A qualitative exploration of facilitators and barriers to adopting a healthy lifestyle among Black, Hispanic, and American Indian males with diabetes or at risk for type 2 diabetes.

OBJECTIVES: Higher prevalence of several chronic diseases occurs in men in the United States, including diabetes and prediabetes. Of the 34 million adults with diabetes and 88 million with prediabetes there is a higher prevalence of both conditions in men compared to women. Black, Hispanic, and American Indian men have some of the highest rates of diabetes and diabetes complications. Adopting a healthy lifestyle including healthy eating and physical activity, is important in preventing type 2 diabetes and diabetes complications. DESIGN: This study included six focus groups that explored facilitators and barriers to adopting a healthy lifestyle in Black, Hispanic, and American Indian men with diabetes or at risk for type 2 diabetes. Thematic analysis was used to identify facilitators and barriers to adopting a healthy lifestyle. RESULTS: Participants included males 18 years of age and older identifying as Black, Hispanic, or American Indian and diagnosed with prediabetes, diabetes, hypertension, or otherwise at risk for type 2 diabetes. Thirty-seven men participated, 19 diagnosed with diabetes and 18 at risk for type 2 diabetes. Fourteen Black, 14 Hispanic, and 9 American Indian men participated. The themes of facilitators to a healthy lifestyle included: family and the social network; psychosocial factors; health status, health priorities and beliefs about aging; knowledge about health and healthy behavior; and healthy community resources. Themes of barriers to a healthy lifestyle also included: mistrust of the health care system, cost, and low socioeconomic status. CONCLUSIONS: This study underscores the complexity of factors involved in adopting a healthy lifestyle for some racial and ethnic minority men with diabetes or at risk for type 2 diabetes.

Effectiveness of Incentives for Improving Diabetes-Related Health Indicators in Chronic Disease Lifestyle Modification Programs: a Systematic Review and Meta-Analysis.

INTRODUCTION: We examined the effectiveness of providing incentives to participants in lifestyle modification programs to improve diabetes-related health indicators: body weight, body mass index (BMI), blood pressure, cholesterol, and hemoglobin A1C (HbA1C). We also examined the potential effect of 4 different incentive domains (ie, type, monetary value, attainment certainty, and schedule) on those indicators. METHODS: We searched Medline, Embase, PsycINFO, and Cochrane Library to identify relevant studies published from January 2008 through August 2021. We used a random-effects model to pool study results and examine between-study heterogeneity by using the I2 statistic and the Cochran Q test. We also conducted moderator analyses by using a mixed-effects model to examine differences between subgroups of incentive domains (eg, incentive type [cash vs other types]). RESULTS: Our search yielded 10,965 articles, of which 19 randomized controlled trials met our selection criteria. The random-effects model revealed that, relative to the control group, the incentive group had significant reductions in weight (-1.85kg; 95% CI, -2.40 to -1.29; P < .001), BMI (-0.47kg/m2; 95% CI, -0.71 to -0.22; P < .001), and both systolic blood pressure (-2.59 mm HG; 95% CI, -4.98 to -0.20; P = .03) and diastolic blood pressure (-2.62 mm Hg; 95% CI, -4.61 to -0.64; P = .01). A reduction in cholesterol level was noted but was not significant (-2.81 mg/dL; 95% CI, -8.89 to -3.28; P = .37). One study found a significant reduction in hemoglobin A1c (-0.17%; 95% CI, -0.30% to -0.05%; P < .05). The moderator analyses showed that the incentive effect did not vary significantly between the subgroups of the incentive domains, except on weight loss for the attainment certainty domain, suggesting that a variety of incentive subgroups could be equally useful. CONCLUSION: Providing incentives in lifestyle modification programs is a promising strategy to decrease weight, BMI, and blood pressure.

Health Care Providers' Knowledge, Attitudes, and Practices and the Association With Referrals to the National Diabetes Prevention Program Lifestyle Change Program.

PURPOSE: To examine how health care providers' knowledge, attitudes, and practices affect their referrals to the National Diabetes Prevention Program. DESIGN: Cross-sectional, self-report data from DocStyles-a web-based survey. SETTING: USA. SAMPLE: Practicing family practitioners, nurse practitioners, pharmacists, and internists, n = 1,503. MEASURES: Questions regarding health care providers' knowledge, attitudes, and practices and their referrals to the National Diabetes Prevention Program. ANALYSIS: Bivariate and multivariate analyses were used to calculate predictive margins and the average marginal effect. RESULTS: Overall, 15.2% of health care providers (n = 1,503) reported making a referral to the National Diabetes Prevention Program. Health care providers were more likely to make referrals if they were familiar with the program (average marginal effect = 36.0%, 95% CI: 29.1%, 42.8%), reported knowledge of its availability (average marginal effect=49.1%, 95% CI: 40.2%, 57.9%), believed it was important to make referrals to the program (average marginal effect = 20.7%, 95% CI: 14.4%, 27.0%), and used electronic health records to manage patients with prediabetes (average marginal effect = 9.1%, 95% CI: 5.4%, 12.7%). Health care providers' demographic characteristics had little to no association with making referrals. CONCLUSION: Making referrals to the National Diabetes Prevention Program was associated with health care providers' knowledge of the program and its reported availability, their attitudes, and their use of the electronic health record system to manage patients with prediabetes.

Iterative guided machine learning-assisted systematic literature reviews: a diabetes case study.

BACKGROUND: Systematic Reviews (SR), studies of studies, use a formal process to evaluate the quality of scientific literature and determine ensuing effectiveness from qualifying articles to establish consensus findings around a hypothesis. Their value is increasing as the conduct and publication of research and evaluation has expanded and the process of identifying key insights becomes more time consuming. Text analytics and machine learning (ML) techniques may help overcome this problem of scale while still maintaining the level of rigor expected of SRs. METHODS: In this article, we discuss an approach that uses existing examples of SRs to build and test a method for assisting the SR title and abstract pre-screening by reducing the initial pool of potential articles down to articles that meet inclusion criteria. Our approach differs from previous approaches to using ML as a SR tool in that it incorporates ML configurations guided by previously conducted SRs, and human confirmation on ML predictions of relevant articles during multiple iterative reviews on smaller tranches of citations. We applied the tailored method to a new SR review effort to validate performance. RESULTS: The case study test of the approach proved a sensitivity (recall) in finding relevant articles during down selection that may rival many traditional processes and show ability to overcome most type II errors. The study achieved a sensitivity of 99.5% (213 out of 214) of total relevant articles while only conducting a human review of 31% of total articles available for review. CONCLUSIONS: We believe this iterative method can help overcome bias in initial ML model training by having humans reinforce ML models with new and relevant information, and is an applied step towards transfer learning for ML in SR.