The Healthy Children, Strong Families 2 randomized controlled trial improved healthy behaviors in American Indian families with young children Emily J. Tomayko 1 , Ronald J. Prince 2 , Kate A. Cronin 3 , KyungMann Kim 4 , Tassy Parker 5 , Alexandra K. Adams 6 1 Nutrition, School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 2 Department of Population Health, School of Medicine and Public Health, University of Wisconsin, Madison, WI 3 Department of Surgery, School of Medicine and Public Health, University of Wisconsin, Madison, WI 4 Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin, Madison, WI 5 Department of Family and Community Medicine, School of Medicine, University of New Mexico, Albuquerque, NM 6 Corresponding author: Alexandra K. Adams, alexandra.adams2@montana.edu; Center for American Indian and Rural Health Equity, Montana State University, 2155 Analysis Dr., P.O. Box 173485, Bozeman, MT, 59717; Tel: (406) 994-6077; Fax: (406) 994-4747. Primary work was conducted in the Department of Family Medicine and Community Health, School of Medicine and Public Health, University of Wisconsin, Madison, WI. List of abbreviations: AI, American Indian; AI/AN, American Indian/Alaska Native; BMI, body mass index; FNPA, Family Nutrition and Physical Activity screener; F/V, fruits/vegetables; HCSF, Healthy Children, Strong Families; HCSF2, Healthy Children, Strong Families 2; MVPA, moderate-to-vigorous physical activity; PSS, Perceived Stress Scale; SSB, sugar sweetened beverage Running Title: HCSF2 improved healthy behaviors in AI families D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 Financial Support: This work was funded by NIH NHLBI R01HL114912 to A.K.A. The trial is registered at ClinicalTrials.gov (NCT01776255). Conflict of Interest and Funding Disclosure: None of the authors have conflicts of interest to report. Abstract Background: American Indian (AI) families experience disproportionate risk for obesity due to complex reasons, including poverty, historic trauma, rural isolation or urban loss of community connections, lack of access to healthy foods and physical activity opportunities, and high stress. Home-based obesity prevention interventions are lacking for these families. Objective: Healthy Children, Strong Families 2 (HCSF2) was a randomized controlled trial of a healthy lifestyle promotion/obesity prevention intervention for American Indian families. Methods: Four hundred and fifty dyads consisting of an adult primary caregiver and a two- to five-year- old child from five AI communities were randomly assigned to a monthly mailed healthy lifestyle intervention toolkit (Wellness Journey) with social support or to a child safety control toolkit (Safety Journey) for one year. The Wellness Journey toolkit targeted increased fruit/vegetable intake, increased physical activity, improved sleep, decreased added sugar intake, decreased screen time, and improved stress management (adults only). Anthropometrics were collected, and health behaviors were assessed via survey at baseline and at the end of Year 1. Adults completed surveys for themselves and the participating child. Repeated measures analysis of variance was used to assess change over the intervention period. Results: Significant improvements in adult and child healthy diet patterns, adult fruit/vegetable intake, adult moderate-to-vigorous physical activity, home nutrition environment, and adult self-efficacy for health behavior change were observed in Wellness Journey compared to Safety Journey families. No changes were observed in adult body mass index (BMI), child BMI z-score, adult stress measures, adult/child sleep, adult/child screen time, or child physical activity. Qualitative feedback suggests the intervention was extremely well-received by the families and our community partners across five participating sites. D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 Conclusions: This multi-site community-engaged intervention addressed key gaps regarding family home-based approaches for early obesity prevention in AI communities and showed several significant improvements in health behaviors. Multiple communities are working to sustain intervention efforts. Keywords: childhood obesity, American Indian, nutrition, physical activity, stress, sleep, prevention, family-based intervention Introduction American Indians (AI) experience severe health inequities, including high rates of cardiovascular disease, type 2 diabetes, and cancer, that are due in part to high rates of obesity (1, 2). Moreover, evidence suggests disparities in obesity begin early in life; AI preschool obesity rates are the highest nationally (21%) and continue to increase despite decreases observed for other population groups (3, 4). As with other minority communities, the reasons for these disparities are complex and include poverty, racism, historic trauma, rural isolation or urban loss of community connections, insufficient health care services, lack of access to healthy foods and physical activity opportunities, and high stress levels (5-7). AI children also have approximately twice the levels of food insecurity and type 2 diabetes relative to the averages for all US children of similar ages (8-10). Given the high rates of early childhood obesity, the early years represent a vital window for promoting healthy habits. Diet and physical activity are commonly identified as a key obesity prevention targets (11), but other factors, such as sleep and stress, have been implicated in increasing the likelihood of weight gain (12, 13). In fact, both maternal stress and child-level stress are associated with an increased likelihood of overweight and obesity in children (13, 14). However, coping responses to stress can be modified to diminish the potential negative effect (15-17), suggesting stress should be considered when designing obesity interventions (9). With regards to sleep, short sleep duration has been associated with obesity in people of all ages, with evidence strongest for children and young adults (18). For young children, the majority of decisions regarding food, activity, and sleep behaviors are significantly influenced by parents/caregivers, highlighting the importance of the family context in understanding and D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 mitigating obesity risk (19, 20). However, recent reviews of best practices in obesity prevention showed a limited number of interventions in the home environment (21-24), with very few interventions conducted in American Indian communities (25) or targeting children <5 years of age (26). In addition, neither stress nor sleep has been addressed in interventions for AI families with young children. Despite facing significant health disparities, AI communities are reviving traditional culture and language, and many AI community-based health programs seek to improve the health of their people by utilizing this resiliency. This interest in and commitment to community health and well-being enabled the research team to successfully partner with multiple AI communities in Wisconsin to test the efficacy of the Healthy Children, Strong Families (HCSF) toolkit, which addressed diet and physical activity targets, to prevent obesity and improve related health behaviors within the family context (27). Based on positive results in HCSF and to address the gap in home-based interventions targeting a wider range of obesity risk factors (28), the HCSF intervention was expanded to also address stress and sleep