Feasibility and Acceptability of Guided Imagery to Sequentially Address Multiple Health Behaviors During Pregnancy.

INTRODUCTION: Pregnancy presents health challenges related to well-being, physical activity, dietary regulation, and body image. There is evidence to support the use of guided imagery to address these concerns during pregnancy. The purpose of this study was to analyze the use and short-term outcomes of a multiple-behavior guided imagery intervention delivered through a mobile health (mHealth) application for pregnant women. METHODS: A single-arm, 5-week feasibility trial was conducted, and participants were instructed to listen to an audio file every day for 35 days on an mHealth application. Measurements included ongoing assessments of the participants' use of the guided imagery audio files and pre- and post-test measures of depression, anxiety, stress, physical activity, food cravings, and body image. Postintervention qualitative interviews were conducted to assess whether participants would continue to use guided imagery. RESULTS: Fifty-eight participants (mean age, 28.5 years) were enrolled from January to June of 2018. Cloud analytics data showed an average of 4.96 audio downloads per week with the Sleep and Relaxation file being the most widely used (mean weekly usage, 5.67) and reported favorite during follow-up interviews. Paired-sample t tests from pre- to post-test showed significant reductions in depression, anxiety, and stress, increased physical activity, and sedentary behavior along with some changes in body image. DISCUSSION: Future scalable guided imagery interventions are justified to test for efficacy. Guided imagery may also be delivered in person by health care providers or by using widely available technologies.

Automated neurite extraction using dynamic programming for high-throughput screening of neuron-based assays.

High-throughput screening (HTS) of cell-based assays has recently emerged as an important tool of drug discovery. The analysis and modeling of HTS microscopy neuron images, however, is particularly challenging. In this paper we present a novel algorithm for extraction and quantification of neurite segments from HTS neuron images. The algorithm is designed to be able to detect and link neurites even with complex neuronal structures and of poor imaging quality. Our proposed algorithm automatically detects initial seed points on a set of grid lines and estimates the ending points of the neurite by iteratively tracing the centerline points along the line path representing the neurite segment. The live-wire method is then applied to link the seed points and the corresponding ending points using dynamic programming techniques, thus enabling the extraction of the centerlines of the neurite segments accurately and robustly against noise, discontinuity, and other image artifacts. A fast implementation of our algorithm using dynamic programming is also provided in the paper. Any thin neurite and its segments with low intensity contrast can be well preserved by detecting the starting and ending points of the neurite. All these properties make the proposed algorithm attractive for high-throughput screening of neuron-based assays.