Remote digital health technologies for improving the care of people with respiratory disorders

Respiratory diseases are a leading cause of morbidity and mortality globally. However, existing systems of care, built around scheduled appointments, are not well designed to support the needs of people with chronic and acute respiratory conditions that can change rapidly and unexpectedly. Home-based and personal digital health technologies (DHTs) allow implementation of new models of care catering to the unique needs of individuals. The high number of respiratory triggers and unique responses to them require a personalised solution for each patient. The real-world, repetitive monitoring capabilities of DHTs enable identification of the normal operating characteristics for each individual and, therefore, recognition of the earliest deviations from that state. However, despite this potential, the number of clinical efficacy studies of DHTs is quite small. Evaluation of clinical effectiveness of DHTs in improving health quality in real-world settings is urgently needed.

Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias

Early identification of atrial fibrillation (AF) can reduce the risk of stroke, heart failure, and other serious cardiovascular outcomes. However, paroxysmal AF may not be detected even after a two-week continuous monitoring period. We developed a model to quantify the risk of near-term AF in a two-week period, based on AF-free ECG intervals of up to 24 h from 459,889 patch-based ambulatory single-lead ECG (modified lead II) recordings of up to 14 days. A deep learning model was used to integrate ECG morphology data with demographic and heart rhythm features toward AF prediction. Observing a 1-day AF-free ECG recording, the model with deep learning features produced the most accurate prediction of near-term AF with an area under the curve AUC = 0.80 (95% confidence interval, CI = 0.79-0.81), significantly improving discrimination compared to demographic metrics alone (AUC 0.67; CI = 0.66-0.68). Our model was able to predict incident AF over a two-week time frame with high discrimination, based on AF-free single-lead ECG recordings of various lengths. Application of the model may enable a digital strategy for improving diagnostic capture of AF by risk stratifying individuals with AF-negative ambulatory monitoring for prolonged or recurrent monitoring, potentially leading to more rapid initiation of treatment.

Educating the healthcare workforce of the future: lessons learned from the development and implementation of a 'Wearables in Healthcare' course

Digital health technologies will play an ever-increasing role in the future of healthcare. It is crucial that the people who will help make that transformation possible have the evidence-based and hands-on training necessary to address the many challenges ahead. To better prepare the future health workforce with the knowledge necessary to support the re-engineering of healthcare in an equitable, person-centric manner, we developed an experiential learning course-Wearables in Healthcare-for advanced undergraduate and graduate university students. Here we describe the components of that course and the lessons learned to help guide others interested in developing similar courses.

Cost-Effectiveness of AF Screening With 2-Week Patch Monitors: The mSToPS Study

BACKGROUND

The mSToPS study (mHealth Screening to Prevent Strokes) reported screening older Americans at risk for atrial fibrillation (AF) and stroke using 2-week patch monitors was associated with increased rates of AF diagnosis and anticoagulant prescription within 1 year and improved clinical outcomes at 3 years relative to matched controls. Cost-effectiveness of this AF screening approach has not been explored.

METHODS

We conducted a US-based health economic analysis of AF screening using patient-level data from mSToPS. Clinical outcomes, resource use, and costs were obtained through 3 years using claims data. Individual costs, survival, and quality-adjusted life years (QALYs) were projected over a lifetime horizon using regression modeling, US life tables, and external data where needed. Adjustment between groups was performed using propensity score bin bootstrapping.

RESULTS

Screening participants (mean age, 74 years, 41% female, median CHA2DS2-VASC score 3) wore on average 1.7 two-week monitors at a mean cost of $614/person. Over 3 years, outpatient visits were more frequent for monitored than unmonitored individuals (difference 190 per 100 patient-years [95% CI, 82-298]), but emergency department visits (-8.3 [95% CI, -12.6 to -4.1]) and hospitalizations (-15.2 [CI, -22 to -8.6]) were less frequent. Total adjusted 3-year costs were slightly higher (mean difference, $1551 [95% CI, -$1047 to $4038]) in the monitoring group. In patient-level projections, the monitoring group had slightly greater quality-adjusted survival (8.81 versus 8.71 QALYs, difference, 0.09 [95% CI, -0.05 to 0.24]) and slightly higher lifetime costs, resulting in an incremental cost-effectiveness ratio of $36 100/QALY gained. With bootstrap resampling, the incremental cost-effectiveness ratio for monitoring was <$50 000/QALY in 64% of study replicates, and <$150 000/QALY in 91%.

CONCLUSIONS

Using lifetime projections derived from the mSToPS study, we found that AF screening using 2-week patch monitors in older Americans was associated with high economic value. Confirmation of these uncertain findings in a randomized trial is warranted.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT02506244.

Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data

Mounting clinical evidence suggests that viral infections can lead to detectable changes in an individual's normal physiologic and behavioral metrics, including heart and respiration rates, heart rate variability, temperature, activity, and sleep prior to symptom onset, potentially even in asymptomatic individuals. While the ability of wearable devices to detect viral infections in a real-world setting has yet to be proven, multiple recent studies have established that individual, continuous data from a range of biometric monitoring technologies can be easily acquired and that through the use of machine learning techniques, physiological signals and warning signs can be identified. In this review, we highlight the existing knowledge base supporting the potential for widespread implementation of biometric data to address existing gaps in the diagnosis and treatment of viral illnesses, with a particular focus on the many important lessons learned from the coronavirus disease 2019 pandemic.

rECHOmmend: An ECG-based Machine-learning Approach for Identifying Patients at High-risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography

Background:

Timely diagnosis of structural heart disease improves patient outcomes, yet many remain underdiagnosed. While population screening with echocardiography is impractical, ECG-based prediction models can help target high-risk patients. We developed a novel ECG-based machine learning approach to predict multiple structural heart conditions, hypothesizing that a composite model would yield higher prevalence and positive predictive values to facilitate meaningful recommendations for echocardiography.

