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International Journal of Nursing Sciences logoLink to International Journal of Nursing Sciences
. 2025 Dec 24;13(1):11–18. doi: 10.1016/j.ijnss.2025.12.010

Effectiveness of a digital technology-assisted personalized exercise prescription in the telerehabilitation of postoperative coronary heart disease patients: A randomized controlled trial

Yue Wu a, Yan Ma b, Chen Zhang c, Chuangshi Wang d, Shumin Zhang a, Mingjing Zhao a, Hongmei Su a, Chang Liu a, Yan Wang e, Xue Feng a,⁎
PMCID: PMC12891784  PMID: 41684613

Abstract

Objective

This study aimed to evaluate whether cardiac rehabilitation with a technology-assisted personalized exercise prescription is superior to traditional remote home-based rehabilitation in improving cardiorespiratory endurance and quality of life in postoperative patients.

Methods

From October 2022 to April 2024, 62 patients who underwent percutaneous coronary intervention for coronary heart disease were recruited from a tertiary hospital in Beijing and randomly assigned to either an intervention group or a control group. After baseline assessments of cardiorespiratory endurance and exercise function, the intervention group received a digitalized personalized exercise prescription combined with remote monitoring rehabilitation. This included an exercise prescription delivered via a mobile application, weekly remote monitoring sessions with an exercise therapist to supervise prescription adherence, provide feedback based on real-time electrocardiographic data, and make personalized adjustments to the exercise prescription based on this information. The control group received an application-delivered exercise movement library and a wearable electrocardiogram device for self-monitoring of exercise intensity. Both groups underwent the 12-week intervention. Changes in maximal oxygen uptake and quality of life were evaluated at enrollment, as well as at 4 weeks, 12 weeks, and 24 weeks after the intervention commenced.

Results

A total of 58 participants completed the study (the intervention group [n=30], the control group [n=28]). Twelve weeks after the interventions, the intervention group showed statistically improvements in VO2AT (Z = 2.247, P = 0.025), general health (Z = 2.126, P = 0.033) and social functioning (Z = 3.349, P = 0.001) compared to the control group. At 24 weeks of follow-up, the intervention group continued to exhibit statistically significant improvements in VO2AT (Z = 2.017, P = 0.044) and social functioning (Z = 3.126, P = 0.002). The exercise duration of patients in the intervention group during the exercise test was significantly prolonged at both 4 weeks (Z= −2.420, P = 0.021), 12 weeks (Z= −2.240, P = 0.029) and 24weeks (Z= −2.300, P = 0.025) showing statistically significant differences compared to the control group.

Conclusions

This study provides new evidence-based support for the practical effectiveness of nurses acting as supervisors of rehabilitation implementation and coordinators of multidisciplinary teams within a remote digital cardiac rehabilitation model, underscoring their significant value in the secondary prevention management system for cardiovascular diseases in the information era.

Keywords: Cardiac rehabilitation, Exercise therapy, Physical therapy, Physical fitness, Telerehabilitation

What is known?

  • •

    Patients diagnosed with coronary heart disease who undergo either home-based rehabilitation or outpatient rehabilitation have been shown to experience significant enhancements in cardiopulmonary endurance and self-management capabilities. They also report positive improvements in their quality of life.

  • •

    The current digital rehabilitation models principally depend on the use of wearable devices to monitor vital signs, including heart rate and electrocardiograms.

  • •

    The development of integrated digital tools for generating personalized exercise prescriptions remains a critical research priority, as the superior clinical efficacy of such multi-modal interventions compared to isolated heart rate monitoring has yet to be conclusively established.

What is new?

  • •

    Digital exercise rehabilitation using personalized video-based exercise program adjustments was demonstrated to enhance participants’ exercise capacity and rehabilitation adherence significantly.

  • •

    Interventions combining wearable device monitoring and personalized exercise programs markedly improved patients’ cardiorespiratory endurance.

1. Introduction

Coronary artery disease (CAD) is a significant cause of mortality on a global scale [1]. Cardiac rehabilitation (CR) is recognized as an effective intervention to reduce the risk of cardiovascular events, enhance the quality of life, and increase patient survival. Studies have demonstrated that patients who participate in cardiac rehabilitation experience significant improvements in cardiac function, physical activity levels, and mental health [2,3].

Despite the evident advantages of cardiac rehabilitation, participation rates remain low, with many patients unable to attend traditional, centralized rehabilitation programs due to factors such as transport, time, and cost [4]. Research in China has shown similar results. Wang et al. found that weekday employment is a factor influencing patient participation in outpatient rehabilitation [5].

Recent years have seen the gradual development of remote monitoring and digital health technologies. Thus, an increased number of cardiovascular patients have access to home-based rehabilitation. Digital Health Programs for Cardiac Rehabilitation (DHP-CR) represent a recent development in the field of cardiac rehabilitation. This emerging trend primarily encompasses the use of digital technology and information and communication technology to enhance the approach, efficiency, and accessibility of cardiac rehabilitation services. The integration of telemedicine, mobile health, wearable devices, health apps, and artificial intelligence is pivotal in this regard.

Some studies have been dedicated to the field of telerehabilitation, with studies typically employing remote monitoring methods, such as the use of wearable devices and mobile applications, to track patients’ physiological parameters, including heart rate, blood pressure, and activity levels [[6], [7], [8]]. The data are then transmitted in real-time to medical professionals [7], who use this information to provide personalized exercise and lifestyle recommendations, thereby assisting patients in achieving their rehabilitation goals [8]. Concurrently, psychological principles and human-computer interaction design are employed to promote behavioral changes and enhance self-management capabilities through motivational and feedback mechanisms within the apps [8].

