Harnessing the Potential of Wearable Activity Trackers for Heart Failure Self-Care

Patients with Heart Failure: Internet Use and Mobile Health Perceptions

BACKGROUND

 Heart failure is a complex clinical syndrome noted on approximately one in eight death certificates in the United States. Vital to reducing complications of heart failure and preventing hospital readmissions is adherence to heart failure self-care routines. Mobile health offers promising opportunities for enhancing self-care behaviors by facilitating tracking and timely reminders.

OBJECTIVES

 We sought to investigate three characteristics of heart failure patients with respect to their heart failure self-care behaviors: (1) internet use to search for heart failure information; (2) familiarity with mobile health apps and devices; and (3) perceptions of using activity trackers or smartwatches to aid in their heart failure self-care.

METHODS

 Forty-nine heart failure patients were asked about their internet and mobile health usage. The structured interview included questions adapted from the Health Information National Trends Survey.

RESULTS

 Over 50% of the patients had utilized the internet to search for heart failure information in the past 12 months, experience using health-related apps, and thoughts that an activity tracker or smartwatch could help them manage heart failure. Qualitative analysis of the interviews revealed six themes: trust in their physicians, alternatives to mobile health apps, lack of need for mobile health devices, financial barriers to activity tracker and smartwatch ownership, benefits of tracking and reminders, and uncertainty of their potential due to lack of knowledge.

CONCLUSION

 Trust in their physicians was a major factor for heart failure patients who reported not searching for health information on the internet. While those who used mobile health technologies found them useful, patients who did not use them were generally unaware of or unknowledgeable about them. Considering patients' preferences for recommendations from their physicians and tendency to search for heart failure information including treatment and management options, patient-provider discussions about mobile health may improve patient knowledge and impact their usage.

Effect of Dapagliflozin Versus Placebo on Symptoms and 6-Minute Walk Distance in Patients With Heart Failure: The DETERMINE Randomized Clinical Trials

BACKGROUND

Sodium-glucose cotransporter 2 inhibitors reduce the risk of worsening heart failure (HF) and cardiovascular death in patients with HF irrespective of left ventricular ejection fraction. It is important to determine whether therapies for HF improve symptoms and functional capacity.

METHODS

The DETERMINE (Dapagliflozin Effect on Exercise Capacity Using a 6-Minute Walk Test in Patients With Heart Failure) double-blind, placebo-controlled, multicenter trials assessed the efficacy of the sodium-glucose cotransporter 2 inhibitor dapagliflozin on the Total Symptom Score (TSS) and Physical Limitation Scale (PLS) of the Kansas City Cardiomyopathy Questionnaire (KCCQ) and 6-minute walk distance (6MWD) in 313 patients with HF with reduced ejection fraction (DETERMINE-Reduced) and in 504 patients with HF with preserved ejection fraction (DETERMINE-Preserved) with New York Heart Association class II or III symptoms and elevated natriuretic peptide levels. The primary outcomes were changes in the KCCQ-TSS, KCCQ-PLS, and 6MWD after 16 weeks of treatment.

RESULTS

Among the 313 randomized patients with HF with reduced ejection fraction, the median placebo-corrected difference in KCCQ-TSS from baseline at 16 weeks was 4.2 (95% CI, 1.0, 8.2; P=0.022) in favor of dapagliflozin. The median placebo-corrected difference in KCCQ-PLS was 4.2 (95% CI, 0.0, 8.3; P=0.058). The median placebo-corrected difference in 6MWD from baseline at 16 weeks was 3.2 meters (95% CI, -6.5, 13.0; P=0.69). In the 504 patients with HF with preserved ejection fraction, the median placebo-corrected 16-week difference in KCCQ-TSS and KCCQ-PLS was 3.2 (95% CI, 0.4, 6.0; P=0.079) and 3.1 (-0.1, 5.4; P=0.23), respectively. The median 16-week difference in 6MWD was 1.6 meters (95% CI, -5.9, 9.0; P=0.67). In an exploratory post hoc analysis of both trials combined (DETERMINE-Pooled), the median placebo-corrected difference from baseline at 16 weeks was 3.7 (1.5, 5.9; P=0.005) for KCCQ-TSS, 4.0 (0.3, 4.9; P=0.036) for KCCQ-PLS, and 2.5 meters (-3.5, 8.4; P=0.50) for 6MWD.

CONCLUSIONS

Dapagliflozin improved the KCCQ-TSS in patients with HF with reduced ejection fraction but did not improve KCCQ-PLS or 6MWD. Dapagliflozin did not improve these outcomes in patients with HF with preserved ejection fraction. In a post hoc analysis including all patients across the full spectrum of ejection fraction, there was a beneficial effect of dapagliflozin on KCCQ-TSS and KCCQ-PLS but not 6MWD.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifiers: NCT03877237 and NCT03877224.