Methods:

Using 2 232 130 ECGs linked to electronic health records and echocardiography reports from 484 765 adults between 1984 to 2021, we trained machine learning models to predict the presence or absence of any of 7 echocardiography-confirmed diseases within 1 year. This composite label included the following: moderate or severe valvular disease (aortic/mitral stenosis or regurgitation, tricuspid regurgitation), reduced ejection fraction <50%, or interventricular septal thickness >15 mm. We tested various combinations of input features (demographics, laboratory values, structured ECG data, ECG traces) and evaluated model performance using 5-fold cross-validation, multisite validation trained on 1 site and tested on 10 independent sites, and simulated retrospective deployment trained on pre-2010 data and deployed in 2010.

Results:

Our composite rECHOmmend model used age, sex, and ECG traces and had a 0.91 area under the receiver operating characteristic curve and a 42% positive predictive value at 90% sensitivity, with a composite label prevalence of 17.9%. Individual disease models had area under the receiver operating characteristic curves from 0.86 to 0.93 and lower positive predictive values from 1% to 31%. Area under the receiver operating characteristic curves for models using different input features ranged from 0.80 to 0.93, increasing with additional features. Multisite validation showed similar results to cross-validation, with an aggregate area under the receiver operating characteristic curve of 0.91 across our independent test set of 10 clinical sites after training on a separate site. Our simulated retrospective deployment showed that for ECGs acquired in patients without preexisting structural heart disease in the year 2010, 11% were classified as high risk and 41% (4.5% of total patients) developed true echocardiography-confirmed disease within 1 year.

Conclusions:

An ECG-based machine learning model using a composite end point can identify a high-risk population for having undiagnosed, clinically significant structural heart disease while outperforming single-disease models and improving practical utility with higher positive predictive values. This approach can facilitate targeted screening with echocardiography to improve underdiagnosis of structural heart disease.

Inter-individual variation in objective measure of reactogenicity following COVID-19 vaccination via smartwatches and fitness bands

The ability to identify who does or does not experience the intended immune response following vaccination could be of great value in not only managing the global trajectory of COVID-19 but also helping guide future vaccine development. Vaccine reactogenicity can potentially lead to detectable physiologic changes, thus we postulated that we could detect an individual's initial physiologic response to a vaccine by tracking changes relative to their pre-vaccine baseline using consumer wearable devices. We explored this possibility using a smartphone app-based research platform that enabled volunteers (39,701 individuals) to share their smartwatch data, as well as self-report, when appropriate, any symptoms, COVID-19 test results, and vaccination information. Of 7728 individuals who reported at least one vaccination dose, 7298 received an mRNA vaccine, and 5674 provided adequate data from the peri-vaccine period for analysis. We found that in most individuals, resting heart rate (RHR) increased with respect to their individual baseline after vaccination, peaked on day 2, and returned to normal by day 6. This increase in RHR was greater than one standard deviation above individuals' normal daily pattern in 47% of participants after their second vaccine dose. Consistent with other reports of subjective reactogenicity following vaccination, we measured a significantly stronger effect after the second dose relative to the first, except those who previously tested positive to COVID-19, and a more pronounced increase for individuals who received the Moderna vaccine. Females, after the first dose only, and those aged <40 years, also experienced a greater objective response after adjusting for possible confounding factors. These early findings show that it is possible to detect subtle, but important changes from an individual's normal as objective evidence of reactogenicity, which, with further work, could prove useful as a surrogate for vaccine-induced immune response.

Passive detection of COVID-19 with wearable sensors and explainable machine learning algorithms

Individual smartwatch or fitness band sensor data in the setting of COVID-19 has shown promise to identify symptomatic and pre-symptomatic infection or the need for hospitalization, correlations between peripheral temperature and self-reported fever, and an association between changes in heart-rate-variability and infection. In our study, a total of 38,911 individuals (61% female, 15% over 65) have been enrolled between March 25, 2020 and April 3, 2021, with 1118 reported testing positive and 7032 negative for COVID-19 by nasopharyngeal PCR swab test. We propose an explainable gradient boosting prediction model based on decision trees for the detection of COVID-19 infection that can adapt to the absence of self-reported symptoms and to the available sensor data, and that can explain the importance of each feature and the post-test-behavior for the individuals. We tested it in a cohort of symptomatic individuals who exhibited an AUC of 0.83 [0.81-0.85], or AUC = 0.78 [0.75-0.80] when considering only data before the test date, outperforming state-of-the-art algorithm in these conditions. The analysis of all individuals (including asymptomatic and pre-symptomatic) when self-reported symptoms were excluded provided an AUC of 0.78 [0.76-0.79], or AUC of 0.70 [0.69-0.72] when considering only data before the test date. Extending the use of predictive algorithms for detection of COVID-19 infection based only on passively monitored data from any device, we showed that it is possible to scale up this platform and apply the algorithm in other settings where self-reported symptoms can not be collected.

Three year clinical outcomes in a nationwide, observational, siteless clinical trial of atrial fibrillation screening-mHealth Screening to Prevent Strokes (mSToPS)

Background

Atrial fibrillation (AF) is common, often without symptoms, and is an independent risk factor for mortality, stroke and heart failure. It is unknown if screening asymptomatic individuals for AF can improve clinical outcomes.

Methods

mSToPS was a pragmatic, direct-to-participant trial that randomized individuals from a single US-wide health plan to either immediate or delayed screening using a continuous-recording ECG patch to be worn for two weeks and 2 occasions, ~3 months apart, to potentially detect undiagnosed AF. The 3-year outcomes component of the trial was designed to compare clinical outcomes in the combined cohort of 1718 individuals who underwent monitoring and 3371 matched observational controls. The prespecified primary outcome was the time to first event of the combined endpoint of death, stroke, systemic embolism, or myocardial infarction among individuals with a new AF diagnosis, which was hypothesized to be the same in the two cohorts but was not realized.

Results

Over the 3 years following the initiation of screening (mean follow-up 29 months), AF was newly diagnosed in 11.4% (n = 196) of screened participants versus 7.7% (n = 261) of observational controls (p<0.01). Among the screened cohort with incident AF, one-third were diagnosed through screening. For all individuals whose AF was first diagnosed clinically, a clinical event was common in the 4 weeks surrounding that diagnosis: 6.6% experienced a stroke,10.2% were newly diagnosed with heart failure, 9.2% had a myocardial infarction, and 1.5% systemic emboli. Cumulatively, 42.9% were hospitalized. For those diagnosed via screening, none experienced a stroke, myocardial infarction or systemic emboli in the period surrounding their AF diagnosis, and only 1 person (2.3%) had a new diagnosis of heart failure. Incidence rate of the prespecified combined primary endpoint was 3.6 per 100 person-years among the actively monitored cohort and 4.5 per 100 person-years in the observational controls.

