An autonomous mobile system for the management of COPD

Effects of Adherence to an mHealth Tool for Self-Management of COPD Exacerbations

Purpose

Poor adherence to COPD mobile health (mHealth) has been reported, but its association with exacerbation-related outcomes is unknown. We explored the effects of mHealth adherence on exacerbation-free weeks and self-management behavior. We also explored differences in self-efficacy and stages of grief between adherent and non-adherent COPD patients.

Patients and Methods

We conducted secondary analyses using data from a recent randomized controlled trial (RCT) that compared the effects of mHealth (intervention) with a paper action plan (comparator) for COPD exacerbation self-management. We used data from the intervention group only to assess differences in exacerbation-free weeks (primary outcome) between patients who were adherent and non-adherent to the mHealth tool. We also assessed differences in the type and timing of self-management actions and scores on self-efficacy and stages of grief (secondary outcomes). We used generalized negative binomial regression analyses with correction for follow-up length to analyze exacerbation-free weeks and multilevel logistic regression analyses with correction for clustering for secondary outcomes.

Results

We included data of 38 patients of whom 13 (34.2%) (mean (SD) age 69.2 (11.2) years) were adherent and 25 (65.8%) (mean (SD) age 68.7 (7.8) years) were non-adherent. Adherent patients did not differ from non-adherent patients in exacerbation-free weeks (mean (SD) 31.5 (14.5) versus 33.5 (10.2); p=0.63). Although statistically not significant, adherent patients increased their bronchodilator use more often and more timely, contacted a healthcare professional and/or initiated prednisolone and/or antibiotics more often, and showed at baseline higher scores of self-efficacy and disease acceptance and lower scores of denial, resistance, and sorrow, compared with non-adherent patients.

Conclusion

Adherence to mHealth may be positively associated with COPD exacerbation self-management behavior, self-efficacy and disease acceptance, but its association with exacerbation-free weeks remains unclear. Our results should be interpreted with caution by this pilot study's explorative nature and small sample size.

Are older adults considered in asthma and chronic obstructive pulmonary disease mobile health research? A scoping review

BACKGROUND

The use of mobile health (mHealth) for asthma and chronic obstructive pulmonary disease (COPD) is rapidly growing and may help address the complex respiratory care needs of our ageing population. However, little is currently known about how airways mHealth is developed and used among older adults (≥65 years).

OBJECTIVE

To identify if and how older adults with asthma and COPD have been incorporated across the mHealth research cycle.

METHODS

We searched Ovid MEDLINE, EMBASE, CINAHL and the Cochrane Central Registry of Controlled Trials for studies pertaining to the development or evaluation of asthma and COPD mHealth for adults published after 2010. Study, participant and mHealth details, including any considerations of older age, were extracted, synthesised and charted.

RESULTS

A total of 334 studies of 191 mHealth tools were identified. Adults ≥65 years old were included in 33.3% of asthma mHealth studies and 85.3% of COPD studies. Discussions of older age focused on barriers to technology use. Methodologic and/or analytic considerations of older age were mostly absent throughout the research cycle. Among the 28 instances quantitative age-related analyses were detailed, 12 described positive mHealth use and satisfaction outcomes in older adults versus negative or equivocal outcomes.

CONCLUSION

We identified an overall lack of consideration for older age throughout the airways mHealth research cycle, even among COPD mHealth studies that predominantly included older adults. We also found a contrast between the perceptions of how older age might negatively influence mHealth use and available quantitative evaluations. Future airways mHealth research must better integrate the needs and concerns of older adults.

Artificial Intelligence-Based Consumer Health Informatics Application: Scoping Review

BACKGROUND

There is no doubt that the recent surge in artificial intelligence (AI) research will change the trajectory of next-generation health care, making it more approachable and accessible to patients. Therefore, it is critical to research patient perceptions and outcomes because this trend will allow patients to be the primary consumers of health technology and decision makers for their own health.

OBJECTIVE

This study aimed to review and analyze papers on AI-based consumer health informatics (CHI) for successful future patient-centered care.

METHODS

We searched for all peer-reviewed papers in PubMed published in English before July 2022. Research on an AI-based CHI tool or system that reports patient outcomes or perceptions was identified for the scoping review.

RESULTS

We identified 20 papers that met our inclusion criteria. The eligible studies were summarized and discussed with respect to the role of the AI-based CHI system, patient outcomes, and patient perceptions. The AI-based CHI systems identified included systems in mobile health (13/20, 65%), robotics (5/20, 25%), and telemedicine (2/20, 10%). All the systems aimed to provide patients with personalized health care. Patient outcomes and perceptions across various clinical disciplines were discussed, demonstrating the potential of an AI-based CHI system to benefit patients.

CONCLUSIONS

This scoping review showed the trend in AI-based CHI systems and their impact on patient outcomes as well as patients' perceptions of these systems. Future studies should also explore how clinicians and health care professionals perceive these consumer-based systems and integrate them into the overall workflow.

Digital health in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) can be prevented and treated through effective care, reducing exacerbations and hospitalizations. Early identification of individuals at high risk of COPD exacerbation is an opportunity for preventive measures. However, many patients struggle to follow their treatment plans because of a lack of knowledge about the disease, limited access to resources, and insufficient clinical support. The growth of digital health-which encompasses advancements in health information technology, artificial intelligence, telehealth, the Internet of Things, mobile health, wearable technology, and digital therapeutics-offers opportunities for improving the early diagnosis and management of COPD. This study reviewed the field of digital health in terms of COPD. The findings showed that despite significant advances in digital health, there are still obstacles impeding its effectiveness. Finally, we highlighted some of the major challenges and possibilities for developing and integrating digital health in COPD management.

Early detection of COPD patients' symptoms with personal environmental sensors: a remote sensing framework using probabilistic latent component analysis with linear dynamic systems

In this study, we present a cohort study involving 106 COPD patients using portable environmental sensor nodes with attached air pollution sensors and activity-related sensors, as well as daily symptom records and peak flow measurements to monitor patients' activity and personal exposure to air pollution. This is the first study which attempts to predict COPD symptoms based on personal air pollution exposure. We developed a system that can detect COPD patients' symptoms one day in advance of symptoms appearing. We proposed using the Probabilistic Latent Component Analysis (PLCA) model based on 3-dimensional and 4-dimensional spectral dictionary tensors for personalised and population monitoring, respectively. The model is combined with Linear Dynamic Systems (LDS) to track the patients' symptoms. We compared the performance of PLCA and PLCA-LDS models against Random Forest models in the identification of COPD patients' symptoms, since tree-based classifiers were used for remote monitoring of COPD patients in the literature. We found that there was a significant difference between the classifiers, symptoms and the personalised versus population factors. Our results show that the proposed PLCA-LDS-3D model outperformed the PLCA and the RF models between 4 and 20% on average. When we used only air pollutants as input, the PLCA-LDS-3D forecasting results in personalised and population models were 48.67 and 36.33% accuracy for worsening of lung capacity and 38.67 and 19% accuracy for exacerbation of COPD patients' symptoms, respectively. We have shown that indicators of the quality of an individual's environment, specifically air pollutants, are as good predictors of the worsening of respiratory symptoms in COPD patients as a direct measurement.

