Content area
Research that examines associations between environments and behavior is often cross-sectional, as is most research based on a social ecologic approach [7-10]. [...]the few studies that examine these associations longitudinally rarely examine multiple behavior settings or microenvironments, such as homes and workplaces, simultaneously [11-13]. [...]for reporting results, we will focus on the final most complex model (Model 4).
Background
Ecologic models are widely acknowledged as powerful tools for understanding behaviors that contribute to obesity [1, 2]. Dietary behaviors, such as fat intake, can be explained by a complex interplay of biological, psychological, cultural and social factors, combined with behavior settings, organizational and community contexts, and public policies [1, 3, 4]. This complexity is difficult to study cross-sectionally and even more so longitudinally [5, 6]. Research that examines associations between environments and behavior is often cross-sectional, as is most research based on a social ecologic approach [7-10]. Moreover, the few studies that examine these associations longitudinally rarely examine multiple behavior settings or microenvironments, such as homes and workplaces, simultaneously [11-13].
The current study explores how dietary fat intake in high-risk midlife adults living in the rural south is influenced by three behavior settings over time. We operationalized the Social Ecological Model for healthy eating by focusing on physical and social aspects of three priority environments in combination with individual factors [14]. Figure 1 presents this conceptual model and includes individual and environmental factors, along with social and physical aspects of home, workplace and church environments. Of particular note, all variables are considered dynamic as they may vary over time. This conceptualization is consistent with the notion of reciprocal determinism from social cognitive theory [15] and the bidirectional and dynamic relationships implied in ecologic models of behavior [14, 16-18]. [ Table Omitted - see PDF ]
Table 1
Baseline demographics (N?=?245)
Number
Percent
Gender
Male
105
42.9
Female
140
57.1
Age
<?50
97
39.6
50-60
101
41.2
??60
47
19.2
Race
White
130
53.1
Black
115
46.9
Marital status
Married/living w/ partner
155
64.6
Separated/divorced
48
20.0
Widowed
13
5.4
Never married
24
10.0
Education
<?HS graduate
28
11.5
HS/GED
72
29.5
Some college
81
33.2
College graduate
44
18.0
Post grad/ prof. degree
19
7.8
Weight status at baseline
Under/normal weight
31
12.9
Overweight
83
34.4
Obese
127
52.7
Smoking status
Daily smoker
75
30.9
Non-daily smoker
17
7.0
Non-smoker
151
62.1
Chronic disease (% yes)
High blood pressure
145
59.4
Coronary heart disease
18
7.5
Stroke
6
2.5
Diabetes
63
26.1
High cholesterol
104
43.7
Description of measures/variables
The conceptual framework presented in Fig. 2 shows the relationship between personal and environmental variables and behavior. [ Table Omitted - see PDF ]
Table 2
Change in individual and environmental variables (N?=?245)
BL
6 months FUP
12 months FUP
Individual Variables
Mean
SD
Mean
SD
Mean
SD
p-value
Fat intake (% of calories from fat)
33.55
2.08
35.59
2.56
35.08
2.51
<0.0001
?BMI (kg/m2)
31.46
6.59
30.99
6.23
30.96
6.14
0.02
Self-efficacy healthy eating (scale 1-5)
3.65
0.87
3.82
0.71
3.75
0.80
0.07
Home Environment
Physical
F&V inventory (out of 7)
3.87
1.61
4.45
1.43
4.02
1.35
0.01
Fat items inventory (out of 3)
2.10
1.15
1.61
1.01
1.65
1.06
<0.0001
Social
Family support for healthy eating (scale 1-4)
1.89
0.80
1.83
0.82
1.78
0.79
0.07
Work Environment (of those working outside the home)
Physical
Lunch facilities (out of 4)
2.80
1.13
Healthy foods available (yes reported)
N
%
N
%
p-value
- cafeteria
53
32.52
45
28.30
0.86
- vending machine
62
38.27
80
51.28
<0.001
- healthy foods at meetings/events
80
51.28
71
47.65
0.11
Health eating programs (yes reported)
- nutrition/healthy eating program
41
24.85
51
32.08
0.11
- weight loss program
32
19.51
44
27.67
<0.001
Social
Mean
SD
Mean
SD
Mean
SD
Social support for healthy eating (scale 1-4)
