Toward the complexities of the development and validation process of digital health interventions for the symptom management for patients with Chronic Kidney Disease: A scoping review based on the UK Medical Research Council Framework

Toward the complexities of the development and validation process of digital health interventions for the symptom management for patients with Chronic Kidney Disease: A scoping review based on the UK Medical Research Council Framework | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Toward the complexities of the development and validation process of digital health interventions for the symptom management for patients with Chronic Kidney Disease: A scoping review based on the UK Medical Research Council Framework Xutong ZHENG, Zhen YANG, Shu LIU, Yuqian LI, Aiping WANG This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4305056/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Dec, 2024 Read the published version in BMC Public Health → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Chronic Kidney Disease (CKD) presents a growing global health issue with a complex symptom phenotype, negatively impacting patients' health-related quality of life and increasing healthcare utilization. While digital health interventions offer promising avenues for improving symptom management in CKD, understanding their development, validation, and effectiveness is crucial for clinical application. Objective: To comprehensively map the existing literature on the development and validation processes of digital health interventions aimed at managing symptoms in patients with Chronic Kidney Disease (CKD), using the UK Medical Research Council's complex intervention framework as a guiding lens. This scoping review aims to identify gaps, challenges, and prospects in this domain, thereby informing future research endeavours and clinical practice guidelines for developing and implementing effective digital health interventions for CKD symptom management. Methods: A scoping review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Databases searched included PubMed, Scopus, Embase, and others, covering literature up to December 2023. Studies were selected based on predefined eligibility criteria focusing on digital health interventions for CKD symptom management. Results: The search yielded 31 studies, with a mix of development and validation studies, predominantly from developed countries. The review highlights the potential of digital interventions in enhancing symptom management, quality of life, and patient engagement in CKD care. However, gaps were identified as follows: 1) Iterative refinement cycles involving multidisciplinary stakeholders enhanced intervention acceptability and usability should be guaranteed, 2) Theory-driven and evidence-based approaches were underutilized in current intervention development, 3) Long-term implementation outcomes and process evaluations were rarely assessed. This review maps an evolving landscape where digital health interventions offer patient-centric solutions for CKD symptom management while highlighting opportunities for methodological advancements. Conclusion: Digital health interventions hold promise for improving symptom management in CKD, yet more research is needed to overcome current limitations and fully realize their potential. Future studies should focus on patient-centred designs, comprehensive validation processes, exploring the underlying mechanism using process evaluation and the integration of these technologies into routine clinical practice. Chronic Kidney Disease digital health interventions symptom management mobile health telemedicine scoping review Figures Figure 1 1 Introduction With the accelerated process of ageing and the increasing incidence of various metabolic syndromes and other chronic diseases, Chronic Kidney Disease (CKD) has been on the rise year by year and has become a global health issue. The Global Burden of Disease (GBD) study has shown that CKD has become a leading cause of death worldwide [ 1 ]. From 1990 to 2017, owing to the improvement in global healthcare level, the mortality rates caused by many chronic non-communicable diseases have declined. For example, cardiovascular diseases have decreased by 30.4%, chronic obstructive pulmonary disease by 41.3%, and cancer by 14.9%. However, the mortality rate of CKD has not shown a similar decline, with a 41.5% increase in age-standardized mortality attributable to CKD [ 2 ]. The GBD report reveals that the ranking of CKD in the list of causes of death continues to rise, ranking 13th in 2016 and 12th in 2017, and is predicted to become the fifth leading cause of death globally by 2040 [ 3 , 4 ]. The diverse symptom phenotype of Chronic Kidney Disease patients could be determined by the complex pathophysiological basis. The physiological functions of the kidneys primarily include filtration and excretion of metabolic waste products and endocrine function. In end-stage renal disease, the accumulation of uremic toxins due to renal failure affects various systems and organs in the body, leading to inflammation, immune dysfunction, vascular disease, increased risk of bleeding, intestinal dysbiosis, abnormal drug metabolism, and other conditions [ 5 ]. Meanwhile, the decline in kidney endocrine function can result in renal malnutrition, renal anaemia, and blood pressure abnormalities. The consensus conference held by Kidney Disease: Improving Global Outcomes (KDIGO) proposed that although hemodialysis can maintain the life of patients, it may not necessarily reduce their symptom burden [ 6 ]. Furthermore, because the disease itself can cause physiological, psychological, and social functional changes in patients, it can also exacerbate the development of symptoms [ 5 ]. Patients with chronic kidney disease typically experience a variety of symptoms, with the incidence of pain, fatigue, itching, and constipation being over 50% [ 7 ]. Other symptoms include bone and joint pain, insomnia, emotional disorders, sexual dysfunction, sensory abnormalities, and nausea [ 8 ]. Symptom burden refers to the overall impact of all disease-related symptoms on the patient's overall health. Existing research has demonstrated that higher levels of symptom burden are associated with decreased health-related quality of life (HRQOL), increased hospitalisation rates, and higher mortality rates [ 9 – 12 ]. The symptom phenotype of chronic kidney disease patients is complex, and how to address and alleviate their symptom burden, while maximizing physiological function and social rehabilitation, is a priority issue [ 13 ]. The KDIGO consensus conference has emphasized that symptom assessment and management are important components of high-quality care for patients with end-stage renal disease. It specifically highlights the need to focus on symptom management strategies and their effectiveness in chronic kidney disease populations, including their impact on relevant patient outcomes such as overall symptom burden, physical function, and health-related quality of life [ 6 ]. Guidelines and consensus statements have recommended the implementation of symptom management for patients to improve their health outcomes [ 6 , 14 – 16 ]. Existing research has identified several characteristic features of implementing symptom management for Chronic Kidney Disease patients: (1) The process requires careful assessment of the availability of local healthcare resources [ 16 ]. (2) The responsibility for symptom management lies with a complex interdisciplinary team: identifying and managing symptoms in maintenance hemodialysis involves multiple individuals and nursing levels, each with overlapping roles [ 16 ]. Routine symptom assessment is not standardized and often complex. (3) Patient-centered care is emphasized, taking into consideration the patient's values, preferences, and wishes when determining treatment and care plans [ 17 – 19 ]. (4) The biopsychosocial medical model is valued, where symptom assessment and management strategies should consider the patient's biological, psychological, and social factors [ 16 , 20 ]. Although symptom management is crucial for reducing the symptom burden of patients, there are currently barriers to clinical symptom assessment and management at both the healthcare and patient levels. On the healthcare level: Firstly, healthcare providers lack sufficient awareness of the importance of symptom management[ 21 ], leading to frequent underestimation of the range and severity of symptoms in hemodialysis patients[ 7 , 22 – 24 ]. Additionally, because symptom management requires multidisciplinary collaboration, the provision of medical services is often fragmented among nephrologists, nephrology nurses, primary healthcare personnel, social workers, etc., resulting in a lack of continuity in care and difficulty for patients in accessing services[ 25 , 26 ]. Furthermore, the current symptom management model neglects the patient-centred approach, with clinical physicians often focusing on laboratory test results rather than the symptoms reported by patients[ 16 ]. Moreover, hemodialysis is a prolonged treatment process that requires attention not only to the patient's disease-related needs [ 16 , 19 , 20 ] but also to the assessment and management of their psychological and social needs. However, current research often overlooks the psychological and social needs of patients, rendering their symptom management strategies ineffective [ 27 , 28 ]. On the patient level: Firstly, patients often conceal their need for symptom management due to fear, lack of knowledge, low health literacy, etc [ 23 ]. Hemodialysis patients often refrain from communicating their symptoms to healthcare personnel and adopt ineffective self-management strategies [ 29 , 30 ]. Secondly, the accessibility of medical resources is also an important influencing factor. Studies have found that patients living far from kidney disease medical resources have low treatment compliance and higher mortality rates [ 31 ]. With the advancement of science and technology, the field of nephrology has witnessed the emergence of remote nephrology, which combines nephrology with telemedicine. Remote nephrology has played a significant role in the diagnosis, treatment, follow-up, and management of kidney disease. Mobile apps, P2P remote consultation meetings, and web-based kidney disease knowledge learning modules designed based on remote nephrology concepts have overcome geographical barriers, improving patient access to medical resources, care continuity, resource allocation optimisation, effective kidney disease management, and patient health outcomes [ 31 – 34 ]. Worldwide, scholars have also developed digital applications for interventions targeting symptom monitoring and symptom management issues faced by chronic kidney disease patients. The development of digital health interventions tailored specifically for symptom management in CKD has garnered substantial attention in recent years. These interventions strive to empower patients through educational resources, self-monitoring tools, symptom-tracking mechanisms, medication adherence support, and remote consultation capabilities [ 35 – 40 ]. By harnessing the potential of digital technologies, healthcare providers can remotely monitor patients' symptoms, evaluate their adherence to treatment regimens, and deliver timely interventions as necessary. This may lead to enhanced symptom control, improved medication compliance, reduced healthcare utilization, and heightened patient satisfaction. Nevertheless, the landscape of digital health interventions for symptom management in CKD is in a phase of continual evolution, necessitating a comprehensive understanding of the current state of research in this domain. Medical Research Council’s complex intervention framework is a rigorous framework for researchers to develop, validate and implement a complex intervention [ 41 ]. Therefore, conducting a scoping review to chart the existing literature on the development of digital health interventions for symptom management in CKD is imperative. Such a review will aid in identifying gaps, challenges, and prospects in this area, thereby informing future research endeavours and clinical practice guidelines. 