Abstract
Aims
The health management of end-stage renal disease patients is a complicated process, and mobile health management technology provides a new choice for the health management of end-stage renal disease patients. The scope of clinical studies on mobile health management for patients with end-stage renal disease was reviewed, and found that about mobile health management problems existing in the literature were identified to provide ideas for subsequent mobile health management research.
Methods
The databases Web of Science, PubMed, The Cochrane Library, Embase, CNKI, Wan Fang Data, BMJ, and VIP were systematically searched for studies on Mobile health management among end-stage renal disease in adult and adolescent patients or children undergoing kidney replacement therapy. The search covered the period from the inception of the databases to June 20, 2023. Two independent reviewers conducted the literature screening process. Following eligibility screening, a total of 38 papers were included for data extraction and descriptive analysis.
Results
A total of 38 studies from 14 countries were finally included. The majority of which were interventional trials. The platforms used in these studies included remote monitoring systems, apps, websites, mobile phones or tablets, and social platforms. These platforms provided patients with a wide range of services, including disease management, behavioral intervention, social support, and follow-up care. Most studies focused on patient clinical indicators, patient experience, quality of life, and healthcare costs.
Conclusion
Our findings that mobile health management has been widely used in disease management of end-stage renal disease patients, with rich management content and many evaluation indicators. Future studies should strengthen the evaluation of patients’ mental health, quality of life, and healthcare costs. Additionally, developing a clinical decision support system would enable mobile health management to play a more effective role in end-stage renal disease patients.
Keywords: end-stage renal disease, mobile health management, MHealth management, ESRD, MHealth
1. Introduction
As the global population ages at an unprecedented rate, the incidence of chronic diseases is soaring, and chronic kidney disease (CKD) is no exception (1). The more patients suffer from CKD, the more develop end-stage renal disease (ESRD) (2). ESRD is diagnosed when kidney function is insufficient to sustain life without kidney transplantation or dialysis (3), and its prevalence is also rising (4). The treatment of ESRD is complex and demanding, requiring long-term dialysis therapy (hemodialysis or peritoneal dialysis), kidney transplantation, and drug management (5). Patients with ESRD also face a variety of health management challenges, including cardiovascular disease (6) and skin disorders (7), etc.
In contrast to traditional medical models, which require patients go to a hospital for treatment, peritoneal dialysis can now be performed at home, while hemodialysis is typically conducted at a dialysis center via an arteriovenous fistula (which requires months to mature before use), an arteriovenous graft (which can be used in as little as 24 h, depending on the graft material), or a central venous catheter (which can be used immediately, but poses the highest risk of infection). Vascular access is essential for both hemodialysis and peritoneal dialysis, but kidney transplantation remains the preferred treatment for ESRD patients (5).
Mobile health (MHealth) management technology has emerged as a promising method in modern medicine, offering patients more convenient and effective ways to manage their health (8). MHealth leverages mobile devices and other technologies to improve patient engagement, monitoring, outreach, and healthcare services. It is accelerating the modernization of medicine and has been widely adopted for the management of various chronic diseases (9), including diabetes (10), hypertension (11), cancer (12), and others. In this context, MHealth management technology provides a new and innovative approach to the health management of ESRD patients.
However, the health management of ESRD patients is a complex and challenging endeavor, requiring close collaboration between medical institutions, patients (13) and IT services. It’s not just about data protection and data safety, it’s also about how much project funding or budget there is, and bureaucracy is also an obstacle. MHealth management technology, interventions that provide health-related information through telecommunications or other wireless technologies, such as smartphones, tablets (14, 15) telemedicine, can provide patients with more convenient and personalized healthcare solutions, enabling them to better monitor their health status and improve their overall health and quality of life (16). A scope review is an ideal tool to determine the scope or coverage of the body of literature on a given topic and to specify the amount of literature and research available as well as an overview (17). Previous studies that reviewed the scope of lifestyle interventions provided by eHealth in chronic kidney disease found that there is currently insufficient evidence to recommend the implementation of specific lifestyle e-health interventions in the clinical care of people with chronic kidney disease (18), funding or budget problems which gives researchers the direction and focus of future research in chronic kidney disease. Therefore, a scope review on MHealth management for ESRD patients is warranted to better manage ESRD patients.
This study aims to review the scope of mobile health management for patients with end-stage renal disease and to synthesize and analyze relevant domestic and international literature. The application landscape, advantages and disadvantages, development trends, and future prospects of mobile health management technology in ESRD patient management were comprehensively studied and analyzed to provide a reference for the practice of mobile health management for ESRD patients. We envision that mobile health management technology for ESRD patients will continue to develop and improve in the future, providing patients with more convenient and personalized health management services. Simultaneously, we hope that medical institutions and researchers will pay greater attention to the health management of ESRD patients and provide more comprehensive and in-depth support for research and application in this area.
2. Methods
In this study, according to PRISMA Extension for Scoping Reviews (PRISMA-SCR) (19) (see Supplementary file 1) and the Arksey and O’Malley (20) framework of mobile health management in patients with end-stage renal disease in the scope of review. All data can be made available upon reasonable request.
