Jacqueline Kerr

National University (California), San Diego, California, United States

Are you Jacqueline Kerr?

Claim your profile

Publications (101)259.29 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: This study aimed to explore the relationship between objectively measured physical activity and cognitive functioning in breast cancer survivors.
    Journal of cancer survivorship : research and practice. 10/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: This study aimed to investigate gender, race/ethnicity, education, and income as moderators of relations of perceived neighborhood crime, pedestrian, and traffic safety to physical activity.
    Medicine and science in sports and exercise. 08/2014; 46(8):1554-1563.
  • [Show abstract] [Hide abstract]
    ABSTRACT: To assess validity of the Personal Activity Location Measurement System (PALMS) for deriving time spent walking/running, bicycling, and in vehicle, using SenseCam as the comparison.
    Medicine and science in sports and exercise. 07/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: To investigate the relation of factors from multiple levels of ecological models (ie, individual, interpersonal and environmental) to active travel to/from school in an observational study of young adolescents. Participants were 294 12-15-year olds living within two miles of their school. Demographic, psychosocial and perceived built environment characteristics around the home were measured by survey, and objective built environment factors around home and school were assessed in Geographic Information Systems (GIS). Mixed effects multinomial regression models tested correlates of engaging in 1-4 (vs 0) and 5-10 (vs 0) active trips/week to/from school, adjusted for distance and other covariates. 64% of participants reported ≥1 active trip/week to/from school. Significant correlates of occasional and/or habitual active travel to/from school included barriers (ORs=0.27 and 0.15), parent modelling of active travel (OR=3.27 for habitual), perceived street connectivity (OR=1.78 for occasional), perceived pedestrian safety around home (OR=2.04 for habitual), objective street connectivity around home (OR=0.97 for occasional), objective residential density around home (ORs=1.10 and 1.11) and objective residential density around school (OR=1.14 for habitual). Parent modelling interacted with pedestrian safety in explaining active travel to/from school. Results supported multilevel correlates of adolescents' active travel to school, consistent with ecological models. Correlates of occasional and habitual active travel to/from school were similar. Built environment attributes around schools, particularly residential density, should be considered when siting new schools and redeveloping neighbourhoods around existing schools.
    British journal of sports medicine 03/2014; · 3.67 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Uncertainty in the relevant spatial context may drive heterogeneity in findings on the built environment and energy balance. To estimate the effect of this uncertainty, we conducted a sensitivity analysis defining intersection and business densities and counts within different buffer sizes and shapes on associations with self-reported walking and body mass index. Linear regression results indicated that the scale and shape of buffers influenced study results and may partly explain the inconsistent findings in the built environment and energy balance literature.
    Health & Place 03/2014; 27C:162-170. · 2.42 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Direct relationships between safety concerns and physical activity have been inconsistently patterned in the literature. To tease out these relationships, crime, pedestrian, and traffic safety were examined as moderators of built environment associations with physical activity. Exploratory analyses used two cross-sectional studies of 2068 adults ages 20-65 and 718 seniors ages 66+ with similar designs and measures. The studies were conducted in the Baltimore, Maryland-Washington, DC and Seattle-King County, Washington regions during 2001-2005 (adults) and 2005-2008 (seniors). Participants were recruited from areas selected to sample high- and low- income and walkability. Independent variables perceived crime, traffic, and pedestrian safety were measured using scales from validated instruments. A GIS-based walkability index was calculated for a street-network buffer around each participant's home address. Outcomes were total physical activity measured using accelerometers and transportation and leisure walking measured with validated self-reports (IPAQ-long). Mixed effects regression models were conducted separately for each sample. Of 36 interactions evaluated across both studies, only 5 were significant (p < .05). Significant interactions did not consistently support a pattern of highest physical activity when safety was rated high and environments were favorable. There was not consistent evidence that safety concerns reduced the beneficial effects of favorable environments on physical activity. Only pedestrian safety showed evidence of a consistent main effect with physical activity outcomes, possibly because pedestrian safety items (e.g., crosswalks, sidewalks) were not as subjective as those on the crime and traffic safety scales. Clear relationships between crime, pedestrian, and traffic safety with physical activity levels remain elusive. The development of more precise safety variables and the use of neighborhood-specific physical activity outcomes may help to elucidate these relationships.
