Matern Child Health J DOI 10.1007/s10995-016-1949-5
Fast Food Intake in Relation to Employment Status, Stress, Depression, and Dietary Behaviors in Low-Income Overweight and Obese Pregnant Women Mei-Wei Chang1 • Roger Brown2 • Susan Nitzke3
Ó Springer Science+Business Media New York 2016
Abstract Objective This study explored fast food intake as a potential mediator of the relationships among employment status; stress; depression; and fruit, vegetable, and fat intakes by race (African American vs. Non-Hispanic White) and body mass index (BMI category: overweight vs. obesity). Methods Low-income overweight and obese pregnant women (N = 332) were recruited from the Special Supplemental Nutrition Program for Women, Infants and Children in Michigan. Path analysis was performed to explore mediation effects by race and BMI category. Results Fast food intake mediated the relationship between employment status and fat intake (p = 0.02) in Non-Hispanic White women, but no mediation effect was detected in African American women. For overweight women, fast food intake mediated the relationship between employment status and fat intake (p = 0.04) and the relationship between depression and vegetable intake (p = 0.01). Also, fast food intake partially mediated the relationship between depression and fat intake (p = 0.003). For obese women, fast food intake mediated the relationship between employment status and fat intake (p = 0.04). Conclusion Fast food is an important topic for nutrition education for overweight and obese pregnant women. Future interventions may be more successful if they address issues associated with employment status (e.g., & Mei-Wei Chang
[email protected] 1
College of Nursing, Ohio State University, 342 Newton Hall, 1585 Neil Avenue, Columbus, OH 43210, USA
2
School of Nursing, University of Wisconsin-Madison, Madison, WI, USA
3
Department of Nutritional Sciences, University of WisconsinMadison, Madison, WI, USA
lack of time to plan and cook healthy meals) and depressive mood (e.g., inability to plan meals or shop for groceries when coping with negative emotions). Keywords Pregnant Obesity Fast food intake Employment Low-income
Significance Previous studies have examined the relationships between fast food intake and employment status; stress; depression; and fruit, vegetable, and fat intakes. Our analyses take the findings from the previous studies one step further by identifying the mediating role of fast food intake on the relationships between employment status; stress; depression; and fruit, vegetable, and fat intakes. Our overall findings suggest that low-income pregnant women with different racial/ethnic backgrounds and body sizes may have important differences in determinants of eating behaviors.
Introduction Prevalence of overweight and obesity is disproportionally high in low-income women. Compared to 45 % of U.S. women [1], over 54 % of low-income women are overweight or obese before becoming pregnant [2]. Overweight and obese women are at 2.6–4.5 times higher risk than normal weight women to develop gestational diabetes [3]. Compared to higher-income women, low-income women, especially those who are overweight or obese, are more likely to experience excessive gestational weight gain, defined as gestational weight gain exceeding that
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recommended by the Institute of Medicine [4]. Excessive gestational weight gain increases risk of adverse maternal outcomes (e.g., gestational diabetes and pregnancy induced hypertension) [5–7] and birth outcomes (e.g., macrosomia) [8–10] and is associated with eating less-healthy foods [11– 14]. Cross-sectional [15–18], longitudinal observation [19– 21] and intervention [22] studies have consistently shown that intake of fast food (food ordered at a counter or at a drive-through window) [23] is positively associated with weight gain and obesity. Also, evidence is clear that fast food intake is positively associated with increased daily caloric intake [24–27]. Eating 1 fast food meal per week is associated with an increase of 56 kcal/day or an extra 1.6 lbs at 3-year follow up among young women (20–45 years old) [22]. Also, eating fast foods at least 2 times per week is associated with an extra 9.9 lbs weight gain in young adults (19–39 years old) at 15-year follow up [19]. Moreover, fast food intake increases risk of adverse metabolic outcomes, i.e., type 2 diabetes [19]; increased serum fasting glucose, insulin [17], and lipids [20]. Studies have consistently shown that fast food intake is positively associated with higher fat intake in adults in general [18, 24], middle aged African Americans [28], young women [22], low-income pregnant women [25], and low-income overweight and obese women [26]. However, the relationships between fast food intake and fruit and vegetable intakes remain unclear. While some studies show that fast food intake is inversely associated with fruit intake in adults [16] and low-income pregnant women [25], other studies show no relationship in adults [18] and young women [22]. Some studies show that fast food intake is inversely associated with vegetable intake in adults [18, 29] and young women [22], but other studies show a positive relationship in low-income pregnant women [25] or no relationship in adults [16] and young women [22]. Stress is associated with poor diet quality in low-income pregnant women [30] but few studies have examined fast food intake in relation to stress and depression. Depression is positively related to fast food intake in middle aged women [31]. A small study of low-income pregnant women reveals that obese women and women who experience a higher level of psychosocial stress or more depression are more likely to eat fast foods than their counterparts [25]. Relationships between fast food intake and demographic characteristics have been examined. Younger adults are more likely to eat fast foods than older adults [22, 26, 28, 32, 33]. Also, low-income adults are more likely to report eating fast food than higher-income adults [15, 22, 34, 35]. Black adults are more likely to eat fast foods than Whites, possibly because fast food restaurants are more concentrated in their neighborhood [19, 31, 34]. Moreover, obese
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adults are more likely to eat fast foods than normal weight adults [16, 31]. Finally, employed adults are more likely to eat unhealthy foods [36] or fast foods [22] than unemployed adults. In summary, the relationships between fast food intake and fruit and vegetable intakes remain unsettled. Limited studies have investigated the relationships among employment status, fast food intake, stress, and depression. Even though prior studies show that young, low-income, obese and black adults are more likely to eat fast foods, there is paucity of studies including young, low-income overweight and obese pregnant women. Low-income women experience stressful daily life situations [37], which negatively affect their ability to eat healthier foods [30, 38– 41]. Being pregnant can amplify young low-income women’s stress level because of heightened concerns, e.g., worry about eating balanced and nutritious meals and about changes associated with motherhood and experience complex spouse/family relationships [38, 39]. Thus, identifying the relationships among employment status, stress, depression, fast food and dietary intakes (fruit, vegetable and fat) among young low-income overweight and obese pregnant women will help researchers target specific eating behaviors (i.e. fast food intake) to potentially decrease the prevalence of excessive gestational weight gain. The present study reports data analyses from a crosssectional study that examined the relationships between stress, depression, sleep, dietary intake, and physical activity in young low-income overweight and obese pregnant women. The objective of this paper is to explore mediation effects of fast food intake on employment status; stress; depression; and fruit, vegetable, and fat intakes (Fig. 1) by race (African American vs. Non-Hispanic White) and body mass index (BMI category; overweight vs. obesity). By examining particular groups, we could provide specific findings for researchers and community planners to tailor intervention to address factors most likely to be important determinants of eating behaviors.