2.1. Inclusion and exclusion criteria
Inclusion criteria ① Subjects: laboratory confirmed patients with end-stage renal disease, regardless of sex, age. ② Study types: randomized controlled trial, non-randomized controlled trial, cohort study, case–control study. ③ Literature sources: journal papers published in peer-reviewed journals and dissertations.
Exclusion criteria ① Repeated publication, full text is not available.
2.2. Search strategy
A systematic paper search was conducted in PubMed, Cochrane Library, CNKI, VIP, Web of Science, EMBASE, BMJ, and Wan fang digital journal full-text database, we included all papers which were listed in the different searching tools until day June 20, 2023. The search strategy was: (end-stage renal disease OR ESRD) AND (telemedicine OR telehealth OR eHealth OR mobile health OR MHealth).
2.3. Literature screening and data extraction
Two researchers (WY and RY) rigorously adhered to the inclusion and exclusion criteria for literature screening. First, they reviewed the titles and abstracts of all identified studies, and then carefully read the full text of any studies that potentially met the inclusion criteria. Any disagreements were resolved through discussion or consultation with a third reviewer. Data were extracted from the included studies, including author, publication year, country, study type, sample size, MHealth management interventions, and evaluation indicators, etc.
3. Results
3.1. Literature search results
A total of 676 papers were retrieved from all databases. After 67 duplicate records were removed, among the remaining 609 relevant studies, 523 were excluded due to being object mismatch, thematic incompatibility, comments, minutes of meeting, book or document. The full text of the remaining 86 studies were read and 48 studies were removed after reading the full text due to object mismatch. The remaining 38 papers were extracted from the corresponding data according to the data extraction requirements. The papers screening process is shown in Figure 1.
Figure 1.
Papers screening process.
3.2. Basic information of literature
The 38 included articles were published between 2003 and 2023, originating from 14 countries, with the United States contributing the most (14 articles, 36.84%), followed by China (7 articles, 18.42%) and Denmark (3 articles, 7.89%). In terms of literature types, 16 were randomized controlled trials, 8 were cohort studies, 11 were qualitative studies, and 3 were meta-analyses/systematic reviews, primarily examining ESRD telecare. According to the treatment methods, 11 studies focused on hemodialysis, 9 on peritoneal dialysis, 11 on kidney transplantation, and 7 on unspecified or multiple dialysis methods (Tables 1, 2).
Table 1.
Basic information of literature.
| Type | First author | Study characteristics | Year | Region | Number of analyzed patients | Age (mean/mean ± sd) | Sex (male %) |
|---|---|---|---|---|---|---|---|
| Hemodialysis | Li Shensen (21) | Randomized Controlled Trial | 2012 | China | 157 | 61.4 ± 13.3 | 54.78% |
| Zhaohui Ni (22) | Randomized Controlled Trial | 2019 | China | 8,392 | 60.5 ± 13.7 | 60.28% | |
| Raquel Scofano (23) | Qualitative research | 2022 | Brazil | 17 | 80 ± 20 | 64% | |
| Dayna E. Minatodani (24) | Randomized Controlled Trial | 2013 | USA | 99 | 45–75 | 55.56% | |
| Jennifer Gabbard (25) | Randomized Controlled Trial | 2021 | USA | 22 | 69.4 ± 6.6 | 36.40% | |
| Neumann, Claas L (26) | Randomized Controlled Trial | 2013 | Germany | 120 | 65.7 ± 14.7 | / | |
| Steven J. Berman (27) | Randomized Controlled Trial | 2011 | USA | 44 | 57–62 | 54.54% | |
| Jessica Dawson (28) | Cohort study | 2020 | Australia | 115 | / | / | |
| Eric D. Weinhandl (29) | Cohort study | 2017 | USA | 606 | 52.4 ± 14.1 | 35% | |
| Nicola Elzabeth Anderson (30) | Qualitative research | 2021 | UK | 22 | > = 18 | / | |
| Mohsen T orabi Khah (31) | Randomised clinical trial. | 2023 | Iran | 35 | 45.26 ± 1.42 | 49% | |
| Kidney transplantation | John W. McGillicuddy (32) | Randomized Controlled Trial | 2020 | USA | 71 | 52 | 69% |
| Nielsen, Charlotte (33) | Qualitative research | 2020 | Denmark | / | / | / | |
| Rachel E. Patzer (34) | Qualitative research | 2016 | USA | 721,339 | / | / | |
| Rachel E. Patzer (35) | Randomized Controlled Trial | 2019 | USA | 470 | 50.6 ± 10.1 | 62.50% | |
| Elisa J. Gordon (36) | Cohort study | 2016 | USA | 63 | 18–75 | / | |
| A. Schmid (37) | Randomized Controlled Trial | 2016 | Germany | 46 | 18–66 | 54.34% | |
| Edward W. Aberger (38) | Randomized Controlled Trial | 2014 | USA | 66 | 54 | 48% | |
| Lieke Wirken (39) | Qualitative research | 2017 | Holland | 13 | 58.8 ± 11.5 | 69% | |
| Rachel E. Patzer (40) | Randomized Controlled Trial | 2016 | USA | 450 | 18–75 | ||
| Charlotte Nielsen (41) | Qualitative research | 2020 | Denmark | 4 | / | / | |
| Alfonso M Cueto-Manzano (42) | Qualitative research | 2015 | México | 23 | 33 | 57% | |
| Peritoneal dialysis | Zheng Pian (43) | Randomized Controlled Trial | 2018 | China | 107 | 45.40 ± 8.90 | 54.21% |
| Fu Qiao Hui (44) | Randomized Controlled Trial | 2022 | China | 100 | 51.96 ± 16.40 | 53.59% | |