Conclusions

At 3 years, screening for AF was associated with a lower rate of clinical events and improved outcomes relative to a matched cohort, although the influence of earlier diagnosis of AF via screening on this finding is unclear. These observational data, including the high event rate surrounding a new clinical diagnosis of AF, support the need for randomized trials to determine whether screening for AF will yield a meaningful protection from strokes and other clinical events.

Trail registration

The mHealth Screening To Prevent Strokes (mSToPS) Trial is registered on ClinicalTrials.gov with the identifier NCT02506244.

Sleep Outcomes From AWAKE-HF: A Randomized Clinical Trial of Sacubitril/Valsartan vs Enalapril in Patients With Heart Failure and Reduced Ejection Fraction

BACKGROUND

Heart failure and sleep-disordered breathing have been increasingly recognized as co-occurring conditions. Their bidirectional relationship warrants investigation into whether heart failure therapy improves sleep and sleep-disordered breathing. We sought to explore the effect of treatment with sacubitril/valsartan on sleep-related endpoints from the AWAKE-HF study.

METHODS AND RESULTS

AWAKE-HF was a randomized, double-blind study conducted in 23 centers in the United States. Study participants with heart failure with reduced rejection fraction and New York Heart Association class II or III symptoms were randomly assigned to receive treatment with either sacubitril/valsartan or enalapril. All endpoints were assessed at baseline and after 8 weeks of treatment. Portable sleep-monitoring equipment was used to measure the apnea-hypopnea index, including obstructive and central events. Total sleep time, wake after sleep onset and sleep efficiency were exploratory measures assessed using wrist actigraphy.

THE RESULTS WERE AS FOLLOWS

140 patients received treatment in the double-blind phase (sacubitril/valsartan, n = 70; enalapril, n = 70). At baseline, 39% and 40% of patients randomly assigned to receive sacubitril/valsartan or enalapril, respectively, presented with undiagnosed, untreated, moderate-to-severe sleep-disordered breathing (≥ 15 events/h), and nearly all had obstructive sleep apnea. After 8 weeks of treatment, the mean 4% apnea-hypopnea index changed minimally from 16.3/h to 15.2/h in the sacubitril/valsartan group and from 16.8/h to 17.6/h in the enalapril group. Mean total sleep time was long at baseline and decreased only slightly in both treatment groups at week 8 (-14 and -11 minutes for sacubitril/valsartan and enalapril, respectively), with small changes in wake after sleep onset and sleep efficiency in both groups.

CONCLUSIONS

In a cohort of patients with heart failure with reduced rejection fraction who met prescribing guidelines for sacubitril/valsartan, one-third had undiagnosed moderate-to-severe obstructive sleep apnea. The addition of sacubitril/valsartan therapy did not significantly improve sleep-disordered breathing or sleep duration or efficiency. Patients who meet indications for treatment with sacubitril/valsartan should be evaluated for sleep-disordered breathing.

A Comprehensive Explanation Framework for Biomedical Time Series Classification

In this study, we propose a post-hoc explainability framework for deep learning models applied to quasi-periodic biomedical time-series classification. As a case study, we focus on the problem of atrial fibrillation (AF) detection from electrocardiography signals, which has strong clinical relevance. Starting from a state-of-the-art pretrained model, we tackle the problem from two different perspectives: global and local explanation. With global explanation, we analyze the model behavior by looking at entire classes of data, showing which regions of the input repetitive patterns have the most influence for a specific outcome of the model. Our explanation results align with the expectations of clinical experts, showing that features crucial for AF detection contribute heavily to the final decision. These features include R-R interval regularity, absence of the P-wave or presence of electrical activity in the isoelectric period. On the other hand, with local explanation, we analyze specific input signals and model outcomes. We present a comprehensive analysis of the network facing different conditions, whether the model has correctly classified the input signal or not. This enables a deeper understanding of the network's behavior, showing the most informative regions that trigger the classification decision and highlighting possible causes of misbehavior.

Assessment of Prolonged Physiological and Behavioral Changes Associated With COVID-19 Infection

This cohort study examines the duration and variation of recovery among COVID-19–positive verses COVID-19–negative individuals.

The Physiologic Response to COVID-19 Vaccination

Two mRNA vaccines and one adenovirus-based vaccine against SARS CoV-2 are currently being distributed at scale in the United States. Objective evidence of a specific individual's physiologic response to that vaccine are not routinely tracked but may offer insights into the acute immune response and personal and/or vaccine characteristics associated with that. We explored this possibility using a smartphone app-based research platform developed early in the pandemic that enabled volunteers (38,911 individuals between 25 March 2020 and 4 April 2021) to share their smartwatch and activity tracker data, as well as self-report, when appropriate, any symptoms, COVID-19 test results and vaccination dates and type. Of 4,110 individuals who reported at least one mRNA vaccination dose, 3,312 provided adequate resting heart rate data from the peri-vaccine period for analysis. We found changes in resting heart rate with respect to an individual baseline increased the days after vaccination, peaked on day 2, and returned to normal on day 6, with a much stronger effect after second dose with respect to first dose (average changes 1.6 versus 0.5 beats per minute). The changes were more pronounced for individuals who received the Moderna vaccine (on both doses), those who previously tested positive to COVID-19 (on dose 1), and for individuals aged <40 years, after adjusting for possible confounding factors. Taking advantage of continuous passive data from personal sensors could potentially enable the identification of a digital fingerprint of inflammation, which might prove useful as a surrogate for vaccine-induced immune response.

The AWAKE-HF Study: Sacubitril/Valsartan Impact on Daily Physical Activity and Sleep in Heart Failure

BACKGROUND

AWAKE-HF evaluated the effect of the initiation of sacubitril/valsartan versus enalapril on activity and sleep using actigraphy in patients who have heart failure with reduced ejection fraction (HFrEF).