    International Journal of Behavioral Nutrition and Physical Activity 02/2014; 11(1):24. · 3.58 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Knowledge on domain-specific physical activity (PA) has the potential to advance public health interventions and inform new policies promoting children's PA. The purpose of this study is to identify and assess domains (leisure, school, transport, home) and subdomains (e.g., recess, playgrounds, and urban green space) for week day moderate to vigorous PA (MVPA) using objective measures and investigate gender and age differences. Participants included 367 Danish children and adolescents (11-16 years, 52% girls) with combined accelerometer and Global Positioning System (GPS) data (mean 2.5 days, 12.7 hrs/day). The Personal Activity and Location Measurement System and a purpose-built database assessed data in 15-second epochs to determine PA and assign epochs to 4 domains and 11 subdomains. Frequencies and proportions of time spent in MVPA were determined and differences assessed using multi-level modeling. More than 90% of MVPA was objectively assigned to domains/subdomains. Boys accumulated more MVPA overall, in leisure, school and transport (all p < 0.05). Children compared with adolescents accumulated more MVPA, primarily through more school MVPA (p < 0.05). Boys spent a large proportion of time accumulating MVPA in playgrounds, active transport, Physical Education, sports facilities, urban green space and school grounds. Girls spent a significant proportion of time accumulating MVPA in active transport and playgrounds. No gender or age differences were found in the home domain. Large variations were found in PA frequency and intensity across domains/subdomains. Significant gender differences were found, with girls being less active in almost all domains and subdomains. Objectively measured patterns of PA across domains/subdomains can be used to better tailor PA interventions and inform future policies for promoting child PA.
    International Journal of Behavioral Nutrition and Physical Activity 01/2014; 11(1):8. · 3.58 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Introduction: Being outdoors has a positive influence on health among children. Evidence in this area is limited and many studies have used self-reported measures. Objective context-specific assessment of physical activity patterns and correlates, such as outdoor time, may progress this field. Aims: To employ novel objective measures to assess age and gender differences in context-specific outdoor weekday behavior patterns among school-children [outdoor time and outdoor moderate to vigorous physical activity (MVPA)] and to investigate associations between context-specific outdoor time and MVPA. Methods: A total of 170 children had at least one weekday of 9 h combined accelerometer and global positioning system data and were included in the analyses. The data were processed using the personal activity and location measurement system (PALMS) and a purpose-built PostgreSQL database resulting in context-specific measures for outdoor time, outdoor MVPA, and overall daily MVPA. In addition, 4 domains (leisure, school, transport, and home) and 11 subdomains (e.g., urban green space and sports facilities) were created and assessed. Multilevel analyses provided results on age and gender differences and the association between outdoor time and MVPA. Results: Girls compared to boys had fewer outdoor minutes (p < 0.05), spent a smaller proportion of their overall daily time outdoors (p < 0.05), had fewer outdoor MVPA minutes during the day (p < 0.001) and in 11 contexts. Children compared to adolescents had more outdoor minutes (p < 0.05). During school and within recess, children compared to adolescents had more outdoor MVPA (p < 0.001) and outdoor time (p < 0.001). A 1-h increase in outdoor time was associated with 9.9 more minutes of MVPA (p < 0.001). Conclusion: A new methodology to assess the context-specific outdoor time and physical activity patterns has been developed and can be expanded to other populations. Different context-specific patterns were found for gender and age, suggesting different strategies may be needed to promote physical activity.
    Frontiers in Public Health 01/2014; 2:20.
  • [Show abstract] [Hide abstract]
    ABSTRACT: Self-report remains the most common method for collecting epidemiological evidence of the links between travel and health outcomes. This study assesses the validity and reliability of a self-reported travel diary (a modified version of a well-established UK travel diary; The National Travel Survey (NTS)) by comparison with wearable camera data. Across four locations (Oxford, UK; Romford, UK; San Diego, USA; and Auckland, New Zealand) we collected 3–4 days of SenseCam (wearable camera) and travel diary data from 84 adult participants (purposive sample). Compliance with the data collection protocol was high and inspection of the crude results suggests acceptable agreement between measures for total days of data collected (diary=278; SenseCam=274), daily journey frequency (diary=4.78; SenseCam=4.64) and average journey duration in minutes (diary=17:46; SenseCam=15:40). Once these data were examined for total daily time spent travelling in minutes agreement was poorer (diary=84:53; SenseCam=72:35). Analysis of matched pairs of journey measurements (n=1127) suggests a positive bias on self-reported journey duration of 2:08 min (95% CI=1:48–2:28; 95% limits-of-agreement=−9:10 to 13:26). Similar analysis of diary days matched to complete SenseCam days (n=201) showed a very small positive bias with a very large limits-of-agreement (1:41 min; 95% CI=−2:00 to 5:24; 95% limits-of-agreement=−50:29 to 53:41). These results suggest self-reported journey and daily travel exposure data are relatively valid at a population level, though corrections according to reported bias could be considered. The large limits of agreement for matched journey and diary summary analysis suggest self-report diaries may be unsuitable for assessment of an individual׳s travel behaviour.
