Summary of evidence on nutritional adherence in cardiac rehabilitation among patients with coronary heart disease after PCI based on mobile healthcare systems

Abstract

Objective

This study aimed to summarize the evidence on nutritional adherence in cardiac rehabilitation among patients with coronary heart disease after PCI based on mobile healthcare systems, providing guidance for clinical practice.

Methods

Researchers systematically searched domestic and international databases based on the “5S” pyramid model. Search types included clinical decisions, guidelines, expert consensuses, evidence summaries, systematic reviews, meta-analysis and RCTs. The search scope covered the period from the database’s inception to August 2025(updated on January 2, 2026). For quality assessment, four researchers used the AGREE II assessment guidelines. Two researchers independently assessed expert consensuses, systematic reviews, and RCTs using the evaluation criteria of the JBI Centre for Evidence-Based Healthcare (2016).

Results

This evidence summary included 12 studies: one clinical decision, two guidelines, four expert consensuses, three systematic reviews, and two RCTs. A total of 26 best evidence items were extracted and categorized into six areas: management team and services of mobile healthcare systems, types and preliminary preparations of mobile healthcare systems, functions of mobile healthcare systems, nutritional screening, assessment, monitoring and data upload of nutritional adherence, and virtual classroom.

Conclusions

This study summarized the evidence on nutritional adherence in cardiac rehabilitation among patients with coronary heart disease after PCI based on mobile healthcare systems. The findings provide clinical reference for healthcare workers to enhance nutritional adherence in cardiac rehabilitation using mobile healthcare systems.

Keywords

after PCI for coronary heart disease mobile healthcare systems cardiac rehabilitation nutritional adherence evidence summary

Introduction

Approximately 252 million people worldwide suffer from coronary heart disease, the most common type of cardiovascular disease.¹ According to the China Cardiovascular Health and Disease Report (2023), the number of patients with coronary heart disease in China has reached 11.39 million, with both incidence and mortality rates continuing to rise.² Percutaneous coronary intervention (PCI) is an effective treatment for coronary heart disease, improving myocardial perfusion and dilating narrowed arterial lumens. However, it cannot reverse or halt the pathological progression of coronary atherosclerosis.³ Therefore, primary focus should be on controlling risk factors for cardiac rehabilitation after PCI.

Cardiac rehabilitation is based on a comprehensive medical assessment and provides comprehensive management services for patients with cardiovascular disease through five core components, encompassing physiological, psychological, and social aspects.⁴ Among these, nutrition is a core component of cardiac rehabilitation.⁵ Nutrition, as a modifiable cardiovascular risk factor,⁶ can be improved by intervention, and its effectiveness largely depends on patients’ adherence. Good nutritional adherence reduces cardiovascular mortality. People with higher nutritional adherence exhibit a 14%–28% reduction in cardiovascular death rates.⁷ Although the benefits of nutritional adherence have been widely demonstrated. However, in cardiac rehabilitation studies, the issue of nutritional adherence is often overlooked. Study showed that nutritional adherence of patients after PCI during stage I cardiac rehabilitation was less than 32%.⁸ Poor nutritional adherence further affects blood pressure and lipid levels, leading to adverse outcomes.⁹ Therefore, improving nutritional adherence in cardiac rehabilitation among patients with coronary heart disease after PCI is of great significance and urgent.

Traditional nutritional management is limited by the number of follow-up visits, and patients cannot receive timely feedback between two counseling sessions, affecting adherence and the sustainability of dietary behavior changes.¹⁰ However, mobile healthcare systems change this situation. Mobile healthcare systems are regarded as an effective tool for promoting behavioral changes in patients with coronary heart disease. A previous randomized controlled trial found that SMS intervention based on mobile healthcare systems improved dietary behaviors in patients with coronary heart disease.¹¹ Mobile healthcare systems enable patients to actively monitor their health and take part in self-management while empowering healthcare workers to provide personalized, continuous care.¹²

In past studies, exercise adherence was frequently tracked using digital technologies. However, there are few research investigating nutritional adherence in cardiac rehabilitation for patients with coronary artery disease after PCI using mobile healthcare systems. The existing evidence is somewhat scattered, and research focuses on different areas. Furthermore, changing dietary behaviors faces challenges, influenced by factors such as the complexity of dietary recording, as well as social and cultural aspects. These issues require systematic research to synthesize the evidence. This study aims to summarize evidence using an evidence-based method and make suggestion for healthcare workers to utilize mobile healthcare systems for nutritional adherence management.

Methods

This evidence summary protocol was registered with the Center for Evidence-Based Nursing at Fudan University (registration number:ES20259479).

Raising questions

This study employed the PIPOST model proposed by the JBI Evidence-Based Nursing Center at Fudan University to formulate evidence-based questions. Population (P): Patients with coronary heart disease after PCI; Intervention(I): Provide nursing interventions related to nutritional adherence in cardiac rehabilitation using mobile healthcare systems; Professional (P): healthcare workers; Outcome (O): nutritional adherence, completion rate, participation rate, etc. Setting (S): Home, community or outpatient clinic; Type of evidence (T): Clinical decisions, Guidelines, expert consensuses, evidence summaries, systematic reviews, meta-analysis, randomized controlled trials (RCTs).

