Skip to main content
Download PDF

A three-arm randomized controlled trial using ecological momentary intervention, community health workers, and video feedback at family meals to improve child cardiovascular health: the Family Matters study design

BMC Public Health volume 23, Article number: 708 (2023) Cite this article

Abstract

Background

Numerous observational studies show associations between family meal frequency and markers of child cardiovascular health including healthful diet quality and lower weight status. Some studies also show the “quality” of family meals, including dietary quality of the food served and the interpersonal atmosphere during meals, is associated with markers of child cardiovascular health. Additionally, prior intervention research indicates that immediate feedback on health behaviors (e.g., ecological momentary intervention (EMI), video feedback) increases the likelihood of behavior change. However, limited studies have tested the combination of these components in a rigorous clinical trial. The main aim of this paper is to describe the Family Matters study design, data collection protocols, measures, intervention components, process evaluation, and analysis plan.

Methods/design

The Family Matters intervention utilizes state-of-the-art intervention methods including EMI, video feedback, and home visiting by Community Health Workers (CHWs) to examine whether increasing the quantity (i.e., frequency) and quality of family meals (i.e., diet quality, interpersonal atmosphere) improves child cardiovascular health. Family Matters is an individual randomized controlled trial that tests combinations of the above factors across three study Arms: (1) EMI; (2) EMI + Virtual Home Visiting with CHW + Video Feedback; and (3) EMI + Hybrid Home Visiting with CHW + Video Feedback. The intervention will be carried out across 6 months with children ages 5–10 (n = 525) with increased risk for cardiovascular disease (i.e., BMI ≥ 75%ile) from low income and racially/ethnically diverse households and their families. Data collection will occur at baseline, post-intervention, and 6 months post-intervention. Primary outcomes include child weight, diet quality, and neck circumference.

Discussion

This study will be the first to our knowledge to use multiple innovative methods simultaneously including ecological momentary intervention, video feedback, and home visiting with CHWs within the novel intervention context of family meals to evaluate which combination of intervention components are most effective in improving child cardiovascular health. The Family Matters intervention has high potential public health impact as it aims to change clinical practice by creating a new model of care for child cardiovascular health in primary care.

Trial registration

This trial is registered in clinicaltrials.gov (Trial ID: NCT02669797). Date recorded 5/02/22.

Background

Cardiovascular disease (CVD) is a highly prevalent public health problem [1, 2]. CVD is the leading cause of death for one in four adults in the US and affects over 30% of minoritized populations [1]. While CVD peaks in middle age, risk factors begin in childhood and may provide a critical window for intervening to mitigate risk [3]. Children ages 7–10 are at a key age when precursors of CVD begin to be observed, but before the manifestation of disease such as high blood pressure, body mass index (BMI), cholesterol [3, 4] and less healthful dietary intake, fewer hours of physical activity, and more sedentary behaviors [5]. To date, there has been low to moderate success with lifestyle behavior interventions for children at risk for CVD and the persistent disparities across race/ethnicity calls for a new and innovative way to intervene [6]. Prior research has identified evidence-based intervention targets and strategies that when combined may provide an innovative approach for improving child cardiovascular health (CVH).

First, over two decades of observational cross-sectional and longitudinal research shows that family meal quantity (i.e., frequency) is associated with child health including higher diet quality, lower prevalence of unhealthy weight control behaviors, better psychosocial health, and reduced risk for childhood obesity—although weight status findings are mixed [7,8,9,10,11,12]. These protective associations have been found across child race/ethnicity, age, sex, and income [13,14,15]. In addition, studies have shown that family meal frequency is associated with better diet quality for adults [9, 13, 16], suggesting family meals may be beneficial for the entire family. However, few studies have tested this association in a randomized controlled trial (RCT) [17].

Prior interventions to increase child CVH have not been anchored around a specific family context/routine such as family meals. Instead, interventions often take a “kitchen sink approach” targeting multiple home environment factors (e.g., eating, physical activity, sedentary behavior, parenting) across multiple contexts (i.e., home, school, daycare). These interventions have had limited success [18]. Family meals are unique in that they create a nexus where multiple parenting and familial behaviors related to childhood obesity occur simultaneously (e.g., parent feeding practices, interpersonal behaviors, availability of healthy foods, portion size, modeling healthy eating) and can be intervened on—which rarely occurs in any other context. Furthermore, intervening on one specific context/routine (i.e., family meals) may also seem more doable to parents [19].

Second, some observational studies have shown the need to examine family meal “quality” (i.e., dietary intake, interpersonal atmosphere), in addition to family meal quantity, to better understand key protective factors of family meals [7, 12, 20]. Specifically, prior studies have shown associations between interpersonal interactions (e.g., non-controlling food parenting practices, positive communication/connection) during meals and better diet quality of foods served at family meals (e.g., fruits/vegetables, whole grains), lower child weight status, and higher child diet quality [20, 21]. The few existing RCTs examining family meal frequency and child CVD risk found that solely increasing the frequency of family meals was not associated with lower weight status in children [17]. Thus, interventions targeting both family meal quality and quantity will have a higher likelihood of improving child CVH.

Additionally, studies have identified barriers to carrying out family meal routines such as busy schedules, parental stress, lack of food prep/cooking skills, and child behaviors (e.g., picky eating) [22, 23]. Research by our team showed that parents experiencing high stress levels earlier in the day, were less likely to have family meals, served less healthy foods at mealtimes, and were more likely to engage in controlling feeding practices later the same day [24, 25]. Interventions including family meal quantity and quality, as well as strategies to reduce barriers (e.g., stress) to carrying out family meals are needed.

Third, research shows that providing immediate feedback on behavior (i.e., ecological momentary intervention (EMI), video feedback) within a specific context (e.g., family meals) results in more behavior change over time [26], compared to solely utilizing parent education [18]. These findings suggest that teaching parents what to do is not enough, rather watching one’s own behavior(s) and receiving feedback that reinforces positive behaviors or prompts different behaviors is necessary. Meta-analyses show that video feedback in parenting interventions is feasible, has low participant burden, results in moderate to large effects on parenting behaviors, and results in sustainable behavior change [27].