    Journal of Transport & Health. 01/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Background: Active travel is an important area in physical activity research, but objective measurement of active travel is still difficult. Automated methods to measure travel behaviors will improve research in this area. In this paper, we present a supervised machine learning method for transportation mode prediction from global positioning system (GPS) and accelerometer data. Methods: We collected a dataset of about 150 h of GPS and accelerometer data from two research assistants following a protocol of prescribed trips consisting of five activities: bicycling, riding in a vehicle, walking, sitting, and standing. We extracted 49 features from 1-min windows of this data. We compared the performance of several machine learning algorithms and chose a random forest algorithm to classify the transportation mode. We used a moving average output filter to smooth the output predictions over time. Results: The random forest algorithm achieved 89.8% cross-validated accuracy on this dataset. Adding the moving average filter to smooth output predictions increased the cross-validated accuracy to 91.9%. Conclusion: Machine learning methods are a viable approach for automating measurement of active travel, particularly for measuring travel activities that traditional accelerometer data processing methods misclassify, such as bicycling and vehicle travel.
    Frontiers in Public Health 01/2014; 2:36.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Background-—Living near major roadways has been linked with increased risk of cardiovascular events and worse prognosis. Residential proximity to major roadways may also be associated with increased risk of hypertension, but few studies have evaluated this hypothesis.
    Journal of the American Heart Association. 01/2014; 3:e000727.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The emergence of portable global positioning system (GPS) receivers over the last 10 years has provided researchers with a means to objectively assess spatial position in free-living conditions. However, the use of GPS in free-living conditions is not without challenges and the aim of this study was to test the dynamic accuracy of a portable GPS device under real-world environmental conditions, for four modes of transport, and using three data collection intervals. We selected four routes on different bearings, passing through a variation of environmental conditions in the City of Copenhagen, Denmark, to test the dynamic accuracy of the Qstarz BT-Q1000XT GPS device. Each route consisted of a walk, bicycle, and vehicle lane in each direction. The actual width of each walking, cycling, and vehicle lane was digitized as accurately as possible using ultra-high-resolution aerial photographs as background. For each trip, we calculated the percentage that actually fell within the lane polygon, and within the 2.5, 5, and 10 m buffers respectively, as well as the mean and median error in meters. Our results showed that 49.6% of all ≈68,000 GPS points fell within 2.5 m of the expected location, 78.7% fell within 10 m and the median error was 2.9 m. The median error during walking trips was 3.9, 2.0 m for bicycle trips, 1.5 m for bus, and 0.5 m for car. The different area types showed considerable variation in the median error: 0.7 m in open areas, 2.6 m in half-open areas, and 5.2 m in urban canyons. The dynamic spatial accuracy of the tested device is not perfect, but we feel that it is within acceptable limits for larger population studies. Longer recording periods, for a larger population are likely to reduce the potentially negative effects of measurement inaccuracy. Furthermore, special care should be taken when the environment in which the study takes place could compromise the GPS signal.
    Frontiers in Public Health 01/2014; 2:21.
