Purpose: Rural residents have higher rates of chronic diseases compared to their urban counterparts, and obesity may be a major contributor to this disparity. This study is the first analysis of ...obesity prevalence in rural and urban adults using body mass index classification with measured height and weight. In addition, demographic, diet, and physical activity correlates of obesity across rural and urban residence are examined.
Methods: Analysis of body mass index (BMI), diet, and physical activity from 7,325 urban and 1,490 rural adults in the 2005‐2008 National Health and Nutrition Examination Survey (NHANES).
Findings: The obesity prevalence was 39.6% (SE = 1.5) among rural adults compared to 33.4% (SE = 1.1) among urban adults (P= .006). Prevalence of obesity remained significantly higher among rural compared to urban adults controlling for demographic, diet, and physical activity variables (odds ratio = 1.18, P= .03). Race/ethnicity and percent kcal from fat were significant correlates of obesity among both rural and urban adults. Being married was associated with obesity only among rural residents, whereas older age, less education, and being inactive was associated with obesity only among urban residents.
Conclusions: Obesity is markedly higher among adults from rural versus urban areas of the United States, with estimates that are much higher than the rates suggested by studies with self‐reported data. Obesity deserves greater attention in rural America.
In analyses of data on graduates from all U.S. medical schools from 1979 through 2013, women were less likely than men to be promoted from assistant to associate professor and from associate to full ...professor. Women were also less likely than men to be appointed to department chair. The sex differences have not diminished over time.
Objective
This study examined the effects of a group phone‐based weight management intervention on change in physical activity as measured via accelerometer and self‐report in rural breast cancer ...survivors. The study also evaluated the role of physical activity on clinically meaningful cut points for weight loss (baseline to 6 months) and weight loss maintenance (6 to 18 months).
Methods
Participants were breast cancer survivors in a weight management intervention who provided valid weight and accelerometer data (N = 142). Participants were categorized into four groups based on weight loss ≥10% and weight regain ≥5% at 18 months.
Results
Accelerometer‐measured moderate‐to‐vigorous physical activity (MVPA) significantly increased from baseline to 6 months (+46.9 minutes). MVPA declined during maintenance but remained significantly greater than baseline. Self‐reported MVPA followed a similar pattern as accelerometer MVPA, but estimates were significantly higher. Participants in the high loss, low regain group had significantly higher MVPA at all points.
Conclusions
A distance‐based weight management intervention for survivors improved physical activity outcomes over 18 months. Self‐reported physical activity was substantially higher than accelerometer measured. Findings highlight the importance of device‐based measurement for characterizing the magnitude of physical activity change as well as the role of physical activity in weight management outcomes.
Despite the incredible progress that has been made against cancer over the last few decades, the demographic trends in the United States predict that we will see significant increases in cancer ...incidence and mortality by the year 2030. This, coupled with an aging cancer workforce, would suggest that we will have major challenges ahead in dealing with the increasing burden from cancer. Clearly a critical part of our strategy must be to focus on cancer prevention and control (CPC) efforts and not solely rely on treatment to mitigate this concerning trend. This review discusses how the University of Kansas Cancer Center has had a longstanding emphasis on CPC and has leveraged this expertise to enhance the effectiveness and impact of our community outreach and engagement efforts.
The unique obesogenic environment may influence the ability to effectively maintain weight loss in rural areas. The aim of this study was to examine the contextual relationship of neighborhood ...disadvantage, distance to supermarkets and supercenters, and fast food, dollar store, and exercise facility environments on weight loss following a weight-loss intervention in the United States. This analysis (n = 1177) linked weight loss outcomes from a rural, primary care-based randomized controlled trial to contextual data collected from residential addresses. Outcomes include 6-month and 24-month percent weight loss. These outcomes were compared across contextual variables, including tract level disadvantage, food accessibility, and food/exercise availability. Covariates were included in ordinary least squares (OLS) multivariable regression models for 6-month and 24-month weight loss measures, across three weight loss interventions. Contextual variables were not significantly related to percent weight loss overall across treatment arms. Participants living in a 5-mile buffer to dollar stores experienced approximately a 2% (p < 0.05) lower weight loss, but only in the least effective counseling arm (individual clinic visits), while controlling for both individual and contextual factors. Our results suggest that specific contextual variables in rural populations may play an important role in moderating weight loss outcomes especially under the conditions of less effective interventions.
•Rural areas present unique food environments for weight loss.•Rural context is related to behavioral weight loss in less effective interventions.•Value stores within 5-miles led to a 2% difference in weight change at follow-up.
Although frequentist paradigm has been the predominant approach to clinical studies for decades, some limitations associated with the frequentist null hypothesis significance testing have been ...recognized. Bayesian approaches can provide additional insights into data interpretation and inference by deriving posterior distributions of model parameters reflecting the clinical interest. In this article, we sought to demonstrate how Bayesian approaches can improve the data interpretation by reanalyzing the Rural Engagement in Primary Care for Optimizing Weight Reduction (REPOWER).
REPOWER is a cluster randomized clinical trial comparing three care delivery models: in-clinic individual visits, in-clinic group visits, and phone-based group visits. The primary endpoint was weight loss at 24 months and the secondary endpoints included the proportions of achieving 5 and 10% weight loss at 24 months. We reanalyzed the data using a three-level Bayesian hierarchical model. The posterior distributions of weight loss at 24 months for each arm were obtained using Hamiltonian Monte Carlo. We then estimated the probability of having a higher weight loss and the probability of having greater proportion achieving 5 and 10% weight loss between groups. Additionally, a four-level hierarchical model was used to assess the partially nested intervention group effect which was not investigated in the original REPOWER analyses.
