Measuring Physical Activity With Implanted Cardiac Devices: A Systematic Review
Background Physical activity is predictive of cardiovascular outcomes in patients with cardiovascular implantable electronic devices, yet it is not regularly assessed in routine care. Current‐generation cardiovascular implantable electronic devices, however, continuously monitor patient activity through a built‐in accelerometer, which provides new opportunities to remotely assess patient activity, detect changes in clinical status, and incorporate these data in risk stratification models. This review critically examines the literature on device‐measured physical activity (D‐PA), with a focus on identifying methodological issues that may affect interpretation of study results.
Methods and Results We conducted a systematic review of D‐PA studies published from January 1 1995 to December 30 2017, identifying 29 studies meeting inclusion criteria, 5 of which were validation reports. Few technical details about D‐PA sensors are reported, and procedures for analyzing and interpreting D‐PA data are heterogeneous. Trends in D‐PA over time and associations with clinical outcomes were reported by 22 studies, and in 7 studies, D‐PA was combined with other device parameters in risk stratification models, demonstrating modest‐to‐good sensitivity in predicting acute heart failure decompensation, hospitalization, and mortality.
Conclusions Current evidence suggests that D‐PA may be useful for assessing physical activity and predicting clinical outcomes in patients with cardiovascular implantable electronic devices when combined with other device parameters. Future work must address challenges related to D‐PA data measurement, interpretation, and generalizability to support expanded clinical applications of this technology.
- cardiovascular implantable electronic device
- device‐derived activity
- implantable cardioverter‐defibrillator
- physical activity
What Is New?
Current‐generation cardiovascular implantable electronic devices continuously collect and store daily physical activity data through a built‐in accelerometer, providing new opportunities to remotely assess patient activity, detect changes in clinical status, and incorporate these data in risk stratification models.
This systematic review summarizes findings from 29 published studies on device‐measured physical activity, including 5 validation reports.
Available evidence suggests that device‐measured physical activity may be useful for assessing physical activity and predicting clinical outcomes in patients with cardiovascular implantable electronic devices when combined with other device parameters.
However, numerous methodological issues related to device‐measured physical activity data measurement, interpretation, and generalizability were identified and warrant further attention.
What Are the Clinical Implications?
These findings support the need for future prospective studies and randomized clinical trials to inform expanded clinical applications of this technology.
Moderate leisure‐time physical activity is safe and clinically recommended for most patients with cardiovascular implantable electronic devices (CIEDs) (ie, implantable cardioverter‐defibrillators [ICDs], cardiac resynchronization therapy [CRT], and pacemakers).1 The benefits of regular physical activity for secondary prevention are also well established,2 and for patients with ICDs in particular, participation in exercise training programs may reduce their risk for ICD shock.3, 4 Alternatively, declines in activity may signal a change in clinical status in patients with heart failure (HF).5
In addition to providing life‐saving therapies for tachyarrhythmias and bradyarrhythmias, modern CIEDs are capable of continuously monitoring patients’ cardiac rhythm (eg, atrial fibrillation [AF] burden and percentage pacing) and measuring intrathoracic impedance to guide HF management, as well as storing these data.6 Most implanted devices also automatically collect and store daily physical activity data obtained from an internal accelerometer, incorporated for the primary purpose of rate‐responsive pacing.6 As the patient moves or accelerates, piezoelectric crystal sensors detect changes in the frequency and amplitude of body motion, generating an electrical signal that is proportional to patient movement.6 Proprietary algorithms then analyze, encrypt, and store device‐measured physical activity (D‐PA) data in device memory, which can be downloaded during routine clinic visits or through remote monitoring.
The potential of the accelerometer feature to assist clinicians in monitoring patient activity longitudinally, detect changes in functional status, and proactively deliver care to prevent adverse events is of great interest and is under active investigation. In preliminary studies, D‐PA has been shown to independently predict ventricular reverse remodeling, hospitalization, and mortality.7 In patients with CRT with HF, D‐PA predicts nonfatal ventricular tachyarrhythmias and short‐term HF events.8 D‐PA data have also been combined with other diagnostic parameters in risk stratification models to predict acute HF decompensation, hospitalization, and mortality.9, 10, 11, 12, 13, 14 However, the specific methods used to measure, analyze, and interpret D‐PA data are not fully described, and they vary considerably between studies. This variation in approach hinders the ability to compare data or synthesize findings, thereby limiting current clinical applications of D‐PA.
