Cost Analysis of Remote Cardiac Rehabilitation Compared With Facility-Based Cardiac Rehabilitation for Coronary Artery Disease.

Remote cardiac rehabilitation (RCR) represents a promising, noninferior alternative to facility-based cardiac rehabilitation (FBCR). The comparable cost of RCR in US populations has yet to be extensively studied. The purpose of this prospective, patient-selected study of traditional FBCR versus a third-party asynchronous RCR platform was to assess whether RCR can be administered at a comparable cost and clinical efficacy to FBCR. Adult insured patients were eligible for enrollment after an admission for a coronary heart disease event. Patients selected either FBCR or Movn RCR, a 12-week telehealth intervention using an app-based platform and internet-capable medical devices. Clinical demographics, intervention adherence, cost-effectiveness, and hospitalizations at 1-year after enrollment were assessed from the Highmark claims database after propensity matching between groups. A total of 260 patients were included and 171 of those eligible (65.8%) received at least 1 cardiac rehabilitation session and half of the patients chose Movn RCR. The propensity matching produced a sample of 41 matched pairs. Movn RCR led to a faster enrollment and higher completion rates (80% vs 50%). The total medical costs were similar between Movn RCR and FBCR, although tended toward cost savings with Movn RCR ($10,574/patient). The cost of cardiac rehabilitation was lower in those enrolled in Movn RCR ($1,377/patient, p = 0.002). The all-cause and cardiovascular-related hospitalizations or emergency department visits in the year after enrollment in both groups were similar. In conclusion, this pragmatic study of patients after a coronary heart disease event led to equivalent total medical costs and lower intervention costs for an asynchronous RCR platform than traditional FBCR while maintaining similar clinically important outcomes.

Effects of a patient-centered digital health intervention in patients referred to cardiac rehabilitation: the Smart HEART clinical trial.

BACKGROUND: Cardiac rehabilitation (CR) improves outcomes in heart disease yet remains vastly underutilized. Remote CR enhanced with a digital health intervention (DHI) may offer higher access and improved patient-centered outcomes over non-technology approaches. We sought to pragmatically determine whether offering a DHI improves CR access, cardiac risk profile, and patient-reported outcome measures. METHODS: Adults referred to CR at a tertiary VA medical center between October 2017 and December 2021 were offered enrollment into a DHI alongside other CR modalities using shared decision-making. The DHI consisted of remote CR with a structured, 3-month home exercise program enhanced with multi-component coaching, a commercial smartphone app, and wearable activity tracker. We measured completion rates among DHI participants and evaluated changes in 6-min walk distance, cardiovascular risk factors, and patient-reported outcomes from pre- to post-intervention. RESULTS: Among 1,643 patients referred to CR, 258 (16%) consented to the DHI where the mean age was 60 ± 9 years, 93% were male, and 48% were black. A majority (90%) of the DHI group completed the program. Over 3-months, significant improvements were seen in 6MWT (mean difference [MD] -29 m; 95% CI, 10 to 49; P < 0.01) and low-density lipoprotein cholesterol (MD -11 mg/dL; 95% CI, -17 to -5; P < 0.01), and the absolute proportion of patients who reported smoking decreased (10% vs 15%; MD, -5%; 95% CI, -8% to -2%; P < 0.01) among DHI participants with available data. No adverse events were reported. CONCLUSIONS: The addition of a DHI-enhanced remote CR program was delivered in 16% of referred veterans and associated with improved CR access, markers of cardiovascular risk, and healthy behaviors in this real-world study. These findings support the continued implementation of DHIs for remote CR in real-world clinical settings. TRIAL REGISTRATION: This trial was registered on ClinicalTrials.gov: NCT02791685 (07/06/2016).

Retrospective Analysis and Forecasted Economic Impact of a Virtual Cardiac Rehabilitation Program in a Third-Party Payer Environment.

Background: Participation in cardiac rehabilitation (CR) is recommended for all patients with coronary artery disease (CAD) following hospitalization for acute coronary syndrome or stenting. Yet, few patients participate due to the inconvenience and high cost of attending a facility-based program, factors which have been magnified during the ongoing COVID pandemic. Based on a retrospective analysis of CR utilization and cost in a third-party payer environment, we forecasted the potential clinical and economic benefits of delivering a home-based, virtual CR program, with the goal of guiding future implementation efforts to expand CR access. Methods: We performed a retrospective cohort study using insurance claims data from a large, third-party payer in the state of Pennsylvania. Primary diagnostic and procedural codes were used to identify patients admitted for CAD between October 1, 2016, and September 30, 2018. Rates of enrollment in facility-based CR, as well as all-cause and cardiovascular hospital readmission and associated costs, were calculated during the 12-months following discharge. Results: Only 37% of the 7,264 identified eligible insured patients enrolled in a facility-based CR program within 12 months, incurring a mean delivery cost of $2,922 per participating patient. The 12-month all-cause readmission rate among these patients was 24%, compared to 31% among patients who did not participate in CR. Furthermore, among those readmitted, CR patients were readmitted less frequently than non-CR patients within this time period. The average per-patient cost from hospital readmissions was $30,814 per annum. Based on these trends, we forecasted that adoption of virtual CR among patients who previously declined CR would result in an annual cost savings between $1 and $9 million in the third-party healthcare system from a combination of increased overall CR enrollment and fewer hospital readmissions among new HBCR participants. Conclusions: Among insured patients eligible for CR in a third-party payer environment, implementation of a home-based virtual CR program is forecasted to yield significant cost savings through a combination of increased CR participation and a consequent reduction in downstream healthcare utilization.

