![](\"https:\/\/asset.jmir.pub\/assets\/4fdd8671-e824-11f0-8e87-4954cf15b3b7.png\")
<\/a>Figure 1. Flowchart outlining the search process for studies across databases, following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. \u200e<\/a>![](\"https:\/\/asset.jmir.pub\/assets\/4ff37f71-e824-11f0-8e87-4954cf15b3b7.png\")
<\/a>Figure 2. (A) The trend analysis of publication dates in the included studies. (B) The specific information on theories and models used to guide the content of mHealth self-management interventions. HD: hemodialysis; mHealth: mobile health; PD: peritoneal dialysis. Table 1. Summary of basic characteristics of the included studies.Author and yearCountryAim of the studyStudy designPopulationTotal sample (experimental\/control group)Saadatifar et al [], 2022IranTo explore the impact of mHealth on treatment adherence in patients undergoing HD.Quasi-experimental studyHD80 (40\/40)Ren et al [], 2019ChinaTo explore the impact of a WeChat-based health education program on the self-management of patients undergoing HD.A longitudinal experimental intervention studyHD85 (49\/36)Park and Kim [], 2019South KoreaTo evaluate the impact of a program based on app and SMS text messaging for patients undergoing HD.Quasi-experimental studyHD84 (42\/42)Pack and Lee [], 2021South KoreaTo develop a mobile app for dietary management and evaluate the impact on patients undergoing HD.RCTHD75 (37\/38)Fakih El Khoury et al [], 2020NetherlandsTo evaluate the efficacy of the app-based dietary intervention for patients undergoing HD.A pilot study: a before-and-after studyHD23Hanifi et al [], 2019IranTo assess the impact of counseling and follow-up phone calls on patients undergoing HD.Quasi-experimental studyHD86 (43\/43)Cho and Park [], 2020South KoreaTo assess the impact of a tablet-based self-management program on patients undergoing HD.Quasi-experimental studyHD46 (23\/23)Chiang et al [], 2021ChinaTo evaluate whether the Assisted Care Program in the app can help patients better control their dietary phosphorus intake.Quasi-experimental studyHD60 (30\/30)Zwi et al [], 2022AustraliaTo evaluate the feasibility of the app and its impact on patients undergoing HD.A mixed methods studyHD61Welch et al [], 2013United StatesTo evaluate the impact of mobile programs on diet and fluid intake in patients undergoing HD.A pilot studyHD33 (16\/17)Thongsunti et al [], 2024ThailandTo evaluate the effectiveness of telemedicine-based management of hyperphosphatemia in patients undergoing HD.RCTHD80 (40\/40)Chung et al [], 2024South KoreaTo assess the impact of adaptive nutrition and education on patients undergoing HD using real electronic medical record data.A decentralized clinical trialHD153 (42\/40\/34\/37)Dawson et al [], 2021AustraliaTo assess the impact of SMS text messaging on patients undergoing HD.A randomized feasibility studyHD115 (78\/37)Fakih El Khoury et al [], 2021NetherlandsTo assess the efficacy of an intervention using the app on phosphorus.A pilot study: a before-and-after studyHD23Hosseini et al [], 2023IranTo determine the effect of an app on self-efficacy and self-care of patients undergoing HD.Quasi-experimental study (longitudinal single-group study)HD54Min and Park [], 2020South KoreaTo assess the impact of a mobile app\u2013based self-management support program on older adults undergoing HD.Quasi-experimental studyHD56 (28\/28)Mollao\u011flu et al [], 2024TurkeyTo evaluate the impact of education and art therapy through a telemedicine approach in patients undergoing HD.RCTHD60 (30\/30)Rocco et al [], 2023United StatesTo evaluate the impact of an app on self-monitoring of daily fluids in patients undergoing HD.A pilot study: a before-and-after studyHD18St-Jules et al [], 2021United StatesTo assess the feasibility and acceptability of mHealth for managing hyperphosphatemia in patients undergoing HD.A feasibility trialHD40 (13\/14\/13)Teong et al [], 2022MalaysiaTo evaluate the effectiveness of an app for nutritional management in patients undergoing HD.RCTHD66 (33\/33)Pungchompoo et al [], 2024ThailandTo evaluate the impact of a home telemedicine model on older patients undergoing HD.A mixed methods studyHD54 (24\/30)Nursalam et al [], 2020IndonesiaTo evaluate the impact of the app on improving fluid restriction adherence in patients undergoing HD.A mixed methods studyHD60 (30\/30)Hayashil et al [], 2017JapanTo evaluate the usefulness of the self-management support system for self-monitoring in patients undergoing HD.A pilot studyHD18 (8\/10)Pinto et al [], 2020BrazilTo evaluate the impact of the app on fluid restriction and dietary control in patients undergoing HD.A randomized, single-center, self-controlled studyHD48Andriati [],2025IndonesiaTo evaluate the impact of the app on adherence and renal function of patients undergoing HD.Quasi-experimental studyHD55Taguiam [],