Method Setting and Participants A detailed description of setting and participants of this study has been published [42]. Briefly, participants were recruited from 4 local Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) agencies in Michigan. WIC is a federally funded program that makes medical/service referrals and provides food vouchers and nutrition consultation to low-income pregnant, breastfeeding and postpartum women and children aged
Matern Child Health J Fig. 1 Model used to explore mediation effects by race and BMI category
0–5. WIC participants who were pregnant, at least 18 years old, English speakers, and overweight/obese (pre pregnancy BMI at least 25.0 kg/m2 calculated using self-reported height and weight) were eligible to participate in the study. Michigan State University Institutional Review Board approved the study procedure. Measures Data were collected via a self-administered pencil-andpaper survey. Participants completed the study survey while waiting for their WIC appointments. Socio-demographics We collected information on race, education, employment and smoking status, age of the participants and their youngest child, and date of the last menstrual period. Stress We used a 9-item perceived stress scale with reported validity and reliability [43]. This survey assesses participants’ perception of stressful life situations during the last month. Participants were asked, for example, thinking about your work and home life in the past month, how often have you felt that you were effectively dealing with important changes that were occurring in your life? (1 = rarely or never, 2 = sometimes, 3 = often, 4 = usually or always). A higher score means perceived less stress. Depression We assessed depression using a 10-item Edinburgh Postnatal Depression Scale with established validity and reliability [44]. Participants were asked, for example, within the
past 7 days, have you been anxious or worried for no good reason (0 = never, 3 = yes, most of the time). A higher score means more depression. Fast Food Intake Fast food intake was assessed using a brief screener (12 items) with established test–retest reliability and criterion validity [23]. For example, participants were asked ‘‘In the past month, how often did you purchase foods at a fast food restaurant where food is ordered at a counter or at a drivethrough window?’’ (0 = never or rarely, 1 = 1 time/per month, 2 = 2–3 times/per month, 3 = 1–2 times/per week, 4 = 3–4 times/per week, 5 = 5–6 times/per week, 6 = 1 time/per day, 7 = 2 times/per day, 8 = 3 or more times/ per day). Fruit, Vegetable, and Fat Intakes We used the 24-item Rapid Food Screener with reported validity and reliability [45] to measure fruit, vegetable, and fat intakes. The correlation (validity) between the screener and food frequency questionnaire ranged from 0.6 to 0.7 (p \ 0. 0001) for fruit, vegetable, total fat, and saturated fat intakes [45]. Two items assessed fruit intake (fruit juice, like orange, apple, grapes, fresh, frozen or canned; fresh or canned fruits). Five items assessed vegetable intake: vegetable juice; green salad; potatoes, in any kind, including baked, mashed or French fries; vegetable soup; any other vegetables. Responses for fruit and vegetable items include 0 = less than 1 time/per week, 1 = 1 time/a week, 2 = 2–3 times/per week, 3 = 4–6 times/per week, 4 = 1 time/per day, and 5 = 2 or more times/per day. Seventeen items were used to assess dietary fat intake (0 = 1 time/per month or less, 1 = 2–3 times/per month, 3 = 1–2 times/ per week, 4 = 3–4 times/per week, and 5 = 5 or more times/per week). A higher score indicates higher fat intake.
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Statistical Analysis All analyses were conducted using Mplus statistical software (version 7.3). Descriptive statistics (mean and percentage) were applied. T test analysis was used for continuous variables and Chi squared analysis was used for categorical variables. For the analysis purpose, we coded employment status as 1 = unemployed (unemployed, homemakers, self-employed, students and others), 2 = part time, 3 = full time. Due to the asymmetry in the fast food measure (skewness = 1.92, D’Agostino skewness test = 10.02, p \ 0.001), Box-Cox power transformation [46] was used to normalize the fast food intake distribution [47]. The transformation used was fast food intake transformation = (fast food intake ? 0.55)^0.39, where 0.55 was the shift parameter and 0.39 was the power transformation (post transformation skewness = 0.02, D’Agostino skewness test = 0.169, p = 0.86). A higher score indicates higher frequency of fast food intake. To determine trimester status (first trimester [B 12 weeks gestation], second trimester [13–27 weeks gestation], and third trimester [28–40 weeks gestation]), we used the date of the last menstrual period minus the date of data collection. Path analysis (Fig. 1) was performed to explore mediation effects by race and BMI category. The fast food intake was the proposed mediating variable for exogenous measurements (employment status, stress, and depression) to 3 endogenous (outcome) measurements (fruit, vegetable, and fat intakes).
