Background: A common hypothesis is that crime is a major barrier to physical activity, but research does not consistently support this assumption. This article advances research on crime-related safety and physical activity by developing a multilevel conceptual framework and reliable measures applicable across age groups. Methods: Criminologists and physical activity researchers collaborated to develop a conceptual framework. Survey development involved qualitative data collection and resulted in 155 items and 26 scales. Intraclass correlation coefficients (ICCs) were computed to assess test–retest reliability in a subsample of participants (N = 176). Analyses were conducted separately by age groups. Results: Test–retest reliability for most scales (63 of 104 ICCs across 4 age groups) was “excellent” or “good” (ICC ≥ .60) and only 18 ICCs were “poor” (ICC < .40). Reliability varied by age group. Adolescents (aged 12–17 y) had ICCs above the .40 threshold for 21 of 26 scales (81%). Young adults (aged 18–39 y) and middle-aged adults (aged 40–65 y) had ICCs above .40 for 24 (92%) and 23 (88%) scales, respectively. Older adults (aged 66 y and older) had ICCs above .40 for 18 of 26 scales (69%). Conclusions: The conceptual framework and reliable measures can be used to clarify the inconclusive relationships between crime-related safety and physical activity.

A common hypothesis is that crime impedes physical activity, but research does not consistently support this assumption. Literature reviews across age groups have revealed inconsistencies in the relationships between crime-related safety and physical activity.14 In a review of 41 studies, 22 reported significant associations in the expected direction (higher crime associated with less physical activity), 16 found no significant associations, and 3 found higher crime correlated with more physical activity.2 Several authors suggested the relationship between crime-related safety and physical activity may vary based on physical activity domain.58 For example, individuals may have to engage in active transportation to get to places regardless of their feelings of safety, but they may choose to avoid recreational physical activity in unsafe places.

Inconsistent findings in the relationship between crime-related safety and physical activity may also be attributable to measurement issues,9 such as whether objective or subjective measures of crime are examined.8 “Objective” measures based on police records can differ in the type and severity of crimes or size of the geographic area included.10 Self-report measures of crime-related safety often lack conceptual specificity, asking ambiguous questions about overall feelings of “safety” without distinguishing between safety from crime and safety from traffic.2 Global or composite scales may not make a distinction among different meanings of safety and include crime- and noncrime-related items.2 Here, we use the term “crime-related safety” to clearly exclude noncrime issues that may impact physical activity, such as unsafe crosswalks or fear of falling.

The impact of crime-related safety on physical activity may be especially germane for lower income and racial/ethnic minority populations, who are more likely to live in higher crime neighborhoods,11 report physical and social disorder,12 and feel unsafe.13,14 Understanding the relationship between crime-related safety and physical activity may partially explain why lower income and racial/ethnic minority populations suffer disproportionate rates of chronic disease15 and may engage in less physical activity.16,17 At least 2 studies of lower income, ethnically diverse adults found fear of crime was related to less physical activity and higher body mass index.17,18 Yet the literature does not consistently show this expected pattern.2,1921 Ross22 found that, despite greater fear of crime, residents of lower income neighborhoods walked more than residents of higher income neighborhoods. A better understanding of the relationship between crime and physical activity may also help to explain why some individuals or populations living in walkable communities, which are otherwise designed to support physical activity, are not engaging in higher levels of physical activity.

Age may also explain the inconsistent relationship between crime-related safety and physical activity.13,23 Several studies found older adults had a heightened fear of crime that was more consistently correlated with less physical activity than for other age groups, but other studies failed to show a moderating effect of age.2,24 A systematic review of studies measuring objective crime and physical activity3 found that most studies of adults (6/10) reported no significant correlation between crime-related safety and physical activity. In contrast, 6 of 11 studies of adolescents reported that higher crime rates are correlated with lower physical activity.3

Sex may moderate the relationship between crime-related safety and physical activity, but findings are inconsistent.2 Adult women tend to report more fear of crime, and they are more likely than men to avoid physical activity due to perceived crime.2,21,23,25 However, these gender-based differences may diminish as neighborhood crime increases and has a more pervasive effect on communities.26 Studies have shown inconsistent relationships between crime and physical activity for adolescent boys and girls and young adult men and women.2729

We propose that inconsistencies in the empirical evidence about crime-related safety and physical activity may result from an overly simplistic conceptual model and measures that insufficiently consider the dynamic interplay of individual, social, and neighborhood factors affecting the crime–physical activity relation. Physical activity researchers have largely neglected criminology literature on fear of crime,30 which has extensively addressed relationships between crime and the community, and crime and the individual.13,31 Lorenc et al9 provided a conceptual overview of crime and health, but neither the physical activity nor the crime literature offers a testable model that considers multilevel dynamics.