| Manya Magnus (45) | Randomized Controlled Trial | 2017 | USA | 200 | 45–64 | 51% | |
| Daphne M. Harrington (46) | Cohort study | 2014 | India | 6 | 24–61 | 50% | |
| Tiantian Ma (47) | Cohort study | 2022 | China | 7,000 | 51.2 ± 14.5 | 53.70% | |
| Brett Tarca (48) | Cohort study | 2021 | Australia | / | / | / | |
| Vishal Dey (49) | Cohort study | 2016 | UK | 22 | 61.6 | 45% | |
| Giusto Viglino (50) | Qualitative research | 2020 | Italy | 107 | 72.2 ± 13.1 | 58.88% | |
| Xiao Xu (51) | Cohort study | 2022 | China | 7,539 | / | / | |
| Not specify the treatment methods | Chi-Sheng Hung (52) | Randomized Controlled Trial | 2018 | Taiwan, China | 715 | 69.7 ± 11.8 | 68.00% |
| Ji-Eun Kim (53) | Qualitative research | 2020 | USA | 16 | 58.18 ± 12.67 | 62.5 | |
| Emily SETO (54) | Qualitative research | 2007 | Canada | 149 | 52 ± 17 | 62% | |
| Abu Bakkar Siddique (55) | Systematic evaluation | 2019 | USA | / | / | / | |
| Manuel Prado (56) | Qualitative research | 2003 | Spain | / | / | / | |
| Meaghan Lunney (57) | Mate analysis | 2018 | / | / | / | / | |
| Priya Ramar (58) | Mate analysis | 2017 | USA | / | / | / |
“/”means not mentioned.
Table 2.
Characteristics of study interventions and evaluation index.
| Type | First author | Intervention duration | Mobile health management measures | Manage the main content or direction | Management mode | Evaluation index |
|---|---|---|---|---|---|---|
| Hemodialysis | Li Shensen (21) | 6 months | Monitoring system | Effectiveness of health management | Active management | 1. Incidence of dialysis complications; 2. Blood pressure, hemoglobin, blood calcium and phosphorus, blood albumin, standard protein breakdown rate, subjective nutrition score (SGA); 3.Urea clearance index 4. Dialysis adequacy. |
| Zhaohui Ni (22) | 28 months | Dialysis registration system based on wechat mobile platform | Anemia monitoring | Automatically push | Hemoglobin and hematocrit levels | |
| Raquel Scofano (23) | 6 months | Assisted home hemodialysis | The role of remote monitoring in improving the relationship between doctors, nurses and patients | / | Remote monitoring experience | |
| Dayna E. Minatodani (24) | 42 months | Remote care nurse–patient contact | Health management | Active management | Number of hospital and emergency department visits, length of stay, and total cost of hospital and emergency room services for all patients | |
| Jennifer Gabbard (25) | 6 months | An iPad-based symptom assessment tool | Evaluation feasibility analysis | / | Ease of use of the system | |
| Neumann, Claas L (26) | 3 months | Body weight telemetry | Weight management | Automatic monitoring followed by active management | Interdialytic weight gain | |
| Steven J. Berman, (27) | 12 months | VitelCare Turtle 500 | Health management outcomes, quality of life, cost–benefit analysis | Automatic monitoring followed by active management | 1. Health outcome measures included hospitalization, emergency room visits, and length of stay. 2. The economic analysis includes total hospital and emergency room costs. 3. Quality of life was measured using the Medical Outcomes Survey tool 36-item Short Form Health Survey (SF-36) |
|
| Jessica Dawson (28) | 6 months | Mobile phone short message | Dietary behavior intervention | Automatic SMS push | 1. They were measured using recruitment and retention rates, acceptability of the intervention, and adherence to dietary recommendations. 2. Secondary findings included information on certain clinical parameters associated with dietary management in patients receiving maintenance hemodialysis |
|
| Eric D. Weinhandl (29) | 1.18 years | Nx2me Interconnect health Platform | To evaluate the mechanisms by which telemedicine platforms improve patient clinical outcomes and patient burden | / | Risk of all-cause attrition, dialysis cessation (i.e., death or transplant) and technical failure | |
| Nicola Elzabeth Anderson (30) | / | Monitoring system | Evaluate the usefulness of patient-reported outcomes collected by the system | / | / | |
| Mohsen T orabi Khah (31) | 1 month | App | Treatment adherence and perception | Automatically push AND Active management | “Treatment adherence and perception | |
| Kidney transplantation | John W. McGillicuddy (32) | 6 months | An electronic medication tray and an mHealth app | Medication adherence intervention | Intelligent reminder | The proportion of patients obtaining normal tacrolimus trough variability |
| Nielsen, Charlotte (33) | / | App | Improve follow-up after renal transplantation | / | / | |
| Rachel E. Patzer (34) | / | A mobile clinical decision aid (iChoose Kidney) | Estimates of risks of death and survival on dialysis compared to kidney transplantation | / | The discriminatory ability of the model for 3-year mortality | |
| Rachel E. Patzer (35) | 12 months | A mobile clinical decision aid (iChoose Kidney) | Improving knowledge about treatment options among transplant candidates | Intelligent reminder | Change in transplant knowledge | |
