Remote daily real-time monitoring in patients with COPD – A feasibility study using a novel device

Remotely supervised spirometry versus laboratory-based spirometry during the COVID-19 pandemic: a retrospective analysis

BACKGROUND

The COVID-19 pandemic has constrained access to spirometry, and the inherent risk of infectious transmission during aerosol-generating procedures has necessitated the rapid development of Remotely Supervised Spirometry (RSS). This innovative approach enables patients to perform spirometry tests at home, using a mobile connected spirometer, all under the real-time supervision of a technician through an online audio or video call.

METHODS

In this retrospective study, we examined the quality of RSS in comparison to conventional Laboratory-based Spirometry (LS), using the same device and technician. Our sample included 242 patients, with 129 undergoing RSS and 113 participating in LS. The RSS group comprised 51 females (39.5%) with a median age of 37 years (range: 13-76 years). The LS group included 63 females (55.8%) with a median age of 36 years (range: 12-80 years).

RESULTS

When comparing the RSS group to the LS group, the percentage of accurate Forced Expiratory Volume in one second (FEV1) measurements was 78% (n = 101) vs. 86% (n = 97), p = 0.177; for Forced Vital Capacity (FVC) it was 77% (n = 99) vs. 82% (n = 93), p = 0.365; and for both FEV1 and FVC, it was 75% (n = 97) vs. 81% (n = 92), p = 0.312, respectively.

CONCLUSIONS

Our findings demonstrate no significant difference in the quality of spirometry testing between RSS and LS, a result that held true across all age groups, including patients aged over 65 years. The principal advantages of remote spirometry include improved access to pulmonary function tests, reduced infectious risk to curtail disease spread, and enhanced convenience for patients.

Remote patient monitoring in the management of chronic obstructive pulmonary disease

Remote patient monitoring allows monitoring high-risk patients through implementation of an expanding number of technologies in coordination with a healthcare team to augment care, with the potential to provide early detection of exacerbation, prompt access to therapy and clinical services, and ultimately improved patient outcomes and decreased healthcare utilization.In this review, we describe the application of remote patient monitoring in chronic obstructive pulmonary disease including the potential benefits and possible barriers to implementation both for the individual and the healthcare system.

Usability of Wearable Multiparameter Technology to Continuously Monitor Free-Living Vital Signs in People Living With Chronic Obstructive Pulmonary Disease: Prospective Observational Study

Background

Vital signs monitoring (VSM) is routine for inpatients, but monitoring during free-living conditions is largely untested in chronic obstructive pulmonary disease (COPD).

Objective

This study investigated the usability and acceptability of continuous VSM for people with COPD using wearable multiparameter technology.

Methods

In total, 50 people following hospitalization for an acute exacerbation of COPD (AECOPD) and 50 people with stable COPD symptoms were asked to wear an Equivital LifeMonitor during waking hours for 6 weeks (42 days). The device recorded heart rate (HR), respiratory rate (RR), skin temperature, and physical activity. Adherence was defined by the number of days the vest was worn and daily wear time. Signal quality was examined, with thresholds of ≥85% for HR and ≥80% for RR, based on the device’s proprietary confidence algorithm. Data quality was calculated as the percentage of wear time with acceptable signal quality. Participant feedback was assessed during follow-up phone calls.

Results

In total, 84% of participants provided data, with average daily wear time of 11.8 (SD 2.2) hours for 32 (SD 11) days (average of study duration 76%, SD 26%). There was greater adherence in the stable group than in the post-AECOPD group (≥5 weeks wear: 71.4% vs 45.7%; P=.02). For all 84 participants, the median HR signal quality was 90% (IQR 80%-94%) and the median RR signal quality was 93% (IQR 92%-95%). The median HR data quality was 81% (IQR 58%-91%), and the median RR data quality was 85% (IQR 77%-91%). Stable group BMI was associated with HR signal quality (r_s=0.45, P=.008) and HR data quality (r_s=0.44, P=.008). For the AECOPD group, RR data quality was associated with waist circumference and BMI (r_s=–0.49, P=.009; r_s=–0.44, P=.02). In total, 36 (74%) participants in the Stable group and 21 (60%) participants in the AECOPD group accepted the technology, but 10 participants (12%) expressed concerns with wearing a device around their chest.

Conclusions

This wearable multiparametric technology showed good user acceptance and was able to measure vital signs in a COPD population. Data quality was generally high but was influenced by body composition. Overall, it was feasible to continuously measure vital signs during free-living conditions in people with COPD symptoms but with additional challenges in the post-AECOPD context.

Identifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are commonly encountered in the primary care setting, though the accurate and timely diagnosis is problematic. Using technology like that employed in speech recognition technology, we developed a smartphone-based algorithm for rapid and accurate diagnosis of AECOPD. The algorithm incorporates patient-reported features (age, fever, and new cough), audio data from five coughs and can be deployed by novice users. We compared the accuracy of the algorithm to expert clinical assessment. In patients with known COPD, the algorithm correctly identified the presence of AECOPD in 82.6% (95% CI: 72.9–89.9%) of subjects (n = 86). The absence of AECOPD was correctly identified in 91.0% (95% CI: 82.4–96.3%) of individuals (n = 78). The diagnostic agreement was maintained in milder cases of AECOPD (PPA: 79.2%, 95% CI: 68.0–87.8%), who typically comprise the cohort presenting to primary care. The algorithm may aid early identification of AECOPD and be incorporated in patient self-management plans.

Remote Patient Monitoring for the Detection of COPD Exacerbations

Background

COPD exacerbations occur more frequently with disease progression and are associated with worse prognosis and higher healthcare expenditure.

Purpose

To utilize a networked system, optimized with statistical process control (SPC), for remote patient monitoring (RPM) and to identify potential predictors of COPD exacerbations.

Methods

Seventeen subjects, mean (SD) age of 69.7 (7.2) years, with moderate to severe COPD received RPM. Over 2618 patient-days (7.17 patient-years) of monitoring, we obtained daily symptom scores, treatment adherence, self-reported activity levels, daily spirometry (SVC, FEV₁, FVC, PEF), inspiratory capacity (IC), and oxygenation (SpO₂). These data were used to identify predictors of exacerbations defined using Anthonisen and other criteria.

Results

After implementation of SPC, concordance analysis showed substantial agreement between FVC (decrease below the 7-day rolling average minus 1.645 SD) and self-reported healthcare utilization events (κ=0.747, P<0.001) as well as between increased use of inhaled short-acting bronchodilators and exacerbations defined by two Anthonisen criteria (κ=0.611, P<0.001) or modified Anthonisen criteria (κ=0.622, P<0.001). There was a moderate agreement between FEV₁ (decrease >1.645 SD below the 7-day rolling average) and self-reported healthcare utilization events (κ=0.475, P<0.001) and between SpO₂ less than 90% and exacerbations defined by two Anthonisen criteria (κ=0.474, P<0.001) or modified Anthonisen criteria (κ=0.564, P<0.001).

Conclusion

Exacerbations were best predicted by FVC and FEV₁ below the one-sided 95% confidence interval derived from SPC but also by increased use of inhaled short-acting bronchodilators and fall in oxygen saturation. An RPM program that captures these parameters may be used to guide appropriate interventions aimed at reducing healthcare utilization in COPD patients.

How stable is lung function in patients with stable chronic obstructive pulmonary disease when monitored using a telehealth system? A longitudinal and home-based study

Background

Many telehealth systems have been designed to identify signs of exacerbations in patients with chronic obstructive pulmonary disease (COPD), but few previous studies have reported the nature of recorded lung function data and what variations to expect in this group of individuals. The aim of the study was to evaluate the nature of individual diurnal, day-to-day and long-term variation in important prognostic markers of COPD exacerbations by employing a telehealth system developed in-house.

Methods

Eight women and five men with COPD performed measurements (spirometry, pulse oximetry and the COPD assessment test (CAT)) three times per week for 4–6 months using the telehealth system. Short-term and long-term individual variations were assessed using the relative density and weekly means respectively. Quality of the spirometry measurements (forced expiratory volume in one second (FEV₁) and inspiratory capacity (IC)) was assessed employing the criteria of American Thoracic Society (ATS)/European Respiratory Society (ERS) guidelines.

Results

Close to 1100 measurements of both FEV₁ and IC were performed during a total of 240 patient weeks. The two standard deviation ranges for intra-individual short-term variation were approximately ±210 mL and ± 350 mL for FEV₁ and IC respectively. In long-term, spirometry values increased and decreased without notable changes in symptoms as reported by CAT, although it was unusual with a decrease of more than 50 mL per measurement of FEV₁ between three consecutive measurement days. No exacerbation occurred. There was a moderate to strong positive correlation between FEV₁ and IC, but weak or absent correlation with the other prognostic markers in the majority of the participants.

Conclusions

Although FEV₁ and IC varied within a noticeable range, no corresponding change in symptoms occurred. Therefore, this study reveals important and, to our knowledge, previously not reported information about short and long-term variability in prognostic markers in stable patients with COPD. The present data are of significance when defining criteria for detecting exacerbations using telehealth strategies.

Statistical Process Control Improves The Feasibility Of Remote Physiological Monitoring In Patients With Chronic Obstructive Pulmonary Disease

Background

Exacerbations of chronic obstructive pulmonary disease (COPD) occur with increasing frequency as the disease progresses and account for poor health status, worse prognosis, and higher healthcare expenditure.