and to include new social support mechanisms to support families to make healthy behavior changes (29, 30). The objective of the Healthy Children, Strong Families 2 (HCSF2) randomized controlled trial was to test the efficacy of the expanded intervention to mitigate obesity risk in both urban and rural American Indian families. The research team partnered nationally with four reservations and one urban site serving AI patients to conduct this trial; sites were selected to include a range of geographic conditions (extreme rural to urban) from among communities with whom the research team had working relationships. An urban site was selected because although a majority of people who identify as AI report living outside of reservations, most federal-level surveys and smaller obesity prevention studies that include AI participants are reservation-based. Consequently, little is known about obesity risk for AI families in urban areas (31). We hypothesized the HCSF2 intervention would improve obesity-associated health behaviors and improve or maintain weight status in American Indian children and adults compared to a child safety focused toolkit control. This paper presents results after Year 1 of the HCSF2 randomized controlled trial. The trial is registered at ClinicalTrials.gov (NCT01776255). D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 Methods Design HCSF2 employed a modified cross-over design whereby adult/child dyads were randomized to an obesity prevention intervention (Wellness Journey) or an active control group focused on child safety (Safety Journey). The study design was described in full previously (29) and briefly here. A sample size of 450 dyads was determined based on changes in adult body mass index (BMI) and child BMI z-score (primary outcomes) to detect an effect size of 0.28 at a significance level of 0.05 with power 0.76–0.81, assuming 10%–20% dropout at Year 1 (27, 29). Stratified randomization by study site was used to balance study arms on child weight status (≥85th BMI percentile vs. <85th percentile) based on child height and weight obtained at the pre-enrollment visit using the randomization module of the REDCap database application (32). Randomization was conducted by a centralized study coordinator after baseline enrollment data were collected by local site coordinators at each study site. Site coordinators were not blinded to study arm for the post-intervention/Year 1 data collection due to in-person delivery of intervention Lesson 1 and administration of the Wellness Journey Facebook group. Data input and analysis were conducted by study personnel who were blinded to group assignment. After Year 1, dyads switched arms so that all families received both Journeys for one year. Dyads randomized to the Wellness Journey first continued to receive social support throughout Year 2; therefore, HCSF2 was not a true cross-over design. This decision was made with community based participatory research principles and was based upon community desires to ensure all families had access to the Wellness Journey intervention. For this reason, we report the results of Year 1 as the randomized controlled trial of the Wellness Journey versus the Safety Journey. Participants Staggered recruitment occurred at four tribal reservations (one in the northeastern US, two in the upper Midwest and one in the northern mountain region) and in one southwestern urban clinic serving a primarily AI population. Primary recruitment strategies included informational flyers sent home with D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 children in tribal-based child care/Head Start and active outreach by community-based site coordinators. Exclusion criteria were minimal to support the inclusiveness valued by the participating communities and the underlying philosophy of our research program. Inclusion criteria were an adult enrolling themselves and a dependent child between the ages of 2 and 5 years and a working cell phone to receive text messages. It was not required that the adult or child be American Indian or that the adult be the biological parent of the child. All study protocols were approved by the University of Wisconsin Institutional Review Board, local tribal councils, and tribal Institutional Review Boards, when requested. Adults provided written consent for themselves and their participating child. Intervention The Wellness Journey toolkit consisted of 12 monthly mailed healthy lifestyle lessons, items, and children‟s books addressing six intervention targets: increase fruit and vegetable consumption, decrease sugar consumption, increase physical activity, decrease screen time, improve sleep habits, and decrease stress (adult only). Each monthly toolkit included (1) printed educational lessons with information and suggestions for activities, (2) supportive items (e.g., measuring cups, recipes, pedometers, games), and (3) a children‟s book relating to one of the intervention targets to foster family interaction. Wellness Journey adult participants were supported by social media engagement via two weekly text messages and invitation to an optional, site-specific Facebook group where intervention targets were discussed. The Safety Journey consisted of 12 monthly mailed safety newsletters and related materials (e.g., safety reflectors for biking, cabinet safety locks). Both the original HCSF and expanded HCSF2 toolkits were developed and tested collaboratively with Extension specialists, content experts, tribal wellness staff, and AI community members, with specific attention to literacy level and to the cultural and social relevance for different tribes of the lesson materials, activities, goals, and incentives (29, 33). Outcomes Physical measurements (height, weight, and waist circumference) were collected according to standardized protocols and converted to age- and sex-specific body mass index (BMI) percentiles for children (34) and adult BMI for adults (35). Adult participants who were pregnant were excluded from D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 weight and waist circumference measures and provided self-reported pre-pregnancy weight (n=27). Health behaviors related to the intervention targets were measured: participating adults completed a packet of validated self-report surveys that assessed adult and child diet (36, 37) and physical activity (38, 39), family home environment (40) [with two validated questions on food security (41)], adult stress and other psychosocial measures (42, 43), and adult cultural identity (44). The research group created or adapted additional surveys to assess adult and child health history, sleep, screen time use, social media use (adult only), and readiness to change health behaviors (adult only). All measures (i.e., anthropometrics and surveys) were completed at baseline and after Year 1. Adults completed all surveys for themselves and the participating child. Families were provided a $50 gift card for each data collection visit. In addition, each participating family was mailed a letter summarizing the baseline results for themselves and their child along with information for follow-up, if desired. Qualitative participant feedback We employed two methods to assess acceptability and utility of the intervention among participating families. First, two open ended questions, “What have you and your child found to be most useful/least useful about the mailed Wellness Journey toolkits?” were asked of participating adults upon study completion. Second, we