Few studies assess built environment correlates of active commuting in low-and-middle-income countries (LMICs), but the different context could yield distinct findings. Policies and investments to promote active commuting remain under-developed in LMICs like India, which grapples with traffic congestion, lack of activity-supportive infrastructure, poor enforcement of traffic rules and regulations, air pollution, and overcrowding. This cross-sectional study investigated associations between home neighborhood environment characteristics and active commuting in Chennai, India. Adults (N = 370, 47.2% female, mean age =37.9 years) were recruited from 155 wards in the metropolitan area of Chennai in southern India between January and June 2015. Participants self-reported their usual mode of commute to work, with responses recoded into three categories: (1) multi-modal or active commuting (walking and bicycling; n = 56); (2) public transit (n = 52); and (3) private transport (n = 111). Environmental attributes around participants' homes were assessed using the Neighborhood Environment Walkability Scale for India (NEWS-India). Associations between environmental characteristics and likelihood of active commuting and public transit use were modeled using logistic regression with private transport (driving alone or carpool) as the reference category, adjusting for age, gender, and household car ownership. Consistent with other international studies, participants living in neighborhoods with a mix of land uses and a transit stop within a 10-minute walk from home were more likely to use active commuting (both
Objective To assess the influence of several potential psychosocial determinants on children's eating behavior. Participants Three hundred fifty-one Mexican-American and non-Hispanic white children (mean age=4.4 years old at baseline) participated in the San Diego Study of Children's Activity and Nutrition for up to 2.5 years. Methods Child's eating behavior was described by 3 dependent variables: total energy, percentage energy from fat, and sodium intake per 1,000kcal. Dietary information was collected 4 days a year using a 24-hour food intake record, which was a combination of direct observation and interviews with food preparers. The 35 predictor variables from child, parental, demographic, and environmental domains were collected by behavioral observation, interviewer-administered questionnaires, and physical measurements. Statistical analyses Bivariate and regression analyses via mixed linear models were performed. Results Variables from the children's domain (such as skinfold thickness and weight) had the strongest associations with energy intake; parental variables (such as fat avoidance behavior and prompts to increase children's food intake) were associated with child's percentage energy from fat and sodium intake. In regression analyses, parsimonious subsets of variables accounted for 46% of variance in energy intake (3 variables), 40% of the variance in percentage of energy from fat (4 variables), and 44% of variance in sodium intake per 1,000 kcal energy (1 variable) in between-subject variance components. Conclusions Fat and sodium intake of children may be improved by improving parents’ nutrition habits and by having parents encourage children to eat a healthful diet. Few modifiable correlates of children's energy intake were identified. J Am Diet Assoc. 1998;98:1282–1289.
Purpose To test the validity of the Ecological Video Identification of Physical Activity (EVIP) computer vision algorithms for automated video-based ecological assessment of physical activity in settings such as parks and schoolyards. Methods Twenty-seven hours of video were collected from stationary overhead video cameras across 22 visits in nine sites capturing organized activities. Each person in the setting wore an accelerometer, and each second was classified as moderate-to-vigorous physical activity or sedentary/light activity. Data with 57,987 s were used to train and test computer vision algorithms for estimating the total number of people in the video and number of people active (in moderate-to-vigorous physical activity) each second. In the testing data set (38,658 s), video-based System for Observing Play and Recreation in Communities (SOPARC) observations were conducted every 5 min (130 observations). Concordance correlation coefficients (CCC) and mean absolute errors (MAE) assessed agreement between (1) EVIP and ground truth (people counts+accelerometry) and (2) SOPARC observation and ground truth. Site and scene-level correlates of error were investigated. Results Agreement between EVIP and ground truth was high for number of people in the scene (CCC = 0.88; MAE = 2.70) and moderate for number of people active (CCC = 0.55; MAE = 2.57). The EVIP error was uncorrelated with camera placement, presence of obstructions or shadows, and setting type. For both number in scene and number active, EVIP outperformed SOPARC observations in estimating ground truth values (CCC were larger by 0.11–0.12 and MAE smaller by 41%–48%). Conclusions Computer vision algorithms are promising for automated assessment of setting-based physical activity. Such tools would require less manpower than human observation, produce more and potentially more accurate data, and allow for ongoing monitoring and feedback to inform interventions.
