Duration: 10/01/2024 to 09/30/2029

Administrative Advisor(s):

NIFA Reps:

Non-Technical Summary

Adolescents in the United States commonly consume less than the recommended amounts of fruits and vegetables and excess amounts of sugary foods, thus potentially contributing to the high obesity rates observed in this group (22.2% among 12-15 year olds). Previous research by our team and others has shown that adolescents who frequently consume food when their parents/caregivers are not around tend to consume less healthy foods and have higher weight status. In our previous research, we also found that adolescents’ consumption of fruits and vegetables was related to parents who made healthy foods available for adolescents to eat. This suggests that if we provide parents with strategies to increase availability of healthy foods, this may result in healthier food intake and weight status among adolescents. The goal of this project is to develop and test nutrition education materials for adolescents (11-14 years old) and their parents/caregivers that will promote healthy eating among adolescents when their parents/caregivers are not around. We plan to disseminate the education materials using one or more technology platforms, like mobile applications (apps), virtual education, and/or artificial intelligence (e.g., ChatGPT), and then evaluate its ability to increase the availability of healthy foods, like fruits and vegetables, for adolescents to eat and the actual consumption of healthy foods among adolescents when they are not around their parents/caregivers. From the information learned through testing materials, we will seek funding to launch the project in a larger group of parents and adolescents.

Statement of Issues and Justification

The need as indicated by stakeholders:

The prevalence of youth obesity in the United States (US) is high (22.2% among 12-15 year-olds, 2017-2020) with disproportionately higher rates among non-Hispanic African American and Hispanic children than other races/ethnicities (Stierman et al., 2021) and among youth from low-income compared to higher income households (Stierman et al., 2021; Ogden et al. 2018). The National Health and Nutrition Examination Survey (NHANES) data from 1999-2016 were used to project that 46% of adolescents would become overweight or obese by 2030 (Wang et al., 2020). Adolescents with overweight or obesity are at greater risk of becoming adults with obesity (Craigie et al., 2011; Simmonds et al., 2016) and developing associated non-communicable health conditions and psychosocial problems in the short- and long-term (Park et al., 2012; Kelsey et al., 2014). Therefore, obesity reduction among adolescents needs to be addressed to reduce the overall burden of adult obesity and associated health risks in the US.

Poor dietary quality increases the risk for obesity (Hruby et al., 2016). More than half of US youth (12-19 years) had poor quality diets based on NHANES surveys from 1999-2016 (Liu et al. 2020) indicating a need for improvement to meet recommendations for health promotion. More specifically, results indicated that 17.5% of total energy intake among US children (2-19 years) was from junk food, with the largest contributions from sweet bakery products (6.4%) and savory snacks (4.6%) (Liu et al., 2021). Marriott et al. (2019) found that US children’s (2-19 years) energy intake from sugar sweetened beverages (SSBs) was 120.5 kcal/day, total sugar intake was 114.2 grams/day, and total energy intake was 2032 kcal/day (NHANES 2015-2016) (Marriott et al., 2019), thus the recommendation of 10% of energy from added sugars was unlikely to be met by many adolescents. Further, Doherty et al. (2021) found that Healthy Eating Index (HEI)-2015 scores were higher among adolescents not consuming SSBs compared to high SSB consumers (53 vs. 46, P < 0.001) (NHANES (2009-2014); although HEI scores for all adolescents were low. USDA Dietary Data Briefs (NHANES 2017-2018) have also shown that only 47% of adolescents (12-19 years) consumed vegetables on a given day (Hoy et al., 2021a) and 35% consumed fruit on a given day (Hoy et al., 2021b). Poor diet can contribute to chronic diseases including obesity (Micha et al., 2017; Liberall et al, 2020), thus factors influencing poor food choices should be examined further, especially among those at highest risk for negative health outcomes.

A socioecological model (SEM) was used to describe the dynamic interrelationships among concentric layers of influence on adolescent obesity, including intrapersonal, interpersonal, community, organization, government, industry and societal domains (Jebeile et al., 2022; Ohri-Vachaspati et al. 2015). Among the intrapersonal and interpersonal layers involving the individual, family and peers, and school, factors such as family socioeconomic position and structure, parenting practices, and food environments can influence consumption of foods and beverages associated with child and adolescent obesity. Cross-sectional data from 560 parents of predominantly low income, minority children 3-18 years were used to show that the SEM layers representing parent characteristics and perceptions of neighborhoods were strong predictors of children’s weight status (Ohri-vachaspati et al. 2015). Parent perceptions of neighborhood environments such as the ability to purchase healthy foods were associated with better child weight outcomes, suggesting that parent perception of availability and accessibility are important for child health (Ohri-vachaspati et al. 2015). Along with the neighborhood and built environment, other social determinants of health (SDOH) domains including economic stability, social and community context, and health care access and quality, together were associated with a 4.38 odds of having a higher BMI category compared to adolescents with the lowest-risk SDOH profiles (Blasingame et al. 2023). These findings suggest that interventions to reduce childhood obesity should also address health equity.

Obesogenic eating behaviors among adolescents (11-14 years) may be more likely to occur during independent eating occasions (iEOs) when parents/caregivers are not present at home, or in other environments away from home including friends’ homes, school, restaurants or convenience stores (Reicks et al. 2015). The frequency of iEOs among adolescents is a concern because food choices during these occasions may be less healthful than when parents/caregivers are present (Reicks et al., 2019), leading to potential development of overweight or obesity (Reicks et al., 2019; Shirasawa et al., 2018). Banna et al. (2020) interviewed low-income early adolescents and their parents to determine characteristics of iEOs based on pictures adolescents took of all eating occasions over a one-day period. Of all eating occasions, 57% were iEOs. Of the iEOs, most occurred at home with many as snack occasions (65%), frequently including sweets, fruit, and dairy foods, which were selected based on preferences, convenience and availability. 

Factors influencing the frequency of adolescent iEOs include the increase in single-parent households and participation of mothers and fathers in the workforce (Mather et al., 2019; US Bureau of Labor Statistics, 2023). These societal shifts may explain a low prevalence of family meals for some adolescents indicated by NHANES data from 2007-2010, which showed that the prevalence of having 0-2, 3-6, and ≥7 family meals per week was 18%, 32% and 50%, respectively among the US population with 2 or more individuals living in the household (Newman, Tumin, Andridge, & Anderson, 2015). A population-based study involving adolescents showed that the percentage consuming shared family meals was lower among lower socioeconomic status groups (Neumark-Sztainer et al., 2013). In addition to a low prevalence of family meals for some adolescents, NHANES data (2011-2014) showed that among US children 12-18 years, the mean daily calorie intake from snacks was 459, based on a mean 2.4 snacks per day with the greatest contributions to snack calorie intake from desserts and sweets, and salty snacks (Dunford and Popkin, 2018). In a large population-based study, frequency of adolescent snack consumption was associated with higher energy and sugar-sweetened beverage (SSB) intakes, lower fruit and vegetable intakes, and more frequent fast-food intake (Larson et al., 2016). Collectively, these factors may contribute to a greater number of adolescent iEOs with less healthy food choices and a greater likelihood of overweight and obesity.

Positive food parenting practices in general are thought to impact adolescent healthy food choices by providing structure (e.g., meal and snack routines, food availability, rules and limits, modeling), and supporting autonomy (e.g., teaching, involving) (Vaughn et al., 2016). In the W-4003 project, our team conducted several foundational studies to improve our understanding of the relationship between parenting practices and adolescent iEO food and beverage consumption. Gunther et al. (2019) examined perspectives on parenting practices that influence iEO food choices based on parent and adolescent interviews. We used these results to create and test a questionnaire based on six parenting practices (Reicks et al., 2020), which was used to collect data from 622 parents of adolescents across the US in the fall of 2021 (Reicks et al., 2023). The findings from these studies and those currently underway will be used to inform the development of future interventions that focus on promoting food parenting practices associated with healthy adolescent iEO food choices.

The importance of the work, and what the consequences are if it is not done:

The limited number of studies that have examined the frequency of adolescent iEOs and eating behaviors have shown that more frequent iEOs are associated with poorer dietary intake and higher weight status among adolescents (Reicks et al., 2019; Shirasawa et al., 2018). Parenting practices associated with adolescent iEO intake were identified by our team in W-4003 among low-income, multiethnic adolescents, with availability and role modeling being the only parenting practices reported by both parents and adolescents that were associated with fruit and vegetable intake (Reicks et al., 2023). An intervention mapping protocol will be employed to design an intervention to promote making healthy foods available for early adolescents during iEOs. Tech-driven approaches such as mobile apps, artificial intelligence (AI), and virtual education approaches will be reviewed to develop an educational module prototype to promote parenting practices that may improve adolescent iEO food choices and assist in childhood obesity prevention (Objective 1). For Objective 2, we will develop and pilot test the intervention for user experience and implementation for feasibility, acceptability, and preliminary health outcome effectiveness and cost effectiveness. For Objective 3, grant proposals will be prepared to scale up the intervention and disseminate findings to health professionals. If the proposed work for W-5003 is not done, the goal of promoting positive parenting practices that improve food choices during adolescent iEOs will not be achieved. Therefore, the risk of adverse consequences of overweight and obesity will not be reduced with respect to health, psychosocial, and economic issues among adolescents and families in the short- and long-term.

