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Protocol for a cross sectional study of cancer risk, environmental exposures and lifestyle behaviors in a diverse community sample: The Community of Mine study

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  • Beckman Research Institute City of Hope

Abstract and Figures

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.
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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 [1315]. 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 [2832]. 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 3580 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
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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 participantshome 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
Countys Department of Environmental Healths 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. 1840 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
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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 Subjects 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
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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 1001951+; 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 participants 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 participantshome 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
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an individuals environment [34,57]. A 1 km buffer
around participantshome and work locations will be
created, and environmental variables contained in that
buffer will be calculated for each participant. Static
characterization of an individuals environment does not
accurately reflect a persons 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 individuals 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 participants 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 participantsreported 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 630660 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.
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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.
Authorscontributions
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. UCSDs Ethics
Committee Approval Number: 140510.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
PublishersNote
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
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... The data that support the findings of this study are available from the corresponding author upon reasonable request. The full study protocol is published elsewhere [20]. Briefly, 602 adult participants (ages 35 to 80) were selected from a stratified random sample from urban and suburban census block groups to maximize variability in built environment walkability (sample enrollment workflow in Supplement Fig. 1). ...
... Briefly, 602 adult participants (ages 35 to 80) were selected from a stratified random sample from urban and suburban census block groups to maximize variability in built environment walkability (sample enrollment workflow in Supplement Fig. 1). Inclusion criteria were participants must have lived in a census block group selected for the study for at least 6 months, be able to walk without human assistance, be able to travel to a study site, 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 [20]. Exclusion criteria were 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, or having a medical condition known to increase inflammation biomarkers [20]. ...
... Inclusion criteria were participants must have lived in a census block group selected for the study for at least 6 months, be able to walk without human assistance, be able to travel to a study site, 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 [20]. Exclusion criteria were 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, or having a medical condition known to increase inflammation biomarkers [20]. ...
Article
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Purpose The association between cardiovascular health (CVH) with perceived quality of life (PQoL) and variations by sex and Hispanic ethnicity is not well understood. Methods This study included 583 participants (42% Hispanic, 56% female, mean age 59 years). Linear regression modeled the covariate-adjusted associations between CVH, using the combined 7 components of Life’s Simple 7 (LS7; ideal and intermediate, compared to poor), and PQoL (total and physical, social, and cognitive health domains). For individual LS7 components, we assessed effect modification by sex and Hispanic ethnicity. Results Compared to individuals with poor CVH, those with intermediate (β [95% CI] = 0.22 [0.09, 0.35]) and ideal (β [95% CI] = 0.22 [0.08, 0.36]) CVH had higher overall PQoL. This effect was dominated by the physical PQoL domain. Of LS7 components, ideal body mass index (BMI) (β [95% CI] = 0.17 [0.03, 0.31]) and physical activity (β [95% CI] = 0.26 [0.12, 0.40]) were associated with overall PQoL. Ideal diet (β [95% CI] = 0.32 [0.08, 0.56]) and fasting plasma glucose (β [95% CI] = 0.32 [0.06, 0.58]) were associated with the physical PQoL domain. A higher PQoL score was associated with intermediate BMI in women, and physical PQoL was associated with smoking for women. A BMI*Hispanic interaction resulted in larger associations between intermediate/ideal BMI and physical PQoL in non-Hispanics. Conclusion Ideal or intermediate CVH health factors and health behaviors were associated with higher PQoL. Sex and ethnicity differences suggest that perceived quality of life is associated with BMI for women and non-Hispanics.
... The Community of Mines protocol is available elsewhere [30], and aspects of the study have been described in other research [20,31]. Briefly, 602 adults aged 35-80 years old (mean age=59 years) who had lived for at least 6 months in a selected census block group completed the study. ...
... Participants were instructed to wear Qstarz GPS devices (Qstarz International Co. Ltd, Taipei, Taiwan) during waking hours to measure their movement [30,31]. The GPS observations, which we also call pings, each had a latitude value, a longitude value, and a time stamp. ...
