Accessibility settings

This paper is in the following e-collection/theme issue:

Nurses Supporting Self-Management of Chronic Diseases (40) Web-based and Mobile Health Interventions (5748) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (2312) Chronic Conditions (545)

Published on in Vol 9 (2026)

This is a member publication of

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/94330, first published .
Nurse shows tablet to elderly patient in hospital bed

Nurse-Led Digital Intervention for Sodium Restriction in Chronic Kidney Disease: Mixed Methods Implementation Study

Nurse-Led Digital Intervention for Sodium Restriction in Chronic Kidney Disease: Mixed Methods Implementation Study

Authors of this article:

Yu Shi1 Author Orcid Image ;   Hongmei Peng1 Author Orcid Image ;   Youying Zhang1 Author Orcid Image ;   Jingyu Zhang2 Author Orcid Image ;   Yang Li1 Author Orcid Image ;   Shi Pu1 Author Orcid Image ;   Xiangchun Tang1 Author Orcid Image ;   Jinghong Zhao1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    1Department of Nephrology, Chongqing Key Laboratory of Prevention and Treatment of Kidney Disease, Chongqing Clinical Research Center of Kidney and Urology Diseases, The Second Affiliated Hospital (Xinqiao Hospital), Army Medical University (Third Military Medical University), 83 Xinqiao Zhengjie, Shapingba District, Chongqing, China

    2State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing, China

    Corresponding Author:

    Jinghong Zhao, PhD


    Background: Digital interventions can support sodium restriction in chronic kidney disease; yet, implementation and engagement barriers remain poorly understood.

    Objective: This mixed methods study evaluated a nurse-led digital program for sodium restriction, focusing on 3 key aspects: implementation, engagement, and patient experience.

    Methods: This study was initially designed as a randomized controlled trial but was revised to a mixed methods implementation study due to high rates of missing primary outcome data. Quantitative measures included 24-hour urinary sodium excretion (the primary outcome of the original randomized controlled trial), knowledge tests, blood pressure measurements, and quality-of-life assessments. Concurrently, we conducted in-depth qualitative interviews with a purposive sample of participants. Quantitative and qualitative data were collected in parallel, analyzed separately, and then integrated to provide a comprehensive understanding of participants’ experiences with the intervention.

    Results: Among 99 enrolled patients (46 in the enhanced digital intervention group and 53 in the minimal digital control group), the program led to a significant improvement in sodium-related knowledge as a secondary exploratory outcome (P=.005). However, the 24-hour urine collection completion rate was only 24.2% (n=24), precluding reliable conclusions regarding the primary outcome. Qualitative findings revealed a “measurement paradox,” in which the gold-standard assessment itself posed a major barrier to adherence. Integrated data illustrated a patient journey marked by early enthusiasm, followed by declining engagement due to digital fatigue, family resistance, and poor fit with daily life. This misalignment created a disconnect between acquired knowledge and measurable behavior change.

    Conclusions: A nurse-led digital intervention was associated with modest improvements in sodium knowledge in chronic kidney disease. However, success requires reducing the monitoring burden and improving real-world fit. The “measurement paradox” highlights the need for patient-centered outcome measures. We propose a “stage-matched intervention” framework to guide adaptive digital tools. Future research should prioritize multicenter pragmatic trials to bridge the knowledge-action gap.

    Trial Registration: Chinese Clinical Trials Registry ChiCTR2200060627; https://tinyurl.com/43sx53j4

    JMIR Nursing 2026;9:e94330

    doi:10.2196/94330

    Keywords

    chronic kidney disease; sodium restriction; digital health; mixed-methods research; nurse-led digital intervention 


    Chronic kidney disease (CKD) affects millions worldwide, and high sodium intake accelerates disease progression [1-3]. Clinical guidelines recommend a daily sodium intake below 2.3 grams or 100 mmol to control blood pressure and preserve kidney function [4-6], but real-world adherence remains poor. Fewer than 30% of patients with CKD meet this target [7-9], revealing a critical knowledge-practice gap in disease management. As frontline providers, nurses are well positioned to leverage digital tools to improve sodium management [10-12]. To systematically address these self-care deficits, this study was guided by Orem’s Self-Care Deficit Nursing Theory, which posits that individuals require nursing support when their self-care capabilities are insufficient [13]. For patients with CKD, inadequate knowledge, poor adherence, and low self-monitoring skills represent key self-care deficits in sodium management. The nurse-led digital intervention was therefore designed to identify these deficits, provide compensatory support, and enhance self-care agency, fully aligning with the core logic of Orem’s theory. Accordingly, we developed a theory-driven, nurse-led mobile digital sodium restriction program embedded within our independently designed Su Yi app (Thinmed Medical Technology (Chongqing) Co, Ltd), integrating nursing expertise into patient education to address these self-care deficits [14,15].

    While digital therapeutics have demonstrated benefits for chronic conditions such as hypertension [16-19], their application in CKD management remains nascent. Most existing studies rely on idealized randomized controlled trial (RCT) designs (Checklist 1) [7,20] that prioritize internal validity but overlook real-world implementation barriers, including digital fatigue, social resistance, and procedural burden. A prime example is the 24-hour urinary sodium test, the gold standard for estimating sodium intake, which imposes a considerable burden on patients due to its cumbersome nature [21]. Yet, its role as a primary end point is rarely reconsidered, creating a critical measurement paradox. Low adherence to such measures renders study outcomes inconclusive, hindering the identification of why interventions fail. Although some studies indicate that digital interventions can improve dietary knowledge among patients with CKD [22,23], the reasons why knowledge gains often do not translate into improved behavior or physiological outcomes remain unclear. Critically, large-scale failures in objective data collection often overshadow patients’ actual experiences with digital tools, obscuring systemic mismatches between intervention design and the realities of daily life.

    Existing literature identifies a key paradox in digital health interventions. Patients often show significant improvements in health-related knowledge; yet, objective behavioral metrics, such as physiological assessment completion rates, remain consistently low. In sodium management programs specifically, knowledge acquisition does not reliably translate into adherence to urine collection protocols used to verify dietary changes. This discrepancy raises a critical question: why do theoretically effective interventions struggle to sustain in real-world settings? Possible explanations include intervention design flaws, insufficient patient motivation, or a fundamental misalignment between digital tools and patients’ daily experiences. The field currently lacks an in-depth understanding of the full engagement process among patients with CKD in digital sodium management. Without insights into patients’ emotional shifts, practical challenges, and evolving needs during intervention, there is a risk of repeatedly developing technically advanced but clinically impractical solutions. To address this gap, advanced practice nurses and clinical nurse researchers conducted a parallel mixed methods study integrating quantitative and qualitative approaches. Led by the nursing team, the intervention was designed and implemented to systematically evaluate both the effectiveness and patient experience of a nurse-developed digital sodium restriction program for patients with CKD.

