Objective: To identify predictors of weight loss in a tri-ethnic population of low-income mothers.

Design: An 8-week dietary and physical activity program was tested. Demographic data were collected at baseline; anthropometric, dietary, physical activity, and psychosocial data were measured at baseline and week 8.

Subjects/setting: A convenience sample of 114 Hispanic, African-American, and white, low-income mothers with a body mass index > or = 25 (calculated as kg/m2) participated in the intervention.

Intervention: Weight-loss classes that incorporated nutrition, physical activity, and behavior modification were administered for 8 weeks.

Main Outcome Measures: Anthropometry (body weight, weight loss).

Statistical Analyses Performed: Analysis of variance, chi2 tests, and Spearman and Pearson correlations were used to test for associations between baseline and change data and total weight loss. Hierarchical regression was employed to assess the marginal importance of factors beyond socioeconomic influences.

Results: Correlates of weight loss included less satisfaction with appearance (r=0.24), greater percentage of energy from protein (r=-0.22), enhanced nutrition knowledge (r=-0.23), and higher scores for benefits of weight loss (r =-0.20) at baseline; and the change in healthful eating attitudes (r=-0.28) and social support (r=-0.21) at 8 weeks. The predictive models of baseline and change variables represented 11.4% and 13.8% of the variance, respectively.

Conclusions: Weight-management programs serving low-income mothers should provide techniques to enhance social support, attitudes toward healthful eating, benefits of weight loss, and nutrition knowledge.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jada.2007.04.016DOI Listing

Publication Analysis

Top Keywords

weight loss
24
low-income mothers
12
physical activity
12
baseline change
12
predictors weight
8
dietary physical
8
nutrition knowledge
8
benefits weight
8
healthful eating
8
social support
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!