This article sought to advance research on the relationship between crime-related safety and physical activity by developing a multilevel conceptual framework and reliable measures to test hypotheses derived from the framework. A systematic, 3-phase process was followed: phase I involved developing a conceptual framework that identified constructs likely to be part of causal pathways involving crime-related safety and physical activity. In phase II, survey items were developed for each construct of the framework, with the goal of generating items suitable for use across age groups. Phase III evaluated test–retest reliability of the new measures in a subsample of a multigenerational sample from neighborhoods with varying crime rates and income levels.

Methods

Phase I: Development of a Conceptual Framework for Crime-Related Safety and Physical Activity

Experts in fields of criminology, public health, physical activity, psychology, law, and epidemiology collaborated to develop the framework in Figure 1. Drawing heavily from criminology, the framework sought to include a complete set of individual-level predictors of fear of crime. The researchers defined crime as an illegal action for which a person can be punished by law, but recognized that fear may be generated by human behavior construed as harmful, but not technically illegal (eg, some types of harassment). Accordingly, respondents were not provided a definition of “crime” before completing the survey. To more closely match the range of constructs deemed pertinent by criminologists, the framework also included a broad range of psychological and behavioral reactions to crime. The framework was designed to explain individual health-related outcomes, primarily physical activity across domains, locations, and time of day. However, the existing theory and evidence is not sufficient to guide hypotheses about the specific linkages, direction, or strength of pathways among the constructs. Subsequent model testing can be pursued to reveal pathways.

Figure 1
Figure 1

—SAFE Study Conceptual Framework.1 Individual-level data used as a proxy for some mesolevel and macrolevel variables when aggregated data were not available. MVPA indicates moderate to vigorous physical activity; NPAQ, Neighborhood Physical Activity Questionnaire32; PA, physical activity; SAFE study, Safe and Fit Environments Study.

Citation: Journal of Physical Activity and Health 16, 10; 10.1123/jpah.2018-0405

The framework organized concepts at multiple ecological levels of influence (ie, macro, meso, and micro), and allowed for methodological holism and individualism.33 That is, it recognized neighborhood context, social dynamics, and individual factors. As depicted in Figure 1, macrolevel inputs capture neighborhood features, such as crime rates and aspects of the built environment that make it more or less conducive for criminal activity (eg, “Crime Prevention through Environmental Design,” features or “CPTED”). Macrolevel inputs also take into consideration the historical and structural dynamics related to racism and discrimination that may give rise to marginalized communities.3436 Macrolevel measures are typically aggregate indicators of neighborhood context (eg, census data) rather than individual-level perceptions.

Mesolevel inputs are social dynamics arising from face-to-face contact and include features of respondents’ local social networks. Collective efficacy37 includes social control (willingness to intervene on behalf of the neighborhood) and social cohesion (trust and solidarity among neighbors). Neighborhood integration refers to the extent to which the individual knows and engages with neighbors.38 Like macrolevel inputs, social dynamics are generally measured with aggregated data, often through the aggregation of individual survey responses.

Microlevel inputs are individual-level factors. The 4 categories of microlevel inputs in Figure 1 (personal experiences, cognitive assessment, emotional response, and behavioral response) are drawn from the framework in DuBow et al.39 DuBow et al distinguished between cognitive assessments of crime (which they divide into “judgments” and “values”) and emotional responses to crime. Emotional responses to crime can include fear for personal safety or safety of others (eg, one’s children). Under the cognitive assessment umbrella, “judgments” are an individual’s evaluation of crime risk—essentially, their perception of the objective crime levels. “Values,” another type of cognitive assessment, relate to personal tolerance of crime or crime-related concerns.39 Asking residents to rate incivilities (eg, “to what extent is gang activity a problem in your neighborhood?”) solicits a value based on individual perceptions of a problem. In Figure 1, judgments and values appear under the concept of “cognitive assessments,” but they remain distinct constructs and can be tested separately.

Personal experiences with crime victimization (personal or vicarious) are included as microlevel inputs because of the relevance to an individual’s cognitive assessments and emotional responses. “Behavioral responses,” the fourth microlevel concept, include 4 main constructs: protective behaviors, avoidant behaviors, community participation, and obligatory behaviors. The construct of “no behavioral response” captures the null set, that is, participants who report that they do not engage in any of these behaviors in response to crime.

Finally, the model includes individual moderators (microlevel inputs), which include respondent demographics and other generally static characteristics, such as access to transportation. These unchangeable features are “exogenous” and distinguishable from “endogenous” features that are more subject to change, like cognitions and emotions.

Phase II: Survey Development

A systematic process was used to develop survey items and a priori scales for the constructs in the conceptual framework. The survey development process was guided by several principles: (1) draw from existing crime, safety, and physical activity measures when possible; (2) develop new items as needed using focus group and expert input; (3) design items to apply across age, sex, and sociodemographic groups; and (4) minimize respondent burden by limiting length of scales and maintaining a common response format.