The mobile health model has been shown to increase patient participation and improve the accessibility and sustainability of rehabilitation [3,9]. Real-time monitoring and personalized guidance enable patients to perform practical rehabilitation exercises in the comfort of their own homes, thereby enhancing both their physical health and their psychological well-being. Studies by Fang et al. [7] and Maddison et al. [2] have also confirmed that remote cardiac rehabilitation programs are effective in improving patients’ exercise capacity and quality of life, while reducing medical costs. The personalized feedback and support provided through this model also help patients maintain a healthy lifestyle after rehabilitation [8].

Exercise therapy constitutes a fundamental element of cardiac rehabilitation; however, patients frequently encounter difficulties in receiving practical movement guidance and in assessing the accuracy of their movements when undertaking exercise training in a domestic environment. This issue has become a matter of urgency within the context of contemporary remote rehabilitation training and requires immediate attention.

This study aimed to validate the efficacy of a personalized cardiac rehabilitation program integrating remote monitoring and digital tools in enhancing exercise capacity among patients with coronary heart disease through clinical trials. This program combined a mobile health application with wearable devices to achieve the dynamic optimization of exercise prescriptions, thereby scientifically verifying its role in improving patients’ exercise endurance.

2. Methods

2.1. Study design

A prospective, randomized, controlled trial (RCT) with two parallel groups was conducted at a research hospital in China (Chinese Clinical Trial Registry Center [http://www.chictr.org.cn], registration number: ChiCTR2500101547). The participants were recruited postoperatively and randomly assigned to either the intervention or control group. This study report follows the Consolidated Standards of Reporting Trials (CONSORT) [10].

2.2. Study setting and participants

Recruitment flyers for this study were distributed in the cardiology ward and cardiac rehabilitation center of the hospital in Beijing. Patients who were interested in the study made a telephone call or had a WeChat session with the researcher, whose contact information was provided on the flyers. The researcher met the potential participants in the ward before recruitment, explained the research, and provided them with the information statement. The potential participants were given two weeks for consideration, as guidelines recommend a cardiopulmonary exercise test (CPET) approximately two weeks post-percutaneous coronary intervention (PCI) to ensure hemodynamic stability [11]. Thus, a CPET was conducted to ascertain the eligibility of prospective participants for participation in the trial. During hospitalization, the potential participants received preliminary demonstrations of the electrocardiogram (ECG) device and mobile application. Upon formal enrollment, those unfamiliar with the tools receive repeated hands-on training to ensure proficiency—a strategy supported by studies on remote monitoring interventions.

Participants were diagnosed with coronary heart disease at admission and had completed PCI at least two weeks; adults aged 18–65; their post-PCI angiography showed no untreated vessels with stenosis >70 %; blood creatine kinase-MB (CK-MB) levels were within the normal range of 0.02–0.13 μg/L; they possessed a mobile device (e.g., smartphone or tablet) with an Internet connection; and were able to access the mobile application WeChat, complete the program and relevant follow-up 24 weeks after enrollment, and provide informed valid consent to participate in the study. The inclusion and exclusion criteria for the study were established in accordance with the 2019 American Association of Cardiovascular and Pulmonary Rehabilitation (AACVPR)/American Heart Association (AHA)/American College of Cardiology (ACC) Scientific Statement on Home-Based Cardiac Rehabilitation [12].

Patients were excluded if they had symptoms of angina or typical ischemic electrocardiographic changes in the CPET, along with the presence of angina symptoms/signs during low-intensity exercise (<5 metabolic equivalents [METs]), and complex arrhythmias during rest or exercise; the integration of cardiogenic shock or heart failure following PCI treatment; were survivors of sudden death or cardiac arrest; had severe psychological disorders; the left ventricular ejection fraction (LVEF) on cardiac ultrasonography was < 40 %; were unable to perform aerobic exercise and cardiopulmonary exercise due to neuromuscular or skeletal diseases; had intermittent claudication complicated with lower extremity arterial disease; other heart diseases, such as valvular disease, large vascular disease, and ventricular aneurysm also treated during PCI; were pregnant or lactating; had rapidly developing neurological disease; or acute or chronic lung function injury, liver failure, or renal failure.

The sample size was calculated using a formula based on the effect size ratio and correlation coefficient of the co-primary outcome, which was the cardiopulmonary function. The effect size of the intervention on the change in oxygen uptake (VO2) was based on data from previous studies (Lee et al. [13], 2013) and was calculated according to changes in METs to be 17.5 (5.0 METs) in the control group and 29.75 (8.5 METs) in the treatment group. An SD of 4, 80 % power, 5 % type I error, n = 21 per group, and a total of 42 participants were assumed. Considering a dropout rate of 10 %, with n = 24 per group and a total of 48 participants, the study ultimately recruited 62 patients in total, 31 in each group [14].

2.3. Ethical considerations

This trial was approved by the Human Research Ethics Committee of Fuwai Hospital (Approval No. 2020-1359). The participants were informed about the study during an enrollment consultation before initiating the intervention. Before the trial began, participants provided written informed consent. The participants’ personal data were used solely for academic purposes and were not disclosed to anyone other than the relevant researchers involved in the study.

2.4. Randomization and masking

The Interactive Web Response System (IWRS) was utilized to ensure a scientifically and statistically sound randomization scheme. The participants were randomly assigned to either the intervention group or the control group at a 1:1 ratio. The randomization statistician designed the randomization parameters within the central randomization system. Investigators logged into the system, and enrolled patients were assigned to treatment groups according to a prespecified randomization schedule. Blinding of the participants and the researcher who delivered the intervention was not possible due to the nature of the intervention. Thus, the outcome evaluation was blinded by a third party, and the statistical analysts were blinded [15]. Baseline assessments and outcome measurements were conducted by a research assistant who had no direct caregiving relationship with the participants.