Sensor-Controlled Digital Game for Heart Failure Self-management: Protocol for a Randomized Controlled Trial

BACKGROUND

Heart failure (HF) is the leading cause of hospitalization among older adults in the United States. There are substantial racial and geographic disparities in HF outcomes, with patients living in southern US states having a mortality rate 69% higher than the national average. Self-management behaviors, particularly daily weight monitoring and physical activity, are extremely important in improving HF outcomes; however, patients typically have particularly low adherence to these behaviors. With the rise of digital technologies to improve health outcomes and motivate health behaviors, sensor-controlled digital games (SCDGs) have become a promising approach. SCDGs, which leverage sensor-connected technologies, offer the benefits of being portable and scalable and allowing for continuous observation and motivation of health behaviors in their real-world contexts. They are also becoming increasingly popular among older adults and offer an immersive and accessible way to measure self-management behaviors and improve adherence. No SCDGs have been designed for older adults or evaluated to test their outcomes.

OBJECTIVE

This randomized clinical trial aims to assess the efficacy of a SCDG in integrating the behavioral data of participants with HF from weight scale and activity tracker sensors to activate game progress, rewards, and feedback and, ultimately, to improve adherence to important self-management behaviors.

METHODS

A total of 200 participants with HF, aged ≥45 years, will be recruited and randomized into 2 groups: the SCDG playing group (intervention group) and sensor-only group (control group). Both groups will receive a weight scale, physical activity tracker, and accompanying app, whereas only the intervention group will play the SCDG. This design, thereby, assesses the contributions of the game. All participants will complete a baseline survey as well as posttests at 6 and 12 weeks to assess the immediate effect of the intervention. They will also complete a third posttest at 24 weeks to assess the maintenance of behavioral changes. Efficacy and benefits will be assessed by measuring improvements in HF-related proximal outcomes (self-management behaviors of daily weight monitoring and physical activity) and distal outcomes (HF hospitalization, quality of life, and functional status) between baseline and weeks 6, 12, and 24. The primary outcome measured will be days with weight monitoring, for which this design provides at least 80% power to detect differences between the 2 groups.

RESULTS

Recruitment began in the fall of 2022, and the first patient was enrolled in the study on November 7, 2022. Recruitment of the last participant is expected in quarter 1 of 2025. Publication of complete results and data from this study is expected in 2026.

CONCLUSIONS

This project will generate insight and guidance for scalable and easy-to-use digital gaming solutions to motivate persistent adherence to HF self-management behaviors and improve health outcomes among individuals with HF.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05056129; https://clinicaltrials.gov/ct2/show/NCT05056129.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/45801.

Geriatric Care Management System Powered by the IoT and Computer Vision Techniques

The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients' data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient's position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff.

Wearable Activity Trackers Objectively Measure Incidental Physical Activity in Older Adults Undergoing Aortic Valve Replacement

BACKGROUND

For older adults with severe aortic stenosis (AS) undergoing aortic valve replacement (AVR), recovery of physical function is important, yet few studies objectively measure it in real-world environments. This exploratory study explored the acceptability and feasibility of using wearable trackers to measure incidental physical activity (PA) in AS patients before and after AVR.

METHODS

Fifteen adults with severe AS wore an activity tracker at baseline, and ten at one month follow-up. Functional capacity (six-minute walk test, 6MWT) and HRQoL (SF 12) were also assessed.

RESULTS

At baseline, AS participants (n = 15, 53.3% female, mean age 82.3 ± 7.0 years) wore the tracker for four consecutive days more than 85% of the total prescribed time, this improved at follow-up. Before AVR, participants demonstrated a wide range of incidental PA (step count median 3437 per day), and functional capacity (6MWT median 272 m). Post-AVR, participants with the lowest incidental PA, functional capacity, and HRQoL at baseline had the greatest improvements within each measure; however, improvements in one measure did not translate to improvements in another.

CONCLUSION

The majority of older AS participants wore the activity trackers for the required time period before and after AVR, and the data attained were useful for understanding AS patients' physical function.

Self-care Practices of Patients With Heart Failure Using Wearable Electronic Devices: A Systematic Review

BACKGROUND

Heart failure (HF) is the fastest growing cardiovascular condition globally; associated management costs and hospitalizations place an immense burden on healthcare systems. Wearable electronic devices (WEDs) may be useful tools to enhance HF management and mitigate negative health outcomes.