2025United StatesTo evaluate the impact of the app on fluid intake management and body weight in patients undergoing HD.A mixed methods studyHD18Lee and Kang [], 2024South KoreaUsing a mobile instant messaging tool to customize diet plans for patients undergoing PD and evaluate outcomes.Quasi-experimental studyPD43 (21\/22)Chae and Kim [], 2024South KoreaTo develop the app for improved self-management and evaluate its impact on patients undergoing PD.RCTPD53 (27\/26)Uchiyama et al [], 2022JapanTo assess the impact of using a remote patient monitoring system on patients undergoing PD.A randomized crossover controlled trialPD15Jung et al [], 2021South KoreaTo evaluate the impact of remote patient monitoring on automated patients undergoing PD.RCTPD50 (28\/22)Lukkanalikitkul et al [], 2022ThailandTo evaluate the availability and impact on patients undergoing PD for app.User-centered design studyPD9Zeng et al [], 2025ChinaTo evaluate the effectiveness of a PD management system in improving adherence and clinical outcomes.A retrospective cohort studyPD127Stark et al [], 2011United StatesTo assess the effectiveness of a PDA-based app for dietary management in patients undergoing dialysis.RCTPD, HDHD:19 (9\/10);
PD: 21 (11\/10)Beer et al [],
2025AustraliaTo evaluate the effectiveness of the app in controlling serum phosphorus levels in patients undergoing dialysis.RCTPD, HD180 (90\/90)<\/p>\n
amHealth: mobile health.<\/p>\n
bHD: hemodialysis.<\/p>\n
cRCT: randomized controlled trial.<\/p>\n
dThe study divided participants into four groups: (1) control (n=42), (2) education intervention (n=40), (3) meal intervention (n=34), and (4) education and meal interventions (n=37).<\/p>\n
eThe grouping is set up as follows: (1) educational videos and handouts (Education; n=13), (2) education intervention plus mobile self-monitoring with email feedback (Monitoring; n=14), or (3) education and monitoring interventions plus social cognitive theory-based behavioral videos (Combined; n=13).<\/p>\n
fPD: peritoneal dialysis.<\/p>\n
gPDA: personal digital assistant.<\/p>\n
Theories or Models Involved<\/p>\n
Among the studies included, 14 [,,,-,,,,,,-] did not explicitly mention the theories or models involved. Of these studies, 5 [,,-] were in patients undergoing PD, and 9 [,-,,,,,] involved patients undergoing hemodialysis. Nine [,-] studies combined 2 or more theories or models, and 11 [,,,,,,,,,,] studies involved only 1 theory or model.<\/p>\n
Among mHealth-based self-management interventions for patients undergoing dialysis, the most frequently incorporated theories included the transtheoretical model (TTM) and stages of change (n=5 [,,,,]), self-efficacy theory (n=4 [,,,]), social cognitive theory (n=4 [,,,]), and Orem\u2019s theory of self-care (n=3 [,,]). Most studies have focused their attention on theories related to behavior change (such as the TTM, the theory of reasoned action [,], the health belief model [,], behavior change frameworks [,], and the behavioral change techniques taxonomy []). Additionally, Bandura self-efficacy theory and Oren self-care theory were often used in combination with other theories. Accordingly, we visualized the theories or models involved in this study ().<\/p>\n
Attributes of Self-Management in Patients Undergoing Dialysis<\/p>\n
We categorized the self-management included in this study into six main themes, which were (1) self-monitoring, (2) diet and fluid management, (3) medication management, (4) exercise management, (5) psychological management, and (6) disease-related knowledge. The components of self-management for patients undergoing dialysis and their corresponding explanations are detailed in .<\/p>\n