METHODS

In this randomized, double-blind study, patients with HFrEF (n = 140) were randomly assigned to sacubitril/valsartan or enalapril for 8 weeks, followed by an 8-week open-label phase with sacubitril/valsartan. Primary endpoint was change from baseline in mean activity counts during the most active 30 min/day at week 8. The key secondary endpoint was change in mean nightly activity counts/minute from baseline to week 8. Kansas City Cardiomyopathy Questionnaire-23 (KCCQ-23) was an exploratory endpoint.

RESULTS

There were no detectable differences between groups in geometric mean ratio of activity counts during the most active 30 min/day at week 8 compared with baseline (0.9456 [sacubitril/valsartan:enalapril]; 95% confidence interval [CI] 0.8863-1.0088; P = 0.0895) or in mean change from baseline in activity during sleep (difference: 2.038 counts/min; 95% CI - 0.062 to 4.138; P = 0.0570). Change from baseline to week 8 in KCCQ-23 was 2.89 for sacubitril/valsartan and 4.19 for enalapril, both nonsignificant.

CONCLUSIONS

In AWAKE-HF, no detectable differences in activity and sleep were observed when comparing sacubitril/valsartan with enalapril in patients with HFrEF using a wearable biosensor.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov, NCT02970669.

Screening for atrial fibrillation: predicted sensitivity of short, intermittent electrocardiogram recordings in an asymptomatic at-risk population

AIMS

Screening for asymptomatic atrial fibrillation (AF) could prevent strokes and save lives, but the AF burden of those detected can impact prognosis. New technologies enable continuous monitoring or intermittent electrocardiogram (ECG) snapshots, however, the relationship between AF detection rates and the burden of AF found with intermittent strategies is unknown. We simulated the likelihood of detecting AF using real-world 2-week continuous ECG recordings and developed a generalizable model for AF detection strategies.

METHODS AND RESULTS

From 1738 asymptomatic screened individuals, ECG data of 69 individuals (mean age 76.3, median burden 1.9%) with new AF found during 14 days continuous monitoring were used to simulate 30 seconds ECG snapshots one to four times daily for 14 days. Based on this simulation, 35-66% of individuals with AF would be detected using intermittent screening. Twice-daily snapshots for 2 weeks missed 48% of those detected by continuous monitoring, but mean burden was 0.68% vs. 4% in those detected (P < 0.001). In a cohort of 6235 patients (mean age 69.2, median burden 4.6%) with paroxysmal AF during clinically indicated monitoring, simulated detection rates were 53-76%. The Markovian model of AF detection using mean episode duration and mean burden simulated actual AF detection with ≤9% error across the range of screening frequencies and durations.

CONCLUSION

Using twice-daily ECG snapshots over 2 weeks would detect only half of individuals discovered to have AF by continuous recordings, but AF burden of those missed was low. A model predicting AF detection, validated using real-world data, could assist development of optimized AF screening programmes.

Association of Sleep Duration and Variability With Body Mass Index: Sleep Measurements in a Large US Population of Wearable Sensor Users

This cohort study examines sleep and demographic data from wearable activity tracking devices to assess for an association of sleep duration and variability with body mass index.

Present and Future of Digital Health in Diabetes and Metabolic Disease

The use of information and communication technology (ICT) in medical and healthcare services goes beyond everyday life. Expectations of a new medical environment, not previously experienced by ICT, exist in the near future. In particular, chronic metabolic diseases such as diabetes and obesity, have a high prevalence and high social and economic burden. In addition, the continuous evaluation and monitoring of daily life is important for effective treatment and management. Therefore, the wide use of ICTbased digital health systems is required for the treatment and management of these diseases. In this article, we compiled a variety of digital health technologies introduced to date in the field of diabetes and metabolic diseases.

Healthcare resource utilization following ECG sensor patch screening for atrial fibrillation

Background

Screening for asymptomatic, undiagnosed atrial fibrillation (AF) has the potential to allow earlier treatment, possibly resulting in prevention of strokes, but also to increase medical resource utilization.

Objective

To compare healthcare utilization rates during the year following initiation of screening among participants screened for AF by electrocardiogram (ECG) sensor patch compared with a matched observational control group.

Methods

A total of 1718 participants recruited from a health care plan based on age and comorbidities who were screened with an ECG patch (actively monitored group) as part of a prospective, pragmatic research trial were matched by age, sex, and CHA₂DS₂-VASc score with 3371 members from the same health plan (observational control group). Healthcare utilization, including visits, prescriptions, procedures, and diagnoses, during the 1 year following screening was compared between the groups using health plan claims data.

Results

Overall, the actively monitored group had significantly higher rates of cardiology visits (adjusted incidence rate ratio [aIRR] [95% confidence interval (CI)]: 1.43 [1.27, 1.60]), no difference in primary care provider visits (aIRR [95% CI]: 1.0 [0.95, 1.05]), but lower rates of emergency department (ED) visits and hospitalizations (aIRR [95% CI]: 0.80 [0.69, 0.92]) compared with controls. Among those with newly diagnosed AF, the reduction in ED visits and hospitalizations was even greater (aIRR [95% CI]: 0.27 [0.17, 0.43]).

Conclusion

AF screening in an asymptomatic, moderate-risk population with an ECG patch was associated with an increase in cardiology outpatient visits but also significantly lower rates of ED visits and hospitalizations over the 1 year following screening.

Inter- and intraindividual variability in daily resting heart rate and its associations with age, sex, sleep, BMI, and time of year: Retrospective, longitudinal cohort study of 92,457 adults

Background

Heart rate is routinely measured as part of the clinical examination but is rarely acted upon unless it is well outside a population-based normal range. With wearable sensor technologies, heart rate can now be continuously measured, making it possible to accurately identify an individual’s “normal” heart rate and potentially important variations in it over time. Our objective is to describe inter- and intra-individual variability in resting heart rate (RHR) collected over the course of two years using a wearable device, studying the variations of resting heart rate as a function of time of year, as well as individuals characteristics like age, sex, average sleep duration, and body mass index (BMI).

