Unick J.L.,The Miriam Hospital |
Neiberg R.H.,Wake forest University |
Hogan P.E.,Wake forest University |
Cheskin L.J.,Global Obesity Prevention Center at Johns Hopkins |
And 6 more authors.
Obesity | Year: 2015
Objective Examine the relationship between 1- and 2-month weight loss (WL) and 8-year WL among participants enrolled in a lifestyle intervention. Methods 2,290 Look AHEAD participants (BMI: 35.65-±-5.93 kg/m2) with type 2 diabetes received an intensive behavioral WL intervention. Results 1- and 2-month WL were associated with yearly WL through Year 8 (P's-<-0.0001). At Month 1, participants losing 2-4% and >4% had 1.62 (95% CI: 1.32, 1.98) and 2.79 (95% CI: 2.21, 3.52) times higher odds of achieving ≥5% WL at Year 4 and 1.28 (95% CI: 1.05,1.58) and 1.77 (95% CI: 1.40, 2.24) times higher odds of achieving ≥5% WL at Year 8, compared to those losing <2% initially. At Month 2, 3-6% WL resulted in greater odds of achieving ≥5% WL at Year 4 (OR-=-1.85; CI: 1.48, 2.32), and >6% WL resulted in the greatest odds of achieving ≥5% WL at Year 4 (OR-=-3.85; CI: 3.05, 4.88) and Year 8 (OR-=-2.28; CI: 1.81, 2.89), compared to those losing <3%. Differences in adherence between WL categories were observed as early as Month 2. Conclusions 1- and 2-month WL was associated with 8-year WL. Future studies should examine whether alternative treatment strategies can be employed to improve treatment outcomes among those with low initial WL. © 2015 The Obesity Society. Source
Diez J.,University of Alcala |
Bilal U.,University of Alcala |
Cebrecos A.,University of Alcala |
Buczynski A.,Future Health |
And 4 more authors.
Preventive Medicine | Year: 2016
Places where we buy food influence dietary patterns, making local food environments a good example of a mass influence on population diets. Cross-cultural studies, using reliable methods, may help understanding the relationship between food environments and diet-related health outcomes. We aimed to understand cross-national differences in the local food environment between Madrid and Baltimore by comparing an average neighborhood in each city in terms of food store types, healthy food availability, and residents' pedestrian access.During 2012-2013, we assessed one neighborhood (~. 15,000 residents) in each city selecting median areas in terms of socio-demographic characteristics (segregation, education, aging, and population density). We collected on-field data on (a) number and types of all food stores, (b) overall healthy food availability and (c) specific availability of fruits & vegetables. Throughout a street network analysis (200 m, 400 m and 800 m) of food stores with high healthy food availability, we estimated residents' pedestrian accessibility.We found 40 stores in Madrid and 14 in Baltimore. Small food stores carrying fresh foods in Madrid contrasted with the high presence of corner and chain convenience stores in Baltimore. In Madrid, 77% of the residents lived within less than 200 m from a food store with high healthy food availability. In contrast, 95% of Baltimore's residents lived further than 400 m from these stores.Our results may help promoting interventions from local city agencies to allocate resources to existing small-sized food stores, and to improve walkable urban environments. These actions may influence food choices, especially for those residents lacking access to private vehicles. © 2016. Source