We conducted a systematic review of published reports on D‐PA and identified methodological issues that may affect interpretation of study results. In addition, we provide recommendations for how to strengthen the evidence base for D‐PA and articulate a framework for how these data could be used to modernize activity measurement in clinical trials and outcomes research and potentially improve evaluation and management of patients with CIEDs.
Literature Search Strategy and Selection Criteria
This systematic review was performed in accordance with the Preferred Reporting Items for Systematic Review and Meta‐Analyses guidelines (Figure S1).15 The data, analytic methods, and study materials will not be made available to other researchers for purposes of reproducing the results or replicating the procedure. We conducted a search of PubMed and the Cochrane databases to identify relevant studies published in peer‐reviewed English‐language journals from January 1 1995 to December 30 2017. A combination of predefined search terms was used: CIEDs (“implantable cardioverter defibrillator” or “cardiac resynchronization therapy” or “pacemaker” or “cardiovascular implantable electronic device”) and “device‐measured activity” or “physical activity” or “accelerometer” or “ICD diagnostic algorithm” or “heart failure device risk stratification model” or “device‐derived activity.” The reference lists from published articles were also examined, and potentially relevant articles were retrieved. Eligible studies included D‐PA validation studies and clinical investigations. Abstracts, case studies, conference presentations, and articles published in non–peer‐reviewed journals were excluded from this review.
Summary of Published Findings
As of December 2017, we identified 29 published studies that included D‐PA, conducted primarily in the United States and Europe. Characteristics of the 29 studies are reported in the Table. More than half (n=17 [59%]) reported on D‐PA data obtained from Medtronic devices, followed by Boston Scientific (n=9 [31%]) and Biotronik (n=2 [7%]). One study compared D‐PA from 2 manufacturers (Medtronic and St Jude).7 Three quarters (n=22) reported on D‐PA trends over time and associations with clinical end points, whereas one quarter (n=7) evaluated D‐PA as a component integrated with other device‐based diagnostic parameters in risk prediction models. With the exception of validation studies (n=5), which generally involved smaller samples, most published reports included ≥100 patients in their sample. Women, on average, represented 27% of patients in each study cohort. Only one study examined D‐PA in patients with pediatric devices.16 Twenty‐one studies included only patients with HF, whereas 8 studies included patients with all devices regardless of cardiac pathology. One study specifically focused on patients with pacemakers and preserved left ventricular function.17 Nearly half of the studies were secondary analyses of clinical trial data (n=12 [41%]). A more detailed discussion of findings from selected studies is provided later, according to activity trends over time and clinical end points.
Measurement of D‐PA by Device Manufacturer
Although the sensor mechanisms involved in ambulatory activity monitoring are relatively similar across device manufacturers, there is considerable variation between companies about how activity data are analyzed, encrypted, stored, and interpreted. In many cases, investigators report few technical details about the accelerometer activity sensor thresholds or signal processing algorithms used in specific CIEDs to quantify activity. Descriptions of D‐PA data analysis procedures are also limited, but we provide the information that is available in published reports (Table S1).
Five validation studies were reported.7, 21, 26, 27, 31 These studies enrolled fewer patients, had a higher percentage of women, and had a shorter duration of follow‐up than the clinical investigations described later.
In 3 studies, D‐PA from CIED accelerometers was compared with data from validated external devices. Two studies used the Actigraph GT3X external accelerometer as the comparison to D‐PA. One study31 found that D‐PA data from Medtronic CIED correlated strongly (r=0.83) with activity data from the Actigraph, whereas the other study26 found that D‐PA from Biotronik CIED correlated only weakly (r=0.37) with metabolic equivalent values derived from Actigraph. In a third study, D‐PA from Medtronic CIEDs was compared with activity data from the AiperMotion 440 external accelerometer, and although strong intraindividual correlations were reported, there were substantial differences in the total amount of daily activity recorded by the devices.27 As noted by the authors, variation in activity captured by implanted versus external accelerometers may be attributable to differences in device placement (pectoral implant versus hip‐ or wrist‐worn accelerometers) and type of activity. It is also possible that external triaxial accelerometers, which use 3‐dimensional measurements to quantify activity, may be better able to discriminate between ambulatory and nonambulatory movement compared with the single‐axis accelerometers in implanted devices.27
Two studies examined the validity of Medtronic and Boston Scientific CRT devices in assessing exercise capacity in ambulatory patients with HF. Both studies compared D‐PA with the 6‐minute walk test (a validated measure of exercise capacity and a strong independent predictor of clinical outcomes in patients with HF5) to determine whether D‐PA could potentially serve as a marker of functional status in these patients.7, 21 Both investigations showed that D‐PA moderately correlated with 6‐minute walking distance (r=0.35–0.49). Furthermore, in multivariate models, D‐PA and the 6‐minute walk test demonstrated similar utility as a univariate predictor of clinical response to CRT.7 These findings suggest that D‐PA may be a valid and reliable indicator of clinical status in patients with HF. Moreover, because CIED accelerometers provide continuous objective activity data in real time, D‐PA might be more clinically useful for assessing functional status than the 6‐minute walk test, which can only assess functionality at a particular point in time.
