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Belitung Nursing Journal logoLink to Belitung Nursing Journal
. 2026 Jan 23;12(1):40–48. doi: 10.33546/bnj.4258

Phone-based motivational interviewing intervention among patients undergoing cardiac rehabilitation: A randomized controlled trial in China

Yu Wang 1,2, Chintana Wacharasin 1,*, Khemaradee Masingboon 1
PMCID: PMC12828558  PMID: 41585839

Abstract

Background

Maintaining regular physical activity (PA) is a critical challenge for patients undergoing cardiac rehabilitation (CR), as inadequate adherence can lead to adverse outcomes, including disease recurrence. Enhancing motivation is key to improving PA adherence. Motivational interviewing (MI) has been shown to overcome psychological barriers by building intrinsic motivation. However, there is a need to explore how to integrate MI techniques with digital technology to enhance intervention accessibility and leverage the strengths of both approaches.

Objective

This study aimed to evaluate the effects of a hybrid intervention model integrating phone-based motivational interviewing with mobile phone-based management on motivation and physical activity in patients undergoing CR.

Methods

This pilot randomized controlled trial was conducted in China between January and June 2024, involving 52 patients undergoing cardiac rehabilitation. The intervention group (n = 25) received usual care plus a 4-week phone-based motivational interviewing intervention, which included a face-to-face session, weekly phone MI sessions (OARS technique), and personalized support via WeChat. The control group (n = 27) received only usual cardiac rehabilitation guidance. Outcomes measured were steps/day (using a smart band) and motivation, assessed using the Behavioral Regulation in Exercise Questionnaire-2 (BREQ-2). Two-way repeated-measures ANOVA was used for data analysis.

Results

Compared with the control group, the intervention group showed a significant interaction effect of Group*Time on both steps/day (p <0.001, ηp2 = 0.706) and the BREQ-2 (RAI) (p <0.001, ηp2 = 0.349). The intervention group’s daily steps significantly increased from 3582.80 ± 649.59 to 9444.79 ± 1413.72 (p <0.001).

Conclusion

This study indicates that a phone-based motivational interviewing intervention delivered by trained researchers can effectively increase physical activity levels and enhance autonomous motivation for PA in CR patients. This approach provides a scalable model for the routine care of cardiac rehabilitation patients, especially in resource-limited settings. For nursing practice, the findings highlight the importance of incorporating MI as a core skill and utilizing a hybrid, technology-supported care model to extend continuous professional support into patients’ homes.

Trial Registry Number

Chinese Clinical Trial Registry (ChiCTR2400079877)

Keywords: motivational interviewing, motivation, physical activity, cardiac rehabilitation, China

Background

Cardiac rehabilitation (CR) programs play a critical role in improving health outcomes among patients with cardiovascular disease. However, maintaining physical activity (PA), a core component of CR, remains a significant challenge. Although extensive clinical evidence supports the effectiveness of CR programs, and the PA component in particular, for patients with cardiovascular disease, global participation rates remain suboptimal (Makita et al., 2022; Sachdev et al., 2023). In China, CR is still in its development phase, and adherence to PA is particularly low among patients with cardiovascular disease (Di et al., 2024). Inadequate engagement in guideline-recommended levels of PA often leads to weight gain, in some cases, disease recurrence within six months of the initial cardiac events (Cunningham et al., 2020; Su et al., 2018).

Evidence suggests that enhancing patients’ motivation exerts a greater impact on their adherence to PA than standard treatment methods (Bailey, 2019; Nogg et al., 2021). Motivational interviewing (MI) is a client-centered communication technique that triggers behavioral change by helping individuals explore and resolve ambivalence or uncertainty regarding specific behavioral change (Miller & Rollnick, 2012). MI has demonstrated effectiveness in overcoming psychological barriers to behavioral change, such as low motivation, ambivalence, and resistance, by building patients’ intrinsic motivation and enhancing their confidence in their ability to adopt and maintain healthier behaviors (Cole et al., 2023; Harkin et al., 2023).

In recent years, digital technology-based health management models have offered new avenues for enhancing intervention accessibility (Wongvibulsin et al., 2021). However, how to organically integrate MI techniques with digital technology to leverage the strengths of both remains an area requiring exploration. This study adopts Self-Determination Theory as its core theoretical framework. This theory posits that fulfilling individuals’ needs for autonomy, competence, and relatedness is key to stimulating intrinsic motivation (Flannery, 2017; Huang et al., 2022). MI is a communication technique that supports autonomy by providing empathic support, avoiding confrontation, and fostering a sense of choice (Zhu et al., 2024). The accumulation of objective data from wearable devices, combined with researchers’ affirmation of patients’ efforts, can also create a sustained positive feedback loop, enhancing patients’ sense of competence and relatedness (De la Torre et al., 2022; Kang et al., 2025). Embedding these strategies is therefore critical for promoting sustainable behavioral change, as they address the underlying psychological and social determinants that shape patients’ commitment to healthier behaviors (Bondaronek et al., 2018). Therefore, this study aimed to evaluate the effects of a hybrid intervention model that integrates MI with mobile phone-based management on motivation and physical activity.

