Skip main navigation

Understanding Baseline Physical Activity in Cardiac Rehabilitation Enrollees Using Mobile Health Technologies

Originally published Cardiovascular Quality and Outcomes. 2022;15



Baseline physical activity in patients when they initiate cardiac rehabilitation is poorly understood. We used mobile health technology to understand baseline physical activity of patients initiating cardiac rehabilitation within a clinical trial to potentially inform personalized care.


The VALENTINE (Virtual Application-Supported Environment to Increase Exercise During Cardiac Rehabilitation Study) is a prospective, randomized-controlled, remotely administered trial designed to evaluate a mobile health intervention to supplement cardiac rehabilitation for low- and moderate-risk patients. All participants receive a smartwatch and usual care. Baseline physical activity was assessed remotely after enrollment and included (1) 6-minute walk distance, (2) daily step count, and (3) daily exercise minutes, both over 7 days and for compliant days, defined by >8 hours of watch wear time. Multivariable linear regression identified patient-level features associated with these 3 measures of baseline physical activity.


From October 2020 to March 2022, 220 participants enrolled in the study. Participants are mostly White (184 [83.6%]); 67 (30.5%) are female and 84 (38.2%) are >65 years old. Most participants enrolled in cardiac rehabilitation after percutaneous coronary intervention (105 [47.7%]) or coronary artery bypass surgery (39 [17.7 %]). Clinical diagnoses include coronary artery disease (78.6%), heart failure (17.3%), and valve repair or replacement (26.4%). Baseline mean 6-minute walk distance was 489.6 (SD, 143.4) meters, daily step count was 6845 (SD, 3353), and exercise minutes was 37.5 (SD, 33.5). In a multivariable model, 6-minute walk distance was significantly associated with age and sex, but not cardiac rehabilitation indication. Sex but not age or cardiac rehabilitation indication was significantly associated with daily step count and exercise minutes.


Baseline physical activity varies substantially in low- and moderate-risk patients enrolled in cardiac rehabilitation. Future studies are warranted to explore whether personalizing cardiac rehabilitation programs using mobile health technologies could optimize recovery.


URL:; Unique identifier: NCT04587882

What Is Known

  • Age and sex are associated with exercise capacity in patients enrolling in cardiac rehabilitation.

  • Mobile health technology is increasingly used to support the delivery of cardiac rehabilitation programs, although patient-level features associated with ambulatory physical activity collected through mobile health technology are not well described.

What the Study Adds

  • There is wide variation in home-based physical activity measures of remote 6-minute walk distance and daily mean step count and exercise minutes collected via mobile health technologies in patients initiating cardiac rehabilitation.

  • Sex and age were associated with remote 6-minute walk distance and sex was associated with daily mean step count and exercise minutes though not cardiac rehabilitation entry diagnosis.

  • Physical activity measures collected through mobile health technologies may be helpful for developing home exercise programs as patients initiate cardiac rehabilitation; this finding warrants further studies in clinical trials.

Cardiovascular disease impacts 9.3% of Americans with a high burden of recurrent events.1 There is thus a need for effective, broadly deployable secondary prevention strategies. Cardiac rehabilitation is a medically supervised risk reduction program for patients recovering from a cardiac event. Center-based cardiac rehabilitation has been shown to reduce hospital readmissions and cardiovascular mortality while improving quality of life.2–5 Despite the overwhelming benefits of cardiac rehabilitation, participation remains low6,7 and adherence to physical activity recommendations declines once patients graduate from cardiac rehabilitation.8,9 In light of the gaps in traditional cardiac rehabilitation, both patients and providers are increasingly utilizing technology as an adjunct to support delivery of their programs.10 For example, many patients are now wearing smartwatches that provide insights into their physical activity when outside of cardiac rehabilitation and which could be used to tailor exercise prescriptions. Availability of patients’ mobile device data is limited, however, and there is also an incomplete understanding of which patient-level features may be associated with ambulatory physical activity. Without such data, it is difficult to understand how to best inform personalized care in this setting.

The VALENTINE study (Virtual Application-Supported Environment to Increase Exercise During Cardiac Rehabilitation Study) is a prospective, randomized-controlled, remotely administered trial designed to evaluate a 6-month mobile health (mHealth) intervention to supplement cardiac rehabilitation for low- and moderate-risk patients. All participants completed remote 6-minute walk tests on enrollment. Here, we describe baseline physical activity for patients initiating cardiac rehabilitation and examine patient-level features associated with remotely assessed baseline physical activity as determined by wearable devices to inform personalized cardiac rehabilitation care.