The technical feasibility of the research:

Research team members for the W-5003 project have successfully collaborated on two or more Agricultural Experiment Station (AES) funded multistate projects (W-1003, W-2003, W-3003, and W-4003) to study influences of parenting practices on parent and adolescent dietary behaviors. The team has previously developed, tested and implemented survey instruments involving parent/adolescent dyads across 10-12 states to identify calcium rich food and beverage (CRF/B) related parenting practices associated with CRF/B intake among adolescents and published results in which all researchers were involved (Edlefsen et al., 2008; Cluskey et al., 2008, 2015; Richards et al., 2014; Vyduna et al. 2016; Reicks et al. 2011, 2012; Banna et al., 2019). The most recent successful collaborative adolescent iEO project (W-4003), in which all researchers were involved, were based on qualitative approaches (Gunther et al., 2019b, 2023; Banna et al., 2020), and quantitative approaches surveying large samples of parent/adolescent dyads (Monroe-Lord et al., 2022a, 2022b; Reicks et al., 2019, 2020, 2023). The diversity of expertise has functioned well for this team in the past and is expected to contribute to the feasibility of developing and pilot testing digital communications for parent/adolescent dyads regarding parenting practices that influence adolescent iEO eating behaviors. For example, within their own institutions, team members have conducted interventions among parent/adolescent dyads involving in-person cooking interventions (Overcash et al., 2018; Gunther et al., 2019), meal planning using meal calendar websites (Jones, 2018; Jones, Evich, & Gaskins, 2017), and experiential learning in a virtual world environment (Meng, Wong, Manore, & Patton-Lopez, 2018). These initiatives have potential application to adolescents’ food consumption during iEOs. The team includes nutrition researchers and Cooperative Extension Nutrition Specialists, as well as two couple and family therapy researchers and a developmental/health psychologist, who all work together to provide expertise from a family social science perspective.

The advantages for doing the work as a multistate effort:

This project has several advantages for being implemented as an AES multistate project. This particular research group has 13 participants in 8 states and the District of Columbia. Researchers have positions as faculty with research experience in community nutrition, eating behavior, and family dynamics including parent-child relationships. Several have appointments within Extension, which provides opportunities for broad access to potential participants. Researchers represent a cross-section of geographic areas within the U.S. with opportunities for reaching low-income, multiethnic groups from which the project can explore, focus and tailor behavior change strategies. Finally, this group has 20+ years of experience working together to conduct cross-disciplinary collaborative investigations reaching a large number of participants. Researchers understand how to successfully assure that all researchers follow an identical protocol and have been successful with this approach in previous multistate projects (W-1003, W-2003, W-3003, and W-4003).

What the likely impacts will be from successfully completing the work:

Parenting practices within the SEM influence adolescent eating behaviors and therefore play an essential role in preventing obesity. The work completed in W-4003 identified parenting practices associated with healthful dietary intake during iEOs among low-income, multiethnic adolescents from both a parent and child perspective (Reicks et al., 2023), providing the rationale for the development of an intervention that promotes use of those parenting practices. The proposed W-5003 project will focus on developing an intervention (Objective 1) that will be pilot-tested (Objective 2) to determine the effectiveness of a tech-driven educational module prototype to impact healthy adolescent iEO food choices. A grant proposal to take the intervention full-scale will be developed based on the results of the pilot test (Objective 3). The ability to build on findings from previous multistate projects (W-1003, W-2003, W-3003, and W-4003) will allow the team to promote parenting practices that result in adolescents choosing healthy foods during iEOs, thereby addressing the need to improve diet quality and prevent obesity among adolescents.

Related, Current and Previous Work

Parental influence on food intake has been well-established (Vaughn et al., 2013; Vaughn et al., 2016). Preliminary work among Hispanic, Asian, and non-Hispanic White adolescents in W-1003 and W-2003 showed that home availability of calcium-rich foods (CRF), parental rules and expectations for their adolescents’ intake of beverages, parents’ consumption of calcium-rich foods, and food role modeling were positively associated with adolescents’ calcium intake (Banna et al., 2019). Associations of parenting practices and children and adolescent’s intake of healthy foods such as fruits and vegetables have been previously reported (Hingle et al., 2012). 

In the W-3003 project, our team addressed a gap in the literature around eating behaviors of adolescents when their parents/caregivers were not around (iEOs) and food-related parenting practices that influence adolescent food choices during iEOs. To explore this topic, the W-3003 team initially used the FLASHE Study data and found that about 20% of adolescents reported experiencing iEOs (often eating alone) (n=343) versus not often eating alone (n=1309). Adjusted odds of adolescents often eating alone were significantly higher for non-Hispanic black compared to non-Hispanic white adolescents (OR=1.7) and for overweight or obese compared to normal or underweight adolescents (OR=1.6). Adjusted odds of adolescents eating alone were significantly lower for those who reported that fruits and vegetables were often/always available in the home (OR=0.65), for those who perceived that parents had expectations about fruit and vegetable intake (OR=0.71), and for those who agreed with parental authority to make rules about intake of junk food/sugary drinks (OR = 0.71). Adjusted odds of adolescents eating alone was significantly higher for those who reported that sweets were often/always available in the home (OR=1.4). Junk food and sugary drink daily intake frequency was positively associated with often eating alone. 

Around the same time, another cross-sectional survey was conducted among 890 seventh grade students in Ina, Japan (2011-2012) to examine whether associations existed between eating dinner alone and being overweight based on measured height and weight (Shirasawa et al. 2018). Among overweight girls, a significantly increased OR (OR=2.78) was observed among girls who ate dinner alone ≥ 1-2 times/week compared to those who did not eat dinner alone; the same association was not observed for boys. The proportion of students who reported eating dinner alone ≥ 1-2 times/week was about 11% for girls and 15% for boys. 

To more specifically describe the frequency, food consumption patterns, and environmental context around iEOs among adolescents and to identify parenting practices influencing adolescent food choices during iEOs, the W-3003 project team asked adolescents (11-14 years) to take photos of all foods and beverages consumed over a 24-hour period. Through interviews, adolescents described the type and source of food and beverages consumed at eating occasions and eating context (location, time, who the child was with). Adolescents were asked open-ended questions regarding parenting practices that influenced intake during these occasions. Parents completed surveys and an interview to evaluate parenting practices during iEOs. Adolescents (n=46) reported  a total of 279 eating occasions with 172 as independent eating occasions (Banna et al., 2018). More than half (65%) of the foods consumed were classified as “snacks.” The most frequent foods consumed during independent eating occasions were sweet snacks (cakes, cookies) (15%), grains (bread, pasta) (13%), fruits (9%), salty snacks (chips) (8%), dairy (milk, cheese) (8%) and sugar-sweetened beverages (7%). Most independent eating occasions occurred at home (72%) while watching TV/surfing the internet (32%), hanging out with a friend (16%) or doing something else (21%). 

Based on the individual interviews, parents commonly reported setting expectations for intake of healthy foods, making healthy foods available and accessible, and teaching children about healthy foods related to iEOs (Gunther et al., 2019). Adolescents reported that their parents controlled what was available and had rules regarding what they could eat during iEOs. Parents who perceived being successful (succeeders) at getting their early adolescents to make healthy food choices when they were not around used strategies of monitoring children’s intake of sweets, high-fat foods, and healthy CRF foods (Richards et al., 2018). Succeeders reported having less availability of sweet and savory convenience foods at home, and that their early adolescents frequently limited sugary drinks when they were not around. Mean BMI z-scores were significantly lower for early adolescents of parent succeeders than strivers. 

With the knowledge that there is a high prevalence of obesity among African American adolescents, the W-4003 team examined the types of parenting practices adopted by African American parents and their children’s food choices during iEOs. Results indicated that adolescents whose parents implemented authoritarian approaches and monitoring and modeling approaches had higher body mass index for age (BMI-for-age) percentiles. There was a positive correlation between reasoning and monitoring parenting approaches and adolescents’ fruit and vegetable consumption. 

Parent and adolescent interview findings were used to develop and test survey items that aimed to assess the frequency of food parenting practices and their associations with adolescents’ dietary intake (Reicks et al., 2020). Cognitive interviews were conducted with 10 parent-adolescent dyads, with revisions made to both surveys based on feedback from parents and adolescents. We then pilot tested the surveys among a sample of 206 parent-adolescent dyads using the online survey system Qualtrics. To ensure racial/ethnic diversity in our sampling, quotas were set in Qualtrics for race/ethnicity for Asian, Black or African American, Hispanic, White or Caucasian, Native American and Hawaiian or Pacific Islander. Quotas were determined based on our work with these racial/ethnic groups in the survey development phase (W-3003). Pilot data collection occurred in January 2019 and data analysis and further revision of survey items occurred from February to September 2019. Following revision based on testing results, the surveys were conducted with a larger sample of parent-adolescent dyads (n=622) in November-December 2021 (Reicks et al., 2023). 

By March 2020, our team was ready to launch final data collection when the COVID-19 pandemic led to a nationwide shutdown. Because adolescents were attending school remotely and some parents were likely working from home remotely, our team expressed uncertainty in how this situation might affect iEOs. Thus, we conducted cross-sectional remote interviews with multiracial/ethnic adolescents  and their parents. About half of the parents indicated that their adolescents had more iEOs during the COVID-19 pandemic and that there were changes in the types of foods consumed during iEOs. In contrast, most adolescents indicated their iEOs had not changed remarkably in frequency or foods consumed since the onset of the pandemic. Most parents reported no change in how they taught their adolescents about healthy food, the rules for foods/beverages permitted during iEOs, or how they monitored what their adolescents ate during iEOs; adolescent reports were in general agreement. Most parents indicated that family members were home together more often during the pandemic, which increased cooking frequency. Based on this work, we modified the demographics section of the parent survey to include a question about how adolescents were attending school and parents were working. And as mentioned previously, we resumed data collection in Fall 2021 when states were moving back to pre-pandemic conditions. 

