Psychosocial Determinants of Food Acquisition and Preparation in Low-Income,Urban African American Households

Abstract

Background. Psychosocial factors are important determinants of health behaviors and diet-related outcomes, yet relatively little work has explored their relation to food-purchasing and preparation behaviors in low-income populations. Aim. To evaluate the psychosocial factors associated with food-related behaviors. Methods. Cross-sectional data collected from 465 low-income African American adult caregivers in the baseline evaluation of the B’more Healthy Communities for Kids obesity prevention trial. Questionnaires were used to assess household sociodemographic characteristics, food sources frequently used, and food preparation and food acquisition behaviors. Multiple linear regression models explored the associations between caregiver psychosocial variables and food-related behaviors, controlling for caregivers’ age, sex, household income, household size, and food assistance participation. Results. Caregivers purchased prepared food at carry-outs on average 3.8 times (standard deviation [SD] = 4.6) within 30 days. Less healthy foods were acquired 2 times more frequently than healthier foods (p < .001). Higher food-related behavioral intention and self-efficacy scores were positively associated with healthier food acquisition (β = 0.7; 95% confidence interval [CI] [0.09, 1.4]; β = 0.04; 95% CI [0.02, 0.06]) and negatively associated with frequency of purchasing at prepared food sources (β = −0.4; 95% CI [−0.6, −0.2]; β = −0.5; 95% CI [−0.7, −0.3]), respectively. Higher nutrition knowledge was associated with lower frequency of purchasing food at prepared food venues (β = −0.7; 95% CI: [−1.2, −0.2]). Discussion. Our findings indicate a positive association between psychosocial determinants and healthier food acquisition and food preparation behaviors. Conclusion. Interventions that affect psychosocial factors (i.e., food-related behavioral intentions and self-efficacy) may have the potential to increase healthier food preparation and food-purchasing practices among low-income African American families.

Keywords

African American food acquisition food preparation intentions nutrition knowledge psychosocial factors race/ethnicity

Obesity remains one of the greatest challenges to public health, with 37.9% of U.S. adults classified as obese (Flegal et al., 2016) and low-income urban communities of color being disproportionally affected (48.5% African American vs. 37.0% European American) (Flegal et al., 2016; Ogden, Carroll, Kit, & Flegal, 2014). The multifactorial causes of obesity are well recognized, with diet and physical activity being the most proximal drivers of obesity (Bleich, Jones-Smith, Wolfson, Zhu, & Story, 2015). Despite recent findings showing a temporal improvement in diet quality among U.S. adults in recent years (Wilson, Reedy, & Krebs-Smith, 2016), African American adults did not increase their diet quality over time, demonstrating that disparities still exist among communities of color (Rehm, Peñalvo, Afshin, & Mozaffarian, 2016).

Food purchasing and food preparation are factors that are highly influenced by a person’s food environment, which may contribute to these disproportionate poor diet and obesity levels (Gordon-Larsen, 2014). The type of food source available has been correlated with the quality of food purchasing of the individual. For instance, a national study conducted in Australia found that individuals living in a neighborhood with higher availability of grocery stores and fruit and vegetable markets were associated with lower odds of fast-food purchasing (Thornton & Kavanagh, 2012). Another cross-sectional study conducted in low-income, predominantly African American neighborhoods in the city of Baltimore suggested that individuals who shopped more frequently at corner stores obtained unhealthier foods than individuals shopping at other food sources, after controlling for socioeconomic characteristics and means of transportation used (D’Angelo, Suratkar, Song, Stauffer, & Gittelsohn, 2011). American families have tended to cook less frequently and eat food from restaurants and carry-outs more frequently in the past decades (Smith, Ng, & Popkin, 2013). Prepared food sources, such as sit-down, fast-food, and carry-out restaurants, often provide high-calorie, high-fat, and high-sodium foods that are linked to increased risk of obesity and chronic diseases (French, Story, & Jeffery, 2001; Lee-Kwan et al., 2013; Wang et al., 2006). On a given day, fast-food restaurant consumption was associated with an additional total daily energy intake of 194 kcal among adults who participated in the National Health and Nutrition Examination Survey (2003–2010) (Nguyen & Powell, 2014). Conversely, adults with increased frequency of home food preparation are less likely to visit prepared food sources and more likely to consume fruits and vegetables (Larson, Neumark-Sztainer, Laska, & Story, 2011; Monsivais, Aggarwal, & Drewnowski, 2014).