METHODS

Study Design

This pilot randomized controlled trial study with a pretest and posttest design was conducted in January and June 2024 at a tertiary hospital in Zhejiang, China, and involved 52 patients undergoing cardiac rehabilitation. An intervention group that received a phone-based motivational interviewing intervention, and a control group that received usual care. Random allocation was performed by the first research assistant (RA1) using a computer-generated random sequence. Sequentially numbered, opaque, and sealed envelopes were used for allocation concealment to prevent researchers from enrolling participants from knowing the assignment.

Sample/Participants

Participants were patients undergoing cardiac rehabilitation at the cardiovascular department of a tertiary-level hospital in Lishui City, Zhejiang Province, China, from January to June 2024. The hospital has a well-established multidisciplinary CR team comprising cardiologists, specialized CR nurses, rehabilitative therapists, dietitians, and psychologists, who typically deliver the first phase of CR during patients’ hospitalization. However, many patients, primarily those residing in nearby towns, face challenges in continuing with the second phase of rehabilitation after discharge. Barriers such as travel distance, medical expenses, and work commitments often prevent them from attending follow-up sessions. These challenges underscore the need for a home-accessible CR program tailored to this population, thereby making them an appropriate target group for the present study.

G*Power Software (version 3.1.9.2) was used for sample size analysis. Based on the finding of Claes et al. (2020), which reported a Cohen’s d of 0.28 for the improvement in daily steps following a home-based CR program for CVD patients. The parameters used were: α (type I error rate) = 0.05, power (1-β) = 0.90, number of groups = 2, number of measurements = 2 (baseline and post-intervention). The total estimated sample size is 58, assuming a dropout rate of 15%. Six participants withdrew because they were referred for a second PCI surgery during the intervention’s implementation.

Consecutive recruitment with a random allocation method was employed in this study. The inclusion criteria were: 1) aged 40 to 60 years; 2) having the ability to use a smartphone; 3) having a primary caregiver (family member or friend) who provides PA support and can assist with their participation in face-to-face interviews; 4) being able to communicate in Mandarin and respond to the researcher’s questions completely; 5) Charlson comorbidity index score < 3 (Charlson et al., 2022); 6) no cognitive and/or mental disorders (as per screening results undertaken by a CR nurse as a part of standard care). The exclusion criteria were: 1) a patient was referred to the CR program after enrolled in the study; 2) a patient experienced episodes of unstable angina, New York Heart Association (NYHA) class IV (Heidenreich et al., 2022), uncontrolled severe arrhythmias, or uncontrolled hypertension; 3) a patient had experienced an acute cardiac event (e.g., malignant arrhythmias, acute myocardial ischemia) with surgery scheduled within three months.

Instruments

Physical activity was monitored by recording the number of steps per day using a smart band, which participants were required to wear continuously throughout the study period. The data recorded were accessible to researchers at any time via the associated mobile application. Participants could also view their own PA data on their phone or smart band, but the data could not be altered or edited. This real-time monitoring allowed researchers to provide timely reminders and feedback, supporting participants’ adherence to the PA as prescribed by their CR program.

The Behavioral Regulation in Exercise Questionnaire–2 (BREQ-2) was used to assess motivation toward physical activity (Markland & Tobin, 2004). The instrument consists of 19 items representing five motivational regulations: amotivation, external regulation, introjected regulation, identified regulation, and intrinsic regulation. Items are rated on a 5-point Likert scale ranging from 0 (not true for me) to 4 (very true for me).

In the present study, only the Relative Autonomy Index (RAI) was used for statistical analysis. The RAI provides a composite indicator of motivational quality along the self-determination continuum and is calculated by weighting each regulation as follows: (amotivation × −3) + (external regulation × −2) + (introjected regulation × −1) + (identified regulation × 2) + (intrinsic regulation × 3) (Verloigne et al., 2011). Higher RAI scores indicate more autonomous motivation. Individual BREQ-2 subscale scores were not analyzed or interpreted separately in this study. The internal consistency of the BREQ-2 items contributing to the Relative Autonomy Index was assessed in this sample. Cronbach’s alpha for the RAI composite was .899 at baseline and .918 at post-intervention, indicating good internal consistency. Permission was obtained from the developers to use the BREQ-2 in this study. All instruments were used in accordance with the terms of the permissions granted.

Interventions

Intervention group

This study implemented a 4-week Phone-Based Motivational Interviewing Intervention for the intervention group. This program combines face-to-face MI with phone-based remote management, aiming to systematically enhance patients’ PA motivation and behavior. The intervention design and reporting adhere to the TIDieR guidelines.

  1. Face-to-Face MI Session: One individualized, 60-minute face-to-face MI session conducted at enrollment. The session was led by a researcher trained in MI, with both the patient and primary caregiver participating. The primary objectives of the initial meeting were to establish trust, explore barriers to PA, and assist the patient in setting PA goals. The core technique employed was OARS: 1) Open Questions: Guide patients to explore their willingness and ability to increase PA. 2) Affirmations: Recognize patients’ efforts toward health and their existing strengths. 3) Reflective Listening: Empathize with and reflect back patients’ conflicting feelings and concerns. 4) Summaries: Periodically review patients’ motivation for change and personal goals to reinforce their commitment.

  2. Phone MI Sessions: During the 4-week intervention period, conduct one telephone MI follow-up session weekly, each lasting 60 minutes. This session employed the OARS technique to review the past week’s smart bracelet activity data with both the patient and primary caregiver. Then, discussion goal attainment, encountered barriers, successful experiences, and adjusting or setting new activity goals were performed.