2 Method 2.1 Methodology & reporting guidelines We used the Arksey & O'Malley as our research framework [ 42 ]. This scoping review was conducted based on the guidelines and principles of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) Checklist [ 43 ]. The scoping review was registered on the OSF (Open Science Framework) and the registration doi is 10.17605/OSF.IO/HAZ9T . The RISMA-ScR Checklist can be found in supplementary file 1. 2.2 Searching strategy Three steps were followed in the search strategy. First, the preliminary search was conducted in the Pubmed database using both MeSH terms and free terms to develop search words. Secondly, two researchers and a medical librarian then devised a search strategy to identify studies reporting on the evidence of the development or validation process of digital health interventions for symptom management for patients with Chronic Kidney Disease. We devised tailored search strategies for each database and tried many times to get an optimal result when it is necessary. Third, we reviewed the references of all the included studies to find potentially eligible supplement literature that may be included. We have searched for grey literature to keep the integrity of searching results. The databases we searched are PubMed, Scopus, Embase, the Cochrane Library, Web of Science, CINAHL (via EBSCO), OpenGrey archive, arXiv, and bioxiv. The searching time limit was from inception to December 2023. The detailed search strategy and results can be found in supplementary file 2. 2.3 Eligibility criteria We used P (Population), I (Intervention), C (comparator), and O (Outcome). Studies were included if they met the following criteria- Population: 1) adult patients with chronic kidney disease at any phase of 1–5; 2) patients undergoing dialysis or conservative management. Patients facing or awaiting kidney transplants were also considered because before completing the transplant process patients were still in the state of chronic kidney disease. Intervention: intervention focusing on e-health. E-health is a broad term that includes telemedicine, electronic health records, telehealth, mobile health applications, and other forms of ICT-based health services. We adopted two sources of e-health to help us define the intervention. E-health is defined by the World Health Organization as “the transfer of health resources and health care by electronic means” [ 44 ]. It encompassed the use of information and communication technologies (ICT) in the health sector, including the delivery of health information, care, and treatment through digital and electronic platforms. The Australian National e-Health strategy further elaborated on this definition by stating that e-Health ensures “that the right health information is provided to the right person at the right place and time in a secure, electronic form to optimize the quality and efficiency of health care delivery” [ 45 ]. Outcome: 1) The focus and aim of this study should include at least one aspect of symptom evaluation, symptom burden or symptom management; 2) For experimental studies, the outcome measure should include at least one measure related to symptoms caused by the chronic kidney disease, the progression of disease or the treatment (dialysis, medicine related to Chronic Kidney Disease, etc. al) rather than caused by other external factors such as anxiety caused by COVID-19 epidemic. Others: were full-text, peer-reviewed articles and published in English. Studies were excluded if they were letters, correspondence, dissertations and conference papers. 2.4 Study selection The first screen is based on titles, abstracts and keywords, and the second round screen is based on full texts. Study selection was performed according to the predetermined eligibility criteria following established guidelines for systematic reviews. We choose to follow the guidelines for systematic reviews because the process of selection of systematic reviews is rigorous and repeatable. All search results were initially imported into the reference management software program Endnote X9. After the duplicates were removed, titles and abstracts were read carefully to identify the eligibility of studies. Then full-text documents were scrutinized to further identify the fulfillment of selection criteria. Before the beginning of the screening process, we hold an online meeting to ascertain the criteria of each step of screening (such as “In what condition should a study be judged as irrelevant and excluded by reading the title and abstract ?”) to prevent the misunderstanding of the eligibility or subjective understanding. After that, the calibration exercise was conducted on the subset of studies to identify any potential discrepancies or areas of confusion among screeners [ 46 ]. One of the researchers chose 50 publications randomly retrieved in the Pubmed database. All reviewers related to the screening process would participate in the exercise to independently evaluate all the literature before the first author of this review compared all their answers and announced the right assessment. At each stage of formal screening, a minimum of two trained reviewers (XT, YZ, LY, YQ) independently read and critically evaluated the eligibility criteria of each study that may be included. Any discrepancies would be discussed to reach a consensus at the pilot and formal screening process stage. 2.5 Data extraction Before the beginning of the extraction, the coordinator (LS) who did not participate in the screening and extraction process conducted the extractor masking to reduce bias in the extraction process. All included papers were transformed to reproducible form and their study-specific details such as the author, study type, affiliations, and journal names were replaced with generic terms (such as author A, affiliations A, journal B) to prevent bias. We used a pre-designed data extraction table to extract the characteristics and content of the evidence, including the first author, publication or update year, type of evidence, the main theme of the evidence, and related evidence of this topic. At least two trained researchers (XT, YQ, YZ) participated in the data extraction process. The extracted evidence was compared with each other and any discrepancies related to the content of the extraction were discussed in interpretation to reach a consensus on the correct extraction. 2.6 Data synthesis Narrative synthesis was conducted from multiple studies to get a descriptive summary and interpret the collective evidence fully [ 47 , 48 ]. Two researchers (XT, and YQ) independently synthesized the evidence and compared the results with each other. Any discrepancies were discussed and resolved by the third researcher (AP). 3 Result 3.1 Search result We retrieved a total of 38,154 articles from the database. After removing duplicates, there were 18,006 remaining articles. Following a review of titles and abstracts, 17,926 irrelevant articles were excluded, leaving 4 articles without full text available and 76 articles requiring full-text review. After excluding articles that did not meet the inclusion criteria, 28 articles remained. The reference lists of these 28 articles were reviewed, resulting in the addition of 3 more references. Ultimately, we included 31 articles. Of the 31 included articles, 7 described the development process of interventions, 27 were validation of interventions, and 3 both described the development of interventions and reported on experimental results. The process of literature retrieval, screening, and evaluation is summarized in Fig. 1. Figure 1 Flow chart for the literature screening 3.2 Characteristics of Validation Studies In 27 validation studies, 9 studies are from the USA [ 37 , 49 – 56 ]. 7 studies are from Iran [ 40 , 57 – 62 ]. 1 study each is from South Korea [ 63 ], Japan [ 64 ], the Netherlands [ 65 ], Poland [ 66 ], UK [ 67 ], Ireland [ 61 ] and United Arab Emirates [ 36 ]. Two studies are from the and China [ 38 , 68 ]. 3 studies are from Australia [ 35 , 69 , 70 ]. Considering the purpose of the study, 13 studies are feasibility, acceptability or utility studies [ 35 , 49 – 54 , 56 , 61 , 64 , 65 , 67 , 69 , 70 ]. 14 studies are efficacy studies [ 36 – 38 , 40 , 55 , 57 – 60 , 62 , 63 , 66 , 68 ]. 22 studies focused specifically on hemodialysis patients [ 35 , 36 , 38 , 40 , 49 , 51 – 54 , 57 – 67 , 69 , 70 ], 2 studies focused on chronic kidney disease [ 50 , 55 ], 2 on kidney transplant candidates [ 37 , 56 ], and 1 on peritoneal dialysis [ 68 ]. The intervention could be categorized into 9 categories: (1) Telephone-based interventions (telenursing/telenursing): 8 studies [ 57 , 37 , 56 , 67 , 60 , 68 , 65 , 36 ]. (2) Mobile health (mHealth) applications/interventions: 6 studies [ 38 , 40 , 58 , 59 , 62 , 64 ]. (3) Virtual reality based interventions: 3 studies [ 49 , 63 , 66 ]. (4) Patient-reported outcome measures (PROMs/EPROMs): 3 studies [ 35 , 51 , 69 ]. (5) Text-message-based: 3 studies [ 50 , 55 , 70 ]. (6) website-based: 2 studies [ 52 , 53 ]. (7) online internet-based platform: 1 study [ 54 ]. (8) wearable monitor study [ 61 ]. The study designs could be categorized as 6 categories: (1) randomized controlled trials: 11 studies [ 37 , 40 , 49 , 55 , 56 , 60 , 62 , 66 – 69 ], (2) quasi-experimental studies: 2 studies [ 36 , 57 ]. (3) Observational studies: 3 studies [ 38 , 61 , 65 ]. (4) Pretest-posttest studies: 5 studies [ 50 , 52 , 53 , 58 , 59 , 63 ]; (5) Mixed methods studies: 3 studies [ 50 , 51 , 54 ]; (6) qualitative studies: 3 studies [ 35 , 64 , 70 ]. The outcome measure could be divided as 6 categories: (1) Feasibility and acceptability outcomes: recruitment and retention rate [ 36 , 49 – 52 , 69 ], completion time and rate [ 51 , 64 , 69 ], usability result [ 53 , 64 , 69 ], adherence rate [ 36 , 49 , 50 , 52 ], barriers and enablers to uptake the system [ 35 ], and participants’ subjective rating [ 35 , 49 , 52 ]. (2) Clinical outcomes: physical fitness and body composition [ 63 ], dialysis weight [ 61 , 62 , 64 ], the occurrence of complications [ 68 ], body mass index [ 55 ]. (3) Patient-reported outcome: quality of life [ 37 , 52 , 54 – 56 , 64 , 67 – 69 ], symptom burden [ 51 , 57 , 68 , 69 ], specific symptom burden such as fatigue [ 37 , 54 , 63 , 65 ], mood related symptom [ 37 , 49 , 54 , 56 , 59 , 60 , 65 – 67 ], hyper/hypovolaemia symptoms [ 61 ], patient activation [ 55 ], medical adherence [ 55 ], sleep quality [ 56 ], social support [ 49 , 65 ], self efficiency [ 58 ]. (4) Laboratory parameters: blood chemistry such as creatinine, potassium, and haemoglobin [ 38 , 40 , 55 , 68 ]. (5) Dietary outcomes: Energy and protein intakes [ 36 ], adherence to fluid and dietary recommendations [ 52 ]. (6) other outcomes: cost-effectiveness [ 67 ], and health service utilization [ 68 ]. 3.3 Characteristics of Development Studies Most interventions involved patient participation and expert guidance in their development process. Some also worked with specific patient communities to incorporate their values and needs. 6 studies [ 50 , 55 , 71 – 74 ] involved patients in the stakeholder input and evaluation process. 3 studies [ 50 , 55 , 71 ] included input from caregivers and family members. 