In summary, 4 of 5 published validation studies showed that activity measured by D‐PA was moderately correlated with external accelerometers and performed as well as an established measure of functional status in predicting intermediate outcomes among patients with CIEDs with HF. Nonetheless, these studies were conducted in small highly selective patient samples, and data are limited to activity detected during walking tasks. No validation studies have examined the accuracy of CIEDs to recognize movement that occurs during a wide range of patient activities (eg, stationary bicycling, swimming, gardening, and household chores) or compared activity detected by devices from multiple manufacturers.
Activity trends over time.
D‐PA data are increasingly being used to understand patterns of activity engagement after ICD implantation; however, investigators have varied in the approach taken to calculate the baseline level of D‐PA,11, 18, 23, 24, 28, 33, 36 making it difficult to compare findings across studies. In some studies, baseline is defined as average activity over a period of several days, 1 to 2 weeks after CIED implantation,18, 22, 24 to account for procedural recovery time; in other studies, baseline is defined as average activity over 7 to 30 days, 1 to 2 months after implantation.11, 23, 28, 36 Despite this variation in approach, D‐PA at baseline is consistently low across studies (<3 hours of total activity per day), regardless of device manufacturer or sample characteristics.23, 24, 28, 36 As expected, D‐PA was lowest immediately after device implantation and increased substantially over the next 90 days.18, 20, 23, 24, 28, 32, 34 After 90 days, some studies showed that activity levels remained stable,22, 23, 24 whereas other studies described a continued increase in D‐PA for up to 6 months after implantation.7, 34 Interpretation of these findings is complicated by the variation in how and when baseline activity was determined.
D‐PA and mortality.
The association between D‐PA and mortality has been a focus of considerable research. Seven studies examined the relationship between D‐PA and all‐cause mortality,7, 8, 11, 17, 23, 24, 32, 36 with one including both all‐cause and cardiac mortality.36 Of these studies, all reported a significant inverse association between D‐PA and all‐cause mortality in unadjusted and adjusted models. One study of patients with pacemakers and preserved left ventricular function17 showed that all‐cause mortality increased as daily D‐PA decreased and that patients with lower D‐PA at baseline (<1 hour of activity per day) had a 7.4‐fold elevated mortality risk at follow‐up, compared with those who were more active (>3 h/d). Early activity (average activity over the first month after implantation) was also found to be inversely associated with all‐cause mortality and cardiac morality.11, 23, 36 Although these data are suggestive, they are observational, and thus D‐PA may have served as a marker for unmeasured factors contributing to mortality risk.
D‐PA and hospitalization.
On the basis of the known connection between physical activity and hospitalization in patients with HF,37, 38 attention has turned to device metrics, such as D‐PA, to determine their potential value as clinical indicators of risk for hospitalization. Seven studies examined D‐PA as a predictor for hospital admissions among patients with CIEDs with HF.7, 9, 11, 12, 30, 35 Using data primarily from Medtronic CRT devices (one study reported on both Medtronic and St Jude CRT devices7), all but one study found that as D‐PA declined, risk for acute HF decompensation and hospitalization increased significantly during the same 30‐day period. One multicenter retrospective cohort study of patients, however, found the association between “low D‐PA” (<30 min/d) and HF hospitalization to be nonsignificant in both unadjusted and adjusted models.35 This discrepant finding may reflect the different activity threshold used to define “low activity” in this study, which was considerably lower than the thresholds used in the other studies (<60–<113 min/d).7, 14, 36 These data highlight an important shortcoming of data‐driven categorization of continuous variables and support the need for evidence‐based clinical thresholds for D‐PA.