\u200e<\/a> The mHealth-based self-management intervention program is focused on the type of mHealth, the content of the mHealth-based intervention, and the duration of the intervention.<\/p>\n The types of mHealth mentioned in this study included app (based on mobile device, tablet personal computer, or personal digital assistant), a remote patient monitoring system, SMS text messaging, and telephones. Apps were divided into 2 categories, such as dialysis-specific software and instant messaging software (eg, WeChat [Tencent Holdings Limited], Line [LY Corporation], Facebook [Meta Platforms, Inc], WhatsApp [Meta Platforms, Inc], and KakaoTalk [Kakao Corp]). Two studies [,] intervened via SMS text messaging only, 1 study [] via telephone only, 4 studies [,,,] via instant messaging software only, and 18 studies [-,-,,,,-,,,,,,] via dialysis-specific software only. Six studies [,,,,,] used 2 or more online interventions. There were only 3 studies [,,] based on a remote patient monitoring system, 1 [] for patients undergoing hemodialysis and the remaining 2 [,] for patients undergoing PD.<\/p>\n This scope review categorized the app\u2019s content into the following dimensions, including intelligent education hub, full-dimensional monitoring system, accurate nutritional management, behavioral interventions (fluid, exercise, and medication adherence), intelligent reminders and alerts, doctor-patient collaboration network, and social support system. Remote patient monitoring systems placed more emphasis on remote monitoring, alarms, and dynamic interventions for patients undergoing dialysis. The details could be found in . Interventions delivered via apps and remote patient monitoring systems were more comprehensive and satisfactory than those delivered via phone and SMS text messaging, even though they showed similar functionalities in certain aspects. illustrates the gap map of core self-management interventions across different mHealth categories for patients undergoing dialysis (blue circles indicate studies with hemodialysis as the research participants; purple circles indicate studies with peritoneal dialysis as the research participants; red circles indicate studies with hemodialysis and peritoneal dialysis as research participants).<\/p>\n Table 2. Type of mobile health and mobile health\u2013based interventions to improve self-management in patients undergoing dialysis.Classification of mHealthNumbersFunction and intervention contentsAppDialysis-specific software24Intelligent education hub amHealth: mobile health.<\/p>\n bPDA: personal digital assistant.<\/p>\n cNFC: near field communication.<\/p>\n dOCR: optical character recognition.<\/p>\n eQ&A: question-and-answer.<\/p>\n \u200e<\/a> The duration of the intervention ranged from 2 to 24 weeks. The included studies preferred interventions for 3 months [,,,,,], 6 months [,,,], 12 weeks [,,,], 2 weeks [,,], 8 weeks [,,].<\/p>\n Key Indicators and Evaluation Tools on Self-Management<\/p>\n Indicators for assessing self-management in patients undergoing dialysis were divided into 2 categories, namely subjective and objective indicators. The subjective indicators mainly involved scales, such as measuring self-management, adherence, self-efficacy, literacy, depression, anxiety, perceived benefits, and quality of life of patients undergoing dialysis. Objective indicators included weight, blood pressure, and laboratory tests. Of these, intradialytic weight gain, serum albumin, serum potassium, and serum phosphorus were the most mentioned indicators in the included studies. Detailed information is provided in . illustrates the gap map of self-management assessment indicators across different mHealth categories for patients undergoing dialysis (blue circles indicate studies with hemodialysis as the research participants; purple circles indicate studies with peritoneal dialysis as the research participants; red circles indicate studies with hemodialysis and peritoneal dialysis as research participants).<\/p>\n Table 3. Mobile health\u2013based indicators for assessing self-management in patients undergoing dialysis.ItemsNumbersToolsSelf-management4The self-management scale for patients undergoing hemodialysis [,] aIDWG: intradialytic weight gain.<\/p>\n biPTH: intact parathyroid hormone.<\/p>\n cPTH: parathyroid hormone.<\/p>\n \u200e<\/a> This scoping review provides the first comprehensive analysis and summary of self-management content for patients undergoing dialysis, types of mHealth, relevant theories and models, the content of mHealth-based interventions, and methods for evaluating their effectiveness. Most studies indicate that mHealth-based interventions significantly improve self-management of patients undergoing dialysis and enhance patient outcomes [,,,,,].