Search strategy

According to the “5S” pyramid model,¹³the search was conducted from top to bottom. This study prioritizes high-level evidence from the top tier of the evidence pyramid, including clinical decision support systems, comprehensive evidence summaries, evidence-based guidelines, and systematic reviews. Where high-level evidence is insufficient or not fully applicable to the research question, primary studies are included to supplement the evidence. The search databases were as follows: BMJ Best Practice, Up To Date, JBI Evidence-Based Healthcare Database, Guidelines International Network (GIN), New Zealand Guideline Group (NZGG), National Institute for Health and Care Excellence (NICE), Scottish Intercollegiate Guidelines Network (SIGN), National Guidelines Clearinghouse (NGC), American Heart Association, Japan Heart Association, Cochrane Library, PubMed, Web of Science, Embase, CINAHL, Yimaitong, CKNI, Wanfang, VIP, Sinomed. Search term:(coronary heart disease OR acute myocardial infarction OR acute coronary syndrome OR percutaneous coronary intervention OR PCI) AND (cardiac rehabilitation OR diet OR nutrition OR food OR dietary adherence OR dietary management OR nutrition prescription OR dietary intervention) AND (guideline OR expert consensus OR clinical decision OR evidence summary OR systematic review OR meta-analysis OR randomized controlled trial OR RCT). This study adopted search strategy that combines medical subject headings and free-text terms, which is adjusted according to the search syntax of different databases. The search strategies for each database were presented in the supplementary material. The search time ranged from the database’s creation to August 2025. (updated on January 2, 2026).

Inclusion and exclusion criteria

Inclusion criteria: Patients aged ≥18 years with coronary heart disease after PCI; The study content focuses on measures to improve nutritional adherence in cardiac rehabilitation using mobile healthcare systems; The types of literature include clinical decisions, guidelines, expert consensuses, evidence summaries, systematic reviews, meta-analyses, and RCTs; Chinese or English literature. Exclusion criteria: Duplicate publications or references without full-text access; Literature that did not pass quality assessment; Meeting summaries or meeting reports; Expert consensus statements that have not been revised or updated.

Literature screening

Two trained researchers independently screened the literature. In the event of any disagreement, the matter will be discussed with a third researcher until a consensus is reached. This study used EndNote 21 to manage the literature. After removing duplicate references, the researchers conducted an initial screening of the article titles and abstracts, then read the full texts and included those that met the predefined inclusion and exclusion criteria.

Quality evaluation

Quality evaluation criteria varied according to the type of research and are listed as follows: Guideline quality was assessed independently by four researchers using the Appraisal of Guidelines for Research and Evaluation II (AGREE II),¹⁴which contains six domains, 23 items, and two overall evaluation items rated on a 7-point scale. If the percentage of standardization in all fields exceeds 60%, it is classified as grade A; If the standardized percentage in ≥3 domains falls between 30% and 60%, it is classified as grade B; If the standardized percentage in three or more domains is less than 30%, it is classified as grade C. Two researchers evaluated expert consensuses, systematic reviews, and RCTs using the JBI Centre for Evidence-Based Healthcare (2016) evaluation tool standards.¹⁵ The quality of the included studies was independently assessed by researchers who had completed evidence-based nursing training. In cases of disagreement, a third researcher was consulted to reach consensus.

Evidence summary and grade

Evidence was independently extracted and summarized by two researchers, with a third verifying the content. Evidence summary adheres to the following principles: When evidence content is complementary, it should be combined. When evidence content is conflicting, prioritize the highest-quality, most recently published literature. Evidence grading using the JBI Evidence Pre-Grading System (2014).¹⁶ This tool categorizes evidence levels into five tiers, with Level 1 the highest and Level 5 the lowest.

Results

Results of literature screening and basic characteristics of included studies

The initial search yielded a total of 4,990 studies, of which 1 was identified through snowball sampling. After removing 1,106 duplicate records, 3,884 studies remained. After reading the titles and abstracts, 78 studies remained. After full-text review,12 studies were eventually included: one clinical decision, two guidelines, four expert consensuses, three systematic reviews, and two RCTs. The screening process for this study is presented as a PRISMA flowchart in Figure 1. Table 1 shows the basic characteristics of the included research.

Figure 1.

PRISMA flowchart for literature screening.

Table 1.

Basic information of included studies(n=12).

Author(year)	Country	Source	Title	Type
Braun et al.¹⁷ (2025)	America	Up to date	Cardiac rehabilitation programs	clinical decision
Chinese medical association, society of physical medicine and rehabilitation¹⁸ (2024)	China	Yimaitong	Evidence-based practice guidelines for coronary heart disease rehabilitation in China(二)	guideline
Bigot et al.¹⁹ (2024)	French	Yimaitong	Cardiac rehabilitation recommendations of the group exercise rehabilitation sports prevention (GERS-P) of the French society of cardiology:2023 update	guideline
Zhang SS et al.¹⁰ (2026)	China	Yimaitong	Chinese expert consensus on the application of digital health in cardiac rehabilitation	expert consensus
Chinese expert consensus working group on home-based rehabilitation for patients with cardiovascular diseases²⁰(2022)	China	Yimaitong	Expert consensus on home rehabilitation for patients with cardiovascular disease in China	expert consensus
Cardiovascular professional committee of Beijing nursing association²¹ (2022)	China	Wang fang	Expert consensus on health education prescription nursing for cardiac rehabilitation in patients with coronary heart disease.	expert consensus
Cardiac interventional therapy and rehabilitation professional committee of Chinese association of rehabilitation medicine et al.²² (2022)	China	CNKI	Consensus of experts on integrated traditional Chinese and Western medicine cardiac rehabilitation after percutaneous coronary intervention	expert consensus
Vanzella et al.²³ (2021)	Brazil	Web of Science	Barriers and facilitators to participant adherence of dietary recommendations within comprehensive cardiac rehabilitation Programmes: a systematic review	systematic review
Kebapci et al.²⁴ (2020)	Turkish	Pubmed	Effects of eHealth-based interventions on adherence to components of cardiac rehabilitation: a systematic review	systematic review
Thom et al.²⁵ (2023)	Canada	pubmed	Impact of mHealth interventions on supporting dietary adherence in cardiovascular disease: a systematic review	systematic review
Cruz-Cobo et al.²⁶ (2024)	Spain	Cochrane	Efficacy of a mobile health app (eMOTIVA) regarding compliance with cardiac rehabilitation guidelines in patients with coronary artery disease: randomized controlled clinical trial	RCT
Bernal-Jiménez et al.²⁷ (2024)	Spain	Web of science	Effectiveness of an interactive mHealth app (EVITE) in improving lifestyle after a coronary event: randomized controlled trial	RCT

Quality evaluation results of literature

Quality evaluation result of clinical decision

This study included one clinical decision¹⁷ from UpToDate, which was regarded as high quality and was directly included in the analysis.