OBJECTIVE

We aimed to perform a systematic review to examine the potential of WEDs to support HF self-care in ambulatory patients at home.

METHODS

Five databases were searched for studies published between 2007 and May 2022, including OVID MEDLINE, EMBASE (OVID), APA PsycINFO (OVID), Cochrane Central Register of Controlled Trials (OVID), and CINAHL Plus with Full Text (Ebsco). After 6210 duplicates were removed, 4045 records were screened and 6 were included for review (2 conference abstracts and 4 full-text citations). All studies used WEDs as 1 component of a larger intervention.

RESULTS

Outcome measures included quality of life, physical activity, self-efficacy, self-care, functional status, time to readmission, social isolation, and mood. Studies were of moderate to high quality and mixed findings were reported. Enhanced exercise habits and motivational behavior to exercise, as well as decreased adverse symptoms of fatigue and dyspnea, were identified in 2 studies. However, improvements in exercise capacity and increased motivational behavior did not lead to exercise adherence in another 2 studies.

CONCLUSIONS

The findings from this review suggest that WEDs may be a viable health behavior improvement strategy for patients with HF. However, studies of higher quality, with the primary intervention being a WED, and consistent outcome measures are needed to replicate the positive findings of studies identified in this review.

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.

Comparison of Different Physical Activity Measures in a Cardiac Rehabilitation Program: A Prospective Study

Concordant assessments of physical activity (PA) and related measures in cardiac rehabilitation (CR) is essential for exercise prescription. This study compared exercise measurement from an in-person walk test; wearable activity tracker; and self-report at CR entry, completion (8-weeks) and follow-up (16-weeks). Forty patients beginning CR completed the Six-Minute Walk Test (6MWT), Physical Activity Scale for the Elderly (PASE), and wore Fitbit-Flex for four consecutive days including two weekend days. The sample mean age was 66 years; 67% were male. Increased exercise capacity at CR completion and follow-up was detected by a 6MWT change in mean distance (39 m and 42 m; p = 0.01, respectively). Increased PA participation at CR completion was detected by Fitbit-Flex mean change in step counts (1794; p = 0.01). Relative changes for Fitbit-Flex step counts and a 6MWT were consistent with previous research, demonstrating Fitbit-Flex's potential as an outcome measure. With four days of data, Fitbit-Flex had acceptable ICC values in measuring step counts and MVPA minutes. Fitbit-Flex steps and 6MWT meters are more responsive to changes in PA patterns following exposure to a cardiac rehabilitation program than Fitbit-Flex or PASE-estimated moderate-vigorous PA (MVPA) minutes. Fitbit-Flex step counts provide a useful additional measure for assessing PA outside of the CR setting and accounts for day-to-day variations. Two weekend days and two weekdays are needed for Fitbit-Flex to estimate PA levels more precisely.

Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables

Detecting stress when performing physical activities is an interesting field that has received relatively little research interest to date. In this paper, we took a first step towards redressing this, through a comprehensive review and the design of a low-cost body area network (BAN) made of a set of wearables that allow physiological signals and human movements to be captured simultaneously. We used four different wearables: OpenBCI and three other open-hardware custom-made designs that communicate via bluetooth low energy (BLE) to an external computer—following the edge-computingconcept—hosting applications for data synchronization and storage. We obtained a large number of physiological signals (electroencephalography (EEG), electrocardiography (ECG), breathing rate (BR), electrodermal activity (EDA), and skin temperature (ST)) with which we analyzed internal states in general, but with a focus on stress. The findings show the reliability and feasibility of the proposed body area network (BAN) according to battery lifetime (greater than 15 h), packet loss rate (0% for our custom-made designs), and signal quality (signal-noise ratio (SNR) of 9.8 dB for the ECG circuit, and 61.6 dB for the EDA). Moreover, we conducted a preliminary experiment to gauge the main ECG features for stress detection during rest.

Chronic Heart Failure Patients’ Attitudes towards Digital Device Data for Self-Documentation and Research in Germany: A Cross-Sectional Survey Study (Preprint)

Background

In recent years, the use of digital mobile measurement devices (DMMDs) for self-documentation in cardiovascular care in Western industrialized health care systems has increased. For patients with chronic heart failure (cHF), digital self-documentation plays an increasingly important role in self-management. Data from DMMDs can also be integrated into telemonitoring programs or data-intensive medical research to collect and evaluate patient-reported outcome measures through data sharing. However, the implementation of data-intensive devices and data sharing poses several challenges for doctors and patients as well as for the ethical governance of data-driven medical research.