1.68
0.77
1.89
0.92
1.78
0.87
0.08
Church Environment (of those attending church at least a few times per year)
Physical
Healthy eating messages (yes reported)
N
%
N
%
- sermon healthy eating
106
49.53
114
55.07
0.33
- church bulletin/newsletter
103
48.58
108
51.43
0.47
Healthy eating programs (yes reported)
42
19.91
43
20.77
0.14
Mean
SD
Mean
SD
Healthy foods available (out of 4)
1.75
0.56
1.88
0.55
1.000
Social
Social support for healthy eating (scale 1-4)
1.40
0.55
1.50
0.68
1.48
0.62
0.07
In the home environment, the household inventories of fruits and vegetables increased significantly over 12 months, with a slight increase from 6 to 12 months. The fat items inventory decreased with a similar pattern, with an average decrease of 0.45 items at the 12 month follow-up. Family social support for healthy eating was not very high (around 1.9 on a 4 point scale) and did not change significantly over time.
In the work environment, changes were significant for the availability of programs to support healthy eating and for one of three indicators of access to healthy food at work. The social support from co-workers was similar to that from family members and did not change significantly within the time frame of this study.
The church environment for healthy eating did not change. There was stability in the number of people reporting healthy eating messages at church (around 50 %) as well as healthy foods available, and healthy eating programs offered (only around 20 %). Social support at church was lower (around 1.5 on a 4 point scale) than for home and work environment and did not change over time.
Factors of baseline fat intake and change in fat intake over time-Hierarchical Linear Growth Model results
The unconditional growth model (Model 1) estimates the average fat intake at baseline and the average change over time. The results show a significant increase in fat intake over time of 1.25 % per year when not including any variables beyond time.
Model fit improved for each subsequent model. Hence, for reporting results, we will focus on the final most complex model (Model 4). It explained 25.6 % of the variance at baseline. The unexplained variance in the baseline differences was still statistically significant, indicating that there are other factors that can explain differences in fat intake. About 16.7 % of the variance in change over time was accounted for and the unexplained variance was also statistically significant. Furthermore, the model explained 22.8 % over the inter-individual variance; the remaining variance was statistically significant. The residuals for the final model were normally distributed.
The final model estimated the adjusted mean fat intake at baseline was at 35.67 % of calories. Table 3 shows that, on average, holding all other things constant, the fat intake increased by 1.55 % per year due to circumstances not modeled. However, this increase was not statistically significant at the 0.05 level, indicating that change over time is explained by the included time-varying predictors as well as cross-level interactions between individual factors and time. The final growth model gives insight into fat intake cross-sectionally, as well change of fat intake over time. [ Table Omitted - see PDF ]
Table 3
HLM Results (N?=?229)
Model 1
Model 2
Model 3
Model 4
Fixed effects
Intercept
33.73***
35.46***
35.46***
35.67***
Level 1-time-varying variables
Time (in years)
1.25***
1.67***
1.81***
1.55[dagger]
Individual
BMI
?0.02
?0.03
Home environment
F&V inventory
0.12*
0.12[dagger]
Fat items inventory
0.20*
0.19*
Level 2-time-invariant variables
Individual
Age (0?=?40 years)
?0.02
0.0004
?0.002
Gender (0?=?male)
?0.55
?0.41
?0.35
Education (0?=?high school/less)
0.25
0.35
0.34
Self-efficacy healthy eating
?0.35*
?0.37*
Home environment
Family support healthy eating
?0.01
?0.01
Church environment
Church social support
?0.12
?0.11
Church messages
0.64*
0.88*
Church healthy food
?1.18**
?1.13*
Church programs
0.32
0.19
Work environment
Work social support
0.43
0.20
Work lunch facilities
0.04
?0.24
Work healthy foods
?0.24
0.37
Work programs