Results Of the sample, about 42 % were African Americans and 41 % were overweight. Tables 1 and 2 present demographic characteristics of the study participants by race (Table 1) and BMI category (Table 2). Table 3 shows frequency of fast food intake by race and BMI category. About 48 % (159/332) of the study participants ate fast food at least 1–2 times per week: African American = 52.5 % (73/139), Non-Hispanic White = 44.6 % (86/193), overweight = 54.0 % (74/137), and obese = 43.6 % (85/195). Compared to Non-Hispanic White pregnant women, African American pregnant women were more likely to report eating fast food, fruit and high fat foods (Table 4). No significant differences were observed between overweight and obese pregnant women (Table 5).
employment status, stress, depression, fast food intake and fruit intake. We did not detect any mediation effects in this group. Non-Hispanic White Women (Fig. 2b). Employed full time Non-Hispanic White women were more likely to eat fast food than their counterparts. Those who reported more depression were more likely to have higher fat intake than women who reported less depression. Higher frequency of fast food intake was associated with more vegetable intake and higher fat intake, but no relationships were observed among stress, fast food intake, and fruit intake. Fast food intake mediated the relationship between employment status and fat intake (p = 0.02); employed full time non-Hispanic White women were more likely to eat fast food, which led to higher fat intake than their counterparts. Overweight Women (Fig. 3a). Employed full time overweight women were more likely to eat fast food than their counterparts. Less stress was associated with higher fat intake. More depression was associated with higher frequency of fast food intake and higher fat intake but less fruit intake. Higher frequency of fast food intake was associated with more fruit and vegetable intakes and higher fat intake. Fast food intake mediated the relationship between employment status and fat intake (p = 0.04); employed full time overweight women were more likely to eat fast food, which led to higher fat intake than their counterparts. Fast food intake mediated the relationship between depression and vegetable intake (p = 0.01); women who reported more depression were more likely to eat fast foods, which led to more vegetable intake than their counterparts. Fast food intake partially mediated the relationship between depression and fat intake (p = 0.003); women who reported more depression were more likely to eat fast food, partially leading to higher fat intake than women reported less depression. Obese Women (Fig. 3b). Employed full time obese women were more likely to eat fast food than their counterparts. Less stress was associated with more fruit intake. No relationships were found between depression; fast food intake; and fruit, vegetable and fat intakes. Higher frequency of fast food intake was associated with more vegetable intake and higher fat intake. Fast food intake mediated the relationship between employment status and fat intake (p = 0.04); employed full time obese women were more likely to eat fast food, which led to higher fat intake than their counterparts.
Discussion Path Analysis African American Women (Fig. 2a). Higher frequency of fast food intake was associated with more vegetable intake and higher fat intake. No relationships were found among
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Previous studies have examined the relationships between fast food intake and employment status; stress; depression; and fruit, vegetable, and fat intakes among adults [16, 18, 29], young women [22], low-income pregnant women [25,
Matern Child Health J Table 1 Demographic characteristics of the study sample by race (N = 332)
Age (years) Age of the youngest child (years)
African American (n = 139)
Non-Hispanic White (n = 193)
Mean
SD
Mean
25.10
5.66
26.15
5.41
0.08
5.56
5.37
4.07
3.94
0.02
SD
n
%
30
21.74
76
39.58
Second trimester
60
43.48
62
32.29
Third trimester
48
34.78
54
28.13
Trimesters First trimester
p value
n
%
\0.01
Education 8th grade or less
1
0.72
4
2.07
Some high school
26
18.71
19
9.84
High school graduate
32
23.02
49
25.39
Some college or technical school
67
48.20
100
51.81
College graduate or higher
13
9.35
21
10.88
Full time
26
18.71
27
13.99
Part time
25
17.99
38
19.69
Unemployed
51
36.69
63
32.64
Homemakers
3
2.16
38
19.69
Self-employed Student
11 20
7.91 14.39
0 20
0.00 10.36
3
2.16
7
3.63
Overweight (25.0–29.9)
61
43.88
76
39.38
Obese class I (30.0–34.9)
41
29.50
65
33.68
Obese class II (35.0–39.9)
16
11.51
32
16.58
Obese class III (40 and greater)
21
15.11
20
10.36
0.18
Employment status
Other
\0.01
Pre-pregnancy body mass index category
30] and middle aged women [31]. Our analyses take the findings from the previous studies one step further by identifying the mediating role of fast food intake on the relationships between employment status; stress; depression; and fruit, vegetable, and fat intakes. Our overall findings suggest that low-income pregnant women with different racial/ethnic backgrounds and body sizes may have important differences in determinants of eating patterns. In terms of mediation effect, there were similarities and a difference between African American and Non-Hispanic White groups. For both racial groups, we did not find that fast food intake mediated the relationship between stress, depression and dietary intakes (fruit, vegetable, and fat). While we did not find that fast food intake mediated the relationship between employment status and fat intake in Africa Americans, we detected this type of mediation effect in Non-Hispanic Whites. More research is needed to
0.28
verify the this finding because of the relatively small sample size of African Americans in the analysis. For both overweight and obese groups, we detected that fast food intake mediated the relationship between employment status and fat intake. However, our results did not show that fast food intake mediated the relationship between stress and dietary intake (fruit, vegetable, and fat) in both groups. Depression was associated with higher BMI and poor dietary quality among low-income adults [48]. We found that the mediation effect differed by BMI groups. Fast food intake mediated the relationship between depression and vegetable intake in overweight but not obese women. Interpretation of this finding is complicated by the fact that some vegetable offerings at fast food restaurants are fried (e.g., fried onions, French fried) and our data did not distinguish among the various vegetable choices. Our results also revealed that fast food intake partially
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Matern Child Health J Table 2 Demographic characteristics of the study sample by BMI category (N = 332)
Overweight (n = 137) Mean Age (years)
25.6
Age of the youngest child (years)
5.18
Trimesters First trimester
Obese (n = 195) SD
Mean
6.05
25.7
5.52
SD
4.33
n
%
41
30.15
p value
5.16
0.89
3.86
0.19
n
%
65
33.51
Second trimester
47
34.56
75
38.66
Third trimester
48
35.29
54
27.84
Non Hispanic Black or African American
61
44.53
78
40.0
Non-Hispanic White
76
55.47
117
60.0
0.35
Race 0.41
Education 8th grade or less
2
1.46
3
1.54
Some high school
24
17.52
21
10.77
High school graduate
38
27.74
43
22.05
Some college or technical school
62
45.26
105
53.85
College graduate or higher
11
8.03
23
11.79
Full time
18
13.14
35
17.95
Part time Unemployed
24 51
17.52 37.23
39 63
20.0 32.31
Homemakers
17
12.41
24
12.31
0.19
Employment status
Self-employed Student Other
Table 3 Frequency of fast food intake by race and BMI category (N = 332)a
Frequency of fast food intake
Never
6
3.08
21
10.77
3
2.19
7
3.59
2 (1.44)
0.77
BMI Category Non-Hispanic White n (%) 1 (0.52)
Overweight n (%) 0 (0)
Obese n (%) 3 (1.54))
3 (2.16)
5 (2.59)
5 (3.65)
3 (1.54)
1 time per month
18 (12.95)
24 (12.44)
19 (13.87)
23 (11.79)
2–3 times per month
43 (30.94)
77 (39.90)
39 (28.47)
81 (41.54)
1–2 times per week
39 (28.06)
65 (33.68)
44 (32.12)
60 (30.77)
3–4 times per week
22 (15.83)
15 (7.77)
22 (16.06)
15 (7.69)
5–6 times per week
5 (3.60)
4 (2.07)
4 (2.92)
5 (2.56)
At least 1 time per day
7 (5.04)
2 (1.04)
4 (2.92)
5 (2.56)
a
African American = 139, Non-Hispanic white = 193, overweight = 137, obese = 195
mediated the relationships between depression and fat intake in overweight but not obese group. It is possible that obese pregnant women were more conscious than overweight pregnant women about what they were eating.
