PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of bmcphBioMed Centralsearchsubmit a manuscriptregisterthis articleBMC Public Health
 
BMC Public Health. 2012; 12: 305.
Published online Jun 15, 2012. doi:  10.1186/1471-2458-12-305
PMCID: PMC3439671
Weight gain prevention among black women in the rural community health center setting: The Shape Program
Perry Foley,corresponding author1 Erica Levine,1 Sandy Askew,1 Elaine Puleo,2 Jessica Whiteley,3 Bryan Batch,4 Daniel Heil,5 Daniel Dix,1 Veronica Lett,1 Michele Lanpher,1 Jade Miller,1 Karen Emmons,6 and Gary Bennett1
1Duke Obesity Prevention Program, Duke Global Health Institute, 2812 Erwin Road, Suite 403 Box 90392, Durham, NC, 27705, USA
2School of Public Health and Health Sciences, University of Massachusetts Amherst, 425 Arnold House 715 North Pleasant Street, Amherst, MA, 01003-9304, USA
3College of Nursing and Health Sciences, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, MA, 02125, USA
4Division of Endocrinology, Metabolism and Nutrition, Duke University Medical Center, 200 Trent Drive, Duke South Orange Zone DUMC, Box 3031, Durham, NC, 27710, USA
5Department of Health & Human Development, Montana State University, H&PE Complex, Hoseaus Room 121, Bozeman, MT, 59717, USA
6Dana-Farber Cancer Institute, 450 Brookline Avenue, LW601, Boston, MA, 02215, USA
corresponding authorCorresponding author.
Perry Foley: perry.foley/at/duke.edu; Erica Levine: erica.levine/at/duke.edu; Sandy Askew: sandy.askew/at/duke.edu; Elaine Puleo: epuleo/at/schoolph.umass.edu; Jessica Whiteley: jessica.whiteley/at/umb.edu; Bryan Batch: bryan.batch/at/duke.edu; Daniel Heil: dheil/at/montana.edu; Daniel Dix: danieldix2/at/gmail.com; Veronica Lett: vmlett/at/gmail.com; Michele Lanpher: michele.lanpher/at/duke.edu; Jade Miller: jade.miller/at/duke.edu; Karen Emmons: Karen_M_Emmons/at/dfci.harvard.edu; Gary Bennett: gary.bennett/at/duke.edu
Received April 10, 2012; Accepted April 26, 2012.
Background
Nearly 60% of black women are obese. Despite their increased risk of obesity and associated chronic diseases, black women have been underrepresented in clinical trials of weight loss interventions, particularly those conducted in the primary care setting. Further, existing obesity treatments are less effective for this population. The promotion of weight maintenance can be achieved at lower treatment intensity than can weight loss and holds promise in reducing obesity-associated chronic disease risk. Weight gain prevention may also be more consistent with the obesity-related sociocultural perspectives of black women than are traditional weight loss approaches.
Methods/Design
We conducted an 18-month randomized controlled trial (the Shape Program) of a weight gain prevention intervention for overweight black female patients in the primary care setting. Participants include 194 premenopausal black women aged 25 to 44 years with a BMI of 25–34.9 kg/m2. Participants were randomized either to usual care or to a 12-month intervention that consisted of: tailored obesogenic behavior change goals, self-monitoring via interactive voice response phone calls, tailored skills training materials, 12 counseling calls with a registered dietitian and a 12-month YMCA membership.
Participants are followed over 18 months, with study visits at baseline, 6-, 12- and 18-months. Anthropometric data, blood pressure, fasting lipids, fasting glucose, and self-administered surveys are collected at each visit. Accelerometer data is collected at baseline and 12-months.
At baseline, participants were an average of 35.4 years old with a mean body mass index of 30.2 kg/m2. Participants were mostly employed and low-income. Almost half of the sample reported a diagnosis of hypertension or prehypertension and 12% reported a diagnosis of diabetes or prediabetes. Almost one-third of participants smoked and over 20% scored above the clinical threshold for depression.
Discussion
The Shape Program utilizes an innovative intervention approach to lower the risk of obesity and obesity-associated chronic disease among black women in the primary care setting. The intervention was informed by behavior change theory and aims to prevent weight gain using inexpensive mobile technologies and existing health center resources. Baseline characteristics reflect a socioeconomically disadvantaged, high-risk population sample in need of evidence-based treatment strategies.
Trial registration
The trial is registered with clinicaltrials.gov NCT00938535.
Keywords: Obesity, Weight, eHealth, Women’s health, Minority health, Primary care, Prevention
The epidemic of obesity in the U.S. shows no signs of abating – presently, almost 70% of the adult U.S. population is either overweight or obese[1]. Black women are disproportionately affected by the condition. Between 1976 and 2008, obesity among black women increased more than 60%[2,3]. Nearly 60% of black women are obese, a rate that is twice that of non-Hispanic white women[1]. Socioeconomic status and obesity are less strongly associated in black women than in other groups. Nevertheless, socioeconomic factors strongly pattern exposure to obesogenic environmental factors [4,5], the adoption of obesogenic risk behaviors[6], the limited availability of weight management resources[7,8], and the efficacy of obesity treatment strategies in the primary care setting[9].
Despite their vastly increased risk of obesity and associated chronic disease[10,11], racial/ethnic minority and socioeconomically disadvantaged populations have been underrepresented in clinical trials of weight loss interventions[11]. This is problematic because promoting weight loss among black women is a long-standing and vexing clinical challenge[12,13]. Evidence-based obesity treatments are consistently less effective and absolute weight losses are generally smaller among black women, compared to other populations[10,11,14]. There is growing recognition that alternative clinical treatment strategies are necessary to contend with the challenge of obesity[14-17]. While it is undeniable that weight loss is the optimal treatment strategy for many obese individuals, weight gain prevention may have considerable clinical utility among overweight and some obese black women.
Weight gain prevention holds promise in reducing risk associated with cardiovascular diseases (CVD), type 2 diabetes, some cancers[18] and perhaps premature mortality[19]. Weight gain prevention may have particular benefits for blacks, who exhibit disproportionately greater rates of adulthood weight gain[20,21] and extreme obesity [2], both of which increase obesity-associated chronic disease risk[22-24]. Relative to whites, black women have weaker associations of adiposity with cardiovascular risk factors[25-28] and mortality from cardiovascular disease[29,30] and all causes. Thus, promoting weight stability within the overweight (BMI = 25-29.9 kg/m2) and lower levels of the Class 1 obesity ranges (BMI = 30-34.9 kg/m2) might be an appropriate chronic disease risk reduction strategy in black women, especially prior to menopause, when weight gains are particularly pronounced[31,32].
