Smart Choices for Healthy Families

Abstract

This pre–post study used a mixed-methods approach to examine the impact of a family-based weight management program among a low-income population. Smart Choices for Healthy Families was developed through an integrated research–practice partnership and piloted with 26 children and parents (50% boys; mean age = 10.5 years; 54% Black) who were referred by their pediatrician. Smart Choices included six biweekly group sessions and six automated telephone-counseling calls over 3 months. Children displayed reduced body mass index z-scores (p < .05), increased lean muscle mass (p < .001), and increased quality of life (p < .0001). Follow-up interviews indicated that physicians valued the lay leaders’ ability to provide lifestyle education, whereas lay leaders extended their reach to more community members. Parents wanted to become positive role models and found that the calls maintained focus on goals. Smart Choices shows promise to initiate weight management for children in low-income families.

Keywords

childhood obesity family-based intervention mixed methods research–practice partnership

The prevalence and severity of childhood overweight has climbed dramatically in the past three decades and is recognized as a serious public health concern that requires urgent action (Ogden et al., 2006; Wang & Lobstein, 2006). Children from low-income families are disproportionately at risk of childhood obesity and have not typically seen the benefit of programs to reduce childhood obesity (Baruch, Fonagy, & Robins, 2007; Kirk, Scott, & Daniels, 2005; Kumanyika & Grier, 2006; Miech et al., 2006; Singh, Kogan, & van Dyck, 2008). As has been recommended by research and practice professionals, a considerable amount of childhood obesity prevention research has been directed to school-based interventions. Unfortunately, the results have been modest in terms of prevention and unsuccessful in terms of treatment for reducing obesity among low-income families (Birch & Ventura, 2009; Doak, 2006; Story, 1999; Veugelers & Fitzgerald, 2005).

Alternatively, physicians have received a call to action to address weight management for overweight and obese children (Ariza, Laslo, Thomson, Seshadri, & Binns, 2009; Spear, 2007). Specifically, physicians have been encouraged to recommend that families with overweight children increase physical activity (PA) and fruit and vegetable consumption while decreasing sedentary behavior and sugar-sweetened beverage consumption. Although there is some support for brief physician counseling and provision of prevention materials at well-child visits (Dennison & Boyer, 2004), the literature does not support the conclusion that simply advising patients to be more active and eat better will induce behavior change and facilitate weight management (Calfas et al., 2002).

Another domain with some supportive evidence is the home environment and family-based interventions. Epstein and colleagues have consistently demonstrated that intensive family-based obesity treatment strategies are efficacious (e.g., Epstein, 1996; Epstein, Paluch, Roemmich, & Beecher, 2007). However, the model developed by Golan and associates (Golan, 2004, 2006; Golan & Weizman, 2001) seems to align more closely with the strategies recommended for childhood obesity treatment in low-income families (Kumanyika & Grier, 2006) in that it targets the parent as the primary agent of change, focuses on home environmental change, includes parenting strategies, and promotes role modeling through parental behavior change. More specifically, the model is based in social–ecological theory that includes an emphasis on modifying the social and physical home environment, providing healthful role modeling, developing family policy related to PA and healthful eating (HE), and targeting family change, rather than child-only change. Golan’s work demonstrated long-term effectiveness in reducing child body mass index (BMI) and parental cardiovascular disease risk (Golan, 2004, 2006; Golan & Weizman, 2001).

Despite the success of these family-based interventions, there is a mismatch in the intensity of both Golan and Epstein’s intervention models (e.g., several in-person sessions plus follow-up and usng personnel resources) and the resources available within clinics that primarily provide care for children from low-income families (Young, Northern, Lister, Drummond, & O’Brien, 2007). In the face of limited evidence of efficacious obesity treatment interventions for low-income families, the combination of physician recommendations and referral to a community-based program has been proposed for low-income audiences (American Academy of Pediatrics, 2003; Young et al., 2007). In addition, family-based childhood obesity treatment programs are recommended for all families—including those with low income (American Academy of Pediatrics, 2003; Epstein et al., 2001; Golan, 2004; Young et al., 2007). However, Kumanyika and Grier (2006) recommended that when considering low-income and other disadvantaged populations, family-based interventions should target parent-fostered changes to the home environment—including changes to parenting skills and personal health behaviors—and be integrated into existing safety net programs available in the community (e.g., Medicaid, WIC [Women, Infants, and Children]). By integrating with existing programs, community childhood obesity programs may be more effective in reaching a low-income audience by overcoming barriers such as lack of trust, time issues, and concern for safety in urban neighborhoods, while benefiting from the perspective of practitioners who engage low-income families on a daily basis (Chatterjee, Blakely, & Barton, 2005; Minkler, 2005).