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Background Heat can vary spatially within an urban area. Individual-level heat exposure may thus depend on an individual’s day-to-day travel patterns (also called mobility patterns or activity space), yet heat exposure is commonly measured based on place of residence. Objective In this study, we compared measures assessing exposure to two heat indicators using place of residence with those defined considering participants’ day-to-day mobility patterns. Methods Participants (n = 599; aged 35-80 years old [mean =59 years]) from San Diego County, California wore a GPS device to measure their day-to-day travel over 14-day intervals between 2014-10-17 and 2017-10-06. We measured exposure to two heat indicators (land-surface temperature [LST] and air temperature) using an approach considering their mobility patterns and an approach considering only their place of residence. We compared participant mean and maximum exposure values from each method for each indicator. Results The overall mobility-based mean LST exposure (34.7 °C) was almost equivalent to the corresponding residence-based mean (34.8 °C; mean difference in means = −0.09 °C). Similarly, the mean difference between the overall mobility-based mean air temperature exposure (19.2 °C) and the corresponding residence-based mean (19.2 °C) was negligible (−0.02 °C). Meaningful differences emerged, however, when comparing maximums, particularly for LST. The mean mobility-based maximum LST was 40.3 °C compared with a mean residence-based maximum of 35.8 °C, a difference of 4.51 °C. The difference in maximums was considerably smaller for air temperature (mean = 0.40 °C; SD = 1.41 °C) but nevertheless greater than the corresponding difference in means. Impact As the climate warms, assessment of heat exposure both at and away from home is important for understanding its health impacts. We compared two approaches to estimate exposure to two heat measures (land surface temperature and air temperature). The first approach only considered exposure at home, and the second considered day-to-day travel. Considering the average exposure estimated by each approach, the results were almost identical. Considering the maximum exposure experienced (specific definition in text), the differences between the two approaches were more considerable, especially for land surface temperature.
... Data collection was completed in 2017. The full details and protocol of the study are described elsewhere [38]. Participants wore accelerometer devices for a 2-week period, attended a clinical visit and blood draw, recorded nightly sleep journals, and completed self-report surveys that included demographic information. ...
... Samples were kept on ice and centrifuged at 4°C to isolate plasma, serum, and buffy coat from both EDTA and serum tubes. The samples were then aliquoted and stored at −80°C [38]. Insulin resistance was calculated using the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) according to the formula: fasting plasma insulin (mlU/L) × fasting plasma glucose (mg/dL)/22.5. ...
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Study Objectives Examining multiple dimensions of sleep health may better capture associations between sleep and health risks, including cardiometabolic disease (CMD). Hispanics have elevated risk for inadequate sleep and CMD biomarkers. Few studies have explored whether associations between sleep and CMD differ by Hispanic ethnicity. Methods Leveraging data from the Community of Mine (CoM) study, a cross-sectional investigation of 602 ethnically diverse participants, we derived accelerometer-measured sleep duration and efficiency, and self-reported sleep quality. Accelerometer-measured sleep exposures were analyzed both as continuous and categorical variables. Multivariate and quantile regression models were used to assess associations between sleep and CMD biomarkers (insulin resistance, systolic blood pressure, and low-density-lipoprotein cholesterol), controlling for age, sex, ethnicity, education, smoking status, and body mass index. We examined the potential effect modification of Hispanic ethnicity. Results We observed mixed results based on CMD biomarkers and sleep exposure. Increased sleep duration was significantly related to low-density lipoprotein cholesterol in adjusted models (estimate = 0.06; 95% CI: 0.02, 0.11). Poor sleep efficiency was associated with greater insulin resistance in the adjusted quantile (estimate = 0.20; 95% CI: 0.04, 0.36) model at the 90th percentile. Self-reported sleep quality was not associated with CMD outcomes. There was no evidence of effect modification by Hispanic ethnicity. Conclusions In this cohort, sleep health measures were found to have mixed and at times opposing effects on CMD outcomes. These effects did not demonstrate an interaction with Hispanic ethnicity.