Psychosocial factors are important determinants of health and eating behaviors (Bandura, 1977). Three common psychosocial factors in the health behavior literature include nutrition knowledge, behavioral intentions, and self-efficacy. Nutrition knowledge is the understanding of foods, their composition and their role in diet-related health outcomes; behavioral intentions refers to what a person plans to do in the future; and self-efficacy reflects how confident a person is in his or her ability to perform a certain behavior (Glanz, Rimer, & Viswanath, 2008). The theory of planned behavior and social cognitive theory, which includes these three factors, are used to explain and guide behavior changes and people’s interactions with their environments (Ajzen, 1991; Bandura, 2001; Glanz et al., 2008; Kramer et al., 2012; Lubans et al., 2010). Several studies have found improvements in the psychosocial factors, knowledge, and intentions, as well as diet, following behavior change interventions (Anderson, Winett, & Wojcik, 2007; Kramer et al., 2012; Luszczynska, Gibbons, Piko, & Tekozel, 2004; Suratkar et al., 2010). Among low-income African-American populations, there is still a limited understanding of the relation between adult psychosocial factors, and food-purchasing and food preparation patterns (Gustat et al., 2017; McGowan et al., 2016).

The purpose of our study was to identify the psychosocial factors influencing the food-purchasing (i.e., frequency of purchasing food in prepared food sources and frequency of healthy/unhealthy food acquisition) and food preparation (cooking methods/techniques) behaviors of adult caregivers in African American low-income urban communities in Baltimore City, Maryland. We hypothesized that higher level of adult psychosocial factors (e.g., intentions to eat healthy food, self-efficacy, and nutrition knowledge) would be associated with healthier food preparation and food-purchasing behaviors for the household. Specifically, we aimed to describe the following (a) the patterns of food purchasing and preparation, (b) the relation between psychosocial factors (self-efficacy, intentions, and knowledge) and healthy and unhealthy food-purchasing behavior, and (c) the relationship between psychosocial factors and food preparation methods among low-income, urban African American adult caregivers.

Method

Study Design

This analysis uses baseline data from the B’more Healthy Communities for Kids (BHCK) trial. BHCK was a multilevel multicomponent childhood obesity prevention study designed to increase the access to and demand for healthy and affordable foods in low-income neighborhoods in Baltimore City through integrated interventions with individuals, peer leaders, recreation centers, small food stores (corner stores), carry-out restaurants, wholesalers, communities, and policymakers (Gittelsohn et al., 2014). The BHCK parent intervention sought to improve the food environment by conducting point-of-purchase promotion of healthier food options in small food stores and carry-out restaurants and delivering nutrition education to children attending intervention recreation centers. The study was conducted in two waves over 5 years, with baseline evaluation taking place at the beginning of each wave: July 2013 to June 2014 (Wave 1) and August 2015 to January 2016 (Wave 2)—each wave in 14 zones. Baseline data from both waves are combined and presented in this article.

Participant Recruitment and Selection

The participants in this study were adult caregivers of a child between the ages of 10 and 14 years (the age range that attends after-school programs in the city and has greater autonomy to purchase foods in corner stores and carry-out restaurants before and after school). They were actively recruited from 28 low-income, predominantly African American zones that were defined as food deserts (Walker, Keane, & Burke, 2010). A list of potential caregivers was created, and they were screened for eligibility. Caregiver eligibility criteria included (a) being an adult (>18 years old) primary caregiver of a child between the ages of 10 and 14 years when recruited; (b) residing within a 1.5-mile radius of a neighborhood recreation center; and (c) having no intentions of moving within the next 2 years. We interviewed a total of 465 youths and their caregivers.

Data Collection and Management

Informed consent was gathered from the adults before the interview, and $20 in gift cards was provided as compensation. The caregiver interviews lasted 60 to 90 minutes. Data collection and data management procedures were identical in both waves. The data collectors, graduate students and staff members, underwent extensive training on instruments through a process of didactic presentation, role playing, observed interviewing, and certification. Data were checked for errors and missing information by the interviewer and a second research staff member following each interview. The data manager provided the data collectors with constructive feedback on completing the questionnaire during the course of data collection. Then, the data were entered and cleaned by a third research analyst staff member. The BHCK study and data collection materials were approved by the Johns Hopkins Bloomberg School of Public Health Institutional Review Board (IRB No. 00004203).