Understanding Contexts and Challenges of Information Management for Epilepsy Care

Epilepsy is a common chronic neurological disease. People with epilepsy (PWE) and their caregivers face several challenges related to their epilepsy management, including quality of care, care coordination, side effects, and stigma management. The sociotechnical issues of the information management contexts and challenges for epilepsy care may be mitigated through effective information management. We conducted 4 focus groups with 5 PWE and 7 caregivers to explore how they manage epilepsy-related information and the challenges they encountered. Primary issues include challenges of finding the right information, complexities of tracking and monitoring data, and limited information sharing. We provide a framework that encompasses three attributes - individual epilepsy symptoms and health conditions, information complexity, and circumstantial constraints. We suggest future design implications to mitigate these challenges and improve epilepsy information management and care coordination.

Machine learning-enabled risk prediction of chronic obstructive pulmonary disease with unbalanced data

BACKGROUND AND OBJECTIVE

Since the early symptoms of chronic obstructive pulmonary disease (COPD) are not obvious, patients are not easily identified, causing improper time for prevention and treatment. In present study, machine learning (ML) methods were employed to construct a risk prediction model for COPD to improve its prediction efficiency.

METHODS

We collected data from a sample of 5807 cases with a complete COPD diagnosis from the 2019 COPD Surveillance Program in Shanxi Province and extracted 34 potentially relevant variables from the dataset. Firstly, we used feature selection methods (i.e., Generalized elastic net, Lasso and Adaptive lasso) to select ten variables. Afterwards, we employed supervised classifiers for class imbalanced data by combining the cost-sensitive learning and SMOTE resampling methods with the ML methods (Logistic Regression, SVM, Random Forest, XGBoost, LightGBM, NGBoost and Stacking), respectively. Last, we assessed their performance.

RESULTS

The cough frequently at age 14 and before and other 9 variables are significant parameters for COPD. The Stacking heterogeneous ensemble model showed relatively good performance in the unbalanced datasets. The Logistic Regression with class weighting enjoyed the best classification performance in the balancing data when these composite indicators (AUC, F1-Score and G-mean) were used as criteria for model comparison. The values of F1-Score and G-mean for the top three ML models were 0.290/0.660 for Logistic Regression with class weighting, 0.288/0.649 for Stacking with synthetic minority oversampling technique (SMOTE), and 0.285/0.648 for LightGBM with SMOTE.

CONCLUSIONS

This paper combining feature selection methods, unbalanced data processing methods and machine learning methods with data from disease surveillance questionnaires and physical measurements to identify people at risk of COPD, concluded that machine learning models based on survey questionnaires could provide an automated identification for patients at risk of COPD, and provide a simple and scientific aid for early identification of COPD.

A mobile-based airway clearance care system using deep learning-based vision technology to support personalized home-based pulmonary rehabilitation for COAD patients: Development and usability testing

Background

Excessive mucus secretion is a serious issue for patients with chronic obstructive airway disease (COAD), which can be effectively managed through postural drainage and percussion (PD + P) during pulmonary rehabilitation (PR). Home-based (H)-PR can be as effective as center-based PR but lacks professional supervision and timely feedback, leading to low motivation and adherence. Telehealth home-based pulmonary (TH-PR) has emerged to assist H-PR, but video conferencing and telephone calls remain the main approaches for COAD patients. Therefore, research on effectively assisting patients in performing PD + P during TH-PR is limited.

Objective

This study developed a mobile-based airway clearance care for chronic obstructive airway disease (COAD-MoAcCare) system to support personalized TH-PR for COAD patients and evaluated its usability through expert validation.

Methods

The COAD-MoAcCare system uses a mobile device through deep learning-based vision technology to monitor, guide, and evaluate COAD patients' PD + P operations in real time during TH-PR programs. Medical personnel can manage and monitor their personalized PD + P and operational statuses through the system to improve TH-PR performance. Respiratory therapists from different hospitals evaluated the system usability using system questionnaires based on the technology acceptance model, system usability scale (SUS), and task load index (NASA-TLX).

Results

Eleven participant therapists were highly satisfied with the COAD-MoAcCare system, rating it between 4.1 and 4.6 out of 5.0 on all scales. The system demonstrated good usability (SUS score of 74.1 out of 100) and a lower task load (NASA-TLX score of 30.0 out of 100). The overall accuracy of PD + P operations reached a high level of 97.5% by comparing evaluation results of the system by experts.

Conclusions

The COAD-MoAcCare system is the first mobile-based method to assist COAD patients in conducting PD + P in TH-PR. It was proven to be usable by respiratory therapists, so it is expected to benefit medical personnel and COAD patients. It will be further evaluated through clinical trials.

Comparing recalled versus experienced symptoms of breathlessness ratings: An ecological assessment study using mobile phone technology

BACKGROUND AND OBJECTIVE

Recall of breathlessness is important for clinical care but might differ from the experienced (momentary) symptoms. This study aimed to characterize the relationship between momentary breathlessness ratings and the recall of the experience. It is hypothesized that recall is influenced by the peak (worst) and end (most recent) ratings of momentary breathlessness (peak-end rule).

METHODS

This study used mobile ecological momentary assessment (mEMA) for assessing breathlessness in daily life through an application installed on participants' mobile phones. Breathlessness ratings (0-10 numerical rating scale) were recorded throughout the day and recalled each night and at the end of the week. Analyses were performed using regular and mixed linear regression.

RESULTS

Eighty-four people participated. Their mean age was 64.4 years, 60% were female and 98% had modified Medical Research Council (mMRC) ≥ 1. The mean number of momentary ratings of breathlessness provided was 7.7 ratings/participant/day. Recalled breathlessness was associated with the mean, peak and end values of the day. The mean was most closely associated with the daily recall. Associations were strong for weekly values: peak breathlessness (beta = 0.95, r2  = 0.57); mean (beta = 0.91, r2  = 0.53); and end (beta = 0.67, r2  = 0.48); p < 0.001 for all. Multivariate analysis showed that peak breathlessness had the strongest influence on the breathlessness recalled at the end of the week.

CONCLUSION

Over 1 week, recalled breathlessness is most strongly influenced by the peak breathlessness; over 1 day, it is mean breathlessness that participants most readily recalled.

