Over a third of adults in the United States have prediabetes, and many of those with prediabetes will progress to type 2 diabetes within 3–5 years. Health insurance status may factor into a proper diagnosis of prediabetes and diabetes. This study sought to determine the associations between health insurance and undiagnosed prediabetes and diabetes in a national sample of American adults. Publicly available data from 13,029 adults aged 18–64 years from the 2005–2016 waves of the National Health and Nutrition Examination Survey were analyzed. Health insurance type (Medicaid, Private, Other, None) was self-reported. Prediabetes and diabetes status were assessed with measures of self-report, glycohemoglobin, fasting plasma glucose, and two-hour glucose. Covariate-adjusted logistic models were used for the analyses. Overall, 5976 (45.8%) participants had undiagnosed prediabetes, while 897 (6.8%) had undiagnosed diabetes. Having health insurance was associated with decreased odds ratios for undiagnosed prediabetes: 0.87 (95% confidence interval (CI: 0.79, 0.95)) for private insurance, 0.84 (CI: 0.73, 0.95) for other insurance, and 0.78 (CI: 0.67, 0.90) for Medicaid. Moreover, having private health insurance was associated with 0.82 (CI: 0.67, 0.99) decreased odds for undiagnosed diabetes. Health insurance coverage and screening opportunities for uninsured individuals may reduce prediabetes and diabetes misclassifications.

Background: Maximal handgrip strength (HGS) could be an incomplete and unidimensional measure of muscle function. This pilot study sought to examine the relationships between maximal HGS, radial and ulnar digit grip strength, submaximal HGS force control, HGS fatigability, neuromuscular HGS steadiness, and HGS asymmetry in older adults. Methods: A digital handgrip dynamometer and accelerometer was used to collect several HGS measurements from 13 adults aged 70.9 ± 4.0 years: maximal strength, radial and ulnar digit grip strength, submaximal force control, fatigability, neuromuscular steadiness, and asymmetry. Pearson correlations determined the relationships between individual HGS measurements. A principal component analysis was used to derive a collection of new uncorrelated variables from the HGS measures we examined. Results: The individual HGS measurements were differentially correlated. Maximal strength (maximal HGS, radial digit strength, ulnar digits strength), contractile steadiness (maximal HGS steadiness, ulnar digit grip strength steadiness), and functional strength (submaximal HGS force control, HGS fatigability, HGS asymmetry, HGS fatigability steadiness) emerged as dimensions from the HGS measurements that we evaluated. Conclusion: Our findings suggest that these additional measures of muscle function may differ from maximal HGS alone. Continued research is warranted for improving how we assess muscle function with more modern technologies, including handgrip dynamometry and accelerometry.