Identification of COPD Patients’ Health Status Using an Intelligent System in the CHRONIOUS Wearable Platform

Bellos, Christos; Παπαδόπουλος, Αθανάσιος; Rosso, Roberto; Fotiadis, Dimitrios I.

doi:10.1109/jbhi.2013.2293172

Cited by 59 publications

(32 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another study employed a neural network to predict falls based on kinematic parameters collected from a smart wearable device [98]. Future mHealth studies should explore the utility of data analytic techniques, potentially using large amount of data from continuous monitoring from smart wearable devices for detecting exacerbation among patients with COPD [99], irregular heart beat in cardiovascular disease [92], and dangerous spikes of blood glucose levels [100,101], to name a few, without resorting to clinicians. Ultimately, new innovative technologies should improve chronic disease care, increase the autonomy of patients, prolong the independence of older adults, and ensure overall well-being rather than being burdensome on patients’ daily lives.…”

Section: Discussionmentioning

confidence: 99%

Smart Devices for Older Adults Managing Chronic Disease: A Scoping Review

Kim¹,

Lee²

2017

JMIR Mhealth Uhealth

182

127

View full text Add to dashboard Cite

BackgroundThe emergence of smartphones and tablets featuring vastly advancing functionalities (eg, sensors, computing power, interactivity) has transformed the way mHealth interventions support chronic disease management for older adults. Baby boomers have begun to widely adopt smart devices and have expressed their desire to incorporate technologies into their chronic care. Although smart devices are actively used in research, little is known about the extent, characteristics, and range of smart device-based interventions.ObjectiveWe conducted a scoping review to (1) understand the nature, extent, and range of smart device-based research activities, (2) identify the limitations of the current research and knowledge gap, and (3) recommend future research directions.MethodsWe used the Arksey and O’Malley framework to conduct a scoping review. We identified relevant studies from MEDLINE, Embase, CINAHL, and Web of Science databases using search terms related to mobile health, chronic disease, and older adults. Selected studies used smart devices, sampled older adults, and were published in 2010 or after. The exclusion criteria were sole reliance on text messaging (short message service, SMS) or interactive voice response, validation of an electronic version of a questionnaire, postoperative monitoring, and evaluation of usability. We reviewed references. We charted quantitative data and analyzed qualitative studies using thematic synthesis. To collate and summarize the data, we used the chronic care model.ResultsA total of 51 articles met the eligibility criteria. Research activity increased steeply in 2014 (17/51, 33%) and preexperimental design predominated (16/50, 32%). Diabetes (16/46, 35%) and heart failure management (9/46, 20%) were most frequently studied. We identified diversity and heterogeneity in the collection of biometrics and patient-reported outcome measures within and between chronic diseases. Across studies, we found 8 self-management supporting strategies and 4 distinct communication channels for supporting the decision-making process. In particular, self-monitoring (38/40, 95%), automated feedback (15/40, 38%), and patient education (13/40, 38%) were commonly used as self-management support strategies. Of the 23 studies that implemented decision support strategies, clinical decision making was delegated to patients in 10 studies (43%). The impact on patient outcomes was consistent with studies that used cellular phones. Patients with heart failure and asthma reported improved quality of life. Qualitative analysis yielded 2 themes of facilitating technology adoption for older adults and 3 themes of barriers.ConclusionsLimitations of current research included a lack of gerontological focus, dominance of preexperimental design, narrow research scope, inadequate support for participants, and insufficient evidence for clinical outcome. Recommendations for future research include generating evidence for smart device-based programs, using patient-generated data for advanced data mining techniques, vali...

show abstract

Section: Discussionmentioning

confidence: 99%

Smart Devices for Older Adults Managing Chronic Disease: A Scoping Review

Kim¹,

Lee²

2017

JMIR Mhealth Uhealth

182

127

View full text Add to dashboard Cite

show abstract

“…Nowadays, miniaturization of sensors, energy efficiency, comfort of use and connectivity are main principles for the design of sensors. While numerous sensors are now commercially available and mobileconnected, mainly for wellness and basic lifestyle assessment, research also focuses on multi-sensorial wearable systems, for the thorough assessment of chronic conditions at home, with minimal burden for the patient [20].…”

Section: Sensor Systemsmentioning

confidence: 99%

Connected health and integrated care: Toward new models for chronic disease management

et al. 2015

View full text Add to dashboard Cite

“…They used data collected through a pulse oximeter, permitting an easy way to the cohort of patients composed of elderly people affected by COPD. The study [13] provided a system, which offers an effective platform for the satisfaction of the clinical and the patient's needs in the area of early diagnosis of patient's health status. The portable system aims at the effective management in the health status of the patients who are suffering from COPD.…”

Section: Related Work 21 Chronic Obstructive Pulmonary Diseasementioning

confidence: 99%

Classifying and Predicting Respiratory Function Based on Gait Analysis

Chan¹,

Liu²,

Chiu³

2016

Advanced Biosignal Processing and Diagnostic Methods

View full text Add to dashboard Cite

The human walking behaviour can express the physiological information of human body, and gait analysis methods can be used to access the human body condition. In addition, the respiratory parameters from pulmonary spirometer are the standard of accessing the body condition of the subjects. Therefore, we want to show the correlation between gait analysis method and the respiratory parameters. We propose a vision sensor-based gait analysis method without wearing any sensors. Our method proposed features such as D ′ υ , V ′ υ and to prove the correlation by classification and prediction experiment. In our experiment, the subjects are divided into three levels depending on the respiratory index. We run classifying and predicting experiment with the extracted features V ′ υ and . In the classifying experiment, the accuracy result is %. In predicting experiment, the correlations of predicting the forced expiratory volume in s FEV and forced vital capacity FVC are . and . , respectively. Therefore, there is a correlation between the pulmonary spirometer and our method. The radar system is a tool using impulse to record the moving of the subjects chest. Combining the features of radar system with our features improves the classification result from to %. In predicting FEV /FVC, the correlation also improves from to %. Therefore, cooperating with radar system improves the correlation.

show abstract

Identification of COPD Patients’ Health Status Using an Intelligent System in the CHRONIOUS Wearable Platform

Cited by 59 publications

References 19 publications

Smart Devices for Older Adults Managing Chronic Disease: A Scoping Review

Smart Devices for Older Adults Managing Chronic Disease: A Scoping Review

Connected health and integrated care: Toward new models for chronic disease management

Classifying and Predicting Respiratory Function Based on Gait Analysis

Contact Info

Product

Resources

About