Purpose: Respiratory rate is assessed less frequently than other vital signs, and documented respiratory rates are often erroneous. This pilot study compared respiratory rates derived from a wearable biosensor to those derived from capnography. Methods: Emergency department patients with respiratory complaints were enrolled and had capnography via nasal cannula and a wireless, wearable biosensor from Philips applied for approximately one hour. Respiratory rates were obtained from both of these methods. We determined the difference between median respiratory rates obtained from the biosensor and capnography and the proportion of biosensor-derived respiratory rates that were within three breaths/minute of the capnography-derived respiratory rates for each patient. A Spearman correlation coefficient was calculated to assess the strength of the correlation between mean respiratory rates derived from both methods. Plots of minute-by-minute respiratory rates, per patient, for each monitoring method were shown to two physicians. The physicians identified time periods in which the respiratory rates appeared invalid. The proportion of time with invalid respiratory rates for each patient, for each method, was calculated and averaged. Results: We analyzed data for 17 patients. Median biosensor-derived respiratory rate was 20 breaths/minute (range: 7–40 breaths/minute) and median capnography-derived respiratory rate was 25 breaths/minute (range: 0–58 breaths/minute). Overall, 72.8% of biosensor-derived respiratory rates were within three breaths per minute of the capnography-derived respiratory rates. Overall mean difference was 3.5 breaths/minute (±5.2 breaths/minute). Respiratory rates appeared invalid 0.7% of the time for the biosensor and 5.0% of the time for capnography. Conclusion: Our pilot study suggests that the Philips wearable biosensor can continuously obtain respiratory rates that are comparable to capnography-derived respiratory rates among emergency department patients with respiratory complaints.

Wearable devices to detect changes in health status are increasingly adopted by consumers, yet hospitals remain slow to assimilate these devices into clinical practice. Despite the clear benefits of capturing clinical information in acutely ill patients, such technology remains difficult to implement in emergency medicine. To improve adoption, barriers must first be removed. In our technical feasibility and acceptability trial, we studied the deployment of a wearable wireless biosensor that collects physiological data. We enrolled 44 adult patients receiving care in an emergency department observation unit. After we consented patients for participation, we applied biosensors to their chest and collected basic demographic and clinical information. We then collected biosensor data on an isolated system and measured patient experience via an exit survey. Throughout this process we documented and studied technical challenges. Overall, the technology was feasible to deploy in the emergency department observation unit and was acceptable to participants. Such technologies have tremendous future operational and clinical implications in settings ranging from emergency to home-care.

BackgroundCertified peer support specialists often use technologies such as smartphone applications to deliver digital peer support in community mental health centers. Certified peer support specialists are individuals with a mental health diagnosis, trained and accredited by their state to provide mental health support services. Digital peer support has shown promising evidence of promoting recovery, hope, social support, and medical and psychiatric self-management among patients with a diagnosis of a serious mental illness. Interest in digital peer support as part of the patient experience has grown. Understanding barriers and facilitators to the implementation process of digital peer support into community mental health centers is a critical next step to facilitate uptake.MethodsSemi-structured qualitative interviews were conducted with 27 patient participants (N = 17 persons with serious mental illness; N = 10 certified peer support specialists) from an urban community mental health center. Participants responded to open-ended questions on the barriers and facilitators of engaging with digital peer support technologies within community mental health centers. The interview guide and the responses were categorized according to the Consolidated Framework for Implementation Science Research (CFIR) constructs.ResultsNine barriers and two facilitators were identified for the implementation of digital peer support in community mental health centers. The overarching domains for the identified barriers included (1) intervention characteristics (i.e., adaptability, complexity, and cost), (2) inner settings (i.e., implementation climate, readiness for implementation, and access to knowledge and information), and (3) characteristics of individuals (i.e., knowledge and beliefs about the intervention and other personal attributes). The two facilitators identified included (1) intervention characteristics (i.e., relative advantage) and (2) outer setting (i.e., patient needs and resources).ConclusionsThe identified barriers and facilitators represent a starting point for developing or modifying digital peer support technology requirements to ease implementation in community mental health centers. Building technology requirements and implementation processes based on these findings may facilitate uptake of digital peer support technologies by people with serious mental illness and certified peer support specialists in community mental health centers.