The survey development process began with a nonsystematic literature review to identify existing measures in the fields of criminology and physical activity that evaluated constructs in the framework. Over the course of a year, an interdisciplinary team met weekly to review existing measures and adapt them as necessary. The experts discussed the language used in existing items, response formats, and psychometrics. Some items were adopted from existing measures without material change, but most were altered in content, format, or both. Many items were revised to address how crime might relate to 3 physical activity outcomes: walking for transportation, walking for leisure, and physical activity in local parks.

To inform item development, researchers conducted qualitative research in the form of focus groups and key informant interviews. A total of 15 focus groups, with 3 to 11 participants per group (N = 86), were conducted to discuss how crime-related safety affects physical activity in the participants’ communities. Researchers used purposeful nonprobability sampling to recruit participants from different age groups and from neighborhoods with varying rates of objective crime and income levels. Focus group participants were recruited using flyers, word of mouth, and by contacting participants from past studies. Two researchers trained in qualitative methods led the focus groups. To standardize protocols, researchers underwent a day-long training with a qualitative research expert and communications specialist. The moderator guide was informed by the conceptual framework. Focus groups were audio recorded and a note taker was present. After each focus group, 2 researchers separately reviewed the notes and recordings to identify themes and commonly used language. The results informed the item development process by generating new terms and items. For example, the term “mugging” was added to help to describe a robbery, and a new item was added to assess fear when in an unfamiliar place.

In addition to focus groups, stakeholder interviews were conducted with community leaders familiar with local crime-related safety problems and concerns. Potential stakeholders were identified by creating a list of city government agencies, neighborhood associations, and nonprofit community organizations with public safety-related missions or programs. Leaders in high-crime and underserved communities were prioritized, as these communities often lack representation in survey development research. As stated in the introduction, the constructs under study may have particular salience among members of marginalized communities, especially racial and ethnic minority groups, making it critical to capture nuanced aspects of their responses to crime. Stakeholders were recruited through direct e-mails or phone calls, or through referral using a snowball method. Recruitment resulted in 7 stakeholder interviews, plus 1 focus group of 4 individuals from a community-based organization. Interviews were conducted by a researcher trained in qualitative methods, and notes were taken. The semistructured interview guide included topics such as residents’ crime-related safety concerns; behavioral responses to crime; and whether crime concerns differed based on age, sex, or income. Interview notes were organized by theme and used to inform the development of survey items. For example, leaders from higher crime neighborhoods provided input on items relating to gang activity and violent crimes. The construct of “street efficacy” was added in response to interview data.

Following the focus groups, interviews, and resulting changes to survey items, the modified survey was pretested on a convenience sample of 8 individuals to assess item clarity and survey length. To accommodate online responses, items were imported into a secure online platform, Qualtrics, and pretested for usability and online appearance. Further modifications to content and format were made where needed.

The final survey included 155 crime-related items, assessing 17 constructs with 26 total scales because some constructs were multidimensional. The scales were defined a priori based on findings from the literature review, the conceptual framework, and specific hypotheses from investigators, rather than by empirical factor analyses. Table 1 describes the constructs, scales, and sources of each scale.

Table 1

Survey Scales With Operational Definitions, Number of Items, Scale Creation Information, Sample Items, Response Options, and Source Information

