- Open Access
Does the neighborhood built environment moderate the effectiveness of a weight-loss intervention for mothers with overweight or obesity? Findings from the Healthy Eating and Active Living Taught at Home (HEALTH) study
International Journal of Behavioral Nutrition and Physical Activity volume 19, Article number: 130 (2022)
Women of childbearing age are vulnerable to weight gain and experience a high prevalence of obesity due to pregnancy and stressors of parenthood. Lifestyle interventions such as the Healthy Eating and Active Living Taught at Home (HEALTH) study have been effective for weight loss; however, little is known about how the built environment (parks, transit, grocery stores, fast food, walkability etc.), where participants live might modify intervention effectiveness. This study examined whether characteristics of the neighborhood built environment modified effectiveness of the HEALTH study on weight loss.
Secondary data analysis was conducted using data from HEALTH. Using GIS, buffers were built around participant addresses to capture distance to and availability of food (grocery store, convenience store, fast food) and urban design and transit (parks, street connectivity, transit) built environment characteristics. Built environment characteristics were dichotomized into low and high density and distance. Likelihood ratio tests for interaction were conducted to determine if built environment characteristics modified intervention effectiveness on Body mass index (BMI) and waist circumference (WC). Mixed effects linear regression models were then run to estimate the effect of the HEALTH intervention on weight outcomes at 24-months across both strata of built environment characteristics.
The analytic sample (n = 151) had baseline mean BMI 34.9 (SD = 5.8) and mean WC 46.0 cm (SD4.9). All urban design and transit and all food environment characteristics modified HEALTH effectiveness on one or both weight outcomes. The built environment modified the HEALTH intervention such that it was mostly effective for mothers residing in neighborhoods with low transit access, low street connectivity, high park access, and low access to grocery stores, convenience stores, and fast food.
Result show the HEALTH was most effective for women residing neighborhoods with built environment characteristics suggestive of suburban neighborhood typology. To maximize impact for mothers residing in all types of neighborhoods, future research should explore scaling up HEALTH in suburban settings, while adapting HEALTH to maximize effectiveness in compact neighborhoods most likely, urban core neighborhoods.
Obesity is an urgent public health problem. An estimated 13% of the global population and 42% of the US adult population have obesity, putting them at higher risk for diabetes, heart disease, hypertension, cancer, Alzheimer’s, psychological conditions, physical impairment, and premature death [1,2,3,4,5,6,7]. Although extensive work has been conducted in understanding the determinants and designing interventions for weight-loss among various sub-population groups, including children and adolescents, older adults, and more recently among some racial and ethnic minorities, less research is available on how to prevent and treat obesity among women of childbearing age (20 to 39 years). It has been documented that during this stage in life women experience an increased vulnerability to weight gain and the development of obesity [8, 9]. Indeed, among women, the prevalence of obesity increases from 20–24% during adolescence (12–19 years old), to 33–57% during the childbearing years . During the childbearing years, women experience an average weight gain per year of one to two pounds . It has also been reported that approximately 23% of women gain an excess of 44 pounds or more above their healthy weight range upper limit between the ages of 18 and 55 years old [8, 9].
One aspect that makes women in this stage of life particularly vulnerable to weight gain is pregnancy, as well as the stressors associated with parenthood. Excessive weight gain during pregnancy and post-partum weight retention can make it difficult for women to return to a healthy weight [10,11,12,13,14,15]. Additionally, early adulthood is a period of life in which many transitions take place (new jobs, marriage, parenthood), increasing responsibilities and roles. During this period, many women have to balance demands from work and childcare, experiencing time constraints and increased financial costs, limiting their ability to maintain healthy lifestyles [8, 16, 17]. Beyond being a concern for women’s health [1,2,3,4,5, 7, 18, 19], weight gain among mothers has been shown to increase the likelihood that offspring will develop overweight and obesity later in life [20,21,22,23]. This intergenerational transmission of obesity is expected to continue to accelerate the obesity epidemic resulting in worsening health outcomes across generations [21, 24].
One way in which weight-loss or weight-gain prevention interventions have been developed and tested to reach women of childbearing age is through home visiting programs. Parents as Teachers (PAT) is a national home visiting organization providing free home visits for families with high needs (i.e., low educational attainment, low income, a parent or child with disabilities/chronic health condition, recent immigrant, parent with mental illness, or unstable housing). The goal of PAT is to promote child development and health through parent education and connection to resources . Recently, PAT partnered with the research team at Washington University to implement the Healthy Eating & Active Living Taught at Home (HEALTH) study .The HEALTH study tested a 24-month lifestyle intervention on improving weight outcomes among mothers with obesity, through a home-delivered adaptation of the Diabetes Prevention Program . When compared to usual care, the intervention group was significantly more likely to achieve and maintain a 5% weight loss at 24 months and show improvements at 12 and 24 months in waist circumference, blood pressure, and behavioral outcomes, defined by eating patterns and physical activity .
The socio-ecological model of health behaviors underscores the influence of environmental factors on individual-level health behaviors, and the interaction across levels of influence (including, for instance, interactions between the inter-personal level, at which interventions like HEALTH take place, and the environmental level) [27, 28]. The built environment includes all aspects of a person’s environment which are human-made or modified, including buildings, spaces and urban design and infrastructure elements . Understanding if the neighborhood built environment plays a moderating role for the effectiveness of the intervention could provide important insights for program tailoring and scale-up. Other studies have reported significant differences in mean BMI and waist circumference by neighborhood built environment characteristics such as access to parks, transit, grocery stores, fast food restaurants, convenience stores, and by walkability scores [30,31,32,33,34,35,36,37]. However, few studies have examined if and how the effectiveness of lifestyle-modification interventions, such as the one tested in the HEALTH study, varies depending on the characteristics of the built environment of neighborhoods where participants live. Among the available studies examining moderation of intervention effectiveness by built environment features, most have focused on either physical activity or nutrition outcomes [38,39,40,41,42,43,44], with fewer studies assessing moderating effects on obesity or weight-related outcomes [45, 46]. Further, almost none of these studies are specific to women of childbearing age. Using an ecological approach, the aim of this study was to examine whether different characteristics of the neighborhood built environment modified the effectiveness of the HEALTH intervention on weight loss outcomes (reductions in BMI and waist circumference).