From the 2021 survey data, we found significant positive associations between a number of adolescent-reported parenting practices including autonomy support, monitoring, indulgence and expectation, and adolescent-reported daily intakes of junk foods, sugary foods, and fruits and vegetables during iEOs (Reicks et al., 2023). Overall, the caregiver-reported and adolescent-reported parenting practice of availability and modeling were weakly associated with daily intakes of less healthy foods and positively associated with fruits and vegetable intake during iEOs (Reicks, 2023). 

With a broader understanding of adolescent behavior during iEOs obtained through the W-4003 project, an intervention developed through the intervention mapping protocol and delivered via a tech-driven (e.g., mobile apps, AI, virtual education) educational module prototype will be explored for parents and adolescents in W-5003 that serve to improve parenting practices and motivate parents to engage in these practices. A tech-driven intervention is proposed because it’s less labor-intensive than an in-person education and has scalability advantages of being deliverable across broad geographic areas, with digital media options and platforms (may be multiple) that are most feasible and effective in reaching the target audience. Existing tech-driven education apps and programs designed to promote adolescent healthy food and beverage choice through parenting practices will be reviewed by researchers and users based on the following criteria: low-cost, scalable, secure, and effective (e.g., easy to use, easy to understand, engaging, low error, and motivates learning).

Several reviews of the literature have provided positive results regarding eHealth and mHealth interventions for parents (Hammersley, Jones, & Okely, 2016), adolescents (Hsu, Rouf, & Allman-Farinelli, 2018) and adults (McCarroll, Eyles, & Ni Mhurchu, 2017). For example, a systematic review of eHealth interventions for parents that focused on child and adolescent (0-18 years) overweight and obesity identified eight studies over a 20-year period that used an eHealth medium in an obesity prevention or treatment trial (Hammersley, Jones, & Okely, 2016). Of the seven studies that reported on dietary outcomes, four showed improvements in at least one dietary measurement compared with the control. Dietary outcomes included fruit and vegetable intake, nutrition knowledge, total energy intake, fat intake and intake of “fattening” foods. In seven of the eight studies, both the parent and child or adolescent were actively involved in the intervention. Intervention duration was typically less than 6 months. Larger, high-quality studies of longer duration were suggested by the authors to determine the effectiveness of eHealth interventions for parents. Another systematic review identified 23 mHealth interventions to promote healthy eating among adults (McCarroll, Eyles, & Ni Mhurchu, 2017). Five of eight trials reported small positive effects on healthy eating. The authors suggested a need for more rigorous methods with longer-term follow-up. Another recent review by Vlahu-Gjorgievska and colleagues of 17 mHealth Apps reported the use of behavior change techniques supported by user interface design patterns in the Apps deployed for obese young adults. 

A study by Brown and colleagues (2022) reviewed nutrition-themed apps targeting children 12 years and younger, and assessed their features and characteristics. Most of the apps were found to have foods and beverages inconsistent to the recommended dietary guidelines. The researchers reported that food game apps were about 3 times more likely to display unhealthy food and beverages. Findings from this study suggest a need for tech-driven intervention approaches targeting children to display food and beverages that meet the recommendation for the dietary guidelines to shape the dietary habits of children.

An example of a mobile application that showed efficacy in a dietary intervention was an app designed to increase vegetable intake by overweight adults (Mummah et al., 2017). The Vegethon mobile app involved goal setting, self-monitoring and feedback and used elements such as fun, surprise, choice, control, social comparison and competition to motivate adults to participate. Compared to the wait-list control group, those using the app increased daily vegetable consumption by two and one servings as assessed by a FFQ or 24-hour recalls, respectively. 

Previous reviews have been conducted to identify mobile apps related to food purchasing and provisioning (Tonkin et al., 2017; Mauch et al., 2018). Tonkin et al. (2017) identified 9 of 47 studies that described smartphone applications addressing food provisioning. These studies primarily described development of food access and food purchasing apps, with a limited focus on a range of food provision processes. Mauch et al. (2018) reviewed mobile apps that offered support for healthy family food provisioning using the Mobile App Rating Scale and assessment of the presence of behavior change techniques (BCTs) (Michie et al. 2013). Apps considered to have the potential to support family food provisioning included those with features related to meal planning, shopping lists, and the ability to share content. Several other recent studies have described mobile apps that assist with identifying food assistance resources (Martin et al., 2022), shopping management, informational resources, and nutrition education for WIC participants (Weber et al., 2018), cooking vegetables (Clarke et al., 2019), and meal planning and preparation with the Cooking Matters program (Garvin et al., 2019).

Robbins, Krebs, Jagannathan, Jean-Louis and Duncan (2017) used data from a national cross-sectional survey of adult mobile phone users in the US without any health conditions to show that about 39% had between one and five health apps. About 21% of those without a health condition reported using a health app two or more times per day. These findings indicated that downloading health apps to a mobile phone or tablet computer is common among US adults. In addition, surveys by the Pew Research Center found that 34% of adults have downloaded an app to their cell phone or tablet computer for a child to use (Lenhart, 2012). Results did not show that downloading apps for children differed by race, ethnicity, or income, whereas other reports suggested that higher-income families were more likely to download and use apps with their children compared to lower-income families. 

A systematic review of social media interventions for adolescents to promote positive nutrition behaviors identified seven interventions for participants 13-18 years (Hsu, Rouf, & Allman-Farinelli, 2018). Of the seven interventions, five showed improvement in at least one nutrition behavior compared to control such as fruit or vegetable intake or sugar-sweetened beverage intake. The authors suggested that better quality interventions were needed with longer-term follow-up.

In addition to mobile apps and social media, virtual parenting coaching/supervision interactions could be used to influence food choices made by adolescents during iEOs. For example, parents and adolescents could have conversations about healthy options via phone calls, texts, video calls, or social media. Food choices and availability could be monitored via programmed virtual reminders or provision via smart home technology, if available. Food, beverage or meals purchased online could be tracked via debit/credit card activity, including fast food purchased and eaten at home, or food delivered to the home. However, to our knowledge, studies are not available regarding the use of virtual parent coaching/supervision to influence adolescent food choices during iEOs. 

A recent study by Carlin et al (2021) tested the feasibility of a family-based health behavior intervention to promote healthy eating and physical activity for parents and their 5-12 year old children using a smart speaker (Amazon Echo) and its linked intelligent personal assistant (Amazon Alexa). The research team remotely accessed the devices to set tasks, prompts and reminders for family members delivered twice a day, for finding healthy recipes, creating a shopping list, and providing tips and reminders for healthy food choices. Families found the devices acceptable and easy to use and felt that the prompts or reminders were useful in promoting healthier behaviors. While the intervention did not test virtual parent-children interactions for healthy eating, parents could use intelligent personal assistants to prompt their children to engage in healthy food behaviors and send reminders about healthy food options during iEOs. 

Digital/virtual cooking experiences have been used with children and adolescents through videos designed to improve cooking skills and cooking self-efficacy. For example, an intervention among school-aged minority children (3 – 5 grade) in Los Angeles schools resulted in improving self-efficacy to cook and eat (Bell et al., 2018). The intervention included gaming sessions and lessons in school and at home using an existing nutrition- and gardening-focused curriculum. Surgenor et al. (2017) also showed that video technology improved comprehension of the cooking process and promoted confidence in cooking skills regarding meal preparation among women. 

A project team member, Dr. Blake Jones, and colleagues conducted focus groups among parents of school-aged children and found that many parents struggled to provide healthy, consistent, or home-cooked meals for their families in the evening because of time constraints (Jones et al., 2023). Thus, Dr. Jones and colleagues developed a web-based meal calendar tool that allowed parents and children to plan their meals for a week in advance using a simple drop-down menu where children could help choose healthier options for meals and snacks from picture options of foods (Jones et al., 2017). When they selected the foods, they were provided with the ingredients, instructions for cooking or preparing the meals, and age-appropriate child tasks. A similar calendar system focused on planning snacks for adolescents during iEOs could improve availability of healthy options in the home and encourage healthier choices by adolescents. 

In the AI landscape, AI general intelligence consists of four methods: 1) Combinatorial through optimization (e.g., Chess, Checkers, puzzle), 2) Knowledge-Intensive through inference over if-then rules and better handling of uncertainty (e.g., medical diagnosis, computational, configuration), 3) Creative @Generative AI through Large Language Models (LLM) (e.g., literature, art, music), and 4) Cognitive through machine learning (e.g., vision, speech, language, robotics) (Tadepalli et al., 2023).  Among those, generative AI and machine learning of real data are most applicable to tech-aided nutrition interventions (Kirk et al., 2022). 

In the digital era, how adolescents consume information, including food, nutrition and health, is very different now even compared to pre-COVID time in 2020.  Information acquisition and exchange happen rapidly as precision nutrition and AI converge to revolutionize personalized health (Antonelli & Donelli, 2023).  Behind the scene, AI influences sensemaking and decision making by shaping the types of information we encounter (Siemens et al., 2022).  AI is revealing growing capability and potentials in detecting and predicting health outcomes in the complex and non-linear interactions with nutrition-related data (Côté & Lamarche, 2021), and promoting better health through physical activity, diet and weight loss (Oh et al., 2021).  An AI adoption survey among 1,006 adolescents aged 13-17 reported 44% of participating adolescents expressed their likelihood of using AI for school work in their upcoming school year, and 22% believed AI eliminates the need to acquire knowledge (Matzinger, 2023).  This emerging digital information consumption behavior and beliefs reveal the need to ride the wave of increasing use of AI tools to make learning fun and engaging to children, with perceived benefits of: 1) improved education through personalized instruction, 2) improved safety through monitoring of children’s online activity and alerting parents of their child’s risky behavior, 3) improved accessibility through sign language translation and reading text aloud, 4) increased creativity with tools and experiences, 5) improved mental health via engaging activities and games to develop social and emotional skills, and 6) stimulated interest in science and technology (Bhambari, 2023).  

