Contemporary Clinical Trials 39 (2014) 201–210

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Contemporary Clinical Trials journal homepage: www.elsevier.com/locate/conclintrial

Enhancing physical and social environments to reduce obesity among public housing residents: Rationale, trial design, and baseline data for the Healthy Families study Lisa M. Quintiliani a,⁎, Michele A. DeBiasse b, Jamie M. Branco c, Sarah Gees Bhosrekar c, Jo-Anna L. Rorie c, Deborah J. Bowen c,1 a b c

Boston University, School of Medicine, 801 Massachusetts Ave, Crosstown Center, 2nd Floor, MISU, Boston, MA 02118, USA Boston University, College of Health & Rehabilitation Sciences, Sargent College, 635 Commonwealth Ave, 4th Floor, Boston, MA 02215, USA Boston University, School of Public Health, Partners in Health and Housing Prevention Research Center, Albany St, Talbot Building 120W, Boston, MA 02118, USA

a r t i c l e

i n f o

Article history: Received 4 April 2014 Received in revised form 9 August 2014 Accepted 11 August 2014 Available online 17 August 2014 Keywords: Public housing Obesity Environmental intervention Family

a b s t r a c t Intervention programs that change environments have the potential for greater population impact on obesity compared to individual-level programs. We began a cluster randomized, multicomponent multi-level intervention to improve weight, diet, and physical activity among lowsocioeconomic status public housing residents. Here we describe the rationale, intervention design, and baseline survey data. After approaching 12 developments, ten were randomized to intervention (n = 5) or assessment-only control (n = 5). All residents in intervention developments are welcome to attend any intervention component: health screenings, mobile food bus, walking groups, cooking demonstrations, and a social media campaign; all of which are facilitated by community health workers who are residents trained in health outreach. To evaluate weight and behavioral outcomes, a subgroup of female residents and their daughters age 8–15 were recruited into an evaluation cohort. In total, 211 households completed the survey (RR = 46.44%). Respondents were Latino (63%), Black (24%), and had ≤ high school education (64%). Respondents reported ≤2 servings of fruits & vegetables/day (62%), visiting fast food restaurants 1+ times/week (32%), and drinking soft drinks daily or more (27%). The only difference between randomized groups was race/ethnicity, with more Black residents in the intervention vs. control group (28% vs. 19%, p=0.0146). Among lowsocioeconomic status urban public housing residents, we successfully recruited and randomized families into a multi-level intervention targeting obesity. If successful, this intervention model could be adopted in other public housing developments or entities that also employ community health workers, such as food assistance programs or hospitals. © 2014 Elsevier Inc. All rights reserved.

1. Introduction The prevalence of overweight/obesity in the U.S. is substantial and is considerably higher in women with lower

⁎ Corresponding author. Tel.: +1 617 638 2777. E-mail addresses: [email protected] (L.M. Quintiliani), [email protected] (M.A. DeBiasse), [email protected] (J.M. Branco), [email protected] (S.G. Bhosrekar), [email protected] (J.-A.L. Rorie), [email protected] (D.J. Bowen). 1 Present address: University of Washington, Seattle, WA, USA.

http://dx.doi.org/10.1016/j.cct.2014.08.005 1551-7144/© 2014 Elsevier Inc. All rights reserved.

socio-economic status compared to other women or in men [1–3]. In urban areas, individuals with low socioeconomic status living in subsidized public housing report nearly two times higher levels of obesity compared with other urban residents [4]. Diet and physical activity behaviors related to obesity are clearly important individual-level factors, but efforts to change these factors on a population-wide basis have been largely unrealized. Research points to the role of environmental conditions in the development of obesity, including availability and marketing of low-cost/energy-dense

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foods and prevalence of areas without easy access to healthy options like places to walk and buy healthful foods [5,6]. Intervention programs that aim to change environments may have greater impact on preventing obesity or further weight gain among a population of residents as opposed to individual, one-on-one intervention programs seeking to modify diet and physical activity behaviors [7,8]. Research studies in public housing have targeted a number of health behaviors, including screening for chronic disease risk [9], cancer screening [10], tobacco use and other environmental hazards [11,12], and chronic disease-related risk factors such as diet [13], and physical activity [14,15]. For example, in the pathways to Health trial, residents of public housing were randomized to receive either an individual-level intervention targeting smoking cessation or fruit and vegetable intakes [13]. The diet-related intervention included receiving culturallyappropriate educational materials and motivational interviewing counseling sessions. Ahluwalia and colleagues reported a significant increase in fruit and vegetable intakes among those receiving the fruit and vegetable intervention compared to those receiving the smoking cessation intervention of 1.58 and 0.78 greater servings of fruit and vegetables at 8 week and 6 month follow-up, respectively [13]. For physical activity, a communitybased intervention study was conducted to promote walking activity in a public housing site in Seattle through walking groups, improvements to walking routes, and advocacy for pedestrian safety [15]. Results showed that self-reported walking increased among walking group participants, from 65 to 109 min/day [15]. Our own pilot work in Boston public housing developments indicates that although residents consider stress and safety/violence to be the top two health issues (endorsed by 43% and 40% of residents, respectively), 27% of residents also endorsed obesity as a health concern [16]. This body of research, along with other research conducted in homes of low-income families [17], establishes the feasibility and efficacy of conducting health behavior interventions in populations living in public housing in general, yet, there is less literature in public housing for programs targeting obesity and multi-component multi-level programs. Intervention programs are needed that target multi-level conditions, that are adapted to individuals at-risk for obesity (e.g., low socioeconomic individuals), and that can be sustained after the active intervention period ends. To meet these goals, the primary aim of this cluster randomized trial is to design, implement, and evaluate a multi-component multi-level intervention to improve weight, diet, and physical activity outcomes among residents of public housing developments in Boston. Here we describe the rationale, intervention design, and baseline survey data.