4 studies [ 56 , 71 – 73 ] involved multidisciplinary experts like clinicians, health providers, policymakers to provide guidance. Most chronic kidney disease interventions underwent iterative cycles of refinement based on input from various stakeholders. 7 studies[ 50 , 56 , 71 – 74 ] refined the interventions based on feedback from stakeholders like patients, experts, nurses etc. This included modifications to content, features, graphics, and colours and correcting errors. 3 studies [ 56 , 71 ] adapted the delivery methods of the interventions based on access needs and preferences. This included changing an in-person program to a teleconference and building an e-health tool. 1 study [ 50 ] highlights the importance of involving end-users in refining health interventions to enhance effectiveness and acceptance. Only 5 studies use the program theory to devise or develop the intervention [ 50 , 56 , 71 – 73 ]. Only 4 studies’ development processes are evidence-based [ 56 , 64 , 71 , 73 ]. 4 Discussion In this scoping review, we comprehensively explored the development and validation of digital health interventions aimed at managing symptoms in patients with Chronic Kidney Disease (CKD). Our findings delineated a multifaceted landscape where these interventions have a significant impact on various aspects of patient care. Firstly, patient engagement has emerged as a critical outcome. Digital interventions, by their inherent accessibility and user-friendliness, have facilitated better engagement of patients in their own care. This aligns with findings from [ 75 ], which demonstrated an increase in self-management behaviours among CKD patients. The interactive nature of these applications, including features like symptom tracking and feedback mechanisms, has empowered patients to take an active role in managing their condition. Secondly, the quality of life for CKD patients has been another focal point. Our review found that well-designed digital interventions could potentially alleviate some of the burdens associated with CKD symptom management. This is particularly relevant considering the chronic nature of CKD, where long-term quality of life is a significant concern. However, the conclusion needs to be provided by systematic review and meta-analysis considering more randomization controlled trials conducted in the future. Studies like [ 60 , 63 , 65 ] have shown that effective symptom management through digital means can lead to improved mental and physical well-being. Lastly, symptom reduction itself is a direct and measurable outcome of these digital interventions. By providing personalized and timely management options, these tools have shown promise in reducing the severity of CKD-related symptoms. This not only improves patient comfort but also potentially delays disease progression, as suggested by [ 35 , 55 , 56 ]. These findings highlight the transformative potential of digital health interventions in CKD care. By improving patient engagement, enhancing the quality of life, and effectively managing symptoms, these tools offer a new paradigm in the management of chronic diseases like CKD. The practical implications of our findings in the realm of digital health interventions for CKD are manifold and significant. For healthcare providers, the integration of these digital tools into routine care emerges as a crucial strategy. This involves not just the adoption of technology but also a shift in the care paradigm to include digital monitoring and patient-reported outcomes as part of standard practice. Considering patient preferences and technological literacy is vital to ensure adherence and effectiveness, as diverse patient populations may have varying levels of comfort and access to technology. This scoping review, using a deductive method based on the Medical Research Council’s complex intervention framework to categorize the previous studies concerning symptom management. By applying this framework, we have identified some gaps that need to be narrowed in the future. Firstly, the implementation outcomes such as fidelity, satisfaction, and adoption needed to be considered in future studies. According to the process evaluation framework, the implementation process could impact the outcome of the intervention. The high adoption, wide reach, and high fidelity of e-health intervention could improve the value of effect size and effectiveness of a trial. Secondly, the programme theory was omitted in some previous studies. Programme theory could describe in what condition could an intervention lead to its effects. It is a key method to understand the underlying mechanism of an intervention. Medical Research Council advised that the programme theory should be developed at the beginning of the research project and refined in successive phases [ 41 ]. Future studies should choose or develop appropriate programme theory to guide the development, trial and implementation of a complex intervention to reduce any uncertainties in the research project. Last but not least, the process evaluation was undermined in most of the studies, only two studies [ 49 , 50 ] used the process evaluation to appraise or validate their theoretical hypothesis. As the process evaluation has been regarded as an essential part of designing and testing the complex intervention [ 76 ], a comprehensive and well-designed process evaluation should be considered and integrated into the study protocol before the research project begins. This scoping review could, to some extent, reveal the inequality of health resources. We found most of the development and validation studies were conducted in developed countries. However, for patients, particularly in remote or undeveloped countries or regions, these digital interventions can be transformative. They offer a means to overcome geographical and logistical barriers, providing consistent and personalized symptom management support [ 33 , 77 – 79 ]. Cultivating a nephrologist is time-consuming, many developing countries reported the number of a nephrologists is insufficient [ 80 ]. This accessibility is crucial in enhancing the overall quality of care for CKD patients who might otherwise have limited access to specialized healthcare services. Policy-wise, the review underscores the necessity of developing guidelines to standardize digital health interventions. This standardization should aim to ensure the quality and efficacy of these tools, making them reliable components of CKD care. Policies should also address issues such as data security, privacy, and interoperability of digital health systems to facilitate their seamless integration into existing healthcare infrastructures [ 81 ]. Furthermore, our findings suggest a need for policy initiatives that support research and development in this field, along with the creation of reimbursement models for digital health interventions. This could encourage innovation while ensuring these tools are accessible and affordable for all CKD patients. In essence, our review highlights a multi-dimensional approach involving healthcare providers, patients, and policymakers, all of whom play a pivotal role in the successful implementation of digital health interventions for CKD symptom management. When comparing our findings with existing literature in the field, a few key areas of convergence and divergence emerge. Our review aligns with studies like [ 75 ], which emphasize the effectiveness of digital interventions in enhancing patient self-management in CKD. However, our review goes further, offering a detailed analysis of how these interventions are developed and validated, a perspective less explored in current literature. One notable divergence is in the area of intervention customization and patient adherence. While existing studies, such as those by [ 82 , 83 ], highlight the potential of digital health interventions in improving clinical outcomes, our review sheds light on the complexities of ensuring consistent patient engagement. This includes challenges in maintaining long-term adherence, which have been less emphasized in previous studies. Another area where our review contributes new insights is in the integration of digital health interventions within the broader healthcare ecosystem for CKD patients. While the literature often focuses on the efficacy of individual digital tools, our review considers how these tools interact with existing healthcare practices and the implications for holistic care. Overall, our review enriches the existing body of literature by providing a more nuanced understanding of the complexities involved in the development and implementation of digital health interventions for CKD, highlighting areas that require further exploration and development. Our scoping review, while comprehensive, acknowledges several limitations inherent in the current body of research on digital health interventions for CKD symptom management. One primary limitation is the significant variability in intervention types. This diversity, spanning from simple mobile applications to complex telehealth systems, poses challenges in drawing generalized conclusions about efficacy and usability. Another critical limitation is the differences in study methodologies. Many studies in this field employ varied research designs, sample sizes, and outcome measures, making it difficult to conduct direct comparisons or meta-analyses. This inconsistency in research design hinders the ability to develop a cohesive understanding of how digital interventions can be most effectively implemented in CKD care. Furthermore, there is a notable gap in long-term outcome data. Most studies focus on short-term outcomes, leaving questions about the sustainability and long-term effectiveness of these interventions. This lack of long-term data is a significant concern in CKD, a chronic condition where the long-term management of symptoms is crucial. Additionally, patient adherence and engagement over extended periods remain under-explored. While initial engagement with digital health interventions may be high, maintaining this engagement over time, particularly in a chronic disease context, is not well understood. Overall, these limitations highlight the need for more standardized, long-term, and comprehensive research in the field to fully understand and optimize the role of digital health interventions in CKD symptom management. Given the limitations and gaps identified in the current research, several recommendations for future research in digital health interventions for CKD symptom management are warranted: There's a critical need for long-term studies that evaluate the sustained impact of digital interventions on CKD symptom management. Such studies should not only assess immediate clinical outcomes but also explore long-term adherence, patient satisfaction, and quality of life. CKD affects patients differently, influenced by factors such as age, disease stage, and comorbid conditions. Future research should focus on specific subgroups of CKD patients to understand how digital interventions can be tailored to meet diverse needs. Research should also explore how digital interventions can be effectively integrated with traditional care models. This includes examining the roles of healthcare providers in supporting these interventions and understanding how digital tools can complement existing treatments. Investigating new technologies and their adaptation in the context of CKD care is essential. This includes exploring the potential of emerging technologies like artificial intelligence and machine learning in personalizing care and predicting symptom fluctuations. Future research should prioritize patient-centred design, assessing the usability and accessibility of digital interventions from the patient’s perspective. Understanding patient preferences, challenges, and barriers to technology use will be crucial in designing effective interventions. Studies