    Initially registered as an RCT (ChiCTR2200060627) with 24-hour urinary sodium as the primary outcome, this study was reconceptualized as an exploratory mixed methods implementation study due to 75.7% (75/99) missing data on the primary outcome. The objectives shifted from efficacy verification to exploring implementation barriers and patient engagement experiences. Guided by Orem’s Self-Care Deficit Nursing Theory, we hypothesized 2 core points. First, the nurse-led digital intervention may improve sodium-related knowledge but is likely to face high attrition in burdensome 24-hour urinary sodium collection due to mismatched self-care capabilities. Second, mapping longitudinal engagement trajectories is expected to identify the core implementation barriers, informing human-centered digital tool development and clarifying the knowledge-behavior disconnect. This study addresses a critical gap in digital nursing research by moving beyond traditional efficacy-focused RCTs to prioritize real-world implementation and user experience. Our findings offer practical guidance for designing adaptive, feasible digital interventions for CKD management.


    Study Design and Setting

    This study was prospectively registered as a parallel-group RCT in the Chinese Clinical Trial Registry (ChiCTR2200060627; registered on June 6, 2022). The initial design aimed to evaluate intervention efficacy, with 24-hour urinary sodium excretion as the primary outcome and secondary outcomes including sodium-related knowledge, blood pressure, and quality of life (QOL). A 3-month follow-up duration was selected based on previous digital health implementation studies in CKD and hypertension, which have demonstrated that 8 to 12 weeks is sufficient to detect changes in intermediate physiological and behavioral outcomes in real-world settings [7,17]. This time frame balances the need to observe meaningful behavior change while minimizing attrition in longitudinal implementation research.

    During the 3-month interim review, only 24.2% (24/99) of participants completed valid 24-hour urine collections, resulting in 75.7% (75/99) missing data for the primary outcome, far below the prespecified acceptable threshold of 60%. Because increasing the sample size could not improve data completeness, the study design was formally revised from an RCT to a single-center, exploratory mixed methods implementation study [24-26].

    This design adaptation was consistent with reporting guidelines for pragmatic implementation research. The original sample of 99 participants remained adequate for mixed methods inquiry, which typically requires at least 80 quantitative participants and 15 to 30 qualitative participants [27]. Accordingly, the analytical focus shifted from confirmatory efficacy testing to exploratory implementation outcomes. The core objective was revised to evaluate implementation processes, participant engagement experiences, and barriers to a nurse-led digital sodium restriction intervention in patients with CKD.

    During the 3-month interim progress review conducted in November 2022, the completion rate for the prespecified primary outcome measurement was merely 24.2% (24/99), far below the expected level. Concurrently, notable participant attrition was observed in both groups. Given the poor feasibility of continuing the original primary outcome evaluation, our research team comprehensively assessed the actual research progress and field conditions. After obtaining formal approval from the ethics committee, we decided to abandon the planned primary outcome efficacy analysis, redefine this project as an exploratory mixed methods implementation study, and shift our core research focus to exploring intervention implementation processes and real-life patient experiences.

    Ethical Considerations

    The research design and study protocol were approved by the Ethics Committee of the Second Affiliated Hospital of Army Medical University (approval number 2022-Research-040-01). The revised study design was subsequently reviewed and approved by the same ethics committee (approval number 2022-Research-515-01). All participants provided written informed consent prior to enrollment. All study procedures were conducted in accordance with the Declaration of Helsinki.

    Participant Recruitment and Screening

    Eligible participants were recruited and screened by trained clinical nurses from inpatients admitted to the nephrology department between June 2022 and August 2022. Inclusion criteria were as follows: age 18 to 65 years; diagnosis of CKD stages 1‐5 [28], including those with hypertension; an estimated glomerular filtration rate stable over the past 3 months; 24-hour urinary sodium excretion >130 mmol/day at baseline; Barthel Index score ≥60; Mini-Mental State Examination (MMSE) score ≥24; proficiency in using smartphones and mobile applications; and willingness to participate. Exclusion criteria included the following: recent acute kidney injury; receipt of renal replacement therapy; major cardiovascular event within 6 months; uncontrolled hypertension (systolic blood pressure >160 mm Hg or diastolic blood pressure >100 mm Hg); prior participation in similar digital health programs; or any condition likely to interfere with study compliance.

    Randomization and Blinding

    Eligible participants were randomly assigned (1:1) to either the intervention or control group using computer-generated random sequences in SPSS (version 24.0; IBM Corp). Allocation was concealed via sequentially numbered, opaque, and sealed envelopes prepared by an independent statistician. Due to the nature of the intervention, participants and the intervention delivery nurse could not be blinded. However, to minimize ascertainment bias, outcome assessors, laboratory technicians, and data analysts remained blinded to group allocation throughout the study. The 46:53 group size discrepancy resulted from random fluctuation of the computer-generated sequence with no manual modification. Such a degree of imbalance falls within the expected range of chance variation in simple randomization and does not compromise the validity of random allocation.

    Interventions

    Control Group (Minimal Digital Intervention)

    Participants received minimal digital intervention [29], which included a single educational session on sodium restriction and access to the Su Yi app—a self-developed mobile platform providing static educational content (1 video and 1 text document) [30]. They were prompted daily via the app to measure and log their morning blood pressure. Both groups used the Su Yi app; however, the intervention content and intensity differed between the groups.

    Intervention Group (Enhanced Digital Intervention)

    Participants received an enhanced digital intervention that was strictly grounded in Orem’s Self-Care Deficit Nursing Theory. The intervention followed three core theoretical components: (1) deficit identification through baseline knowledge assessment, (2) compensatory nursing support via structured education and gamified learning, and (3) self-care agency enhancement using personalized feedback and autonomous practice. The 4-week intervention was delivered in four progressive stages (Figure 1): (1) initiation (knowledge learning), (2) advancement (quizzes and peer competition), (3) stabilization (behavioral practice), and (4) outcome (personalized reporting). This staged structure directly operationalized Orem’s theoretical logic of identifying deficits, providing compensatory support, and promoting self-care capacity. Gamified modules, in-app quizzes, peer competition, and standardized behavioral tasks were integrated to enhance engagement. Upon completion of the 4-week intervention, each participant received a personalized report illustrating knowledge mastery, salt intake goal achievement, trends in 24-hour urinary sodium excretion, and blood pressure trajectories. All activities were completed autonomously via the app, minimizing clinical burden while maintaining consistent nursing oversight through remote monitoring.

    Both groups attended a follow-up visit 3 months post intervention for outcome assessment.

    ‎
    Figure 1. Formation and application process of the digital sodium control system. CKD: chronic kidney disease.

    Sample Size Calculation

    The sample size was calculated using PASS (version 15.0; IBM Corp) software based on the anticipated between-group difference in 24-hour urinary sodium excretion at the 3-month follow-up. Data from a previous randomized trial suggested a mean excretion of approximately 173.5 mmol/day in control groups and 147.5 mmol/day in intervention groups, indicating a potential difference of 26 mmol/day [31]. A common SD of 34.0 mmol/day was assumed based on the same study. Using a 2-sample, 2-tailed t test with α=.05% and 90% power, 37 participants per group were required. Accounting for an estimated 20% dropout rate, the final target sample size was set at 47 per group (total n=94).