<\/p>\n Compared to face-to-face training, mHealth-based interventions have a greater impact on patient adherence and laboratory outcomes []. In this study, apps and remote monitoring systems were more common. SMS text messaging and phone calls could be used as an aid to promote self-management in patients undergoing dialysis []. Due to differences in mHealth-based intervention content and evaluation indicators, quantitative analysis of apps, remote monitoring systems, phones, and SMS text messaging is extremely challenging. Overall, the vast majority of apps demonstrate strong potential for improving medication adherence, enhancing care efficiency, and increasing patient satisfaction and treatment outcomes [,].<\/p>\n Special Considerations for Specific Groups Among Patients Undergoing Dialysis<\/p>\n Currently, there are relatively few apps available on the market for patients undergoing dialysis. Existing research has identified 12 Android apps, 11 iOS apps, and 5 dual-platform apps closely associated with kidney disease []. In addition, middle-aged and older people are less receptive to apps than younger people, which is particularly reflected in lower usage rates and more negative attitudes toward apps []. Since middle-aged and older patients are less receptive to new things and have declining eyesight, the app should be designed to meet the special needs and usage habits of middle-aged and older patients. A survey of requirements for the app in middle-aged and older patients with CKD based on the Kano model showed that the app first had to protect privacy, followed by simplifying the data entry process, simplifying in-app navigation, and simplifying the function and number of buttons []. Navigation for beginners, appropriate text size, using color to distinguish different options or icons, and ensuring all buttons maintain consistency in size, labels, and spacing are also details that should be considered during software design []. It is also worth noting that there are also barriers that prevent the promotion of apps, such as information barriers, trustworthiness, security, compatibility, complexity, time constraints, and low mHealth literacy []. Therefore, in future research, user profiles can be incorporated based on patient age, dialysis modality, and digital literacy levels to automatically match interfaces []. During initial use, an interactive tutorial should be set up to guide patients. Beyond traditional touchscreen operations, additional features, such as voice commands, remote assistance, and offline mode, can be added to accommodate the diverse needs and usage habits of patients undergoing dialysis.<\/p>\n Key Areas for Self-Management in Patients Undergoing Dialysis Using mHealth<\/p>\n In terms of app self-management content, CKD disease-related knowledge, symptom management, medication management, provision of health insurance information, diet management, exercise guidance, and psychosocial support may be the content they need more (arranged according to the patient\u2019s needs) []. However, among the apps for patients undergoing dialysis in this study, the provision of health insurance information was what they lacked. Moreover, the app in this study focused more on disease knowledge, self-monitoring, diet, and medication management and lacked sufficient attention to exercise and psychological support. Compared to self-monitoring, dietary self-management becomes significantly more challenging for patients undergoing dialysis. Strict dietary restrictions, dynamic adjustments to meal plans (based on laboratory indicators), the need for specialized knowledge, lack of external supervision (eg, hospital and home), and the influence of long-standing dietary habits and psychological factors further compound the complexity of implementing dietary management for patients undergoing dialysis. Currently, mHealth\u2019s promotion offers multiple solutions for dietary management among patients undergoing dialysis. The current app can automatically calculate the calories, protein, sodium, phosphorus, and potassium of foods consumed by patients undergoing dialysis, typically achieved through a nutritional database and barcode scanning functionality []. A study has also monitored the diet of patients undergoing dialysis by sending photos of food through an app and having them evaluated by the expert []. Food analysis and feedback also include the health system score, which is an easy-to-understand health score calculated by the app based on the phosphorus-to-protein ratio of the food []. Health care professionals provide personalized advice through dietary records and laboratory tests to promote changes in dietary behavior in patients undergoing dialysis.