should also include economic evaluations to assess the cost-effectiveness of digital interventions. This is vital for policymakers and healthcare providers in making informed decisions about the allocation of resources and reimbursement policies. These recommendations aim to address the current limitations and pave the way for more robust, effective, and patient-centric digital health interventions in CKD symptom management. In conclusion, this scoping review illuminates the intricate landscape of digital health interventions in CKD symptom management, emphasizing their potential to transform patient care. The review not only highlights the current state of these interventions but also sheds light on the complexities involved in their development and validation. The findings underscore the need for more standardized, patient-centred approaches and the integration of these technologies into broader CKD care strategies. Looking forward, the research paves the way for future explorations that can further refine and enhance the effectiveness of digital health interventions in chronic disease management. This work stands as a testament to the evolving nature of healthcare delivery and the critical role of technology in shaping future patient care paradigms. Declarations Authorship Xutong ZHENG: Conceptualization; Data curation; Formal analysis; Methodology; Project administration; Software; Supervision; Validation; Visualization; Roles/Writing - original draft. Zhen YANG: Project administration; Resources; Software; Supervision Shu LIU: Project administration; Resources; Software; Supervision Yuqian LI: Software; Supervision; Validation; Visualization Aiping WANG: Conceptualization; review & editing Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Funding Not applicable. Acknowledgements Not applicable. Conflict of Interest None. Data Availability Statement Research data will be shared with reasonable requests when contacting the corresponding author. Funding None. ORCID Xutong Zheng: 0000-0002-9236-1764 References GBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Lond Engl. 2018;392:1736–88. Bikbov B, Purcell CA, Levey AS, Smith M, Abdoli A, Abebe M, et al. 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You need a team: perspectives on interdisciplinary symptom management using patient-reported outcome measures in hemodialysis care-a qualitative study. J Patient-Rep Outcomes. 2023;7:3. Himmelfarb J, Vanholder R, Mehrotra R, Tonelli M. The current and future landscape of dialysis. Nat Rev Nephrol. 2020;16:573–85. Daugirdas JT, Depner TA, Inrig J, Mehrotra R, Rocco MV, Suri RS, et al. KDOQI Clinical Practice Guideline for Hemodialysis Adequacy: 2015 update. Am J Kidney Dis. 2015;66:884–930. Stevens PE. Evaluation and Management of Chronic Kidney Disease: Synopsis of the Kidney Disease: Improving Global Outcomes 2012 Clinical Practice Guideline. Ann Intern Med. 2013;158:825. Mehrotra R, Davison SN, Farrington K, Flythe JE, Foo M, Madero M et al. Managing the symptom burden associated with maintenance dialysis: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int. 2023;104:441–54. Manns B, Hemmelgarn B, Lillie E, Dip SCPG, Cyr A, Gladish M, et al. Setting Research Priorities for Patients on or Nearing Dialysis. Clin J Am Soc Nephrol. 2014;9:1813–21. Kalantar-Zadeh K, Lockwood MB, Rhee CM, Tantisattamo E, Andreoli S, Balducci A, et al. Patient-centred approaches for the management of unpleasant symptoms in kidney disease. Nat Rev Nephrol. 2022;18:185–98. Rhee CM, Edwards D, Ahdoot RS, Burton JO, Conway PT, Fishbane S et al. Living Well With Kidney Disease and Effective Symptom Management: Consensus Conference Proceedings. Kidney Int Rep. 2022;7:1951–63. Ng MSN, Brown EA, Cheung M, Figueiredo AE, Hurst H, King JM et al. The Role of Nephrology Nurses in Symptom Management - Reflections on the Kidney Disease: Improving Global Outcomes Controversies Conference on Symptom-Based Complications in Dialysis Care. Kidney Int Rep. 2023;8:1903–6. Feldman R, Berman N, Reid MC, Roberts J, Shengelia R, Christianer K, et al. 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Technology-assisted cognitive-behavioral therapy intervention for end-stage renal disease. Transl Behav Med. 2020;10:657–63. Nelson RG, Pankratz VS, Ghahate DM, Bobelu J, Faber T, Shah VO. Home-Based Kidney Care, Patient Activation, and Risk Factors for CKD Progression in Zuni Indians: A Randomized, Controlled Clinical Trial. Clin J Am Soc Nephrol. 2018;13:1801–9. Reilly-Spong M, Reibel D, Pearson T, Koppa P, Gross CR. Telephone-adapted mindfulness-based stress reduction (tMBSR) for patients awaiting kidney transplantation: Trial design, rationale and feasibility. Contemp Clin Trials. 2015;42:169–84. Fallahpour S, Nasiri M, Fotokian Z, Alipoor ZJ, Hajiahmadi M. The Effects of Telephone-Based Telenursing on Perceived Stressors among Older Adults Receiving Hemodialysis. Nurs Midwifery Stud. 2020;9:201. Hosseini A, Jackson AC, Chegini N, Dehghan MF, Mazloum D, Haghani S, et al. The effect of an educational app on hemodialysis patients’ self-efficacy and self-care: A quasi-experimental longitudinal study. Chronic Illn. 2023;19:383–94. Hosseini T, Hooshmandja M, Noaparast M, Mojtahedzadeh R, Mohammadi A. Virtual reality exposure therapy to decrease anxiety before surgical invasive procedures in hemodialysis patients: an interventional study. BMC Nephrol. 2024;25:30. Kargar Jahromi M, Javadpour S, Taheri L, Poorgholami F. Effect of Nurse-Led Telephone Follow ups (Tele-Nursing) on Depression, Anxiety and Stress in Hemodialysis Patients. Glob J Health Sci. 2015;8:168. Sandys V, Edwards C, McAleese P, O’Hare E, O’Seaghdha C. Protocol of a pilot-scale, single-arm, observational study to assess the utility and acceptability of a wearable hydration monitor in haemodialysis patients. Pilot Feasibility Stud. 2022;8:17. Torabi Khah M, Farsi Z, Sajadi SA. Comparing the effects of mHealth application based on micro-learning method and face-to-face training on treatment adherence and perception in haemodialysis patients: a randomised clinical trial. BMJ Open. 2023;13:e071982. Cho H, Sohng K-Y. The effect of a virtual reality exercise program on physical fitness, body composition, and fatigue in hemodialysis patients. J Phys Ther Sci. 2014;26:1661–5. Hayashi A, Yamaguchi S, Waki K, Fujiu K, Hanafusa N, Nishi T, et al. Testing the Feasibility and Usability of a Novel Smartphone-Based Self-Management Support System for Dialysis Patients: A Pilot Study. JMIR Res Protoc. 2017;6:e63. Brys ADH, Stifft F, Van Heugten CM, Bossola M, Gambaro G, Lenaert B. mHealth-based experience sampling method to identify fatigue in the context of daily life in haemodialysis patients. Clin Kidney J. 2021;14:245–54. Turoń-Skrzypińska A, Tomska N, Mosiejczuk H, Rył A, Szylińska A, Marchelek-Myśliwiec M, et al. Impact of virtual reality exercises on anxiety and depression in hemodialysis. Sci Rep. 2023;13:12435. Hudson JL, Moss-Morris R, Norton S, Picariello F, Game D, Carroll A, et al. Tailored online cognitive behavioural therapy with or without therapist support calls to target psychological distress in adults receiving haemodialysis: A feasibility randomised controlled trial. J Psychosom Res. 2017;102:61–70. Li J, Wang H, Xie H, Mei G, Cai W, Ye J, et al. Effects of post-discharge nurse-led telephone supportive care for patients with chronic kidney disease undergoing peritoneal dialysis in China: a randomized controlled trial. Perit Dial Int J Int Soc Perit Dial. 2014;34:278–88. Agarwal N, Shah KK, Dansie K, Bennett PN, Greenham L, Brown C, et al. Feasibility of Symptom monitoring WIth Feedback Trial (SWIFT) for adults on hemodialysis: a registry-based cluster randomized pilot trial. BMC Nephrol. 2023;24:345. Dawson J, Tong A, Matus Gonzalez A, Campbell KL, Craig JC, Lee VW. Patients’ experiences and perspectives of a mobile phone text messaging intervention to improve dietary behaviours in haemodialysis. Nutr Diet J Dietit Assoc Aust. 2021;78:516–23. Donald M, Beanlands H, Straus S, Ronksley P, Tam-Tham H, Finlay J, et al. Preferences for a self-management e-health tool for patients with chronic kidney disease: results of a patient-oriented consensus workshop. CMAJ Open. 2019;7:E713–20. Feizalahzadeh H, Tafreshi MZ, Moghaddasi H, Farahani MA, Khosrovshahi HT, Zareh Z, et al. Development and validation of a theory-based multimedia application for educating Persian patients on hemodialysis. Comput Inf Nurs CIN. 2014;32:242–7. Lightfoot CJ, Wilkinson TJ, Hadjiconstantinou M, Graham-Brown M, Barratt J, Brough C, et al. The Codevelopment of My Kidneys & Me: A Digital Self-management Program for People With Chronic Kidney Disease. J Med Internet Res. 2022;24:e39657. Silva SSD, Sipolatti WGR, Fiorin BH, Massaroni L, Lopes AB, Fioresi M, et al. Content validation and development of a software for hemodialysis. Acta Paul Enferm. 2021;34:eAPE02571. Donald M, Kahlon BK, Beanlands H, Straus S, Ronksley P, Herrington G, et al. Self-management interventions for adults with chronic kidney disease: a scoping review. BMJ Open. 2018;8:e019814. Moore GF, Audrey S, Barker M, Bond L, Bonell C, Hardeman W, et al. Process evaluation of complex interventions: Medical Research Council guidance. BMJ. 2015;350:h1258. Hailey D. Telehealth in Nephrology Care-Promises and Challenges. Am J Kidney Dis Off J Natl Kidney Found. 2016;68 1:5–7. Hussein WF, Bennett PN, Abra G, Watson E, Schiller B. Integrating Patient Activation Into Dialysis Care. Am J Kidney Dis Off J Natl Kidney Found. 2022;79:105–12. Tan J, Mehrotra A, Nadkarni GN, He JC, Langhoff E, Post J, et al. Telenephrology: Providing Healthcare to Remotely Located Patients with Chronic Kidney Disease. Am J Nephrol. 2018;47:200–7. 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The Global Burden of Disease (GBD) study has shown that CKD has become a leading cause of death worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. From 1990 to 2017, owing to the improvement in global healthcare level, the mortality rates caused by many chronic non-communicable diseases have declined. For example, cardiovascular diseases have decreased by 30.4%, chronic obstructive pulmonary disease by 41.3%, and cancer by 14.9%. However, the mortality rate of CKD has not shown a similar decline, with a 41.5% increase in age-standardized mortality attributable to CKD [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The GBD report reveals that the ranking of CKD in the list of causes of death continues to rise, ranking 13th in 2016 and 12th in 2017, and is predicted to become the fifth leading cause of death globally by 2040 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe diverse symptom phenotype of Chronic Kidney Disease patients could be determined by the complex pathophysiological basis. The physiological functions of the kidneys primarily include filtration and excretion of metabolic waste products and endocrine function. In end-stage renal disease, the accumulation of uremic toxins due to renal failure affects various systems and organs in the body, leading to inflammation, immune dysfunction, vascular disease, increased risk of bleeding, intestinal dysbiosis, abnormal drug metabolism, and other conditions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Meanwhile, the decline in kidney endocrine function can result in renal malnutrition, renal anaemia, and blood pressure abnormalities. The consensus conference held by Kidney Disease: Improving Global Outcomes (KDIGO) proposed that although hemodialysis can maintain the life of patients, it may not necessarily reduce their symptom burden [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, because the disease itself can cause physiological, psychological, and social functional changes in patients, it can also exacerbate the development of symptoms [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Patients with chronic kidney disease typically experience a variety of symptoms, with the incidence of pain, fatigue, itching, and constipation being over 50% [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Other symptoms include bone and joint pain, insomnia, emotional disorders, sexual dysfunction, sensory abnormalities, and nausea [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Symptom burden refers to the overall impact of all disease-related symptoms on the patient's overall health. Existing research has demonstrated that higher levels of symptom burden are associated with decreased health-related quality of life (HRQOL), increased hospitalisation rates, and higher mortality rates [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The symptom phenotype of chronic kidney disease patients is complex, and how to address and alleviate their symptom burden, while maximizing physiological function and social rehabilitation, is a priority issue [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe KDIGO consensus conference has emphasized that symptom assessment and management are important components of high-quality care for patients with end-stage renal disease. It specifically highlights the need to focus on symptom management strategies and their effectiveness in chronic kidney disease populations, including their impact on relevant patient outcomes such as overall symptom burden, physical function, and health-related quality of life [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Guidelines and consensus statements have recommended the implementation of symptom management for patients to improve their health outcomes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Existing research has identified several characteristic features of implementing symptom management for Chronic Kidney Disease patients: (1) The process requires careful assessment of the availability of local healthcare resources [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. (2) The responsibility for symptom management lies with a complex interdisciplinary team: identifying and managing symptoms in maintenance hemodialysis involves multiple individuals and nursing levels, each with overlapping roles [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Routine symptom assessment is not standardized and often complex. (3) Patient-centered care is emphasized, taking into consideration the patient's values, preferences, and wishes when determining treatment and care plans [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. (4) The biopsychosocial medical model is valued, where symptom assessment and management strategies should consider the patient's biological, psychological, and social factors [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough symptom management is crucial for reducing the symptom burden of patients, there are currently barriers to clinical symptom assessment and management at both the healthcare and patient levels. On the healthcare level: Firstly, healthcare providers lack sufficient awareness of the importance of symptom management[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], leading to frequent underestimation of the range and severity of symptoms in hemodialysis patients[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, because symptom management requires multidisciplinary collaboration, the provision of medical services is often fragmented among nephrologists, nephrology nurses, primary healthcare personnel, social workers, etc., resulting in a lack of continuity in care and difficulty for patients in accessing services[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Furthermore, the current symptom management model neglects the patient-centred approach, with clinical physicians often focusing on laboratory test results rather than the symptoms reported by patients[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Moreover, hemodialysis is a prolonged treatment process that requires attention not only to the patient's disease-related needs [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] but also to the assessment and management of their psychological and social needs. However, current research often overlooks the psychological and social needs of patients, rendering their symptom management strategies ineffective [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the patient level: Firstly, patients often conceal their need for symptom management due to fear, lack of knowledge, low health literacy, etc [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Hemodialysis patients often refrain from communicating their symptoms to healthcare personnel and adopt ineffective self-management strategies [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Secondly, the accessibility of medical resources is also an important influencing factor. Studies have found that patients living far from kidney disease medical resources have low treatment compliance and higher mortality rates [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWith the advancement of science and technology, the field of nephrology has witnessed the emergence of remote nephrology, which combines nephrology with telemedicine. Remote nephrology has played a significant role in the diagnosis, treatment, follow-up, and management of kidney disease. Mobile apps, P2P remote consultation meetings, and web-based kidney disease knowledge learning modules designed based on remote nephrology concepts have overcome geographical barriers, improving patient access to medical resources, care continuity, resource allocation optimisation, effective kidney disease management, and patient health outcomes [\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Worldwide, scholars have also developed digital applications for interventions targeting symptom monitoring and symptom management issues faced by chronic kidney disease patients.\u003c/p\u003e \u003cp\u003eThe development of digital health interventions tailored specifically for symptom management in CKD has garnered substantial attention in recent years. These interventions strive to empower patients through educational resources, self-monitoring tools, symptom-tracking mechanisms, medication adherence support, and remote consultation capabilities [\u003cspan additionalcitationids=\"CR36 CR37 CR38 CR39\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. By harnessing the potential of digital technologies, healthcare providers can remotely monitor patients' symptoms, evaluate their adherence to treatment regimens, and deliver timely interventions as necessary. This may lead to enhanced symptom control, improved medication compliance, reduced healthcare utilization, and heightened patient satisfaction.\u003c/p\u003e \u003cp\u003eNevertheless, the landscape of digital health interventions for symptom management in CKD is in a phase of continual evolution, necessitating a comprehensive understanding of the current state of research in this domain. Medical Research Council\u0026rsquo;s complex intervention framework is a rigorous framework for researchers to develop, validate and implement a complex intervention [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Therefore, conducting a scoping review to chart the existing literature on the development of digital health interventions for symptom management in CKD is imperative. Such a review will aid in identifying gaps, challenges, and prospects in this area, thereby informing future research endeavours and clinical practice guidelines.\u003c/p\u003e"},{"header":"2 Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Methodology \u0026amp; reporting guidelines\u003c/h2\u003e \u003cp\u003eWe used the Arksey \u0026amp; O'Malley as our research framework [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This scoping review was conducted based on the guidelines and principles of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) Checklist [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The scoping review was registered on the OSF (Open Science Framework) and the registration doi is \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.17605/OSF.IO/HAZ9T\u003c/span\u003e\u003cspan address=\"10.17605/OSF.IO/HAZ9T\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The RISMA-ScR Checklist can be found in supplementary file 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Searching strategy\u003c/h2\u003e \u003cp\u003eThree steps were followed in the search strategy. First, the preliminary search was conducted in the Pubmed database using both MeSH terms and free terms to develop search words. Secondly, two researchers and a medical librarian then devised a search strategy to identify studies reporting on the evidence of the development or validation process of digital health interventions for symptom management for patients with Chronic Kidney Disease. We devised tailored search strategies for each database and tried many times to get an optimal result when it is necessary. Third, we reviewed the references of all the included studies to find potentially eligible supplement literature that may be included. We have searched for grey literature to keep the integrity of searching results. The databases we searched are PubMed, Scopus, Embase, the Cochrane Library, Web of Science, CINAHL (via EBSCO), OpenGrey archive, arXiv, and bioxiv. The searching time limit was from inception to December 2023. The detailed search strategy and results can be found in supplementary file 2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Eligibility criteria\u003c/h2\u003e \u003cp\u003eWe used P (Population), I (Intervention), C (comparator), and O (Outcome). Studies were included if they met the following criteria- Population: 1) adult patients with chronic kidney disease at any phase of 1\u0026ndash;5; 2) patients undergoing dialysis or conservative management. Patients facing or awaiting kidney transplants were also considered because before completing the transplant process patients were still in the state of chronic kidney disease. Intervention: intervention focusing on e-health. E-health is a broad term that includes telemedicine, electronic health records, telehealth, mobile health applications, and other forms of ICT-based health services. We adopted two sources of e-health to help us define the intervention. E-health is defined by the World Health Organization as \u0026ldquo;the transfer of health resources and health care by electronic means\u0026rdquo; [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. It encompassed the use of information and communication technologies (ICT) in the health sector, including the delivery of health information, care, and treatment through digital and electronic platforms. The Australian National e-Health strategy further elaborated on this definition by stating that e-Health ensures \u0026ldquo;that the right health information is provided to the right person at the right place and time in a secure, electronic form to optimize the quality and efficiency of health care delivery\u0026rdquo; [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Outcome: 1) The focus and aim of this study should include at least one aspect of symptom evaluation, symptom burden or symptom management; 2) For experimental studies, the outcome measure should include at least one measure related to symptoms caused by the chronic kidney disease, the progression of disease or the treatment (dialysis, medicine related to Chronic Kidney Disease, etc. al) rather than caused by other external factors such as anxiety caused by COVID-19 epidemic. Others: were full-text, peer-reviewed articles and published in English. Studies were excluded if they were letters, correspondence, dissertations and conference papers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Study selection\u003c/h2\u003e \u003cp\u003eThe first screen is based on titles, abstracts and keywords, and the second round screen is based on full texts. Study selection was performed according to the predetermined eligibility criteria following established guidelines for systematic reviews. We choose to follow the guidelines for systematic reviews because the process of selection of systematic reviews is rigorous and repeatable. All search results were initially imported into the reference management software program Endnote X9. After the duplicates were removed, titles and abstracts were read carefully to identify the eligibility of studies. Then full-text documents were scrutinized to further identify the fulfillment of selection criteria. Before the beginning of the screening process, we hold an online meeting to ascertain the criteria of each step of screening (such as \u0026ldquo;In what condition should a study be judged as irrelevant and excluded by reading the title and abstract ?