... The Community of Mine (CoM) study enrolled 602 participants aged 35-80 years living in San Diego County from 2014-2017. The study collected behavioral, clinical, and biomarker outcomes related to cancer risk with the main study aim of advancing cancer risk exposure assessment; full details have been published elsewhere [31]. The crosssectional study recruited adults able to walk without human assistance and willing to wear study sensors for two weeks, with a goal of oversampling participants identifying as Hispanic/Latino. ...
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Background Sedentary behavior has been identified as a significant risk factor for Metabolic Syndrome (MetS). However, it is unclear if the sedentary pattern measurement approach (posture vs. movement) impacts observed associations or if associations differ for Hispanic/Latino communities, who have higher risk of MetS. Methods Participants from the Community of Mine (CoM) study (N = 602) wore hip-based accelerometers for 14 days and completed MetS-associated biomarker assessment (triglycerides, blood pressure, fasting glucose, HDL cholesterol, waist circumference). Sedentary patterns were classified using both cutpoints (movement-based) and the Convolutional Neural Network Hip Accelerometer Posture (CHAP) algorithm (posture-based). We used logistic regression to estimate associations between MetS with sedentary patterns overall and stratified by Hispanic/Latino ethnicity. Results CHAP and cutpoint sedentary patterns were consistently associated with MetS. When controlling for total sedentary time and moderate to vigorous physical activity, only CHAP-measured median sedentary bout duration (OR = 1.15, CI: 1.04, 1.28) was significant. In stratified analysis, CHAP-measured median bout duration and time spent in sedentary bouts ≥ 30 min were each associated with increased odds of MetS, but the respective associations were stronger for Hispanic/Latino ethnicity (OR = 1.71 and 1.48; CI = 1.28–2.31 and 1.12–1.98) than for non-Hispanic/Latino ethnicity (OR = 1.43 and 1.40; CI = 1.10–1.87 and 1.06–1.87). Conclusions The way sedentary patterns are measured can impact the strength and precision of associations with MetS. These differences may be larger in Hispanic/Latino ethnic groups and warrants further research to inform sedentary behavioral interventions in these populations.
... Further details on the study protocol and design can be found elsewhere. 24 This study was approved by the University of California, San Diego Institutional Review Board (protocol #140510), and all participants provided signed informed consent. In this study, a complete case analysis was utilized, where those with missing information related to the NDVI exposure, metabolic biomarker outcomes, and confounders were removed (see Table S1; http://links.lww.com/EE/A293 ...
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Introduction Growing evidence exists that greenspace exposure can reduce metabolic syndrome risk, a growing public health concern with well-documented inequities across population subgroups. We capitalize on the use of g-computation to simulate the influence of multiple possible interventions on residential greenspace on nine metabolic biomarkers and metabolic syndrome in adults (N = 555) from the 2014–2017 Community of Mine Study living in San Diego County, California. Methods Normalized difference vegetation index (NDVI) exposure from 2017 was averaged across a 400-m buffer around the participants’ residential addresses. Participants’ fasting plasma glucose, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglyceride concentrations, systolic and diastolic blood pressure, hemoglobin A1c (%), waist circumference, and metabolic syndrome were assessed as outcomes of interest. Using parametric g-computation, we calculated risk differences for participants being exposed to each decile of the participant NDVI distribution compared to minimum NDVI. Differential health impacts from NDVI exposure by sex, ethnicity, income, and age were examined. Results We found that a hypothetical increase in NDVI exposure led to a decrease in hemoglobin A1c (%), glucose, and high-density lipoprotein cholesterol concentrations, an increase in fasting total cholesterol, low-density lipoprotein cholesterol, and triglyceride concentrations, and minimal changes to systolic and diastolic blood pressure, waist circumference, and metabolic syndrome. The impact of NDVI changes was greater in women, Hispanic individuals, and those under 65 years old. Conclusions G-computation helps to simulate the potential health benefits of differential NDVI exposure and identifies which subpopulations can benefit most from targeted interventions aimed at minimizing health disparities.