Quality evaluation results of guidelines

This study included two guidelines.^18,19 One guideline¹⁸ has a recommendation level of A, with an intraclass correlation coefficient of 0.963. Another guideline¹⁹ has a recommendation level of B, with an intraclass correlation coefficient of 0.960. Detailed results are provided in supplementary table 1.

Quality evaluation results of expert consensuses

This study included four expert consensuses.^10,20–22 Four expert consensuses^10,20–22 were assessed as “No” for item 6, and “Yes” for all other items. The overall quality of the studies was high, making it appropriate for inclusion. Detailed results are provided in supplementary table 2.

Quality assessment results of systematic reviews

This study included three systematic reviews.^23–25 One systematic review²⁵ was rated “Yes” for all items. Two systematic reviews^23,24 were assessed as “No” for item 9, and “Yes” for all other items. The included studies were of good quality and deed eligible for inclusion. The detailed results are shown in supplementary table 3.

Quality assessment results of RCTs

This study included two RCTs.^26,27 Two RCTs^26,27 were assessed as “unclear” for item 2, “No” for item 4, “unclear” for item 5 and “No” for item 8. The studies were of high overall quality and were considered appropriate for inclusion. Detailed results are provided in supplementary table 4.

Summary of evidence

This study summarized 26 pieces of evidence across six dimensions: management team and services of mobile healthcare systems, types and preliminary preparations of mobile healthcare systems, functions of mobile healthcare systems, nutritional screening, assessment, monitoring and data upload of nutritional adherence, and virtual classroom. As shown in Table 2.

Table 2.

Summary of best evidence on nutritional adherence in cardiac rehabilitation among patients with coronary heart disease after PCI based on mobile healthcare systems.

Category	Content of evidence	Level
Management team and services of mobile healthcare systems	1.The various components of the cardiac rehabilitation program are delivered by a specialized team comprising physicians, nurses, exercise specialists, nutritionists, and psychologists.¹⁷	5b
	2.It is recommended that registered dietitians develop personalized dietary plans based on patients’ dietary preferences, cultural background and body weight et al.¹⁷	4b
	3.To provide patients (appropriate family members/spouses) with guidance on dietary goals and methods for achieving them,^17,20 and adjust dietary goals based on individual health conditions.¹⁰	5b
	4. Healthcare workers provider for patients with standard salt spoons, oil can, and other nutritional tools at discharge.²⁰	5b
	5.Healthcare workers conduct regular follow-ups, contacting patients by phone or text message every 1–2 weeks to improve adherence.^10,20,22	5b
Types and preliminary preparations of mobile healthcare systems	6.It is suggested to adopt mobile healthcare and web-based applications as platforms for managing nutritional adherence in cardiac rehabilitation.^24,25	1a
	7.Mobile healthcare systems provide privacy access. Patients log in with a username and password. Data are stored anonymously in a web-based system with automatic backup.^26,27	1c
	8.Patients sign informed consent forms before enrollment.^26,27	1c
	9.It is recommended that patients complete online training on mobile healthcare systems before enrollment to ensure that patients or their family members are proficient in its various functions.²⁷	1c
Functions of mobile healthcare systems	10.Family members monitor patients’ dietary habits to reduce unhealthy preferences and improve nutritional adherence.²⁰	5b
	11.Mobile healthcare systems provide consultation services, allowing patients to contact nurses through this section to address any questions.^10,26,27	1c
	12.When a patient’s blood pressure, weight, or health status changes abnormally, the mobile healthcare systems will display a red alert.^26,27	1c
	13.It is recommended that a mobile healthcare systems deliver one randomly generated health habit reminder per week at a fixed time via pop-up notifications.^26,27	1c
	14.It is suggested to use different colors in the mobile healthcare systems indicate the patient’s progress toward goals and areas needing improvement: green indicates the goal has been met, yellow indicates the goal has been partially met, and red indicates the goal has not been met.^26,27	1c
	15.It is recommended that personalized feedback be provided based on the patient’s progress toward last week’s goals. This feedback should appear as a pop-up window via the mobile healthcare systems at a set time each week.²⁷	1c
	16.The mobile healthcare systems use gamification to award different levels of badges based on patients’ dietary adherence during follow-up visits, thereby encouraging them to improve their dietary adherence.²⁶	1c
	17. It is suggested that the mobile healthcare systems generate detailed progress charts weekly for self-comparison.^26,27	1c
Nutritional screening	18. Assessing patient nutritional status using the Nutritional Risk Screening NRS2002, healthy eating index and biological sample indicators.^18,20	1a
Assessment, monitoring and data upload of nutritional adherence	19. Assessing factors affecting patient nutritional adherence: individual level (economic status, personal reasons, time constraints, psychological factors, knowledge, age, etc.), family level (family support), social level (dietary habits, social support).²³	1
	20.The most commonly used method for assessing nutritional adherence is self-reported data:24-hour recall method, dietary diary, photo upload, food frequency questionnaire, and mediterranean diet adherence scale.^20,26,27	1c
	21.Indicators of nutritional adherence: total daily calorie intake, cholesterol, sodium, and nutrient intake; dietary habits (including consumption of whole grains, fish, vegetables, and fruits); frequency of main meals and snacks; frequency of eating out; and alcohol consumption.¹⁷	5b
	22.It is recommended to patients record their weight, BMI, blood pressure, blood sugar, blood lipid and upload it to the system.^26,27	1c
Virtual Classroom	23.It is suggested to provides downloadable and printable materials, such as weekly menus, nutrition-related charts (food pyramids) and health education information.²⁶	1c
	24. Educational content: mediterranean diet, heart-healthy diet, evidence-based dietary recommendations for secondary prevention of cardiovascular disease, reading nutrition labels, identifying hidden salt et al.^18,19 The content is simplified and presented in plain, easy-to-understand language.¹⁰	1b
	25.Teaching methods: workshops, hands-on demonstrations, small-group instruction, science outreach activities, and videos.²¹	5b
	26. It is recommended to set up a questionnaire to assess patient learning outcomes.²⁶	1c