  3. WeChat Platform Support: Researchers send participants personalized activity data summaries and motivational messages twice weekly. PA instructional videos are also distributed via WeChat. Messages include specific feedback on step counts, exercise duration, and acknowledgment of participants’ efforts. Participants can ask questions or feedback via WeChat at any time.

  4. Activity Monitoring and Compliance: All participants were provided with smart bands to monitor daily step count, exercise duration, and energy expenditure. They were required to wear the band throughout the day for the entire 4-week intervention period, removing it only for bathing or charging. Adherence of the intervention was identified as participation in ≥3 (75%) scheduled phone sessions and consistent wear of the wristband (defined as ≥5 days per week with valid data).

  5. Intervention Fidelity: All MI sessions were audio-recorded. PA data from all participants was collected via smart wristbands and downloaded/recorded by RA2 in the backend system.

Control group

The control group received usual care, developed by the cardiologist and CR nurse at the CR Centre in collaboration with the patient. This typically involved 3-5 sessions of moderate-intensity PA per week. After hospital discharge, the CR nurse made follow-up calls to ensure participants understood how to implement their PA and to gather feedback. Participants also wore smart bands and sent their PA records to RA2 via self-apps. The content of PA for participants in both the intervention and control groups was managed by a cardiologist, and the principles for choosing the PA format were the same. Both groups received identical PA safety monitoring throughout the intervention.

Data Collection

A total of 52 eligible patients undergoing CR were enrolled in the study. RA1 recruited eligible patients who had completed phase I CR at a tertiary hospital. After screening, 52 participants were enrolled and randomly assigned using computer-generated codes to either the intervention (n = 25) or control group (n = 27). Due to the nature of the behavioral intervention, participants were aware of their group allocation. Outcome data were collected by a research assistant (RA1) who was not involved in intervention delivery. A second research assistant (RA2) performed data collection. For the intervention group, the outcome (PA: steps; motivation: BREQ-2) was measured at pre-intervention (the day before the intervention) and post-intervention (at the end of the day of the intervention), as well as participants’ characteristics. The researcher provided a phone-based motivational interviewing intervention and usual care to CR patients included in the intervention group. The control group participants received usual care. RA2 also asked them to complete the same questionnaires as the intervention group at pre-intervention and post-intervention.

Data Analysis

SPSS version 27 was used to analyze the data for this study, with an overall significance level of 0.05. All data and analyses were verified twice to check for errors. Participant characteristics were described using descriptive statistics. Fisher’s exact test and the chi-square were employed to compare characteristics between the two groups. A two-way repeated-measures ANOVA was used to analyze the primary outcomes. Every statistical assumption was examined before inferential analyses were carried out. The Shapiro-Wilk test and visual examination of Q-Q plots were used to determine normality. Levene’s test was used to assess the homogeneity of variances. Confidence intervals represent 95% CIs for the mean based on standard errors.

Ethical Considerations

The research was approved by the Institutional Review Board of Burapha University in Thailand (IRB: HS071/2566, October 24, 2024), and Lishui People’s Hospital, China (IRB: 2023-042-01, November 9, 2023). Before the study commenced, the principal investigator provided eligible CR patients with comprehensive information about the study, including its objectives, assessment plan, participation protocol, timeline, and potential risks and benefits. Participant confidentiality was strictly maintained, with data access limited to the principal investigator. Participants were informed of their right to decline participation or withdraw from the study at any time without any impact on their cardiovascular treatment. After confirming their understanding of the research protocol and reviewing the consent information, those who agreed to participate in the study were invited to sign the consent form, which had also been approved by the Institutional Review Board (IRB). This study is registered with the Chinese Clinical Trial Registry (Number ChiCTR2400079877). The registration was prospective, as it was completed before the enrollment of the first participant.

Results

Patients’ Characteristics

A total of 95 patients undergoing cardiac rehabilitation were screened for eligibility. Of these, 37 were excluded (29 did not meet the inclusion criteria, 5 declined participations, and three were unable to participate due to distance). Fifty-eight patients were randomized to either the intervention group (n = 28) or the control group (n = 30). During the intervention period, six participants withdrew due to referral for a second percutaneous coronary intervention (PCI). The final analysis, therefore, included 52 participants: 25 in the intervention group and 27 in the control group (Figure 1).

Figure 1.

Figure 1

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A CONSORT flow of the sample allocation

There were 25 CR patients in the intervention group, with a mean age of 53.8 years (SD = 6.423, range = 40-60). The majority of CR patients were male (60%), had less than a high school education (52%), were married (92%), and were full-time employed (64%). The age range of the 27 CR patients in the control group was 43–60 years old, with a mean age of 51.15 (SD = 5.468). The majority of patients undergoing CR were male (77.8%), had less than a high school diploma (63%), and were married (85.2%). The characteristics of both groups were not different (Table 1).

Table 1.