?0.06
?0.65
Cross-level Interactions-predicting change in fat intake over time
Individual
Time*Age
?0.05**
?0.05**
?0.05**
Time*Gender
0.70*
0.67*
0.62*
Time*Education
?0.38
?0.46
?0.50
Time*Self-efficacy healthy eating
0.06
Home environment
Time*Family social support
0.02
Church environment
Time*Church social support
?0.01
Time*Church messages
?0.46
Time*Church healthy food
?0.05
Time*Church programs
0.36
Work environment
Time*Work social support
0.35
Time*Work lunch facilities
0.40
Time*Work healthy foods
?0.90*
Time*Work programs
?0.86*
Time*Work healthy foods change
0.63[dagger]
Time*Work programs change
?0.24
Random effects
??00 (intercept)
2.09***
2.13***
1.67***
1.68***
??11 (Time)
0.78*
0.59*
0.74*
0.65*
??2
3.28***
3.25***
3.19***
3.19***
Model fit
?Reduction in ?00
7.4 %
5.7 %
26.0 %
25.6 %
?Reduction in ?11
24.4 %
5.1 %
16.7 %
?Reduction in ?2
20.6 %
21.3 %
22.8 %
22.8 %
Deviance
3170.8
3165.0
2974.0
2947.3
AIC
3176.8
3171.1
2980.0
2953.3
BIC
3187.3
3181.5
2990.3
2963.6
Note: All models account for clustering of participants in counties. [dagger] p?<?0.10 * p?<?0.05 ** p?<?0.01 *** p?<?0.0001
Factors of baseline fat intake
There were several individual variables of baseline fat intake modeled. However, only self-efficacy for healthy eating was significantly related to fat intake, with higher self-efficacy resulting in lower fat intake (??=??0.37, SE?=?0.17). Age, gender, and education did not explain differences of fat intake at baseline.
In the home environment, the fat items inventory was a significant factor in explaining fat intake. While the number of unhealthy high-fat items decreased over time (Table 2), having a larger variety of unhealthy high-fat items in the home was related to higher fat intake and vice versa (??=?0.19, SE?=?0.09). Neither the fruit and vegetable inventory nor family support for healthy eating is predictive of fat intake.
Some of the church environment variables predicted fat intake. Participants who were exposed to messages about healthy eating at church had a higher fat intake than those who did not hear such messages at church or did not attend church (??=?0.87, SE?=?0.34). Those reporting better access to healthy food at church had a significantly lower fat intake (??=??1.13, SE?=?0.47).
Neither physical nor social components of the work environment had a significant effect on the average fat intake.
Factors of change in fat intake
The final model also shows which variables have an impact on change in fat intake. When looking at individual variables, age (??=??0.05, SE?=?0.02) and gender (??=?0.62, SE?=?0.30) have a significant effect on the change in fat intake over time. Older participants increased their fat intake over time, but less so than younger ones. In addition, women increased their fat intake more than men, by 0.62 % over 12 months. Education and self-efficacy for healthy eating did not predict change in fat intake.
In the home environment, the fat item inventory (as mentioned earlier) had an impact on fat intake (??=?0.19, SE?=?0.09) with having more unhealthy items in the home predicting higher fat intake. No other home environment factors were significantly influencing change in fat intake.
In the work environment, there was a positive influence of having healthy foods available (??=??0.90, SE?=?0.38) on the change in fat intake. Participants who had access to healthy foods at work had a lower increase in fat intake (by 0.90 % points over 12 months) than those who did not. In addition, participants who had access to work programs regarding healthy eating had a lower increase in fat intake (??=??0.86, SE?=?0.41). Participants who had both access to healthy foods at work and work programs for healthy eating decreased their fat intake over time.
None of the variables from the church environment explained change in fat intake.
Results at a glance
Figure 2 shows all factors investigated in this study. The diagram shows which environments and also which variables within each environment changed over time. Significant determinants of fat intake and change in fat intake are noted as well.