Additionally, we suspect that weight gain prevention strategies may be more consistent with the sociocultural experiences of black women, compared to traditional weight loss approaches. While some opposing data exist, most studies have shown that black women are more tolerant of heavier body weights, as compared to white women[33]. Blacks have a greater social acceptance of overweight, less body weight dissatisfaction, and higher body weight ideals than do whites[12,33-40]. A number of studies have shown that overweight blacks are less likely to perceive themselves to be overweight, compared to whites and Hispanics[41-43]. Perceived body image and attractiveness are not as strongly linked with weight in black women, compared to white women. Moreover, a majority of blacks do not consider overweight to be unhealthy[43]. Given that black women’s views about attractiveness and health are not closely associated with their weight status, weight loss messages[44-46] – which emphasize the importance of thinness – may have limited effectiveness among obese women. Intervention messages that emphasize weight gain prevention or enhancement of one’s current shape may have greater sociocultural relevance, thus enhancing participant receptivity[47,48].
Given the challenges associated with promoting weight loss among black women, particularly in the primary care setting, alternative treatment strategies are necessary. Weight gain prevention among overweight and Class 1 obese individuals is one such approach, one that requires relatively low treatment intensity and might be more consistent with the sociocultural experiences of black women. We suspect that its lower intensity and greater consistency with sociocultural norms may heighten participant responsiveness, improve intervention engagement, and enhance intervention outcomes among black women.
We conduct the Shape Program (Shape), an 18-month randomized controlled trial of a weight gain prevention intervention for overweight and Class 1 obese (BMI: 25–34.9 kg/m2) black female patients in the primary care setting. The primary outcome is weight maintenance over 12 months; secondary outcomes are change in obesity risk behaviors and obesity-related biomarkers, as well as maintenance of outcomes through 18-months. The primary hypothesis is that baseline BMI levels will be maintained in participants randomized to the intervention, while BMI levels will increase in those assigned to usual care.
All study procedures and protocols were approved by the Duke University Institutional Review Board and the Piedmont Health Board of Advisors.
Shape is conducted in six CHCs operated by Piedmont Health, a private, non-profit community health system that operates six health centers in a seven-county service area in central North Carolina. Each Piedmont Health center offers primary care services, with additional site-specific services (e.g., laboratory, dentistry, pharmacy) that address local needs. Registered dietitians based at each health center provide WIC counseling, diabetes education, and medical nutrition therapy. Piedmont Health has a patient population of nearly 40,000 with over 123,900 medical/dental visits in 2010. Patients are predominately racial/ethnic minority (77%), 98% are <200% of the federal poverty level, and most are either uninsured, underinsured, or hold public insurance (59% uninsured, 31% Medicaid/Medicare).
Participants
Participants include 194 premenopausal black women, aged 25 to 44 years, with a BMI of 25-34.9 kg/m2. Additional inclusion criteria are: at least one visit to a Piedmont Health center in the prior 24 months, North Carolina residency, and the ability to read and write in English. Exclusion criteria include: current pregnancy, being  12 months postpartum, a history of myocardial infarction or stroke in the prior two years, and profound cognitive, developmental or psychiatric disorders.
Recruitment of participants occurred between September 2009 and February 2011. Piedmont Health staff used electronic medical record (EMR) data to generate lists of potentially eligible patients from each health center. Study staff abstracted patients’ heights and weights from paper medical charts to assess BMI eligibility (25–34.9 kg/m2).
Potential participants were sent invitation letters (signed by the respective health center medical director and the study principal investigator) and study brochures via postal mail. Patients could opt out of the study by calling the toll-free number provided in the recruitment letter. No patients opted out of the recruitment process. After one week, study staff called potentially eligible patients to invite participation, perform an initial eligibility assessment, and schedule a screening evaluation visit.
Randomization
Randomization occurred at the baseline visit, using a computer-based algorithm that was triggered after participants completed the baseline questionnaire battery. The randomization algorithm allocated participants equally (1:1) across treatment arms. We assigned participants to one of two research assistants and participants randomized to the intervention arm were randomly assigned to one of two interventionists. The intervention design precluded blinding either patients or interventionists to treatment assignment.
Sample size
The study is designed to detect a difference of 1.03 kg/m2 in BMI at the 0.05 alpha level and 80% statistical power using a two-tailed test for differences. We increased the target sample size to account for examination of effect modifiers and mediators.
Usual care
Usual care participants received the current standard of care offered by their primary care providers. In addition, usual care participants received semi-annual newsletters from our study team over the 12-month project period. These newsletters covered topics (e.g., finances, the environment) that were relevant to women in the target age group but did not relate to weight, nutrition, or physical activity.
Weight maintenance intervention
Theoretical framework
Social Cognitive Theory (SCT) [49,50] informed the intervention’s design. From SCT, self-efficacy was selected as the primary psychosocial mediator. There is strong and consistent evidence that self-efficacy is positively associated with weight loss intentions, initiation, and maintenance[51-53]. The intervention was designed to target each of the four factors that Bandura identified as influencing self-efficacy: [54] mastery experiences, social modeling, social persuasion, and somatic and emotional reactions. Social Cognitive Theory also indicates that behavior change can be facilitated through a number of self-regulatory processes that were built into the intervention, including self-monitoring[55,56], goal setting[53,57], and social support[58]. The intervention was designed to support these self-regulatory processes, which should further increase self-efficacy.
Intervention design
The intervention contained five components (Table (Table1):1): 1) obesogenic behavior change goals; 2) self-monitoring via interactive voice response (IVR) phone calls; 3) tailored skills training materials; 4) 12 interpersonal counseling calls; and 5) a 12-month YMCA membership.
Table 1
Table 1
Intervention design
Behavior change goals
The intervention utilized the interactive obesity treatment approach (iOTA), which creates an energy deficit sufficient to produce weight change through the modification of routine obesogenic lifestyle behaviors[59,60]. Participants were assigned 3 behavior change goals from the iOTA library using an algorithm that considers a participant’s need for change, self-efficacy, readiness, and the goal’s intended caloric deficit. The iOTA goal library contains over 21 obesogenic behavior change goals (e.g., five or more fruits and vegetables/day, no fast food, no sugar sweetened beverages, walking 7,000 steps/day) that were selected based on their: 1) empirical support; 2) population relevance; 3) ease of self-monitoring; and 4) concreteness. Participants were assigned new goals at months two and four to maintain motivation and facilitate goal mastery. At the beginning of each new goal assignment (every two months), participants received printed personalized feedback reports that detailed results of the previous goal assignments as well as provided tailored prescriptions for new assignments.