Ecological momentary intervention (EMI), or mobile health (mHealth), uses smartphones to send text messages to participants to intervene on behaviors in real-time as they unfold, moment-by-moment, over time and across contexts [28]. For example, a participant responds to a text earlier in the day regarding their stress level and source(s) of stress (e.g., too many things to do, demands from family, fatigue) then, an EMI message is sent providing suggestions to support them in making a healthful choice for family meals in the face of stress (e.g., tip for making a quick pasta meal more healthful by adding a vegetable stir in) [29, 30]. EMI studies from other fields have shown significant improvement in targeted behaviors (e.g., medication compliance, smoking cessation) [31, 32], high feasibility [32], validity and reliability [33, 34], few logistical problems [26], and low burden [35].

Fourth, interventions utilizing community health workers (CHWs) who can meet participants “where they are at,” both with regard to readiness for change and in their own environment (i.e., home visiting) are associated with better outcomes [36]. CHWs link care across clinic and home contexts and have high success with addressing obesity [36], diabetes [36] and other chronic conditions [36]. In addition, given the rise of virtual technology during the COVID-19 pandemic, the need to test virtual versus in-person measurement and delivery of home visiting interventions in a rigorous RCT is key to confirm the benefits of these approaches [37].

The main aim of the Family Matters Intervention is to target a well-documented family context associated with child CVH (i.e., family meals) using innovative real-time methods (i.e., EMI, video feedback) with CHWs in both virtual and in-person delivery modes to increase child CVH using a three-arm RCT (see Fig. 1). The three Arms include: EMI (Arm 1); EMI + Virtual Home Visiting (HV) with CHW + Video Feedback (Arm 2); and EMI + Hybrid HV with CHW + Video Feedback (Arm 3). Our overall hypothesis is that increasing both the quantity and quality of family meals will improve child CVH. Our main study hypotheses include (see Fig. 1):

  • Hypothesis 1: BMI percentile (%ile) and neck circumference will decrease and diet quality will increase in children in Arm 3 compared to children in Arms 1 or 2.

  • Hypothesis 2: Family meal quantity and quality will increase, controlling food parenting practices (e.g., restriction) will decrease, and parent coping skills will increase in parents in Arm 3 compared to parents in Arms 1 or 2.

  • Hypothesis 3: BMI %ile will decrease in siblings in Arm 3 compared to siblings in Arms 1 or 2.

Fig. 1
figure 1

Family Matters Intervention Study

Full size image

Theoretical framework

Family Systems Theory (FST) [38] guides the current study. According to FST, the family environment is the most proximal influence on child CVH [39, 40]. FST suggests that intervening on individual-level behavior (e.g., dietary intake) has limited success unless the family-level behavior sustaining or overriding the individual-level behavior (e.g., fruits/vegetables served at family meals, food parenting practices) changes too [39, 41]. FST also suggests that healthful behaviors learned in one family context (e.g., family dinner meal) will generalize to other family contexts (e.g., breakfast, lunch, snacks) [41, 42]. Thus, in the current study it is expected that positive parenting practices learned in the family meal context will generalize to other eating occasions and contribute to child CVH overall. Also, including multiple family members (e.g., parents, grandparents, siblings) in the intervention increases the likelihood of sustainable family-level change [7, 20].

Methods

The current study protocol was written following the guidelines of the Standard Protocol Items Recommendations for Interventional Trials (SPIRIT) checklist (Additional file 1). A SPIRIT figure is also provided below to demonstrate the flow of the study (see Fig. 2).

Fig. 2
figure 2

The SPIRIT diagram

Full size image

Study design

The Family Matters intervention is a single site RCT with child as the unit of randomization and analysis (see Fig. 3). The study is funded by the National Institutes of Health (HL151978) and is registered at clinicaltrial.gov (Trial ID: NCT02669797; May 2, 2022). This RCT lasts 12 months for each family, with a four month active intervention phase, a two month maintenance phase, and data collection at baseline, 6 months (i.e., post-intervention), and 12 months (i.e., 6 months post-intervention). All study materials are created in both English and Spanish.

Fig. 3
figure 3

The Family Matters Intervention Flowchart

Full size image

Study recruitment

Children (n = 525) and their families are recruited via family medicine and pediatric primary care clinics in Minneapolis and St. Paul, MN. Recruitment is ongoing for 42 months. Eligible children receive a letter inviting participation. Parents then fill out a REDCap survey assessing eligibility criteria.

Inclusion criteria

  • Children ages 5–10, their primary caregiver (e.g., parent, grandparent, aunt) and at least one sibling.

  • Children at high risk for CVD, defined as BMI ≥ 75th percentile [43].

  • Children from African American/Black, Asian, Hispanic, Native American, or White households who speak Spanish or English.

  • Children who consume ≤ 3 family meals per week [12].

Exclusion criteria

  • Children with medically necessary dietary restrictions (e.g., feeding tubes) or who are developmentally unable to participate (e.g., non-verbal).

  • Non-custodial parent who lives with the child < 50% of the time.

  • Children participating in a weight management study.

Study arms and randomization

Families are randomized into one of three intervention Arms: (1) EMI; (2) EMI + Virtual HV with a CHW + Video Feedback; and (3) EMI + Hybrid HV with a CHW + Video Feedback. All Arms receive 16 weeks (4 months) of EMI stress reduction and family meal tip messages via smartphones. Arms 2 and 3 additionally receive eight home visits by CHWs focused on family meal quantity and quality, a meal preparation activity, and video feedback on their family meal behaviors/patterns every-other-week for 16 weeks. Arm 2 receives all of these components virtually and Arm 3 receives these components half in person and half virtual (hybrid). In between weeks, families in Arms 2 and 3 complete a Try-it-Yourself activity to apply the new skills/behaviors they have been taught. All Arms receive an 8-week (2 months) maintenance phase allowing for progressively less support so they can increase self-efficacy and sustainability of behavior change.

Once participants complete their baseline data collection visit, they are randomized into one of three study Arms (n = 175 per Arm). If households have multiple eligible children, one child is randomly selected to minimize bias that could affect generalizability due to parent selection. Randomization is stratified by five racial/ethnic groups (African American, Hispanic, Native American, Asian American, White; n≈105 per race group). Block randomization schedules were produced in PASS 2021 (Kaysville, Utah) to account for the racial/ethnic stratification. Schedules are maintained by the biostatistician to keep team members blinded.