Baseline characteristics of participants in the intervention and control groups

Characteristic Intervention group (n = 25) Control group (n = 27) Test statistic p
Age (years), M ± SD 53.8 ± 6.423 51.15 ± 5.468 t = 1.607 0.114
Gender, n (%) χ2 = 1.926 0.165
Male 15 (60.0) 21 (77.8)
Female 10 (40.0) 6 (22.2)
Marital status, n (%) χ2 = 0.924 0.630
Single 1 (4.0) 3 (11.1)
Married 23 (92.0) 23 (85.2)
Widowed 1 (4.0) 1 (3.7)
Educational level, n (%) χ2 = 5.703 0.127
Below high school 13 (52.0) 17 (63.0)
High school 4 (16.0) 8 (29.6)
Bachelor’s degree 6 (24.0) 1 (3.7)
Master’s degree or above 2 (8.0) 1 (3.7)
Employment status, n (%) χ2 = 4.964 0.291
Unemployed 1 (4.0) 4 (14.8)
Freelance 7 (28.0) 12 (44.4)
Full-time employment or retired 16 (64.0) 10 (37.0)
Part-time employment 1 (4.0) 1 (3.7)
Monthly household income (RMB), n (%) χ2 = 0.822 0.844
< 4,000 6 (24.0) 4 (14.8)
4,000–6,000 10 (40.0) 11 (40.7)
6,000–8,000 7 (28.0) 9 (33.3)
> 8,000 2 (8.0) 3 (11.1)
Place of residence, n (%) χ2 = 3.462 0.177
City 17 (68.0) 13 (48.1)
County 5 (20.0) 5 (18.5)
Rural 3 (12.0) 9 (33.3)
Distance to hospital, n (%) χ2 = 0.367 0.947
< 1 km 3 (12.0) 4 (14.8)
1–5 km 10 (40.0) 9 (33.3)
6–10 km 2 (8.0) 3 (11.1)
> 10 km 10 (40.0) 11 (40.7)
Family living arrangement, n (%) χ2 = 1.285 0.733
Living with partner 18 (72.0) 19 (70.4)
Living with partner’s children 4 (16.0) 4 (14.8)
Living with children 2 (8.0) 1 (3.7)
Living alone 1 (4.0) 3 (11.1)
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Note. Values are presented as n (%) unless otherwise indicated. Test statistics are reported as t or χ2 as appropriate. Continuous variables were analyzed using independent-samples t tests; categorical variables were analyzed using chi-square (χ2) tests.

Effects of Intervention

A two-way repeated-measures ANOVA (one between, one within) was used to examine the effects of the intervention on the outcomes between the intervention and control groups across the two time points (pre- and post-intervention). The data met the prerequisite assumptions for this analysis. First, the residuals were approximately normally distributed. Second, Levene’s test indicated homogeneity of variances between the groups at baseline (p > 0.05). As there were only two levels of the repeated-measures factor (Time), the assumption of sphericity was not applicable and therefore not tested. Independent-samples t-tests confirmed that there were no statistically significant differences between the intervention and control groups for any of the variables at pre-intervention (p > 0.05). For Steps/day, the ANOVA revealed a significant Time*Group interaction, F(1, 50) = 120.267, p <0.001, ηp2 = 0.706. Significant main effects were also found for Time, F(1, 50) = 248.473, p <0.001, ηp2 = 0.832, and for Group, F(1, 50) = 86.123, p <0.001, ηp2 = 0.633. For the BREQ-2 (RAI), a significant Time*Group interaction was also observed, F(1, 50) = 26.813, p <0.001, ηp2 = 0.349. Significant main effects were found for Time, F(1, 50) = 16.000, p <0.001, ηp2 = 0.242, and for Group, F(1, 50) = 4.730, p = 0.034, ηp2 = 0.086 (Table 2). Post-hoc analyses (Table 3) were conducted to decompose this interaction. Results showed that the intervention group significantly increased their steps/day and BREQ-2 (RAI) scores from pre-intervention to post-intervention (p < 0.05). These results suggest that the intervention effectively promoted a shift toward more self-determined forms of motivation, demonstrating the benefits of integrating MI with telephone-based support.

Table 2.

Repeated-measures ANOVA results for steps per day and BREQ-2 (RAI)

Outcome Source of variation F df p ηp2 95% CI for ηp2
Steps/day Time 248.473 1 <0.001 0.832 [0.776, 0.874]
Group 86.123 1 <0.001 0.633 [0.544, 0.704]
Time × Group 120.267 1 <0.001 0.706 [0.626, 0.769]
BREQ-2 (RAI) Time 16.000 1 <0.001 0.242 [0.138, 0.337]
Group 4.730 1 0.034 0.086 [0.009, 0.182]
Time × Group 26.813 1 <0.001 0.349 [0.242, 0.446]
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Note. ηp2 = partial eta squared; df = degrees of freedom

Table 3.

Descriptive statistics and post hoc comparisons for steps per day and BREQ-2 (RAI)

Outcome Group Pre-intervention (M ± SD) Post-intervention (M ± SD) 95% CI for post-intervention mean t p Cohen’s d
Steps/day Intervention 3582.804 ± 649.593 9444.788 ± 1413.719 [8861.233, 10028.343] -18.249 <0.001 3.650
Control 3588.538 ± 773.136 5880.24 ± 3182.116 [4566.73, 7193.75] -4.291 <0.001 0.826
t 0.040 5.370
p 0.969 <0.001
BREQ-2 (RAI) Intervention 12.40 ± 20.710 34.04 ± 16.285 [27.32, 40.76] -4.613 <0.001 0.923
Control 15.92 ± 19.296 12.92 ± 17.279 [5.79, 20.05] 0.023 0.045 0.404
t -0.423 4.740
p 0.674 <0.001
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Note. Values are presented as mean ± standard deviation unless otherwise indicated. Cohen’s d reflects the standardized mean change from pre- to post-intervention, calculated using the standard deviation of the difference scores (not the pre-intervention SD). This is equivalent to d= t/√n, where t is the paired-samples t statistic and n is the sample size. This approach accounts for the correlation between repeated measures and is recommended for within-subject designs (Lakens, 2013).