2. Material and methods 2.1. Participants and data collection Public housing in Boston is administered by the Boston Housing Authority, a public agency that provides subsidized housing to low- and moderate-income individuals and families, disabled individuals, and elderly individuals. There are 64 public housing developments, 37 are designated as elderly/disabled developments and 27 are designated as family developments

[18]. Approximately 27,000 people are housed under the public housing program [18]. Family (vs. elderly) designated housing developments with more than 200 residents that were not undergoing renovations that require residents to move out of the development for a period of time in the city of Boston were eligible to participate (n = 24). Our goal was to have 10 developments participate in the Healthy Families study; 5 serving as intervention developments and 5 serving as control developments. Initially study staff sent an email request to meet with development managers and Boston Housing Authority employees at each development. In that email, research staff attached a question and answer document detailing the project, steps managers would need to take, and how the programs would operate. Then study staff met face to face with the managers and received permission to either move forward and bring in the project or was refused. If the project was approved, staff then met with tenant associations and development leaders and went through the same process with them; we called this “community consent” (manuscript under review). Developments were then randomly assigned to either condition, in matched pairs for size of development and existence of health activities in the development. Housing developments randomized to the intervention group received all intervention components (see Intervention section below) and developments randomized to the control group did not receive any intervention components. All residents in the 5 intervention housing developments are allowed to participate in any intervention activities. The 10 developments are spread fairly evenly throughout the city of Boston, representing 8 neighborhoods. We chose an assessment only control group in place of an attention placebo control group in order to achieve the maximum difference in change in outcomes between the intervention and control groups. In both intervention and control group housing developments, a subgroup of female residents and their daughters were recruited into an evaluation cohort to examine study outcomes. We selected mothers because in the family developments over 80% of heads of household were women. We selected daughters aged 8–15 because of the development of obesity that occurs during this time period for females, and obesity prevalence is particularly high among African American girls [19]. To be eligible, participants must be female, age 18– 72, live in public housing and plan to do so for two years, have responsibility for a girl age 8–15 (also living in public housing), be English or Spanish speaking, and be able to make changes to their diet and physical activity habits if desired. Exclusion criteria are if the adult is not able to complete the survey tools or is not interested in participating. All study materials were available and used in both English and Spanish; materials in Spanish were reviewed by a Health Living Advocate (see Intervention section for description of Healthy Living Advocates) to check if the content would resonate with residents. Mothers were given a $10 gift card for their time at baseline. Survey assistants approached randomly selected apartment units within each of the 10 housing developments. Sequential numbers were assigned to each unit in a development starting with number 1 and a 20% sample of units were then chosen using a random number generator (stattrek.com) until enrollment minimums were reached. Using a standardized protocol, a trained team of two, composed one survey assistant and one

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Resident Health Advocate (see Intervention section) knocked on randomly selected doors to introduce themselves, to explain the study, and to assess individual's interest in participating and eligibility. Instructions for the data collection teams included how many times to knock on a resident's door and how many times to return if no one was home. If interested and eligible, the participant provided their written informed consent to participate, girls aged 12–15 provided written assent, and girls aged 8–11 gave verbal assent. If there was more than one girl in the age range, then a participant was still eligible. If a girl did not provide assent, the mother–daughter pair would not have been enrolled. The data collection team then administered the baseline survey (see Measures section) to the adult participant and recorded the adult participant's responses. After completing the survey, the women and girl's height and weight were measured and recorded. Therefore, the only evaluation measure completed by the child is measured height and weight. Data collection teams return to re-assess the original evaluation cohort at one- and two-year follow-up intervals. 2.2. Intervention Healthy Families was initiated under the administrative structure of the Boston University Partners in Health and Housing Prevention Research Center (PHH-PRC), a CDC-funded Prevention Research Center focused exclusively on health promotion research in public housing. This 3-year multicomponent multi-level intervention package is called ‘Healthy Families’. Adapted from the model by Glanz and colleagues [20], our conceptual model (see Fig. 1) depicts the mother– daughter pairs who are at the center of our intervention surrounded by three main environmental-level categories of influence: community, organizational, and consumer nutrition and physical activity environments. Government and industry policies and the information environment are depicted as