Description of Adult Impact Questionnaire

The survey instrument was a 176-item Adult Impact Questionnaire (AIQ) developed from similar instruments used in previous intervention trials and tailored to this population (Budd et al., 2015; Gittelsohn et al., 2006; Kramer et al., 2012; Suratkar et al., 2010). The questionnaire was finalized after pilot testing and assessment of face validity within the community (Surkan et al., 2011). The AIQ elicits information on household socio-demographic characteristics (e.g. caregiver education, marital status, employment status, household income, housing arrangement of the primary caregiver, and household participation in food assistance programs) as well as food sources, food acquisition, and psychosocial factors. Cronbach’s alpha was used to calculate internal consistency reliability for the scales.

For household frequency of purchasing at prepared food sources, the caregivers reported the number of times they purchased or got food from different food sources 30 days prior to the interview date (Trude et al., 2016; Vedovato et al., 2016). A list of 18 different food sources (e.g. farmers’ market, urban farm, street vendor, public market, corner store, supermarket, carry-out, restaurant, family/friends, etc.) was provided to assess purchasing frequency at each food source (Supplemental Appendix 1, available with the article online). Additionally, healthier/less healthy food household acquisition was determined based on how often the caregiver acquired selected foods over the past 30 days (e.g., “How many times did you get these foods?”). A list of 31 healthier and 23 less healthy foods was provided. Less healthy items were higher in fat and/or sugar. Examples of less healthy food items included whole milk, regular sodas, hot dog, bacon, sugary cereals, white bread, chips, cookies, ice cream, and ketchup. Healthier items were foods and drinks that were lower in fat and/or sugar, or were “light” or “diet” versions of less healthy foods and beverages. Examples of healthier items included water, pretzels, sugar-free fruit drink, yogurt, low-sugar cereal, fruits, and vegetables (Vedovato et al., 2016).

For household food preparation (i.e., cooking methods) behavior, the caregivers were asked to rank the top three most common cooking methods used when they prepared chicken, turkey (including ground turkey and turkey bacon), pork (including bacon), ground beef, fish, eggs, greens (excluding lettuce), and potatoes. The most common cooking methods included baking/broiling, boiling, pan frying in oil/fat, pan frying and draining, deep frying, grilling, steaming, cooking with cooking spray, microwaving, draining and rinsing, and raw. Information about the frequency of household food preparation in the 30 days prior to the interview date was also gathered.

The AIQ additionally assessed psychosocial factors: food-related self-efficacy, behavioral intentions, and knowledge, including label reading. Food related self-efficacy questions included perceptions regarding how easy or difficult it was to make healthy choices related to food preparation, purchasing, and consumption. Questions about food-related behavioral intentions enquired as to whether healthier options (promoted during the intervention) would be chosen the next time the respondents prepared, purchased, and consumed food. Eleven questions assessed food-related knowledge regarding fats, calories, and sugar content in food.

Adult Caregiver Scale Construction

Nutrition knowledge was assessed from questions that asked the respondents to identify the lower-fat, lower-calorie, higher-fiber, and lower-sugar options for 11 different food items. Each correct answer was given 1 point and each incorrect answer a 0. The responses were then summed to provide the nutrition knowledge score. This scale also included three additional questions associated with reading a food label.

Self-efficacy for healthy eating was based on 10 questions about the perceived ease or difficulty of healthy food purchasing, preparation, and consumption (scores were on a 3-point Likert-type scale, ranging from 3 for very easy through 0 for impossible). Questions related to the participants’ confidence in performing a certain behavior (e.g., difficulty regularly using 100% whole wheat bread) in the context of their lives right now.Behavioral intentions for healthy eating were based on nine questions where the participants were asked to hypothesize future food-related behaviors using a forced- choice format. An example question would be “The next time you make eggs, would you use . . . ?”—with answer choices of (a) cooking spray, (b) vegetable oil, or (c) shortening/butter/lard. The responses were graded by assigning 2 points to the healthiest option (in this example, cooking spray), 1 point to the second healthy option (vegetable oil), and 0 otherwise (shortening/butter/lard).

