Activity data from wearables as an indicator of functional capacity in patients with cardiovascular disease

The future of valvular heart disease assessment and therapy

Valvular heart disease (VHD) is becoming more prevalent in an ageing population, leading to challenges in diagnosis and management. This two-part Series offers a comprehensive review of changing concepts in VHD, covering diagnosis, intervention timing, novel management strategies, and the current state of research. The first paper highlights the remarkable progress made in imaging and transcatheter techniques, effectively addressing the treatment paradox wherein populations at the highest risk of VHD often receive the least treatment. These advances have attracted the attention of clinicians, researchers, engineers, device manufacturers, and investors, leading to the exploration and proposal of treatment approaches grounded in pathophysiology and multidisciplinary strategies for VHD management. This Series paper focuses on innovations involving computational, pharmacological, and bioengineering approaches that are transforming the diagnosis and management of patients with VHD. Artificial intelligence and digital methods are enhancing screening, diagnosis, and planning procedures, and the integration of imaging and clinical data is improving the classification of VHD severity. The emergence of artificial intelligence techniques, including so-called digital twins-eg, computer-generated replicas of the heart-is aiding the development of new strategies for enhanced risk stratification, prognostication, and individualised therapeutic targeting. Various new molecular targets and novel pharmacological strategies are being developed, including multiomics-ie, analytical methods used to integrate complex biological big data to find novel pathways to halt the progression of VHD. In addition, efforts have been undertaken to engineer heart valve tissue and provide a living valve conduit capable of growth and biological integration. Overall, these advances emphasise the importance of early detection, personalised management, and cutting-edge interventions to optimise outcomes amid the evolving landscape of VHD. Although several challenges must be overcome, these breakthroughs represent opportunities to advance patient-centred investigations.

Test-retest reliability of the six-minute walking distance measurements using FeetMe insoles by completely unassisted healthy adults in their homes

Wearable technology provides an opportunity for new ways of monitoring patient gait remotely, through at-home self-administered six-minute walk tests (6MWTs). The purpose of this study was to evaluate the test-retest reliability of FeetMe insoles, a wearable gait assessment device, for measuring the six-minute walking distance (6MWD) during tests conducted with a one-week interval by completely unassisted healthy adults in their homes. Participants (n = 21) performed two 6MWTs at home while wearing the FeetMe insoles, and two 6MWTs at hospital while wearing FeetMe insoles and being assessed by a rater. All assessments were performed with a one-week interval between tests, no assistance was provided to the participants at home. The agreement between the 6MWD measurements made at baseline and at Week 1 was good for all test configurations and was highest for the at-home FeetMe measurements, with an intraclass correlation coefficient (ICC) of 0.95, standard error of the measurement (SEM) of 15.02 m and coefficient of variation (CV) of 3.33%, compared to ICCs of 0.79 and 0.78, SEMs of 25.65 and 26.65 and CVs of 6.24% and 6.10% for the rater and FeetMe measurements at hospital, respectively. Our work demonstrates that the FeetMe system could provide a reliable solution allowing individuals to self-administer 6MWTs independently at home.

A randomized trial of a mobile health intervention to augment cardiac rehabilitation

Mobile health (mHealth) interventions may enhance positive health behaviors, but randomized trials evaluating their efficacy are uncommon. Our goal was to determine if a mHealth intervention augmented and extended benefits of center-based cardiac rehabilitation (CR) for physical activity levels at 6-months. We delivered a randomized clinical trial to low and moderate risk patients with a compatible smartphone enrolled in CR at two health systems. All participants received a compatible smartwatch and usual CR care. Intervention participants received a mHealth intervention that included a just-in-time-adaptive intervention (JITAI) as text messages. The primary outcome was change in remote 6-minute walk distance at 6-months stratified by device type. Here we report the results for 220 participants enrolled in the study (mean [SD]: age 59.6 [10.6] years; 67 [30.5%] women). For our primary outcome at 6 months, there is no significant difference in the change in 6 min walk distance across smartwatch types (Intervention versus control: +31.1 meters Apple Watch, -7.4 meters Fitbit; p = 0.28). Secondary outcomes show no difference in mean step counts between the first and final weeks of the study, but a change in 6 min walk distance at 3 months for Fitbit users. Amongst patients enrolled in center-based CR, a mHealth intervention did not improve 6-month outcomes but suggested differences at 3 months in some users.

Perceptions of Mobile Health Technology in Elective Surgery: A Qualitative Study of North American Surgeons

OBJECTIVES

To explore the surgeon-perceived added value of mobile health technologies (mHealth), and determine facilitators of and barriers to implementing mHealth.

BACKGROUND

Despite the growing popularity of mHealth and evidence of meaningful use of patient-generated health data in surgery, implementation remains limited.

METHODS

This was an exploratory qualitative study following the Consolidated Criteria for Reporting Qualitative Research. Purposive sampling was used to identify surgeons across the United States and Canada. The Consolidated Framework for Implementation Research informed development of a semistructured interview guide. Video-based interviews were conducted (September-November 2020) and interview transcripts were thematically analyzed.