Methods and findings

Our retrospective, longitudinal cohort study includes 92,457 de-identified individuals from the United States (all 50 states), who consistently—over at least 35 weeks in the period from March 2016 to February 2018, for at least 2 days per week, and at least 20 hours per day—wore a heart rate wrist-worn tracker. In this study, we report daily RHR and its association with age, BMI, sex, and sleep duration, and its variation over time. Individual daily RHR was available for a median of 320 days, providing nearly 33 million daily RHR values. We also explored the range in daily RHR variability between individuals, and the long- and short-term changes in the trajectory of an individual’s daily RHR.

Mean daily RHR was 65 beats per minute (bpm), with a range of 40 to 109 bpm among all individuals. The mean RHR differed significantly by age, sex, BMI, and average sleep duration. Time of year variations were also noted, with a minimum in July and maximum in January. For most subjects, RHR remained relatively stable over the short term, but 20% experienced at least 1 week in which their RHR fluctuated by 10 bpm or more.

Conclusions

Individuals have a daily RHR that is normal for them but can differ from another individual’s normal by as much as 70 bpm. Within individuals, RHR was much more consistent over time, with a small but significant seasonal trend, and detectable discrete and infrequent episodes outside their norms.

On the Effectiveness of Deep Representation Learning: the Atrial Fibrillation Case

The automatic and unsupervised analysis of biomedical time series is of primary importance for diagnostic and preventive medicine, enabling fast and reliable data processing to reveal clinical insights without the need for human intervention. Representation learning (RL) methods perform an automatic extraction of meaningful features that can be used, e.g., for a subsequent classification of the measured data. The goal of this study is to explore and quantify the benefits of RL techniques of varying degrees of complexity, focusing on modern deep learning (DL) architectures. We focus on the automatic classification of atrial fibrillation (AF) events from noisy single-lead electrocardiographic signals (ECG) obtained from wireless sensors. This is an important task as it allows the detection of sub-clinical AF which is hard to diagnose with a short in-clinic 12-lead ECG. The effectiveness of the considered architectures is quantified and discussed in terms of classification performance, memory/data efficiency and computational complexity.

Usability of a Wrist-Worn Smartwatch in a Direct-to-Participant Randomized Pragmatic Clinical Trial

BACKGROUND

The availability of a wide range of innovative wearable sensor technologies today allows for the ability to capture and collect potentially important health-related data in ways not previously possible. These sensors can be adopted in digitalized clinical trials, i.e., clinical trials conducted outside the clinic to capture data about study participants in their day-to-day life. However, having participants activate, charge, and wear the digital sensors for long hours may prove to be a significant obstacle to the success of these trials.

OBJECTIVE

This study explores a broad question of wrist-wearable sensor effectiveness in terms of data collection as well as data that are analyzable per individual. The individuals who had already consented to be part of an asymptomatic atrial fibrillation screening trial were directly sent a wrist-wearable activity and heart rate tracker device to be activated and used in a home-based setting.

METHODS

A total of 230 participants with a median age of 71 years were asked to wear the wristband as frequently as possible, night and day, for at least a 4-month monitoring period, especially to track heart rhythm during sleep.

RESULTS

Of the individuals who received the device, 43% never transmitted any data. Those who used the device wore it a median of ∼15 weeks (IQR 2-24) and for 5.3 days (IQR 3.2-6.5) per week. For rhythm detection purposes, only 5.6% of all recorded data from individuals were analyzable (with beat-to-beat intervals reported).

CONCLUSIONS

This study provides some important learnings. It showed that in an older population, despite initial enthusiasm to receive a consumer-quality wrist-based fitness device, a large proportion of individuals never activated the device. However, it also found that for a majority of participants it was possible to successfully collect wearable sensor data without clinical oversight inside a home environment, and that once used, ongoing wear time was high. This suggests that a critical barrier to overcome when incorporating a wearable device into clinical research is making its initiation of use as easy as possible for the participant.

Impact of Clopidogrel Therapy on Mortality and Cancer in Patients With Cardiovascular and Cerebrovascular Disease: A Patient-Level Meta-Analysis

BACKGROUND

Clinical trial data associate extended clopidogrel therapy with increased mortality and cancer. We sought to determine the impact of continued clopidogrel use on mortality and cancer within a patient-level meta-analysis of randomized clinical trials.

METHODS AND RESULTS

Meta-analytic clinical event rates for all-cause, cardiovascular, noncardiovascular, and cancer-related mortality; cancer; myocardial infarction; stroke; and fatal and major nonfatal bleeding were generated using patient-level data from 6 randomized trials comparing prolonged versus no or short-duration clopidogrel on a background of aspirin in patients with cardiovascular and cerebrovascular disease. Among 48 817 randomized patients (median follow-up 546 days), there was no difference in all-cause (7.23% versus 7.26%; P=0.97), cardiovascular (5.25% versus 5.22%; P=0.86), noncardiovascular (1.98% versus 2.03%; P=0.73), and cancer-related (0.93% versus 0.99%; P=0.59) mortality or in new cancer diagnoses (2.97% versus 2.96%; P>0.99). Rates of myocardial infarction (3.21% versus 4.05%; P<0.0001) and stroke (3.04% versus 3.75%; P<0.0001) were significantly lower in patients receiving continued clopidogrel. Fatal bleeding was more common with continued clopidogrel use (0.39% versus 0.27%; P=0.03), as were major nonfatal bleeding (4.06% versus 2.68%; P<0.0001) and intracranial hemorrhage (0.43% versus 0.30%; P=0.02).

CONCLUSIONS

Across trials of cardiovascular and cerebrovascular disease, extended-duration clopidogrel on a background of aspirin has no overall effect on mortality or cancer but does reduce rates of myocardial infarction and stroke and increase rates of bleeding. These findings emphasize the need for selective use of extended clopidogrel therapy in patients in whom the risks of ischemia are not fully counterbalanced by the risks of bleeding.