<\/p>\n Among the included studies, there were fewer studies for patients undergoing PD compared to patients undergoing hemodialysis. This may be related to the late start of PD. Self-management is an important influence on the quality of life and outcomes of patients undergoing PD []. Patients undergoing PD who undergo inappropriate operations face a higher risk of developing peritonitis []. If patients undergoing PD experience a technical failure, they must be treated with hemodialysis. Compared with PD, long-term regular hemodialysis significantly impacts patients\u2019 quality of life, hinders their social reintegration, and increases the risk of complications [,]. Therefore, for patients undergoing PD, self-management is necessary for them to master. The focus of self-management in patients undergoing PD is not the same as in patients undergoing hemodialysis. As patients undergoing PD need to perform peritoneal dialysis-related operations at home, training in relevant knowledge and operational skills is particularly important []. The training of skills mainly includes aseptic operation, change of peritoneal fluid and peritoneal dialysis catheter, and outlet care []. Patients undergoing PD also need to learn the calculation of ultrafiltration []. All of these can be learned and managed remotely based on mHealth. In the event of an emergency (eg, contamination or disconnection of the peritoneal dialysis tubing), patients should immediately contact a health care professional for on-site guidance. Thus, establishing emergency contact channels within the app is particularly crucial.<\/p>\n Theoretical or Model Support for mHealth Intervention Programs in Patients Undergoing Dialysis<\/p>\n Many theories and models have been used to guide practice in the self-management of patients undergoing dialysis. TTM, self-efficacy theory, and social cognitive theory are the most commonly used theories [,,,]. Currently, most studies use a single theory as their guiding framework, with fewer adopting 2 or more theories for guidance. Taking the TTM as an example, it emphasizes guiding patients through staged behavioral shifts but overlooks individual differences (such as cultural background and cognitive level) [,,,,]. Self-care theory, on the other hand, emphasizes personalized care needs and can be combined with TTM. Additionally, the maintenance phase of TTM is prone to behavioral relapse, particularly among patients requiring lifelong treatment (eg, dialysis). A single theory cannot sustainably motivate patients. Integrating other theories (such as the PERMA [Positive Emotion, Engagement, Relationship, Meaning and Accomplishment] model []) during this phase can further reinforce patients\u2019 behavior. Therefore, future research should focus more on integrating multiple theories rather than relying on a single one. In addition, goal-setting theory, the information-motivation-behavioral skills model, the chronic care model, the ecological model of health behavior, and the theory of planned behavior can also be applied in subsequent research.<\/p>\n Implications for Future Research<\/p>\n Future research on self-management software for patients undergoing dialysis will focus on integrating the strengths of existing tools and promoting their synergistic use. Software development should break down barriers between tools, such as deeply integrating the real-time data collection capabilities of remote patient monitoring systems, the personalized analysis functions of intelligent decision support systems, and the instant communication advantages of phone or SMS text messaging, to provide patients with more comprehensive self-management support.<\/p>\n A combination of artificial intelligence can be considered for use in the remote management of patients undergoing dialysis (eg, wearable devices). Smart wristbands can monitor data, such as heart rate, blood pressure, activity, and sleep [] and automatically transmit the data to the app for analysis and storage. Some researchers have implemented data linkage through apps using near field communication and optical character recognition. Data from measuring devices, such as sphygmomanometers and weight scales, can be automatically transmitted to the app via near field communication []. Alternatively, the numbers from the sphygmomanometer can be captured using the phone\u2019s camera and imported into the app []. These not only improve the efficiency of patients undergoing dialysis but also increase the accuracy of the records. In conclusion, there is still a lot of room to explore the use of artificial intelligence in combination with mHealth in patients undergoing dialysis.