Barriers and Facilitators in Implementing a Telemonitoring Application for Patients With Chronic Kidney Disease and Health Professionals: Ancillary Implementation Study of the NeLLY (New Health e-Link in the Lyon Region) Stepped-Wedge Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The use of telemonitoring to manage renal function in patients with chronic kidney disease (CKD) is recommended by health authorities. However, despite these recommendations, the adoption of telemonitoring by both health care professionals and patients faces numerous challenges. OBJECTIVE: This study aims to identify barriers and facilitators in the implementation of a telemonitoring program for patients with CKD, as perceived by health care professionals and patients, and to explore factors associated with the adoption of the program. This study serves as a process evaluation conducted alongside the cost-effectiveness NeLLY (New Health e-Link in the Lyon Region) trial. METHODS: A mixed methods approach combining a quantitative questionnaire and semistructured interviews was conducted among nurses, nephrologists, and patients with stages 3 and 4 CKD across 10 renal care centers in France that have implemented telemonitoring. The Technology Acceptance Model (TAM) and the Consolidated Framework for Implementation Research (CFIR) were used to design the questionnaires and interview guides. The dimensions investigated included ease of use, perceived usefulness, and intention to use (TAM), as well as characteristics of the intervention, local and general context, individual factors, and processes (CFIR). The adoption of telemonitoring was assessed based on the frequency with which patients connected to the telemonitoring device. Determinants of telemonitoring use were analyzed using nonparametric tests, specifically the Wilcoxon-Mann-Whitney and Kruskal-Wallis tests. Thematic analysis was conducted on the transcriptions of semistructured interviews. Both quantitative and qualitative results, including data from patients and professionals, were integrated to provide a comprehensive understanding of the factors associated with the use of remote monitoring in CKD. RESULTS: A total of 42 professionals and 128 patients with CKD responded to our questionnaire. Among these, 11 professionals and 13 patients participated in interviews. Nurses, who were responsible for patient follow-up, regularly used telemonitoring (8/13, 62%, at least once a month), while nephrologists, who were responsible for prescribing it, were primarily occasional users (5/8, 63%, using it less than once a month). Among professionals, the main obstacles identified were the heavy workload generated by telemonitoring, lack of training, and insufficient support for nurses. Among the 128 patients, 46 (35.9%) reported using the application at least once a week. The main barriers for patients were issues related to computer use, as well as the lack of feedback and communication with health care professionals. The main facilitators identified by both professionals and patients for using telemonitoring were the empowerment of patients in managing their health and the reduction of the burden associated with CKD. CONCLUSIONS: Improving adherence to telemonitoring in the context of CKD requires collaborative efforts from both professionals and patients. Our results provide insights that can inform the design of effective, theory-driven interventions aimed at improving telemonitoring adoption and usage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".