Discussion

Management team and services of mobile healthcare systems

Coronary heart disease is frequently accompanied by conditions such as hypertension and hyperglycemia, leading to a clinical course that is complex and prone to change. Therefore, it requires collaboration across disciplines.²⁸ Mobile healthcare systems provide a platform for enhancing communication and information sharing among medical teams, thereby fostering more informed decisions and delivering high quality care services.²⁹ Nutrition plans formulated by dietitians are a crucial component in assessing the quality of cardiac rehabilitation. When formulating a nutrition plan, dietitians should take into account the patient’s dietary culture, preferences, and set specific, achievable goals to enhance the patient’s sense of self-efficacy, thereby improving nutritional adherence.³⁰ The achievement of more significant results is achieved by setting goals that include short-term and long-term aims.³¹ It is worth noting that patients often find it difficult to control their salt intake at home. To improve the accuracy of salt intake assessments, experts recommend providing patients with standardized salt spoons at discharge. This recommendation is particularly pertinent given findings by Zhang et al.³² that only 10.7% of Chinese residents use salt spoons, highlighting a significant opportunity for educational intervention and tool distribution by the mobile healthcare team. In addition, telephone follow-ups are an important way to reinforce patients’ target behaviors, thereby promptly identify and address potential barriers that arise during the implementation of interventions. However, this method involves higher labor costs. It is necessary to explore AI-based automated follow-up systems to optimize resource allocation.

Types and preliminary preparations of mobile healthcare systems

Mobile healthcare systems take various forms, including remote monitoring, telephone, and telemedicine. Mobile healthcare and web-based applications have been shown to improve nutritional adherence.^24,25 Data security, privacy and confidentiality remain the primary concerns for patients using mobile healthcare systems.³³ According to statistics,³⁴ between 2009 and 2024, the United States experienced a cumulative total of 392 million healthcare data breaches. To reduce potential risks, it is recommended that mobile healthcare systems strengthen user security management and system-level data protection. Future research should validate the effectiveness of artificial intelligence technologies in identifying and dynamically managing potential data risks, with the aim of establishing a data security protection system. Additionally, we respect patients’ autonomy and protect them from harm. Therefore, patients sign an informed consent form before enrollment. To further enhance patients’ understanding of informed consent content, researchers use digital tools such as videos and interactive media.³⁵ For patients, mobile healthcare systems are new tool. Therefore, patients should be trained via pre-recorded online courses before using it to ensure they are proficient in its features.

Functions of mobile healthcare systems

The functions of mobile healthcare systems are monitoring, consultation, alerts, reminders, identification, feedback, rewards, and charts. Patients are more likely to choose unhealthy foods on weekdays or when dining out with friends.²³ Using the mobile healthcare systems, family members can remotely monitor patients’ dietary behaviors, provide behavioral guidance and emotional support to promote nutritional compliance. Family members or patients can also consult experts for personalized nutrition advice. Research shows that personalized dietary counseling improves metabolic outcomes and may lower risk of complication.³⁶ Indicators such as blood pressure, blood glucose, and body weight are closely associated with the occurrence of adverse cardiovascular events.³⁷ For these indicators, a tiered alert system is recommended to accurately identify high-risk patients by setting clear thresholds.³⁸ However, warning systems that rely on fixed thresholds have limitations in complex data analysis and struggle to capture dynamic changes. In comparison, machine learning algorithms have better predictive accuracy and are more effective at identifying high-risk patients.^39,40 Reminders assist patients focus on nutritional issues, and make healthier food choices, thereby reducing risk.⁴¹ However, frequent reminders might cause information overload and notification fatigue, reducing patient compliance.⁴² Thus, it is recommended that mobile healthcare systems reminder be scheduled weekly, with automatic delivery within a fixed time window to improve read rates and avoid excessive disruption. Reminders also help reinforce patients’ behavioral memories and improve nutritional adherence,⁴³ thereby promoting the achievement of dietary goals. To make progress toward goals more easily identifiable, a striking color scheme is used, such as traffic light color coding. Moreover, the mobile healthcare systems are suggested to give kind and encouraging comments weekly based on the patients’ nutrition target achievement status. Such feedback enhances the emotional bond between the participants and target behavior, forming a positive feedback loop that fosters compliance.⁴⁴ However, current evidence on feedback frequency is limited.⁴⁵ Future study will explore the dose-response relationship between feedback frequency and nutritional adherence. Patients may feel annoyed if feedback is frequent. Gamification features make the process more entertaining, increasing adherence and motivating patients to improve their nutrition habits.⁴⁶ Research has found that coupons, points, and ranking systems are favored by patients.⁴⁶ However, the effectiveness of gamification shows differences in various age groups.⁴⁷ More importantly, overreliance on external rewards may undermine patients’ intrinsic motivation, affecting the long-term maintenance of behavioral change.⁴⁴ When developing gamification, it is recommended to adjust game elements based on age differences and emphasize the cultivation of intrinsic motivation to promote long-term adherence by strengthening autonomy-based support, providing continuous feedback, and setting phased goals. In addition, presenting information clearly and effectively is important to help patients understand it. Visualization enables complex data to be presented in a more intuitive way. Among other, charts are one of the most used visualizing methods with 24%.⁴⁸ Vibrant colors attract attention and enhance the readability of chart information.⁴⁸ The technology acceptance model suggests that perceived usefulness and perceived ease of use increase patients’ willingness to adopt mobile healthcare systems. Visual design reduces the difficulty of understanding information and enhances visual intuitiveness, thereby improving patients’ perceived usefulness and perceived ease of use.⁴⁹ For the design of mobile healthcare systems, it is important to consider the preferences of colors and styles of charts for patients from different age groups.⁵⁰