Clinical predictors of D‐PA.
Eight studies examined clinical and demographic predictors of D‐PA. Of these studies, most have found that older adults, women, and those who are more frail engage in less daily activity than patients who are younger, men, and less frail.20, 23, 25, 34 Comorbidities, such as AF, diabetes mellitus, peripheral artery disease, stroke, and diuretic use at baseline, have also been associated with lower levels of D‐PA in the months after CIED implantation.11, 36 In contrast, findings on the relationship between activity and New York Heart Association functional class, left ventricular ejection fraction (%), device type, and ischemic cause have been inconsistent.11, 18, 31, 34, 36 Taken together, the independent contribution of demographic and baseline clinical characteristics to D‐PA remains poorly understood, and known risk factors for inactivity, such as chronic pain, osteoporosis, and depression, have not been examined.
Effect of arrhythmias and device therapies on D‐PA.
Three investigations examined the temporal relationship between D‐PA and arrhythmias. Variation in daily D‐PA before, during, and after an episode of persistent AF was examined in one study of patients with dual‐chamber ICD.19 Results showed that in most patients, D‐PA declined at the onset of persistent AF, remained low during AF, and returned to baseline by 12 weeks after AF termination. Another study examined the short‐term effects of ICD shock and ATP on D‐PA trends over a 30‐day period.28 Results showed that D‐PA significantly declined after ICD shock and that as the number of shocks increased, D‐PA decreased. In contrast, D‐PA did not significantly decline after ATP therapies.28 Another longitudinal investigation of a large, multicenter, international cohort of patients with ICD prospectively examined the short‐term and lasting effects of ICD shock on objective behaviors (ie, D‐PA) and subjective quality‐of‐life outcomes (ie, self‐reported quality of life and shock anxiety).29 Results from this study showed that D‐PA significantly declined after ICD shock (−23.7 min/d when corrected for device type, time since implantation, and the effect of hospitalization) and gradually recovered to a normal level after ≈90 days. Similar to the EMPIRIC (Comparison of Empiric to Physician‐Tailored Programming of Implantable Cardioverter‐Defibrillators) trial,28 an inverse relationship between D‐PA and the number of prior shocks was reported; however, as an extension of these findings, this investigation showed that activity reduction did not differ significantly between appropriate and inappropriate shocks.
D‐PA in risk stratification models.
Developing cost‐effective tools to predict and potentially prevent clinical decompensation and hospitalization in patients with HF is a priority for patients, providers, and healthcare systems. Accordingly, in 7 studies, D‐PA was combined with other clinical and device data to develop, test, and validate clinically useful tools for ambulatory risk assessment in patients with HF.9, 10, 11, 12, 13, 14, 30 Nearly all risk assessment tools were developed retrospectively, using data from completed clinical trials, and study samples included predominantly older white men. Risk models were mostly developed to predict acute HF decompensation and hospitalization within a 30‐day period, although 2 studies tested risk prediction models for HF hospitalization and all‐cause mortality.11, 13 Of the 7 studies, all found that models including D‐PA provided modest‐to‐good sensitivity in the prediction of hospitalization and/or death in patients with HF. Still, it remains to be seen whether device‐based risk stratification models improve clinical outcomes in patients with CIEDs with HF. Moreover, because women and racial and ethnic minorities are underrepresented in published studies, the predictive value of D‐PA–based risk models for these patient groups is unknown. This is concerning, because traditional cardiovascular risk assessment tools have been shown to underestimate risk in women and minority patients.39, 40
To our knowledge, this review is the first to examine the current state of the D‐PA literature. The strengths and limitations of D‐PA technology and extant research are summarized in Table S2. Most studies showed significant associations between D‐PA and relevant clinical outcomes, and when combined with other device parameters, D‐PA–based models improve ambulatory risk assessment in patients with HF. However, we identified important methodological challenges, including inconsistency in D‐PA baseline measurement, observational/cohort study designs, infrequent use of relevant covariates, and homogeneous patient samples, that limit our ability to draw conclusions from the data, compare results between studies, or synthesize findings to inform clinical applications of this technology. Indeed, one of the most striking findings was that published studies provide relatively few technical details about how specific CIED accelerometers detect, analyze, and interpret activity data, and available information suggests that activity sensor thresholds and signal processing algorithms differ considerably between manufacturers. Thus, activity captured by one device may not reflect the same amount of activity captured by another device. Addressing these issues is critical to strengthening the evidence base for D‐PA and supporting future clinical applications of this technology.