Trial Design

The VALENTINE study is a prospective, randomized-controlled, remotely administered trial. The study was designed to evaluate a just-in-time adaptive intervention through text-based notifications as an adjunct to cardiac rehabilitation for low- and moderate-risk patients. This analysis reports on a secondary study objective to collect mHealth data from patients enrolled in cardiac rehabilitation to better understand baseline physical activity. The full study protocol has been published.11 The authors are solely responsible for the design and conduct of this study, all study analyses, the drafting and editing of the paper, and its final contents. The study was approved by the Michigan Medicine Institutional Review Board (HUM00162365). The data that support the findings of this study are available from the corresponding author upon reasonable request.


The study was designed to recruit low- and moderate-risk patients as guided by the American Association of Cardiovascular and Pulmonary Rehabilitation criteria.12 Patients were considered eligible for the study if ages 18 to 74 years, enrolled in cardiac rehabilitation at one of two study sites with a qualifying diagnosis, completed at least 2 cardiac rehabilitation sessions, and spoke English. We included participants who owned an Android or Apple phone with study-supported operating software to enhance generalizability. Patients unable to safely exercise without supervision or those with high-risk conditions were excluded from the study. These included severe valvular stenosis or regurgitation, unrevascularized obstructive left main or proximal left anterior descending coronary artery disease, exercise-induced ventricular tachycardia, New York Heart Association Class III or IV symptoms, and an ejection fraction <40%. Full inclusion and exclusion criteria are available in Table S1.

Trial Procedures

The VALENTINE study launched in October 2020 at Michigan Medicine and was expanded to include patients from Spectrum Health in July 2021. Michigan Medicine is a quaternary referral center located in southeastern Michigan with 2 cardiac rehabilitation facilities, whereas Spectrum Health is a large community health system in western Michigan with 5 facilities.

Recruitment was conducted remotely through a combination of targeted emails and phone calls and through flyers at cardiac rehabilitation sites. Informed consent occurred primarily by telephone with use of videoconferencing as needed. Once consented, participants were mailed an Apple Watch Series 4 or Fitbit Versa 2 in accordance with their smartphone ownership. Participants had the option, however, of using their own devices if they owned an equivalent model smartwatch or newer. Participants were then randomized to the intervention or control arms of the study, stratified by smartwatch. Remote enrollment appointments were conducted by telephone with use of videoconferencing as needed during which time participants were assisted in pairing their watches with their smartphones, assigned baseline study tasks, and oriented to the mobile study application. Participants were also assigned general and disease-specific quality of life questionnaires to be completed within the mobile study application within 7 days of enrollment. Both the intervention and control arms of the study received a compatible smartwatch and usual care, including cardiac rehabilitation as initially prescribed.

The study intervention has been previously described in full.11 In brief, the core of the intervention is the delivery of a just-in-time adaptive intervention to increase physical activity for patients enrolled in cardiac rehabilitation with the goal of augmenting and extending the benefits of cardiac rehabilitation. Just-in-time adaptive interventions leverage contextual data from wearable devices to deliver tailored support to users at times most likely to modify behavior.13,14 In the VALENTINE study, participants receive contextually tailored notifications promoting low-level physical activity and exercise. Additional components of the study intervention include access to a mobile study application and weekly emails to participants and their cardiac rehabilitation exercise physiologists with summaries of their wearable device data. The study intervention began one week after enrollment to allow for baseline data collection before intervention delivery for all participants. Participants will be followed for 6 months with outcome assessments at baseline, 3 months, and 6 months.

Data Collection and Study End Points

All mobile data were collected within the mobile application MyDataHelps, a commercially available iOS or Android research application developed by CareEvolution and available for download. Participants opt into data sharing on enrollment. Data are pulled into MyDataHelps through a secure, direct application programming interface for Fitbit devices and through Apple HealthKit for Apple Watch devices.