    Data Collection Procedures

    Quantitative data were collected at baseline (T0) and at the 3-month follow-up (T1). Outcomes of interest included 24-hour urinary sodium excretion [32] and blood pressure [33] as potential indicators of physiological response. Blood pressure was defined as the average of 3 consecutive office blood pressure measurements. Additional outcomes comprised sodium control knowledge, QOL, and laboratory parameters. Sodium control knowledge was assessed using a 10-item questionnaire developed by the research team. The questionnaire was based on CKD sodium restriction guidelines, refined through 3 rounds of expert review, with a content validity index of 0.92, and validated via pilot testing with 20 participants and a Cronbach α of 0.78, and the questionnaire is provided in Multimedia Appendix 1. QOL was measured using KDQOL-SF (Kidney Disease Quality of Life-Short Form) 1.3 [34], and laboratory parameters included serum creatinine, urea, uric acid, electrolytes, hemoglobin, and albumin. Adherence to 24-hour urine collection was anticipated to present challenges; therefore, this measure was analyzed exploratorily and interpreted cautiously, with due consideration of completion rates.

    Qualitative data were collected through semistructured in-depth interviews with a purposive subsample of 23 participants. Participants who discontinued the intervention were not included due to practical constraints related to time and research resources. This subsample comprised only intervention completers; accordingly, all qualitative findings were limited to participants who completed the full intervention program.

    All interviews were conducted in Mandarin Chinese by 2 nurse researchers with master’s degrees who had received systematic training in qualitative research (Checklist 2). These researchers were not involved in intervention delivery to minimize potential bias. Face-to-face interviews were conducted in the outpatient CKD management clinic, with each session lasting 20 to 40 minutes. All interviews were audio-recorded, transcribed verbatim, and anonymized. The transcripts were then translated into English using dual translation and back-translation to ensure linguistic and conceptual fidelity. Regular debriefing sessions were held throughout data collection to enhance reflexivity and analytic rigor. The interview guide was developed under the core framework of Orem’s Self-Care Deficit Nursing Theory, with the integration of key concepts from Empowerment Theory. The guide was designed to explore participants’ experiences across multiple dimensions, including task execution, emotional responses, barriers encountered, and evolving needs. Thematic analysis was used to analyze the interview transcripts. To ensure coding reliability, 2 researchers independently coded 20% of the data, yielding strong intercoder agreement with a Cohen κ value of 0.72. Discrepancies were resolved through consensus discussion. The finalized codebook, which included code definitions, thematic hierarchies, representative quotes, and decision rules, was then applied to the full dataset by the primary researcher. The codebook is provided in Multimedia Appendix 2. Sensitivity analyses were conducted to assess the potential influence of selection bias on qualitative interpretations. We hypothesized that participants who discontinued the intervention may hold distinct perspectives related to higher intervention burden, digital fatigue, and competing time demands. Such systematic differences may lead to overestimation of participant engagement and underestimation of real-world implementation barriers if noncompleters are excluded from analysis.

    Statistical and Analytical Approaches

    Quantitative Analysis

    During manuscript revision, we consulted a biostatistician regarding the handling of the substantial missing data in 24-hour urinary sodium excretion. It was determined that traditional inferential statistics (eg, t tests, ANOVA) would yield unreliable estimates due to severe attrition (75/99, 75.8% missing) and potential selection bias. Consequently, we have revised the manuscript to reflect a mixed methods evaluation framework, removed causal interpretations of urinary sodium outcomes, and focused the discussion on implementation insights and patient-reported experiences.

    Continuous variables were tested for normality using the Shapiro-Wilk test. Normally distributed data were presented as mean (SD) and compared using independent samples t tests. Nonnormally distributed variables were reported as median (IQR) and analyzed using Mann-Whitney U tests. Categorical variables were expressed as frequencies (%) and compared using chi-square or Fisher exact tests. Intention-to-treat (ITT) analysis was applied to all primary outcomes. Statistical significance was set at P<.05. Analyses were performed using SAS software (version 9.4; SAS Institute Inc). Given the substantial missingness in 24-hour urinary sodium excretion (>60%), we carefully evaluated the data quality and implemented the following handling strategies: (1) ITT analysis was applied to all available data to maintain the randomized design’s integrity; (2) between-group inferential comparisons were not performed, even for completers, due to the high risk of selection bias; and (3) multiple imputation was not adopted as it would require untestable assumptions about the missing values, for example, missing at random, given the extreme missing rate. We interpret this outcome exploratorily, focusing on implementation insights rather than efficacy conclusions.

    Qualitative Analysis

    Transcripts were analyzed using thematic analysis guided by Empowerment Theory. A hybrid deductive-inductive approach was used: initial coding was informed by predefined domains (tasks, emotions, pain points, and needs), while emergent themes were iteratively refined. Two researchers independently coded 20% of the transcripts to assess reliability, achieving consensus through discussion. NVivo 12 (QSR International) was used for data management.

    Integration of Mixed Methods

    A convergent parallel mixed methods design was used in accordance with the GRAMMS (Good Reporting of A Mixed Methods Study) guidelines (Checklist 3) [35]. Quantitative and qualitative data were collected in parallel, analyzed independently, and subsequently integrated using a joint display matrix. Quantitative outcomes provided the primary analytical framework, while qualitative findings were used to explain, supplement, and contextualize the quantitative results. To ensure integration rigor, 2 researchers conducted independent interpretive integration; discrepancies were resolved through consensus discussions, and a third researcher reviewed the entire integration process.


    Participant Flow

    Of 303 screened patients, 99 were randomized: 46 to the enhanced digital intervention group and 53 to the minimal digital control group. Baseline characteristics were similar between groups (Table 1), confirming balanced randomization. At the 3-month follow-up, 52 (98.1%) in the control group and 45 (97.8%) in the intervention group completed blood pressure and blood component analysis, with the exception of 1 death in the intervention group and 1 withdrawal in the control group for personal reasons. However, only 13 (24.5%) in the control group and 11 (23.9%) in the intervention group completed the 24-hour urine collection (Figure 2), resulting in an overall completion rate of 24.2% (24/99) for this outcome measure.