Digital Medicine in Thyroidology: A New Era of Managing Thyroid Disease

Digital medicine has the capacity to affect all aspects of medicine, including disease prediction, prevention, diagnosis, treatment, and post-treatment management. In the field of thyroidology, researchers are also investigating potential applications of digital technology for the thyroid disease. Recent studies using artificial intelligence (AI)/machine learning (ML) have reported reasonable performance for the classification of thyroid nodules based on ultrasonographic (US) images. AI/ML-based methods have also shown good diagnostic accuracy for distinguishing between benign and malignant thyroid lesions based on cytopathologic findings. Assistance from AI/ML methods could overcome the limitations of conventional thyroid US and fine-needle aspiration cytology. A web-based database has been developed for thyroid cancer care. In addition to its role as a nationwide registry of thyroid cancer, it is expected to serve as a clinical platform to facilitate better thyroid cancer care and as a research platform providing comprehensive disease-specific big data. Evidence has been found that biosignal monitoring with wearable devices may predict thyroid dysfunction. This real-world thyroid function monitoring could aid in the management and early detection of thyroid dysfunction. In the thyroidology field, research involving the range of digital medicine technologies and their clinical applications is expected to be even more active in the future.

Connected Health Technology for Cardiovascular Disease Prevention and Management

PURPOSE OF THE REVIEW

Advances in computing power and wireless technologies have reshaped our approach to patient monitoring. Medical grade sensors and apps that were once restricted to hospitals and specialized clinic are now widely available. Here, we review the current evidence supporting the use of connected health technologies for the prevention and management of cardiovascular disease in an effort to highlight gaps and future opportunities for innovation.

RECENT FINDINGS

Initial studies in connected health for cardiovascular disease prevention and management focused primarily on activity tracking and blood pressure monitoring but have since expanded to include a full panoply of novel sensors and pioneering smartphone apps with targeted interventions in diet, lipid management and risk assessment, smoking cessation, cardiac rehabilitation, heart failure, and arrhythmias. While outfitting patients with sensors and devices alone is infrequently a lasting solution, monitoring programs that include personalized insights based on patient-level data are more likely to lead to improved outcomes. Advances in this space have been driven by patients and researchers while healthcare systems remain slow to fully integrate and adequately adapt these new technologies into their workflows. Cardiovascular disease prevention and management continue to be key focus areas for clinicians and researchers in the connected health space. Exciting progress has been made though studies continue to suffer from small sample size and limited follow-up. Efforts that combine home patient monitoring, engagement, and personalized feedback are the most promising. Ultimately, combining patient-level ambulatory sensor data, electronic health records, and genomics using machine learning analytics will bring precision medicine closer to reality.

Real world usage characteristics of a novel mobile health self-monitoring device: Results from the Scanadu Consumer Health Outcomes (SCOUT) Study

A wide range of personal wireless health-related sensor devices are being developed with hope of improving health management. Factors related to effective user engagement, however, are not well-known. We sought to identify factors associated with consistent long-term use of the Scanadu Scout multi-parameter vital sign monitor among individuals who invested in the device through a crowd-funding campaign. Email invitations to join the study were sent to 4525 crowd-funding participants from the US. Those completing a baseline survey were sent a device with follow-up surveys at 3, 12, and 18 months. Of 3872 participants receiving a device, 3473 used it during Week 1, decreasing to 1633 (47 percent) in Week 2. Median time from first use of the device to last use was 17 weeks (IQR: 5-51 weeks) and median uses per week was 1.0 (IQR: 0.6-2.0). Consistent long-term use (defined as remaining in the study at least 26 weeks with at least 3 recordings per week during at least 80% of weeks) was associated with older age, not having children in the household, and frequent use of other medical devices. In the subset of participants answering the 12-month survey (n = 1222), consistent long-term users were more likely to consider the device easy to use and to share results with a healthcare provider. Thirty percent of this subset overall reported improved diet or exercise habits and 25 percent considered medication changes in response to device results. The study shows that even among investors in a device, frequency of device usage fell off rapidly. Understanding how to improve the value of information from personal health-related sensors will be critical to their successful implementation in care.

Characteristics of the modern-day physician house call

Many barriers to primary healthcare accessibility in the United States exist including an increased opportunity cost associated with seeking primary care. New models of healthcare delivery aimed at addressing these problems are emerging. The potential impact that on-demand primary care physician house calls services can have on healthcare accessibility, patient care, and satisfaction by both patients and physicians is poorly characterized.We performed a retrospective observational analysis on data from 13,849 patients who utilized Heal, Inc, an application (app)-based, on-demand house calls platform between August 2016 and July 2017. We assessed house call wait time and visit duration, diagnoses by International Classification of Diseases, tenth revision, Inc (ICD10) codes, and house call outcomes by post-visit prescription and lab requests, and patient satisfaction survey.Patients who utilized this physician house call service had a bimodal age distribution peaking at age 1 year and 36 years. Same day acute sick exams (93.9% of pediatric (Ped) and 66.9% of adult requests) for fever and/or acute upper respiratory infection represented the most common use. The mean wait time for as soon as possible house calls were 96.1 minutes, with an overall mean house call duration of 27.1 minutes. A house call was primarily chosen over an Urgent Care Clinic or Doctor's office (46.2% and 41.6% of respondents, respectively), due to convenience or fastest appointment available (69.6% and 33.8% of respondents, respectively). Most survey respondents (94.2%) would schedule house calls again.On-demand physician house calls programs can expand access options to primary healthcare, primarily used by younger individuals with acute illness and preference for a smartphone app-based home visit.

Digital recruitment and enrollment in a remote nationwide trial of screening for undiagnosed atrial fibrillation: Lessons from the randomized, controlled mSToPS trial

Objectives

The advent of large databases, wearable technology, and novel communications methods has the potential to expand the pool of candidate research participants and offer them the flexibility and convenience of participating in remote research. However, reports of their effectiveness are sparse. We assessed the use of various forms of outreach within a nationwide randomized clinical trial being conducted entirely by remote means.

Methods

Candidate participants at possibly higher risk for atrial fibrillation were identified by means of a large insurance claims database and invited to participate in the study by their insurance provider. Enrolled participants were randomly assigned to one of two groups testing a wearable sensor device for detection of the arrhythmia.

