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

User Engagement With mHealth Interventions to Promote Treatment Adherence and Self-Management in People With Chronic Health Conditions: Systematic Review

BACKGROUND

There are numerous mobile health (mHealth) interventions for treatment adherence and self-management; yet, little is known about user engagement or interaction with these technologies.

OBJECTIVE

This systematic review aimed to answer the following questions: (1) How is user engagement defined and measured in studies of mHealth interventions to promote adherence to prescribed medical or health regimens or self-management among people living with a health condition? (2) To what degree are patients engaging with these mHealth interventions? (3) What is the association between user engagement with mHealth interventions and adherence or self-management outcomes? (4) How often is user engagement a research end point?

METHODS

Scientific database (Ovid MEDLINE, Embase, Web of Science, PsycINFO, and CINAHL) search results (2016-2021) were screened for inclusion and exclusion criteria. Data were extracted in a standardized electronic form. No risk-of-bias assessment was conducted because this review aimed to characterize user engagement measurement rather than certainty in primary study results. The results were synthesized descriptively and thematically.

RESULTS

A total of 292 studies were included for data extraction. The median number of participants per study was 77 (IQR 34-164). Most of the mHealth interventions were evaluated in nonrandomized studies (157/292, 53.8%), involved people with diabetes (51/292, 17.5%), targeted medication adherence (98/292, 33.6%), and comprised apps (220/292, 75.3%). The principal findings were as follows: (1) >60 unique terms were used to define user engagement; "use" (102/292, 34.9%) and "engagement" (94/292, 32.2%) were the most common; (2) a total of 11 distinct user engagement measurement approaches were identified; the use of objective user log-in data from an app or web portal (160/292, 54.8%) was the most common; (3) although engagement was inconsistently evaluated, most of the studies (99/195, 50.8%) reported >1 level of engagement due to the use of multiple measurement methods or analyses, decreased engagement across time (76/99, 77%), and results and conclusions suggesting that higher engagement was associated with positive adherence or self-management (60/103, 58.3%); and (4) user engagement was a research end point in only 19.2% (56/292) of the studies.

CONCLUSIONS

The results revealed major limitations in the literature reviewed, including significant variability in how user engagement is defined, a tendency to rely on user log-in data over other measurements, and critical gaps in how user engagement is evaluated (infrequently evaluated over time or in relation to adherence or self-management outcomes and rarely considered a research end point). Recommendations are outlined in response to our findings with the goal of improving research rigor in this area.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42022289693; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022289693.

Remote Patient Monitoring and Machine Learning in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Dual Systematic Literature Review and Narrative Synthesis

BACKGROUND

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with high mortality, morbidity, and poor quality of life and constitute a substantial burden to patients and health care systems. New approaches to prevent or reduce the severity of AECOPD are urgently needed. Internationally, this has prompted increased interest in the potential of remote patient monitoring (RPM) and digital medicine. RPM refers to the direct transmission of patient-reported outcomes, physiological, and functional data, including heart rate, weight, blood pressure, oxygen saturation, physical activity, and lung function (spirometry), directly to health care professionals through automation, web-based data entry, or phone-based data entry. Machine learning has the potential to enhance RPM in chronic obstructive pulmonary disease by increasing the accuracy and precision of AECOPD prediction systems.

OBJECTIVE

This study aimed to conduct a dual systematic review. The first review focuses on randomized controlled trials where RPM was used as an intervention to treat or improve AECOPD. The second review examines studies that combined machine learning with RPM to predict AECOPD. We review the evidence and concepts behind RPM and machine learning and discuss the strengths, limitations, and clinical use of available systems. We have generated a list of recommendations needed to deliver patient and health care system benefits.

METHODS

A comprehensive search strategy, encompassing the Scopus and Web of Science databases, was used to identify relevant studies. A total of 2 independent reviewers (HMGG and CM) conducted study selection, data extraction, and quality assessment, with discrepancies resolved through consensus. Data synthesis involved evidence assessment using a Critical Appraisal Skills Programme checklist and a narrative synthesis. Reporting followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.

RESULTS

These narrative syntheses suggest that 57% (16/28) of the randomized controlled trials for RPM interventions fail to achieve the required level of evidence for better outcomes in AECOPD. However, the integration of machine learning into RPM demonstrates promise for increasing the predictive accuracy of AECOPD and, therefore, early intervention.

CONCLUSIONS

This review suggests a transition toward the integration of machine learning into RPM for predicting AECOPD. We discuss particular RPM indices that have the potential to improve AECOPD prediction and highlight research gaps concerning patient factors and the maintained adoption of RPM. Furthermore, we emphasize the importance of a more comprehensive examination of patient and health care burdens associated with RPM, along with the development of practical solutions.

Effect of telemonitoring and telerehabilitation on physical activity, exercise capacity, health-related quality of life and healthcare use in patients with chronic lung diseases or COVID-19: A scoping review

BACKGROUND

Telemonitoring and telerehabilitation can support home-based pulmonary rehabilitation (PR) and benefit patients with lung diseases or COVID-19. This study aimed to (1) identify which telemonitoring and telerehabilitation interventions (e.g. videoconferencing) are used to provide telehealth care for people with chronic respiratory conditions or COVID-19, and (2) provide an overview of the effects of telemonitoring and telerehabilitation on exercise capacity, physical activity, health-related QoL (HRQoL), and healthcare use in patients with lung diseases or COVID-19.