    Table 1. Baseline demographic and clinical characteristics of participants (N=99).
    VariablesControl (minimal digital; n=53)Intervention (enhanced digital; n=46)zChi-square (df)P value
    Age (y), median (IQR)48 (37‐54)44 (31-52)−1.24—a.21
    Sex, n (%)  —1.56 (1).21
    Male32 (60.3)22 (47.8)   
    Female21 (39.6)24 (52.1)   
    Educational level  —1.47 (4).83
    Primary education or below11 (20.7)7 (15.2)   
    Junior middle school19 (35.8)15 (32.6)   
    Senior high school7 (13.2)9 (19.5)   
    Associate degree8 (15.1)6 (13.0)   
    Bachelor’s degree or above8 (15.1)9 (19.5)   
    Hypertension  —1.51 (1).22
     No20 (37.7)23 (50)   
     Yes33 (62.2)23 (50)   
    CKDb stage  —7.20 (4).13
     113 (24.5)20 (43.4)   
     210 (18.8)12 (26.1)   
     313 (24.5)6 (13.0)   
     48 (15.1)3 (6.5)   
     59 (16.9)5 (10.8)   
    BMI (kg/m²), median (IQR)24.6 (21.5-26.9)24.0 (22.1-26.8)−0.15—.88
    Waist circumference (cm), median (IQR)85 (78-94)81 (77-92)−1.55—.12
    Urine protein to creatinine ratio (g/g), median (IQR)1.8 (0.6-2.4)1.2 (0.3-3.9)−0.52—.60
    Serum creatinine (μmol/L), median (IQR)146.7 (85.3-315.5)94.3 (94.3-56.6)−1.90—.06
    Urea (mmol/L), median (IQR)9.2 (5.8-12.8)6.1 (4.6-11.7)−1.44—.15
    Uric acid (μmol/L), median (IQR)392.3 (329.8-482.6)400.3 (308.0-481.0)−0.69—.49
    Glucose (mmol/L), median (IQR)5.2 (4.7-5.6)5.2 (4.8-5.9)0.29—.78
    Serum sodium (mmol/L), median (IQR)139.0 (137.6-140.2)138.6 (137.6-140.0)−0.85 —.40
    Serum potassium (mmol/L), median (IQR)4.1 (4.0-4.5)4.1 (3.9-4.4)−0.46 —.64
    Serum phosphate (mmol/L), median (IQR)1.2 (1.0-1.3)1.2 (1.0-1.3)−0.29 —.77
    Serum calcium (mmol/L), median (IQR)2.2 (2.1-2.3)2.2 (2.1-2.3)−1.21 —.22
    Total protein (g/L), median (IQR)65.1 (59.1-72.4)65.9 (58.1-73.6)−0.14 —.89
    Hemoglobin (g/L), median (IQR)126.0 (113.0-142.0)129.0 (106.0-138.0)−0.40 —.80
    Triglycerides (mmol/L), median (IQR)1.8 (1.2-2.8)1.6 (1.3-2.2)−0.40 —.69
    Total cholesterol (mmol/L), median (IQR)4.9 (4.4-6.0)5.0 (4.1-6.0)＜0.01 —>.99
    Systolic blood pressure (mm Hg), median (IQR)130 (116-148)127 (115-145)−0.07 —.95
    Diastolic blood pressure (mm Hg), median (IQR)82 (77-93)82 (75-90)−0.24 —.81
    Heart rate (beats/min), median (IQR)82 (80-87)81 (78-85)−0.84 —.40
    Urinary sodium (mmol/24 h), median (IQR)156.3 (141.4-184.6)166.3 (140.8-206.7)−0.84 —.42
    Sodium control knowledge score3 (2-5)3 (2-5)1.13 —.26
    SF-36c, median (IQR)2295 (2025-2500)2247 (2247-1935)−0.34—.73
    KDTAd, median (IQR)3126 (2673-3440)3165 (2758-3483)0.29 —.77

    a—: not applicable.

    bCKD: chronic kidney disease.

    cSF-36: 36-Item Short Form Health Survey.

    dKDTA: kidney disease targeted areas.

    ‎
    Figure 2. Flow diagram of participant enrollment. KDQOL-SF: Kidney Disease Quality of Life-Short Form.

    Baseline Characteristics

    Baseline demographic and clinical characteristics were balanced between groups (Table 1). The median age was 48 (IQR 37‐54) years in the control group and 44 (IQR 31-52) years in the intervention group (P=.21). There were no significant differences in sex distribution, CKD stage, comorbidities, or laboratory parameters, confirming successful randomization.

    Quantitative Outcomes

    As shown in Table 2, the completion rate for 24-hour urinary sodium excretion was critically low (24/99, 24.2%), precluding reliable estimation of between-group differences or definitive conclusions about behavioral change. Among the small subset of completers (n=24), median sodium excretion appeared lower in the intervention group (97.3, IQR 73.7‐124.0) compared to controls (154.4 mmol/day, IQR 123.2‐190.9). However, inferential testing was not conducted due to insufficient power and selection bias concerns.

    However, a significant improvement was observed in the secondary outcome of sodium control knowledge: intervention group participants scored 8 (IQR 7-8) versus 6 (IQR 6-8) in controls (z=2.84, P=.005). No significant differences were found in blood pressure, QOL (36-Item Short Form Health Survey [SF-36] and kidney disease targeted areas), or other physiological markers.

    Table 2. Analysis of outcomes at 3-month follow-up by study group (N=97)a.
    OutcomesControl (minimal digital; n=52)Intervention (enhanced digital; n=45)zP value
    nbMedian (IQR)nbMedian (IQR)
    Sodium control knowledge score506 (6-8)408 (7-8)2.84.005
    SF-36c492515 (2355-2640)402535 (2315-2780)1.06.30
    KDTAd493375 (3048-3755)403535 (3082-3939)0.78.44
    Systolic blood pressure (mm Hg)52126 (112-141)45125 (112-139)−0.22.83
    Diastolic blood pressure (mm Hg)5278 (72-88)4580 (73-88)0.45.66
    Heart rate (beats/min)5280 (78-85)4580 (78-86)0.38.70

    aCompletion rate for 24-hour urinary sodium excretion was 24.2% (24/99). Between-group comparisons were not performed due to low adherence and risk of selection bias.

    bNumber of participants with available data for each outcome.

    cSF-36: 36-Item Short Form Health Survey.

    dKDTA: kidney disease targeted areas.

    Qualitative Findings

    Overview of the Patient Journey Map

    Thematic analysis was conducted among 23 intervention completers; noncompleters were not interviewed due to resource constraints. Sensitivity analyses indicated that the exclusion of noncompleters may reduce the representativeness of the patient journey map. Consequently, all findings are restricted to participants who completed the enhanced digital intervention. Data saturation was assessed retrospectively after coding all 23 interview transcripts and was reached at the 18th interview, with no new themes identified in the subsequent 5 interviews. The analysis revealed a dynamic patient journey, summarized in Table 3. A patient journey map typically consists of a horizontal axis (timeline) and a vertical axis (task axis). Guided by Empowerment Theory, this study divides the typical journey of patients with CKD in a digital sodium restriction intervention along the horizontal axis into three phases: (1) initial contact and expectation phase (week 1); (2) sustained interaction and empowerment phase (weeks 2‐4); and (3) platform-facilitated relationship evolution phase (week 5 and beyond). The vertical axis encompasses 4 dimensions—tasks, emotions, pain points, and needs—yielding a total of 28 themes. These themes capture the realities, emotional shifts, barriers, and needs faced by patients at each stage of the digital sodium restriction intervention, comprehensively mapping the storyline across different dimensions throughout each phase. This reveals the dynamic evolution of patients from adherence to self-empowerment during the digital sodium restriction intervention, ultimately forming a typical patient journey map for patients with CKD in such interventions, as shown in Figure 3.