2.5. Interventions

The intervention commenced after the baseline evaluation. For patients who received PCI, both groups received standard rehabilitative care, primarily involving physical exercise risk evaluation and education, as well as out-of-hospital exercise training. After the cardiovascular disease exercise risk assessment, patients who met the low exercise risk conditions and the inclusion criteria were taught the functions of the portable ECG devices and the Fitness Guidance Application.

In this study, nine researchers administered rehabilitation therapy to participants (two exercise science specialists, two cardiologists, two physiotherapists, and three cardiac rehabilitation specialist nurses). The primary responsibility of the cardiologists was to screen participants in accordance with the predetermined inclusion and exclusion criteria. This was followed by conducting exercise risk assessments, evaluating psychological disorders, and reviewing exercise prescriptions. The physiotherapists played a crucial role in developing exercise prescriptions for the participants, encompassing both aerobic exercise prescriptions and the design of home exercise programs. The exercise physiologists were responsible for remotely monitoring participants’ exercise precision, exercise heart rate, and ECG readings during home-based exercise to ensure that they remained within safe parameters. Cardiac rehabilitation specialist nurses play a crucial role in managing cardiac rehabilitation programs. They were responsible for participant recruitment, instructing participants on the proper use of portable ECG devices, guiding software installation and usage, providing home rehabilitation precautions, and conducting routine follow-ups. Before study initiation, all research personnel were required to undergo two training sessions. The objective of the sessions was to ensure that the research personnel possessed the necessary proficiency in the research protocol, the operation of the application software, and the health education techniques.

The HaopengyouTM online pro app was designed to enable portable ECG devices to upload and record heart rates and ECG data. The YiDongTM app is a software program that provides exercise prescription decision support. The exercise prescription database features video demonstrations of over 2,000 prescribed movements, exercises, and stretches targeting more than 70 muscle groups, as well as rehabilitation programs for over 50 standard conditions. One researcher (Y. Ma) was the exercise training specialist involved in developing the app’s exercise prescriptions for coronary heart disease. Consequently, this study employed the app as the intervention tool.

2.5.1. Intervention group

Initially, the researchers conducted a comprehensive exercise assessment of the participants. Following the CPET and functional movement screening of the patient, the exercise therapist formulated an individualized exercise prescription tailored to the patient’s test performance.

Individual exercise training protocols included specifications regarding exercise duration, target heart rate, and the use of a heart rate alarm. The target heart rate was defined as (heart rate max − heart rate rest) × (40–60) % + heart rate rest. The heart rate alarm was defined as heart rate target + 20 beats per minute (bpm). The enrolled patients were encouraged to engage actively in aerobic exercises. They were recommended to choose forms such as brisk walking, jogging, cycling on an exercise bike, and using rowing machines, five times a week. Exercise sessions began with a 10 min warm-up to prevent exercise-induced injuries, followed by an average session duration of 30–45 min at the target heart rate, and concluded with a 10 min relaxation and stretching period. The total exercise duration was at least 150 min every week, and the entire exercise program lasted for 12 weeks. The specific types of exercise and their detailed arrangements are shown in Table 1.

Table 1.

Weekly exercise schedule.

Exercise sessions Frequency Intensity Time Type
Aerobic exercises 3–5 days/week Moderate intensity:heart ratetarget =(heart ratemax-heart raterest) × (40–60) %+heart raterest 150 min/week Brisk walking, jogging, cycling on an exercise bike, and using rowing machines, etc
Resistance training 3 days/week Moderate loads (8–12 repetition maximum); volume was structured around 2–4 sets per exercise, with rest intervals of 60–90 s between sets to balance metabolic stress and recovery 45–60 min ① The gluteal and lower-limb muscles: leg press, lunges, squats, calf raises, side-lying hip abduction; ② core (abdominal and lumbar regions): glute bridge, superman, straight-arm pulldown, seated row; ③ upper-body muscles: dumbbell flye, bench press, front raise, bicep curl, triceps pushdown
Flexibility training Everyday Borg 4–6 10–15 min Stretch the neck and chest. For the hips and legs, perform lunge stretches, quadriceps stretches, and seated forward bends. Stretch the biceps and triceps, and finally, use the Child’s Pose for relaxation.
Balance training Everyday Borg 4–6 10–15 min ① Single-leg stance: hold for 20–30 s, then switch legs; ②tandem stance: hold this position for 30 s to a minute; ③ heel-to-toe walk: take 20–30 steps. ④ single-leg deadlift: perform 8–10 repetitions per side.
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The intervention group received a telerehabilitation program delivered via the YiDongTM app, in addition to routine care. In addition to standard telerehabilitation care, patients in the intervention group received digital, personalized telerehabilitation, which was a home-based, patient-tailored, mobile application-guided, and supervised program utilizing an ECG recording device. Two key real-time adjustments demonstrated the customized nature of the exercise prescription in the intervention group. First, while patients were implementing the exercise prescription online each week, the exercise therapist assessed whether their exercise heart rate was within the target heart rate zone. If it fell outside this range, the therapist promptly adjusted the exercise intensity. Second, if a patient was unable to perform the prescribed movements with precision, the therapist modified the exercise content in real time. The personalized exercise prescription within the intervention group protocol entails exercise specialists providing participants with tailored exercise regimens (Appendix A). The participants were required to schedule weekly online training sessions with specialists who observed their workouts via a video link and offered verbal guidance (Appendix B). The WeChat app was introduced to enable participants to communicate with the nurse and rehabilitation therapist, and to support patient adherence. Patients could make appointments for training sessions and share records of their daily activities.