Discussion

Compared with the control group, the intervention group achieved statistically significant and clinically meaningful improvements in steps/day and motivation. The results of this pilot study indicate that Phone-Based Motivational Interviewing Intervention can improve both PA levels and motivation for PA among CR patients. This study innovatively combines MI, continuous monitoring, and support facilitated by WeChat. While MI alone has been validated in fostering intrinsic motivation, its long-term sustainability may be limited (Brouwer-Goossensen et al., 2022). The intervention innovatively establishes a dynamic feedback loop: 1) MI techniques (open-ended questioning, reflective listening, positive feedback) help patients explore and resolve cognitive dissonance, fostering intrinsic motivation for PA; 2) Smart band monitoring provides objective, real-time PA data, enabling purposeful tracking and timely feedback; 3) the WeChat platform enabled continuous professional support and peer interaction.

Patients in the intervention group showed a substantial increase in daily step count compared to the control group. Research indicated that at least 7,500 steps per day is necessary to halt the progression of coronary artery disease (Hacke & Weisser, 2021). In this study, patients in the intervention group averaged more than 9,000 steps per day. This finding is consistent with other studies that demonstrated increased step counts following behavioral counseling during CR (Cruz-Cobo et al., 2022). This intervention successfully elevated patients’ PA to a level associated with halting disease progression and fostered the autonomous motivation necessary for long-term adherence. In this study, the smart band provided real-time PA data, enabling participants to monitor their progress and determine whether they were achieving their goals. Such immediate feedback, combined with self-monitoring and timely prompting, has been identified as an essential behavioral support mechanism (Van Rhoon et al., 2020). The device not only informed participants of discrepancies between their current behavior and intended goals but also allowed healthcare professionals to monitor activity patterns remotely, delivering targeted guidance when necessary. In clinical practice, leveraging these low-cost, consumer-oriented smart devices as adjunct tools for remote patient monitoring and incorporating a brief review of patient-recorded activity data (e.g., weekly step trends) into follow-up consultations enables objective assessment and personalized goal-setting, thereby moving beyond reliance on subjective patient self-report alone.

Participants’ enthusiasm for continuing physical activity increased as a result of the intervention’s successful facilitation of a change toward more self-determined motivation. This result shows that our intervention effectively promoted patients’ advancement along the autonomy continuum, which aligns perfectly with Self-Determination Theory (Ntoumanis & Moller, 2025). MI techniques are particularly effective in helping patients connect PA with personal health beliefs, which are key motivators for individuals in CR. The intervention combined face-to-face and phone-based MI, enabling participants to share their PA experiences during rehabilitation without the need for frequent hospital visits. MI’s core strength lies in providing empathic understanding and a supportive space for patients to explore and resolve ambivalence or barriers through techniques such as open-ended questions, reflective listening, and positive feedback. This process helps patients identify discrepancies between their current behaviors and desired goals, fostering intrinsic motivation and ultimately promoting behavioral change (Samdal et al., 2017). The intervention began by exploring participants’ motivations for engaging in PA, including preventing disease recurrence, regaining independence, and extending life expectancy (Fraser et al., 2022; Warehime et al., 2020). Once key motivations were clarified, the researcher assisted each participant in setting achievable short- and long-term PA goals. Healthcare institutions may consider integrating core motivational interviewing techniques into nursing professional development programs to help nurses effectively address patient ambivalence and strengthen patients’ intrinsic motivation for health behavior change during routine interactions.

The success of this study can also be attributed to the alignment of the intervention design with the challenges inherent in the development of CR in China. Research indicates that despite the substantial burden of cardiovascular diseases, the implementation of CR programs in China remains underdeveloped and exhibits significant regional disparities (Di et al., 2024). In response to this reality, the present study adopted a hybrid model integrating remote support and smart band monitoring, delivering CR services directly to patients’ homes via telephone and WeChat, thereby addressing some of the systemic barriers to CR development in China. At the level of patient cognition, the issue is far more complex than a simple “lack of knowledge.” The pervasive belief that “rest over exercise” is preferable among patients, coupled with the misconception that surgical intervention equates to full recovery, constitutes a significant barrier to CR (Zou et al., 2025). Motivational interviewing (MI) effectively addresses this by exploring patients’ illness perceptions and reshaping their misconceptions, an approach whose efficacy has been validated within Chinese patient populations (Li et al., 2020). Concurrently, the objective biofeedback provided by the smart bands helped reduce patients’ anxiety about exercise-related risks and enhanced their exercise self-efficacy, thereby supporting the maintenance of their PA behavior.

Strengths and Limitations

This study had several notable strengths. First, participants engaged actively during the MI sessions, often discussing in depth the barriers they encountered in adhering to their PA program. The extended duration and richness of these conversations reflected both high participant engagement and the acceptability of delivering this type of intervention. Second, the combination of face-to-face and phone-based MI enhanced both the reach and impact of the program. This dual-delivery model offered flexible, continuous professional support and encouragement, ensuring sustained guidance throughout recovery and maximizing the intervention’s effectiveness.