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3.65 13.87
Race African American n (%)
Missing
5 19
Comparisons of our study findings to findings of previous studies are challenging because this is the first study investigating whether fast food intake mediated the relationship between employment status; stress; depression; and fruit, vegetable, and fat intakes. We found that
Matern Child Health J Table 4 Key Variables by Race (N = 332)
Key Variables
Stress
a
Depression b
African American (n = 139)
Non-Hispanic White (n = 193)
Mean
Mean
SD
24.21
3.04
24.79
3.01
0.08
8.33
5.63
7.75
4.85
0.31
12.87
9.11
9.16
6.13
\0.01
Fruit intake
5.80
2.76
5.21
2.41
0.04
Vegetable intake
7.90
4.90
7.06
4.17
0.09
26.81
11.29
22.19
9.17
\0.01
Fast food intake
Fat intake a
A higher score means less stress
b
Probability value based on transformed values
Table 5 Key variables by BMI Category (N = 332) Key variables
Stress
a
Depression
Overweight (n = 137)
Obese (n = 195)
Mean
Mean
24.28
SD 3.16
24.73
p value
SD 2.93
0.18
8.57
5.02
7.59
5.29
0.09
11.46
7.17
10.18
8.08
0.07
Fruit intake
5.29
2.66
5.58
2.51
0.30
Vegetable intake
7.29
4.58
7.50
4.45
0.66
25.29
10.34
23.30
10.30
0.08
Fast food intakeb
Fat intake
SD
p value
a
A higher score means less stress
b
Probability value based on transformed values
employed full time non-Hispanic White, overweight and obese women were more likely to eat fast foods, which led to higher fat intake than their counterparts. Previous research showed that employed mothers tend to eat unhealthy [36] and fast foods [49]. Reasons to eat fast foods included time pressure because of long hours at work, unusual working hours, family commitments [36, 50] and involvement in children’s extra-curricular activities [49]. Low-income employed mothers reported being constantly in a rush, thus it’s easier to ‘‘just grab fast food and go’’ [38, 50, 51]. Also, they felt lack of energy or time after coming home from work and had little discretionary time. When unexpected events occurred or they were running late, they gave up cooking because having food on the table was more important than eating healthier [51]. Low-income employed mothers also identified ways to find time to prepare meals at home, e.g., having a schedule and structure time for cooking, planning meals, cooking in larger batches for the week for expected events, and multitasking (e.g., cooking while doing laundry) [51]. A recent study of employed mothers found that time pressure was not associated with but lack of confidence in meal
preparation was associated with unhealthy eating [52]. Other reasons to eat fast foods included a perception that fast food was quick and convenient [32, 38, 52, 53], tasted good, was inexpensive [38, 53], and was enjoyed by family members [53]; and dislike of meal planning or cooking [32, 38, 54]. We did not find that fast food intake mediated the relationship between stress and fruit, vegetable, and fat intakes regardless race or BMI category. This may be due to a measurement issue because the stress scale measured perception of life situation in general without specific reference to prenatal stress. Previous studies have shown that transition to parenthood could be a very stressful time for couples [55]. For example, relationship satisfaction decreased [56–59] as parents adjusted to new roles and responsibilities [55, 60]. Among all racial and BMI groups, we only observed that overweight women who reported eating fast food more frequently were likely to eat more fruits than their counterparts. However, when the juice item was removed from the analysis, the finding became non significant (data not shown). Similarly, we observed a relationship between fast food and vegetable intakes that changed from significant to non significant in non-Hispanic White and obese women when we removed ‘potato’ items from the analysis (data not shown). Our findings suggest that measurement issues may play a role on the inconsistent relationships between fast food, fruit and vegetable intakes in the prior studies [16, 18, 22, 25]. Consistent with previous research [18, 22, 24–26, 28], we found that low-income pregnant women regardless of race or BMI category who reported eating fast foods more frequently were more likely to eat high fat foods than their counterparts. Our participants were young and busy mothers. Previous studies showed that younger adults were more likely to eat fast foods than older adults [28, 32, 61]. Fast food restaurants are more likely to locate in low-income than higherincome neighborhoods [34]. These may partially explain
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(a)
(b)
Fig. 2 Path analysis by race. *p B 0.05; **p B 0.01. Thicker lines mediation effect. Employment status—1 unemployed, homemakers, self-employed, students and others; 2 part time; 3 full time. Stress a higher score means less stress. Depression a higher score means more
depression. Fast food intake a higher score indicates higher frequently of fast food intake. Fat intake a higher score means higher fat intake. Fruit intake a higher score indicates more fruit intake. Vegetable intake a higher score indicates more vegetable intake
that 48 % of our sample ate fast foods at least 1–2 times per week. Eating fast food frequently is a great concern because pregnancy is a critical time for women to eat high quality diets for optimal fetal development. Compared to higher-income women, low-income women, especially those who were overweight or obese, were more likely to eat less healthy foods, e.g., high fat and fast foods [16, 18, 22, 35, 62]. Also, low-income overweight and obese pregnant women craved unhealthy foods and were strongly encouraged to ‘‘eat for two’’ by their spouses and other family members [38]. Excessive gestational weight gain was associated with eating less healthy foods [11, 13]. Recent studies show that about 60–85 % of overweight and obese women experienced excessive gestational weight gain [63–66]. Evidence shows that excessive gestational weight gain predicts postpartum weight
