- Email: [email protected]
Impact of a Behavioral Intervention on Diet, Eating Patterns, Self-Efficacy, and Social Support
ARTICLE IN PRESS Research Brief Impact of a Behavioral Intervention on Diet, Eating Patterns, Self-Efficacy, and Social Support Matthew Lee Smith, PhD1,2,3; Shinduk Lee, DrPH1; Samuel D. Towne Jr, PhD1,2,4,5; ~ a-Purcell, PhD1,2,7; Gang Han, PhD6; Cindy Quinn, MS1; Ninfa C. Pen Marcia G. Ory, PhD1,2 ABSTRACT Objective: To examine the effectiveness of a structured multimodal behavioral intervention to change dietary behaviors, as well as self-efficacy and social support for engaging in healthier diets. Methods: A quasi-experimental design was used to assign sites into intervention and comparison groups. Data were collected at baseline, 3 months, and 6 months. The intervention group participated in Texercise Select, a 12-week lifestyle enhancement program. Multiple mixed-effects models were used to examine nutrition-related changes over time. Results: For the intervention group, significant improvements were observed for fast food consumption (P = .011), fruit/vegetable consumption (P = .008), water consumption (P = .009), and social support (P < .001) from baseline to 3 months. The magnitude of these improvements was significantly greater than changes in the comparison group. Conclusions and Implications: Findings suggest the intervention’s ability to improve diet-related outcomes among older adults; however, additional efforts are needed to maintain changes over longer periods. Key Words: healthy diet, evidence-based program, program evaluation, lifestyle, intervention (J Nutr Educ Behav. 2019;000:1−7.) Accepted June 7, 2019.
INTRODUCTION The 2015−2020 Dietary Guidelines for Americans call for increasing fruit and vegetable intake while simultaneously reducing the intake of saturated fats, salt, and sugars.1 However, a substantial proportion of adults do not consume the recommended servings of fruits and vegetables.2,3
Furthermore, many adults consume diets that exceed the recommended limits for saturated fats, sodium, and added sugars.2 Consequently, fast food consumption (including sugarsweetened beverages) is associated with obesity and cardiovascular risk.4 Small steps can be beneficial to modify dietary behavior as well as create balance in food choices and total
1
Center for Population Health and Aging, Texas A&M University, College Station, TX Department of Environmental and Occupational Health, School of Public Health, Texas A&M University, College Station, TX 3 Department of Health Promotion and Behavior, College of Public Health, The University of Georgia, Athens, GA 4 Department of Health Management & Informatics, University of Central Florida, Orlando, FL 5 Aging & Technology Faculty Cluster Initiative, University of Central Florida, Orlando, FL 6 Department of Epidemiology & Biostatistics, School of Public Health, Texas A&M University, College Station, TX 7 Family and Community Health, AgriLife Extension Service, Texas A&M University, College Station, TX Conflict of Interest Disclosure: The authors have not stated any conflicts of interest. Address for correspondence: Matthew Lee Smith, Center for Population Health and Aging, Texas A&M University, 212 Adriance Lab Rd, College Station, Texas 77843; E-mail: [email protected] Ó 2019 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved. https://doi.org/10.1016/j.jneb.2019.06.008 2
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caloric intake.5 Dietary habits are influenced by individual characteristics (eg, sociodemographics), interpersonal interactions (eg, social support), and settings that individuals visit in a community (eg, facilities hosting health-promoting programs).6−9 An individual’s perceived confidence (ie, self-efficacy) to change dietary patterns can be influenced.10 Based on a multimodal intervention approach, this quasi-experimental study examined the effectiveness of a structured behavioral intervention to change diet, eating patterns, selfefficacy, and social support among middle-aged and older adults. The intervention, Texercise Select, has been shown to improve fruit and vegetable consumption, self-efficacy to improve nutrition, and general social support for lifestyle/behavior using a singlegroup, pretest/post-test design.11 The current study builds upon these findings by using a more rigorous study design to examine initial and sustained diet-related outcomes. It is hypothesized that significant initial and sustained improvements will be observed among Texercise Select participants relative to participants in
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the comparison group (ie, those not receiving the intervention).
METHODS Participants and Procedures This study used longitudinal data collected from nonequivalent groups in a quasi-experimental design (ie, intervention and comparison groups) from May 2015 to September 2017. This study included 9 intervention and 14 comparison sites, which were recruited through local partnerships with multiple organizations in the aging and healthcare sectors. More comparison sites were recruited to account for greater participant attrition within the comparison group. Between 13 and 26 participants were recruited from each intervention site, and between 8 to 41 participants were recruited from each comparison site. All sites were either exposed to the intervention or treated as comparison sites. There was no mixing in terms of treatment arm within a particular site location. Sites were assigned to the intervention or comparison condition using nonrandomized methods based on the organizations’ comparability in terms of size, client base, and their readiness to offer the intervention. The intervention group participated in Texercise Select, a 12-week lifestyle enhancement program offered in a small group education format in community settings. The comparison group did not receive the intervention during the study period but were invited to join an evidence-based health promotion program after the end of the study. Examples of topics covered in the sessions include components of a healthy diet; components of a balanced diet; portion sizes; food labels; cooking modifications to maximize nutritional intake; and healthy choices when eating outside the home. Examples of session activities include keeping a nutrition log, making action plans, brainstorming about ways to change their diet, creating healthy meals/menus, and staying committed to nutritional goals and healthy eating. Specific details on program activities are described elsewhere.12 Study participants were recruited using convenience sampling methods by community partners in multiple
Journal of Nutrition Education and Behavior Volume 000, Number 000, 2019 locations across Texas who work with middle-aged or older adults. The inclusion criteria for the study participants were: (1) being at least 45 years old; (2) agreeing to participate in the study (consent given by way of an information sheet); and (3) completing and returning the baseline survey to the research team. For the intervention group, participants were excluded from the study if they did not attend the first or second session of the Texercise Select workshop. To be ethical and accommodate community partners’ needs, those who did not attend the first or second session were still allowed to participate in the workshops; however, their data were excluded from analyses. A total of 9 Texercise Select workshops were hosted in community settings, including senior centers, faith-based facilities, senior housing facilities, and all-purpose community centers. Data were collected from participants at baseline, 3 months, and 6 months. Instruments at all 3-time points included items related to self-reported dietary behaviors, self-efficacy related to engaging in a healthy diet, and perceived social support for engaging in a healthy diet. This study was approved by the Texas A&M University Institutional Review Board.