The AI integration into the proposed intervention design (Objective 1) may involve co-creation with the intervention parents and/or children, which often results in greater adoption of new habits/behaviors and engagement in maintaining the new habits/behaviors.  Siemens et al. (2022) recommend revisiting the Bloom’s Taxonomy to account for generative AI tool capabilities and distinctive human skills.  For example, learning and assessment can be amended at the ‘analyze’ level by having AI ‘compare and contrast data, infer trends and themes, compute, predict’ while having the parent/child learners practice critical thinking and reasoning, interpret and relate to authentic problems, decisions, and choices.’  This AI-assisted approach may further support children’s personalized nutrition, including during IEOs, through a data-driven approach to promote healthy food availability, informed food choices and positive food experience (plan, get, cook, eat) and prevent negative health outcomes through early detection.

 

Objectives

1. Design a theory-based nutrition education intervention to promote making healthy foods available for early adolescents (11-14 years) during independent eating occasions using the Intervention Mapping Protocol to be delivered through an educational module prototype (i.e., app elements, AI, virtual education, etc.) and to explore technologies to deliver protocol as an educational module prototype (i.e., app elements, AI, virtual education, etc.) (October 2024-Sept 2025).
   
2. Pilot test the nutrition education module prototype for user experience and implementation for feasibility, acceptability, and preliminary health outcome effectiveness and cost-effectiveness to promote positive parenting practices during iEOs and to improve early adolescent dietary intake during iEOs. (Oct 2025-Sept 2028).
   
3. Prepare grant proposals to scale up intervention and disseminate findings to health professionals. (Oct 2028-Sept 2029).
   

Methods

Objective 1:

Intervention Mapping Protocol: The Intervention Mapping Protocol [Bartholomew et al 1998; Bartholomew et al 2011] will be utilized in the design of the intervention. Formulation of proximal program objectives will occur as the first step in the mapping process. Based on the current evidence demonstrating that ‘making healthy foods available’ is the most widely used food parenting practice by caregivers of early adolescents during iEOs [Gunther et al 2019], and that it has exclusively positive effects on dietary choices during iEOs (i.e., greater fruit/vegetable intake) [Reicks et al 2023] – as reported by both parents and adolescents – the target behavior of the intervention will be availability. Additional rationale for selecting availability include the relevance of availability vs other parenting practices (e.g., modeling) in the context of iEOs when parents are not with their child. The following program objectives were formulated based on the evidence linking availability of foods in the home with child diet quality during iEOs and overall:

1. Improve the dietary quality of foods available in the home by significantly increasing the number of healthy foods and decreasing the number of less healthy foods available in the home over the past 7 days;
2. Improve child diet quality during iEOs by significantly increasing Healthy Eating Index (HEI) scores and daily servings of fruits/vegetables to the 2020-2025 Dietary Guidelines for Americans recommendations (5/d) and significantly decreasing daily servings of sugar-sweetened beverages; 

Matrices containing the behavioral performance objectives relating to each of the program objectives (1 and 2 above) will be created for each level of intervention: individual (child) and interpersonal (caregiver). Development of the performance objectives will be guided by the evidence-based 2020-2025 Dietary Guidelines for Americans for families and children [Department of Health and Human Services, 2020].  After formulation of performance objectives, a list of personal determinants for each performance objective will be generated based on the theoretical foundation of this research – i.e., the Social Cognitive Theory, which posits that behavior change is a function of a reciprocal relationship between personal (e.g., behavioral capabilities and cognitive factors, such as self-efficacy and self-evaluation) and environmental (e.g., norms, modeling, and reinforcement) factors (Bandura 1991; Bandura 2004). Next, personal determinants will be selected for children at the individual level and caregivers at the interpersonal level based on importance (i.e., strength of the association of the determinant with the behavior) and changeability (i.e., likelihood that the intervention may impact the determinant) (Bartholomew 1998). 

The performance objectives will then be coupled with the selected determinants, which will result in matrices of change objectives. The change objectives will state precisely what needs to change in the determinants’ behavioral outcomes in order to accomplish the performance objectives. They will be developed using action words and followed by a statement of what is expected to result from the intervention (Bartholomew et al 1998; Bartholomew et al 2011). Because two target groups are selected, two different matrices of change will be developed under each program objective. 

Next, theory-based methods to influence change in the determinants at the individual (child) and interpersonal (caregiver) level will be selected based on the theoretical framework of the intervention (Social Cognitive Theory)(Bandura 1989; Bandura 2004) and in reference to methods described by Bartholomew et al. (1998, 2011). A list of all change objectives linked with a specific determinant will be made, and the theoretical methods will then be matched with the corresponding determinant. Finally, practical strategies will be designed to put the theoretical methods into practice.  

Previous data collected by our team (unpublished) demonstrate that caregivers of adolescents are most interested in 1) having access to a snack planner or learning games (online or app) (86%), 2) text message reminders (86%), and 3) doing cooking classes with their children to support them in their efforts to help their child have better (healthier) independent eating occasions (93%). Combined with results from the intervention mapping, these data will inform the design of the nutrition education module prototype.   

Educational Module Prototype: The nutrition education content for the intervention, as identified through the intervention mapping process, will be delivered through an educational module prototype. Given the fast pace changes in the technological and virtual spaces, we will evaluate three potential education modalities: 1) mobile phone applications (apps), 2) virtual education, and 3) AI. We expect that our nutrition education module will be a combination of the three modalities, the goal to determine the usability, accessibility, and availability of the potential modalities. We will evaluate existing mobile apps, existing virtual education, and AI options. We will map the existing technology onto our intervention map. 

Mobile Apps

We will use an iterative process as outlined by MacMillan Uribe et al. (2023) to select a small number of mobile apps for evaluation (< 10 apps). We will select apps based on appropriateness of content to address behavior change objectives identified through the Intervention Mapping protocol and also alignment with nutrition education being delivered to low-income families by the Expanded Food and Nutrition Education Program (EFNEP) and the Supplemental Nutrition Assistance Program-Education (SNAP-Ed). Appropriate search terms will be identified through pilot searches based on key terms related to the audience, content, and procedures involved in meeting behavioral objectives. Research teams will identify apps through iterative searches in the App Store for iOS and Google Play for Android search engines using smartphones based on progressive sets of exclusion criteria.

The research team will evaluate the selected apps using close-ended items from the App Quality Eval (AQEL) tool for 6 domains (behavior change potential, knowledge support, skill development potential, app functionality, meeting the intended purpose, and appropriateness for the audience and their needs) (DiFilippo et al., 2017). The AQEL will be modified to revise original items to evaluate relevance to the target audience and behavioral objectives. All W-5003 project members will download each app, spend 10-15 minutes becoming familiar with it and its features, and complete the revised AQEL for each app via Qualtrics (an online survey platform). 

For each AQEL domain, we will first calculate an average sum score and then convert all sum scores to a 10-point scale to account for an unequal number of questions for each AQEL domain. We will consider a score of > 8 as high quality on the basis of previous studies using AQEL (DeFilippo et al., 2017; DeFilippo et al., 2018). We will calculate interrater reliability using 2-way random, absolute intraclass correlations on the average measures. The minimum acceptable score will be > 0.70, a good score will be > 0.80 (Cicchetti, 1994).

Virtual Education

We will evaluate pre-existing virtual education materials such as those created by members on our team, those available through SNAP-Ed and EFNEP, and on platforms like YouTube using similar methods as outlined above for mobile apps. We will use the same key terms for selecting relevant educational materials and will evaluate the education for the 6 domains on the AQEL (DeFilippo et al., 2017). However, we will adapt the AQEL tool from evaluating apps to evaluating virtual nutrition education materials. 

Artificial Intelligence (AI)

Disclosure: In this writing process, generative AI platform Claude.ai (version 2.1) was used with human oversight and control to suggest tech-driven approaches to promote positive parenting practices during iEOs and to improve early adolescent dietary intake during iEOs.  Some of the suggestions were considered and then illustrated by the human author.

The AI integration will involve two implementation components: (1) backend (administrators and researchers) and (2) frontend (adult and child research participants)

(1) Backend (Administrators and Researchers)

AI Personas

To predict the target population’s response and intervention outcomes, our team will use AI to build personas of parents and children that reflect the demography (such as age, race/ethnicity, socioeconomic status), home and school food environments, family dynamics, values and beliefs, lifestyle, personalized nutrition, learning styles, gamer motivations (i.e., Action-Social vs. Mastery-Achievement vs. Immersion-Creativity) (Yee, 2021) and etc. to predict user learning experience, intervention implementation feasibility, and acceptability of the educational module prototype.  

Stretch Goal: If funding is available to include wearable sensors to increase accuracy in assessments (McClung et al., 2018), we will explore building the Digital Twin that digitally replicates in the virtual environment the physical asset (functionality, features and behavior) of the community-based intervention to optimize the intervention, e.g., by identifying potential pitfalls and simulating solutions early.  The AI personas generated/predicted intervention outcomes will provide insights for the researchers to fine-tuning the project hypotheses regarding the impact of artificial cognition on knowledge processes such as learning, sensemaking, and decision making in the context of children’s IEO food choices.  They will also provide insight for researchers regarding the optimal relationship between which cognitive activities should be handed off to the machine, and which should remain in the domain of human performance, and how these two should then be integrated when outputs are passed from one cognitive system (human or artificial) to the other (Siemens et al., 2022).

