machine learning techniques, the random forest-based method had the highest accuracy, with a coefficient of determination (R) of 0.98, Root Mean Square Error (RMSE) of 0.001, bias of 0, and Relative Root Mean Square Error (RRMSE) of 11.16%. We applied this model to compute land surface SH using data from 2018 to 2019 and found that it had high accuracy (R = 0.927, RMSE = 0.002, bias = 0, RRMSE = 28.35%). Although the data used in this study were limited, the model was able to accurately represent a small region based on an ensemble of satellite and model data, demonstrating its potential to address important issues related to SH measurements from satellites. [ABSTRACT FROM AUTHOR]