Procedures and data collection

Virtual data collection

Once child eligibility is confirmed, baseline data collection occurs via a virtual zoom visit including: guided anthropometry [43] and neck circumference measurements, a child 24 h. dietary recall, registration for two weeks of Ecological Momentary Assessment (EMA) on their phone [44], and training on video recording of family meals. Following the virtual visit, a 14-day observational period ensues including a parent online survey, two additional 24-h dietary recalls, ten days of EMA measuring parent stress and parenting practices, and a 2-day video-recorded family meal observation period (1 weeknight, 1 weekend night) measuring family meal quality (i.e., dietary, interpersonal) [45]. Virtual data collection occurs at baseline, 6 months (post-intervention), and 12 months (6-month post-intervention). Primary and secondary outcome measures are described in Table 1 and are collected at all three data collection time points in all Arms. Virtual protocols are based on ours [37] and other’s [7, 37] prior studies. Data collection tools and databases (i.e., REDCap) include features to support HIPAA compliance and allows for data checks to ensure data quality during data entry. Access to data collection tools and databases including REDCap and Box are strictly limited and regulated through personal user profiles. Both of these platforms are password protected and all data are regularly backed up into a password-protected database.

Table 1 Family matters intervention primary and secondary outcome measures and EMI survey questions used in intervention
Full size table

Measures

This study has three primary child outcomes: BMI%ile [46], neck circumference [47], and diet quality [48, 50]. Secondary outcomes include family meal quantity, meal dietary quality [21], meal interpersonal quality [51], parent outcomes: BMI [52], neck circumference [47], food-related parent practices [53], coping skills, sibling BMI%ile, and others [8, 13, 39, 54,55,56,57,58,59,60, 62] (see Table 1).

Blinding and investigator allocation concealment

As with most behavioral interventions, it is not possible to double blind this RCT. However, this study incorporates measurement staff and investigator blinding as much as possible to minimize bias. For example, the intervention is administered by CHWs who are not involved with measurement team responsibilities or meetings and measurement team members are blinded to participant study Arm assignment and are not involved with intervention team responsibilities or meetings. The biostatistician is the only completely unblinded member of the research team and will be overseeing data management and analyses throughout the trial and will have restricted access to the final study dataset.

Measurement team training and supervision

Measurement team members are trained, engage in role-plays, conduct mock visits, and are closely supervised by the measurement team director according to best practice [7, 63]. Table 2 describes these processes in depth. All practice, certification, and data collection visits are video recorded to allow for thorough supervision of visits where both the measurement team member and their supervisor gives feedback.

Table 2 Training, certification, recertification, and ongoing supervision for measurement team members and intervention community health workers
Full size table

Measurement team members are also trained on the Iowa Family Interaction Rating Scale (IFIRS) for video coding of family meals and the Nutrition Data System for Research (NDS-R) for dietary recalls [49]. Staff only code families in which they did not participate in the measurement visit [7, 20, 21]. Practice videos are used until coders reach 95% inter-rater reliability and 100% after consensus meetings; 25% of videos are double coded and checked at a 1:5 ratio to ensure high inter-rater reliability and fidelity to protocols. For NDS-R, quality assurance is conducted on 100% of recalls [48].

Retention plan

To minimize attrition in all study Arms, the following retention strategies are used, based on our successful prior studies with > 95% retention rate and best practice [63, 64]: (1) gather extensive participant contact information (e.g., phone numbers, email addresses, home/work addresses, emergency contacts); (2) tailor preferred forms of contact to participants (e.g., texts, phone, email); (3) utilize primary care electronic medical record (EMR) databases for updated contact information; (4) send tracking postcards during important cultural celebrations (e.g., Hispanic Heritage Month, Native American Heritage Day); and (5) use ongoing tracking databases (e.g., LexisNexis, White Pages). Additionally, at 9 months families are sent a small gift (e.g., reusable grocery bag with the Family Matters logo) and a short survey asking them to update their contact information.

Ethical considerations

The University of Minnesota’s Institutional Review Board (IRB) Human Subjects Committee approved all protocols used in the study. Prior to enrollment into the study, participants are provided with detailed information about the study by our research team via consent and assent forms including study aims and detailed procedures. Participants are informed that their participation is voluntary and that they have the right to withdraw from the study without any consequences at any point. They will be assured of anonymity in participation and confidentially of any data they provide throughout the study, through the use of study IDs and the storage of sensitive information in secure online platforms (i.e., REDCap and Box). Participants can be enrolled into the study only after they have provided written consent and assent forms to our research team.

Regulatory oversight/monitoring

All study modifications will be communicated with and regulated by the IRB. Even though the study is expected to pose minimal risk, the Data Safety Monitoring Board (DSMB), in collaboration with the study investigators will closely monitor recruitment, process evaluation, and retention activities. The DSMB will meet yearly with the study investigators and staff, or more often as needed, for oversight of the study. Any adverse events will be reported to the NHLBI and the IRB at the University of Minnesota. This trial is also registered in the OnCore clinical trial management system and is audited by the Medical School at the University of Minnesota.

Intervention

The Family Matters three-arm intervention, known as the Family Matters Program to our study families, components and dose are described below.

Study arm #1: EMI

Parents randomized to study Arm 1 receive EMI text messages twice a day for 16 weeks via their smartphone. A study smartphone is provided for use if needed.

EMI

Our prior research showed parental stress early in the day was associated with more controlling food parenting practices and serving more unhealthful foods (e.g., fast food) at dinner the same night [24]. Therefore, in all Arms, parents receive EMI text messages to their phones that include two parts. First, the parent is sent a text message with a survey link, between 11am-2pm to report their stress level (i.e., scale of 0-10) and sources of stress (e.g., child demands, busy at home/work, social media; see Table 1). Second, a text message is sent back to the participant from a bank of tips (approximately 50 tips per source of stress) for the particular source of stress they reported. This tailored tip is intended to help them cope with the reported stress and increase the likelihood that they will still carry out a family meal the same evening in the face of stress [24]. After the tip is sent, parents are also asked to report whether or not the tip was helpful, which then adjusts their individual EMI algorithm so there is an increased likelihood of them receiving more or less of these types of tips. If parents report no stress, they receive a tip to facilitate having a family meal (e.g., recipe ideas, meal prep tips, mealtime conversation starters).

Study arm #2: EMI + Virtual Home Visiting (HV) with CHW + Video feedback

Parents randomized to study Arm 2 receive all elements of study Arm 1, in addition to home visiting by a CHW. Visits by CHWs are virtual via zoom and occur every-other-week (8 total) simultaneously with the 16 weeks of EMI. In between CHW home visits, families complete “Try-it-Yourself” activities (8 total) to reinforce new behaviors and meal preparation skills (e.g., batch cooking recipe, shopping scavenger hunt, stress reduction coping skills).