METHODS

A search was performed in the electronic databases of Ovid MEDLINE, EMBASE, and Cinahl through 15 June 2021. Subject heading and keywords were used to reflect the concepts of telemonitoring, telerehabilitation, chronic lung diseases, and COVID-19. Studies that explored the effect of a telerehabilitation and/or telemonitoring intervention, in patients with a chronic lung disease such as asthma, chronic obstructive pulmonary diseases (COPD), or COVID-19, and reported the effect of the intervention in one or more of our outcomes of interest were included. Excluding criteria included evaluation of new technological components, teleconsultation or one-time patient assessment.

RESULTS

This scoping review included 44 publications reporting the effect of telemonitoring (25 studies), telerehabilitation (8 studies) or both (11 studies) on patients with COPD (35 studies), asthma (5 studies), COPD and asthma (1 study), and COVID-19 (2 studies). Patients who received telemonitoring and/or telerehabilitation had improvements in exercise capacity in 9 out of 11 (82%) articles, better HRQoL in 21 out of 25 (84%), and fewer health care use in 3 out of 3 (100%) articles compared to pre-intervention. Compared to controls, no statistically significant differences were found in the intervention groups' exercise capacity in 5 out 6 (83%) articles, physical activity in 3 out of 3 (100%) articles, HRQoL in 21 out of 25 (84%) articles, and healthcare use in 15 out of 20 (75%) articles. The main limitation of the study was the high variability between the characteristics of the studies, such as the number and age of the patients, the outcome measures, the duration of the intervention, the technological components involved, and the additional elements included in the interventions that may influence the generalization of the results.

CONCLUSION

Telemonitoring and telerehabilitation interventions had a positive effect on patient outcomes and appeared to be as effective as standard care. Therefore, they are promising alternatives to support remote home-based rehabilitation in patients with chronic lung diseases or COVID-19.

Towards evidence-based practice 2.0: leveraging artificial intelligence in healthcare

Background

Evidence-based practice (EBP) involves making clinical decisions based on three sources of information: evidence, clinical experience and patient preferences. Despite popularization of EBP, research has shown that there are many barriers to achieving the goals of the EBP model. The use of artificial intelligence (AI) in healthcare has been proposed as a means to improve clinical decision-making. The aim of this paper was to pinpoint key challenges pertaining to the three pillars of EBP and to investigate the potential of AI in surmounting these challenges and contributing to a more evidence-based healthcare practice. We conducted a selective review of the literature on EBP and the integration of AI in healthcare to achieve this.

Challenges with the three components of EBP

Clinical decision-making in line with the EBP model presents several challenges. The availability and existence of robust evidence sometimes pose limitations due to slow generation and dissemination processes, as well as the scarcity of high-quality evidence. Direct application of evidence is not always viable because studies often involve patient groups distinct from those encountered in routine healthcare. Clinicians need to rely on their clinical experience to interpret the relevance of evidence and contextualize it within the unique needs of their patients. Moreover, clinical decision-making might be influenced by cognitive and implicit biases. Achieving patient involvement and shared decision-making between clinicians and patients remains challenging in routine healthcare practice due to factors such as low levels of health literacy among patients and their reluctance to actively participate, barriers rooted in clinicians' attitudes, scepticism towards patient knowledge and ineffective communication strategies, busy healthcare environments and limited resources.

AI assistance for the three components of EBP

AI presents a promising solution to address several challenges inherent in the research process, from conducting studies, generating evidence, synthesizing findings, and disseminating crucial information to clinicians to implementing these findings into routine practice. AI systems have a distinct advantage over human clinicians in processing specific types of data and information. The use of AI has shown great promise in areas such as image analysis. AI presents promising avenues to enhance patient engagement by saving time for clinicians and has the potential to increase patient autonomy although there is a lack of research on this issue.

Conclusion

This review underscores AI's potential to augment evidence-based healthcare practices, potentially marking the emergence of EBP 2.0. However, there are also uncertainties regarding how AI will contribute to a more evidence-based healthcare. Hence, empirical research is essential to validate and substantiate various aspects of AI use in healthcare.

Content, Mechanism, and Outcome of Effective Telehealth Solutions for Management of Chronic Obstructive Pulmonary Diseases: A Narrative Review

Telehealth (TH) solutions for Chronic Obstructive Pulmonary Disease (COPD) are promising behavioral therapeutic interventions and can help individuals living with COPD to improve their health status. The linking content, mechanism, and outcome of TH interventions reported in the literature related to COPD care are unknown. This paper aims to summarize the existing literature about structured TH solutions in COPD care. We conducted an electronic search of the literature related to TH solutions for COPD management up to October 2023. Thirty papers presented TH solutions as an innovative treatment to manage COPD. TH and digital health solutions are used interchangeably in the literature, but both have the potential to improve care, accessibility, and quality of life. To date, current TH solutions in COPD care have a variety of content, mechanisms, and outcomes. TH solutions can enhance education as well as provide remote monitoring. The content of TH solutions can be summarized as symptom management, prompt physical activity, and psychological support. The mechanism of TH solutions is manipulated by factors such as content, mode of delivery, strategy, and intensity. The most common outcome measures with TH solutions were adherence to treatment, health status, and quality of life. Implementing effective TH with a COPD care bundle must consider important determinants such as patient's needs, familiarity with the technology, healthcare professional support, and data privacy. The development of effective TH solutions for COPD management also must consider patient engagement as a positive approach to optimizing implementation and effectiveness.

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.