Primary Outcome Measures Dietary behavior. Using slightly modified measures of dietary behavior from the Starting the Conversation diet instrument,13 participants were asked about the following dietary behaviors over the past 7 days: number of times consumed a fast food meal or snack; number of soda and sugar-sweetened drinks (regular, not diet) consumed each day; number of fruit or vegetable servings consumed each day. For each of these items, responses were: 1, 2, 3, 4, and 5 or more. Participants were also asked to report how many cups of water they drink on an average day. For this item, responses were: 0, 1, 2, 3, 4, 5, 6, 7, and 8 or more. Self-efficacy. Participants were asked to indicate their degree of confidence using a 4-point Likert scale to (1) set a healthy eating goal; (2) read food labels; (3) identify recommended portion size for different foods, and (4)
identify social services for obtaining healthy foods. The valid response range for this self-efficacy scale was 4 to 16, with higher scores indicating greater self-efficacy related to keeping a healthy diet. The internal reliability for this scale was strong for the comparison (Cronbach’s alpha = 0.87) and intervention (Cronbach’s alpha = 0.88) group.14 Social support. Participants were asked how frequently they received social support for the following at baseline and 3 months using a 4-point Likert scale: (1) planning dietary goals; (2) keeping dietary goals; (3) reducing barriers to healthy eating; and (4) eating healthy meals. The valid response range for this scale was 4 to 16, with higher scores indicating greater perceived social support to keep a healthy diet. The internal reliability for this scale was good for the comparison (Cronbach’s alpha = 0.80) and intervention (Cronbach’s alpha = 0.77) group.14 Other covariate measures. The baseline survey included sociodemographic information (eg, age, sex, race/ethnicity, and education) and self-reported chronic conditions (ie, from a list of 9 conditions). Participants who selfreported ≥2 conditions were identified as having disease comorbidity. Race/ ethnicity was collapsed into 2 groups: non-Hispanic White and Other. Three levels of education were defined: highschool graduate or lower; some college or technical school; and college graduate or higher.
Statistical Analyses Bivariate analyses were used to compare the participants’ sociodemographic and baseline characteristics among the study participants between the comparison and intervention groups. Pearson’s chi-squared tests were used for categorical variables, independent sample t tests were used for interval variables, and WilcoxonMann Whitney tests were used for ordinal variables. Multiple mixedeffects models (ie, linear for interval variables; ordinal logistic for ordinal variables assuming proportional odds) were used to examine the changes in outcome variables from baseline to
ARTICLE IN PRESS Journal of Nutrition Education and Behavior Volume 000, Number 000, 2019 3 months and baseline to 6 months. In these models, nesting within sites (using a site ID variable) and individuals over time (using unique participant IDs) were incorporated in the model. Statistical independence was assumed at the participant-level, and over-time effects were assumed to be nested within the subject effect. Despite the low intra-class correlations within each site (ranged from <0.001 to 0.059), the random effects from sites were incorporated in the models. Given the nonlinear trend of the changes over time (ie, baseline, 3 months, and 6 months), time was included in the model as a categorical variable instead of a continuous variable. All models included time, group (intervention or comparison), and the interaction between time and group. Additionally, all models were performed after controlling for age, sex, race/ethnicity, education, and disease comorbidity. For each mixedeffect model, all participants with outcome data at ≥1 time points were included in the analyses.
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However, only participants with complete data for all independent and control variables were included in the analyses.
RESULTS A CONSORT diagram depicting participant flow for this study is published elsewhere.15 A total of 430 studyeligible participants were recruited (ie, intervention group = 163 [37.9%]; comparison group = 267 [62.1%]). In the intervention group, about 77% of study-eligible participants completed the 3-month follow-up, and 45% completed the 6 months follow-up. In the comparison group, about 65% of study-eligible participants completed the 3-month follow-up, and 40% completed the 6-month follow-up. Table 1 reports participants’ sociodemographic characteristics and baseline values for outcome variables by treatment group. On average, participants were age 74.49 (standard deviation, 8.95). Most of the study participants were female (77.4%), had ≥2 comorbid
conditions (66.4%), and had cardiovascular risk (72.6%). About half (47.3%) of participants were non-Hispanic white, and 61.3% had more than a high-school education. Relative to the comparison group, the intervention group had a significantly larger proportion of non-Hispanic white participants (34.7% vs. 67.7%). At baseline, the comparison group consumed significantly more sugar-sweetened beverages relative to the intervention group (P < .005). On average, intervention group participants attended 14.8 (§5.47) of the 20 workshop sessions.
Program Effects Table 2 presents outcomes from the adjusted models for all study-eligible participants. For the intervention group, significant improvements were observed for fast food consumption (P = .011), fruit/vegetable consumption (P = .008), water consumption (P = .009), and social support (P < .001) from baseline to 3 months. For the comparison group, no statistically
Table 1. Baseline Characteristics of the Study Participants Mean (SD), Median [IQR], or Frequency (%)
Demographic Characteristics Age (years) Female Non-Hispanic white Education High-school graduate or lower Some college College graduate or higher Disease comorbitity Cardiovascular risk Baseline diet behavior Fast food consumption (times in the past 7 days) Fruit/vegetable consumption (servings per day in the past 7 days) Soda/sugar drink consumption (drinks per day in the past 7 days) Water (drinks per day) Baseline social support Baseline self-efficacy Workshop characteristics Number of attended sessions (max = 20)
Overall (n = 430)
Intervention (n = 163)
Comparison (n = 267)
P Values (Intervention vs Comparison)
74.49 (8.95) 333 (77.4%) 192 (47.3%)
74.84 (7.70) 129 (79.1%) 105 (67.7%)
74.28 (9.65) 204 (76.4%) 87 (34.7%)
.505 .510 <.001
166 (38.7%) 134 (31.2%) 129 (30.1%) 277 (66.4%) 312 (72.6%)
56 (34.4%) 62 (38.0%) 45 (27.6%) 111 (68.5%) 119 (73.0%)
110 (41.4%) 72 (27.1%) 84 (31.6%) 166 (65.1%) 193 (72.3%)
.058
.471 .871
2 [1, 3]
2 [1, 3]
2 [1, 3]
.509
3 [2, 4]
3 [2, 5]
3 [2, 4]
.553
0 [0, 2]
0 [0, 1]
1 [0, 2]
.005
5 [4, 7] 9.93 (3.39) 12.23 (2.53) —
IQR indicates interquartile range; SD, standard deviation.