retention [11, 12, 66–69], leading to long-term weight gain and obesity [70, 71]. It is possible that fast food intake plays a role on excessive gestational weight gain because eating 1 fast food meal per week has been reported to contribute an additional 56 kcal/day [22]. Thus, we recommend that nutrition interventions including specific effective strategies (discussed below) to help low-income overweight and obese pregnant women eating less fast food and maintain the change after delivery. In our work with low-income overweight and obese women for the past 15 years, we have learned the importance of numerous issues that are unrelated to personal motivation influencing dietary intake. Lack of awareness of eating pattern and challenging daily life situations prevent many of these women from achieving the healthy eating behaviors that they would like to adopt. For example, this
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(a)
(b)
Fig. 3 Path analysis by BMI. *p B 0.05; **p B 0.01. Thicker lines mediation effect. Employment status—1 unemployed, homemakers, self-employed, students and others; 2 part time; 3 full time. Stress a higher score means less stress. Depression a higher score means more
depression. Fast food intake a higher score indicates higher frequently of fast food intake. Fat intake a higher score means higher fat intake. Fruit intake a higher score indicates more fruit intake. Vegetable intake a higher score indicates more vegetable intake
population perceives the cost a single fast food per meal to be cheap but they may not be aware that frequent reliance on fast food meals may be more expensive than eating foods prepared at home. While many may not be aware of adverse effects of eating fast foods (e.g., weight gain and increase risk of health hazards), others are aware but do not know efficient ways to prepare meals for themselves and their families. Many women perceive that they already eat very healthfully but are not aware that the type and amount of foods intake typically eaten per day do not meet accepted nutrition recommendations. They may not have found ways to budget their time and identify opportunities to plan meals, shop for groceries, and prepare healthy and
quick meals at home. Some women did not know how to prepare meals at home and found it embarrassing to admit their lack of meal preparation skills. Other women did not have proper tools and equipment (e.g., a sharp knife or a working stove). To help low-income overweight and obese pregnant women reduce fast food intake, we recommend the following approaches to nutrition education and behavioral interventions for this population. Researchers and community intervention planners may consider utilizing peers to deliver intervention. Our ongoing intervention study (Mothers In Motion, MIM) for low-income non pregnant overweight and obese WIC mothers [42] utilized peers’
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testimonies and demonstrations in educational DVDs. We also asked open questions to help women consider their current life situations. We have learned that this population enjoys receiving tips from their peers because they perceive their peers understand their challenging life situations—if their peers could do it, it would be worth trying. Practical recommendations from professionals may be dismissed as less feasible than tips from other mothers in similar circumstances. We have received numerous anecdotes from our study participants that they were not aware of their current life situations (e.g., monthly expenditures on fast food type and amount of foods eaten on a typical day, and amount of time spent on non-essential activities). The tone and wording of probing questions is important when encouraging a person to consider their current practices and plan healthful behavior changes. For example, asking ‘‘how often do you eat fast foods?’’ and ‘‘how much money do you spend on fast food each week or month?’’ allows the person to decide whether they want to make improvements over their current behaviors. In contrast, telling the target audience what they ‘‘should’’ do may trigger defensiveness and resistance. Asking permission before giving suggestions or educational advice may increase acceptability and likelihood that suggestions will be implemented. Example tips include using a crock pot, cooking frozen vegetables in the microwave, packing a lunch and reminding oneself to take the lunch to work, and cooking batches of foods when time permits. Providing cooking utensils as incentives (e.g. sharp knives or a drawing for a crock pot) for program participation is an effective way to reinforce messages related to meal preparation. There are limitations to this study. The cross-sectional design precludes causal interpretations. The assessment of fruit intake was based on 2 items, possibly leading to underestimations of fruit intake. Our analysis did not take trimester status into consideration. Our participants were all from one state and may not be representative of broader populations of low-income pregnant women or women who were overweight or obese before becoming pregnant.
Conclusion This study documents the prevalence of fast food intake and the role that fast food intake plays as a factor affecting relationships among employment status and dietary intakes for low-income overweight and obese pregnant women. Interventions focusing on promotion of healthy eating in this population may be strengthened by including time management, emphasizing the importance of meal planning, and providing tips to manage negative feelings. Future prospective/longitudinal studies are needed to
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clarify relationships among employment status, fast food intake, and dietary intake. Compliance with Ethical Standards Conflict of interest The authors declare no potential competing or conflict interest. Also, study procedure was approval by the Michigan State University Institute Review Board and we adhered to the approved procedure.