... The Community of Mine Study was an observational study conducted from 2014 to 2017 in San Diego County, CA. The protocol and inclusion criteria have been described previously (Jankowska et al., 2019). This study includes 602 adults aged 35-80 years old living for at least 6 months in study census block group, which were selected to maximize environmental variation. ...
Article
Air pollution and noise exposure may synergistically contribute to increased cardiometabolic disorders; however, few studies have examined this potential interaction nor considered exposures beyond residential location. This study investigates the combined impact of dynamic air pollution and transportation noise on cardiometabolic disorders in San Diego County. Using the Community of Mine Study (2014-2017), 602 ethnically diverse participants were assessed for obesity, dyslipidemia, hypertension, and metabolic syndrome (MetS) using anthropometric measurements and biomarkers from blood samples. Time-weighted measures of exposure to PM2.5, NO2, road and aircraft noise were calculated using global positioning system (GPS) mobility data and Kernel Density Estimation. Generalized estimating equation models were used to analyze associations. Interactions were assessed on the multiplicative and additive scales using relative excess risk due to interaction (RERI). We found that air pollution and noise interact to affect metabolic disorders on both multiplicative and additive scales. The effect of noise on obesity and MetS was higher when air pollution was higher. The RERI of aircraft noise and NO2 on obesity and MetS were 0.13 (95%CI 0.03, 0.22) and 0.13 (95%CI 0.02, 0.25), respectively. This finding suggests that aircraft noise and air pollution may have synergistic effects on obesity and MetS.
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Global Positioning System (GPS) technology is increasingly used in health research to capture individual mobility and contextual and environmental exposures. However, the tools, techniques and decisions for using GPS data vary from study to study, making comparisons and reproducibility challenging. Objectives The objectives of this systematic review were to (1) identify best practices for GPS data collection and processing; (2) quantify reporting of best practices in published studies; and (3) discuss examples found in reviewed manuscripts that future researchers may employ for reporting GPS data usage, processing and linkage of GPS data in health studies. Design A systematic review. Data sources Electronic databases searched (24 October 2023) were PubMed, Scopus and Web of Science (PROSPERO ID: CRD42022322166). Eligibility criteria Included peer-reviewed studies published in English met at least one of the criteria: (1) protocols involving GPS for exposure/context and human health research purposes and containing empirical data; (2) linkage of GPS data to other data intended for research on contextual influences on health; (3) associations between GPS-measured mobility or exposures and health; (4) derived variable methods using GPS data in health research; or (5) comparison of GPS tracking with other methods (eg, travel diary). Data extraction and synthesis We examined 157 manuscripts for reporting of best practices including wear time, sampling frequency, data validity, noise/signal loss and data linkage to assess risk of bias. Results We found that 6% of the studies did not disclose the GPS device model used, only 12.1% reported the per cent of GPS data lost by signal loss, only 15.7% reported the per cent of GPS data considered to be noise and only 68.2% reported the inclusion criteria for their data. Conclusions Our recommendations for reporting on GPS usage, processing and linkage may be transferrable to other geospatial devices, with the hope of promoting transparency and reproducibility in this research. PROSPERO registration number CRD42022322166.