5 [4, 7] 9.75 (3.30) 12.30 (2.59) 14.83 (5.47)
5 [4, 7] 10.03 (3.45) 12.19 (2.50) —
3
.940 .414 .682 —
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Table 2. Adjusted Immediate and Sustained Program Effects Among the Overall Study Participants
Adjusted Differences (D) or Adjusted ORa (95% CI), Changes From Baseline
Adjusted Means (95% CI)
Variables
Self-Efficacy Comparison Intervention
Immediate Program Effects: Compare T1 vs T2
Sustained Program Effects: Compare T1 vs T3
Sustained Program Effects: Compare T1 vs T3
P Values (“group x time” Interaction) .046b,c .184d
− −
− −
− −
OR = 1.04 (0.77−1.40) OR = 0.66 (0.48−0.91)
OR = 0.97 (0.66−1.42) OR = 0.65 (0.42−1.02)
.820 .011
.886 .062 .006b,c .656d
− −
− −
− −
OR = 0.83 (0.60−1.15) OR = 0.74 (0.50−1.09) OR = 1.68 (1.15−2.47) OR = 0.84 (0.53−1.35)
.263 .008
.123 .483 .393c .710d
− −
− −
− −
OR = 1.09 (0.82−1.44) OR = 0.88 (0.60−1.30)
OR = 0.82 (0.58−1.16) OR = 0.73 (0.45−1.19)
.567 .524
.267 .209 .204c
− −
− −
− −
OR = 1.14 (0.89−1.47) OR = 1.46 (1.10−1.94)
NA
.299 .009
NA
NA
D = 0.49 (0.06−1.04)
NA
.079
NA
NA
D = 1.79 (1.16−2.42)
.002b,c 9.78 (9.31−10.25) 10.08 (9.53−10.63)
10.27 (9.72−10.83) 11.87 (11.27−12.48)
<.001 .167c
12.23 (11.86−12.59) 12.24 (11.81−12.67) 12.43 (11.97−12.89) 12.95 (12.44−13.47)
NA NA
D = 0.01 ( 0.46−0.48) D = 0.52 ( 0.03−1.07)
NA
.957 .063
NA
CI indicates confidence interval; NA, not available; OR, odds ratio; T1, baseline; T2, 3-month follow-up; T3, 6-month follow-up. a The likelihood of having a higher response category (ie, more fast food, more fruit/vegetables, more soda, or more water) were modeled; bIntervention group showed greater improvement over time than the control group; cStatistical significance of differences in changes from the baseline to 3-month follow-up between the intervention and control groups; dStatistical significance of differences in changes from the baseline to 6-month follow-up between the intervention and control groups. Note: All models were controlled for age, sex, race/ethnicity, education, and disease comorbidity
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Intervention
T1. Baseline
T3. 6-Month Follow-Up
Immediate Program Effects: Compare T1 vs T2
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Fast Food Consumption (times in the past 7 days) Comparison Intervention Fruit/Vegetable Consumption (servings in past 7 days) Comparison Intervention Soda/Sugar Drink (drinks in past 7 days) Comparison Intervention Water consumption Comparison Intervention Social support Comparison
T2. 3-Month Follow-Up
P Values (Changes From Baseline to Immediate Post or 6-Month Post-Test)
ARTICLE IN PRESS Journal of Nutrition Education and Behavior Volume 000, Number 000, 2019 significant changes were observed. Relative to the comparison group, the magnitude of change was significantly greater for the intervention group in terms of fast food consumption (odds ratio [OR] = 1.04 vs. OR = 0.66; P = .046), fruit/vegetable consumption (OR = 0.83 vs. OR = 1.68; P = .006), and social support (D = 0.49 vs. D = 1.79; P = .002) from baseline to 3 months.
DISCUSSION Texercise Select has the potential to improve diet-related outcomes among older and potentially at-risk adults. Relative to comparison group participants, those in the intervention group significantly improved in terms of their fast food and fruit/vegetable consumption, and social support from baseline to 3 months. Texercise Select participants reduced their fast food consumption from baseline to 3 months. Fast food and dining out are common challenges for individuals wanting to engage in a healthier diet.16 Findings suggest that Texercise Select sessions addressing these topics were influential in helping participants change the frequency of their fast food consumption. Of particular note, Texercise Select participants as a group reduced their fast food consumption from baseline to 3 months. Fast food and dining out are common challenges for individuals wanting to engage in a healthier diet.16 Findings suggest that Texercise Select sessions addressing these topics were influential in helping participants change the frequency of their fast food consumption. Foods that are most conveniently purchased are not always those that are most healthy. While fast food restaurants may be easily available, accessible, and affordable,4,17 they are not the only food option. Despite perceptions that purchasing healthy foods is inconvenient or cost-prohibitive,18,19 dietary recommendations support the consumption of fresh, frozen, or canned fruits and vegetables,20 which makes having healthy foods “on-hand” easier because of widespread purchasing locations and the ability to store foods in the home for longer periods. However, it should be acknowledged that many fast food restaurants now
carry healthier options (eg, milk, bottled water, apple slices instead of French fries), thus individuals can better control their dietary intake despite the existing negative stigma around fast food consumption.21,22 Although most of the initial dietrelated improvements were not sustained at 6 months, the overall findings are encouraging. The general tapering effect of improvements observed in this study suggests that participants could benefit from additional or ongoing intervention. Strategies to sustain initial gains can include hosting brief booster sessions (eg, one hour, once a month), initiating telephone or text communication, and creating social media groups for participants after the workshop concludes. Another strategy to maintain program effects could be to sequentially enroll Texercise Select participants into other evidence-based disease prevention and health promotion interventions.23,24 These findings have implications for participant recruitment to enhance intervention effectiveness by enrolling at-risk, older adults. Recruitment efforts could focus on engaging community organizations that serve older adults (eg, senior centers, residential facilities, area Agencies on Aging) or healthcare facilities that serve heart disease or diabetes patients (eg, hospitals and dialysis clinics). While these sites may or may not be able to adopt and host the workshops themselves, a referral protocol could be created to ensure eligible participants are aware of, and able to access, the intervention. Prior to introducing an intervention into a community, a variety of key stakeholders (eg, community leaders, representatives from potential delivery sites, potential leaders, potential participants) should be approached to learn more about their needs and what they value as successes.25 This may include tailoring marketing material to the target population, being responsive to unique needs of target delivery sites (eg, literacy and language), and engaging community members to volunteer as facilitators to lead programs in their community. Further, identifying and engaging key stakeholders early in the planning process will increase
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community buy-in, improve organizational adoption, and ultimately promote intervention scalability and sustainability.26 Texercise Select was able to reach and retain individuals in need of sustainable interventions targeted at improved nutrition, a key component in overall health. The intervention effects suggest Texercise Select is a replicable, successful strategy to modify health behaviors into sustainable healthy lifestyles. Although only limited areas throughout Texas have received the program thus far, the successes of the program set the stage for wider implementation and dissemination across the state (and beyond), thereby seeking to ameliorate adverse health linked to poor nutrition facing millions of middle-aged and older adults across the US.