Study design and parent study
We conducted a secondary analysis using HEALTH study data. Briefly, the HEALTH study aimed to improve weight outcomes for mothers of preschool children (ages 3–5) and took place between 2012 and 2016. Additional inclusion criteria for the mother included having obesity and residing in the St. Louis, Missouri region. Families that were already enrolled in the PAT program were recruited via a flyer at events, through providers, at childcare, or mailed. Families interested in participating were asked to contact the research team. Families were randomized to usual care (PAT program) or intervention (PAT + HEALTH intensive lifestyle intervention), which consisted of education and counseling aimed at physical activity and dietary behavior change . The HEALTH intensive lifestyle intervention was modeled after the Diabetes Prevention Program and included strategies targeting intrapersonal (e.g., self-monitoring), interpersonal (e.g., family meal time), and home environment (e.g., food access at home) factors related to health behavior change. Behavioral change strategies used in the intervention included goal setting (e.g., set goal to reduce inactive time), nutritional and physical activity education (e.g., learn the benefits of eating a healthy breakfast), stimulus control (e.g., search home for problem food cues and make changes), and skill development (e.g., read food labels). Extensive information about the parent study, including details about the behavior change strategies implemented, has been published elsewhere (see Additional file 1 Appendix Table 1of cited article by Haire-Joshu and colleagues for full description of the behavior change strategies included in the intervention) . Our sample for this secondary analysis examining the moderating effects of neighborhood built environment characteristics on the HEALTH intervention consisted of all HEALTH study participants with complete outcome data at baseline and 24 months, and whose addresses were available for geocoding. In the parent study, 23% of participants for which data were collected at baseline moved or could not be located at follow-up. These participants did not complete the 24-month follow-up, and as such, were not included in the analysis reported in this paper, which includes only participants that resided in the same location throughout the study period.
Height, weight, and waist circumference were objectively measured following standard procedures from the National Health and Nutrition Examination Survey . Height and weight were used to obtain BMI using the standard formula . For this analysis, BMI and waist circumference are treated as continuous variables.
Built environment measures
After geocoding all participant addresses, we built a series of participant centric buffers of varying radii (250, 500, 100, 1500) in meters. Given that adults walk at an average speed of 5 km per hour, these buffer radii represent approximate walking times ranging from 2 to 20 min . Two types of buffers were used, including Euclidian buffers, measured as the crow flies, as well as network buffers, measured through underlying road networks to find all possible routes from the participant to the end point (Fig. 1a). In addition to buffer-based variables, some distance-based variables were calculated using the road network. We chose buffer-based and distance-based variables to represent two different dimensions of geospatial access. Buffer-based variables measure availability (i.e., diversity and number of options) of a built environment feature within the home neighborhood (i.e., buffer) of a participant (Fig. 1b). For example, how many different options does a participant have in their neighborhood built environment for food? The distance-based variables measure accessibility (i.e., distance traveled along road networks) to the nearest built environment feature from the participants home (Fig. 1c). For example, how easy is it for a participant to reach the nearest park to their home?
We built a series of buffer-based indicators at each of the buffer types (Euclidian, network) and sizes (250, 500, 1000, 1500 m). Next, we conducted a sensitivity analysis to determine for each built environment buffer-based measure, which specific buffer (type and size) was most strongly correlated with the outcomes of interest (see Additional file 1. Appendix Sect. 1). Based on this analysis, number of parks, Metrobus stops, Metrolink stops, all-transit stops, fast food locations, convenience stores, and grocery stores within each buffer were computed. Street connectivity was measured as counts of three-or-more-way street intersections within each buffer. This is the most frequently used indicator for assessing street connectivity in the US and in international studies, since restricting the indicator to include only 4-or-more-way intersections would result in reduced environmental variability due to the amount of urban sprawl typical of US cities . Finally, population density was estimated using census block group total population size data and intersecting it with each participant's buffer. Through spatial apportionment, the slices of each census block group intersecting the buffer were used to compute a weighted average, to estimate total population size for each participant-centric buffer. This number was divided by the buffer area to estimate population density per buffer and expressed as total population per Kilometer squared.
We measured distance-based variables by calculating the nearest route in meters from participant address to nearest built environment feature using the road network system. These distance-based variables included distances along the road network to the nearest grocery store, park, transit stop of any kind, fast food location, and convenience store.
For analyses, all buffer-based and distance-based variables were dichotomized. Buffer-based variables of parks, convenience stores, grocery stores, and Metrolink stops were dichotomized as less than one or greater than or equal to one. For buffer-based variables with higher counts the median was used. Fast food was dichotomized at the median of four, and three-or-more-way intersections, all transit stops and Metrobus stops were dichotomized at the median of 21. All distance-based variables were dichotomized at the median, which ranged from a low of 742 m to a high of 1,344 m. It was decided to dichotomize all built environment variables to allow for easier interpretation. The main goal of our work with this analysis was to answer the question of whether, for example: “does the HEALTH intervention work better for people that have access to more parks in their neighborhood than for those who have less parks?”. Hence, built environment variable dichotomization allowed for splicing the modeling results accordingly, across the two levels of the moderating variable. Future work focused on examining the shape of the associations should consider using continuous variables. All spatial measures were built using ArcGIS 10.6 (ESRI Inc).
All of the raw spatial data used to build indicators of the food and built environment were sourced from Environmental Systems Research Institute (ESRI)’s data package, which in itself includes data from both public (e.g., US Census, US Department of Transportation, State and Local Departments of Parks and Recreation, etc.), and proprietary data sources (e.g., ESRI Business Analyst – from which all our food environment data were sourced).
We conducted descriptive analyses by computing counts and percentages for outcomes of interest and built environment measures. For all outcome variables, normality was assessed using skewness and kurtosis statistics, as well as visual assessments of histograms. Since both variables were found to be normally distributed, no data transformations were necessary. Next, we ran a series of likelihood ratio tests for interaction to determine if built environment variables modified the effect of the intervention on BMI and/or on waist circumference. We considered any p-value lower than 0.15 as evidence of potential effect measure modification, warranting further exploration of stratum specific estimates . Mixed effects linear regression models, accounting for the clustering effect of study sites, were run to estimate the effect of the HEALTH intervention, relative to the usual care arm, on the outcomes at 24-months (while adjusting for the outcome at baseline) [51, 52] across both strata of the given built environment variable. Of the 179 participants in the parent study, 156 completed the 24-month follow up assessment, and among them, 151 provided valid residential addresses for geocoding. The analytic sample did not significantly differ from the full parent study sample in terms of sociodemographic characteristics, except for income, which was significantly higher among the analytic sample. Further, income as well as population density, are often highly correlated with built environment characteristics [53,54,55,56]. Because of this, we ran unadjusted, density-adjusted, and income-adjusted models. All statistical analyses were conducted using SAS version 9.5 (SAS Institute Inc).