ScaleOperational definition, number of items, scale creation method (eg, mean, sum), sample item, and response optionsItem sources
Macrolevel: neighborhood context
  CPTEDReducing opportunities for crime and attractiveness of targets through surveillance (features that maximize visibility, such as windows, lighting, and police patrols); maintenance (upkeep of homes, buildings, landscaping, parks); access control (features that deny access, like locked gates); and territorial reinforcement (barriers, like signage or fencing, that reinforce a sense of ownership). 1 = disagree strongly, 2 = disagree somewhat, 3 = agree somewhat, 4 = agree stronglyConcepts drawn from Refs. 40, 41, 42
 • CPTED: surveillance5 items; mean. “When I walk in my neighborhood, I know there are residents or business owners watching the streets.”
 • CPTED: maintenance3 items; mean. “The homes, buildings, and landscaping in my neighborhood are well maintained.”
 • CPTED: access control2 items; mean. “A lot of the homes or apartment buildings in my neighborhood have fences, locked gates, entrances and/or metal security doors to keep out criminals.”
 • CPTED: territorial reinforcement2 items; mean. “A lot of my neighbors have signs on their property signaling for people to keep out (eg, ‘private property,’ ‘no trespassing,’ ‘security system,’ ‘no soliciting,’ or ‘neighborhood watch’).”
Mesolevel: social dynamics
 Collective efficacyAssesses informal social control (willingness to intervene on behalf of the neighborhood and maintain public order) and social cohesion and trust (mutual trust and solidarity among neighbors)Taken from Ref. 37
9 items; mean. “People in my neighborhood can be trusted.” 1 = disagree strongly . . . 4 = agree strongly
 Neighborhood integrationExtent to which the individual knows and engages with neighbors; relates to helpful neighboring and reciprocal relationships.Adapted from Refs.  38, 43, 44
6 items; mean. “I know many of the people in my neighborhood by name.” 1 = disagree strongly . . . 4 = agree strongly
Microlevel: individual factors (personal experiences)
 VictimizationAssesses recent victimization (≤12 months), past victimization (>12 months), witnessing neighborhood crime, or hearing about neighborhood crime. Four scales. never = 0, 1 time = 1, 2–5 times = 3.5, 6 or more times = 6Concepts drawn from Ref.  45
 • Recent victimization4 items; mean. “In the past 12 months, how many times have YOU been the victim of any of the following . . . A shooting or attempted shooting?”
 • Past victimization4 items; mean. “PRIOR to the past 12 months, how many times have YOU ever been the victim of any of the following . . . Property crimes (including theft, motor vehicle theft, burglary, vandalism)?”
 • Witnessing crime4 items; mean. “In the past 12 months, how many times have you WITNESSED any of the following happening to SOMEONE ELSE in your neighborhood . . . Harassment, verbal abuse, or bullying?”
 • Hearing about crime4 items; mean. “In the past 12 months, how many times have you HEARD (or seen evidence) of any of the following happening to SOMEONE ELSE in your neighborhood . . . Other personal crimes (including being beat up, robbed, mugged, sexually assaulted, or attacked)?”
 Crime information sourcesAssesses where the respondent obtains crime information (eg, media sources, word of mouth, community meetings).Concepts drawn from Ref. 46
10 items; sum. “Please CIRCLE whether you get information about CRIME IN YOUR NEIGHBORHOOD from the following sources . . . radio.” yes = 1, no = 0
Microlevel: individual factors (cognitive assessment of crime)
 Evaluation of riskA cognitive assessment of the likelihood of crime.Adapted from Refs. 4749
12 items; mean. “How likely is it that in the next year, you will be a victim of crime when you are in a local park?” 1 = very unlikely, 2 = somewhat unlikely, 3 = somewhat likely, 4 = very likely
 Values/incivilitiesConcerns relating to the personal tolerance of the respondent to crime and incivilities.Adapted from Refs. 13, 49, 50
17 items; mean. “Please circle to what extent the issue is a problem in your neighborhood . . . gang activity.” 1 = not present in my neighborhood; 2 = present, but not a problem; 3 = present, somewhat a problem; 4 = present, big problem
 Street efficacyConfidence in the ability to avoid crime or to find ways to be safe.Concepts drawn from Ref. 51
4 items; mean. “I am confident I can avoid crime because I am good at fitting in.” 1 = disagree strongly . . . 4 = agree strongly
Microlevel: individual factors (emotional responses to crime)
 Fear of crimeEmotional response related to fear, worry, helplessness, and uneasy feelings.Adapted from Refs. 45, 48, 52, 53
13 items; mean. “I am fearful of being a victim of crime when walking for recreation, health, or fitness in my neighborhood.” 1 = disagree strongly . . . 4 = agree strongly
Microlevel: individual factors (behavioral responses to crime)
 Protective behaviorsMeasures taken to make victimization more challenging for the offender by minimizing the chances of being targeted or minimizing harm if victimized.Adapted from Refs. 54, 55
13 items; mean. “In the past 12 months when you have gone outside, how often have you done the following things to reduce your chances of becoming a victim of crime . . . taken someone with you for safety in numbers?” 1 = never, 2 = rarely, 3 = sometimes, 4 = often
 Avoidant behaviors:Measures taken to decrease exposure to crime. Changing when, where, or what you are doing to avoid encountering crime. Four scales. Sample items below. 1 = never . . . 4 = oftenAdapted from Refs. 45, 56, 57
 • Avoidant behaviors, daylight, alone5 items; mean. “In the past 12 months, how often have you avoided going outside in your neighborhood to reduce your chance of becoming a victim of crime . . . when it’s daylight and you are alone?”
 • Avoidant behaviors, daylight, with others5 items; mean. “In the past 12 months, how often have you avoided being in a local park to reduce your chance of becoming a victim of crime . . . when it’s daylight and you are with other people?”
 • Avoidant behaviors, dark, alone6 items; mean. “In the past 12 months, how often have you avoided walking in places with poor lighting in your neighborhood AFTER DARK to reduce your chance of becoming a victim of crime . . . when it’s dark and you are alone?”
 • Avoidant behaviors, dark, with others6 items; mean. “In the past 12 months, how often have you avoided walking for recreation, health, or fitness in your neighborhood to reduce your chance of becoming a victim of crime . . . when it’s dark and you are with others?”
 Positive avoidant behaviorAvoidant behaviors that may result in increased physical activity (eg, walking a longer route to avoid a dangerous neighborhood).Concepts drawn from Ref. 57
8 items; sum. “In the past 12 months, have you . . . Planned to be home before dark to avoid crime when you do outdoor activities?” yes = 1, no = 0
 News-related avoidant behaviorChanging or avoiding outdoor activities in response to crime-related news.Concepts drawn from Ref. 46
3 items; sum. “I have changed or avoided outdoor activities in response to a crime-related news story that happened in my city.” yes = 1, no = 0
 Obligatory behaviorsEngaging in physical activity, regardless of crime-related perceptions, due to lack of an alternate form of transportation.New items
3 items; sum. “I have to walk or bike because I don’t have a car to use all the time.” yes = 1, no = 0
 Community participationCivic engagement and involvement in neighborhood safety programs.Concepts drawn from Ref. 56
2 items; sum. “In the past 12 months, have you attended a neighborhood meeting to address a neighborhood crime-related problem?” yes = 1, no = 0
 No behavioral responseThis construct captures the null set – ie, participants who report they do not have any behavioral responses to crimeNew items
2 items; mean. “Crime affects my physical activity (walking, biking, jogging) in my neighborhood.” 1 = disagree strongly . . . 4 = agree strongly
Outcome
 Getting out of the houseFrequency an individual goes shopping, spends evenings away from home, and rides public transportation.Adapted from Ref. 58
3 items; sum. “Please circle the number of days per week you usually engage in each of the following activities . . . go shopping.” 0–7 days

