Access to this full-text is provided by Springer Nature.
Content available from BMC Public Health
This content is subject to copyright. Terms and conditions apply.
S T U D Y P R O T O C O L Open Access
Protocol for a cross sectional study of
cancer risk, environmental exposures and
lifestyle behaviors in a diverse community
sample: the Community of Mine study
Marta M. Jankowska
1*
, Dorothy D. Sears
2
, Loki Natarajan
3,4
, Elena Martinez
4
, Cheryl A. M. Anderson
3
,
James F. Sallis
3
, Stephen A. Matthews
5
, Katie Crist
3
, Lindsay Dillon
3
, Eileen Johnson
3
, Angelica Barrera-Ng
3
,
Kelsey Full
3
, Suneeta Godbole
3
and Jacqueline Kerr
3,4
Abstract
Background: Physical inactivity and unhealthy diet are modifiable behaviors that lead to several cancers.
Biologically, these behaviors are linked to cancer through obesity-related insulin resistance, inflammation, and
oxidative stress. Individual strategies to change physical activity and diet are often short lived with limited effects.
Interventions are expected to be more successful when guided by multi-level frameworks that include environmental
components for supporting lifestyle changes. Understanding the role of environment in the pathways between
behavior and cancer can help identify what environmental conditions are needed for individual behavioral change
approaches to be successful, and better recognize how environments may be fueling underlying racial and ethnic
cancer disparities.
Methods: This cross-sectional study was designed to select participants (n= 602 adults, 40% Hispanic, in San Diego
County) from a range of neighborhoods ensuring environmental variability in walkability and food access. Biomarkers
measuring cancer risk were measured with fasting blood draw including insulin resistance (fasting plasma insulin and
glucose levels), systemic inflammation (levels of CRP), and oxidative stress measured from urine samples. Objective
physical activity, sedentary behavior, and sleep were measured by participants wearing a GT3X+ ActiGraph on the hip
and wrist. Objective measures of locations were obtained through participants wearing a Qstarz Global Positioning
System (GPS) device on the waist. Dietary measures were based on a 24-h food recall collected on two days (weekday
and weekend). Environmental exposure will be calculated using static measures around the home and work, and
dynamic measures of mobility derived from GPS traces. Associations of environment with physical activity, obesity, diet,
and biomarkers will be measured using generalized estimating equation models.
Discussion: Our study is the largest study of objectively measured physical activity, dietary behaviors, environmental
context/exposure, and cancer-related biomarkers in a Hispanic population. It is the first to perform high quality
measures of physical activity, sedentary behavior, sleep, diet and locations in which these behaviors occur in relation to
cancer-associated biomarkers including insulin resistance, inflammation, impaired lipid metabolism, and oxidative stress.
Results will add to the evidence-base of how behaviors and the built environment interact to influence biomarkers that
increase cancer risk.
Trial registration: ClinicalTrials.gov NCT02094170, 03/21/2014.
Keywords: Cancer, Built environment, Physical activity, Sleep, Diet, Insulin resistance, Inflammation, Global positioning
system, Accelerometry
* Correspondence: majankowska@ucsd.edu
1
Calit2/Qualcomm Institute, UCSD, 9500 Gilman Dr, La Jolla, CA 92093, USA
Full list of author information is available at the end of the article
© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. 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.
Jankowska et al. BMC Public Health (2019) 19:186
https://doi.org/10.1186/s12889-019-6501-2
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Background
Physical inactivity, an unhealthy diet, and obesity are
related to several cancers. In the United States, approxi-
mately 85,000 new cancer cases per year are related to
obesity [1]. Research has found that as body mass index
increased by 5 kg/m
2
, cancer mortality increased by 10%
[2]. A healthy dietary pattern that is high in fiber and
low in fat can reduce cancer risk [3,4]. Physical activity
(PA) has also been shown to be effective in the primary
prevention of cancer, to prevent cancer recurrence, and
to improve treatment effects [5,6]. The largest review to
date of the effects of PA on cancer outcomes found that
individuals who participated in the most PA had 17%
reduced risk for all cancer mortality in the general popu-
lation and among cancer survivors [7]. More recently,
sedentary behavior, independent of PA, has been related
to cancer [8].
There are numerous biological mechanisms by which
cancer is related to obesity and lifestyle factors. Obesity-
related insulin resistance is associated with chronic
systemic and tissue-localized inflammation and oxidative
stress, which are thought to increase cancer risk [9].
Inflammation is known to promote the initiation and
progression of cancer, and several insulin resistance and
associated inflammation biomarkers have been shown to
be biomarkers of cancer risk [10]. These include hyper-
insulinemia [11], the HOMA-IR index [12], CRP, adipo-
kines, and cytokines [13–15]. PA, sedentary behavior,
diet and obesity are key, modifiable factors that influence
insulin resistance, inflammation, and other cancer
related biomarkers [16]. As such, understanding these
modifiable lifestyle behaviors and their relationship to
cancer is critical for minimizing incidence of obesity-as-
sociated cancers in high risk populations.
A 1998 review of the impact of PA interventions on
population levels of behavior found that the majority of
interventions focused on individual behavior change
strategies had short term and limited effects on PA [17].
Reviews of individually-focused weight loss interventions
also indicate that long term weight loss maintenance
remains a challenge [18]. The Ecological Model of
Behavior Change, which posits that individual health be-
haviors and outcomes exist within multiple levels of
influence (individual, family, community, and policy), of-
fers a lens through which to understand modifiable life-
style behaviors beyond the individual [19,20]. Research
and worldwide health organizations (including WHO,
CDC, IOM) have recommended an ecological approach
to PA and healthy diet promotion and obesity preven-
tion, including system level changes in policy and built
environments [21]. Disparities in cancer mortality have
also been considered from an ecological perspective
including patient-, provider-, and health system-level
factors [22,23]. In Latino populations, research shows
that PA levels are lower in racially segregated neighbor-
hoods [24], and several studies have linked residential
segregation and neighborhood-level SES to obesity
[25,26]. Understanding the role of environments in bio-
logical pathways leading to cancer can help identify envir-
onmental supports that facilitate the success of individual
behavioral change approaches, identify locations where
built environment changes are needed before individual
efforts are implemented, and better recognize how
environments may be fueling underlying ethnic and racial
cancer disparities [27].