Embedded system design for classification of COPD and pneumonia patients by lung sound analysis

Chronic obstructive pulmonary disease (COPD) and pneumonia are lethal pulmonary illnesses with equivocal nature of abnormal pulmonic acoustics. Using lung sound signals, the classification of pulmonary abnormalities is a difficult task. A standalone system was conceived for screening COPD and Pneumonia patients through signal processing and machine learning methodologies. The proposed system will assist practitioners and pulmonologists in the accurate classification of disease. In this research work, ICBHI's and self-collected lung sound (LS) databases are used to investigate COPD and pneumonia patient. In this scheme, empirical mode decomposition (EMD), discrete wavelet transform (DWT), and analysis of variance (ANOVA) techniques are employed for segmentation, noise elimination, and feature selection, respectively. To overcome the inherent limitation of ICBHI's LS database, the adaptive synthetic (ADASYN) sampling technique is used to eradicate class imbalance. Lung sound features are used to train fine Gaussian support vector machine (FG-SVM) for classification of COPD, pneumonia, and heathy healthy subjects. This machine learning scheme is implemented on low cost and portable Raspberry pi 3 model B+ (Cortex-A53 (ARMv8) 64-bit SoC @ 1.4 GHz through hardware-supported language. Resultant hardware is capable of screening COPD and pneumonia patients accurately and assist health professionals.

Predictive performance and impact of algorithms in remote monitoring of chronic conditions: A systematic review and meta-analysis

BACKGROUND

The use of telehealth interventions, such as the remote monitoring of patient clinical data (e.g. blood pressure, blood glucose, heart rate, medication use), has been proposed as a strategy to better manage chronic conditions and to reduce the impact on patients and healthcare systems. The use of algorithms for data acquisition, analysis, transmission, communication and visualisation are now common in remote patient monitoring. However, their use and impact on chronic disease management has not been systematically investigated.

OBJECTIVES

To investigate the use, impact, and performance of remote monitoring algorithms across various types of chronic conditions.

METHODS

A literature search of MEDLINE complete, CINHAL complete, and EMBASE was performed using search terms relating to the concepts of remote monitoring, chronic conditions, and data processing algorithms. Comparable outcomes from studies describing the impact on process measures and clinical and patient-reported outcomes were pooled for a summary effect and meta-analyses. A comparison of studies reporting the predictive performance of algorithms was also conducted using the Youden Index.

RESULTS

A total of 89 articles were included in the review. There was no evidence of a positive impact on healthcare utilisation [OR 1.09 (0.90 to 1.31); P = .35] and mortality [OR 0.83 (0.63 to 1.10); P = .208], but there was a positive effect on generic health status [SDM 0.2912 (0.06 to 0.51); P = .010] and diabetes control [SDM -0.53 (-0.74 to -0.33); P < .001; I² = 15.71] (with two of the three diabetes studies being identified as having a high risk of bias). While the majority of impact studies made use of heuristic threshold-based algorithms (n = 27,87%), most performance studies (n = 36, 62%) analysed non-sequential machine learning methods. There was considerable variance in the quality, sample size and performance amongst these studies. Overall, algorithms involved in diagnosis (n = 22, 47%) had superior performance to those involved in predicting a future event (n = 25, 53%). Detection of arrythmia and ischaemia utilising ECG data showed particularly promising results.

CONCLUSION

The performance of data processing algorithms for the diagnosis of a current condition, particularly those related to the detection of arrythmia and ischaemia, is promising. However, there appears to exist minimal testing in experimental studies, with only two included impact studies citing a performance study as support for the intervention algorithm used. Because of the disconnect between performance and impact studies, there is currently limited evidence of the effect of integrating advanced inference algorithms in remote monitoring interventions. If the field of remote patient monitoring is to progress, future impact studies should address this disconnect by evaluating high performance validated algorithms in robust clinical trials.

Development of a smartphone application for Korean patients with chronic obstructive pulmonary disease: Self-monitoring based action plans

AIM

The purpose of this study was to develop and evaluate a smartphone application (app) of a COPD action plan (AP) based on symptom self-monitoring (SM) [AP-SM Sapp] to support the early detection of, and response to, symptoms.

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is one of the most prevalent respiratory diseases worldwide. Disease control is important to prevent progression of COPD caused by exacerbations; action plans are a successful strategy to prevent and manage COPD exacerbations. However, the digital literacy that COPD patients need to support technology-based COPD action plans is poorly understood.

METHODS

A systematic literature review identified components for the app's development. Content validity testing with 12 clinical experts identified 35 critical components for inclusion in the app's development. The app was then submitted to user experience evaluation by thirteen technology experts and nine COPD patients.

RESULTS

In user evaluation of the app, experts evaluated the AP-SM Sapp as a good quality app (57.37 ± 9.13) and COPD patients as an average quality app (44.44 ± 3.94) (range 0-69; higher scores indicating greater endorsement of app quality). Revisions based on these critiques produced a final version.

CONCLUSION

The app was developed to support COPD patients in the early detection of symptoms so that exacerbations could be prevented or managed appropriately. Although the app used simple messages and pictographs to enhance digital literacy (thus narrowing the digital literacy gap), efficient onboarding will be important if barriers to app use are to be further reduced.

Adherence to an eHealth Self-Management Intervention for Patients with Both COPD and Heart Failure: Results of a Pilot Study

Background

Chronic obstructive pulmonary disease (COPD) and chronic heart failure (CHF) often coexist and share periods of symptom deterioration. Electronic health (eHealth) might play an important role in adherence to interventions for the self-management of COPD and CHF symptoms by facilitating and supporting home-based care.

Methods

In this pilot study, an eHealth self-management intervention was developed based on paper versions of multi-morbid exacerbation action plans and evaluated in patients with both COPD and CHF. Self-reporting of increased symptoms in diaries was linked to an automated decision support system that generated self-management actions, which was communicated via an eHealth application on a tablet. After participating in self-management training sessions, patients used the intervention for a maximum of four months. Adherence to daily symptom diary completion and follow-up of actions were analyzed. An add-on sensorized (Respiro^®) inhaler was used to analyze inhaled medication adherence and inhalation technique.

Results

In total, 1148 (91%) of the daily diaries were completed on the same day by 11 participating patients (mean age 66.8 ± 2.9 years; moderate (55%) to severe (45%) COPD; 46% midrange left ventricular function (LVF) and 27% reduced LVF). Seven patients received a total of 24 advised actions because of increased symptoms of which 11 (46%) were followed-up. Of the 13 (54%) unperformed advised actions, six were “call the case manager”. Adherence to inhaled medication was 98.4%, but 51.9% of inhalations were performed incorrectly, with “inhaling too shortly” (<1.25 s) being the most frequent error (79.6%).

Discussion

Whereas adherence to completing daily diaries was high, advised actions were inadequately followed-up, particularly the action “call the case manager”. Inhaled medication adherence was high, but inhalations were poorly performed. Future research is needed to identify adherence barriers, further tailor the intervention to the individual patient and analyse the intervention effects on health outcomes.