For this study, we collected data on the following 3 remote measures of baseline physical activity: 6-minute walk distance, daily mean step count, and daily mean exercise minutes. Six-minute walk distance was measured remotely using the mobile study application and smartwatch. We chose these three different aspects of baseline physical activity as they represent different activity axes with 6-minute walk distance representing functional capacity, step count representing daily movement, and exercise minutes representing planned physical activity. Tasks were assigned to participants on enrollment. Participants were asked to complete their 6-minute walk test within 7 days though were given up to 30 days after enrollment to complete the test. We excluded distances <50 meters from the analysis due to concerns for incorrect performance (n=8). If these were within 30 days of enrollment, participants were reassigned another 6-minute walk test (n=7), of which 5 were completed within 30 days of enrollment. All 6-minute walk tests completed >30 days after enrollment or <50 meters were excluded from the analysis (n=9). Of 211 valid tests, 179 (84.8%) were completed within 7 days of enrollment. Baseline mean physical activity measures of daily step count and daily exercise minutes were included for compliant days during the first 7 days of the study with compliance defined by at least 8 hours of daily watch wear time. Mean step count and exercise minutes were calculated per participant and then averaged across the study population.

Statistical Analysis

Baseline clinical characteristics are described as means and SD for continuous symmetrical variables and median with interquartile range for skewed continuous variables. Categorical variables are presented as counts and percentages. We performed Student t tests for bivariate comparisons between continuous variables and χ2 tests for comparisons across categorical variables.

We used multivariable generalized linear models to estimate the associations between key demographic and clinical features and baseline physical activity measures of 6-minute walk distance, daily step count, and daily exercise minutes. Features within the models were selected a priori based on clinical expertise and included: device ownership (Fitbit, Apple Watch), age, race, sex, indication for cardiac rehabilitation, clinical site (Michigan Medicine, Spectrum Health), and clinical conditions common amongst the cardiovascular disease population including heart failure, coronary artery disease, and history of valve repair or replacement. Participants with missing data were excluded. Clinical conditions were determined by a cardiologist (J.R.G.) upon review of the electronic medical record at the time of study consent. All analyses were conducted using SAS version 9.4 (SAS Institute, Inc, Cary, NC).


Study Population

Between October 2020 and March 2022, 940 patients were screened for the VALENTINE study across two study sites, of whom 442 (47.0%) patients were eligible for the study. Of these, 224 (50.7%) patients consented and 220 (49.8%) subsequently enrolled in the VALENTINE study (Figure). Participants enrolled a mean of 20.7 (SD, 12.7) days after starting cardiac rehabilitation.


Figure. Consort diagram.

CAD indicates coronary artery disease; and LAD, left anterior descending. *Patients may be listed as ineligible for more than one reason.

The majority of participants enrolled in cardiac rehabilitation after undergoing coronary revascularization by either percutaneous coronary intervention (105 [47.7%]) or coronary artery bypass grafting (39 [17.7 %]) or after valve repair or replacement (49 [22.3%]; Table 1). One hundred seventy-five patients (79.5%) participated in cardiac rehabilitation at Michigan Medicine and 45 patients (20.5%) at Spectrum Health. Participants’ mean age is 59.6 (10.6) years with 84 (38.2%) 65 years of age or older. Clinical diagnoses include coronary artery disease (78.6%), heart failure (17.3%), and history of valve repair or replacement (26.4%). Most participants (138 [62.7%]) have an iPhone and were provided with an Apple Watch. Baseline general and disease-specific quality of life scores are presented in Table 1. Over the first 7 days of the study, participants wore their watches a mean of 6.6 (SD, 1.2) compliant days with mean watch wear time 15.3 (SD, 3.9) hours per compliant day, with compliant days defined as days which participants wore their watches for a least 8 hours.

Table 1. Baseline Demographic and Clinical Characteristics

NMean (SD) or %
 Age, y22059.6 (10.6)
 Phone type
  iPhone (Apple Watch Series 4)13862.7%
  Android phone (Fitbit Versa 2)8237.3%
 BMI classification
  Normal weight2611.8%
 Indication for cardiac rehabilitation
  Valve repair or replacement4922.3%
  PCI or CABG and valve repair or replacement62.7%
  CAD or ACS, not revascularized219.5%
Clinical diagnoses
 Coronary artery disease17378.6%
 Heart failure3817.3%
 History of valve repair or replacement5826.4%
Quality of life
 KCCQ, overall summary score8380.7 (16.0)
 KCCQ, clinical summary score8383.7 (15.3)
 SAQ, physical limitation score16787.6 (16.7)
 EuroQol Visual Analog Scale21173.2 (17.9)

Baseline data for 220 study participants. Values displayed as mean (SD) or %. ACS indicates acute coronary syndrome; BMI, body mass index; CAD, coronary artery disease; CABG, coronary artery bypass grafting; KCCQ, Kansas City Cardiomyopathy Questionnaire; PCI, percutaneous coronary intervention; and SAQ, Seattle Angina Questionnaire.