Objective

This study aims to explore the potential and challenges of digital device data in cardiology research from patients’ perspectives. Leading research questions of the study concerned the attitudes of patients with cHF toward health-related data collected in the use of digital devices for self-documentation as well as sharing these data and consenting to data sharing for research purposes.

Methods

A cross-sectional survey of patients of a research in cardiology was conducted at a German university medical center (N=159) in 2020 (March to July). Eligible participants were German-speaking adult patients with cHF at that center. A pen-and-pencil questionnaire was sent by mail.

Results

Most participants (77/105, 73.3%) approved digital documentation, as they expected the device data to help them observe their body and its functions more objectively. Digital device data were believed to provide cognitive support, both for patients’ self-assessment and doctors’ evaluation of their patients’ current health condition. Interestingly, positive attitudes toward DMMD data providing cognitive support were, in particular, voiced by older patients aged >65 years. However, approximately half of the participants (56/105, 53.3%) also reported difficulty in dealing with self-documented data that lay outside the optimal medical target range. Furthermore, our findings revealed preferences for the self-management of DMMD data disclosed for data-intensive medical research among German patients with cHF, which are best implemented with a dynamic consent model.

Conclusions

Our findings provide potentially valuable insights for introducing DMMD in cardiovascular research in the German context. They have several practical implications, such as a high divergence in attitudes among patients with cHF toward different data-receiving organizations as well as a large variance in preferences for the modes of receiving information included in the consenting procedure for data sharing for research. We suggest addressing patients’ multiple views on consenting and data sharing in institutional normative governance frameworks for data-intensive medical research.

Impact of COVID-19 on Heart Failure Self-Care: A Qualitative Study

BACKGROUND

Difficulties in coping with and self-managing heart failure (HF) are well known. The COVID-19 pandemic may further complicate self-care practices associated with HF.

OBJECTIVE

The aim of this study was to understand COVID-19's impact on HF self-care, as well as related coping adaptations that may blunt the impact of COVID-19 on HF health outcomes.

METHODS

A qualitative study using phone interviews, guided by the framework of vulnerability analysis for sustainability, was used to explore HF self-care among older adults in central Texas during the late spring of 2020. Qualitative data were analyzed using directed content analysis.

RESULTS

Seventeen older adults with HF participated (mean [SD] age, 68 [9.1] years; 62% female, 68% White, 40% below poverty line, 35% from rural areas). Overall, the COVID-19 pandemic had an adverse impact on the HF self-care behavior of physical activity. Themes of social isolation, financial concerns, and disruptions in access to medications and food indicated exposure, and rural residence and source of income increased sensitivity, whereas adaptations by healthcare system, health-promoting activities, socializing via technology, and spiritual connections increased resilience to the COVID-19 pandemic.

CONCLUSIONS

The study's findings have implications for identifying vulnerabilities in sustaining HF self-care by older adults and empowering older adults with coping strategies to improve overall satisfaction with care and quality of life.

Digital Health: Implications for Heart Failure Management

Digital health encompasses the use of information and communications technology and the use of advanced computing sciences in healthcare. This review covers the application of digital health in heart failure patients, focusing on teleconsultation, remote monitoring and apps and wearables, looking at how these technologies can be used to support care and improve outcomes. Interest in and use of these technologies, particularly teleconsultation, have been accelerated by the coronavirus disease 2019 pandemic. Remote monitoring of heart failure patients, to identify those patients at high risk of hospitalisation and to support clinical stability, has been studied with mixed results. Remote monitoring of pulmonary artery pressure has a consistent effect on reducing hospitalisation rates for patients with moderately severe symptoms and multiparameter monitoring shows promise for the future. Wearable devices and apps are increasingly used by patients for health and lifestyle support. Some wearable technologies have shown promise in AF detection, and others may be useful in supporting self-care and guiding prognosis, but more evidence is required to guide their optimal use. Support for patients and clinicians wishing to use these technologies is important, along with consideration of data validity and privacy and appropriate recording of decision-making.

The left ventricular assist device as a patient monitoring system

Technological progress of left ventricular assist devices (LVADs) towards rotary blood pumps and the optimization of medical management contributed to the significant improvements in patient survival as well as LVAD support duration. Even though LVAD therapy is now well-established for end-stage heart failure patients, the long-term occurrence of adverse events (AE) such as bleeding, infection or stroke, still represent a relevant burden. An early detection of AE, before onset of major symptoms, can lead to further optimization of patient treatment and thus mitigate the burden of AE. Continuous patient monitoring facilitates identification of pathophysiological states and allows anticipation of AE to improve patient management. In this paper, methods, algorithms and possibilities for continuous patient monitoring based on LVAD data are reviewed. While experience with continuous LVAD monitoring is currently limited to a few centers worldwide, the pace of developments in this field is fast and we expect these technologies to have a global impact on the well-being of LVAD patients.

