Digital Health as a Mechanism to Reduce Neonatal Intensive Care Unit Admissions: Retrospective Cohort Study.

Background: Admission to the neonatal intensive care unit (NICU) is costly and has been associated with financial and emotional stress among families. Digital health may be well equipped to impact modifiable health factors that contribute to NICU admission rates.

Objective: The aim of the study is to investigate how the use of a comprehensive prenatal digital health platform is associated with gestational age at birth and mechanisms to reduce the risk of admission to the NICU.

Digital health utilization during pregnancy and the likelihood of preterm birth.

Objective: Given the complex nature of preterm birth, interventions to reduce rates of preterm birth should be multifaceted. This analysis aimed to explore the association between the duration of using Maven, a digital health platform for women's and family health, and the odds of preterm birth.

Methods: Data came from 3326 pregnant, nulliparous Maven users who enrolled in Maven during their pregnancy between January 2020 and September 2022.

Digital health's influence on the association between birth preference and vaginal birth.

Background: Women's preferred mode of birth during pregnancy is predictive of their actual mode of birth. Digital prenatal care services are a promising method for educating women on mode of birth to reduce elective cesareans. This study aimed to evaluate the influence of digital health on the association between birth preference and mode of birth.

Association Between the Use of a Digital Health Platform During Pregnancy and Helping Users Avoid Emergency and In-Person Care: Retrospective Observational Study.

Background: Almost one-third of pregnant people visit the emergency room during pregnancy. Although some emergency care is necessary, gaps in patient education and inaccessibility of preventive services have been identified as key reasons for high-cost, low-value care in pregnancy. Digital platforms present a promising solution for providing resources to supplement routine prenatal care, thereby reducing the use of low-value in-person services.

Utilization of digital prenatal services and management of depression and anxiety during pregnancy: A retrospective observational study.

Introduction: We examined how utilization of Maven, a digital healthcare platform that provides virtual prenatal services, is associated with improvements in perceived management of anxiety and depression during pregnancy, and how medical knowledge and support may influence this association.

Materials And Methods: In this retrospective study we used adjusted logistic regression to examine the relationship between digital platform use in pregnancy and perceived mental health management, and how perceived management of mental health is affected by user-reported improvements in medical knowledge and feeling supported by the platform. Effects were evaluated separately among users with and without a mental health condition.

Understanding social needs in pregnancy: Prospective validation of a digital short-form screening tool and patient survey.

Background: Social determinants of health significantly affect health outcomes, yet are infrequently addressed in prenatal care.

Objective: This study aimed to improve the efficiency and experience of addressing social needs in pregnancy through: (1) testing a digital short-form screening tool; and (2) characterizing pregnant people's preferences for social needs screening and management.

Study Design: We developed a digital short-form social determinants of health screening tool from PRAPARE (Protocol for Responding to and Assessing Patients' Assets, Risks, and Experiences), and a survey to query patients' preferences for addressing social needs.

Myoelectric signal from below the level of spinal cord injury as a command source for an implanted upper extremity neuroprosthesis - a case report.

Implanted motor neuroprostheses offer significant restoration of function for individuals with spinal cord injury. Providing adequate user control for these devices is a challenge but is crucial for successful performance. Electromyographic (EMG) signals can serve as effective control sources, but the number of above-injury muscles suitable to provide EMG-based control signals is very limited.

Training to improve volitional muscle activity in clinically paralyzed muscles for neuroprosthesis control.

Neuroprostheses are devices that use electrical stimulation to activate paralyzed muscles in a coordinated manner to restore functional movements. These systems utilize a voluntarily-generated command signal for control of function. Current command signals include electromyographic (EMG) activity from muscles above the injury level that remain under volitional control.

A novel command signal for motor neuroprosthetic control.

Background: Neuroprostheses can restore functions such as hand grasp or standing to individuals with spinal cord injury (SCI) using electrical stimulation to elicit movements in paralyzed muscles. Implanted neuroprostheses currently use electromyographic (EMG) activity from muscles above the lesion that remain under volitional control as a command input. Systems in development use a networked approach and will allow for restoration of multiple functions but will require additional command signals to control the system, especially in individuals with high-level tetraplegia.