Several limitations should be acknowledged. The open-label design may have introduced performance bias, while the use of wearable smart bands, though offering objective outcome measures, may induce a Hawthorne effect, whereby participants modify their behavior due to being observed. The generalizability of the findings is constrained by the use of a limited age range of participants (40-60 years). Furthermore, although the results demonstrate promising short-term effects, the lack of long-term follow-up data necessitates further research to confirm the intervention’s sustainability. Future studies are recommended to employ a multicenter design and extended follow-up periods to validate and expand upon the present findings. The intervention included weekly 60-minute motivational interviewing telephone sessions combined with continuous digital support, which may be more intensive than what is feasible in routine clinical practice. As this was a pilot study, future research should evaluate the effectiveness of lower-intensity or stepped-care versions of this intervention to enhance scalability. The use of the Relative Autonomy Index as the sole motivational outcome was intended to provide a theoretically grounded and parsimonious indicator of motivational quality while avoiding inappropriate aggregation or misinterpretation of distinct BREQ-2 subscales.

Implications for Nursing Practice

The findings of this study have several important implications for nursing practice in the CR context. First, the significant improvements observed in both physical activity levels (steps/day) and autonomous motivation (BREQ-2 RAI) highlight the critical role nurses can play in supporting sustained behavior change beyond hospital discharge. As frontline providers in CR programs, nurses are uniquely positioned to incorporate MI techniques into routine patient education, follow-up, and counseling to address motivational barriers to physical activity.

Second, the demonstrated effectiveness of phone-based MI combined with digital support suggests that nurses can extend professional care into patients’ home environments through remote modalities. Regular telephone MI sessions and asynchronous communication via platforms such as WeChat enabled continuous engagement, personalized feedback, and timely problem-solving, which are often difficult to achieve through traditional, center-based CR alone. Integrating these strategies into nursing follow-up protocols may help overcome common barriers to CR participation, including travel distance, time constraints, and limited access to specialized services, particularly in resource-limited or geographically dispersed settings.

Third, the use of wearable smart bands to objectively monitor physical activity provides nurses with actionable data to guide individualized goal-setting, reinforce patient progress, and enhance patients’ sense of competence. The combination of real-time activity feedback and empathic MI-based communication allows nurses to move beyond prescriptive advice toward a collaborative, autonomy-supportive care model. This approach aligns closely with Self-Determination Theory and supports the development of self-regulated, long-term adherence to physical activity.

Finally, the study highlights the importance of building MI competency within the nursing workforce. Training nurses in core MI skills, such as open-ended questioning, reflective listening, affirmations, and collaborative goal-setting, may enhance the effectiveness of CR nursing interventions and improve patient outcomes. Embedding MI as a core nursing skill and leveraging technology-supported care models can strengthen continuity of care, promote patient empowerment, and advance nurses’ roles as key agents of behavioral change in cardiac rehabilitation.

Conclusion

This study indicates that implementing MI combined with phone-based management is both feasible and well-received by patients undergoing CR. The high levels of satisfaction and acceptance observed suggest that this approach holds promise as a supportive strategy in CR. However, future research is required to assess its long-term effectiveness and evaluate its applicability across diverse patient age groups undergoing CR.

Acknowledgment

The authors would like to express their heartfelt gratitude to all participants who contributed significantly to this study, as well as to the medical and nursing staff of the Department of Cardiology and the Cardiac Rehabilitation Center at Lishui People’s Hospital, Zhejiang Province, China.

Funding Statement

Funding None.

Declaration of Conflicting Interest

No conflict of interest to declare in this study.

Author Contribution

Data collection: Y.W; Study Design, Data analysis, Manuscript writing and revisions for important intellectual content, and Final approval: Y.W., C.W., and K.M.

Author Biography

Yu Wang, RN, is a PhD candidate at the Faculty of Nursing, Burapha University, Chonburi, Thailand. She also works at the Department of Nursing, the School of Medicine at Lishui University, Lishui, China.

Chintana Wacharasin, RN, PhD, is a Professor at the Faculty of Nursing, Burapha University, Chonburi, Thailand.

Khemaradee Masingboon, RN, PhD, is an Assistant Professor at the Faculty of Nursing, Burapha University, Chonburi, Thailand.

Data Availability

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

Declaration of Use of AI in Scientific Writing

The authors used ChatGPT/Gemini in the writing process to improve readability and remove grammatical errors. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the published article.