The Bayesian analyses estimated 99.5% probability that in-clinic group visits, compared with in-clinic individual visits, resulted in a higher percent weight loss (posterior mean difference: 1.8%95% CrI: 0.5,3.2%), a greater probability of achieving 5% threshold (posterior mean difference: 9.2% 95% CrI: 2.4, 16.0%) and 10% threshold (posterior mean difference: 6.6% 95% CrI: 1.7, 11.5%). The phone-based group visits had similar result. We also concluded that including intervention group did not impact model fit significantly.
We unified the analyses of continuous (the primary endpoint) and categorical measures (the secondary endpoints) of weight loss with one single Bayesian hierarchical model. This approach gained statistical power for the dichotomized endpoints by leveraging the information in the continuous data. Furthermore, the Bayesian analysis enabled additional insights into data interpretation and inference by providing posterior distributions for parameters of interest and posterior probabilities of different hypotheses that were not available with the frequentist approach.
ClinicalTrials.gov Identifier NCT02456636 ; date of registry: May 28, 2015.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
IMPORTANCE: Rural populations have a higher prevalence of obesity and poor access to weight loss programs. Effective models for treating obesity in rural clinical practice are needed. OBJECTIVE: To ...compare the Medicare Intensive Behavioral Therapy for Obesity fee-for-service model with 2 alternatives: in-clinic group visits based on a patient-centered medical home model and telephone-based group visits based on a disease management model. DESIGN, SETTING, AND PARTICIPANTS: Cluster randomized trial conducted in 36 primary care practices in the rural Midwestern US. Inclusion criteria included age 20 to 75 years and body mass index of 30 to 45. Participants were enrolled from February 2016 to October 2017. Final follow-up occurred in December 2019. INTERVENTIONS: All participants received a lifestyle intervention focused on diet, physical activity, and behavior change strategies. In the fee-for-service intervention (n = 473), practice-employed clinicians provided 15-minute in-clinic individual visits at a frequency similar to that reimbursed by Medicare (weekly for 1 month, biweekly for 5 months, and monthly thereafter). In the in-clinic group intervention (n = 468), practice-employed clinicians delivered group visits that were weekly for 3 months, biweekly for 3 months, and monthly thereafter. In the telephone group intervention (n = 466), patients received the same intervention as the in-clinic group intervention, but sessions were delivered remotely via conference calls by centralized staff. MAIN OUTCOMES AND MEASURES: The primary outcome was weight change at 24 months. A minimum clinically important difference was defined as 2.75 kg. RESULTS: Among 1407 participants (mean age, 54.7 SD, 11.8 years; baseline body mass index, 36.7 SD, 4.0; 1081 77% women), 1220 (87%) completed the trial. Mean weight loss at 24 months was –4.4 kg (95% CI, –5.5 to –3.4 kg) in the in-clinic group intervention, –3.9 kg (95% CI, –5.0 to –2.9 kg) in the telephone group intervention, and –2.6 kg (95% CI, –3.6 to –1.5 kg) in the in-clinic individual intervention. Compared with the in-clinic individual intervention, the mean difference in weight change was –1.9 kg (97.5% CI, –3.5 to –0.2 kg; P = .01) for the in-clinic group intervention and –1.4 kg (97.5% CI, –3.0 to 0.3 kg; P = .06) for the telephone group intervention. CONCLUSIONS AND RELEVANCE: Among patients with obesity in rural primary care clinics, in-clinic group visits but not telephone-based group visits, compared with in-clinic individual visits, resulted in statistically significantly greater weight loss at 24 months. However, the differences were small in magnitude and of uncertain clinical importance. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02456636
Obesity, depression, and anxiety often co-occur, but research on weight change and mental health status is limited. This analysis examined how the mental component score (MCS-12) from the Short Form ...health survey changed over 24 months in weight loss trial participants with vs. without treatment seeking for affective symptoms (TxASx) and by weight change quintiles.
Participants with complete data (n = 1163) were analyzed from enrollees in a cluster-randomized, behavioral weight loss trial in rural U.S. Midwestern primary care practices. Participants received a lifestyle intervention with different delivery models, including in-clinic individual, in-clinic group, or telephone group counseling visits. Participants were stratified by baseline TxASx status and 24-month weight change quintiles. Mixed models were used to estimate MCS-12 scores.
There was a significant group-by-time interaction at the 24-month follow-up. The largest 0–24 month increase in MCS-12 scores (+5.3 points 12 %) was observed in participants with TxASx who lost the most weight during the trial, while the largest decrease in MCS-12 scores (−1.8 points −3 %) was observed in participants without TxASx who gained the most weight (p < 0.001).
Notable limitations included self-reported mental health, the observational analytical design, and a largely homogenous source population, as well as the possibility of reverse causation biasing some findings.
Mental health status generally improved, particularly among participants with TxASx who experienced significant weight loss. Those without TxASx who gained weight, however, had a decline in mental health status over 24 months. Replication of these findings is warranted.
•Mental health of adults with obesity generally improved during a 24-month behavioral weight loss trial.•Mental health improved most in adults with affective disorder symptoms who lost significant weight over 24-months.•Mental health declined most in adults without affective disorder symptoms who gained weight over 24-months.
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