Research that considers built and social environmental
factors like walkability, crime, parks, greenspace, and
food environments in relation to lifestyle behaviors like
PA and diet have presented mixed results [28–32]. Over-
all, the strongest relation has been found between
walkability and walking for transportation [33]. Results
have been less consistent for other environmental fea-
tures such as parks and other behaviors such as total
PA, with findings varying by population age, income and
race [25,29,34]. Studies that relate walkability to obesity
or BMI are inconsistent, especially in minority groups
[35]. Food environment studies have focused mostly on
access to foods in the neighborhood and demonstrate
income inequalities in access with inconsistent associa-
tions with behaviors [36,37]. Only two studies have
assessed built environment and cancer risk factors such
as insulin and diabetes [38,39], with findings indicating
that PA supportive environments are associated with
better insulin and diabetes outcomes. The inconsistent
findings to date may be due to: 1) poorly powered stud-
ies, 2) samples that lack exposure variation, 3) studies in
limited population groups, 4) measurement error in
behavior and environment, 5) lack of specificity in the
hypothesized relations, 6) a focus on the residential
neighborhood only, and 7) no inclusion of time spent in
locations [34].
This paper describes the protocol of a cross-sectional
study of objective measures of PA, sleep, and environ-
ments, high quality measures of dietary intake, and bio-
markers in Hispanic and non-Hispanic adults. The study
completed recruitment in October of 2017, and is now
in analysis phase. In total, 602 adults aged 35–80 years
old were recruited, with 40% of the sample being
Hispanic. The primary objective of the Community of
Mine study is to advance methods of cancer risk expos-
ure assessment by measuring both neighborhood access
and total exposure to healthy environments by integrat-
ing Global Positioning System (GPS) data with
Geographical Information System (GIS) data across the
full day. These measures of exposure are expected to be
associated with breast and colon cancer risk factors
assessed through biomarkers, as well as PA and diet be-
haviors measured through accelerometry and food
Jankowska et al. BMC Public Health (2019) 19:186 Page 2 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
recalls. We hypothesize that dynamic minute-level GPS
based measures of environmental exposures will be
more strongly related to behavior, as well as insulin and
inflammation biomarkers than static environmental
measures based on participants’home and work address.
Figure 1shows the hypothesized relationships between
environments, behaviors, and biomarkers.
Methodology
Study design
This is a cross-sectional observational study conducted
in San Diego County, California. Participants were ran-
domly sampled from urban census block groups pur-
posefully selected to maximize environmental variability
in walkability and fast food access (as a proxy for healthy
eating environments), and to strengthen our ability to
uncover associations with cancer related outcomes.
Walkability was derived from the San Diego Association
of Governments’(SANDAG) 2012 Health Atlas [40],
which calculates a walkability index from net residential
density, intersection density, retail floor area ratio, and
land use mix. Versions of this index have been validated
[41]. Data on food retail was obtained from San Diego
County’s Department of Environmental Health’s Food
Facility Inspection database in 2013. Terciles for walk-
ability and fast food were calculated, and four categories
were created using the highest and lowest terciles result-
ing in 764 census block groups for participant recruit-
ment out of 1794 total in San Diego County: high fast
food/low walkability (38 block groups), high fast food/
high walkability (318), low fast food/low walkability
(319), and low fast food/high walkability (89). No more
than 10 participants per census block group were re-
cruited. Recruitment began in 2014 and ended in 2017.
Participants and eligibility
Participants include adults 35 to 80 years of age, of any
ethnicity or race living for at least 6 months in a census
block group selected for study. This middle aged and
older adult population was selected because they likely
have a more homogenous cancer risk profile (versus a
younger group e.g. 18–40 years), but also provide in-
creased exposure variability by including adults who are
employed and likely to spend time outside of their home
neighborhood. Adults over 80 years were not included as
they are more likely to be home bound.
To be eligible, participants must be able to walk with-
out human assistance, travel to a study visit, have a
phone, be able to read and write fluently in English or
Spanish, be able to give informed consent and comply
with the protocol, and be willing and able to complete
all assessments. Ineligible criteria are being pregnant or
nursing, having a mental state that would preclude
complete understanding of the protocol or compliance,
having a medical condition that would affect PA or diet
behavior, or having a medical condition known to
increase inflammation biomarkers.
Fig. 1 Hypothesized relationships between environments, behaviors, and biomarkers for the Community of Mine Study
Jankowska et al. BMC Public Health (2019) 19:186 Page 3 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Recruitment for the study was conducted across the
four walkability/fast food neighborhood types. Recruit-
ment goals for each neighborhood type included 50%
Hispanic, 50% female, and 50% in lower age range (35–
60 years). Recruitment was initiated by obtaining name,
age, gender, home address, and telephone number for
individuals living within target census block groups from
a marketing company. A letter was sent to potential
participants informing them about the study and telling
them to expect a phone call from study staff. Partici-
pants were contacted by experienced phone recruiters
and given additional information on the study in either
English or Spanish as appropriate. Participants who were
interested and eligible were enrolled. All participants
were provided with a copy of the study consent and
HIPAA form and Subject’s Bill of Rights. These forms
were reviewed section by section over the phone by
trained research staff. Signed informed consent was
obtained from all participants who enroll in the study.
Study procedure
Once eligibility criteria were confirmed and the signed
consent form was received, participants were scheduled
for a clinical visit. A packet with devices, wear instruc-
tions, and instructions for the visit were delivered to
participants approximately 1 week prior to the scheduled
visit time. Participants were asked to wear an accelerom-
eter on both the hip and wrist and a GPS device on a
belt with the hip accelerometer for 14 days, with a mini-
mum of 10 h of wear time per day. Writs accelerometers
were requested to be worn at all times including during
sleep. They were asked to complete a sleep log for each
night. Participants were asked to fast for 12 h prior to
the visit for a 45 mL blood draw and urine (~ 50 mL)
sample. At the visit, blood pressure, height, weight, hip
and waist circumference, temperature, and respiratory
rate were recorded. Blood and urine samples were held
on ice (EDTA tubes for 60 minutes, urine samples for 60
minutes, serum tubes for 30 minutes after 30 minutes at
room temp to allow clotting) before centrifugation and
aliquoting. Plasma, serum, and buffy coat from blood
drawn into EDTA and serum tubes was isolated by cen-
trifugation at 4 °C, then aliquoted and stored at −80 °C.