    Table 3. Participant characteristics (qualitative study).
    NumberSexAge (y)Educational levelProfessionCKDa stage
    P1Female43Primary education or belowUnemployed5
    P2Male52Junior middle schoolFarmer2
    P3Female29Bachelor’s degree or aboveOffice worker1
    P4Female24Associate degreeProfessionals1
    P5Female37Bachelor’s degree or aboveCivil servant1
    P6Male61Bachelor’s degree or aboveProfessionals5
    P7Male38Junior middle schoolMigrant worker1
    P8Female55Senior high schoolRetired1
    P9Female31Bachelor’s degree or aboveCivil servant1
    P10Female56Primary education or belowOffice worker2
    P11Female22Junior middle schoolOffice worker1
    P12Male45Associate degreeCivil servant2
    P13Female45Junior middle schoolUnemployed3
    P14Female28Bachelor’s degree or aboveUnemployed1
    P15Male47Senior high schoolMigrant worker2
    P16Female24Junior middle schoolOffice worker1
    P17Female47Junior middle schoolUnemployed1
    P18Male43Junior middle schoolMigrant worker3
    P19Male27Associate degreeCivil servant4
    P20Male50Senior high schoolMigrant worker4
    P21Male26Associate degreeProfessionals1
    P22Female37Bachelor’s degree or aboveTeacher2
    P23Male20Associate degreeStudent1

    aCKD: chronic kidney disease.

    ‎
    Figure 3. Patient empowerment journey among enhanced digital intervention completers.
    Initial Contact and Expectation Phase (Learning-Adapting-Depending)

    Participants described initial excitement and hope (“I wanted to try anything that could help my kidneys” [P5/13]), which was quickly tempered by information overload and technical anxiety (“So many messages… I didn’t know what to do first” [P7]). Key barriers included difficulty navigating the app interface and uncertainty about correct data entry. Emotional dependency on nurse feedback emerged as a critical motivator for early adherence.

    Sustained Interaction and Empowerment Phase (From Passive to Active Management)

    As familiarity grew, participants transitioned from passive compliance to active self-monitoring and habit formation. Many reported checking food labels spontaneously (“Now I always look at the nutrition facts” [P5/7/21]) and using salt substitutes. Personalized feedback and peer comparison features enhanced motivation. However, digital fatigue (“It felt repetitive after a while” [P18]) and social resistance (“My family said the food was too bland” [P1/9]) emerged as significant challenges.

    Platform-Facilitated Relationship Evolution Phase (Identity Integration)

    In later stages, participants began identifying as health managers rather than passive patients. They shared knowledge with family members (“My kids now remind me not to eat pickles” [P13/16/20]) and advocated for healthier meals. Despite these positive shifts, motivational decay and content monotony threatened long-term engagement. Participants expressed strong desires for expanded educational content (eg, protein and potassium management) and ongoing community support.

    Mixed Methods Integration

    Integrated analyses were performed using a joint display matrix (Table 4) following GRAMMS guidelines for mixed methods reporting. Three patterns of integration were identified. First, convergent findings demonstrated significant improvements in sodium-related knowledge in the enhanced digital intervention group (z=2.84, P=.005). These quantitative improvements were supported by qualitative accounts of clear, structured learning and enhanced confidence in sodium management, providing consistent support for the improvement in knowledge gains observed in the quantitative analysis. Second, supplementary findings highlighted a critical measurement paradox: completion of 24-hour urinary sodium collection was only 24.2% (24/99). Qualitative data further identified substantial procedural burden and poor compatibility with daily routines as key barriers, indicating that the gold-standard assessment itself impeded adherence. Third, explanatory findings clarified the absence of significant between-group differences in blood pressure or QOL (P>.05). Identified barriers, including family resistance, digital fatigue, and limited long-term support, explained why improvements in knowledge did not translate into measurable changes in clinical or patient-reported outcomes. No divergent findings were observed. The integrated synthesis indicates that although the intervention improved sodium-related knowledge, translation into behavioral and clinical changes was limited by implementation barriers and measurement burden.

    Table 4. Joint display matrix for mixed methods integration.
    Quantitative outcomeQualitative findingIntegration typeIntegrated interpretation
    Sodium control knowledge: intervention 8 (7-8), control 6 (6-8); z=2.84, P=.005Participants reported clear learning, easy-to-understand modules, and increased confidence in sodium management.ConvergenceThe intervention effectively improved sodium-related knowledge, consistent with participant experience.
    24 h urine completion: 24.2% (24/99); 75.7% (75/99) missingParticipants reported high burden, time intensity, and poor fit with daily life; urine collection was the top barrier.SupplementationLow completion is explained by substantial patient burden, revealing a “measurement paradox” in gold-standard outcome assessment.
    BPa or QoLb: no significant between-group differences (P>.05)Barriers included family resistance, digital fatigue, repetitive tasks, and insufficient long-term support.ExplanationNeutral clinical outcomes are explained by real-world implementation barriers that prevented knowledge from translating to behavior change.

    aBP: blood pressure.

    bQOL: quality of life.


    Principal Findings

    This study found that the nurse-led digital intervention improved participants’ sodium-related knowledge as a secondary exploratory outcome. However, the completion rate of the primary outcome (24-h urinary sodium collection) was only 24.2% (24/99), which precluded evaluation of the intervention’s true efficacy. These findings are consistent with Orem Self-Care Deficit Nursing Theory. Qualitative interviews identified a 3-phase patient engagement journey: initial enthusiasm, active participation, and subsequent disengagement driven by digital fatigue and family barriers. Overall, while digital interventions can enhance health knowledge, high-burden assessment tools and poor adaptation to daily life significantly hinder sustained behavioral changes.

    From Information Delivery to Multidimensional Empowerment, Bridging the Knowledge-Action Gap

    The improvement in knowledge without obvious behavioral changes supports Orem theory, which holds that self-care agency requires adequate knowledge, skills, motivation, and environmental support [36-39]. This reflects a typical knowledge-action gap. Digital tools can efficiently disseminate standardized health information on a large scale, but knowledge alone cannot drive long-term behavioral changes [40-43]. Sodium restriction is affected by family dynamics, cooking habits, and taste preferences—factors far more complex than simple information delivery [44,45]. Therefore, future interventions should shift from one-way knowledge popularization to multidimensional empowerment, comprehensively addressing both structural and interpersonal barriers [46-48].

    Selecting Scientifically Sound and Feasible Outcome Measures

    This study revealed a measurement paradox. Although 24-hour urine collection is the gold standard for sodium intake assessment [49], its complicated operation and heavy burden lead to low patient adherence and high measurement error [50,51]. This phenomenon aligns with Orem's view that self-care interventions must match patients’ actual capabilities, which limits its application in clinical practice and research. By contrast, the spot urine sodium-to-creatinine ratio is simple to operate and has been validated to predict disease progression in multiple studies [52,53]. AI-based real-time urine testing may balance accuracy and convenience; however, its reliability across different patient groups requires further verification before large-scale application [54-56]. We therefore suggest researchers prioritize outcome measures that offer both scientific rigor and real-world feasibility, rather than pursuing methodological purity at the expense of practicality, to improve the clinical translation of research findings.