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Evidence linking traffic noise to insulin resistance and diabetes remains scarce and unanswered questions remain regarding the potential effect modification by neighborhood socioeconomic status (nSES). We aimed to assess socioeconomic inequalities in noise exposure, whether road and aircraft noise exposures were associated with insulin resistance or diabetes, and whether nSES modified these relationships. Among the Community of Mine Study in San Diego County, road and aircraft noise exposure at enrollment was calculated based on the static (participant's administrative boundary, and circular buffer around participant homes), and dynamic (mobility data by global positioning system, GPS) spatial aggregation methods. Associations of noise with insulin resistance (HOMA-IR) or type 2 diabetes (T2DM) were quantified using generalized estimating equation models adjusted for sex, age, ethnicity, individual income, and air pollution (nitrogen dioxide) exposure. Additive interaction between noise and nSES was assessed. Among 573 participants (mean age 58.7 y), participants living in low nSES were exposed to higher levels of aircraft and road noise using noise level at the census tract, circular buffer, or Kernel Density Estimation (KDE) of GPS data. Participants exposed to road noise greater or equal to the median (53 dB(A)) at the census tract and living in low nSES had increased level of insulin resistance (β = 0.15, 95%CI: -0.04, 0.34) and higher odds of T2DM (Odds Ratio = 2.34, 95%CI: 1.12, 4.90). A positive additive interaction was found as participants living in low nSES had higher odds of T2DM. The impact of noise exposure on insulin resistance and T2DM differs substantially by nSES. Public health benefits of reducing exposure to road or aircraft noise would be larger in individuals living in low nSES.
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This scoping review identified observational studies of adults that utilized accelerometry to assess physical activity and sedentary behavior. Key elements on accelerometry data collection were abstracted to describe current practices and completeness of reporting. We searched three databases (PubMed, Web of Science, and SPORTDiscus) on June 1, 2021 for articles published up to that date. We included studies of non-institutionalized adults with an analytic sample size of at least 500. The search returned 5686 unique records. After reviewing 1027 full-text publications, we identified and abstracted accelerometry characteristics on 155 unique observational studies (154 cross-sectional/cohort studies and 1 case control study). The countries with the highest number of studies included the United States, the United Kingdom, and Japan. Fewer studies were identified from the continent of Africa. Five of these studies were distributed donor studies, where participants connected their devices to an application and voluntarily shared data with researchers. Data collection occurred between 1999 to 2019. Most studies used one accelerometer (94.2%), but 8 studies (5.2%) used 2 accelerometers and 1 study (0.6%) used 4 accelerometers. Accelerometers were more commonly worn on the hip (48.4%) as compared to the wrist (22.3%), thigh (5.4%), other locations (14.9%), or not reported (9.0%). Overall, 12.7% of the accelerometers collected raw accelerations and 44.6% were worn for 24 hours/day throughout the collection period. The review identified 155 observational studies of adults that collected accelerometry, utilizing a wide range of accelerometer data processing methods. Researchers inconsistently reported key aspects of the process from collection to analysis, which needs addressing to support accurate comparisons across studies.
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The main purpose of the present study was to assess the impact of global positioning system (GPS) signal lapse on physical activity analyses, discover any existing associations between missing GPS data and environmental and demographics attributes, and to determine whether imputation is an accurate and viable method for correcting GPS data loss. Accelerometer and GPS data of 782 participants from 8 studies were pooled to represent a range of lifestyles and interactions with the built environment. Periods of GPS signal lapse were identified and extracted. Generalised linear mixed models were run with the number of lapses and the length of lapses as outcomes. The signal lapses were imputed using a simple ruleset, and imputation was validated against person-worn camera imagery. A final generalised linear mixed model was used to identify the difference between the amount of GPS minutes pre- and post-imputation for the activity categories of sedentary, light, and moderate-to-vigorous physical activity. Over 17% of the dataset was comprised of GPS data lapses. No strong associations were found between increasing lapse length and number of lapses and the demographic and built environment variables. A significant difference was found between the pre- and postimputation minutes for each activity category. No demographic or environmental bias was found for length or number of lapses, but imputation of GPS data may make a significant difference for inclusion of physical activity data that occurred during a lapse. Imputing GPS data lapses is a viable technique for returning spatial context to accelerometer data and improving the completeness of the dataset.