Using Mobiles to Monitor Respiratory Diseases

In this work, a mobile application is developed to assist patients suffering from chronic obstructive pulmonary disease (COPD) or Asthma that will reduce the dependency on hospital and clinic based tests and enable users to better manage their disease through increased self-involvement. Due to the pervasiveness of smartphones, it is proposed to make use of their built-in sensors and ever increasing computational capabilities to provide patients with a mobile-based spirometer capable of diagnosing COPD or asthma in a reliable and cost effective manner. Data collected using an experimental setup consisting of an airflow source, an anemometer, and a smartphone is used to develop a mathematical model that relates exhalation frequency to air flow rate. This model allows for the computation of two key parameters known as forced vital capacity (FVC) and forced expiratory volume in one second (FEV1) that are used in the diagnosis of respiratory diseases. The developed platform has been validated using data collected from 25 subjects with various conditions. Results show that an excellent match is achieved between the FVC and FEV1 values computed using a clinical spirometer and those returned by the model embedded in the mobile application.

Using Mobile Health Technology to Deliver a Community-Based Closed-Loop Management System for Chronic Obstructive Pulmonary Disease Patients in Remote Areas of China: Development and Prospective Observational Study

Background

Mobile health (mHealth) technology is an increasingly recognized and effective method for disease management and has the potential to intervene in pulmonary function, exacerbation risk, and psychological status of patients with chronic obstructive pulmonary disease (COPD).

Objective

This study aimed to investigate the feasibility of an mHealth-based COPD management system designed for Chinese remote areas with many potential COPD patients but limited medical resources.

Methods

The system was implemented based on a tailored closed-loop care pathway that breaks the heavy management tasks into detailed pieces to be quantified and executed by computers. Low-cost COPD evaluation and questionnaire-based psychological intervention are the 2 main characteristics of the pathway. A 6-month prospective observational study at the community level was performed to evaluate the effect of the system. Primary outcomes included changes in peak expiratory flow values, quality of life measured using the COPD assessment test scale, and psychological condition. Acute exacerbations, compliance, and adverse events were also measured during the study. Compliance was defined as the ratio of the actual frequency of self-monitoring records to the prescribed number.

Results

A total of 56 patients was enrolled; 39 patients completed the 6-month study. There was no significant difference in the mean peak expiratory flow value before and after the 6-month period (366.1, SD 106.7 versus 313.1, SD 116.6; P=.11). Psychological condition significantly improved after 6 months, especially for depression, as measured using the Patient Health Questionnaire-9 scale (median 6.0, IQR 3.0-9.0 versus median 4.0, IQR 0.0-6.0; P=.001). The COPD assessment test score after 6 months of intervention was also lower than that at the baseline, and the difference was significant (median 4.0, IQR 1.0-6.0 versus median 3.0, IQR 0.0-6.0; P=.003). The median overall compliance was 91.1% (IQR 67%-100%). In terms of acute exacerbation, 110 exacerbations were detected and confirmed by health care providers (per 6 months, median 2.0, IQR 1.0-5.0). Moreover, 72 adverse events occurred during the study, including 1 death, 19 hospitalizations, and 52 clinic visits due to persistent respiratory symptoms.

Conclusions

We designed and validated a feasible mHealth-based method to manage COPD in remote Chinese areas with limited medical resources. The proposed closed-loop care pathway was effective at the community level. Proper education and frequent communication with health care providers may encourage patients’ acceptance and use of smartphones to support COPD self-management. In addition, WeChat might play an important role in improving patient compliance and psychological distress. Further research might explore the effect of such systems on a larger scale and at a higher evidence level.

A two-layer probabilistic model to predict COPD exacerbations for patients in telehealth

Conventional one-layer models have yet to achieve clinically relevant classification rates in predicting exacerbations for patients with COPD. The present study investigates whether a two-layer probabilistic model can increase classification rates compared to a one-layer model. Continuous measurements of oxygen saturation, pulse rate, and blood pressure from nine patients with COPD were structured into 17 prodromal exacerbation periods and 398 control periods. A one-layer model was compared to a two-layer model based on prior probabilities using double cross-validation. The two models were compared by the area under the receiver operating characteristics curve and sensitivity at an arbitrarily set specificity of 0.95. This comparison was carried out across nine different classification algorithms. The area under the receiver operating characteristics curve was increased across all nine classification algorithms and by a mean value of 0.11. Sensitivity at an arbitrarily set specificity of 0.95 was also increased by a mean value of 0.13. In conclusion, a two-layer probabilistic model for predicting COPD exacerbations can increase classification rates compared to a one-layer model, and to a level of clinical relevance, for patients in telehealth.

Compliance and Utility of a Smartphone App for the Detection of Exacerbations in Patients With Chronic Obstructive Pulmonary Disease: Cohort Study

Background

In recent years, mobile health (mHealth)–related apps have been developed to help manage chronic diseases. Apps may allow patients with a chronic disease characterized by exacerbations, such as chronic obstructive pulmonary disease (COPD), to track and even suspect disease exacerbations, thereby facilitating self-management and prompt intervention. Nevertheless, there is insufficient evidence regarding patient compliance in the daily use of mHealth apps for chronic disease monitoring.

Objective

This study aimed to provide further evidence in support of prospectively recording daily symptoms as a useful strategy to detect COPD exacerbations through the smartphone app, Prevexair. It also aimed to analyze daily compliance and the frequency and characteristics of acute exacerbations of COPD recorded using Prevexair.

Methods

This is a multicenter cohort study with prospective case recruitment including 116 patients with COPD who had a documented history of frequent exacerbations and were monitored over the course of 6 months. At recruitment, the Prevexair app was installed on their smartphones, and patients were instructed on how to use the app. The information recorded in the app included symptom changes, use of medication, and use of health care resources. The patients received messages on healthy lifestyle behaviors and a record of their cumulative symptoms in the app. There was no regular contact with the research team and no mentoring process. An exacerbation was considered reported if medical attention was sought and considered unreported if it was not reported to a health care professional.

Results

Overall, compliance with daily records in the app was 66.6% (120/180), with a duration compliance of 78.8%, which was similar across disease severity, age, and comorbidity variables. However, patients who were active smokers, with greater dyspnea and a diagnosis of depression and obesity had lower compliance (P<.05). During the study, the patients experienced a total of 262 exacerbations according to daily records in the app, 99 (37.8%) of which were reported exacerbations and 163 (62.2%) were unreported exacerbations. None of the subject-related variables were found to be significantly associated with reporting. The duration of the event and number of symptoms present during the first day were strongly associated with reporting. Despite substantial variations in the COPD Assessment Test (CAT), there was improvement only among patients with no exacerbation and those with reported exacerbations. Nevertheless, CAT scores deteriorated among patients with unreported exacerbations.

Conclusions

The daily use of the Prevexair app is feasible and acceptable for patients with COPD who are motivated in their self-care because of frequent exacerbations of their disease. Monitoring through the Prevexair app showed great potential for the implementation of self-care plans and offered a better diagnosis of their chronic condition.

A Smart Mobile Health Tool Versus a Paper Action Plan to Support Self-Management of Chronic Obstructive Pulmonary Disease Exacerbations: Randomized Controlled Trial

Background

Many patients with chronic obstructive pulmonary disease (COPD) suffer from exacerbations, a worsening of their respiratory symptoms that warrants medical treatment. Exacerbations are often poorly recognized or managed by patients, leading to increased disease burden and health care costs.