Mobile heath applications for self-management in chronic lung disease: a systematic review

Integration of mobile health (mHealth) applications (apps) into chronic lung disease management is becoming increasingly popular. MHealth apps may support adoption of self-management behaviors to assist people in symptoms control and quality of life enhancement. However, mHealth apps' designs, features, and content are inconsistently reported, making it difficult to determine which were the effective components. Therefore, this review aims to summarize the characteristics and features of published mHealth apps for chronic lung diseases. A structured search strategy across five databases (CINAHL, Medline, Embase, Scopus and Cochrane) was performed. Randomized controlled trials investigating interactive mHealth apps in adults with chronic lung disease were included. Screening and full-text reviews were completed by three reviewers using Research Screener and Covidence. Data extraction followed the mHealth Index and Navigation Database (MIND) Evaluation Framework (https://mindapps.org/), a tool designed to help clinicians determine the best mHealth apps to address patients' needs. Over 90,000 articles were screened, with 16 papers included. Fifteen distinct apps were identified, 8 for chronic obstructive pulmonary disease (53%) and 7 for asthma (46%) self-management. Different resources informed app design approaches, accompanied with varying qualities and features across studies. Common reported features included symptom tracking, medication reminders, education, and clinical support. There was insufficient information to answer MIND questions regarding security and privacy, and only five apps had additional publications to support their clinical foundation. Current studies reported designs and features of self-management apps differently. These app design variations create challenges in determining their effectiveness and suitability for chronic lung disease self-management. Registration: PROSPERO (CRD42021260205).

Supplementary Information

The online version contains supplementary material available at 10.1007/s13721-023-00419-0.

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.

Usability of a mobile application for the clinical follow-up of patients with chronic obstructive pulmonary disease and home oxygen therapy

BACKGROUND

Technological health tools (e-Health) may potentially facilitate the treatment of patients with chronic diseases through development of self-management and -care skills in patients and caregivers. However, these tools are usually marketed without prior analysis and without providing any context to final users, which frequently results in low adherence to their use.

PURPOSE

To determine the usability of and satisfaction toward a mobile app for the clinical monitoring of patients with chronic obstructive pulmonary disease (COPD) receiving oxygen therapy at home.

METHODS

This was a participative-qualitative study focused on final users-with direct intervention by patients and professionals-consisting of three phases as follows: (i) medium-fidelity mockups design, (ii) development of a usability test for each user profile, and (iii) assessment of the satisfaction level regarding the usability of the mobile app. A sample was established and selected through non-probability convenience sampling and was divided into two groups as follows: healthcare professionals (n = 13) and patients (n = 7). Each participant received a smartphone with mockup designs. The "think-aloud" method was applied in the usability test. Participants were audio recorded and the anonymous transcriptions were analyzed, highlighting fragments about mockups characteristics and the usability test. The difficulty level of the tasks was assessed with a scale from 1 (very easy) to 5 (too difficult), and task non-completion was considered a critical mistake. The satisfaction level related to test usability was assessed with a 4-score Likert scale ranging from 4 (totally agree) to 1 (totally disagree).

RESULTS

Regarding the difficulty level, >60% of professionals described most tasks as "very easy" and 70% of patients as "easy." No participant made critical mistakes and both groups reported a high satisfaction level regarding the usability variables. The patient and professionals group required 18 and 11 min to complete all tasks, respectively.

CONCLUSIONS

Participants described the app as intuitive and easy to use. The usability satisfaction results show a high level of satisfaction for both groups. This positive assessment and performance in user tests showed that the mobile application was able to be apprehended and used by participants in the circumstances of use in the usability tests. Usability evaluation through satisfaction surveys and qualitative data analysis allows for greater insight into the use of mobile applications in healthcare.

Features and characteristics of publicly available mHealth apps for self-management in chronic obstructive pulmonary disease

Mobile health applications (mHealth apps) may be able to support people living with chronic obstructive pulmonary disease (COPD) to develop the appropriate skills and routines for adequate self-management. Given the wide variety of publicly available mHealth apps, it is important to be aware of their characteristics to optimize their use and mitigate potential harms.

Objective

To report the characteristics and features of publicly available apps for COPD self-management.

Methods

MHealth apps designed for patients' COPD self-management were searched in the Google Play and Apple app stores. Two reviewers trialed and assessed the eligible apps using the MHealth Index and Navigation Database framework to describe the characteristics, qualities, and features of mHealth apps across five domains.

Results

From the Google Play and Apple stores, thirteen apps were identified and eligible for further evaluation. All thirteen apps were available for Android devices, but only seven were available for Apple devices. Most apps were developed by for-profit organizations (8/13), non-profit organizations (2/13), and unknown developers (3/13). Many apps had privacy policies (9/13), but only three apps described their security systems and two mentioned compliance with local health information and data usage laws. Education was the common app feature; additional features were medication reminders, symptom tracking, journaling, and action planning. None provided clinical evidence to support their use.

Conclusions

Publicly available COPD apps vary in their designs, features, and overall quality. These apps lack evidence to support their clinical use and cannot be recommended at this time.

Patients, caregivers, and healthcare professionals' needs when designing the content of a mobile application for the clinical monitoring of patients with chronic obstructive pulmonary disease and home oxygen therapy: A user-centered design

Background

Patients with chronic respiratory diseases require oxygen supply in a considerable amount to reduce their symptoms and increase their survival. The development of abilities for the self-management of chronic diseases has been shown to be essential to decrease exacerbation of symptoms. Therefore, the design and development of health mobile applications (apps) that aid in educating and training for disease self-management are cost-effective strategies.

Objective

The purpose of this research was to describe the main characteristics that, according to final users, should be included in a mobile application for monitoring patients prescribed home oxygen therapy.

Methods

A participative-qualitative design was used, involving direct participation of patients, caregivers, and healthcare professionals. Focus groups were conducted to identify the needs and perspectives related to the app. A card sorting method was used to determine the contents and basic architecture of the app.