One community program that provides resources for low-income families is the Supplemental Nutrition Assistance Program Education (SNAP-ED) delivered by Cooperative Extension. This program employs lay leaders from low-income, indigenous populations, with the intention that their life experiences will enhance their rapport and credibility with the target audience (Bremner, Campbell, & Sobal, 1994; Cooperative State Research, Education, and Extension Service, 2009). Large trials indicate that programs lead by community lay leaders are effective in improving obesity-related behavioral outcomes (i.e., nutrition, PA) for youth and may provide a cost-effective method to prevent diet-related chronic health conditions (Janicke et al., 2008; Rajgopal, Cox, Lambur, & Lewis, 2002; Townsend, Johns, Shilts, & Farfan-Ramirez, 2006). Furthermore, it has been demonstrated the Cooperative Extension can successfully deliver obesity treatment strategies to adults in both face-to-face and telephone modalities (Perri et al., 2008). Finally, Janicke and colleagues (2008) also demonstrated the efficacy of a family-based childhood obesity treatment program in rural families.

Even with improved feasibility demonstrated by interventions partnering with community-based program, some still require a number of resources. For example, an intervention that integrated with cooperative extension used up to twelve 90-minute sessions delivered by primarily master’s-level cooperative extension agents (Janicke et al., 2008). In Virginia, lay leaders—often with only high school education—deliver SNAP-ED, and the program typically includes six lessons and rarely works with families (i.e., programs are for either children or adults). Therefore, the implementation of previously efficacious interventions in which parents are provided a comprehensive and intensive weight-control program (e.g., Epstein et al., 2001; Golan, 2004: Janicke et al., 2008) may not be feasible using available community and clinical settings because of the heavy use of clinical time and highly trained professionals (Kalarchian et al., 2009; Schwartz et al., 2007). Therefore, there is a need to develop and assess interventions that are feasible and can be delivered on a broad scale and efficiently but are still of the frequency and duration necessary to initiate behavior and weight status changes.

One way to deliver feasible interventions that can be disseminated more widely includes the use of automated telephone calling (ATC) systems. ATC systems have been advantageous as a tool to extend intervention contact for a number of years (Piette, McPhee, Weinberger, Mah, & Kraemer, 1999; Pinto et al., 2002). Some of the advantages of using ATC technologies include reaching a larger number of people with relatively little staffing increases, self-paced and tailored learning, rapid updates once distributed, accessibility from any location, and feasibility and effectiveness on treatment outcomes (Piette et al., 1999). ATC technology is described as a potentially feasible and advantageous addition to health behavior programs (Glasgow, Bull, Piette, & Steiner, 2004) and has had similar results in nutrition education outcomes as other delivery methods in a community based program (Luccia, Kunkel, & Cason, 2003; Perri et al., 2008). Recently, members of our research team combined ATC and Golan’s model to deliver a family-based intervention (Estabrooks et al., 2009). In that study, a large proportion of parents completed all of the key content and saw reductions in their children’s BMI z-scores at 6 months and those changes were sustained at 12 months—6 months after the intervention was completed. That trial provided information on how Golan’s strategies could be adapted for ATC but was not tested in low-income families (Estabrooks et al., 2009).

The primary objective of this mixed-methods study was to explore the feasibility and effectiveness of a family-based intervention to treat childhood obesity that was designed for, and by, clinical and the Cooperative Extension personnel, who provide services for low-income families. Identification of an intervention’s external validity (i.e., reach, implementation, feasibility) is a largely not reported in the literature yet is central to disseminating successful interventions into wider practice (Glasgow, Klesges, Dzewaltowski, Bull, & Estabrooks, 2004). As such, quantitative methods were used to determine the reach, implementation, and effectiveness of the intervention (Glasgow, Vogt, & Boles, 1999; Klesges, Dzewaltowski, & Glasgow, 2008), whereas qualitative research methods were used to determine feasibility (i.e., post–pilot key informant interviews).

Method

The intervention and study design for this pilot mixed quantitative and qualitative methods project were developed using an integrated research–practice partnership in South-west Virginia (Estabrooks & Glasgow, 2006). As such, an interdisciplinary team of researchers, clinicians, Virginia Cooperative Extension specialists, and lay leaders from SNAP-ED met with the goal of developing a local and sustainable model for childhood obesity treatment in low-income families. This a priori inclusion of a range of stakeholders was intended to enhance the likelihood of generalizability and sustainability by integrating the intervention, and its testing, within existing local clinical and community resources that could deliver lifestyle behavior change programs in a low-income population (Evashwick & Ory, 2003). Furthermore, having the specialists and lay leaders participate in the development and delivery of the program increased the probability that the program characteristics would align with the Virginia Cooperative Extension/SNAP-ED organizational missions and reach a limited-resource audience (Norris & Baker, 1998).