Objective

This study aimed to examine the effects of a smart mobile health (mHealth) tool that supports COPD patients in the self-management of exacerbations by providing predictions of early exacerbation onset and timely treatment advice without the interference of health care professionals.

Methods

In a multicenter, 2-arm randomized controlled trial with 12-months follow-up, patients with COPD used the smart mHealth tool (intervention group) or a paper action plan (control group) when they experienced worsening of respiratory symptoms. For our primary outcome exacerbation-free time, expressed as weeks without exacerbation, we used an automated telephone questionnaire system to measure weekly respiratory symptoms and treatment actions. Secondary outcomes were health status, self-efficacy, self-management behavior, health care utilization, and usability. For our analyses, we used negative binomial regression, multilevel logistic regression, and generalized estimating equation regression models.

Results

Of the 87 patients with COPD recruited from primary and secondary care centers, 43 were randomized to the intervention group. We found no statistically significant differences between the intervention group and the control group in exacerbation-free weeks (mean 30.6, SD 13.3 vs mean 28.0, SD 14.8 weeks, respectively; rate ratio 1.21; 95% CI 0.77-1.91) or in health status, self-efficacy, self-management behavior, and health care utilization. Patients using the mHealth tool valued it as a more supportive tool than patients using the paper action plan. Patients considered the usability of the mHealth tool as good.

Conclusions

This study did not show beneficial effects of a smart mHealth tool on exacerbation-free time, health status, self-efficacy, self-management behavior, and health care utilization in patients with COPD compared with the use of a paper action plan. Participants were positive about the supportive function and the usability of the mHealth tool. mHealth may be a valuable alternative for COPD patients who prefer a digital tool instead of a paper action plan.

Trial Registration

ClinicalTrials.gov NCT02553096; https://clinicaltrials.gov/ct2/show/NCT02553096.

Personalized medicine for patients with COPD: where are we?

Chronic airflow limitation is the common denominator of patients with chronic obstructive pulmonary disease (COPD). However, it is not possible to predict morbidity and mortality of individual patients based on the degree of lung function impairment, nor does the degree of airflow limitation allow guidance regarding therapies. Over the last decades, understanding of the factors contributing to the heterogeneity of disease trajectories, clinical presentation, and response to existing therapies has greatly advanced. Indeed, diagnostic assessment and treatment algorithms for COPD have become more personalized. In addition to the pulmonary abnormalities and inhaler therapies, extra-pulmonary features and comorbidities have been studied and are considered essential components of comprehensive disease management, including lifestyle interventions. Despite these advances, predicting and/or modifying the course of the disease remains currently impossible, and selection of patients with a beneficial response to specific interventions is unsatisfactory. Consequently, non-response to pharmacologic and non-pharmacologic treatments is common, and many patients have refractory symptoms. Thus, there is an ongoing urgency for a more targeted and holistic management of the disease, incorporating the basic principles of P4 medicine (predictive, preventive, personalized, and participatory). This review describes the current status and unmet needs regarding personalized medicine for patients with COPD. Also, it proposes a systems medicine approach, integrating genetic, environmental, (micro)biological, and clinical factors in experimental and computational models in order to decipher the multilevel complexity of COPD. Ultimately, the acquired insights will enable the development of clinical decision support systems and advance personalized medicine for patients with COPD.

Predicting forced vital capacity (FVC) using support vector regression (SVR)

OBJECTIVE

Spirometry, as the gold standard approach in the diagnosis of chronic obstructive pulmonary disease (COPD), has strict end of test (EOT) criteria (e.g. complete exhalation), which cannot be met by patients with compromised health states. Thus, significant parameters measured by spirometry, such as forced vital capacity (FVC), have limited accuracies. To address this issue, the present study aimed to develop models based on support vector regression (SVR) to predict values of FVC under the condition that the EOT criteria were not fully met.

APPROACH

The prediction models for the quantification of FVC were developed based on SVR. A total of 354 subjects underwent conventional spirometry (CS), and the resulting data of forced expiratory volumes in 1 s (FEV1), peak expiratory flow (PEF), age and gender were used as input features, while the resulting values of the FVC were used as the target feature in the prediction models. Next, three prediction models (mixed model, normal model and abnormal model) were established according to the criterion in the diagnosis of COPD that a postbronchodilator shows an FEV1/FVC ratio lower than 0.70. Then, 35 subjects were recruited to be tested using both CS and a low-degree-of-EOT criteria spirometry (LDCS), which did not fully meet the EOT criteria of CS. In LDCS, subjects were allowed to terminate the procedure at their own will at any time after the technicians had assumed that both acceptable values of FEV1 and PEF had been obtained. Quantified values of FVC derived from both CS and LDCS were compared to validate the performances of the developed prediction models.

MAIN RESULTS

The FVC prediction performances of the normal model and abnormal model were better than that of the mixed model. The root mean squared error are lower than 0.35 l and the accuracies are higher up to 95%. One-tailed t test results demonstrate that the absolute differences in the measured and predicted values are not significantly different from 0.15 l for both the abnormal model and the normal model.

SIGNIFICANCE

Our study shows the possibility of predicting FVC with acceptable precision in cases where the EOT criteria of spirometry were not fully met, which can be beneficial for patients who cannot or did not achieve full exhalation in spirometry.

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.

Design of a Study Assessing Disease Behaviour During the Peri-Diagnostic Period in Patients with Interstitial Lung Disease: The STARLINER Study

Background/Objectives

This study will aim to characterise disease behaviour during the peri-diagnostic period in patients with suspected interstitial lung disease (ILD), including idiopathic pulmonary fibrosis (IPF), using daily home spirometry and accelerometry. Additionally, this study will aim to increase collaboration between secondary and tertiary centres using a digital collaboration platform.

Methods

The STARLINER study (NCT03261037) will enrol approximately 180 symptomatic patients aged 50 years or more with radiological evidence of ILD/IPF from community and tertiary centres in Canada and Europe. Approximately two-thirds of sites will be community centres. Patients will be followed during pre-diagnosis (inclusion to diagnosis; up to a maximum of 12 months) and post-diagnosis (diagnosis to treatment initiation; up to a maximum of 6 months). The study will be facilitated by a digital ecosystem consisting of the devices used for home-based assessments and a digital collaboration platform enabling communication between community and tertiary centres, and between clinicians and patients.

Planned Outcomes

The primary endpoint will be time-adjusted semi-annual change in forced vital capacity (FVC; in millilitres) during the peri-diagnostic period. Physical functional capacity and patient-reported outcomes (PROs) will also be assessed. FVC and physical functional capacity will be measured using daily home spirometry and accelerometry, and at site visits using spirometry and the 6-min walk test. PROs will be assessed prior to, or during, site visits and will always be completed in the same order.