Urine samples were aliquoted and stored at −80 °C.
During the clinical visit, participants completed a
medical history interview that includes any current med-
ications. Demographic characteristics (e.g., age, gender,
race/ethnicity) were collected via self-report survey.
Standard surveys measured health conditions, depressive
symptoms, quality of life, sleep quality, fear of falling,
and neighborhood perceptions. Participants received
new devices at the clinic visit due to memory limitations
on the devices. They were instructed to mail the devices
back and, if at the end of the study they did not have at
least 7 days of data, they were asked to re-wear the
devices. Participants completed two 24-h dietary recall
assessments (one weekday, one weekend). One dietary
assessment was completed at the visit and the other was
scheduled over the phone a few days after the visit.
Participants received reminder calls to wear the devices
and to allow them to ask questions. Once devices were
returned participants were paid $100 for their participa-
tion. Participants also received $25 the day of their
clinical visit to cover travel expenses and parking is
provided.
Biological outcomes
Insulin resistance and inflammation are the primary
biomarker outcomes [10]. Insulin resistance is gauged by
fasting plasma insulin and glucose levels, and the
HOMA-IR index (fasting plasma insulin x fasting plasma
glucose/22.5) [12]. Average 24 h circulating glucose
levels were assessed by measures of HbA1c. Adipose
tissue inflammation and insulin resistance are gauged by
measuring the adipokines, adiponectin and leptin [42].
Systemic inflammation is gauged by levels of CRP
(assayed in a multiplex panel with ICAM-1, VCAM1,
and SAA –also markers of inflammation in insulin
resistant subjects), [13,43] IL-6, TNFα, and IL-10
(assayed in a multiplex panel with INFγ, IL-1β, IL-8, and
IL-12p70 –also markers of inflammation in insulin
resistant subjects), and MCP-1. IGF-1 axis is assessed
measuring plasma IGF-1 and IGFBP - 3 levels. Impaired
lipid metabolism is assessed by lipid panel measures of
total, HDL and LDL cholesterol and triglycerides. Oxida-
tive stress is assessed by urinary markers 8-oxo-dG and
F2-isoprostanes [9,44].
Anthropometric measures were taken in duplicate at
the clinic visit. Height was measured without shoes
using a stadiometer. Weight was measured without
shoes using a bariatric digital scale, and waist circumfer-
ence was measured along the midaxillary line using a
flexible, non-stretchable measuring tape. Blood pressure
was measured 3 times with 1 min between readings. A
4th measure was taken if two of three previous readings
were > 5 mmHg different.
Behavioral outcomes
Objective PA and sedentary behavior was measured by
participants wearing a GT3X+ ActiGraph accelerometer
(ActiGraph, LLC; Pensacola, FL) on the hip. Raw accel-
erometer data at 30 Hz is collected on 3 axes. Actigraph
GT3X+ data are aggregated to the minute level using
the low frequency extension. Participants wore the
device for waking hours. During analysis, non-wear time
is excluded using the validated Choi algorithm in Actilife
6 that clarifies invalid data as 90 consecutive minutes of
zero counts with a 2 min tolerance and a 30 min small
Jankowska et al. BMC Public Health (2019) 19:186 Page 4 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
window to detect artificial movement [45]. The hip worn
PA variables are created from collected accelerometer
data employing traditional accelerometer count per
minute (cpm) cut offs (e.g. moderate-vigorous PA 1952
+; light intensity PA 100–1951+; and sedentary < 100)
[46,47], to be able to compare results to previous stud-
ies using these methods. However, we will also employ
new machine learning techniques to classify specific
behaviors related to built environments [48,49]. These
include walking/running, biking, sitting, standing still,
standing moving, and sitting in vehicle. A random forest
algorithm has been developed and tested by our group
using a 10-fold cross validation on a training set with
performance at 90% accuracy [50].
Objective sleep was measured by participants wearing
a GT3X+ ActiGraph (ActiGraph, LLC; Pensacola, FL) on
the non-dominant wrist for 24 h. Raw accelerometer
data at 30 Hz is collected on 3 axes. Non-wear time is
removed after being classified by 60 min of consecutive
zeros on the vector magnitude [45]. Sleep duration and
quality will be scored by visual inspection by trained
raters, using the Actilife sleep analysis tab with the input
of self-reported in-bed and out-of-bed times from par-
ticipant sleep logs. After in-bed and out-of-bed time is
determined by coder visual inspection, the Cole-Kripke
algorithm [51] will be used to assess minute by minute
sleep versus wake state. Variables include daily sleep
duration, sleep onset time, sleep offset time, sleep effi-
ciency, and minutes awake after sleep onset (WASO).
Objective measures of locations were obtained through
participants wearing a Qstarz GPS device (BT-
Q1000XT) attached to a belt worn on the waist. The de-
vice records data every 15 s. Missing data are imputed
from a validated algorithm [52]. The GPS device logs
geographic location coordinates, distance, speed, eleva-
tion, and time. The Qstarz has an industry reported
accuracy of 3 m, and independent validation has shown
that median error of the device varies by behavior (from
3.9 m for walking to 0.5 m for driving) and environment
(from 5.2 m in urban canyons to 0.7 m in open areas)
[53]. Participants were instructed to charge the GPS
device overnight. The GPS data will be processed and
joined to the accelerometer data using the Personal Ac-
tivity and Location Measurement System (PALMS) [54].
Data will be aggregated and merged at the minute level.
Indoor/outdoor locations will also be calculated using
validated algorithms in PALMS [55]. The GPS device
collects the signal to noise ratio of satellites in commu-
nication with the device. Higher ratios indicate more
interference and likelihood of an indoor location. We
have tested and validated a threshold of 250, which
differentiates indoor and outdoor locations with over
85% accuracy compared to in person observations and
coded images captures by person worn cameras.