The analytic sample (n = 151, usual care = 83, intervention = 68) included participants with a mean age of 33.0 (SD = 5.5) years, a mean BMI at baseline of 34.9 (SD = 5.8) kg/m2, and mean waist circumference at baseline of 46.0 cm (SD = 4.9). The sample was predominantly highly educated (86.8% had college or more) and white (61.6%). Full sociodemographic characteristics of the sample are shown in Table 1. No significant differences were observed between usual care and intervention groups with respect to sociodemographic characteristics and baseline BMI and waist circumference values.
Home neighborhood built environment characteristics
Urban design and transit
Among study participants, slightly less than half (48.3%) lived in neighborhoods with high road connectivity (\(\ge\) 21 three-way intersections within a 500 m Euclidean buffer, approximately 5–10 min walking distance). Around 45.7% lived in neighborhoods with high availability of transit stops (\(\ge\) 21 transit stops within a 1500 m Euclidean buffer, approximately 15–20 min walking time). Meanwhile, more than half (62.9%) lived in areas which had high park density (\(\ge\) 1 park within a 1000 m Euclidean buffer, approximately 10–15 min walking time). The average road-network distance to the nearest park from participant’s homes was 2,200 m (approximately 26.5 min walking time) (SD = 2213.9), while the average road-network distance to the nearest transit stop was of 5,211 m (approximately 63 min walking time) (SD = 7228.7). No significant differences in urban design and transit environment characteristics were observed between participants in the intervention and usual care groups at baseline (Table 2).
More than half (57%) of the study participants lived in neighborhoods with high grocery store density (\(\ge\) 1 store within a 1000 m Euclidean buffer, approximately 10–15 min walking time). Around 56% lived in neighborhoods with high convenience store density (\(\ge\) 1 store within a 1000 m Euclidean buffer, approximately 10–15 min walking time), and a little under half (47.6%) lived in neighborhoods with high fast food density (\(\ge\) 4 stores within a 1500 m network buffer, approximately 18 min walking time). The average distance to the nearest grocery store was 1,836 m (approximately 22 min walking time) (SD = 1891.6). The average distance to the nearest convenience store was 1,795 m (approximately 22 min walking time) (SD = 1748.6), while the average distance to the nearest fast food restaurant was 1,273 m (approximately 15 min walking time) (SD = 1305.4). Between usual care and intervention groups, no significant differences were found for any of the food environment characteristics.
Effect modification of the impact of the HEALTH intervention on weight reduction outcomes by neighborhood environment characteristics
Our test for interactions revealed potential effect measure modification of the HEALTH intervention on one or both outcomes of interest (BMI, waist circumference) by all neighborhood environment characteristics examined (Table 3). Therefore, we proceeded to determine the effect of the HEALTH intervention (relative to the usual care arm) across different strata of neighborhood environment features. Population density-adjusted models did not meaningfully change the results when compared to unadjusted models (Additional file 1. Online Appendix). However, there was evidence of confounding by income level for some of the models. Therefore, results are presented for both unadjusted and income-adjusted models (Table 4).
Modification by urban design and transit neighborhood environment characteristics
Table 4 shows the stratum specific estimates of the effect of the HEALTH intervention across strata of urban design and transit environmental neighborhood characteristics.
We found Road-network connectivity (number of three-way intersections in the home neighborhood, representing walkability), moderated the effect of the intervention on the outcome of waist circumference. The intervention was only effective in achieving significant reductions in waist circumference among participants living in neighborhoods with low connectivity (p < 0.01), but not for those residing in highly connected neighborhoods (p = 0.06).
In terms of access to parks, both park density and distance to the nearest park were found to modify the effectiveness of the intervention for both outcomes of interest (reductions in BMI and waist circumference). For the case of park density (number of parks in the home neighborhood, representing park availability), the intervention was only effective in achieving significant reductions in BMI and waist circumference for women living in areas with high park density (p < 0.01). For the case of park distance (close and far distances to the nearest park, representing park accessibility), we found the intervention was effective in achieving significant reductions in BMI for participants, regardless of park distance. However, there was a stronger effect for those living closer to the nearest park (beta -2.09, SE 0.74, p < 0.01) relative to those living farther to their nearest park (beta -1.54, SE 0.70, p = 0.03). On the other hand, the intervention was only effective in achieving significant reductions in waist circumference for participants living closer to the nearest park (p < 0.01) compared to participants living farther away from the nearest park (p < 0.08).
Our findings examining potential effect modification of the intervention by access to public transit revealed that Metrolink density (representing transit availability, and as such, the degree to which there are a variety of options within close range), moderated the effect of the intervention on the outcome of BMI. The intervention only achieved significant reductions in BMI for participants living in areas with a low density of Metrolink stops (p < 0.01) compared to participants living in areas with a high density of Metrolink stops (p = 0.59). Similarly, Metrobus density, all-transit density, and Metrolink density moderated the effect of the intervention on the outcome of waist circumference, such that the intervention only achieved significant reductions in waist circumference for participants living in areas with low density transit characteristics (p < 0.01). Along the same lines, for transit stop distance (representing accessibility), we found that the intervention only achieved significant reductions in waist circumference for participants living farther away from their nearest transit stop (p < 0.01) compared to participants living closer to the nearest transit stop (p < 0.17).
Modification by food neighborhood environment characteristics
Table 4 shows the stratum-specific estimates of the effect of the intervention across strata of food environmental neighborhood characteristics.
In terms of access to grocery stores, we found that grocery store density (representing grocery store availability, and as such, the degree to which there are a variety of options within close range), moderated the effect of the intervention for both outcomes. Specifically, the intervention only achieved significant reductions in BMI and waist circumference for participants living in areas with low grocery store density (p < 0.01). Similarly, distance to the nearest grocery store (representing accessibility), the intervention was found to be effective at reducing BMI and waist circumference only for participants who lived far away from their nearest grocery store (p < 0.01).
