the stanford healthy neighborhood discovery tool
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The Stanford Healthy NeighborhoodDiscovery Tool
A Computerized Tool to AssessActive Living Environments
Matthew P. Buman, PhD, Sandra J. Winter, PhD, Jylana L. Sheats, PhD, MPH,Eric B. Hekler, PhD, Jennifer J. Otten, PhD, RD, Lauren A. Grieco, PhD, Abby C. King, PhD
Background: The built environment can influence physical activity, particularly among olderpopulations with impaired mobility. Existing tools to assess environmental features associated withwalkability are often cumbersome, require extensive training, and are not readily available for use bycommunity residents.
Purpose: This project aimed to develop and evaluate the utility of a computerized, tablet-basedparticipatory tool designed to engage older residents in identifying neighborhood elements thataffect active living opportunities.
Methods: Following formative testing, the tool was used by older adults (aged �65 years, in 2011)to record common walking routes (tracked using built-in GPS) and geocoded audio narratives andphotographs of the local neighborhood environment. Residents (N�27; 73%women; 77%with somecollege education; 42%used assistive devices) from three low-income communal senior housing sitesused the tool while navigating their usual walking route in their neighborhood.Datawere analyzed in2012.
Results: Elements (from 464 audio narratives and photographs) identifıed as affecting activeliving were commensurate with the existing literature (e.g., sidewalk features, aesthetics, parks/playgrounds, crosswalks). However, within each housing site, the profıle of environmentalelements identifıed was distinct, reflecting the importance of granular-level information col-lected by the tool. Additionally, consensus among residents was reached regarding whichelements affected active living opportunities.
Conclusions: This tool serves to complement other assessments and assist decision makers inconsensus-building processes for environmental change.(Am J Prev Med 2013;44(4):e41–e47) © 2013 American Journal of Preventive Medicine
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Background
There is growing national consensus that the builtenvironment must be redesigned to optimize ac-tive living.1–5 However, it remains unclear which
nvironmental features best support this goal6 and howto prioritize environmental changes so that they reflect
From the Stanford Prevention Research Center (Buman, Winter, Sheats,Hekler, Otten, Grieco, King), the Department of Health Research andPolicy (King), Stanford University School of Medicine, Stanford, Califor-nia; and the School of Nutrition and Health Promotion (Buman, Hekler),Arizona State University, Phoenix, Arizona
Address correspondence to: Matthew P. Buman, PhD, Arizona StateUniversity, School of Nutrition and Health Promotion, 500 N. 3rd Street,Mail Code 3020, Phoenix AZ 85004-2135. E-mail: [email protected].
0749-3797/$36.00http://dx.doi.org/10.1016/j.amepre.2012.11.028
© 2013 American Journal of Preventive Medicine • Published by Elsev
the needs of the local community. New methods of cost-effıcient data collection are needed that can generate lo-cal, relevant data to foster community-focused planningand policy solutions.7 Unfortunately, tools used to assessctive living environments are often insuffıcient for in-orming local-level change: they are often cumbersomend can require intensive training8,9; self-report mea-ures often are not correlated with objective measures10;GIS-based measures are often inaccessible, and mi-croscale data unavailable to identify specifıc commu-nity needs.This article reports on the development and deploy-
ment of the Stanford Healthy Neighborhood DiscoveryTool (referred to hereafter as the Discovery Tool), a
computerized, tablet-based participatory tool designed toier Inc. Am J Prev Med 2013;44(4):e41–e47 e41
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assist residents in identifying neighborhood features thataffect active living. Specifıcally, this paper reports on thefollowing aspects of the Discovery Tool: (1) the develop-ment and design process; (2) the process of identifıcationof neighborhood features by residents; and (3) its perfor-mance in capturing consensus among residents.
MethodsTool Development
A community-based participatory research11 orientation and aniterative user-centered design process12were used for development(Figure 1). The planning group included an academic–communitypartnership with the San Mateo County CA Health Departmentand other local offıcials in transportation, aging, and housing, inaddition to local housing coalitions, public housing management,and community residents.The target user group was low-income, racial/ethnic-minority
older adults (aged �65 years), the segment of the U.S. populationthat is most physically inactive.13,14 This population has beenshown to have higher levels of physical activity when living inpedestrian-friendly “walkable” urban neighborhoods and yet haveunique challenges with many urban elements, such as traffıc andsafety levels.15–19 Initial design was driven by informal observa-ions of community-conducted PhotoVoice activities20 and tradi-tional pen-and-paper audits7 with residents and local decisionmakers. Paper prototypes21 and an electronic prototype of theDiscovery Tool underwent user testing (with six residents) to im-prove the user interface, device wearability, and user training.