| Elisa J. Gordon (36) | 3 weeks | A Website | Increase Knowledge About Living Kidney Donation and Transplantation Among Hispanic/Latino Dialysis Patients | / | Participants’ knowledge scores | |
| A. Schmid (37) | 12 months | Telemedicine support | Optimize Routine Evidence-Based Aftercare | Custom management | Medical outcomes, adherence, quality of life and costs | |
| Edward W. Aberger (38) | 6 months | Telemedicine systems and electronic blood pressure monitoring systems | Enhancing Patient Engagement and Blood Pressure Management | Automatically push AND Active management | Systolic, diastolic, and pulse rate | |
| Lieke Wirken (39) | / | internet | A guided and tailored internet-based cognitive behavioral therapy (ICBT) intervention for donors and donor candidates was developed and the feasibility and perceived effectiveness were evaluated. | Custom management | Health related quality of life, anxiety and depression | |
| Rachel E. Patzer (40) | 8 months | iChoose Kidney | Improve access to individualized prognosis information comparing dialysis and transplantation outcomes | Auxiliary management | 1. Change in knowledge; 2. Change in treatment preferences,; 3. Improved decisional conflict, and increased access to kidney transplantation |
|
| Charlotte Nielsen (41) | / | APP | Development of a telehealth solution to improve the kidney transplantation process | Active management | / | |
| Alfonso M Cueto-Manzano (42) | 4 months | Mobile phone short message | Improve lifestyle and adherence of patients | Automatically push AND Active management | The usefulness of the text messages, the medication reminders, the appointment reminders | |
| Peritoneal dialysis | Zheng Pian (43) | 6 months | APP | Explore the application effect of mobile medical app in the follow-up management of peritoneal dialysis patients | Automatically push AND Active management | 1. Incidence of complications: peritonitis incidence, catheter outlet infection rate, tunnel infection rate, hospitalization rate 2. Daily record of indicator changes: weight. Bmi. Blood pressure, Ultrafiltration, Urine Volume 3. Test indicators: Hemoglobin (Hb), Albumin (Alb), hemoglobin (HB), albumin (ALB),Serum creatinine (Scr), Calcium (Ca), Phosphorus (P), serum Creatinine (SCR),Blood urea nitrogen (BUN), Intact parathyroid hormone (iPTH) and urea clearance (Kt/V) indicators |
| Fu Qiao Hui (44) | 12 months | Internet Plus cloud platform | Evaluate the management effect of various management modes | Automatically push AND Active management | Clinical data, laboratory test indicators, peritonitis incidence, tube drift incidence, tube blockage incidence, dropout rate, Duration of peritoneal dialysis treatment, average length of stay in patients exiting peritoneal dialysis” |
|
| Manya Magnus (45) | 2 times | Specific educational online videos | Understand patient satisfaction with telemedicine | / | Blood pressure, weight, glucose and peritoneal dialysis (PD)-specific educational online videos for ESRD patients using PD | |
| Daphne M. Harrington (46) | 251 days | A Tablet Computer Platform | The Use of a Tablet Computer Platform to optimize the Care of Patients Receiving to assess their usage in a pilot trial | Active management | Compliance with the applications ranged from 51–92%. No major adverse events were recorded. The overall impression of the interface was 5.2 out of 10 | |
| Tiantian Ma (47) | / | The PD telemedicine App called Manburs | To explore potential predictors and their effects on patient survival, technique survival, and the occurrence of infectious and noninfectious complications. | Automatically push AND Active management | Patient survival, technique survival, hospitalization, and the occurrence of infectious and noninfectious complications. | |
| Brett Tarca (48) | 7 days | Ecological momentary assessment mobile application | Explore the real-time relationships between fatigue, mood and physical activity in people with ESKD receiving peritoneal dialysis. | / | Fatigue and mood | |
| Vishal Dey (49) | 15 months | Computer tablets | To explore patient acceptability of technology and evaluate its effect on clinical interventions and quality of life in patients undergoing peritoneal dialysis |
Automatically push AND Active management | QUEST and QOL outcomes: Satisfaction scores retention rates Clinical interventions: admissions and supporting patients to self-manage from the comfort of their home. |
|
| Giusto Viglino (50) | 19 ± 12.9 months | Video dialysis | To overcome physical, cognitive and psychological barriers to PD. | Active management | Peritonitis incidence Assisted PD patients, with a family member/live-in carer patients selfcare patients | |
| Xiao Xu (51) | at least 20 months | The Peritoneal Dialysis Telemedicine-assisted Platform and TM app (Manburs) | Aimed to explore the long-term effects of TM on the mortality and technique failure | Automatically push AND Active management | All-cause mortality | |