Procedures

Recruitment included a brief physician recommendation (either by letter or by in-office visit) to Medicaid-eligible patients for a program that could help families improve HE and regular PA. Electronic medical records of the children between the ages of 8 and 12 from Carilion Clinic to identify potential eligibility based on Medicaid eligibility and child BMI. Physicians were notified of potentially eligible children who could be referred through either an upcoming visit or a letter. Parents who declined participation were asked to complete a short telephone survey that included basic demographic questions to determine if the final sample was representative of the referred population (see Figure 1 for flow diagram). Measurements were collected before the program, 1 month into the program, and after the 3-month program. Participants received $25 reimbursement for their time at each measurement session but were not paid an incentive for attending program sessions. Post–pilot key informant interviews were conducted with a physician partner, the two SNAP-ED lay leaders, and parents. Selection was based on maximum variation sampling to purposively select a heterogeneous sample (Patton, 2002). All measurement sessions, including post–pilot qualitative interviews were held at the same neutral location near the physician’s office to be convenient for the participants.

Figure 1.

Flow of participants

Sample

Participants were parent–child dyads (n = 26 dyads; we use the term parent to reflect parent or primary caregiver) recruited from the Carilion Clinic Children’s Hospital in Roanoke, Virginia, through physician referral. Similar to other family-based interventions (Estabrooks et al., 2009; Golan & Weizman, 2001; Janicke et al., 2008), children were between the ages of 8 and 12 years to match the program content in terms of being young enough to focus on parental involvement (Golan, 2004) as well as not too young to focus on the children’s individual behavior education (Flodmark, Lissau, Moreno, Pietrobelli, & Widhalm, 2004). Children also had a BMI between the 90th and 99th percentile for age and gender, and parents were English speaking, had telephone access, and lived within the geographic area. Exclusion criteria included patients with genetic/metabolic growth syndromes (e.g., Prader Willi syndrome) or being given medication(s) that would alter appetite in either direction (e.g., SSRIs [selective serotonin reuptake inhibitors], stimulant medications, Abilify®).

Of the 668 potentially eligible participants drawn from the electronic medical records database, 293 did not meet the BMI inclusion criteria, whereas others were ineligible because of being non–English speaking, the primary physician being outside of the participating practices, and living outside of the geographic area (Figure 1). Potentially eligible participants are those individuals whose medical records indicated they met eligibility criteria; 109 of these potentially eligible participants were eliminated once they were contacted either because of low BMI or not meeting other criteria, hence, there was some incorrect information in the medical record. Of eligible patients, we were not able to contact 87, resulting in a denominator of eligible and contacted potential participants of 177. Twenty-six parents agreed to participate in the study, reflecting a 15% proportional reach.

Participants were compared with those who declined on several demographic variables. There were significantly more people living in the homes of those who declined (M = 3.95, SD = 1.31) than in the homes of those who agreed to participate (M = 3.08, SD = 1.32; p < .05), and those who agreed to participate had a significantly higher household income (p < .05). Moreover, those who declined were more likely to participate in federally funded supplemental nutrition programs (62% vs. 35%; p < .05). Similarly, those who declined were more likely to access food pantries (49% vs. 4%; p < .01).

The mean age of the children was 10.5 years (SD = 1.2), 54% of the children were Black, 42% were White, and 4% were of Hispanic origin. The mean age of the parents was 39.5 years (SD = 12.3) and included two biological grandmothers who were the primary caregivers of the child participants. The racial and ethnicity composition was the same as the children, the median parental income was between $15,000 and $20,000 a year, and most parents were unemployed (n = 14).

Intervention

The intervention, Smart Choices for Healthy Families (Smart Choices), focused on providing support for parent behavior change, parenting strategies, role modeling, and home environmental changes to support HE and regular PA (Golan & Weizman, 2001). In addition, behavioral targets included increasing PA and HE (American Academy of Pediatrics, 2003). The research practice partnership agreed to use Golan and associates’ underlying theoretical model as the basis for Smart Choices. Using this framework, the research–practice partnership adapted and integrated three programs: (a) Healthy Weights for Healthy Kids—a Cooperative Extension–based program for children (Burkett et al., 2007), (b) Eat Smart, Move More—a program for low-income parents (http://www.eatsmartmovemorenc.com/), and (c) Family Connections—an evidence-based childhood obesity treatment intervention (Estabrooks et al., 2009). The initial two programs were chosen because they had a history of delivery through Virginia SNAP-ED, were popular with low-income families, were both delivered in a group setting, and addressed key obesity-related behavioral content (i.e., fruits and vegetables, sugar-sweetened beverages, PA, and screen time). The third program was selected because it demonstrated efficacy in decreasing child BMI z-scores and had adapted Golan’s model using ATC to reduce travel and time barriers for parents (Table 1 presents a description of the lessons).

Table 1.