2.5.2. Control group

The control group received usual cardiac telerehabilitation. The exercise prescription, meticulously tailored to the needs of patients with coronary heart disease, was delivered to the participants using the YiDongTM app. Video materials focused on strength training and combined aerobic movement training were also provided for patients to follow, aiming to improve muscle strength, balance, and flexibility. The participants could opt to engage in this exercise program or select an alternative form of physical activity that better aligned with their personal preferences. Participants in the control group did not receive online verbal guidance or movement adjustments from an exercise trainer while following the exercise videos. Additionally, portable ECG monitoring devices were provided for both patient groups to monitor heart rate responses during exercise and record any abnormal ECGs. The devices would issue alerts when the patients’ heart rates exceeded the target heart rates, guaranteeing safety while exercising. Abnormal ECG signals triggered software alerts. These prompted researchers to review and verify the accuracy of the ECG interpretation.

2.6. Measures

Baseline characteristics, including demographic information and clinical variables, were assessed using a self-designed questionnaire.

Cardiorespiratory endurance [16]. The primary outcome was defined as the difference between baseline and 12 weeks in VO2peak, as measured by the CPET using a breath-by-breath analysis. VO2peak was determined as the highest VO2 measured when the patients were completely exhausted, with a respiratory exchange ratio of ≥1.10 or an abnormal ECG or heart rate observed during the exercise test. VO2AT was determined as the VO2 at the anaerobic threshold. To account for the modest absolute increase in oxygen uptake, the increase ratio was additionally calculated for intergroup comparison. The increase ratio is calculated by dividing the difference between the increased value and the baseline value by the baseline value. In this study, “exercise time” is defined as the actual duration of exercise during the load-increasing phase of the CPET. “Workloadpeak” refers to the highest level of exercise intensity achieved during the test, typically measured in watts. Both of these parameters were directly recorded as objective measures by the CPET.

Health-related quality of life (HRQoL). The HRQoL was measured with the Short Form Health Survey-36 (SF-36), which the developted by the American Boston Health Research Institute [17]. The Chinese version of the SF-36 used in this study was translated and cross-culturally adapted by the Department of Social Medicine at Zhejiang University School of Medicine [18]. The SF-36v1 questionnaire is available for both commercial and non-commercial use without requiring a license. The questionnaire consists of eight subscales. Due to differences in the number of items and scoring ranges across dimensions, raw scores cannot be directly compared. Therefore, the following formula is applied to convert them into a standardized score ranging from 0 to 100. A physical component score is based on physical functioning, role-physical, bodily pain, and general health. In contrast, a mental component score is based on vitality, social functioning, role-emotion, and mental health. Higher SF-36 scores reflect better health status. The SF-36 scale has demonstrated good reliability and validity in patients with various cardiovascular diseases, including chronic heart failure and coronary heart disease, with an internal consistency reliability (Cronbach’s α coefficient) ranging from 0.738 to 0.919 [19,20].

2.7. Data collection

Cardiorespiratory endurance and HRQoL were measured four times. The final evaluation of outcomes was scheduled for week 12 to align with existing guidelines that posit 12 weeks as a standard exercise therapy cycle. A mid-term assessment at 4 weeks facilitated necessary modifications to the exercise protocol. Participants were determined to meet the inclusion criteria through a risk assessment process at baseline (T0). The following time points were considered: 4 weeks post-intervention (T1), 12 weeks post-intervention (T2), and 24 weeks during follow-up (T3) [11]. As the intervention aimed to improve patients’ cardiorespiratory endurance, the primary outcome of the present study was the change in oxygen uptake (VO2) from T0 to T2 during cardiopulmonary exercise testing. The secondary outcomes were changes in patient-reported HRQoL from T0 to T2, as well as changes in all outcome variables from T0 to T3. The research data for this study were collected with the assistance of research assistants who were not involved in the intervention during patient follow-up visits. As the primary outcome measure of this trial was CPET, participants were scheduled for their assessments at the cardiac rehabilitation center at the corresponding time points during the intervention and follow-up periods. Specifically, they were notified to complete the CPET within one week of each designated assessment point. While awaiting their test results, a research assistant assisted the participants in completing the relevant questionnaires.

2.8. Data analysis

The researchers established a database based on the collected materials, and double-entry was conducted to guarantee input accuracy. Data analyses were conducted using IBM@ SPSS Statistics version 27.0 (IBM Corp, 2020). Descriptive continuous variables were presented as the mean (SD) or median (interquartile range); categorical variables were expressed as the frequency and percentage, which were analyzed using the Pearson’s chi-square test or Fisher’s exact test. Changes from baseline to follow-up within each group were compared using the paired t-test for normally distributed data and the Wilcoxon signed-rank test for non-normally distributed data. Changes from baseline to follow-up between the two groups were analyzed using the independent-samples t-test and the Mann–Whitney U test, as appropriate. Generalized estimating equations (GEE) were used to compare the differential changes in each outcome at T1, T2 and T3 with respect to T0 between groups. Two-sided P-values < 0.05 were considered statistically significant. For the participants who dropped out midway, their data were included in the final efficacy analysis. For the primary outcome measure, the last observation carried forward (LOCF) method [21] was applied, using their previous available CPET and SF questionnaire data as the observation values carried forward to the trial endpoint for analysis.

3. Results

Data were collected between October 2022 and April 2024. Overall, 78 patients were assessed for eligibility, and 62 patients completed the trial, with 31 patients in the intervention group and 31 patients in the control group. One patient in the intervention group dropped out of the study due to a gout attack, and three patients in the control group were automatically withdrawn from the study because of a loss of contact. Fifty-eight participants were included in the data analysis. Appendix C presents the participant flowchart.

3.1. Participants’ characteristics

This randomized controlled trial enrolled 62 patients who had undergone surgery for coronary heart disease. They were randomly allocated to an intervention group (n = 31) or a control group (n = 31). The trial was completed by 30 participants in the intervention group and 28 in the control group. Attrition included three dropouts due to unwillingness to continue participation and one discontinuation in the intervention group because of a stroke. Baseline characteristics, including age, gender, education, height, weight, smoking history, and cardiovascular high-risk factors, were comparable between the groups with no statistically significant differences (Appendix D). No complications related to the interventions occurred. No severe adverse reactions or readmission of cardiovascular events occurred during the intervention period.

