BackgroundSickle cell disease (SCD) is an inherited red blood cell disorder affecting millions worldwide, and it results in many potential medical complications throughout the life course. The hallmark of SCD is pain. Many patients experience daily chronic pain as well as intermittent, unpredictable acute vaso-occlusive painful episodes called pain crises. These pain crises often require acute medical care through the day hospital or emergency department. Following presentation, a number of these patients are subsequently admitted with continued efforts of treatment focused on palliative pain control and hydration for management. Mitigating pain crises is challenging for both the patients and their providers, given the perceived unpredictability and subjective nature of pain.ObjectiveThe objective of this study was to show the feasibility of using objective, physiologic measurements obtained from a wearable device during an acute pain crisis to predict patient-reported pain scores (in an app and to nursing staff) using machine learning techniques.MethodsFor this feasibility study, we enrolled 27 adult patients presenting to the day hospital with acute pain. At the beginning of pain treatment, each participant was given a wearable device (Microsoft Band 2) that collected physiologic measurements. Pain scores from our mobile app, Technology Resources to Understand Pain Assessment in Patients with Pain, and those obtained by nursing staff were both used with wearable signals to complete time stamp matching and feature extraction and selection. Following this, we constructed regression and classification machine learning algorithms to build between-subject pain prediction models.ResultsPatients were monitored for an average of 3.79 (SD 2.23) hours, with an average of 5826 (SD 2667) objective data values per patient. As expected, we found that pain scores and heart rate decreased for most patients during the course of their stay. Using the wearable sensor data and pain scores, we were able to create a regression model to predict subjective pain scores with a root mean square error of 1.430 and correlation between observations and predictions of 0.706. Furthermore, we verified the hypothesis that the regression model outperformed the classification model by comparing the performances of the support vector machines (SVM) and the SVM for regression.ConclusionsThe Microsoft Band 2 allowed easy collection of objective, physiologic markers during an acute pain crisis in adults with SCD. Features can be extracted from these data signals and matched with pain scores. Machine learning models can then use these features to feasibly predict patient pain scores.

Background Pediatric blood and marrow transplant (PBMT) patients experience significant symptom distress. Mobile health (mHealth) technologies can be leveraged to improve understanding of the patient’s symptom experience by providing continuous, real-time, in situ, patient-generated symptom data. This rich data stream can subsequently enhance symptom management strategies. However, limited research has been conducted in this area. Objectives This pilot study seeks to (a) explore the feasibility of integrating mHealth technologies to monitor symptom data for PBMT patients and (b) evaluate the study design, measures, and procedures. Methods An exploratory longitudinal design was employed to assess the feasibility of monitoring 10 PBMT patients’ symptoms using data from two mHealth technologies: (a) a smartphone mHealth application (app) to collect symptom data and (b) a wearable tracking device (Apple watch) to collect physiological data. Feasibility was measured as usability and acceptability. Monthly patient interviews and an end-of-study feasibility survey were employed and analyzed to further understand reasons for sustained interest in and attrition from the study. Results Overall usability of the wearable was 51%, and app was 56%. Children reported devices were easy to use and acceptable. The study demonstrated acceptability with an enrollment rate of 83% and an attrition rate of 30%, with 70% of the children remaining in the study for at least 40 days. Discussion This pilot study is among the first to explore the feasibility of using mobile technologies to longitudinally obtain patient-generated symptom data to enhance understanding of the PBMT symptom experience. In addition, it will improve our understanding of how these data present, interact, and cluster together throughout the posttransplant period.