(2) Frontend (Adult and Child Research Subjects/Participants)

We will help the parents-child dyads understand how AI works, and empower them to use AI to make healthy food available at home easier.  This could be conducted solo, in pairs, and with the other participating families. 

We want to leverage teaching with the strengths of AI relating to precision nutrition, i.e., data analysis and pattern recognition, personalized dietary recommendations, real-time monitoring and adaptation, uncovering novel insights and biomarkers, predictive models and decision support, and scalability and efficiency (Antonelli & Donelli, 2023).  The following activities with AI application could be explored:

• Begin with evaluating the home food environment through computer vision that analyzes food pantry photos.  It could involve augmented reality where the user wears a pair of AI glasses, selects the most frequently available food at home, and labels his/her top 10 favorite food to eat during iEOs
• Teach with a chatbot to personalize learning interests.  The chatbot can also be trained social-emotional learning support (e.g., Woebot developed by Fitzpatrick et al., 2017)
• Virtual field trips using augmented reality or virtual reality and interactive moments.
• Automate assessments with graphic representations such as eating behavior (Romero-Tapiador et al., 2023) in perspective to goal achievement and impacts on human health and planet health.
• Through natural language processing, identify topics through records of parent and child conversations to inform the intervention content.
• Use AI to navigate through vast food databases and large amounts of nutritional information to generate guidance on individuals's informed, personalized, and healthy food choices (Everloo & Martinović, 2023).
• Through machine learning, predict childhood obesity based on single/multiple well-child visit data (Mondal et al., 2023) and who within the family will be most at risk for a specific health outcome/disease or disorder using parent and child data collected with permission over time.
• Drive learning to the highest taxonomy of creation by encouraging parents and children to improvise a familiar recipe with alternative ingredients, such as a replacement to plant-based and local ingredients, and generate imaginary photos of the recipe using the Google AIY Projects and DIY hardware kits (Bhambari, 2023).
• Collaborate and troubleshoot through Role Play Games.  Variations may include: switch up parent and child role to make healthy food available, different demographic or health profiles (culture, ethnicity, income level, immigration level; food allergies, intolerance, chronic diseases), social-emotional safety (the number of intimate family members/friends around, micro-aggression, hostility, asking why they eat/not eat, and why do they choose this food), a point in time (past, present, future), a location (distance from home, indoor vs. outdoor, traveling on land/sea/air), and consequence of their food choices at individual health level (predicted risk of diseases) and planet health level (e.g., impacts on climate change).
• Recognizing the limitations of AI, i.e., data reliability and quality, lack of individualization because population level data was used to train current LLMs, ethical concerns, limited human interaction, overreliance on technology and generalizability issues (Antonelli & Donelli, 2023), the following preventive measures will be taken:
• We will provide parents and children with specific question prompts to generate shopping lists, meal planning ideas, and/or recipes related to iEOs.  Then we test different question prompts through the intervention AI chatbot (equivalent to ChatGPT interface) and evaluate the content for accuracy, relevancy, and usefulness.  If the content quality is high, we will train the AI system to identify misinformation and misconceptions in popular nutrition topics.
• To ensure the participants only receive credible resources throughout the intervention, we will train our intervention AI chatbot to inference to a closed knowledge ecosystem/database.  Prior to that, we will determine how AI can deliver added value compared to other learning modalities, particularly mobile apps and virtual education.
• To protect confidentiality and minimize risk of breaching any sensitive data, we could use AI (such as Gradle AI and LLaMA2) to create synthetic data that is non-reversible and analyze the data in local servers. 
• We will adopt viable natural language processing and machine learning approaches to minimize respondent’s burden in logging food intake where the survey automatically predicts food category and nutrition quality (Hu et al., 2022).

Building the intervention prototype for further testing 

It is possible that elements from different pre-existing apps, virtual education materials, and AI will be deemed relevant to the intervention objectives, thus leading us to build our own protocol for integrating the different elements into one intervention protocol. We also anticipate the pre-existing materials may not focus specifically on iEOs, thus we will need to build into the protocol instructions for parent-adolescent dyads on how to tailor the education components to iEOs. 

Objective 2: 

The educational module prototype will undergo pilot testing with parent-adolescent (11-14 years old) dyads, as outlined below. 

Parent-adolescent Dyads

Recruitment:

Upon approval by individual Institutional Review Boards (IRB), each site will recruit a convenience sample of parent/adolescent (11-14 years) dyads according to the sampling approach described below. Inclusion criteria for parents are: 1) being the parent of an early adolescent (11-14 years) who resides in the household at least 3 days a week, 2) being the adult responsible for food acquisition and preparation in the early adolescent's household, 3) earning 185% or below the poverty guidelines in the past year and/or eligible for food assistance programs, 4) being comfortable reading and speaking English, and 5) household smartphone and internet access. Parental consent and youth assent will be obtained prior to data collection. The type of IRB-approved incentive/compensation for participation may vary in different sites.

Sampling approach:

Sample size calculations will be completed for pilot testing the intervention including consideration of the particular intervention component(s), outcomes of interest, and what the W-5003 team considers a meaningful difference in the outcome variables and variability. In other studies this multistate group has conducted (e.g., Banna et al., 2018; Banna et al., 2020; Cluskey et al., 2015; Gunther et al., 2023; Monroe-Lord et al, 2022a and 2022b; Reicks et al., 2011; Reicks et al., 2019, 2020 and 2023), a recruitment plan has been developed with partner states indicating the number and type of participants they could recruit based on geographic location and availability of participants by sociodemographic characteristics such as sex, age and race/ethnicity. Therefore, the sampling approach for Objective 2 will take into account the total sample size needed based on sample size calculations, recruitment strategies, the ability of individual states to recruit a specified number of participants according to inclusion criteria, and the capacity of individual states to implement pilot testing. A standardized protocol will be developed for the pilot test to maintain consistency of implementation across sites. A subgroup of states may elect to implement one or more intervention programs depending on their capacity and interests.

Mobile apps

If mobile apps are incorporated in the module, we will train parent-child dyads to use the mobile application rating scale (MARS) developed by Stoyanov et al. (2015). After training, over a period of three months, the dyads will use the selected apps and fill out the MARS. The working group will incorporate the MARS feedback into a Must, Should, Could and Won’t (MoSCoW) matrix to prioritize what aspects of the selected apps are needed, desired, and what apps should be eliminated. The dyads will be prompted to complete the MARS every two weeks. Based on user experience data we will then outline our priorities on the MoSCoW. This testing will occur through an iterative process with selected apps and dyads to narrow what apps and app features are most useful.

The selected apps will be retained in the module if the MARS rating at the end of 3 months of use is greater than a 3.93 (based on ratings of 50 health related apps; p< 0.05 and mean> 3.93) (Stoyanov et al., 2015). If the app does not meet our criteria it will be removed from the module we will update the MoSCoW matrices. 

Virtual Education

If virtual educational materials are incorporated into the educational module prototype, the W5003 team will evaluate users’ experience with these materials through the System Usability Scale (SUS) (Banger et al., 2008). SUS provides users with ten questions to evaluate intended frequency of use, ease or complexity of using the product (prototype), overall functionality of the product, perceived level of learning gained through the product, and confidence in using the product (Brooke, 1995; Banger et al., 2008). 

Response options are a 5-point Likert scale from strongly agree to strongly disagree, Responses are individually scored, from a scale of 0 to 4, summed, and then multiplied by 2.5 (Brooke, 1995). A total score > 70 is considered acceptable (Isgin-Atici et al., 2020). 

Health Outcome and Cost-Effectiveness

In addition to the feasibility and acceptability of the educational module prototype, we will also measure health- and cost-effectiveness outcomes, as follows:

(1) Evaluating the frequency of supportive parenting practices around availability and monitoring during iEOs assessed based on parenting practice availability scales that were developed and tested as part of W-4003 at baseline and post-intervention among parent- adolescent dyads.

(2) Assessing eating behavior change among early adolescents by collecting and comparing baseline and post-intervention data based on the FLASHE Study 27-item food frequency questionnaire (NCI, 2017) that we adapted to iEOs in W-4003 (Reicks, 2023). These questions assess intake over the past 7 days.

(3) Determining the potential moderating role of general parenting (Comprehensive General Parenting Questionnaire; Sleddens et al., 2014) on the relationship between eating behavior change (change in FFQ from pre-to-post-intervention) and parenting practices (availability and monitoring). 

(4) Measuring the costs associated with implementation of the educational module prototype compared to the costs associated with a comparable in-person education class relative to the change in the primary intervention outcomes. Specifically, we will conduct an incremental cost-effectiveness ratio analysis whereby we will calculate the mean change in the primary intervention outcomes for each mode of delivery (prototype and in-person), and then calculate the difference in costs divided by the difference in outcomes:

(Costtest-Costcontrol)/(Outcometest-Outcomecontrol)

Results will be visualized graphically by plotting the effect on the x-axis and cost on the y-axis.

Objective 3. 

Grant proposals informed by findings from W-5003 objectives 1 and 2 will be prepared and reviewed during Oct. 2028 to Sept. 2029. We will explore federal funding opportunities within USDA (AFRI) and NIH (Office of Nutrition Research, ONR). NIH ONR's new director has indicated interest in teen nutrition research, specifically relating to nutritional choices and physical activity and the influence of environments outside the home.

