Symptom clusters in chronic obstructive pulmonary disease: A systematic review.

AIM: To conduct a comprehensive literature review to identify symptom clusters commonly present in Chronic obstructive pulmonary disease (COPD) patients. BACKGROUND: COPD is the fourth leading cause of death worldwide. Substantial research has been studied regarding single symptoms that burden patients with this disease and the profound impacts that these symptoms can have on physical and psychological health. However, these symptoms rarely occur in isolation and limited research has been conducted identifying clinically significant relationships or clusters of symptoms associated with COPD afflicted patients. METHODS: PubMed, Web of Science, and Embase databases were used to identify potential articles limited to records published between 2005 and 2018 with human-conducted trials on adults with COPD, examining symptom clusters in this population. Only 5 studies met inclusion criteria. RESULTS: Across the five studies, 596 participants were included with a mean age of 70.49. Two themes emerged including psychological symptom clusters and respiratory-related symptom clusters. Anxiety-related symptoms appeared to be a common theme among psychological symptom clusters and varied greatly based on instrument selection. Inconsistent results were found in respiratory-related symptom clusters, but included difficulty breathing as a common symptom component. Only one study examined for stability of symptoms over time. CONCLUSION: There were inconsistent results across all studies which may be contributed to the heterogeneity amongst patients, instruments administered, and statistical approach. Future research should be conducted to further elucidate COPD related symptom clusters, their effects on somatic and cognitive health, and the stability of these symptom clusters over time.

TussisWatch: A Smart-Phone System to Identify Cough Episodes as Early Symptoms of Chronic Obstructive Pulmonary Disease and Congestive Heart Failure.

Chronic obstructive pulmonary disease (COPD) and congestive heart failure (CHF) are leading chronic health concerns among the aging population today. They are both typically characterized by episodes of cough that share similarities. In this paper, we design TussisWatch, a smart-phone-based system to record and process cough episodes for early identification of COPD or CHF. In our technique, for each cough episode, we do the following: 1) filter noise; 2) use domain expertise to partition each cough episode into multiple segments, indicative of disease or otherwise; 3) identify a limited number of audio features for each cough segment; 4) remove inherent biases as a result of sample size differences; and 5) design a two-level classification scheme, based on the idea of Random Forests, to process a recorded cough segment. Our classifier, at the first-level, identifies whether or not a given cough segment indicates a disease. If yes, the second-level classifier identifies the cough segment as symptomatic of COPD or CHF. Testing with a cohort of 9 COPD, 9 CHF, and 18 CONTROLS subjects spread across both the genders, races, and ages, our system achieves good performance in terms of Sensitivity, Specificity, Accuracy, and Area under ROC curve. The proposed system has the potential to aid early access to healthcare, and may be also used to educate patients on self-care at home.

"Mobile technology to improve heart failure outcomes: A proof of concept paper".

BACKGROUND: Heart failure (HF) causes significant symptom burden and human suffering with considerable economic burden due to hospital readmissions. Targeted interventions to encourage and support self-management behavior is warranted. AIM: To test the proof of concept of a mobile application (HeartMapp) in improving self-care management of patients with heart failure. METHOD: An exploratory inquiry used a field study strategy with purposeful sampling and constant comparative analysis to test the proof of concept of HeartMapp using The Business Model Canvas framework. RESULTS: A total of 125 individuals, who were identified as potential candidates to use the HeartMapp completed the interview over a seven-week period in 2016. Constant comparative analysis indicated themes that Skilled Nursing Facilities had increased readmissions. Participants from Skilled Nursing Facilities reported concern on lack of staffing, star rating, and malpractice claims. Two types of patients were identified as early adapters of technology and those in denial. Health care facilities reported challenges on transitional care, nurses struggle with engagement of patients on self-care management. To avoid readmission penalty, hospitals task home care agencies to keep the patients home for 30-days. While home care agencies rely on remote telemonitoring reported that current telemonitoring devices are costly to maintain, thus exploring novel technology. CONCLUSION: The Business Model Canvas provided directions for future testing of HeartMapp for its usability as an adjunct device in home health setting to improve self-management and enhance communication with providers, and ultimately reduce readmissions.