Structured weight-loss programs: meta-analysis of weight loss at 24 weeks and assessment of effects of intervention intensity.

Autor: Anderson JW, Luan J, Høie LH, Anderson, James W, Luan, Jingyu, Høie, Lars H
Zdroj: Advances in Therapy; Mar2004, Vol. 21 Issue 2, p61-75, 15p
Abstrakt: Obesity is increasing in epidemic proportions globally while current therapies continue to be suboptimal. In this investigation, weight loss in obese individuals after 24 weeks with different nutrition interventions was compared. The impact of intervention intensity was assessed. Inclusion criteria were established and a comprehensive literature review was performed. These nutrition interventions were identified and analyzable: meal replacements (MRs); energy-restricted (>1500 kcal/d) diets (ERDs); low-energy (800-1500 kcal/d) diets (LEDs); soy very low energy (<800 kcal/d) diets (VLEDs) referred to as SOYs; and VLEDs. Intensity was assessed using the following parameters: physician visits, clinic visits, and hours of class over 24 weeks; an intensity score represents an adjusted sum of the values. Weight losses at 24 weeks as percentage of baseline weights (95%, confidence intervals) were as follows: MRs, 9.1% (5.7-12.5); ERDs, 8.5% (4.9-12.1); LEDs, 11.4% (8.9-13.1); SOYs, 16.5% (13.9-19.1); and VLEDs, 21.3% (20.1-22.5). Weight loss with SOYs was significantly greater than with MRs and ERDs; weight loss with VLEDs was significantly greater than with any other diet. Energy intake was the most significant (P<.0001) regression variable related to weight loss; however, the intensity of intervention (P=.0003) was significantly stronger than initial body weight or duration of treatment. Medically supervised VLEDs are the most effective intervention for facilitating substantial weight loss over 24 weeks. SOY may promote more rapid weight loss over the first 8 weeks than other interventions. MRs appear to be equally effective with ERDs and LEDs with lower levels of intervention intensity. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index