Behavior change strategies
Self-monitoring
We recommended that participants self-monitor their iOTA behavior change goals daily using paper tracking logs. Participants were given pedometers to facilitate daily monitoring of physical activity. Participants relayed the self-monitoring data recorded on their tracking logs to the study team during weekly interactive voice response (IVR) calls. Interactive voice response calls allow one to interact with a computer system using a telephone by typing on the keypad or via speech. Participants received weekly IVR calls throughout the 12-month intervention. Self-monitoring data collected via IVR were visible to coaches to inform counseling activities during monthly coaching calls.
After self-monitoring data was collected, tailored feedback was immediately provided through IVR. Feedback messages described trends in participant progress, reinforced successes, and/or offered motivational strategies. Short skills training tips were also provided.
Shape utilized telephonic technologies for several reasons. First, IVR helps to overcome the literacy/numeracy barriers associated with detailed paper records. These systems have high reach[61,62], as mobile phone penetration is very high in the target population. In contrast to web-based approaches, telephony is easily accessible, low-cost, quickly used, and requires no expert knowledge. Finally, IVR is inexpensive to develop, simple to tailor and immediately scalable.
Skills training materials
A major point of innovation in the Shape design is that, unlike traditional weight loss strategies, our approach suggests that participants maintain their current weight (and shape). This approach inherently embraces long-held social norms and aesthetic values. The intervention’s focus resulted from qualitative pilot work designed to assess the acceptability of print materials and to test narrative messages.
Shape print materials were tailored at several levels. Participants received skills training content that corresponded to their behavior change goals. At baseline visits, participants were provided with a set of tailored intervention materials to be utilized over the first two months of the intervention. For example, individualized “Shape Tracking Logs” included tailored narratives based on each participant’s unique set of goals. Additional materials were sent via postal mail every two months. We sent cycle-specific materials every two months in order to keep participants focused on the goals of current assignment and to heighten feelings of novelty and connection with the program. Participants also received quarterly newsletters with additional skills training information (e.g., appropriate portion sizes, food shopping tips, healthy recipes).
Telephone counseling calls
Each month, 20-minute counseling calls were delivered by Piedmont Health registered dietitians (“coaches”) trained in motivational interviewing principles[63]. The coach calls were designed to enhance self-efficacy by guiding participants through identification of barriers to behavior change and resulting ambivalence towards change efforts. They also provided skills training and helped participants utilize goal-setting as a problem solving strategy.
Coaches used a web application that presented each session’s call script, allowed for note taking, and provided access to participant self-monitoring data. The system recorded calls and automatically stored process data (e.g., date/time, call disposition, duration). Prior to the monthly counseling calls, coaches reviewed the participant self-monitored IVR goal tracking data that was held on the centralized data management system. The coaches used this data to guide discussions of participant progress towards assigned goals and to discuss readiness for behavior change and barriers to change. Data from the coaching calls were stored on the secure study server with all other study data for monitoring and data collection purposes.
Shape coaches participated in a 2-day training session at baseline and received biannual refresher trainings. Shape staff monitored IVR data for completeness and reviewed 5% of coaching calls for adherence to protocol. Weekly supervision with the intervention coordinator ensured appropriate delivery of the coaching component of the intervention.
YMCA
Participants in the target population have limited options for safe and affordable physical activity. We addressed this barrier and promoted participant motivation for physical activity by providing intervention participants with 12-month memberships to local YMCAs.
Data collection
At the screening visit, research assistants oriented participants to the study, gathered informed consent, and collected anthropometric data to confirm BMI eligibility. The anthropometric and blood data collection activities were conducted at baseline and again at study follow-up visits at 6, 12, and 18 months.
Anthropometric data
Participants changed into hospital gowns and their body heights were measured to the nearest 0.1 cm using a calibrated wall-mounted stadiometer (Seca 214) [64] and body weights were measured to the nearest 0.1 kg using a portable electronic scale (Seca Model 876) [64]. Waist circumference was measured to the nearest 0.1 cm using a vinyl, retractable tape measure (AccuFitness MyoTape) where circumference was measured horizontally from the highest point of the iliac crest at minimal respiration. The Omron HEM 907XL, a microprocessor controlled, noninvasive device that automatically measures systolic pressure, diastolic pressure, and pulse rate for adults, was used to measure blood pressure three times at 1-minute intervals after five minutes of quiet sitting. Participants were advised not to smoke or to consume any caffeine within 30 minutes of their study visits.
Cardiometabolic biomarkers
Participants were instructed to fast for at least eight hours prior to their study visits. Each participant had a fasting glucose and lipid panel analyzed using fingerstick blood specimens collected in 40μl capillary tubes (Cholestech LDX; Cholestech Corporation, Hayward, CA, USA).
Physical activity
All participants (Intervention and Usual Care) wore accelerometers (Actical; Philips Respironics, Inc., Bend, OR USA) on their non-dominant wrists[65] to provide estimates of free-living physical activity before baseline and after 12-month visits. Participants were instructed to wear the monitors continuously until their return visits approximately 14 days later. Upon its return, the activity monitor was removed from the wrist and data was downloaded to a computer and visually screened for compliance and collection errors. Complete files were defined as those in which the monitor had been worn for ≥10 days (i.e., complete compliance).
Activity monitor files were first transformed from 15-sec to 1-min epochs and then “smoothed” using a protocol validated for wrist monitoring of physical activity in overweight and obese adults[66]. Next, the resulting data were transformed into units of activity energy expenditure (AEE; kcals/kg/min) using a 2R calibration algorithm[67] and then summarized into outcome variables that included time (T; mins/week) and activity energy expenditure (AEE; kcals/week) engaged in light (TL and AEEL, respectively) and moderate-vigorous (TMV and AEEMV, respectively) activity intensities. The cut point used to distinguish light from moderate intensity activities was 0.0385 kcals/kg/min, which is the same value defined previously for overweight and obese adults[68]. Finally, both TMV and AEEMV variables were summarized in activity bouts of 1 and 10 minutes. These data-screening and processing procedures were consistent with those recently recommended for use with accelerometry-based physical activity data[69].
Survey data
Participants complete self-report surveys at baseline and 6-, 12-, and 18-month follow-up. Surveys are administered via computer using an online survey tool (http://www.surveygizmo.com). Demographic variables collected at baseline include age, race/ethnicity, marital status, occupational status, educational attainment, income, and co-morbidities.
We used validated measures to assess a range of relevant constructs, including:
Body image
The 14-item Figure Rating Scale (FRS) is designed to assess current and past body size as well as attractiveness of body figure drawings [70].
Quality of life
The 5-item EuroQol instrument (EQ-5D) assesses mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. The EuroQol visual analog scale (EQ-VAS) is similar to a health thermometer and is designed to assess self-rated health quality of life[71,72].
