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- Wang, Ximin, et al.
(författare)
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Reliability and validity of the Chinese version of the LMC Skills, Confidence & Preparedness Index (SCPI) in patients with type 2 diabetes
- 2021
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Ingår i: Health and Quality of Life Outcomes. - : BioMed Central. - 1477-7525. ; 19:1
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: A variety of diabetes self-management instruments have been developed but few of them consist of the preparedness for diabetes self-management behavior. The novel psychometric evaluation tool "the LMC Skills, Confidence & Preparedness Index (SCPI)" measures three key aspects of a patient's diabetes self-management: knowledge of the skill, confidence in being able to perform skill and preparedness to implement the skill. The objective of this study was to translate, adapt and validate the SCPI for use in Chinese adult patients with type 2 diabetes.METHODS: This study followed the guideline recommended by the American Academy of Orthopaedic Surgeons Evidence Based Medicine Committee (AAOS) to indigenize the scale. Forward and back translation, and cross-cultural language debugging were completed according to the recommended steps. A convenience sample of Chinese patients with type 2 diabetes (n = 375) were recruited from a university-affiliated hospital in Shanghai. The validity (criterion, discriminant validity, and construct validity), reliability (internal consistency and test-retest reliability) and the interpretability of the instrument were examined. The content validity was calculated by experts' evaluation.RESULTS: The Chinese version of SCPI (C-SCPI) has good internal consistency with a Cronbach's alpha of 0.92. The ceiling effects of the preparedness subscales is 21%. The criterion validity of three dimensions of C-SCPI was established with significantly moderate correlations between the DKT, DES-SF and SDSCA (p < 0.05). The S-CVI of the whole scale was 0.83. Except for entry 21, the I-CVI values of all entries were greater than 0.78. The C-SCPI has also shown good discriminative validity with statistically significant differences between the patients with good and poor glycemic control. Confirmatory factor analysis showed that modified results indicate that the fitting degree of the model is good, chi(2)/df = 2.775, RMSEA = 0.069, CFI = 0.903, GFI = 0.873, TLI = 0.889, IFI = 0.904. The test-retest reliability coefficient was 0.61 (p < 0.01).CONCLUSION: We established a Chinese version of SCPI through translation and cross-cultural adaptation. The C-SCPI is reliable and valid for assessment of the level of self-management in Chinese patients with type 2 diabetes.
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| 2. |
- Middeldorp, Christel M., et al.
(författare)
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The Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia : design, results and future prospects
- 2019
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Ingår i: European Journal of Epidemiology. - : Springer Science and Business Media LLC. - 0393-2990 .- 1573-7284. ; 34:3, s. 279-300
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Tidskriftsartikel (refereegranskat)abstract
- The impact of many unfavorable childhood traits or diseases, such as low birth weight and mental disorders, is not limited to childhood and adolescence, as they are also associated with poor outcomes in adulthood, such as cardiovascular disease. Insight into the genetic etiology of childhood and adolescent traits and disorders may therefore provide new perspectives, not only on how to improve wellbeing during childhood, but also how to prevent later adverse outcomes. To achieve the sample sizes required for genetic research, the Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia were established. The majority of the participating cohorts are longitudinal population-based samples, but other cohorts with data on early childhood phenotypes are also involved. Cohorts often have a broad focus and collect(ed) data on various somatic and psychiatric traits as well as environmental factors. Genetic variants have been successfully identified for multiple traits, for example, birth weight, atopic dermatitis, childhood BMI, allergic sensitization, and pubertal growth. Furthermore, the results have shown that genetic factors also partly underlie the association with adult traits. As sample sizes are still increasing, it is expected that future analyses will identify additional variants. This, in combination with the development of innovative statistical methods, will provide detailed insight on the mechanisms underlying the transition from childhood to adult disorders. Both consortia welcome new collaborations. Policies and contact details are available from the corresponding authors of this manuscript and/or the consortium websites.
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| 3. |
- Playdon, Mary C., et al.
(författare)
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Metabolomics Analytics Workflow for Epidemiological Research : Perspectives from the Consortium of Metabolomics Studies (COMETS)
- 2019
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Ingår i: Metabolites. - : MDPI. - 2218-1989. ; 9:7
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Tidskriftsartikel (refereegranskat)abstract
- The application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting of study findings. To inform the development of such guidelines, we conducted a survey of 47 cohort representatives from the Consortium of Metabolomics Studies (COMETS) to gain insights into the current strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide. The results indicated a variety of applied analytical strategies, from biospecimen and data pre-processing and quality control to statistical analysis and reporting of study findings. These strategies included methods commonly used within the metabolomics community and applied in epidemiological research, as well as novel approaches to pre-processing pipelines and data analysis. To help with these discrepancies, we propose use of open-source initiatives such as the online web-based tool COMETS Analytics, which includes helpful tools to guide analytical workflow and the standardized reporting of findings from metabolomics analyses within epidemiological studies. Ultimately, this will improve the quality of statistical analyses, research findings, and study reproducibility.
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