3.2. Primary outcomes

After 12 weeks of intervention, the anaerobic threshold oxygen uptake level of the intervention group increased 2.00 (−0.40, 3.72), and the difference was statistically significant (Z = 2.247, P = 0.025) compared with the control group −0.05 (−1.45,1.65). Increases in the anaerobic threshold oxygen uptake ratio at 12 weeks in intervention group 13.07 (−2.34, 28.58) and control group −0.49 (−9.63, 15.00) was statistically significant (Z = 2.107, P = 0.035). Changes in the primary outcome are shown in Table 2. The GEE analysis showed a significant main effect of time on VO2peak (wald χ2 = 10.39, P = 0.006). Additionally, there was a significant main effect of group on the increase ratio of VO2peak (wald χ2 = 11.23, P = 0.004). Both time (wald χ2 = 6.434, P = 0.04) and group (wald χ2 = 5.051, P = 0.025) also demonstrated significant main effects on the increase ratio of VO2AT. In the CPET, improvements were observed in exercise duration at 4, 12, and 24 weeks in the intervention group, which differed from those in the control group (all P < 0. 05) (Appendix E).

Table 2.

Changes in anaerobic threshold oxygen uptake and peak oxygen uptake.

Variables Intervention group (n = 30) Control group (n = 28) Z P
VO2peak, mL/kg·min
 Difference (T1–T0) 1.00 (−0.85, 2.60) 0.20 (−1.40, 2.00) 1.517 0.129
 Difference (T2–T0) 1.90 (−0.60, 3.85) 0.30 (−0.70, 2.30) 1.533 0.125
 Difference (T3–T0) 1.00 (−1.75, 2.20) −0.60 (−1.80, 0.80) 1.271 0.204
VO2AT, mL/kg·min
 Difference (T1–T0) 0.85 (−0.80, 2.12) 1.00 (−1.80, 1.75) 0.664 0.507
 Difference (T2–T0) 2.00 (−0.40, 3.72) −0.05 (−1.45, 1.65) 2.247 0.025
 Difference (T3–T0) 1.30 (−0.22, 2.17) −0.50 (−1.60, 1.65) 2.017 0.044
VO2peak increase ratio (%)
 Difference (T1–T0) 4.55 (−3.24, 13.18) 0.90 (−9.32, 8.73) 1.599 0.110
 Difference (T2–T0) 6.54 (−2.11, 17.35) 1.92 (−3.49, 10.41) 1.402 0.161
 Difference (T3–T0) 4.27 (−8.31, 11.19) −3.49 (−9.74, 3.64) 1.468 0.142
VO2AT increase ratio (%)
 Difference (T1–T0) 5.92 (−5.17, 17.86) 8.40 (−15.13, 14.29) 0.902 0.367
 Difference (T2–T0) 13.07 (−2.34, 28.58) −0.49 (−9.63, 15.00) 2.107 0.035
 Difference (T3–T0) 8.23 (−1.37, 15.24) −3.76 (−13.45, 13.47) 1.894 0.058
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Note: Data are Median (P25, P75). AT = anaerobic threshold. VO2peak = peak oxygen uptake. VO2AT = oxygen uptake at AT. T0: pre-intervention. T1: four weeks after intervention. T2: 12 weeks after intervention. T3: 24 weeks after intervention.

3.3. Secondary outcomes

At the 4-week intervention mark, the SF-36 questionnaire indicated that the general health dimension score was higher in the intervention group compared to the control group, showing a statistically difference (t = −2.010, P = 0.049). At 12 weeks, the general health score between the intervention group and control group remaining statistically significant (Z = 2.126, P = 0.033). Also at 12 weeks, for the social functioning dimension, the scores of the intervention group and the control group indicating a marked difference between the groups (Z = 3.349, P = 0.001). This significant difference persisted at the 24-week follow-up (Z = 3.126, P = 0.002). For the remaining dimensions of the SF-36, no statistically significant differences were observed between the two groups at any time point (all P > 0.05). (Appendix F). The GEE analysis revealed a significant main effect of group on social function scores (wald χ2 = 19.920, P < 0.001). The control group experienced a decrease in scores from T0 to T3, while the intervention group did not show such a decline. For mental health, these main effects of the group (wald χ2 = 3.848, P = 0.050) and group-by-time interaction (wald χ2 = 8.240, P = 0.016) were observed. This indicates that the intervention group’s scores improved over time compared to the control group. No significant differences were observed in the other dimensions.

4. Discussion

The objective of this study was to investigate the efficacy of a personalized exercise program, dynamically adjusted based on data from wearable devices, and to compare it with a standardized, guideline-based home rehabilitation program. The comparison focused on immediate (during intervention) and short-term (12 weeks after intervention) improvements in cardiopulmonary fitness and exercise capacity among patients who had undergone PCI.

Contrary to our hypothesis, the improvement in maximal oxygen uptake was not significantly different between the intervention group and the control group, which was essentially similar to the results of a study by Avila et al. [22]. Those in the home group received exercise data uploads and weekly telephone or e-mail feedback.

The VO2peak improved at 12 weeks in the group of participants who received an individualized exercise prescription and supervision in this study with 1.9 mL/kg·min in intervention group compared with 0.3 mL/kg·min in the control group. These results demonstrate that supervised telerehabilitation has superior short-term effects on enhancing oxygen uptake compared to traditional home-based rehabilitation. The GEE analysis indicated a significant temporal effect on changes in VO2peak, implying that ongoing intervention is essential for maintaining cardiorespiratory fitness. Previous studies [22,23] reported that participantss with supervised telerehabilitation maintained their training better at the end of the intervention than those with unsupervised training. A previous study showed that each 3.5 mL/kg·min increase in cardiorespiratory endurance was associated with a 14 % reduction in mortality [24]. The results of this study support the clinical value of a supervised personalized exercise prescription for rehabilitation outcomes in patients with coronary artery disease.