Dietary measures were based on a 24-h multiple-pass
food recall collected on 2 days by trained research staff
using the NDSR (Nutrition Data System for Research)
Software versions 2015 and 2016, a Windows-based pro-
gram for food intake data entry and analysis developed
by the Nutrition Coordinating Center at the University
of Minnesota. The recalls were done on one weekday
and one weekend day. Participants were given food por-
tion visuals to aid in recall of portion sizes. NDSR allows
research staff to collect a participant’s food intake for
the previous day from midnight to midnight. The soft-
ware guides the interviewer through its completion
using a dynamic user interface. It is available in English
and Spanish. A research staff member completed the
master training course at the University of Minnesota on
how to use the software and train others to administer
the NDSR. Research staff participated in a rigorous
training, and were assigned a set number of practices be-
fore completing the standardized test for certification.
Ongoing monthly quality control checks of 10% of
NDSRs were scheduled to maintain the quality of food
interviews and data entry. Quality checks and staff feed-
back were conducted by the master trainer. All micro-
and macronutrients are obtained, as well as data on
foods and food groups. Reports per participant from
NDSR will be used for primary analyses, and will include
information on total calories, fat calories, fiber, macro-
nutrients, and fruits and vegetables. Dietary outcomes
from NDSR data include total calories, fat calories, fiber,
and fruit and vegetable intake [56].
Objective environmental exposures
The goal of the study is to better understand how envir-
onmental exposure to PA and diet-related environments
impacts both behaviors and cancer-related biomarkers.
Exposure in the study is measured in two ways: static
measures around participants’home and work locations,
and dynamic measures using GPS traces of participants
throughout the day. These two methods of measuring
exposure will be compared to advance cancer exposure
risk assessment. GIS data of the environment includes
metrics of various land uses (retail, park, beaches, indus-
trial, vacant), food outlet locations (grocery, fast food,
restaurant, convenience store), walkability, recreation,
public transportation, greenness, and crime. The data
will be derived from the local government agency,
SANDAG, as well as commercial data from Environ-
mental Sciences Research Institute (ESRI) and Dun &
Bradstreet.
Static measures of exposure are derived from buffers
around participant homes as well as work places for
those who work. The majority of studies that examine
the relationship between environment and behaviors
utilize a 400 m –2 km buffer around a location to define
Jankowska et al. BMC Public Health (2019) 19:186 Page 5 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
an individual’s environment [34,57]. A 1 km buffer
around participants’home and work locations will be
created, and environmental variables contained in that
buffer will be calculated for each participant. Static
characterization of an individual’s environment does not
accurately reflect a person’s daily mobility path and
exposures that occur outside of the home and work en-
vironments, limiting the variance in environmental ex-
posures over the study data collection period [58,59].
Furthermore, single location estimates of exposure
underestimate associations between environment and
cancer risk factors when related to behaviors accumu-
lated over the course of entire days, such as daily phys-
ical activity or total calories consumed [60,61]. GPS
devices offer a potential solution to these problems by
providing a daily trace of an individual’s movement,
which can be converted into a total dynamic exposure
metric across the day. GPS-based dynamic measures of
exposure will be calculated with the Kernel Density
Equation (KDE) tool in ArcGIS. All GPS points for the
entire participant’s wear time will be used to create a
total exposure surface, which can then be multiplied by
environmental variables to derive a total exposure meas-
ure that accounts for locations visited and the time spent
in all locations [39].
Self-report measures and covariates
Though the focus of this study is objective measurement,
standard surveys items were administered for compari-
son with prior studies, because some perceptions e.g.
safety and aesthetics are not available in objective GIS
layers, and to assess important covariates. Surveys were
provided in Spanish and English depending on language
preference of the participant.
Demographic covariates include age, income, gender,
ethnicity, education, employment status, employment
location, marital status, number of children in the
household, automobile ownership, driving status, type of
residence, and years living at address. Family history of
cancer was assessed with a standard population based
surveillance survey [62], and employed as a covariate in
analysis. Subjects were asked to record the names of all
medications (prescription and over-the-counter) they
currently take, and the doses of each (amount per day
etc.). Participants were asked if a health professional ever
diagnosed them with a chronic disease or risk factors.
The Center for Epidemiologic Studies Depression Scale
was included to assess symptoms of depression [63].
International Physical Activity Questionnaire (IPAQ -
long form) assessed participants’reported PA over the
last 7 days. The IPAQ estimates PA frequency and
duration per week for housework, occupation, transpor-
tation, and recreation [64]. The IPAQ includes a meas-
ure of total duration of weekday and weekend sitting
time. Frequency and duration of sedentary behaviors
were also be assessed by a separate 7-item measure [65].
Sleep disturbances was assessed using the NIH PROMIS
Sleep Disturbance 6a Short Form and the MAP Index/
Survey Screen for Sleep Apnea. Quality of sleep was
assessed using the Epworth Sleepiness Scale.
Due to the importance of the neighborhood design, an
11-item self-selection survey was deployed that mea-
sures reasons for moving to a neighborhood, which is a
commonly cited potential bias in cross-sectional envir-
onmental studies [66]. Social interactions was measured
using items from standard surveys assessing social isola-
tion, cohesion, and support [67,68]. Late Life Function-
ing Disability Instrument (LLFDI) was used to determine
self-reported functional performance and physical dis-
ability [69]. The validated and widely used NEWS [70]
was used to assess perceptions of neighborhood environ-
ment variables including subscales for aesthetics, and
perceived safety from traffic and crime. The NEWS has
good test-retest reliability (median ICC = .79) and
construct validity by discriminating known neighbor-
hood types and showing convergent relations to PA.
Participants completed the NEWS scale for both home
and work locations. For those who work, surveys were
given related to support in the workplace for healthy
food, physical activity and standing. The home sleep
environment was assessed by the Functional Outcomes
of Sleep Questionnaire (FOS-Q). Home sedentary envir-
onment included number of TVs in the bedroom etc.
Sample size
Sample-size estimates were computed to achieve ≥80%
power to detect meaningful differences in associations
between outcomes and dynamic versus static GIS expo-
sures. Specifically, a sample-size of 648 would achieve
80% power to detect a 0.32 correlation between out-
come, Y, and Dynamic GIS-based neighborhood attri-
butes (X) versus a 0.15 correlation between the outcome
Y and Static GIS exposure (W). These calculations,
based on the methods of Dunn and Clark [71] for
dependent correlations, assumed a conservative correl-
ation of 0.2 between the static and dynamic GIS-mea-
sures, and used a stringent significance level (α=0.01
2-sided) to account for the multiple outcomes (BMI,
MVPA, walking, and biomarkers). Further, accounting for
census-block clustering effects with ICC = 0.05 to 0.1, we
would require 630–660 participants. Recruitment was
targeted at 700 individuals. Due to the intense data collec-
tion requirements, limited recruitment options due to
environmental limitations (walkability and fast food
access), and difficulty in recruiting Hispanic participants,
recruitment fell short of the targeted 700 and the study
was completed with 602 participants.