Results

By means of the focus groups, we could identify nine basic functions of the app for the clinical monitoring of patients under home oxygen therapy. For both profiles, the app structure was determined by identifying the most frequent contents among participants.

Conclusions

The implementation of a user-centered design allowed for the detection of the functions, contents, and basic architecture of the app by identifying healthcare professionals and patients' needs and preferences regarding the self-management and monitoring of home oxygen prescriptions.

Trial Registration

The study is registered in ClinicalTrials.gov: NCT04820790.

Quality Standard Position Statements for Health System Policy Changes in Diagnosis and Management of COPD: A Global Perspective

Introduction

Despite being a leading cause of death worldwide, chronic obstructive pulmonary disease (COPD) is underdiagnosed and underprioritized within healthcare systems. Existing healthcare policies should be revisited to include COPD prevention and management as a global priority. Here, we propose and describe health system quality standard position statements that should be implemented as a consistent standard of care for patients with COPD.

Methods

A multidisciplinary group of clinicians with expertise in COPD management together with patient advocates from eight countries participated in a quality standards review meeting convened in April 2021. The principal objective was to achieve consensus on global health system priorities to ensure consistent standards of care for COPD. These quality standard position statements were either evidence-based or reflected the combined views of the panel.

Results

On the basis of discussions, the experts adopted five quality standard position statements, including the rationale for their inclusion, supporting clinical evidence, and essential criteria for quality metrics. These quality standard position statements emphasize the core elements of COPD care, including (1) diagnosis, (2) adequate patient and caregiver education, (3) access to medical and nonmedical treatments aligned with the latest evidence-based recommendations and appropriate management by a respiratory specialist when required, (4) appropriate management of acute COPD exacerbations, and (5) regular patient and caregiver follow-up for care plan reviews.

Conclusions

These practical quality standards may be applicable to and implemented at both local and national levels. While universally applicable to the core elements of appropriate COPD care, they can be adapted to consider differences in healthcare resources and priorities, organizational structure, and care delivery capabilities of individual healthcare systems. We encourage the adoption of these global quality standards by policymakers and healthcare practitioners alike to inform national and regional health system policy revisions to improve the quality and consistency of COPD care worldwide.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12325-022-02137-x.

Effectiveness of remote home monitoring for patients with Chronic Obstructive Pulmonary Disease (COPD): systematic review

Background

Although remote home monitoring (RHM) has the capacity to prevent exacerbations in patients with chronic obstructive pulmonary disease (COPD), evidence regarding its effectiveness remains unclear. The objective of this study was to determine the effectiveness of RHM in patients with COPD.

Methods

A systematic review of the scholarly literature published within the last 10 years was conducted using internationally recognized guidelines. Search strategies were applied to several electronic databases and clinical trial registries through March 2020 to identify studies comparing RHM to ‘no remote home monitoring’ (no RHM) or comparing RHM with provider’s feedback to RHM without feedback. To critically appraise the included randomized studies, the Cochrane Collaboration risk of bias tool (ROB) was used. The quality of included non-randomized interventional and comparative observational studies was evaluated using the ACROBAT-NRSI tool from the Cochrane Collaboration. The quality of evidence relating to key outcomes was assessed using Grading of Recommendations, Assessment, Development and Evaluations (GRADE) on the following: health-related quality of life (HRQoL), patient experience and number of exacerbations, number of emergency room (ER) visits, COPD-related hospital admissions, and adherence as the proportion of patients who completed the study. Three independent reviewers assessed methodologic quality and reviewed the studies.

Results

Seventeen randomized controlled trials (RCTs) and two comparative observational studies were included in the review. The primary finding of this systematic review is that a considerable amount of evidence relating to the efficacy/effectiveness of RHM exists, but its quality is low. Although RHM is safe, it does not appear to improve HRQoL (regardless of the type of RHM), lung function or self-efficacy, or to reduce depression, anxiety, or healthcare resource utilization. The inclusion of regular feedback from providers may reduce COPD-related hospital admissions. Though adherence RHM remains unclear, both patient and provider satisfaction were high with the intervention.

Conclusions

Although a considerable amount of evidence to the effectiveness of RHM exists, due to heterogeneity of care settings and the low-quality evidence, they should be interpreted with caution.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-07938-y.

Evaluation of m‐Health‐rehabilitation for respiratory disorders: A systematic review

Background and Aims

Chronic respiratory diseases are prominent causes of morbidity worldwide that impose significant social and economic burdens on individuals and communities. Pulmonary rehabilitation is one of the main aspects of medical rehabilitation. Nowadays, mobile health apps deliver pulmonary rehabilitation support via smartphones. This article presents a systematic review of the literature on m‐Health apps used in respiration disorders rehabilitation.

Methods

A systematic search was performed on MEDLINE (through PubMed), Web of Science, and Scopus in May 2021 without any date limitation. This study was using a combination of keywords and MeSH terms associated with pulmonary rehabilitation. Relevant studies were selected by two independents and were categorized studies results. The inclusion criterion was m‐Health apps for pulmonary rehabilitation and exclusion criteria mobile‐based interventions, by voice call or short message service and cardiopulmonary articles.

Results

Searching scientific databases yielded 161 relevant articles. Then, 27 articles were included in the study with a complete evaluation of the articles. Sixty percent of them were related to patients with chronic obstructive pulmonary disease (COPD). Rehabilitation aiming to improve the quality of life, promote self‐management, encourage physical activity, and reduce the symptoms as the most common goals of pulmonary rehabilitation using m‐Health apps; 89% of these studies showed that m‐Health apps can be effective in improving pulmonary rehabilitation. In addition, 37% of studies reported high usability and acceptance. However, the results of some studies show that adherence to apps decreases in the long run.