Outline of Group Lessons

Lesson Contents and Topic and Completion	Child Component	Parent Component	Automated Telephone Counseling
Lesson 1
Contents	MyPyramid relay game Smart foods: constructing MyPyramid and Color Your Way	Eating smart through the life cycle: Parenting as it relates to diet and physical activity	Parenting skills: consistency, communication, role modeling, rewards
	Smart foods: constructing MyPyramid and Color Your Way
Topic	Basic nutrition	Food and activity
Percentage complete	81		74
Lesson 2
Contents	Milk jug volleyball	Making smart drink choices	Physical activity, nutrition, stress management
Contents	Smart drinks: Be a drink detective
Topic	Healthy drinks	Healthy drinks
Percentage complete	77		61
Lesson 3
Contents	Freeze tag	Moving more, everyday, everywhere	Home environment for physical activity and healthy eating
	Smart activities: Activity charades and activitudes
Topic	Physical activity	Physical activity
Percentage complete	74		61
Lesson 4
Contents	MyPyramid hopskotch	Smart-size your portions	Engaged role modeling for physical activity and healthy eating
	Smart Choices: Pyramid practice
Topic	Sensible portion sizes	Sensible portion sizes
Percentage complete	58		52
Lesson 5
Contents	Upset the fruit basket	Limit TV	Resources for physical activity, healthy eating, and parenting skill development
	Smart image: What normal supposed to look like?
Topic	Positive body image	Physical activity
Percentage attendance	62		65
Lesson 6
Topics	Broccoli, broccoli, carrot	Eating smart on the run	Relapse prevention
	Smart snacks: Commercializing ourselves
Topic	Media and food	Fruits and vegetables
Percentage complete	58		49

The resultant program was a 12-week program: initiated through physician recommendation to parents of potentially eligible children, taught by SNAP-ED lay leaders, and supported by ATC. Two SNAP-ED lay leaders delivered the Smart Choices small group sessions. Both lay leaders were involved in the development of the program and activities: One focused on delivering the intervention to the child participants whereas the other focused on the parent participants. No other staff was involved in delivering the intervention, though the lead author monitored the group sessions and automated telephone calls to ensure the intervention was delivered as intended. Small group sessions were delivered biweekly in a location near the physician’s office and adjacent to a bus stop for convenience (same location as measurement sessions). No transportation to and from sessions was provided. Parent sessions used goal setting and feedback with an emphasis on the home environment, whereas children engaged in activities focusing on the same major topic. At the end of each class, children and parents participated in a shared activity and healthy recipe sampling. Each group session was approximately 90 minutes. A member of the research team used a checklist of activities and topics to be covered each class to document treatment fidelity. On the alternate week, the ATC session included a review of the previous week’s goal, tailored feedback related to the degree of goal success, a brief lesson, and the development of a new goal to be achieved prior to the next group session (Estabrooks et al., 2009). These ATC calls were automated and tailored based on responses given in the form of numbered choices and on average took 10 minutes to complete. Attendance for both the group sessions and completion rate for the ATC calls are reported in Table 1.

Measures

Child body weight and composition

The children’s body weight and height were collected through a standard scale and stadiometer to calculate BMI for age and gender (Kuczmarski et al., 2002). Height was measured to the nearest eighth of an inch and weight was measured to the nearest half of a pound. Both were collected at each measurement time point. Objective measures of body composition and weight were obtained through dual-energy X-ray absorptiometry (Sopher, Shen, & Pietrobelli, 2005).

Child health behaviors

PA (outside playtime), screen time, fruit and vegetable consumption, and sweetened-beverage consumption were measured using the SNAP-ED evaluation tool. We selected this tool because it was relatively brief (21-item) and had been used in a similar population previously demonstrating reliability and validity (Gresock, 2004).

Child body image

The Kids’ Eating Disorders Survey is a 14-item questionnaire used as a screening tool for eating disorders in children (Childress, Jarrell, & Brewerton, 1993). Responses are categorized into weight dissatisfaction, restricting/purging, binge eating, and body dissatisfaction components. The scale has high internal consistency (α = .73; Childress et al., 1993).

Child quality of life

The Pediatric Quality of Life 4.0 Generic Core Scale measures health-related quality of life (HRQL) in children, has 23 items, and is applicable for healthy school and community populations (Varni, Seid, & Kurtin, 2001). This scale has summary, physical, social, emotional, and school scores that show adequate reliability and validity (α = .80-.90; Varni et al., 2001).

Parent physical activity

The Rapid Assessment Physical Activity Scale includes nine yes/no items assessing the type (strength and cardiovascular training) and amount of PA in which adults engage (Topolski et al., 2006). In validation studies it compares well with longer scales (Topolski et al., 2006).

Parent health behaviors

The same short questionnaire that the children completed was also completed by the parents to assess screen time, fruit and vegetable consumption, sugar-sweetened beverage consumption, and healthy cooking behaviors (Gresock, 2004). There were 12 items that included 2 added items to the beverage section.