The household healthy food acquisition frequency score was an additive scale that included 31 different foods (and food groups) promoted as part of the BHCK (e.g., baked chips, low-fat milk, high-fiber cereals, fruits, and vegetables). The respondents were asked to recall the number of times they had acquired each food in the previous 30 days. For example, if the participant had purchased low-fat milk four times (roughly once per week) and high-fiber cereal four times, then those numbers were summed with the frequency of purchasing for the other 29 foods for the final score.

The household healthy food preparation score reflected the healthfulness of the cooking methods used based on the top three most common cooking methods used when the respondent prepared food in the home. Cooking methods were assigned scores as follows: deep fried or pan fried with oil (−2); pan fried and drained or use of cooking spray (−1); not prepared in the past 30 days (0); pan fried, drained, and rinsed with hot water (+1); broiled/baked, grilled, or steamed, or boiled, raw, or microwaved (+2). The top three methods were then weighted taking into account the following proportions: 60% (for the first method most commonly used), 30% (second method), and 10% (third method) (Suratkar et al., 2010). The scores for the eight foods were calculated separately. For example, if eggs were most commonly pan fried, second most commonly boiled, and third most commonly cooked with cooking spray, the score was calculated as (0.6 × −2) + (0.3 × 2) + (0.1 × −1), as an indicator of the overall healthiness of egg preparation. A higher score represented healthier preparation methods.

The household’s frequency of purchasing at a prepared food source score was the total number of times the respondent reported buying prepared food at a local carry-out restaurant, at a fast-food restaurant, at a sit-down restaurant, from a street food vendor, or from a public market (including deliveries) in the past 30 days. Scores ranged from 0 to 94, with a mean of 9.6 and standard deviation (SD) = 9.4.

Data Analysis

Statistical analysis of the data was conducted using the software STATA 13.1 (College Station, TX, 2013). Two-tailed Pearson chi-square and Kendall rank correlation tests were used to examine the associations between frequency of caregiver characteristics (sex, age, income, education level, food assistance, and weight status) by tertiles of healthy food acquisition frequency (low frequency, mean = 21.5 ± 6.4; medium frequency, mean = 38.9 ± 5.3; high frequency, mean = 80.2 ± 36.6). On the basis of our conceptual framework (Figure 1) and hypothesis, we conducted multiple linear regression models to analyze the association between caregiver psychosocial characteristics (healthy eating self-efficacy, intentions, and knowledge) and food-related behaviors (frequency of healthy food purchasing, frequency of purchasing at a prepared food source, and healthiness of cooking methods). Potentially confounding socio-demographic variables (caregiver’s age and sex, household income, number of individuals living in the household, and participation in a food assistance program) were included as covariates in all the adjusted models. Assumptions of normally distributed residuals were investigated for each outcome of interest (frequency of healthy food acquisition, healthiness of cooking method, and frequency of purchasing at prepared food sources). Based on the Shapiro–Wilk test, the assumptions of normality for frequency of healthy food acquisition and frequency of purchasing at prepared food sources were violated. Thus, we conducted the regressions using the log-transformed frequency of healthy food acquisition and square root of the frequency of purchasing at prepared food sources to fix the residual distribution. To improve interpretation of the results, regression results are presented in their nontransformed form, but the p value is from the transformed regressions. We calculated the variance inflation factor for each model to check for collinearity, which were all less than 10. For all the models, we performed the specification error test to check whether our model included all the relevant predictors and if their linear combination was sufficient, indicating no specification error. For all the analyses, statistical significance was defined by a p value of <.05.

Figure 1.

Conceptual framework to understand the relationship between psychosocial factors and food-related behaviors.

Results

Sample Characteristics

In total, 77% of the caregivers interviewed reported purchasing healthy food/beverage items at least 60 times in the previous 30 days. Ninety percent of the caregivers were female, with a mean age of 39 (SD = ±9.5) and mean body mass index of 33.6 kg/m² (Table 1). Forty percent of the caregivers interviewed had a high-school degree, and most had a mean annual income between $20,000 and $30,000. More individuals in the low tertile for healthy food acquisition frequency were low income and had lower educational levels than those in the medium and high tertiles (Kendall correlation: 0.04 and 0.041, respectively).