Nutritional screening, assessment, monitoring and data upload of nutritional adherence

Malnutrition is associated with adverse clinical outcomes, including higher morbidity and mortality, prolonged hospital stays, and an increased risk of complications.⁵¹ A study reported that approximately two-thirds of elderly patients undergoing PCI exhibit varying degrees of malnutrition.⁵² Early identification and intervention of patients’ nutritional status play a crucial role in improving prognosis. Therefore, it is suggested that patients undergo nutritional screening after PCI to assess their nutritional status. The European Society for Enteral Nutrition recommends the Nutritional Risk Screening Tool 2002 (NRS-2002) as the preferred screening tool due to its ease of use, and it has been endorsed by multiple expert consensus groups.^20,53,54 Biological parameters such as serum albumin and urine analysis provide a more objective reflection of a patient’s nutritional status and are relatively reliable, serving as an important supplement to screening results.⁵⁵ In addition, nutritional adherence in cardiac rehabilitation patients is influenced by a variety of personal, family, and social factors, so we ought to take these factors into account when implementing interventions. To gain a more comprehensive understanding of a patient’s nutritional adherence, multiple indicators are usually required for assessment. Currently, the most commonly used assessment methods rely primarily on subjective reports, such as dietary diary, 24-hour recall, and questionnaire surveys.⁵⁶ These methods are low-cost but are affected by participants’ memory biases and subjective judgments, limiting data reliability. Among them, the dietary diary provides detailed information, but the recording process is time-consuming, resulting in low participant acceptability.⁵⁷ Standardized questionnaires are commonly used to evaluate nutritional adherence. However, existing scales are designed for the general population, and there is currently no scale specifically for assessing nutritional adherence in cardiac rehabilitation patients. With the development of machine learning and image recognition technologies, artificial intelligence plays a significant role in assessing nutritional intake and adherence.⁵⁸ Participants use their phone cameras to take photos. Food features are extracted by AI technology, which then utilizes web databases to identify food types, portion sizes, and calorie counts, making dietary records more convenient and efficient.⁵⁹ But the accuracy of AI recognition of food still needs further study.

Virtual classroom

Research indicates that patients recovery to unhealthy eating habits six months after completing a cardiac rehabilitation program.³⁶ Given this, it is recommended that mobile healthcare establish a virtual classroom and provide downloadable educational materials to enable patients to learn about nutrition at any time. Kumar et al. confirmed that virtual education enhances adherence to the Mediterranean diet.⁶⁰ Moreover, current health education lacks theoretical guidance to explain changes in nutritional adherence behavior. The Health Belief Model is a theoretical framework for promoting behavioral change in health. It systematically explains the relevant behavioral mechanisms, with its core components being perceived susceptibility, perceived severity, perceived benefits, perceived barriers, behavioral cues, and self-efficacy.⁶¹ Therefore, virtual classroom design is recommended to combine the Health Belief Model. Additionally, nurses are the primary providers of nutrition education.⁶² Educational content is evidence-based, ensuring scientific rigor and standardization of information. But depending simply on static educational resources may not be sufficient to meet patients’ diverse needs. We employ more dynamic and interactive digital education strategies, such as using short animated videos to improve understanding for people with low health literacy, incorporating quizzes into mobile healthcare to reinforce knowledge retention, and implementing AI-powered chatbots to provide personalized nutrition counseling around the clock.

Limitations

This study has the following limitations: First, because most of the evidence comes from English-language literature, there are differences between China’s dietary patterns and those of Western countries, as well as differences in cultural backgrounds.

The Chinese population generally has a high sodium intake.⁶³ These differences in dietary patterns may reduce the applicability of the Mediterranean diet in our country.

Furthermore, despite the high penetration rate of smartphones. Yet, the prevalence of coronary heart disease is relatively high among the elderly. The group lacks digital health literacy and may have reservations about recommendations regarding mobile healthcare systems, which affect adherence to the intervention.⁶⁴ Therefore, during the process of clinical translation, medical professionals are advised to adapt the protocol to local conditions by taking into account the characteristics of the Chinese diet, the patients’ specific circumstances, and expert judgment in order to enhance its feasibility and applicability. Secondly, the limited number of studies and RCTs may restrict the generalizability of the conclusions. Furthermore, due to significant heterogeneity among the studies (interventions and outcome measures), this study did not conduct a meta-analysis. Further large-scale randomized controlled trials and meta-analysis are needed in future research. Thirdly, although this study has taken steps to control for methodological bias, there remains a risk of publication bias.