Access to convenience stores modified the effect of the intervention on both outcomes. Density of convenience stores (representing availability, i.e., variety of options within close range), moderated the effect of the intervention, such that the intervention only achieved significant reductions in BMI and waist circumference for participants living in areas with low convenience store density (p < 0.01). Similarly, distance to the nearest convenience store (representing accessibility) was also significant moderator of the interventions, which was only effective for participants living far away from their nearest convenience store (p < 0.01).
In terms of fast food density (representing availability), the intervention only resulted in significant reductions in waist circumference for participants living in areas with low density of fast food restaurants (p < 0.01), compared to participants living in areas with a high density of fast food restaurants (p = 0.13). For fast food distance (representing accessibility), we found the intervention only achieved significant reductions in BMI, for participants living farther away from the nearest fast food restaurant (p < 0.01) compared to participants living closer to the nearest fast food restaurant (p = 0.71). Meanwhile, we found the intervention was effective in achieving significant reductions in waist circumference for all participants, regardless of their distance to fast food outlets. However, there was a stronger effect for those living farther away from their nearest fast food restaurant (beta -2.63, SE 0.88, p < 0.01) relative to those living closer to their nearest fast food restaurant (beta -2.01, SE 0.94, p = 0.03).
This study presents evidence from the HEALTH study showing that the characteristics of residential neighborhood environments can modify the effectiveness of weight-loss interventions for mothers with overweight or obesity. Our results show that while the main effects of the HEALTH intervention on BMI and waist circumference at 24-months were significant and in the intended direction, effective weight loss occurred mostly among women residing in low-density, low-connectivity neighborhoods with low access to food outlets and transit stops, and high access to parks.
One possible explanation for our findings is that weight-loss interventions, like HEALTH, may work best for “resource deprived” areas, where a small “nudge” can go a long way. Nudge theory, proposed by behavioral economists, posits that people’s choices are bounded by their context, and through minimal changes to these contexts, peoples behaviors can change . Hence, it is possible that lifestyle interventions, like HEALTH, can have an outsized effects on behavioral outcomes for participants residing in areas with minimal supports for active living. For example, the HEALTH intervention provided participants with goal-setting techniques for gradually increasing their discretionary physical activity, as well as problem solving skills to meet these goals. These types of strategies may have aided participants in low-resourced neighborhoods in overcoming some of these environmental barriers to healthy living. Other investigators examining the potential moderating role of neighborhood environments on the effectiveness of lifestyle interventions have reported similar results. For instance, Kerr et al. found that their weight-loss intervention for adult men and women was most successful in increasing physical activity levels among participants of less walkable neighborhoods . This interpretation is in line with our results, which revealed that the intervention worked the best for mothers residing in areas with low walkability, access to transit, and access to food stores and services. It must be highlighted that effectively mitigating environmental constraints for healthy living is indeed a central aim of the HEALTH study [57, 59].
Another complementary explanation for these findings is that there may be neighborhood typologies which help optimize the effectiveness of lifestyle interventions. It is possible that in its current form, the HEALTH intervention is most effective in suburban neighborhoods, and less so in inner city, urban core neighborhoods. Although our analysis did not focus on examining whether certain neighborhood environment characteristics clustered in space, our results are supportive of this hypothesis. Our analysis, consisting of a series of single-environment variable models, revealed that the intervention was mostly effective for those residing in areas with low access to grocery stores, low access to restaurants, low access to public transit, low walkability, and high access to parks. These are all known characteristics of a suburban neighborhood typology. On the other hand, our results showed that the effectiveness of the intervention was either weaker or non-significant for mothers living in neighborhoods with high access to grocery stores, restaurants, transit, walkability, and low access to parks (all typical of inner city, urban core neighborhoods). Importantly, our results are not explained by differences in socioeconomic status, as this was accounted for in the analysis. Other studies examining the moderating effect of the built environment on lifestyle behavioral interventions have reported consistent findings , but we are the first to do so for weight-loss intervention designed for young mothers. For example, King et al. found that age-related declines in mobility and physical function among older adults were significantly less pronounced among those living in suburban type neighborhoods, when compared to those residing in more urban, compact areas .
One important element to consider when designing behavioral interventions intended to be implemented in different types of neighborhoods, is the behavioral domain that they intend to modify, and if and how that matches the place-based characteristics of the participants. For example, many physical activity interventions, including the HEALTH intervention, focus on discretionary (leisure-time or recreational) physical activity [60,61,62], which is more likely to be supported by neighborhood environments with low residential density, traffic, noise, pollution, and high access to green space. These types of features are most commonly found in the suburbs of US cities, where it has been reported that there are safer and more aesthetically pleasing opportunities for recreational physical activity (e.g., leisure walking) than in higher-density urban areas [41, 63,64,65,66]. Conversely, previous evidence on the role of compact, walkable neighborhoods with high density of services and transit (i.e., urban neighborhood typologies) on physical activity (one of the behaviors that influences weight management), is mainly observed as being due to increased utilitarian (e.g., walking or cycling for transport) physical activity in these settings [67,68,69,70]. Hence, although it may seem counter-intuitive that lifestyle interventions, such as HEALTH, are less effective in “highly walkable” areas, it may in fact be due to the behavioral domains being targeted by the intervention and their match with the places in which it takes place.
In regard to the food environment, it is worth highlighting that our findings with regards to intervention effectiveness in neighborhoods with high access to fast food restaurant and convenience stores are in the expected direction (the intervention was less effective in these settings). However, this was not the case for supermarket access, which other studies have reported as being directly associated with healthy eating and healthy weight outcomes, while in our study, we found that the intervention was less effective among participants residing in neighborhoods with high access to supermarkets, relative to those with lower access [71, 72]. However, although access to food stores and outlets of any type (healthy or unhealthy) is lower in the suburbs than in the inner-city core, especially when operationalizing access based on walking distances, driving distances to access supermarket are still generally short. In the US, most households own at least one motor vehicle and most people drive to purchase groceries [73,74,75]. As such, distance to supermarkets may not be as much of a barrier to healthy food access in the suburbs. Additionally, home cooking, which has been associated with healthier eating and better weight outcomes, may be more common in suburban type neighborhoods given their lower access to restaurants and fast food [76, 77]. Altogether, these findings appear to support the suburban neighborhood typology hypothesis – i.e., that it is not individual features (like a supermarket or a fast food store) which are moderating the effectiveness of the intervention, but rather, that the intervention seems to be more effective for people residing in suburban type areas, relative to those in the inner core; all whilst adjusting for socioeconomic level and population density.