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Background: Environmental factors may influence breast cancer, however most studies have measured environmental exposure in neighborhoods around home residences (static exposure). We hypothesize that tracking environmental exposures over time and space (dynamic exposure) is key to assessing total exposure. This study compares breast cancer survivors' exposure to walkable and recreation-promoting environments using dynamic Global Positioning System (GPS) and static home-based measures of exposure in relation to insulin resistance. Methods: GPS data from 249 breast cancer survivors living in San Diego County were collected for one week along with fasting blood draw. Exposure to recreation spaces and walkability was measured for each woman's home address within an 800m buffer (static), and using a kernel density weight of GPS tracks (dynamic). Participants' exposure estimates were related to insulin resistance (using the homeostatic model assessment of insulin resistance, HOMA-IR) controlled by age and BMI in linear regression models. Results: The dynamic measurement method resulted in greater variability in built environment exposure values than did the static method. Regression results showed no association between HOMA-IR and home-based, static measures of walkability and recreation area exposure. GPS-based dynamic measures of both walkability and recreation area were significantly associated with lower HOMA-IR (p<0.05). Conclusions: Dynamic exposure measurements may provide important evidence for community- and individual-level interventions that can address cancer risk inequities arising from environments wherein breast cancer survivors live and engage. Impact: This is the first study to compare associations of dynamic versus static built environment exposure measures with insulin outcomes in breast cancer survivors.
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To address the current obesity and inactivity epidemics, public health researchers have attempted to identify spatial factors that influence physical inactivity and obesity. Technologic and methodologic developments have led to a revolutionary ability to examine dynamic, high-resolution measures of temporally matched location and behavior data through GPS, accelerometry, and GIS. These advances allow the investigation of spatial energetics, high-spatiotemporal resolution data on location and time-matched energetics, to examine how environmental characteristics, space, and time are linked to activity-related health behaviors with far more robust and detailed data than in previous work. Although the transdisciplinary field of spatial energetics demonstrates promise to provide novel insights on how individuals and populations interact with their environment, there remain significant conceptual, technical, analytical, and ethical challenges stemming from the complex data streams that spatial energetics research generates. First, it is essential to better understand what spatial energetics data represent, the relevant spatial context of analysis for these data, and if spatial energetics can establish causality for development of spatially relevant interventions. Second, there are significant technical problems for analysis of voluminous and complex data that may require development of spatially aware scalable computational infrastructures. Third, the field must come to agreement on appropriate statistical methodologies to account for multiple observations per person. Finally, these challenges must be considered within the context of maintaining participant privacy and security. This article describes gaps in current practice and understanding and suggests solutions to move this promising area of research forward.
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Importance Rates of obesity and diabetes have increased substantially in recent decades; however, the potential role of the built environment in mitigating these trends is unclear. Objective To examine whether walkable urban neighborhoods are associated with a slower increase in overweight, obesity, and diabetes than less walkable ones. Design, Setting, and Participants Time-series analysis (2001-2012) using annual provincial health care (N ≈ 3 million per year) and biennial Canadian Community Health Survey (N ≈ 5500 per cycle) data for adults (30-64 years) living in Southern Ontario cities. Exposures Neighborhood walkability derived from a validated index, with standardized scores ranging from 0 to 100, with higher scores indicating more walkability. Neighborhoods were ranked and classified into quintiles from lowest (quintile 1) to highest (quintile 5) walkability. Main Outcomes and Measures Annual prevalence of overweight, obesity, and diabetes incidence, adjusted for age, sex, area income, and ethnicity. Results Among the 8777 