Jankowska et al. BMC Public Health (2019) 19:186 Page 6 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Statistical approach
Our main objective is to compare Dynamic and Static
GIS measures of PA and food environments and their
associations with PA behaviors, BMI, diet, and biological
markers. To test if the Dynamic GIS exposures are more
strongly correlated with outcomes than the Static mea-
sures, we will apply the methods of Dunn and Clark [71]
for dependent correlations (e.g., comparing two correla-
tions which have a common variable, i.e., the PA behav-
iors or biomarkers). In addition, we will fit generalized
estimating equations (GEE) models to examine associa-
tions between exposure to PA supportive environments
(main effect) and outcomes (PA, BMI, diet, biomarkers)
with adjustment for individual level confounding covari-
ates (e.g. demographics, medication use for biomarker
analyses), and block group clustering effects. We will use
the multiple-informant approach developed by Horton
et al. [72] to test if regression coefficients between PA
and outcomes differ for the dynamic vs static GIS mea-
sures. We will examine interactions between covariates
(e.g., ethnicity, age) and GIS measures on outcomes, to
test if these factors moderate exposure-outcome associa-
tions. Similar analyses will be conducted for the
self-reported outcomes, and Static versus Dynamic GIS
measures of healthy eating promoting environments.
Data storage and dissemination
Due to the sensitive nature of the GPS data collected, as
well as the significant volume of data generated from the
sensor devices, a secure geodatabase is used to store and
process the GPS and accelerometer data. The geodata-
base is housed in a HIPAA and FISMA-compliant
(HIPAA Privacy Rule establishes national standards for
protection individual medical records and personal
health information) private computing cloud housed at
the San Diego Supercomputer Center called Sherlock
(http://sherlock.sdsc.edu). The geodatabase employs
PostgreSQL to organize spatial data, and allow them to
be directly exported to spatial software for visualization
purposes [73]. All other data will be stored in a secure
database, stripped of identifiers. Methods for data man-
agement and coding can be obtained by contacting the
corresponding author. The results of the study will be
disseminated through publications, reports, and confer-
ence presentations.
Discussion
Understanding PA and dietary behaviors in the context
of their environments can provide evidence leading to
the design of potentially more effective multi-level
interventions that can be designed to mitigate disparities
in cancer outcomes. There are, however, still many limi-
tations in the design and implementation of cross-sec-
tional studies that examine PA and dietary behaviors in
environmental contexts. There are no studies with
extensive objective measures of behavior, environment,
and exposure that also assess cancer-related biomarkers.
The study is cross-sectional, limiting inferences of causality
between outcomes. However, the environmental design and
objective measurement of environmental exposures allows
for more accurate assessment of associations between en-
vironment, behavior, and cancer-related biomarkers.
The Community of Mine study collected minute-level
PA, sleep, and location data, food recalls, biomarkers,
and survey information for 602 participants through a
purposeful neighborhood-based sampling design, as well
as extensive environmental information for the study
area. The study protocol has completed data collection
and is now moving into the data analysis phase of the
study. It is important to note that there were significant
challenges involved with data collection stemming from
the large demands placed on participants (sensor device
wear and clinical visits), limitations on location of re-
cruitment (census block group walkability and fast food
access selection design), and recruitment of Hispanic in-
dividuals. The location component of recruitment was
purposeful to attempt to expand the variability in types
of environments that participants lived in. However, with
study results we hope to demonstrate that this may be
an unnecessary recruitment limitation when the addition
of GPS data may provide significant environmental
exposure variability. While recruitment fell short of the
700 participant goal, the resulting data set is rich in ob-
jective measurements of health behaviors and biomarker
outcomes. The main goal of the study is to advance
methods of cancer risk exposure assessment by compar-
ing the utility of static versus dynamic environmental ex-
posures assessed by integrating GPS and GIS data. The
data collected will allow for several other analyses of en-
vironmental exposure effects, such as time spent indoors
and outdoors, interlinking effects of sleep duration/qual-
ity, PA, and diet, as well as environmental and racial
inequalities as related to cancer risk.
Abbreviations
CPM: Count per minute; ESRI: Environmental Sciences Research Institute;
FOS-Q: Functional Outcomes of Sleep Questionnaire; GEE: Generalized
estimating equations; GPS: Global positioning system; IPAQ: International
physical activity questionnaire; KDE: Kernel Density Equation; LLFDI: Late Life
Functioning Disability Instrument; NDSR: Nutrition data system research;
PA: Physical Activity; PALMS: Personal Activity and Location Measurement
System; SANDAG: San Diego Associations of Governments
Acknowledgements
Not applicable.
Funding
This work was supported by the National Institutes of Health grants, National
Cancer Institute R01CA179977. The Project described was partially supported
by the National Institutes of Health, Grant UL1TR001442 of CTSA. The
content is solely the responsibility of the authors and does not necessarily
represent the official views of the NIH.
Jankowska et al. BMC Public Health (2019) 19:186 Page 7 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Availability of data and materials
The datasets generated from the study are not publicly available due to the
need to protect participant privacy, but they are available from the
corresponding author on reasonable request.
Authors’contributions
MMJ contributed to the study design, data collection tools, and statistical
analysis plan, as well as wrote the initial draft and revised the paper. She is
guarantor. LN developed the statistical design of the study and revised the
draft paper. EM, CAMA, JFS, and SAM contributed to the design of the study,
helped with development of data collection tools, and revised the draft
paper. KC, LD, EJ, AB-N, KF, and SG developed data collection tools, assisted
with the statistical plan, ran data collection for the study, cleaned data, and
contributed to the drafting and revision of the paper. JK initiated the project,
conceived and designed the study, developed data collection tools, moni-
tored data collection, wrote the initial draft, and revised the paper. DDS con-
ceived biomarker data collection, organized and oversaw clinical data
collection, and revised the draft paper. All authors made contributions to the
final draft and approved its submission.
