Developing a Tailored eHealth Self-Management Intervention for Patients With Chronic Kidney Disease in China: Intervention Mapping Approach

Introduction

Background

Chronic kidney disease (CKD) is a serious public health problem [1-5]. The burden of CKD is highest in low- and middle-income countries [6,7]. In China, around 132 million patients are affected by CKD—accounting for one-fifth of the global burden of CKD [8]. Interventions supporting self-management can improve patient self-management behaviors [9-11] and health outcomes [12] and even slow down disease progression [13-15]. Optimizing CKD self-management is vital to reduce disease burden and control health care expenditures [13].

eHealth-based interventions are being developed to support CKD self-management [16-18]. There is previous evidence suggesting that eHealth self-management interventions can promote healthy behaviors and improve the health outcomes of patients with CKD [19-21] and are feasible and acceptable for patients with CKD and health care professionals (HCPs) [22]. An example of a rigorously studied and effective eHealth CKD self-management intervention is Medical Dashboard (MD) [23-25]. MD was developed in the Netherlands, enabling patients and HCPs to monitor and track healthy behaviors and disease parameters. This platform has been used in the Kidney Diseases and Transplantation Outpatient Clinic at Leiden University Medical Center since February 2016. In a randomized controlled trial, the use of MD effectively improved patients’ adherence to sodium intake restriction and blood pressure control [23]. In addition, patients and HCPs were satisfied with the MD self-management intervention [24].

Improving CKD self-management using digital innovations has been named a top priority in China [26,27]. Policy makers and health care experts in China launched the national health strategy Healthy China 2030 [28]. This strategy describes eHealth technology as an essential pillar to improve disease self-management and the accessibility and cost-effectiveness of care in (rural) China—where >558 million people have access to a mobile phone. In rural areas in China, an estimated 8.6% of adults have CKD [29]. There is a lack of a strong primary care system in rural China to provide adequate health care for patients with CKD; most of the high-quality resources in medical care such as human capital and modern diagnostic and therapeutic technologies are concentrated in Chinese hospitals. The long distance to health care facilities is a substantial problem for patients with CKD in rural areas. eHealth interventions provide great potential to address these challenges, such as so-called “internet hospitals” that allow patients to receive high-quality care from a top-tier hospital from either their own home or a local clinic through a video or telephone connection [30]. In addition, patients with CKD and HCPs in China feel highly positive toward using eHealth to support CKD self-management [31]. Thus, there is a high need and significant momentum for implementing effective eHealth-based interventions—such as MD—to support CKD self-management in China.

The implementation of self-management interventions should take into account the patients’ needs regarding interventions or the fact that self-management occurs in a social context [32]. Hence, applying a “one-size-fits-all” approach and simply translating effective CKD self-management interventions to a different context is not sufficient. According to the recently published SETTING (Setting Exploration Treasure Trail to Inform Implementation Strategies) tool, mapping local context characteristics and needs before translating health interventions is essential for successful implementation [33]. By doing so, context-specific, tailored intervention development and adaptation can be performed, ensuring an optimal fit with the local context.

Implementation science provides guidance on assessing and integrating knowledge about local contexts into the eHealth intervention development [34,35]. For instance, the intervention mapping (IM) method [36] ensures a theory-based approach from the recognition of local needs to the identification of tailored intervention and implementation strategies and has been used to develop and adapt evidence-based interventions [37,38]. However, evidence regarding how IM may guide the translation of a complex eHealth self-management intervention for patients with CKD from high-resource to low-resource settings is still limited.

eHealth self-management interventions have the potential to fundamentally improve the quality of life and health outcomes of patients with CKD in China. The MD-based self-management intervention has been researched extensively and proven effective. In addition, it matches the needs identified in our earlier studies among Chinese patients with CKD [31,39]. Moreover, we are in close contact with the developers and, therefore, able to discuss amendments with the team and allowed to upscale a tailored version of the intervention to the Chinese context. Therefore, we aimed to use IM to develop a tailored eHealth self-management intervention for patients with CKD in China based on the Dutch MD intervention (clinical trial registration: ClinicalTrials.gov NCT04212923). Further details on the methodology and design of the extensive study can be found elsewhere [40].