Wasserman and MoIlroy [25] used the anaerobic threshold (AT) as a measure of exercise intensity to assess the exercise capabilities of cardiac patients clinically. The AT oxygen uptake is closely related to muscle metabolic capacity. Enhancement of the AT can be achieved through systematic endurance training. This study demonstrated a significant increase in the VO2AT of participants in the intervention group at both 12 and 24 weeks, with increases of 2.00 mL/kg·min and 1.30 mL/kg·min, respectively. The ratio of the rise in VO2AT compared with that at baseline was 13.07 % and 8.23 %. Individualized exercise prescriptions are effective in improving muscle mass and capacity. GEE analysis highlighted the critical role of personalized intervention strategies in enhancing VO2AT. It is also conceivable that participants in the intervention group exhibited greater accuracy in their movements and superior exercise performance when the supervisor provided guidance via video during the execution of the exercise prescription. A comparable outcome was observed in a previous study [26]. In the present study, the researchers validated the training of the participants via video conferencing. In the intervention group, the VO2AT of the patients was 13.7 ± 3.3 mL/kg·min at baseline, which significantly increased to 14.9 ± 3.5 mL/kg·min (P < 0.05) three weeks later.

This study recorded and compared the exercise performance indicators during CPET between the two groups. No differences were found in the peak power of the participants’ exercise. However, significant differences were observed in exercise duration times at 4, 12, and 24 weeks. Patients in the intervention group exercised for a longer period than those in the control group. This may be related to improvements in aerobic endurance.

The statistically significant improvements in cardiorespiratory endurance and exercise metrics observed in this study may indicate that a digital technology-based personalized exercise prescription using the app contributed to enhanced patient engagement, motivation, and training effectiveness. These findings are consistent with those of previous digital rehabilitation studies [23,27]. Currently, a study [28] indicate that high-intensity interval training is more effective than moderate-intensity continuous training in improving VO2max. In light of the circumstances surrounding home training, the clinical team must conduct further research to better determine the optimal means of balancing safety and effectiveness.

The objective of enhancing exercise endurance in patients with cardiovascular disease is to improve the HRQoL. Thus, the SF-36 questionnaire was employed to assess the objective changes in the quality of life. At 4 weeks, the intervention group exhibited notable increases in GH scores, indicating enhanced satisfaction with the improvement of their health status. GH indicators exhibited sustained increases in the intervention group throughout the intervention period, which may be attributed to the fact that participants in the intervention group engaged in more exercise movements, providing greater encouragement to the patients. In contrast, the control group demonstrated a declining trend in mean values at 4 weeks and 12 weeks.

The control group exhibited lower SF scores than at baseline. At 12 weeks, a significant difference was observed between the two groups (P < 0.01). The superior performance of the intervention group may be attributed to the support and encouragement provided by the researcher in supervising and assisting with the implementation of the exercise program, which instilled greater confidence in these patients regarding their participation in social activities. The GEE indicated that our intervention significantly improved patients’ overall level of social functioning, suggesting that it may help alleviate their sense of social isolation caused by illness and facilitate their reintegration into family and social roles.

The intervention group demonstrated a trend of gradually rising MH scores during the intervention (P < 0.05). The magnitude of this increase was greater than that observed in the control group. However, no significant difference was noted between the two groups during the intervention and follow-up period.

A study by Raquel et al. [29] included an intermediate-risk ischemic heart disease population. Participants in the training group who used a combination of home monitoring and outpatient rehabilitation showed a notable improvement in total SF-36 scores compared to those in the outpatient home care group. However, the study did not present specific scores for each SF-36 dimension. In the study by Fang [7], the quality of life was evaluated using the SF-36, indicating that the Physical Component Summary Scale and the Mental Component Summary Scale scores of the two groups were higher than those at the baseline. Furthermore, the scores of the intervention group were higher than those of the control group, with a statistically significant difference (P < 0.05). However, the specific dimensions were not presented.

The present study [30] provides empirical evidence in support of digital technology-assisted personalized exercise prescription as a means of facilitating exercise rehabilitation in patients who have undergone treatment for coronary artery disease. The data analysis demonstrated the clinical significance of the intervention program that employed an app, video-format exercise instruction, and real-time ECG monitoring in enhancing patients’ cardiorespiratory endurance, exercise duration, and quality of life compared to a conventional home exercise prescription.

5. Limitations

This study was conducted at a single center, which may have reduced confounding variables caused by the use of different exercise rehabilitation protocols in other hospitals. This study excluded participants over 65 years of age for the following reasons. Age itself is a significant independent risk factor for cardiovascular events, which can influence an individual's tolerance to exercise and may influence compliance and the quality of the data obtained. Thus, the equivalency and generalizability of this study’s intervention in populations aged 65 and older require further exploration. Furthermore, the exercise ECG and the app used in the study employed different devices, resulting in an inability to correlate heart rate fluctuations with specific movements.

6. Conclusion

The findings of this study provide support for using a mobile health (mHealth) application to deliver exercise instruction to low-risk populations after coronary artery disease surgery. The provision of remote exercise rehabilitation instruction based on digital technology can facilitate the delivery of rehabilitation services to individuals who are unable to access hospital facilities due to geographical constraints. The nurses on this research team played a pivotal role in remote monitoring, tracking the progress of patients’ rehabilitation implementation, communicating with patients, and assisting them in completing the exercise program. With the advent of technology, extended care and nursing health education can also be enhanced on the rehabilitation management platform. This study lays the groundwork for advancing digital nursing. It also substantiated the potential advantages of mobile rehabilitation in improving quality of life, thereby providing clinical evidence for the advancement of digital care.