Conclusions

Findings from this study may help to facilitate the early and accurate diagnosis of ILDs by increasing knowledge about disease progression, enabling collaboration between community and tertiary centres and improving communication between clinicians and patients.

Trial Registration Number

NCT03261037.

Funding

F. Hoffmann-La Roche, Ltd., Basel, Switzerland.

Plain Language Summary

Plain language summary available for this article.

Electronic supplementary material

The online version of this article (10.1007/s12325-018-0845-3) contains supplementary material, which is available to authorized users.

Validation of ACCESS: an automated tool to support self-management of COPD exacerbations

Background

To support patients with COPD in their self-management of symptom worsening, we developed Adaptive Computerized COPD Exacerbation Self-management Support (ACCESS), an innovative software application that provides automated treatment advice without the interference of a health care professional. Exacerbation detection is based on 12 symptom-related yes-or-no questions and the measurement of peripheral capillary oxygen saturation (SpO₂), forced expiratory volume in one second (FEV₁), and body temperature. Automated treatment advice is based on a decision model built by clinical expert panel opinion and Bayesian network modeling. The current paper describes the validity of ACCESS.

Methods

We performed secondary analyses on data from a 3-month prospective observational study in which patients with COPD registered respiratory symptoms daily on diary cards and measured SpO₂, FEV₁, and body temperature. We examined the validity of the most important treatment advice of ACCESS, ie, to contact the health care professional, against symptom- and event-based exacerbations.

Results

Fifty-four patients completed 2,928 diary cards. One or more of the different pieces of ACCESS advice were provided in 71.7% of all cases. We identified 115 symptom-based exacerbations. Cross-tabulation showed a sensitivity of 97.4% (95% CI 92.0-99.3), specificity of 65.6% (95% CI 63.5-67.6), and positive and negative predictive value of 13.4% (95% CI 11.2-15.9) and 99.8% (95% CI 99.3-99.9), respectively, for ACCESS' advice to contact a health care professional in case of an exacerbation.

Conclusion

In many cases (71.7%), ACCESS gave at least one self-management advice to lower symptom burden, showing that ACCES provides self-management support for both day-to-day symptom variations and exacerbations. High sensitivity shows that if there is an exacerbation, ACCESS will advise patients to contact a health care professional. The high negative predictive value leads us to conclude that when ACCES does not provide the advice to contact a health care professional, the risk of an exacerbation is very low. Thus, ACCESS can safely be used in patients with COPD to support self-management in case of an exacerbation.

Modeling asynchronous event sequences with RNNs

Sequences of events have often been modeled with computational techniques, but typical preprocessing steps and problem settings do not explicitly address the ramifications of timestamped events. Clinical data, such as is found in electronic health records (EHRs), typically comes with timestamp information. In this work, we define event sequences and their properties: synchronicity, evenness, and co-cardinality; we then show how asynchronous, uneven, and multi-cardinal problem settings can support explicit accountings of relative time. Our evaluation uses the temporally sensitive clinical use case of pediatric asthma, which is a chronic disease with symptoms (and lack thereof) evolving over time. We show several approaches to explicitly incorporating relative time into a recurrent neural network (RNN) model that improve the overall classification of patients into those with no asthma, those with persistent asthma, those in long-term remission, and those who have experienced relapse. We also compare and contrast these results with those in an inpatient intensive care setting.

The Association Between Technology Use and Health Status in a Chronic Obstructive Pulmonary Disease Cohort: Multi-Method Study

Background

Telemedicine and electronic health (eHealth) interventions have been proposed to improve management of chronic obstructive pulmonary disease (COPD) for patients between traditional clinic and hospital visits to reduce complications. However, the effectiveness of such interventions may depend on patients’ comfort with technology.

Objective

The aim was to describe the relationship between patient demographics and COPD disease severity and the use of communication-related technology.

Methods

We administered a structured survey about the use of communication technologies to a cohort of persons in the COPDGene study at one midwestern hospital in the United States. Survey results were combined with clinical and demographic data previously collected as part of the cohort study. A subsample of patients also completed eHealth simulation tasks. We used logistic or linear regression to determine the relationship between patient demographics and COPD disease severity and reported use of communication-related technology and the results from our simulated eHealth-related tasks.

Results

A total of 686 patients completed the survey and 100 participated in the eHealth simulation. Overall, those who reported using communication technology were younger (P=.005) and had higher incomes (P=.03). Men appeared less likely to engage in text messaging (P<.001) than women. Patients who spent more time on tasks in the eHealth simulation had greater odds of a COPD Assessment Test score >10 (P=.02) and walked shorter distances in their 6-minute walk tests (P=.003) than those who took less time.

Conclusions

Older patients, patients with lower incomes, and less healthy patients were less likely to report using communication technology, and they did not perform as well on our simulated eHealth tasks. Thus, eHealth-based interventions may not be as effective in these populations, and additional training in communication technology may be needed.

Continuous remote monitoring of COPD patients-justification and explanation of the requirements and a survey of the available technologies

Remote patient monitoring should reduce mortality rates, improve care, and reduce costs. We present an overview of the available technologies for the remote monitoring of chronic obstructive pulmonary disease (COPD) patients, together with the most important medical information regarding COPD in a language that is adapted for engineers. Our aim is to bridge the gap between the technical and medical worlds and to facilitate and motivate future research in the field. We also present a justification, motivation, and explanation of how to monitor the most important parameters for COPD patients, together with pointers for the challenges that remain. Additionally, we propose and justify the importance of electrocardiograms (ECGs) and the arterial carbon dioxide partial pressure (PaCO2) as two crucial physiological parameters that have not been used so far to any great extent in the monitoring of COPD patients. We cover four possibilities for the remote monitoring of COPD patients: continuous monitoring during normal daily activities for the prediction and early detection of exacerbations and life-threatening events, monitoring during the home treatment of mild exacerbations, monitoring oxygen therapy applications, and monitoring exercise. We also present and discuss the current approaches to decision support at remote locations and list the normal and pathological values/ranges for all the relevant physiological parameters. The paper concludes with our insights into the future developments and remaining challenges for improvements to continuous remote monitoring systems. Graphical abstract ᅟ.

Decision Making Concepts for the Remote, Personalized Evaluation of COPD Patients’ Health Status

Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Biosignal Interpretation: Advanced Methods for Neural Signals and Images”.

Objectives: This paper presents the main concepts of a decision making approach for the remote management of COPD patients based on the early detection of disease exacerbation episodes.

Methods: An e-diary card is defined to evaluate a number of physiological variables and clinical parameters acquired remotely by means of wearable and environmental sensors deployed in patients’ long-stay settings. The automatic evaluation of the card results in a so-called Chronic Status Index (CSI) whose computation is tailored to patients’ specific manifestation of the disease (i.e., patient’s phenotype). The decision support method relies on a parameterized analysis of CSI variations so as to early detect worsening changes, identify exacerbation severity and track the patterns of recovery.