Home visiting

CHWs carry out the 60-90-minute HVs using Motivational Interviewing (MI) [65, 66] and psychoeducation [35]. The visits focus on family meal quantity and quality factors [7, 20, 21] known to be associated with child CVH. Family members are taught specific skills through didactic and interactive session activities (e.g., AHA Slides, Figma games) to improve family meal processes and behaviors. Session content and activities are described in Table 3. A SMART goal (i.e., specific, measurable, achievable, relevant and time-bound) [67] is set at the end of each session related to the content delivered in the home visit and their video feedback.

Table 3 Intervention session titles, content, and interactive activities for the Family Matters program
Full size table

Video feedback

Every other week, starting during home visit three, families video-record and upload one family meal via their smartphone (6 total meals). CHWs watch videos in between home visits to identify specific clips to show family members at the next visit that highlight both strengths and growth areas regarding interpersonal interactions and dietary patterns. During HVs, CHWs engage family members in a conversation—using MI skills [65] where both the CHW and family members identify positive behaviors seen in the videos and areas for growth, based on session content that families have been learning.

“Try-it-Yourself” activities

Families are given food-related (e.g., recipes, meal planning strategies) and interpersonal (e.g., food prep with kids, family meal communication game, stress reduction) activities to try out in between visits to increase their self-efficacy in preparing family meals on their own and reinforce messages they are taught during HVs. The study child and all siblings in the home are also given an activity book with games that reinforce session content.

Study arm #3: EMI + Hybrid HV with CHW + Video feedback

Parents randomized to study Arm 3 receive all elements of study Arm 2, but they are delivered hybrid. Specifically, CHWs meet in-person with families every other HV and then virtually via zoom on the other weeks. Families also engage in two cooking demonstration activities with the CHW during in-person HVs to reinforce messages taught, share easy recipes (e.g., batch cooking, one ingredient for multiple meals), and teach food prep skills to increase family’s self-efficacy for having family meals. This Arm is important to examine whether relationship building and creating an atmosphere conducive to health behavior change requires an in-person component. This Arm is also critical to examine COVID’s impact on moving research to virtual modes.

Maintenance phase

After completing four months of the intervention, all study Arms transition to a two-month maintenance phase, based on best practice [68]. For all study Arms, EMI family meal tips are reduced to the three days per week that parents reported their highest stress levels during the 16-week active intervention phase. Stress profiles corresponding to the high risk stress days are created for each participant to maximize intervention uptake and subsequent sustainability [24, 25].

Community Health Workers (CHWs) training and supervision

Interventionists are racially/ethnically diverse CHWs, with half being Spanish speaking. CHWs are trained/certified in MI [65], SMART goals [67], the intervention content for eight HV sessions, video feedback skills [7, 27], and HV protocols. The CHW supervision process provides multiple levels of supervision throughout training and intervention delivery (see Table 2). Co-investigators who provide supervision and the intervention director (MA) are trained in MI and are licensed mental health clinicians (JB, TM) or registered dietitians (KL, DNS). All role-plays, certification, and family intervention visits in all study Arms are video-recorded. Both the CHW and supervisor watch and give feedback on the video-recordings, which allows for thorough feedback. Just as families receive video feedback on their recorded family meals from the CHWs as part of the intervention, the CHWs are given feedback as well, thus creating a parallel process that models to the CHW how to give feedback that is collaborative, focusing on both strengths and areas of growth, with their intervention families.

Process evaluation

A robust feasibility and process evaluation protocol was designed for this intervention (see Table 4), to ensure feasibility, generalizability, and dissemination into primary care and other health care settings [68, 69].

Table 4 Process evaluation plan based on the national institutes of health treatment fidelity framework [68]
Full size table

Statistical analysis plan

Overview

This study is powered for three pairs of tests [70] to evaluate intervention effectiveness: (a) Arm 2 vs. 1, (b) Arm 3 vs. 1, and (c) Arm 2 vs. 3 over three time points. Multi-level, general linear mixed models (MLMM) with a clinic random intercept that nests participants within clinics to address any clinic differences in the recruitment populations, with participant random slopes for time to examine intervention treatment effects, and conditional fixed effects regression models (within-child analytic contrast against baseline), are the primary analytical models for all study hypotheses. Participants’ randomized condition will be examined irrespective of adherence to the study protocol in accordance with an intent-to-treat (ITT) analysis. After the intervention has been fully administered, data will be assessed for balance across arms, outliers, missingness, and other modeling assumptions. Although randomization is expected to produce balance on measured and unmeasured characteristics, variables will be considered for inclusion as controls in adjusted analyses to reduce test statistic variance [71]. We expect little missing data based on our prior work, but if needed, we will employ methods recommended for clinical trials to minimize analytical assumptions required when missing data are present (e.g., follow up all randomized participants prior to unblinding [72], evaluating if results from the primary analysis differ when sensitivity analyses are performed [73]). Reasons for participant withdrawal and non-adherence will be analyzed and reported in the final ITT analysis [74].

Sample size and power computations

Study design features were accounted for in powering the study that required increases in sample size to minimize an inflated experiment-wise error rate (EER) due to three pairwise tests between each treatment arm for three primary outcomes (i.e., BMI%ile, neck circumference, and the Healthy Eating Index (HEI)). Accounting for these nine tests, sample size was determined using a conservative two-sided critical value of z = 2.77 (P = 0.006) to achieve experiment-wise Type I error of 0.05. Our power calculations were based on prior studies showing that a decline of two BMI%ile points was a clinically meaningful difference in children with overweight/obesity [46]. BMI%ile is a continuous outcome with a variance of 18.8. Eighteen-month follow up data with a comparable cohort provided intraclass correlation coefficient estimates to inform sample size determination (BMI%ile ICC \(=0.716\)). At 80% power and multiple-outcome and pairwise testing corrected EER of 0.05, with a sample size of 525, we will be able to detect a minimum average difference in BMI%ile as small as 1.67 (or 0.38 SD) with 15% attrition. This magnitude translates to approximately a 2.8 lb difference in a six year-old boy who is 3.8 feet tall and 45 lbs, or approximately a 7.5 oz per month change in weight by the post-intervention endpoint.