Medical history
Twelve items measure general health and previous diagnosis and perceived risk of diabetes and high blood pressure.
Physical activity
Four items are used to assess current stage of change in relation to physical activity. A 6-item scale from the Behavioral Risk Factor Surveillance System (BRFSS) is designed to assess the amount and frequency of moderate and vigorous activity[73,74].
Tobacco use
Three items assess current smoking behaviors and previous quit attempts. This measure is derived from the National Health Interview Survey (NHIS) [75].
Sleep behavior
The Medical Outcomes Study (MOS) Sleep is a 12-item measure designed to assess minutes to fall asleep, total hours of sleep each night, and difficulties with sleep. The questionnaire assesses six dimensions: sleep disturbance, sleep adequacy, daytime somnolence, snoring, short of breath, and quantity of sleep[76].
Self-efficacy for exercise
Self-efficacy for exercise is measured using five items that assess confidence in ability to exercise when tired, in a bad mood, don’t have time, on vacation, or when it is raining/snowing[77].
Genetic causal beliefs
An 8-item scale is designed to assess perceptions of risk for obesity, diabetes, and heart disease (i.e., genetic or behavioral lifestyle habits) [78,79].
Dietary restraint
The Three Factor Eating Questionnaire is designed to assess different dimensions of eating behavior. For the current study, we used the 18-item revised version (TFEQ-R18) to measure three domains: Cognitive Restraint (6 items), Uncontrolled Eating (9 items), and Emotional Eating (3 items) [80,81].
Social support
A 19-item subscale from the Medical Outcomes Study (MOS-SSS) assesses availability of social support. Four subscales are used: emotional/informational, tangible, affectionate, and positive social interaction[82].
Perceived weight
A 12-item scale is designed to assess perceptions of past, current, and future weight, self-perceived weight class (i.e., underweight, average weight, overweight), and body satisfaction.
Negative life events
A 16-item questionnaire measures frequency of stressful life events[83].
Depression
The 8-item Patient Health Questionnaire (PHQ-8) is designed to assess the presence of depressive symptoms[84].
Health literacy
A 3-item questionnaire is used to screen for limited health literacy[85].
Neighborhood environment
The physical activity environment questions include items adapted from the Neighborhood Environment Walkability Scale (NEWS) on perceptions of the built environment, land use mix, and community support for physical activity [86].
Food security
An adaptation of the USDA Household Food Security Scale (6 items) is designed to assess household food security (money for food, food affordability, skipped meals) [87-89].
Racial identity
The 8-item Centrality subscale of the Multidimensional Inventory of Black Identity (MIBI) is used to measure African-American or black racial identity[90].
Absenteeism and presenteeism
The 11 items from the World Health Organization Health and Work Performance Questionnaire (HPQ)-Short Form assess number of hours worked, expected hours of work, missed work, total hours of work over the past 4 weeks, and perceived job performance[91,92].
Data analysis
Shape will be evaluated using the RE-AIM planning and evaluation framework[93] (Table (Table2).2). The RE-AIM framework addresses five issues related to both internal and external validity by comprehensively evaluating the success of interventions on issues key for translation from research to practice and dissemination: 1) Reach and representativeness of individuals who participate; 2) Effectiveness/Efficacy of the intervention on the primary outcomes at the individual level; 3) Adoption at the organizational/CHC level; 4) Implementation measured at the CHC provider/staff level; and 5) Maintenance at both the individual participant and provider level.
Table 2
Table 2
RE-AIM Measures
Baseline characteristics
A total of 194 black female patients were randomized to treatment arms. Five participants became ineligible after randomization due to pregnancy or diagnosis of cancer. At baseline (Table (Table3),3), participants were an average of 35.4 (SD = 5.5) years old with an average BMI of 30.2 (SD = 2.6) kg/m2. Participants were mostly employed (70.4%) and low-income – 73.5% had an annual household income  $29,999 and one-third lived beneath the federal poverty threshold. Participants were supporting an average of 3.2 (SD = 1.3) persons with their household income. Most participants (72.5%) did not live with partners in the household. One-third of the sample had a high school diploma, GED or less and only 7% had completed college.
Table 3
Table 3
Baseline characteristics of the Shape Program analytic sample (n = 189)
Accelerometers placed at screening visits were returned an average of 15.1 days after initial placement. Complete baseline accelerometer data was collected for 87.6% of the participants (n = 170). Nearly a third (31.6%) of participants met the federal guideline of  150 minutes of 10-minute bouts of physical activity each week. Participants averaged 166.9 (SD = 265.5) minutes a week of moderate physical activity in 10-minute bouts and no (0.0) minutes a week of vigorous activity in 10-minute bouts.
Almost half (46.0%) of the sample reported a diagnosis of hypertension or prehypertension and 12% reported a diagnosis of diabetes or prediabetes. Almost one-third of participants smoked and over one-fifth scored above the PHQ clinical threshold for depression. Mean blood pressure measurements were: SBP = 123.2 (SD = 14.8)/DBP = 80.6 (SD = 11.0). Mean lipid levels were optimal or close to optimal.
Despite the recent plateau of the obesity epidemic in the U.S. adult population [1], black women still have dramatically higher obesity risk compared to other groups. Given evidence suggesting that overweight (and Class 1 obesity) is less health damaging for black women compared to other racial/ethnic groups, maintaining weight status among black women in the BMI  35 kg/m2 range may hold promise as an alternative obesity treatment strategy. Strategies are needed that can prevent weight gain for women in the BMI  35 kg/m2 range, those who might benefit most from weight gain prevention approaches. Fortunately, weight gain prevention can be accomplished at lower treatment intensity than can weight loss [96]. A major advantage for this population is that weight gain prevention builds upon existing weight-related sociocultural norms among black women instead of challenging them. In stark contrast to the focus of weight loss interventions, Shape’s content focused on “maintaining shapes” and “showing off curves” in order to validate sociocultural norms about body image and to reinforce self-affirming weight maintenance messages.
Furthermore, Shape was designed for primary care practice – a particularly meaningful setting for addressing the obesity epidemic and one in which relatively few weight-related trials are conducted. More specifically, the intervention was developed for implementation in community health centers, critical primary care delivery systems for our nation’s most medically vulnerable populations. Indeed, the baseline characteristics of the Shape sample reflect a group that is at extremely high risk for obesity and obesity-associated chronic disease. The sample was composed of largely rural, black women who are unmarried, supporting several family members, and struggling to make ends meet. About half of the sample was diagnosed with hypertension or prehypertension. Participants demonstrated elevated levels of moderate physical activity (that was likely due primarily to occupational pursuits) and no vigorous activity, which suggests little intentional exercise. Many women (over 20%) in our sample were above the clinical threshold for depression. Together, this group is one at extremely high disease risk and, yet, one for which we have few evidence-based intervention approaches.
