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.

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Background User-centered design (UCD) is a powerful framework for creating useful, easy-to-use, and satisfying mobile health (mHealth) apps. However, the literature seldom reports the practical challenges of implementing UCD, particularly in the field of mHealth. Objective This study aims to characterize the practical challenges encountered and propose strategies when implementing UCD for mHealth. Methods Our multidisciplinary team implemented a UCD process to design and evaluate a mobile app for older adults with heart failure. During and after this process, we documented the challenges the team encountered and the strategies they used or considered using to address those challenges. Results We identified 12 challenges, 3 about UCD as a whole and 9 across the UCD stages of formative research, design, and evaluation. Challenges included the timing of stakeholder involvement, overcoming designers’ assumptions, adapting methods to end users, and managing heterogeneity among stakeholders. To address these challenges, practical recommendations are provided to UCD researchers and practitioners. Conclusions UCD is a gold standard approach that is increasingly adopted for mHealth projects. Although UCD methods are well-described and easily accessible, practical challenges and strategies for implementing them are underreported. To improve the implementation of UCD for mHealth, we must tell and learn from these traditionally untold stories.

The study findings show that PHR use had minimal impact on intermediate health outcomes and no significant impact on patient engagement among CAD patients.

Background: Every day, older adults living with heart failure make decisions regarding their health that may ultimately affect their disease trajectory. Experts describe these decisions as instances of naturalistic decision making influenced by the surrounding social and physical environment and involving shifting goals, high stakes, and the involvement of others. Objective: This study applied a naturalistic decision-making approach to better understand everyday decision making by older adults with heart failure. Methods: We present a cross-sectional qualitative field research study using a naturalistic decision-making conceptual model and critical incident technique to study health-related decision making. The study recruited 24 older adults with heart failure and 14 of their accompanying support persons from an ambulatory cardiology center. Critical incident interviews were performed and qualitatively analyzed to understand in depth how individuals made everyday health-related decisions. Results: White, male (66.7%), older adults' decision making accorded with a preliminary conceptual model of naturalistic decision making occurring in phases of monitoring, interpreting, and acting, both independently and in sequence, for various decisions. Analyses also uncovered that there are barriers and strategies affecting the performance of these phases, other actors can play important roles, and health decisions are made in the context of personal priorities, values, and emotions. Conclusions: Study findings lead to an expanded conceptual model of naturalistic decision making by older adults with heart failure. In turn, the model bears implications for future research and the design of interventions grounded in the realities of everyday decision making.