Dyadic Relationship Scale: A Measure of the Impact of the Provision and Receipt of Family Care

Purpose: This study evaluated the psychometric properties of the Dyadic Relationship Scale (DRS), which measures negative and positive dyadic interactions from the perspective of both the patient and the family caregiver. An important aspect of evaluating the DRS was that it be statistically sound and meaningful for both members of the dyad. Design and Methods: The study used a cross-sectional design. Survey packages were mailed to home health care patients and their family caregivers. The unit of analysis was the dyad, and exploratory and confirmatory factor analyses were conducted. We examined the reliability, discriminant, and concurrent validities of the instrument. Results: The data supported a two-factor DRS that included negative dyadic strain (patient α = .84; caregiver α = .89) and positive dyadic interaction (patient α = .86; caregiver α = .85). The analysis supported the DRS\u27s construct, discriminant, and concurrent validity, as well as its reliability for both patients and family caregivers. Implications: Using the DRS to measure the impact of family care on positive and negative interactions inclusive of patients and caregivers can assist in identifying areas of difficulty and guide interventions to improve outcomes for both members of the dyad

Care Management of Patients With Complex Health Care Needs

Explores how patients' complexity of healthcare needs, vulnerability, and age affect the cost and quality of their health care. Examines the potential for care management to improve quality of care and reduce costs, elements of success, and challenges

Motivational Interviewing Impact on Cardiovascular Disease

abstract: Harm reduction in cardiovascular disease is a significant problem worldwide. Providers, families, and healthcare agencies are feeling the burdens imparted by these diseases. Not to mention missed days of work and caregiver strain, the losses are insurmountable. Motivational interviewing (MI) is gaining momentum as a method of stimulating change through intrinsic motivation by resolving ambivalence toward change (Ma, Zhou, Zhou, & Huang, 2014). If practitioners can find methods of educating the public in a culturally-appropriate and sensitive manner, and if they can work with community stakeholders to organize our resources to make them more accessible to the people, we may find that simple lifestyle changes can lead to risk reduction of cardiovascular diseases. By working with our community leaders and identifying barriers unique to each population, we can make positive impacts on a wide range of issues that markedly impact our healthcare systems

Telehealth Coaching to Improve Exercise Self-Efficacy in Diabetics

Abstract Background and Review of Literature: Exercise is essential to the self-management of Type 2 Diabetes Mellitus (T2DM). The literature reveals that many patients with difficulty in the self-management of exercise improve with coaching. The literature also reveals that an adequate exercise routine results in improved blood glucose control and the prevention of complications. Purpose: This DNP project aims to demonstrate the positive effect of low-cost telehealth coaching to improve self-efficacy in an exercise regimen for T2DM patients. Methods: A one-group pretest-posttest design was used in this project. Telephone calls and text messaging were used to coach patients to improve exercise self-efficacy A self-efficacy scale was used to measure the pre- and post-intervention self-efficacy. Implementation plan/Procedure: This project was implemented by recruiting participants from the community and administering three weeks of remote coaching via phone calls and text messaging. Implication/Conclusion: Evidence from the literature supports the positive effect of telehealth coaching in improving self-efficacy in patients with T2DM. The project’s outcome was a significant improvement in exercise self-efficacy in the participants which far exceeded 5% on the self-efficacy scale after three weeks of coaching. The results showed that telehealth coaching via phone calls and text messaging is an effective, low-cost means of helping T2DM patients improve self-efficacy in maintaining an exercise routine due to the behavior modification that occurred from the intervention. Keywords: Type 2 diabetes, diabetes and exercise, remote health coaching, blood glucose control, telehealth, behavior change in chronic disease, exercise self-efficacy, telephone coaching

Health And Wellness Coaching Utilization And Perspectives Of Health Practitioners Working In American Indian Communities