Tool Description
The Discovery Tool is a computerized, handheld tablet–basedenvironment assessment tool that provides contextual (i.e., mi-croscale) data about walking routes; environmental features (e.g.,sidewalks, crosswalks); and destinations. Users are prompted tocollect geocoded audio narratives and photographs about neigh-borhood features they perceive as affecting their active livingchoices. Quantitative data also were collected via a built-in 25-item
Pilot research project
Community-led environmental
audit
Paper and initial working
prototype development
Alpha version
Initial user testing
Desigmodifica
Initial community
working group
Stanford researchersCommunity residents
Community residents
Figure 1. Stanford Healthy Neighborhood Discovery Tool d
post-assessment survey.
The Discovery Tool operates on Android 2.3 and newer Googlelatforms and was optimized for viewing on the Samsung Galaxyab 7.7. The Discovery Tool was based on traditional PhotoVoiceethodology but leverages mobile technology to track routesalked using GPS and records geocoded audio narratives andhotographs. Users reported preferring the 7-inch tablet to amartphone (which is harder to grasp and has small buttons) orull-sized tablet (which ismore cumbersome to carry). Tabletswerequippedwith wired, clip-onmicrophone Skullcandy® earbuds foraudio instructions and were worn around the neck in a plastic andwaterproof MZ Services protective case to allow users to walkhands-free.
Participants
Participants qualifıed for U.S. Department of Housing and UrbanDevelopment low-income housing and resided in three seniorhousing sites in three San Francisco peninsula-region cities: SouthSan Francisco (40 units); Menlo Park (93 units); and San Mateo(200 units). Participants were recruited after onsite demonstrationof the tool and explanation of the study. Residents learned aboutthese demonstrations via housing management and flyers postedonsite. This project was ruled exempt by the local IRB given itsfocus on neighborhood characteristics (no private, identifıable in-formation was collected).
Neighborhood Assessment Procedures
Residents conducted environmental assessment of the neighbor-hood surrounding their housing site. Their participation lasted1hour andconsistedof (1) trainingonhow to use theDiscoveryTool(15minutes); (2) the environmental assessment (up to 30minutes);(3) data collection review (5 minutes); and (4) post-assessmentsurvey (10 minutes). Residents were asked to conduct the assess-ment by navigating their “usual” walking route through theirneighborhood (i.e., routes used for leisure, to access transportationor destinations), originating from their housing site. Participantswere given a $10 gift card for participation. Data were collected in
Iterative designprocess (IDP)
Community partners
onfirmatory testing
Beta version
Senior housing
deployment
Map-based prototype for
decisionmakers
Local decisionmakers
xpert panel
Stanford researchers
Housing management
lopment process
n tions
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2011.
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Data Analysis
Audio narratives and photographs were downloaded for data anal-ysis. The researchers used both inductive and deductive analyticstrategies22–24 to code the audio narratives and photographs, andthey followed recommended procedures25,26 to ensure that thecoded elements were “believable, accurate, and right.”26 Two cod-ers with expertise in environmental research and not involved inthe data collection process independently provided an initial re-view of the raw data and then met to discuss similarities anddifferences observed in data coding. A coding schema was gener-ated based on these discussions and existing environmental audittools.27–29 Two additional coders reviewed the schema for con-truct validity, and four additional coders joined researchmeetingso discuss the schema.Data were divided evenly across eight coders such that each audioarrative and photograph was reviewed independently twice (coderspent 4–6 hours to complete this process). A full discussion of thenter-rater reliability of the coding is beyond the scope of this papernd is discussed elsewhere.30 Briefly, for both the audio narratives andphotographs, inter-rater reliabilitywashigh,withobserved agreementof�90%, and prevalence- and bias-adjusted kappa�0.80.The quantitative analyses of the audio narrative and photographic
lementsweredescriptive. Total frequency refers to overall prevalencef coded neighborhood elements, with more than one instance of aoded element included for each participant. Subject-level frequencyefers to the number of participants recording the coded element (forhis variable, only one instance of an element was allowed for eacharticipant). Site-level frequency, or “consensus,” was defıned in leg-slative procedure terms31 such that a supermajority (�67%) of par-ticipants were needed to identify the coded element for positive con-sensus, and �33% of participants were needed to identify the codedelement for negative consensus. Data were analyzed in 2012.