Objectives

In concordance with IM, this study comprised two parts: (1) phase 1: needs, beliefs, and perceptions (step 1 of IM)—examine the needs, beliefs, and perceptions of both patients with CKD and HCPs regarding CKD self-management and related eHealth interventions; and (2) phase 2: intervention and implementation development and planning (steps 2-5 of IM)—complement the core components of the MD self-management intervention for patients with CKD and adapt them to the Chinese context.

Methods

Overview

IM consists of six steps: (1) a needs assessment, (2) preparation of change objective matrices, (3) selection of theory-informed intervention methods and strategies, (4) development of a tailored intervention, (5) implementation, and (6) evaluation plans. For our study, steps 1 to 5 were completed between 2017 and 2023. The tasks performed to complete each step are described in the following paragraphs.

Phase 1: Needs, Beliefs, and Perceptions

Intervention Monitoring Group

First, an intervention monitoring group including both Dutch and Chinese experts and other key stakeholders was established. This group consisted of 2 researchers, 1 nephrologist, 1 nurse in CKD practice, and 1 implementation specialist. The expert group has ample experience with CKD care and implementing (eHealth) self-management interventions. The intervention monitoring group discussed the progress and the execution of major steps, such as the needs assessment and intervention development.

Identify Key Factors Influencing the (Potential) Implementation of eHealth CKD Self-Management Interventions

Evidence in Previous Studies

Evidence regarding key factors influencing the implementation of eHealth CKD self-management interventions was gathered from our systematic review [21] and qualitative studies conducted in the Chinese context [31,39].

A systematic review was conducted to summarize the evidence regarding the implementation and effectiveness of eHealth self-management interventions for patients with CKD. A total of 24 articles were included. Details on the review are published elsewhere [21].

In addition, 2 qualitative studies were conducted. The following is a short description of the methods used in the 2 studies. The inclusion and exclusion criteria for patients with CKD are shown in Textbox 1. Further details can be found elsewhere [31,39].

In the first study, the perceptions and needs of patients with CKD and HCPs regarding CKD self-management in China were examined. A basic interpretive, cross-sectional qualitative study comprising semistructured interviews and observations was conducted with 11 patients with CKD and 10 HCPs in the Department of Nephrology at the First Affiliated Hospital of Zhengzhou University in Henan province, China.

The second qualitative study examined the perceptions, attitudes, and needs of patients with CKD and HCPs regarding eHealth-based (self-management) interventions in general and the Dutch MD intervention specifically. A basic interpretive, cross-sectional qualitative study was conducted comprising semistructured interviews with 11 patients with CKD and 10 HCPs and 2 focus group discussions with 9 patients with CKD in the Department of Nephrology at the First Affiliated Hospital of Zhengzhou University in Henan province, China.

Key Factors Identified

The key factors (ie, barriers and facilitators) identified through the review and qualitative studies were gathered and categorized following the 5 domains of the Consolidated Framework for Implementation Research (CFIR) [41,42]. The CFIR provides a pragmatic structure for identifying potential implementation strategies for interventions in health systems at multiple levels [43-47]. It has also been successfully used to identify determinants of behavior change and optimize the design and effectiveness of self-management interventions [48].

Next, the intervention monitoring group decided which factors were the most important and changeable in each domain of the CFIR and summarized key lessons in designing eHealth CKD self-management interventions in the Chinese context.

Phase 2: Intervention and Implementation Development and Planning (Steps 2-5 of IM)

Step 2: Preparing Matrices of Change Objectives

First, we formulated program outcomes [36] based on the socioecological model [49]. Second, we subdivided program outcomes into performance objectives (POs). The POs are the required actions to accomplish change in the behavioral and environmental outcomes [36], which were linked to key determinants identified in the needs assessment [36]. We used the Theoretical Domains Framework to identify and select relevant determinants of behavior [50]. In total, 2 researchers identified and linked the determinants, and discussions resolved discrepancies. In addition, the intervention monitoring group evaluated the selected determinants for relevance and changeability. Finally, based on the determinants identified, we specified change objectives [36].

