Assessment of Heart Failure Patients' Interest in Mobile Health Apps for Self-Care: Survey Study.

BackgroundHeart failure is a serious public health concern that afflicts millions of individuals in the United States. Development of behaviors that promote heart failure self-care may be imperative to reduce complications and avoid hospital re-admissions. Mobile health solutions, such as activity trackers and smartphone apps, could potentially help to promote self-care through remote tracking and issuing reminders.ObjectiveThe objective of this study was to ascertain heart failure patients' interest in a smartphone app to assist them in managing their treatment and symptoms and to determine factors that influence their interest in such an app.MethodsIn the clinic waiting room on the day of their outpatient clinic appointments, 50 heart failure patients participated in a self-administered survey. The survey comprised 139 questions from previously published, institutional review board-approved questionnaires. The survey measured patients' interest in and experience using technology as well as their function, heart failure symptoms, and heart failure self-care behaviors. The Minnesota Living with Heart Failure Questionnaire (MLHFQ) was among the 11 questionnaires and was used to measure the heart failure patients' health-related quality of life through patient-reported outcomes.ResultsParticipants were aged 64.5 years on average, 32% (16/50) of the participants were women, and 91% (41/45) of the participants were determined to be New York Heart Association Class II or higher. More than 60% (30/50) of the survey participants expressed interest in several potential features of a smartphone app designed for heart failure patients. Participant age correlated negatively with interest in tracking, tips, and reminders in multivariate regression analysis (P<.05). In contrast, MLHFQ scores (worse health status) produced positive correlations with these interests (P<.05).ConclusionsThe majority of heart failure patients showed interest in activity tracking, heart failure symptom management tips, and reminder features of a smartphone app. Desirable features and an understanding of factors that influence patient interest in a smartphone app for heart failure self-care may allow researchers to address common concerns and to develop apps that demonstrate the potential benefits of mobile technology

Measuring health status using wearable devices for patients undergoing radical cystectomy

Wearable devices (WDs) are an untapped resource for measuring patient health status during the peri-operative period. The overarching aim of this thesis is to explore the potential for WDs to be used in the clinical setting for patients undergoing radical cystectomy (RC) for bladder cancer. The lack of consensus regarding the optimal approach for RC presents an opportunity to design an RCT comparing open (ORC) and robotic (RARC) RC, in which a wearable device sub-study can be embedded. While the intracorporeal Robotic vs Open Cystectomy (iROC) trial will address the comparison between ORC and RARC, my thesis focuses on exploring the clinical utility of WDs. I present the results of a systematic review of RCTs comparing ORC and RARC. Meta-analysis shows no significant difference in peri-operative and oncological outcomes between ORC and RARC. Additionally, I systematically review healthcare studies using WDs and highlight the findings, device choices and device metrics used. Step-count is the most frequently collected WD metric, and chronic health conditions are the focus of majority of studies. Findings from these systematic reviews guided the design of the iROC trial protocol. I present the pre-planned interim analysis of the iROC trial, and explore associations between WD data and pre-operative health measures including cardiopulmonary exercise testing (CPET). Step-count correlates with the CPET variables (p < 0.01) routinely used to risk-stratify patients undergoing RC, and is the only predictor of major complications following RC in a logistic regression model. Finally, I evaluate recovery of baseline step-count at three months post-operatively as a predictor of overall survival. Applying a threshold of 50% recovery at 3 months, step-count predicts one-year survival to a sensitivity and specificity of 100% and 93% respectively. My findings highlight the potential of WDs in peri-operative care, and my post-doctoral work will progress this work further

Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance

One in twenty-five patients admitted to a hospital will suffer from a hospital acquired infection. If we can intelligently track healthcare staff, patients, and visitors, we can better understand the sources of such infections. We envision a smart hospital capable of increasing operational efficiency and improving patient care with less spending. In this paper, we propose a non-intrusive vision-based system for tracking people's activity in hospitals. We evaluate our method for the problem of measuring hand hygiene compliance. Empirically, our method outperforms existing solutions such as proximity-based techniques and covert in-person observational studies. We present intuitive, qualitative results that analyze human movement patterns and conduct spatial analytics which convey our method's interpretability. This work is a step towards a computer-vision based smart hospital and demonstrates promising results for reducing hospital acquired infections.Comment: Machine Learning for Healthcare Conference (MLHC

Patient characteristics associated with objective measures of digital health tool use in the United States: A literature review.