Patient-Level Predictors of Baseline Activity

Data were available on 6-minute walk distance for 211 (95.9%) participants and on step count and exercise minutes for 217 (98.6%) participants. For the entire population, baseline physical activity included mean 6-minute walk distance of 489.6 (SD, 143.4) meters, mean daily step count of 6845 (SD, 3353), and mean daily exercise minutes of 37.5 (SD, 33.5; Table 2). There was wide variation in all three measures of daily physical activity across indications for cardiac rehabilitation (Table 3).

Table 2. Baseline Functional Data

Overall (N=220)Apple Watch (N=138)Fitbit versa 2 (N=82)
NMean (SD)NMean (SD)NMean (SD)
6-minute walk distance, m211489.6 (143.4)133502.1 (119.6)78468.4 (175.5)
Average daily step count2176845 (3353)1366764 (3146)816979 (3693)
Exercise minutes21737.5 (33.5)13635.8 (27.0)8140.4(42.3)

Values are displayed as mean (SD). Daily step count and exercise minutes are measured over the first 7 d of the study. Data presented for compliant days, defined by at least 8 h of watch wear time.

Table 3. Baseline Functional Data by Cardiac Rehabilitation Indication

PCI (N=105)CABG (N=39)Valve repair or replacement (N=49)PCI or CABG and valve repair or replacement (N=6)CAD or ACS, not revascularized (N=21)
6-minute walk distance, m503.1 (148.8)488.8 (157.0)457.7 (139.0)507.9 (61.0)492.5 (114.8)
Average daily step count7750 (3693)6687 (2713)5690 (3104)5713 (946)5696 (2183)
Exercise minutes45.9 (41.6)33.2 (22.9)28.9 (21.1)32.8 (16.2)24.5 (17.8)

Mean (SD) for functional data by indication for cardiac rehabilitation. ACS indicates acute coronary syndrome; CAD, coronary artery disease; CABG, coronary artery bypass grafting; and PCI, percutaneous coronary intervention.

Multivariable models were constructed for each of the 3 measures of baseline physical activity and included the key demographic and clinical features noted earlier. In a multivariable model of 6-minute walk distance, sex, age, race, and heart failure diagnosis were all significantly associated with changes in 6-minute walk distance (Table 4). Six-minute walk distance declined with age and was significantly lower in participants who were female, Black, or who had a diagnosis of heart failure. In separate multivariable models of mean daily step count and mean daily exercise minutes, only female sex and race were significant (Tables 5 and 6) with no association noted with age and clinical diagnoses. Female and Black or Other race participants took fewer steps. Similarly, female and Black participants exercised for fewer minutes. Indication for cardiac rehabilitation was not associated with any of the 3 measures of baseline physical activity.

Table 4. Multivariable Predictors of 6-Minute Walk Distance

EstimateSEP value
Female sex (ref=male)−54.9122.750.02
Race (ref=White)0.04
Device (ref=Fitbit)39.5921.250.06
Indication for cardiac rehabilitation (ref=PCI)0.85
 Valve repair or replacement−48.6267.56
 PCI or CABG and valve repair or replacement11.8380.61
 ACS or CAD, not revascularized5.1535.78
Study site (ref=Michigan Medicine)16.5225.920.52
Coronary artery disease (ref=absent)−12.5648.670.80
Heart failure (ref=absent)−72.4727.210.01
Valve repair or replacement (ref=absent)7.7955.860.89

ACS indicates acute coronary syndrome; CAD, coronary artery disease; CABG, coronary artery bypass grafting; and PCI, percutaneous coronary intervention.

Table 5. Multivariable Predictors of Mean Daily Step Count

EstimateSEP value
Female sex (ref=Male)−1310.76513.160.01
Race (ref=White)0.03
Device (ref=Fitbit)−100.09478.470.83
Indication for cardiac rehabilitation (ref=PCI)0.17
 Valve repair or replacement−2345.481553.18
 PCI or CABG and valve repair or replacement−1573.841942.79
 ACS or CAD, not revascularized−1445.32817.86
Study site (ref=Michigan Medicine)−67.91599.680.91
Coronary artery disease (ref=absent)−1098.881124.780.33
Heart failure (ref=absent)−340.69610.160.58
Valve repair or replacement (ref=absent)−295.891281.140.82

ACS indicates acute coronary syndrome; CAD, coronary artery disease; CABG, coronary artery bypass grafting; and PCI, percutaneous coronary intervention.