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influencing the three main environmental-level categories as well as on eating and physical activity patterns directly. Individual-level factors, socio-demographics and psychosocial factors, are shown as influencing eating and physical activity patterns, but the main emphasis of the model is on various multi-level influences. Healthy Families contains six intervention components: health screenings, walking groups, nutrition and cooking demonstrations, healthy purchasing options, a social media campaign, and neighborhood resource maps. We chose these components to comprise our intervention since they can be implemented within a reasonable period of time, is built on research already conducted by the PHH-PRC [9,21–23], and can be sustained by the developments at the conclusion of our project. Data collection and intervention components are facilitated and coordinated by residents of public housing who have been trained in community health outreach, which is a longstanding program (12+ years) provided by the PHH-PRC [9]. Upon completion of a 14-week training, these residents, called Resident Health Advocates, completed a six month paid internship in their public housing development, distributing health information on a variety of topics of concern to residents and helping to link residents to neighborhood health- and clinical-related resources. As described above, several Resident Health Advocates were selected and paid as members of the data collection teams. A subset of all previously trained Resident Health Advocates were also recruited to receive additional training in obesity-specific knowledge and procedures to serve as a Healthy Living Advocates in the Healthy Families intervention. We began by contacting the Resident Health Advocates living in each of our chosen intervention developments and encouraging them to apply. We interviewed all applicants and chose the most qualified candidate. These individuals completed a 3-day training covering research

Fig. 1. Conceptual model and associated intervention activities of the Healthy Families multi-level intervention approach to target obesity among mother–daughter pairs in Boston public housing.

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processes (e.g., protecting participant privacy), study-specific protocols, and obesity management (e.g., health implications of obesity, effective approaches to supporting weight loss) and passed a post-training assessment to demonstrate their knowledge. All of those who began the training, completed it. Upon completion, the Healthy Living Advocates (n = 5) served to coordinate the Healthy Family activities, described below, in the 5 intervention developments. They were paid an hourly wage according to the University's recommended pay scale [22].

2.2.1. Health screenings Screening for risk factors for chronic disease is one of the most widely used strategies to prevent mortality and morbidity for chronic disease in modern industrialized countries. Identification of risk factors, measured in blood or other body fluids, or by body function, like blood pressure, offers information about a person's overall risk for development of chronic diseases, like cardiovascular disease and diabetes [24,25]. Among public housing residents, we found in previous work that both rates of participation in screenings and the rates of positive screens for chronic disease were high among residents when the screenings were held inside the housing developments [9]. Therefore, we offered monthly screenings for blood pressure, smoking, and diabetes risk for individuals to learn about their chronic disease risk and be referred to a program within the Healthy Families intervention, to their own provider, or given information about how to obtain a provider if they do not have one. Due to expanded public insurance options, 94–97% of non-elderly adults in Massachusetts have health insurance [26], removing this classic barrier to care. Research team members staff the screenings for 3–4 h in duration, with a screening for an individual resident lasting 5 min or less. They are held in a shared space in the development, are advertised for 2 weeks prior, and are administered by research study staff. HLAs also attend screenings to assist with linking residents to the primary care system as needed.

2.2.2. Access to healthy food Increasing access to healthy foods is a key component of improving the food environment, yet, adding supermarkets or changing the existing store resources (e.g., adding refrigerated units to accommodate fruits and vegetables) can take years to perform and are often expensive [27]. Additional options, such as farmer's markets and urban gardening, can be limited by their seasonal nature and may have differential patterns of use in different areas of the United States [27,28]. To provide immediate, affordable, and easy access to healthy choices, we selected to provide access by contracting with a company that operates a retrofitted school bus to sell fruits and vegetables to residents of public housing at or slightly below market value. The company works with the same distributors that work with major area supermarkets and provides more than 30 different kinds of fruits and vegetables, as well as nuts, whole grains, healthy snacks and other food items. The bus visits each intervention housing development weekly. HLAs at each development promote the offerings and are present while the mobile food bus is parked on-site.