Limitations Data used in these analyses were selfreported. Thus, actual food consumption or changes in social support could not be verified through observation or other objective measures. However, many of the items have been used in other research and practice settings and reflect the pragmatic nature of this study.27,28 Although this study examined 2 positive (ie, fruit/vegetable and water) and 2 negatives (ie, fast food and sugar-sweetened beverages) measures of diet, including more dietrelated measures, may have allowed for a more robust assessment of participants’ eating patterns over time. Although the measures for self-efficacy and social support showed strong internal reliability (Cronbach’s alpha), intra- and interrater reliabilities were not examined. This design was quasiexperimental, which represents an improvement over a 1-group, pre-post design. However, because there was no randomization, group assignment was imperfect and resulted in some baseline differences between the groups. To minimize baseline differences, the adjusted models control for age, sex, race/ethnicity, education, and disease comorbidity, as well as including a random effect for site differences. Future studies should consider using propensity score or frequency matching methods for analyses. Because of the realities of
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pragmatic community-based intervention research, the overall sample size was modest. Therefore, some analyses may have been underpowered to detect meaningful changes. Although the socioecological model29 provided a guiding framework for identifying different intervention touch points, in no way was the full model or spheres of influence included or measured.
IMPLICATIONS FOR RESEARCH AND PRACTICE Texercise Select is a structured intervention that draws upon multiple spheres of the socioecological framework.29 At the individual level, the intervention content provides participants with practical knowledge about food choices, raises awareness about common barriers/pitfalls, and teaches skills to identify local resources to promote a healthy diet. Concurrently, the intervention enables participants to practice problem solving and goal setting week after week. This iterative trialand-error process is skill building and improves self-efficacy in a safe environment. At the interpersonal level, intervention activities (eg, identifying resources) and the small group format (including lay leader facilitators) provide social support. Often participants in community-based interventions begin to form friendships and function as an informal support group or buddy systems, even after the intervention ends.30,31 Furthermore, the information gained during the intervention and the participants’ dietary goals are often shared with others (eg, family members, co-workers, healthcare providers), which further garners support. At the organization/setting level, introducing and embedding interventions in communities can begin to change organizational policy, inter-agency partnership, and social/cultural norms and values.1 In short, Texercise Select adheres to the 2015−2020 Dietary Guidelines for Americans in that it delivers “information in a way that is compelling, inspiring, empowering, and actionable for individuals. . .” and focuses “on individuals where they are making food and beverage choices.”1
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ACKNOWLEDGMENTS The authors recognize the Texas Health and Human Services Commission, for supporting the creation and evaluation of Texercise Select, and the Texas A&M School of Public Health for helping to standardize and implement Texercise Select. We thank the delivery sites, class facilitators, and participants for their role in the study.
REFERENCES 1. US Department of Health and Human Services and US Department of Agriculture. Dietary Guidelines for Americans 2015−2020. 8th ed. https://health.gov/ dietaryguidelines/2015/guidelines/. Accessed December 20, 2018. 2. What We Eat in America, NHANES 2007−2010 for average intakes by agesex group. Healthy U.S.-Style Food Patterns, which vary based on age, sex, and activity level, for recommended intakes and limits. Beltsville, MD: Agricultural Research Service, US Department of Agriculture; 2010. 3. Centers for Disease Control and Prevention. Nutrition, physical activity, and obesity: data, trends and maps. https://www.cdc.gov/nccdphp/dnpao/ data-trends-maps/index.html. Accessed December 20, 2018. 4. Bowman SA, Vinyard BT. Fast food consumption of U.S. adults: impact on energy and nutrient intakes and overweight status. J Am Col Nutr. 2004;23: 163–168. 5. Ory MG, Smith ML, Mier N, Wernicke MM. The science of sustaining health behavior change: the health maintenance consortium. Am J Health Behav. 2010;34: 647–659. 6. Golden SD, Earp JAL. Social ecological approaches to individuals and their contexts: twenty years of health education & behavior health promotion interventions. Health Educ Behav. 2012;39:364–372. 7. Robinson T. Applying the socio-ecological model to improving fruit and vegetable intake among low-income African Americans. J Commun Health. 2008;33:395–406. 8. Jr Barrera M, DJ Toobert, Angell KL, Glasgow RE, MacKinnon DP. Social support and social-ecological resources as mediators of lifestyle intervention effects for type 2 diabetes. J Health Psychol. 2006;11:483–495.
9. Barrera M Jr, Strycker LA, MacKinnon DP, Toobert DJ. Social-ecological resources as mediators of two-year diet and physical activity outcomes in type 2 diabetes patients. Health Psychol. 2008;27: s118–s125. 10. AbuSabha R, Achterberg C. Review of self-efficacy and locus of control for nutrition- and health-related behavior. J Am Diet Assoc. 1997;97:1122– 1132. 11. Smith ML, Ory MG, Jiang L, et al. Texercise Select effectiveness: an examination of physical activity and nutrition outcomes. Transl Behav Med. 2015;5: 433–442. 12. Ory MG, Smith ML, Howell D, et al. The conversion of a practice-based lifestyle enhancement program into a formalized, testable program: from Texercise Classic to Texercise Select. Front Public Health. 2014;2:291. 13. Paxton AE, Strycker LA, Toobert DJ, Ammerman AS, Glasgow RE. Starting the conversation: performance of a brief dietary assessment and intervention tool for health professionals. J Prev Med. 2011;40:67–71. 14. Nunnaly J. Psychometric Theory. New York, NY: McGraw-Hill; 1978. 15. Ory MG, Lee S, Han G, et al. Effectiveness of a lifestyle intervention on social support, self-efficacy, and physical activity among older adults: evaluation of Texercise Select. Int J Environ Res Public Health. 2018;15:234. 16. Welch N, McNaughton SA, Hunter W, Hume C, Crawford D. Is the perception of time pressure a barrier to healthy eating and physical activity among women. Public Health Nutr. 2009;12:888–895. 17. Paeratakul S, Ferdinand DP, Champagne CM, Ryan DH, Bray GA. Fastfood consumption among US adults and children: dietary and nutrient intake profile. J Am Diet Assoc. 2003; 103:1332–1338. 18. Walker RE, Keane CR, Burke JG. Disparities and access to healthy food in the United States: A review of food deserts literature. Health Place. 2010; 16:876–884. 19. Rao M, Afshin A, Singh G, Mozaffarian D. Do healthier foods and diet patterns cost more than less healthy options? A systematic review and meta-analysis. BMJ Open. 2014;3: e004277. 20. Centers for Disease Control and Prevention. Healthy eating for a healthy
ARTICLE IN PRESS Journal of Nutrition Education and Behavior Volume 000, Number 000, 2019 weight. Atlanta, GA: Centers for Disease Control and Prevention; 2016 https:// www.cdc.gov/healthyweight/healthy_ eating/index.html. Accessed December 20, 2018. 21. Kraak VI. US restaurant sector can promote healthy food environments to reduce obesity risk. Am J Clin Nutr. 2018;107:288–290. 22. Min J, Jahns L, Xue H, Kandiah J, Wang Y. Americans’ perceptions about fast food and how they associate with its consumption and obesity risk. Adv Nutr. 2018;9:590–601. 23. Towne SD Jr, Smith ML, Ahn S, et al. National dissemination of multiple evidence-based disease prevention programs: Reach to vulnerable older adults. Front Public Health. 2014;2:156.