Results: A preliminary study, carried out in real settings with 30 COPD patients monitored at home, has shown the validity and sensitivity of the method proposed, which was effectively able to timely and correctly identify patients’ critical situation.

Conclusion: The preliminary results showed that the proposed e-diary card, which presents several novel features with respect to other solutions presented in the literature, can be practically used to remotely monitor COPD patients.

Oxygen saturation measurements in telemonitoring of patients with COPD: a systematic review

INTRODUCTION

Telemonitoring applications are expected to become a key component in future healthcare. Despite the frequent use of SpO₂ measurements in telemonitoring of patients with chronic obstructive pulmonary disease (COPD), no profound overview is available about these measurements. Areas covered: A systematic search identified 71 articles that performed SpO₂ measurements in COPD telemonitoring. The results indicate that long-term follow-up of COPD patients using daily SpO₂ spot checks is practically feasible. Very few studies specified protocols for performing these measurements. In many studies, deviating SpO₂ values were used to raise alerts that led to immediate action from healthcare professionals. However, little information was available about the exact implementation and performance of these alerts. Therefore, no firm conclusions can be drawn about the real value of SpO₂ measurements. Future research could optimize performance of alerts using individualized, time-dependent thresholds or predictive algorithms to account for individual differences and SpO₂ baseline changes. Additionally, the value of performing continuous measurements should be examined. Expert commentary: Standardization of the measurements, data science techniques and advancing technology can still boost performance of telemonitoring applications. All these opportunities should be thoroughly explored to assess the real value of SpO₂ in COPD telemonitoring.

Design of wearable and wireless multi-parameter monitoring system for evaluating cardiopulmonary function

The 6-minute walking test (6MWT) is the test most commonly used to evaluate cardiopulmonary function in patients with respiratory or heart disease. However, there was previously no integrated monitoring system available to simultaneously record both the real-time cardiopulmonary physiological parameters and the walking information (i.e., walking distance, speed, and acceleration) during the 6MWT. In this study, then, a wearable and wireless multi-parameter monitoring system was proposed to simultaneously monitor oxygen saturation (SpO₂), heart rhythm, and the walking information during the 6MWT. A multi-parameter detection algorithm was also designed to estimate the heart rate effectively. The results of the study indicate that this system was able to reveal the dynamic changes and differences in walking speed and acceleration during the 6MWT. As such, the system has the potential to provide a more integrated approach to monitoring cardiopulmonary parameters and walking information simultaneously during the 6MWT. The proposed system warrants further investigation as an assistive assessment tool in evaluating cardiopulmonary function and may be widely applied in cardiopulmonary-related and sports medicine applications in the future.

Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network

In the last three decades, researchers have examined extensively how context-aware systems can assist people, specifically those suffering from incurable diseases, to help them cope with their medical illness. Over the years, a huge number of studies on Chronic Obstructive Pulmonary Disease (COPD) have been published. However, how to derive relevant attributes and early detection of COPD exacerbations remains a challenge. In this research work, we will use an efficient algorithm to select relevant attributes where there is no proper approach in this domain. Such algorithm predicts exacerbations with high accuracy by adding discretization process, and organizes the pertinent attributes in priority order based on their impact to facilitate the emergency medical treatment. In this paper, we propose an extension of our existing Helper Context-Aware Engine System (HCES) for COPD. This project uses Bayesian network algorithm to depict the dependency between the COPD symptoms (attributes) in order to overcome the insufficiency and the independency hypothesis of naïve Bayesian. In addition, the dependency in Bayesian network is realized using TAN algorithm rather than consulting pneumologists. All these combined algorithms (discretization, selection, dependency, and the ordering of the relevant attributes) constitute an effective prediction model, comparing to effective ones. Moreover, an investigation and comparison of different scenarios of these algorithms are also done to verify which sequence of steps of prediction model gives more accurate results. Finally, we designed and validated a computer-aided support application to integrate different steps of this model. The findings of our system HCES has shown promising results using Area Under Receiver Operating Characteristic (AUC = 81.5%).

Features of a Mobile Support App for Patients With Chronic Obstructive Pulmonary Disease: Literature Review and Current Applications

Background

Chronic obstructive pulmonary disease (COPD) is a serious long-term lung disease in which the airflow from the lungs is progressively reduced. By 2030, COPD will become the third cause of mortality and seventh cause of morbidity worldwide. With advances in technology and mobile communications, significant progress in the mobile health (mHealth) sector has been recently observed. Mobile phones with app capabilities (smartphones) are now considered as potential media for the self-management of certain types of diseases such as asthma, cancer, COPD, or cardiovascular diseases. While many mobile apps for patients with COPD are currently found on the market, there is little published material on the effectiveness of most of them, their features, and their adoption in health care settings.

Objectives

The aim of this study was to search the literature for current systems related to COPD and identify any missing links and studies that were carried out to evaluate the effectiveness of COPD mobile apps. In addition, we reviewed existing mHealth apps from different stores in order to identify features that can be considered in the initial design of a COPD support tool to improve health care services and patient outcomes.

Methods

In total, 206 articles related to COPD management systems were identified from different databases. Irrelevant materials and duplicates were excluded. Of those, 38 articles were reviewed to extract important features. We identified 214 apps from online stores. Following exclusion of irrelevant apps, 48 were selected and 20 of them were downloaded to review some of their common features.

Results

Our review found that out of the 20 apps downloaded, 13 (65%, 13/20) had an education section, 5 (25%, 5/20) consisted of medication and guidelines, 6 (30%, 6/20) included a calendar or diary and other features such as reminders or symptom tracking. There was little published material on the effectiveness of the identified COPD apps. Features such as (1) a social networking tool; (2) personalized education; (3) feedback; (4) e-coaching; and (5) psychological motivation to enhance behavioral change were found to be missing in many of the downloaded apps.

Conclusions

This paper summarizes the features of a COPD patient-support mobile app that can be taken into consideration for the initial design of an integrated care system to encourage the self-management of their condition at home.

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.

The role of interdisciplinary research team in the impact of health apps in health and computer science publications: a systematic review

BACKGROUND

Several studies have estimated the potential economic and social impact of the mHealth development. Considering the latest study by Institute for Healthcare Informatics, more than 165.000 apps of health and medicine are offered including all the stores from different platforms. Thus, the global mHealth market was an estimated $10.5 billion in 2014 and is expected to grow 33.5 percent annually between 2015 and 2020s. In fact, apps of Health have become the third-fastest growing category, only after games and utilities.

METHODS

This study aims to identify, study and evaluate the role of interdisciplinary research teams in the development of articles and applications in the field of mHealth. It also aims to evaluate the impact that the development of mHealth has had on the health and computer science field, through the study of publications in specific databases for each area which have been published until nowadays.

RESULTS

Interdisciplinary nature is strongly connected to the scientific quality of the journal in which the work is published. This way, there are significant differences in those works that are made up by an interdisciplinary research team because of they achieve to publish in journals with higher quartiles. There are already studies that warn of methodological deficits in some studies in mHealth, low accuracy and no reproducibility. Studies of low precision and poor reproducibility, coupled with the low evidence, provide low degrees of recommendation of the interventions targeted and therefore low applicability.