Abbreviations: CPTED, Crime Prevention Through Environmental Design; SAFE study, Safe and Fit Environments Study. Note: Items were “taken from” a source if the previously created scale was used without material change. Items were “adapted from” a source if used with some material changes. “Concepts were drawn” from a source if the new items were loosely based on previous scales or research findings, but comprise mostly new language. “New items” are original items developed through the SAFE study survey development process. The complete survey and more information item adaptation from original sources are available online at http://sallis.ucsd.edu/.

Phase III: Test–Retest Reliability Evaluation

Recruitment

The survey was administered to study participants of the Safe and Fit Environments Study (SAFE study) recruited from 4 metropolitan US regions: Baltimore, MD/Washington DC; Seattle and King County, WA; San Diego County, CA; and Phoenix, AZ. Most participants were rerecruited from one of 4 previous studies conducted by the same research team: the Neighborhood Quality of Life Study,59 the Senior Neighborhood Quality of Life Study,60 the Teen Environment and Neighborhood Study,61 and the Neighborhood Impact on Kids Study.62,63 Though not necessary for the test–retest sample, the pool of past participants was supplemented with new participants, recruited from purchased marketing lists of residents who met targeted age and crime criteria and lived in the San Diego, Baltimore, or Seattle regions. The full SAFE study sample also included participants from Phoenix, AZ, who completed the SAFE study survey as part of the WalkIT Arizona study.64 Methods for the studies listed earlier are reported in full elsewhere, as cited. Ethics approval was obtained from the University of California San Diego Human Protections Program, and participant consent (or assent for adolescents) was obtained.

Participant sampling was targeted to achieve a balance of participants from high- and low-crime neighborhoods and across 4 age groups: older adults (aged 66 y and older), middle-age adults (aged 40–65 y), younger adults (aged 18–39 y), and adolescents (aged 12–17 y). Crime was based on each participant’s census block group CrimeRisk value, an index of Uniform Crime Report data modeled to the prior 5 years and population weighted by jurisdiction.65 Personal crimes included murder, rape, robbery, and aggravated assault. Property crimes included burglary, theft, and motor vehicle theft. The CrimeRisk index was normalized so a value of 100 represented the national average. Block groups were categorized as “high” and “low” crime based on median splits of the region-specific CrimeRisk values.

To assess test–retest reliability of the survey items, all participants from the beginning of recruitment were invited to repeat the survey 2 to 5 weeks after completing the survey for the first time. Recruitment for the test–retest subsample continued on a first come, first served basis until 45 test–retest completions in each of the 4 age groups was achieved. Of those asked to participate in the retest, 38% agreed. The test–retest sample did not include participants recruited from the WalkIT Arizona study or via marketing data because they joined the study after the test–retest was completed. The final test–retest subset of participants was considered a nonprobability, or convenience, sample (total N = 176). Demographics of the test–retest sample are reported in Table 2. Participants received $30 for participation in the main study and $15 for the retest.

