ArticleJMIR mHealth and uHealth2020
Limitations of Existing Dialysis Diet Apps in Promoting User Engagement and Patient Self-Management: Quantitative Content Analysis Study.
Article in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Mobile Application Based Dietary Self Management Intervention in Chinese Chronic Kidney Disease Patients: a Multi-Center Randomized Controlled Study
Clinical Effectiveness of MyNutriKidney® for Dietary Self-management in Chronic Kidney Disease Patients: Protocol for a Pragmatic Randomized Controlled Trial
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness of Mobile Apps in Improving Medication Adherence Among Chronic Kidney Disease Patients: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Efficacy Trial of a Mobile Application for Fluid Intake Management in Patients Receiving Chronic Hemodialysis Therapy.Kidney medicine · 2026Article
- Personalized nutrition in haemodialysis: a scoping review of studies published between 2015 and 2025.Clinical kidney journal · 2026Review
- Nutritional self-management in chronic diseases: a conceptual analysis.Frontiers in public health · 2025Review
- Weight management personas of breast cancer patients undergoing chemotherapy in China: a multi-method study.BMC medical informatics and decision making · 2024Article
- Chronic kidney disease and mobile health: quality of renal nutritional APPs in Italy.Acta bio-medica : Atenei Parmensis · 2023Review
- Automated Diet Capture Using Voice Alerts and Speech Recognition on Smartphones: Pilot Usability and Acceptability Study.JMIR formative research · 2023Article
- Interventions, Participative Role, Barriers, and Facilitators for Involvement in eHealth Communication for People Undergoing Hemodialysis: Protocol for a Scoping Review.JMIR research protocols · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the unprecedented growth of mobile technology, a plethora of dialysis diet apps have been developed to promote patient dietary self-management. Nevertheless, the utility of such apps remains questionable.
objectiveThis study aimed to evaluate the content, features, and quality of commercial dialysis diet apps for adult dialysis patients.
methodsThis study consisted of a quantitative content analysis of commercial dialysis diet apps downloaded from Google Play and the Apple App Store available in the Asian marketplace, searched for using the following keywords in English: dialysis diet and diet for kidney disease. Free and paid apps available in English that provide nutrition information for adult dialysis patients were included. Apps that were not relevant to the dialysis diet, not meant for patient self-management, or redundant were excluded. Apps were evaluated for language medium (subscore=1), credibility (subscore=1), food database (subscore=1), valuable features (subscore=12), health-behavior theory constructs (subscore=60), and technical quality (subscore=25). The relationships among the variables of interest were determined by Pearson correlation. Stepwise multiple linear regression analysis was performed to identify the features that contribute to greater technical quality of dialysis diet apps. Statistical significance was defined as P<.05.
resultsA total of 22 out of 253 apps (8.7%) were eligible for evaluation. Based on a 100-point scale, the mean overall score of the apps was 31.30 (SD 14.28). Only 5% (1/22) of the apps offered relevant language options, and 46% (10/22) contained food databases. In addition, 54% (12/22) of the apps were not credible. The mean score for valuable features was 3.45 (SD 1.63) out of 12, in which general education (16/22, 73%), free download (15/22, 68%), and usability (13/22, 59%) were the three most popular features. However, the apps scored a mean of 13.41 (SD 11.56) out of 60 for health-behavior theory constructs. The overall app technical quality was considered poor, with a mean score of 2.70 (SD 0.41) out of 5. The scores of valuable features (r=.65, P=.001) and health-behavior theory constructs (r=.55, P=.009) were positively correlated with the overall technical quality of the commercial dialysis diet apps. Features such as free download (β=.43, P=.03) and usability (β=.41, P=.03) could significantly determine the functional quality of the apps. Health-behavior theory constructs such as self-monitoring could significantly predict both the subjective quality (β=.55, P=.008) and the engagement quality (β=.66, P=.001) of the apps, whereas the information quality domain could be determined by plan or orders (β=.48, P=.007) and knowledge (β=.45, P=.01).
conclusionsAlthough most of the available commercial dialysis diet apps are free and easy to use, they are subject to theory deficiency, limited language options, and a lack of food databases, credibility, tailored education, and overall technical quality.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.