The combination of clinical findings and CRP is a useful tool to transition children with osteoarticular infections to oral therapy. Complicated outcomes were associated with higher early CRP at diagnosis and lower CRP at the end of parenteral therapy, suggesting that clinicians were more conservative with prolonged initial parenteral therapy in this group.

Objective To describe a configurable mobile health (mHealth) framework for integration of physiologic and environmental sensors to be used in studies focusing on the domain of pediatric asthma. Materials and Methods The Biomedical REAl-Time Health Evaluation (BREATHE) platform connects different sensors and data streams, contextualizing an individual’s symptoms and daily activities over time to understand pediatric asthma’s presentation and its management. A smartwatch/smartphone combination serves as a hub for personal/wearable sensing devices collecting data on health (eg, heart rate, spirometry, medications), motion, and personal exposures (eg, particulate matter, ozone); securely transmitting information to BREATHE’s servers; and interacting with the user (eg, ecological momentary assessments). Server-side integration of electronic health record data and spatiotemporally correlated information (eg, weather, traffic) elaborates on these observations. An initial panel study involving pediatric asthma patients was conducted to assess BREATHE. Results Twenty subjects were enrolled, during which BREATHE accrued seven consecutive days of continuous data per individual. The data were used to confirm knowledge about asthma (use of controller inhalers, time-activity behaviors, personal air pollution exposure), and additional analyses provided insights into within-day associations of environmental triggers and asthma exacerbations. Exit surveys focusing on mHealth usability, while positive, noted several translational challenges. Discussion Based on these promising results, a longitudinal panel study to evaluate individual microenvironments and exposures is ongoing. Lessons learned thus far reflect the need to address various usability aspects, including convenience and ongoing engagement. Conclusion BREATHE enables multi-sensor mHealth studies, capturing new types of information alongside an evolving understanding of personal exposomes.

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Advanced statistical machine learning approaches can be powerful tools for discovery of phenotypes based on treatment response and can aid in asthma control prediction in complex medical conditions such as asthma.

Our findings suggest that PPC consultation for patients in the pediatric ward and PICU is more likely among patients with a greater number of LT-CCCs, and is associated with increased Physician Orders for Life-Sustaining Treatment preparation and documentation of mental health disorders. Patients at risk to not receive PPC consultation are those with acute illness and patients in the NICU.