Although Shape is a novel approach executed in an understudied population, we are encouraged by our initial success with recruitment and retention. To optimize participant recruitment and retention, the study team employed several strategies designed to accommodate the busy lives of study participants, most of whom work, are socioeconomically disadvantaged, and are solely responsible for children in the household. Study visits were offered in a number of locations convenient to participants, including in the Piedmont Health centers, at all times of day on weekdays and weekends. Participants who notified the study team about transportation difficulties were offered home visits and taxi vouchers for evaluation visits.
The research team worked closely and collaboratively with Piedmont Health partners on Shape design and implementation issues. The research team designed recruitment and data collection activities to be minimally burdensome on health center practices, while harnessing the strengths of the Piedmont Health system. Financial mechanisms were negotiated that adequately reimbursed Piedmont Health for its involvement and for the staff time of its registered dietitians, who functioned as study coaches. These types of strategies are crucial in ensuring that CHC-based research benefits patients without overburdening already-strained clinical operations.
In conclusion, black women are disproportionally affected by the epidemic of obesity and, consequently, the risk of comorbidities associated with being obese. In previous trials, low-income black women have not been as well represented as other groups; when included, their weight loss outcomes have been suboptimal. Shape was designed to take an innovative approach to managing obesity and its health consequences among black women by integrating behavior change theory, building upon an understanding of sociocultural norms and utilizing health information technologies. In these ways, Shape was designed to be responsive to calls for interventions that have dissemination potential in real world practice settings. Such considerations are particularly pressing for socioeconomically disadvantaged populations given their dramatically increased risk of obesity and obesity-associated chronic diseases and the limited availability of obesity treatment options.
Abbreviation
CVD = Cardiovascular disease; CHC = Community health center; EMR = Electronic medical record; SCT = Social cognitive theory; iOTA = Interactive obesity treatment approach; IVR = Interactive voice response; AEE = Activity energy expenditure; T = Time; FRS = Figure rating scale; BRFSS = Behavioral risk factor surveillance system; NHIS = National health interview survey; MOS = Medical outcomes study; PHQ = Patient health questionnaire; MIBI = Multidimensional inventory of black identity.
Competing interests
The authors declare that they have no competing interests.
PF managed study design and execution and drafted the manuscript for publication. EL and JW coordinated intervention design. BB consulted on data safety and execution of the study. DH consulted on the accelerometer data collection protocols and analyzed accelerometer data. EP and SA participated in study design and conducted statistical analysis. DD, VL, ML and JM conducted primary data collection and participated in study design. KE participated in study conceptualization and design. GB conceived of the study, acquired study funding, participated in study design and coordination and drafted the manuscript for publication. All authors read and approved the final manuscript.
Authors’ information
DD is now with Quintiles Transnational in Durham, NC and VL is now with the School of Social Work at the University of North Carolina, Chapel Hill, NC.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This trial is funded by grant R01DK078798. Dr. Emmons was supported by K05CA124415 and Dr. Bennett was supported by K22CA126992. We express deep gratitude to the administration and staff of Piedmont Health for their continued collaboration and participation in the Shape Program. In particular, we would like to thank Brian Toomey, MSW, Tom Wroth, MD, MPH, Heather Miranda, RD, LDN, Marni Holder, RN, FNP-BC, Ashley Brewer, RD, LDN, Greg Wheatley, MPH, RD, LDN, Kristen Norton, MA, RD, LDN, Marianne Ratcliffe, MHA and staff at the PHS health centers for their support. We are grateful to Martha Zorn, MS at the University of Massachusetts-Amherst for her assistance with data analysis and to Lisa Englert for her assistance with manuscript preparation. Lastly, we would like to especially thank the women participating in Shape.
  • Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of Body Mass Index among US adults, 1999–2010. JAMA. 2012;307(5):491–497. doi: 10.1001/jama.2012.39. [PubMed] [Cross Ref]
  • Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999–2008. JAMA. 2010;303(3):235–241. doi: 10.1001/jama.2009.2014. [PubMed] [Cross Ref]
  • Centers for Disease Control. Trends in the Health of Americans. 2004.
  • Morland K, Wing S. Diez Roux A, Poole C: Neighborhood characteristics associated with the location of food stores and food service places. Am J Prev Med. 2002;22(1):23–29. doi: 10.1016/S0749-3797(01)00403-2. [PubMed] [Cross Ref]
  • Morland K, Wing S, Roux AD. The contextual effect of the local food environment on residents' diets: the atherosclerosis risk in communities study. Am J Public Health. 2002;92(11):1761. doi: 10.2105/AJPH.92.11.1761. [PubMed] [Cross Ref]
  • Ogden CL, National Center for Health Statistics (US) Obesity and socioeconomic status in adults: United States, 2005–2008. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics; 2010.