2.2.3. Walking groups Research suggests that the built environment is a powerful influence on behaviors [29]. Significant environmental barriers to walking, particularly among multi-cultural low-income women, have been well-documented and include lack of sense of safety and lack of places to exercise [30–32]. While research examining the specific aspects of the built environment, such as population density, design, and connectivity, is equivocal due to methodological and study design considerations [33,34], it stands to reason that developing a walking program to overcome perceived barriers to walking among the low-income multi-cultural women of public housing may encourage total physical activity. Our own pilot work indicates that walking groups are well-accepted by public housing residents, with about 20% of residents participating across 4 housing complexes, indicating self-reported increases in days of walking and social cohesion [21]. Walking groups were held weekly. HLAs serve as walk group leaders and promote the groups in the developments via flyers and word of mouth. HLAs are also provided a pedometer to track number of steps taken (participants are not given a pedometer). 2.2.4. Cooking demonstration/nutrition education Numerous successful multi-component interventions with the aim of reducing overweight and obesity have used cooking demonstrations as part of their programming [35–37]. While it is usually not possible to isolate their effects from the other intervention components, cooking demonstrations offer the opportunity to provide both nutrition education as well as shape social norms around healthful food practices among friends, family, and neighbors. A common theme of cooking demonstrations has been tailoring recipes to fit the targeted cultural context [38]. Other research has shown that among African American families, having meals prepared by a caregiver using healthier cooking methods was associated with reduced risk of adolescent overweight/obesity [39]. In preparation for the Healthy Families project, nearly all residents and management at targeted developments reported a desire to bring cooking classes to their neighborhoods as they were thought of as family friendly activities and positive social events in which neighbors could get together and work towards a common goal. The cooking demonstrations are held four times per year per development, are led by a Registered Dietitian, and are promoted beforehand by the HLAs using flyers and word of mouth, as well as via social media. The dietitian compiled a number of culturally diverse recipes which included foods that are available for purchase from the mobile food bus truck and are SNAP (Supplemental Nutrition Assistance Program)-eligible and WIC (Women Infants Children Program)-approved. At each demonstration, the dietitian prepares two recipes and provides nutrition education on a number of topics such as whole grains and sodium. 2.2.5. Social media The information environment generally includes different types of media such as mass media, social media, and small media. The Community Preventive Services Task Force recently recommended that health communication campaigns use multiple channels in conjunction with low-cost health-related

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products [40]. In Healthy Families, we focused on changing the small media inside the housing developments, not mass media which markets messages to the broader environment, by implementing a social media campaign. For this campaign, two channels of social media were chosen for their breadth of exposure as well as the literature support of their effectiveness: Facebook© and text/SMS messaging [41–43]. Facebook© is one of the most widely used online social networking platforms with 1.11 billion monthly active users as of March 31, 2013 [44]. In addition to providing an outlet for communication, Facebook© is a means to create social support and networking among groups of people who share connections. Literature describing the use of social support via Internet platforms suggests that these groups are effective in delivering weight loss programs [45] and may be effective in promoting weight maintenance [46]. With the advent of mobile technology via cellular phones, Short Messaging Service (i.e., texting) has become highly prevalent [47] and is an emerging outlet for delivering weight management programs [42,48]. In a survey of U.S. adults, the majority owns a cell phone (91%), and of those, the majority sends text messages (81%) [47]. While texting prevalence is significantly higher among those with higher education and income compared to those with less education and income, texting remains prevalent among all groups (e.g., 71% among those without a high school diploma and 78% among those with a household income of b $30,000/ year) [47]. In Healthy Families, research staff administrate a Facebook page with a target of at least one post each weekday. Post topics include health messages (e.g., healthy recipes, information regarding specific nutrients or physical activity) or scheduling information for upcoming intervention activities for the intervention developments (e.g., dates of cooking demonstrations, health screenings). Recruitment of participants for the Facebook© page takes place at group events, through word-ofmouth by HLAs, and by promoting the page on informational flyers. Use of text messaging is intended mainly for information dissemination regarding events. Similar to the Facebook© page, text/SMS message content is informed by CDC social marketing guidelines [49]. Text messages are delivered by research staff using a commercially available service (MessageMedia©) with a goal rate of at least two messages per intervention event, such as a cooking demonstration (one about a week before the event, and another about 24 h before an event). As there is a fee charged for participants to send text messages, and to receive text messages for some cell phone plans, participants are allowed to opt out at any time. Recruitment for text messaging participation occurs at group events and via word-of-mouth through the HLAs.

2.2.6. Resource maps Maps listing local health related resources (such as local gyms, walking parks, and healthy buying and eating establishments) are made available to development leaders and to all interested participants at health screenings, mobile food bus sessions, walking groups, and cooking demonstrations. Our pilot work showed that residents were very excited about these maps and had concrete suggestions for making them readable and useful [23].