Discussion
This longitudinal observational study offers insight into determinants of fat intake in general as well as change in fat intake in a rural population in the Southeast region of the US. Overall, the participants increased their fat intake over the 12 month period of the study. Several interesting observations can be made when looking at the change in individual factors as well as different environmental factors of fat intake over the period of 1 year. Individually, there was an average decrease in BMI from self-report. However, the physical environments of the home (inventories of fruits and vegetables as well as unhealthy snacks) changed significantly over the 12 month study period, which might have increased the intake of low-energy dense foods. The church environments were stable both in physical and social factors related to fat intake. There were some notable changes in the work environments: an increase in healthy foods in vending machines and weight loss programs at work sites.
The multilevel model allows us to draw conclusions on how these factors interact and help explain fat intake in general as well as change in fat intake over time.
When looking at fat intake at baseline, we found an impact of the home and the church environment, but not the work environment. A recent research study conducted in Australia suggests that food environments near work places might influence eating behaviors [53]. It would be meaningful to investigate impact of the food environment at the work facility in conjunction with access to food in the workplace neighborhood on eating behaviors, distinguishing between food prepared away from home from food prepared at home and taken to work due to significant nutritional disadvantages of food prepared away from home [54].
Not surprisingly, we found having fatty items in the home is related to higher fat intake. This means that changing the home environment by reducing the number (and amount) of high-fat items has a positive impact on the eating behavior resulting in lower fat intake. This finding confirms similar findings from many other studies [4, 55].
In addition, higher self-efficacy for healthy eating is associated with lower fat intake. Social support in all three settings was relatively modest and we did not see an impact for family, church or workplace social support on fat intake. While interventions should include components aimed at increasing self-efficacy for healthy eating, it would be also of interest to see if collective social support [56] might have an impact on fat intake rather than family social support. Our lack of social support findings may also be related to measurement as our church and workplace social support measures were shortened considerably from the original scales. Interestingly, while prior research has generally shown an effect of social support on healthy eating, another one of our studies in the same region found no association between family social support for healthy eating and nutrition behavior [27, 35, 57].
While we did not see an impact of the work environment on fat intake, having healthy food available at church was associated with lower fat intake. Several evidence based programs to improve the church eating environment are available [58, 59].
Because there was a significant change in fat intake during this 1 year study, it allows a look at what individual and environmental factors predict change in fat intake. While the overall trend showed people increasing their fat intake, there were several time-invariant factors that were associated with a decrease in fat intake over time from three areas: individual factors, as well as the work environment. When looking at individual factors of change in fat intake, we found that older people decreased their fat intake more than younger people and women increased their fat intake more than men. Positive physical eating environments at work (offering healthy foods and programs for healthy eating) also had positive impact on change in fat intake. Neither the home nor the church environments were predictive of the change in fat intake.
The current analysis also indicates that there are additional variables of fat intake in general and change in fat intake over time that we have not measured. Collecting more in-depth dietary intake data, for example through 24 dietary recalls would allow to investigate the relationships between fat intake, fruit and vegetable intake, and BMI more in-depth. Future research should investigate additional variables on the individual level as well as from the three environments discussed here. In addition, other environments such as homes of family members and friends might play a significant role in fat intake. Interventions might aim at improving the physical environments and investigate the impact on healthy eating. Churches seem to provide very stable environments and change could reach many people.
In addition, this study shows stability and variability in the physical and social home, church, and work environments related to eating behaviors.
The lack of change in many of the environmental variables such as social support environments is of interest. Improving the social support for healthy eating in one or several of these environments might have an impact on fat intake, but more research is needed. In addition, there was no change in the physical church environment reported. As mentioned earlier, there are evidence based programs for improving the healthy eating environments in churches available focusing on fruit and vegetable intake [58, 60, 61]. Future research should investigate long-term impact of such changes on fat intake.