RESULTS

Thirty surgeons from 8 specialties and 6 North American regions were interviewed. Surgeons identified opportunities to integrate mHealth data pre- operatively (eg, expectation-setting, decision-making) and during recovery (eg, remote monitoring, earlier detection of adverse events) among higher risk patients. Perceived advantages of mHealth data compared with surgical and patient-reported outcomes included easier data collection, higher interpretability and objectivity of mHealth data, and the potential to develop more patientcentered and functional measures of health. Surgeons identified a variety of implementation facilitators and barriers around surgeon- and patient buy-in, integration with electronic medical records, regulatory/reimbursement concerns, and personnel responsible for mHealth data. Surgeons described similar considerations regarding perceptions of mHealth among patients, including the potential to address or worsen existing disparities in surgical care.

CONCLUSIONS

These findings have the potential to inform the effective and equitable implementation of mHealth for the purposes of supporting patients and surgical care teams throughout the delivery of surgical care.

Construct Validity and Test-Retest Reliability of a Free Mobile Application to Evaluate Aerobic Capacity and Endurance in Post-COVID-19 Syndrome Patients-A Pilot Study

Introduction: Disability associated with the symptoms of post-COVID-19 syndrome is one of its main features and can have a considerable impact on care and rehabilitation units. This, linked to a decreased aerobic capacity and endurance in post-COVID-19 syndrome patients, increases interest in studying the potential of mobile applications to assess performance capacity. The purpose of this research was to study how a free mobile application assesses aerobic capacity and endurance and its relationship with aerobic capacity, test-retest reliability, and endurance evaluated by a conventional test, along with fatigue and health-related quality of life. Methods: An observational study was conducted. RUNZI®, a free mobile application, was used by mounting a Samsung Galaxy S8 smartphone using a strap on the right forearm while all participants simultaneously performed a 6-Minute Walking Test (6MWT). Construct validity between the 6MWT and the total distance performed evaluated by RUNZI® was explored. Additionally, evaluation scales to assess fatigue (MFIS) and health-related quality of life (SF-36) were used to analyze the construct validity of RUNZI®. For test−retest reliability of the app, the same instructions about the 6MWT and procedure with the app were given to all participants at two different time periods. Results: A total of 16 post-COVID-19 syndrome patients (15 females and 1 male) completed the procedure. Distance measured with the RUNZI® showed an excellent correlation with the 6MWT assessed conventionally (p < 0.0001; r = 0.851). No statistical correlations were found between the distance assessed by the RUNZI® app with MFIS and the SF-36. Test−retest reliability was found to be close to statistical significance (p = 0.058) for distance (m) measured by RUNZI® with an ICC of 0.482. Conclusions: Instrumental 6MWT assessed by the RUNZI® app for the Android® operating system showed an excellent correlation with conventional 6MWT, indicating its construct validity in post-COVID-19 syndrome patients. Further, values for the test−retest reliability for the free mobile application were close to statistical significance with a reliability considered poor in an indoor setting.

Recording context matters: Differences in gait parameters collected by the OneStep smartphone application

BACKGROUND

Detailed understanding of impairments that underlie walking dysfunction through objective measures is essential to diagnosis, evaluation and care planning. Despite significant developments in motion tracking technologies, there is a dearth of research about the influence of remote monitoring context on performance. The objective of this study was to determine whether gait parameters collected by the OneStep smartphone application differ based on the recording condition.

METHODS

Retrospective repeated measures univariate analysis was performed on data extracted based on detected activity, either spontaneous (background recording) or consciously initiated (in app) walks, of 25 patients enrolled in a physical therapy program.

FINDINGS

Across 7227 walking bouts, significant differences between the two paradigms in velocity (g = 0.48), double support (g = 0.37), stride length (g = 0.37) and step length of the affected side (g = 0.32) were revealed. Overall, the passively recorded walks presented a less clinically favorable spatiotemporal pattern for each of these variables.

INTERPRETATION

The recording context of walks that were used for analysis appears to significantly affect the biomechanical output of the OneStep application. It is unclear whether the disparity found would impact functional recovery of individuals undergoing rehabilitation due to neurological or musculoskeletal disorder. Clinicians may consider this information when incorporating remotely-acquired quantitative gait analysis and interpreting care outcomes as part of therapeutic practice. Future work can further investigate the behavioral and environmental factors contributing to how movement occurs in specific clinical populations when monitored via mobile health systems.

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.