CONCLUSIONS

From the evidence of this study, working in interdisciplinary groups from different areas greatly enhances the quality of research work as well as the quality of the publications derived from its results.

ChronicOnline: Implementing a mHealth solution for monitoring and early alerting in chronic obstructive pulmonary disease

Lack of time or economic difficulties prevent chronic obstructive pulmonary disease patients from communicating regularly with their physicians, thus inducing exacerbation of their chronic condition and possible hospitalization. Enhancing Chronic patients’ Health Online proposes a new, sustainable and innovative business model that provides at low cost and at significant savings to the national health system, a preventive health service for chronic obstructive pulmonary disease patients, by combining human medical expertise with state-of-the-art online service delivery based on cloud computing, service-oriented architecture, data analytics, and mobile applications. In this article, we implement the frontend applications of the Enhancing Chronic patients’ Health Online system and describe their functionality and the interfaces available to the users.

Novel developments in mobile sensing based on the integration of microfluidic devices and smartphones

Portable electronic devices and wireless communication systems enable a broad range of applications such as environmental and food safety monitoring, personalized medicine and healthcare management. Particularly, hybrid smartphone and microfluidic devices provide an integrated solution for the new generation of mobile sensing applications. Such mobile sensing based on microfluidic devices (broadly defined) and smartphones (MS(2)) offers a mobile laboratory for performing a wide range of bio-chemical detection and analysis functions such as water and food quality analysis, routine health tests and disease diagnosis. MS(2) offers significant advantages over traditional platforms in terms of test speed and control, low cost, mobility, ease-of-operation and data management. These improvements put MS(2) in a promising position in the fields of interdisciplinary basic and applied research. In particular, MS(2) enables applications to remote in-field testing, homecare, and healthcare in low-resource areas. The marriage of smartphones and microfluidic devices offers a powerful on-chip operating platform to enable various bio-chemical tests, remote sensing, data analysis and management in a mobile fashion. The implications of such integration are beyond telecommunication and microfluidic-related research and technology development. In this review, we will first provide the general background of microfluidic-based sensing, smartphone-based sensing, and their integration. Then, we will focus on several key application areas of MS(2) by systematically reviewing the important literature in each area. We will conclude by discussing our perspectives on the opportunities, issues and future directions of this emerging novel field.

A Survey of Mobile Phone Sensing, Self-Reporting, and Social Sharing for Pervasive Healthcare

The current institution-based model for healthcare service delivery faces enormous challenges posed by an aging population and the prevalence of chronic diseases. For this reason, pervasive healthcare, i.e., the provision of healthcare services to individuals anytime anywhere, has become a major focus for the research community. In this paper, we map out the current state of pervasive healthcare research by presenting an overview of three emerging areas in personalized health monitoring, namely: 1) mobile phone sensing via in-built or external sensors, 2) self-reporting for manually captured health information, such as symptoms and behaviors, and 3) social sharing of health information within the individual's community. Systems deployed in a real-life setting as well as proofs-of-concept for achieving pervasive health are presented, in order to identify shortcomings and increase our understanding of the requirements for the next generation of pervasive healthcare systems addressing these three areas.

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.

A smartphone-based system for the automated management of point-of-care test results in hospitals

OBJECTIVE

Managing test results is an important issue in hospitals because of the increasing use of point-of-care testing (POCT). Here, we propose a smartphone-based system for automatically managing POCT test results.

MATERIALS AND METHODS

We developed the system to provide convenience to the medical staffs. The system recognizes the patient identification or prescription number of the test by reading barcodes and provides a countdown to indicate when the results will be ready. When the countdown in finished, a picture of the test result is transferred to the electronic medical record server using the Health Level 7 protocol. Human immunodeficiency virus (HIV) kits were selected in this research because HIV is a life-threatening infectious virus, especially for the medical staff who treat undiagnosed patients. The performance of the system was verified from a survey of the users.

RESULTS

The performance of the system was tested at the emergency room (ER) for 10 months using commercially available POCT kits for detecting HIV. The survey showed that, in total, 80% and 0% of users reported positive or negative feedback, respectively. The staff also reported that the system reduced total processing time by approximately 32 min, in addition to reducing workload.

CONCLUSIONS

The developed automated management system was successfully tested at an ER for 10 months. The survey results show that the system is effective and that medical staff members who used the system are satisfied with using the system at the ER.

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.

Learning Bayesian networks for clinical time series analysis

INTRODUCTION

Autonomous chronic disease management requires models that are able to interpret time series data from patients. However, construction of such models by means of machine learning requires the availability of costly health-care data, often resulting in small samples. We analysed data from chronic obstructive pulmonary disease (COPD) patients with the goal of constructing a model to predict the occurrence of exacerbation events, i.e., episodes of decreased pulmonary health status.

METHODS

Data from 10 COPD patients, gathered with our home monitoring system, were used for temporal Bayesian network learning, combined with bootstrapping methods for data analysis of small data samples. For comparison a temporal variant of augmented naive Bayes models and a temporal nodes Bayesian network (TNBN) were constructed. The performances of the methods were first tested with synthetic data. Subsequently, different COPD models were compared to each other using an external validation data set.

RESULTS

The model learning methods are capable of finding good predictive models for our COPD data. Model averaging over models based on bootstrap replications is able to find a good balance between true and false positive rates on predicting COPD exacerbation events. Temporal naive Bayes offers an alternative that trades some performance for a reduction in computation time and easier interpretation.

Describing disease processes using a probabilistic logic of qualitative time

BACKGROUND

Clinical knowledge about progress of diseases is characterised by temporal information as well as uncertainty. However, precise timing information is often unavailable in medicine. In previous research this problem has been tackled using Allen's qualitative algebra of time, which, despite successful medical application, does not deal with the associated uncertainty.

OBJECTIVES

It is investigated whether and how Allen's temporal algebra can be extended to handle uncertainty to better fit available knowledge and data of disease processes.

METHODS

To bridge the gap between probability theory and qualitative time reasoning, methods from probabilistic logic are explored. The relation between the probabilistic logic representation and dynamic Bayesian networks is analysed. By studying a typical, and clinically relevant problem, the detection of exacerbations of chronic obstructive pulmonary disease (COPD), it is determined whether the developed probabilistic logic of qualitative time is medically useful.

RESULTS

The probabilistic logic extension of Allen's temporal algebra, called Qualitative Time CP-logic provides a tool to model disease processes at a natural level of abstraction and is sufficiently powerful to reason with imprecise, uncertain knowledge. The representation of the COPD disease process gives evidence that the framework can be applied functionally to a clinical problem.

CONCLUSION

The combination of qualitative time and probabilistic logic offers a useful framework for modelling knowledge and data to describe disease processes in clinical medicine.