    Constructing a Dynamic, Stage-Matched Intervention Framework

    Family resistance, lifestyle constraints, and digital fatigue are key barriers to long-term intervention effects [57-59]. Digital health success relies on continuous patient engagement rather than short-term adoption [60-62]. Based on Orem's self-care theory, we propose a stage-matched intervention framework that includes 3 core components: deficit identification, compensatory support, and self-care capacity improvement [63]. In the initial stage, medical staff identify patients’ knowledge gaps and practical obstacles to build trust. In the middle stage, personalized digital guidance is used to improve patients’ self-efficacy and bridge capability gaps. The later stage helps patients maintain healthy behaviors through community interaction and independent self-care management. This framework transforms static, one-size-fits-all intervention modes into adaptive, dynamic support that addresses the multidimensional self-care deficits in CKD sodium management. Future mixed methods studies should further explore the mechanisms of patient engagement to inform the development of truly patient-centered digital intervention solutions.

    Strengths and Limitations

    This study adopted a parallel mixed methods design. Combining quantitative data and qualitative narratives clarifies intervention effects and their underlying mechanisms—an integrated approach that remains underused in digital therapeutics research. Conducted in routine outpatient settings without additional resource input, the study demonstrates good ecological validity and practicality. The proposed patient empowerment pathway also provides a new perspective for exploring digital health engagement among patients with CKD.

    Several limitations of this study should be acknowledged. First, the 24.2% (24/99) completion rate of 24-hour urine collection resulted in massive missing data, rendering ITT analysis and primary outcome testing invalid. Consequently, the true intervention effect could not be confirmed. Second, qualitative participants were limited to intervention completers, which may introduce selection bias, potentially overestimating engagement and underestimating implementation barriers, even after sensitivity analysis. Third, the single-center setting and small sample size limit the generalizability of findings across geographic, demographic, and statistical dimensions. Fourth, we did not analyze outcome differences across CKD stages 1 to 5, further limiting the applicability of the results.

    Conclusions

    This exploratory mixed methods study indicated that the nurse-led digital sodium restriction intervention was associated with modest gains in participants’ sodium-related knowledge as a secondary outcome. However, this study encountered prominent practical difficulties in implementing high-load outcome monitoring, with the completion rate of 24-hour urine collection reaching only 24.2% (24/99). The misalignment between idealized trial protocols and patients’ daily realities underscores the need for more feasible, patient-centered outcome measures and adaptive intervention designs. Rather than drawing conclusions about efficacy, this study provides critical insights into implementation barriers and informs the development of future person-centered digital tools. Digital health interventions may be improved via technical enhancements and better adaptation to patients’ daily lives. Future research should use implementation science and mixed methods to explore intervention mechanisms, thereby narrowing the gap between health knowledge and self-management in chronic disease management.

    Acknowledgments

    We would like to express our sincere thanks to all the participants who participated in this project and helped us complete the entire study. We also thank Senmei Company (Chongqing) for providing us with technical support and the Department of Nephrology at the Second Affiliated Hospital of the Army Military Medical University for providing us with the research platform, which ensured that our research was successfully completed. Generative AI was used for post-text verification.

    Funding

    This study was supported by the Chongqing Technology Innovation and Application Development (Key Research and Development Project; grant number CSTB2023TIAD-KPX0060) and Chongqing Municipal Science-Technology Bureau and Health Commission jointly funded the project (grant number 2022MSXM026).

    Authors' Contributions

    Conceptualization: J Zhao, YS

    Data curation: HP, SP, XT, YL, YZ

    Formal analysis: J Zhang, YS

    Funding acquisition: J Zhao, YS

    Investigation: HP, J Zhang, SP, XT, YL, YS, YZ

    Methodology: HP, J Zhao, YS,

    Project administration: J Zhang, J Zhao, YS

    Resources: HP

    Supervision: J Zhao

    Validation: YS

    Visualization: HP

    Writing – original draft: YS

    Writing – review & editing: J Zhao, YS

    The first author is the principal investigator of this funded research project and takes full responsibility for study design, data collection, statistical analysis, manuscript drafting and final submission. All authors confirm that there are no commercial benefits, industry sponsorships, paid consulting relationships or other potential financial conflicts of interest that may bias the research findings.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Sodium salt knowledge questionnaire.

    PDF File, 109 KB

    Multimedia Appendix 2

    Patient interview guide and coding codebook.

    DOCX File, 37 KB

    Checklist 1

    CONSORT-eHEALTH checklist (V 1.6).

    PDF File, 193 KB

    Checklist 2

    COREQ checklist.

    PDF File, 98 KB

    Checklist 3

    GRAMMS checklist.