Table 6. Multivariable Predictors of Mean Daily Exercise Minutes

EstimateSEP value
Female sex (ref=male)−16.385.14<0.01
Race (ref=White)0.02
Device (ref=Fitbit)−5.704.490.24
Indication for cardiac rehabilitation (ref=PCI)0.09
 Valve repair or replacement−25.4815.56
 PCI or CABG and valve repair or replacement−16.6819.46
 ACS or CAD, not revascularized−15.078.19
Study site (ref=Michigan Medicine)4.966.010.41
Coronary artery disease (ref=absent)−5.8811.270.60
Heart failure (ref=absent)−4.806.110.43
Valve repair or replacement (ref=absent)9.8912.830.44

ACS indicates acute coronary syndrome; CAD, coronary artery disease; CABG, coronary artery bypass grafting; and PCI, percutaneous coronary intervention.


Adoption of mHealth technology is increasing broadly across age, race, and socioeconomic groups.15 Eighty-five percent of Americans now own a smartphone, and over 50% of American use mobile devices to monitor their health.15,16 As patients with cardiovascular disease spend greater than 5000 waking hours each year independent of medical providers,17 mHealth technologies are increasingly used to provide a window into patients’ daily lives, particularly for lifestyle behaviors like physical activity.18

Here, we describe wearable device–based physical activity data for patients initiating cardiac rehabilitation. We saw substantial variation in baseline physical activity within this cohort despite all patients being classified as low- or moderate-risk by standard cardiac rehabilitation entry criteria. Physical activity was associated with sex but not indication for cardiac rehabilitation across multivariable models for remotely assessed 6-minute walk distance, mean daily step count, and mean daily exercise minutes.

These results have significant implications for delivery of cardiac rehabilitation programs. Prior studies have demonstrated that age and sex are associated with measures of exercise capacity amongst those entering cardiac rehabilitation.19 Other studies have evaluated exercise capacity at the time of cardiac rehabilitation initiation by entry diagnosis.19–21 In one study, peak VO2 was measured in 2896 patients initiating cardiac rehabilitation between 1996 and 2004.19 Peak VO2 was higher in men than women, diminished progressively between the third and eighth decades, and differed by cardiac diagnosis with lower peak VO2’s in patients who had undergone coronary artery bypass grafting. Longer duration between patients’ hospitalizations and exercise tests was also associated with a lower peak VO2, although this differed by age, gender, and cardiac diagnoses. A second study looked at gender differences in self-reported physical activity following an acute myocardial infarction. In a multivariable model which accounted for physical activity at 3 time points post–acute myocardial infarction, women were more likely to have insufficient physical activity than men. Additionally, when controlling for other factors, Black or Other race, current smoker, history of diabetes, hypertension, and sedentary or walking workplace were significantly associated with being less physically active.22 Finally, a retrospective cohort study evaluated factors associated with peak VO2 on cardiopulmonary exercise testing across 1217 patients referred to cardiac rehabilitation for ischemic heart disease, valvular heart disease, or heart failure.20 In bivariate analyses, peak VO2 was associated with sex, age, left ventricular ejection fraction, Charlson Comorbidity Index, and beta-blocker use but not index event.

Our study builds on these findings by evaluating remote measures of baseline physical activity that were obtained through digital health tools. Although some of our findings correspond to those from prior studies, they additionally provide new insights into physical activity that can be used to tailor cardiac rehabilitation programs. Importantly, the association between cardiac rehabilitation indication and wearable device–based physical activity has not previously been explored. The latter is more representative of patients’ home-based physical activity and maybe more informative for constructing exercise prescriptions than traditional center-based measures collected during formal exercise testing or in clinical settings. We found, for example, that age was significantly associated with remotely assessed 6-minute walk distance, a measure of exercise capacity, but not daily step count or exercise minutes. Notably, cardiac rehabilitation entry diagnosis was not associated with any of the three measures of baseline physical activity, which were selected as they each represent unique aspects of physical activity. These findings suggest that cardiac rehabilitation programs could tailor activity prescriptions on objective measures of home physical activity obtained from mobile devices and not take a one size fits all approach to developing exercise programs based on factors such as indication for cardiac rehabilitation.