References 1. Gunderson, E. P. (2009). Childbearing and obesity in women: Weight before, during, and after pregnancy. Obstetrics and Gynecology Clinics of North America, 36(2), 317–332. doi:10. 1016/j.ogc.2009.04.001. 2. Hinkle, S. N., Sharma, A. J., Kim, S. Y., et al. (2012). Prepregnancy obesity trends among low-income women, United States, 1999–2008. Maternal and Child Health Journal, 16(7), 1339–1348. doi:10.1007/s10995-011-0898-2. 3. Zhang, C., Tobias, D. K., Chavarro, J. E., et al. (2014). Adherence to healthy lifestyle and risk of gestational diabetes mellitus: Prospective cohort study. BMJ, 349, g5450. doi:10.1136/bmj. g5450. 4. Insitute of Medicine. (2009). Weight gain during pregnancy: Reexaming the guidelines. http://www.iom.edu/pregnancy weightgain. July, 2009. 5. Heslehurst, N., Simpson, H., Ells, L. J., et al. (2008). The impact of maternal BMI status on pregnancy outcomes with immediate short-term obstetric resource implications: a meta-analysis. Obesity Reviews, 9(6), 635–683. doi:10.1111/j.1467-789X.2008. 00511.x. 6. Koebnick, C., Smith, N., Huang, K., et al. (2012). The prevalence of obesity and obesity-related health conditions in a large, multiethnic cohort of young adults in California. Annals of Epidemiology, 22(9), 609–616. doi:10.1016/j.annepidem.2012.05. 006. 7. Gaillard, R., Durmus, B., Hofman, A., et al. (2013). Risk factors and outcomes of maternal obesity and excessive weight gain during pregnancy. Obesity, 21(5), 1046–1055. doi:10.1002/oby. 20088. 8. Bodnar, L. M., Parks, W. T., Perkins, K., et al. (2015). Maternal prepregnancy obesity and cause-specific stillbirth. The American Journal of Clinical Nutrition, 102(4), 858–864. doi:10.3945/ajcn. 115.112250. 9. Johansson, S., Villamor, E., Altman, M., et al. (2014). Maternal overweight and obesity in early pregnancy and risk of infant mortality: A population based cohort study in Sweden. BMJ, 349, g6572. doi:10.1136/bmj.g6572. 10. Vesco, K. K., Sharma, A. J., Dietz, P. M., et al. (2011). Newborn size among obese women with weight gain outside the 2009 Institute of Medicine recommendation. Obstetrics and Gynecology, 117(4), 812–818. doi:10.1097/AOG.0b013e3182113ae4. 11. Olson, C. M., Strawderman, M. S., Hinton, P. S., et al. (2003). Gestational weight gain and postpartum behaviors associated with weight change from early pregnancy to 1 y postpartum. International Journal of Obesity and Related Metabolic Disorders: Journal of the International Association for the Study of Obesity, 27(1), 117–127. 12. Ostbye, T., Peterson, B. L., Krause, K. M., et al. (2012). Predictors of postpartum weight change among overweight and obese women: results from the Active Mothers Postpartum study. Journal of Women’s Health, 21(2), 215–222. doi:10.1089/jwh.2011.2947.
Matern Child Health J 13. Stuebe, A. M., Oken, E., & Gillman, M. W. (2009). Associations of diet and physical activity during pregnancy with risk for excessive gestational weight gain. American Journal of Obstetrics and Gynecology, 201(1), 58 e1–58 e8. doi:10.1016/j.ajog. 2009.02.025. 14. Shin, D., Lee, K. W., & Song, W. O. (2015). Dietary patterns during pregnancy are associated with risk of gestational diabetes mellitus. Nutrients, 7(11), 9369–9382. doi:10.3390/nu7115472. 15. Moore, L. V., Diez Roux, A. V., Nettleton, J. A., et al. (2009). Fast-food consumption, diet quality, and neighborhood exposure to fast food: The multi-ethnic study of atherosclerosis. American Journal of Epidemiology, 170(1), 29–36. doi:10.1093/aje/kwp090. 16. Schroder, H., Fito, M., Covas, M. I., et al. (2007). Association of fast food consumption with energy intake, diet quality, body mass index and the risk of obesity in a representative Mediterranean population. The British Journal of Nutrition, 98(6), 1274–1280. doi:10.1017/S0007114507781436. 17. Smith, K. J., Blizzard, L., McNaughton, S. A., et al. (2012). Takeaway food consumption and cardio-metabolic risk factors in young adults. European Journal of Clinical Nutrition, 66(5), 577–584. doi:10.1038/ejcn.2011.202. 18. Jeffery, R. W., Baxter, J., McGuire, M., et al. (2006). Are fast food restaurants an environmental risk factor for obesity? The international Journal of Behavioral Nutrition and Physical Activity, 3, 2. doi:10.1186/1479-5868-3-2. 19. Pereira, M. A., Kartashov, A. I., Ebbeling, C. B., et al. (2005). Fast-food habits, weight gain, and insulin resistance (the CARDIA study): 15-year prospective analysis. Lancet, 365(9453), 36–42. doi:10.1016/S0140-6736(04)17663-0. 20. Duffey, K. J., Gordon-Larsen, P., Steffen, L. M., et al. (2009). Regular consumption from fast food establishments relative to other restaurants is differentially associated with metabolic outcomes in young adults. The Journal of Nutrition, 139(11), 2113–2118. doi:10.3945/jn.109.109520. 21. Duffey, K. J., Gordon-Larsen, P., Jacobs, D. R, Jr, et al. (2007). Differential associations of fast food and restaurant food consumption with 3-y change in body mass index: The Coronary Artery Risk Development in Young Adults Study. The American Journal of Clinical Nutrition, 85(1), 201–208. 