The changes observed in the work environment (i.e. availability of healthier food options and weight loss programs) are promising as is their impact on change in fat intake. Strengthening these efforts and widening their use might be a viable avenue for reducing fat intake. It would also be of interest to look the food environment in work place neighborhoods.
This study has several limitations. Sampling was not random and locations chosen to recruit participants might have resulted in a biased sample. In addition, the longitudinal study only included high-risk participants. Hence, the results are neither generalizable to the general population in rural southwest Georgia nor other areas in the United States. The self-report nature of data collection might have introduced reporting biases, including social desirability bias. Measures of variables were brief, in order to be acceptable to respondents. Furthermore, there are many other variables, both individual and in the three key environments, that could have been measured and enhanced the understanding of the determinants of fat intake and change thereof.
Conclusion
Despite its limitations, this study contributes important knowledge about home, work, and church environments' impact on fat intake. Very few studies have looked at the interplay of influences from all three environments over time, especially for an at-risk population. Future studies in different populations are needed to investigate the combined influence of multiple key environments on nutrition behaviors.
Declarations
Acknowledgements
The authors thank members of the Emory Prevention Research Center Community Advisory Board for their guidance in the design and implementation of this research and the Cancer Coalition of South Georgia for local study coordination. We also thank our study staff and study participants for their valuable contributions to this research.
This publication was supported by Cooperative Agreements #U48DP000043 and# 5U48DP001909 from the U.S. Centers for Disease Control and Prevention. The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the U.S. Centers for Disease Control and Prevention.
Funding
Funding for this research was provided through cooperative agreements #U48DP 000043 and 5U48DP001909 from the Centers for Disease Control and Prevention (CDC) for the Emory Prevention Research Center (EPRC).
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
RH has conducted the statistical analysis, interpreted the data, and drafted the manuscript. AA contributed to acquisition of data and drafting of the manuscript. IA contributed to conception and design of the study, acquisition of data and drafting of the manuscript. KG contributed to conception and design of the study, interpretation of the data and drafting of the manuscript. MK contributed to conception and design of the study, interpretation of data and drafting of the manuscript. All authors read and approved the final manuscript.
Boehmer TK, Lovegreen SL, Haire-Joshu D, Brownson RC. What constitutes an obesogenic environment in rural communities? Am J Health Promot. 2006;20(6):411-21.
Richard L, Gauvin L, Raine K. Ecological models revisited: their uses and evolution in health promotion over two decades. Annu Rev Public Health. 2011;32:307-26. doi:10.1146/annurev-publhealth-031210-101141.
Booth SL, Sallis JF, Ritenbaugh C, Hill JO, Birch LL, Frank LD, et al. Environmental and societal factors affect food choice and physical activity: rationale, influences, and leverage points. Nutr Rev. 2001;59(3 Pt 2):S21-39. discussion S57-65.
Story M, Kaphingst KM, Robinson-O'Brien R, Glanz K. Creating healthy food and eating environments: policy and environmental approaches. Annu Rev Public Health. 2008;29:253-72. doi:10.1146/annurev.publhealth.29.020907.090926.
Nasuti G, Blanchard C, Naylor PJ, Levy-Milne R, Warburton DE, Benoit C, et al. Comparison of the dietary intakes of New parents, second-time parents, and nonparents: a longitudinal cohort study. J Acad Nutr Diet. 2013. doi:10.1016/j.jand.2013.07.042.
Steptoe A, Wardle J, Cui W, Bellisle F, Zotti AM, Baranyai R, et al. Trends in smoking, diet, physical exercise, and attitudes toward health in European university students from 13 countries, 1990-2000. Prev Med. 2002;35(2):97-104.
Longacre MR, Drake KM, MacKenzie TA, Gibson L, Owens P, Titus LJ, et al. Fast-food environments and family fast-food intake in nonmetropolitan areas. Am J Prev Med. 2012;42(6):579-87. doi:10.1016/j.amepre.2012.02.017.
Saelens BE, Sallis JF, Frank LD, Couch SC, Zhou C, Colburn T, et al. Obesogenic neighborhood environments, child and parent obesity: the Neighborhood Impact on Kids study. Am J Prev Med. 2012;42(5):e57-64. doi:10.1016/j.amepre.2012.02.008.