Recommendations and Future Directions
Moving forward, it is imperative that future investigations provide sufficient technical details about specific CIED accelerometers, including how motion is detected and analyzed by signal processing algorithms, and the methods used for D‐PA data analysis. This will allow for replication and development of a standardized approach to D‐PA use in clinical research and real‐world applications. Second, validation studies need to be conducted in larger, more diverse, patient samples and examine a wide range of patient activities (eg, bicycling, swimming, and household chores) using devices from multiple manufacturers. This would enhance generalizability of D‐PA data. Third, patients with CIEDs are a heterogeneous population, and more research is needed to identify patient groups (eg, women, racial and ethnic minorities, patients without HF, and patients <50 years old) and critical events (eg, ICD shock and hospitalization) that increase risk for inactivity. This would help define the utility of D‐PA as an outcome measure in clinical trials designed to increase activity in specific patient groups.
Prospective research is also required to define potential therapeutic targets for daily activity measured by CIED accelerometer, and clinical trials need to evaluate whether a rapid clinical response to a D‐PA–defined reduction in activity prevents hospitalizations, improves survival, or reduces healthcare expenditures. These data will be critical to guide risk evaluation and treatment and to help educate patients about physical activity. On the other hand, D‐PA may have the greatest value as a parameter combined with other clinical and device diagnostic data to identify patients at high risk for adverse events. As multiple‐parameter device‐based risk stratification tools continue to be developed and tested, issues related to clinical adoption and implementation also need to be considered (eg, choosing which risk model to use, who will use it, when to use it, and thresholds for intervention).
D‐PA data are not currently provided directly to patients, which limits their clinical utility for engaging patients in their care, encouraging self‐monitoring, or promoting health behavior change.41 Nonetheless, these data are presently available to healthcare providers and routinely included in device interrogation reports. Providers could use these activity summaries to initiate clinically meaningful discussions and engage in shared decision making with their patients about the importance of physical activity and how to increase/maintain optimal levels of activity to achieve desired health and quality‐of‐life goals, the associated risks of certain types of activity (eg, competitive sports in those with contraindications), preventative strategies to minimize potential risks (eg, optimal medical therapy and activity recommendations on the basis of individual risk profile), and the benefits of regular physical activity for secondary prevention.4
Advances in device technology provide new opportunities to continuously monitor patient's physical activity and use these data to develop cost‐effective resource‐efficient strategies to proactively prevent patient decompensation and hospitalization. Preliminary data are encouraging, but issues of data accuracy and generalizability must first be addressed. Clinical trials are also needed to evaluate the utility of D‐PA data alone or in combination with other clinical and device‐based markers of disease, in predicting patient outcomes. This work will provide important insights into how to best use D‐PA data to inform patient care, device guidelines, and future activity interventions.
Lampert reports research funding from Medtronic, Abbott, and GE Medical; and advisory committee/honoraria from Medtronic. Sears reports honoraria/consulting fees from Medtronic, Boston Scientific, Spectranetics, St Jude Medical, and Zoll Medical; and research grants from Medtronic and Zoll Medical. All research funds are directed to East Carolina University. The remaining authors have no disclosures to report.
Table S1. Measurement of Physical Activity by Device Manufacturer*
Table S2. Summary of the Strengths and Limitations of D‐PA Technology and Extant Research
Figure S1. PRISMA diagram.
- ↵Pelliccia A, Fagard R, Bjørnstad HH, Anastassakis A, Arbustini E, Assanelli D, Biffi A, Borjesson M, Carrè F, Corrado D, Delise P, Dorwarth U, Hirth A, Heidbuchel H, Hoffmann E, Mellwig KP, Panhuyzen‐Goedkoop N, Pisani A, Solberg EE, van‐Buuren F, Vanhees L, Blomstrom‐Lundqvist C, Deligiannis A, Dugmore D, Glikson M, Hoff PI, Hoffmann A, Hoffmann E, Horstkotte D, Nordrehaug JE, Oudhof J, McKenna WJ, Penco M, Priori S, Reybrouck T, Senden J, Spataro A, Thiene G. Recommendations for competitive sports participation in athletes with cardiovascular disease consensus document from the Study Group of Sports Cardiology of the Working Group of Cardiac Rehabilitation and Exercise Physiology and the Working Group of Myocardial and Pericardial Diseases of the European Society of Cardiology. Eur Heart J. 2005;26:1422–1445.