Quantifying Human Movement Using the Movn Smartphone App: Validation and Field Study.

BACKGROUND: The use of embedded smartphone sensors offers opportunities to measure physical activity (PA) and human movement. Big data-which includes billions of digital traces-offers scientists a new lens to examine PA in fine-grained detail and allows us to track people's geocoded movement patterns to determine their interaction with the environment. OBJECTIVE: The objective of this study was to examine the validity of the Movn smartphone app (Moving Analytics) for collecting PA and human movement data. METHODS: The criterion and convergent validity of the Movn smartphone app for estimating energy expenditure (EE) were assessed in both laboratory and free-living settings, compared with indirect calorimetry (criterion reference) and a stand-alone accelerometer that is commonly used in PA research (GT1m, ActiGraph Corp, convergent reference). A supporting cross-validation study assessed the consistency of activity data when collected across different smartphone devices. Global positioning system (GPS) and accelerometer data were integrated with geographical information software to demonstrate the feasibility of geospatial analysis of human movement. RESULTS: A total of 21 participants contributed to linear regression analysis to estimate EE from Movn activity counts (standard error of estimation [SEE]=1.94 kcal/min). The equation was cross-validated in an independent sample (N=42, SEE=1.10 kcal/min). During laboratory-based treadmill exercise, EE from Movn was comparable to calorimetry (bias=0.36 [-0.07 to 0.78] kcal/min, t82=1.66, P=.10) but overestimated as compared with the ActiGraph accelerometer (bias=0.93 [0.58-1.29] kcal/min, t89=5.27, P<.001). The absolute magnitude of criterion biases increased as a function of locomotive speed (F1,4=7.54, P<.001) but was relatively consistent for the convergent comparison (F1,4=1.26, P<.29). Furthermore, 95% limits of agreement were consistent for criterion and convergent biases, and EE from Movn was strongly correlated with both reference measures (criterion r=.91, convergent r=.92, both P<.001). Movn overestimated EE during free-living activities (bias=1.00 [0.98-1.02] kcal/min, t6123=101.49, P<.001), and biases were larger during high-intensity activities (F3,6120=1550.51, P<.001). In addition, 95% limits of agreement for convergent biases were heterogeneous across free-living activity intensity levels, but Movn and ActiGraph measures were strongly correlated (r=.87, P<.001). Integration of GPS and accelerometer data within a geographic information system (GIS) enabled creation of individual temporospatial maps. CONCLUSIONS: The Movn smartphone app can provide valid passive measurement of EE and can enrich these data with contextualizing temporospatial information. Although enhanced understanding of geographic and temporal variation in human movement patterns could inform intervention development, it also presents challenges for data processing and analytics.

Hierarchical approaches to estimate energy expenditure using phone-based accelerometers.

Physical inactivity is linked with increase in risk of cancer, heart disease, stroke, and diabetes. Walking is an easily available activity to reduce sedentary time. Objective methods to accurately assess energy expenditure from walking that is normalized to an individual would allow tailored interventions. Current techniques rely on normalization by weight scaling or fitting a polynomial function of weight and speed. Using the example of steady-state treadmill walking, we present a set of algorithms that extend previous work to include an arbitrary number of anthropometric descriptors. We specifically focus on predicting energy expenditure using movement measured by mobile phone-based accelerometers. The models tested include nearest neighbor models, weight-scaled models, a set of hierarchical linear models, multivariate models, and speed-based approaches. These are compared for prediction accuracy as measured by normalized average root mean-squared error across all participants. Nearest neighbor models showed highest errors. Feature combinations corresponding to sedentary energy expenditure, sedentary heart rate, and sex alone resulted in errors that were higher than speed-based models and nearest-neighbor models. Size-based features such as BMI, weight, and height produced lower errors. Hierarchical models performed better than multivariate models when size-based features were used. We used the hierarchical linear model to determine the best individual feature to describe a person. Weight was the best individual descriptor followed by height. We also test models for their ability to predict energy expenditure with limited training data. Hierarchical models outperformed personal models when a low amount of training data were available. Speed-based models showed poor interpolation capability, whereas hierarchical models showed uniform interpolation capabilities across speeds.

A study of position independent algorithms for phone-based gait frequency detection.