Conclusion

Our study shows that m‐Health pulmonary rehabilitation apps are effective in improving the quality of life, self‐management, and physical activity. According to the results, it seems that using the m‐Health apps for pulmonary rehabilitation can be useful in the COVID‐19 pandemic and help reduce respiratory disorders in patients with COVID‐19 disease.

Validity and Reliability of the Korean Version of the Health Information Technology Usability Evaluation Scale: Psychometric Evaluation

Background

Rigorous development of mobile technologies requires the use of validated instruments to evaluate the usability of these tools, which has become more relevant with the expansion of these technologies. Although various usability evaluation tools have been developed, there are relatively few simple evaluation instruments that have been validated across diseases and languages in mobile health (mHealth) information technology for use in multiple diseases.

Objective

The purpose of this study is to validate the Korean version of the Health Information Technology Usability Evaluation Scale (Korean Health-ITUES) and assess its applicability for different health conditions.

Methods

To develop the Korean Health-ITUES, we used a validation process involving the following 3 steps: (1) customization of the Health-ITUES for menstrual symptoms, (2) translation of the Health-ITUES from English into Korean, and (3) examination of the reliability and validity of the instrument. The translation process adhered to the World Health Organization (WHO) guidelines for translation and back-translation, expert review, and reconciliation.

Results

The Korean Health-ITUES showed reliable internal consistency with Cronbach α=.951; meanwhile, factor loadings of the 20 items in the 4 subscales ranged from 0.416 to 0.892.

Conclusions

The Health-ITUES demonstrated reliability and validity for its use in assessing mHealth apps’ usability in young Korean women with menstrual discomfort. Given the strong psychometric properties of this tool in Korean and English and across 2 different health conditions, the Health-ITUES is a valid and reliable instrument for assessing the usability of mHealth apps. The Health-ITUES is also a valid instrument for evaluating mHealth technologies, which are widely used by patients to self-manage their health and by providers to improve health care delivery.

The Role of Digital Tools in the Timely Diagnosis and Prevention of Acute Exacerbations of COPD: A Comprehensive Review of the Literature

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory disease of the airways and lung parenchyma with multiple systemic manifestations. Exacerbations of COPD are important events during the course of the disease, as they are associated with increased mortality, severe impairment of health-related quality of life, accelerated decline in lung function, significant reduction in physical activity, and substantial economic burden. Telemedicine is the use of communication technologies to transmit medical data over short or long distances and to deliver healthcare services. The need to limit in-person appointments during the COVID-19 pandemic has caused a rapid increase in telemedicine services. In the present review of the literature covering published randomized controlled trials reporting results regarding the use of digital tools in acute exacerbations of COPD, we attempt to clarify the effectiveness of telemedicine for identifying, preventing, and reducing COPD exacerbations and improving other clinically relevant outcomes, while describing in detail the specific telemedicine interventions used.

Development features and study characteristics of mobile health apps in the management of chronic conditions: a systematic review of randomised trials

COVID-19 pandemic challenges have accelerated the reliance on digital health fuelling the expanded incorporation of mobile apps into healthcare services, particularly for the management of long-term conditions such as chronic diseases (CDs). However, the impact of health apps on outcomes for CD remains unclear, potentially owing to both the poor adoption of formal development standards in the design process and the methodological quality of studies. A systematic search of randomised trials was performed on Medline, ScienceDirect, the Cochrane Library and Scopus to provide a comprehensive outlook and review the impact of health apps on CD. We identified 69 studies on diabetes (n = 29), cardiovascular diseases (n = 13), chronic respiratory diseases (n = 13), cancer (n = 10) or their combinations (n = 4). The apps rarely adopted developmental factors in the design stage, with only around one-third of studies reporting user or healthcare professional engagement. Apps differed significantly in content, with a median of eight behaviour change techniques adopted, most frequently pertaining to the ‘Feedback and monitoring’ (91%) and ‘Shaping knowledge’ (72%) categories. As for the study methodologies, all studies adopted a traditional randomised control trial (RCT) design, with relatively short follow-ups and limited sample sizes. Findings were not significant for the majority of studies across all CD, with most RCTs revealing a high risk of bias. To support the adoption of apps for CD management, this review reinforces the need for more robust development and appropriate study characteristics to sustain evidence generation and elucidate whether study results reflect the true benefits of apps or a biased estimate due to unsuitable designs.

Effectiveness of Telemonitoring for Reducing Exacerbation Occurrence in COPD Patients With Past Exacerbation History: A Systematic Review and Meta-Analysis

Background: Although an increasing number of studies have reported that telemonitoring (TM) in patients with chronic obstructive pulmonary disease (COPD) can be useful and efficacious for hospitalizations and quality of life, its actual utility in detecting and managing acute exacerbation of COPD (AECOPD) is less established. This meta-analysis aimed to identify the best available evidence on the effectiveness of TM targeting the early and optimized management of AECOPD in patients with a history of past AECOPD compared with a control group without TM intervention.

Methods: We systematically searched PubMed, Embase, and the Cochrane Library for randomized controlled trials published from 1990 to May 2020. Primary endpoints included emergency room visits and exacerbation-related readmissions. P-values, risk ratios, odds ratios, and mean differences with 95% confidence intervals were calculated.