\u0026rdquo;) to prevent the misunderstanding of the eligibility or subjective understanding. After that, the calibration exercise was conducted on the subset of studies to identify any potential discrepancies or areas of confusion among screeners [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. One of the researchers chose 50 publications randomly retrieved in the Pubmed database. All reviewers related to the screening process would participate in the exercise to independently evaluate all the literature before the first author of this review compared all their answers and announced the right assessment. At each stage of formal screening, a minimum of two trained reviewers (XT, YZ, LY, YQ) independently read and critically evaluated the eligibility criteria of each study that may be included. Any discrepancies would be discussed to reach a consensus at the pilot and formal screening process stage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data extraction\u003c/h2\u003e \u003cp\u003eBefore the beginning of the extraction, the coordinator (LS) who did not participate in the screening and extraction process conducted the extractor masking to reduce bias in the extraction process. All included papers were transformed to reproducible form and their study-specific details such as the author, study type, affiliations, and journal names were replaced with generic terms (such as author A, affiliations A, journal B) to prevent bias. We used a pre-designed data extraction table to extract the characteristics and content of the evidence, including the first author, publication or update year, type of evidence, the main theme of the evidence, and related evidence of this topic. At least two trained researchers (XT, YQ, YZ) participated in the data extraction process. The extracted evidence was compared with each other and any discrepancies related to the content of the extraction were discussed in interpretation to reach a consensus on the correct extraction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Data synthesis\u003c/h2\u003e \u003cp\u003eNarrative synthesis was conducted from multiple studies to get a descriptive summary and interpret the collective evidence fully [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Two researchers (XT, and YQ) independently synthesized the evidence and compared the results with each other. Any discrepancies were discussed and resolved by the third researcher (AP).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Search result\u003c/h2\u003e \u003cp\u003eWe retrieved a total of 38,154 articles from the database. After removing duplicates, there were 18,006 remaining articles. Following a review of titles and abstracts, 17,926 irrelevant articles were excluded, leaving 4 articles without full text available and 76 articles requiring full-text review. After excluding articles that did not meet the inclusion criteria, 28 articles remained. The reference lists of these 28 articles were reviewed, resulting in the addition of 3 more references. Ultimately, we included 31 articles. Of the 31 included articles, 7 described the development process of interventions, 27 were validation of interventions, and 3 both described the development of interventions and reported on experimental results. The process of literature retrieval, screening, and evaluation is summarized in Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eFigure 1 Flow chart for the literature screening\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Characteristics of Validation Studies\u003c/h2\u003e \u003cp\u003eIn 27 validation studies, 9 studies are from the USA [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan additionalcitationids=\"CR50 CR51 CR52 CR53 CR54 CR55\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. 7 studies are from Iran [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan additionalcitationids=\"CR58 CR59 CR60 CR61\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. 1 study each is from South Korea [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], Japan [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], the Netherlands [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], Poland [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], UK [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], Ireland [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] and United Arab Emirates [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Two studies are from the and China [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. 3 studies are from Australia [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsidering the purpose of the study, 13 studies are feasibility, acceptability or utility studies [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan additionalcitationids=\"CR50 CR51 CR52 CR53\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. 14 studies are efficacy studies [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan additionalcitationids=\"CR58 CR59\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e22 studies focused specifically on hemodialysis patients [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan additionalcitationids=\"CR52 CR53\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan additionalcitationids=\"CR58 CR59 CR60 CR61 CR62 CR63 CR64 CR65 CR66\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], 2 studies focused on chronic kidney disease [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], 2 on kidney transplant candidates [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], and 1 on peritoneal dialysis [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe intervention could be categorized into 9 categories: (1) Telephone-based interventions (telenursing/telenursing): 8 studies [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. (2) Mobile health (mHealth) applications/interventions: 6 studies [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. (3) Virtual reality based interventions: 3 studies [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. (4) Patient-reported outcome measures (PROMs/EPROMs): 3 studies [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. (5) Text-message-based: 3 studies [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. (6) website-based: 2 studies [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. (7) online internet-based platform: 1 study [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. (8) wearable monitor study [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study designs could be categorized as 6 categories: (1) randomized controlled trials: 11 studies [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan additionalcitationids=\"CR67 CR68\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], (2) quasi-experimental studies: 2 studies [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. (3) Observational studies: 3 studies [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. (4) Pretest-posttest studies: 5 studies [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]; (5) Mixed methods studies: 3 studies [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]; (6) qualitative studies: 3 studies [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe outcome measure could be divided as 6 categories: (1) Feasibility and acceptability outcomes: recruitment and retention rate [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan additionalcitationids=\"CR50 CR51\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], completion time and rate [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], usability result [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], adherence rate [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], barriers and enablers to uptake the system [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], and participants\u0026rsquo; subjective rating [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. (2) Clinical outcomes: physical fitness and body composition [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], dialysis weight [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], the occurrence of complications [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], body mass index [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. (3) Patient-reported outcome: quality of life [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan additionalcitationids=\"CR68\" citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], symptom burden [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], specific symptom burden such as fatigue [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], mood related symptom [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], hyper/hypovolaemia symptoms [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], patient activation [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], medical adherence [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], sleep quality [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], social support [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], self efficiency [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. (4) Laboratory parameters: blood chemistry such as creatinine, potassium, and haemoglobin [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. (5) Dietary outcomes: Energy and protein intakes [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], adherence to fluid and dietary recommendations [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. (6) other outcomes: cost-effectiveness [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], and health service utilization [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Characteristics of Development Studies\u003c/h2\u003e \u003cp\u003eMost interventions involved patient participation and expert guidance in their development process. Some also worked with specific patient communities to incorporate their values and needs. 6 studies [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan additionalcitationids=\"CR72 CR73\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e] involved patients in the stakeholder input and evaluation process. 3 studies [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e] included input from caregivers and family members. 4 studies [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan additionalcitationids=\"CR72\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e] involved multidisciplinary experts like clinicians, health providers, policymakers to provide guidance.\u003c/p\u003e \u003cp\u003eMost chronic kidney disease interventions underwent iterative cycles of refinement based on input from various stakeholders. 