Step 3: Selecting Theory-Informed Intervention Methods and Practical Strategies

The matrices of change objectives guided the selection of theoretical methods and practical intervention strategies [36]. Relevant theoretical methods such as the Theory of Planned Behavior (TPB) [51], the Health Belief Model (HBM) [52], the IM book [36], and the taxonomy of behavior change techniques (BCTs) [53] were identified through a literature review. These theoretical methods or BCTs were translated into practical strategies tailored to each change objective. In addition, the intervention monitoring group discussed the acceptability and feasibility of these practical strategies based on existing CKD self-management interventions and results from the needs assessment.

Step 4: Developing a Tailored MD-Based Intervention (Plan)

First, based on the results of steps 1 to 3, the intervention monitoring group formulated guiding principles to complement the MD and adapt it into a tailored self-management intervention for the Chinese context. A logic model was also built to map and structure the causal mechanisms of the intervention by providing detailed evidence and strategies [54,55].

Second, the intervention components were adapted for the Chinese context. To ensure the effectiveness of the self-management intervention, we did not change the core self-management intervention components of MD that underlie its effectiveness. In addition, the intervention monitoring group used the technology functionality framework [56,57] to prioritize eHealth features, app functionalities, and strategies to reduce the complexity and improve the accessibility and usability of the app content. Low-fidelity prototypes [58] were developed to visualize design solutions and determine intervention requirements.

Step 5: Developing an Adoption and Implementation Plan

The intervention monitoring group pragmatically used the CFIR–Expert Recommendations for Implementing Change (ERIC) Matching Tool [59] to generate a list of potential implementation strategies. They also discussed the output of the implementation strategies to ensure that these strategies would be best placed to deliver the adapted mobile app for CKD self-management.

Ethical Considerations

This study has been approved by the ethical board of the Guangzhou Medical University (reference L202212020). All participants gave written informed consent. All data were anonymized. The participants volunteered to participate in this study, and no compensation was provided.

Results

Phase 1: Needs, Beliefs, and Perceptions

Identification of Key Barriers and Facilitators

Evidence in Previous Studies

Our review [21] identified promising intervention components for CKD self-management and related implementation determinants. For instance, self-monitoring and using messages or alerts to nudge patients toward displaying healthy behaviors were found to optimize patient self-management skills. In addition, the determinant ability of HCPs to monitor and, if necessary, anticipate patient measurements online influenced patients’ adherence to interventions. The review results are published elsewhere [21].

Furthermore, the first qualitative study revealed a paternalistic patient-HCP relationship. Barriers, facilitators, and needs regarding CKD self-management were frequently related to (a lack of) knowledge and environmental context and resources. The second qualitative study highlighted factors such as a lack of information, perceived trustworthiness and safety of eHealth interventions, clinical compatibility and complexity of eHealth, time constraints, and eHealth literacy. Suggestions for adapting and implementing the Dutch MD intervention in China focused on improving the functionalities and content of MD. Further details can be found elsewhere [31,39].

Key Factors Identified Using a Theoretical Framework

Key factors gathered from the aforementioned studies [21,31,39] were structured and categorized following the 5 domains of the CFIR. Figure 1 provides an overview of the key factors per CFIR domain that facilitate and hinder implementation.

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Lessons Learned

Several key lessons were summarized for designing eHealth CKD self-management interventions in the Chinese context. For instance, to ensure that the eHealth app is time saving, eHealth functionalities must be simple and easy to use, and eHealth navigation must be clear. Other key lessons are summarized in Multimedia Appendix 1.

Phase 2: Intervention and Implementation Development and Planning (Steps 2-5 of IM)

Step 2: Preparing Matrices of Change Objectives

The program outcomes were specified based on the socioecological model [49] (refer to Textbox 2 for the specific outcomes). In addition, a total of 12 POs and 124 accompanying change objectives were identified (for details, refer to Multimedia Appendix 2). The POs are shown in Textbox 3.