24. Lee S, Smith ML, Towne SD Jr, Ory MG. Effects of sequential participation in evidence-based health and wellness programs among older adults. Innov Aging. 2018;2:1–12. 25. Stevens AB, Thiel SB, Thorud JL, et al. Increasing the availability of physical activity programs for older adults: Lessons learned from Texercise stakeholders. J Aging Phys Act. 2016;24:39–44. 26. Smith ML, Schneider EC, Byers IN, et al. Reported systems changes and sustainability perceptions of three state Departments of Health implementing multi-faceted evidence-based fall prevention efforts. Front Public Health. 2017;5:120. 27. Glasgow RE. What does it mean to be pragmatic? Pragmatic methods,
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measures, and models to facilitate research translation. Health Educ Behav. 2013;40:257–265. Glasgow RE, Riley WT. Pragmatic measures: what they are and why we need them. Am J Prev Med. 2013;45:237–243. McLeroy KR, Bibeau D, Steckler A, Glanz K. An ecological perspective on health promotion programs. Health Educ Behav. 1988;15:351–377. Boutaugh ML, Jenkins SM, Kulinski KP, Lorig KL, Ory MG, Smith ML. Closing the disparity: The work of the Administration on Aging. Generations. 2014;38:107–118. Ory MG, Smith ML, editors. EvidenceBased Programming for Older Adults, Lausanne, Switzerland: Frontiers Media; 2015.
INTRODUCTION The 2015−2020 Dietary Guidelines for Americans call for increasing fruit and vegetable intake while simultaneously reducing the intake of saturated fats, salt, and sugars.1 However, a substantial proportion of adults do not consume the recommended servings of fruits and vegetables.2,3
Furthermore, many adults consume diets that exceed the recommended limits for saturated fats, sodium, and added sugars.2 Consequently, fast food consumption (including sugarsweetened beverages) is associated with obesity and cardiovascular risk.4 Small steps can be beneficial to modify dietary behavior as well as create balance in food choices and total
1
Center for Population Health and Aging, Texas A&M University, College Station, TX Department of Environmental and Occupational Health, School of Public Health, Texas A&M University, College Station, TX 3 Department of Health Promotion and Behavior, College of Public Health, The University of Georgia, Athens, GA 4 Department of Health Management & Informatics, University of Central Florida, Orlando, FL 5 Aging & Technology Faculty Cluster Initiative, University of Central Florida, Orlando, FL 6 Department of Epidemiology & Biostatistics, School of Public Health, Texas A&M University, College Station, TX 7 Family and Community Health, AgriLife Extension Service, Texas A&M University, College Station, TX Conflict of Interest Disclosure: The authors have not stated any conflicts of interest. Address for correspondence: Matthew Lee Smith, Center for Population Health and Aging, Texas A&M University, 212 Adriance Lab Rd, College Station, Texas 77843; E-mail: [email protected] Ó 2019 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved. https://doi.org/10.1016/j.jneb.2019.06.008 2
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caloric intake.5 Dietary habits are influenced by individual characteristics (eg, sociodemographics), interpersonal interactions (eg, social support), and settings that individuals visit in a community (eg, facilities hosting health-promoting programs).6−9 An individual’s perceived confidence (ie, self-efficacy) to change dietary patterns can be influenced.10 Based on a multimodal intervention approach, this quasi-experimental study examined the effectiveness of a structured behavioral intervention to change diet, eating patterns, selfefficacy, and social support among middle-aged and older adults. The intervention, Texercise Select, has been shown to improve fruit and vegetable consumption, self-efficacy to improve nutrition, and general social support for lifestyle/behavior using a singlegroup, pretest/post-test design.11 The current study builds upon these findings by using a more rigorous study design to examine initial and sustained diet-related outcomes. It is hypothesized that significant initial and sustained improvements will be observed among Texercise Select participants relative to participants in
1
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the comparison group (ie, those not receiving the intervention).
METHODS Participants and Procedures This study used longitudinal data collected from nonequivalent groups in a quasi-experimental design (ie, intervention and comparison groups) from May 2015 to September 2017. This study included 9 intervention and 14 comparison sites, which were recruited through local partnerships with multiple organizations in the aging and healthcare sectors. More comparison sites were recruited to account for greater participant attrition within the comparison group. Between 13 and 26 participants were recruited from each intervention site, and between 8 to 41 participants were recruited from each comparison site. All sites were either exposed to the intervention or treated as comparison sites. There was no mixing in terms of treatment arm within a particular site location. Sites were assigned to the intervention or comparison condition using nonrandomized methods based on the organizations’ comparability in terms of size, client base, and their readiness to offer the intervention. The intervention group participated in Texercise Select, a 12-week lifestyle enhancement program offered in a small group education format in community settings. The comparison group did not receive the intervention during the study period but were invited to join an evidence-based health promotion program after the end of the study. Examples of topics covered in the sessions include components of a healthy diet; components of a balanced diet; portion sizes; food labels; cooking modifications to maximize nutritional intake; and healthy choices when eating outside the home. Examples of session activities include keeping a nutrition log, making action plans, brainstorming about ways to change their diet, creating healthy meals/menus, and staying committed to nutritional goals and healthy eating. Specific details on program activities are described elsewhere.12 Study participants were recruited using convenience sampling methods by community partners in multiple
Journal of Nutrition Education and Behavior Volume 000, Number 000, 2019 locations across Texas who work with middle-aged or older adults. The inclusion criteria for the study participants were: (1) being at least 45 years old; (2) agreeing to participate in the study (consent given by way of an information sheet); and (3) completing and returning the baseline survey to the research team. For the intervention group, participants were excluded from the study if they did not attend the first or second session of the Texercise Select workshop. To be ethical and accommodate community partners’ needs, those who did not attend the first or second session were still allowed to participate in the workshops; however, their data were excluded from analyses. A total of 9 Texercise Select workshops were hosted in community settings, including senior centers, faith-based facilities, senior housing facilities, and all-purpose community centers. Data were collected from participants at baseline, 3 months, and 6 months. Instruments at all 3-time points included items related to self-reported dietary behaviors, self-efficacy related to engaging in a healthy diet, and perceived social support for engaging in a healthy diet. This study was approved by the Texas A&M University Institutional Review Board.