| Not specify the treatment methods | Chi-Sheng Hung (52) | 24 weeks | Internet-based platform | Aimed to evaluate the effect of renal function status on hospitalization among patients receiving this program and to evaluate the relationship between contract compliance rate to the program and risk of hospitalization in patients with CKD | Automatically push AND Active management | 1. Contract Compliance Rate to the Telehealth Program 2. Renal Function and Hospitalization 3. Interaction Between Renal Status and Contract Compliance Rate With Telehealth |
| Ji-Eun Kim (53) | / | A personalized mobile dialysis device | To examine patients’ and caregivers’ design preferences and feature considerations for an Ambulatory Kidney to Improve Vitality | / | / | |
| Emily Seto (54) | / | Internet use | To ascertain the prevalence and predictors of Internet use by ESRD patients among different dialysis modalities. | / | The prevalence and predictors of Internet use | |
| Abu Bakkar Siddique (55) | / | Mobile Apps | To comprehensively evaluate mobile apps used for medication compliance and nutrition tracking for possible use by CKD and ESRD patients | / | Mobile App Rating Scale | |
| Manuel Prado (56) | / | A novel telehealth care system for ESRD patients called VCRS |
/ | / | / | |
| Meaghan Lunney (57) | / | Telephone, telemetry or video conferencing | Systematically reviewed studies that examined the effectiveness of telehealth versus or in addition to usual care for ESRD management |
/ | / | |
| Priya Ramar (58) | / | Remote monitoring | Effects of Different Models of Dialysis Care on Patient-Important Outcomes | / | The effect of interventions on mortality and hospitalizations |
“/“means not mentioned.
3.3. Types of MHealth management
3.3.1. Remote monitoring system
Of the 38 studies included in the review, 13 (34.21%) mentioned remote monitoring systems. The following is a description of these systems by treatment method:
Hemodialysis: there were a total of 7 literature, four of which assessed the effectiveness of remote monitoring for health management, i.e., the use of a remote monitoring system to track patients’ vital signs, such as blood pressure and weight. Secondly, the monitoring content includes quality of life detection, mainly focusing on the quality of life and mental health of patients; and monitoring the patient’s medical burden, such as medical costs. And two of the studies examined the potential of remote monitoring to improve doctor-patient relationships and the patient-reported outcomes collected through this modality. One of the study just focused on remote weight monitoring.
Peritoneal dialysis: one study evaluated the management effectiveness of various peritoneal dialysis modalities.
Kidney transplantation: two studies focused on kidney transplantation, with one focusing on optimizing routine evidence-based aftercare and the other on enhancing patient engagement and blood pressure management.
Unspecified treatment methods: three studies did not specify the treatment methods used. One study evaluated the impact of hospitalization on patients receiving telemedicine and the relationship between compliance and hospitalization risk. The second study explored the impact of different dialysis nursing modes on patient outcomes.
3.3.2. App
Of the 38 studies included in the review, 9 (23.68%) mentioned mobile app. These studies were categorized by treatment method as follows:
Hemodialysis: the study focused on changes in patients’ treatment adherence and perception though app and face-to-face training, and the result showed that such improvements were detected much more in the patients trained with APP based on the micro-learning method than face-to- face training.
Kidney transplantation: three studies focused on kidney transplantation, with one assessing medication adherence and two examining improved follow-up after kidney transplantation.
Peritoneal dialysis: four studies focused on peritoneal dialysis. Two of these studies, named “Manburs,” explored potential predictors of peritoneal dialysis patients and their effects on patient survival, technical survival, infectious disease occurrence, non-infectious complications, and long-term mortality. The other two studies examined the effectiveness of follow-up management in peritoneal dialysis patients and the real-time relationship between fatigue, mood, and physical activity in ESRD patients undergoing peritoneal dialysis.
Unspecified treatment methods: one review systematically evaluated the impact of mobile apps on medication adherence and nutrition tracking.
3.3.3. Phone or tablet
Among the 38 literatures, a total of 6 (15.78%) mentioned the use of mobile phones or tablet for health management, as follows:
Hemodialysis: there were two studies, and one of which, adopted mobile phone short message to intervene eating behavior regularly. Another one analyzed the feasibility of data collected by iPad.
Kidney transplantation: there was one used mobile phone text messages, regular text messages to improve the patient’s lifestyle and persistence
Peritoneal dialysis: there were 2 papers used a tablet computer, which mainly focused on the satisfaction and retention rate.
Unspecified treatment methods: there was one study to adopted mobile phone short message to intervene eating behavior regularly.
