Utilizing a Homecare Platform for Remote Monitoring of Patients with Idiopathic Pulmonary Fibrosis. In: Vlamos P, editor. GeNeDis 2016. Cham: Springer International Publishing; 2017. pp. 177-87. [DOI: 10.1007/978-3-319-57348-9_15]

Current Implementation of Digital Health in Chronic Disease Management: Scoping Review

BACKGROUND

Approximately 1 in 3 adults live with multiple chronic diseases. Digital health is being harnessed to improve continuity of care and management of chronic diseases. However, meaningful uptake of digital health for chronic disease management remains low. It is unclear how these innovations have been implemented and evaluated.

OBJECTIVE

This scoping review aims to identify how digital health innovations for chronic disease management have been implemented and evaluated: what implementation frameworks, methods, and strategies were used; how successful these strategies were; key barriers and enablers to implementation; and lessons learned and recommendations shared by study authors.

METHODS

We used the Joanna Briggs Institute methodology for scoping reviews. Five databases were searched for studies published between January 2015 and March 2023: PubMed, Scopus, CINAHL, PsycINFO, and IEEE Xplore. We included primary studies of any study design with any type of digital health innovations for chronic diseases that benefit patients, caregivers, or health care professionals. We extracted study characteristics; type of digital health innovation; implementation frameworks, strategies, and outcome measures used; barriers and enablers to implementation; lessons learned; and recommendations reported by study authors. We used established taxonomies to synthesize extracted data. Extracted barriers and enablers were grouped into categories for reporting. Descriptive statistics were used to consolidate extracted data.

RESULTS

A total of 252 studies were included, comprising mainly mobile health (107/252, 42.5%), eHealth (61/252, 24.2%), and telehealth (97/252, 38.5%), with some studies involving more than 1 innovation. Only 23 studies (23/252, 9.1%) reported using an implementation science theory, model, or framework; the most common were implementation theories, classic theories, and determinant frameworks, with 7 studies each. Of 252 studies, 144 (57.1%) used 2 to 5 implementation strategies. Frequently used strategies were "obtain and use patient or consumer feedback" (196/252, 77.8%); "audit and provide feedback" (106/252, 42.1%); and piloting before implementation or "stage implementation scale-up" (85/252, 33.7%). Commonly measured implementation outcomes were acceptability, feasibility, and adoption of the digital innovation. Of 252 studies, 247 studies (98%) did not measure service outcomes, while patient health outcomes were measured in 89 studies (35.3%). The main method used to assess outcomes was surveys (173/252, 68.7%), followed by interviews (95/252, 37.7%). Key barriers impacting implementation were data privacy concerns and patient preference for in-person consultations. Key enablers were training for health care workers and personalization of digital health features to patient needs.

CONCLUSIONS

This review generated a summary of how digital health in chronic disease management is currently implemented and evaluated and serves as a useful resource for clinicians, researchers, health system managers, and policy makers planning real-world implementation. Future studies should investigate whether using implementation science frameworks, including how well they are used, would yield better outcomes compared to not using them.

Current challenges in the diagnosis and management of idiopathic pulmonary fibrosis in Japan

Idiopathic pulmonary fibrosis (IPF) is the archetypal interstitial lung disease. It is a chronic progressive condition that is challenging to manage as the clinical course of the disease is often difficult to predict. The prevalence of IPF is rising globally and in Japan, where it is estimated to affect 27 individuals per 100,000 of the population. Greater patient numbers and the poor prognosis associated with IPF diagnosis mean that there is a growing need for disease management approaches that can slow or even reverse disease progression and improve survival. Considerable progress has been made in recent years, with the approval of two antifibrotic therapies for IPF (pirfenidone and nintedanib), the availability of Japanese treatment guidelines, and the creation of global and Japanese disease registries. Despite this, significant unmet needs remain with respect to the diagnosis, treatment, and management of this complex disease. Each of these challenges will be discussed in this review, including making a timely and differential diagnosis of IPF, uptake and adherence to antifibrotic therapy, patient access to pulmonary rehabilitation, lung transplantation and palliative care, and optimal strategies for monitoring and staging disease progression, with a particular focus on the status in Japan. In addition, the review will reflect upon how ongoing research, clinical trials of novel therapies, and technologic advancements (including artificial intelligence, biomarkers, and genomic classification) may help address these challenges in the future.