Despite increased interest in client-directed counseling methods to manage chronic disease, limited data exists about utilization of counseling skills and attitudes towards these methods among health care providers, specifically those providing care to at risk populations. The aim of this qualitative study was to explore the beliefs and practices of a multidisciplinary group of health practitioners who were trained in health and wellness coaching (HWC) that included motivational interviewing (MI) techniques. The training was sponsored by the Bemidji Area Indian Health Service Health Promotion Disease Prevention (BAO IHS HPDP) program. Forty-seven trained coaches from the BAO IHS HPDP working with American Indian health programs in Illinois, Minnesota, Michigan, and Wisconsin were invited to participate. Participants completed an anonymous, online survey exploring perceptions about and use of HWC in practice. Twenty-seven of the 47 IHS Bemidji Area coaches who qualified for the study completed it. Participants represented nine different health practice areas with the majority working for Tribal Health Programs (22 of 27). Frequent use of coaching techniques, high self-efficacy with the use of HWC skills, and observed improved adherence to treatment and client outcomes were reported. Practitioners believed HWC was an effective method for providing care to patients participating in American Indian health programs. Future research is needed to examine relationships between HWC practice and patient outcomes in populations who are from diverse cultures

Improving personalized elderly care: an approach using cognitive agents to better assist elderly people

Tesis por compendio de publicaciones[ES]El envejecimiento de la población a nivel global es una constante cada vez más presente en el día a día y las consecuencias derivadas de este problema son cada vez más impactantes para el correcto funcionamiento y estructuración de la sociedad. En este contexto, hablamos de consecuencias a nivel de crecimiento económico, estilos de vida (y jubilación), relaciones familiares, recursos disponibles por el gobierno a la franja etaria más anciana e inevitablemente la prevalencia de enfermedades crónicas. Es ante esta realidad que surge la necesidad de desarrollo y promoción de estrategias eficaces en el acompañamiento, prevención y estímulo al envejecimiento activo y saludable de la población para garantizar que las personas ancianas continúen teniendo un papel relevante en la sociedad en lugar de someterse al aislamiento y fácil deterioro de las capacidades físicas, cognitivas, emocionales y sociales. De esta forma, tiene todo el sentido aprovechar todos los desarrollos tecnológicos verificados en los últimos años, principalmente en lo que se refiere a avances en las áreas de dispositivos móviles, inteligencia artificial y sistemas de monitoreo y crear soluciones capaces de brindar apoyo diariamente al recopilar datos e indicadores del estado de salud y, en respuesta, proporcionar diversas acciones personalizadas que motiven la adopción de mejores hábitos de salud y medios para lograr este envejecimiento activo y saludable. El desafío consiste en motivar a esta población a conciliar su día a día con el interés y la voluntad de utilizar aplicaciones y sistemas que brinden este apoyo personalizado. Algunas de las abordajes recientemente explorados en la literatura con este objetivo y que han alcanzado resultados prometedores se basan en la utilización de técnicas de gamificación e incentivo al cumplimiento de desafíos a nivel de salud (como si la persona estuviera jugando un juego) y la utilización de interacciones personalizadas con objetos (ya sean físicos como robots o virtuales como avatares) capaces de brindar feedback más personal, creando así una conexión más cercana entre ambas entidades. El trabajo aquí presentado combina estas ideas y resulta en un enfoque inteligente para la promoción del bienestar de la población anciana a través de un sistema de cuidados de salud personalizado. Este sistema incorpora diversas técnicas de gamificación para la promoción de mejores hábitos y comportamientos, y la utilización de un asistente virtual cognitivo capaz de entender las necesidades e intereses del usuario para posibilitar un feedback e interacción personalizados con el fin de ayudar y motivar al cumplimiento de los diferentes desafíos y objetivos que se identifiquen. El enfoque propuesto fue validado a través de un estudio con 12 usuarios ancianos y se lograron resultados significativos en términos de usabilidad, aceptación y efectos de salud. Específicamente, los resultados obtenidos permiten respaldar la importancia y el efecto positivo de combinar técnicas de gamificación e interacción con un asistente virtual cognitivo que traduzca el progreso del estado de salud del usuario, ya que se lograron mejoras significativas en los resultados de salud después de la intervención. Además, los resultados de usabilidad obtenidos mediante la cumplimentación de un cuestionario de usabilidad confirmaron la buena adhesión a el enfoque presentado. Estos resultados validan la hipótesis de la investigación estudiada en el desarrollo de esta disertación