22. French, S. A., Harnack, L., & Jeffery, R. W. (2000). Fast food restaurant use among women in the Pound of Prevention study: dietary, behavioral and demographic correlates. International Journal of Obesity and Related Metabolic Disorders: Journal of the International Association for the Study of Obesity, 24(10), 1353–1359. 23. Nelson, M. C., & Lytle, L. A. (2009). Development and evaluation of a brief screener to estimate fast-food and beverage consumption among adolescents. Journal of the American Dietetic Association, 109(4), 730–734. doi:10.1016/j.jada.2008.12. 027. 24. Paeratakul, S., Ferdinand, D. P., Champagne, C. M., et al. (2003). Fast-food consumption among US adults and children: Dietary and nutrient intake profile. Journal of the American Dietetic Association, 103(10), 1332–1338. 25. Fowles, E. R., Timmerman, G. M., Bryant, M., et al. (2011). Eating at fast-food restaurants and dietary quality in low-income pregnant women. Western Journal of Nursing Research, 33(5), 630–651. doi:10.1177/0193945910389083. 26. Wilcox, S., Sharpe, P. A., Turner-McGrievy, G., et al. (2013). Frequency of consumption at fast-food restaurants is associated with dietary intake in overweight and obese women recruited from financially disadvantaged neighborhoods. Nutrition Research, 33(8), 636–646. doi:10.1016/j.nutres.2013.05.007. 27. Nguyen, B. T., & Powell, L. M. (2014). The impact of restaurant consumption among US adults: Effects on energy and nutrient
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
intakes. Public Health Nutrition, 17(11), 2445–2452. doi:10. 1017/S1368980014001153. Satia, J. A., Galanko, J. A., & Siega-Riz, A. M. (2004). Eating at fast-food restaurants is associated with dietary intake, demographic, psychosocial and behavioural factors among African Americans in North Carolina. Public Health Nutrition, 7(8), 1089–1096. doi:10.1079/PHN2004662. Bowman, S. A., & Vinyard, B. T. (2004). Fast food consumption of U.S. adults: Impact on energy and nutrient intakes and overweight status. Journal of the American College of Nutrition, 23(2), 163–168. Fowles, E. R., Stang, J., Bryant, M., et al. (2012). Stress, depression, social support, and eating habits reduce diet quality in the first trimester in low-income women: A pilot study. Journal of the Academy of Nutrition and Dietetics, 112(10), 1619–1625. doi:10.1016/j.jand.2012.07.002. Crawford, G. B., Khedkar, A., Flaws, J. A., et al. (2011). Depressive symptoms and self-reported fast-food intake in midlife women. Preventive Medicine, 52(3–4), 254–257. doi:10. 1016/j.ypmed.2011.01.006. Dave, J. M., An, L. C., Jeffery, R. W., et al. (2009). Relationship of attitudes toward fast food and frequency of fast-food intake in adults. Obesity, 17(6), 1164–1170. doi:10.1038/oby.2009.26. Smith, C., Gray, A. R., Fleming, E. A., et al. (2013). Characteristics of fast-food/takeaway-food and restaurant/cafe-food consumers among New Zealand adults. Public Health Nutrition,. doi:10.1017/S1368980013002681. Block, J. P., Scribner, R. A., & DeSalvo, K. B. (2004). Fast food, race/ethnicity, and income: A geographic analysis. American Journal of Preventive Medicine, 27(3), 211–217. doi:10.1016/j. amepre.2004.06.007. Thornton, L. E., Crawford, D. A., & Ball, K. (2010). Neighbourhood-socioeconomic variation in women’s diet: The role of nutrition environments. European Journal of Clinical Nutrition, 64(12), 1423–1432. doi:10.1038/ejcn.2010.174. Welch, N., McNaughton, S. A., Hunter, W., et al. (2009). Is the perception of time pressure a barrier to healthy eating and physical activity among women? Public Health Nutrition, 12(7), 888–895. doi:10.1017/S1368980008003066. Adler, N. E., & Ostrove, J. M. (1999). Socioeconomic status and health: What we know and what we don’t. Annals of the New York Academy of Sciences, 896, 3–15. Chang, M. W., Nitzke, S., Buist, D., et al. (2015). I am pregnant and want to do better but i can’t: Focus groups with low-income overweight and obese pregnant women. Maternal and Child Health Journal, 19(5), 1060–1070. doi:10.1007/s10995-0141605-x. Thomas, M., Vieten, C., Adler, N., et al. (2014). Potential for a stress reduction intervention to promote healthy gestational weight gain: Focus groups with low-income pregnant women. Womens Health Issues., 24(3), e305–e311. doi:10.1016/j.whi. 2014.02.004. Chang, M., Nitzke, S., Guilford, E., et al. (2008). Motivators and barriers to healthful eating and physical activity among low-income overweight and obese mothers. Journal of the American Dietetic Association, 108(6), 1023–1028. George, G. C., Milani, T. J., Hanss-Nuss, H., et al. (2005). Compliance with dietary guidelines and relationship to psychosocial factors in low-income women in late postpartum. Journal of the American Dietetic Association, 105(6), 916–926. doi:10.1016/j.jada.2005.03.009. Chang, M. W., Brown, R., Nitzke, S., et al. (2015). Stress, sleep, depression and dietary intakes among low-income overweight and obese pregnant women. Maternal and Child Health Journal, 19(5), 1047–1059. doi:10.1007/s10995-014-1604-y.