Table 2

Demographics of Test–Retest Sample (N = 176) by Age Group

Adolescents (11–17 y)Young adults (18–39 y)Middle-aged adults (40–65 y)Older adults (66 y and older)
Number of participantsn = 45n = 41n = 45n = 45
Age, mean (SD)16.1 (1.3)22.3 (4.5)57.5 (6.3)76.1 (6.3)
Sex, % female60.068.353.346.7
White, non-Hispanic, %53.382.982.282.2
Household income (median)$110K$70K$90K$45K
Census block group median household income:
 High income (% above median)55.650.048.943.2
 Low income (% below median)44.450.051.156.8
Crime risk score for block groups:
 High-crime category (% above median)53.328.948.931.8
 Low-crime category (% below median)46.771.151.168.2
Days between test–retest surveys (median)24161721

Statistical Analysis

Mean (SD) were calculated for each of the 26 scales at both time points (initial and retest). To assess test–retest reliability of the scales, intraclass correlation coefficients (ICCs) were computed using SPSS (version 21; IBM Corp, Armonk, NY). The SPSS scale/reliability procedure was used to compute ICCs within each scale using the 2-way mixed (absolute) model for single measures. Analyses were conducted separately by age group to reflect the study design. Magnitudes of the ICCs were summarized using Cicchetti’s66 numeric ranges and descriptors. ICCs were classified as indicating “excellent” (ICC ≥ .75), “good” (.60–.74), “fair” (.40–.59), or “poor” (<.40) test–retest reliability.

Results

Table 3 shows the test–retest reliability ICCs and descriptive statistics (mean [SD] at both time points) for all 26 scales in each of the 4 age groups, producing 104 ICCs. Using Cicchetti’s classifications,66 the test–retest reliability for the majority of the scales (63 of 104 ICCs, 61%) was “excellent” or “good.” Only 18 ICCs (17%) across all age groups were in the “poor” category (ICC below .40), with most of these being in older adults (8 ICCs) or adolescents (5 ICCs). In all 4 age groups, the median ICC across all 26 scales was “good” (median ICC > .60).

Table 3

SAFE Study Scale Reliability Results and Mean Scores at Test and Retest, by Age Group

Adolescents (n = 45)Young adults (n = 41)Middle-aged adults (n = 45)Older adults (n = 45)
ScaleICCMean (SD)

Test and retest
ICCMean (SD)

Test and retest
ICCMean (SD)

Test and retest
ICCMean (SD)