The proposals will include plans to revise the intervention based on the pilot test findings, implement the successful elements in a full-scale intervention within the participating states and the District of Columbia and disseminate findings to health professionals. A writing team of at least 4 researchers will be responsible for developing/writing proposals. All W-5003 team members will provide input.

 

Measurement of Progress and Results

Outputs

• Education module prototype using tech modalities, such as mobile apps, virtual nutrition education, and AI, that can be used to promote parenting practices that positively influence eating behaviors among adolescents during iEOs.
• Pilot intervention results based on implementation of the education module prototype to promote parenting practices that positively influence eating behaviors among adolescents during iEOs scalable to a large population.
• Grant proposals to fund a large-scale intervention based on pilot intervention results.

Outcomes or Projected Impacts

• Identify and assess the effectiveness of the education module prototype that is related to eating behaviors, snacking, and mealtime planning. Describe best practices and effectiveness of these apps and what features they include.
• Create an education module prototype to help parents and adolescents plan healthy snacking behaviors during independent eating occasions.
• Improvement in eating behaviors during iEOs among adolescents including reductions in intake of junk food, convenience/fast food, sugar-sweetened beverages, sugary foods, fatty meats and increases in intake of fruits and vegetables and beneficial foods (based on the FLASHE Study defined food groups) (NCI, 2017).

Milestones

(2024):To complete W-4003: Analyze quantitative survey data and prepare manuscripts peer-reviewed publications. Develop intervention strategies through the intervention mapping process and exploring technologies (such as apps, virtual nutrition education, AI) to deliver intervention through an educational module prototype.

(2025):Pilot test the education module prototype to examine how the prototype is working and get initial feedback on the prototype.

(2026):Make any necessary updates or changes to the prototype and prepare to do a full assessment of its effectiveness at improving snacking and eating behaviors in early adolescents.

(2027):Finish conducting the pilot testing to evaluate effectiveness of the education module prototype and begin data analysis. .

(2028):Continue data analysis of intervention data and present findings at conferences. Write research publications about the findings of this intervention.

(2029):Continue to publish findings and do analysis of intervention. Share educational materials and resources with others in cooperation with state partners. Finalize grant proposals at the annual 2029 meeting and submit to potential funding sources.

Projected Participation

View Appendix E: Participation

Outreach Plan

All states and the District of Columbia will contribute to the development of communications to disseminate findings, intervention protocols and materials from the W5003 project to Extension and other organizations. Findings will be disseminated in the following ways: 

1. Best practices based on the findings from W-5003 will be developed by the group and disseminated through multiple avenues, including NIFA Extension – specifically Connect Extension, for use by students, researchers, clinicians, professors, practitioners and the general public. Best practices, applied findings, products, and professional development opportunities resulting from this project will also be made available through 4-H and NEAFCS professionals and stakeholders in Extension and through national 4-H conferences, events, and publications (e.g., Journal of Youth Development). 
2. Study intervention findings will be disseminated via webcasts, podcasts, and other social media outlets linked to Connect Extension. 
3. Findings will be included as new knowledge content in the areas of nutrition and physical activity for children (grades 5th - 8th) on Connect Extension. 
4. Childhood obesity prevention articles will be published on Connect Extension website and cross-listed with other related CoPs (e.g., Community Nutrition Education, Family Caregiving, and Parenting). Audiences will be alerted to the articles via social media (Facebook, Instagram, Twitter, Pinterest) after consideration of privacy concerns are addressed. 
5. Research findings from this project will be disseminated through presentations made at annual meetings of nutrition, health, and education professional societies and via manuscripts in refereed publications in nutrition and health journals.

Organization/Governance

An executive committee will be formed annually by group consensus methods with a Chair and Reporter. The Chair manages meetings, submits reports and plans the agenda for the annual meeting. The Reporter provides minutes describing the discussion and actions suggested during the meetings. Administrative guidance will be provided by an assigned Administrative Advisor and a CSREES Representative.

Each approved member will cooperate in the design of the project, collection and analysis of data, and co-authoring of publications and presentations that result from this work. The W-5003 team will use common protocols to accomplish each objective. For Objective 1, subgroups of researchers will lead the development of the intervention mapping process and evaluation/selection of apps, virtual nutrition education and/or use of AI, with subsequent review by the entire W-5003 team. Data for Objective 2 will be collected in individual states and aggregated for analysis by a subgroup of researchers as applicable to the study design, specific intervention components and target audience. Research team subgroups will develop several manuscripts, reports and presentations, with all W-5003 members providing a critical review before submission.

Literature Cited

Antonelli M, Davide Donelli. Precision Nutrition and Artificial Intelligence Mobile Apps: A Narrative Review. Biology and Life Sciences Forum. 2023;29(25). doi:https://doi.org/10.3390/iecn2023-15532

Bangor A, Kortum PT, Miller JM. An Empirical Evaluation of the System Usability Scale. Int J Hum-Comput Int. 2008;24(6):574-594. doi: 10.1080/10447310802205776.

Banna J, Driscoll J, Boushey C, Auld G, Olson B, Cluskey M, Ballejos M, Bruhn C, Misner S, Reicks M, Wong SS, Zaghloul S. Parent and household influences on calcium intake among early adolescents. BMC Public Health 2019;18:1390. doi: 10.1186/s12889-018-6297-5.

Banna J, Richards R, Jones B, Anderson A, Reicks M, Cluskey M, Gunther C, Hongu NK, Lora K, Misner S, Monroe-Lord L, Topham G, Wong S, Lim E. Describing independent eating occasions among low-income adolescents. Int J Env Res Public Health 2020;17:981. doi: 10.3390/ijerph17030981.

Bhambari K. AI Tools For Kids To Make Learning Fun And Engaging. SKOOL OF CODE. Published January 4, 2023. Accessed January 12, 2024. https://skoolofcode.us/blog/ai-tools-for-kids-making-learning-fun-and-engaging/

Blasingame M, Samuels LR, Heerman WJ. The combined effects of social determinants of health on childhood overweight and obesity. Child Obes. 2023; 29 Mar 2023 https://doi.org/10.1089/chi.2022.0222

Brooke J. SUS - A quick and dirty usability scale. 1995. Available at: https://www.researchgate.net/publication/228593520_SUS_A_quick_and_dirty_usability_scale Accessed 1-9-2024. 

Brown JM, Franco-Arellano B, Froome H, Siddiqi A, Mahmood A, Arcand J. The content, quality, and behavior change techniques in nutrition-themed mobile apps for children in Canada: app review and evaluation study. JMIR Mhealth Uhealth 2022;10(2):e31537. doi: 10.2196/31537

Carlin A, Logue C, Flynn J, Murphy MH, Gallagher AM. Development and feasibility of a family-based health behavior intervention using intelligent personal assistants: Randomized controlled trial. JMIR Form Res. 2021;5(1):e17501) doi: 10.2196/17501

Chung LMY, Fong SSM, Law QPS. Younger adults are more likely to increase fruit and vegetable consumption and decrease sugar intake with the application of dietary monitoring. Nutrients 2021;13(2):333.

Cicchetti DV. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess. 1994;6:284-290.

Clarke P, Evans SH, Neffa-Creech D. Mobile app increases vegetable-based preparations by low-income household cooks: a randomized controlled trial. Public Health Nutr. 2019;22(4):714-725. doi: 10.1017/S1368980018003117.

Cluskey M, Edlefsen M, Olson B, Reicks M, Goldberg DL, Auld GW, Bock A, Boushey CJ, Bruhn CM, Misner SL, Olson BH, Wang C, Zaghloul S. At home and away-from-home eating patterns influencing preadolescents’ intake of calcium rich foods as perceived by Asian, Hispanic and non-Hispanic White parents. J Nutr Educ Behav. 2008;40:72-79.

Cluskey, M., Wong, S. S., Ballejos, M., Reicks, M., Richards, R., Auld, G., Boushey, C., Bruhn, C., Misner, S., Olson, B., & Zaghloul, S. Dietary sources of calcium among parents and their early adolescent children in the United States by parent race/ethnicity and place of birth. J Immigr Minor Health. 2015;17(2): 432-440.

Côté M, Lamarche B. Artificial intelligence in nutrition research: perspectives on current and future applications. Applied Physiology, Nutrition, and Metabolism. 2021;47:1-8. doi:https://doi.org/10.1139/apnm-2021-0448

Craigie AM, Lake AA, Kelly SA, Adamson AJ, Mathers JC. Tracking of obesity-related behaviors from childhood to adulthood: A systematic review. Maturitas. 2011;70(3):266-84.

Department of Health and Human Services. Dietary Guidelines for Americans, 2020-2025. 9th Edition. December 2020.

DiFilippo KN, Huang W, Chapman-Novakofski KM. A new tool for nutrition App Quality Evaluation (AQEL): development, validation, and reliability testing. JMIR Mhealth UHealth 2017;5(10):e163.

DiFilippo KN, Huang W-HD, Chapman-Novakofski KM. Mobile apps for the Dietary Approaches to Stop Hypertension (DASH): App Quality Evaluation. J Nutr Educ Behav. 2018;50:620-625.

Doherty AM, Lacko AM, Popkin BM. Sugar-sweetened beverage (SSB) consumption is associated with lower quality of the non-SSB diet in US adolescents and young adults. Am J Clin Nutr. 2021;113(3):657-664. doi: 10.1093/ajcn/nqaa342.

Dunford E, Popkin B. 37 year snacking trends for US children 1977-2014. Pediatr Obes.,2018;13(4):247-255.

