In this study, we examined parents' (n = 260) perceptions of their own and their children's use of social media and other types of communication technologies in the beginning stages of coronavirus disease 2019 (COVID-19) related sanctions (e.g., social distancing) in the United States. We also examined associations between social media and technology use and anxiety. On average, parents reported that both they and their children (especially teenagers aged 13-18) had increased technology and social media use since the beginning of social distancing. Moreover, even after controlling for demographic factors, structural equation models showed that parents and children with higher levels of anxiety (as reported by parents) were more likely to increase their technology use and use social media and phones to connect. Among parents, higher anxiety was related to using social media for both social support and information seeking. Based on these results, we advocate for the utilization of social media by public health officials for collecting, collating, and dispersing accurate crisisrelated information. As social media use is widespread, and there is potential for false rumors to cause erroneous behavioral action and/or undue stress and anxiety, we also suggest that social media campaigns be thoughtfully designed to account for individual differences in developmental stages and psychological vulnerabilities.

Recent advances in small inexpensive sensors, low-power processing, and activity modeling have enabled applications that use on-body sensing and machine learning to infer people's activities throughout everyday life. To address the growing rate of sedentary lifestyles, we have developed a system, UbiFit Garden, which uses these technologies and a personal, mobile display to encourage physical activity. We conducted a 3-week field trial in which 12 participants used the system and report findings focusing on their experiences with the sensing and activity inference. We discuss key implications for systems that use on-body sensing and activity inference to encourage physical activity.

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Objectives: Using predictive modeling techniques, we developed and compared appointment no-show prediction models to better understand appointment adherence in underserved populations. Methods and Materials: We collected electronic health record (EHR) data and appointment data including patient, provider and clinical visit characteristics over a 3-year period. All patient data came from an urban system of community health centers (CHCs) with 10 facilities. We sought to identify critical variables through logistic regression, artificial neural network, and naïve Bayes classifier models to predict missed appointments. We used 10-fold cross-validation to assess the models’ ability to identify patients missing their appointments. Results: Following data preprocessing and cleaning, the final dataset included 73811 unique appointments with 12,392 missed appointments. Predictors of missed appointments versus attended appointments included lead time (time between scheduling and the appointment), patient prior missed appointments, cell phone ownership, tobacco use and the number of days since last appointment. Models had a relatively high area under the curve for all 3 models (e.g., 0.86 for naïve Bayes classifier). Discussion: Patient appointment adherence varies across clinics within a healthcare system. Data analytics results demonstrate the value of existing clinical and operational data to address important operational and management issues. Conclusion: EHR data including patient and scheduling information predicted the missed appointments of underserved populations in urban CHCs. Our application of predictive modeling techniques helped prioritize the design and implementation of interventions that may improve efficiency in community health centers for more timely access to care. CHCs would benefit from investing in the technical resources needed to make these data readily available as a means to inform important operational and policy questions.