3.3.4. Website
Peritoneal dialysis: one web-based study assessed patient satisfaction with telemedicine.
Kidney transplantation: five studies used this approach, including one web-based study that disseminated kidney transplantation knowledge and one qualitative study that explored the evaluation of interventions for kidney donors and donor candidates. Three studies used a mobile clinical decision aid called “iChoose Kidney,” which helped patients discuss treatment plans at the onset of ESRD and improved their knowledge of kidney transplantation, which could influence their decision-making.
Unspecified treatment methods: one study analyzed patients’ internet use.
3.3.5. Social media
For hemodialysis, there has a collection system based on WeChat to monitor anemia.
3.3.6. Other types
One article described the use of video dialysis to train peritoneal dialysis patients, providing them with essential information about PD and improving the quality of their training. Another article reported on the design of a personalized mobile dialysis device to enhance the vitality of dialysis devices.
3.4. Content of mobile health management
The overwhelming majority of studies (28 articles, 73.68%) used electronic archives to ascertain baseline information, including age, sex, and geographic region. Further data monitoring and management were conducted based on electronic records.
3.4.1. Disease management
Disease management is primarily reflected in the monitoring of objective indicators, including clinical and physical parameters. Physical parameters include blood pressure, weight, and so on; clinical indicators include laboratory and clinical findings. Specific examples are as follows:
Hemodialysis: three studies reported on real-time guidance and personalized intervention through disease monitoring. For example, in studies Li Shensen (21) and Minatodani (24), Berman (27), data uploaded to the network in real time through the remote monitoring system were monitored, analyzed, and evaluated by medical staff, who then provided personalized feedback and guidance to patients.
Peritoneal dialysis: five studies described timely intervention and treatment by medical staff after automatic monitoring.
Kidney transplantation: seven studies reported on personalized guidance and management by doctors. For example, Nielsen’s (33), Schmid’s (37) and Aberger’s (38) studies proposed allowing consultations via telephone, video, or online, or introducing training courses for patients.
Unspecified treatment methods: Hung’s (52) study exemplified the automatic monitoring push and personalized guidance of disease management.
3.4.2. Behavioral intervention
Behavioral intervention is primarily reflected in health behaviors, such as medication adherence and dietary compliance. Nine studies reported on behavioral intervention.
Hemodialysis: two studies focused on healthy behaviors, including weight management monitoring to guide patients in weight control and dietary advice.
Peritoneal dialysis: three studies mentioned behavioral intervention, such as diet advice and weight management (43, 44, 49).
Kidney transplantation: three studies evaluated interventions to improve compliance. For example, two studies (32, 42) reported improved medication adherence through remote intervention management, and one study (37) showed a reduction in non-compliance through remote monitoring.
Unspecified treatment methods: Huang’s study (52) exemplified behavioral intervention, with nurse case managers communicating with patients daily over the phone as needed to ensure medication and medical instruction adherence.
3.4.3. Social support
Social support encompasses patient-clinician communication and question-and-answer sessions, as well as the support provided by clinicians to patients through remote monitoring and behavioral interventions. Notably, Viglino’s study (50) found that video dialysis enhanced patients’ confidence in peritoneal dialysis (PD).
3.4.4. Self-management and reminders
Here are 10 articles on follow-up management, and they are distributed across different treatment modalities. For hemodialysis, there are three studies mentioned follow-up management (21, 24, 27), for peritoneal dialysis, there are four studies mentioned follow-up management (43, 44, 47, 51), for kidney transplantation, follow-up management was mentioned in two studies (38, 40), for unspecified treatment methods, One study mentioned follow-up management (52). Follow-up management and reminders primarily involve the regular monitoring and communication with patients to assess their overall self-management behavior, provide medication reminders, dietary guidance, and exercise guidance, and schedule follow-up appointments. Ten studies reported using the internet, mini-programs, phone calls, or text messages for follow-up management and reminders.
3.5. Evaluation index of health management
3.5.1. Clinical indicators
A diverse range of studies have investigated the clinical indicators associated with m-health management. The main indicators include:
Dialysis indicators: dialysis adequacy
Complication indicators: complication rate
Daily recording indicators: body weight, BMI, blood pressure, ultrafiltration, urine volume
Assay parameters: hemoglobin, albumin, calcium, phosphorus, serum creatinine, blood urea nitrogen, intact parathyroid hormone (iPTH), urea clearance
Medical indicators: length of hospital stay, average length of hospital stay, number of emergency department visits, treatment duration
Survival situation indexes: survival rate, survival time, life expectancy.
3.5.2. Quality of life indicators
Two studies assessed the impact of MHealth management on quality of life.
In hemodialysis, Berman (27) used the 36-item Short Form Health Survey (SF-36) to measure quality of life. In peritoneal dialysis, Dey (49) assessed quality of life before and after a MHealth management intervention.
3.5.3. Cost index
For hemodialysis, Berman (27) Economic analysis of total hospital and emergency room costs; for kidney transplantation, Schmid (37) involved the reduction of nursing cost.