Table 1.

Sociodemographic Baseline Characteristics of the BHCK Adult Sample by Levels of Healthy Food Acquisition (n = 465).

		Healthy food-purchasing frequency (tertiles)
Individual characteristics of caregiver	Total, n (%)	Low,^a n (%)	Medium,^b n (%)	High,^c n (%)
Sex
Male	45 (9.7)	11 (2.4)	15 (3.2)	19 (4.0)
Female	420 (90.3)	156 (33.5)	129 (27.7)	135 (29.0)
Age (years)
20–29	56 (12.1)	25 (5.4)	15 (3.2)	16 (3.5)
30–39	241 (52.0)	87 (18.8)	74 (16.0)	80 (17.3)
40–49	102 (22.0)	35 (7.5)	34 (7.3)	33 (7.1)
>50	64 (13.9)	19 (4.1)	20 (4.3)	25 (5.4)
Annual income (US$)
0–10,000	107 (23.0)	46 (9.9)*	31 (6.7)**	30 (6.5)**
10,001–20,000	102 (21.9)	37 (7.9)	32 (6.8)	33 (7.1)
20,001–30,000	86 (18.5)	33 (7.1)	24 (5.1)	29 (6.3)
>30,000	170 (36.6)	51 (10.9)	57 (12.3)	62 (13.3)
Caregiver education level
<High school	79 (17.0)	42 (9.0)*	18 (3.9)**	19 (4.1)**
High school	185 (39.8)	68 (14.6)	57 (12.3)	60 (12.9)
>High school	201 (43.2)	57 (12.3)	69 (14.8)	75 (16.1)
Food assistance participation
WIC	102 (21.9)	27 (5.8)	34 (7.3)	41 (8.8)
SNAP	340 (73.1)	127 (27.3)	104 (31.0)	154 (33.1)
Body mass index classification^d
Normal weight	60 (13.1)	21 (4.6)	18 (3.9)	21 (4.6)
Overweight	88 (19.2)	34 (7.4)	28 (6.1)	26 (5.7)
Obese	309 (67.6)	108 (23.6)	96 (21.0)	152 (32.3)

Note. BHCK = B’more Healthy Communities for Kids; WIC = The Special Supplemental Nutrition Program for Women, Infants, and Children; SNAP = Supplemental Nutrition Assistance Program.

Low tertile of healthy food purchasing: mean frequency 21.5 (±6.4), range 3-30. ^bMedium tertile of healthy food purchasing: mean frequency 39.9 (±5.4), range 31-49. ^cHigh tertile of healthy food purchasing: mean frequency 80 (±36.6), range 50-350. ^dAccording to World Health Organization cutoff points.

*,**

Are statistically different when comparing adult characteristics between tertiles using Pearson chi square (two-sided test) or Kendall rank correlation coefficient (ordinal variables).

Patterns of Food-Purchasing and Preparation Methods

Food Purchasing

On average, the foods bought most often within the 30-day period were high-fat and high-sugar snack foods, such as regular soda (5.0, SD = 8.4), fruit-flavored drinks (4.3, SD = 8.3), and regular potato chips (4.1, SD = 6.6) (Table 2). The healthier items most often acquired included water (3.9, SD = 6.7), bananas (3.1, SD = 4.2), and fresh vegetables (3.1, SD = 3.7). Less healthy food options were acquired two times more frequently than fruits and vegetables (p < .01).

Table 2.

Mean Frequency of Food Acquisition Comparing Healthier and Less Healthy Food Items.

Healthier food item (Mean ± SD)	Less healthy food item (Mean ± SD)	p ^a
Water (3.9 ± 6.7)	Regular soda (4.9 ± 8.4)	.01
Pretzels (1.2 ± 3.1)	Regular potato chips (4.2 ± 6.6)	<.001
Sugar-free fruit drink (0.8 ± 1.7)	Fruit drink (4.2 ± 8.3)	<.001
Yogurt (1.9 ± 3.2)	Ice cream (2.3 ± 4.6)	.17
Low-sugar cereal (1.3 ± 1.5)	Sugary cereal (2.3 ± 2.6)	<.001
Total daily acquisition of fruits and vegetables^b (0.68 ± 0.6)	Total daily acquisition of less healthy foods^c (1.3 ± 1.1)	<.001

Two-sided t test comparing the means between the groups. ^bTotal frequency of fruits and vegetables acquisition (100% fruit juice, apples, bananas, oranges, other fresh fruits, frozen fruit, canned fruit, fresh vegetables, frozen vegetables, and canned vegetables) divided by 30 days. ^cTotal frequency of less healthy food acquisition (whole milk, regular soda, fruit drink, sweet ice tea, tuna in oil, pork hotdog, baked beans, sugary cereal, white bread, sweet oats, regular chips, cookie, candy, ice cream, popsicles, butter, oil, regular butter, mayo) divided by 30 days.