Test and retest
Macrolevel: neighborhood context
 CPTED: surveillance.6142.5 (0.5).5882.5 (0.4).3632.9 (0.4).6452.8 (0.5)
2.6 (0.5)2.6 (0.4)2.8 (0.4)2.8 (0.4)
 CPTED: maintenance.6563.1 (0.6).7903.2 (0.7).4753.3 (0.5).3973.4 (0.5)
3.2 (0.6)3.1 (0.6)3.4 (0.5)3.6 (0.4)
 CPTED: access control.5122.1 (0.9).3601.9 (0.6).5561.6 (0.7).2972.0 (0.8)
2.1 (0.7)1.9 (0.6)1.7 (0.6)1.9 (0.9)
 CPTED: territorial reinforcement.6502.0 (0.7).4221.5 (0.5).6161.7 (0.7).5111.7 (0.6)
2.1 (0.8)1.7 (0.6)1.6 (0.6)1.7 (0.6)
Mesolevel: social dynamics
 Collective efficacy.5592.8 (0.4).6852.5 (0.5).6202.7 (0.4).7602.8 (0.4)
2.9 (0.4)2.6 (0.4)2.8 (0.3)2.8 (0.3)
 Neighborhood integration.8542.4 (0.8).8272.1 (0.7).8832.6 (0.8).8852.4 (0.7)
2.5 (0.7)2.2 (0.7)2.7 (0.7)2.5 (0.8)
Microlevel: individual factors (personal experiences)
 Victimization: recent−.0450.1 (0.3)−.0660.2 (0.3)−.0860.1 (0.2)−.0810.1 (0.2)
0.1 (0.1)0.2 (0.3)0.1 (0.2)0.0 (0.1)
 Victimization: past.7080.2 (0.4).6590.4 (0.5).6190.3 (0.5).1690.3 (0.5)
0.1 (0.2)0.4 (0.4)0.5 (0.5)0.2 (0.2)
 Victimization: witnessing crime.3610.1 (0.3).5170.2 (0.3).7370.1 (0.2).0410.1 (0.3)
0.2 (0.4)0.2 (0.4)0.1 (0.2)0.0 (0.1)
 Victimization: hearing about crime.2190.5 (0.7).7570.7 (0.7).7230.4 (0.5).3740.4 (0.5)
0.4 (0.4)0.7 (0.7)0.5 (0.7)0.2 (0.3)
 Crime information sources.5803.7 (2.4).7094.8 (2.1).7304.3 (2.2).5824.4 (2.3)
3.8 (2.4)4.8 (2.0)4.2 (2.0)4.2 (2.1)
Microlevel: individual factors (cognitive assessment of crime)
 Evaluation of risk.6641.3 (0.5).6041.3 (0.4).8471.3 (0.4).3411.3 (0.4)
1.4 (0.4)1.4 (0.5)1.3 (0.4)1.2 (0.3)
 Values/incivilities.8871.5 (0.5).9031.6 (0.5).8521.4 (0.3).7841.3 (0.3)
1.5 (0.5)1.6 (0.5)1.4 (0.4)1.2 (0.3)
 Street efficacy.6323.0 (0.6).8152.7 (0.7).5242.7 (0.7).6512.8 (0.7)
3.0 (0.6)2.8 (0.7)2.5 (0.7)2.8 (0.8)
Microlevel: individual factors (emotional responses to crime)
 Fear of crime.7241.8 (0.6).8461.7 (0.4).7421.5 (0.4).7421.5 (0.5)
1.8 (0.6)1.7 (0.5)1.4 (0.4)1.5 (0.5)
Microlevel: individual factors (behavioral responses to crime)
 Protective behaviors.7451.8 (0.7)0.8301.9 (0.6)0.7021.6 (0.5)0.8611.6 (0.5)
1.9 (0.6)1.8 (0.6)1.7 (0.5)1.6 (0.5)
 Avoidant behavior: daylight, alone.4201.4 (0.7).5561.2 (0.3).8061.2 (0.4).6261.2 (0.5)
1.5 (0.7)1.3 (0.6)1.1 (0.4)1.1 (0.4)
 Avoidant behavior: daylight, with others.1771.2 (0.4).4671.1 (0.3).2201.1 (0.3).6651.1 (0.4)
1.3 (0.5)1.2 (0.5)1.1 (0.3)1.1 (0.4)
 Avoidant behavior: dark, alone.6772.2 (1.0).8281.9 (1.0).7651.6 (0.8).7381.8 (0.9)
2.2 (1.0)1.9 (0.9)1.7 (0.9)1.6 (0.7)
 Avoidant behavior: dark, with others.5411.8 (0.8).7971.6 (0.8).8161.4 (0.6).7971.5 (0.8)
1.8 (0.8)1.5 (0.7)1.3 (0.7)1.4 (0.6)
 Positive avoidant behavior.4891.8 (2.1).7171.5 (1.9).8381.5 (2.1).6151.3 (1.7)
2.4 (2.5)1.8 (2.2)1.4 (2.2)1.0 (1.7)
 News-related avoidant behavior.5550.5 (0.8).6060.5 (0.8).5560.3 (0.6).2560.3 (0.7)
0.5 (0.9)0.7 (0.9)0.4 (0.7)0.2 (0.6)
 Obligatory behavior.8000.8 (1.0).8540.2 (0.7).5100.2 (0.5).6660.2 (0.7)
1.0 (1.2)0.2 (0.6)0.3 (0.8)0.2 (0.7)
 Community participation.0430.2 (0.4).6170.1 (0.3).5880.2 (0.5).7240.2 (0.4)
0.1 (0.4)0.2 (0.4)0.3 (0.6)0.2 (0.5)
 No behavioral response.7281.4 (0.7).9041.5 (0.6).8081.4 (0.7).4981.3 (0.6)
1.5 (0.7)1.4 (0.6)1.3 (0.6)1.2 (0.4)
Outcome
 Getting out of the house.6014.2 (2.6).5664.9 (3.0).5595.2 (2.8).6745.0 (3.1)
3.9 (2.6)4.7 (2.7)5.7 (3.7)4.7 (2.4)
Median ICCs.608.697.661.636

Abbreviations: CPTED, Crime Prevention Through Environmental Design; ICC, intraclass correlation coefficient; SAFE study, Safe and Fit Environments Study.

Of the 18 ICCs below the .40 threshold, half (9) of the scales measured victimization (all with low frequencies of occurrence), 4 measured Crime Prevention through Environmental Design, 3 measured avoidant behaviors, 1 measured evaluation of risk, and 1 measured community participation.

Reliability varied by age group (Table 4). Adolescents had ICCs above the .40 “poor” threshold for 21 of the 26 scales (81%). Young and middle-aged adults had ICCs above the .40 threshold for 24 (92%) and 23 (88%) of the 26 scales, respectively. Older adults had ICCs above the .40 threshold for 18 of the 26 scales (69%). Older adult respondents had the poorest test–retest reliability, accounting for 8 of the 18 scales (44%) with ICCs below .40. Adolescents accounted for 5 of the 18 scales (28%) with ICCs below .40. Young and middle-aged adults combined had 5 scales (28%) out of 18 scales with ICCs below .40.