7 studies[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan additionalcitationids=\"CR72 CR73\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e] refined the interventions based on feedback from stakeholders like patients, experts, nurses etc. This included modifications to content, features, graphics, and colours and correcting errors. 3 studies [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e] adapted the delivery methods of the interventions based on access needs and preferences. This included changing an in-person program to a teleconference and building an e-health tool. 1 study [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] highlights the importance of involving end-users in refining health interventions to enhance effectiveness and acceptance.\u003c/p\u003e \u003cp\u003eOnly 5 studies use the program theory to devise or develop the intervention [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan additionalcitationids=\"CR72\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Only 4 studies\u0026rsquo; development processes are evidence-based [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn this scoping review, we comprehensively explored the development and validation of digital health interventions aimed at managing symptoms in patients with Chronic Kidney Disease (CKD). Our findings delineated a multifaceted landscape where these interventions have a significant impact on various aspects of patient care.\u003c/p\u003e \u003cp\u003eFirstly, patient engagement has emerged as a critical outcome. Digital interventions, by their inherent accessibility and user-friendliness, have facilitated better engagement of patients in their own care. This aligns with findings from [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], which demonstrated an increase in self-management behaviours among CKD patients. The interactive nature of these applications, including features like symptom tracking and feedback mechanisms, has empowered patients to take an active role in managing their condition. Secondly, the quality of life for CKD patients has been another focal point. Our review found that well-designed digital interventions could potentially alleviate some of the burdens associated with CKD symptom management. This is particularly relevant considering the chronic nature of CKD, where long-term quality of life is a significant concern. However, the conclusion needs to be provided by systematic review and meta-analysis considering more randomization controlled trials conducted in the future. Studies like [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] have shown that effective symptom management through digital means can lead to improved mental and physical well-being. Lastly, symptom reduction itself is a direct and measurable outcome of these digital interventions. By providing personalized and timely management options, these tools have shown promise in reducing the severity of CKD-related symptoms. This not only improves patient comfort but also potentially delays disease progression, as suggested by [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. These findings highlight the transformative potential of digital health interventions in CKD care. By improving patient engagement, enhancing the quality of life, and effectively managing symptoms, these tools offer a new paradigm in the management of chronic diseases like CKD.\u003c/p\u003e \u003cp\u003eThe practical implications of our findings in the realm of digital health interventions for CKD are manifold and significant. For healthcare providers, the integration of these digital tools into routine care emerges as a crucial strategy. This involves not just the adoption of technology but also a shift in the care paradigm to include digital monitoring and patient-reported outcomes as part of standard practice. Considering patient preferences and technological literacy is vital to ensure adherence and effectiveness, as diverse patient populations may have varying levels of comfort and access to technology.\u003c/p\u003e \u003cp\u003eThis scoping review, using a deductive method based on the Medical Research Council\u0026rsquo;s complex intervention framework to categorize the previous studies concerning symptom management. By applying this framework, we have identified some gaps that need to be narrowed in the future. Firstly, the implementation outcomes such as fidelity, satisfaction, and adoption needed to be considered in future studies. According to the process evaluation framework, the implementation process could impact the outcome of the intervention. The high adoption, wide reach, and high fidelity of e-health intervention could improve the value of effect size and effectiveness of a trial. Secondly, the programme theory was omitted in some previous studies. Programme theory could describe in what condition could an intervention lead to its effects. It is a key method to understand the underlying mechanism of an intervention. Medical Research Council advised that the programme theory should be developed at the beginning of the research project and refined in successive phases [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Future studies should choose or develop appropriate programme theory to guide the development, trial and implementation of a complex intervention to reduce any uncertainties in the research project. Last but not least, the process evaluation was undermined in most of the studies, only two studies [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] used the process evaluation to appraise or validate their theoretical hypothesis. As the process evaluation has been regarded as an essential part of designing and testing the complex intervention [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e], a comprehensive and well-designed process evaluation should be considered and integrated into the study protocol before the research project begins.\u003c/p\u003e \u003cp\u003eThis scoping review could, to some extent, reveal the inequality of health resources. We found most of the development and validation studies were conducted in developed countries. However, for patients, particularly in remote or undeveloped countries or regions, these digital interventions can be transformative. They offer a means to overcome geographical and logistical barriers, providing consistent and personalized symptom management support [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan additionalcitationids=\"CR78\" citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Cultivating a nephrologist is time-consuming, many developing countries reported the number of a nephrologists is insufficient [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. This accessibility is crucial in enhancing the overall quality of care for CKD patients who might otherwise have limited access to specialized healthcare services. Policy-wise, the review underscores the necessity of developing guidelines to standardize digital health interventions. This standardization should aim to ensure the quality and efficacy of these tools, making them reliable components of CKD care. Policies should also address issues such as data security, privacy, and interoperability of digital health systems to facilitate their seamless integration into existing healthcare infrastructures [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Furthermore, our findings suggest a need for policy initiatives that support research and development in this field, along with the creation of reimbursement models for digital health interventions. This could encourage innovation while ensuring these tools are accessible and affordable for all CKD patients.\u003c/p\u003e \u003cp\u003eIn essence, our review highlights a multi-dimensional approach involving healthcare providers, patients, and policymakers, all of whom play a pivotal role in the successful implementation of digital health interventions for CKD symptom management.\u003c/p\u003e \u003cp\u003eWhen comparing our findings with existing literature in the field, a few key areas of convergence and divergence emerge. Our review aligns with studies like [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], which emphasize the effectiveness of digital interventions in enhancing patient self-management in CKD. However, our review goes further, offering a detailed analysis of how these interventions are developed and validated, a perspective less explored in current literature. One notable divergence is in the area of intervention customization and patient adherence. While existing studies, such as those by [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e], highlight the potential of digital health interventions in improving clinical outcomes, our review sheds light on the complexities of ensuring consistent patient engagement. This includes challenges in maintaining long-term adherence, which have been less emphasized in previous studies. Another area where our review contributes new insights is in the integration of digital health interventions within the broader healthcare ecosystem for CKD patients. While the literature often focuses on the efficacy of individual digital tools, our review considers how these tools interact with existing healthcare practices and the implications for holistic care. Overall, our review enriches the existing body of literature by providing a more nuanced understanding of the complexities involved in the development and implementation of digital health interventions for CKD, highlighting areas that require further exploration and development.\u003c/p\u003e \u003cp\u003eOur scoping review, while comprehensive, acknowledges several limitations inherent in the current body of research on digital health interventions for CKD symptom management. One primary limitation is the significant variability in intervention types. This diversity, spanning from simple mobile applications to complex telehealth systems, poses challenges in drawing generalized conclusions about efficacy and usability. Another critical limitation is the differences in study methodologies. Many studies in this field employ varied research designs, sample sizes, and outcome measures, making it difficult to conduct direct comparisons or meta-analyses. This inconsistency in research design hinders the ability to develop a cohesive understanding of how digital interventions can be most effectively implemented in CKD care. Furthermore, there is a notable gap in long-term outcome data. Most studies focus on short-term outcomes, leaving questions about the sustainability and long-term effectiveness of these interventions. This lack of long-term data is a significant concern in CKD, a chronic condition where the long-term management of symptoms is crucial. Additionally, patient adherence and engagement over extended periods remain under-explored. While initial engagement with digital health interventions may be high, maintaining this engagement over time, particularly in a chronic disease context, is not well understood. Overall, these limitations highlight the need for more standardized, long-term, and comprehensive research in the field to fully understand and optimize the role of digital health interventions in CKD symptom management. Given the limitations and gaps identified in the current research, several recommendations for future research in digital health interventions for CKD symptom management are warranted:\u003c/p\u003e \u003cp\u003eThere's a critical need for long-term studies that evaluate the sustained impact of digital interventions on CKD symptom management. Such studies should not only assess immediate clinical outcomes but also explore long-term adherence, patient satisfaction, and quality of life. CKD affects patients differently, influenced by factors such as age, disease stage, and comorbid conditions. Future research should focus on specific subgroups of CKD patients to understand how digital interventions can be tailored to meet diverse needs. Research should also explore how digital interventions can be effectively integrated with traditional care models. This includes examining the roles of healthcare providers in supporting these interventions and understanding how digital tools can complement existing treatments. Investigating new technologies and their adaptation in the context of CKD care is essential. This includes exploring the potential of emerging technologies like artificial intelligence and machine learning in personalizing care and predicting symptom fluctuations. Future research should prioritize patient-centred design, assessing the usability and accessibility of digital interventions from the patient\u0026rsquo;s perspective. Understanding patient preferences, challenges, and barriers to technology use will be crucial in designing effective interventions. Studies should also include economic evaluations to assess the cost-effectiveness of digital interventions. This is vital for policymakers and healthcare providers in making informed decisions about the allocation of resources and reimbursement policies. These recommendations aim to address the current limitations and pave the way for more robust, effective, and patient-centric digital health interventions in CKD symptom management.