Step 3: Selecting Theory-Informed Intervention Methods and Practical Strategies

The selected theoretical methods and practical strategies applied to specific determinants are presented in Multimedia Appendix 3. For instance, the determinant Beliefs about capabilities and the PO Patients improve their CKD self-management behavior after a 9-month intervention period were linked to the change objective Patients express confidence in their ability to manage food intake following care providers’ suggestions. The BCTs that target this change objective include feedback, self-monitoring of the behavior, and behavioral practice. A diary to self-monitor and review progress is included as a practice strategy. Examples of theories mapping to change objectives and translated into practical strategies are shown in Table 1.

Step 4: Develop a Tailored MD-Based Intervention (Plan)

Guiding Principles

Guiding principles are formulated for developing and adapting the MD-based self-management intervention (plan) for the Chinese context. For instance, the tailored MD-based intervention should meet individual patient needs, perceptions, and preferences regarding CKD self-management and focus on the knowledge of, motivation toward, and skills for CKD self-management. Details are summarized in Multimedia Appendix 4.

Intervention Description

In China, it is a regular practice that all patients with CKD (stages 1-5 and dialysis) visit a nephrologist in a hospital setting. Patients undergoing a kidney transplant visit a different department, named the Department of Kidney Transplantation. In our needs assessment to prepare for the intervention, we included patients who visited the Department of Nephrology (hence, those in stages 1-5 and undergoing dialysis). As a result, our intervention targets patients with Kidney Disease: Improving Global Outcomes staging of CKD (stages G1-G5 and dialysis) and not patients undergoing kidney transplantation. In addition, as all patients with CKD report a multitude of symptoms and fairly high disease burden, the needs of patients with CKD (regardless of the CKD stage) are basic and similar for self-management interventions. For instance, we found that patients expressed the need for better access to and provision of disease-related knowledge. The level of knowledge of patients with CKD in China is much lower than that of those living in high-income countries. Therefore, in contrast to high-income countries, in China, we need a fairly basic self-management intervention focusing on knowledge, awareness, and basic skills that is suitable for all patients with CKD in stages 1 to 5.

A logic model explains how a tailored MD-based self-management intervention (plan) contributes to a chain of results (short-, medium-, and long-term outcomes) that achieve specific program outcomes of our intervention. On the basis of logic model guidance [60], in our program logic model, the short-term outcomes examine changes directly connected to intervention implementation, typically including knowledge, skills, or attitudes that contribute to medium-term outcomes. These are the prerequisite changes expected as a result of the intervention implementation. Medium-term outcomes are specific, measurable changes in things such as certain behaviors, decision-making practices, and community resources, acting as a bridge between short- and long-term outcomes. Long-term outcomes are ultimate changes or impacts, typically including improved health behavior and health conditions, increased capacity, and changes in programmatic reach. The logic model components were developed based on evidence from all previous steps, and a consensus was reached during intervention monitoring group meetings (Figure 2). Details on logic model definitions and examples are shown in Multimedia Appendix 5. On the basis of previous literature, the main concept of a logic model is a cascading effect of the outcomes; long-term outcomes are built (and depend) on medium-term outcomes, which are, in turn, built on short-term outcomes. For outcome evaluation, we selected an intervention duration of 9 months to evaluate long-term outcomes as previous literature provides evidence that this intervention duration is sufficient to demonstrate the impact on several self-management outcome indicators [61,62].

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A Chinese MD-based self-management intervention (plan) was built based on the Dutch MD self-management intervention (components; refer to the Dutch MD intervention in Multimedia Appendix 6). Examples of intervention adaptations and their rationale are described in Table 2 (details are provided in Multimedia Appendix 7). Surface-level adaptations included customizing intervention content, messages, and approaches to the local patient population’s cultural characteristics. Our main surface adaptations included (1) providing CKD information translated into Chinese, (2) providing video or voice call options on the dashboard for interactions with HCPs, (3) extending the intervention delivery medium to a mobile phone app combined with a wireless tracker to collect measurements automatically, and (4) a user interface platform for data visualization and progress review.

