Differential Patterns of Social Media Use Associated with Loneliness and Health Outcomes in Selected Socioeconomic Groups.

Unlabelled: Loneliness has emerged as a chronic and persistent problem for a considerable fraction of the general population in the developed world. Concurrently, use of online social media by the same societies has steadily increased over the past two decades. The present study analyzed a recent large country-wide loneliness survey of 20,096 adults in the US using an unsupervised approach for systematic identification of clusters of respondents in terms of their social media use and representation among different socioeconomic subgroups.

Geographically-explicit Ecological Momentary Assessment (GEMA) Architecture and Components: Lessons Learned from PMOMS.

Geographically explicit Ecological Momentary Assessment (GEMA), an extension of Ecological Momentary Assessment (EMA), allows to record time-stamped geographic location information for behavioral data in the every-day environments of study participants. Considering that GEMA studies are continually gaining the attention of researchers and currently there is no single approach in collecting GEMA data, in this paper, we propose and present a GEMA architecture that can be used to conduct any GEMA study based on our experience developing and maintaining the Postpartum Mothers Mobile Study (PMOMS). Our GEMA client-server architecture can be customized to meet the specific requirements of each GEMA study.

Understanding Pregnancy and Postpartum Health Using Ecological Momentary Assessment and Mobile Technology: Protocol for the Postpartum Mothers Mobile Study.

Background: There are significant racial disparities in pregnancy and postpartum health outcomes, including postpartum weight retention and cardiometabolic risk. These racial disparities are a result of a complex interplay between contextual, environmental, behavioral, and psychosocial factors.

Objective: This protocol provides a description of the development and infrastructure for the Postpartum Mothers Mobile Study (PMOMS), designed to better capture women's daily experiences and exposures from late pregnancy through 1 year postpartum.

An Artificial Neural Network for Movement Pattern Analysis to Estimate Blood Alcohol Content Level.

Impairments in gait occur after alcohol consumption, and, if detected in real-time, could guide the delivery of "just-in-time" injury prevention interventions. We aimed to identify the salient features of gait that could be used for estimating blood alcohol content (BAC) level in a typical drinking environment. We recruited 10 young adults with a history of heavy drinking to test our research app.

Using phone sensors and an artificial neural network to detect gait changes during drinking episodes in the natural environment.

Background: Phone sensors could be useful in assessing changes in gait that occur with alcohol consumption. This study determined (1) feasibility of collecting gait-related data during drinking occasions in the natural environment, and (2) how gait-related features measured by phone sensors relate to estimated blood alcohol concentration (eBAC).

Methods: Ten young adult heavy drinkers were prompted to complete a 5-step gait task every hour from 8pm to 12am over four consecutive weekends.