Conclusions

This study summarized the evidence on nutritional adherence in cardiac rehabilitation among patients with coronary heart disease after PCI based on mobile healthcare systems. The evidence summary provides recommendations for clinical practice guidance for healthcare workers. However, the feasibility and effectiveness of these strategies still need to be validated in clinical practice.

Supplemental material

Supplemental material - Summary of evidence on nutritional adherence in cardiac rehabilitation among patients with coronary heart disease after PCI based on mobile healthcare systems

Supplemental material for Summary of evidence on nutritional adherence in cardiac rehabilitation among patients with coronary heart disease after PCI based on mobile healthcare systems by Yaqin Hu, Dongyan Lu, Jinghua Yi, Mengying Wang, Jingya Liang, and Jing Li in Digital Health.

Footnotes

Acknowledgements

We extend our gratitude to all experts who took the time to guide this study.

ORCID iD

Yaqin Hu

Author contributions

HYQ: Drafting, literature search, assessing the quality of the literature, extracting and grading evidence, reviewing.

LDY: Literature search, assessing the quality of the literature, extracting and grading evidence.

YJH: Assessing the quality of the literature, extracting and grading evidence.

WMY: Assessing the quality of the literature.

LJY: Assessing the quality of the literature.

LJ: Evidence extraction and grading, methodological guidance, review.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the Lishui Municipal Science and Technology Bureau Public Welfare Project (2024GYX54).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Guarantor

Guarantor LJ.

Supplemental material

Supplemental material for this article is available online.

References

Martin

Aday

Allen

, et al. Correction to: 2025 heart disease and stroke statistics: a report of US and global data from the American Heart Association. Circulation 2025; 151: e1096e1096. https://doi.org/10.1161/cir.0000000000001345

Liu

Yang

, et al. Key points from the 2023 China cardiovascular health and disease report. China J Cardiovasc 2024; 29: 305–324.

Khan

Ludman

. Percutaneous coronary intervention. Medicine 2022; 50: 437–444. https://doi.org/10.1016/j.mpmed.2022.04.008

Liu

Zhao

Shen

, et al. Advances in theories and models of cardiac rehabilitation after acute myocardial infarction: A narrative review. Medicine 2024; 103: e39755. https://doi.org/10.1097/md.0000000000039755

Vassiliou

Tsampasian

Abreu

, et al. Promotion of healthy nutrition in primary and secondary cardiovascular disease prevention: a clinical consensus statement from the European Association of Preventive Cardiology. Eur J Prev Cardiol 2023; 30: 696–706. https://doi.org/10.1093/eurjpc/zwad057

Small

Iglesies-Grau

Gariepy

, et al. Time-restricted eating: a novel dietary strategy for cardiac rehabilitation. Can J Cardiol 2023; 39: S384–S394. https://doi.org/10.1016/j.cjca.2023.09.017

Lichtenstein

Appel

Vadiveloo

, et al. 2021 dietary guidance to improve cardiovascular health: a scientific statement from the American Heart Association. Circulation 2021; 144: e472–e487. https://doi.org/10.1161/cir.0000000000001031

Chai

Fan

Gong

, et al. Adherence to phase I cardiac rehabilitation in post-PCI patients: a latent class analysis. Front Cardiovasc Med 2025; 12: 1460855. https://doi.org/10.3389/fcvm.2025.1460855

Daidone

Di Chiara

Del Cuore

, et al. Mediterranean diet and hypertension: relationship between adherence to a Mediterranean diet and arterial hypertension. BMC nutr 2025; 11: 44. https://doi.org/10.1186/s40795-025-01025-1

10.

Zhang

Cao

Shen

, et al. Chinese expert consensus on the application of digital health in cardiac rehabilitation. Journal of Rehabilitation, 2026. https://m.medlive.cn/guide/1/37668 (accessed 10 May 2026).

11.

Bae

Woo

Lee

, et al. mHealth interventions for lifestyle and risk factor modification in coronary heart disease: randomized controlled trial. JMIR mHealth uHealth 2021; 9: e29928. https://doi.org/10.2196/29928

12.

Ullah

Hamayun

Wahab

, et al. Smart technologies used as smart tools in the management of cardiovascular disease and their future perspective. Curr Probl Cardio 2023; 48: 101922. https://doi.org/10.1016/j.cpcardiol.2023.101922

13.

Xing

Zhou

Zhu

, et al. The evolution of the evidence pyramid: from the “6S” model to the “5S” model. J Nurs Train 2025; 40: 134–1348.

14.

The AGREE Next Steps Consortium . The AGREE II instrument. https://www.agreetrust.org/resource-centre/agree-ii/agree-ii-training-tools/ (2017), accessed 26 December 2025).

15.

Xiang

Yoon

Low

AHL

, et al. Approaches to improving symptom appraisal: a systematic literature review. BMJ open 2022; 12: e064521. https://doi.org/10.1136/bmjopen-2022-064521

16.

Wang

. JBI evidence pre-grading and recommendation rating system (2014 Edition). J Nurse Train 2015; 30: 964–967.

17.

Braun

Wenger

Rosenson

. Cardiac rehabilitation programs. Up To date. https://www.uptodate.com/contents/cardiac-rehabilitation-programs (2025), accessed 26 December 2025).

18.

Chinese medical association, society of physical medicine and rehabilitation . Evidence-based practice guidelines for coronary heart disease rehabilitation in China. China J Phys Med Rehabil 2024; 46: 577–586.

19.

Bigot

Guy

Monpere

, et al. Cardiac rehabilitation recommendations of the Group Exercise Rehabilitation Sports–Prevention (GERS-P) of the French Society of Cardiology: 2023 update. Arch Cardiovasc Dis 2024; 117: 521–541. https://doi.org/10.1016/j.acvd.2024.05.119

20.