Personalized features were added, such as visual aids, pictograms, and customized videos (eg, videos covering tailored information about CKD and its treatment, such as a video on fluid restrictions and dialysis procedure for patients with end-stage renal disease or success stories of self-management for newly diagnosed patients with CKD). In addition, the core self-management intervention components that underlie its effectiveness, such as self-monitoring, were not changed. On the basis of the technology functionality framework, priorities of eHealth features of Chinese MD and possible app functionalities were also depicted (Table 3).

Furthermore, the intervention monitoring group intended to develop a mobile app to enhance self-management in patients with CKD based on patients’ and HCPs’ needs. The final adapted mobile app contained different modules, as illustrated in Table 4. Considering the low health literacy level in some vulnerable groups, strategies such as keyword searching and animations or videos without medical terminology were used to simplify the content, reduce the complexity, and improve the accessibility and usability of the app content.

Low-fidelity prototypes were used to develop the flow, content, and design styles of the app, with examples shown in Figures 3 and 4.

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Step 5: Develop an Adoption and Implementation Plan

The CFIR-ERIC Matching Tool [59] generates a list of potential strategies to address determinants. An output table (condensed to include highly endorsed strategies) lists the CFIR determinants across the top of the table, with CFIR-ERIC implementation strategies in the first column. Strategies are sorted by cumulative level of endorsement. “Cumulative percent” indicates the strength of endorsement for that strategy across all CFIR determinants (Multimedia Appendix 8). For example, “Identify and prepare champions” has the highest cumulative endorsement (407%) for this strategy to address all determinants. This strategy is color coded in green for all determinants, indicating that most respondents endorsed this strategy for those determinants. The yellow color coded cells indicate that at least 20% of respondents endorsed that strategy to address each indicated barrier. After discussion with the intervention monitoring group, the finalized implementation strategies are detailed in Textbox 4. For instance, to optimize the complexity and compatibility with HCPs’ workflows, ERIC implementation strategies including “develop a formal implementation blueprint,” “conduct local consensus discussions,” “organize clinician implementation team meetings,” and “provide clinical supervision” can be used. These implementation strategies can improve the implementation practice by assessing intervention-workflow compatibility (eg, staff working patterns and practice management) before and during the development and implementation of eHealth interventions and replacing existing care elements instead of adding elements to care.

Discussion

Principal Findings

This study used the IM approach to develop a tailored eHealth self-management intervention for patients with CKD in China based on the Dutch MD intervention. The adapted core intervention components comprise education, motivational interviewing, modeling, self-monitoring, combined home and hospital measurements, blended information support, and coaching. The tailored mobile health (mHealth) self-management intervention has potential for use by patients and HCPs in China, improving patient self-management and implementation success. Our study contributes to implementation research on eHealth self-management interventions in CKD care, addressing patient and HCP needs and priorities. It also provides insights into potential barriers and facilitators specific to an eHealth CKD self-management intervention in the Chinese context, which may be relevant for countries with similar contextual characteristics.

Comparison With Prior Work

Identification of Key Barriers and Facilitators

Our findings on key barriers and facilitators of eHealth interventions and self-management in the Chinese context align with those of similar studies in Western contexts. A lack of knowledge among patients was a barrier to CKD self-management, consistent with previous literature [63]. In addition, our finding that patients were generally convinced of the good quality and advantage of eHealth interventions has been corroborated by other research [64]. These similarities suggest that certain challenges such as clinical compatibility persist across different regions and time in the rapidly evolving field of eHealth.

However, our findings also revealed differences from studies in Western settings [21], namely, “Cultural context” (ie, paternalistic patient-HCPs relationship) and “Needs and resources” (ie, patients’ and HCPs’ specific needs in the Chinese context and infrastructure of [primary] health care) in the domain “Outer setting.” For instance, patient autonomy is a core principle of patient-physician interaction in Western cultures [65,66]. However, the appreciated paternalistic relationship in our study can be valuable and even essential to improving health outcomes and treatment adherence in some cultural contexts [67,68]; for instance, if patients prefer and express needs for a paternalistic approach over autonomy [67,68], HCPs can provide guidance on self-management, raising awareness of its importance and potential benefits. This can optimize the effectiveness of the self-management intervention.