<\/p>\n Different studies have different insights into the evaluation criteria for self-management. Most of the studies used objective indicators as one of the evaluation criteria for self-management. For the evaluation of knowledge of patients undergoing dialysis, most studies have assessed it using self-developed questionnaires and lacked uniform criteria for judging. Various scales are currently available for assessing self-management, adherence, self-efficacy, and quality of life. The use of these assessment tools varies across different studies. Existing research lacks a gold standard for evaluating self-management in patients undergoing dialysis. Therefore, there is a need to standardize the criteria for evaluating self-management in patients undergoing dialysis in future studies.<\/p>\n This study provides guidance for the development of subsequent dialysis-related software for patients, including the design of functional modules, user experience optimization, and the integration of clinical indicators and assessment tools. Our findings will enhance the ability of patients undergoing dialysis to self-manage their health at home, improving both the effectiveness of their dialysis treatment and their quality of life. In the future design of the app, attention should also be paid to the usage needs of special groups, strengthening adaptive software development, deeply integrating artificial intelligence technology, and establishing standardized self-management evaluation criteria.<\/p>\n Limitation<\/p>\n This scoping review also has some limitations. First, the broad scope of scoping reviews and the complexity of search strategies may lead to the omission of relevant studies. These challenges persist despite strict adherence to the PRISMA-ScR guidelines for greater rigor and transparency. Second, self-management assessment tools incorporate numerous subjective and objective indicators, which complicate data integration and comparability. Third, publication bias was one of the limitations of this study. This review only covered relevant studies published in English. Most of the included studies were limited to Asia and North America, which may have resulted in limited global generalizability. Future studies should use standardized, integrated measures to improve consistency and reliability. Additionally, these shortcomings can be remedied by improving search strategies, expanding database coverage, and removing language restrictions to include a more diverse patient population from different continents.<\/p>\n Conclusion<\/p>\n This study conducted a scoping review of the existing literature on mHealth-based self-management among patients undergoing dialysis. Although mHealth holds potential advantages for self-management in patients undergoing dialysis, it has not been widely adopted and integrated into standard renal care, requiring further optimization and refinement. This study provides theoretical and practical guidance for subsequent research, helping to enhance self-management of patients undergoing dialysis and improve their quality of life, ultimately offering insights for digital transformation in chronic disease management.<\/p>\n Multimedia Appendix 1<\/p>\n Search strategy<\/p>\n DOCX File, 17 KB<\/a>Multimedia Appendix 2<\/p>\n Critical appraisal of the selected studies using the Mixed Methods Appraisal Tool (MMAT).<\/p>\n XLSX File, 72 KB<\/a>Multimedia Appendix 3<\/p>\n Types of mobile health, core intervention content, intervention time, and evaluation indicators for self-management in patients undergoing dialysis.<\/p>\n DOCX File, 45 KB<\/a>Checklist 1<\/p>\n PRISMA-ScR and PRISMA-S checklists.<\/p>\n PDF File, 315 KB<\/a><\/p>\n We are grateful to the editors and reviewers who helped with this study.<\/p>\n This work was supported by grants from the Tongji Hospital Research Fund Project (2024D10, 2022D22). The funder participated in the research design, data collection, analysis, interpretation, and manuscript writing.<\/p>\n All data generated or analyzed during this study are included in this published article and its supplementary information files.<\/p>\n None declared.<\/p>\n Edited by Stefano Brini; submitted 03.May.2025; peer-reviewed by Dinda Iryawati, Yu Shi; accepted 17.Nov.2025; published 02.Jan.2026.<\/p>\n \u00a9 Qin Xu, Yulin Xu, Xiaoqin Liu, Xiaolin Ma. Originally published in the Journal of Medical Internet Research (https:\/\/www.jmir.org), 2.Jan.2026. <\/p>\n This is an open-access article distributed under the terms of the Creative Commons Attribution License (https:\/\/creativecommons.org\/licenses\/by\/4.0\/<\/a>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https:\/\/www.jmir.org\/<\/a>, as well as this copyright and license information must be included.