\u003c/p\u003e \u003cp\u003eIn conclusion, this scoping review illuminates the intricate landscape of digital health interventions in CKD symptom management, emphasizing their potential to transform patient care. The review not only highlights the current state of these interventions but also sheds light on the complexities involved in their development and validation. The findings underscore the need for more standardized, patient-centred approaches and the integration of these technologies into broader CKD care strategies. Looking forward, the research paves the way for future explorations that can further refine and enhance the effectiveness of digital health interventions in chronic disease management. This work stands as a testament to the evolving nature of healthcare delivery and the critical role of technology in shaping future patient care paradigms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthorship\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXutong ZHENG: Conceptualization; Data curation; Formal analysis; Methodology; Project administration; Software; Supervision; Validation; Visualization; Roles/Writing - original draft.\u003c/p\u003e\n\u003cp\u003eZhen YANG: Project administration; Resources; Software; Supervision\u003c/p\u003e\n\u003cp\u003eShu LIU: Project administration; Resources; Software; Supervision\u003c/p\u003e\n\u003cp\u003eYuqian LI: Software; Supervision; Validation; Visualization\u003c/p\u003e\n\u003cp\u003eAiping WANG: Conceptualization; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch data will be shared with reasonable requests when contacting the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXutong Zheng: 0000-0002-9236-1764\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGBD 2017 Causes of Death Collaborators. 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Feasibility of Symptom monitoring WIth Feedback Trial (SWIFT) for adults on hemodialysis: a registry-based cluster randomized pilot trial. BMC Nephrol. 2023;24:345.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDawson J, Tong A, Matus Gonzalez A, Campbell KL, Craig JC, Lee VW. Patients\u0026rsquo; experiences and perspectives of a mobile phone text messaging intervention to improve dietary behaviours in haemodialysis. Nutr Diet J Dietit Assoc Aust. 2021;78:516\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonald M, Beanlands H, Straus S, Ronksley P, Tam-Tham H, Finlay J, et al. Preferences for a self-management e-health tool for patients with chronic kidney disease: results of a patient-oriented consensus workshop. CMAJ Open. 2019;7:E713\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeizalahzadeh H, Tafreshi MZ, Moghaddasi H, Farahani MA, Khosrovshahi HT, Zareh Z, et al. Development and validation of a theory-based multimedia application for educating Persian patients on hemodialysis. Comput Inf Nurs CIN. 2014;32:242\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLightfoot CJ, Wilkinson TJ, Hadjiconstantinou M, Graham-Brown M, Barratt J, Brough C, et al. The Codevelopment of My Kidneys \u0026amp; Me: A Digital Self-management Program for People With Chronic Kidney Disease. J Med Internet Res. 2022;24:e39657.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilva SSD, Sipolatti WGR, Fiorin BH, Massaroni L, Lopes AB, Fioresi M, et al. Content validation and development of a software for hemodialysis. Acta Paul Enferm. 2021;34:eAPE02571.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonald M, Kahlon BK, Beanlands H, Straus S, Ronksley P, Herrington G, et al. Self-management interventions for adults with chronic kidney disease: a scoping review. BMJ Open. 2018;8:e019814.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore GF, Audrey S, Barker M, Bond L, Bonell C, Hardeman W, et al. Process evaluation of complex interventions: Medical Research Council guidance. BMJ. 2015;350:h1258.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHailey D. Telehealth in Nephrology Care-Promises and Challenges. Am J Kidney Dis Off J Natl Kidney Found. 2016;68 1:5\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHussein WF, Bennett PN, Abra G, Watson E, Schiller B. Integrating Patient Activation Into Dialysis Care. Am J Kidney Dis Off J Natl Kidney Found. 2022;79:105\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan J, Mehrotra A, Nadkarni GN, He JC, Langhoff E, Post J, et al. Telenephrology: Providing Healthcare to Remotely Located Patients with Chronic Kidney Disease. Am J Nephrol. 2018;47:200\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsman MA, Alrukhaimi M, Ashuntantang GE, Bellorin-Font E, Benghanem Gharbi M, Braam B, et al. Global nephrology workforce: gaps and opportunities toward a sustainable kidney care system. Kidney Int Suppl. 2018;8:52\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDawson J, Lambert K, Campbell K, Kelly J. Incorporating digital platforms into nutritional care in chronic kidney disease. Semin Dial. 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/sdi.12998\u003c/span\u003e\u003cspan address=\"10.1111/sdi.12998\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Almeida OAE, de Lima MEF, Santos WS, Silva BLM. Telehealth strategies in the care of people with chronic kidney disease: integrative review. Rev Lat Am Enfermagem. 2023;31:e4049.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrawz PE, Archdeacon P, McDonald CJ, Powe NR, Smith KA, Norton J, et al. CKD as a Model for Improving Chronic Disease Care through Electronic Health Records. Clin J Am Soc Nephrol CJASN. 2015;10:1488\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Chronic Kidney Disease, digital health interventions, symptom management, mobile health, telemedicine, scoping review","lastPublishedDoi":"10.21203/rs.3.rs-4305056/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4305056/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Chronic Kidney Disease (CKD) presents a growing global health issue with a complex symptom phenotype, negatively impacting patients' health-related quality of life and increasing healthcare utilization. While digital health interventions offer promising avenues for improving symptom management in CKD, understanding their development, validation, and effectiveness is crucial for clinical application.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo comprehensively map the existing literature on the development and validation processes of digital health interventions aimed at managing symptoms in patients with Chronic Kidney Disease (CKD), using the UK Medical Research Council's complex intervention framework as a guiding lens. This scoping review aims to identify gaps, challenges, and prospects in this domain, thereby informing future research endeavours and clinical practice guidelines for developing and implementing effective digital health interventions for CKD symptom management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA scoping review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Databases searched included PubMed, Scopus, Embase, and others, covering literature up to December 2023. Studies were selected based on predefined eligibility criteria focusing on digital health interventions for CKD symptom management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe search yielded 31 studies, with a mix of development and validation studies, predominantly from developed countries. The review highlights the potential of digital interventions in enhancing symptom management, quality of life, and patient engagement in CKD care. However, gaps were identified as follows: 1) Iterative refinement cycles involving multidisciplinary stakeholders enhanced intervention acceptability and usability should be guaranteed, 2) Theory-driven and evidence-based approaches were underutilized in current intervention development, 3) Long-term implementation outcomes and process evaluations were rarely assessed. This review maps an evolving landscape where digital health interventions offer patient-centric solutions for CKD symptom management while highlighting opportunities for methodological advancements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eDigital health interventions hold promise for improving symptom management in CKD, yet more research is needed to overcome current limitations and fully realize their potential. Future studies should focus on patient-centred designs, comprehensive validation processes, exploring the underlying mechanism using process evaluation and the integration of these technologies into routine clinical practice.\u003c/p\u003e","manuscriptTitle":"Toward the complexities of the development and validation process of digital health interventions for the symptom management for patients with Chronic Kidney Disease: A scoping review based on the UK Medical Research Council Framework","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 23:02:13","doi":"10.21203/rs.3.rs-4305056/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-29T05:05:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-28T06:45:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2024-10-07T06:23:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-15T15:26:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15004184512934568886293684396205935562","date":"2024-08-15T12:31:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-29T17:20:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-08T13:10:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-26T08:19:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-26T06:57:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-04-26T06:55:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"562fa88f-74b1-40dd-9191-9d197b20a39d","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T16:03:47+00:00","versionOfRecord":{"articleIdentity":"rs-4305056","link":"https://doi.org/10.1186/s12889-024-20871-5","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2024-12-19 15:58:02","publishedOnDateReadable":"December 19th, 2024"},"versionCreatedAt":"2024-06-07 23:02:13","video":"","vorDoi":"10.1186/s12889-024-20871-5","vorDoiUrl":"https://doi.org/10.1186/s12889-024-20871-5","workflowStages":[]},"version":"v1","identity":"rs-4305056","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4305056","identity":"rs-4305056","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}