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The Web of Risk Factors for Excessive Gestational Weight Gain in Low Income Women
Author(s) -
Paul Keriann H.,
Graham Meredith L.,
Olson Christine M.
Publication year - 2011
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.25.1_supplement.30.1
Subject(s) - weight gain , psychological intervention , environmental health , low income , pregnancy , gerontology , physical activity , psychology , medicine , demography , developmental psychology , body weight , demographic economics , economics , endocrinology , physical therapy , biology , psychiatry , sociology , genetics
Identifying psychological and socio‐ecological risk factors of excessive gestational weight gain (GWG) has been difficult in quantitative studies due to interactions between underlying risk factors and household income. Qualitative studies are useful for initial descriptions of such complex phenomena. We conducted 9 focus groups with high (n=4 groups) and low (n=5 groups) income pregnant women aged 18–35 years to discuss health, GWG, diet and physical activity. Low income women, in contrast to high income, had higher BMIs, had more children, and were African American. Diet and physical activity behaviors of low income women were more likely to promote positive energy balance than were those of high income women. We developed a conceptual framework for excessive GWG in low income women based on underlying factors related to these weight gain‐promoting behaviors. Experiencing multiple risk factors leads to 1) engaging in several behavior changes during pregnancy unrelated to weight and 2) holding more weight gain‐promoting beliefs than weight‐maintaining beliefs. These 2 factors then inhibit diet and physical activity behaviors and/or behavior changes that promote energy balance and in combination, result in excessive GWG. Low income women experience multiple risk factors for excessive GWG and successful interventions will need to recognize the complex web of influences. NIH Grant No. HL096760‐2.