Aim 1 (Primary Outcomes): examine intervention effects on markers of child CVH including BMI%ile, diet quality, neck circumference

Treatment condition mean differences on the three primary outcomes will be examined at the post-intervention primary endpoint (6 months after baseline). Sample size determination allows for primary outcome standardized effect size assessment of all three outcomes of at least 0.38, which is a small-to-moderate minimum detectable effect.

Aim 2 (Secondary Outcomes): examine intervention effects on family, parental, and sibling factors

Family meal quantity and quality, food parenting practices and stress, and sibling BMI %ile outcomes are powered at similar levels as in Aim 1 with the ability to detect standardized effect sizes as small as 0.38.

Sub-group exploratory analyses

Analyses exploring whether interaction effects depend on participant sex, race/ethnicity, and seasonality will also be conducted. These post-hoc analyses will examine whether the intervention has synergistic effects in specific populations or during different seasonal contexts. Post-hoc analyses will be conducted to explore the interaction of child/parent sex and baseline weight status on intervention treatment effects to determine whether the intervention is particularly effective in certain subpopulations.

Other exploratory hypotheses

A model incorporating an interaction effect of treatment arm crossed with the change in family meal quality and quantity between observation periods will be used to evaluate if increases (or decreases) in the quality and quantity of family meals correspond with synergistically favorable (or unfavorable) child outcomes. This analysis will inform whether intervention effects depend on participants’ changes in family meal quality and quantity. This analysis is powered to detect a between-within intervention slope difference over the 6-month intervention period of as little as 9.6 oz per month, depending on whether participants had high or low change in the moderating variables. Seasonality robustness checks will also be performed to evaluate whether results differ substantively for participants who received the intensive intervention during the summer months.

Discussion

The Family Matters intervention has high potential public health impact as it aims to change clinical practice by creating a new model of care for child CVH in primary care. Research in this field is needed given the low to moderate success of lifestyle behavior interventions for children at risk for CVD and the persistent high prevalence of disparities across race/ethnicity groups. The state-of-the-art measures being used including EMA, EMI, and video feedback combined with the novel intervention context of family meals and CHWs as interventionists will greatly advance the field. In addition, the three-arm study design will allow for testing which combinations of intervention components are most effective in improving child CVH by race/ethnicity as well as whether a virtual or hybrid Arm is more effective. Dependent on study findings, this intervention will be disseminated to other primary care settings.

Availability of data and materials

The datasets generated from the study will be available from the corresponding author upon reasonable request.

Abbreviations

CVH:

Cardiovascular Health

EMI:

Ecological Momentary Intervention

CHW:

Community Health Worker

HV:

Home Visiting

FST:

Family Systems Theory

EMA:

Ecological Momentary Assessment

IFIRS:

Iowa Family Interaction Rating Scale

NDS-R:

Nutrition Data System for Research

EER:

Experiment-Wise Error Rate

HEI:

Healthy Eating Index

References

  1. Benjamin E, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, et al. Heart disease and stroke statistics-2019 update: a report from the American heart association. Circulation. 2019;139(10):e56-528.

    Article  PubMed  Google Scholar 

  2. Einarson TR, Acs A, Ludwig C, Panton UH. Prevalence of cardiovascular disease in type 2 diabetes: A systematic literature review of scientific evidence from across the world in 2007–2017. Cardiovasc Diabetol. 2018;17:83 (BioMed Central Ltd.).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Araújo J, Barros H, Ramos E, Li L. Trajectories of total and central adiposity throughout adolescence and cardiometabolic factors in early adulthood. Int J Obes. 2016;40(12):1899–905.

    Article  Google Scholar 

  4. Hannon TS, Arslanian SA. The changing face of diabetes in youth: Lessons learned from studies of type 2 diabetes. Ann N Y Acad Sci. 2015;1353(1):113–37.

    Article  CAS  PubMed  Google Scholar 

  5. Berge JM, Fertig A, Tate A, Trofholz A, Neumark SD. Who is meeting the Healthy People 2020 objectives?: Comparisons between racially/ethnically diverse and immigrant children and adults. Fam Syst Health. 2018;36(4):451–70.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Palmer RC, Ismond D, Rodriquez EJ, Kaufman JS. Social determinants of health: future directions for health disparities research. Am J Public Health. 2019;109(S1):S70–1. Available from: http://europepmc.org/abstract/MED/30699027.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Berge JM, Rowley S, Trofholz A, Hanson C, Rueter M, MacLehose RF, et al. Childhood obesity and interpersonal dynamics during family meals. Pediatrics. 2014;134(5):923–32. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25311603.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Rollins BY, Belue RZ, Francis LA. The beneficial effect of family meals on obesity differs by race, sex, and household education: the national survey of children’s health, 2003–2004. J Am Diet Assoc. 2010;110(9):1335–9.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Chan JC, Sobal J. Family meals and body weight analysis of multiple family members in family units. Appetite. 2011;57(2):517–24.

    Article  PubMed  Google Scholar 

  10. Fulkerson JA, Kubik MY, Story M, Lytle L, Arcan C. Are There Nutritional and Other Benefits Associated with Family Meals Among At-Risk Youth? J Adolesc Health. 2009;45(4):389–95.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Hammons AJ, Fiese BH. Is frequency of shared family meals related to the nutritional health of children and adolescents? Pediatrics. 2011;127:e1565–74.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Berge JM, Wall MM, Hsueh TF, Fulkerson JA, Larson N, Neumark-Sztainer D. The protective role of family meals for youth obesity: 10-year longitudinal associations. J Pediatr. 2015;166(2):296–301.

    Article  PubMed  Google Scholar 

  13. Berge JM, MacLehose RF, Loth KA, Eisenberg ME, Fulkerson JA, Neumark-Sztainer D. Family meals. Associations with weight and eating behaviors among mothers and fathers. Appetite. 2012;58(3):1128–35.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Neumark-Sztainer D, Hannan PJ, Story M, Croll J, Perry C. Family meal patterns: Associations with sociodemographic characteristics and improved dietary intake among adolescents. J Am Diet Assoc. 2003;103(3):317–22.

    Article  PubMed  Google Scholar 

  15. Frazier L, Field AE. Family dinner and diet quality among older children and adolescents Article in Archives of Family Medicine · April 2000.