Wyse R, Campbell E, Nathan N, Wolfenden L. Associations between characteristics of the home food environment and fruit and vegetable intake in preschool children: a cross-sectional study. BMC Public Health. 2011;11:938. doi:10.1186/1471-2458-11-938.
Zick CD, Smith KR, Fan JX, Brown BB, Yamada I, Kowaleski-Jones L. Running to the store? The relationship between neighborhood environments and the risk of obesity. Soc Sci Med. 2009;69(10):1493-500. doi:10.1016/j.socscimed.2009.08.032.
Jeffery RW, Utter J. The changing environment and population obesity in the United States. Obes Res. 2003;11(Suppl):12S-22. doi:10.1038/oby.2003.221.
Shier V, An R, Sturm R. Is there a robust relationship between neighbourhood food environment and childhood obesity in the USA? Public Health. 2012;126(9):723-30. doi:10.1016/j.puhe.2012.06.009.
Wang MC, Cubbin C, Ahn D, Winkleby MA. Changes in neighbourhood food store environment, food behaviour and body mass index, 1981-1990. Public Health Nutr. 2008;11(9):963-70. doi:10.1017/s136898000700105x.
McLeroy KR, Bibeau D, Steckler A, Glanz K. An ecological perspective on health promotion programs. Health Educ Q. 1988;15(4):351-77.
Bandura A. Social foundations of thought and action: a social cognitive theory. New York: Prentice-Hall; 1986.
Bronfenbrenner U. Toward an experimental ecology of human development. Am Psychol. 1977;32(7):513-31. doi:10.1037/0003-066x.32.7.513.
Cohen DA, Scribner RA, Farley TA. A structural model of health behavior: a pragmatic approach to explain and influence health behaviors at the population level. Prev Med. 2000;30(2):146-54. doi:10.1006/pmed.1999.0609.
Flay BR, Snyder JF, Petraitis J. The theory of triadic influence. In: DiClemente RJ, Crosby RA, Kegler MC, editors. Emerging theories in health promotion practice and research. 2nd ed. San Francisco, CA: Jossey-Bass Wiley; 2009. p. 451-510.
Aranceta J, Perez-Rodrigo C. Recommended dietary reference intakes, nutritional goals and dietary guidelines for fat and fatty acids: a systematic review. Br J Nutr. 2012;107 Suppl 2:S8-22. doi:10.1017/s0007114512001444.
World Health Organization. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. 2000. Report No.: 0512-3054 (Print) 0512-3054 (Linking).
Bray GA, Popkin BM. Dietary fat intake does affect obesity! Am J Clin Nutr. 1998;68(6):1157-73.
de Castro JM, King GA, Duarte-Gardea M, Gonzalez-Ayala S, Kooshian CH. Overweight and obese humans overeat away from home. Appetite. 2012;59(2):204-11. doi:10.1016/j.appet.2012.04.020.
Cullen KW, Baranowski T, Owens E, Marsh T, Rittenberry L, de Moor C. Availability, accessibility, and preferences for fruit, 100% fruit juice, and vegetables influence children's dietary behavior. Health Educ Behav. 2003;30(5):615-26.
Gattshall ML, Shoup JA, Marshall JA, Crane LA, Estabrooks PA. Validation of a survey instrument to assess home environments for physical activity and healthy eating in overweight children. Int J Behav Nutr Phys Act. 2008;5:3. doi:10.1186/1479-5868-5-3.
Patterson RE, Kristal AR, Shannon J, Hunt JR, White E. Using a brief household food inventory as an environmental indicator of individual dietary practices. Am J Public Health. 1997;87(2):272-5.
Lubans DR, Plotnikoff RC, Morgan PJ, Dewar D, Costigan S, Collins CE. Explaining dietary intake in adolescent girls from disadvantaged secondary schools. A test of Social Cognitive Theory. Appetite. 2012;58(2):517-24. doi:10.1016/j.appet.2011.12.012.