- ↵Darden D, Richardson C, Jackson EA. Physical activity and exercise for secondary prevention among patients with cardiovascular disease. Curr Cardiovasc Risk Rep. 2013;7:411–416.
- ↵Lampert R. No further question: cardiac rehabilitation benefits patients with implantable‐cardioverter defibrillators: insurers, are you listening? JACC Clin Electrophysiol. 2017;3:127–128.
- ↵Pandey A, Parashar A, Moore C, Ngo C, Salahuddin U, Bhargava M, Kumbhani DJ, Piccini JP, Fonarow GC, Berry JD. Safety and efficacy of exercise training in patients with an implantable cardioverter‐defibrillator: a meta‐analysis. JACC Clin Electrophysiol. 2017;3:117–126.
- ↵Kaszala K, Ellenbogen KA. Device sensing: sensors and algorithms for pacemakers and implantable cardioverter defibrillators. Circulation. 2010;122:1328–1340.
- ↵Vegh EM, Kandala J, Orencole M, Upadhyay GA, Sharma A, Miller A, Merkely B, Parks KA, Singh JP. Device‐measured physical activity versus six‐minute walk test as a predictor of reverse remodeling and outcome after cardiac resynchronization therapy for heart failure. Am J Cardiol. 2014;113:1523–1528.
- ↵Jamé S, Kutyifa V, Polonsky B, McNitt S, Al‐Ahmad A, Moss AJ, Zareba W, Wang PJ. Predictive value of device‐derived activity level for short‐term outcomes in MADIT‐CRT. Heart Rhythm. 2017;14:1081–1086.
- ↵Adamson PB, Smith AL, Abraham WT, Kleckner KJ, Stadler RW, Shih A, Rhodes MM. Continuous autonomic assessment in patients with symptomatic heart failure: prognostic value of heart rate variability measured by an implanted cardiac resynchronization device. Circulation. 2004;110:2389–2394.
- ↵Boehmer JP, Hariharan R, Devecchi FG, Smith AL, Molon G, Capucci A, An Q, Averina V, Stolen CM, Thakur PH, Thompson JA, Wariar R, Zhang Y, Singh JP. A multisensor algorithm predicts heart failure events in patients with implanted devices: results from the multiSENSE study. JACC Heart Fail. 2017;5:216–225.
- ↵Conraads VM, Spruit MA, Braunschweig F, Cowie MR, Tavazzi L, Borggrefe M, Hill MR, Jacobs S, Gerritse B, van Veldhuisen DJ. Physical activity measured with implanted devices predicts patient outcome in chronic heart failure. Circ Heart Fail. 2014;7:279–287.
- ↵Cowie MR, Sarkar S, Koehler J, Whellan DJ, Crossley GH, Tang WH, Abraham WT, Sharma V, Santini M. Development and validation of an integrated diagnostic algorithm derived from parameters monitored in implantable devices for identifying patients at risk for heart failure hospitalization in an ambulatory setting. Eur Heart J. 2013;34:2472–2480.
- ↵Whellan DJ, Ousdigian KT, Al‐Khatib SM, Pu W, Sarkar S, Porter CB, Pavri BB, O'Connor CM. Combined heart failure device diagnostics identify patients at higher risk of subsequent heart failure hospitalizations: results from PARTNERS HF (program to access and review trending information and evaluate correlation to symptoms in patients with heart failure) study. J Am Coll Cardiol. 2010;55:1803–1810.
- ↵de la Uz CM, Burch AE, Gunderson B, Koehler J, Sears SF. How active are young cardiac device patients? Objective assessment of activity in children with cardiac devices. Pacing Clin Electrophysiol. 2017;40:1286–1290.
- ↵Tyagi S, Curley M, Berger M, Fox J, Strath SJ, Rubenstein J, Roth J, Widlansky ME. Pacemaker quantified physical activity predicts all‐cause mortality. J Am Coll Cardiol. 2015;66:754–755.
- ↵Braunschweig F, Mortensen PT, Gras D, Reiser W, Lawo T, Mansour H, Sogaard P, Stegemann B, Bruns HJ, Linde C. Monitoring of physical activity and heart rate variability in patients with chronic heart failure using cardiac resynchronization devices. Am J Cardiol. 2005;95:1104–1107.