    Google Scholar 

  16. Utter J, Larson N, Berge JM, Eisenberg ME, Fulkerson JA, Neumark-Sztainer D. Family meals among parents: associations with nutritional, social and emotional wellbeing. Prev Med. 2018;113:7–12.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Fulkerson JA, Friend S, Horning M, Flattum C, Draxten M, Neumark-Sztainer D, et al. Family home food environment and nutrition-related parent and child personal and behavioral outcomes of the Healthy Home Offerings via the Mealtime Environment (HOME) plus program: a randomized controlled trial. J Acad Nutr Diet. 2018;118(2):240–51.

    Article  PubMed  Google Scholar 

  18. Seo DC, Sa J. A meta-analysis of obesity interventions among U.S. minority children. J Adolesc Health. 2010;46:309–23.

    Article  PubMed  Google Scholar 

  19. Berge JM, Trofholz A, Schulte A, Conger K, Neumark-Sztainer D. A Qualitative Investigation of Parents' Perspectives About Feeding Practices With Siblings Among Racially/Ethnically and Socioeconomically Diverse Households. J Nutr Educ Behav. 2016;48(7):496-504.e1. https://doi.org/10.1016/j.jneb.2016.05.002.

  20. Berge JM, Jin SW, Hannan P, Neumark-Sztainer D. Structural and interpersonal characteristics of family meals: associations with adolescent body mass index and dietary patterns. J Acad Nutr Diet. 2013;113(6):816–22. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23567247.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Trofholz AC, Tate AD, Draxten ML, Rowley SS, Schulte AK, Neumark-Sztainer D, et al. What’s Being Served for Dinner? An Exploratory Investigation of the Associations between the Healthfulness of Family Meals and Child Dietary Intake. J Acad Nutr Diet. 2017;117(1):102–9.

    Article  PubMed  Google Scholar 

  22. Neumark-Sztainer D, MacLehose R, Loth K, Fulkerson JA, Eisenberg ME, Berge J. What’s for dinner? Types of food served at family dinner differ across parent and family characteristics. Public Health Nutr. 2014;17(1):145–55.

    Article  PubMed  Google Scholar 

  23. Fulkerson JA, Larson N, Horning M, Neumark-Sztainer D. A review of associations between family or shared meal frequency and dietary and weight status outcomes across the lifespan. J Nutr Educ Behav. 2014;46(1):2–19.

    Article  PubMed  Google Scholar 

  24. Berge JM, Tate A, Trofholz A, Fertig AR, Miner M, Crow S, et al. Momentary parental stress and food-related parenting practices. Pediatrics. 2017;140(6):e20172295.

    Article  PubMed  Google Scholar 

  25. Berge JM, Tate A, Trofholz A, Fertig A, Crow S, Neumark-Sztainer D, et al. Examining within- and across-day relationships between transient and chronic stress and parent food-related parenting practices in a racially/ethnically diverse and immigrant population : Stress types and food-related parenting practices. Int J Behav Nutr Phys Act. 2018;15(1):7.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Dunton GF, Liao Y, Intille SS, Spruijt-Metz D, Pentz M. Investigating children’s physical activity and sedentary behavior using ecological momentary assessment with mobile phones. Obesity. 2011;19(6):1205–12.

    Article  PubMed  Google Scholar 

  27. Fukkink RG. Video feedback in widescreen: a meta-analysis of family programs. Clin Psychol Rev. 2008;28:904–16.

    Article  PubMed  Google Scholar 

  28. Heron KE, Smyth JM. Ecological momentary interventions: Incorporating mobile technology into psychosocial and health behaviour treatments. Br J Health Psychol. 2010;15(1):1–39.

    Article  PubMed  Google Scholar 

  29. Fertig AR, Loth KA, Trofholz AC, et al. Compared to Pre-prepared Meals, Fully and Partly Home-Cooked Meals in Diverse Families with Young Children Are More Likely to Include Nutritious Ingredients. J Acad Nutr Diet. 2019;119(5):818–30. https://doi.org/10.1016/j.jand.2018.12.006.

  30. Noar SM, Grant Harrington N, van Stee SK, Shemanski AR. Tailored health communication to change lifestyle behaviors. Am J Lifestyle Med. 2011;5:112–22.

    Article  Google Scholar 

  31. Ehrenreich B, Righter B, Rocke DA, Dixon L, Himelhoch S. Are mobile phones and handheld computers being used to enhance delivery of psychiatric treatment?: a systematic review. J Nervous Mental Disease. 2011;199:886–91.

    Article  Google Scholar 

  32. Businelle MS, Ma P, Kendzor DE, Frank SG, Wetter DW, Vidrine DJ. Using intensive longitudinal data collected via mobile phone to detect imminent lapse in smokers undergoing a scheduled quit attempt. J Med Internet Res. 2016;18(10):e275.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Anestis MD, Selby EA, Crosby RD, Wonderlich SA, Engel SG, Joiner TE. A comparison of retrospective self-report versus ecological momentary assessment measures of affective lability in the examination of its relationship with bulimic symptomatology. Behav Res Ther. 2010;48(7):607–13.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Beech B, Klesges R, Kumanyika S, Murray D, Klesges L, Mcclanahan B, et al. Child- and parent-targeted interventions: The Memphis GEMS Pilot Study. Ethn Dis. 2003;1(13):S40-53.

    Google Scholar 

  35. Bell IH, Lim MH, Rossell SL, Thomas N. Ecological momentary assessment and intervention in the treatment of psychotic disorders: a systematic review. Psychiatr Serv. 2017;68:1172–81 American Psychiatric Association.

    Article  PubMed  Google Scholar 

  36. Schroeder K, McCormick R, Perez A, Lipman TH. The role and impact of community health workers in childhood obesity interventions: a systematic review and meta-analysis. Obes Rev. 2018;19:1371–84. Blackwell Publishing Ltd.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Barkin S, Verbiest S, Berge J. Using the tools of today to advance the life course interventions of tomorrow. Pediatrics. 2022;149(Suppl 5):e2021053509N.

    Article  PubMed  Google Scholar 

  38. Whitchurch G, Constantine L. System Theory. In Boss, P., Doherty, W.J., LaRossa, R., Schumm, W.R., Steinmetz, S.K. In: Sourcebook on Family Theories and Methods: A contextual Approach. Boston: Springer; 1993. p. 325–55.

    Chapter  Google Scholar 

  39. Berge JM, Wall M, Larson N, Loth KA, Neumark-Sztainer D. Family functioning: associations with weight status, eating behaviors, and physical activity in adolescents. J Adolesc Health. 2013;52(3):351–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23299010.