Results

Over 10 months, the various outreach methods used resulted in enrollment of 2659 participants meeting eligibility criteria. Starting with a baseline enrollment rate of 0.8% in response to an email invitation, the recruitment campaign was iteratively optimized to ultimately include website changes and the use of a five-step outreach process (three short, personalized emails and two direct mailers) that highlighted the appeal of new technology used in the study, resulting in an enrollment rate of 9.4%. Messaging that highlighted access to new technology outperformed both appeals to altruism and appeals that highlighted accessing personal health information.

Conclusions

Targeted outreach, enrollment, and management of large remote clinical trials is feasible and can be improved with an iterative approach, although more work is needed to learn how to best recruit and retain potential research participants.

Trial registration

Clinicaltrials.govNCT02506244. Registered 23 July 2015.

A cross-sectional study of physical activity participation among adults with chronic conditions participating in a digital health program

Objective

Many American adults are insufficiently active. Digital health programs are designed to motivate this population to engage in regular physical activity and often rely on wearable devices and apps to objectively measure physical activity for a large number of participants. The purpose of this epidemiological study was to analyze the rates of physical activity among participants in a digital health program.

Method

We conducted a cross-sectional study of participants enrolled in a digital health program between January 2014 and December 2016. All activity data were objectively collected through wearable devices.

Results

Participants (n = 241,013) were on average 39.7 years old and 65.7% were female. Participants walked on average 3.72 miles per day. Overall, 5.3% and 21.8% of participants were being treated with diabetes and cardiovascular medications respectively, but these rates varied across young, middle and older adults. Participants of all ages being treated with cardiovascular and/or diabetes medications walked significantly less than those not being treated for these conditions.

Conclusion

The feasibility of using a large database containing data from consumer-grade activity trackers was demonstrated through this epidemiological study of physical activity rates across age and condition status of participants. The approach and findings described may inform future research as the information age brings about new opportunities to manage and study massive amounts of data generated by connected devices.

Identification of paroxysmal atrial fibrillation subtypes in over 13,000 individuals

BACKGROUND

Paroxysmal atrial fibrillation (PAF) is broadly defined despite high variability in the occurrence and duration of PAF episodes.

OBJECTIVE

The purpose of this study was to identify rhythm patterns in a large cohort of individuals with PAF who wore an ambulatory single-lead electrocardiogram (ECG) patch sensor as part of standard clinical care.

METHODS

We performed a retrospective analysis of longitudinal rhythm data obtained from 13,293 individuals with PAF.

RESULTS

In this study, 7934 men and 5359 women with PAF wore an ambulatory single-lead ECG patch sensor for 11.4 days on average, experiencing 1,041,504 PAF episodes. The median daily rate of PAF was 1.21 episodes per day (interquartile range [IQR] 0.31-4.99), and the median maximum duration per individual was 7.5 hours (IQR 2.4-18.6 hours). There was an inverse relationship between the duration of PAF episodes and the frequency in which they occurred, which became pronounced at moderate and high overall burdens of AF. This produced a spectrum of PAF flanked by 2 distinct subtypes of the disease: the staccato subtype, characterized by many, short AF episodes; and the legato subtype, characterized by fewer, longer episodes. Longer but less frequent episodes became more common with increasing age. Only 49.4% of individuals experienced an episode in the first 24 hours of monitoring, increasing to 89.7% after 1 week of monitoring.

CONCLUSION

We identified subtypes of the disease that we labeled staccato and legato. Although further study is required, these subtypes may result from differing elements of pathophysiology and disease progression, and may confer differing stroke risks.

Effect of a Home-Based Wearable Continuous ECG Monitoring Patch on Detection of Undiagnosed Atrial Fibrillation: The mSToPS Randomized Clinical Trial

IMPORTANCE

Opportunistic screening for atrial fibrillation (AF) is recommended, and improved methods of early identification could allow for the initiation of appropriate therapies to prevent the adverse health outcomes associated with AF.

OBJECTIVE

To determine the effect of a self-applied wearable electrocardiogram (ECG) patch in detecting AF and the clinical consequences associated with such a detection strategy.

DESIGN, SETTING, AND PARTICIPANTS

A direct-to-participant randomized clinical trial and prospective matched observational cohort study were conducted among members of a large national health plan. Recruitment began November 17, 2015, and was completed on October 4, 2016, and 1-year claims-based follow-up concluded in January 2018. For the clinical trial, 2659 individuals were randomized to active home-based monitoring to start immediately or delayed by 4 months. For the observational study, 2 deidentified age-, sex- and CHA2DS2-VASc-matched controls were selected for each actively monitored individual.

INTERVENTIONS

The actively monitored cohort wore a self-applied continuous ECG monitoring patch at home during routine activities for up to 4 weeks, initiated either immediately after enrolling (n = 1364) or delayed for 4 months after enrollment (n = 1291).

MAIN OUTCOMES AND MEASURES

The primary end point was the incidence of a new diagnosis of AF at 4 months among those randomized to immediate monitoring vs delayed monitoring. A secondary end point was new AF diagnosis at 1 year in the combined actively monitored groups vs matched observational controls. Other outcomes included new prescriptions for anticoagulants and health care utilization (outpatient cardiology visits, primary care visits, or AF-related emergency department visits and hospitalizations) at 1 year.

RESULTS

The randomized groups included 2659 participants (mean [SD] age, 72.4 [7.3] years; 38.6% women), of whom 1738 (65.4%) completed active monitoring. The observational study comprised 5214 (mean [SD] age, 73.7 [7.0] years; 40.5% women; median CHA2DS2-VASc score, 3.0), including 1738 actively monitored individuals from the randomized trial and 3476 matched controls. In the randomized study, new AF was identified by 4 months in 3.9% (53/1366) of the immediate group vs 0.9% (12/1293) in the delayed group (absolute difference, 3.0% [95% CI, 1.8%-4.1%]). At 1 year, AF was newly diagnosed in 109 monitored (6.7 per 100 person-years) and 81 unmonitored (2.6 per 100 person-years; difference, 4.1 [95% CI, 3.9-4.2]) individuals. Active monitoring was associated with increased initiation of anticoagulants (5.7 vs 3.7 per 100 person-years; difference, 2.0 [95% CI, 1.9-2.2]), outpatient cardiology visits (33.5 vs 26.0 per 100 person-years; difference, 7.5 [95% CI, 7.2-7.9), and primary care visits (83.5 vs 82.6 per 100 person-years; difference, 0.9 [95% CI, 0.4-1.5]). There was no difference in AF-related emergency department visits and hospitalizations (1.3 vs 1.4 per 100 person-years; difference, 0.1 [95% CI, -0.1 to 0]).