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2.3. Measures 2.3.1. Baseline survey Participants completed standard questions about sociodemographics (e.g., age, race/ethnicity, highest level of education completed, self-rated health), psychosocial, and behavioral variables. Our selection of diet and physical activity behaviors on which to focus was guided by evidenced-based guidelines for health promotion and weight management [50,51], as well as our previous work that pointed to specific interventions that have high acceptability among our target populations (e.g., increasing moderate activity via walking groups [21]). Dietary intake is difficult and time consuming to measure, and so in the interest of decreasing participant burden, we decided to measure key single behaviors related to obesity and for which there were existing measures. We assessed three nutrition behaviors: fruits and vegetables (“How many servings of fruits and vegetables do you eat each day?” with 12 responses ranging from 0 to 11 or more, which was prefaced with pictures representing portion sizes) [52]; soda (“How often do you drink soft drinks or soda pop (regular or diet)?” with 6 responses ranging from never to 2 or more times per day) [53]; mindless eating (“How often do you eat food (meals or snacks) while doing another activity, for example, watching TV, working at a computer, reading, driving, playing video games?” with 5 responses ranging from never to always) [53]; and fast food (“Thinking about how often you eat out, how many times in a week or month do you eat breakfast, lunch or dinner in a place such as McDonald's®, Burger King®, Wendy's®, Arby's®, Pizza Hut®, or Kentucky Fried Chicken®” with 3 responses asking participants to write in number of times per week, times per month, or times per year) [54]. To assess self-efficacy to eat more healthfully, we asked “On a scale of 0 to 10, how sure are you that you will eat less sugar and fat during the next year?” with 11 responses ranging from 0 (not sure) to 10 (very sure) [55]. The same measurement difficulties exist for physical activity, and so we used the same strategy to select key single items that have been used before in research studies and compared well with longer measures. We assessed walking physical activity behavior for recreation, transport, or exercise during the past week with two questions: (1) “During the last 7 days, on how many days did you walk for at least 10 min at a time in your neighborhood?” with responses ranging from no walking for more than 10 min at a time or the option to fill in number of days per week and number of minutes per day [56]. Number of days/week was multiplied by minutes/day to calculate minutes of walking per week and then multiplied by 3.3 to calculate Metabolic Equivalents (METS). To assess walking on a typical day, we then asked: (2) “On a typical day how many minutes do you walk in your neighborhood?” with the option to fill in minutes per day [56]. Finally, the survey assistant measured the height and weight of both the mother and daughter using a scale. This was used to calculate body mass index (BMI, kg/m2). Survey assistants were trained to have participants remove their shoes and subtract 3 lb from the scale measurement to account for clothing weight. Electronic scales were used (Health o Meter, Model HDM770DQ1-05 E097BN Sunbeam Products operating as Jarden Consumer Solutions Boca Raton Florida). If a resident weighed over 300 lb (maximum measurement for the electronic scale), we used a second scale (Tanita Body Composition Analyzer, Model TBF-300A Tanita

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Corporation of America Inc., Arlington Heights, Illinois) that could weigh individuals up to 500 lb. The survey assistants would return to the participant's home the following day and weigh participants again if this scale was needed. Height was measured using a basic tape measure, which the survey assistants taped to the wall. Participants were asked to remove their shoes and stand with their back facing the wall; survey assistants then measured height in inches and converted it to inches and feet. 2.3.2. Process measures Data on indicators of intervention implementation were collected on three forms (1) Group Activity Form, which gathers information on date, type of event, which development, HLA name, number of attendees, number of steps (if walking group), walk route (if walking group), and participant contact information; (2) HLA Weekly Report, which gathers information on all outreach activities conducted by the HLAs in order to encourage residents to attend intervention events; and (3) Participant Evaluation Form, which gathers information on what participants did and did not like about each of the intervention activities. Data on number of Facebook posts, views, text messages sent, text messages successfully delivered, and replies to sent text messages will also be collected. 2.4. Analysis plan Survey data was hand written on standardized forms by survey assistants at the time of data collection and then entered into and managed using Research Electronic Data Capture (REDCap) electronic data capture tool hosted at Boston University [57]. REDCap is a secure, web-based application designed to support data management for research studies, providing: 1) an intuitive interface for validated data entry; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for importing data from external sources. Our plan is to compare the difference of the difference between participants in intervention and control sites from baseline to follow-up on obesity, dietary behaviors, and physical activity. We will begin with making statistical comparisons of socio-demographic and behavioral variables as measured at baseline between randomized groups using chi-square tests for categorical variables and independent t-tests for continuous variables. These findings are reported in this manuscript. Next, we will use GEE and mixed effects regression models to compare the change in weight, walking, fruit and vegetable intakes, soda consumption, fast food consumption, and frequency of mindless eating behaviors from baseline to one, two, and three year follow-up assessments. We will also take into account the grouped nature of our study design (group being the development) in final analyses, which will be reported in a future manuscript. 2.4.1. Sample size We have enrolled women participants and their daughters from 10 public housing developments in roughly equal numbers for the evaluation cohort. Sample size and power considerations for this study are based on 4 parameters that require some estimation: 1) the strength of ‘clustering’ by