Methods

We developed a networked system for remote physiological monitoring (RPM) at home and optimized it with statistical process control (SPC) with the goal of earlier detection of COPD exacerbations. We enrolled 17 patients with moderate to severe COPD with a mean (SD) age of 71.1 (7.2) years. We obtained daily symptom scores, treatment adherence and activity levels using a programmable device, and measured daily slow and forced spirometry (FEV₁, FVC, PEF), inspiratory capacity (IC) and oxygenation (SpO₂). To identify exacerbations, we developed rolling prediction intervals for FVC, FEV₁, IC and SpO₂ using SPC.

Results

The time taken to perform daily monitoring was reduced from 12.7 (5.4) minutes to 6.5 (2.6) minutes through software refinements during the study. Adherence to forced and slow spirometry was 62.6% and 62.4%, respectively. The within-subject coefficients of variation for FEV₁, PEF and IC were 12.2%, 16.2%, and 13.1%, respectively. Event rates per patient-year for exacerbations were: self-reported 0.42, 2/3 Anthonisen Criteria (AC) 0.42, modified AC 2.23, systemic corticosteroid use 0.56, and antibiotic use 0.56.

Conclusion

We successfully implemented a networked system for RPM of symptoms, treatment adherence, and physiology at home in patients with COPD. We demonstrated that SPC improves the feasibility of RPM in COPD patients which may increase the likelihood of detecting COPD exacerbations.

Simplification of Validated Patient-Reported Outcome Instruments in Inflammatory Bowel Disease

Objectives

Improving health-related quality of life (HRQoL) is a major treatment goal for inflammatory bowel disease (IBD) patients. Tools to measure HRQoL, including the Pittsburgh Sleep Quality Index and the Short Inflammatory Bowel Diseases Questionnaire, are lengthy and rely on recall. This prospective, proof-of-concept pilot study assessed the feasibility, reliability, and validity of daily, simplified HRQoL and sleep quality data collection using mobile technologies in patients with IBD.

Methods

Adult IBD patients were recruited from our center. Daily HRQoL and sleep quality were assessed using visual analog scale (VAS) surveys, and pain was assessed using the Wong-Baker FACES Pain Rating Scale (WBS). HRQoL and sleep were assessed on days 1, 14, and 28 of the study using the short IBD Questionnaire (SIBDQ) and Pittsburgh Sleep Quality Index (PSQI). Correlation between the daily instruments and the biweekly validated instruments were assessed using the Pearson correlation coefficient.

Results

One hundred patients were enrolled. The correlation between mean global assessment VAS score over 2 weeks and PSQI score taken at 14 days was moderate (r = 0.62, P &lt; 0.0001). The correlation over the same time interval between mean WBS score and SIBDQ score was strong (r = −0.71, P &lt; 0.0001), and the correlation between mean sleep VAS score and PSQI score was moderate (r = −0.55, P &lt; 0.0001).

Conclusions

This study demonstrates the potential for electronic quality of life, sleep quality, and pain assessments as feasible, reliable, and valid tools in IBD patients. Intermittent administration of these simplified electronic assessments may be useful in further reducing patient survey burden without significantly compromising their utility.

Acceptance, adherence and dropout rates of individuals with COPD approached in telehealth interventions: a protocol for systematic review and meta-analysis

INTRODUCTION

Telehealth interventions have the potential of improving exacerbation and health outcomes for individuals with chronic obstructive pulmonary disease (COPD), by delivering care in between clinical visits. However, the precise impact on avoiding exacerbation and reducing the incidence of hospital readmissions remains inconclusive. This lack of knowledge on the effectiveness of telehealth for COPD care might be due to non-adherence or partial adherence to intervention programmes and/or the withdrawal of participants over the course of previous studies.

OBJECTIVES

To conduct a systematic review of trials of telehealth interventions (including randomised control trials (RCT), crossover and pre-post studies) to: (1) estimate the acceptance, adherence and dropout rates; (2) identify the reasons for dropout from telehealth interventions among individuals with COPD; (3) evaluate the impact of trial-related, sociodemographic and intervention-related factors on the acceptance, adherence and dropout rates and (4) estimate the extent to which the acceptance, adherence and dropout rates impact outcomes in comparison with usual monitoring.

METHODS AND ANALYSIS

A systematic literature review of four databases from earliest records to November 2018 will be carried out using CINAHL, Medline (Ovid), Cochrane Library and Embase. Randomised and non-randomised control studies will be included, in addition to crossover and pre-studies post-studies comparing telehealth with standard monitoring among individuals with COPD only. Two independent reviewers will screen all relevant abstracts and full-text studies to determine eligibility, assess the risk of bias and extract the data using structured forms. If the included studies are sufficiently homogenous in terms of interventions, populations and objectives, a meta-analysis will be performed.