CRediT authorship contribution statement

Yue Wu: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Project administration. Yan Ma: Conceptualization, Methodology, Validation, Investigation, Resources, Data curation. Chen Zhang: Conceptualization, Methodology, Supervision. Chuangshi Wang: Methodology, Validation, Formal analysis. Shumin Zhang: Formal analysis, Investigation, Data curation. Mingjing Zhao: Investigation, Data curation. Hongmei Su: Investigation, Data curation. Chang Liu: Investigation, Data curation. Yan Wang: Conceptualization, Methodology, Project administration. Xue Feng: Conceptualization, Methodology, Validation, Formal analysis, Funding acquisition, Supervision, Project administration.

Data availability statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Declaration of competing interest

The authors declare there is no conflict of interest.

Footnotes

Peer review under responsibility of Chinese Nursing Association.

Appendices

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijnss.2025.12.010.

Appendices. Supplementary data

The following are the Supplementary data to this article:

Multimedia component 1
mmc1.docx (14.3KB, docx)
Multimedia component 2
mmc2.docx (235.2KB, docx)

References

  • 1.Global burden of cardiovascular diseases and risks 2023 collaborators. Global, regional, and national burden of cardiovascular diseases and risk factors in 204 countries and territories, 1990-2023. J Am Coll Cardiol. 2025;86(22):2167–2243. doi: 10.1016/j.jacc.2025.08.015. [DOI] [PubMed] [Google Scholar]
  • 2.Maddison R., Rawstorn J.C., Stewart R.A.H., Benatar J., Whittaker R., Rolleston A., et al. Effects and costs of real-time cardiac telerehabilitation: randomised controlled non-inferiority trial. Heart. 2019;105(2):122–129. doi: 10.1136/heartjnl-2018-313189. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Avila A., Claes J., Buys R., Azzawi M., Vanhees L., Cornelissen V. Home-based exercise with telemonitoring guidance in patients with coronary artery disease: does it improve long-term physical fitness. Eur J Prev Cardiol. 2020;27(4):367–377. doi: 10.1177/2047487319892201. [DOI] [PubMed] [Google Scholar]
  • 4.Brouwers R.W.M., van der Poort E.K.J., Kemps H.M.C., van den Akker-van Marle M.E., Kraal J.J. Cost-effectiveness of cardiac telerehabilitation with relapse prevention for the treatment of patients with coronary artery disease in the Netherlands. JAMA Netw Open. 2021;4(12) doi: 10.1001/jamanetworkopen.2021.36652. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Wang Y., Jiang L.Y., Wu Y., Zhang S.M., Wang C.S., Feng X. Completion rate of phase II cardiac rehabilitation and its influencing factors in patients with cardiovascular disease. Chin Circul J. 2021;36(10):1003–1008. doi: 10.3969/j.issn.1000-3614.2021.10.010. [In Chinese] [DOI] [Google Scholar]
  • 6.Ramachandran H.J., Jiang Y., Teo J.Y.C., Yeo T.J., Wang W. Technology acceptance of home-based cardiac telerehabilitation programs in patients with coronary heart disease: systematic scoping review. J Med Internet Res. 2022;24(1) doi: 10.2196/34657. Published 2022 Jan 7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Fang J.Y., Huang B., Xu D.M., Li J.L., Au W.W. Innovative application of a home-based and remote sensing cardiac rehabilitation protocol in Chinese patients after percutaneous coronary intervention. Telemed E Health. 2019;25(4):288–293. doi: 10.1089/tmj.2018.0064. [DOI] [PubMed] [Google Scholar]
  • 8.Sankaran S., Dendale P., Coninx K. Evaluating the impact of the HeartHab app on motivation, physical activity, quality of life, and risk factors of coronary artery disease patients: multidisciplinary crossover study. JMIR mHealth uHealth. 2019;7(4) doi: 10.2196/10874. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Szalewska D., Zieliński P., Tomaszewski J., Kusiak-Kaczmarek M., Łepska L., Gierat-Haponiuk K., et al. Effects of outpatient followed by home-based telemonitored cardiac rehabilitation in patients with coronary artery disease. Kardiol Pol. 2015;73(11):1101–1107. doi: 10.5603/kp.a2015.0095. [DOI] [PubMed] [Google Scholar]
  • 10.Hopewell S., Chan A.-W., Collins G.S., Hróbjartsson A., Moher D., Schulz K.F., et al. CONSORT 2025 statement: updated guideline for reporting randomised trials. Br Med J. 2025 doi: 10.1136/bmj-2024-081123. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Brown T.M., Pack Q.R., Aberegg E., Brewer L.C., Ford Y.R., Forman D.E., et al. Core components of cardiac rehabilitation programs: 2024 update: a scientific statement from the American Heart Association and the American Association of Cardiovascular and Pulmonary Rehabilitation. Circulation. 2024;150(18):e328–e347. doi: 10.1161/CIR.0000000000001289. [DOI] [PubMed] [Google Scholar]
  • 12.Thomas R.J., Beatty A.L., Beckie T.M., Brewer L.C., Brown T.M., Forman D.E., 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. Circulation. 2019;140(1):e69–e89. doi: 10.1161/cir.0000000000000663. [DOI] [PubMed] [Google Scholar]
  • 13.Lee Y.H., Hur S.H., Sohn J., Lee H.M., Park N.H., Cho Y.K., et al. Impact of home-based exercise training with wireless monitoring on patients with acute coronary syndrome undergoing percutaneous coronary intervention. J Kor Med Sci. 2013;28(4):564. doi: 10.3346/jkms.2013.28.4.564. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Julious S.A., Campbell M.J. Tutorial in biostatistics: sample sizes for parallel group clinical trials with binary data. Stat Med. 2012;31(24):2904–2936. doi: 10.1002/sim.5381. [DOI] [PubMed] [Google Scholar]