Chinese expert consensus working group on home-based rehabilitation for pa-tients with cardio-vascular diseases . Expert consensus on home rehabilitation for patients with cardiovascular disease in China. China J Circul 2022; 37: 108–121.

21.

Cardiovascular professional committee of Beijing nursing association . Expert consensus on health education pre-scription nursing for cardiac rehabilitation in patients with coronary heart disease. China J Mod Nurs 2022; 28: 1121–1127.

22.

Cardiac interventional therapy and rehabilitation professional committee of Chinese association of rehabilitation medicine . World federation of Chinese medicine societies cardiac rehabilitation committee, Interventional cardiology committee of the world federation of Chinese medicine societies. Consensus of experts on integrated traditional Chinese and Western medicine cardiac rehabilitation after percutaneous coronary intervention. China J Rehabil Med 2022; 37:1517–1528.

23.

Vanzella

Rouse

Ajwani

, et al. Barriers and facilitators to participant adherence of dietary recommendations within comprehensive cardiac rehabilitation programmes: a systematic review. Public Health Nutr 2021; 24: 4823–4839. https://doi.org/10.1017/s1368980021002962

24.

Kebapci

Ozkaynak

Lareau

. Effects of eHealth-based interventions on adherence to components of cardiac rehabilitation: a systematic review. J Cardiovasc Nurs 2020; 35: 74–85. https://doi.org/10.1097/JCN.0000000000000619

25.

Thom

SJM

Sivakumar

Ayodele

, et al. Impact of mHealth interventions on supporting dietary adherence in cardiovascular disease: a systematic review. J Nutr Educ Behav 2023; 55: 419–436. https://doi.org/10.1016/j.jneb.2023.03.004

26.

Cruz-Cobo

Bernal-Jiménez

Germán

, et al. Efficacy of a Mobile Health App (eMOTIVA) Regarding Compliance with Cardiac Rehabilitation Guidelines in Patients with Coronary Artery Disease: Randomized Controlled Clinical Trial. JMIR mHealth uHealth 2024; 12: e55421. https://doi.org/10.2196/55421

27.

Bernal-Jimenez

Calle

Barrios

, et al. Effectiveness of an interactive mHealth app (EVITE) in improving lifestyle after a coronary event: randomized controlled trial. JMIR mHealth uHealth 2024; 12: e48756. https://doi.org/10.2196/48756

28.

Andersen

Jensen

Vestergaard

, et al. The multidisciplinary team in diagnosing and treatment of patients with diabetes and comorbidities: a scoping review. J multimorb comorbid 2023; 13: 26333565231165966. https://doi.org/10.1177/26335565231165966

29.

Teo

JYC

Wang

. Interdisciplinary collaboration for chronic illness prevention and management through digital health interventions. Interdiscip Nurs Res 2024; 3: 199–200. https://doi.org/10.1097/nr9.0000000000000073

30.

Buss

Varnfield

Harris

, et al. A mobile app for prevention of cardiovascular disease and type 2 diabetes mellitus: development and usability study. JMIR Hum Factors 2022; 9: e35065. https://doi.org/10.2196/35065

31.

Miller

Brickman

. A model of future-oriented motivation and self-regulation. Educ Psychol Rev 2004; 16: 9–33. https://doi.org/10.1023/b:edpr.0000012343.96370.39

32.

Zhang

Neupane

Zhao

, et al. Knowledge and practices related to salt consumption in China: findings from a national representative cross-sectional survey. J Hum Hypertens 2024; 38: 155–167. https://doi.org/10.1038/s41371-023-00861-7

33.

Alhammad

Alejano

Abd-Alrazaq

, et al. Patients’ perspectives on the data confidentiality, privacy, and security of mHealth apps: systematic review. J Med Internet Res 2024; 26: e50715. https://doi.org/10.2196/50715

34.

Koczkodaj

Nowacki

Pedrycz

, et al. Text mining analysis of over 392 million compromised healthcare records. Adv Sci Technol Res J 2025; 19: 73–81. https://doi.org/10.12913/22998624/195690

35.

Gesualdo

Daverio

Palazzani

, et al. Digital tools in the informed consent process: a systematic review. BMC Med Ethics 2021; 22: 18. https://doi.org/10.1186/s12910-021-00585-8

36.

Popiolek-Kalisz

Mazur

Perone

. The role of dietary education in cardiac rehabilitation. Nutrients 2025; 17: 1082. https://doi.org/10.3390/nu17061082

37.

Vaduganathan

Mensah

Turco

, et al. The global burden of cardiovascular diseases and risk: a compass for future health. J Am Coll Cardiol 2022; 80: 2361–2371. https://doi.org/10.1016/j.jacc.2022.11.005

38.

Indraratna

Biswas

McVeigh

, et al. A smartphone-based model of care to support patients with cardiac disease transitioning from hospital to the community (TeleClinical Care): pilot randomized controlled trial. JMIR mHealth uHealth 2022; 10: e32554. https://doi.org/10.2196/32554

39.

Dhanka

Kumar

Maini

, et al. Padding interpolation, median imputation, RobustScalar, and particle swarm optimization with heterogeneous classifiers: a robust combination for effective heart disease diagnosis. Front Med 2025; 12: 1721740. https://doi.org/10.3389/fmed.2025.1721740

40.

Dhanka

Kumar

Sharma

, et al. Comparative Analysis of Computational Intelligence Techniques for Predicting Coronary Artery Heart Disease: A Study on Logistic Regression, Support Vector Machine, and K-Nearest Neighbor. Int J Comput Inf Syst Ind Manag Appl 2025; 17: 565–582. https://doi.org/10.70917/ijcisim-2025-0036

41.