ETHICS AND DISSEMINATION

Ethical considerations are not required for this research.

TRIAL REGISTRATION NUMBER

CRD42017078541.

A comparison between discrete and continuous time Bayesian networks in learning from clinical time series data with irregularity

BACKGROUND

Recently, mobile devices, such as smartphones, have been introduced into healthcare research to substitute paper diaries as data-collection tools in the home environment. Such devices support collecting patient data at different time points over a long period, resulting in clinical time-series data with high temporal complexity, such as time irregularities. Analysis of such time series poses new challenges for machine-learning techniques. The clinical context for the research discussed in this paper is home monitoring in chronic obstructive pulmonary disease (COPD).

OBJECTIVE

The goal of the present research is to find out which properties of temporal Bayesian network models allow to cope best with irregularly spaced multivariate clinical time-series data.

METHODS

Two mainstream temporal Bayesian network models of multivariate clinical time series are studied: dynamic Bayesian networks, where the system is described as a snapshot at discrete time points, and continuous time Bayesian networks, where transitions between states are modeled in continuous time. Their capability of learning from clinical time series that vary in nature are extensively studied. In order to compare the two temporal Bayesian network types for regularly and irregularly spaced time-series data, three typical ways of observing time-series data were investigated: (1) regularly spaced in time with a fixed rate; (2) irregularly spaced and missing completely at random at discrete time points; (3) irregularly spaced and missing at random at discrete time points. In addition, similar experiments were carried out using real-world COPD patient data where observations are unevenly spaced.

RESULTS

For regularly spaced time series, the dynamic Bayesian network models outperform the continuous time Bayesian networks. Similarly, if the data is missing completely at random, discrete-time models outperform continuous time models in most situations. For more realistic settings where data is not missing completely at random, the situation is more complicated. In simulation experiments, both models perform similarly if there is strong prior knowledge available about the missing data distribution. Otherwise, continuous time Bayesian networks perform better. In experiments with unevenly spaced real-world data, we surprisingly found that a dynamic Bayesian network where time is ignored performs similar to a continuous time Bayesian network.

CONCLUSION

The results confirm conventional wisdom that discrete-time Bayesian networks are appropriate when learning from regularly spaced clinical time series. Similarly, we found that time series where the missingness occurs completely at random, dynamic Bayesian networks are an appropriate choice. However, for complex clinical time-series data that motivated this research, the continuous-time models are at least competitive and sometimes better than their discrete-time counterparts. Furthermore, continuous-time models provide additional benefits of being able to provide more fine-grained predictions than discrete-time models, which will be of practical relevance in clinical applications.

Effort Oxygen Saturation and Effort Heart Rate to Detect Exacerbations of Chronic Obstructive Pulmonary Disease or Congestive Heart Failure

Background: current algorithms for the detection of heart failure (HF) and chronic obstructive pulmonary disease (COPD) exacerbations have poor performance. Methods: this study was designed as a prospective longitudinal trial. Physiological parameters were evaluated at rest and effort (walking) in patients who were in the exacerbation or stable phases of HF or COPD. Parameters with relevant discriminatory power (sensitivity (Sn) or specificity (Sp) ≥ 80%, and Youden index ≥ 0.2) were integrated into diagnostic algorithms. Results: the study included 127 patients (COPD: 56, HF: 54, both: 17). The best algorithm for COPD included: oxygen saturation (SaO₂) decrease ≥ 2% in minutes 1 to 3 of effort, end-of-effort heart rate (HR) increase ≥ 10 beats/min and walking distance decrease ≥ 35 m (presence of one criterion showed Sn: 0.90 (95%, CI(confidence interval): 0.75–0.97), Sp: 0.89 (95%, CI: 0.72–0.96), and area under the curve (AUC): 0.92 (95%, CI: 0.85–0.995)); and for HF: SaO₂ decrease ≥ 2% in the mean-of-effort, HR increase ≥ 10 beats/min in the mean-of-effort, and walking distance decrease ≥ 40 m (presence of one criterion showed Sn: 0.85 (95%, CI: 0.69–0.93), Sp: 0.75 (95%, CI: 0.57–0.87) and AUC 0.84 (95%, CI: 0.74–0.94)). Conclusions: effort situations improve the validity of physiological parameters for detection of HF and COPD exacerbation episodes.

A multicentre, randomized controlled trial of telehealth for the management of COPD

BACKGROUND

Evidence is needed to determine the role of telehealth (TH) in COPD management.

METHODS

PROMETE II was a multicentre, randomized, 12-month trial. Severe COPD patients in stable condition were randomized to a specific monitoring protocol with TH or routine clinical practice (RCP). The primary objective was to reduce the number of COPD exacerbations leading to ER visits/hospital admissions between groups.