  • Sinclair J, Lawson B, Burge F. Which patients receive advice on diet and exercise? Canadian Family Physician. 2008;54(3):404–412. [PMC free article] [PubMed]
  • Bleich SN, Pickett-Blakely O, Cooper LA. Physician practice patterns of obesity diagnosis and weight-related counseling. Patient Educ Couns. 2011;82(1):123–129. doi: 10.1016/j.pec.2010.02.018. [PMC free article] [PubMed] [Cross Ref]
  • Bennett GG, Warner ET, Glasgow RE, Askew S, Goldman J, Ritzwoller DP, Emmons KM, Rosner BA, Colditz GA. Obesity Treatment for Socioeconomically Disadvantaged Patients in Primary Care Practice. Arch Intern Med. 2012;172(7):565–574. doi: 10.1001/archinternmed.2012.1. [PMC free article] [PubMed] [Cross Ref]
  • Osei-Assibey G, Kyrou I, Adi Y, Kumar S, Matyka K. Dietary and lifestyle interventions for weight management in adults from minority ethnic/non‒White groups: a systematic review. Obes Rev. 2010;11(11):769–776. doi: 10.1111/j.1467-789X.2009.00695.x. [PubMed] [Cross Ref]
  • Yancey AK, Kumanyika SK, Ponce NA, McCarthy WJ, Fielding JE, Leslie JP, Akbar J. Population-based interventions engaging communities of color in healthy eating and active living: a review. Preventing Chronic Disease. 2004;1(1) [PMC free article] [PubMed]
  • Kumanyika S, Wilson JF, Guilford-Davenport M. Weight-related attitudes and behaviors of black women. J Am Diet Assoc. 1993;93(4):416–422. doi: 10.1016/0002-8223(93)92287-8. [PubMed] [Cross Ref]
  • Neve M, Morgan PJ, Jones P, Collins C. Effectiveness of web‒based interventions in achieving weight loss and weight loss maintenance in overweight and obese adults: a systematic review with meta‒analysis. Obes Rev. 2010;11(4):306–321. doi: 10.1111/j.1467-789X.2009.00646.x. [PubMed] [Cross Ref]
  • Kumanyika SK, Gary TL, Lancaster KJ, Samuel-Hodge CD, Banks-Wallace J, Beech BM, Hughes-Halbert C, Karanja N, Odoms-Young AM, Prewitt TE. Achieving healthy weight in African-American communities: research perspectives and priorities. Obesity. 2005;13(12):2037–2047. doi: 10.1038/oby.2005.251. [PubMed] [Cross Ref]
  • Wareham NJ, van Sluijs EMF, Ekelund U. Physical activity and obesity prevention: a review of the current evidence. Proc Nutr Soc. 2005;64(02):229–247. doi: 10.1079/PNS2005423. [PubMed] [Cross Ref]
  • World Health Organization. Obesity: preventing and managing the global epidemic. World Health Organization Technical Report. 2000. [PubMed]
  • Gill TP. Key issues in the prevention of obesity. Br Med Bull. 1997;53(2):359. doi: 10.1093/oxfordjournals.bmb.a011618. [PubMed] [Cross Ref]
  • Mokdad AH, Ford ES, Bowman BA, Dietz WH, Vinicor F, Bales VS, Marks JS. Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA. 2003;289(1):76–79. doi: 10.1001/jama.289.1.76. [PubMed] [Cross Ref]
  • Visscher T, Kromhout D, Seidell J. Long-term and recent time trends in the prevalence of obesity among Dutch men and women. Int J Obes. 2002;26(9):1218–1224. doi: 10.1038/sj.ijo.0802016. [PubMed] [Cross Ref]
  • Ogden CL, Flegal KM, Carroll MD, Johnson CL. Prevalence and trends in overweight among US children and adolescents, 1999–2000. JAMA. 2002;288(14):1728–1732. doi: 10.1001/jama.288.14.1728. [PubMed] [Cross Ref]
  • Carpenter C, Ross R, Paganini-Hill A, Bernstein L. Lifetime exercise activity and breast cancer risk among post-menopausal women. Br J Cancer. 1999;80(11):1852. doi: 10.1038/sj.bjc.6690610. [PMC free article] [PubMed] [Cross Ref]
  • Hu FB, Stampfer MJ, Solomon C, Liu S, Colditz GA, Speizer FE, Willett WC, Manson JAE. Physical activity and risk for cardiovascular events in diabetic women. Ann Intern Med. 2001;134(2):96–105. [PubMed]
  • Roberts RE, Deleger S, Strawbridge WJ, Kaplan GA. Prospective association between obesity and depression: evidence from the Alameda County Study. Int J Obes. 2003;27(4):514–521. doi: 10.1038/sj.ijo.0802204. [PubMed] [Cross Ref]
  • Vainio H, Bianchini F. Weight control and physical activity. World Health Organization; 2002.
  • Stevens J, Cai J, Jones DW. The effect of decision rules on the choice of a body mass index cutoff for obesity: examples from African American and white women. Am J Clin Nutr. 2002;75(6):986–992. [PubMed]
  • Dustan HP. Obesity and hypertension in blacks. Cardiovascular drugs and therapy. 1990;4:395–402. doi: 10.1007/BF02603183. [PubMed] [Cross Ref]
  • Pan SY, Johnson KC, Ugnat AM, Wen SW, Mao Y. Association of obesity and cancer risk in Canada. Am J Epidemiol. 2004;159(3):259–268. doi: 10.1093/aje/kwh041. [PubMed] [Cross Ref]
  • Pan WH, Flegal KM, Chang HY, Yeh WT, Yeh CJ, Lee WC. Body mass index and obesity-related metabolic disorders in Taiwanese and US whites and blacks: implications for definitions of overweight and obesity for Asians. Am J Clin Nutr. 2004;79(1):31. [PubMed]
  • Stevens J, Plankey MW, Williamson DF, Thun MJ, Rust PF, Palesch Y, O'Neil PM. The body mass index-mortality relationship in white and African American women. Obes Res. 1998;6(4):268–277. [PubMed]
  • Stevens J, Keil JE, Rust PF, Tyroler H, Davis C, Gazes PC. Body mass index and body girths as predictors of mortality in black and white women. Arch Intern Med. 1992;152(6):1257. doi: 10.1001/archinte.1992.00400180111018. [PubMed] [Cross Ref]
  • Wing RR, Matthews KA, Kuller LH, Meilahn EN, Plantinga PL. Weight gain at the time of menopause. Arch Intern Med. 1991;151(1):97. doi: 10.1001/archinte.1991.00400010111016. [PubMed] [Cross Ref]
  • Despres JP, Moorjani S, Lupien PJ, Tremblay A, Nadeau A, Bouchard C. Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease. Arterioscler Thromb Vasc Biol. 1990;10(4):497–511. doi: 10.1161/01.ATV.10.4.497. [PubMed] [Cross Ref]
  • Flynn KJ, Fitzgibbon M. Body images and obesity risk among black females: a review of the literature. Annals of behavioral medicine. 1998;20(1):13–24. doi: 10.1007/BF02893804. [PubMed] [Cross Ref]
  • Roberts A, Cash TF, Feingold A, Johnson BT. Are black-white differences in females' body dissatisfaction decreasing? A meta-analytic review. J Consult Clin Psychol. 2006;74(6):1121. [PubMed]
  • Sabik NJ, Cole ER, Ward LM. Are all minority women equally buffered from negative body image? Intra-ethnic moderators of the buffering hypothesis. Psychol Women Q. 2010;34(2):139–151. doi: 10.1111/j.1471-6402.2010.01557.x. [Cross Ref]