ResultsParticipantsCommunity residents (N�27) conducted neighbor-hood assessments around three senior housing sites inthe San Francisco peninsula region: South San Fran-cisco (n�7); Menlo Park (n�9); and San Mateo(n�11). Participants were English-speaking; primarilywomen (73%); aged 65–89 years (92%, with two aged�90 years); non-Hispanic white (58%); and with somecollege education (77%). A number of participants(42%) reported using an assistive device (e.g., walker,scooter, wheelchair) and did so while completing theirneighborhood assessment.
Descriptive Geographic, Audio Narrative, andPhotographic DataResidents walked, on average, 1.0�0.6 km (0.6�0.4miles), and the large majority of residents (89%) did nottravel beyond a four-block radius from their residence. Atotal of 116 audio narratives and 126 photographs werecaptured. Participants recorded, on average, 3.5�3.5 au-dio narratives (mean length: 29.7�17.7 seconds) and
4.9�3.6 photographs during their neighborhoodApril 2013
assessment. The qualitative analysis yielded a total of 19coded elements: nine present in the audio narratives andphotographs (shared elements); fıve in audio narrativesonly (unique elements); and fıve in photographs only(unique elements). Coded elements had both positive(i.e., facilitators of active living) and negative (i.e., barri-ers to active living) valences. Because many audio narra-tives and photographs contained multiple coded ele-ments, 464 total coded elements were included in theanalyses.
Neighborhood Elements IdentifiedTable 1 provides descriptive information about the con-tent of the audio narratives and photographs. Using theaudio narratives, participants recorded a relatively equalnumber of facilitators (52%) and barriers (48%). The mostcommon audio-captured facilitators were aesthetics (e.g.,presence of trees, flowers); parks/playgrounds (e.g., walkingpaths, public garden); amenities/destinations (e.g., shops,restaurants, public services); personal safety (e.g., “crimefree,” “upscale living”); and sidewalk features (e.g., conve-nient routes, well kept).The most common barriers were negative sidewalk
features (e.g., cracks, unevenness); personal safety issues(e.g., afraid of being hit by a vehicle); disability issues (e.g.,street not suitable for wheelchair or walker, lack oframps); crosswalk limitations (e.g., cars do not stop, sig-nals inoperable); and road safety (e.g., speeding cars,blind driveways). Using photographs, participants re-corded more facilitators (76%) than barriers (24%). Themost common facilitators were aesthetics, sidewalk fea-tures, crosswalks, parks/playground, and trails/paths,respectively. The predominant barrier was negativesidewalk features.
Housing Site–Level Comparisons and LocalConsensusSubject-level frequencies and percentages of coded ele-ments are displayed by housing site in Table 2. Superma-jority consensus (�67% of residents at the given siterecording the element) was reached for fıve housing site–specifıc elements overall: one at South San Francisco,three at Menlo Park, and one at San Mateo. These ele-ments included positive aesthetics, attractive amenities/destinations, and deleterious sidewalk features. Negativeconsensus (�33% of residents at the given site recordingthe element) was high for all three sites, indicating thatresidents could largely agree on what was not importantfor active living (e.g., public transportation, crime, park-ing). Total consensus (reflective of combining elementsreaching either supermajority or negative consensus) was�90% at South San Francisco and Menlo Park sites, and
�70% for the San Mateo site.p
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DiscussionParticipatory data collection methods offer a potentiallycost-effıcient way to capture data on neighborhood envi-ronments. The Discovery Tool engages individuals in theenvironmental assessment process and can help themreach consensus in identifying active living facilitatorsand barriers. The Discovery Tool produced similar re-sults to those that have been identifıed through more-complex and time-intensive methods.The most common features identifıed were aesthetics,
sidewalk features, and parks/playgrounds. Pleasant scen-ery (i.e., aesthetics)32,33 and access to suitable walkingaths34 have been found to be primary drivers of physical
activity in midlife and among older adults. Finally, given
Table 1. Total frequencya of coded elements for audio na
Audio narratives
Facilitators Barri
n 166 15
Shared elementsc
Sidewalk features 10 (6.0) 49 (3
Public transportation 3 (1.8) 3 (2
Stop light or stop sign 0 (0.0) 5 (3
Aesthetics 60 (36.1) 5 (3
Traffic volume 1 (0.6) 4 (2
Crosswalk 8 (4.8) 12 (7
Park/playground 30 (18.1) 9 (6
Amenities/destinations 27 (16.3) 3 (2
Disability issues 4 (2.4) 18 (1
Unique elementsd
Crime 3 (1.8) 1 (0
Street lighting 3 (1.8) 2 (1
Road safety 2 (1.2) 10 (6
Parking 4 (2.4) 6 (4
Personal safety 11 (6.6) 24 (1
Another person — —
Private residence — —
Street features — —
Construction — —
Trail/path — —
aTotal frequency refers to overall prevalence of coded elements, withbIn total, 116 audio narratives and 126 photographs were captured bnarratives and photographs.