Our study has multiple strengths. First, we enrolled patients from both academic and community medical centers increasing diversity of the patient population and generalizability of study findings. Second, we utilized an inclusive study design with respect to smartwatch ownership. Many digital health studies have required that participants own a particular smartwatch or smartphone. We enrolled patients who own either an Android or an Apple phone and provided them with a compatible smartwatch. Although utilizing two study devices presented initial challenges in the study design, we believe it allows for a more inclusive enrollment strategy. Third, we had high rates of compliance with participants wearing their watches a mean of 6.6 complaint days during the first week of the study. Fourth, we enrolled an older population with 38.2% of participants 65 years of age or older, demonstrating that older adults typical of those enrolled in cardiac rehabilitation are capable of using wearable devices. Finally, we examined different measures of baseline physical activity across different axes that represented functional capacity, daily movement, and planned physical activity. This provided unique insights as some features like age were associated with functional capacity but not daily movement or planned physical activity.

Our study has several important limitations. First, this is a digital clinical trial that enrolled low- and moderate-risk patients who completed at least 2 center-based cardiac rehabilitation sessions within one of two large medical centers within the state of Michigan. There are thus issues of generalizability related to participation in a center-based cardiac rehabilitation program, though we required participants to complete only two center-based cardiac rehabilitation sessions, as well as to willingness to participate in a clinical trial using mHealth technology. Although only 47.0% of patients were eligible for the study, nearly 50% of eligible patients enrolled in the study with 38.2% of participants ≥65 years of age. These results, however, may not be indicative of all cardiovascular disease populations, particularly those with high-risk conditions whom we excluded due to concerns regarding safety for unsupervised exercise, as well as patients enrolled in home-based cardiac rehabilitation programs. Additionally, the majority of participants were from the academic medical center, which may impact generalizability through enrollment of a more affluent population. In a recent Pew survey, however, 76% of US adults earning less than US $30,000 per year and 75% of high school graduates reported owning a smartphone.15 Future work will have to ensure these findings also apply to more disadvantaged populations.

Second, as part of study participation, we encouraged participants to exercise independently without supervision. As a result, we required that participants complete at least two supervised cardiac rehabilitation sessions before enrolling in the study. As such, baseline physical activity in this study refers to that on study initiation and not necessarily upon initiating cardiac rehabilitation. Participants consented to participate in the study, however, a mean of 20.7 days after initiating cardiac rehabilitation. Third, the sample size of 220 participants was selected to detect a difference in 6-minute walk distance between the intervention and control arms of the study at 6 months. As such, this study may be underpowered in this observational analysis to detect differences in baseline physical activity based on demographic or clinical characteristics and larger studies will be necessary to confirm these results. Finally, we defined compliant days as those in which participants wore their watches for at least 8 hours. There is thus the potential that participants were physically active at times that they did not wear their smartwatches, and this would not be reflected in mean daily physical activity. We encouraged participants on enrollment, however, to wear their smartwatches during all exercise sessions. Participants, therefore, had a mean of 6.6 compliant days during the first week of the study and wore their watches for over 15 hours on compliant days. Furthermore, in earlier work, we demonstrated only small differences in wearable device–based physical activity when participants in a community setting wore their watches for 12 rather than 8 hours per day.18

In conclusion, in this analysis of a wearable device study of low- and moderate-risk patients enrolling in cardiac rehabilitation, we found wide variation in baseline physical activity though this was not associated with indication for cardiac rehabilitation. Future studies are warranted to explore whether personalizing cardiac rehabilitation programs using mHealth technologies could optimize recovery.

Article Information


We thank Samantha Fink, Joseph Bryant, and Rebecca Chappell for their critical assistance in study design and execution.

Supplemental Material

Table S1

Nonstandard Abbreviations and Acronyms


mobile health


Virtual Application-Supported Environment to Increase Exercise During Cardiac Rehabilitation Study

Disclosures Dr Nallamothu is a principal investigator or coinvestigator on research grants from the National Institutes of Health (NIH), Veterans Affairs Health Services Research & Development, the American Heart Association, and Apple, Inc. He also receives compensation as Editor-in-Chief of Circulation: Cardiovascular Quality and Outcomes, a journal of the American Heart Association. Finally, he is a coinventor on US Utility Patent Number US15/356,012 (US20170148158A1) entitled Automated Analysis of Vasculature in Coronary Angiograms that uses software technology with signal processing and machine learning to automate the reading of coronary angiograms, held by the University of Michigan. The patent is licensed to AngioInsight, Inc, in which Dr Nallamothu holds ownership shares and receives consultancy fees. Dr Kheterpal is a principal investigator or coinvestigator on research grants from the US NIH, Blue Cross Blue Shield of Michigan, the American Heart Association, Apple, Merck & Co, and Becton Dickinson & Company; and is a coinventor on US patent number 62/791,257 entitled Automated System To Medical Procedures, which is held by the University of Michigan. Dr Klasnja is a principal investigator or a coinvestigator on research grants from NIH and the American Heart Association. Dr Golbus receives funding from the NIH (L30HL143700) and receives salary support by an American Heart Association grant (grant number 20SFRN35370008). The other authors report no conflicts.