Primary Outcome Measures Dietary behavior. Using slightly modified measures of dietary behavior from the Starting the Conversation diet instrument,13 participants were asked about the following dietary behaviors over the past 7 days: number of times consumed a fast food meal or snack; number of soda and sugar-sweetened drinks (regular, not diet) consumed each day; number of fruit or vegetable servings consumed each day. For each of these items, responses were: 1, 2, 3, 4, and 5 or more. Participants were also asked to report how many cups of water they drink on an average day. For this item, responses were: 0, 1, 2, 3, 4, 5, 6, 7, and 8 or more. Self-efficacy. Participants were asked to indicate their degree of confidence using a 4-point Likert scale to (1) set a healthy eating goal; (2) read food labels; (3) identify recommended portion size for different foods, and (4)
identify social services for obtaining healthy foods. The valid response range for this self-efficacy scale was 4 to 16, with higher scores indicating greater self-efficacy related to keeping a healthy diet. The internal reliability for this scale was strong for the comparison (Cronbach’s alpha = 0.87) and intervention (Cronbach’s alpha = 0.88) group.14 Social support. Participants were asked how frequently they received social support for the following at baseline and 3 months using a 4-point Likert scale: (1) planning dietary goals; (2) keeping dietary goals; (3) reducing barriers to healthy eating; and (4) eating healthy meals. The valid response range for this scale was 4 to 16, with higher scores indicating greater perceived social support to keep a healthy diet. The internal reliability for this scale was good for the comparison (Cronbach’s alpha = 0.80) and intervention (Cronbach’s alpha = 0.77) group.14 Other covariate measures. The baseline survey included sociodemographic information (eg, age, sex, race/ethnicity, and education) and self-reported chronic conditions (ie, from a list of 9 conditions). Participants who selfreported ≥2 conditions were identified as having disease comorbidity. Race/ ethnicity was collapsed into 2 groups: non-Hispanic White and Other. Three levels of education were defined: highschool graduate or lower; some college or technical school; and college graduate or higher.
Statistical Analyses Bivariate analyses were used to compare the participants’ sociodemographic and baseline characteristics among the study participants between the comparison and intervention groups. Pearson’s chi-squared tests were used for categorical variables, independent sample t tests were used for interval variables, and WilcoxonMann Whitney tests were used for ordinal variables. Multiple mixedeffects models (ie, linear for interval variables; ordinal logistic for ordinal variables assuming proportional odds) were used to examine the changes in outcome variables from baseline to
ARTICLE IN PRESS Journal of Nutrition Education and Behavior Volume 000, Number 000, 2019 3 months and baseline to 6 months. In these models, nesting within sites (using a site ID variable) and individuals over time (using unique participant IDs) were incorporated in the model. Statistical independence was assumed at the participant-level, and over-time effects were assumed to be nested within the subject effect. Despite the low intra-class correlations within each site (ranged from <0.001 to 0.059), the random effects from sites were incorporated in the models. Given the nonlinear trend of the changes over time (ie, baseline, 3 months, and 6 months), time was included in the model as a categorical variable instead of a continuous variable. All models included time, group (intervention or comparison), and the interaction between time and group. Additionally, all models were performed after controlling for age, sex, race/ethnicity, education, and disease comorbidity. For each mixedeffect model, all participants with outcome data at ≥1 time points were included in the analyses.
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However, only participants with complete data for all independent and control variables were included in the analyses.
RESULTS A CONSORT diagram depicting participant flow for this study is published elsewhere.15 A total of 430 studyeligible participants were recruited (ie, intervention group = 163 [37.9%]; comparison group = 267 [62.1%]). In the intervention group, about 77% of study-eligible participants completed the 3-month follow-up, and 45% completed the 6 months follow-up. In the comparison group, about 65% of study-eligible participants completed the 3-month follow-up, and 40% completed the 6-month follow-up. Table 1 reports participants’ sociodemographic characteristics and baseline values for outcome variables by treatment group. On average, participants were age 74.49 (standard deviation, 8.95). Most of the study participants were female (77.4%), had ≥2 comorbid
conditions (66.4%), and had cardiovascular risk (72.6%). About half (47.3%) of participants were non-Hispanic white, and 61.3% had more than a high-school education. Relative to the comparison group, the intervention group had a significantly larger proportion of non-Hispanic white participants (34.7% vs. 67.7%). At baseline, the comparison group consumed significantly more sugar-sweetened beverages relative to the intervention group (P < .005). On average, intervention group participants attended 14.8 (§5.47) of the 20 workshop sessions.
Program Effects Table 2 presents outcomes from the adjusted models for all study-eligible participants. For the intervention group, significant improvements were observed for fast food consumption (P = .011), fruit/vegetable consumption (P = .008), water consumption (P = .009), and social support (P < .001) from baseline to 3 months. For the comparison group, no statistically
Table 1. Baseline Characteristics of the Study Participants Mean (SD), Median [IQR], or Frequency (%)
Demographic Characteristics Age (years) Female Non-Hispanic white Education High-school graduate or lower Some college College graduate or higher Disease comorbitity Cardiovascular risk Baseline diet behavior Fast food consumption (times in the past 7 days) Fruit/vegetable consumption (servings per day in the past 7 days) Soda/sugar drink consumption (drinks per day in the past 7 days) Water (drinks per day) Baseline social support Baseline self-efficacy Workshop characteristics Number of attended sessions (max = 20)
Overall (n = 430)
Intervention (n = 163)
Comparison (n = 267)
P Values (Intervention vs Comparison)
74.49 (8.95) 333 (77.4%) 192 (47.3%)
74.84 (7.70) 129 (79.1%) 105 (67.7%)
74.28 (9.65) 204 (76.4%) 87 (34.7%)
.505 .510 <.001
166 (38.7%) 134 (31.2%) 129 (30.1%) 277 (66.4%) 312 (72.6%)
56 (34.4%) 62 (38.0%) 45 (27.6%) 111 (68.5%) 119 (73.0%)
110 (41.4%) 72 (27.1%) 84 (31.6%) 166 (65.1%) 193 (72.3%)
.058
.471 .871
2 [1, 3]
2 [1, 3]
2 [1, 3]
.509
3 [2, 4]
3 [2, 5]
3 [2, 4]
.553
0 [0, 2]
0 [0, 1]
1 [0, 2]
.005
5 [4, 7] 9.93 (3.39) 12.23 (2.53) —
IQR indicates interquartile range; SD, standard deviation.