123
Matern Child Health J 43. Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24(4), 385–396. 44. Cox, J. L., Holden, J. M., & Sagovsky, R. (1987). Detection of postnatal depression. Development of the 10-item Edinburgh Postnatal Depression Scale. British Journal of Psychiatry, 150, 782–786. 45. Block, G., Gillespie, C., Rosenbaum, E. H., et al. (2000). A rapid food screener to assess fat and fruit and vegetable intake. American Journal of Preventive Medicine, 18(4), 284–288. 46. Box, G. E. P., & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society, 26(2), 211–252. 47. D’Agostino, R. B., Belanger, A., & D’Agostino, R. B, Jr. (1990). A suggestion for using powerful and informative tests of normality. The American Statistician, 44(4), 316–321. 48. Florez, K. R., Dubowitz, T., Ghosh-Dastidar, M. B., et al. (2015). Associations between depressive symptomatology, diet, and body mass index among participants in the Supplemental Nutrition Assistance Program. Journal of the Academy of Nutrition and Dietetics,. doi:10.1016/j.jand.2015.01.001. 49. Slater, J., Sevenhuysen, G., Edginton, B., et al. (2012). ‘Trying to make it all come together’: Structuration and employed mothers’ experience of family food provisioning in Canada. Health Promotion International, 27(3), 405–415. doi:10.1093/heapro/ dar037. 50. Devine, C. M., Connors, M. M., Sobal, J., et al. (2003). Sandwiching it in: spillover of work onto food choices and family roles in low- and moderate-income urban households. Social Science and Medicine, 56(3), 617–630. 51. Jabs, J., Devine, C. M., Bisogni, C. A., et al. (2007). Trying to find the quickest way: employed mothers’ constructions of time for food. Journal of Nutrition Education and Behavior, 39(1), 18–25. doi:10.1016/j.jneb.2006.08.011. 52. Beshara, M., Hutchinson, A., & Wilson, C. (2010). Preparing meals under time stress. The experience of working mothers. Appetite, 55(3), 695–700. doi:10.1016/j.appet.2010.10.003. 53. Rydell, S. A., Harnack, L. J., Oakes, J. M., et al. (2008). Why eat at fast-food restaurants: Reported reasons among frequent consumers. Journal of the American Dietetic Association, 108(12), 2066–2070. doi:10.1016/j.jada.2008.09.008. 54. Crawford, D., Ball, K., Mishra, G., et al. (2007). Which foodrelated behaviours are associated with healthier intakes of fruits and vegetables among women? Public Health Nutrition, 10(3), 256–265. doi:10.1017/S1368980007246798. 55. Cowan, C., & Cowan, P. A. (2000). When partners become parents: The big life change for couples. Mahwah, NJ: Lawrence Erlbaum Assocaites Publishers. 56. Doss, B. D., Rhoades, G. K., Stanley, S. M., et al. (2009). The effect of the transition to parenthood on relationship quality: An 8-year prospective study. Journal of Personality and Social Psychology, 96(3), 601–619. doi:10.1037/a0013969. 57. Charles, P., & Perreira, K. M. (2007). Intimate partner violence during pregnancy and 1-year post-partum. Journal of Family Violence, 22, 609–619. 58. Mitnick, D. M., Heyman, R. E., & Smith Slep, A. M. (2009). Changes in relationship satisfaction across the transition to parenthood: A meta-analysis. Journal of Family Psychology: Journal of the Division of Family Psychology of the American
123
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
70.
71.
Psychological Association., 23(6), 848–852. doi:10.1037/ a0017004. Stampfel, C. C., Chapman, D. A., & Alvarez, A. E. (2010). Intimate partner violence and posttraumatic stress disorder among high-risk women: Does pregnancy matter? Violence Against Women., 16, 426–443. Perren, S., von Wyl, A., Burgin, D., et al. (2005). Depressive symptoms and psychosocial stress across the transition to parenthood: Associations with parental psychopathology and child difficulty. Journal of Psychosomatic Obstetrics and Gynaecology, 26(3), 173–183. Blanck, H. M., Yaroch, A. L., Atienza, A. A., et al. (2009). Factors influencing lunchtime food choices among working Americans. Health Education & Behavior: The Official Publication of the Society for Public Health Education, 36(2), 289–301. doi:10.1177/1090198107303308. Jeffery, R. W., Linde, J. A., Simon, G. E., et al. (2009). Reported food choices in older women in relation to body mass index and depressive symptoms. Appetite, 52(1), 238–240. doi:10.1016/j. appet.2008.08.008. Kraschnewski, J. L., Chuang, C. H., Downs, D. S., et al. (2013). Association of prenatal physical activity and gestational weight gain: Results from the first baby study. Womens Health Issues, 23(4), e233–e238. doi:10.1016/j.whi.2013.04.004. Ferrari, R. M., & Siega-Riz, A. M. (2013). Provider advice about pregnancy weight gain and adequacy of weight gain. Maternal and Child Health Journal, 17(2), 256–264. doi:10.1007/s10995012-0969-z. Gould Rothberg, B. E., Magriples, U., Kershaw, T. S., et al. (2011). Gestational weight gain and subsequent postpartum weight loss among young, low-income, ethnic minority women. American Journal of Obstetrics and Gynecology, 204(1), 52 e1–52 e11. doi:10.1016/j.ajog.2010.08.028. Endres, L. K., Straub, H., McKinney, C., et al. (2015). Postpartum weight retention risk factors and relationship to obesity at 1 year. Obstetrics and Gynecology, 125(1), 144–152. doi:10.1097/ AOG.0000000000000565. Siega-Riz, A. M., Herring, A. H., Carrier, K., et al. (2010). Sociodemographic, perinatal, behavioral, and psychosocial predictors of weight retention at 3 and 12 months postpartum. Obesity, 18(10), 1996–2003. doi:10.1038/oby.2009.458. Ostbye, T., Krause, K. M., Lovelady, C. A., et al. (2009). Active mothers postpartum: A randomized controlled weight-loss intervention trial. American Journal of Preventive Medicine, 37(3), 173–180. doi:10.1016/j.amepre.2009.05.016. Phelan, S., Phipps, M. G., Abrams, B., et al. (2014). Does behavioral intervention in pregnancy reduce postpartum weight retention? Twelve-month outcomes of the fit for delivery randomized trial. American Journal of Clinical Nutrition, 99(2), 302–311. doi:10.3945/ajcn.113.070151. Amorim, A. R., Rossner, S., Neovius, M., et al. (2007). Does excess pregnancy weight gain constitute a major risk for increasing long-term BMI? Obesity, 15(5), 1278–1286. doi:10. 1038/oby.2007.149. Nehring, I., Schmoll, S., Beyerlein, A., et al. (2011). Gestational weight gain and long-term postpartum weight retention: A metaanalysis. American Journal of Clinical Nutrition, 94(5), 1225–1231. doi:10.3945/ajcn.111.015289.