  • Striegel-Moore RH, Wilfley DE, Caldwell MB, Needham ML, Brownell KD. Weight-related attitudes and behaviors of women who diet to lose weight: a comparison of black dieters and white dieters. Obes Res. 1996;4(2):109–116. [PubMed]
  • Stevens J, Kumanyika SK, Keil JE. Attitudes toward body size and dieting: differences between elderly black and white women. Am J Public Health. 1994;84(8):1322. doi: 10.2105/AJPH.84.8.1322. [PubMed] [Cross Ref]
  • Smith DE, Thompson JK, Raczynski JM, Hilner JE. Body image among men and women in a biracial cohort: the CARDIA Study. Int J Eat Disord. 1999;25(1):71–82. doi: 10.1002/(SICI)1098-108X(199901)25:1<71::AID-EAT9>3.0.CO;2-3. [PubMed] [Cross Ref]
  • Powell AD, Kahn AS. Racial differences in women's desires to be thin. Int J Eat Disord. 1995;17(2):191–195. doi: 10.1002/1098-108X(199503)17:2<191::AID-EAT2260170213>3.0.CO;2-Z. [PubMed] [Cross Ref]
  • Altabe M. Ethnicity and body image: quantitative and qualitative analysis. Int J Eat Disord. 1998;23(2):153–159. doi: 10.1002/(SICI)1098-108X(199803)23:2<153::AID-EAT5>3.0.CO;2-J. [PubMed] [Cross Ref]
  • Kuchler F, Variyam J. Mistakes were made: misperception as a barrier to reducing overweight. Int J Obes. 2003;27(7):856–861. doi: 10.1038/sj.ijo.0802293. [PubMed] [Cross Ref]
  • Paeratakul S, White MA, Williamson DA, Ryan DH, Bray GA. Sex, race/ethnicity, socioeconomic status, and BMI in relation to self-perception of overweight. Obesity. 2002;10(5):345–350. doi: 10.1038/oby.2002.48. [PubMed] [Cross Ref]
  • Bennett GG, Wolin KY, Goodman M, Samplin-Salgado M, Carter P, Dutton S, Hill R, Emmons K. Attitudes regarding overweight, exercise, and health among blacks (United States) Cancer Causes and Control. 2006;17(1):95–101. doi: 10.1007/s10552-005-0412-5. [PubMed] [Cross Ref]
  • Malpede CZ, Greene LF, Fitzpatrick SL, Jefferson WK, Shewchuk RM, Baskin ML, Ard JD. Racial influences associated with weight-related beliefs in African American and Caucasian women. Ethn Dis. 2007;17(1):1–5. [PubMed]
  • Gluck ME, Geliebter A. Racial/ethnic differences in body image and eating behaviors. Eat Behav. 2002;3(2):143–151. doi: 10.1016/S1471-0153(01)00052-6. [PubMed] [Cross Ref]
  • Ard JD. Unique perspectives on the obesogenic environment. Journal of general internal medicine. 2007;22(7):1058–1060. doi: 10.1007/s11606-007-0243-z. [PMC free article] [PubMed] [Cross Ref]
  • Blixen CE, Singh A, Xu M, Thacker H, Mascha E. What women want: understanding obesity and preferences for primary care weight reduction interventions among African-American and Caucasian women. J Natl Med Assoc. 2006;98(7):1160. [PMC free article] [PubMed]
  • Viner R, Haines M, Taylor S, Head J, Booy R, Stansfeld S. Body mass, weight control behaviours, weight perception and emotional well being in a multiethnic sample of early adolescents. Int J Obes. 2006;30(10):1514–1521. doi: 10.1038/sj.ijo.0803352. [PubMed] [Cross Ref]
  • Bandura A. Self-efficacy: The Exercise of Control. Freeman, New York; 1997.
  • Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 1977;84(2):191. [PubMed]
  • Linde JA, Rothman AJ, Baldwin AS, Jeffery RW. The impact of self-efficacy on behavior change and weight change among overweight participants in a weight loss trial. Heal Psychol. 2006;25(3):282. [PubMed]
  • Richman RM, Loughnan GT, Droulers AM, Steinbeck KS, Caterson ID. Self-efficacy in relation to eating behaviour among obese and non-obese women. Int J Obes Relat Metab Disord. 2001;25(6):907–913. [PubMed]
  • Elfhag K, Rossner S. Who succeeds in maintaining weight loss? A conceptual review of factors associated with weight loss maintenance and weight regain. Obes Rev. 2005;6(1):67–85. doi: 10.1111/j.1467-789X.2005.00170.x. [PubMed] [Cross Ref]
  • Bandura A. Health promotion from the perspective of social cognitive theory. Psychol Heal. 1998;13(4):623–649. doi: 10.1080/08870449808407422. [Cross Ref]
  • Levitsky D, Garay J, Nausbaum M, Neighbors L, Dellavalle D. Monitoring weight daily blocks the freshman weight gain: a model for combating the epidemic of obesity. Int J Obes. 2006;30(6):1003–1010. doi: 10.1038/sj.ijo.0803221. [PubMed] [Cross Ref]
  • Klem ML, Wing RR, McGuire MT, Seagle HM, Hill JO. A descriptive study of individuals successful at long-term maintenance of substantial weight loss. Am J Clin Nutr. 1997;66(2):239–246. [PubMed]
  • Strecher V. Internet methods for delivering behavioral and health-related interventions (eHealth) Annu Rev Clin Psychol. 2007;3:53–76. doi: 10.1146/annurev.clinpsy.3.022806.091428. [PubMed] [Cross Ref]
  • Wing RR, Jeffery RW. Benefits of recruiting participants with friends and increasing social support for weight loss and maintenance. J Consult Clin Psychol. 1999;67(1):132–138. [PubMed]
  • Bennett GG, Herring SJ, Puleo E, Stein EK, Emmons KM, Gillman MW. Web-based weight loss in primary care: a randomized controlled trial. Obesity. 2009;18(2):308–313. [PubMed]
  • Greaney ML, Quintiliani LM, Warner ET, King DK, Emmons KM, Colditz GA, Glasgow RE, Bennett GG. Weight management among patients at community health centers: The “Be Fit, Be Well” study. Obesity and Weight Management. 2009;5(5):222–228. doi: 10.1089/obe.2009.0507. [Cross Ref]
  • Piette JD. Enhancing support via interactive technologies. Curr Diab Rep. 2002;2(2):160–165. doi: 10.1007/s11892-002-0076-4. [PubMed] [Cross Ref]
  • Ramelson HZ, Bassey B, Friedman RH. The use of computer telephony to provide interactive health information. AMIA Annu Symp Proc. 2003;2003:539–543. [PMC free article] [PubMed]
  • Miller W, Rollnick S. Motivational Interviewing: Preparing People To Change Addictive Behaviors. Guilford Press, New York; 1993.
  • Centers for Disease Control and Prevention. National Health and Nutrition Examination Survey Protocol. Hyattsville; 2007.
  • Heil DP, Bennett GG, Bond KS, Webster MD, Wolin KY. Influence of activity monitor location and bout duration on free-living physical activity. Res Q Exerc Sport. 2009;80(3):424–433. doi: 10.5641/027013609X13088500159048. [PubMed] [Cross Ref]
  • Heil DP, Hymel AM, Martin CK. Predicting free-living activity energy expenditure with hip and wrist accelerometry versus double labeled water. Medicine & Science in Sports & Exercise. 2009;41(5):447.