Table 4

Summary of Test–Retest ICCs: Number of Scales Per Classification From “Excellent” to “Poor,” by Age Groupa

Adolescents (aged 12–17 y)Young adults (aged 18–39 y)Middle-aged adults (aged 40–65 y)Older adults (aged 65 y and older)Total no. of scales
“Excellent”: ICC ≥ .753118527 (26%)
“Good”: ICC .60–.7411781036 (35%)
“Fair”: ICC .40–.59767323 (22%)
“Poor”: ICC < .40523818 (17%)
Total26262626104

Abbreviation: ICC, intraclass correlation coefficient.

aCicchetti.66

Discussion

The present study developed a conceptual framework and reliable measures to use in studies that can improve the understanding of the relationship between crime-related safety and physical activity. The systematic survey development process used in the SAFE study included a literature review, creation of a transdisciplinary conceptual framework, qualitative data collection, consideration of existing items and adaptation and/or development of new items, and evaluation of test–retest reliability in 4 age groups. Results showed the vast majority of SAFE study scales were reliable when completed by participants ranging in age from adolescence to older adulthood. Twenty-six scales were tested for reliability in 4 age groups, producing 104 ICC scores. Of those, 86 ICC scores (83%) were above the commonly defined “fair” threshold of .40.66,67

The need for the present study was driven by inconsistencies in the literature that make it unclear how crime-related safety and physical activity are related.2,3 Evaluation of a more complete and diverse set of constructs that better incorporate criminology scholarship may help to elucidate nuances of whether and how crime affects physical activity for different people under different circumstances. The development of a multilevel conceptual framework and reliable measures provides a critical first step to help to resolve inconsistencies through improved future research.

Several scales had test–retest reliability below the .40 threshold. As ICCs are relative measures that depend on the variability of the sample, low ICCs may be attributable to the lack of response variability for certain items and/or small sample size.68 Many of the scales with low ICCs had a low frequency of occurrences. For example, reports of crime victimization were very low in these test–retest reliability participants, and it is notable that half (9 of 18) of the low ICCs were for crime victimization scales. Because of the small sample size used in the reliability analyses, scales with low ICCs were retained for testing of the conceptual model in the full SAFE study sample.

Low ICCs were observed more often in the extreme age groups of older adults and adolescents. Perhaps participants in these age groups were less reliable reporters. For example, items relating to victimization prior to the past 12 months had low test–retest reliability in the older adult sample. This may be because it was harder for respondents to remember events that occurred over a year ago.

Study strengths included the development of a transdisciplinary conceptual framework and the implementation of a systematic survey development process. Item development was enriched by conducting focus groups and stakeholder interviews. Recruiting participants across 4 age groups, from 3 US regions, and from neighborhoods selected to vary in crime risk and socioeconomic status, enhanced generalizability of the findings. Several US regions, such as the southeastern states, were not included in the sample. Other study limitations included a small test–retest sample that was balanced across age groups but not other factors such as race/ethnicity and geography. The small test–retest sample precluded confirmatory factor analysis. However, confirmatory factor analysis is planned for the full SAFE study sample.

Conclusions

Concerns and fear about crime are often mentioned as barriers to physical activity, and may be especially relevant among subgroups at risk for low leisure-time physical activity,18 yet several reviews have concluded the literature on the relationship between crime-related safety and physical activity is inconsistent and inconclusive.2,3 To overcome limitations of prior studies, a more comprehensive conceptual framework was proposed, drawing on concepts and evidence from criminology. Measures were developed that would allow the framework to be evaluated. Assessing test–retest reliability was the first step in evaluating the performance of scales designed to capture a broad range of crime-related safety constructs. Results showed the majority of SAFE study scales were reliable for use in participants aged 12 years or older. We recommend further assessing test–retest reliability in more diverse samples, especially focusing on demographic groups that experience high risk of crime. Although the proposed model is extensive, there are likely to be additional constructs that could be fruitfully incorporated in future studies. These could include community relationships with police, residential segregation, and recent news reports about local crimes.

Acknowledgments

Carrie M. Geremia and Edith A. Bonilla (UC San Diego) managed data, recruitment, and study administration. This work was supported by NIH Grant R01 HL117884.

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Patch is with the University of California, San Diego, CA, USA; and the Joint Doctoral Program in Public Health, Health Behavior, San Diego State University, CA, USA. Roman and Taylor are with the Department of Criminal Justice, Temple University, Philadelphia, PA, USA. Conway, Gavand, Cain, Engelberg, and Sallis are with the Department of Family Medicine and Public Health, University of California, San Diego, CA, USA. Saelens is with the Department of Pediatrics, University of Washington & Seattle Children’s Research Institute, Seattle, WA, USA. Adams is with the School of Nutrition and Health Promotion (SNHP), Arizona State University, Phoenix, AZ, USA. Mayes is with the Department of Criminology, Vancouver Island University, Nanaimo, British Columbia, Canada. Roesch is with the Department of Psychology, San Diego State University, San Diego, CA, USA.

Patch (cmt@ucsd.edu) is corresponding author.
Journal of Physical Activity and Health
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    —SAFE Study Conceptual Framework.1 Individual-level data used as a proxy for some mesolevel and macrolevel variables when aggregated data were not available. MVPA indicates moderate to vigorous physical activity; NPAQ, Neighborhood Physical Activity Questionnaire32; PA, physical activity; SAFE study, Safe and Fit Environments Study.

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