Parent quality of life

The Centers for Disease Control and Prevention’s Healthy Day measure that was used to assess quality of life in the parents is a four-item measure that has been shown to be valid and reliable (Barger, Burke, & Limbert, 2007).

Reach, representativeness, and implementation

The RE-AIM (Reach, Efficacy, Adoption, Implementation, and Maintenance) framework includes factors that balance the internal and external validity of a study. Implementation is the intervention agents’ fidelity to the various elements of an intervention’s protocol that highlights the need to determine the degree to which the intervention was delivered as intended. We operationalized this dimension as participant attendance at the group sessions and parent completions of each ATC call. Rates were calculated as the proportion of attended classes and completed calls divided by the total number of sessions and calls available. In addition, reach was determined by calculating the proportion of eligible families that participated. The denominator included children that were identified as having an eligible BMI percentile. The representativeness included a comparison between those that agreed to participate and those that declined. The variables that were compared between the two groups included the child’s age, gender, race, BMI, BMI percentile; the number of people living in the home; and the respondent’s gender, age, ethnicity, race, education level, participation in food assistance programs, and family income.

Qualitative Data Collection

A mixed-methods approach was selected for this study to determine the feasibility of Smart Choices and provide a richer understanding of the feasibility from the recruitment and delivery perspective, participant’s perceptions of the program, and adoption of new behaviors (Creswell, 2009). Interviews were conducted by the lead author in the same location where group and measurement sessions were held and took on average 30 minutes. These interviews followed a semistructured format with a grounded theory approach that allows some flexibility in the interview format and does not set any a priori hypothesis but, rather, allows themes and categories to emerge once the data are coded (Henderson, 2006; Patton, 2002). Rather than beginning by researching and developing a hypothesis, the first step is data collection, through a variety of methods, and to develop theory inductively (Henderson, 2006).

The physician was asked questions such as “What strategies do you have to address childhood obesity with families currently in your practice?” and “What do you think about physician referral to community programs to learn health behaviors or engage in a weight-loss program?” The lay leaders were asked questions such as “What programs do you typically deliver to address obesity?” and “If you were to develop partnerships in the community to support reversing the childhood obesity epidemic, what would it look like and who would be involved?” and questions regarding the Smart Choices program. The parents were asked questions such as “What were your expectations for the program?” and “What do you think about cooking regularly for your family?” and questions regarding changes they may have made as a result of the Smart Choices program.

Statistical Analysis

Data were entered into SPSS Statistical Software, Version 16 (SPSS Inc, Chicago, IL) and tested for normality and with normally distributed variables, and paired-samples t tests were performed between baseline and 1 month and between baseline and 3 months. With variables that were not normally distributed, Wilcoxon’s rank order tests were conducted. Because of the pilot nature of the study, a present-at-follow-up analytic approach was used. BMI z-scores were calculated as recommended by the U.S. Centers for Disease Control and Prevention (2001). The interviews were recorded digitally and transcribed verbatim, then coded for meaning by multiple coders (lead author and trained graduate research assistants). Each interview was member checked by the interviewee to confirm that the meaning shown in the document was indeed what they had meant. Following the grounded theory approach, themes and categories in the data were coded inductively (Patton, 2002). This method includes grouping of meaning units (i.e., a word, phrase, or statement with a single meaning) into higher order categories and then grouping categories into higher order themes through consensus.

Results

Quantitative Outcomes

Between baseline (see Table 2) and 1 month, there was a significant decrease in BMI z-score (t = −3.44, p < .01), decrease in the child’s consumption of sweetened beverages (t = −2.00, p < .05), decrease in negative body image (t = 4.42, p < .01), improvement in the child’s overall HRQL (t = −3.95, p < .01), improvement in the child’s physical HRQL (t = −3.11, p < .01), improvement in the child’s school HRQL (t = −3.12, p < .01), increase in the parent’s vegetable intake (t = −2.18, p < .05), and an increase in parental meal regularity (t = 11.85, p < .01).

Table 2.