    Article  PubMed  Google Scholar 

  40. Rhee K. Childhood overweight and the relationship between parent behaviors, parenting style, and family functioning. Ann Am Acad Pol Soc Sci. 2008;615(1):11–37. https://doi.org/10.1177/0002716207308400.

    Article  Google Scholar 

  41. Berge JM, Meyer C, MacLehose RF, Crichlow R, Neumark-Sztainer D. All in the family: correlations between parents’ and adolescent siblings’ weight and weight-related behaviors. Obesity (Silver Spring). 2015;23(4):833–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25820257.

    Article  PubMed  Google Scholar 

  42. Pachucki MC, Lovenheim MF, Harding M. Within-family obesity associations: evaluation of parent, child, and sibling relationships. Am J Prev Med. 2014;47(4):382–91. https://doi.org/10.1016/j.amepre.2014.05.018.

    Article  PubMed  Google Scholar 

  43. Centers for Disease Control and Preventions, National Center for Health Statistics. CDC Growth Charts: United States. 2000. [cited 2022 Oct 3]. Available from: http://www.cdc.gov/growthcharts/.

  44. Trofholz A, Tate A, Janowiec M, Fertig A, Loth K, de Brito JN, et al. Ecological momentary assessment of weight-related behaviors in the home environment of children from low-income and racially and ethnically diverse households: development and usability study. JMIR Res Protoc. 2021;10(12):e30525.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Tate A, Trofholz A, Miner M, Berge J. Days needed to characterize the healthfulness of a typical dinner meal in direct observational research: Mixed methods study. JMIR Pediatr Parent. 2021;4(1):e22541.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Reinehr T, Lass N, Toschke C, Rothermel J, Lanzinger S, Holl RW. Which amount of BMI-SDS reduction is necessary to improve cardiovascular risk factors in overweight children? J Clin Endocrinol Metab. 2016;101(8):3171–9.

    Article  CAS  PubMed  Google Scholar 

  47. Valencia-Sosa E, González-Pérez GJ, Martínez-López E, Rodriguez-Echevarria R. Neck and wrist circumferences as indicators of metabolic alterations in the pediatric population: a scoping review. Children (Basel). 2021;8(4):297.

    PubMed  Google Scholar 

  48. McPherson RS, Hoelscher DM, Alexander M, Scanlon KS, Serdula MK. Dietary assessment methods among school-aged children: Validity and reliability. Prev Med Baltim. 2000;31(2 II):S11-33.

    Article  Google Scholar 

  49. University of Minnesota, Nutrition Coordinating Center. NDSR Software. http://www.ncc.umn.edu/products/. Accessed Jan 2023.

  50. Guenther PM, Casavale KO, Reedy J, Kirkpatrick SI, Hiza HAB, Kuczynski KJ, et al. Update of the healthy eating index: HEI-2010. J Acad Nutr Diet. 2013;113(4):569–80. https://doi.org/10.1016/j.jand.2012.12.016.

    Article  PubMed  Google Scholar 

  51. Melby JN, Conger RD. The Iowa Family Interaction Rating Scales: Instrument Summary. Mahwah: Lawrence Erlbaum Associates, Inc.; 2001.

    Google Scholar 

  52. Centers for Disease Control and Prevention. About Adult BMI. 2022. [cited 2023 Jan 22]. Available from: https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html

  53. Vaughn AE, Ward DS, Fisher JO, et al. Fundamental constructs in food parenting practices: a content map to guide future research. Nutr Rev. 2016;74(2):98–117.

    Article  PubMed  Google Scholar 

  54. Sherwood NE, Wall M, Neumark-Sztainer D, Story M. Effect of Socioeconomic Status on Weight Change Patterns in Adolescents. Prev Chronic Dis. 2009;6(1):A19. Available from: http://www.cdc.gov/pcd/issues/2009/jan/07_0226.htm. Cited 2022 Oct 4.

    PubMed  Google Scholar 

  55. Patterson JG, Jabson JM, Bowen DJ. Measuring sexual and gender minority populations in health surveillance. LGBT Health. 2017;4(2):82–105. https://doi.org/10.1089/lgbt.2016.0026.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Berge JM, MacLehose RF, Loth KA, Eisenberg ME, Fulkerson JA, Neumark-Sztainer D. Parent-adolescent conversations about eating, physical activity and weight: prevalence across sociodemographic characteristics and associations with adolescent weight and weight-related behaviors. J Behav Med. 2015;38(1):122–35. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24997555.

    Article  PubMed  Google Scholar 

  57. Unger JB, Gallaher P, Shakib S, Ritt-Olson A, Palmer PH, Johnson CA. The AHIMSA Acculturation Scale: A new measure of acculturation for adolescents in a multicultural society. J Early Adolesc. 2002;22:225–51.

    Article  Google Scholar 

  58. Blumberg SJBKHWBR. The effectiveness of a short form of the Household Food Security Scale. Am J Public Health. 1999;89(8):1231–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Berge JM, MacLehose RF, Larson N, Laska M, Neumark-Sztainer D. Family food preparation and its effects on adolescent dietary quality and eating patterns. J Adolesc Health. 2016;59(5):530–6.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Crawford D, Ball K, Mishra GD, Salmon J, Timperio A. Which food-related behaviours are associated with healthier intakes of fruits and vegetables among women? Public Health Nutr. 2007;10:256–65.

    Article  PubMed  Google Scholar 

  61. Netemeyer RG, Boles JS, McMurrian R. Development and validation of work-family conflict and family-work conflict scales. J Appl Psychol. 1996;81(4):400–10.

    Article  Google Scholar 

  62. Kessler RC, Green JG, Gruber MJ, Sampson NA, Bromet E, Cuitan M, et al. Screening for serious mental illness in the general population with the K6 screening scale: Results from the WHO World Mental Health (WMH) survey initiative. Int J Methods Psychiatr Res. 2010;19(SUPPL. 1):4–22.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Berge JM, Trofholz A, Tate A, Beebe M, Fertig A, Miner M, et al. Examining unanswered questions about the home environment and childhood obesity disparities using an incremental, mixed-methods longitudinal study design: The Family MAtters study. Contemp Clin Trials. 2017;62:61–76.