development, 2) the difference in mean weight loss for the two study groups, 3) the standard deviation of weight loss, and 4) the loss-to-follow-up rate. Several sources suggest an ICC between 0.005 and 0.01 as reasonable estimates. Literature suggests that the intervention might lead to an average weight loss of 5 lb, and suggests a standard deviation for weight loss of between 6 and 10 lbs. A meta-analysis of 12 trials reported sufficient data to analyze group differences in short-term BMI change after a behavioral intervention [58]. The analysis found an average difference in BMI change of 1.22 kg/m2 between treatment and control groups (CI 0.75, 1.69) with statistical heterogeneity (I2 — 84.3%). Our primary outcome is change from baseline to Month 12 in BMI. Assuming a recruitment of 18 subjects per development and using an ICC of 0.01, the study has better than 80% power of detecting a 5 pound weight loss in the intervention vs. control groups for all combinations of ICC and standard deviation except for the worst-case scenario of an ICC of 0.05 and a standard deviation of 0.10. Therefore, a followed sample of 18 subjects per development (which gives 18 × 5 = 90 subjects per group or 180 total) can support the study. 3. Results and discussion We approached 12 developments to see if they would be interested in participating to arrive at our final sample of 10 developments (development response rate = 83%). Two developments were not interested. For the baseline survey, survey assistants approached a total of 3080 households to assess their interest and eligibility. Of these, 1953 were not eligible (no women lived in the household = 180, female child b8 years old = 257, female child N15 years old = 338, no female children = 995, woman N 72 years old = 25, 134 households did not speak English or Spanish = 134, vacant unit = 17, resident moving within 2 years = 5, no unit/ deceased = 2); 789 had unknown eligibility (no response = 559, declined enrollment = 214, not enrolled by study staff = 14 [often meaning that the data collection team encountered an unsafe situation], missing data = 2); and 338 were eligible (declined enrollment = 56, rescheduled [data collection team encountered difficulty in scheduling baseline assessment] = 27, woman/child not home to be assessed = 44, enrolled = 211). In total, there are 211 baseline survey respondents (response rate = 46.44%) [59]. Socio-demographic and behavioral variables for the sample of participants are shown in Table 1. Overall, the majority of respondents are Latino (63%), rely on public health insurance (Medicaid or Medicare, 79%), and have a high school education or less (64%). Respondents reported a mean consumption of 2.33 (SD=1.68)servings of fruits and vegetables per day, visiting fast food restaurants 1 or more times/week (31%), and drinking soft drinks daily or more (27%). Mean minutes of walking/day was 19.7 (SD =33.7). The majority of adults and girls were overweight or obese (83% of adults and 48% of girls). Of the variables listed in Table 1, the only difference between randomized groups was race/ethnicity, with more Black or African American residents in the intervention vs. the control group (28% vs. 19%, p=0.0146). We successfully recruited, enrolled, and randomized a cohort of residents of public housing with a low-income multi-cultural background in an ongoing cluster randomized

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Table 1 Baseline socio-demographic and behavioral variables of mother–daughter pairs living in public housing randomized to intervention (n = 5 developments) and control (n = 5 developments) groups in the Healthy Families study.

Socio-demographic variables Age, years, mean (SD) Race/ethnicityb,⁎ Asian Black or African American Hispanic/Latino White Other More than one Language spoken at home English Spanish Other Born in the U.S. Yes No Highest level of education b High school High school graduate/GED Some college or technical college College graduate Other Health insuranceb Private insurance Medicaid, MassHealth or Commonwealth Care Medicare Free care Other None More than one Behavioral variables Self-rated health Excellent Very good Good Fair Poor Adult BMI, kg/m2, mean (SD)c Obese Overweight Normal weight Underweight Physical activityd Inactive Minimally (sufficiently) active More (highly active) Walking in neighborhood, min/day, mean (SD)c Fruits & vegetables, servings/day 0 1 2 3 4 5+ Soft drinks consumption 2+ times/day About once/day About once/week bOnce/week 2–5 times/week Never Fast food visitsc Never b1 time/week 1–2 times/week N2 times/week

Totala n = 211

Controla n = 95

Interventiona n = 116

38.1 (7.6)

37.1 (6.8)

38.8 (8.2)

3 (1.4) 50 (23.7) 134 (63.5) 8 (3.8) 9 (4.3) 7 (3.3)

2 (2.1) 18 (18.9) 60 (63.2) 8 (8.4) 4 (4.2) 3 (3.2)

1 (0.9) 32 (27.6) 74 (63.8) 0 (0) 5 (4.3) 4 (3.4)

83 (39.3) 104 (49.3) 24 (11.4)

32 (33.7) 48 (50.5) 15 (15.8)

51 (44) 56 (48.3) 9 (7.7)

72 (34.1) 139 (65.9)

29 (30.5) 66 (69.5)

43 (37.1) 73 (62.9)

60 (28.4) 75 (35.6) 48 (22.7) 25 (11.9) 3 (1.4)

23 (24.2) 40 (42.1) 22 (23.3) 9 (9.4) 1 (1)

