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1.
J Behav Med ; 45(4): 580-588, 2022 08.
Artigo em Inglês | MEDLINE | ID: mdl-35124742

RESUMO

Extended-care interventions have been demonstrated to improve maintenance of weight loss after the end of initial obesity treatment; however, it is unclear whether these programs are similarly effective for African American versus White participants. The current study examined differences in effectiveness of individual versus group telephone-based extended-care on weight regain, compared to educational control, in 410 African American (n = 82) and White (n = 328) adults with obesity (mean ± SD age = 55.6 ± 10.3 years, BMI = 36.4 ± 3.7 kg/m2). After controlling for initial weight loss, multivariate linear models demonstrated a significant interaction between treatment condition and race, p = .048. Randomization to the individual telephone condition produced the least amount of weight regain in White participants, while the group condition produced the least amount of weight regain in African American participants. Future research should investigate the role of social support in regain for African American versus White participants and examine whether tailoring delivery format by race may improve long-term outcomes.


Assuntos
Negro ou Afro-Americano , Telemedicina , Adulto , Idoso , Humanos , Pessoa de Meia-Idade , Obesidade/terapia , Aumento de Peso , Redução de Peso
2.
JAMA Netw Open ; 3(6): e206764, 2020 06 01.
Artigo em Inglês | MEDLINE | ID: mdl-32539150

RESUMO

Importance: Lifestyle interventions for obesity produce reductions in body weight that can decrease risk for diabetes and cardiovascular disease but are limited by suboptimal maintenance of lost weight and inadequate dissemination in low-resource communities. Objective: To evaluate the effectiveness of extended care programs for obesity management delivered remotely in rural communities through the US Cooperative Extension System. Design, Setting, and Participants: This randomized clinical trial was conducted from October 21, 2013, to December 21, 2018, in Cooperative Extension Service offices of 14 counties in Florida. A total of 851 individuals were screened for participation; 220 individuals did not meet eligibility criteria, and 103 individuals declined to participate. Of 528 individuals who initiated a 4-month lifestyle intervention, 445 qualified for randomization. Data were analyzed from August 22 to October 21, 2019. Interventions: Participants were randomly assigned to extended care delivered via individual or group telephone counseling or an education control program delivered via email. All participants received 18 modules with posttreatment recommendations for maintaining lost weight. In the telephone-based interventions, health coaches provided participants with 18 individual or group sessions focused on problem solving for obstacles to the maintenance of weight loss. Main Outcomes and Measures: The primary outcome was change in body weight from the conclusion of initial intervention (month 4) to final follow-up (month 22). An additional outcome was the proportion of participants achieving at least 10% body weight reduction at follow-up. Results: Among 445 participants (mean [SD] age, 55.4 [10.2] years; 368 [82.7%] women; 329 [73.9%] white), 149 participants (33.5%) were randomized to individual telephone counseling, 143 participants (32.1%) were randomized to group telephone counseling, and 153 participants (34.4%) were randomized to the email education control. Mean (SD) baseline weight was 99.9 (14.6) kg, and mean (SD) weight loss after the initial intervention was 8.3 (4.9) kg. Mean weight regains at follow-up were 2.3 (95% credible interval [CrI], 1.2-3.4) kg in the individual telephone counseling group, 2.8 (95% CrI, 1.4-4.2) kg for the group telephone counseling group, and 4.1 (95% CrI, 3.1-5.0) kg for the education control group, with a significantly smaller weight regain observed in the individual telephone counseling group vs control group (posterior probability >.99). A larger proportion of participants in the individual telephone counseling group achieved at least 10% weight reductions (31.5% [95% CrI, 24.1%-40.0%]) than in the control group (19.1% [95% CrI, 14.1%-24.9%]) (posterior probability >.99). Conclusions and Relevance: This randomized clinical trial found that providing extended care for obesity management in rural communities via individual telephone counseling decreased weight regain and increased the proportion of participants who sustained clinically meaningful weight losses. Trial Registration: ClinicalTrials.gov Identifier: NCT02054624.


Assuntos
Obesidade/psicologia , População Rural/estatística & dados numéricos , Telemedicina/estatística & dados numéricos , Redução de Peso/fisiologia , Idoso , Doenças Cardiovasculares/prevenção & controle , Estudos de Casos e Controles , Aconselhamento/métodos , Diabetes Mellitus/prevenção & controle , Correio Eletrônico/instrumentação , Feminino , Florida/epidemiologia , Humanos , Estilo de Vida , Assistência de Longa Duração/tendências , Masculino , Pessoa de Meia-Idade , Administração dos Cuidados ao Paciente/tendências , Educação de Pacientes como Assunto/métodos , Comportamento de Redução do Risco , Telemedicina/instrumentação , Telefone/instrumentação
3.
Bayesian Anal ; 15(3): 759-780, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-33692872

RESUMO

For many biomedical, environmental, and economic studies, the single index model provides a practical dimension reaction as well as a good physical interpretation of the unknown nonlinear relationship between the response and its multiple predictors. However, widespread uses of existing Bayesian analysis for such models are lacking in practice due to some major impediments, including slow mixing of the Markov Chain Monte Carlo (MCMC), the inability to deal with missing covariates and a lack of theoretical justification of the rate of convergence of Bayesian estimates. We present a new Bayesian single index model with an associated MCMC algorithm that incorporates an efficient Metropolis-Hastings (MH) step for the conditional distribution of the index vector. Our method leads to a model with good interpretations and prediction, implementable Bayesian inference, fast convergence of the MCMC and a first-time extension to accommodate missing covariates. We also obtain, for the first time, the set of sufficient conditions for obtaining the optimal rate of posterior convergence of the overall regression function. We illustrate the practical advantages of our method and computational tool via reanalysis of an environmental study.

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