Ethics approval and consent to participate
The Internal Review Board of the University of California San Diego approved
all study procedures. Each participant provided written informed consent for
participation in the study at the time of enrollment. UCSD’s Ethics
Committee Approval Number: 140510.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Publisher’sNote
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Author details
1
Calit2/Qualcomm Institute, UCSD, 9500 Gilman Dr, La Jolla, CA 92093, USA.
2
Nutrition, College of Health Solutions, Arizona State University, 445 N 5th
Street, Phoenix, AZ 85004, USA.
3
Department of Family Medicine and Public
Health, UCSD, 9500 Gilman Dr, La Jolla, CA 92093, USA.
4
UCSD Moores
Cancer Center, 3855 Health Sciences Dr, La Jolla, CA 92093, USA.
5
Department of Sociology & Criminology, Department of Anthropology,
Population Research Institute, Old Main, State College, PA 16801, USA.
Received: 21 August 2018 Accepted: 31 January 2019
References
1. Jia H, Lubetkin EI. Obesity-related quality-adjusted life years lost in the U.S.
from 1993 to 2008. Am J Prev Med. 2010;39:220–7. https://doi.org/10.1016/j.
amepre.2010.03.026.
2. Calle EE, Rodriguez C, Walker-Thurmond K, Thun MJ. Overweight, obesity,
and mortality from cancer in a prospectively studied cohort of U.S. adults. N
Engl J Med. 2003;348:1625–38. https://doi.org/10.1056/NEJMoa021423.
3. Kushi LH, Doyle C, McCullough M, Rock CL, Demark-Wahnefried W, Bandera
EV, et al. American Cancer Society guidelines on nutrition and physical
activity for cancer prevention: reducing the risk of cancer with healthy food
choices and physical activity. CA Cancer J Clin. 2012;62:30–67.
https://doi.org/10.3322/caac.20140.
4. Su LJ. Diet, epigenetics, and cancer. Methods Mol Biol (Clifton, NJ). 2012;863:
377–93.
5. Anzuini F, Battistella A, Izzotti A. Physical activity and cancer prevention: a
review of current evidence and biological mechanisms. J Prev Med Hyg.
2011;52:174–80 http://www.ncbi.nlm.nih.gov/pubmed/22442921.
6. Moore SC, Patel AV, Matthews CE, Berrington de Gonzalez A, Park Y, Katki
HA, et al. Leisure time physical activity of moderate to vigorous intensity
and mortality: a large pooled cohort analysis. PLoS Med. 2012;9:e1001335.
7. Li T, Wei S, Shi Y, Pang S, Qin Q, Yin J, et al. The dose-response effect of
physical activity on cancer mortality: findings from 71 prospective cohort
studies. Br J Sports Med. 2016;50:339–45. https://doi.org/10.1136/bjsports-
2015-094927.
8. Lynch BM. Sedentary behavior and cancer: a systematic review of the
literature and proposed biological mechanisms. Cancer Epidemiol Biomark
Prev. 2010;19:2691–709.
9. Forte V, Pandey A, Abdelmessih R, Forte G, Whaley-Connell A, Sowers JR, et
al. Obesity, diabetes, the Cardiorenal syndrome, and risk for cancer.
Cardiorenal Med. 2012;2:143–62. https://doi.org/10.1159/000337314.
10. Ballard-Barbash R, Friedenreich CM, Courneya KS, Siddiqi SM, McTiernan A,
Alfano CM. Physical activity, biomarkers, and disease outcomes in cancer
survivors: a systematic review. J Natl Cancer Inst. 2012;104:815–40.
https://doi.org/10.1093/jnci/djs207.
11. Gunter MJ, Hoover DR, Yu H, Wassertheil-Smoller S, Rohan TE, Manson JE, et
al. Insulin, insulin-like growth factor-I, and risk of breast cancer in
postmenopausal women. J Natl Cancer Inst. 2009;101:48–60.
12. Friedenreich CM, Langley AR, Speidel TP, Lau DCW, Courneya KS, Csizmadi I,
et al. Case-control study of markers of insulin resistance and endometrial
cancer risk. Endocr Relat Cancer. 2012;19:785–92.
13. Touvier M, Fezeu L, Ahluwalia N, Julia C, Charnaux N, Sutton A, et al.
Association between Prediagnostic biomarkers of inflammation and
endothelial function and Cancer risk: a nested case-control study. Am J
Epidemiol. 2013;177:3–13. https://doi.org/10.1093/aje/kws359.
14. Ko Y-J, Kwon Y-M, Kim KH, Choi H-C, Chun SH, Yoon H-J, et al. High-
sensitivity C-reactive protein levels and cancer mortality. Cancer Epidemiol
Biomark Prev. 2012;21:2076–86. https://doi.org/10.1158/1055-9965.EPI-12-0611.
15. Hursting SD, Hursting MJ. Growth signals, inflammation, and vascular
perturbations: mechanistic links between obesity, metabolic syndrome, and
cancer. Arterioscler Thromb Vasc Biol. 2012;32:1766–70.
16. Auchincloss AH, Diez Roux AV, Mujahid MS, Shen M, Bertoni AG, Carnethon
MR. Neighborhood resources for physical activity and healthy foods and
incidence of type 2 diabetes mellitus: the multi-ethnic study of
atherosclerosis. Arch Intern Med. 2009;169:1698–704. https://doi.org/10.
1001/archinternmed.2009.302.
17. Mayne SL, Auchincloss AH, Michael YL. Impact of policy and built
environment changes on obesity-related outcomes: a systematic review of
naturally occurring experiments. Obes Rev. 2015;16(5):362–75.
18. Sumithran P, Proietto J. Maintaining weight loss: an ongoing challenge. Curr
Obes Rep. 2016;5(4):383–85.
19. Sallis JF, Owen N, Fisher EB. Ecological models of health behavior. In: Glanz K,
Rimer B, Viswanath K, editors. Health behavior and health education: theory,
research, and practice. 4th ed. San Francisco: Jossey-Bass; 2008. p. 465–85.
