This study in preterm infants was designed to characterize the prognostic potential of several features of heart rate variability (HRV), respiration, and (infant) motion for the predictive monitoring of late-onset sepsis (LOS). In a neonatal intensive care setting, the cardiorespiratory waveforms of infants with blood-culture positive LOS were analyzed to characterize the prognostic potential of 22 features for discriminating control- from sepsis-state, using the Naive Bayes algorithm. Historical data of the subjects acquired from a period sufficiently before the clinical suspicion of LOS was used as control-state whereas data from the 24 hours preceding the clinical suspicion of LOS were used as sepsis-state (test-data). The overall prognostic potential of all features was quantified at three-hourly intervals for the period corresponding to test data by calculating the area under the receiver operating characteristics curve. For the 49 infants studied, features of HRV, respiration, and movement showed characteristic changes in the hours leading up to the clinical suspicion of sepsis, namely an increased propensity towards pathological heart rate decelerations, increased respiratory instability and a decrease in spontaneous infant activity, i.e., lethargy. While features characterizing HRV and respiration can be used to probe the state of the autonomic nervous system, those characterizing movement probe the state of the motor system - dysregulation of both reflects an increased likelihood of sepsis. By using readily interpretable features derived from cardiorespiratory monitoring, opportunities for preemptively identifying and treating LOS can be developed.

There are different ways to deliver Cognitive Behavioral Therapy for Insomnia (CBT-I), of which in-person (face to face) is the traditional delivery method. However, the scalability of in-person therapy is low. Digital Cognitive Behavioral Therapy for Insomnia (dCBT-I) is an alternative and there are tools on the market that are validated in clinical studies. In this paper, we provide a review of the existing evidence-based CBT-I apps and a summary of the published usability-oriented studies of these apps. The goal is to explore the range of interaction methods commonly applied in dCBT-I platforms, the potential impact for the users, and the design elements applied to achieve engagement. Six commercially available CBT-I apps tested by scientifically valid methods were accessed and reviewed. Commonalities were identified and categorized into interactive elements, CBT-I-related components, managerial features, and supportive motivational features. The dCBT-I apps were effectively assisting the users, and the type of interactions promoted engagement. The apps’ features were based on design principles from interactive product design, experience design, online social media, and serious gaming. This study contributes to the field by providing a critical summary of the existing dCBT-I apps that could guide future developers in the field to achieve a high engagement.