Although research has shown the known benefits of compact, walkable neighborhoods with access to services and food vending locations on physical activity and healthy eating [32,33,34, 78, 79], it may be the case that the suburban area typology with low-density, low-connectivity, and low access to food and transit stops is more supportive for interpersonal lifestyle interventions. Since our analysis was restricted to looking at the possible moderating role of individual neighborhood environmental factors, and not in determining their co-occurrence, future work should examine in more depth the role of neighborhood typologies in the context of lifestyle intervention effectiveness. This will require developing standardized, replicable methods for categorizing neighborhoods based on their clustering environmental factors.
Limitations and strengths
The findings presented must be interpreted in light of the limitations of the study. Secondary data from public data sources were used to build the geospatial variables, and these data were collected with other intentions beyond public health research (city planning and management, etc.), precluding a comprehensive assessment of data completeness and quality. Some relevant environmental measures were not readily available, including measures of crime or safety, which would have enhanced our analysis. While all attempts were made to temporally-match source built environment data to the time of baseline data collection (2012–2013), exact time-matching was not possible in all instances due to variations on available source GIS data. We did, however, achieve time-matching of the GIS data used for this analysis to be within a range of up to 5-years post-baseline (i.e., built environment variables reflect the ground truth between 2012 and 2017). While built environments do change over time (e.g., the number of fast food restaurants in a neighborhood can change over time), it is highly unlikely that any given neighborhood environment would change to a degree such that it would modify the assigned classification across dichotomized built environment measures (e.g., turn a neighborhood classified as having a high number of parks to become one that has a low number of parks, or vice versa). We measured access to built environment characteristics through density and distance-based measures but did not assess the quality of these features, nor participant perceptions of their neighborhood environment. Another possible limitation is that we dichotomized the built environment variables examined as potential moderators. It was decided to do so to allow for easier interpretation of the moderating effects of the built environment on the intervention by a broad public health audience, and, because of the exploratory nature of this analysis. Further, we dichotomized built environment features via a median-split approach. As such, “low” and “high” access categories are defined by the available range of values in the City of St. Louis, and may not be reflective of relative high versus low access to environmental assets in other contexts. The scope of this analysis was limited to using changes in BMI and waist circumference as the primary outcomes of interest; however, future analyses should explore the potential moderating roles of neighborhood environmental features on changes in intermediary behavioral outcomes, such as measures of physical activity and dietary behaviors. Additionally, we did not have an optimal measure of urbanicity available and relied on a measure of population density to approximate this complex construct. Another limitation of the study is the potential introduction of selection bias due to differential losses to follow-up across arms in the parent study. Finally, the scope of this study focused on assessing individual built environment features, and did not analyze the co-occurrence of these features or other environmental factors which influence physical activity and healthy eating (i.e., food prices, social support, crime, neighborhood aesthetics). Future research should be conducted to explore the impact of co-occurring features on study effectiveness as well as the impact of other environmental factors not explored in this analysis.
Our study also had some key strengths which are important to underscore. We included two outcomes for weight status (BMI score and waist circumference). Waist circumference is a more sensitive measure of weight loss and specifically weight loss associated with better health outcomes [80, 81]. Also, we provide a unique contribution to the literature around the role of the built environment on interpersonal interventions for weight loss among overweight or obese young women. Few, if any studies have explored this relationship among women of preschool children with overweight or obesity, limiting our knowledge around how effective interpersonal interventions are in varying built environment contexts for this population.
This study sheds light with respect to the characteristics of neighborhoods where the HEALTH study intervention is most effective. Specifically, we found the intervention to be most effective for women residing in low-density, low-connectivity neighborhoods, with low access to food outlets and transit stops, and high access to parks (i.e., suggestive of a suburban neighborhood typology). Further steps should be taken to scale up the HEALTH intervention to these types of settings.
Future research should also look toward adapting the intervention to maximize effectiveness in compact neighborhoods with high access to restaurants, grocery stores, with high connectivity, and high access to public transit but low access to parks (most likely to be inner city, urban core neighborhoods). Additional mixed-methods research is required to identify the barriers operating in these settings, which may be limiting intervention effectiveness. This will allow for new iterations of the intervention to become better tailored, allowing to maximize its impact for mothers residing in all types of neighborhoods, thus ensuring it benefits the greatest possible number of people and contributes to reducing health inequities.
Availability of data and materials
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
Healthy Eating and Active Living Taught at Home
Parents as Teachers
Flegal KM, Kit BK, Orpana H. Association of All-Cause Mortality. J Am Med Assoc. 2013;309(1):71–82. https://doi.org/10.1001/jama.2012.113905.
Jaffe RB. Cause-specific excess deaths associated with underweight, overweight, and obesity: Commentary. JAMA. 2008;63(3):157–9. https://doi.org/10.1097/01.ogx.0000305202.93507.2a.
Borrell LN, Samuel L. Body mass index categories and mortality risk in US adults: The effect of overweight and obesity on advancing death. Am J Public Health. 2014;104(3):512–9. https://doi.org/10.2105/AJPH.2013.301597.
Hruby A, Manson JAE, Qi L, et al. Determinants and consequences of obesity. Am J Public Health. 2016;106(9):1656–62. https://doi.org/10.2105/AJPH.2016.303326.
Field AE, Coakley EH, Must A, et al. Impact of overweight on the risk of developing common chronic diseases during a 10-year period. Arch Intern Med. 2001;161(13):1581–6. https://doi.org/10.1001/archinte.161.13.1581.
Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of Obesity Among Adults and Youth: United States, 2015–2016. NCHS Data Brief, No 288. Hyattsville: National Center for Health Statistics; 2017. https://www.cdc.gov/nchs/products/databriefs/db288.htm.
Swinburn BA, Sacks G, Hall KD, et al. The global obesity pandemic: Shaped by global drivers and local environments. Lancet. 2011;378(9793):804–14. https://doi.org/10.1016/S0140-6736(11)60813-1.
Dietz WH. Obesity and ExcessiveWeight Gain in Young Adults NewTargets for Prevention. JAMA - J Am Med Assoc. 2017;318(3):255–69. https://doi.org/10.1001/jama.2017.7092.