Furthermore, improved primary health care infrastructure is needed to support CKD self-management in China. In China, the care for patients with CKD relies heavily on nephrology departments, and the lack of a strong primary care system in rural areas poses a significant challenge due to the distance to health care facilities. Therefore, eHealth interaction plays a crucial role in enabling access to care. These factors can be used to accelerate the implementation of eHealth CKD self-management interventions in countries with similar contextual characteristics to those of China.

Theoretical Foundation

Previous studies recommend that a solid theoretical foundation is necessary to plan, design, evaluate, and implement eHealth self-management interventions [69]. However, our review found that only a few eHealth CKD self-management interventions reported the use of a specific theory [21], thereby limiting the understanding of mechanisms of action and hindering replication of eHealth intervention implementation. Our study highlighted the strengths of the theories used.

The IM method guides the development and implementation of interventions by making evidence-based decisions and matching methods to change objectives. It provides an overview of these methods [36]. In addition, the IM-based design involves analyzing relevant constructs in theory, mapping them to stakeholders’ needs, and conceptualizing intervention components. These components are then converted into different functionalities and incorporated into various function modules in the mobile app.

Furthermore, to translate interventions into different contexts (eg, health care system and population), it is crucial to adapt and align these interventions with context characteristics. The HBM and TPB are 2 highly cited social psychological theories focusing on individuals’ perceptions, attitudes, needs, and sociocultural context [70,71]. In step 1, we conducted a needs assessment based on elements mainly from the HBM and TPB to improve intervention alignment with the target population’s needs and priorities. In step 2, we used the CFIR to identify key factors influencing the implementation of eHealth CKD self-management interventions. The CFIR provides a pragmatic structure for identifying potential intervention components and implementation strategies in health systems at multiple levels. In addition, it helped address the black-box problem common in many adaptation models, in which the procedures used to carry out the adaptation process with stakeholders are often vague and unspecified and, thus, difficult to replicate in the new target population and local setting.

Stakeholder Involvement in Intervention Development and Adaptation

Researchers and intervention developers integrated the perceptions, attitudes, and needs of patients and HCPs to adapt a tailored eHealth self-management intervention for patients with CKD in China. To our knowledge, our study is the first to consider patients’ and HCPs’ needs and preferences in designing and implementing an eHealth intervention for CKD self-management in China. However, to ensure a successful and sustainable implementation of eHealth interventions within a medical organization, it is important to follow patient-oriented principles and have active patient involvement in the research process [72]. In addition, using cocreation methods and engaging patient stakeholders as research partners should be done throughout intervention development and adaptation [73-75].

MD Self-Management Intervention Adaptation in China

To ensure the effectiveness of the Dutch MD–based self-management intervention, we did not change the core self-management intervention components of MD that underlie its effectiveness, such as provision of information support. In addition, we made surface-level cultural adaptations to customize the intervention content, messages, and approaches to the local patient population, enhancing its appeal and feasibility. Our adaptations focused on the following areas.

First, self-management is often misunderstood as adherence to medical treatment by patients with CKD and HCPs in China [39]. To address this, we included specific self-management training for patients and HCPs in the adapted intervention. Second, Chinese society is more collective compared to most Western countries, emphasizing the importance of good relationships with family and the community. Therefore, the adapted intervention provides information on locally available resources, such as newsletters or role-modeling stories of CKD self-management offered by the community or patient associations.

Third, we adapted the intervention delivery medium to a mobile phone app. mHealth using technologies such as trusted medical knowledge resources can improve telecommunication and behavior monitoring and offer timely and accessible primary health care on a wide scale. This could reduce the burden of CKD, particularly in China, which has numerous internet and mobile phone users. This finding is reflected in previous literature on the development of a user-centered eHealth app in China [76].