Garcia

. ChatGPT as a virtual dietitian: Exploring its potential as a tool for improving nutrition knowledge. Appl Syst Innov 2023; 6: 96. https://doi.org/10.3390/asi6050096

42.

Kim

Park

. Empowering individual preferences in mobile notifications: A balanced approach to cognitive load and information needs. IEEE Access 2025; 13: 44936–44950. https://doi.org/10.1109/access.2025.3549033

43.

Karagöz

Sarıca

. Patient compliance to dietary recommendations: tips and tricks to improve compliance rates. World J Urol 2023; 41: 1261–1268. https://doi.org/10.1007/s00345-023-04318-x

44.

Yang

Han

. Digital nudges and gamification: how the China state promotes citizens to manage chronic diseases in neighborhoods. BMC Public Health 2025; 25: 3928. https://doi.org/10.1186/s12889-025-25381-6

45.

Krukowski

Denton

König

. Impact of feedback generation and presentation on self-monitoring behaviors, dietary intake, physical activity, and weight: a systematic review and meta-analysis. Int J Behav Nutr Phys Act 2024; 21: 3–21. https://doi.org/10.1186/s12966-023-01555-6

46.

Berger

Jung

. Gamification preferences in nutrition apps: Toward healthier diets and food choices. Digital health 2024; 10: 20552076241260482. https://doi.org/10.1177/20552076241260482

47.

Kaißer

Kleffmann

Schaaff

. One Size Doesn't Fit All: Age-Aware Gamification Mechanics for Multimedia Learning Environments. In: 025 International Symposium on Multimedia (ISM). IEEE, 2025, pp. 182–189.

48.

Svenšek

Lorber

Gosak

, et al. The role of visualization in estimating cardiovascular disease risk: scoping review. JMIR public health surveill 2024; 10: e60128. https://doi.org/10.2196/60128

49.

Davis

. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 1989; 13: 319–340. https://doi.org/10.2307/249008

50.

Chan

Nwagu

Odenigbo

, et al. The shape of mobile health: a systematic review of health visualization on mobile devices. Int J Hum-Comput Interact 2025; 41: 1154–1172. https://doi.org/10.1080/10447318.2024.2313282

51.

Chen

Huang

, et al. Impact of the malnutrition on mortality in elderly patients undergoing percutaneous coronary intervention. Clin Interv Aging 2021; 16: 1347–1356. https://doi.org/10.2147/cia.s308569

52.

Zhu

Yang

Zhang

, et al. Comparative analysis of four nutritional scores predicting the incidence of MACE in older adults with acute coronary syndromes after PCI. Sci Rep 2023; 13: 20333. https://doi.org/10.1038/s41598-023-47793-3

53.

Selcuk

Arslan

Aydın

, et al. Which screening tool performs best in identifying malnutrition risk among hospitalized older adults with cardiovascular disease? A diagnostic accuracy study comparing six different screening tools with GLIM criteria. Eur Geriatr Med 2025; 16: 1493–1505. https://doi.org/10.1007/s41999-025-01187-y

54.

Cardiovascular disease prevention and rehabilitation professional committee of Chinese association of rehabilitation medicine . China heart alliance cardiovascular disease prevention and rehabilitation professional committee rehabilitation therapist nursing alliance. Consensus of Chinese experts on rehabilitation treatment and nursing practice for stable coronary heart disease. China J Intern Med 2023; 62:1406–1417.

55.

Serón-Arbeloa

Labarta-Monzón

Puzo-Foncillas

, et al. Malnutrition screening and assessment. Nutrients 2022; 14: 2392. https://doi.org/10.3390/nu14122392

56.

Cinthia

CBB

. Basic protocols in foods and nutrition. 1th.ed.: Humana Press, 2022.

57.

König

Attig

Franke

, et al. Barriers to and facilitators for using nutrition apps: systematic review and conceptual framework. JMIR mHealth uHealth 2021; 9: e20037. https://doi.org/10.2196/20037

58.

Dhanka

Kumar

Sharma

, et al. Advances in machine learning and deep learning for hormonal disorder diagnosis: an exhaustive review on PCOS, thyroid, and optimization techniques. Arch Comput Methods Eng 2026; 33: 2785–2829. https://doi.org/10.1007/s11831-025-10380-y

59.

Kassem

Beevi

Basheer

, et al. Investigation and assessment of AI’s role in nutrition—an updated narrative review of the evidence. Nutrients 2025; 17: 190. https://doi.org/10.3390/nu17010190

60.

Kumar

Hazra

Gharat

. Long-Term Outcomes of Digital Cardiac Rehabilitation. JACC: Asia 2025; 5: 1225–1238. https://doi.org/10.1016/j.jacasi.2025.08.007

61.

Jones

Smith

Llewellyn

. Evaluating the effectiveness of health belief model interventions in improving adherence: a systematic review. Health psychol Rev 2014; 8: 253–269. https://doi.org/10.1080/17437199.2013.802623

62.

James

Butler

Nichols

, et al. Provision of dietary education in UK-based cardiac rehabilitation: a cross-sectional survey conducted in conjunction with the British Association for Cardiovascular Prevention and Rehabilitation. Br Jo Nutr 2024; 131: 880–893. https://doi.org/10.1017/s0007114523002374

63.

Fang

, et al. Dietary sodium intake and food sources among Chinese adults: Data from the CNNHS 2010–2012. Nutrients 2020; 12: 453. https://doi.org/10.3390/nu12020453

64.

Liu

Zhao

, et al. Current status and influencing factors of digital health literacy among community-dwelling older adults in Southwest China: a cross-sectional study. BMC Public Health 2022; 22: 996. https://doi.org/10.1186/s12889-022-13378-4

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.17 MB