    Article  PubMed  PubMed Central  Google Scholar 

  64. French SA, Sherwood NE, Veblen-Mortenson S, Crain AL, JaKa MM, Mitchell NR, et al. Multicomponent obesity prevention intervention in low-income preschoolers: primary and subgroup analyses of the NET-Works randomized clinical trial, 2012–2017. Am J Public Health. 2018;108(12):1695–706.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Miller WRRS. Motivational Interviewing, Preparing People for Change. 2nd ed. New York: Guilford Press; 2002.

    Google Scholar 

  66. Irby M, Kaplan S, Garner-Edwards D, Kolbash S, Skelton JA. Motivational Interviewing in a Family-Based Pediatric Obesity Program: A Case Study. Fam Syst Health. 2010;28(3):236–46.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Sherwood NE, French SA, Veblen-Mortenson S, Crain AL, Berge J, Kunin-Batson A, et al. NET-Works: Linking families, communities and primary care to prevent obesity in preschool-age children. Contemp Clin Trials. 2013;36(2):544–54.

    Article  PubMed  PubMed Central  Google Scholar 

  68. JaKa MM, Haapala JL, Trapl ES, et al. Reporting of treatment fidelity in behavioural paediatric obesity intervention trials: a systematic review. Obes Rev. 2016;17(12):1287–300. https://doi.org/10.1111/obr.12464.

  69. Moyers TB, Rowell LN, Manuel JK, Ernst D, Houck JM. The Motivational Interviewing Treatment Integrity Code (MITI 4): Rationale, Preliminary Reliability and Validity. J Subst Abuse Treat. 2016;1(65):36–42.

    Article  Google Scholar 

  70. Chang YW, Tsong Y, Dong X, Zhao Z. Sample Size Determination for a Three-Arm Equivalence Trial of Normally Distributed Responses. J Biopharm Stat. 2014;24(6):1190–202. https://doi.org/10.1080/10543406.2014.948552.

    Article  PubMed  Google Scholar 

  71. Friedman L, Furberg C, DeMets D, Reboussin D, Granger C. Fundamentals of Clinical Trials. 5th ed. New York: Springer; 2015.

    Book  Google Scholar 

  72. Shih WJ. Current Controlled Trials in Cardiovascular Medicine Problems in dealing with missing data and informative censoring in clinical trials. Curr Control Trials Cardiovasc Med. 2002;3:4. Available from: http://cvm.controlled-trials.com/content/3/1/4.

    Article  PubMed  PubMed Central  Google Scholar 

  73. White IRHNCJPS. Strategy for intention to treat analysis in randomised trials with missing outcome data. BMJ. 2011;342: d40.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Schafer J. Analysis of Incomplete Multivariate Data. New York: Chapman & Hall/CRC; 1997.

    Book  Google Scholar 

Download references

Acknowledgements

The success of the Family Matters Program is only possible through the generous time of our family participants and our talented staff and community health workers including: Jeffrey Barrera, Hanna Boccheciamp, Isaac Nicholl, Katrina Mraz, Mia Pylkkanen, and Zachary Clark.

Funding

Research is supported by grant number R61/33 HL151978 from the National Heart, Lung, and Blood Institute (PI: Jerica Berge). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung and Blood Institute or the National Institutes of Health.

Author information

Authors and Affiliations

  1. Department of Family Medicine and Community Health, University of Minnesota Medical School, 717 Delaware Street SE, Room 425, Minneapolis, MN, 55455, USA

    Jerica M. Berge, Amanda C. Trofholz, Marah Aqeel, Kristin Norderud & Katie Loth

  2. College of Public Health, University of Georgia, Athens, GA, USA

    Allan Tate

  3. University of Minnesota, Humphrey School of Public Affairs, Minneapolis, MN, USA

    Angela R. Fertig

  4. Department of Family Social Science, University of Minnesota, Minneapolis, MN, USA

    Tai Mendenhall

  5. School of Public Health, Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA

    Dianne Neumark-Sztainer

Authors
  1. Jerica M. Berge
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amanda C. Trofholz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marah Aqeel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kristin Norderud
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Allan Tate
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Angela R. Fertig
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Katie Loth
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Tai Mendenhall
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Dianne Neumark-Sztainer
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JB is the principal investigator of this trial. JB conceptualized the paper and wrote all drafts. ACT and KN assisted with writing the procedures and data collection plan and critically reviewed the paper. MA assisted with the intervention design and writing the process evaluation plan and critically reviewed the paper. AT and AF assisted with the conceptualization of the paper and the data analysis plan. KL, TM, and DNS critically reviewed the paper. All authors approved the final manuscript.

Authors’ information

JB is a Professor and Vice Chair for Research in the Department of Family Medicine and Community Health at the University of Minnesota; the Principal Investigator of the Family Matters study; the Director of the Healthy Eating and Activity across the Lifespan (HEAL) Center; and Director of the Center for Women’s Health Research.

ACT is a Research Manager over the Family Matters study in the Department of Family Medicine and Community Health at the University of Minnesota and the Administrative Director of the Healthy Eating and Activity across the Lifespan (HEAL) Center.

KN is an Evaluation Director for the Family Matters Intervention study in the Department of Family Medicine and Community Health at the University of Minnesota.

MA is an Intervention Director for the Family Matters Intervention study in the Department of Family Medicine and Community Health at the University of Minnesota.

AF is an Assistant Professor in the Humphries School of Public Affairs at the University of Minnesota.

KL is an Assistant Professor in the Department of Family Medicine and Community Health at the University of Minnesota.

TM is a Professor in the Department of Family Social Science at the University of Minnesota.

DNS is a Professor and Division Head at the School of Public Health, Division of Epidemiology and Community Health at the University of Minnesota.

Corresponding author

Correspondence to Jerica M. Berge.

Ethics declarations

Ethics approval and consent to participate

The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Institutional Review Board Human Subjects Committee at the University of Minnesota (IRB ID: STUDY00014632). All participants provide their written informed consent or assent before participating in the study. For minors, a signed assent form will be obtained from their parents/or legal guardians before participating in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1.

Completed SPIRIT guidelines checklist for the Family Matters Intervention study.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Berge, J.M., Trofholz, A.C., Aqeel, M. et al. A three-arm randomized controlled trial using ecological momentary intervention, community health workers, and video feedback at family meals to improve child cardiovascular health: the Family Matters study design. BMC Public Health 23, 708 (2023). https://doi.org/10.1186/s12889-023-15504-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-023-15504-2

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us