Wave of Wearables: Clinical Management of Patients and the Future of Connected Medicine

Study Protocol for the Artificial Intelligence-Driven Evaluation of Structural Heart Diseases Using Wearable Electrocardiogram (ID-SHD)

Introduction

Portable devices capable of electrocardiogram (ECG) acquisition have the potential to enhance structural heart disease (SHD) management by enabling early detection through artificial intelligence-ECG (AI-ECG) algorithms. However, the performance of these AI algorithms for identifying SHD in a real-world screening setting is unknown. To address this gap, we aim to evaluate the validity of our wearable-adapted AI algorithm, which has been previously developed and validated for detecting SHD from single-lead portable ECGs in patients undergoing routine echocardiograms in the Yale New Haven Hospital (YNHH).

Research Methods and Analysis

This is the protocol for a cross-sectional study in the echocardiographic laboratories of YNHH. The study will enroll 585 patients referred for outpatient transthoracic echocardiogram (TTE) as part of their routine clinical care. Patients expressing interest in participating in the study will undergo a screening interview, followed by enrollment upon meeting eligibility criteria and providing informed consent. During their routine visit, patients will undergo a 1-lead ECG with two devices - one with an Apple Watch and the second with another portable 1-lead ECG device. With participant consent, these 1-lead ECG data will be linked to participant demographic and clinical data recorded in the YNHH electronic health records (EHR). The study will assess the performance of the AI-ECG algorithm in identifying SHD, including left ventricular systolic dysfunction (LVSD), valvular disease and severe left ventricular hypertrophy (LVH), by comparing the algorithm's results with data obtained from TTE, which is the established gold standard for diagnosing SHD.

Ethics and Dissemination

All patient EHR data required for assessing eligibility and conducting the AI-ECG will be accessed through secure servers approved for protected health information. Data will be maintained on secure, encrypted servers for a minimum of five years after the publication of our findings in a peer-reviewed journal, and any unanticipated adverse events or risks will be reported by the principal investigator to the Yale Institutional Review Board, which has reviewed and approved this protocol (Protocol Number: 2000035532).

Cardiology in a Digital Age: Opportunities and Challenges for e-Health: A Literature Review

To date, mortality rates associated with heart diseases are dangerously increasing, making them the leading cause of death globally. From this point of view, digital technologies can provide health systems with the necessary support to increase prevention and monitoring, and improve care delivery. The present study proposes a review of the literature to understand the state of the art and the outcomes of international experiences. A reference framework is defined to develop reflections to optimize the use of resources and technologies, favoring the development of new organizational models and intervention strategies. Findings highlight the potential significance of e-health and telemedicine in supporting novel solutions and organizational models for cardiac illnesses as a response to the requirements and restrictions of patients and health systems. While privacy concerns and technology-acceptance-related issues arise, new avenues for research and clinical practice emerge, with the need to study ad hoc managerial models according to the type of patient and disease.

Use of Wearable Devices in Individuals With or at Risk for Cardiovascular Disease in the US, 2019 to 2020

Importance

Wearable devices may be able to improve cardiovascular health, but the current adoption of these devices could be skewed in ways that could exacerbate disparities.

Objective

To assess sociodemographic patterns of use of wearable devices among adults with or at risk for cardiovascular disease (CVD) in the US population in 2019 to 2020.

Design, Setting, and Participants

This population-based cross-sectional study included a nationally representative sample of the US adults from the Health Information National Trends Survey (HINTS). Data were analyzed from June 1 to November 15, 2022.

Exposures

Self-reported CVD (history of heart attack, angina, or congestive heart failure) and CVD risk factors (≥1 risk factor among hypertension, diabetes, obesity, or cigarette smoking).

Main Outcomes and Measures

Self-reported access to wearable devices, frequency of use, and willingness to share health data with clinicians (referred to as health care providers in the survey).