The household healthy food acquisition frequency scores ranged from 3 to 350, with a mean of 46.3 (SD = 32.9, α = .81).

Food Preparation

The most common cooking methods utilized by low-income African Americans in Baltimore were baking (28.1%), boiling (18.4%), and pan frying with oil or fat (16.7%) (Table 3). Raw (0.5%) and pan frying then draining and/or rinsing (0.8%) were the least common methods of preparation used for the eight foods listed. Frequency of the cooking method used varied by the food being prepared. For example, when preparing ground beef, the participants usually pan fried and drained it (51%)—one of the three most common cooking methods. Fish and chicken were usually baked, and eggs were either pan fried in oil/fat or boiled. Greens, for example, collard greens or spinach, were boiled or steamed when prepared at home. Cooking spray was mainly used when making eggs (12%)—one of the three most common preparation methods.

Table 3.

Main Methods of Food Preparation in the Past 30 Days of Eight Foods Selected for Data Collection, n = 465.

Food item	Baking/broiling (n)	Boiling (n)	Pan frying in oil/fat (n)	Pan frying and draining (n)	Deep frying (n)	Grilling (n)	Steaming (n)	Cooking spray (n)	Microwaving (n)	Draining and rinsing (n)	Raw (n)
Ground beef	133	9	44	241	4	33	6	6	0	19	0
Chicken	373	59	90	72	161	130	10	7	4	3	0
Pork^a	166	17	67	148	27	25	3	2	17	2	0
Fish^b	265	4	107	58	87	40	12	9	1	2	0
Turkey	167	15	57	128	2	20	5	12	15	8	0
Potatoes	200	253	115	38	47	3	19	4	23	3	2
Eggs	11	250	287	29	4	1	5	58	8	1	0
Greens^c	13	263	25	6	0	4	129	3	12	0	23
Total^d	1,328	870	792	720	332	256	189	101	80	38	25
Frequency (%)	28.1	18.4	16.7	15.2	7.0	5.4	3.9	2.1	1.7	0.8	0.5

Note. This table represents the combined preparation frequencies for each method for the sample.

Included bacon and sausage. ^bIncluded canned tuna. ^cExcluded lettuce. Cumulative frequency of cooking methods used.

Respondents’ food preparation scores ranged from −1 to 2.1, with a mean of −0.5 and SD = 0.8.

Prepared Food Purchasing

During the past 30 days, prepared food was purchased on average 3.8 times from fast-food restaurants (SD = 4.6), 3.1 times at carry-outs (SD = 4.7), 2.3 times from a public market (SD = 3.9), and 0.5 times from a street food vendor (SD = 1.9). The household’s frequency of purchasing at a prepared food source scores ranged from 0 to 94, with a mean of 9.6 and SD = 9.4.

Psychosocial Correlates of Household Food-Related Behaviors

Nutrition knowledge scores ranged from 0 to 11, with a mean of 6.9 (SD = 1.8, Cronbach’s α = .44). Self-efficacy for healthy eating scores ranged from 11 to 30, with a mean of 24.8 (SD = 3.8, α = .68). Behavioral intentions for healthy eating scores ranged from 1 to 18, with a mean of 9.7 (SD = 4.2, α = .71) (Table 4).

Table 4.

Psychosocial Factors Determinant of Household Food-Related Behavior.

