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

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. We studied the underlying population heterogeneity with a computational pipeline that was developed to gain insights into cluster- or group-specific patterns of loneliness. In particular, distributions of high loneliness were observed in groups of female users of Facebook and YouTube of certain age, race, marital, and socioeconomic status. For instance, among the group of predominantly YouTube users, we noted that non-Hispanic white female respondents of age 25-44 years who have high school or less education level and are single or never married have more significant high loneliness distribution. In fact, their high loneliness scores also seem to be associated with self-reported poorer physical and mental health outcomes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s41347-021-00208-4.

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. Key features of our proposed GEMA architecture include: utilization of widely used smartphones to make GEMA studies practical; alleviation of the burden of activities on participants by designing clients (mobile applications) that are very lightweight and servers that are heavyweight in terms of functionality; utilization of at least one positioning sensor to determine EMA contexts marked with locations; and communication through the Internet. We believe that our proposed GEMA architecture, with the illustrated foundation for GEMA studies in our exemplar study (PMOMS), will help researchers from any field conduct GEMA studies efficiently and effectively.

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. The primary aims of PMOMS are to understand the contextual, psychosocial, and behavioral factors contributing to racial disparities in postpartum weight and cardiometabolic health, with a focus on the daily experiences of stress and racism, as well as contextual forms of stress (eg, neighborhood stress and structural racism). METHODS: PMOMS is a longitudinal observation study that is ancillary to an existing randomized control trial, GDM2 (Comparison of Two Screening Strategies for Gestational Diabetes). PMOMS uses an efficient and cost-effective approach for recruitment by leveraging the infrastructure of GDM2, facilitating enrollment of participants while consolidating staff support from both studies. The primary data collection method is ecological momentary assessment (EMA) and through smart technology (ie, smartphones and scales). The development of the study includes: (1) the pilot phase and development of the smartphone app; (2) feedback and further development of the app including selection of key measures; and (3) implementation, recruitment, and retention. RESULTS: PMOMS aims to recruit 350 participants during pregnancy, to be followed through the first year after delivery. Recruitment and data collection started in December 2017 and are expected to continue through September 2020. Initial results are expected in December 2020. As of early May 2019, PMOMS recruited a total of 305 participants. Key strengths and features of PMOMS have included data collection via smartphone technology to reduce the burden of multiple on-site visits, low attrition rate because of participation in an ongoing trial in which women are already motivated and enrolled, high EMA survey completion and the use of EMA as a unique data collection method to understand daily experiences, and shorter than expected timeframe for enrollment because of the infrastructure of the GDM2 trial. CONCLUSIONS: This protocol outlines the development of the PMOMS, one of the first published studies to use an ongoing EMA and mobile technology protocol during pregnancy and throughout 1 year postpartum to understand the health of childbearing populations and enduring racial disparities in postpartum weight and cardiometabolic health. Our findings will contribute to the improvement of data collection methods, particularly the role of EMA in capturing multiple exposures and knowledge in real time. Furthermore, the results of the study will inform future studies investigating weight and cardiometabolic health during pregnancy and the postpartum period, including how social determinants produce population disparities in these outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/13569.

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. We collected 3-axis accelerometer, gyroscope, and magnetometer data from phone sensors, and computed 24 gait-related features using a sliding window technique. eBAC levels were calculated at each time point based on Ecological Momentary Assessment (EMA) of alcohol use. We used an artificial neural network model to analyze associations between sensor features and eBACs in training (70% of the data) and validation and test (30% of the data) datasets. RESULTS: We analyzed 128 data points where both eBAC and gait-related sensor data were captured, either when not drinking (n=60), while eBAC was ascending (n=55) or eBAC was descending (n=13). 21 data points were captured at times when the eBAC was greater than the legal limit (0.08mg/dl). Using a Bayesian regularized neural network, gait-related phone sensor features showed a high correlation with eBAC (Pearson's r>0.9), and >95% of estimated eBAC would fall between -0.012 and +0.012 of actual eBAC. CONCLUSIONS: It is feasible to collect gait-related data from smartphone sensors during drinking occasions in the natural environment. Sensor-based features can be used to infer gait changes associated with elevated blood alcohol content.

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. During four consecutive Fridays and Saturdays, every hour from 8 p.m. to 12 a.m., they were prompted to use the app to report alcohol consumption and complete a 5-step straight-line walking task, during which 3-axis acceleration and angular velocity data was sampled at a frequency of 100 Hz. BAC for each subject was calculated. From sensor signals, 24 features were calculated using a sliding window technique, including energy, mean, and standard deviation. Using an artificial neural network (ANN), we performed regression analysis to define a model determining association between gait features and BACs. Part (70%) of the data was then used as a training dataset, and the results tested and validated using the rest of the samples. We evaluated different training algorithms for the neural network and the result showed that a Bayesian regularization neural network (BRNN) was the most efficient and accurate. Analyses support the use of the tandem gait task paired with our approach to reliably estimate BAC based on gait features. Results from this work could be useful in designing effective prevention interventions to reduce risky behaviors during periods of alcohol consumption.