Results

Of the overall 9303 HINTS participants representing 247.3 million US adults (mean [SD] age, 48.8 [17.9] years; 51% [95% CI, 49%-53%] women), 933 (10.0%) representing 20.3 million US adults had CVD (mean [SD] age, 62.2 [17.0] years; 43% [95% CI, 37%-49%] women), and 5185 (55.7%) representing 134.9 million US adults were at risk for CVD (mean [SD] age, 51.4 [16.9] years; 43% [95% CI, 37%-49%] women). In nationally weighted assessments, an estimated 3.6 million US adults with CVD (18% [95% CI, 14%-23%]) and 34.5 million at risk for CVD (26% [95% CI, 24%-28%]) used wearable devices compared with an estimated 29% (95% CI, 27%-30%) of the overall US adult population. After accounting for differences in demographic characteristics, cardiovascular risk factor profile, and socioeconomic features, older age (odds ratio [OR], 0.35 [95% CI, 0.26-0.48]), lower educational attainment (OR, 0.35 [95% CI, 0.24-0.52]), and lower household income (OR, 0.42 [95% CI, 0.29-0.60]) were independently associated with lower use of wearable devices in US adults at risk for CVD. Among wearable device users, a smaller proportion of adults with CVD reported using wearable devices every day (38% [95% CI, 26%-50%]) compared with overall (49% [95% CI, 45%-53%]) and at-risk (48% [95% CI, 43%-53%]) populations. Among wearable device users, an estimated 83% (95% CI, 70%-92%) of US adults with CVD and 81% (95% CI, 76%-85%) at risk for CVD favored sharing wearable device data with their clinicians to improve care.

Conclusions and Relevance

Among individuals with or at risk for CVD, fewer than 1 in 4 use wearable devices, with only half of those reporting consistent daily use. As wearable devices emerge as tools that can improve cardiovascular health, the current use patterns could exacerbate disparities unless there are strategies to ensure equitable adoption.

The grade of instability in fragility fractures of the pelvis correlates with impaired early mobilization

PURPOSE

This study aimed to investigate whether gait patterns of patients with fragility fractures of the pelvis (FFP) comply with the grade of fracture instability, defined by radiological patterns.

PATIENTS AND METHODS

This prospective, single-center, observational study included 39 patients with an FFP. Gait analysis was performed with a wearable insole force sensor (Loadsol® by Novel, Munich, Germany) 4-7 days after admission. Patients were divided in two groups: Group A included FFP type 1 fractures, which affect the anterior pelvic ring only, Group B contained FFP type 2-4 fractures with an involvement of the posterior pelvic ring. Primary outcome parameter was the FTI ratio (force-time integral (N*s)).

RESULTS

The mean age was 85.08 years (SD ± 6.45), 94.9% (37/39) of the patients were female. The most common fracture type was an FFP 2b (64.1%, 25/39). Group A showed a significantly higher FTI ratio (45.12%, SD ± 4.19%) than Group B (38.45%, SD ± 5.97%, p = 0.002). Further, a significant correlation of the FTI ratio and the average (r = 0.570, p < 0.001) and maximum (r = 0.394, p = 0.013) peak force was observed.

CONCLUSION

The gait pattern of patients with an FFP type 2-4 was more imbalanced than of patients with an FFP type 1 fracture. These findings match with the radiological classification of FFP, which indicates higher instability, when the posterior pelvis is affected. Gait analysis might offer earlier functional diagnostics and may accelerate the treatment decision with shorter periods of immobility in future. Especially in cross-border cases, early gait analysis could be beneficial to clarify the indication for or against surgery.

Recommendations for the safe, effective use of adaptive CDS in the US healthcare system: an AMIA position paper

The development and implementation of clinical decision support (CDS) that trains itself and adapts its algorithms based on new data-here referred to as Adaptive CDS-present unique challenges and considerations. Although Adaptive CDS represents an expected progression from earlier work, the activities needed to appropriately manage and support the establishment and evolution of Adaptive CDS require new, coordinated initiatives and oversight that do not currently exist. In this AMIA position paper, the authors describe current and emerging challenges to the safe use of Adaptive CDS and lay out recommendations for the effective management and monitoring of Adaptive CDS.

mHealth for pediatric chronic pain: state of the art and future directions

INTRODUCTION

Chronic pain conditions are common among children and engender cascading effects across social, emotional, and behavioral domains for the child and family. Mobile health (mHealth) describes the practice of delivering healthcare via mobile devices and may be an ideal solution to increase access and reach of evidence-based behavioral health interventions.

AREAS COVERED

The aim of this narrative review is to present a state-of-the-art overview of evidence-based mHealth efforts within the field of pediatric chronic pain and consider new and promising directions for study. Given the nascent nature of the field, published mHealth interventions in all stages of development are discussed. Literature was identified through a non-systematic search in PubMed and Google Scholar, and a review of reference lists of papers that were identified as particularly relevant or foundational (within and outside of the chronic pain literature).

EXPERT OPINION

mHealth is a promising interventional modality with early evidence suggesting it is primed to enhance behavioral health delivery and patient outcomes. There are many exciting future directions to be explored including drawing inspiration from digital health technology to generate new ways of thinking about the optimal treatment of pediatric chronic pain.