- ↵Chelu MG, Gunderson BD, Koehler J, Ziegler PD, Sears SF. Patient activity decreases and mortality increases after the onset of persistent atrial fibrillation in patients with implantable cardioverter‐defibrillators. JACC Clin Electrophysiol. 2016;2:518–523.
- ↵Kramer DB, Mitchell SL, Monteiro J, Jones PW, Normand SL, Hayes DL, Reynolds MR. Patient activity and survival following implantable cardioverter‐defibrillator implantation: the ALTITUDE activity study. J Am Heart Assoc. 2015;4:e001775. DOI: 10.1161/JAHA.115.001775.
- ↵Kramer DB, Jones PW, Rogers T, Mitchell SL, Reynolds MR. Patterns of physical activity and survival following cardiac resynchronization therapy implantation: the ALTITUDE activity study. Europace. 2017;19:1841–1847.
- ↵Kramer DB, Tsai T, Natarajan P, Tewksbury E, Mitchell SL, Travison TG. Frailty, physical activity, and mobility in patients with cardiac implantable electrical devices. J Am Heart Assoc. 2017;6:e004659. DOI: 10.1161/JAHA.116.004659.
- ↵Melczer C, Melczer L, Goják I, Oláh A, Ács P. A comparative analysis between external accelerometer and internal accelerometer's physical activity data from implanted resynchronization devices in patients with heart failure. Eur J Integr Med. 2016;8(suppl 2):18–22.
- ↵Sears SF, Rosman L, Sasaki S, Kondo Y, Sterns LD, Schloss EJ, Kurita T, Meijer A, Raijmakers J, Gerritse B, Auricchio A. Defibrillator shocks and their impact on objective and subjective patient outcomes: results from the PainFree SST clinical trial. Heart Rhythm. 2017. Available at: https://www.sciencedirect.com/science/article/pii/S1547527117314601. Accessed April 26, 2018.
- ↵Shoemaker MJ, Cartwright K, Hanson K, Serba D, Dickinson MG, Kowalk A. Concurrent validity of daily activity data from Medtronic ICD/CRT devices and the actigraph GT3X triaxial accelerometer: a pilot study. Cardiopulm Phys Ther J. 2017;28:3–11.
- ↵Shoemaker MJ, Roper SE, Calkins TN. Seasonal variation of daily physical activity in individuals with heart failure. Heart Int. 2016;11:e25–e31.
- ↵Shoemaker MJ, Curtis AB, Paul R, Dickinson MG, Polso A, Sinha K. Longitudinal daily activity patterns in individuals with heart failure: derivation and association with prognosis and clinical characteristics. Cardiopulm Phys Ther J. 2013;24:25–33.
- ↵Small RS, Wickemeyer W, Germany R, Hoppe B, Andrulli J, Brady PA, Labeau M, Koehler J, Sarkar S, Hettrick DA, Tang WH. Changes in intrathoracic impedance are associated with subsequent risk of hospitalizations for acute decompensated heart failure: clinical utility of implanted device monitoring without a patient alert. J Card Fail. 2009;15:475–481.
- ↵Zhao S, Chen K, Su Y, Hua W, Chen S, Liang Z, Xu W, Dai Y, Liu Z, Fan X, Hou C, Zhang S. Association between patient activity and long‐term cardiac death in patients with implantable cardioverter‐defibrillators and cardiac resynchronization therapy defibrillators. Eur J Prev Cardiol. 2017;24:760–767.
- ↵Piepoli MF, Davos C, Francis DP, Coats AJ. Exercise training meta‐analysis of trials in patients with chronic heart failure (ExTraMATCH). BMJ. 2004;328:189.
- ↵Taylor RS, Sagar VA, Davies EJ, Briscoe S, Coats AJ, Dalal H, Lough F, Rees K, Singh S. Exercise‐based rehabilitation for heart failure. Cochrane Database Syst Rev. 2014:CD003331.
- ↵Park KE, Pepine CJ. Assessing cardiovascular risk in women: looking beyond traditional risk factors. Trends Cardiovasc Med. 2015;25:152–153.
- ↵Mody P, Gupta A, Bikdeli B, Lampropulos JF, Dharmarajan K. Most important articles on cardiovascular disease among racial and ethnic minorities. Circ Cardiovasc Qual Outcomes. 2012;5:e33–e41.
- ↵Nash IS. It's my heart: why not my data? Circulation. 2018;137:4–6.