Physical inactivity is responsible for major health and economic costs in the United States. Despite widespread recognition of the scope and importance of the problem of physical inactivity, only modest progress has been made in improving overall physical activity in the U.S. population. This paper applies a combined economic and public health perspective to better understand physical activity behavior and to guide a search for promising new economically oriented interventions to increase physical activity at the population level. This perspective is operationalized as the SLOTH model—a time-budget model incorporating Sleep, Leisure, Occupation, Transportation, and Home-based activities. Key economic forces that may influence individuals' choices about utilization of time and physical activity are identified. Potential interventions are proposed in response to each of the important forces and are evaluated on four criteria: (1) economic efficiency, (2) equity, (3) effectiveness, and (4) feasibility. The SLOTH model provides guidance regarding interventions that might increase physical activity in each of the four nonsleep domains. Economic intervention strategies are proposed and compared to economic and public health criteria. The results provide a starting point for selecting and evaluating potentially effective and feasible economic interventions that might be implemented as part of a larger effort to address the health crisis of inactive lifestyles and obesity.
According to ecological models, the physical environment plays a major role in determining individual health behaviors. As such, researchers have started targeting the consumer nutrition environment of large-scale foodservice operations when implementing obesity-prevention programs. In 2010, the American Hospital Association released a call-to-action encouraging health care facilities to join in this movement and improve their facilities' consumer nutrition environments. The Hospital Nutrition Environment Scan (HNES) for Cafeterias, Vending Machines, and Gift Shops was developed in 2011, and the present study evaluated the inter-rater reliability of this instrument. Two trained raters visited 39 hospitals in southern California and completed the HNES. Percent agreement, kappa statistics, and intraclass correlation coefficients were calculated. Percent agreement between raters ranged from 74.4% to 100% and kappa statistics ranged from 0.458 to 1.0. The intraclass correlation coefficient for the overall nutrition composite scores was 0.961. Given these results, the HNES demonstrated acceptable reliability metrics and can now be disseminated to assess the current state of hospital consumer nutrition environments.
Background: Physical activity declines during childhood and determinants of this decline are poorly understood. The purpose of this prospective study was to examine modifiable psychological and social correlates of physical activity change over 20 months. Subjects: 370 girls and 362 boys in public schools, studied through the 4th and 5th grades. Measures: A physical activity index was computed from child reports, parent reports, and objective activity monitoring. Predictor variables were assessed by child and parent surveys. Results: In multiple regressions, after adjustment for demographic variables, psychological variables explained 4% of the variance in boys’ physical activity change, parent variables explained 8%, and interactions with skinfolds explained 3%, for a total of 15% (unadjusted). Regarding change in girls’ physical activity, psychological variables explained 3%, parent variables explained 1.5%, and interactions with skinfold explained 1.5%, for a total of 6% explained variance (unadjusted). There were differences in predictors among children with high and low levels of body fat. Conclusions: Children’s preferences for physical activity and frequency of parents transporting children to activity locations explained significant proportions of variance for girls and boys. The results support a dynamic model of the determinants of children’s physical activity, but most of the variance was left unexplained.
Some attributes of neighborhood environments are associated with physical activity among older adults. This study examined whether the associations were moderated by driving status. Older adults from neighborhoods differing in walkability and income completed written surveys and wore accelerometers (N = 880, mean age = 75 years, 56% women). Neighborhood environments were measured by geographic information systems and validated questionnaires. Driving status was defined on the basis of a driver’s license, car ownership, and feeling comfortable to drive. Outcome variables included accelerometer-based physical activity and self-reported transport and leisure walking. Multilevel generalized linear regression was used. There was no significant Neighborhood Attribute × Driving Status interaction with objective physical activity or reported transport walking. For leisure walking, almost all environmental attributes were positive and significant among driving older adults but not among nondriving older adults (five significant interactions at p < .05). The findings suggest that driving status is likely to moderate the association between neighborhood environments and older adults’ leisure walking.
Background To date, nearly all research on physical activity and the built environment is based on self-reported physical activity and perceived assessment of the built environment. Objective To assess how objectively measured levels of physical activity are related with objectively measured aspects of the physical environment around each participant’s home while controlling for sociodemographic covariates. Methods Objective measures of the built environment unique to each household’s physical location were developed within a geographic information system to assess land-use mix, residential density, and street connectivity. These measures were then combined into a walkability index. Accelerometers were deployed over a 2-day period to capture objective levels of physical activity in 357 adults. Results Measures of land-use mix, residential density, and intersection density were positively related with number of minutes of moderate physical activity per day. A combined walkability index of these urban form factors was significant (p =0.002) and explained additional variation in the number of minutes of moderate activity per day over sociodemographic covariates. Thirty-seven percent of individuals in the highest walkability index quartile met the ≥30 minutes of physical activity recommended, compared to only 18% of individuals in the lowest walkability quartile. Individuals in the highest walkability quartile were 2.4 times more likely (confidence interval=1.18–4.88) than individuals in the lowest walkability quartile to meet the recommended ≥30 minutes of moderate physical activity per day. Conclusions This research supports the hypothesis that community design is significantly associated with moderate levels of physical activity. These results support the rationale for the development of policy that promotes increased levels of land-use mix, street connectivity, and residential density as interventions that can have lasting public health benefits.