conducted five focus group sessions after study completion of approximately 5-7 adult participants per session (n=15 total from two rural sites, n=20 total from the urban site) regarding their experience with the intervention and supports and barriers to making healthy lifestyle changes. A focus group discussion guide was developed prior to the sessions to ensure consistency, and sessions were moderated by community site coordinators and study staff. Discussions were transcribed by a third-party and analyzed for major themes using an inductive approach (45). Statistical analyses Analysis of continuous variables was conducted using repeated measures analysis of variance with study arm (Wellness Journey versus Safety Journey) as the between subjects factor. Because of the large number of diet variables produced by the screener, the multivariate technique of principal component analysis was used to determine optimal groupings of variables to maximize the amount of variance D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 explained through such combinations (46, 47). Additional weighting using oblique rotation was applied to maximize the correlation of components with their constituent variables and to reduce the correlation with non-constituent variables. The method of Trude et al. (48) was used to construct the dietary pattern scales. All analyses were performed using SPSS v.23 (IBM Statistics) with significance set at p<0.05. Results During recruitment, 659 adults completed interest forms and 25 failed the initial screen, resulting in 502 eligible participants. Of these, we were unable to collect complete baseline data on 52 families, resulting in 450 adult/child dyads enrolled (100% of recruitment goal). Baseline data were collected between February 2013 and April 2015 and Year 1 data between February 2015 and April 2017. At the end of Year 1, dropout was 16.4% and did not differ by study arm. Dropouts refer to families who failed to complete the Year 1 data collection visit, who voluntarily withdrew from the study, or for whom mailed materials were returned; dropout was attributed primarily to unavoidable circumstances (e.g., relocation, incarceration, loss of custody). At baseline, mean child age was 3.3±1.1 years (50.2% female), with 39.8% overweight/obese; mean adult age was 31.4±8.4 years (94.7 % female), with 82% overweight/obese. Among all families, 57% reported family income <$20,000/year, with a high prevalence of reported household food insecurity (61%). Baseline demographics by study arm, Safety Journey vs. Wellness Journey, are shown in Table 1. Baseline variables and predictors of weight status have been reported in detail elsewhere (49) and did not differ between study arms at baseline. Healthy Lifestyle Outcomes Diet. At baseline, six patterns were determined for adults („fast food‟, „healthy food‟, „sweets‟, „cereal and milk‟, „animal protein‟, „other‟) that explained 82.5% of the model variance and 4 for children („non- healthy foods‟, „healthy foods‟, „non-healthy beverages‟, „healthy beverages‟) that explained 56% of the model variance. Comparable categories were significantly correlated between adult and child data (e.g., adult „fast food‟ with child „non-healthy foods‟, r=0.519, p<0.05; adult „healthy foods‟ with child „healthy foods‟, r=0.543, p<0.05). When the food patterns were combined to create an overall healthy diet pattern D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 variable, the Wellness Journey group had a significantly greater post-intervention improvement compared to the Safety Journey group for both adults (-0.02±0.76 to 0.20±0.68 [change of +0.21] for Wellness Journey and 0.05±0.65 to 0.04±0.62 [change of -0.005] for Safety Journey at baseline and Year 1, respectively; p=0.009) and children (-0.01±0.72 to 0.15±0.70 [change of +0.16] for Wellness Journey and 0.05±0.63 to 0.05±0.07 [change of +0.007] for Safety Journey at baseline and Year 1, respectively; p=0.008). Adults in the Wellness Journey reported a significant increase in servings/week (seven days) of fruits and vegetables (F/V) from 16.2±12.0 to 18.5±14.0 compared to baseline and Year 1 values for Safety Journey adults of 16.1±13.4 and 14.9±10.2 (p=0.007). There were no significant intervention effects for adult sugar sweetened beverage (SSB) intake. Wellness Journey children had a non-significant mean increase in F/V servings/week of 1.6 (18.0±12.1 to 19.6±13.3) vs. 0.6 (15.4±10.4 to 16.0±9.8) in Safety Journey children as well as a decrease of 1.2 servings/week of SSB (9.8±10.4 to 8.6±8.9) vs. no change in Safety Journey children (8.0±8.5 to 8.1±8.5). Physical activity, screen time, and sleep. Adults in the Wellness Journey self-reported a significant increase in 15-minute bouts of moderate/vigorous physical activity compared to adults in the Safety Journey (3.60±3.79 to 4.91±3.78 for Wellness Journey and 3.85±3.65 to 3.90±3.51 for Safety Journey at baseline and Year 1, respectively; p<0.001). There were no significant differences between study arms for child physical activity, adult/child screen time, or adult/child minutes of weekday and weekend sleep (Table 2). Child total sleep time among all participants was significantly below national norms (50). Weight status. There was no difference between Wellness or Safety Journey groups for adult BMI or child BMI z-score after Year 1. Values are listed in Table 2. However, child BMI z-score was stable with a trend towards a decrease in the Wellness Journey group (non-significant). In addition, food secure Wellness children had a loss of -0.07 BMI z-score while food insecure Wellness children had a gain of 0.12 BMI z-score) (non-significant). D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 Psychosocial factors (Table 3). Wellness Journey adults reported significant increases in readiness to change the following health behaviors compared to Safety Journey adults: increase physical activity (p=0.019), increase fruit and vegetable consumption (p=0.037), decrease screen time (p<0.001), and obtain adequate sleep (p=0.012), with a trend for significance for decrease added sugar (p=0.074). Wellness Journey families also had a trend toward significant improvement in the family environment for nutrition-related behaviors compared to Safety Journey families (p=.057) as assessed by the Family Nutrition and Physical Activity Screening Tool (FNPA) (40). Changes in self-rated quality of life and perceived stress, assessed in adults only, were not significantly different between Wellness Journey and Safety Journey adults. Qualitative Outcomes: Family Response to Intervention. Results from five focus groups and from written participant feedback forms indicated a high level of satisfaction with the intervention, including the Facebook and text message components. Wellness Journey families reported spending more time together as a family reading and doing activities after the HCSF2 intervention. Parents reported they appreciated receiving the materials by mail because it got their child excited to receive a package, which facilitated child engagement with the lessons. Moreover, mailed lessons allowed them to learn at their own pace without requiring them to attend a meeting or class. Getting a book with every lesson was especially appreciated, and parents reported spending more time reading with their child, particularly on topics related to health. Participants also related high satisfaction with the Safety Journey and may not have distinguished between the two