    PDF File, 97 KB
    1. Rafferty Q, Stafford LK, Vos T, GBD 2023 Kidney Failure with Replacement Therapy Collaborators, et al. Global, regional, and national prevalence of kidney failure with replacement therapy and associated aetiologies, 1990–2023: a systematic analysis for the global burden of disease study 2023. Lancet Glob Health. Aug 2025;13:e1378-e1395. [CrossRef]
    2. Chan W. Chronic kidney disease and nutrition support. Nutr Clin Pract. Apr 2021;36(2):312-330. [CrossRef] [Medline]
    3. Romagnani P, Agarwal R, Chan JCN, et al. Chronic kidney disease. Nat Rev Dis Primers. Jan 30, 2025;11(1):8. [CrossRef] [Medline]
    4. Borrelli S, Provenzano M, Gagliardi I, et al. Sodium intake and chronic kidney disease. Int J Mol Sci. Jul 3, 2020;21(13):4744. [CrossRef] [Medline]
    5. Kang M, Kang E, Ryu H, et al. Measured sodium excretion is associated with CKD progression: results from the KNOW-CKD study. Nephrol Dial Transplant. Feb 20, 2021;36(3):512-519. [CrossRef] [Medline]
    6. Nerbass FB, Calice-Silva V, Pecoits-Filho R. Sodium intake and blood pressure in patients with chronic kidney disease: a salty relationship. Blood Purif. 2018;45(1-3):166-172. [CrossRef] [Medline]
    7. Schrauben SJ, Inamdar A, Yule C, et al. Effects of dietary app-supported tele-counseling on sodium intake, diet quality, and blood pressure in patients with diabetes and kidney disease. J Ren Nutr. Jan 2022;32(1):39-50. [CrossRef] [Medline]
    8. Sun L, Duan S, Zuo C, et al. Characteristics of urinary sodium excretion in patients with chronic kidney disease in Jiangsu, China. J Clin Hypertens (Greenwich). Jul 2021;23(7):1372-1379. [CrossRef] [Medline]
    9. Xing XY, Zhao Y, Sam NB, et al. Salt reduction behavior of adults in Anhui province in 2019: a cross-sectional survey of 3,378 participants. Front Public Health. 2023;11:1242969. [CrossRef] [Medline]
    10. Digital therapeutics definition and core principles. Digital Therapeutics Alliance (DTA); 2019. URL: https://dtxalliance.org/wp-content/uploads/2021/01/DTA_DTx-Definition-and-Core-Principles.pdf [Accessed 2026-06-26]
    11. Hong JS, Wasden C, Han DH. Introduction of digital therapeutics. Comput Methods Programs Biomed. Sep 2021;209:106319. [CrossRef] [Medline]
    12. Pereira AP, Janela D, Areias AC, et al. Innovating care for postmenopausal women using a digital approach for pelvic floor dysfunctions: prospective longitudinal cohort study. JMIR Mhealth Uhealth. Apr 2, 2025;13:e68242. [CrossRef] [Medline]
    13. Hartweg DL, Metcalfe SA. Orem’s Self-Care Deficit Nursing Theory: relevance and need for refinement. Nurs Sci Q. Jan 2022;35(1):70-76. [CrossRef] [Medline]
    14. Pongpoottipatchara R, Anonjarn K, Prawatwong W, Singha-Dong N. Implementation of digital device-assisted and nurse-led case management to promote self-management in adults with noncommunicable diseases: protocol for a single-arm intervention study. JMIR Res Protoc. Jan 22, 2026;15:e80083. [CrossRef] [Medline]
    15. Hua J, Sun D, Wang H, et al. Development of a nurse-led mHealth intervention framework for patients with chronic diseases: a systematic review and Delphi study. Digit Health. 2025;11:20552076251387050. [CrossRef] [Medline]
    16. Forma F, Pratiwadi R, El-Moustaid F, Smith N, Thorndike F, Velez F. Network meta-analysis comparing the effectiveness of a prescription digital therapeutic for chronic insomnia to medications and face-to-face cognitive behavioral therapy in adults. Curr Med Res Opin. Oct 2022;38(10):1727-1738. [CrossRef] [Medline]
    17. Kario K, Harada N, Okura A. Digital therapeutics in hypertension: evidence and perspectives. Hypertension. Oct 2022;79(10):2148-2158. [CrossRef] [Medline]
    18. Kario K, Nomura A, Harada N, et al. Efficacy of a digital therapeutics system in the management of essential hypertension: the HERB-DH1 pivotal trial. Eur Heart J. Oct 21, 2021;42(40):4111-4122. [CrossRef] [Medline]
    19. Lutz J, Offidani E, Taraboanta L, Lakhan SE, Campellone TR. Appropriate controls for digital therapeutic clinical trials: a narrative review of control conditions in clinical trials of digital therapeutics (DTx) deploying psychosocial, cognitive, or behavioral content. Front Digit Health. 2022;4:823977. [CrossRef] [Medline]
    20. Greenwood SA, Young HML, Briggs J, et al. Evaluating the effect of a digital health intervention to enhance physical activity in people with chronic kidney disease (Kidney BEAM): a multicentre, randomised controlled trial in the UK. Lancet Digit Health. Jan 2024;6(1):e23-e32. [CrossRef] [Medline]
    21. McLean RM. Measuring population sodium intake: a review of methods. Nutrients. Oct 28, 2014;6(11):4651-4662. [CrossRef] [Medline]
    22. Liu W, Yu X, Wang J, et al. Improving kidney outcomes in patients with nondiabetic chronic kidney disease through an artificial intelligence-based health coaching mobile app: retrospective cohort study. JMIR Mhealth Uhealth. Jun 1, 2023;11:e45531. [CrossRef] [Medline]
    23. Ma J, Wang J, Ying J, et al. Long-term efficacy of an AI-based health coaching mobile app in slowing the progression of nondialysis-dependent chronic kidney disease: retrospective cohort study. J Med Internet Res. Nov 25, 2024;26:e54206. [CrossRef] [Medline]
    24. Lopez-Alcalde J, Wieland LS, Yan Y, et al. Methodological challenges in randomized controlled trials of mhealth interventions: cross-sectional survey study and consensus-based recommendations. J Med Internet Res. Dec 19, 2024;26:e53187. [CrossRef] [Medline]
    25. Steinman L, Chadwick K, Chavez Santos E, et al. Remote evidence-based programs for health promotion to support older adults during the COVID-19 pandemic and beyond: mixed methods outcome evaluation. JMIR Aging. Jun 13, 2024;7:e52069. [CrossRef] [Medline]
    26. Younas A, Kvist T, Stolt M. Framework and key considerations for designing and conducting critical mixed-methods research (cMMR). J Epidemiol Community Health. Dec 10, 2025;80(1):55-61. [CrossRef] [Medline]
    27. Östlund U, Kidd L, Wengström Y, Rowa-Dewar N. Combining qualitative and quantitative research within mixed method research designs: a methodological review. Int J Nurs Stud. Mar 2011;48(3):369-383. [CrossRef] [Medline]
    28. Inker LA, Eneanya ND, Coresh J, et al. New creatinine- and cystatin C-based equations to estimate GFR without race. N Engl J Med. Nov 4, 2021;385(19):1737-1749. [CrossRef] [Medline]
    29. Shi Y, Peng H, Chen Y, et al. Implementation of cluster-based management strategies for patients with chronic kidney disease. J Nurse Pract. Oct 2020;16(9):689-694. [CrossRef]
    30. Shi Y, Pu S, Peng H, et al. Impact of mobile application and outpatient follow-up on renal endpoints and physiological indices in patients with chronic kidney disease: a retrospective cohort study in Southwest China. BMC Med Inform Decis Mak. Jun 12, 2024;24(1):163. [CrossRef] [Medline]
    31. Humalda JK, Klaassen G, de Vries H, et al. A self-management approach for dietary sodium restriction in patients with CKD: a randomized controlled trial. Am J Kidney Dis. Jun 2020;75(6):847-856. [CrossRef] [Medline]
    32. Cogswell ME, Wang CY, Chen TC, et al. Validity of predictive equations for 24-h urinary sodium excretion in adults aged 18-39 y. Am J Clin Nutr. Dec 2013;98(6):1502-1513. [CrossRef] [Medline]
    33. Muntner P, Shimbo D, Carey RM, et al. Measurement of blood pressure in humans: a scientific statement from the American Heart Association. Hypertension. May 2019;73(5):e35-e66. [CrossRef] [Medline]