Estimating gait frequency is an important component in the detection and diagnosis of various medical conditions. Smartphone-based kinematic sensors offer a window of opportunity in free-living gait frequency estimation. The main issue with smartphone-based gait frequency estimation algorithms is how to adjust for variations in orientation and location of the phone on the human body. While numerous algorithms have been implemented to account for these differences, little work has been done in comparing these algorithms. In this study, we compare various position independent algorithms to determine which are more suited to robust gait frequency estimation. Using sensor data collected from volunteers walking with a smartphone, we examine the effect of using three different time series with the magnitude, weighted sum, and closest vertical component algorithms described in the paper. We also test two different methods of extracting step frequency: time domain peak counting and spectral analysis. The results show that the choice of time series does not significantly affect the accuracy of frequency measurements. Furthermore, both time domain and spectral approaches show comparable results. However, time domain approaches are sensitive to false-positives while spectral approaches require a minimum set of repetitive measurements. Our study suggests a hybrid approach where both time-domain and spectral approaches be used together to complement each other's shortcomings.

An inertial sensor-based system to develop motor capacity in children with cerebral palsy.

Learning to communicate with alternative augmentative communication devices can be difficult because of the difficulty of achieving controlled interaction while simultaneously learning to communicate. What is needed is a device that harnesses a child's natural motor capabilities and provides the means to reinforce them. We present a kinematic sensor-based system that learns a child's natural gestural capability and allows him/her to practice those capabilities in the context of a game. Movement is captured with a single kinematic sensor that can be worn anywhere on the body. A gesture recognition algorithm interactively learns gesture models using kinematic data with the help of a nearby teacher. Learned gesture models are applied in the context of a game to help the child practice gestures to gain better consistency. The system was successfully tested with a child over two sessions. The system learned four candidate gestures: lift hand, sweep right, twist right and punch forward. These were then used in a game. The child showed better consistency in performing the gestures as each session progressed. We aim to expand on this work by developing qualitative scores of movement quality and quantifying algorithm accuracy on a larger population over long periods of time.

Hierarchical Linear Models for Energy Prediction using Inertial Sensors: A Comparative Study for Treadmill Walking.

Walking is a commonly available activity to maintain a healthy lifestyle. Accurately tracking and measuring calories expended during walking can improve user feedback and intervention measures. Inertial sensors are a promising measurement tool to achieve this purpose. An important aspect in mapping inertial sensor data to energy expenditure is the question of normalizing across physiological parameters. Common approaches such as weight scaling require validation for each new population. An alternative is to use a hierarchical approach to model subject-specific parameters at one level and cross-subject parameters connected by physiological variables at a higher level. In this paper, we evaluate an inertial sensor-based hierarchical model to measure energy expenditure across a target population. We first determine the optimal movement and physiological features set to represent data. Periodicity based features are more accurate (p<0.1 per subject) when generalizing across populations. Weight is the most accurate parameter (p<0.1 per subject) measured as percentage prediction error. We also compare the hierarchical model with a subject-specific regression model and weight exponent scaled models. Subject-specific models perform significantly better (p<0.1 per subject) than weight exponent scaled models at all exponent scales whereas the hierarchical model performed worse than both. However, using an informed prior from the hierarchical model produces similar errors to using a subject-specific model with large amounts of training data (p<0.1 per subject). The results provide evidence that hierarchical modeling is a promising technique for generalized prediction energy expenditure prediction across a target population in a clinical setting.

Determining energy expenditure from treadmill walking using hip-worn inertial sensors: an experimental study.

We describe an experimental study to estimate energy expenditure during treadmill walking using a single hip-mounted inertial sensor (triaxial accelerometer and triaxial gyroscope). Typical physical-activity characterization using commercial monitors use proprietary counts that do not have a physically interpretable meaning. This paper emphasizes the role of probabilistic techniques in conjunction with inertial data modeling to accurately predict energy expenditure for steady-state treadmill walking. We represent the cyclic nature of walking with a Fourier transform and show how to map this representation to energy expenditure (VO(2), mL/min) using three regression techniques. A comparative analysis of the accuracy of sensor streams in predicting energy expenditure reveals that using triaxial information leads to more accurate energy-expenditure prediction compared to only using one axis. Combining accelerometer and gyroscope information leads to improved accuracy compared to using either sensor alone. Nonlinear regression methods showed better prediction accuracy compared to linear methods but required an order of higher magnitude run time.

Energy estimation of treadmill walking using on-body accelerometers and gyroscopes.

Walking is the most common activity among people who are physically active. Standard practice physical activity characterization from body-mounted inertial sensors uses accelerometer-generated counts. There are two problems with this - imprecison (due to usage of proprietary counts) and incompleteness (due to incomplete description of motion). We address both these problems by directly predicting energy expenditure during steady-state treadmill walking from a hip-mounted inertial sensor comprised of a tri-axial accelerometer and a tri-axial gyroscope. We use Bayesian Linear Regression to predict energy expenditure based on modelling joint probabilities of streaming data. The prediction is significantly better with data from a 6 axis sensor as compared with streaming data from only 2 linear accelerations as is common in current practice. We also show how counts from a commercially available accelerometer can be reproduced from raw streaming acceleration data (up to a linear transformation) with high correlation (.9787 ± .0089 for the X-axis and .9141 ± .0460 for the Y-axis acceleration streams). The paper emphasizes the role of probabilistic techniques in conjunction with joint modeling of tri-axial accelerations and rotational rates to improve energy expenditure prediction for steady-state treadmill walking.