5 [4, 7] 9.75 (3.30) 12.30 (2.59) 14.83 (5.47)
5 [4, 7] 10.03 (3.45) 12.19 (2.50) —
3
.940 .414 .682 —
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Table 2. Adjusted Immediate and Sustained Program Effects Among the Overall Study Participants
Adjusted Differences (D) or Adjusted ORa (95% CI), Changes From Baseline
Adjusted Means (95% CI)
Variables
Self-Efficacy Comparison Intervention
Immediate Program Effects: Compare T1 vs T2
Sustained Program Effects: Compare T1 vs T3
Sustained Program Effects: Compare T1 vs T3
P Values (“group x time” Interaction) .046b,c .184d
− −
− −
− −
OR = 1.04 (0.77−1.40) OR = 0.66 (0.48−0.91)
OR = 0.97 (0.66−1.42) OR = 0.65 (0.42−1.02)
.820 .011
.886 .062 .006b,c .656d
− −
− −
− −
OR = 0.83 (0.60−1.15) OR = 0.74 (0.50−1.09) OR = 1.68 (1.15−2.47) OR = 0.84 (0.53−1.35)
.263 .008
.123 .483 .393c .710d
− −
− −
− −
OR = 1.09 (0.82−1.44) OR = 0.88 (0.60−1.30)
OR = 0.82 (0.58−1.16) OR = 0.73 (0.45−1.19)
.567 .524
.267 .209 .204c
− −
− −
− −
OR = 1.14 (0.89−1.47) OR = 1.46 (1.10−1.94)
NA
.299 .009
NA
NA
D = 0.49 (0.06−1.04)
NA
.079
NA
NA
D = 1.79 (1.16−2.42)
.002b,c 9.78 (9.31−10.25) 10.08 (9.53−10.63)
10.27 (9.72−10.83) 11.87 (11.27−12.48)
<.001 .167c
12.23 (11.86−12.59) 12.24 (11.81−12.67) 12.43 (11.97−12.89) 12.95 (12.44−13.47)
NA NA
D = 0.01 ( 0.46−0.48) D = 0.52 ( 0.03−1.07)
NA
.957 .063
NA
CI indicates confidence interval; NA, not available; OR, odds ratio; T1, baseline; T2, 3-month follow-up; T3, 6-month follow-up. a The likelihood of having a higher response category (ie, more fast food, more fruit/vegetables, more soda, or more water) were modeled; bIntervention group showed greater improvement over time than the control group; cStatistical significance of differences in changes from the baseline to 3-month follow-up between the intervention and control groups; dStatistical significance of differences in changes from the baseline to 6-month follow-up between the intervention and control groups. Note: All models were controlled for age, sex, race/ethnicity, education, and disease comorbidity
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Intervention
T1. Baseline
T3. 6-Month Follow-Up
Immediate Program Effects: Compare T1 vs T2
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Fast Food Consumption (times in the past 7 days) Comparison Intervention Fruit/Vegetable Consumption (servings in past 7 days) Comparison Intervention Soda/Sugar Drink (drinks in past 7 days) Comparison Intervention Water consumption Comparison Intervention Social support Comparison
T2. 3-Month Follow-Up
P Values (Changes From Baseline to Immediate Post or 6-Month Post-Test)
ARTICLE IN PRESS Journal of Nutrition Education and Behavior Volume 000, Number 000, 2019 significant changes were observed. Relative to the comparison group, the magnitude of change was significantly greater for the intervention group in terms of fast food consumption (odds ratio [OR] = 1.04 vs. OR = 0.66; P = .046), fruit/vegetable consumption (OR = 0.83 vs. OR = 1.68; P = .006), and social support (D = 0.49 vs. D = 1.79; P = .002) from baseline to 3 months.
DISCUSSION Texercise Select has the potential to improve diet-related outcomes among older and potentially at-risk adults. Relative to comparison group participants, those in the intervention group significantly improved in terms of their fast food and fruit/vegetable consumption, and social support from baseline to 3 months. Texercise Select participants reduced their fast food consumption from baseline to 3 months. Fast food and dining out are common challenges for individuals wanting to engage in a healthier diet.16 Findings suggest that Texercise Select sessions addressing these topics were influential in helping participants change the frequency of their fast food consumption. Of particular note, Texercise Select participants as a group reduced their fast food consumption from baseline to 3 months. Fast food and dining out are common challenges for individuals wanting to engage in a healthier diet.16 Findings suggest that Texercise Select sessions addressing these topics were influential in helping participants change the frequency of their fast food consumption. Foods that are most conveniently purchased are not always those that are most healthy. While fast food restaurants may be easily available, accessible, and affordable,4,17 they are not the only food option. Despite perceptions that purchasing healthy foods is inconvenient or cost-prohibitive,18,19 dietary recommendations support the consumption of fresh, frozen, or canned fruits and vegetables,20 which makes having healthy foods “on-hand” easier because of widespread purchasing locations and the ability to store foods in the home for longer periods. However, it should be acknowledged that many fast food restaurants now
carry healthier options (eg, milk, bottled water, apple slices instead of French fries), thus individuals can better control their dietary intake despite the existing negative stigma around fast food consumption.21,22 Although most of the initial dietrelated improvements were not sustained at 6 months, the overall findings are encouraging. The general tapering effect of improvements observed in this study suggests that participants could benefit from additional or ongoing intervention. Strategies to sustain initial gains can include hosting brief booster sessions (eg, one hour, once a month), initiating telephone or text communication, and creating social media groups for participants after the workshop concludes. Another strategy to maintain program effects could be to sequentially enroll Texercise Select participants into other evidence-based disease prevention and health promotion interventions.23,24 These findings have implications for participant recruitment to enhance intervention effectiveness by enrolling at-risk, older adults. Recruitment efforts could focus on engaging community organizations that serve older adults (eg, senior centers, residential facilities, area Agencies on Aging) or healthcare facilities that serve heart disease or diabetes patients (eg, hospitals and dialysis clinics). While these sites may or may not be able to adopt and host the workshops themselves, a referral protocol could be created to ensure eligible participants are aware of, and able to access, the intervention. Prior to introducing an intervention into a community, a variety of key stakeholders (eg, community leaders, representatives from potential delivery sites, potential leaders, potential participants) should be approached to learn more about their needs and what they value as successes.25 This may include tailoring marketing material to the target population, being responsive to unique needs of target delivery sites (eg, literacy and language), and engaging community members to volunteer as facilitators to lead programs in their community. Further, identifying and engaging key stakeholders early in the planning process will increase
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community buy-in, improve organizational adoption, and ultimately promote intervention scalability and sustainability.26 Texercise Select was able to reach and retain individuals in need of sustainable interventions targeted at improved nutrition, a key component in overall health. The intervention effects suggest Texercise Select is a replicable, successful strategy to modify health behaviors into sustainable healthy lifestyles. Although only limited areas throughout Texas have received the program thus far, the successes of the program set the stage for wider implementation and dissemination across the state (and beyond), thereby seeking to ameliorate adverse health linked to poor nutrition facing millions of middle-aged and older adults across the US.
