A Lifestyle Intervention to Delay Early Chronic Kidney Disease in African Americans With Diabetic Kidney Disease: Pre-Post Pilot Study
Bibliographic record
Abstract
Background Behavioral factors, such as lifestyle, have been shown to explain approximately 24% of the excess risk of chronic kidney disease (CKD) among African Americans. However, there are limited intervention studies culturally tailored to African Americans with type 2 diabetes mellitus and CKD. Objective The main objective of this study was to examine the feasibility and preliminary efficacy of a culturally tailored lifestyle intervention among African Americans with type 2 diabetes mellitus and CKD. Methods A pre-post design was used to test the feasibility of a lifestyle intervention in 30 African American adults recruited from the Medical University of South Carolina between January 2017 and February 2017. A research nurse delivered the manualized study intervention weekly for 6 weeks. Clinical outcomes (hemoglobin A1c, blood pressure, and estimated glomerular filtration rate [eGFR]) were measured at baseline and postintervention. Disease knowledge, self-care, and behavior outcomes were also measured using validated structured questionnaires at baseline and postintervention. Descriptive statistics and effect sizes were calculated to determine clinically important changes from baseline. Results Significant pre-post mean differences and decreases were observed for hemoglobin A1c (mean 0.75%, 95% CI 0.16-1.34; P=.01), total cholesterol (mean 16.38 mg/dL, 95% CI 5.82-26.94; P=.004), low-density lipoprotein (mean 13.73 mg/dL, 95% CI 3.91-23.54; P=.008), and eGFR (mean 6.73 mL/min/1.73m2, 95% CI 0.97-12.48; P=.02). Significant pre-post mean differences and increases were observed for CKD self-efficacy (mean −11.15, 95% CI −21.55 to −0.75; P=.03), CKD knowledge (mean −2.62, 95% CI −3.98 to −1.25; P<.001), exercise behavior (mean −1.21, 95% CI −1.96 to −0.46; P=.003), and blood sugar testing (mean −2.15, 95% CI −3.47 to −0.83; P=.003). Conclusions This study provides preliminary data for a large-scale appropriately powered randomized controlled trial to examine a culturally tailored lifestyle intervention in African Americans with type 2 diabetes mellitus and CKD in order to improve clinical, knowledge, self-care, and behavior outcomes in this population.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".