CONCLUSIONS AND RELEVANCE

Among individuals at high risk for AF, immediate monitoring with a home-based wearable ECG sensor patch, compared with delayed monitoring, resulted in a higher rate of AF diagnosis after 4 months. Monitored individuals, compared with nonmonitored controls, had higher rates of AF diagnosis, greater initiation of anticoagulants, but also increased health care resource utilization at 1 year.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02506244.

2017 Cardiovascular and Stroke Endpoint Definitions for Clinical Trials

This publication describes uniform definitions for cardiovascular and stroke outcomes developed by the Standardized Data Collection for Cardiovascular Trials Initiative and the US Food and Drug Administration (FDA). The FDA established the Standardized Data Collection for Cardiovascular Trials Initiative in 2009 to simplify the design and conduct of clinical trials intended to support marketing applications. The writing committee recognizes that these definitions may be used in other types of clinical trials and clinical care processes where appropriate. Use of these definitions at the FDA has enhanced the ability to aggregate data within and across medical product development programs, conduct meta-analyses to evaluate cardiovascular safety, integrate data from multiple trials, and compare effectiveness of drugs and devices. Further study is needed to determine whether prospective data collection using these common definitions improves the design, conduct, and interpretability of the results of clinical trials.

Home Monitoring of Blood Pressure: Short-Term Changes During Serial Measurements for 56398 Subjects

Hypertension is one of the greatest contributors to premature morbidity and mortality worldwide. It has been demonstrated that lowering blood pressure (BP) by just a few mmHg can bring substantial clinical benefits, reducing the risk of stroke and ischemic heart disease. Properly managing high BP is one of the most pressing global health issues, but accurate methods to continuously monitoring BP at home are still under discussion. Indeed, the BP for any given individual can fluctuate significantly during intervals as short as a few minutes. In clinical settings, the guidelines suggest to wait for 5 or 10 minutes in seated rest before taking the measure, in order to alleviate the effect of the stress induced by the clinical environment. Alternatively, BP measured in the home environment is thought to provide a more accurate measure free of the stress of a clinical environment, but there is currently a lack of extensive studies on the trajectory of serial BP measurements over minutes in the home setting. In this paper, we aim at filling this gap by analyzing a large dataset of more than 16 million BP measurements taken at home with commercial BP monitoring devices. In particular, we propose new techniques to analyze this dataset, taking into account the limitations due to the uncontrolled data collection, and we study the characteristics of the BP trajectory for consecutive measures over several minutes. We show that the BP values significantly decrease after 10 minutes minutes from the initial measurement (4.1 and 6.6 mmHg for the diastolic and systolic BP, respectively), and continue to decrease for about 25 minutes. We also describe statistically the clinical relevance of this change, observing more than 50% misclassifications for measurements in the hypertension region. We then propose a model to study the inter-subject variability, showing significant variations in the expected decrease in systolic BP. These results may provide the initial evidence for future large clinical studies using participant-monitored BP.

High-Definition Medicine

The foundation for a new era of data-driven medicine has been set by recent technological advances that enable the assessment and management of human health at an unprecedented level of resolution-what we refer to as high-definition medicine. Our ability to assess human health in high definition is enabled, in part, by advances in DNA sequencing, physiological and environmental monitoring, advanced imaging, and behavioral tracking. Our ability to understand and act upon these observations at equally high precision is driven by advances in genome editing, cellular reprogramming, tissue engineering, and information technologies, especially artificial intelligence. In this review, we will examine the core disciplines that enable high-definition medicine and project how these technologies will alter the future of medicine.

Medication burden in patients with acute coronary syndromes

OBJECTIVES

Cardioprotective medications improve outcomes following acute coronary syndromes (ACS) but add to medication complexity. We set out to describe the use of these medications and quantify medication changes in patients admitted and discharged for ACS.

STUDY DESIGN

Retrospective cohort study.

METHODS

Using archived data from the electronic health record (EHR), we evaluated patients with ACS admitted to 1 of 2 hospitals between January 2008 and December 2012. Patients aged 18 to 89 years who were discharged with a principal diagnosis of ACS were included in the study. Descriptive statistics were compiled and medication use was compared at 3 time points: admission, discharge, and within 90 days post discharge.

RESULTS

This study included 4767 patients. The mean number of total medications increased from 8.6 ± 6.5 to 11.4 ± 5.4 from admission to discharge, dropping slightly within 90 days post discharge (11.1 ± 5.2). Patients taking medications at least twice daily increased from 6.4 of 10 at admission to 9 of 10 at discharge. Cardioprotective medication use increased by a relative 76% for aspirin, 72% for statins, 85% for beta-blockers, and 29% for angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers from admission to discharge, whereas P2Y12 receptor inhibitor use increased 4-fold.

CONCLUSIONS

Medication complexity among patients with ACS are high, with notable changes from admission to discharge. Awareness of the extent of medication burden provides clinicians and policy makers with insight to help address medication use during the ACS peri-hospitalization period.

Characterizing Physicians Practice Phenotype from Unstructured Electronic Health Records

Clinical practice varies among physicians in ways that could lead to variation in what is documented in a patient's electronic health records (EHR) and act as a source of bias to predictive model performance that is independent of patient health status. We used EHR encounter note data on 5,187primary care patients 50 to 85 years of age selected for a separate case-control study covering 144 unique primary care physicians (PCPs). A validated text extractor tool was used to identify mentions of Framingham heartfailure signs and symptoms (FHFSS) from the notes. Hierarchical clustering analyses were performed on the encounter note data for finding subgroups of PCPs with distinct FHFSS documentation behaviors. Three distinct PCP groups were identified that differed in the rate of documenting assertions and denials of mentions. Physician subgroup differences were not explained by patient disease burden, medication use, or other factors related to health.