Journeys. Saturation in themes was reached with the five focus groups. When asked at the end of Year 1 how long they spent reviewing the lessons each month, 38.9% of participants said 30 minutes or more, 40.9% said 15-30 minutes, and 20.2% said 5-15 minutes. When asked how much time they spent doing the activities described in the lessons, 49.0% said 30 minutes or more, 34.8% said 15-30 minutes, 15.2% said 5-15 minutes, and 1% said they did not do the activities. An open-ended question on the feedback form asked if there was anything prevented healthier choices; 41% D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 of participants who responded said “time”; 18% said “money”; and 41% identified some other issue, such as physical limitations, uncooperative family members, old habits, and lack of willpower. Multiple participants requested opportunities to connect with other families participating in HCSF2 in a “real world” setting, highlighting that social and environmental supports are needed to reinforce family learning for making and sustaining healthy lifestyle behaviors. With social media, there was variability in engagement among sites, with the urban site being much more engaged than the rural sites; this difference may be due to Facebook bullying that community partners have anecdotally reported to occur in smaller communities. Participants indicated the most useful posts were about recipes and expressed a desire to learn more about how to cook with fruits and vegetables and how to preserve food (freezing/canning). Text messages were consistently reported to be helpful as a reminder to stay on track, and participants also liked the motivational messaging. Table 4 presents representative participant focus group comments by theme. Discussion Healthy Children, Strong Families 2 is the first trial of a family-based healthy lifestyle intervention for AI families with young children and represents one of the largest obesity prevention intervention trials among AI participants. Our approach was novel for the use of a home-delivered healthy lifestyle intervention aimed at both adults and young children, which promoted family interaction and early health literacy and was highly acceptable to families. Moreover, HCSF2 is the first obesity prevention trial to include both urban and rural AI families. This factor is significant, as we previously demonstrated significant differences between urban and rural families in this sample with regards to food insecurity, diet patterns, and obesity (51). The significant changes in some key health behaviors and readiness to change these behaviors seen for families after participating in the HCSF2 Wellness Journey are encouraging and are similar to other low-intensity interventions (52, 53). D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 After the intervention, we observed significant improvements in adult and child diet patterns, adult fruit/vegetable intake, and in the home nutrition environment. Non-significant but important improvements in child F/V intake and SSB consumption also were observed. For diet variables, we used a method previously employed in AI communities (48) to look at overall diet patterns among participants. The construction of dietary patterns using this method depends on the foods included in the model, and the resulting value is more difficult to interpret than standardized measures, such as the Healthy Eating Index (54). For this reason, data on specific food groups that were targets of the intervention (i.e., fruits, vegetables, SSBs) also are reported in addition to the diet pattern scores, as these specific foods are easier to interpret in servings per week. Given the potentially small biological significance of the observed changes (e.g., increase of approximately one third of a serving of fruits and vegetables per day for Wellness adults), the principal component analysis method was employed to provide indication of change in broader dietary patterns that may not be captured at the individual food or food group level. In addition to health behavior changes, we observed significant improvements in readiness to change multiple health behaviors for the Wellness Journey families. Previous work suggests this variable is associated with significant levels of subsequent behavior change (55-57). Families were highly engaged with the HCSF2 intervention materials as evidenced by comments in both the end of study surveys and the focus groups. This high level of engagement may partially explain the increase in both health behaviors and in the self-efficacy for health behavior change. Similar to HCSF1, families reported spending more time together as a result of the intervention (27). This significant increase in readiness to change health behaviors among families receiving the intervention may indicate that additional behavior change could be expected beyond the Year 1 timeline reported here. Maintenance of weight status (i.e., prevention of weight gain or weight status crossing) or weight loss were hypothesized outcomes as a result of study participation. Importantly, child weight status was stable during Year 1 of the intervention, with a trend for a decreased BMI z-score in Wellness Journey children. Of note, HCSF2 was not designed as a weight loss study: current weight status was not an inclusion/exclusion criterion for the study, and 60% of the children were within normal weight range at D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 baseline. The Wellness Journey therefore emphasized health and wellness rather than focusing on weight. Moreover, recent evidence suggests body mass index may not be a sensitive indicator of adiposity in overweight and obese children (58) or an appropriate marker of changes in body fat over time (59). Other methods, such as dual x-ray absorptiometry, may be more accurate but are difficult to implement in community-based research studies due to cost, participant burden, and geographic distribution in study sites. Less expensive methods, including skinfold thickness and waist circumference, are subject to large error in pediatric populations (59, 60). Biomarkers of both body composition and diet intake that can be used within community research settings are needed to overcome limitations of commonly used approaches. A home-based obesity prevention intervention for Latino preschoolers reported by Taverno Ross and colleagues reported very similar findings to ours of no changes in adult or child weight parameters despite significant improvements in adult and child health behaviors (52). Another study, the CHILE multi-level obesity prevention trial targeting preschool American Indian and Hispanic children in Head Start settings, found no changes in BMI z-score after a two-year intervention (61); although this study included a family engagement component, adult-level variables were not assessed. Other studies have shown reductions in BMI percentile in older children after a family-focused obesity intervention (62). Of note, the US Preventive Services Task Force recommends a total of 26 or more high intensity clinical contact hours over a period of 2 to 12 months for successful weight loss to occur for children (63). In light of this recommendation, the behavior changes seen in HCSF2 are reasonable given the intervention intensity as well as the severe resource constraints of many AI families and communities that might prevent access to high intensity clinical contact. Specific to resources constraints, our findings