Zheng Y, Manson JE, Yuan C, et al. Associations ofweight gain from early to middle adulthood with major health outcomes later in life. JAMA - J Am Med Assoc. 2017;318(3):255–69. https://doi.org/10.1001/jama.2017.7092.
Rong K, Yu K, Han X, et al. Pre-pregnancy BMI, gestational weight gain and postpartum weight retention: A meta-analysis of observational studies. Public Health Nutr. 2015;18(12):2172–82. https://doi.org/10.1017/S1368980014002523.
Leddy MA, Power ML, Schulkin J. The impact of maternal obesity on maternal and fetal health. Rev Obstet Gynecol. 2008;1(4):170–8. http://www.ncbi.nlm.nih.gov/pubmed/19173021%0Ahttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC2621047
Nehring I, Schmoll S, Beyerlein A, Hauner H, Von Kries R. Gestational weight gain and long-term postpartum weight retention: A meta-analysis. Am J Clin Nutr. 2011;94(5):1225–31. https://doi.org/10.3945/ajcn.111.015289.
Lisonkova S, Muraca GM, Potts J, et al. Association between prepregnancy body mass index and severe maternal morbidity. J Am Med Assoc. 2017;318(18):1777–86. https://doi.org/10.1001/jama.2017.16191.
Linné Y, Dye L, Barkeling B, Rössner S. Long-term weight development in women: A 15-year follow-up of the effects of pregnancy. Obes Res. 2004;12(7):1166–78. https://doi.org/10.1038/oby.2004.146.
Keppel KG, Taffel SM. Pregnancy-related weight gain and retention: Implications of the 1990 Institute of Medicine guidelines. Am J Public Health. 1993;83(8):1100–3. https://doi.org/10.2105/AJPH.83.8.1100.
Ivers LC, Cullen KA. Food insecurity: Special considerations for women. Am J Clin Nutr. 2011;94(6):1740–4. https://doi.org/10.3945/ajcn.111.012617.
Bellows-Riecken KH, Rhodes RE. A birth of inactivity? A review of physical activity and parenthood. Prev Med (Baltim). 2008;46(2):99–110. https://doi.org/10.1016/j.ypmed.2007.08.003.
Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of obesity among adults and youth: United States, 2015–2016. US Department of Health and Human Services, Centers for Disease Control and Prevention. National Center for Health Statistics, Data Brief 288; 2017. p. 1–8. https://www.cdc.gov/nchs/products/databriefs/db360.htm.
Wing RR, Espeland MA, Tate DF, et al. Changes in Cardiovascular Risk Factors Over 6 Years in Young Adults in a Randomized Trial of Weight Gain Prevention. Obesity. 2020;28(12):2323–30. https://doi.org/10.1002/oby.23003.
Haire-Joshu D, Tabak R. Preventing Obesity Across Generations: Evidence for Early Life Intervention. Annu Rev Public Health. 2016;37:253–71. https://doi.org/10.1146/annurev-publhealth-032315-021859.
Heslehurst N, Vieira R, Akhter Z, et al. The association between maternal body mass index and child obesity: A systematic review and meta-analysis. PLoS Med. 2019;16(6):1–20. https://doi.org/10.1371/journal.pmed.1002817.
Jääskeläinen A, Pussinen J, Nuutinen O, et al. Intergenerational transmission of overweight among Finnish adolescents and their parents: A 16-year follow-up study. Int J Obes. 2011;35(10):1289–94. https://doi.org/10.1038/ijo.2011.150.
Branje S, Geeraerts S, de Zeeuw EL, et al. Intergenerational transmission: Theoretical and methodological issues and an introduction to four Dutch cohorts. Dev Cogn Neurosci. 2020;45:100835. https://doi.org/10.1016/j.dcn.2020.100835.
Ma R, Popkin B. Intergenerational diabetes and obesity—A cycle to break? PLoS Med. 2017;14(10):1–5. https://doi.org/10.1371/journal.pmed.1002415.
Parents as Teachers National Center Inc. Parents As Teachers. https://parentsasteachers.org/. Accessed 10 Nov 2019.
Haire-Joshu D, Schwarz CD, Steger-May K, et al. A Randomized Trial of Weight Change in a National Home Visiting Program. Am J Prev Med. 2019;54(3):341–51. https://doi.org/10.1016/j.amepre.2017.12.012.A.
Glanz K, Bishop DB. The Role of Behavioral Science Theory in Development and Implementation of Public Health Interventions. Annu Rev Public Health. 2010;31(1):399–418. https://doi.org/10.1146/annurev.publhealth.012809.103604.
Moffatt S, Kohler N. Conceptualizing the built environment as a social-ecological system. Build Res Inf. 2008;36(3):248–68. https://doi.org/10.1080/09613210801928131.
Rao M, Prasad S, Adshead F, Tissera H. The built environment and health. Lancet. 2007;370(9593):1111–3. https://doi.org/10.1016/S0140-6736(07)61260-4.
National Research Council (US). Committee on Physical Activity, Transportation, Land Use, Transportation Research Board and Institute of Medicine, 2005. Does the Built Environment Influence Physical Activity? Examining the Evidence--Special Report 282, Vol. 282. Transportation Research Board.
Papas MA, Alberg AJ, Ewing R, Helzlsouer KJ, Gary TL, Klassen AC. The built environment and obesity. Epidemiol Rev. 2007;29(1):129–43. https://doi.org/10.1093/epirev/mxm009.
Sallis JF, Floyd MF, Rodríguez DA, Saelens BE. Role of built environments in physical activity, obesity, and cardiovascular disease. Circulation. 2012;125(5):729–37. https://doi.org/10.1161/CIRCULATIONAHA.110.969022.
Feng J, Glass TA, Curriero FC, Stewart WF, Schwartz BS. The built environment and obesity: A systematic review of the epidemiologic evidence. Heal Place. 2010;16(2):175–90. https://doi.org/10.1016/j.healthplace.2009.09.008.
Renalds A, Smith TH, Hale PJ. A systematic review of built environment and health. Fam Community Heal. 2010;33(1):68–78. https://doi.org/10.1097/FCH.0b013e3181c4e2e5.
Handy SL, Boarnet MG, Ewing R, Killingsworth RE. How the built environment affects physical activity: Views from urban planning. Am J Prev Med. 2002;23(2 SUPPL. 1):64–73. https://doi.org/10.1016/S0749-3797(02)00475-0.