3.5.4. Patient experience
Among the included studies, 7 (18.42%) assessed patient experience and satisfaction, including the availability of remote monitoring systems, apps, or professional websites, and the feasibility of MHealth management measures.
Hemodialysis: one study (55) evaluated the patient experience of remote monitoring, one study (25) assessed the system’s usability, and one study (30) evaluated the system’s role in collecting patient reports.
Peritoneal dialysis: one study (45) analyzed the satisfaction of nurses with remote monitoring, one study (46) assessed the satisfaction with the interface, and one study (49) evaluated the satisfaction with remote assistive technology.
Unspecified treatment methods: one study (53) found that research is conducive to improving the efficiency, effectiveness, and user satisfaction of AKTIV prototypes and products.
3.6. Other
For example, four studies (35, 36, 39, 42) assessed the acquisition of transplanted knowledge, and one study (48) investigated the real-time relationships between fatigue, mood, and physical activity in people with ESRD receiving peritoneal dialysis.
4. Discussion
The scope review commences with an examination of MHealth management types, MHealth management content, MHealth management evaluation indices, and other relevant aspects. The studies included in this review utilized various platforms such as remote monitoring systems, apps, websites, mobile phones or tablets, and social platforms to offer patients a wide array of services encompassing disease management, behavioral intervention, social support, and follow-up care. These studies primarily focused on patient clinical indicators, patient experience, quality of life improvements, and healthcare cost. It is discussed from the following aspects.
4.1. MHealth has been widely used in ESRD patients
This study found that the volume of literature on MHealth management for ESRD has steadily increased since 2003, reflecting the growing convergence of mobile internet technology and medicine. In the early stages, patient management was primarily conducted via phone, text messages, and other simple modalities. However, in recent years, research has focused on developing app-and mini-program-based interventions that leverage the internet and monitoring systems to facilitate personalized interventions based on automatically uploaded health data and automated push or early warning notifications. This study demonstrates that MHealth management has been widely adopted in kidney disease management, with a diverse range of applications. Similarly to the review (18), both demonstrate the breadth of e-health interventions used to provide lifestyle interventions in the CKD population.
4.2. MHealth has obvious advantages in ESRD patients
Overall, MHealth offers several advantages for ESRD patients, enabling comprehensive multi-platform management from the dissemination of relevant knowledge to the monitoring of physiological parameters and disease intervention. MHealth also facilitates effective doctor-patient communication. We analyzed different treatment methods separately. From the perspective of mobile management carrier types, hemodialysis research is relatively comprehensive. In terms of research content, the peritoneal dialysis system in China, a telemedicine-assisted platform and telemedicine app (47, 51), has demonstrated promising results in a large-sample cohort study, which was real-world associations between telemedicine use and reduced survival and technology survival in peritoneal dialysis patients. Among kidney transplantation methods, iChoose Kidney (34, 35, 40) from the United States is a prominent mobile health management platform that not only provides transplant-related knowledge, but also predicts the mortality risk of dialysis and transplantation, and aids decision-making for kidney transplantation. Notably, iChoose Kidney offers outstanding functional features, but lacks follow-up management after kidney transplantation, while other MHealth management functions are relatively basic, focusing on the dissemination of transplant knowledge. From the perspective of evaluation indicators, most studies focus on clinical and patient experience indicators. We observed that most studies paid more attention to the physical health status of patients, with vital signs and kidney function being the primary monitoring indicators and evaluation outcomes. Additionally, we found that most studies monitored the health data of ESRD patients through mobile health management, and background medical staff analyzed and evaluated the data, providing further interventions for patients with abnormal conditions, such as adjusting medication or recommending outpatient clinic visits.
4.3. Several existing problems of MHealth in ESRD patients
4.3.1. Lack of research on social media platforms
The application of peritoneal dialysis and kidney transplantation lacks research on social media platforms, which are commonly used and familiar to us. Strengthening the interaction with social media platforms could enhance effective communication between medical staff and patients, improve patient engagement, and boost management efficiency.
4.3.2. Single-sample management
Single-sample management studies are still present in hemodialysis, such as those that monitor and manage only weight (26) or anemia (22).
4.3.3. Lack of evaluation of quality-of-life and cost indicators
Further research is needed to determine whether mobile health management can improve patients’ quality of life and reduce costs.
ESRD can seriously affect patients’ quality of life (59). It involves a variety of physical and emotional challenges, including frequent medical interventions, dialysis treatments, dietary restrictions, and limitations in daily activities. In this context, MHealth applications can play a key role in providing personalized care, symptom management and support. By integrating these technologies into the healthcare ecosystem, patients can better self-manage, reduce hospital admissions, and improve overall well-being. ESRD and its associated treatments, such as dialysis and transplantation, can be a financial burden on individuals and healthcare systems (60). Given the long-term nature of ESRD management, cost-effectiveness is an important issue. MHealth applications have the potential to optimize the allocation of medical resources, streamline care processes, and reduce unnecessary expenses. By allowing patients to actively participate in the medical process, these technologies enable more efficient use of resources, resulting in cost savings for patients and providers.