<\/p>\n","protected":false},"excerpt":{"rendered":"Introduction End-stage renal disease (ESRD) refers to the end stage of various chronic kidney diseases (CKDs). The global…\n","protected":false},"author":2,"featured_media":384478,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[49,48,163572,61],"class_list":["post-384477","post","type-post","status-publish","format-standard","has-post-thumbnail","category-technology","tag-ca","tag-canada","tag-mhealth-mobile-health-self-management-peritoneal-dialysis-hemodialysis-kidney","tag-technology"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts\/384477","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/comments?post=384477"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts\/384477\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/media\/384478"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/media?parent=384477"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/categories?post=384477"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/tags?post=384477"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}<\/a>Figure 3. Components of self-management for patients undergoing dialysis. AVF: arteriovenous fistula; AVG: arteriovenous graft; IDWG: intradialytic weight gain; iPTH: intact parathyroid hormone; PTH: parathyroid hormone. mHealth-Based Self-Management Intervention Program<\/p>\n
Disease knowledge base: kidney function, dialysis principles, complication prevention, medications, and lifestyle guidance presented through animated videos, podcasts, manuals, and charts
Operating system training: training for new patients (including equipment operation instruction) and periodic knowledge reinforcement.
Full-dimensional monitoring system
Automatic acquisition and manual entry of data: physiological indicators, dialysis parameters, symptom logs, and medication records
Visualization analysis: trend analysis and correlation analysis.
Accurate nutritional management
Database support: 500+kinds of kidney disease-specific food nutrients, nutrition calculator (real-time display of phosphorus, sodium, potassium, and protein)
Recording function: barcode scanning to enter food, manual recording of intake
Analysis and feedback: nutritional value calculation for each meal, health scoring system (weekly or monthly summary), electrolyte excess warning (sodium, potassium, and phosphorus)
Behavioral interventions: personalized dynamic recipe recommendations (adjusted based on lab data), daily water intake limits (residual urine volume algorithm)
Behavioral interventions (fluid, exercise, and medication adherence)
Personalized goal management: goal setting, progress tracking (instant values + trend charts + health scores)
Behavior shaping tools: badge reward mechanism (continuous recording of achievements)
Intelligent reminders and alerts
Treatment reminder: dialysis time and follow-up appointment
Medication reminder: medication time, dosage, and drug interaction
Early warning system: set thresholds to trigger abnormal alerts and notify patients and health care at the same time.
Doctor-patient collaboration network
Prescription cloud adjustment, test result synchronization (direct connection to electronic medical records), and equipment interconnection.
Social support system
Patient community: experience sharing (recipes or exercise programs)
Family linkage: caregiver collaborative recording function and family health data sharing.
System Infrastructure
System compatibility: mobile (iOS\/Android)+Web+PDA compatibility, offline data caching, and synchronization
Intelligent devices: support joint NFC, OCR, and PDA terminals
Multimodal recording: support voice recording, image recognition, and manual supplementation.
Instant messaging software5Knowledge delivery and personalized guidance: basic health manuals, video educational resources, and customized content delivery
Dietary interventions and guidance
Psychological intervention: structured facilitation (motivational interviewing), psychological facilitation (drawing healing-experts\u2019 video guidance), and emotional support (full psychological status tracking)
Instant interaction: online Q&A with health care, regular phone consultations (psychological support and health guidance), and videoconference support (group discussions or personalized guidance).
