Feasibility of a wearable self-management application for patients with COPD at home: a pilot study.

BACKGROUND: Among people with COPD, smartphone and wearable technology may provide an effective method to improve care at home by supporting, encouraging, and sustaining self-management. The current study was conducted to determine if patients with COPD will use a dedicated smartphone and smartwatch app to help manage their COPD and to determine the effects on their self-management. METHODS: We developed a COPD self-management application for smartphones and smartwatches. Participants were provided with the app on a smartphone and a smartwatch, as well as a cellular data plan and followed for 6 months. We measured usage of the different smartphone app functions. For the primary outcome, we examined the change in self-management from baseline to the end of follow up. Secondary outcomes include changes in self-efficacy, quality of life, and COPD disease control. RESULTS: Thirty-four patients were enrolled and followed. Mean age was 69.8 years, and half of the participants were women. The most used functions were recording steps through the smartwatch, entering a daily symptom questionnaire, checking oxygen saturation, and performing breathing exercises. There was no significant difference in the primary outcome of change in self-management after use of the app or in overall total scores of health-related quality of life, disease control or self-efficacy. CONCLUSION: We found older patients with COPD would engage with a COPD smartphone and smartwatch application, but this did not result in improved self-management. More research is needed to determine if a smartphone and smartwatch application can improve self-management in people with COPD. TRIAL REGISTRATION: ClinicalTrials.Gov NCT03857061, First Posted February 27, 2019.

Remote COPD Severity and Exacerbation Detection Using Heart Rate and Activity Data Measured from a Wearable Device.

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of human mortality worldwide. Traditionally, estimating COPD severity has been done in controlled clinical conditions using cough sounds, respiration, and heart rate variability, with the latter reporting insights on the autonomic dysfunction caused by the disease. Advancements in remote monitoring and wearable device technologies, in turn, have allowed for remote COPD monitoring in daily life conditions. In this study, we explore the potential for predicting COPD severity and exacerbation using a low-cost wearable device that measures heart rate and activity data. We collected smartwatch sensor data from 35 COPD patients over a period of three months. Our evaluation shows that future trajectory of the disease can be predicted using only the first few days of continuous unobtrusive wearable data collected from COPD patients. Using features extracted from wearable device an Isolation Forest was able to predict exacerbation with an area under curve (AUC) 0.69 thus showing improvement over a random choice classifier.

Sharing is Caring: Exploring machine learning-enabled methods for regional medical imaging exchange using procedure metadata.

Seamless sharing between imaging facilities of medical images obtained on the same patient is crucial in providing accurate and efficient care to patients. However, the terminology used to describe semantically similar examinations can vary widely between facilities. Current practice is manual table-based mapping to a standard terminology, which has substantial potential for mislabelled and missing examinations. In this work, we establish several baseline methods for automating the mapping of radiology imaging procedure descriptions to a SNOMED CT based standard terminology. Our best performing baseline, consisting of a bag of words representation and shallow neural network, achieved 96.3% accuracy. In addition, we explore an unsupervised clustering method that explores relevancy matching without the need for an intervening standard. Lastly, we make the procedure name dataset used in this work available to encourage extension of this application.