of high food insecurity and poverty among HCSF2 families reflect the difficulty of making healthy lifestyle changes within significantly economically challenged communities. In particular, food insecurity was associated with less optimal diet patterns in HCSF2 families (51) and in other populations (64) and in other health-related behavior changes in response to the intervention. Other issues include the importance of community support for healthy behavior change D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 among families, as multi-component interventions (65-67) have been shown to be more successful than individual interventions. A review of three AI multi-level environmental interventions revealed that AI chronic disease prevention strategies appeared to be successful when they functioned at multiple levels, including environmental and individual levels, and when local stakeholders were engaged (68). Although not specific to AI communities, another recent study indicated that communities with high levels of community-based programs and policies supporting healthy behaviors were associated with lower BMI in children (69). Given the scope of this HCSF2 trial in five states, it was not possible to implement both home- and community-based approaches during the intervention. However, this represents an important future direction. HCSF2 revealed promising healthy behavior changes, improved caregiver readiness to change, and created a highly acceptable toolkit for positive family engagement. This work adds to the scant literature regarding (1) home- and family-based obesity prevention interventions for (2) young children in (3) American Indian communities. Moreover, the inclusion of both adults and children, the geographic distribution of study sites from extreme rural to urban, the comprehensive intervention targets, and the high engagement of community partners throughout all phases of the project and of families during the intervention represent significant strengths of HCSF2. Limitations include the lack of objective measures of health behavior (e.g., accelerometry rather than physical activity surveys), and possible contamination from implementing a randomized trial in small communities. Other limitations include potential participant bias, as the local site coordinator often was personally known to the rural participants, and response bias, as survey packets were sometimes completed by participants in the presence of the site coordinator. However, >60% of the total number of families with children in the target age range were recruited from some of the rural communities, which may have minimized potential selection bias. Future interventions for AI families with young children need to address both individual- and community- level support for change, particularly in addressing food insecurity and the role of historic and current trauma. Most of the participating communities are continuing to work on childhood obesity prevention, with one community implementing individual sleep and healthy behavior interventions; another working D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 on community-wide policy, system, and environmental changes to support families; and the urban site working to sustain the program by continuing to provide intervention materials (e.g., toolkit lessons, books) in the clinic. The intervention team also is actively working on creative ways to disseminate the materials to communities to support additional families in their journey towards health based on the promising findings of the HCSF2 trial. Acknowledgements We gratefully acknowledge all the communities and families who participated in the design, development, and implementation of the Healthy Children, Strong Families 2 intervention. We also are indebted to the site coordinators who worked so hard to recruit and retain participants. Author’s Contributions A.K.A., R.J.P., K.A.C., and T.A. designed the research; A.K.A., K.A.C., and E.T. conducted the research; R.J.P. and K.K. analyzed the data; A.K.A. and E.T. wrote the paper; A.K.A. had primary responsibility for the final content. All authors have read and approved the final manuscript. References 1. Story M, Evans M, Fabsitz RR, Clay TE, Rock BH, Broussard B. Epidemic of obesity in American Indian communities and the need for childhood obesity-prevention programs. Am J Clin Nutr 1999;69(4):747S-54. 2. Hutchinson RN, Shin S. Systematic review of health disparities for cardiovascular diseases and associated factors among American Indian and Alaska Native populations. PLoS One 2014;9(1):e80973. doi: 10.1371/journal.pone.0080973. 3. Story M, Stevens J, Himes J, Stone E, Holy Rock B, Ethelbah B, Davis S. Obesity in American- Indian children: prevalence, consequences, and prevention. Prev Med 2003;37(Supplement 1):S3- S12. 4. Anderson SE, Whitaker RC. Prevalence of Obesity Among US Preschool Children in Different Racial and Ethnic Groups. Arch Pediatr Adolesc Med 2009;163(4):344-8. doi: 10.1001/archpediatrics.2009.18. D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 5. Castor ML, Smyser MS, Taualii MM, Park AN, Lawson SA, Forquera RA. A nationwide population-based study identifying health disparities between American Indians/Alaska Natives and the general populations living in select urban counties. American journal of public health 2006;96(8):1478-84. doi: 10.2105/AJPH.2004.053942. 6. Lillie-Blanton M, Roubideaux Y. Understanding and addressing the health care needs of American Indians and Alaska Natives. Am J Public Health 2005;95(5):759-61. 7. Cobb N, Espey D, King J. Health Behaviors and Risk Factors Among American Indians and Alaska Natives, 2000–2010. American journal of public health 2014;104(Suppl 3):S481-S9. doi: 10.2105/AJPH.2014.301879. 8. Gordon A, Oddo V. Adressing Child Hunger and Obesity in Indian Country: Report to Congress (Final Report). Mathematica Policy Research, 2012. 9. Halpern P. Obesity and American Indians/Alaska Natives United States Department of Health and Human Services. Office of the Assistant Secretary for Planning and Evaluation, 2007. 10. Schell LM, Gallo MV. Overweight and obesity among North American Indian infants, children, and youth. American journal of human biology : the official journal of the Human Biology Council 2012;24(3):302-13. doi: 10.1002/ajhb.22257. 11. Institute of Medicine (2012) Accelerating Progress in Obesity PRevention: Solving the Weight of the Nation. Recommendations. . 12. Yoong SL, Chai LK, Williams CM, Wiggers J, Finch M, Wolfenden L. Systematic review and meta-analysis of interventions targeting sleep and their impact on child body mass index, diet, and physical activity. Obesity (Silver Spring) 2016;24(5):1140-7. doi: 10.1002/oby.21459. 13. Gundersen C, Mahatmya D, Garasky S, Lohman B. Linking psychosocial stressors and childhood obesity. Obes Rev 2011;12(5):e54-63. doi: 10.1111/j.1467-789X.2010.00813.x. 14. Tate EB, Wood W, Liao Y, Dunton GF. Do stressed mothers have heavier children? A meta- analysis on the relationship between maternal stress and child body mass index. Obes Rev 2015;16(5):351-61. doi: 10.1111/obr.12262. D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 15. Compas BE. Coping with stress during childhood and adolescence. Psychol Bull 1987;101(3):393-403. 16. Compas BE, Howell DC, Phares V, Williams RA, CT G. Risk factors for emotional/behavioral problems in young adolescents: a prospective analysis of adolescent and parental stress and symptoms. J Consult Clin Psychol 1989;57(6):732-40. 