37 (31.9) 35 (30.2) 26 (22.4) 16 (13.8) 2 (1.7)

16 (7.6) 164 (77.7) 3 (1.4) 10 (4.7) 5 (2.4) 1 (0.5) 12 (5.7)

4 (4.2) 76 (80) 0 (0) 3 (3.2) 4 (4.2) 1 (1.1) 7 (7.3)

12 (10.3) 88 (75.9) 3 (2.6) 7 (6.0) 1 (0.9) 0 (0) 5 (4.3)

28 (13.3) 31 (14.7) 86 (40.7) 58 (27.5) 8 (3.8) 31.1 (7.7) 103 (48.8) 73 (34.6) 31 (14.7) 4 (1.9)

14 (14.7) 13 (13.7) 38 (40) 26 (27.4) 4 (4.2) 31.8 (7.7) 49 (51.6) 29 (30.5) 15 (15.8) 2 (2.1)

14 (12.1) 18 (15.5) 48 (41.4) 32 (27.6) 4 (3.5) 30.6 (7.7) 54 (46.6) 42 (36.2) 16 (13.8) 2 (1.7)

181 (85.8) 28 (13.3) 2 (0.9) 19.7 (33.7)

78 (82.1) 17 (17.9) 0 (0) 19.6 (24.6)

103 (88.8) 11 (9.5) 2 (1.7) 19.8 (39.8)

14 (6.6) 56 (26.6) 61 (28.9) 46 (21.8) 19 (9) 15 (7.1)

8 (8.4) 19 (20) 29 (30.5) 25 (26.3) 9 (9.5) 5 (5.3)

6 (5.2) 37 (31.9) 32 (27.6) 21 (18.1) 10 (8.6) 10 (8.6)

38 (18) 20 (9.5) 40 (19) 41 (19.4) 24 (11.4) 48 (22.7)

21 (22.1) 7 (7.4) 20 (21.1) 15 (15.8) 8 (8.4) 24 (25.3)

17 (14.7) 13 (11.2) 20 (17.2) 26 (22.4) 16 (13.8) 24 (20.7)

24 (11.4) 119 (56.7) 55 (26.2) 11 (5.2)

12 (12.8) 52 (55.3) 24 (25.5) 6 (6.4)

12 (10.3) 67 (57.8) 32 (27.6) 5 (4.3) (continued on next page)

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Table 11 (continued) (continued) Totala n = 211 Eating while doing other activities Always Most of the time Sometimes Seldom Never Self-efficacy to eat less sugar and fat, mean (SD) Smoking status Everyday Some days Not at all Child BMIe Obese Overweight Normal Underweight

Controla n = 95

Interventiona n = 116

28 (13.3) 29 (13.7) 86 (40.8) 46 (21.8) 22 (10.4) 5.99 (3.3)

16 (16.8) 14 (14.7) 38 (40) 16 (16.8) 11 (11.6) 5.6 (3.4)

12 (10.3) 15 (12.9) 48 (41.4) 30 (25.9) 11 (9.5) 6.3 (3.3)

32 (15.2) 9 (4.3) 170 (80.5)

15 (15.8) 3 (3.2) 77 (81)

17 (14.7) 6 (5.2) 93 (80.1)

53 (25.1) 49 (23.2) 102 (48.4) 7 (3.3)

25 (26.3) 22 (23.2) 46 (48.4) 2 (2.1)

28 (24.2) 27 (23.3) 56 (48.3) 5 (4.3)

Growth Charts and the height, weight, and age-in-months of our study subjects. BMI categories were assigned based on the CDC's definitions[69]: underweight — less than the 5th percentile; healthy weight — 5th percentile to less than the 85th percentile; overweight — 85th to less than the 95th percentile; and obese — equal to or greater than the 95th percentile. Three subjects were missing weight or height information, 4 subjects were found to have been under the age of 8 at the time of enrollment, and one subject had a recorded birthday that conflicted with her recorded age. These subjects are excluded from this analysis. ⁎ Difference between intervention and control groups is statistically significant at p b 0.05. a Numbers represent n (%) unless otherwise noted. b Participants were able to choose multiple answers. c One person missing from BMI adult average, 4 people missing from walking in neighborhood average, and one person missing from fast food visits. d Physical activity categories were calculated by multiplying the number of days/week × minutes / day × 3.3 (MET intensity level for walking), results were then grouped into the following 3 categories: inactive (answer = 0), minimally/sufficiently active (495–3000), and more/highly active (N3000) [68]. e Childhood BMI-for-age was calculated using the CDC's SAS Program for the 2000 CDC.