  • Heil DP. Predicting activity energy expenditure using the Actical activity monitor. Res Q Exerc Sport. 2006;77(1):64–80. doi: 10.5641/027013606X13080769703920. [PubMed] [Cross Ref]
  • Heil DP, Whitt-Glover MC, Brubaker P. H, Mori Y: Influence of moderate intensity cut point on free-living physical activity outcome variables. Med Sci Sports Exerc. 2007;39(suppl 5):S185.
  • Heil DP, Brage S, Rothney MP. Modeling physical activity outcomes from wearable monitors. Med Sci Sports Exerc. 2012;44(1 Suppl 1):S50–S60. [PubMed]
  • Stunkard AJ, Sørensen T, Schulsinger F. Use of the Danish Adoption Register for the study of obesity and thinness. Research publications-Association for Research in Nervous and Mental Disease. 1983;60:115–120. [PubMed]
  • Brooks R. EuroQol: the current state of play. Health Policy. 1996;37(1):53–72. doi: 10.1016/0168-8510(96)00822-6. [PubMed] [Cross Ref]
  • Dolan P. Modeling valuations for EuroQol health states. Med Care. 1997;35(11):1095–1108. doi: 10.1097/00005650-199711000-00002. [PubMed] [Cross Ref]
  • Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System user's guide. , Atlanta; 1998.
  • Marcus B. Forsyth LA: Motivating People to be Physically Active: Champaign. Human Kinetics, IL; 2003.
  • National Center for Health Statistics. Data file documentation, National Health Interview Survey, 2005. 2006.
  • Hays RD, Stewart AL. In: Measuring functioning and well-being: The Medical Outcomes Study approach. Stewart AL, Ware JE, editor. Duke University Press, Durham, N.C; 1992. Sleep measures; pp. 235–259.
  • Marcus BH, Selby VC, Niaura RS, Rossi JS. Self-efficacy and the stages of exercise behavior change. Research quarterly for exercise and sport. 1992;63(1):60–66. [PubMed]
  • Ashida S, Goodman M, Pandya C, Koehly L, Lachance C, Stafford J, Kaphingst K. Age differences in genetic knowledge, health literacy and causal beliefs for health conditions. Public Health Genomics. 2011;14(4–5):307–316. [PMC free article] [PubMed]
  • Weinman J, Petrie KJ, Moss-Morris R, Horne R. The Illness Perception Questionnaire: A new method for assessing the cognitive representation of illness. Psychol Health. 1996;11:431–445. doi: 10.1080/08870449608400270. [Cross Ref]
  • Karlsson J, Persson LO, Sjostrom L, Sullivan M. Psychometric properties and factor structure of the Three-Factor Eating Questionnaire (TFEQ) in obese men and women. Results from the Swedish Obese Subjects (SOS) study. Int J Obes Relat Metab Disord. 2000;24(12):1715–1725. doi: 10.1038/sj.ijo.0801442. [PubMed] [Cross Ref]
  • Stunkard AJ, Messick S. The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. J Psychosom Res. 1985;29(1):71–83. doi: 10.1016/0022-3999(85)90010-8. [PubMed] [Cross Ref]
  • Sherbourne CD, Stewart AL. The MOS Social Support Survey. Social Science & Medicine. 1991;32(6):705–714. doi: 10.1016/0277-9536(91)90150-B. [PubMed] [Cross Ref]
  • Friedman GD, Cutter GR, Donahue RP, Hughes GH, Hulley SB, Jacobs DR, Liu K, Savage PJ. CARDIA: study design, recruitment, and some characteristics of the examined subjects. J Clin Epidemiol. 1988;41(11):1105–1116. doi: 10.1016/0895-4356(88)90080-7. [PubMed] [Cross Ref]
  • Kroenke K, Strine TW, Spitzer RL, Williams JBW, Berry JT, Mokdad AH. The PHQ-8 as a measure of current depression in the general population. J Affect Disord. 2009;114(1–3):163–173. [PubMed]
  • Chew LD, Bradley KA, Boyko EJ. Brief questions to identify patients with inadequate health literacy. Fam Med. 2004;36(8):588–594. [PubMed]
  • Saelens BE, Sallis JF, Black JB, Chen D. Neighborhood-based differences in physical activity: an environment scale evaluation. Journal Information. 2003;93(9) [PubMed]
  • Nord M. Household food security in the United States, 2009. DIANE Publishing; 2010.
  • Bickel G, Nord M. Guide to Measuring Household Food Security. Rev. 2000.
  • Carlson SJ, Andrews MS, Bickel GW. J Nutr. 1999. pp. 510S–516S. [PubMed]
  • Sellers RM, Rowley SA, Chavous TM, Shelton JN, Smith MA. Multidimensional inventory of Black Identity: Preliminary investigation of reliability and construct validity. J Personal Soc Psychol. 1997;73(4):805–815.
  • Kessler RC, Barber C, Beck A, Berglund P, Cleary PD, McKenas D, Pronk N, Simon G, Stang P, Ustun TB, Wang P. The World Health Organization Health and Work Performance Questionnaire (HPQ) J Occup Environ Med. 2003;45(2):156–174. doi: 10.1097/01.jom.0000052967.43131.51. [PubMed] [Cross Ref]
  • Kessler RC, Ames M, Hymel PA, Loeppke R, McKenas DK, Richling DE, Stang PE, Ustun TB. Using the World Health Organization Health and Work Performance Questionnaire (HPQ) to evaluate the indirect workplace costs of illness. J Occup Environ Med. 2004;46(6 Suppl):S23–S37. [PubMed]
  • Glasgow RE, Klesges LM, Dzewaltowski DA, Estabrooks PA, Vogt TM. Evaluating the impact of health promotion programs: using the RE-AIM framework to form summary measures for decision making involving complex issues. Health Educ Res. 2006;21(5):688. doi: 10.1093/her/cyl081. [PubMed] [Cross Ref]
  • Glasgow RE, McKay HG, Piette JD, Reynolds KD. The RE-AIM framework for evaluating interventions: what can it tell us about approaches to chronic illness management? Patient Educ Couns. 2001;44(2):119–127. doi: 10.1016/S0738-3991(00)00186-5. [PubMed] [Cross Ref]
  • Glasgow RE, Vogt TM, Boles SM. Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am J Public Health. 1999;89(9):1322–1327. doi: 10.2105/AJPH.89.9.1322. [PubMed] [Cross Ref]
  • Hill JO, Wyatt HR, Reed GW, Peters JC. Obesity and the environment: where do we go from here? Science. 2003;299(5608):853–855. doi: 10.1126/science.1079857. [PubMed] [Cross Ref]
Articles from BMC Public Health are provided here courtesy of
BioMed Central