RESULTS

Overall, 237 COPD patients were screened, and 229 (96.6%) were randomized to TH (n = 115) or RCP (n = 114), with age of 71 ± 8 years and 80% were men. Overall, 169 completed the full follow-up period. There were no statistical differences at one year between groups in the proportion of participants who had a COPD exacerbation (60% in TH vs. 53.5% in RCP; p = 0.321). There was, however, a marked but non-significant trend towards a shorter duration of hospitalization and days in ICU in the TH group (18.9 ± 16.0 and 6.0 ± 4.6 days) compared to the RCP group (22.4 ± 19.5 and 13.3 ± 11.1 days). The number of all-cause deaths was comparable between groups (12 in TH vs. 13 in RCP) as was total resource utilization cost (7912€ in TH vs. 8918€ in RCP). Telehealth was evaluated highly positively by patients and doctors.

CONCLUSIONS

Remote patient management did not reduce COPD-related ER visits or hospital admissions compared to RCP within 12 months.

Feasibility of Using a Smartwatch to Intensively Monitor Patients With Chronic Obstructive Pulmonary Disease: Prospective Cohort Study

Background

Acute exacerbations of chronic obstructive pulmonary disease (COPD) are associated with accelerated decline in lung function, diminished quality of life, and higher mortality. Proactively monitoring patients for early signs of an exacerbation and treating them early could prevent these outcomes. The emergence of affordable wearable technology allows for nearly continuous monitoring of heart rate and physical activity as well as recording of audio which can detect features such as coughing. These signals may be able to be used with predictive analytics to detect early exacerbations. Prior to full development, however, it is important to determine the feasibility of using wearable devices such as smartwatches to intensively monitor patients with COPD.

Objective

We conducted a feasibility study to determine if patients with COPD would wear and maintain a smartwatch consistently and whether they would reliably collect and transmit sensor data.

Methods

Patients with COPD were recruited from 3 hospitals and were provided with a smartwatch that recorded audio, heart rate, and accelerations. They were asked to wear and charge it daily for 90 days. They were also asked to complete a daily symptom diary. At the end of the study period, participants were asked what would motivate them to regularly use a wearable for monitoring of their COPD.

Results

Of 28 patients enrolled, 16 participants completed the full 90 days. The average age of participants was 68.5 years, and 36% (10/28) were women. Survey, heart rate, and activity data were available for an average of 64.5, 65.1, and 60.2 days respectively. Technical issues caused heart rate and activity data to be unavailable for approximately 13 and 17 days, respectively. Feedback provided by participants indicated that they wanted to actively engage with the smartwatch and receive feedback about their activity, heart rate, and how to better manage their COPD.

Conclusions

Some patients with COPD will wear and maintain smartwatches that passively monitor audio, heart rate, and physical activity, and wearables were able to reliably capture near-continuous patient data. Further work is necessary to increase acceptability and improve the patient experience.

Monitoring of Physiological Parameters to Predict Exacerbations of Chronic Obstructive Pulmonary Disease (COPD): A Systematic Review

INTRODUCTION

The value of monitoring physiological parameters to predict chronic obstructive pulmonary disease (COPD) exacerbations is controversial. A few studies have suggested benefit from domiciliary monitoring of vital signs, and/or lung function but there is no existing systematic review.

OBJECTIVES

To conduct a systematic review of the effectiveness of monitoring physiological parameters to predict COPD exacerbation.

METHODS

An electronic systematic search compliant with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted. The search was updated to April 6, 2016. Five databases were examined: Medical Literature Analysis and Retrieval System Online, or MEDLARS Online (Medline), Excerpta Medica dataBASE (Embase), Allied and Complementary Medicine Database (AMED), Cumulative Index of Nursing and Allied Health Literature (CINAHL) and the Cochrane clinical trials database.

RESULTS

Sixteen articles met the pre-specified inclusion criteria. Fifteen of these articules reported positive results in predicting COPD exacerbation via monitoring of physiological parameters. Nine studies showed a reduction in peripheral oxygen saturation (SpO₂%) prior to exacerbation onset. Three studies for peak flow, and two studies for respiratory rate reported a significant variation prior to or at exacerbation onset. A particular challenge is accounting for baseline heterogeneity in parameters between patients.

CONCLUSION

There is currently insufficient information on how physiological parameters vary prior to exacerbation to support routine domiciliary monitoring for the prediction of exacerbations in COPD. However, the method remains promising.

Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: A systematic review

Major reported factors associated with the limited effectiveness of home telemonitoring interventions in chronic respiratory conditions include the lack of useful early predictors, poor patient compliance and the poor performance of conventional algorithms for detecting deteriorations. This article provides a systematic review of existing algorithms and the factors associated with their performance in detecting exacerbations and supporting clinical decisions in patients with chronic obstructive pulmonary disease (COPD) or asthma. An electronic literature search in Medline, Scopus, Web of Science and Cochrane library was conducted to identify relevant articles published between 2005 and July 2015. A total of 20 studies (16 COPD, 4 asthma) that included research about the use of algorithms in telemonitoring interventions in asthma and COPD were selected. Differences on the applied definition of exacerbation, telemonitoring duration, acquired physiological signals and symptoms, type of technology deployed and algorithms used were found. Predictive models with good clinically reliability have yet to be defined, and are an important goal for the future development of telehealth in chronic respiratory conditions. New predictive models incorporating both symptoms and physiological signals are being tested in telemonitoring interventions with positive outcomes. However, the underpinning algorithms behind these models need be validated in larger samples of patients, for longer periods of time and with well-established protocols. In addition, further research is needed to identify novel predictors that enable the early detection of deteriorations, especially in COPD. Only then will telemonitoring achieve the aim of preventing hospital admissions, contributing to the reduction of health resource utilization and improving the quality of life of patients.

Temporal Informative Analysis in Smart-ICU Monitoring: M-HealthCare Perspective

The rapid introduction of Internet of Things (IoT) Technology has boosted the service deliverance aspects of health sector in terms of m-health, and remote patient monitoring. IoT Technology is not only capable of sensing the acute details of sensitive events from wider perspectives, but it also provides a means to deliver services in time sensitive and efficient manner. Henceforth, IoT Technology has been efficiently adopted in different fields of the healthcare domain. In this paper, a framework for IoT based patient monitoring in Intensive Care Unit (ICU) is presented to enhance the deliverance of curative services. Though ICUs remained a center of attraction for high quality care among researchers, still number of studies have depicted the vulnerability to a patient's life during ICU stay. The work presented in this study addresses such concerns in terms of efficient monitoring of various events (and anomalies) with temporal associations, followed by time sensitive alert generation procedure. In order to validate the system, it was deployed in 3 ICU room facilities for 30 days in which nearly 81 patients were monitored during their ICU stay. The results obtained after implementation depicts that IoT equipped ICUs are more efficient in monitoring sensitive events as compared to manual monitoring and traditional Tele-ICU monitoring. Moreover, the adopted methodology for alert generation with information presentation further enhances the utility of the system.

Computerised Analysis of Telemonitored Respiratory Sounds for Predicting Acute Exacerbations of COPD

Chronic obstructive pulmonary disease (COPD) is one of the commonest causes of death in the world and poses a substantial burden on healthcare systems and patients' quality of life. The largest component of the related healthcare costs is attributable to admissions due to acute exacerbation (AECOPD). The evidence that might support the effectiveness of the telemonitoring interventions in COPD is limited partially due to the lack of useful predictors for the early detection of AECOPD. Electronic stethoscopes and computerised analyses of respiratory sounds (CARS) techniques provide an opportunity for substantial improvement in the management of respiratory diseases. This exploratory study aimed to evaluate the feasibility of using: (a) a respiratory sensor embedded in a self-tailored housing for ageing users; (b) a telehealth framework; (c) CARS and (d) machine learning techniques for the remote early detection of the AECOPD. In a 6-month pilot study, 16 patients with COPD were equipped with a home base-station and a sensor to daily record their respiratory sounds. Principal component analysis (PCA) and a support vector machine (SVM) classifier was designed to predict AECOPD. 75.8% exacerbations were early detected with an average of 5 ± 1.9 days in advance at medical attention. The proposed method could provide support to patients, physicians and healthcare systems.

Detecting COPD exacerbations early using daily telemonitoring of symptoms and k-means clustering: a pilot study

COPD places an enormous burden on the healthcare systems and causes diminished health-related quality of life. The highest proportion of human and economic cost is associated with admissions for acute exacerbation of respiratory symptoms (AECOPD). Since prompt detection and treatment of exacerbations may improve outcomes, early detection of AECOPD is a critical issue. This pilot study was aimed to determine whether a mobile health system could enable early detection of AECOPD on a day-to-day basis. A novel electronic questionnaire for the early detection of COPD exacerbations was evaluated during a 6-months field trial in a group of 16 patients. Pattern recognition techniques were applied. A k-means clustering algorithm was trained and validated, and its accuracy in detecting AECOPD was assessed. Sensitivity and specificity were 74.6 and 89.7 %, respectively, and area under the receiver operating characteristic curve was 0.84. 31 out of 33 AECOPD were early identified with an average of 4.5 ± 2.1 days prior to the onset of the exacerbation that was considered the day of medical attendance. Based on the findings of this preliminary pilot study, the proposed electronic questionnaire and the applied methodology could help to early detect COPD exacerbations on a day-to-day basis and therefore could provide support to patients and physicians.

