The DPP lifestyle intervention showed that meeting weight loss (WL) and activity goals can prevent/delay T2D. The success of community translated versions of it (e.g., Group Lifestyle Balance, DPP-GLB) has led to Medicare reimbursement based on session attendance (at 6 and 12 months) and participant success at meeting a 5% WL goal at 12 months. Although attendance has been suggested to be related to WL success, the impact of the 6 monthly maintenance sessions (mos 7-12) after the more frequent 16 core sessions (mos 0-6) on one-year WL is not known. This effort examines how maintenance session attendance and WL goal status at 6 months in DPP-GLB affects achievement of the 12-month 5% WL goal. Data were combined from two identical intervention trials that delivered the 12-month DPP-GLB in a community setting to overweight/obese adults (mean age 62 years, 76% women) with prediabetes and/or metabolic syndrome. Participants who attended ≥4 core sessions during the first 6 months and had complete data on maintenance attendance and WL were included (n=238). Meeting the 6-month WL goal was associated with greater odds of meeting the 12-month WL goal (p<.0001). Odds of meeting the 12-month 5% WL goal were significantly greater for those who attended ≥4 maintenance sessions vs. those who did not (OR=6.0, 95% CI: 2.4-15.0). When stratified by achieving the 5% WL goal at 6 months, those who attended ≥4 maintenance sessions and met the 6-month WL goal were more likely to meet the WL goal at 12 months (OR=11.4, CI: 3.2- 40.7) compared to those who failed to meet the WL goal at 6 months (OR=1.5, CI: 0.3-7.5). Adjustment for age, gender and 6-month physical activity yielded similar results. Attending DPP-GLB maintenance sessions and meeting the 6-month WL goal increased the odds of meeting ≥5% WL at 12 months. For those who failed to meet the 6-month WL goal, attending maintenance sessions did not significantly improve their chance of 12-month WL success.
Structure–activity relationship (SAR) models can be used to predict the biological activity of potential developmental toxicants whose adverse effects include death, structural abnormalities, altered growth and functional deficiencies in the developing organism. Physico-chemical descriptors of spatial, electronic and lipophilic properties were used to derive SAR models by two modeling approaches, logistic regression and Classification and Regression Tree (CART), using a new developmental database of 293 chemicals (FDA/TERIS). Both single models and ensembles of models (termed bagging) were derived to predict toxicity. Assessment of the empirical distributions of the prediction measures was performed by repeated random partitioning of the data set. Results showed that both the decision tree and logistic regression derived developmental SAR models exhibited modest prediction accuracy. Bagging tended to enhance the prediction accuracy and reduced the variability of prediction measures compared to the single model for CART-based models but not consistently for logistic-based models. Prediction accuracy of single logistic-based models was higher than single CART-based models but bagged CART-based models were more predictive. Descriptor selection in SAR for the understanding of the developmental mechanism was highly dependent on the modeling approach. Although prediction accuracy was similar in the two modeling approaches, there was inconsistency in the model descriptors.