17. Rudolph KD, Hammen C. Age and gender as determinants of stress exposure, generation, and reactions in youngsters: a transactional perspective. Child Dev 1999;70(3):660-77. 18. Cappuccio F, Taggart F, Kandala N, Currie A, Pelie E, Stranges S, Miller M. Meta-analysis of short sleep duration and obesity in children and adults. Sleep 2008;31(5):619-26. 19. Golan M, Crow S. Parents are key players in the prevention and treatment of weight-related problems. Nutr Rev 2004;62(1):39-50. 20. Anderson SE, Whitaker RC. Household routines and obesity in US preschool-aged children. Pediatrics 2010;125(3):420-8. doi: 10.1542/peds.2009-0417. 21. Flynn MAT, McNeil DA, Maloff B, Mutasingwa D, Wu M, Ford C, Tough SC. Reducing obesity and related chronic disease risk in children and youth: a synthesis of evidence with 'best practice' recommendations. Obesity research 2006;7 ((Suppl. 1)):7-66. 22. Skouteris H, McCabe M, Swinburn B, Newgreen V, Sacher P, Chadwick P. Parental influence and obesity prevention in pre-schoolers: a systematic review of interventions. Obes Rev 2010;12(5):315-28. 23. Monasta L, Batty GD, Macaluso A, Ronfani L, Lutje V, Bavcar A, van Lenthe FJ, Brug J, Cattaneo A. Interventions for the prevention of overweight and obesity in preschool children: a systematic review of randomized controlled trials. Obes Rev 2011;12(5):e107-18. doi: 10.1111/j.1467-789X.2010.00774.x. 24. Showell NN, Fawole O, Segal J, Wilson RF, Cheskin LJ, Bleich SN, Wu Y, Lau B, Wang Y. A systematic review of home-based childhood obesity prevention studies. Pediatrics 2013;132(1):e193-200. doi: 10.1542/peds.2013-0786. D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 25. Laws R, Campbell KJ, van der Pligt P, Russell G, Ball K, Lynch J, Crawford D, Taylor R, Askew D, Denney-Wilson E. The impact of interventions to prevent obesity or improve obesity related behaviours in children (0-5 years) from socioeconomically disadvantaged and/or indigenous families: a systematic review. BMC Public Health 2014;14:779. doi: 10.1186/1471-2458-14-779. 26. Nigg CR, Anwar MM, Braun KL, Mercado J, Fialkowski MK. A Review of Promising Multicomponent Environmental Child Obesity Prevention Intervention Strategies by the Children‟s Healthy Living Program. J Env Health 2016;79.3:18-26. 27. Tomayko EJ, Prince RJ, Cronin KA, Adams AK. The Healthy Children, Strong Families intervention promotes improvements in nutrition, activity and body weight in American Indian families with young children. Public Health Nutr 2016;19(15):2850-9. doi: 10.1017/S1368980016001014. 28. Ash T, Agaronov A, Young T, Aftosmes-Tobio A, Davison KK. Family-based childhood obesity prevention interventions: a systematic review and quantitative content analysis. Int J Behav Nutr Phys Act 2017;14(1):113. doi: 10.1186/s12966-017-0571-2. 29. Tomayko EJ, Prince RJ, Cronin KA, Parker T, Kim K, Grant VM, Sheche JN, Adams AK. Healthy Children, Strong Families 2: A randomized controlled trial of a healthy lifestyle intervention for American Indian families designed using community-based approaches. Clin Trials 2017;14(2):152-61. doi: doi:10.1177/1740774516685699. 30. Berns RM, Tomayko EJ, Cronin KA, Prince RJ, Parker T, Adams AK. Development of a Culturally Informed Child Safety Curriculum for American Indian Families. The Journal of Primary Prevention 2017;38(1):195-205. doi: 10.1007/s10935-016-0459-y. 31. Tomayko EJ, Weinert BA, Godfrey L, Adams AK, Hanrahan LP. Using Electronic Health Records to Examine Disease Risk in Small Populations: Obesity Among American Indian Children, Wisconsin, 2007-2012. Prev Chronic Dis 2016;13:E29. doi: 10.5888/pcd13.150479. 32. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 translational research informatics support. Journal of biomedical informatics 2009;42(2):377-81. doi: 10.1016/j.jbi.2008.08.010. 33. LaRowe TL, Wubben DP, Cronin KA, Vannatter SM, Adams AK. Development of a culturally appropriate, home-based nutrition and physical activity curriculum for Wisconsin American Indian families. Prev Chronic Dis 2007;4(4):A109. 34. Kuczmarski RJ, Ogden CL, Grummer LM, et al. CDC growth charts: United States, Advance data from vital and health statistics. Health Statistics 2000;314:2-27. 35. CDC. Division of Nutrition, Physical Activity, and Obesity, National Center for Chronic Disease Prevention and Health Promotion. Defining Adult Overweight and Obesity. https://www.cdc.gov/obesity/adult/defining.html. Accessed 7/24/2017. 36. Katula JA, Vitolins MZ, Morgan TM, Lawlor MS, Blackwell CS, Isom SP, Pedley CF, Goff DC, Jr. The Healthy Living Partnerships to Prevent Diabetes study: 2-year outcomes of a randomized controlled trial. Am J Prev Med 2013;44(4 Suppl 4):S324-32. doi: 10.1016/j.amepre.2012.12.015. 37. Eaton DK, Olsen EO, Brener ND, Scanlon KS, Kim SA, Demissie Z, Yaroch AL. A comparison of fruit and vegetable intake estimates from three survey question sets to estimates from 24-hour dietary recall interviews. J Acad Nutr Diet 2013;113(9):1165-74. doi: 10.1016/j.jand.2013.05.013. 38. Amireault S, Godin G. The Godin-Shephard leisure-time physical activity questionnaire: validity evidence supporting its use for classifying healthy adults into active and insufficiently active categories. Perceptual and motor skills 2015;120(2):604-22. doi: 10.2466/03.27.PMS.120v19x7. 39. Janz KF, Broffitt B, Levy SM. Validation evidence for the Netherlands physical activity questionnaire for young children: the Iowa bone development study. Res Q Exerc Sport 2005;76(3):363-9. doi: 10.1080/02701367.2005.10599308. 40. Ihmels MA, Welk GJ, Eisenmann JC, Nusser SM. Development and preliminary validation of a Family Nutrition and Physical Activity (FNPA) screening tool. Int J Behav Nutr Phys Act 2009;6:14. doi: 10.1186/1479-5868-6-14. D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 41. Hager ER, Quigg AM, Black MM, Coleman SM, Heeren T, Rose-Jacobs R, Cook JT, de Cuba SA, Casey PH, Chilton M, et al. Development and validity of a 2-item screen to identify families at risk for food insecurity. Pediatrics 2010;126(1):e26-32. doi: 10.1542/peds.2009-3146. 42. Latimer L, Walker LO, Kim S, Pasch KE, Sterling BS. Self-efficacy Scale for Weight Loss among Multi-ethnic Women of Lower Income: A Psychometric Evaluation. Journal of Nutrition Education and Behavior 2011;43(4):279-83. doi: 10.1016/j.jneb.2010.09.007. 43. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav 1983;24(4):385-96. 44. Goins RT, Spencer SM, McGuire LC, Goldberg J, Wen Y, Henderson JA. Adult caregiving among American Indians: the role of cultural factors. The Gerontologist 2011;51(3):310-20. 45. Silverman D. Data Analysis. Edtion ed. Interpreting qualitative data: A guide to the principles of qualitative research. London: SAGE Publications, 2011. 46. Previdelli ÁN, de Andrade SC, Fisberg RM, Marchioni DM. Using Two Different Approaches to Assess Dietary Patterns: Hypothesis-Driven and Data-Driven Analysis. Nutrients 2016;8(10):593. doi: 10.3390/nu8100593. 47. Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol 2002;13(1):3-9. 48. Trude AC, Kharmats A, Jock B, Liu D, Lee K, Martins PA, Pardilla M, Swartz J, Gittelsohn J. Patterns of Food Consumption are Associated with Obesity, Self-Reported Diabetes and Cardiovascular Disease in Five American Indian Communities. Ecol Food Nutr 2015;54(5):437- 54. doi: 10.1080/03670244.2014.922070. 