Edlefsen M, Reicks M, Goldberg DL, Auld GW, Bock A, Boushey CJ, Bruhn CM, Cluskey M, Misner SL, Olson BH, Wang C, Zaghloul S. Strategies based on parental roles to influence intake of calcium-rich foods by Asian, Hispanic, and non-Hispanic white early adolescents. Prev Chronic Dis. 2008;5(4) October. http://www2.cdc.gov/pcd/issues/2008/oct/07_0174.htm

Everloo E. Personalized Nutrition — Because You Can’t Manage What You Don’t Measure» PeakBridge VC. PeakBridge VC. Published September 6, 2022. Accessed January 12, 2024. https://peakbridge.vc/personalized-nutrition-because-you-cant-manage-what-you-dont-measure/

Everloo E, Martinović A.  Bytes to Bites part three - Wellness wired: Leveraging AI to digitize nutrition and health. AgFunderNews. Published October 12, 2023. https://agfundernews.com/bytes-to-bites-part-three-wellness-wired-leveraging-ai-to-digitize-nutrition-and-health

Fitzpatrick KK, Darcy A, Vierhile M. Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial. JMIR Mental Health. 2017;4(2):e19. doi:https://doi.org/10.2196/mental.7785

Garvin TM, Chiappone A, Boyd L, Stern K, Panichelli J, Edwards Hall LA, Yaroch AL. Cooking Matters Mobile Application: a meal planning and preparation tool for low-income parents. Public Health Nutr. 2019;22(12):2220-2227. doi: 10.1017/S1368980019001101.

Gunther, C., Rogers, C., Holloman, C. et al. Child diet and health outcomes of the simple suppers program: a 10-week, 2-group quasi-experimental family meals trial. BMC Public Health 2019a;19;1657. https://doi.org/10.1186/s12889-019-7930-7

Gunther C, Reicks M, Banna J, Suzuki A, Topham G, Richards R, Jones B, Penicka C, Hopkins LC, Kojo Anderson A, da Silva V, Lora K, Monroe-Lord L, Hongu N, Wong SS, Cluskey M. Perceived parent practices to influence independent eating occasions among early adolescents: a qualitative study of low-income parents and children. J Nutr Educ Behav. 2019b;51:993−1002. doi: 10.1016/j.jneb.2019.05.597.

Gunther C, Richards R, Banna J, Reicks M, Wong SS, Ballejos M, Lora KR, Topham G, Jones B, Park C, Schier H, Monroe-Lord L, Anderson A, Hopkins L. Adolescent independent eating occasions (iEOs), dietary intake, and parenting practices during the COVID-19 pandemic: a qualitative study of parents and adolescents from low-income households. J Nutr Educ Behav. 2023;55:634-643.

Harakeh Z, Preuhs K, Eekhout I, Lanting C, Klein Velderman M, van Empelen P. Behavior change techniques that prevent or decrease obesity in youth with a low socioeconomic status: A systematic review and meta-analysis. Child Obes. 2023 May 18. doi: 10.1089/chi.2022.0172. Epub ahead of print. PMID: 37204322.

Hoy MK, Clemens JC, Moshfegh AJ. Intake of vegetables by children and adolescents in the U.S.: What We Eat in America, NHANES 2017-2018. Food Surveys Research Group Dietary Data Brief No. 40. June 2021a.

Hoy MK, Clemens JC, Moshfegh AJ. Intake of fruit by children and adolescents in the U.S.: What We Eat in America, NHANES 2017-2018. Food Surveys Research Group Dietary Data Brief No. 38. June 2021b.

Hruby A, Manson JE, Qi L, et al. Determinants and consequences of obesity. Am J Public Health. 2016;106(9):1656-1662. doi:10.2105/AJPH.2016.303326.

Hu G, Ahmed M, L’Abbé MR. Natural language processing and machine learning approaches for food categorization and nutrition quality prediction compared with traditional methods. The American Journal of Clinical Nutrition. 2023;117(3):553-563. doi:10.1016/j.ajcnut.2022.11.022

Isgin-Atici K, Ozkan A, Celikcan U, et al. Usability Study of a Novel Tool: The Virtual Cafeteria in Nutrition Education. J Nutr Educ Behav. 2020;52:1058-1065. 

Jebeile H, Kelly AS, O'Malley G, Baur LA. Obesity in children and adolescents: epidemiology, causes, assessment, and management. Lancet Diabetes Endocrinol. 2022;10(5):351-365. doi: 10.1016/S2213-8587(22)00047-X. 

Jones BL, Evich C, Gaskins A. Barriers and solutions to having healthy mealtime routines for children: a focus group and pilot study of a meal calendar website. Presented at the Biennial Meeting of the Society for Research on Child Development (SRCD) in Austin, TX. 2017.

Jones BL, Orton A, Tindall S, Christensen J, Enosakhare O, Russell K, et al. 2023. Barriers to Healthy Family dinners and preventing child obesity: Focus group discussions with parents of 5-to-8-year-old children. Children. 10(6):952.

Jones BL. Making time for family meals: parental influences, home eating environments, barriers and protective factors. Physiol Behav. 2018;193:248-251.

Kelsey MM, Zaepfel A, Bjornstad P, Nadeau KJ. Age-related consequences of childhood obesity. Gerontology 2014;60:222-228.

Kirk D, Kok E, Tufano M, Tekinerdogan B, Feskens EJM, Camps G. Machine Learning in Nutrition Research. Advances in Nutrition. 2022;13(6). doi:https://doi.org/10.1093/advances/nmac103

Larson NI, Miller JM, Watts AW, Story MT, Neumark-Stzainer DR. Adolescent snacking behaviors are associated with dietary intake and weight status. J Nutr. 2016;146(7):1348-1355.

Liberall R, Kupek E, Alternburg de Assis, MA. Dietary patterns and childhood obesity risk: A systematic review. Childhood Obes. 2020;16(2):70-85.

Liu J, Lee Y, Micha R, Li Y, Mozaffarian D. Trends in junk food consumption among US children and adults, 2001-2018. Am J Clin Nutr. 2021;114(3):1039-1048. doi: 10.1093/ajcn/nqab129.

Liu J, Rehm CD, Onopa J, Mozaffarian D. Trends in diet quality among youth in the United States, 1999-2016. JAMA. 2020;323(12):1161–1174. doi:10.1001/jama.2020.0878

MacMillan Uribe AL, DiFillippo KN, Jarnell K, Patterson JA. Evaluation of commercially available infant-feeding mobile applications using App Quality Evaluation Tool. J Nutr Educ Behav. 2023;000:1-8.

Marriott BP, Hunt KJ, Malek AM, Newman JC. Trends in intake of energy and total sugar from sugar-sweetened beverages in the United States among children and adults, NHANES 2003-2016. Nutrients. 2019;11(9):2004. doi: 10.3390/nu11092004.

Martin NM, Barnett DJ, Poirier L, Sundermeir SM, Reznar MM, Gittelsohn J. Moving food assistance into the digital age: a scoping review. Int J Environ Res Public Health. 2022;19(3):1328. doi: 10.3390/ijerph19031328.

Matzinger K. The Rising Trend of Teens Using AI for Schoolwork. Junior Achievement USA. Published July 27, 2023. Accessed January 12, 2024. https://jausa.ja.org/news/blog/the-rising-trend-of-teens-using-ai-for-schoolwork

Mather M, Jacobsen LA, Jarosz B, Kilduff L, Lee A, Pollard KM, Scommegna P, VanOrman A. Population Bulletin: America’s Changing Population. Population Reference Bureau 2019;74(1):1-21. www.prb.org 

Mauch CE, Wycherley TP, Laws RA, et al. Mobile apps to support healthy family food provision: systemic assessment of popular commercially available apps. JMIR Mhealth and Uhealth 2018;6:e11867.

McClung HL, Ptomey LT, Shook RP, et al. Dietary Intake and Physical Activity Assessment: Current Tools, Techniques, and Technologies for Use in Adult Populations. American Journal of Preventive Medicine. 2018;55(4):e93-e104. doi:https://doi.org/10.1016/j.amepre.2018.06.011

Meng Y, Wong SS, Manore MM, Patton-Lopez M. WAVE~Ripples for Change obesity two-year intervention in high school soccer players: process evaluation, best practices, and youth engagement. Nutrients, 2018;10(6):pii: E711.

Micha R, Peñalvo JL, Cudhea F, Imamura F, Rehm CD, Mozaffarian D. Association between dietary factors and mortality from heart disease, stroke, and type 2 diabetes in the United States.  JAMA. 2017;317(9):912-924. doi:10.1001/jama.2017.0947

Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med 2013 Aug;46(1):81-95.

Mondal PK, Foysal KH, Norman BA, Gittner LS. Predicting Childhood Obesity Based on Single and Multiple Well-Child Visit Data Using Machine Learning Classifiers. Sensors (14248220). 2023;23(2):759. doi:https://doi.org/10.3390/s23020759

Monroe-Lord L, Anderson A, Jones BL, Richards R, Reicks M, Gunther C, Banna J, Topham GL, Lora KR, Wong SS, Ballejos M, Hopkins L, Ardakani A. Relationship between family racial/ethnic backgrounds, parenting practices and styles, and adolescent eating behaviors. Int J Environ Res Public Health 2022a;19:7388. https://doi.org/10.3390/ ijerph19127388

Monroe-Lord L, Jones BL, Richards R, Reicks M, Gunther C, Banna J, Topham G, Anderson A, Lora K, Wong SS, Ballejos M, Hopkins L, Ardakani A. Parenting practices and adolescent eating behaviors in African American families. Int J Env Res Public Health 2022b;19:110. https://doi.org/10.3390/ ijerph19010110

Mummah SA, King AC, Gardner CD, Sutton S. Iterative development of Vegethon: a theory-based mobile app intervention to increase vegetable consumption. Int J Behav Nutr Phys Act. 2016;13:90.  https://pubmed.ncbi.nlm.nih.gov/27501724/

Mummah SA, Robinson TN, Mathur M, Farzinkhou S, Sutton S, Gardner CD. Effect of a mobile app intervention on vegetable consumption in overweight adults: a randomized controlled trial. Int J Behav Nutr Phys Act. 2017;14(1):125.