  • 15.Boutron I., Moher D., Altman D.G., Schulz K.F., Ravaud P., Group| FTC Methods and processes of the CONSORT group: example of an extension for trials assessing nonpharmacologic treatments. Ann Intern Med. 2008;148(4) doi: 10.7326/0003-4819-148-4-200802190-00008-w1. W-60-W-66. [DOI] [PubMed] [Google Scholar]
  • 16.Ross R., Blair S.N., Arena R., Church T.S., Després J.P., Franklin B.A., et al. Importance of assessing cardiorespiratory fitness in clinical practice: a case for fitness as a clinical vital sign: a scientific statement from the American Heart Association. Circulation. 2016;134(24):e653–e699. doi: 10.1161/cir.0000000000000461. [DOI] [PubMed] [Google Scholar]
  • 17.Ware J.E., Snow K.K., Kosinski M., Candek B. SF-36 Health Survey Manual and Interpretation Guide. Boston: New: England Medical Center the Health Institute; 1993 https://www.researchgate.net/publication/247503121_SF36_Health_Survey_Manual_and_Interpretation_Guide [Accessed 1 October 2025] [Google Scholar]
  • 18.Li L., Wang H.M., Shen Y. Development and psychometric tests of a Chinese version of the SF-36 health survey scales. Chin J Prev Med. 2022;36(2):109–113. doi: 10.3760/j:issn:0253-9624.2002.02.011. [in Chinese] [DOI] [PubMed] [Google Scholar]
  • 19.Yang L., Du X.P. Research on the quality of life in patients with stable coronary artery disease under standardized management in community. Chin J General Pract. 2023;21(11):1898–1902. doi: 10.16766/j.cnki.issn.1674-4152.003253. [In Chinese] [DOI] [Google Scholar]
  • 20.Gong K.Z., Zhang Z.G., Zhu N, Sun HG, Feng YL. Evaluation of reliability and validity of SF-36 in patients with chronic heart failure. Chin J Rehabil Med. 2004;19(3):182–184. doi: 10.3969/j.issn.1001-1242.2004.03.008. [in Chinese] [DOI] [Google Scholar]
  • 21.Davis M.P. Missing data and the last observation carried forward. J Pain Symptom Manag. 2024;67(6):e921–e922. doi: 10.1016/j.jpainsymman.2024.02.019. [DOI] [PubMed] [Google Scholar]
  • 22.Avila A., Claes J., Goetschalckx K., Buys R., Azzawi M., Vanhees L., et al. Home-based rehabilitation with telemonitoring guidance for patients with coronary artery disease (short-term results of the TRiCH study): randomized controlled trial. J Med Internet Res. 2018;20(6) doi: 10.2196/jmir.9943. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Zhu Y.N., Zhao Y.H., Wu Y. Effectiveness of mobile health applications on clinical outcomes and health behaviors in patients with coronary heart disease: a systematic review and meta-analysis. Int J Nurs Sci. 2024;11(2):258–275. doi: 10.1016/j.ijnss.2024.03.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Kokkinos P., Faselis C., Samuel I.B.H., Pittaras A., Doumas M., Murphy R., et al. Cardiorespiratory fitness and mortality risk across the spectra of age, race, and sex. J Am Coll Cardiol. 2022;80(6):598–609. doi: 10.1016/j.jacc.2022.05.031. [DOI] [PubMed] [Google Scholar]
  • 25.Wasserman K., McIlroy M.B. Detecting the threshold of anaerobic metabolism in cardiac patients during exercise. Am J Cardiol. 1964;14(6):844–852. doi: 10.1016/0002-9149(64)90012-8. [DOI] [PubMed] [Google Scholar]
  • 26.Batalik L., Dosbaba F., Hartman M., Konecny V., Batalikova K., Spinar J. Long-term exercise effects after cardiac telerehabilitation in patients with coronary artery disease: 1-year follow-up results of the randomized study. Eur J Phys Rehabil Med. 2021;57(5):807–814. doi: 10.23736/s1973-9087.21.06653-3. [DOI] [PubMed] [Google Scholar]
  • 27.Wang Q.L., Hunter S., Lee R.L., Chan S.W. The effectiveness of a mobile application-based programme for rehabilitation after total hip or knee arthroplasty: a randomised controlled trial. Int J Nurs Stud. 2023;140 doi: 10.1016/j.ijnurstu.2023.104455. [DOI] [PubMed] [Google Scholar]
  • 28.Dibben G.O., Faulkner J., Oldridge N., Rees K., Thompson D.R., Zwisler A.D., et al. Exercise-based cardiac rehabilitation for coronary heart disease: a meta-analysis. Eur Heart J. 2023;44(6):452–469. doi: 10.1093/eurheartj/ehac747. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Bravo-Escobar R., González-Represas A., Gómez-González A.M., Montiel-Trujillo A., Aguilar-Jimenez R., Carrasco-Ruíz R., et al. Effectiveness and safety of a home-based cardiac rehabilitation programme of mixed surveillance in patients with ischemic heart disease at moderate cardiovascular risk: a randomised, controlled clinical trial. BMC Cardiovasc Disord. 2017;17(1):66. doi: 10.1186/s12872-017-0499-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Li Y., Feng X., Chen B.Y., Liu H.P. Retrospective analysis of exercise capacity in patients with coronary artery disease after percutaneous coronary intervention or coronary artery bypass graft. Int J Nurs Sci. 2021;8(3):257–263. doi: 10.1016/j.ijnss.2021.05.008. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Multimedia component 1
mmc1.docx (14.3KB, docx)
Multimedia component 2
mmc2.docx (235.2KB, docx)

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Articles from International Journal of Nursing Sciences are provided here courtesy of Chinese Nursing Association

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