Results: Of 505 identified citations, 17 original articles with both TM intervention and a control group were selected for the final analysis (N = 3,001 participants). TM was found to reduce emergency room visits [mean difference (MD) −0.70, 95% confidence interval (CI) −1.36 to −0.03], exacerbation-related readmissions (risk ratio 0.74, 95% CI 0.60–0.92), exacerbation-related hospital days (MD −0.60, 95% CI −1.06 to −0.13), mortality (odds ratio 0.71, 95% CI 0.54–0.93), and the St. George's Respiratory Questionnaire (SGRQ) score (MD −3.72, 95% CI −7.18 to −0.26) but did not make a difference with respect to all-cause readmissions, the rate of exacerbation-related readmissions, all-cause hospital days, time to first hospital readmission, anxiety and depression, and exercise capacity. Furthermore, the subgroup analysis by observation period showed that longer TM (≥12 months) was more effective in reducing readmissions.

Conclusions: TM can reduce emergency room visits and exacerbation-related readmissions, as well as acute exacerbation (AE)-related hospital days, mortality, and the SGRQ score. The implementation of TM intervention is thus a potential protective therapeutic strategy that could facilitate the long-term management of AECOPD.

Systematic Review Registration: This systematic review and meta-analysis is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement and was registered at International Prospective Register of Systematic Reviews (number: CRD42020181459).

A Systematic Review and Meta-Analysis of Telemonitoring Interventions on Severe COPD Exacerbations

This systematic review and meta-analysis aimed to provide current evidence regarding the effectiveness of telemonitoring for preventing COPD exacerbations, focusing on severe exacerbations requiring hospitalisation or emergency room (ER) visits. We systematically searched for randomised controlled trials using nine databases from August to September 2020 following the Cochrane Collaboration Guidelines. Of 2159 records identified, 22 studies involving 2906 participants met the inclusion criteria. The participants in 55% and 59% of studies had severe airflow limitations and severe exacerbation histories in the preceding year, respectively. The most commonly telemonitored data were oxygen saturation (91%) and symptoms (73%). A meta-analysis showed that telemonitoring did not reduce the number of admissions (12 studies) but decreased the number of ER visits due to severe exacerbations [7 studies combined, standardised mean difference (SMD) = −0.14; 95% confidence interval (CI): −0.28, −0.01]. Most studies reported no benefit in mortality, quality of life, or cost-effectiveness. All eight studies that surveyed participant satisfaction reported high satisfaction levels. Our review suggested that adding telemonitoring to usual care reduced unnecessary ER visits but was unlikely to prevent hospitalisations due to COPD exacerbations and that telemonitoring was well-accepted by patients with COPD and could be easily integrated into their existing care.

Using COPD action plans to support outcomes after discharge

ABSTRACT

In 2017, the World Health Organization reported that chronic obstructive pulmonary disease (COPD) impacted 251 million individuals and was responsible for 3.17 million deaths globally. To educate hospitalized patients with COPD about self-management at home, nurses require an action plan to use as part of discharge instructions. This article discusses the benefits of COPD action plans revealed in the literature and describes the creation and use of such an action plan by the author.

Digital interventions for the management of chronic obstructive pulmonary disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is associated with dyspnoea, cough or sputum production (or both) and affects quality of life and functional status. More efficient approaches to alternative management that may include patients themselves managing their condition need further exploration in order to reduce the impact on both patients and healthcare services. Digital interventions may potentially impact on health behaviours and encourage patient engagement.

OBJECTIVES

To assess benefits and harms of digital interventions for managing COPD and apply Behaviour Change Technique (BCT) taxonomy to describe and explore intervention content.

SEARCH METHODS

We identified randomised controlled trials (RCTs) from the Cochrane Airways Trials Register (date of last search 28 April 2020). We found other trials at web-based clinical trials registers.

SELECTION CRITERIA

We included RCTs comparing digital technology interventions with or without routine supported self-management to usual care, or control treatment for self-management. Multi-component interventions (of which one component was digital self-management) compared with usual care, standard care or control treatment were included.

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methods. Two review authors independently selected trials for inclusion, extracted data, and assessed risk of bias. Discrepancies were resolved with a third review author. We assessed certainty of the evidence using the GRADE approach. Primary outcomes were impact on health behaviours, self-efficacy, exacerbations and quality of life, including the St George's Respiratory Questionnaire (SGRQ). The minimally important difference (MID) for the SGRQ is 4 points. Two review authors independently applied BCT taxonomy to identify mechanisms in the digital interventions that influence behaviours.

MAIN RESULTS

Fourteen studies were included in the meta-analyses (1518 participants) ranging from 13 to 52 weeks duration. Participants had mild to very severe COPD. Risk of bias was high due to lack of blinding. GRADE ratings were low to very low certainty due to lack of blinding and imprecision. Common BCT clusters identified as behaviour change mechanisms in interventions were goals and planning, feedback and monitoring, social support, shaping knowledge and antecedents. Digital technology intervention with or without routine supported self-management Interventions included mobile phone (three studies), smartphone applications (one study), and web or Internet-based (five studies). Evidence is very uncertain about effects on impact on health behaviours as measured by six-minute walk distance (6MWD) at 13 weeks (mean difference (MD) 26.20, 95% confidence interval (CI) -21.70 to 74.10; participants = 122; studies = 2) or 23 to 26 weeks (MD 14.31, 95% CI -19.41 to 48.03; participants = 164; studies = 3). There may be improvement in 6MWD at 52 weeks (MD 54.33 95% CI -35.47 to 144.12; participants = 204; studies = 2) but studies were varied (very low certainty). There may be no difference in self-efficacy on managing Chronic Disease Scale (SEMCD) or pulmonary rehabilitation adapted index of self-efficacy tool (PRAISE). Evidence is very uncertain. Quality of life may be slightly improved on the chronic respiratory disease questionnaire (CRQ) at 13 weeks (MD 0.45, 95% CI 0.01 to 0.90; participants = 123; studies = 2; low certainty), but is not clinically important (MID 0.5). There may be little or no difference at 23 or 52 weeks (low to very low certainty). There may be a clinical improvement on SGRQ total at 52 weeks (MD -26.57, 95% CI -34.09 to -19.05; participants = 120; studies = 1; low certainty). Evidence for COPD assessment test (CAT) and Clinical COPD Questionnaire (CCQ) is very uncertain. There may be little or no difference in dyspnoea symptoms (CRQ dyspnoea) at 13, 23 weeks or 52 weeks (low to very low certainty evidence) or mean number of exacerbations at 26 weeks (low-certainty evidence). There was no evidence for the number of people experiencing adverse events. Multi-component interventions Digital components included mobile phone (one study), and web or internet-based (four studies). Evidence is very uncertain about effects on impact on health behaviour (6MWD) at 13 weeks (MD 99.60, 95% CI -15.23 to 214.43; participants = 20; studies = 1). No evidence was found for self-efficacy. Four studies reported effects on quality of life (SGRQ and CCQ scales). The evidence is very uncertain. There may be no difference in the number of people experiencing exacerbations or mean days to first exacerbation at 52 weeks with a multi-component intervention compared to standard care. Evidence is very uncertain about effects on the number of people experiencing adverse events at 52 weeks.