Neumark-Sztainer D, Wall M, Fulkerson JA, Larson N. Changes in the frequency of family meals from 1999 to 2010 in the homes of adolescents: Trends by sociodemographic characteristics. J Adolesc Health 2013;52(2):201–206.

Newman SL, Tumin R, Andridge R, Anderson SE. Family meal frequency and association with household food availability in United States multi-person households: National Health and Nutrition Examination Survey 2007-2010. PLoS One. 2015;10(12):e0144330. doi: 10.1371/journal.pone.0144330.

Ogden CL, Carroll MD, Fakhouri TH, et al. Prevalence of obesity among youths by household income and education level of head of household – United States 2011-2014. MMWR Morb Mortal Wkly Rep. 2018;67(6):186-189.

Oh YJ, Zhang J, Fang ML, Fukuoka Y. A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss. International Journal of Behavioral Nutrition and Physical Activity. 2021;18(1). doi:https://doi.org/10.1186/s12966-021-01224-6

Ohri-Vachaspati P, DeLia D, DeWeese RS, Crespo NC, Todd M, Yedidia MJ. The relative contribution of layers of the Social Ecological Model to childhood obesity. Public Health Nutr. 2015;18(11):2055-66. doi: 10.1017/S1368980014002365

Overcash F, Ritter A, Mann T, Mykerezi E, Redden J, Rendahl A, Vickers Z, Reicks M. Impacts of a vegetable cooking skills program among low-income parents and children. J Nutr Educ Behav. 2018;50(8):795-802.

Park MH, Falconer C, Viner RM, Kinra S. The impact of childhood obesity on morbidity and mortality in adulthood: a systematic review. Obes Rev. 2012;13:985–1000.

Pliner P, Bell R. A table for one: the pain and pleasure of eating alone. In: Meals in Science and Practice: Interdisciplinary Research and Business Applications. pp. 169-189. 2009.

Reicks M, Edlefsen M, Goodell LS, Gunther C, Richards R, Wong SS, Auld G, Boushey C, Bruhn C, Cluskey M, Misner S, Olson B, Zaghloul S. Individual and family correlates of calcium-rich food intake among parents of early adolescent children. J Am Diet Assoc. 2011;111:376-384.

Reicks M, Degeneffe D, Ghosh K, Bruhn C, Goodell S, Gunther C, Auld G, Ballejos M, Boushey C, Cluskey M, Edlefsen M, Misner S, Olson B, Richards R, Wong SS, Zaghloul S. Parent calcium-rich-food practices/perceptions are associated with calcium intake among parents and their early adolescent children. Public Health Nutr. 2012;15(2):331-340.

Reicks M, Banna J, Cluskey M, Gunther C, Hongu N, Richards R, Topham G, Wong SS. Influence of parenting practices on eating behaviors of early adolescents during independent eating occasions: implications for obesity prevention. Nutrients 2015;7(10):8783-8801. doi: 10.3390/nu7105431.

Reicks M, Davey C, Anderson AK, Banna J, Cluskey M, Gunther C, Jones B, Richards R, Topham G, Wong SS. Frequency of eating alone is associated with adolescent dietary intake, perceived food-related parenting practices and weight status: cross-sectional FLASHE Study results. Public Health Nutr. 2019;22(9):1555-1566. Doi: 0.1017/S1368980019000107.

Reicks M, Banna J, Anderson A, Gunther C, Hongu N, Jones B, Lora K, Monroe-Lord L, Richards R, Topham G, Wong S. Development and testing of a questionnaire to assess frequency of food parenting practices to influence adolescent intake during independent eating occasions. J Nutr Educ Behav. 2020;52(3):307-313. doi: 10.1016/j.jneb.2019.10.018

Reicks M, Lora K, Jin, Y, Kojo Anderson A, Monroe-Lord L, Jones B, Topham G, Banna J, Gunther C, Hopkins L, Richards R, Wong SS. Associations among independent eating occasion-specific parenting practices and adolescent dietary intake during these occasions. J Acad Nutr Diet, 2023 May 23;S2212-2672(23)00276-9. doi: 10.1016/j.jand.2023.05.016.

Richards R, Reicks M, Wong SS, Gunther C, Ballejos M, Bruhn C, Cluskey M, Misner S, Watters C. Perceptions of how parents of early adolescents will personally benefit from calcium-rich food and beverage parenting practices. J Nutr Educ Behav. 2014;46(6):595-601.

Robbins R, Krebs P, Jagannathan R, Jean-Louis G, Duncan DT. Health App Use Among US Mobile Phone Users: Analysis of Trends by Chronic Disease Status. JMIR Mhealth Uhealth. 2017 Dec 19;5(12):e197. doi: 10.2196/mhealth.7832. PMID: 29258981; PMCID: PMC5750424.

Romero-Tapiador S, Rubén Tolosana, Morales A, et al. AI4Food-NutritionFW: A Novel Framework for the Automatic Synthesis and Analysis of Eating Behaviours. arXiv (Cornell University). 2023;11. doi:https://doi.org/10.1109/access.2023.3322770

Shirasawa T, Ochiai H, Yoshimoto T, Matoba M, Sunaga Y, Hoshino H, Kokaze A. Effects of eating dinner alone on overweight in Japanese adolescents: a cross-sectional study. BMC Pediatrics, 2018;18(1):36.

Siemens G, Marmolejo-Ramos F, Gabriel F, et al. Human and artificial cognition. Computers and Education: Artificial Intelligence. 2022;3:100107. doi:https://doi.org/10.1016/j.caeai.2022.100107

Simmonds M, Llewellyn A, Owen CG, Woolacott N. Predicting adult obesity from childhood obesity: a systematic review and meta-analysis. Obes Rev. 2016;17:95-107.

Sleddens EF, O'Connor TM, Watson KB, Hughes SO, Power TG, Thijs C, De Vries NK, Kremers SP. Development of the Comprehensive General Parenting Questionnaire for caregivers of 5-13 year olds. Int J Behav Nutr Phys Act. 2014 Feb 10;11:15. doi: 10.1186/1479-5868-11-15. PMID: 24512450; PMCID: PMC3926334.

Stierman B, Afful J, Carroll MD, et al. National Health and Nutrition Examination Survey 2017–March 2020 prepandemic data files development of files and prevalence estimates for selected health outcomes. National Health Statistics Reports. 2021;158:1-20. https://stacks.cdc.gov/view/cdc/106273

Stoyanov, S. R. et al. Mobile App Rating Scale: A New Tool for Assessing the Quality of Health Mobile Apps. JMIR MHealth UHealth. 2015;3:e27.

Tadepalli P, Shademyer C, Rio W, Semore S, Wiseman M, Dykeman C. Accelerating your research using AI. OSU MediaSpace. Published October 31, 2023. Accessed January 12, 2024. https://media.oregonstate.edu/media/t/1_gn8g6jdk 

Tonkin E, Brimblecombe J, Wycherley TP. Characteristics of smartphone applications for nutrition improvement in community settings: a scoping review. Adv Nutr 2017;8:308-322.

US Bureau of Labor Statistics, Department of Labor. Employment Characteristics of Families - 2022. USDL-23-0723. https://www.bls.gov/news.release/pdf/famee.pdf

Vlahu-Gjorgievska E, Burazor A, Win KT, Trajkovik V. mHealth apps targeting obesity and overweight in young people: App review analysis. JMIR Mhealth Uhealth 2023;11:e37716. https://pubmed.ncbi.nlm.nih.gov/36656624/  

Vaughn AE, Ward DS, Fisher JO, Faith MS, Hughes SO, Kremers SP, Musher-Eizenman DR, O’Connor RM, Patrick H, Power TG. Fundamental constructs in food parenting practices: a content map to guide future research. Nutr Rev. 2016;74(2):98-117.

Vyduna JL, Boushey CJ, Auld GW, Bruhn CM, Cluskey M, Edlefsen M, Misner S, Olson B, Reicks M, Schram J, Wang C, Zaghloul S. Field testing a questionnaire to identify parental psychosocial factors related to consumption of calcium-rich foods of their early adolescent children. Ecol Food Nutr. 2016;55(1):1-15.

Weber SJ, Dawson D, Greene H, Hull PC. Mobile phone apps for low-income participants in a public health nutrition program for women, infants, and children (WIC): review and analysis of features. JMIR Mhealth Uhealth. 2018;6(11):e12261. doi: 10.2196/12261

Wang Y, Beydoun MA, Min J, Xue H, Kaminsky LA, Cheskin LJ. Has the prevalence of overweight, obesity and central obesity leveled off in the United States? Trends, patterns, disparities, and future projections for the obesity epidemic. Int J Epidemiol. 2020;49(3):810-823. doi: 10.1093/ije/dyz273.

Yee N. A Closer Look into the 12 Gamer Motivations. Medium. Published March 6, 2021. Accessed January 12, 2024. https://medium.com/ironsource-levelup/a-closer-look-into-the-12-gamer-motivations-8d156ff0151a

 

Attachments

Land Grant Participating States/Institutions

DC, GA, HI, KS, OH, OR, WY

Non Land Grant Participating States/Institutions

Baldwin Wallace University, Brigham Young University, Ohio State University, The George Washington University