Adult psychosocial factors	Healthy food acquisition score	Healthy food preparation methods score	Frequency of purchasing at prepared food sources
Adult psychosocial factors	β [95% CI]	β [95% CI]	β [95% CI]
Nutrition knowledge	0.01 [−1.7, 1.7]	0.03 [−0.02, 0.07]	−0.7 [−1.2, −0.2]*
Food-related self-efficacy	0.4 [−0.4, 1.2]	0.04 [0.02, 0.06]*	−0.5 [−0.7, −0.3]*
Intentions for healthy eating	0.7 [0.09, 1.4]*	0.04 [0.02, 0.06]*	−0.4 [−0.6, −0.2]*

Note. Multiple linear regression analysis on food-related behaviors. Adjusted model for caregiver’s age and sex, number of individuals living in the household, Supplemental Nutrition Assistance Program recipient household, and caregiver education level. Food knowledge ranged from 0 to 11 points; intentions for healthy eating ranged from 1 to 18 points; food-related self-efficacy ranged from 11 to 30 points.

p < .05.

Higher food-related behavioral intention scores were marginally significantly associated with healthier food acquisition (β = 0.7; 95% CI [0.09, 1.4]; p = .002), healthier food preparation methods (β = 0.04; 95% CI [0.02, 0.06]; p < .001), and lower frequency of purchasing at prepared food sources (β = −0.4; 95% CI [−0.6, −0.2]; p = .003). Greater food-related self-efficacy was positively associated with healthy food preparation methods (β = 0.04; 95% CI [0.02, 0.06]; p < .001) and negatively associated with purchasing at prepared food sources (β = −0.5; 95% CI [−0.7, −0.3]; p < .001). Higher nutrition knowledge was only associated with lower frequency of purchasing at prepared food sources (β = −0.7; 95% CI [−1.2, −0.2]; p = .007).

Discussion

This study is one of the first to examine the relationships between healthy food preparation and acquisition and adults’ psychosocial characteristics among low-income African Americans in Baltimore City. Our findings indicate a positive association between psychosocial determinants and healthier food acquisition and food preparation behaviors. Higher behavioral intentions and self-efficacy were associated with healthier food preparation methods and lower frequency of purchasing at prepared food venues. Higher intentions for healthy eating was also associated with healthier food acquisition, whereas higher nutrition knowledge was associated with only lower frequency of purchasing at prepared food venues. These findings support the literature that finds nutrition knowledge to be more distal to behavior change than intentions or self-efficacy (Fila & Smith, 2006; Godin & Kok, 1996), informed by the theory of planned behavior (Ajzen, 2005).

We found a positive association between food-related behavioral intentions and healthier food acquisition and food preparation. These findings are similar to those of other studies in Baltimore (Gittelsohn et al., 2006). For example, a cross-sectional study among low-income African Americans found a significant positive association between adult food intentions and healthier food preparation methods (Suratkar et al., 2010). That is, participants from that study who were characterized as food insecure as well as having higher intentions for healthy eating were more likely to have higher healthy food preparation scores (Suratkar et al., 2010). These results also mirror results from a study in Florida with a similar population of low-income African Americans, of older age, for whom intentions were found to be associated with higher consumption of fruits and vegetables (O’Neal et al., 2014).

Our results also suggest that adults with greater levels of self-efficacy are more likely to use healthier food preparation methods. These results align with social cognitive theory and the theory of planned behavior in that greater self-efficacy would be expected to increase behavior change (Ajzen, 2005; Bandura, 2001; Glanz et al., 2008)). A cross-sectional analysis has also hypothesized that low-income adults may be more likely to prepare healthier meals if there is an improvement in cooking skills, confidence, and knowledge. (Smith et al., 2013). Our results provide support for obesity interventions and efforts to increase efficacy through promoting healthier food preparation behaviors among adults.

Our study found that adults with more nutrition knowledge are less likely to purchase food at prepared food sources. However, there was no association between nutrition knowledge and food preparation methods or food acquisition. In another study with African American adults and children, however, nutrition knowledge was positively significantly associated with the likelihood of home food preparation (Kramer et al., 2012). The low reliability of the nutrition knowledge scale may have contributed to the nonsignificant results observed with that measure in our study. Another potential reason for the nonsignificant results with other food-related behaviors is that nutrition knowledge questions were not contextualized in terms of the respondents’ lives, as it was done for self-efficacy and intentions.

Our research supports the previous literature that psychosocial factors, more specifically food-related behavioral intentions and self-efficacy, have the potential to increase healthier food-purchasing and healthier food preparation practices (McAlister, Perry, & Parcel, 2008).