AUTHORS' CONCLUSIONS

There is insufficient evidence to demonstrate a clear benefit or harm of digital technology interventions with or without supported self-management, or multi-component interventions compared to usual care in improving the 6MWD or self-efficacy. We found there may be some short-term improvement in quality of life with digital interventions, but there is no evidence about whether the effect is sustained long term. Dyspnoea symptoms may improve over a longer duration of digital intervention use. The evidence for multi-component interventions is very uncertain and as there is little or no evidence for adverse events, we cannot determine the benefit or harm of these interventions. The evidence base is predominantly of very low certainty with concerns around high risk of bias due to lack of blinding. Given that variation of interventions and blinding is likely to be a concern, future, larger studies are needed taking these limitations in consideration. Future studies are needed to determine whether the small improvements observed in this review can be applied to the general COPD population. A clear understanding of behaviour change through the BCT classification is important to gauge uptake of digital interventions and health outcomes in people with varying severity of COPD. Currently there is no guidance for interpreting BCT components of a digital intervention for changes to health outcomes. We could not interpret the BCT findings to the health outcomes we were investigating due to limited evidence that was of very low certainty. In future research, standardised approaches need to be considered when designing protocols to investigate effectiveness of digital interventions by including a standardised approach to BCT classification in addition to validated behavioural outcome measures that may reflect changes in behaviour.

Early-Stage Feasibility of a Mobile Health Intervention (Copilot) to Enhance Exacerbation-Related Self-Management in Patients With Chronic Obstructive Pulmonary Disease: Multimethods Approach

BACKGROUND

There is an emergence of mobile health (mHealth) interventions to support self-management in patients with chronic obstructive pulmonary disease (COPD). Recently, an evidence-driven mHealth intervention has been developed to support patients with COPD in exacerbation-related self-management: the Copilot app. Health care providers (HCPs) are important stakeholders as they are the ones who have to provide the app to patients, personalize the app, and review the app. It is, therefore, important to investigate at an early stage whether the app is feasible in the daily practice of the HCPs.

OBJECTIVE

The aim of this study is to evaluate the perceived feasibility of the Copilot app in the daily practice of HCPs.

METHODS

A multimethods design was used to investigate how HCPs experience working with the app and how they perceive the feasibility of the app in their daily practice. The feasibility areas described by Bowen et al were used for guidance. HCPs were observed while performing tasks in the app and asked to think aloud. The System Usability Scale was used to investigate the usability of the app, and semistructured interviews were conducted to explore the feasibility of the app. The study was conducted in primary, secondary, and tertiary care settings in the Netherlands from February 2019 to September 2019.

RESULTS

In total, 14 HCPs participated in this study-8 nurses, 5 physicians, and 1 physician assistant. The HCPs found the app acceptable to use. The expected key benefits of the app were an increased insight into patient symptoms, more structured patient conversations, and more tailored self-management support. The app especially fits within the available time and workflow of nurses. The use of the app will be influenced by the autonomy of the professional, the focus of the organization on eHealth, costs associated with the app, and compatibility with the current systems used. Most HCPs expressed that there are conditions that must be met to be able to use the app. The app can be integrated into the existing care paths of primary, secondary, and tertiary health care settings. Individual organizational factors must be taken into account when integrating the app into daily practice.

CONCLUSIONS

This early-stage feasibility study shows that the Copilot app is feasible to use in the daily practice of HCPs and can be integrated into primary, secondary, and tertiary health care settings in the Netherlands. The app was considered to best fit the role of the nurses. The app will be less feasible for those organizations in which many conditions need to be met to use the app. This study provides a new approach to evaluate the perceived feasibility of mHealth interventions at an early stage and provides valuable insights for further feasibility testing.

The effects of nurse-driven self-management programs on chronic obstructive pulmonary disease: A systematic review and meta-analysis

AIMS

To analyse the effects of nurse-driven self-management (SM) programs on physical and psychosocial health variables in people with chronic obstructive pulmonary disease (COPD).

DESIGN

A systematic review and meta-analysis.

DATA SOURCES

An exhaustive scanning of PubMed, Cochrane Controlled Register of Trials, CINAHL, ScienceDirect and Medline databases between January 2010-December 2019 was conducted for this meta-analysis.

REVIEW METHODS

Randomized controlled trials (RCTs) related to nurse-driven SM programs in COPD population were included. The standardized mean differences with 95% confidence intervals were determined for the main variables and heterogeneity was analysed using the I² test. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) was used.

