This study examined the effects of social and physical neighborhood disorder on television, computer, and video game use in a large cross-sectional sample of Canadian youth. High social neighborhood disorder was associated with a 35-45% increased risk of high television, computer, and video game use. Physical neighborhood disorder was not associated with screen time activities after adjusting for social neighborhood disorder. However, high social and physical neighborhood disorder combined was associated with approximately 40-60% increased risk of high television, computer, and video game use.
To our knowledge, seven previous studies have examined the influence of certain aspects of social and physical neighborhood disorder on screen time among youth. The findings of these studies are mixed. In terms of social neighborhood disorder, four studies reported that parental perceptions of crime [22, 23] and safety [7, 24] had little or no influence on screen time use among school-aged children and adolescents. Whereas, two studies reported modest overall associations between high crime rates  and low parental perceptions of neighborhood safety  with screen time. Additionally, one study reported a link between physical disorder and television viewing among 5-year-olds  .
The inconsistency in findings across the previous literature may be due to the measure of neighborhood disorder used. For example, six out of the seven previous studies only considered one dimension of neighborhood disorder such as safety or crime . Thus, these studies may not have fully conceptualized the measure of neighborhood disorder . In addition, all seven previous studies used either a subjective measure (i.e., parent perceptions) or an objective measure (i.e., crime statistics) of neighborhood disorder. However, several investigators have suggested that in order to make a comprehensive assessment of the environment’s condition and accurate inferences regarding the environment’s effect, both objective measures and perceptions of the environment should be considered [30, 33, 44–47], as was done in our study.
The prevalence ratios for screen time observed in the highest social and physical neighborhood disorder quartiles in our study were in the order of 1.13 to 1.60, which would be considered weak to modest effect sizes by epidemiological standards . With that being said, it is important to recognize that exposures measured at the area-level, such as neighborhood disorder in the present study, tend to have smaller effect sizes than individual-level exposures . When compared to the influence that other area-level exposures have on screen time use (e.g., urban-rural status  or neighborhood SES [51, 52]) the risk estimates for social neighborhood disorder observed here and of previous studies [7, 25] were of a similar order of magnitude.
Our findings are supported by ecological models, which recognize the importance of multiple levels of influence, including the neighborhood environment, on health behaviors . Furthermore, our findings are supported by the neighborhood disorder model . High neighborhood disorder has consistently been linked with psychological distress . The premise of the neighborhood disorder model is that high neighborhood disorder negatively influences mental health partly through fear . It has been suggested that people may cope with this fear and distress by minimizing or avoiding their exposure to their neighborhood environment . Thus, youth in the present study that lived in neighborhoods with high neighborhood disorder may have been more inclined to stay indoors to avoid dangerous situations and other deviant behavior. When young people are indoors they are more likely to engage in screen time activities because they are highly accessible .
The present study suggests that social neighborhood disorder is more strongly associated with screen time than physical neighborhood disorder. It is important to note that social neighborhood disorder was assessed through subjective measures (i.e., perceptions), while physical neighborhood disorder was assessed through objective measures. Therefore, differences in findings could be due to differences in measures. However, Molnar and colleagues, who assessed both social and physical neighborhood disorder through objective measures, also reported that social neighborhood disorder was more strongly associated with youths’ recreational physical activity than physical neighborhood disorder . Combined, these observations suggest that high social neighborhood disorder may have a greater influence on whether youth stay indoors, compared to high physical neighborhood disorder. However, the greatest influence on screen time use was observed when examining the combined effects of the variables. Participants living in neighborhoods with both high social and physical neighborhood disorders were approximately 40-60% more likely to be high television, computer, and video game users compared to participants in neighborhoods of low social and physical disorder.
The majority of screen time reduction interventions conducted thus far have been individual or family-focused . However, according to a recent systematic review, these interventions have been largely ineffective in reducing screen time . While there were several methodological concerns with the available evidence in the systematic review , future research is still needed to better understand the environmental influences on screen time and to determine whether interventions can be more successful at reducing screen time among young people if they also take into account relevant area-level factors.
The findings from this study suggest intervening upon high social and physical neighborhood disorder may be one relevant area-level factor to consider for future interventions. However, social and physical neighborhood disorder is a multifaceted issue that has many causes and consequences; therefore, the reduction of social and physical neighborhood disorder will require coordinated efforts from community members, law enforcement, and various other government departments . One example of a coordinated effort between community members and law enforcement aimed at decreasing crime in some communities in Canada is the Neighbourhood Watch program . This program is designed to strengthen community ties by having neighbors look out for other neighbors . Implementing this program or similar programs along with other initiatives to lower fences and increase street lighting in neighbourhoods with high disorder may be one potential intervention strategy .
Along with efforts to decrease neighbourhood disorder, providing safe alternative opportunities to indoor screen time activities for youth who live in neighbourhoods with high disorder should also be considered. The after-school period has been identified as a key window of time for targeting reduction in screen time activities . Therefore, the implementation of affordable community and/or school-based supervised after-school programs in neighbourhoods with high disorder may be another potential intervention strategy. While many youth in Canada (~80%) do not have access to a supervised after-school program , providing programs in areas with high neighbourhood disorder may be especially important for screen time reduction.
The multi-level analyses, the comprehensive measure of neighborhood disorder, the use of a large population-based sample, and the confirmatory sensitivity analyses are strengths of this study. A limitation of this study is the cross-sectional design, which limits the ability to make causal inferences about the relationships observed. Also, the use of self-report data for the screen time measures may have resulted in information bias. Similarly, some inaccuracies with the GIS data may have resulted in information bias of the physical neighbourhood disorder exposure variable. Any biases associated with these measures were likely non-differential, which would have led to the under-estimation of true associations . Furthermore, other potentially important dimensions of neighborhood disorder were not included such as prostitution. Finally, the final sample was no longer representative of the population in terms of age, gender, ethnicity, SES, and urban-rural location.