Limitations

This study has several limitations. First, the cross-sectional nature of the study precludes us from understanding the causal associations. Second, the data for analysis were self-reported, which may have been influenced by social desirability as well as error in recalling standardized sizes and the quality of the food items purchased. Third, our survey was administered to caregivers under the assumption that they purchase or acquire most of the food and cook for their family members. However, some caregivers may not be the primary food purchasers for their households. Fourth, we only investigated the frequency of food being purchased at different types of food venues and did not take into consideration the quality or quantity of the acquired food. Fifth, our survey included a predefined list of food preparation methods, which did not allow the participants from reporting other food preparation methods. Finally, although we recognize that individuals may also purchase prepared foods from grocery stores/supermarkets, our instrument did not capture the frequency of prepared and nonprepared foods being acquired from every food source. Therefore, we only included in our analysis food sources that sell primarily prepared foods.

Implications for Research and Practice

The current study suggests that higher healthy food intentions and self-efficacy are associated with healthier food preparation methods and purchases in low-income African American households. Changes in food-related behavior intentions could result in healthier adults, as well as healthier youth because these behaviors may be learned by children (Kramer et al., 2012; Surkan et al., 2011). Interventions and programs aimed at promoting healthier food purchase and preparation could improve adults’ self-efficacy and intentions by providing in-store taste tests of healthier prepared foods and community cooking classes (Liberato, Bailie, & Brimblecombe, 2014). Whereas most public health nutrition messages aim specifically at improving nutrition knowledge, our results demonstrated the importance of using experiential learning approaches to also improve self-efficacy and intentions, as these psychosocial factors have a stronger correlation with food-related behaviors. It is worth noting that various external elements (food availability, food quality, habit, cooking skills, food preferences, etc.) can affect these psychosocial factors. Thus, exploring these elements, in future interventions may help improve behavioral intentions and increase opportunities for behavior change. Further research should also explore the time available for preparing food in the household and the availability of healthy food resources, which could provide more insight into the associations between quantities of prepared foods and healthy food purchasing. Given that American families get one third of their daily calories from foods eaten outside the household and that the amount of time available for and frequency of cooking have decreased in the past decades (Smith et al., 2013), it is imperative that future programs and policies explore ways to improve the quality of prepared foods offered and that low-fat cooking methods are used when preparing food in the home. We found that improving psychosocial factors (food intentions and self-efficacy) is important to improve food-related behaviors and, ultimately, diet quality among low-income African American adults.

Supplemental Material

HEB760686_Supplemental_Appendix_1 – Supplemental material for Psychosocial Determinants of Food Acquisition and Preparation in Low-Income, Urban African American Households

Supplemental material, HEB760686_Supplemental_Appendix_1 for Psychosocial Determinants of Food Acquisition and Preparation in Low-Income, Urban African American Households by JaWanna L. Henry, Angela C. B. Trude, Pamela J. Surkan, Elizabeth Anderson Steeves, Laura C. Hopkins, and Joel Gittelsohn inHealth Education & Behavior

Footnotes

Acknowledgements

We would like to thank the families interviewed and the following students, staff, and volunteers who assisted in the BHCK data collection: Cara Shipley, Kelleigh Eastman, Melissa Sattler, Jenny Brooks, Selma Pourzal, Teresa Schwendler, Anna Kharmats, Sarah Rastatter, Kate Perepezko, Lisa Poirier, Thomas Eckmann, Maria Jose Mejia, Yeeli Mui, Priscila Sato, Bengucan Gunen, Courtney Turner, Whitney Kim, Shruti Patel, Ellen Sheehan, Ryan Wooley, Donna Dennis, Elizabeth Chen, Kiara James, Latecia Williams, and Nandita Krishnan.

Authors’ Note

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the National Institute of Child Health and Human Development (NICHD), or the Office of Behavioral and Social Sciences Research.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research reported in this publication was supported by the Global Obesity Prevention Center (GOPC) at Johns Hopkins, the Eunice Kennedy Shriver National Institute of CHild Health and Human Development (NICHD), and the Office of the Director, National Institutes of Health (OD) under award number U54HD070725. A.C.B.T. is supported by a doctoral fellowship from CNPq (259316/2013-7).

Supplemental Material

Supplemental Appendix 1 is available in the online version of this article at .

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