neighborhoods included in this study, the median walkability index was 16.8, ranging from 10.1 in quintile 1 to 35.2 in quintile 5. Resident characteristics were generally similar across neighborhoods; however, poverty rates were higher in high- vs low-walkability areas. In 2001, the adjusted prevalence of overweight and obesity was lower in quintile 5 vs quintile 1 (43.3% vs 53.5%; P < .001). Between 2001 and 2012, the prevalence increased in less walkable neighborhoods (absolute change, 5.4% [95% CI, 2.1%-8.8%] in quintile 1, 6.7% [95% CI, 2.3%-11.1%] in quintile 2, and 9.2% [95% CI, 6.2%-12.1%] in quintile 3). The prevalence of overweight and obesity did not significantly change in areas of higher walkability (2.8% [95% CI, −1.4% to 7.0%] in quintile 4 and 2.1% [95% CI, −1.4% to 5.5%] in quintile 5). In 2001, the adjusted diabetes incidence was lower in quintile 5 than other quintiles and declined by 2012 from 7.7 to 6.2 per 1000 persons in quintile 5 (absolute change, −1.5 [95% CI, −2.6 to −0.4]) and 8.7 to 7.6 in quintile 4 (absolute change, −1.1 [95% CI, −2.2 to −0.05]). In contrast, diabetes incidence did not change significantly in less walkable areas (change, −0.65 in quintile 1 [95% CI, −1.65 to 0.39], −0.5 in quintile 2 [95% CI, −1.5 to 0.5], and −0.9 in quintile 3 [95% CI, −1.9 to 0.02]). Rates of walking or cycling and public transit use were significantly higher and that of car use lower in quintile 5 vs quintile 1 at each time point, although daily walking and cycling frequencies increased only modestly from 2001 to 2011 in highly walkable areas. Leisure-time physical activity, diet, and smoking patterns did not vary by walkability (P > .05 for quintile 1 vs quintile 5 for each outcome) and were relatively stable over time. Conclusions and Relevance In Ontario, Canada, higher neighborhood walkability was associated with decreased prevalence of overweight and obesity and decreased incidence of diabetes between 2001 and 2012. However, the ecologic nature of these findings and the lack of evidence that more walkable urban neighborhood design was associated with increased physical activity suggest that further research is necessary to assess whether the observed associations are causal.
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Background: The WHO recommends moderate physical activity to combat the increasing risk of death from chronic diseases. We conducted a meta-analysis to assess the association between physical activity and cancer mortality and the WHO recommendations to reduce the latter. Methods: MEDLINE and EMBASE were searched up until May 2014 for cohort studies examining physical activity and cancer mortality in the general population and cancer survivors. Combined HRs were estimated using fixed-effect or random-effect meta-analysis of binary analysis. Associated HRs with defined increments and recommended levels of recreational physical activity were estimated by two-stage random-effects dose-response meta-analysis. Results: A total of 71 cohort studies met the inclusion criteria and were analysed. Binary analyses determined that individuals who participated in the most physical activity had an HR of 0.83 (95% CI 0.79 to 0.87) and 0.78 (95% CI 0.74 to 0.84) for cancer mortality in the general population and among cancer survivors, respectively. There was an inverse non-linear dose-response between the effects of physical activity and cancer mortality. In the general population, a minimum of 2.5 h/week of moderate-intensity activity led to a significant 13% reduction in cancer mortality. Cancer survivors who completed 15 metabolic equivalents of task (MET)-h/week of physical activity had a 27% lower risk of cancer mortality. A greater protective effect occurred in cancer survivors undertaking physical activity postdiagnosis versus prediagnosis, where 15 MET-h/week decreased the risk by 35% and 21%, respectively. Conclusions: Our meta-analysis supports that current physical activity recommendations from WHO reduce cancer mortality in both the general population and cancer survivors. We infer that physical activity after a cancer diagnosis may result in significant protection among cancer survivors.
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The intent of this chapter is to enhance understanding of various dietary assessment methods so that the most appropriate method for a particular need is chosen. This review focuses only on individual-level food intake assessment. It is intended to serve as a resource for those who wish to assess diet in a research study using individual measurements for group-level analysis. The chapter reviews major dietary assessment methods, their advantages and disadvantages, and validity; describes which dietary assessment methods are appropriate for different types of studies and populations; and discusses specific issues that relate to all methods.