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.

Sensor-based surveillance for digitising real-time COVID-19 tracking in the USA (DETECT): a multivariable, population-based, modelling study.

BACKGROUND: Traditional viral illness surveillance relies on in-person clinical or laboratory data, paper-based data collection, and outdated technology for data transfer and aggregation. We aimed to assess whether continuous sensor data can provide an early warning signal for COVID-19 activity as individual physiological and behavioural changes might precede symptom onset, care seeking, and diagnostic testing. METHODS: This multivariable, population-based, modelling study recruited adult (aged ≥18 years) participants living in the USA who had a smartwatch or fitness tracker on any device that connected to Apple HealthKit or Google Fit and had joined the DETECT study by downloading the MyDataHelps app. In the model development cohort, we included people who had participated in DETECT between April 1, 2020, and Jan 14, 2022. In the validation cohort, we included individuals who had participated between Jan 15 and Feb 15, 2022. When a participant joins DETECT, they fill out an intake survey of demographic information, including their ZIP code (postal code), and surveys on symptoms, symptom onset, and viral illness test dates and results, if they become unwell. When a participant connects their device, historical sensor data are collected, if available. Sensor data continue to be collected unless a participant withdraws from the study. Using sensor data, we collected each participant's daily resting heart rate and step count during the entire study period and identified anomalous sensor days, in which resting heart rate was higher than, and step count was lower than, a specified threshold calculated for each individual by use of their baseline data. The proportion of users with anomalous data each day was used to create a 7-day moving average. For the main cohort, a negative binomial model predicting 7-day moving averages for COVID-19 case counts, as reported by the Centers for Disease Control and Prevention (CDC), in real time, 6 days in the future, and 12 days in the future in the USA and California was fitted with CDC-reported data from 3 days before alone (H0) or in combination with anomalous sensor data (H1). We compared the predictions with Pearson correlation. We then validated the model in the validation cohort. FINDINGS: Between April 1, 2020, and Jan 14, 2022, 35 842 participants enrolled in DETECT, of whom 4006 in California and 28 527 in the USA were included in our main cohort. The H1 model significantly outperformed the H0 model in predicting the 7-day moving average COVID-19 case counts in California and the USA. For example, Pearson correlation coefficients for predictions 12 days in the future increased by 32·9% in California (from 0·70 [95% CI 0·65-0·73] to 0·93 [0·92-0·94]) and by 12·2% (from 0·82 [0·79-0·84] to 0·92 [0·91-0·93]) in the USA from the H0 model to the H1 model. Our validation model also showed significant correlations for predictions in real time, 6 days in the future, and 12 days in the future. INTERPRETATION: Our study showed that passively collected sensor data from consenting participants can provide real-time disease tracking and forecasting. With a growing population of wearable technology users, these sensor data could be integrated into viral surveillance programmes. FUNDING: The National Center for Advancing Translational Sciences of the US National Institutes of Health, The Rockefeller Foundation, and Amazon Web Services.

rECHOmmend: An ECG-Based Machine Learning Approach for Identifying Patients at Increased 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.

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.

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.

Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients.

The COVID-19 pandemic has accelerated the adoption of innovative healthcare methods, including remote patient monitoring. In the setting of limited healthcare resources, outpatient management of individuals newly diagnosed with COVID-19 was commonly implemented, some taking advantage of various personal health technologies, but only rarely using a multi-parameter chest-patch for continuous monitoring. Here we describe the development and validation of a COVID-19 decompensation index (CDI) model based on chest patch-derived continuous sensor data to predict COVID-19 hospitalizations in outpatient-managed COVID-19 positive individuals, achieving an overall AUC of the ROC Curve of 0.84 on 308 event negative participants, and 22 event positive participants, out of an overall study cohort of 400 participants. We retrospectively compare the performance of CDI to standard of care modalities, finding that the machine learning model outperforms the standard of care modalities in terms of both numbers of events identified and with a lower false alarm rate. While only a pilot phase study, the CDI represents a promising application of machine learning within a continuous remote patient monitoring system.

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.

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.

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.

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.

Wearable sensor data and self-reported symptoms for COVID-19 detection.

Traditional screening for COVID-19 typically includes survey questions about symptoms and travel history, as well as temperature measurements. Here, we explore whether personal sensor data collected over time may help identify subtle changes indicating an infection, such as in patients with COVID-19. We have developed a smartphone app that collects smartwatch and activity tracker data, as well as self-reported symptoms and diagnostic testing results, from individuals in the United States, and have assessed whether symptom and sensor data can differentiate COVID-19 positive versus negative cases in symptomatic individuals. We enrolled 30,529 participants between 25 March and 7 June 2020, of whom 3,811 reported symptoms. Of these symptomatic individuals, 54 reported testing positive and 279 negative for COVID-19. We found that a combination of symptom and sensor data resulted in an area under the curve (AUC) of 0.80 (interquartile range (IQR): 0.73-0.86) for discriminating between symptomatic individuals who were positive or negative for COVID-19, a performance that is significantly better (P < 0.01) than a model1 that considers symptoms alone (AUC = 0.71; IQR: 0.63-0.79). Such continuous, passively captured data may be complementary to virus testing, which is generally a one-off or infrequent sampling assay.

Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study.

BACKGROUND: Acute infections can cause an individual to have an elevated resting heart rate (RHR) and change their routine daily activities due to the physiological response to the inflammatory insult. Consequently, we aimed to evaluate if population trends of seasonal respiratory infections, such as influenza, could be identified through wearable sensors that collect RHR and sleep data. METHODS: We obtained de-identified sensor data from 200 000 individuals who used a Fitbit wearable device from March 1, 2016, to March 1, 2018, in the USA. We included users who wore a Fitbit for at least 60 days and used the same wearable throughout the entire period, and focused on the top five states with the most Fitbit users in the dataset: California, Texas, New York, Illinois, and Pennsylvania. Inclusion criteria included having a self-reported birth year between 1930 and 2004, height greater than 1 m, and weight greater than 20 kg. We excluded daily measurements with missing RHR, missing wear time, and wear time less than 1000 min per day. We compared sensor data with weekly estimates of influenza-like illness (ILI) rates at the state level, as reported by the US Centers for Disease Control and Prevention (CDC), by identifying weeks in which Fitbit users displayed elevated RHRs and increased sleep levels. For each state, we modelled ILI case counts with a negative binomial model that included 3-week lagged CDC ILI rate data (null model) and the proportion of weekly Fitbit users with elevated RHR and increased sleep duration above a specified threshold (full model). We also evaluated weekly change in ILI rate by linear regression using change in proportion of elevated Fitbit data. Pearson correlation was used to compare predicted versus CDC reported ILI rates. FINDINGS: We identified 47 249 users in the top five states who wore a Fitbit consistently during the study period, including more than 13·3 million total RHR and sleep measures. We found the Fitbit data significantly improved ILI predictions in all five states, with an average increase in Pearson correlation of 0·12 (SD 0·07) over baseline models, corresponding to an improvement of 6·3-32·9%. Correlations of the final models with the CDC ILI rates ranged from 0·84 to 0·97. Week-to-week changes in the proportion of Fitbit users with abnormal data were associated with week-to-week changes in ILI rates in most cases. INTERPRETATION: Activity and physiological trackers are increasingly used in the USA and globally to monitor individual health. By accessing these data, it could be possible to improve real-time and geographically refined influenza surveillance. This information could be vital to enact timely outbreak response measures to prevent further transmission of influenza cases during outbreaks. FUNDING: Partly supported by the US National Institutes of Health National Center for Advancing Translational Sciences.