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Record W4415908175 · doi:10.2196/75306

Evaluating the Safety and Performance of the KidneYou App for Chronic Kidney Disease: Protocol for an Italian Multicenter, Randomized, Open-Label, Premarket Study

2025· article· en· W4415908175 on OpenAlexvenueno aff
Cesira Cafiero, Marco Gnesi, Anna Rita Maurizi, Giorgia Campilongo, Marco Fiorentino, Paola Maria Acquaviva, Francesca Mastromauro, Marco Gorini, Loreto Gesualdo

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsKidney diseaseProtocol (science)Clinical trialmHealthDigital healthMEDLINEDiseaseChronic disease

Abstract

fetched live from OpenAlex

Background: Chronic kidney disease (CKD) is characterized by long-term structural or functional kidney abnormalities, often progressing over decades and potentially leading to kidney failure, treatable only by dialysis or transplantation. Nutritional programs (NPs), physical activity (PA) programs, and mindfulness programs (MPs) can play a key role in conservative CKD management, aiming to slow the progression of symptoms, decrease drug load, reduce stress, and delay dialysis. KidneYou (Advice Pharma Group Srl) integrates nutrition, exercise, and MPs to improve the health of patients with CKD by promoting lifestyle changes and stress reduction. As an innovative medical tool, KidneYou is designed to address an unmet need by providing a nonpharmacological approach for better managing CKD, empowering patients to manage their condition more effectively and sustain a healthier lifestyle, which could ultimately lead to improved disease outcomes. Objective: This study aims to evaluate the efficacy and safety of KidneYou, a digital medical device that delivers personalized NPs, PA programs, and MPs to patients with CKD, in comparison with the standard of care. Methods: This is a multicenter, open-label, randomized, parallel-arm trial aimed at evaluating health improvement in patients with CKD exposed to a nonpharmacological treatment involving an NP, PA program, and MP delivered to the patient by digital technology (investigational arm) or a standard approach (paper diary and control arm) for 3 months. The primary aim of the study is to evaluate the efficacy of KidneYou, defined by the achievement of at least 1 of the 3 conditions described in the composite primary end point: a reduction of at least 10% of azoturia (gram per 24 h), an increase of at least 15% of distance (meters in the 6-min walk test), or a decrease of at least 10% of perceived stress in CKD KidneYou app users compared to CKD KidneYou app nonusers after 3 months. Results: Data will be analyzed and presented in accordance with international CONSORT (Consolidated Standards of Reporting Trials) guidelines. Recruitment began in July 2022 and stopped at all sites in April 2024. Data analysis is currently ongoing. The results are expected to be published in early 2026. Conclusions: The results of this trial will guide further studies on the impact of digital devices to help patients with CKD in improving their condition more effectively and sustain a healthier lifestyle, which could ultimately lead to improved disease outcomes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.027
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.020
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0010.002
Science and technology studies0.0020.003
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0260.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.410
GPT teacher head0.680
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreProtocol

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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