Wickrama KA, Ralston PA, O'Neal CW, Ilich JZ, Harris CM, Coccia C, et al. Life dissatisfaction and eating behaviors among older African Americans: the protective role of social support. J Nutr Health Aging. 2012;16(9):749-53. doi:10.1007/s12603-012-0404-6.
Emmons KM, Linnan LA, Shadel WG, Marcus B, Abrams DB. The Working Healthy Project: a worksite health-promotion trial targeting physical activity, diet, and smoking. J Occup Environ Med. 1999;41(7):545-55.
Proper KI, Koning M, van der Beek AJ, Hildebrandt VH, Bosscher RJ, van Mechelen W. The effectiveness of worksite physical activity programs on physical activity, physical fitness, and health. Clin J Sport Med. 2003;13(2):106-17.
Roos E, Sarlio-Lahteenkorva S, Lallukka T. Having lunch at a staff canteen is associated with recommended food habits. Public Health Nutr. 2004;7(1):53-61.
Ni Mhurchu C, Aston LM, Jebb SA. Effects of worksite health promotion interventions on employee diets: a systematic review. BMC Public Health. 2010;10:62. doi:10.1186/1471-2458-10-62.
Hart Jr A, Tinker L, Bowen DJ, Longton G, Beresford SA. Correlates of fat intake behaviors in participants in the eating for a healthy life study. J Am Diet Assoc. 2006;106(10):1605-13. doi:10.1016/j.jada.2006.07.006.
Baruth M, Wilcox S, Condrasky MD. Perceived environmental church support is associated with dietary practices among African-American adults. J Am Diet Assoc. 2011;111(6):889-93. doi:10.1016/j.jada.2011.03.014.
Roozen DA. American congregations 2005. Hartford, CT: Hartford Seminary: Hartford Institute for Religion Research; 2007. Contract No.: Dec.
Kegler MC, Escoffery C, Alcantara IC, Hinman J, Addison A, Glanz K. Perceptions of social and environmental support for healthy eating and physical activity in rural southern churches. J Relig Health. 2010. doi:10.1007/s10943-010-9394-z.
Campbell MK, Hudson MA, Resnicow K, Blakeney N, Paxton A, Baskin M. Church-based health promotion interventions: evidence and lessons learned. Annu Rev Public Health. 2007;28:213-34. doi:10.1146/annurev.publhealth.28.021406.144016.
Escoffery C, Kegler MC, Alcantara I, Wilson M, Glanz K. A qualitative examination of the role of small, rural worksites in obesity prevention. Prev Chronic Dis. 2011;8(4):A75.
Williams RM, Glanz K, Kegler MC, Davis Jr E. A study of rural church health promotion environments: leaders' and members' perspectives. J Relig Health. 2009. doi:10.1007/s10943-009-9306-2.
Sharkey JR, Dean WR, St John JA, Huber Jr JC. Using direct observations on multiple occasions to measure household food availability among low-income Mexicano residents in Texas colonias. BMC Public Health. 2010;10:445. doi:10.1186/1471-2458-10-445.
Hermstad AK, Swan DW, Kegler MC, Barnette JK, Glanz K. Individual and environmental correlates of dietary fat intake in rural communities: a structural equation model analysis. Soc Sci Med. 2010;71(1):93-101. doi:10.1016/j.socscimed.2010.03.028.
Kegler MC, Swan DW, Alcantara I, Wrensford L, Glanz K. Environmental Influences on Physical Activity in Rural Adults: The Relative Contributions of Home, Church and Work Settings. J Phys Act Health. 2011;9(7):996-1003.
Kegler M, Escoffery C, Alcantara I, Ballard D, Glanz K. A qualitative examination of home and neighborhood environments for obesity prevention in rural adults. Int J Behav Nutr Phys Act. 2008;5:65.