49. Adams AK, Tomayko EJ, K AC, R JP, Kim K, Carmichael L, Parker T. Predictors of Overweight and Obesity in American Indian Families With Young Children. J Nutr Educ Behav 2018. doi: 10.1016/j.jneb.2018.07.011. D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 50. Ingram DG, Irish LA, Tomayko EJ, Prince RJ, Cronin KA, Parker T, Kim K, Carmichael L, Grant VM, Sheche JN, et al. Overnight sleep duration and obesity in 2-5 year-old American Indian children. Pediatr Obes 2018;13(7):406-12. doi: 10.1111/ijpo.12281. 51. Tomayko EJ, Mosso KL, Cronin KA, Carmichael L, Kim K, Parker T, Yaroch AL, Adams AK. Household food insecurity and dietary patterns in rural and urban American Indian families with young children. BMC Public Health 2017;17(1):611. doi: 10.1186/s12889-017-4498-y. 52. Taverno Ross SE, Barone Gibbs B, Documet PI, Pate RR. ANDALE Pittsburgh: results of a promotora-led, home-based intervention to promote a healthy weight in Latino preschool children. BMC Public Health 2018;18(1):360. doi: 10.1186/s12889-018-5266-3. 53. Hodder RK, O'Brien KM, Stacey FG, Wyse RJ, Clinton-McHarg T, Tzelepis F, James EL, Bartlem KM, Nathan NK, Sutherland R, et al. Interventions for increasing fruit and vegetable consumption in children aged five years and under. Cochrane Database Syst Rev 2018;5:CD008552. doi: 10.1002/14651858.CD008552.pub5. 54. Guenther PM, Casavale KO, Reedy J, Kirkpatrick SI, Hiza HA, Kuczynski KJ, Kahle LL, Krebs- Smith SM. Update of the Healthy Eating Index: HEI-2010. J Acad Nutr Diet 2013;113(4):569-80. doi: 10.1016/j.jand.2012.12.016. 55. De Nooijer J, Van Assema P, De Vet E, Brug J. How stable are stages of change for nutrition behaviors in the Netherlands? Health Promot Int 2005;20(1):27-32. doi: 10.1093/heapro/dah504. 56. Guess N, Vasantharajah L, Gulliford M, Viberti G, Gnudi L, Karalliedde J, Wijesuriya M. Improvements in stage of change correlate to changes in dietary intake and clinical outcomes in a 5-year lifestyle intervention in young high-risk Sri Lankans. Prev Med 2016;90:193-200. doi: 10.1016/j.ypmed.2016.07.011. 57. Mastellos N, Gunn LH, Felix LM, Car J, Majeed A. Transtheoretical model stages of change for dietary and physical exercise modification in weight loss management for overweight and obese adults. Cochrane Db Syst Rev 2014(2). doi: 10.1002/14651858.CD008066.pub3. D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 58. Vanderwall C, Randall Clark R, Eickhoff J, Carrel AL. BMI is a poor predictor of adiposity in young overweight and obese children. BMC Pediatr 2017;17(1):135. doi: 10.1186/s12887-017- 0891-z. 59. Vanderwall C, Eickhoff J, Randall Clark R, Carrel AL. BMI z-score in obese children is a poor predictor of adiposity changes over time. BMC Pediatr 2018;18(1):187. doi: 10.1186/s12887- 018-1160-5. 60. Sijtsma A, Bocca G, L'Abee C, Liem ET, Sauer PJ, Corpeleijn E. Waist-to-height ratio, waist circumference and BMI as indicators of percentage fat mass and cardiometabolic risk factors in children aged 3-7 years. Clinical nutrition 2014;33(2):311-5. doi: 10.1016/j.clnu.2013.05.010. 61. Davis SM, Myers OB, Cruz TH, Morshed AB, Canaca GF, Keane PC, O'Donald ER. CHILE: Outcomes of a group randomized controlled trial of an intervention to prevent obesity in preschool Hispanic and American Indian children. Prev Med 2016;89:162-8. doi: 10.1016/j.ypmed.2016.05.018. 62. Falbe J, Cadiz AA, Tantoco NK, Thompson HR, Madsen KA. Active and Healthy Families: A Randomized Controlled Trial of a Culturally Tailored Obesity Intervention for Latino Children. Acad Pediatr 2015;15(4):386-95. doi: 10.1016/j.acap.2015.02.004. 63. USPSTF. Screening for obesity in children and adolescents: US Preventive Services Task Force Recommendation Statement. JAMA 2017;317(23):2417-26. doi: 10.1001/jama.2017.6803. 64. Hanson KL, Connor LM. Food insecurity and dietary quality in US adults and children: a systematic review. Am J Clin Nutr 2014;100(2):684-92. doi: 10.3945/ajcn.114.084525. 65. Economos CD, Hyatt RR, Goldberg JP, Must A, Naumova EN, Collins JJ, Nelson ME. A community intervention reduces BMI z-score in children: Shape Up Somerville first year results. Obesity 2007;15(5):1325-36. doi: 10.1038/oby.2007.155. 66. Economos CD, Hyatt RR, Must A, Goldberg JP, Kuder J, Naumova EN, Collins JJ, Nelson ME. Shape Up Somerville two-year results: a community-based environmental change intervention D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 sustains weight reduction in children. Prev Med 2013;57(4):322-7. doi: 10.1016/j.ypmed.2013.06.001. 67. Gittelsohn J, Trude A. Environmental Interventions for Obesity and Chronic Disease Prevention. J Nutr Sci Vitaminol (Tokyo) 2015;61 Suppl:S15-6. doi: 10.3177/jnsv.61.S15. 68. Gittelsohn J, Rowan M. Preventing diabetes and obesity in American Indian communities: the potential of environmental interventions. Am J Clin Nutr 2011;93(5):1179S-83S. doi: 10.3945/ajcn.110.003509. 69. Strauss WJ, Nagaraja J, Landgraf AJ, Arteaga SS, Fawcett SB, Ritchie LD, John LV, Gregoriou M, Frongillo EA, Loria CM, et al. The longitudinal relationship between community programmes and policies to prevent childhood obesity and BMI in children: the Healthy Communities Study. Pediatr Obes 2018. doi: 10.1111/ijpo.12266. D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 Table 1. Selected participant demographics by study arm at baseline (Safety Journey versus Wellness Journey) Safety Journey (n=225 dyads) Wellness Journey (n=225 dyads) Adult age, years (mean±SD) 31.3±9.1 31.4±7.8 Child age, months (mean±SD) 44.1±13.2 45.9±12.8 Adult sex, female (n, %) 213, 94.7% 213, 94.7% Child sex, female (n, %) 111, 49.3% 115, 51.1% Adult education (n, %) High school equivalent or less 86, 38.2% 83, 36.9% Some college or Associate Degree 115, 51.1% 120, 53.3% College degree or post-graduate 24, 10.7% 22, 9.8% Family income (n, %) <$5,000 63, 28.4% 69, 31.7% $5,000-19,999 63, 28.4% 61, 28.0% $20,000-24,999 49, 22.1% 45, 20.6% >$35,000 47, 21.2% 43, 19.7% Adult Race (n, %) AI/AN 177, 78.7% 191, 84.9% White 42, 18.7% 35, 15.6% Other 6, 2.6% 5, 2.2% Adult Ethnicity, Hispanic/Latino (n, %) 30, 13.3% 21, 9.3% Child Race (n, %) AI/AN 190, 84.4% 200, 88.9% White 44, 19.6% 45, 20% Other 14, 6.2% 10, 4.4% Child Ethnicity, Hispanic/Latino (n, %) 46, 20.4% 34, 15.1% Adult BMI, kg/m 2 (mean±SD) 31.8±7.0 32.3±8.7 Child BMI percentile (mean±SD) 72.2±26.7 69.9±27.0 Table 2. Health behaviors at baseline and after Year 1 for Safety Journey and Wellness Journey Families Safety Journey, Baseline Safety Journey, Year 1 Wellness Journey, Baseline Wellness Journey, Year 1 p- value Adult MVPA, 15- minute bouts per week 3.85±3.65 3.90±3.51 3.60±3.79 4.91±3.78 0.001 Screen time, total min/day 173.52±157.6 157.61±157.2 198.91±209.8 161.88±165.2 0.305 Weekday sleep, hours 8.00±1.42 8.13±1.46 8.04±1.57 8.16±1.46 0.660 Weekend sleep, hours 8.66±1.50 8.49±1.48 8.48±1.62 8.54±1.54 0.181 SSB intake, servings/week 13.3±11.7 12.1±10.8 15.2±13.3 13.02±11.8 0.472 F/V intake, servings/week 16.1±13.4 14.9±10.2 16.2±12.0 18.5±14.0 0.007 BMI, kg/m 2 31.64±6.94 31.54±6.70 32.64±9.05 32.96±9.29 0.46 D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 Child Physical activity score 24.19±3.40 24.22±3.60 24.03±3.87 24.11±4.07 0.950 Screen time, total min/day 122.91±115.0 119.60±126.3 124.35±116.8 109.07±95.3 0.319 Weekday sleep, hours 10.16±1.08 10.14±0.97 10.08±1.08 10.10±0.87 0.422 Weekend sleep, hours 10.26±1.07 10.19±1.14 10.27±1.14 10.25±1.06 0.656 SSB intake, servings/week 8.0±8.5 8.1±8.5 9.8±10.4 8.6±8.9 0.209 F/V intake, servings/week 15.4±10.4 16.0±9.8 18.0±12.1 20.0±13.3 0.414 BMI z-score 0.80±1.11 0.80±1.10 0.78±1.06 0.76±1.04 0.513 All data are mean±SD unless otherwise noted. Min, minutes; MVPA, moderate