This manuscript was sent to Dennis T. Ko, Senior Guest Editor, for review by expert referees, editorial decision, and final disposition.

Supplemental Material is available at

This work was presented as an abstract at the Quality of Care and Outcomes Research Scientific Sessions, May 13–May 14, 2022.

For Sources of Funding and Disclosures, see page 482 & 483.

Correspondence to: Jessica R. Golbus, MD, MS, Department of Internal Medicine, Division of Cardiovascular Medicine, 2723 Cardiovascular Center, 1500 E. Medical Center Dr, SPC 5853, Ann Arbor, Michigan 48109-5853. Email


  • 1. Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, Boehme AK, Buxton AE, Carson AP, Commodore-Mensah Y, et al. Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association.Circulation. 2022; 145:e153–e639. doi: 10.1161/CIR.0000000000001052LinkGoogle Scholar
  • 2. Dunlay SM, Pack QR, Thomas RJ, Killian JM, Roger VL. Participation in cardiac rehabilitation, readmissions, and death after acute myocardial infarction.Am J Med. 2014; 127:538–546. doi: 10.1016/j.amjmed.2014.02.008CrossrefMedlineGoogle Scholar
  • 3. Eijsvogels TMH, Maessen MFH, Bakker EA, Meindersma EP, van Gorp N, Pijnenburg N, Thompson PD, Hopman MTE. Association of cardiac rehabilitation with all-cause mortality among patients with cardiovascular disease in the Netherlands.JAMA Netw Open. 2020; 3:e2011686. doi: 10.1001/jamanetworkopen.2020.11686CrossrefMedlineGoogle Scholar
  • 4. Dibben G, Faulkner J, Oldridge N, Rees K, Thompson DR, Zwisler AD, Taylor RS. Exercise-based cardiac rehabilitation for coronary heart disease.Cochrane Database Syst Rev. 2021; 11:CD001800. doi: 10.1002/14651858.CD001800.pub4MedlineGoogle Scholar
  • 5. Taylor RS, Long L, Mordi IR, Madsen MT, Davies EJ, Dalal H, Rees K, Singh SJ, Gluud C, Zwisler A-D. Exercise-based rehabilitation for heart failure: cochrane systematic review, meta-analysis, and trial sequential analysis.JACC: Heart Fail. 2019; 7:691–705. doi: 10.1016/j.jchf.2019.04.023CrossrefMedlineGoogle Scholar
  • 6. Beatty AL, Brown TM, Corbett M, Diersing D, Keteyian SJ, Mola A, Stolp H, Wall HK, Sperling LS. Million hearts cardiac rehabilitation think tank: accelerating new care models.Circ Cardiovasc Qual Outcomes. 2021; 14:e008215. doi: 10.1161/CIRCOUTCOMES.121.008215LinkGoogle Scholar
  • 7. Sukul D, Seth M, Barnes GD, Dupree JM, Syrjamaki JD, Dixon SR, Madder RD, Lee D, Gurm HS. Cardiac rehabilitation use after percutaneous coronary intervention.J Am Coll Cardiol. 2019; 73:3148–3152. doi: 10.1016/j.jacc.2019.03.515CrossrefMedlineGoogle Scholar
  • 8. Hansen D, Dendale P, Raskin A, Schoonis A, Berger J, Vlassak I, Meeusen R. Long-term effect of rehabilitation in coronary artery disease patients: randomized clinical trial of the impact of exercise volume.Clin Rehabil. 2010; 24:319–327. doi: 10.1177/0269215509353262CrossrefMedlineGoogle Scholar
  • 9. Gupta R, Sanderson BK, Bittner V. Outcomes at one-year follow-up of women and men with coronary artery disease discharged from cardiac rehabilitation: what benefits are maintained?J Cardiopulm Rehabil Prev. 2007; 27:11–8; quiz 19. doi: 10.1097/01.hcr.0000265015.44210.bfCrossrefMedlineGoogle Scholar