Quantitative Findings at 1 and 3 Months

Variable	Baseline M (SD)	1-Month M (SD)	3-Month M (SD)
Child anthropometrics
BMI z-score	−3.03 × 10−¹⁶ (1.0)	−0.25 (0.91)**	−0.26 (0.99)*
Percentage body fat	38.2 (6.3)	37.9 (6.9)	38.0 (7.2)
Lean muscle mass (lbs)	68.9 (10.1)	68.3 (11.2)	69.1 (10.4)**
Weight (lbs)	116.1 (18.8)	114.3 (18.5)	116.1 (17.7)
Height (inches)	57.8 (3.3)	58.0 (3.7)	58.2 (3.8)
Child variables
Fruit servings	1.3 (0.5)	1.4 (0.8)	1.6 (0.6)
Vegetable servings	1.4 (1.0)	1.3 (0.7)	1.3 (0.8)
Soda consumption	0.8 (0.7)	0.8 (1.2)*	1.1 (1.2)
Outside playtime	0.8 (0.7)	0.9 (0.7)	1 (0.8)
Screen time	1.0 (0.4)	0.8 (0.4)	0.9 (0.3)
Body dissatisfaction	2.3 (1.4)	1.8 (1.1)**	1.8 (1.3)
HRQL: total	86.2 (6.8)	90.1 (6.6)**	90.7 (6.4)**
HRQL: physical	89.7 (5.3)	93.0 (6.1)**	95.1 (4.4)**
HRQL: emotional	84.2 (9.9)	88.3 (12.7)	86.0 (11.0)
HRQL: social	88.5 (11.6)	90.2 (11.2)	91.1 (11.4)
HRQL: school	83.0 (12.0)	86.9 (11.5)**	88.1 (10.4)**
Parent variables
BMI	42.8 (11.5)	34.9 (13.8)	31.0 (11.1)
Quality of life	2.1 (1.1)	2.1 (0.5)	1.9 (0.9)
Fruit consumption	1.2 (0.5)	1.5 (0.9)	1.7 (0.8)
Vegetable consumption	1.4 (0.6)	1.8 (0.8)*	1.9 (0.7)
Meal regularity	0.8 (0.6)	0.7 (0.6)**	0.6 (0.3)
RAPA: cardio	1 (1.2)	1.1 (1.1)	1.8 (1.1)
RAPA: strength	0.6 (1.1)	0.6 (1.0)	1.1 (0.9)
Sweetened-beverage consumption	1.8 (1.5)	1.5 (1.1)	1.5 (1.0)
Screen time	1.9 (0.7)	1.4 (0.6)	1 (0.5)*
Healthy cooking	3.6 (0.6)	3.7 (0.6)	3.9 (0.5)**

Note. BMI = body mass index; HRQL = health-related quality of life; RAPA = Rapid Assessment Physical Activity Scale.

p < .05. **p < .01.

Between baseline and 3 months, there was a significant decrease in BMI z-score (t = −3.28, p < .01), increase in lean muscle mass (t = −4.03, p < .01), improvement in overall HRQL (t = −4.23, p < .01), improvement in physical HRQL (t = −4.26, p < .01), and improvement in school HRQL (t = −4.02, p < .01). For parents who participated in the program there was a decrease in screen time (t = −2.49, p < .05) and increase in reports of healthy cooking (t = −3.89, p < .01).

Qualitative Results

The physicians explained that because of their lack of time and resources, they have not had a high level of success in treating childhood obesity in their patients. Therefore, the partnership with SNAP-ED and the lay leaders’ ability to provide healthy lifestyle education is valued by the physicians (see Table 3). The SNAP-ED lay leaders also valued the partnership with the physicians because they were able to reach more members of the community, and involving parent–child dyads was a novel approach that helped enhance the impact of their programming. Although transportation was a barrier to attendance, the parents reported that their enjoyment of the program and the excitement that their children expressed was a motivation to keep attending. Motivation to enroll in the program included the parent’s desire to become better role models for their children to support healthy nutrition and PA.

Table 3.