20. Dunn AL, Andersen RE, Jakicic JM. Lifestyle physical activity interventions.
History, short- and long-term effects, and recommendations. Am J Prev
Med. 1998;15:398–412. https://doi.org/10.1016/S0749-3797(98)00084-1.
21. CDC. CDC’s Built Environment and Health Initiative. 2015.
22. Blackman DJ, Masi CM. Racial and ethnic disparities in breast cancer
mortality: are we doing enough to address the root causes? J Clin Oncol.
2006;24:2170–8.
23. Masi CM, Olopade OI. Racial and ethnic disparities in breast cancer: a
multilevel perspective. Med Clin N Am. 2005;89:753–70.
24. Powell LM, Slater S, Chaloupka FJ, Harper D. Availability of physical activity-
related facilities and neighborhood demographic and socioeconomic
characteristics: a national study. Am J Public Health. 2006;96:1676–80.
25. Fields R, Kaczynski A, Bopp M, Fallon E. Built environment associations with
health behaviors among Hispanics. J Phys Act Health. 2013;10:355–42.
26. Gallo LC, Fortmann AL, de los Monteros KE, Mills PJ, Barrett-Connor E,
Roesch SC, et al. Individual and neighborhood socioeconomic status and
inflammation in middle-aged Mexican-American women: what is the role of
obesity. Psychosom Med. 2012;74:535–42.
27. Osypuk TL, Acevedo-Garcia D. Beyond individual neighborhoods: a
geography of opportunity perspective for understanding racial/ethnic
health disparities. Heal Place. 2010;16:1113–23.
28. Sallis JF, Floyd MF, Rodriguez DA, Saelens BE. The role of built environments
in physical activity, obesity, and CVD. Circulation. 2012;125:729–37. https://
doi.org/10.1161/CIRCULATIONAHA.110.969022.
29. Kerr J, Sallis J, Owen N, De Bourdeaudhuij I, Cerin E, Sugiyama T, et al.
Advancing science and policy through a coordinated international study of
physical activity and built environments: IPEN adult methods. J Phys Act
Health. 2013;10:581–601.
30. Caspi CE, Sorensen G, Subramanian SV, Kawachi I. The local food
environment and diet: a systematic review. Health Place. 2012;18:1172–87.
Jankowska et al. BMC Public Health (2019) 19:186 Page 8 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
31. Bancroft C, Joshi S, Rundle A, Hutson M, Chong C, Weiss CC, et al.
Association of proximity and density of parks and objectively measured
physical activity in the United States: a systematic review. Soc Sci Med.
2015;138:22–30. https://doi.org/10.1016/j.socscimed.2015.05.034.
32. Ferdinand AO, Sen B, Rahurkar S, Engler S, Menachemi N. The relationship
between built environments and physical activity: a systematic review. Am J
Public Health. 2012;102.
33. Saelens BE, Handy SL. Built environment correlates of walking: a review.
Med Sci Sports Exerc. 2008;40(7):SUPPL.1.
34. Ding D, Gebel K. Built environment, physical activity, and obesity: what have
we learned from reviewing the literature? Health Place. 2012;18:100–5.
https://doi.org/10.1016/j.healthplace.2011.08.021.
35. Feng J, T a G, Curriero FC, Stewart WF, Schwartz BS. The built environment
and obesity: a systematic review of the epidemiologic evidence. Health
Place. 2010;16:175–90. https://doi.org/10.1016/j.healthplace.2009.09.008.
36. Walker RE, Keane CR, Burke JG. Disparities and access to healthy food in the
United States: a review of food deserts literature. Health Place. 2010;16:876–
84. https://doi.org/10.1016/j.healthplace.2010.04.013.
37. Creatore MI, Glazier RH, Moineddin R, Fazli GS, Johns A, Gozdyra P, et al.
Association of neighborhood walkability with change in overweight,
obesity, and diabetes. JAMA J Am Med Assoc. 2016;315:2211–20.
38. Auchincloss AH, Diez Roux AV, Brown DG, Erdmann CA, Bertoni AG.
Neighborhood resources for physical activity and healthy foods and their
association with insulin resistance. Epidemiology. 2008;19:146–57. https://
doi.org/10.1097/EDE.0b013e31815c480.
39. Jankowska MM, Natarajan L, Godbole S, Meseck K, Sears DD, Patterson RE,
et al. Kernel density estimation as a measure of environmental exposure
related to insulin resistance in breast Cancer survivors. Cancer Epidemiol
Biomark Prev. 2017; In Press.
40. San Diego Association of Governments. SANDAG Healty Communities
Assessment Tool. 2012. http://www.sandag.org/index.asp?classid=
12&projectid=482&fuseaction=projects.detail. Accessed 3 Jan 2014.
41. Frank LD, Sallis JF, Saelens BE, Leary L, Cain K, Conway TL, et al. The
development of a walkability index: application to the neighborhood
quality of life study. Br J Sports Med. 2010;44:924–33. https://doi.org/10.
1136/bjsm.2009.058701.
42. Chen M-W, Ye S, Zhao L-L, Wang S-Y, Li Y-X, Yu C-J, et al. Association of
plasma total and high-molecular-weight adiponectin with risk of colorectal
cancer: an observational study in Chinese male. Med Oncol. 2012;29:1–7.
https://doi.org/10.1007/s12032-012-0280-2.
43. Toriola AT, T-YD C, Neuhouser ML, Wener MH, Zheng Y, Brown E, et al.
Biomarkers of inflammation are associated with colorectal cancer risk in
women but are not suitable as early detection markers. Int J Cancer. 2013;
132:2648–58. https://doi.org/10.1002/ijc.27942 .
44. Gago-Dominguez M, Jiang X, Castelao JE. Lipid peroxidation, oxidative
stress genes and dietary factors in breast cancer protection: a hypothesis.
Breast Cancer Res. 2007;9:201. https://doi.org/10.1186/bcr1628.
45. Choi L, Ward SC, Schnelle JF, Buchowski MS. Assessment of wear/nonwear
time classification algorithms for triaxial accelerometer. Med Sci Sports
Exerc. 2012;44:2009–16.
46. Copeland JL, Esliger DW. Accelerometer assessment of physical activity in
active, healthy older adults. J Aging Phys Act. 2009;17:17–30.