Smart Home Technology Solutions for Cardiovascular Diseases: A Systematic Review

Cardiovascular diseases (CVD) are the leading cause of mortality globally. Despite improvement in therapies, people with CVD lack support for monitoring and managing their condition at home and out of hospital settings. Smart Home Technologies have potential to monitor health status and support people with CVD in their homes. We explored the Smart Home Technologies available for CVD monitoring and management in people with CVD and acceptance of the available technologies to end-users. We systematically searched four databases, namely Medline, Web of Science, Embase, and IEEE, from 1990 to 2020 (search date 18 March 2020). “Smart-Home” was defined as a system using integrated sensor technologies. We included studies using sensors, such as wearable and non-wearable devices, to capture vital signs relevant to CVD at home settings and to transfer the data using communication systems, including the gateway. We categorised the articles for parameters monitored, communication systems and data sharing, end-user applications, regulations, and user acceptance. The initial search yielded 2462 articles, and the elimination of duplicates resulted in 1760 articles. Of the 36 articles eligible for full-text screening, we selected five Smart Home Technology studies for CVD management with sensor devices connected to a gateway and having a web-based user interface. We observed that the participants of all the studies were people with heart failure. A total of three main categories—Smart Home Technology for CVD management, user acceptance, and the role of regulatory agencies—were developed and discussed. There is an imperative need to monitor CVD patients’ vital parameters regularly. However, limited Smart Home Technology is available to address CVD patients’ needs and monitor health risks. Our review suggests the need to develop and test Smart Home Technology for people with CVD. Our findings provide insights and guidelines into critical issues, including Smart Home Technology for CVD management, user acceptance, and regulatory agency’s role to be followed when designing, developing, and deploying Smart Home Technology for CVD.

Wearable Devices for Physical Monitoring of Heart: A Review

Cardiovascular diseases (CVDs) are the leading cause of death globally. An effective strategy to mitigate the burden of CVDs has been to monitor patients’ biomedical variables during daily activities with wearable technology. Nowadays, technological advance has contributed to wearables technology by reducing the size of the devices, improving the accuracy of sensing biomedical variables to be devices with relatively low energy consumption that can manage security and privacy of the patient’s medical information, have adaptability to any data storage system, and have reasonable costs with regard to the traditional scheme where the patient must go to a hospital for an electrocardiogram, thus contributing a serious option in diagnosis and treatment of CVDs. In this work, we review commercial and noncommercial wearable devices used to monitor CVD biomedical variables. Our main findings revealed that commercial wearables usually include smart wristbands, patches, and smartwatches, and they generally monitor variables such as heart rate, blood oxygen saturation, and electrocardiogram data. Noncommercial wearables focus on monitoring electrocardiogram and photoplethysmography data, and they mostly include accelerometers and smartwatches for detecting atrial fibrillation and heart failure. However, using wearable devices without healthy personal habits will cause disappointing results in the patient’s health.

The association between heart rate behavior and gait performance: The moderating effect of frailty

Introduction

Research suggests that frailty not only influence individual systems, but also it affects the interconnection between them. However, no study exists to show how the interplay between cardiovascular and motor performance is compromised with frailty.

Aim

To investigate the effect of frailty on the association between heart rate (HR) dynamics and gait performance.

Methods

Eighty-five older adults (≥65 years and able to walk 9.14 meters) were recruited (October 2016—March 2018) and categorized into 26 non-frail (age = 78.65±7.46 years) and 59 pre-frail/frail individuals (age = 81.01±8.17) based on the Fried frailty phenotype. Participants performed gait tasks while equipped with a wearable electrocardiogram (ECG) sensor attached to the chest, as well as wearable gyroscopes for gait assessment. HR dynamic parameters were extracted, including time to peak HR and percentage increase in HR in response to walking. Using the gyroscope sensors gait parameters were recorded including stride length, stride velocity, mean swing velocity, and double support.

Results

Among the pre-frail/frail group, time to peak HR was significantly correlated with all gait parameters (p<0.0001, r = 0.51–0.59); however, for the non-frail group, none of the correlations between HR dynamics and gait performance parameters were significant (p>0.45, r = 0.03–0.15). The moderation analysis of time to peak HR, demonstrated a significant interaction effect of HR dynamics and frailty status on walking velocity (p<0.01), and the interaction effect was marginally non-significant for other gait parameters (p>0.10).

Conclusions

Current findings, for the first time, suggest that a compromised motor and cardiac autonomic interaction exist among pre-frail/frail older adults; an impaired HR performance (i.e., slower increase of HR in response to stressors) may lead to a slower walking performance. Assessing physical performance and its corresponding HR behavior should be studied as a tool for frailty screening and providing insights about the underlying cardiovascular-related mechanism leading to physical frailty.

Enhancing Patient-Centered Surgical Care With Mobile Health Technology

From smartphones or wearables to portable physiologic sensors and apps, healthcare is witnessing an exponential growth in mHealth-digital health tools used to support medical and surgical care, as well as public health. In surgery, there is interest in harnessing the capabilities of mHealth to improve the quality of patient-centered care delivery. Digitally delivered surveys have enhanced patient-reported outcome measurement and patient engagement throughout care. Wearable devices and sensors have allowed for the assessment of physical fitness before surgery and during recovery. Smartphone-based digital phenotyping has introduced novel methods of integrating multiple data streams (accelerometer, global positioning system, call and text logs) to create multidimensional digital health footprints for patients following surgery. Yet, with all the technological sophistication and 'big data' mHealth provides, widespread implementation has been elusive. Do clinicians and patients find these data valuable or clinically actionable? How can mHealth become integrated into the day-to-day workflows of surgical systems? Do these data represent opportunities to address disparities of care or worsen them? In this review, we discuss experiences and future opportunities to use mHealth to enhance patient-centered surgical care.