The Role of Wearable Devices in Chronic Disease Monitoring and Patient Care: A Comprehensive Review

Wearable health devices are becoming vital in chronic disease management because they offer real-time monitoring and personalized care. This review explores their effectiveness and challenges across medical fields, including cardiology, respiratory health, neurology, endocrinology, orthopedics, oncology, and mental health. A thorough literature search identified studies focusing on wearable devices' impact on patient outcomes. In cardiology, wearables have proven effective for monitoring hypertension, detecting arrhythmias, and aiding cardiac rehabilitation. In respiratory health, these devices enhance asthma management and continuous monitoring of critical parameters. Neurological applications include seizure detection and Parkinson's disease management, with wearables showing promising results in improving patient outcomes. In endocrinology, wearable technology advances thyroid dysfunction monitoring, fertility tracking, and diabetes management. Orthopedic applications include improved postsurgical recovery and rehabilitation, while wearables help in early complication detection in oncology. Mental health benefits include anxiety detection, post-traumatic stress disorder management, and stress reduction through wearable biofeedback. In conclusion, wearable health devices offer transformative potential for managing chronic illnesses by enhancing real-time monitoring and patient engagement. Despite significant improvements in adherence and outcomes, challenges with data accuracy and privacy persist. However, with ongoing innovation and collaboration, we can all be part of the solution to maximize the benefits of wearable technologies in healthcare.

Road Toward a New Model of Care for Idiopathic Pulmonary Fibrosis in the Lazio Region

A timely, confirmed diagnosis of Idiopathic Pulmonary Fibrosis (IPF) has a significant impact on the evolution of the disease. The current model of care in the Lazio region (in Italy) was assessed on the basis of real-world data provided by the four reference centers responsible for diagnosing and treating IPF. The 5-year, population-based, retrospective longitudinal study provided the data that is at the basis of the current proposal for a new clinical and therapeutic pathway (DTCP) and has been shared with regional decision makers. A DTCP must be defined and based on four pillars: GPs, pulmonologists, IPF centers, and telemedicine. Each must play a role within a sort of hub-and-spoke model. IPF centers remain the hubs, while spokes are identified in trained GPs and pulmonologists.

The Current and Future Role of Technology in Respiratory Care

Over the past few decades, technology and improvements in artificial intelligence have dramatically changed major sectors of our day-to-day lives, including the field of healthcare. E-health includes a wide range of subdomains, such as wearables, smart-inhalers, portable electronic spirometers, digital stethoscopes, and clinical decision support systems. E-health has been consistently shown to enhance the quality of care, improve adherence to therapy, and allow early detection of worsening in chronic pulmonary diseases. The present review addresses the current and potential future role of major e-health tools and approaches in respiratory medicine, with the aim of providing readers with trustful and updated evidence to increase their awareness of the topic, and to allow them to optimally benefit from the latest innovation technology. Collected literature evidence shows that the potential of technology tools in respiratory medicine mainly relies on three fundamental interactions: between clinicians, between clinician and patient, and between patient and health technology. However, it would be desirable to establish widely agreed and adopted standards for conducting trials and reporting results in this area, as well as to take into proper consideration potentially relevant pitfalls related to privacy protection and compliance with regulatory procedures.

Patterns of digital information and communication technology use among patients at primary health care centres in Colombia: Phase I of the DIADA project

OBJECTIVE

Assess the prevalence and types of digital technology use, as well as the extent to which patients use the internet and mobile devises. Evaluate the socioeconomic characteristics of patients and the possible relation to patterns of technology use in Colombia. Understand the nature of patient technology use in primary care for finding medical information.