47. Matthews CE, Chen KY, Freedson PS, Buchowski MS, Beech BM, Pate RR, et
al. Amount of time spent in sedentary behaviors in the United States, 2003-
2004. Am J Epidemiol. 2008;167:875–81.
48. Heil DP, Brage S, Rothney MP. Modeling physical activity outcomes from
wearable monitors. Med Sci Sports Exerc. 2012;44(1 Suppl 1):S50–60.
49. Staudenmayer J, Zhu W, Catellier DJ. Statistical considerations in the analysis
of accelerometry-based activity monitor data. Med Sci Sports Exerc. 2012;
44(SUPPL. 1).
50. Ellis K, Godbole S, Marshall S, Lanckriet G, Staudenmayer J, Kerr J. Identifying
active travel behaviors in challenging environments using GPS,
accelerometers, and machine learning algorithms. Front public Heal. 2014;2
April:36. doi:https://doi.org/10.3389/fpubh.2014.00036.
51. Cole RJ, Kripke DF, Gruen W, Mullaney DJ, Gillin JC. Automatic sleep/wake
identification from wrist activity. Sleep. 1992;15:461–9.
52. Meseck K, Jankowska MM, Schipperijn J, Natarajan L, Godbole S, Carlson JA, et
al. Is missing geographic position system (GPS) data in accelerometry studies a
problem, and is imputation the solution? Geospat Health. 2016;11:157–63.
53. Schipperijn J, Kerr J, Duncan S, Madsen T, Klinker CD, Troelsen J. Dynamic
accuracy of GPS receivers for use in Health Research: a novel method to
assess GPS accuracy in real-world settings. Front Public Heal. 2014;2:21.
https://doi.org/10.3389/fpubh.2014.00021.
54. Carlson JA, Jankowska MM, Meseck K, Godbole S, Natarajan L, Raab F, et al.
Validity of PALMS GPS scoring of active and passive travel compared with
SenseCam. Med Sci Sports Exerc. 2015;47:662–7.
55. Lam MS, Godbole S, Chen J, Oliver M, Badland H, Marshall S, et al.
Measuring time spent outdoors using a wearable camera and GPS. In:
Proceedings of the 4th International SenseCam & Pervasive Imaging
Conference; 2013. p. 1–7. ACM New York, NY
56. Frances E. Thompson AFS. Dietary assessment methodology. 2008. doi:
https://doi.org/10.1016/B978-0-12-391884-0.00001-9.
57. Larson N, Story M. A review of environmental influences on food choices.
Ann Behav Med. 2009;38:56–73.
58. Jankowska MM, Schipperijn J, Kerr J. A framework for using GPS data in
physical activity and sedentary behavior studies. Exerc Sport Sci Rev. 2015;
43:48–56.
59. James P, Jankowska MM, Marx C, Hart JE, Berrigan D, Kerr J, et al. “Spatial
energetics”: integrating data from GPS, Accelerometry, and GIS to address
obesity and inactivity. Am J Prev Med. 2016.
60. Hurvitz PM, Moudon AV, Kang B, Saelens BE, Duncan GE. Emerging
technologies for assessing physical activity behaviors in space and time.
Front Public Heal 2014;2:2. doi:https://doi.org/10.3389/fpubh.2014.00002.
61. Inagami S, D a C, Finch BK. Non-residential neighborhood exposures
suppress neighborhood effects on self-rated health. Soc Sci Med. 2007;65:
1779–91. https://doi.org/10.1016/j.socscimed.2007.05.051.
62. Mai PL, Garceau AO, Graubard BI, Dunn M, McNeel TS, Gonsalves L, et al.
Confirmation of family cancer history reported in a population-based
survey. J Natl Cancer Inst. 2011;103:788–97.
63. Eaton WW, Muntaner C, Smith C, Tien A, Ybarra M. Center for Epidemiologic
Studies Depression Scale: review and revision (CESD and CESDR). In: The use
of psychological testing for treatment planning and outcomes assessment.
Vol 3: instruments for adults; 2004. p. 363–78.
64. Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et
al. International physical activity questionnaire: 12-country reliability and
validity. Med Sci Sports Exerc. 2003;35:1381–95.
65. Marshall SJ, Biddle SJH, Sallis JF, McKenzie TL, Conway TL. Clustering of
sedentary Behaviours and physical activity among youth. Med Sci Sport
Exerc. 2002;34:129. https://doi.org/10.1097/00005768-200205001-01827.
66. Frank LD, Saelens BE, Powell KE, Chapman JE. Stepping towards causation:
do built environments or neighborhood and travel preferences explain
physical activity, driving, and obesity? Soc Sci Med. 2007;65:1898–914.
67. Shankar A, McMunn A, Banks J, Steptoe A. Loneliness, social isolation, and
behavioral and biological health indicators in older adults. Health Psychol.
2011;30:377–85. https://doi.org/10.1037/a0022826.
68. Bøen H, Dalgard OS, Bjertness E. The importance of social support in the
associations between psychological distress and somatic health problems and
socio-economic factors among older adults living at home: a cross sectional
study. BMC Geriatr. 2012;12:27. https://doi.org/10.1186/1471-2318-12-27.
69. Haley SM, Jette AM, Coster WJ, Kooyoomjian JT, Levenson S, Heeren T, et al.
Late life function and disability instrument: II. Development and evaluation
of the function component. J Gerontol A Biol Sci Med Sci. 2002;57:M217–22.
https://doi.org/10.1093/gerona/57.4.M217.
70. Cerin E, Saelens BE, Sallis JF, Frank LD. Neighborhood environment
walkability scale: validity and development of a short form. Med Sci Sports
Exerc. 2006;38:1682–91.
71. Dunn OJ, Clark V. Correlation coefficients measured on the same individuals.
J Am Stat Assoc. 1969;64:366–77.
72. Horton NJ, Fitzmaurice GM. Regression analysis of multiple source and
multiple informant data from complex survey samples. Stat Med. 2004;23:
2911–33.
73. Jankowska MM, Schipperjin J, Kerr J, Altintas I. SPACES: An applied CyberGIS
in the age of complex spatial health data. In: GIScience 2016. Montreal,
Canada; 2016.
Jankowska et al. BMC Public Health (2019) 19:186 Page 9 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
Available via license: CC BY 4.0
Content may be subject to copyright.