Remote, real-time monitoring and analysis of vital signs of neonatal graduate infants

This paper presents a system for the remote monitoring of a newborn infant's physiological data outside the Neonatal Intensive Care Unit. By providing a simple means for parents to enable monitoring, and physicians a simple mobile application to monitor live and historical physiological information, this system provides the insight once only possible in an Intensive Care Unit. The system utilizes a variety of connectivity means such as Wi-Fi and 3G to facilitate the communication between a multitude of industry standard vital sign monitor and a remote server. A system trial monitoring an infant to simulate neonatal graduate monitoring has determined the system was able to successfully transmit 99.99% of data generated from the vital sign monitor.

Telemedicine in COPD: time to pause

There is increasing interest in the use of telemedicine to assist in the management of chronic diseases. Telemedicine possibilities for patients with COPD include medical consultations, in-home patient monitoring, and remote rehabilitation. Teleconsultations have been used successfully, saving time and travel costs for patients with only a few subsequently requiring face-to-face visits. Despite many reports, the impact of telemonitoring on the detection of exacerbations, reductions in health-care utilization, and cost savings is equivocal. Given the health-care costs and commitment involved in telemonitoring, well-designed longer-term multicenter studies with appropriate follow-up are required prior to its more widespread application. Emerging evidence from preliminary trials of telerehabilitation for the pulmonary patient is encouraging. It may represent a useful tool for increasing access and building capacity, especially in remote areas.

A pilot study of a mobile-phone-based home monitoring system to assist in remote interventions in cases of acute exacerbation of COPD

We conducted a six-month feasibility study of a mobile-phone-based home monitoring system, called M-COPD. Patients with a history of moderate Acute Exacerbation of COPD (AECOPD) were given a mobile phone to record major symptoms (dyspnoea, sputum colour and volume), minor symptoms (cough and wheezing) and vital signs. A care team remotely monitored the recorded data and provided clinical interventions. Eight patients (mean age 65 years) completed the trial. Ten acute exacerbations occurred during the trial and were successfully treated at home. Prior to the AECOPD episode, the combined score of the major symptoms increased significantly (P < 0.05). Following the intervention, it decreased significantly (P < 0.05) within two weeks and returned to the baseline. The score of the minor symptoms also increased significantly (P < 0.05), but the decrease following the intervention was not significant. There were significantly fewer hospital admissions during the trial, fewer ED presentations and fewer GP visits than in a six-month matched period in the preceding year. The results demonstrate the potential of home monitoring for analysing respiratory symptoms for early intervention of AECOPD.

A novel multimodal tool for telemonitoring patients with COPD

Introduction: Among factors that underlie high rates of non-participation reported in telehealth interventions are the low older users' acceptance of information technologies and the low levels of non-compliance with therapy of chronic patients. Therefore, inclusion of potential users into design stages of assistive technologies is challenging. In this paper, the design, implementation and evaluation of a multimodal mobile application for telemonitoring chronic obstructive pulmonary disease (COPD) is presented. The goal of the study was to assess the usability and feasibility of the designed tool. Methods: An iterative user-centered design methodology was applied to implement a prototype that satisfied users' requirements. Feasibility (compliance, COPD knowledge and satisfaction) of the application was assessed in a 6-month field trial with COPD patients. Results: A usable, effective and efficient prototype was released after the development process. A high compliance (86.1%) and an increasing in COPD knowledge were achieved in the field trial. Conclusions: The findings reveal the importance of integrating usability in the design development processes to improve adherence to routine tasks and to reduce the high rates of non-participation reported in recent evaluation studies of telehealth interventions. The presented tool can help to recognize early symptoms of deterioration and to support patients in COPD self-management.

Patients' use of self-monitored readings for managing everyday life with COPD: a qualitative study

OBJECTIVE

Effects of self-monitoring depend on how patients engage with readings and how this engagement is used for managing chronic disease. This article reports on a study of how chronic obstructive pulmonary disease (COPD) patients made use of readings during 16 weeks of self-monitoring.

PATIENTS AND METHODS

Semistructured interviews were conducted with 22 COPD patients three times each: at the beginning, halfway through, and after the monitoring device was collected. Spouses of nine interviewees were present during one or more interviews. The analysis of how patients used self-monitored readings was based on critical psychology.

RESULTS

Patients used readings to monitor bodily condition, to judge whether and when to contact health professionals, to communicate with health professionals about health management, to revise planned activities, to engage close relatives, and to motivate exercise and other health behavior. Self-monitoring can produce a sense of security as readings provide grounds for explaining symptoms and widen the scope of possibilities for taking action. Patients experienced readings as encouraging, reassuring, depressing, worrisome, and at times disturbing. A few patients involved themselves with readings in ways that are emotionally challenging and contrary to medical advice. Reasons for not making use of self-monitoring include good health or not regarding monitoring as relevant at the time.