Deep learning-based ambient assisted living for self-management of cardiovascular conditions

According to the World Health Organization, cardiovascular diseases contribute to 17.7 million deaths per year and are rising with a growing ageing population. In order to handle these challenges, the evolved countries are now evolving workable solutions based on new communication technologies such as ambient assisted living. In these solutions, the most well-known solutions are wearable devices for patient monitoring, telemedicine and mHealth systems. This systematic literature review presents the detailed literature on ambient assisted living solutions and helps to understand how ambient assisted living helps and motivates patients with cardiovascular diseases for self-management to reduce associated morbidity and mortalities. Preferred reporting items for systematic reviews and meta-analyses technique are used to answer the research questions. The paper is divided into four main themes, including self-monitoring wearable systems, ambient assisted living in aged populations, clinician management systems and deep learning-based systems for cardiovascular diagnosis. For each theme, a detailed investigation shows (1) how these new technologies are nowadays integrated into diagnostic systems and (2) how new technologies like IoT sensors, cloud models, machine and deep learning strategies can be used to improve the medical services. This study helps to identify the strengths and weaknesses of novel ambient assisted living environments for medical applications. Besides, this review assists in reducing the dependence on caregivers and the healthcare systems.

Measuring physical activity with activity monitors in patients with heart failure: from literature to practice. A position paper from the Committee on Exercise Physiology and Training of the Heart Failure Association of the European Society of Cardiology

The aims of this paper were to provide an overview of available activity monitors used in research in patients with heart failure and to identify the key criteria in the selection of the most appropriate activity monitor for collecting, reporting, and analysing physical activity in heart failure research. This study was conducted in three parts. First, the literature was systematically reviewed to identify physical activity concepts and activity monitors used in heart failure research. Second, an additional scoping literature search for validation of these activity monitors was conducted. Third, the most appropriate criteria in the selection of activity monitors were identified. Nine activity monitors were evaluated in terms of size, weight, placement, costs, data storage, water resistance, outcomes and validation, and cut‐off points for physical activity intensity levels were discussed. The choice of a monitor should depend on the research aims, study population and design regarding physical activity. If the aim is to motivate patients to be active or set goals, a less rigorously tested tool can be considered. On the other hand, if the aim is to measure physical activity and its changes over time or following treatment adjustment, it is important to choose a valid activity monitor with a storage and battery longevity of at least one week. The device should provide raw data and valid cut‐off points should be chosen for analysing physical activity intensity levels. Other considerations in choosing an activity monitor should include data storage location and ownership and the upfront costs of the device.

Assessment of a Personal Interactive Carbon Monoxide Breath Sensor in People Who Smoke Cigarettes: Single-Arm Cohort Study

Background

Tobacco use is the leading cause of preventable morbidity and mortality. Existing evidence-based treatments are underutilized and have seen little recent innovation. The success of personal biofeedback interventions in other disease states portends a similar opportunity in smoking cessation. The Pivot Breath Sensor is a personal interactive FDA-cleared (over-the-counter) device that measures carbon monoxide (CO) in exhaled breath, enabling users to link their smoking behavior and CO values, and track their progress in reducing or quitting smoking.

Objective

The objective of this study is to assess the Pivot Breath Sensor in people who smoke cigarettes, evaluating changes in attitudes toward quitting smoking, changes in smoking behavior, and use experience.

Methods

US adults (18-80 years of age, ≥10 cigarettes per day [CPD]) were recruited online for this remote 12-week study. Participants completed a screening call, informed consent, and baseline questionnaire, and then were mailed their sensor. Participants were asked to submit 4 or more breath samples per day and complete questionnaires at 1-4, 8, and 12 weeks. Outcomes included attitudes toward quitting smoking (Stage of Change, success to quit, and perceived difficulty of quitting), smoking behavior (quit attempts, CPD reduction, and 7-, 30-day point prevalence abstinence [PPA]), and use experience (impact and learning).