Limitations Data used in these analyses were selfreported. Thus, actual food consumption or changes in social support could not be verified through observation or other objective measures. However, many of the items have been used in other research and practice settings and reflect the pragmatic nature of this study.27,28 Although this study examined 2 positive (ie, fruit/vegetable and water) and 2 negatives (ie, fast food and sugar-sweetened beverages) measures of diet, including more dietrelated measures, may have allowed for a more robust assessment of participants’ eating patterns over time. Although the measures for self-efficacy and social support showed strong internal reliability (Cronbach’s alpha), intra- and interrater reliabilities were not examined. This design was quasiexperimental, which represents an improvement over a 1-group, pre-post design. However, because there was no randomization, group assignment was imperfect and resulted in some baseline differences between the groups. To minimize baseline differences, the adjusted models control for age, sex, race/ethnicity, education, and disease comorbidity, as well as including a random effect for site differences. Future studies should consider using propensity score or frequency matching methods for analyses. Because of the realities of
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pragmatic community-based intervention research, the overall sample size was modest. Therefore, some analyses may have been underpowered to detect meaningful changes. Although the socioecological model29 provided a guiding framework for identifying different intervention touch points, in no way was the full model or spheres of influence included or measured.
IMPLICATIONS FOR RESEARCH AND PRACTICE Texercise Select is a structured intervention that draws upon multiple spheres of the socioecological framework.29 At the individual level, the intervention content provides participants with practical knowledge about food choices, raises awareness about common barriers/pitfalls, and teaches skills to identify local resources to promote a healthy diet. Concurrently, the intervention enables participants to practice problem solving and goal setting week after week. This iterative trialand-error process is skill building and improves self-efficacy in a safe environment. At the interpersonal level, intervention activities (eg, identifying resources) and the small group format (including lay leader facilitators) provide social support. Often participants in community-based interventions begin to form friendships and function as an informal support group or buddy systems, even after the intervention ends.30,31 Furthermore, the information gained during the intervention and the participants’ dietary goals are often shared with others (eg, family members, co-workers, healthcare providers), which further garners support. At the organization/setting level, introducing and embedding interventions in communities can begin to change organizational policy, inter-agency partnership, and social/cultural norms and values.1 In short, Texercise Select adheres to the 2015−2020 Dietary Guidelines for Americans in that it delivers “information in a way that is compelling, inspiring, empowering, and actionable for individuals. . .” and focuses “on individuals where they are making food and beverage choices.”1
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ACKNOWLEDGMENTS The authors recognize the Texas Health and Human Services Commission, for supporting the creation and evaluation of Texercise Select, and the Texas A&M School of Public Health for helping to standardize and implement Texercise Select. We thank the delivery sites, class facilitators, and participants for their role in the study.
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9. Barrera M Jr, Strycker LA, MacKinnon DP, Toobert DJ. Social-ecological resources as mediators of two-year diet and physical activity outcomes in type 2 diabetes patients. Health Psychol. 2008;27: s118–s125. 10. AbuSabha R, Achterberg C. Review of self-efficacy and locus of control for nutrition- and health-related behavior. J Am Diet Assoc. 1997;97:1122– 1132. 11. Smith ML, Ory MG, Jiang L, et al. Texercise Select effectiveness: an examination of physical activity and nutrition outcomes. Transl Behav Med. 2015;5: 433–442. 12. Ory MG, Smith ML, Howell D, et al. The conversion of a practice-based lifestyle enhancement program into a formalized, testable program: from Texercise Classic to Texercise Select. Front Public Health. 2014;2:291. 13. Paxton AE, Strycker LA, Toobert DJ, Ammerman AS, Glasgow RE. Starting the conversation: performance of a brief dietary assessment and intervention tool for health professionals. J Prev Med. 2011;40:67–71. 14. Nunnaly J. Psychometric Theory. New York, NY: McGraw-Hill; 1978. 15. Ory MG, Lee S, Han G, et al. Effectiveness of a lifestyle intervention on social support, self-efficacy, and physical activity among older adults: evaluation of Texercise Select. Int J Environ Res Public Health. 2018;15:234. 16. Welch N, McNaughton SA, Hunter W, Hume C, Crawford D. Is the perception of time pressure a barrier to healthy eating and physical activity among women. Public Health Nutr. 2009;12:888–895. 17. Paeratakul S, Ferdinand DP, Champagne CM, Ryan DH, Bray GA. Fastfood consumption among US adults and children: dietary and nutrient intake profile. J Am Diet Assoc. 2003; 103:1332–1338. 18. Walker RE, Keane CR, Burke JG. Disparities and access to healthy food in the United States: A review of food deserts literature. Health Place. 2010; 16:876–884. 19. Rao M, Afshin A, Singh G, Mozaffarian D. Do healthier foods and diet patterns cost more than less healthy options? A systematic review and meta-analysis. BMJ Open. 2014;3: e004277. 20. Centers for Disease Control and Prevention. Healthy eating for a healthy
ARTICLE IN PRESS Journal of Nutrition Education and Behavior Volume 000, Number 000, 2019 weight. Atlanta, GA: Centers for Disease Control and Prevention; 2016 https:// www.cdc.gov/healthyweight/healthy_ eating/index.html. Accessed December 20, 2018. 21. Kraak VI. US restaurant sector can promote healthy food environments to reduce obesity risk. Am J Clin Nutr. 2018;107:288–290. 22. Min J, Jahns L, Xue H, Kandiah J, Wang Y. Americans’ perceptions about fast food and how they associate with its consumption and obesity risk. Adv Nutr. 2018;9:590–601. 23. Towne SD Jr, Smith ML, Ahn S, et al. National dissemination of multiple evidence-based disease prevention programs: Reach to vulnerable older adults. Front Public Health. 2014;2:156.
24. Lee S, Smith ML, Towne SD Jr, Ory MG. Effects of sequential participation in evidence-based health and wellness programs among older adults. Innov Aging. 2018;2:1–12. 25. Stevens AB, Thiel SB, Thorud JL, et al. Increasing the availability of physical activity programs for older adults: Lessons learned from Texercise stakeholders. J Aging Phys Act. 2016;24:39–44. 26. Smith ML, Schneider EC, Byers IN, et al. Reported systems changes and sustainability perceptions of three state Departments of Health implementing multi-faceted evidence-based fall prevention efforts. Front Public Health. 2017;5:120. 27. Glasgow RE. What does it mean to be pragmatic? Pragmatic methods,
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measures, and models to facilitate research translation. Health Educ Behav. 2013;40:257–265. Glasgow RE, Riley WT. Pragmatic measures: what they are and why we need them. Am J Prev Med. 2013;45:237–243. McLeroy KR, Bibeau D, Steckler A, Glanz K. An ecological perspective on health promotion programs. Health Educ Behav. 1988;15:351–377. Boutaugh ML, Jenkins SM, Kulinski KP, Lorig KL, Ory MG, Smith ML. Closing the disparity: The work of the Administration on Aging. Generations. 2014;38:107–118. Ory MG, Smith ML, editors. EvidenceBased Programming for Older Adults, Lausanne, Switzerland: Frontiers Media; 2015.