Social and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review

Background: There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been little research into how to make the most of SBDH information from EHRs. Methods: A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided. Results: Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, and several NLP approaches for extracting SDOH from clinical literature. Discussion: The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using Natural Language Processing (NLP) technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues. Conclusion: Despite known associations between SBDH and disease, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, and ultimately promoting health and health equity. Keywords: Social and Behavioral Determinants of Health, Artificial Intelligence, Electronic Health Records, Natural Language Processing, Predictive ModelComment: 32 pages, 5 figure

Smart Healthcare solutions in China and Europe, an international business perspective

The thesis is part of the Marie Curie Fellowship project addressing health related challenges with IoT solutions. The author tries to address the challenge for the implementation of telehealth solutions by finding out the demand of the telehealth solution in selected European economies and in China (chapter 1), analyzing the emerging business models for telehealth solution ecosystems in China (chapter 2), how to integrate telehealth solutions with institutional stakeholders (chapter 3) and why are elderly users willing to use telehealth solutions in China. Chapter 1 and chapter 2 form the theoretical background for empirical work in chapter 3 and chapter 4. The thesis addressed four research questions, namely “Which societal and social-economics unmet needs that Internet of Healthcare Things can help to resolve?”, “What are the business model innovation for tech companies in China for the smart health industry?”, “What are the facilitators and hurdles for implementing telehealth solutions”, “Are elderly users willing to use telehealth solutions in China?”. Both qualitative study and quantitative analysis has been made based on data collected by in depth interviews with stakeholders, focus group study work with urban and rural residents in China. The digital platform framework was used in chapter 2 as the theoretical framework where as the stakeholder power mapping framework was used in chapter 3. The discretion choice experiment was used in chapter 4 to design questionnaire study while ordered logit regression was used to analyze the data. Telehealth solutions have great potential to fill in the gap for lack of community healthcare and ensuring health continuity between home care setting, community healthcare and hospitals. There is strong demand for such solutions if they can prove the medical value in managing chronic disease by raising health awareness and lowering health risks by changing the patients’ lifestyle. Analyzing how to realize the value for preventive healthcare by proving the health-economic value of digital health solutions (telehealth solutions) is the focus of research. There remain hurdles to build trust for telehealth solutions and the use of AI in healthcare. Next step of research can also be extended to addressing such challenges by analyzing how to improve the transparency of algorithms by disclosing the data source, and how the algorithms were built. Further research can be done on data interoperability between the EHR systems and telehealth solutions. The medical value of telehealth solutions can improve if doctors could interpret data collected from telehealth solutions; furthermore, if doctors could make diagnosis and provide treatment, adjust healthcare management plans based on such data, telehealth solutions then can be included in insurance packages, making them more accessible

Systematic review of context-aware digital behavior change interventions to improve health

Health risk behaviors are leading contributors to morbidity, premature mortality associated with chronic diseases, and escalating health costs. However, traditional interventions to change health behaviors often have modest effects, and limited applicability and scale. To better support health improvement goals across the care continuum, new approaches incorporating various smart technologies are being utilized to create more individualized digital behavior change interventions (DBCIs). The purpose of this study is to identify context-aware DBCIs that provide individualized interventions to improve health. A systematic review of published literature (2013-2020) was conducted from multiple databases and manual searches. All included DBCIs were context-aware, automated digital health technologies, whereby user input, activity, or location influenced the intervention. Included studies addressed explicit health behaviors and reported data of behavior change outcomes. Data extracted from studies included study design, type of intervention, including its functions and technologies used, behavior change techniques, and target health behavior and outcomes data. Thirty-three articles were included, comprising mobile health (mHealth) applications, Internet of Things wearables/sensors, and internet-based web applications. The most frequently adopted behavior change techniques were in the groupings of feedback and monitoring, shaping knowledge, associations, and goals and planning. Technologies used to apply these in a context-aware, automated fashion included analytic and artificial intelligence (e.g., machine learning and symbolic reasoning) methods requiring various degrees of access to data. Studies demonstrated improvements in physical activity, dietary behaviors, medication adherence, and sun protection practices. Context-aware DBCIs effectively supported behavior change to improve users' health behaviors