RESULTS

Twelve studies were included. The results indicated that significant difference in physical health scores based on COPD Assessment Tool (CAT) and walking distance according to the 6-min walk distance (6MWD) test in the intervention groups compared with the control groups. About psychosocial health findings, the quality of life increased and the Hospital Anxiety and Depression Scale (HADS) scores decreased following SM programs. All of the studies had good quality (varying from 5-8 points) according to The Modified Jadad Scale.

CONCLUSION

Nurse-driven SM programs may contribute to prognosis in patients with COPD. Due to methodological weaknesses in the included trials, high-quality RCTs are needed to better determine the effects of nurse-driven SM programs in the management of COPD. Nurse-driven SM programs may be employed as a useful strategy to improve health status and QOL and psychosocial health in the COPD population, as well.

IMPACT

Current evidence shows that nurse-driven SM programs could be safely integrated into the clinical practice for patients with COPD. Future studies are warranted that evaluating the effects of nurse-driven SM programs on other frequently observed COPD symptoms such as dyspnoea, fatigue and sleep disturbance.

Development and Preliminary Evaluation of the Effects of an mHealth Web-Based Platform (HappyAir) on Adherence to a Maintenance Program After Pulmonary Rehabilitation in Patients With Chronic Obstructive Pulmonary Disease: Randomized Controlled Trial

Background

Pulmonary rehabilitation is one of the main interventions to reduce the use of health resources, and it promotes a reduction in chronic obstructive pulmonary disease (COPD) costs. mHealth systems in COPD aim to improve adherence to maintenance programs after pulmonary rehabilitation by promoting the change in attitude and behavior necessary for patient involvement in the management of the disease.

Objective

This study aimed to assess the effects of an integrated care plan based on an mHealth web-based platform (HappyAir) on adherence to a 1-year maintenance program applied after pulmonary rehabilitation in COPD patients.

Methods

COPD patients from three hospitals were randomized to a control group or an intervention group (HappyAir group). Patients from both groups received an 8-week program of pulmonary rehabilitation and educational sessions about their illness. After completion of the process, only the HappyAir group completed an integrated care plan for 10 months, supervised by an mHealth system and therapeutic educator. The control group only underwent the scheduled check-ups. Adherence to the program was rated using a respiratory physiotherapy adherence self-report (CAP FISIO) questionnaire. Other variables analyzed were adherence to physical activity (Morisky-Green Test), quality of life (Chronic Obstructive Pulmonary Disease Assessment Test, St. George’s Respiratory Questionnaire, and EuroQOL-5D), exercise capacity (6-Minute Walk Test), and lung function.

Results

In total, 44 patients were recruited and randomized in the control group (n=24) and HappyAir group (n=20). Eight patients dropped out for various reasons. The CAP FISIO questionnaire results showed an improvement in adherence during follow-up period for the HappyAir group, which was statistically different compared with the control group at 12 months (56.1 [SD 4.0] vs 44.0 [SD 13.6]; P=.004) after pulmonary rehabilitation.

Conclusions

mHealth systems designed for COPD patients improve adherence to maintenance programs as long as they are accompanied by disease awareness and patient involvement in management.

Trial Registration

ClinicalTrials.gov NCT04479930; https://clinicaltrials.gov/ct2/show/NCT04479930

Technology-Enabled Self-Management of Chronic Obstructive Pulmonary Disease With or Without Asynchronous Remote Monitoring: Randomized Controlled Trial

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality and leads to frequent hospital admissions and emergency department (ED) visits. COPD exacerbations are an important patient outcome, and reducing their frequency would result in significant cost savings. Remote monitoring and self-monitoring could both help patients manage their symptoms and reduce the frequency of exacerbations, but they have different resource implications and have not been directly compared.

OBJECTIVE

This study aims to compare the effectiveness of implementing a technology-enabled self-monitoring program versus a technology-enabled remote monitoring program in patients with COPD compared with a standard care group.

METHODS

We conducted a 3-arm randomized controlled trial evaluating the effectiveness of a remote monitoring and a self-monitoring program relative to standard care. Patients with COPD were recruited from outpatient clinics and a pulmonary rehabilitation program. Patients in both interventions used a Bluetooth-enabled device kit to monitor oxygen saturation, blood pressure, temperature, weight, and symptoms, but only patients in the remote monitoring group were monitored by a respiratory therapist. All patients were assessed at baseline and at 3 and 6 months after program initiation. Outcomes included self-management skills, as measured by the Partners in Health (PIH) Scale; patient symptoms measured with the St George's Respiratory Questionnaire (SGRQ); and the Bristol COPD Knowledge Questionnaire (BCKQ). Patients were also asked to self-report on health system use, and data on health use were collected from the hospital.

RESULTS

A total of 122 patients participated in the study: 40 in the standard care, 41 in the self-monitoring, and 41 in the remote monitoring groups. Although all 3 groups improved in PIH scores, BCKQ scores, and SGRQ impact scores, there were no significant differences among any of the groups. No effects were observed on the SGRQ activity or symptom scores or on hospitalizations, ED visits, or clinic visits.

CONCLUSIONS

Despite regular use of the technology, patients with COPD assigned to remote monitoring or self-monitoring did not have any improvement in patient outcomes such as self-management skills, knowledge, or symptoms, or in health care use compared with each other or with a standard care group. This may be owing to low health care use at baseline, the lack of structured educational components in the intervention groups, and the lack of integration of the action plan with the technology.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03741855; https://clinicaltrials.gov/ct2/show/ NCT03741855.