controlled trial of a multi-level intervention. Overall, the total sample self-reported health behaviors that are below recommended guidelines [50,60] and also sub-optimal compared to nationwide prevalence estimates. For example, for nutrition behaviors, 93% reported eating less than 5 servings of fruits and vegetables per day (compared to 76.5% nationwide [61]); 27.5% consumed soft drinks daily or more (compared to 21% of adults in a nationwide survey [62]); and 31% visited fast food restaurants 1–2 times/week or more (compared to 29.7% ≥1 time/week in a multi-site U.S. survey [63]). For physical activity, the American College of Sports Medicine advocates for at least 150 min/week of moderate intensity physical activity to prevent weight gain over time, with larger amounts recommended for individuals who need to lose weight [60]. In the Healthy Families study, more than three-quarters reported being physically inactive, as measured by walking (compared to 48.4% nationwide who report being below recommended guidelines when considering any type of moderate/vigorous activity [64]). Residents also provided relatively low ratings of their health, with 28% of residents reported being in excellent or very good health compared with 51.2% nationwide [65]. Although these estimates are not directly comparable to our data due to differences in gender, socio-economic status, and other factors, they do illustrate the prevalence of at-risk health behaviors among our sample of urban public housing residents. Multi-level level interventions are needed to counteract the limitations of one-to-one individual-level interventions, which are often time and resource intensive, expensive, and have limited reach. Interventions that target multiple environments, such as communities (e.g., social norms around walking in the neighborhood); organizations (e.g., interacting with HLAs to promote healthy eating among residents); consumer

(e.g., accessibility of affordable produce); and information (e.g., belonging to a social networking site targeting health promotion), can enhance residents' perceptions of available resources and facilitate beneficial eating and physical activity patterns for weight management. This approach can be applied to a variety of public housing developments, which differ according to size and number of residents and location within the city. While the relationship between obesity and healthful eating habits and food environments is not without controversy [66,67], clearly intervention programs are needed to both increase access to healthful environments and to encourage usage of existing environmental resources. Indeed, our previous work has shown that residents may perceive that they have limited access to health promotion resources even if they are objectively within close proximity to resources [16]. In these cases, environmental resources are still needed for residents to perceive that resources are available to them and adapted to their needs, which can be facilitated by the HLAs. Our plans for the multi-level intervention components are to create opportunities for each of these activities to be sustained and encouraged in developments by community partners. For example, walking groups could be sustained by the residents themselves; however, other components would be more difficult to maintain after the intervention ends without additional monetary and logistical support, such as the mobile food bus. We intend to produce protocols and resources to help these organizations continue with activities in the event that this intervention is successful in helping residents reduce weight. At the conclusion of the study, these protocols and resources will be made available to all developments (intervention and control) to facilitate the process of requesting and implementing intervention activities.

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Limitations to this study include the self-reported nature of the assessments. We chose assessments carefully, balancing the use of validated instruments with the need for brief measurements that would not be overly burdensome. In addition, we cannot exclude the possibility that individuals from control housing developments were exposed to activities at intervention developments, however anecdotally, our previous research experiences have revealed that residents are unlikely to attend activities held outside of their development of residence. It is also possible that low-income residents of public housing may not be representative of other lowincome populations in urban areas, such as homeless populations. Thus caution should be taken when generalizing our intervention approach outside of public housing populations. However, because the public housing infrastructure is coordinated at a federal level, it is possible that our intervention approach would have good transferability to public housing programs in other U.S. cities, even those without pre-existing Healthy Living Advocate programs. Although this study is on-going, we are able to comment on challenges faced in intervention deployment. First, although we intended to have the bus providing fruits and vegetables to residents year-round, we were unable to contract with a vendor who would be able to provide services over the winter months. Thus, resident access to the bus is limited to the spring, summer, and fall seasons. Secondly, walking outdoors in Boston is difficult during the winter, but we identified indoor projects that could occur during the winter months. This flexibility and substitution when necessary was critical to maintain participants' interest. 4. Conclusions In summary, we are undertaking a cluster randomized trial to improve social, physical, and information environments among low-income, multi-cultural women living in urban public housing developments. Our multi-level intervention approach is designed to have wide reach and potential sustainability after the conclusion of the research study. While there are factors that may position environmental-level interventions as more difficult to design and implement (e.g., ensuring a consistent dose of intervention received by participants; difficulty changing established organizational policies) as compared to individual-level interventions; in the long-term, multi-level interventions have more potential for population change and sustainability. Future work will entail an examination of change in nutrition and physical activity behaviors and weight, as well as secondary examinations including: technology availability and use by residents and acceptability of the intervention as perceived by the residents. Acknowledgements This work was conducted with the support from the Partners in Health and Housing Prevention Research Center (U48DP001922), funded by the Centers for Disease Control and Prevention. The authors thank the Resident Health Advocates, Healthy Living Advocates, and public housing administration and residents who supported and participated in this research project.

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Enhancing physical and social environments to reduce obesity among public housing residents: rationale, trial design, and baseline data for the Healthy Families study.

Intervention programs that change environments have the potential for greater population impact on obesity compared to individual-level programs. We b...
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