  • 10. Thomas RJ, Beatty AL, Beckie TM, Brewer LC, Brown TM, Forman DE, Franklin BA, Keteyian SJ, Kitzman DW, Regensteiner JG, et al. Home-based cardiac rehabilitation: a scientific statement from the American Association of Cardiovascular and Pulmonary Rehabilitation, the American Heart Association, and the American College of Cardiology.J Am Coll Cardiol. 2019; 74:133–153. doi: 10.1016/j.jacc.2019.03.008CrossrefMedlineGoogle Scholar
  • 11. Jeganathan VS, Golbus JR, Gupta K, Luff E, Dempsey W, Boyden T, Rubenfire M, Mukherjee B, Klasnja P, Kheterpal S, et al. Virtual Application-Supported Environment to Increase Exercise (VALENTINE) during cardiac rehabilitation study: Rationale and design.Am Heart J. 2022; 248:53–62. doi: 10.1016/j.ahj.2022.02.012CrossrefMedlineGoogle Scholar
  • 12. Silva AK, Barbosa MP, Bernardo AF, Vanderlei FM, Pacagnelli FL, Vanderlei LC. Cardiac risk stratification in cardiac rehabilitation programs: a review of protocols.Rev Bras Cir Cardiovasc. 2014; 29:255–265. doi: 10.5935/1678-9741.20140067MedlineGoogle Scholar
  • 13. Klasnja P, Hekler EB, Shiffman S, Boruvka A, Almirall D, Tewari A, Murphy SA. Microrandomized trials: an experimental design for developing just-in-time adaptive interventions.Health Psychol. 2015; 34S:1220–1228. doi: 10.1037/hea0000305CrossrefMedlineGoogle Scholar
  • 14. Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, Murphy SA. Just-in-Time Adaptive Interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support.Ann Behav Med. 2018; 52:446–462. doi: 10.1007/s12160-016-9830-8CrossrefMedlineGoogle Scholar
  • 15. Greenwood S. Mobile technology and home broadband 2021 [Internet].Pew Research Center: Internet, Science & Tech. Accessed March 1, 2022. Scholar
  • 16. Desilva J, Prensky-Pomaranz R, Zweig M. Digital Health Consumer Adoption Report 2020 [Internet].Rock Health. Scholar
  • 17. Asch DA, Muller RW, Volpp KG. Automated hovering in health care–watching over the 5000 hours.N Engl J Med. 2012; 367:1–3. doi: 10.1056/NEJMp1203869CrossrefMedlineGoogle Scholar
  • 18. Golbus JR, Pescatore NA, Nallamothu BK, Shah N, Kheterpal S. Wearable device signals and home blood pressure data across age, sex, race, ethnicity, and clinical phenotypes in the Michigan Predictive Activity & Clinical Trajectories in Health (MIPACT) study: a prospective, community-based observational study.Lancet Digit Health. 2021; 3:e707–e715. doi: 10.1016/S2589-7500(21)00138-2CrossrefMedlineGoogle Scholar
  • 19. Ades PA, Savage PD, Brawner CA, Lyon CE, Ehrman JK, Bunn JY, Keteyian SJ. Aerobic capacity in patients entering cardiac rehabilitation.Circulation. 2006; 113:2706–2712. doi: 10.1161/CIRCULATIONAHA.105.606624LinkGoogle Scholar
  • 20. Mikkelsen N, Dall CH, Frederiksen M, Holdgaard A, Rasmusen H, Prescott E. Depression, socioeconomic factors, and ethnicity as predictors of cardiorespiratory fitness before and after cardiac rehabilitation.J Cardiopulm Rehabil Prev. 2019; 39:E1–E6. doi: 10.1097/HCR.0000000000000367CrossrefMedlineGoogle Scholar
  • 21. Rengo JL, Khadanga S, Savage PD, Ades PA. Response to exercise training during cardiac rehabilitation differs by sex.J Cardiopulm Rehabil Prev. 2020; 40:319–324. doi: 10.1097/HCR.0000000000000536CrossrefMedlineGoogle Scholar
  • 22. Minges KE, Strait KM, Owen N, Dunstan DW, Camhi SM, Lichtman J, Geda M, Dreyer RP, Bueno H, Beltrame JF, et al. Gender differences in physical activity following acute myocardial infarction in adults: a prospective, observational study.Eur J Prev Cardiol. 2017; 24:192–203. doi: 10.1177/2047487316679905CrossrefMedlineGoogle Scholar