Furthermore, we improved accessibility of self-management support for most patients, including vulnerable groups such as people with a lower educational level and eHealth illiteracy and of older age. While eHealth has been proven effective in improving health care locally, regionally, and worldwide [30], some vulnerable populations may be disadvantaged by eHealth; for instance, people who are unemployed or with low education benefit less from web-based interventions [77]. As face-to-face contact remains necessary to optimize medical care, adherence, and treatment outcomes, a “blended or hybrid care model” [78] is applied when necessary, combining eHealth with face-to-face support to provide patients with in-person training on the use of the mHealth app in addition to on-demand web-based help, phone support, or support from family and friends.

Transferability and Recommendations

Our research extends the translation of potentially effective mHealth interventions to different local contexts, increasing their generalizability. The development and adaptation process encompassed high user involvement, stakeholder input, and robust theories from established eHealth self-management interventions, enhancing the intervention’s potential effectiveness. In addition, the methodological approach and findings of our study are relevant to the Chinese context as well as other countries with similar contextual characteristics such as the lack of a strong primary care system, long traveling distances to health care facilities, a limited understanding and knowledge of CKD (self-management), a more prominent paternalistic patient-HCP relationship, and wide-scale use of mobile phone apps.

The lessons learned from our study can guide future research on intervention adaptation. First, instead of a “one-size-fits-all” approach, researchers and eHealth intervention developers should be aware of context-specific factors in the local settings. Second, this study serves as an example of preparation for intervention adaptation by leveraging the experiences of clinicians and multidisciplinary teams to produce the initial prototype app.

Strengths and Limitations

Our study has several strengths. To our knowledge, it is the first to use the IM approach to develop and adapt an eHealth intervention for CKD self-management in China. The IM approach incorporates theory and evidence, providing guidance for local adaptation. Second, we provide an overview of barriers and facilitators specific to eHealth self-management interventions in the Chinese context. Third, our research team is multidisciplinary, including academic general practitioners, a nephrologist, health services researchers, nurses, and a behavioral scientist. The involvement of a multidisciplinary intervention monitoring group ensured the intervention’s feasibility and alignment with existing processes.

Nevertheless, there are also limitations. First, we did not include primary care HCPs in the qualitative interviews. In rural China, where a robust primary care system is lacking, the responsibility for CKD care primarily lies with HCPs in nephrology departments at city hospitals. As a result, patients with CKD have limited or no contact with their primary care providers. Second, we performed a needs assessment to identify perceptions, attitudes, and needs of patients and HCPs regarding eHealth self-management interventions for CKD. The participation of those that matter is essential for IM and greatly determines the success of the intervention in practice. However, due to limited time and resources, we unfortunately were not able to include patients and caregivers in the intervention monitoring group. For future research, we will include them in the monitoring group, such as using the “member check” method to explore patients’ and caregivers’ views on the intervention plan and provide an in-depth understanding of its usability, functionality, and acceptability. Third, the intervention development study is based exclusively on formative research methods, and a pilot test is currently underway to optimize the MD-based self-management intervention in preparation for a full-scale randomized controlled trial.

Conclusions

eHealth interventions such as the Dutch MD self-management intervention could be widely used to improve the health of people with CKD. To increase the reach of these interventions in the public health system, especially in low- and middle-income countries, adaptations to the local context are needed. This paper details the development and adaptation process of the Dutch MD–based CKD self-management intervention to the Chinese setting. By systematically applying an IM approach collaborating with experts and stakeholders, an mHealth-based CKD self-management intervention was developed that provides a good fit with patients’ needs and priorities and is easy to use and well integrated into HCPs’ workflows. To translate the implementation of effective eHealth self-management interventions for patients in local settings, we recommend that future researchers and intervention developers explore the intersection of factors that influence the use of eHealth (eg, previous experience of eHealth use and preferences) to discern tailored intervention adaptation. In addition, the iterative Plan-Do-Study-Act cycle is needed for continuous intervention improvement in daily practice that aims to identify barriers and needed changes in implementation and outcome efforts.