Qualitative Themes and Quotes

Topic	Theme	Details and Quotes
Physician respondent
Previous approach to treat childhood obesity	Physician felt unsuccessful in attempts to address childhood obesity in patients	Lack of time and resources, ineffective messages for parents and children, families do not have a good attendance rate for appointment referrals that are set to discuss health behaviors
Previous approach to treat childhood obesity	Physician felt unsuccessful in attempts to address childhood obesity in patients	“So if they are really overweight I will try and talk to the parent, but I am not very successful (. . .) it also depends upon a little bit, if I have time to try and talk about exercise and activity levels sometimes I’m able to give targeted guidance along those line. But I sort of anticipate that most of the work is going to be done at the follow-up visit.”
Partnership	Physician valued partnership	Physicians recognized importance of their referral to the community-based program and the benefits their patients would see by participating and receiving the educational component that they did not have time to provide
Lay leaders
Partnership	Lay leaders valued partnership	Lay leaders also recognized the value of a physician referral to their program and being able to help more people in the community
Partnership	Lay leaders found approach of parent–child dyad novel and beneficial for greater impact	“I think the biggest barrier [when delivered in schools] is just going in, because they are so low-income that it’s really hard to get the message to the parent from the kid, unless it’s just a change that the kid tries to make on his own, the biggest barrier is getting that from the school to the families. I think you’ve got to make it family oriented for it to work. Versus, me, I could go in all day and do lessons for the kids, but unless they actually can take it home and get mom and dad on board, you never really know how far it goes.”
Program feedback	Automated telephone calling component was accepted	“I think that’s a good idea, basically because it keep them, number one its accountability and number two it brings them, its keeps them in mind, because a lot of times when you are trying to make a change to your diet or a health situation, you need that constant reminder. So I think that going two weeks in between sessions might have been a little bit too long, so that weekly call that they got in the middle was that little ‘hey, I’m still here, don’t forget’ to kind of remind and to look forward to what’s coming the following week.”
Changes as a result of the program	Positive changes in diet	“The parents would come back the next week and say they had tried such and such recipe, or this week, we cut back on sodas.”
Parent participants
Partnership	Parents found approach of parent–child dyad beneficial for family learning	“I think it was a success because the kids were involved, they came back and told the parents what we learned.”
Program feedback	One reason parents sign up for program is to become better role models for children	“I thought it would be something good for the grandchildren since I am overweight, and my grandchildren were getting overweight, I thought it would be a good idea for us to go through this together.”
Barriers	Main barrier to attendance was transportation	“Sending a bus to come and pick us up. That would make it so much easier to get over there.”
Program feedback	Favorite components of the program were hands-on activities and sampling recipes	“They liked the actual fact that they could taste the recipes before trying them, they loved that.”
Program feedback	Attendance was encouraged by children’s enjoyment of and excitement for the program	“Well, um, I liked em [the classes], but it was mainly her attitude towards them, she loves it here, she was always excited about coming, and she still is.”
		“The children were excited about learning, how to eat.”
Program feedback	Main recommendations: more behavior change strategies	“Umm, but as far as actually doing what I learned, I haven’t actually applied what I learned like I should have.”
		“We’ve been talking about going in and walking in the mornings before we get up, but for some reason, we have just gotten to bed so late, and getting up so late and we are rushing and it’s so much quicker just to get her a 99-cent biscuit on the way to school. Rather than having a bagel and a piece of fruit or something that’s healthier. I’m sorry.”
Program feedback	Automated telephone calling component was accepted	“It was nice to be reminded and keep us on track.”
Changes as a result of the program	Positive changes in diet	“I started to use those wraps more often and we are trying to increase our vegetables.”
		“We are eating a lot more vegetables, the kids like broccoli, cauliflower and carrots. Now my grandbabies like them raw, but I prefer them cooked. Also, we are using less fat in the cooking, we used to fry things and put lots of butter on em, now we are limiting the fat we use.”
		“. . . and we had smaller plates, we don’t eat off these plates (gesture to larger plate size), we eat off like saucers now.”

Beyond the quantitative results, the interviews suggested several behavior changes that improved nutritional quality (e.g., increased fruit and vegetable intake) and portion sizes (e.g., reducing dinner portions for all family members) as well as increased levels of the families’ PA. Although many of the parents did report positive changes, those who struggled described a need for greater behavior change strategies such as further goal setting and feedback to help implement their newly obtained knowledge. Finally, parents indicated that the ATC component was a valuable tool to maintain focus on goals and receive feedback.

Discussion

This pilot study provided an example of a feasible model to treat childhood obesity in a low-income population. It is important to track recruitment reach and representativeness, and for Smart Choices, those who declined to participate in the program were of a slightly lower income and reported more participation in food assistance program than those who agreed to participate. While our participants were low-income families, this finding aligns with previous research that documented that the lower the income, the less likely a family is to participate in research (Yancey, Ortega, & Kumanyika, 2006) and also less likely to recognize that their child is overweight or to feel it is necessary to intervene on the child’s eating and activity habits (Baughcum et al., 2001). In addition, it has been suggested that low-income families may be less likely to participate in disease prevention interventions, because of the higher levels of stress and likelihood of being a single parent or having lower education levels (Gross, Julion, & Fogg, 2001).

Effectiveness for Smart Choices was supported quantitatively with a mean decrease in the primary outcome: children’s BMI z-score. In addition, child lean muscle mass increased; however, the overall percent body fat did not decrease significantly. Beyond simply having a small sample size and difficulty in behavior change, perhaps these results are partially because of the age-group that participated in the study, 8 to 12 years, which is an age of naturally occurring metabolic changes along with increasing behavioral risk factors for obesity (Jasik & Lustig, 2008). However, in previous studies with this age-group, children who did not receive intervention materials showed an increase in BMI z-scores (Estabrooks et al., 2009). Still, when a single-group design is used, developmental metabolic changes may present a barrier to intervention effectiveness, but the design also provides an opportunity to address these changes with specific intervention content. This naturally occurring barrier to intervention effectiveness presents a challenge for future interventions and perhaps an opportunity to address this age-group considering puberty.