Block JP, Christakis NA, O’Malley AJ, Subramanian SV. Proximity to food establishments and body mass index in the framingham heart study offspring cohort over 30 years. Am J Epidemiol. 2011;174(10):1108–14. https://doi.org/10.1093/aje/kwr244.
Morland K, Diez Roux AV, Wing S. Supermarkets, other food stores, and obesity: The Atherosclerosis Risk in Communities Study. Am J Prev Med. 2006;30(4):333–9. https://doi.org/10.1016/j.amepre.2005.11.003.
McCormack GR, Patterson M, Frehlich L, Lorenzetti DL. The association between the built environment and intervention - facilitated physical activity: a narrative systematic review. Int J Behav Nutr Phys Act. 2022;19(1):1–21.
Gustafson AA, Sharkey J, Samuel-Hodge CD, Jones-Smith JC, Cai J, Ammerman AS. Food store environment modifies intervention effect on fruit and vegetable intake among low-income women in north carolina. J Nutr Metab. 2012;2012:932653. https://doi.org/10.1155/2012/932653.
Kerr J, Norman GJ, Adams MA, et al. Do neighborhood environments moderate the effect of physical activity lifestyle interventions in adults? Health Place. 2010;16(5):903–8. https://doi.org/10.1016/j.healthplace.2010.05.002.
King AC, Salvo D, Banda JA, et al. Preserving Older Adults’ Routine Outdoor Activities in Contrasting Neighborhood Environments through a Physical Activity Intervention. Prev Med. 2017;96:87–93. https://doi.org/10.1016/j.ypmed.2016.12.049.Preserving.
Merom D, Bauman A, Phongsavan P, et al. Can a motivational intervention overcome an unsupportive environment for walking-findings from the step-by-step study. Ann Behav Med. 2009;38(2):137–46. https://doi.org/10.1007/s12160-009-9138-z.
Perez LG, Kerr J, Sallis JF, et al. Perceived Neighborhood Environmental Factors That Maximize the Effectiveness of a Multilevel Intervention Promoting Physical Activity Among Latinas. Am J Heal Promot. 2018;32(2):334–43. https://doi.org/10.1177/0890117117742999.
Schoeny ME, Fogg L, Buchholz SW, Miller A, Wilbur JE. Barriers to physical activity as moderators of intervention effects. Prev Med Reports. 2017;5:57–64. https://doi.org/10.1016/j.pmedr.2016.11.008.
Jilcott Pitts SB, Keyserling TC, Johnston LF, et al. Examining the Association between Intervention-Related Changes in Diet, Physical Activity, and Weight as Moderated by the Food and Physical Activity Environments among Rural. Southern Adults J Acad Nutr Diet. 2017;117(10):1618–27. https://doi.org/10.1016/j.jand.2017.04.012.
Mendez DD, Gary-Webb TL, Goode R, et al. Neighborhood factors and six-month weight change among overweight individuals in a weight loss intervention. Prev Med Reports. 2016;4:569–73. https://doi.org/10.1016/j.pmedr.2016.10.004.
National Health and Nutrition Examinatory Survey (NHANES). Anthropometry Procedures Manual. 2007. https://www.cdc.gov/nchs/data/nhanes/nhanes_07_08/manual_an.pdf.
Bhattacharya T, Kulik L, Bailey J. Extracting significant places from mobile user GPS trajectories: a bearing change based approach. In: Proceedings of the 20th International Conference on Advances in Geographic Information Systems; 2012. p. 398–401.
Adams MA, Frank LD, Schipperijn J, et al. International variation in neighborhood walkability, transit, and recreation environments using geographic information systems: The IPEN adult study. Int J Health Geogr. 2014;13(1):43. https://doi.org/10.1186/1476-072X-13-43.
Pocock SJ. Further Aspects of Data Analysis. In: Clinical Trials: A Practical Approach. New York: John Wiley and Sons; 2013. p. 213–5.
Kessler RC. The use of change scores as criteria in longitudinal survey research. Qual Quant. 1977;11(1):43–66. https://doi.org/10.1007/BF00143986.
Markus G. Models for the Analysis of Panel Data. Beverly Hills: Sage; 1980.
Sallis JF, Saelens BE, Frank LD, et al. Neighborhood built environment and income: Examining multiple health outcomes. Soc Sci Med. 2009;68(7):1285–93. https://doi.org/10.1016/j.socscimed.2009.01.017.
Boone-Heinonen J, Evenson KR, Song Y, Gordon-Larsen P. Built and socioeconomic environments: Patterning and associations with physical activity in U.S. adolescents. Int J Behav Nutr Phys Act. 2010;7:45. https://doi.org/10.1186/1479-5868-7-45.
Ewing R, Meakins G, Hamidi S, Nelson AC. Relationship between urban sprawl and physical activity, obesity, and morbidity - Update and refinement. Heal Place. 2014;26:118–26. https://doi.org/10.1016/j.healthplace.2013.12.008.
Gordon-Larsen P, Nelson MC, Page P, Popkin BM. Inequality in the built environment underlies key health disparities in physical activity and obesity. Pediatrics. 2006;117(2):417–24. https://doi.org/10.1542/peds.2005-0058.
Ledderer L, Kjær M, Madsen EK, Busch J, Fage-Butler A. Nudging in Public Health Lifestyle Interventions: A Systematic Literature Review and Metasynthesis. Heal Educ Behav. 2020;47(5):749–64. https://doi.org/10.1177/1090198120931788.
Kerr J, Norman GJ, Adams MA, et al. Do neighborhood environments moderate the effect of physical activity lifestyle interventions in adults? Heal Place. 2010;16(5):903–8. https://doi.org/10.1016/j.healthplace.2010.05.002.
Arno A, Thomas S. The efficacy of nudge theory strategies in influencing adult dietary behaviour: A systematic review and meta-analysis. BMC Public Health. 2016;16(1):1–11. https://doi.org/10.1186/s12889-016-3272-x.
Goldberg JH, King AC. Physical activity and weight management across the lifespan. Annu Rev Public Health. 2007;28:145–70. https://doi.org/10.1146/annurev.publhealth.28.021406.144105.
Goodpaster BH, DeLany JP, Otto AD, et al. Effects of diet and physical activity interventions on weight loss and cardiometabolic risk factors in severely obese adults: A randomized trial. JAMA - J Am Med Assoc. 2010;304(16):1795–802. https://doi.org/10.1001/jama.2010.1505.