References

  1. Bailey, R. R. (2019). Self-efficacy, self-regulation, social support, and outcomes expectations for daily physical activity in adults with chronic stroke: A descriptive, exploratory study. Occupational Therapy in Health Care, 33(2), 129-141. 10.1080/07380577.2018.1558326 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bondaronek, P., Alkhaldi, G., Slee, A., Hamilton, F. L., & Murray, E. (2018). Quality of publicly available physical activity apps: Review and content analysis. JMIR mHealth and uHealth, 6(3), e9069. 10.2196/mhealth.9069 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Brouwer-Goossensen, D., Scheele, M., van Genugten, L., Lingsma, H. F., Dippel, D. W. J., Koudstaal, P. J., & den Hertog, H. M. (2022). Motivational interviewing in a nurse-led outpatient clinic to support lifestyle behaviour change after admission to a stroke unit: A randomized controlled trial. European Journal of Cardiovascular Nursing, 21(1), 36-45. 10.1093/eurjcn/zvab001 [DOI] [PubMed] [Google Scholar]
  4. Charlson, M. E., Carrozzino, D., Guidi, J., & Patierno, C. (2022). Charlson comorbidity index: A critical review of clinimetric properties. Psychotherapy and Psychosomatics, 91(1), 8-35. 10.1159/000521288 [DOI] [PubMed] [Google Scholar]
  5. Claes, J., Cornelissen, V., McDermott, C., Moyna, N., Pattyn, N., Cornelis, N., Gallagher, A., McCormack, C., Newton, H., & Gillain, A. (2020). Feasibility, acceptability, and clinical effectiveness of a technology-enabled cardiac rehabilitation platform (physical activity toward health-I): Randomized controlled trial. Journal of Medical Internet Research, 22(2), e14221. 10.2196/14221 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Cole, S. A., Sannidhi, D., Jadotte, Y. T., & Rozanski, A. (2023). Using motivational interviewing and brief action planning for adopting and maintaining positive health behaviors. Progress in Cardiovascular Diseases, 77, 86-94. 10.1016/j.pcad.2023.02.003 [DOI] [PubMed] [Google Scholar]
  7. Cruz-Cobo, C., Bernal-Jiménez, M. Á., Vazquez-Garcia, R., & Santi-Cano, M. J. (2022). Effectiveness of mHealth interventions in the control of lifestyle and cardiovascular risk factors in patients after a coronary event: Systematic review and meta-analysis. JMIR mHealth and uHealth, 10(12), e39593. 10.2196/39593 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cunningham, C., O'Sullivan, R., Caserotti, P., & Tully, M. A. (2020). Consequences of physical inactivity in older adults: A systematic review of reviews and meta‐analyses. Scandinavian Journal of Medicine & Science in Sports, 30(5), 816-827. 10.1111/sms.13616 [DOI] [PubMed] [Google Scholar]
  9. De la Torre, S., Spruijt-Metz, D., & Farias, A. J. (2022). Survivors’ health competence mediates the association between wearable activity tracker use and self-rated health: HINTS analysis. Journal of Cancer Survivorship, 16(6), 1268-1278. 10.1007/s11764-021-01112-9 [DOI] [PubMed] [Google Scholar]
  10. Di, W., Ming, T., Yongmei, C., Yu, C., Hemei, W., & Lu, Z. (2024). Evidence summary of phase Ⅱ cardiac rehabilitation for patients with chronic heart failure. Chinese Journal of Emergency and Critical Care Nursing, 5(3), 222-229. 10.3761/j.issn.2096-7446.2024.03.005 [DOI] [Google Scholar]
  11. Flannery, M. (2017). Self-determination theory: Intrinsic motivation and behavioral change. Oncology Nursing Forum, 44(2), 155-156. 10.1188/17.onf.155-156 [DOI] [PubMed] [Google Scholar]
  12. Fraser, M. J., Leslie, S. J., Gorely, T., Foster, E., & Walters, R. (2022). Barriers and facilitators to participating in cardiac rehabilitation and physical activity: A cross-sectional survey. World Journal of Cardiology, 14(2), 83-95. 10.4330/wjc.v14.i2.83 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Hacke, C., & Weisser, B. (2021). Sport and exercise in prevention and therapy of cardiovascular diseases. Deutsche Medizinische Wochenschrift (1946), 146(6), 381-388. 10.1055/a-1342-3070 [DOI] [PubMed] [Google Scholar]
  14. Harkin, K., Apostolopoulos, V., Tangalakis, K., Irvine, S., Tripodi, N., & Feehan, J. (2023). The impact of motivational interviewing on behavioural change and health outcomes in cancer patients and survivors. A systematic review and meta-analysis. Maturitas, 170, 9-21. 10.1016/j.maturitas.2023.01.004 [DOI] [PubMed] [Google Scholar]
  15. Heidenreich, P. A., Bozkurt, B., Aguilar, D., Allen, L. A., Byun, J. J., Colvin, M. M., Deswal, A., Drazner, M. H., Dunlay, S. M., Evers, L. R., Fang, J. C., Fedson, S. E., Fonarow, G. C., Hayek, S. S., Hernandez, A. F., Khazanie, P., Kittleson, M. M., Lee, C. S., Link, M. S., . . . Yancy, C. W. (2022). 2022 AHA/ACC/HFSA Guideline for the management of heart failure: A report of the American College of Cardiology/American Heart Association Joint Committee on clinical practice guidelines. Circulation, 145(18), e895-e1032. 10.1161/CIR.0000000000001063 [DOI] [PubMed] [Google Scholar]