The study sought to determine which patient characteristics are associated with the use of patient-facing digital health tools in the United States.We conducted a literature review of studies of patient-facing digital health tools that objectively evaluated use (eg, system/platform data representing frequency of use) by patient characteristics (eg, age, race or ethnicity, income, digital literacy). We included any type of patient-facing digital health tool except patient portals. We reran results using the subset of studies identified as having robust methodology to detect differences in patient characteristics.We included 29 studies; 13 had robust methodology. Most studies examined smartphone apps and text messaging programs for chronic disease management and evaluated only 1-3 patient characteristics, primarily age and gender. Overall, the majority of studies found no association between patient characteristics and use. Among the subset with robust methodology, white race and poor health status appeared to be associated with higher use.Given the substantial investment in digital health tools, it is surprising how little is known about the types of patients who use them. Strategies that engage diverse populations in digital health tool use appear to be needed.Few studies evaluate objective measures of digital health tool use by patient characteristics, and those that do include a narrow range of characteristics. Evidence suggests that resources and need drive use

Effect of Values and Technology Use on Exercise: Implications for Personalized Behavior Change Interventions

Technology has recently been recruited in the war against the ongoing obesity crisis; however, the adoption of Health & Fitness applications for regular exercise is a struggle. In this study, we present a unique demographically representative dataset of 15k US residents that combines technology use logs with surveys on moral views, human values, and emotional contagion. Combining these data, we provide a holistic view of individuals to model their physical exercise behavior. First, we show which values determine the adoption of Health & Fitness mobile applications, finding that users who prioritize the value of purity and de-emphasize values of conformity, hedonism, and security are more likely to use such apps. Further, we achieve a weighted AUROC of .673 in predicting whether individual exercises, and we also show that the application usage data allows for substantially better classification performance (.608) compared to using basic demographics (.513) or internet browsing data (.546). We also find a strong link of exercise to respondent socioeconomic status, as well as the value of happiness. Using these insights, we propose actionable design guidelines for persuasive technologies targeting health behavior modification

Secure and Privacy-Preserving Automated Machine Learning Operations into End-to-End Integrated IoT-Edge-Artificial Intelligence-Blockchain Monitoring System for Diabetes Mellitus Prediction

Diabetes Mellitus, one of the leading causes of death worldwide, has no cure to date and can lead to severe health complications, such as retinopathy, limb amputation, cardiovascular diseases, and neuronal disease, if left untreated. Consequently, it becomes crucial to take precautionary measures to avoid/predict the occurrence of diabetes. Machine learning approaches have been proposed and evaluated in the literature for diabetes prediction. This paper proposes an IoT-edge-Artificial Intelligence (AI)-blockchain system for diabetes prediction based on risk factors. The proposed system is underpinned by the blockchain to obtain a cohesive view of the risk factors data from patients across different hospitals and to ensure security and privacy of the user's data. Furthermore, we provide a comparative analysis of different medical sensors, devices, and methods to measure and collect the risk factors values in the system. Numerical experiments and comparative analysis were carried out between our proposed system, using the most accurate random forest (RF) model, and the two most used state-of-the-art machine learning approaches, Logistic Regression (LR) and Support Vector Machine (SVM), using three real-life diabetes datasets. The results show that the proposed system using RF predicts diabetes with 4.57% more accuracy on average compared to LR and SVM, with 2.87 times more execution time. Data balancing without feature selection does not show significant improvement. The performance is improved by 1.14% and 0.02% after feature selection for PIMA Indian and Sylhet datasets respectively, while it reduces by 0.89% for MIMIC III