eHealth in Chronic Diseases

This book provides a review of the management of chronic diseases (evaluation and treatment) through eHealth. Studies that examine how eHealth can help to prevent, evaluate, or treat chronic diseases and their outcomes are included

Methods and measures used to evaluate patient-operated mobile health interventions:Scoping literature review

Background: Despite the prevalence of mobile health (mHealth) technologies and observations of their impacts on patients’ health, there is still no consensus on how best to evaluate these tools for patient self-management of chronic conditions. Researchers currently do not have guidelines on which qualitative or quantitative factors to measure or how to gather these reliable data. Objective: This study aimed to document the methods and both qualitative and quantitative measures used to assess mHealth apps and systems intended for use by patients for the self-management of chronic noncommunicable diseases. Methods: A scoping review was performed, and PubMed, MEDLINE, Google Scholar, and ProQuest Research Library were searched for literature published in English between January 1, 2015, and January 18, 2019. Search terms included combinations of the description of the intention of the intervention (eg, self-efficacy and self-management) and description of the intervention platform (eg, mobile app and sensor). Article selection was based on whether the intervention described a patient with a chronic noncommunicable disease as the primary user of a tool or system that would always be available for self-management. The extracted data included study design, health conditions, participants, intervention type (app or system), methods used, and measured qualitative and quantitative data. Results: A total of 31 studies met the eligibility criteria. Studies were classified as either those that evaluated mHealth apps (ie, single devices; n=15) or mHealth systems (ie, more than one tool; n=17), and one study evaluated both apps and systems. App interventions mainly targeted mental health conditions (including Post-Traumatic Stress Disorder), followed by diabetes and cardiovascular and heart diseases; among the 17 studies that described mHealth systems, most involved patients diagnosed with cardiovascular and heart disease, followed by diabetes, respiratory disease, mental health conditions, cancer, and multiple illnesses. The most common evaluation method was collection of usage logs (n=21), followed by standardized questionnaires (n=18) and ad-hoc questionnaires (n=13). The most common measure was app interaction (n=19), followed by usability/feasibility (n=17) and patient-reported health data via the app (n=15). Conclusions: This review demonstrates that health intervention studies are taking advantage of the additional resources that mHealth technologies provide. As mHealth technologies become more prevalent, the call for evidence includes the impacts on patients’ self-efficacy and engagement, in addition to traditional measures. However, considering the unstructured data forms, diverse use, and various platforms of mHealth, it can be challenging to select the right methods and measures to evaluate mHealth technologies. The inclusion of app usage logs, patient-involved methods, and other approaches to determine the impact of mHealth is an important step forward in health intervention research. We hope that this overview will become a catalogue of the possible ways in which mHealth has been and can be integrated into research practice

Text Message Intervention for Latino Adults To Improve Diabetes Outcomes in an Urban Free Clinic Setting

Introduction: This text message intervention sought to help patients at a free clinic in the Southeastern U.S. that have uncontrolled diabetes (DM) (A1C \u3e or = 7) improve their DM clinical and behavioral outcomes, and thereby help them to live healthier, more hopeful and productive lives as they deal daily with this chronic illness. Eight weeks of educational text messages were sent to help improve DM care and outcomes. Method: Free clinic patient Latino adults with DM (n=25) pre-post one group design. Results: Statistically significant results (p \u3c .05) were seen in three (SKILLD, p=.001, DSES, p = .000, and SDSCA, p = .042) of the four tools/surveys administered. A1C improvements were significant from the pre-intervention (M = 9.10, SD = 1.51) and the trended post-intervention values/results (M=8.26, SD = 1.29, t [21] = 2.79, p = .0110). Discussion: Does personalized communication, education and follow up for patients at the free clinic improve diabetes knowledge, self-efficacy and self-care? This text message intervention shows great promise to improve outcomes for diabetes self-management

Análisis bibliométrico de información en salud basado en PubMed disponible en las redes sociales: un estudio de La India

Social networks have long been used to disseminate health-related information and help, and this use has increased with the emergence of online social media. The goal of this study is to conduct a bibliometric analysis of health information in the context of India. The literature available in PubMed is the source of the study. The objective of this paper is to develop a better insight into the literature on social media-based health information using bibliometric analysis in the context of India. The software used for bibliometric analysis is profile research networking software from Harvard University and Vosviewer. From the study, it is clear that social media is important in the context of public health. We also found out that although the number of publications in journals is highest but video-audio content has been cited more. Although there is a significant increase in publication during 2020, but number of researchers are still very few. It is clear that social media is of greater importance for marginalized people; health care providers and regulators must take precautions to avoid possible negative outcomes.Las redes sociales se han utilizado durante mucho tiempo para difundir información y ayuda relacionadas con la salud, y este uso ha aumentado con la aparición de las redes sociales en línea. El objetivo de este estudio es realizar un análisis bibliométrico de la información sanitaria en el contexto de la India. La literatura disponible en PubMed es la fuente del estudio. El objetivo de este artículo es desarrollar una mejor comprensión de la literatura sobre la información de salud basada en las redes sociales utilizando el análisis bibliométrico en el contexto de la India. El software utilizado para el análisis bibliométrico es un software de redes de investigación de perfiles de la Universidad de Harvard y Vosviewer. Del estudio, queda claro que las redes sociales son importantes en el contexto de la salud pública. También descubrimos que aunque el número de publicaciones en revistas es mayor, se ha citado más contenido de video-audio. Aunque hay un aumento significativo de la publicación durante 2020, el número de investigadores sigue siendo muy reducido. Está claro que las redes sociales son de mayor importancia para las personas marginadas. Los proveedores de atención médica y los reguladores deben tomar precauciones para evitar posibles resultados negativos