  16. Huang, H., Wang, H. H. X., Donaghy, E., Henderson, D., & Mercer, S. W. (2022). Using self‐determination theory in research and evaluation in primary care. Health Expectations, 25(6), 2700-2708. 10.1111/hex.13620 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Kang, H., Yang, T., Banu, N., Ng, S. W. T., & Lee, J. K. (2025). Exploring smart health wearable adoption among Singaporean older adults based on self-determination theory: Web-based survey study. JMIR Aging, 8(1), e69008. 10.2196/69008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, 863. 10.3389/fpsyg.2013.00863 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Li, X., Yang, S., Wang, Y., Yang, B., & Zhang, J. (2020). Effects of a transtheoretical model-based intervention and motivational interviewing on the management of depression in hospitalized patients with coronary heart disease: A randomized controlled trial. BMC Public Health, 20(1), 420. 10.1186/s12889-020-08568-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Makita, S., Yasu, T., Akashi, Y. J., Adachi, H., Izawa, H., Ishihara, S., Iso, Y., Ohuchi, H., Omiya, K., & Ohya, Y. (2022). JCS/JACR 2021 guideline on rehabilitation in patients with cardiovascular disease. Circulation Journal, 87(1), 155-235. 10.1253/circj.CJ-22-0234 [DOI] [PubMed] [Google Scholar]
  21. Markland, D., & Tobin, V. (2004). A modification to the behavioural regulation in exercise questionnaire to include an assessment of amotivation. Journal of Sport and Exercise Psychology, 26(2), 191-196. 10.1123/jsep.26.2.191 [DOI] [Google Scholar]
  22. Miller, W. R., & Rollnick, S. (2012). Motivational Interviewing: Helping People Change. Guilford Publications. https://books.google.co.th/books?id=o1-ZpM7QqVQC [Google Scholar]
  23. Nogg, K. A., Vaughn, A. A., Levy, S. S., & Blashill, A. J. (2021). Motivation for physical activity among US adolescents: A self-determination theory perspective. Annals of Behavioral Medicine, 55(2), 133-143. 10.1093/abm/kaaa037 [DOI] [PubMed] [Google Scholar]
  24. Ntoumanis, N., & Moller, A. C. (2025). Self-determination theory informed research for promoting physical activity: Contributions, debates, and future directions. Psychology of Sport and Exercise, 102879. 10.1016/j.psychsport.2025.102879 [DOI] [PubMed] [Google Scholar]
  25. Sachdev, V., Sharma, K., Keteyian, S. J., Alcain, C. F., Desvigne-Nickens, P., Fleg, J. L., Florea, V. G., Franklin, B. A., Guglin, M., & Halle, M. (2023). Supervised exercise training for chronic heart failure with preserved ejection fraction: A scientific statement from the American Heart Association and American College of Cardiology. Circulation, 147(16), e699-e715. 10.1161/CIR.0000000000001122 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Samdal, G. B., Eide, G. E., Barth, T., Williams, G., & Meland, E. (2017). Effective behaviour change techniques for physical activity and healthy eating in overweight and obese adults; Systematic review and meta-regression analyses. International Journal of Behavioral Nutrition and Physical Activity, 14(1), 42. 10.1186/s12966-017-0494-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Su, G., Xu, Y., Xu, X., Xu, H., Lu, L., Marrone, G., Lindholm, B., Wen, Z., Liu, X., & Johnson, D. W. (2018). Association between reduced renal function and cardiovascular mortality in patients hospitalized with infection: A multi-center cohort study. European Journal of Internal Medicine, 57, 32-38. 10.1016/j.ejim.2018.06.017 [DOI] [PubMed] [Google Scholar]
  28. Van Rhoon, L., Byrne, M., Morrissey, E., Murphy, J., & McSharry, J. (2020). A systematic review of the behaviour change techniques and digital features in technology-driven type 2 diabetes prevention interventions. Digital Health, 6, 2055207620914427. 10.1177/2055207620914427 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Verloigne, M., De Bourdeaudhuij, I., Tanghe, A., D'Hondt, E., Theuwis, L., Vansteenkiste, M., & Deforche, B. (2011). Self-determined motivation towards physical activity in adolescents treated for obesity: An observational study. International Journal of Behavioral Nutrition and Physical Activity, 8(1), 97. 10.1186/1479-5868-8-97 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Warehime, S., Dinkel, D., Alonso, W., & Pozehl, B. (2020). Long-term exercise adherence in patients with heart failure: A qualitative study. Heart & Lung, 49(6), 696-701. 10.1016/j.hrtlng.2020.08.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Wongvibulsin, S., Habeos, E. E., Huynh, P. P., Xun, H., Shan, R., Porosnicu Rodriguez, K. A., Wang, J., Gandapur, Y. K., Osuji, N., & Shah, L. M. (2021). Digital health interventions for cardiac rehabilitation: Systematic literature review. Journal of Medical Internet Research, 23(2), e18773. 10.2196/18773 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Zhu, S., Sinha, D., Kirk, M., Michalopoulou, M., Hajizadeh, A., Wren, G., Doody, P., Mackillop, L., Smith, R., & Jebb, S. A. (2024). Effectiveness of behavioural interventions with motivational interviewing on physical activity outcomes in adults: Systematic review and meta-analysis. BMJ, 386, e078713. 10.1136/bmj-2023-078713 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Zou, Y., Janus, S., Du, J., Qu, N., Zuidema, K., Burger, H., Ahaus, K., Guo, Z., & Zuidema, S. (2025). Barriers and facilitators of cardiac rehabilitation in a middle-income country: A qualitative study from China. International Journal of Environmental Research and Public Health, 22(4), 574. 10.3390/ijerph22040574 [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.

Data Availability Statement

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

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