A remote patient monitoring system3Intelligent monitoring hub
Device interconnection: bidirectional communication with automatic peritoneal dialysis devices through a cloud platform
Full-dimensional data collection: real-time access to dialysis parameters, device alarm logs, and patient physiological indicators.
Remote dynamic intervention
Intelligent alarms: yellow and red alarms, instantly triggering phone interventions;
Prescription cloud adjustment: physicians remotely optimize automated peritoneal dialysis prescription parameters based on real-time data
Physician-patient collaboration platform: support real-time treatment issues through system messages and phone calls.
Telephones4Health education
Treatment adherence assessment
Personalized dietary recommendations
Psychological support
Problem solving
SMS text messaging4Personalized health education
Regular collection of patients\u2019 feedback
Regular medication reminders
Positive motivational text messages<\/p>\n<\/a>Figure 4. Gap map of core self-management interventions across different mobile health (mHealth) categories for patients undergoing dialysis. <\/p>\n
The self-care performance questionnaire []
The Perceived Medical Condition Self-Management Scale (PKDSMS) []
The Patient Activation Measure-13 (PAM-13) []
Adherence17The End-Stage Renal Disease Adherence Questionnaire (ESRD-AQ) []
Hemodialysis Compliance Questionnaire []
The Simplified Medication Adherence Questionnaire (SMAQ) []
The Dialysis Diet and Fluid Nonadherence Questionnaire (DDFQ) []
The Modified Morisky Scale (MMS)
The Compliance of Patient Role Behavior Tool []
The Sick-role Behavior Adherence []
The Adherence Questionnaire (self-developed or revised version)
Self-efficacy14The 15-item dietary self-efficacy questionnaire []
The decision self-efficacy scale []
The 11-item Fluid Self-Efficacy Scale (FSES) []
The 6-item Chronic Disease Self-Efficacy Scale (CDSES) []
The Self-Efficacy for Appropriate Medication Use Scale (SEAMS) []
The strategies used by people to promote health
The Self-Efficacy Scale (self-developed or revised version) []
Perceived benefits2The Benefits of Sodium Adherence (BSA) [,]
The 9-item Benefits of Fluid Adherence Scale [,]
Literacy2The Media Health Literacy Questionnaire (MeHLit) []
The Health Literacy Questionnaire (HLQ) []
Knowledge9Self-developed or revised knowledge-related questionnaires
Quality of life12Kidney Disease Quality of Life Instrument-Short Form (KDQOL-SF) []
The Kidney Disease Quality of Life (KDQOL-36) []
Short Form 36 (SF-36)
EuroQol Five Dimensions Questionnaire (EQ-5D)
Health-Related Quality of Life (HRQoL)
9-item Thai Health Status Assessment Instrument (9-THAI) []
Diet and nutrition924-hour dietary recall method
3-day dietary recall method
App automatic calculation
Food Frequency Questionnaires (FFQ)
Malnutrition Inflammation Score (MIS)
Healthy Eating Index (HEI-20)
Psychosocial3Beck Depression Inventory (BDI)
\ufe0f Beck Anxiety Inventory (BAI)
Weight, dry weight5IDWG12Blood pressure3Blood urea nitrogen4Creatinine5Urea2KT\/V, dialysis adequacy5Serum albumin14Serum sodium2Serum calcium5Serum potassium14Serum phosphorus20Serum aluminum1Serum iron1Hemoglobin6Glycosylated hemoglobin1Hematocrit1Bicarbonate1Alkaline phosphatase1iPTH2PTH4Brain natriuretic peptide1C-reactive protein1<\/p>\n<\/a>Figure 5. Gap map of self-management assessment indicators across different mobile health (mHealth) categories for patients undergoing dialysis.
DiscussionPrincipal Findings<\/p>\n