cElements that were present in both the audio narratives and photodElements that were present in only the audio narratives or only the
that a relatively high proportion of the sample relied on
an assistive device for ambulation (42%), sidewalk qualitywas cited as a common barrier. Lighting conditions didnot emerge as an important feature, perhaps due to thetime of day assessments were conducted or the fact thatmobility-impaired older adults may not venture into theneighborhood at night.Although distinctions occurred among housing sites,
there was consensus among residents within housingsites concerning which features affected active living. Forexample, at the Menlo Park site, only two facilitators(aesthetics and amenities/destinations) and one barrier(sidewalk features) were identifıed by �67% of residents,suggesting a high level of consensus for the importance of arelatively few number of elements. Conversely, there was a
ves and photographs,b n (%)
Photographs
TotalFacilitators Barriers
111 36 464
14 (12.6) 28 (77.8) 101 (21.8)
0 (0.0) 0 (0.0) 6 (1.3)
7 (6.3) 0 (0.0) 12 (2.6)
34 (30.6) 3 (8.3) 102 (22.0)
2 (1.8) 1 (2.8) 8 (1.7)
12 (10.8) 1 (2.8) 33 (7.1)
12 (10.8) 0 (0.0) 51 (11.0)
7 (6.3) 1 (2.8) 38 (8.2)
5 (4.5) 0 (0.0) 27 (5.8)
— — 4 (0.9)
— — 5 (1.1)
— — 12 (2.6)
— — 10 (2.2)
— — 35 (7.5)
1 (0.9) 1 (2.8) 2 (0.4)
4 (3.6) 0 (0.0) 4 (0.9)
2 (1.8) 0 (0.0) 2 (0.4)
0 (0.0) 1 (2.8) 1 (0.2)
11 (9.9) 0 (0.0) 11 (2.4)
than one instance of a coded element included for each participant.icipants. Multiple coded elements were present in each of the audio
sographs
rrati
ers
1
2.5)
.0)
.3)
.3)
.6)
.9)
.0)
.0)
1.9)
.7)
.3)
.6)
.0)
5.9)
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large percentage of elements (84%) deemed unimportant to
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Buman et al / Am J Prev Med 2013;44(4):e41–e47 e45
active living (i.e., negative consensus), suggesting that re-sources should not be expended within these areas. Finally,consensus was not reached, either positive or negative, foronly 8%of elements, suggesting that therewas little ambigu-ity as towhichelementswereandwerenot important.Thesetypes of metrics give decision makers clear messages aboutthe key areas where improvements are needed.
LimitationsLimitations of the study are small sample size, both
Table 2. Subject-level frequencies (%)a and consensus ra
South San Francisco (n�7)
Facilitators Barriers
Shared elements
Sidewalk features 2 (28.6) 3 (42.9)
Public transportation 1 (14.3) 1 (14.3)
Stop light or stop sign 1 (14.3) 0 (0.0)
Aesthetics 6 (85.7) 2 (28.6)
Traffic volume 0 (0.0) 2 (28.6)
Crosswalk 2 (28.6) 0 (0.0)
Park/playground 3 (42.9) 0 (0.0)
Amenities/destinations 4 (57.1) 2 (28.6)
Disability issues 1 (14.3) 0 (0.0)
Unique elements
Crime 1 (14.3) 0 (0.0)
Street lighting 0 (0.0) 0 (0.0)
Road safety 1 (14.3) 1 (14.3)
Parking 0 (0.0) 0 (0.0)
Personal safety 2 (28.6) 0 (0.0)
Another person 0 (0.0) 0 (0.0)
Private residence 1 (14.3) 0 (0.0)
Street features 0 (0.0) 0 (0.0)
Construction 0 (0.0) 0 (0.0)
Trail/path 3 (42.9) 0 (0.0)
Consensus
Positivec 1 (5.3) 0 (0.0)
Negatived 15 (78.9) 18 (94.7)
Totale 33 (89.5)
aNumber of participants recording the coded elementbAudio narrative and photographs have been collapsed together.cRecorded by supermajority (�67%) of residentsdRecorded by �33% of residentsePositive and negative consensus combined, collapsed across facili
within neighborhoods and for housing sites, thus limiting
April 2013
generalizability. Moreover, although the sample was eth-nically diverse and of lower SES, participants had a rela-tively high education level. Finally, because residentswere instructed to take their “usual” walking route, allresidents did not travel the same path; thus, the authorsare unable to make conclusions about specifıc street seg-ments or less-traveled portions of a neighborhood.