to vigorous physical activity. Child physical activity score was determined from the Netherlands Physical Activity Questionnaire for Young Children, with a higher score indicating higher activity. Sample sizes ranged from 172-199 for Safety Journey and 176-199 for Wellness Journey, depending on the variable. The p- value indicates significance of the time by group interaction term as determined by repeated measures analysis of variance, with intervention arm/group as the between subjects factor. D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 Table 3. Psychosocial factors at baseline and after Year 1 for Safety Journey and Wellness Journey Families Safety Journey, Baseline Safety Journey, Year 1 Wellness Journey, Baseline Wellness Journey, Year 1 p-value Readiness to Change Increase Physical Activity 3.63±1.05 3.70±1.01 3.63±1.08 4.04±0.83 0.019 Increase Fruits/Vegetables 3.82±0.90 3.98±0.88 3.90±0.90 4.31±0.68 0.037 Decrease Screen Time 3.07±1.31 3.28±1.31 3.01±1.29 3.73±1.18 <0.001 Decrease Sugar Intake 3.72±1.11 3.85±1.10 3.70±1.18 4.15±0.90 0.074 Manage Stress 3.60±1.06 3.75±1.03 3.70±1.10 3.88±0.94 0.689 Obtain Adequate Sleep 3.62±1.08 3.67±0.99 3.60±1.08 3.96±1.00 0.012 Adult Perceived Stress (PSS) 16.20±6.04 14.60±6.66 16.48±6.33 15.02±6.56 0.826 Adult SF-12 Score Physical health component 50.31±7.42 49.85±7.67 48.53±8.52 48.52±8.85 0.567 Mental health component 46.82±9.21 48.71±9.82 46.66±10.44 48.87±9.84 0.767 Home Environment (FNPA) Total score 61.33±7.41 62.84±7.13 61.72±7.55 64.20±6.50 0.136 Nutrition domain 3.21±0.38 3.24±0.34 3.23±0.35 3.32±0.31 0.057 Physical activity domain 2.89±0.47 3.03±0.45 2.92±0.50 3.08±0.43 0.539 All data are mean±SD. PSS, Perceived Stress Scale; FNPA, Family Nutrition and Physical Activity Screening Tool. Sample sizes ranged from 172-199 for Safety Journey and 176-199 for Wellness Journey, depending on the variable. The p-value indicates significance of the time by group interaction term as determined by repeated measures analysis of variance, with intervention arm/group as the between subjects factor. D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 Table 4. Themes and sample participant comments from focus group sessions. Families reported meaningful benefits from HCSF2 participation.  I have grown to love this program because this program has been there for me when I had no clue of what to do or know to go about life. This program has helped me to be a better parent to my children and it has better my view of life. It has given me a very good chance to have a great recovery from alcohol. It helped me to figure out what to do with my children. It has given me great ideas about how to recap my connection with my children. It up lifted my spirit knowing that I'm not the only one with many problems. But this has given my outlook in life back to me. I thank you for the support in everything that this program has given to me and my child. I feel so much better within myself. And so does my child. This program has given me and taught me many things that I have lost when I was a drunk. So thank you for helping me out to be a better person and showing me a different and a healthier life style.  I learned a lot and this program helped keep me motivated to stay eating healthy and be more physically active. I now eat veggies every day and I cook dinner more and watch a lot less TV and actually we don't even watch TV more than 2 times a week.  I want to say I am glad I got to participate in this program. It has been a lot of fun, we all learned from it, and it brought us closer together as a family. Thanks to this wonderful program, we have many new activities/games for the summer. We have new recipes that are healthy, the kids can help with, and that we actually like! This program has not only helped my partner and me, but my youngest too! Both boys and I have learned so much! Thank you for helping us become closer and grow as a family.  We loved how activities kept us active and always having something to do. My daughter always liked getting the mail from HCSF. Keeps her super busy with things to do and cook! Mmmm! Thank you so much! Intervention materials were well-received.  Good choices on books…we enjoyed reading them together…and my daughter was engaged and asked questions.  My daughter loved playing all the games; we would talk about the lesson, and then play the game. She also loved the cooking and being able to help.  I found this program to be very helpful and I loved all the information I received, the fun activities to do, and all the recipes to try. Families reported commitments to making healthy choices for their family and desire to serve as positive role models.  So, I wanted to teach them how not to eat all that junk food, and to replace it with healthy foods and then the activities for them.  The program was a nice reminder that it‟s important for me to be the role model for my kids – but also that they help keep me on track…like they ask before they eat things „is this healthy?‟ and they ask for water or milk instead of pop when we go out  You have to think of them and their future. I think the name of the program is good, “Healthy Children, Strong Families” because if I‟m not teaching them good habits now, what will their family look like in the future?  I am very pleased with this program. It taught me a lot about healthy habits...it was hard at times when I realized that I needed to change and work on being healthier without using any excuses or blaming others.  Now I know to make sure that half their plate is for fruits and vegetables… and they are eating them, so that‟s one thing I like.  It was like very small, little steps, even with the healthy food, the eating healthy and cooking using fresh vegetables…you weren‟t asking us to change D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019 everything all at once. And it was fun to have the kids help decide new things they want to try when we‟re at the grocery store. Families are setting limits for screen time and recognizing the link between screen time, sleep and the amount of food advertising on TV.  What really helped me about the sleep schedule was actually being thoughtful about transition time…so we start winding down and turn off everything so we have time to brush teeth and get out our books and generally have quiet time so we‟re all ready for bed.  We were more aware that you have these commercials coming on – for food, for McDonald‟s, so you have the urge to go there…and the kids want to stop there when you‟re out driving around. I think turning off the TV has helped us avoid that.  The Sleep Tight lesson…it helped me a lot, getting my kids to go to bed, because I have one that doesn‟t really like to follow bedtime rules and routines, but that lesson really helped and now he goes to sleep with no problem. Families face significant challenges to healthy lifestyles, including the high cost of food and stress.  It's hard to change to eating healthy meals because healthy foods are expensive.  Hard to afford consistent supply of fruits and veggies in the house, especially at the end of the month  I‟m just used to being stressed out all the time.  It is always harder to make healthy lifestyle choices when I am sleep-deprived or have a lot of work to do.  We had a rough situation the past few months that have made it difficult. D ow nloaded from https://academ ic.oup.com /cdn/advance-article-abstract/doi/10.1093/cdn/nzy087/5185110 by M ontana State U niversity Library user on 24 April 2019