Rock CL, Flatt SW, Byers TE, et al. Results of the exercise and nutrition to enhance recovery and good health for you (ENERGY) trial: A behavioral weight loss intervention in overweight or obese breast cancer survivors. J Clin Oncol. 2015;33(28):3169–76. https://doi.org/10.1200/JCO.2015.61.1095.
McCormack GR. Neighbourhood built environment characteristics associated with different types of physical activity in Canadian adults. Heal Promot Chronic Dis Prev Canada. 2017;37(6):175–85. https://doi.org/10.24095/hpcdp.37.6.01.
Jáuregui A, Salvo D, Lamadrid-Figueroa H, Hernández B, Rivera JA, Pratt M. Perceived neighborhood environmental attributes associated with leisure-time and transport physical activity in Mexican adults. Prev Med (Baltim). 2017;103:S21–6. https://doi.org/10.1016/j.ypmed.2016.11.014.
Van Holle V, Deforche B, Van Cauwenberg J, et al. Relationship between the physical environment and different domains of physical activity in European adults: a systematic review. BMC Public Health. 2012;12:807. https://doi.org/10.1186/1471-2458-12-807.
Van Cauwenberg J, De Bourdeaudhuij I, De Meester F, et al. Relationship between the physical environment and physical activity in older adults: A systematic review. Heal Place. 2011;17(2):458–69. https://doi.org/10.1016/j.healthplace.2010.11.010.
Sallis JF, Cerin E, Kerr J, et al. Built environment, physical activity, and obesity: Findings from the international physical activity and environment network (IPEN) adult study. Annu Rev Public Health. 2019;41:119–39. https://doi.org/10.1146/annurev-publhealth-040218-043657.
Christiansen LB, Cerin E, Badland H, et al. International comparisons of the associations between objective measures of the built environment and transport-related walking and cycling: IPEN adult study. J Transp Heal. 2016;3(4):467–78. https://doi.org/10.1016/j.jth.2016.02.010.
Frank LD, Sallis JF, Saelens BE, et al. The development of a walkability index: Application to the neighborhood quality of life study. Br J Sports Med. 2010;44(13):924–33. https://doi.org/10.1136/bjsm.2009.058701.
Frank LD, Andresen MA, Schmid TL. Obesity relationships with community design, physical activity, and time spent in cars. Am J Prev Med. 2004;27(2):87–96. https://doi.org/10.1016/j.amepre.2004.04.011.
Wilkins E, Radley D, Morris M, et al. A systematic review employing the GeoFERN framework to examine methods, reporting quality and associations between the retail food environment and obesity. Heal Place. 2018;2019(57):186–99. https://doi.org/10.1016/j.healthplace.2019.02.007.
Cobb LK, Appel LJ, Franco M, Jones-Smith JC, Nur A, Anderson CAM. The relationship of the local food environment with obesity: A systematic review of methods, study quality, and results. Obesity. 2015;23(7):1331–44. https://doi.org/10.1002/oby.21118.
Ver Ploeg M, Mancino L, Todd JE, Clay DM, Scharadin B. Where do Americans usually shop for food and how do they travel to get there? Initial findings from the National Household Food Acquisition and Purchase Survey (No. 1476-2017-3882). 2015. https://doi.org/10.22004/ag.econ.262116.
United States Department of Transportation website. Bureau of Transportation Statistics. Automobile Profile. https://www.bts.gov/content/automobile-profile. Accessed 23 Aug 2021.
United States Census Bureau website. Census Bureau Reports: 236,000 Workers Commute into St. Louis County, Mo., Each Day. https://www.census.gov/newsroom/archives/2013-pr/cb13-r24.html. Published 2013. Accessed 9 Sep 2021.
Pinho MGM, Mackenbach JD, Charreire H, et al. Spatial access to restaurants and grocery stores in relation to frequency of home cooking. Int J Behav Nutr Phys Act. 2018;15(1):1–9. https://doi.org/10.1186/s12966-017-0640-6.
Caspi CE, Sorensen G, Subramanian SV, Kawachi I. The local food environment and diet: A systematic review. Heal Place. 2012;18(5):1172–87. https://doi.org/10.1016/j.healthplace.2012.05.006.
Sallis JF, Cain KL, Conway TL, et al. Is your neighborhood designed to support physical activity? A brief streetscape audit tool. Prev Chronic Dis. 2015;12(9):E141. https://doi.org/10.5888/pcd12.150098.
Saelens BE, Handy SL. Built environment correlates of walking: A review. Med Sci Sports Exerc. 2008;40(7 SUPPL 1):S550–66. https://doi.org/10.1249/MSS.0b013e31817c67a4.
Janssen I, Katzmarzyk PT, Ross R. Waist circumference and not body mass index explains obesity-related health risk. Am J Clin Nutr. 2004;79(3):379–84. https://doi.org/10.1093/ajcn/79.3.379.
Fox CS, Massaro JM, Hoffmann U, et al. Abdominal visceral and subcutaneous adipose tissue compartments: Association with metabolic risk factors in the framingham heart study. Circulation. 2007;116(1):39–48. https://doi.org/10.1161/CIRCULATIONAHA.106.675355.
This publication was made possible by Grant Number T32 HL130357 from the National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
This study was funded by the Centers for Disease Control and Prevention U48DP006395, the National Heart, Lung, and Blood Institute (NHLBI) Grant Number T32 HL130357, the National Institute of Diabetes and Digestive and Kidney Diseases of NIH R18DK089461 and The National Institute of Diabetes and Digestive and Kidney Diseases Center for Diabetes Translation Research P30DK092950. The funders had no role in data collection, data analysis, or input on the final manuscript.
Ethics approval and consent to participate
The study was by the Institutional Review Board at Washington University in St. Louis (IRB# 201103147). All participants were provided with the reason for the study. Participants provided written consent. Consent was approved by the ethics committee.
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
About this article
Cite this article
Gilbert, A.S., Salvo, D., Tabak, R.G. et al. Does the neighborhood built environment moderate the effectiveness of a weight-loss intervention for mothers with overweight or obesity? Findings from the Healthy Eating and Active Living Taught at Home (HEALTH) study. Int J Behav Nutr Phys Act 19, 130 (2022). https://doi.org/10.1186/s12966-022-01368-z
- Built Environment
- Health behaviors
- Lifestyle intervention