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Aims/Introduction: We analyzed patient‐reported outcomes of people with type 2 diabetes to better understand perceptions and experiences contributing to treatment adherence. Materials and Methods: In the ongoing International Diabetes Management Practices Study, we collected patient‐reported outcomes data from structured questionnaires (chronic treatment acceptance questionnaire and Diabetes Self‐Management Questionnaire) and free‐text answers to open‐ended questions to assess perceptions of treatment value and side‐effects, as well as barriers to, and enablers for, adherence and self‐management. Free‐text answers were analyzed by natural language processing. Results: In 2018–2020, we recruited 2,475 patients with type 2 diabetes (43.3% insulin‐treated, glycated hemoglobin (HbA1c) 8.0 ± 1.8%; 30.9% with HbA1c <7%) from 13 countries across Africa, the Middle East, Europe, Latin America and Asia. Mean ± standard deviation scores of chronic treatment acceptance questionnaire (acceptance of medication, rated out of 100) and Diabetes Self‐Management Questionnaire (self‐management, rated out of 10) were 87.8 ± 24.5 and 3.3 ± 0.9, respectively. Based on free‐text analysis and coded responses, one in three patients reported treatment non‐adherence. Overall, although most patients accepted treatment values and side‐effects, self‐management was suboptimal. Treatment duration, regimen complexity and disruption of daily routines were major barriers to adherence, whereas habit formation was a key enabler. Treatment‐adherent patients were older (60 ± 11.6 vs 55 ± 11.7 years, P < 0.001), and more likely to have longer disease duration (12 ± 8.6 vs 10 ± 7.7 years, P < 0.001), exposure to diabetes education (73.1% vs 67.8%, P < 0.05), lower HbA1c (7.9 ± 1.8% vs 8.3 ± 1.9%, P < 0.001) and attainment of HbA1c <7% (29.7% vs 23.3%, P < 0.01). Conclusions: Patient perceptions/experiences influence treatment adherence and self‐management. Patient‐centered education and support programs that consider patient‐reported outcomes aimed at promoting empowerment and developing new routines might improve glycemic control. [ABSTRACT FROM AUTHOR]

The exploration of heath data by clustering algorithms allows to better describe the populations of interest by seeking the sub-profiles that compose it. This therefore reinforces medical knowledge, whether it is about a disease or a targeted population in real life. Nevertheless, contrary to the so-called conventional biostatistical methods where numerous guidelines exist, the standardization of data science approaches in clinical research remains a little discussed subject. This results in a significant variability in the execution of data science projects, whether in terms of algorithms used, reliability and credibility of the designed approach. Taking the path of parsimonious and judicious choice of both algorithms and implementations at each stage, this article proposes Qluster, a practical workflow for performing clustering tasks. Indeed, this workflow makes a compromise between (1) genericity of applications (e.g. usable on small or big data, on continuous, categorical or mixed variables, on database of high-dimensionality or not), (2) ease of implementation (need for few packages, few algorithms, few parameters, ...), and (3) robustness (e.g. use of proven algorithms and robust packages, evaluation of the stability of clusters, management of noise and multicollinearity). This workflow can be easily automated and/or routinely applied on a wide range of clustering projects. It can be useful both for data scientists with little experience in the field to make data clustering easier and more robust, and for more experienced data scientists who are looking for a straightforward and reliable solution to routinely perform preliminary data mining. A synthesis of the literature on data clustering as well as the scientific rationale supporting the proposed workflow is also provided. Finally, a detailed application of the workflow on a concrete use case is provided, along with a practical discussion for data scientists. An implementation on the Dataiku platform is available upon request to the authors., Competing Interests: CE, MR, and PG were employed by Quinten. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Esnault, Rollot, Guilmin and Zucker.)