Several behavioral risks for obesity decreased in both the children and the parents reflected in the quantitative data and corroborated with qualitative responses. The children showed a decrease in sugar-sweetened beverage consumption whereas parents showed an increase in vegetable intake, meal regularity, and healthy cooking, as well as a decrease in screen time. This parallels the parents’ initial reason for signing up for the program—to help their children and to become better role models in terms of nutrition and PA—and shows the resulting behavior changes from that motivation. This is supported in other interviews with mothers of obese children, where the mothers indicated that they believed that their child’s weight was a family, rather than individual, problem and intended to initiate a range of strategies (Jackson, Mannix, Faga, & McDonald, 2005).

Children showed improved body image and HRQL. These results are important to acknowledge since obese children experience decreasing levels of self-esteem and HRQL are more likely to experience depression and engage in high-risk behaviors such as smoking or consuming alcohol (O’Dea, 2005). Impaired HRQL has been identified in obese children at much higher rates than nonobese children and has many important implications such as physical limitations and social adjustment (Schwimmer, Burwinkle, & Varni, 2003). Furthermore, interventions addressing childhood obesity need to be health centered (Golan & Weizman, 2001) and cautious in monitoring body image and other potentially negative outcomes from childhood obesity intervention so as to “first do no harm” (O’Dea, 2005).

The qualitative data confirmed the positive impact Smart Choices had on the participating families. Once the families started attending the classes, the children’s enjoyment of the activities was described as a motivating factor for adherence, especially because of the hands-on activities. The parents reported both positive dietary changes and PA changes from the program. Although some of these reported changes were reflected in the quantitative data, not all changes were. Perhaps the measurement tools used were not sensitive to change since a short and simple assessment of nutrition and PA behavior changes was selected. In dietary behavior change research, there is a documented discrepancy between self-perceived diet and actual diet (Kristal, Glanz, Curry, & Patterson, 2009). Therefore, perhaps these parents were making positive changes to their family’s diet, but it may be that this behavior change was still in progress and they were still learning the necessary skills to perform the behavior change to the fullest (Kristal et al., 2009). With that said, individuals also reported that integrating more behavior change strategies within the program would be helpful. Creating a balance between feasibility and dose of the behavior change strategies is an area that requires more research, perhaps following a stepped-care approach that allows participants who are having more difficulty to receive a higher level of intervention support (Patrick et al., 2006).

The effectiveness of Smart Choices relies on the authority that the physicians have with their patients and the experience and level of rapport that the lay leaders were able to develop with the participants. Using the physicians for referral to a community-based program may initiate behavior change in their patients and benefit from the resources available in these community programs. The lay leaders’ ability to address the cultural sensitivity of the program is largely because they serve as change agents that are representative of the target population. As Rogers (2003) describes, representativeness (homophily) is the degree to which pairs of individuals who interact are similar, and homophily between the change agent and audience is predictive of adoption. The use of lay leaders is recommended, and what they lack in technical expertise they make up for in social expertness and the development of close personal relationships with a base of trust (Rogers, 2003). This translates to other community-based settings by highlighting the value of intervention agents that come from within the target population and are guided by an integrated research–practice partnership approach.

The finding from the qualitative follow-up supported the research–practice partnership between SNAP-ED, the clinic, and researchers. The physician and the SNAP-ED lay leaders both acknowledged the value in having lifestyle behaviors related to childhood obesity taught to families outside of the clinic because of limited time and resources in a medical setting. Furthermore, the lay leaders acknowledged the value of the novel approach of having both parent and child involved in group sessions rather than relying solely on the children to make the change. Future implications include a recommendation for health care organizations to develop partnerships with community-based programs such as SNAP-ED.

The final piece of feedback from the participants was that they enjoyed the ATC calls and found them helpful. This demonstrates acceptability of the ATC call format to extend reach of short-term programs to other low-income target populations and is aligned with the completion of the calls; of those who attended the previous class, 79% completed the call that followed. Future studies may want to investigate the influence of ATC calls on the main outcome through multiple-group designs. Future programs treating childhood obesity in a low-income population could benefit from the inclusion of ATC and the use of community-based resources in terms of programming.

The central limitations of the current study are the small sample size and lack of control group. The difficulty in recruitment highlights the need for further work in recruitment and retention of high risk populations. This small sample size limited the ability to detect changes in some outcomes and did not allow for the inclusion of a control group or extended follow-up. An additional limitation also relates to the power to detect changes: the use of simple not highly sensitive measures. However, the brevity of using simple, short measures suited the purpose of the current pilot study, which was enhanced by the qualitative data. Ultimately, we were able to demonstrate the feasibility and initial success of a potentially sustainable model for childhood obesity treatment in low-income families.

Footnotes

Declaration of Conflicting Interests

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

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article:

Smart Choices for Healthy Families was funded through the Carilion Clinic Research Acceleration Program (PI: Estabrooks & Pinard) to support clinical/community integrated pediatric obesity studies.

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