Thompson FE, Midthune D, Subar AF, Kahle LL, Schatzkin A, Kipnis V. Performance of a short tool to assess dietary intakes of fruits and vegetables, percentage energy from fat and fibre. Public Health Nutr. 2004;7(8):1097-105. doi:10.1079/PHN2004642.
Centers for Disease Control and Prevention (CDC). In: U.S. Department of Health and Human Services, editor. Behavioral risk factor surveillance system survey questionnaire. Atlanta, Georgia: Centers for Disease Control and Prevention; 2005.
Sallis JF, Pinski RB, Grossman RM, Patterson TL, Nader PR. The development of self-efficacy scales for health-related diet and exercise behaviors. Health Educ Res. 1988;3(3):283-92.
Sallis JF, Grossman RM, Pinski RB, Patterson TL, Nader PR. The development of scales to measure social support for diet and exercise behaviors. Prev Med. 1987;16(6):825-36.
Sallis JF, Johnson MF, Calfas KJ, Caparosa S, Nichols JF. Assessing perceived physical environmental variables that may influence physical activity. Res Q Exerc Sport. 1997;68(4):345-51.
Monterey County Health Department. Steps to a healthier Salinas. Steps to a healthier Salinas: 2004 community partner baseline survey results: Monterey County Health Department. 2004.
Institute of Medicine. Priority areas for national action: transforming healthcare quality. Washington, DC: National Academy Press; 2003.
Oldenburg B, Sallis JF, Harris D, Owen N. Checklist of Health Promotion Environments at Worksites (CHEW): development and measurement characteristics. Am J Health Promot. 2002;16(5):288-99. doi:http://dx.doi.org/10.4278/0890-1171-16.5.288.
Raudenbush SW, Bryk AS. Hierarchical linear models: applications and data analysis methods. 2nd ed. Thousand Oaks, CA: Sage; 2002.
Singer JD, Willett JB. Applied longitudinal data analysis: modeling change and event occurrence. New York, NY: Oxford University Press; 2003.
Thornton LE, Lamb KE, Ball K. Employment status, residential and workplace food environments: associations with women's eating behaviours. Health Place. 2013;24:80-9. doi:10.1016/j.healthplace.2013.08.006.
Lin B-H, Guthrie J. Nutritional quality of food prepared at home and away from home, 1977-2008: U.S. Department of Agriculture, Economic Research Service. 2012. December Contract No.: EIB-105.
Gorin AA, Phelan S, Raynor H, Wing RR. Home food and exercise environments of normal-weight and overweight adults. Am J Health Behav. 2011;35(5):618-26.
Cohen DA, Finch BK, Bower A, Sastry N. Collective efficacy and obesity: the potential influence of social factors on health. Soc Sci Med. 2006;62(3):769-78. doi:10.1016/j.socscimed.2005.06.033.
Kegler M, Alcantara I, Haardoerfer R, Gazmararian J, Ballard D, Sabbs D. The influence of home food environments on eating behaviors of overweight and obese women. J Nutr Educ Behav. 2014;46(3):188-96.
Resnicow K, Campbell MK, Carr C, McCarty F, Wang T, Periasamy S, et al. Body and soul. A dietary intervention conducted through African-American churches. Am J Prev Med. 2004;27(2):97-105. doi:10.1016/j.amepre.2004.04.009.
Wilcox S, Parrott A, Baruth M, Laken M, Condrasky M, Saunders R, et al. The faith, activity, and nutrition program: a randomized controlled trial in African-American churches. Am J Prev Med. 2013;44(2):122-31. doi:http://dx.doi.org/10.1016/j.amepre.2012.09.062.
Williams E. Cooking with soul: a look into faith-based wellness programs: interview conducted by Tony Peregrin. J Am Diet Assoc. 2006;106(7):1016-20. doi:10.1016/j.jada.2006.05.263.
Campbell MK, Resnicow K, Carr C, Wang T, Williams A. Process evaluation of an effective church-based diet intervention: body & soul. Health Educ Behav. 2007;34(6):864-80. doi:10.1177/1090198106292020.
Copyright BioMed Central 2016