Results

Participants comprised 234 smokers, mean age 39.9 (SD 11.3) years, 52.6% (123/234) female, mean CPD 20.3 (SD 8.0). The 4- and 12-week questionnaires were completed by 92.3% (216/234) and 91.9% (215/234) of participants, respectively. Concerning attitude outcomes, at baseline, 15.4% (36/234) were seriously thinking of quitting in the next 30 days, increasing to 38.9% (84/216) at 4 weeks and 47.9% (103/215) at 12 weeks (both P<.001). At 12 weeks, motivation to quit was increased in 39.1% (84/215), unchanged in 54.9% (118/215), and decreased in 6.0% (13/215; P<.001). Additional attitudes toward quitting improved from baseline to 12 weeks: success to quit 3.3 versus 5.0 (P<.001) and difficulty of quitting 2.8 versus 4.3 (P<.001). Regarding smoking behavior, at 4 weeks, 28.2% (66/234) had made 1 or more quit attempts (≥1 day of abstinence), increasing to 48.3% (113/234) at 12 weeks. At 4 weeks, 23.1% (54/234) had reduced CPD by 50% or more, increasing to 38.5% (90/234) at 12 weeks. At 12 weeks, CPD decreased by 41.1% from baseline (P<.001), and 7- and 30-day PPA were 12.0% (28/234) and 6.0% (14/234), respectively. Concerning use experience, 75.3% (171/227) reported the sensor increased their motivation to quit. More than 90% (>196/214) indicated the sensor taught them about their CO levels and smoking behavior, and 73.1% (166/227) reported that seeing their CO values made them want to quit smoking.

Conclusions

Use of the Pivot Breath Sensor resulted in a significant increase in motivation to quit, a reduction in CPD, and favorable quit attempt rates. These outcomes confer increased likelihood of quitting smoking. Accordingly, the results support a role for biofeedback via personal CO breath sampling in smoking cessation.

Trial Registration

ClinicalTrials.gov NCT04133064; https://clinicaltrials.gov/ct2/show/NCT04133064

Wearable health devices and personal area networks: can they improve outcomes in haemodialysis patients?

Digitization of healthcare will be a major innovation driver in the coming decade. Also, enabled by technological advancements and electronics miniaturization, wearable health device (WHD) applications are expected to grow exponentially. This, in turn, may make 4P medicine (predictive, precise, preventive and personalized) a more attainable goal within dialysis patient care. This article discusses different use cases where WHD could be of relevance for dialysis patient care, i.e. measurement of heart rate, arrhythmia detection, blood pressure, hyperkalaemia, fluid overload and physical activity. After adequate validation of the different WHD in this specific population, data obtained from WHD could form part of a body area network (BAN), which could serve different purposes such as feedback on actionable parameters like physical inactivity, fluid overload, danger signalling or event prediction. For a BAN to become clinical reality, not only must technical issues, cybersecurity and data privacy be addressed, but also adequate models based on artificial intelligence and mathematical analysis need to be developed for signal optimization, data representation, data reliability labelling and interpretation. Moreover, the potential of WHD and BAN can only be fulfilled if they are part of a transformative healthcare system with a shared responsibility between patients, healthcare providers and the payors, using a step-up approach that may include digital assistants and dedicated ‘digital clinics’. The coming decade will be critical in observing how these developments will impact and transform dialysis patient care and will undoubtedly ask for an increased ‘digital literacy’ for all those implicated in their care.

The Role of Wearables in Heart Failure

PURPOSE OF REVIEW

This review discusses how wearable devices-sensors externally applied to the body to measure a physiological signal-can be used in heart failure (HF) care.

RECENT FINDINGS

Most wearables are marketed to consumers and can measure movement, heart rate, and blood pressure; detect and monitor arrhythmia; and support exercise training and rehabilitation. Wearable devices targeted at healthcare professionals include ECG patch recorders and vests, patches, and textiles with in-built sensors for improved prognostication and the early detection of acute decompensation. Integrating data from wearables into clinical decision-making has been slow due to clinical inertia and concerns regarding data security and validity, lack of evidence of meaningful impact, interoperability, regulatory and reimbursement issues, and legal liability. Although few studies have assessed how best to integrate wearable technologies into clinical practice, their use is rapidly expanding and may support improved decision-making by patients and healthcare professionals along the whole patient pathway.

Effect of Smartphone-Based Telemonitored Exercise Rehabilitation among Patients with Coronary Heart Disease

The aim of this study was to investigate the effects of telemonitored exercise rehabilitation on patients with coronary heart disease (CHD) in China. Ninety-six patients with stable CHD were included and analyzed (48 in telemonitored group and 48 in control group). All patients received routine follow-up, and patients in telemonitored group participated in smartphone-based telemonitored cardiac rehabilitation. Patients' demographic information, medical history, diagnosis and treatment of CHD, and laboratory results were collected. The difference of cardiopulmonary exercise testing (CPET), blood test, and echocardiographic parameters; exercise habits; control rate of blood lipid and blood glucose; and incidence of adverse events between the two groups during 6 months of follow-up was analyzed. After intervention, the subjects in the telemonitored group performed significantly better in VO2peak, exercise compliance, and some other parameters than those in the control group. Telemonitored exercise rehabilitation is an effective rehabilitation mode for CHD patients in China.