Future DirectionsFuture directions for the Discovery Tool are to explore
of the coded elements,b by housing site
Menlo Park (n�9) San Mateo (n�11)
acilitators Barriers Facilitators Barriers
5 (55.6) 7 (77.8) 8 (72.7) 5 (45.5)
1 (11.1) 0 (0.0) 0 (0.0) 1 (9.1)
3 (33.3) 1 (11.1) 2 (18.2) 2 (18.2)
8 (88.9) 3 (33.3) 7 (63.6) 2 (18.2)
2 (22.2) 1 (11.1) 1 (9.1) 0 (0.0)
4 (44.4) 3 (33.3) 4 (36.4) 4 (36.4)
3 (33.3) 2 (22.2) 7 (63.6) 3 (27.3)
6 (66.7) 1 (11.1) 4 (36.4) 1 (9.1)
1 (11.1) 2 (22.2) 5 (45.5) 2 (18.2)
0 (0.0) 0 (0.0) 1 (9.1) 1 (9.1)
1 (11.1) 1 (11.1) 0 (0.0) 0 (0.0)
0 (0.0) 2 (22.2) 0 (0.0) 2 (18.2)
0 (0.0) 1 (11.1) 0 (0.0) 1 (9.1)
1 (11.1) 5 (55.6) 2 (18.2) 5 (45.5)
0 (0.0) 0 (0.0) 1 (9.1) 1 (9.1)
1 (11.1) 0 (0.0) 1 (9.1) 0 (0.0)
1 (11.1) 0 (0.0) 0 (0.0) 0 (0.0)
0 (0.0) 0 (0.0) 0 (0.0) 1 (9.1)
0 (0.0) 0 (0.0) 3 (27.3) 0 (0.0)
2 (10.5) 1 (5.3) 1 (5.3) 0 (0.0)
15 (78.9) 17 (89.5) 12 (63.2) 16 (84.2)
32 (92.1) 28 (74.4)
s and barriers
tings
F
alternative methods for classifying the audio narratives
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
3
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and photographs, such as utilizing “lay coders” throughcrowd-sourced methods (e.g., AmazonMechanical Turk)or asking users to “self-classify” data based on predefınedcategories. Also, additional modules could be added thatassess various behaviors and contexts (e.g., food, home,school, worksite); population subgroups; and platforms(e.g., basic cellular phones, smartphones). Further refıne-ments of theDiscoveryTool are already underway, andbetatesting is ongoing. TheDiscovery Tool subsequently will beavailable to researchers as a licensed product and interestedreaders may contact the authors directly.
ConclusionTheDiscoveryTool represents a new type of environmentalassessment tool that complements existing tools by engag-ing participants in the data collection process, thereby gen-erating commonwalking routes and geocoded audio narra-tives and photographs. These new types of data-capturetools can help to provide the compelling illustrative exam-ples needed to prioritize and build consensus for change.
The project described was supported by the National Center forResearchResourcesandtheNationalCenter forAdvancingTrans-lational Sciences, NIH, through UL1 RR025744 (PI: King). MPB,JLS, EBH, and LAGwere supported by U.S. Public Health ServiceGrant 5T32HL007034 from the National Heart, Lung, and BloodInstitute. JJO was supported by a Nutrilite training grant. Thecontent is solely the responsibility of the authors and does notnecessarily represent the offıcial views of the NIH.The authors thank ElizabethMezias for programming work,
Amy Woof and Kate Youngman for their assistance in thecoding process, Cathleen Baker and colleagues at the SanMateoCountyHealth System for their feedback on the development ofthe Discovery Tool, and the management staff and residents ofthe housing sites that participated in the study.No fınancial disclosures were reported by the authors of this
paper.
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