METHODS

A survey was applied to adult patients who attended primary health care centers systems in 6 Colombian cities. The survey inquired about demographic characteristics, insurance, access to services, cell phone use, internet access, and the use of such technology to access health-related services and information. Data was collected and managed using REDCap. Summary statistics on each survey item were calculated and the differences between discrete variables were analyzed using chi-square. Multivariate analyses were performed using logistic regression analysis for binary dependent variables.

RESULTS

A total of 1580 patients were surveyed across the six study sites. 93% of the patients reported they have a cell phone. Patients from urban healthcare centers showed a higher use of the Internet on their phone than less urban settings. Around half of the surveyed patients reported Internet use (49.7%). Among Internet users, 65% of participants use the Internet looking for health care information. Around one-third of patients use cellphones to arrange clinic visits. Around 24% of participants answered positively for both Whooley's questions. Of those who screened positive on the Whooley questions, 43% reported being moderately anxious, 47% reported being very anxious. 51% reported having moderate pain; 52% reported having severe pain.

CONCLUSIONS

The patterns of technology use identified in this study are essential for developing future health interventions based on ICT. The design of ICT clinical interventions must take into account the cellphone payment plans, availability of internet connection, advantages, and disadvantages of messenger services, including SMS as a possible alternative to people who do not have smartphones.

Real-time integration of emotion analysis into homecare platforms

The scientific and technological advancements in the area of affective computing enable the development of services in various domains that facilitate the interaction between humans and computers, and can considerably improve decision-making. This work presents the integration and operation of an emotion analysis service in a homecare/mHealth application. In the described approach, the developed emotion analysis service follows the IoT paradigm and it is combined with features for connectivity such as biosignal sensors and wearables, while it is fully integrated in the WebRTC video communication functionality offered by the homecare platform. Thus, it supports the medical experts to perform real-time analysis of their patients' emotional status during interactive video-conference sessions. The paper discusses the technical details of the implementation and the integration of the proposed service and provides initial results from its operation in practice.

An Intelligent Cloud-Based Platform for Effective Monitoring of Patients with Psychotic Disorders

The therapy of patients with psychotic disorders (i.e., bipolar disorder and schizophrenia) could benefit from the constant monitoring of their physiological and motor parameters. In this paper, we present an innovative and advanced cloud based platform that facilitates the effective monitoring of such patients. A commodity smartwatch is used for biosignal and motion data collection at a 24/7 basis. The paper describes the technical details of the implemented application both on the smartwatch and the cloud server side. Technical challenges regarding the upload, the storage and the battery constraints of the smartwatch are also discussed, along with the initial results regarding data visualization and processing.

Real-Time Mobile-Based Electrocardiogram System for Remote Monitoring of Patients with Cardiac Arrhythmias

In this study, we propose an electrocardiogram (ECG) system for the simultaneous and remote monitoring of multiple heart patients. It consists of three main components: patient, sever, and monitoring units. The patient unit uses a wearable miniature sensor that continuously measures ECG signals and sends them to a smart mobile phone via a Bluetooth connection. In the mobile device, the ECG signals can be stored, displayed on screen, and automatically transmitted to a distant server unit over the internet; the server stores ECG data from several patients. Health care stakeholders use a monitoring unit to retrieve the ECG signals of multiple patients at any time from the server for display and real-time automatic analysis. The analysis includes segmentation of the ECG signal into separate heartbeats followed by arrhythmia detection and classification. When compared to existing real-time ECG systems, where the detection of abnormalities is usually performed using simple rules, the proposed system implements a real-time classification module that is based on a support vector machine (SVM) classifier. Extensive experimental results on ECG data obtained from a TechPatientTM simulator, a real person, and 20 records from the MIT arrhythmia database are reported and discussed.