Abstract Background Group lifestyle sessions with phone maintenance could improve weight, health, and function in vulnerable older adults. Methods Community-dwelling adults (N = 322) with body mass index (BMI, kg/m2) ≥27 and additional risk factors received 12 one-hour in-person behavioral weight management group sessions then were randomized to 8 half-hour telephone sessions (n = 162) or newsletter control (n = 160) from 4 to 12 months with no treatment contact thereafter. Primary outcome was 0- to 12-month weight change. Cardiometabolic, short physical performance battery (SPPB), and self-reported activity changes were assessed at 12 and 24 months. Results At baseline, the mean (SD) age was 71.2 (4.3) and BMI was 33.8 (5.1). Participants were 77% women, 13% Black, 85% retired, averaging 4 medical conditions, and taking blood pressure (67.4%) and lipid-lowering (51.6%) medications. At 12 months, a greater proportion of the phone group (66.0%) achieved ≥5% weight loss compared with newsletter control (53.2%; p = .02). Mean (95% CI) weight loss was greater for phone (−6.6 kg [−7.5, −5.8]) than newsletter (−5.1 kg [−7.2, −3.0]); p = .01. Modest lipid, glucose, and blood pressure improvements were found, but did not differ significantly between groups. Small SPPB and activity improvements were maintained at 12 and 24 months in both groups. Conclusions Brief phone contacts compared to newsletters enhanced weight loss maintenance among older high-risk adults at 1 year, but not cardiometabolic outcomes. Modest functional improvements were observed in both. Lower-intensity maintenance contacts (phone or newsletter) for weight, health, and physical function in older adults warrant further study. Clinical Trials Registration Number NCT03192475
Generalized additive models (GAMs) with natural cubic splines (NS) as smoothing functions have become standard analytical tools in time series studies of health effects of air pollution. A GAM with NS as a smoother is reduced to a generalized linear model and is denoted by GLM+NS in literature. The amount of smoothing is controlled by the parameter degrees of freedom (df) in the fitted NS. While a large amount of smoothing could result in a less biased parameter estimate, over–smoothing may attenuate important signals in the data. In practice, this issue is often addressed by sensitivity analyses with different df values. Smoothing can also be achieved by assuming the parameters of the splines as random effects with an appropriate distribution. In time series studies of health effects of air pollution the outcome variable, daily mortality (or morbidity), is commonly assumed to follow a Poisson distribution. With assuming parameters of the natural cubic spline smoother random, a generalized linear mixed model is resulted and, denoted by GLMM+NS. We investigate the validity of this mixed modeling approach through a simulation study. Our simulation results show that for small true pollution effects, fitting a GLMM+NS model results in absolute biases similar to those obtained from the fitting of a GLM+NS, but provides larger empirical standard deviations than a GLM+NS. It can be noted that as the variability in the observed time series data can be different over the study period, the assumption of a variable amount of smoothing over a study period is more realistic and the larger standard deviation may reflect reality. We provide an application of GLMM+NS utilizing data from the Allegheny County Air Pollution Study with interest in estimating the relative risk of cardiopulmonary hospital admissions for a 20 μg/m3 increase in PM10.
Background: Models that address the needs of patients with diabetes mellitus (DM) in primary care (PC) are needed, as health systems move to value-based care. To support DM patients at high risk, an insurer-based program that paired nurse practice care managers (PCM) with certified diabetes educators (CDE) was designed to improve outcomes for DM patients with complex needs. Objective: To assess the durability of glycemic improvement after diabetes self-management education and support (DSMES) intervention within a model that relies on a PCM to identify, refer, and provide ongoing support to complex patients who received a CDE intervention in PC. Methods: 2 CDEs, serving rural and urban areas, were introduced as team members into PC practices. CDEs provided DM training to PCMs who then proactively identified and referred patients under clear criteria (DM related ER visits, hospitalizations, HbA1c>9, reported barriers to care) for DSMES in collaboration with PC provider and PCM. Post CDE intervention, the PCM was available for follow-up and ongoing support. HbA1c was monitored every 3 months after intervention and compared to baseline value to assess durability of improvement in glycemic control. Results: Of 222 patients referred, 1had 6 and 80 had 12-month data for analysis. Patients were 52% female; mean age 57 (SD 13.43). Mean HbA1c decreased from 9.6 to 8.4 over 6 months and 9.2 to 8.1 over 12 months (p<0.001). Improvement in glycemic control was maintained for at least 1 year after intervention. There was no significant change in BMI over this time. Conclusions: A model where CDEs partner with PCMs, who identify, refer, and provide ongoing support to patients post-CDE delivered DSMES, is an effective and feasible intervention to improve and sustain DM outcomes in PC. This collaborative approach expands opportunities to meet complex needs of DM patients and can contribute to the ability of practices and health plans to provide an effective intervention with ongoing patient support. Disclosure M. Zupa: None. V.C. Arena: None. M.B. Thearle: None. P.A. Johnson: None. L.M. Siminerio: None.