An exploration of the potential contribution of a medication management app in heart failure outpatients’ care: the experiences of staff and older patients

Background: Managing the care of older adults with Heart Failure (HF) largely centres on symptom and medication management. Medication management in patients with HF is challenging due to frequent medication adjustments in response to changes in their symptomatology and polypharmacy. Some patients with HF typically take on average 10-25 tablets daily. Given the complexity of HF self-management, assisting older adults in managing their own care at home is critical to the success of HF management. Aim: To explore the role of a medication management app in supporting the care of older adults attending a HF outpatients ‘clinic and the impact of this new intervention on staff working practices. Methods: Mixed methods sequential design to test the feasibility of a medication app with HF patients. Observations of clinical practice were conducted followed by semi-structured interviews with healthcare professionals (HCPs) and patients pre- and post-intervention. Interviews were transcribed and analysed using thematic analysis, the Normalisation Process Theory (NPT) framework was used to capture challenges and facilitators to technology use in phase three. A systematic search of apps was also conducted to identify commercially available apps with a medication functionality, followed by an evaluation of apps using a validated tool. The optimal app was selected and implemented in a three-month intervention with patients attending the HF clinic. A case study strategy was used to present the experiences and opinions of HCPs and patients using the app. Findings: Patients normalised the use of the app and found it easy to use after training for medication self-management at home. HCPs found the use of the app to empower patients and to assist them in maintaining an up-to-date medication list and concluded that the use of the app was beneficial to both HCPs and patients. However, several challenges need to be overcome before implementing and scaling up this intervention. Some of the barriers to technology uptake identified in this study were: HCPs attitudes towards older people using technology, lack of managerial support and the need for training and ongoing technical support for older adults Conclusion: The use of the NPT framework captured individual and organisational barriers and facilitators to the normalisation of the use of the medication app with HF older patients. These barriers need to be overcome to enable the implementation and scaling up of this intervention. The findings of this feasibility study are encouraging and warrant further investigation to test the effectiveness of a medication app with HF older adults at a larger scale in future studies

A Descriptive Correlational Study of Rate and Determinants of Parental mHealth Adherence to Symptom Home Monitoring for Infants with Congenital Heart Disease during the Single Ventricle Interstage Period: The DOMAIN Study

Title from PDF of title page viewed June 9, 2020Dissertation advisor: Cynthia L. RussellVitaIncludes bibliographical references (pages 166-188)Thesis (Ph.D.)--School of Nursing and Health Studies. University of Missouri--Kansas City, 2020Single ventricle heart disease care in the ambulatory setting affects approximately 4,000 infants in the United States annually. Treatment typically involves a three-staged surgical strategy over the first three years of life with parental home monitoring of the infant during the interstage period, which is the time between the first two surgeries. Symptom home monitoring during the interstage period increasingly requires technology to maximize patient outcomes. Mobile health, or mHealth, transfers infant hemodynamic monitoring data captured by parents from the home to designated registered nurse coordinators who monitor the data remotely. Parental mHealth symptom home monitoring adherence is critical to improve morbidity and reduce mortality in infants during this high-risk period. However, rates and determinants of mHealth adherence have yet to be studied. The purpose of this research was to quantify the rate of parental mHealth adherence and to describe the relationship between patient-related, family-related, community-related, and healthcare system-related determinants of parental mHealth adherence for infants with congenital heart disease during the single ventricle interstage period. The pediatric self-management conceptual framework was used with a retrospective, descriptive, correlational research design. De-identified data from 312 infants treated at nine pediatric hospitals between March 2014-September 2019 were included from the Cardiac High Acuity Monitoring Program multi-site registry. This registry was developed in 2014 by Children’s Mercy Kansas City and includes patient, family, and medical record data. SPSS AMOS software was used to refine a model to develop a theoretically identified, recursive structural equation model. The rate of parental mHealth adherence-data days was 75.54%. The overall model variance was 24.0%, with good local and global fit. A higher parental age (p<.001) and Medicaid insurance (p=.009) were positively associated with parental mHealth adherence. Higher rates of implementation of oxygen saturation symptom home monitoring were associated with lower clinic visits (p< .001) and increased education levels (p=.001). Adherence to mHealth video use was associated with increased healthcare team driven communications (p=.047). Future research areas proposed from these findings include determining mHealth adherence rates associated with optimized clinical outcomes and ways to reduce parental mHealth non-adherence.Introduction -- Review of literature -- Methodology -- Results -- Discussion -- Appendi