A Machine Learning Approach to Classifying Self-Reported Health Status in a Cohort of Patients With Heart Disease Using Activity Tracker Data

Constructing statistical models using personal sensor data could allow for tracking health status over time, thereby enabling the possibility of early intervention. The goal of this study was to use machine learning algorithms to classify patient-reported outcomes (PROs) using activity tracker data in a cohort of patients with stable ischemic heart disease (SIHD). A population of 182 patients with SIHD were monitored over a period of 12 weeks. Each subject received a Fitbit Charge 2 device to record daily activity data, and each subject completed eight Patient-Reported Outcomes Measurement Information Systems short form at the end of each week as a self-assessment of their health status. Two models were built to classify PRO scores using activity tracker data. The first model treated each week independently, whereas the second used a hidden Markov model (HMM) to take advantage of correlations between successive weeks. Retrospective analysis compared the classification accuracy of the two models and the importance of each feature. In the independent model, a random forest classifier achieved a mean area under curve (AUC) of 0.76 for classifying the physical function PRO. The HMM model achieved significantly better AUCs for all PROs (p < 0.05) other than Fatigue and Sleep Disturbance, with a highest mean AUC of 0.79 for the physical function-short form 10a. Our study demonstrates the ability of activity tracker data to classify health status over time. These results suggest that patient outcomes can be monitored in real time using activity trackers.

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

BACKGROUND

Heart 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.

OBJECTIVE

The 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.

METHODS

In 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.

RESULTS

Participants 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).

CONCLUSIONS

The 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.

Feedback on Physical Activity Through a Wearable Device Connected to a Mobile Phone App in Patients With Metabolic Syndrome: Pilot Study

Background

Little is known of the effect of wearable devices on metabolic impairments in clinical settings. We hypothesized that a wearable device that can monitor and provide feedback on physical activity may help resolve metabolic syndrome.

Objective

This study aimed to examine the objective effects of the use of these devices on metabolic syndrome resolution.

Methods

Patients diagnosed with metabolic syndrome were recruited. Participants were prescribed regular walking using a wearable device (Coffee WALKIE +Dv.3, GC Healthcare CI, Korea) on their wrist for 12 weeks. Participants received self-feedback on the amount of their exercise through an app on their mobile phone. The information on physical activities of the participants was uploaded automatically to a website. Thus, a trained nurse could provide individuals with feedback regarding the physical activity via telephone consultation on alternate weeks. Blood pressure (BP), body composition, fasting plasma glucose, and lipid profiles were recorded. The primary outcome was metabolic syndrome resolution. The secondary outcome was an improvement in the components of metabolic impairment.

Results

Of the 53 participants recruited, 20 participants with a median age of 46 (range 36-50) years completed the trial. There was no significant difference in the amount of calorie expenditure at weeks 4, 8, and 12. After 12 weeks, metabolic syndrome was resolved in 9 of 20 participants (45%), and the mean number of metabolic impairment components per person decreased from 3.4 to 2.9. Particularly, the mean systolic and diastolic BP decreased from mean 136.6 (SD 18.5) mm Hg to mean 127.4 (SD 19.5) mm Hg and from mean 84.0 (SD 8.1) mm Hg to mean 77.4 (SD 14.4) mm Hg (both P=.02), respectively.

Conclusions

This study found that a 12-week intervention via feedback, based on a wearable physical activity monitor, helped metabolic syndrome patients to be more engaged in regular walking and it improved impaired metabolic components, especially in BP. However, some practical challenges regarding patients’ adherence and sustained engagement were observed.

Correlates of Self-Care Behaviors in Adults With Type 2 Diabetes and Comorbid Heart Failure

PURPOSE

The purpose of this study was to describe sociodemographic, clinical, and psychosocial correlates of diabetes self-care behaviors in adults with type 2 diabetes mellitus (T2D) and comorbid heart failure (HF).

METHOD

Baseline data of 180 participants from a clinical trial that tested a 6-month integrated self-care intervention for persons with comorbid HF and T2D were analyzed. Correlational bivariate and multiple logistic regression analyses were performed to examine correlates of diabetes self-care behaviors.