4.3.4. Mental health is rarely considered in management
Depression is the most common psychiatric disorder in patients with ESRD, with a prevalence of 22.8 to 39.3% in the dialysis population (61). However, in this study, few people paid attention to mental health, and we found that the psychological management of mobile health management is becoming more and more abundant. For example, Chou’s research (62) found that chatbots can promote the mental health of the elderly and reduce depressive symptoms. Therefore, integrating it into mobile health management and offering enhanced psychological support represents a key future direction for mobile health management in ESRD patients.
4.3.5. Lack of a big data-driven clinical decision intervention system
These interventions are rarely based on big data decision support systems, lack accurate evidence-based feedback, and lack clinical decision support. However, sometimes data security and privacy concerns affect the development of decision support systems (63). Clinical decision support refers to the integration of electronic medical records and other clinical information through computer technology, automatic processing of patient data, and intelligent medical and nursing recommendations to provide the best plan to help patients make the best clinical decision (64). Clinical decision support is used in the management of many chronic diseases, such as hypertension (65) and advanced heart failure (66). In this study, only the “iChoose Kidney” tool in the kidney transplantation domain exhibited decision-making capabilities. There are studies of biomedical based remote diagnosis of kidney disease, for example, electrochemical creatinine (Bio) sensors for point-of-care diagnosis of renal malfunction and CKD (67). Studies have also been conducted through the development and validation of mixed Brillouin-Raman spectroscopy for non-contact assessment of the mechanochemical properties of urinary proteins as biomarkers for kidney disease (68). Therefore, developing a mobile management decision support system with diversified functions to provide optimal clinical decision support to patients is a key area for future development, and based on biomedical remote instant of ESRD disease diagnosis is also worth exploring, In the context of ESRD, clinical decision support systems can help healthcare providers make timely and informed decisions about treatment choices, medication management, and care planning. By integrating patient-specific data from mobile health apps, clinical decision support systems can enhance clinical decision making, improve treatment outcomes, and potentially reduce the occurrence of medical errors. And how to develop and adopt a standardized set of evaluation metrics and evaluation methods to compare different MHealth applications and platforms. This may include validation based on assessments of enablement and functionality, user satisfaction surveys, and clinical trials.
4.3.6. Lack of other patients MHealth management support, such as patients with disabilities or the elderly (digital newbies) or poor people
At the same time, there is a lack of relevant research on MHealth management for special ESRD population, especially applicability and accessibility of MHealth applications. This may include specific features and interface designs for these patients to ensure they can easily use these apps. Also can consider exploring how assistive technologies and technical support can be used to help these patients overcome barriers to use. And, how the elderly (digital newbies) or individuals from low-income backgrounds can access and benefit from MHealth treatments is also a question. This entails providing technical training and support, enhancing digital engagement capabilities, and improving accessibility to devices and networks. Simultaneously, there is a need to increase public policies and initiatives to ensure that individuals from low-income backgrounds have equitable access to necessary medical treatment and support.
5. Conclusion
Following the scoping review reporting framework of Arksey and O’Malley (20), this study reviewed relevant studies on MHealth management for ESRD patients to synthesize the types, contents, and evaluation indicators of MHealth management. The findings revealed that MHealth management has been widely adopted in the disease management of ESRD patients, encompassing a diverse range of management content and numerous evaluation indicators. Future research should focus on enhancing the evaluation of patients’ mental health, quality of life, and costs, as well as developing a clinical decision support system to better realize the potential of MHealth management in ESRD patients.
6. Limitation
There was a suggestion at the June 2019 Consensus meeting on Improving Global Results in Kidney Disease (KDIGO) to use “kidney failure” and appropriately describe whether symptoms, signs and treatments are present, rather than “end-stage kidney disease, “but since it is limited to English (nuances can be difficult to translate) (69), Therefore, “end-stage renal disease” was still used for the search in this review, resulted in 38 number of papers in this scope review. Additionally, this scope review starts from types of MHealth management, content of mobile health management, evaluation index of health management, and Others four aspects have been reported, lack of the responsibility of the government to establish modern medicine, including MHealth products and other aspects of sorting. Lastly, quality assessment of included studies is not a primary component of a scoping review (18), therefore critical appraisal is not provided.
Author contributions
YW: Writing – original draft, Writing – review & editing. YR: Writing – original draft, Writing – review & editing. YY: Writing – original draft, Writing – review & editing.
Funding Statement
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research were funded by the National Natural Science Foundation of China (Grant No. 82200837), the Sichuan Province Science and Technology Program (Grant No. 2021YFS0163).
Abbreviations
ESRD, End-stage renal disease; MHealth, Mobile health; CKD, Chronic kidney disease; PRISMA-SCR, Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews.; eHealth, Electronic health; PD, Peritoneal dialysis; iPTH, Intact parathyroid hormone; SF-36, The 36-item Short Form Health Survey; AKTIV, Ambulatory Kidney to Improve Vitality; BMI, Body mass index.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmed.2024.1366362/full#supplementary-material
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