CONCLUSIONS

Results can qualify the support health professionals offer in relation to patients' self-monitoring and self-management.

Day-to-day variability of oscillatory impedance and spirometry in asthma and COPD

Variability in airway function may be a marker of disease activity in COPD and asthma. The aim was to determine the effects of repeatability and airway obstruction on day-to-day variability in respiratory system resistance (Rrs) and reactance (Xrs) measured by forced oscillation technique (FOT). Three groups of 10 subjects; normals, stable asthmatic and stable COPD subjects underwent daily FOT recordings for 7 days. Mean total and inspiratory Rrs and Xrs, and expiratory flow limitation (EFL) Index (inspiratory - expiratory Xrs), were calculated. The ICC's were high for all parameters in all groups. Repeatability, in terms of absolute units, correlated with airway obstruction and was therefore lowest in COPD. Day-to-day variability was due mostly to repeatability, with a small contribution from the mean value for some parameters. FOT measures are highly repeatable in health, stable asthma and COPD in relation to the wide range of measures between subjects. For home monitoring in asthma and COPD, either the coefficient of variation or individualized SDs could be used to define day-to-day variability.

A novel respiratory symptom scoring system for CF pulmonary exacerbations

BACKGROUND

There is currently no simple scoring system to evaluate change in symptoms during a pulmonary exacerbation (PEx) in adult cystic fibrosis (CF) patients.

PATIENTS AND METHODS

We evaluated 265 episodes in 58 adult CF patients. A simple symptom score was administered at the start and the end of each PEx. The score evaluated four symptoms: cough, sputum, breathlessness and fatigue. Each symptom was scored from one (mild symptoms) to four (severe symptoms). The total symptom score was the summation of all the four symptoms. The total symptom score was compared with CF Respiratory Questionnaire (CFRQ) and with spirometry.

RESULTS

There was significant internal correlation between scores for each pair of symptoms. The total symptom score correlated with the functional activity score and the respiratory score domains and with the summary score for CFRQ. The total symptom score correlated with spirometry values. Symptom score improved after 2-week treatment with intravenous (IV) antibiotics in 88.3%, remained unchanged in 7.3% and worsened in 4.4% of all episodes. Changes in symptom score after IV treatment correlated with changes of all main spirometry measurements.

CONCLUSION

This new symptom score is simple and sensitive to change over a short period. It correlates with established quality-of-life questionnaires and with spirometry. The changes of symptom score over a short period correlate with changes in spirometry. This score can be used as an added tool to assess the outcome of CF PExs.

Enhancing cost-effective care with a patient-centric chronic obstructive pulmonary disease program

Coronary obstructive pulmonary disease (COPD) is an escalating health problem for individuals, their families, and the public at large, resulting n considerable morbidity and mortality. A 1-year pilot program was conducted at a managed care medical group to empower COPD patients with self-management skills and improve their quality of life through enhancing cost-effective care. A total of 141 COPD patients were enrolled in the intervention group that imparted self-management principles, and provided telephonic nursing outreach and an action plan for symptom exacerbation. The same number of patients in the control group accessed care from their physician or urgently through emergency departments. At the conclusion of this program, paid claims in the intervention group were significantly (P  <  0.001) decreased compared to the control group. Primary care physician visits were also significantly (P  <  0.001) greater in the intervention group than in the control group. Although not statistically significant, hospital admissions, bed-days, and emergency department visits showed downward trends in the intervention group. Working with their clinical team, motivated patients can gain health benefits through self-management in an era of rising COPD prevalence and cost of care.

Telemonitoring in chronic obstructive airway disease and adult patients with cystic fibrosis

We compared the use of telemonitoring in patients with chronic obstructive pulmonary disease (COPD) and adult patients with cystic fibrosis (CF). Seventy patients (51 CF and 19 COPD) were enrolled in two studies of six months' duration. Patients used a personal data assistant (PDA) attached to a spirometer to score symptoms and to perform daily spirometry. Criteria for diagnosis of exacerbations of COPD and CF were pre-defined. When exacerbations were detected, patients were offered treatment according to a pre-designed protocol. Thirty-two (63%) CF patients and one (5%) COPD patient withdrew from the studies due to lack of adherence to daily recording. For those who remained in the study, COPD patients recorded more study days (139) than CF patients (113), P = 0.03. The median number of exacerbations detected during the study was greater in COPD than in CF patients, although this was not significant. The median number of device-detected exacerbations in the COPD group was significantly greater than in the CF group, P = 0.024. When compared to a parallel period in the previous year, the number of hospitalisations for COPD exacerbations was reduced, whereas the number of intravenous antibiotics in CF patients did not differ. Adherence to telemonitoring was much greater for COPD than CF patients and the results appear to be more favourable for COPD patients than for CF patients.