    34. Korevaar JC, Merkus MP, Jansen MAM, et al. Validation of the KDQOL-SF: a dialysis-targeted health measure. Qual Life Res. Aug 2002;11(5):437-447. [CrossRef] [Medline]
    35. O’Cathain A, Murphy E, Nicholl J. The quality of mixed methods studies in health services research. J Health Serv Res Policy. Apr 2008;13(2):92-98. [CrossRef] [Medline]
    36. Conner M, Wilding S, Wright CE, Sheeran P. How does self-control promote health behaviors? A multi-behavior test of five potential pathways. Ann Behav Med. Apr 12, 2023;57(4):313-322. [CrossRef] [Medline]
    37. Kelly MP, Barker M. Why is changing health-related behaviour so difficult? Public Health. Jul 2016;136:109-116. [CrossRef] [Medline]
    38. Phipps DJ, Hagger MS, Hamilton K. Evidence inhibitory self-control moderates effects of habit on complex but not simple health behaviors. Appl Psychol Health Well Being. Feb 2025;17(1):e12642. [CrossRef] [Medline]
    39. Yang MZ, Conner M, Sheeran P. The role of affect regulation in health behavior change. Health Psychol. Sep 2025;44(9):876-885. [CrossRef] [Medline]
    40. Krebs P, Duncan DT. Health app use among US mobile phone owners: a national survey. JMIR Mhealth Uhealth. Nov 4, 2015;3(4):e101. [CrossRef] [Medline]
    41. Lattie EG, Adkins EC, Winquist N, Stiles-Shields C, Wafford QE, Graham AK. Digital mental health interventions for depression, anxiety, and enhancement of psychological well-being among college students: systematic review. J Med Internet Res. Jul 22, 2019;21(7):e12869. [CrossRef] [Medline]
    42. Wang Y, Min J, Khuri J, et al. Effectiveness of mobile health interventions on diabetes and obesity treatment and management: systematic review of systematic reviews. JMIR Mhealth Uhealth. Apr 28, 2020;8(4):e15400. [CrossRef] [Medline]
    43. Zhong C, Luo X, Tan M, et al. Digital health interventions to improve mental health in patients with cancer: umbrella review. J Med Internet Res. Feb 21, 2025;27:e69621. [CrossRef] [Medline]
    44. Bandura A. Health promotion by social cognitive means. Health Educ Behav. Apr 2004;31(2):143-164. [CrossRef] [Medline]
    45. Palmer SM, Knoblauch ST, Winham DM, Hiller MB, Shelley MC. Putting knowledge into practice: low-income women talk about food choice decisions. Int J Environ Res Public Health. Jul 15, 2020;17(14):5092. [CrossRef] [Medline]
    46. Gao J, Pan Q, Li SL, et al. Influencing factors of health promotion behaviour in patients with aortic dissection: a qualitative study using the COM-B model. BMJ Open. Mar 5, 2025;15(3):e076181. [CrossRef] [Medline]
    47. Liu X, Zhang J, Guan X, Cao Y. Barriers and facilitators to cognitive function interventions in rural diabetic older adults: using the COM-B model and theoretical domains framework. J Adv Nurs. Mar 2026;82(3):2320-2331. [CrossRef] [Medline]
    48. Reedman SE, Jayan L, Boyd RN, Ziviani J, Elliott C, Sakzewski L. Descriptive contents analysis of ParticiPAte CP: a participation-focused intervention to promote physical activity participation in children with cerebral palsy. Disabil Rehabil. Nov 2022;44(23):7167-7177. [CrossRef] [Medline]
    49. Ogata S, Akashi Y, Sakusabe T, et al. A multiple 24-hour urine collection study indicates that kidney function decline is related to urinary sodium and potassium excretion in patients with chronic kidney disease. Kidney Int. Jan 2022;101(1):164-173. [CrossRef] [Medline]
    50. Carnes K, Howe A, Feustel PJ, Listman JA, White M, Kogan BA. 24-hour urine collection for first time pediatric stone formers: is it worth it? J Pediatr Urol. Jun 2021;17(3):387. [CrossRef] [Medline]
    51. Santos JA, Bolton KA, Rosewarne E, et al. Agreement between 24-hour urine and 24-hour food recall in measuring salt intake in primary school children in Australia. Nutr J. Nov 15, 2022;21(1):68. [CrossRef] [Medline]
    52. Wan ER, Cross J, Sofat R, Walsh SB. 24-hour vs. spot urinary sodium and potassium measurements in adult hypertensive patients: a cohort validation study. Am J Hypertens. Sep 24, 2019;32(10):983-991. [CrossRef] [Medline]
    53. Tagawa K, Tsuru Y, Yokoi K, Aonuma T, Hashimoto J. Being overweight worsens the relationship between urinary sodium excretion and albuminuria: the Wakuya study. Eur J Clin Nutr. Nov 2023;77(11):1044-1050. [CrossRef] [Medline]
    54. Bentzen SM. Artificial intelligence in health care: a rallying cry for critical clinical research and ethical thinking. Clin Oncol. May 2025;41:103798. [CrossRef]
    55. Inagaki K, Tsuriya D, Hashimoto T, Nakamura K. Verification of the reliability of an automated urine test strip colorimetric program using colorimetric analysis: survey study. JMIR Form Res. Jan 14, 2025;9:e62772. [CrossRef] [Medline]
    56. Xu H, Hua Y, Li H, Wang J, Liu G, Li X. Smartphone-based colorimetric correction analysis system for long-term urine monitoring of chronic kidney disease. Anal Biochem. Jul 2025;702:115824. [CrossRef] [Medline]
    57. Duffy A, Boroumandzad N, Sherman AL, Christie G, Riadi I, Moreno S. Examining challenges to co-design digital health interventions with end users: systematic review. J Med Internet Res. Mar 14, 2025;27:e50178. [CrossRef] [Medline]
    58. Main A, McCartney H, Ibrar M, et al. Patients’ experiences of digital health interventions for the self-management of chronic pain: systematic review and thematic synthesis. J Med Internet Res. Mar 18, 2025;27:e69100. [CrossRef] [Medline]
    59. Perski O, Blandford A, West R, Michie S. Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Transl Behav Med. Jun 2017;7(2):254-267. [CrossRef] [Medline]
    60. Nahum-Shani I, Hekler EB, Spruijt-Metz D. Building health behavior models to guide the development of just-in-time adaptive interventions: a pragmatic framework. Health Psychol. Dec 2015;34S:1209-1219. [CrossRef] [Medline]
    61. van Genugten CR, Thong MSY, van Ballegooijen W, et al. Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review. Front Digit Health. 2025;7:1460167. [CrossRef] [Medline]
    62. Willms A, Liu S. Exploring the feasibility of using ChatGPT to create just-in-time adaptive physical activity mHealth intervention content: case study. JMIR Med Educ. Feb 29, 2024;10:e51426. [CrossRef] [Medline]
    63. Mohr DC, Lyon AR, Lattie EG, Reddy M, Schueller SM. Accelerating digital mental health research from early design and creation to successful implementation and sustainment. J Med Internet Res. May 10, 2017;19(5):e153. [CrossRef] [Medline]

    ‎
    CKD: chronic kidney disease
    GRAMMS: Good Reporting of A Mixed Methods Study
    ITT: intention-to-treat
    KDQOL-SF: Kidney Disease Quality of Life-Short Form
    MMSE: Mini-Mental State Examination
    QOL: quality of life
    RCT: randomized controlled trial
    SF-36: 36-Item Short Form Survey

    Edited by Elizabeth Borycki; submitted 27.Feb.2026; peer-reviewed by Feng-Jung Yang, Hajira Arooj, Qiumei Jiang-Siebert; final revised version received 13.Jun.2026; accepted 17.Jun.2026; published 20.Jul.2026.

    Copyright

    © Yu Shi, Hongmei Peng, Youying Zhang, Jingyu Zhang, Yang Li, Shi Pu, Xiangchun Tang, Jinghong Zhao. Originally published in JMIR Nursing (https://nursing.jmir.org), 20.Jul.2026.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Nursing, is properly cited. The complete bibliographic information, a link to the original publication on https://nursing.jmir.org/, as well as this copyright and license information must be included.