RESULT

Participants had a mean age of 58 ± 11years; the majority were male (n = 118, 66%) and African American (n = 119, 66%). Number of comorbidities >2 (P < .001), having more than a high school education (P < .05), and African American ethnicity (P < .05) predicted better exercise, self-monitoring blood glucose (SMBG), and foot care behaviors, respectively. The use of an aldosterone inhibitor (P < .05) predicted worse exercise performance, higher Charlson Comorbidity Index scores (P < .01) predicted worse SMBG, and treatment with diet plus medication (P < .05) and dyslipidemia (P < .001) predicted worse foot care.

CONCLUSION

Findings from this study provide new insights into the complex self-care requirements for T2D patients with comorbid HF. Integrated self-care interventions are clearly warranted in persons living with multiple chronic conditions for optimal health outcomes and the prevention of complications. Our sample of predominately African American men showed they had better T2D self-care behaviors than whites than previously reported. Additional research is needed to determine racial and gender differences on health outcomes in persons with T2D and comorbid HF.

Data management and wearables in older adults: A systematic review

BACKGROUND

Wearable trackers as research or clinical tools are increasingly used to support the care of older adults, due to their practicality in self-monitoring and potential to promote healthy lifestyle behaviours. However, there is limited understanding of appropriate data collection and analysis methods in different contexts.

AIM

To summarise evidence on wearable data generation and management in older adults, focusing on physical activity (PA), electrocardiogram (ECG), and vital signs monitoring. In addition to examine the accuracy and utility of wearable trackers in the care of older people.

METHODS

A systematic search of CINAHL, MEDLINE, PubMed and a manual search were conducted. Twenty studies on the use of wearable trackers by older adults met the inclusion criteria.

RESULTS

Methodological designs for data collection and analysis were heterogeneous, with diverse definitions of wear and no-wear time, the number and type of valid days, and proprietary algorithms. Wearable trackers had adequate accuracy for measuring step counts, moderate to vigorous physical activity (MVPA), ECG and heart rate (HR), but not for respiratory rate. Participants reported ease of use and had high-level adherence over daily long-term use. Moreover, wearable trackers encouraged users to increase their daily level of physical activity and decrease waist circumference, facilitating atrial fibrillation (AF) diagnoses and predicting length of stay.

CONCLUSION

Wearable trackers are multi-dimensional technologies offering a viable and promising approach for sustained and scaled monitoring of older people's health. Frameworks and/or guidelines, including standards for the design, data management and application of use specifically for older adults, are required to enhance validity and reliability.

Can activity monitors predict outcomes in patients with heart failure? A systematic review

Actigraphy is increasingly incorporated into clinical practice to monitor intervention effectiveness and patient health in congestive heart failure (CHF). We explored the prognostic impact of actigraphy-quantified physical activity (AQPA) on CHF outcomes. PubMed and Medline databases were systematically searched for cross-sectional studies, cohort studies or randomised controlled trials from January 2007 to December 2017. We included studies that used validated actigraphs to predict outcomes in adult HF patients. Study selection and data extraction were performed by two independent reviewers. A total of 17 studies (15 cohort, 1 cross-sectional, 1 randomised controlled trial) were included, reporting on 2,759 CHF patients (22-89 years, 27.7% female). Overall, AQPA showed a strong inverse relationship with mortality and predictive utility when combined with established risk scores, and prognostic roles in morbidity, predicting cognitive function, New York Heart Association functional class and intercurrent events (e.g. hospitalisation), but weak relationships with health-related quality of life scores. Studies lacked consensus regarding device choice, time points and thresholds of PA measurement, which rendered quantitative comparisons between studies difficult. AQPA has a strong prognostic role in CHF. Multiple sampling time points would allow calculation of AQPA changes for incorporation into risk models. Consensus is needed regarding device choice and AQPA thresholds, while data management strategies are required to fully utilise generated data. Big data and machine learning strategies will potentially yield better predictive value of AQPA in CHF patients.

Evaluating utility and compliance in a patient-based eHealth study using continuous-time heart rate and activity trackers

Telemedicine has been used to remotely diagnose and treat patients, yet previously applied telemonitoring approaches have been fraught with adherence issues. The primary goal of this study was to evaluate the adherence rates using a consumer-grade continuous-time heart rate and activity tracker in a mid-risk cardiovascular patient population. As a secondary analysis, we show the ability to utilize the information provided by this device to identify information about a patient's state by correlating tracker information with patient-reported outcome survey scores. We showed that using continuous-time activity trackers with heart rate monitors can be effective in a telemonitoring application, as patients had a high level of adherence (90.0% median usage) and low attrition (0.09% decrease per day) over a 90-day period. Furthermore, data collected correlated significantly with clinically relevant patient surveys (r2=0.15 for PROMIS global health scores, p < .00001), and therefore might provide an effective signal for identifying patients in need of intervention.