  • [Show abstract] [Hide abstract]
    ABSTRACT: Objective: The main study objective was to examine the moderating effects of perceived enjoyment, barriers/benefits, perceived social support and self-efficacy, on the associations of perceived environmental attributes with walking for recreation and leisure-time moderate-to-vigorous physical activity, and whether these potential moderating effects differed by gender and study site. Methods: Data from three observational studies in the United States (Seattle and Baltimore), Australia (Adelaide), and Belgium (Ghent) were pooled. In total, 6014 adults (20-65 years, 55.7% women) were recruited in high-/low-walkable and high-/low-income neighborhoods. All participants completed the Neighborhood Environment Walkability Scale, a validated questionnaire on psychosocial attributes, and the International Physical Activity Questionnaire. General additive mixed models were conducted in R. Results: Enjoyment of physical activity, perceived barriers to physical activity, perceived benefits of physical activity, social support from family and friends, and self-efficacy for physical activity moderated the relationships of specific perceived environmental characteristics with walking for recreation and/or leisure-time moderate-to-vigorous physical activity. Overall, moderating effects were in the same direction: environmental perceptions were positively associated with leisure-time activity, but associations were strongest in adults with less positive scores on psychosocial attributes. The findings were fairly consistent across gender and study sites. Conclusions: The present study findings are promising, as it seems that those who might benefit most from environmental interventions to promote physical activity, may mainly be adults at risk of being insufficiently active or those difficult to reach through individual health promotion programs. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
    Health Psychology 11/2013; · 3.95 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Machine learning techniques are used to improve accelerometer-based measures of physical activity. Most studies have used laboratory-collected data to develop algorithms to classify behaviors, but studies of free-living activity are needed to improve the ecological validity of these methods. With this aim, we collected a novel free-living dataset that uses SenseCams to obtain ground-truth annotations of physical activities. We trained a classifier on free-living data and compare it to a classifier trained on prescribed activities. The classifier predicts five activity classes: bicycling, riding in a vehicle, sitting, standing, and walking/running. When testing on free-living data, classifiers trained on free-living data significantly outperform those trained on a controlled dataset (89.2% vs. 70.9% accuracy).
    Proceedings of the 4th International SenseCam & Pervasive Imaging Conference; 11/2013
  • [Show abstract] [Hide abstract]
    ABSTRACT: This study explores the use of SenseCam images to measure the environment. SenseCam images were collected in two neighbourhoods and annotated using a comprehensive list of features in the built, natural, and social environments. Several issues arose during this process. Some, were common to all SenseCam use (time to annotate images, obscured images, annotation error), and others were specific to using SenseCam to assess environmental features (difficult to identify features, directionality, annotator familiarity, uncertainty about which features to annotate, assessing quantity/density of features). Despite these issues SenseCam images complement existing methods of measuring the environment and allow researchers to capture the environment the wearer is exposed to. This data can then be linked to behaviour data and data from other wearable sensors.
    Proceedings of the 4th International SenseCam & Pervasive Imaging Conference; 11/2013
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The SenseCam data can be used to estimate time spent in specific episodes of sedentary behaviors, as well as some dimensions of sedentary behaviors. However, it is unknown whether SenseCam data can be aggregated to provide an objective estimate of total sedentary time accumulated during a single day. We compared SenseCam-derived day-level estimates to self-report estimates of time spent in sedentary behaviors using 39 days of concurrent SenseCam and self-report data from a sample of university employed adults (age 18--70 years). We also examined whether SenseCam data can be used to compute day-level estimates of specific dimensions of sedentary behavior (e.g., co-occurring sedentary behaviors and social context). Twenty-four percent of the days of SenseCam image data collected did not have enough image data (i.e., ≥8 hours of data) to generate day-level estimates. Further, the day-level agreement between the SenseCam and self-report estimates of time spent in sedentary behaviors varied considerably by device wear time. In terms of dimensions of sedentary behaviors measured by the SenseCam, over one-third of the total sedentary time involved a social interaction and the majority (71%) of the estimated sedentary time was spent in one behavior. Overall, SenseCam data can be used to compute day-level estimates of time spent in specific episodes of sedentary behaviors and the images provide data on critical dimensions of these behaviors; however, device wear-time significantly influences the accuracy of day-level estimates.
    Proceedings of the 4th International SenseCam & Pervasive Imaging Conference; 11/2013
  • [Show abstract] [Hide abstract]
    ABSTRACT: Introduction: We used data from Global Positioning Systems (GPS) to describe active transportation (AT) and recreational walking (RW; i.e. in a park) trips among older adults with mobility disabilities (i.e. that use assistive devices). Little is known about these patterns in this growing segment of the population. Methods: Participants (N = 35; Mean age = 67, 30 white, 26 females) wore a Qstarz BT1000XT GPS device for 3 days. Data were processed and analyzed using the Physical Activity Location Measurement System (PALMS; UCSD) and Global Information Systems (GIS) from which we classified trips as vehicle, AT or RW. We compared Walkscore.com values (based on their home address as a proxy for built environment walkability) for those using vehicle trips to those with an active trip (AT or WFR) from their home. Results: Among participants with usable GPS data (N = 28), 25% (N = 7) used AT and another 21% (N = 6) did RW. Of AT and RW trips, 39% used a vehicle to get to a destination where they then made their active trip. Participants whose active trip was from home had higher walkscores (mean = 82) than those who did not (mean = 66; p = .05). Conclusions: People with mobility disabilities are able to use active modes of transportation, particularly when the built environment is supportive. It was fairly common to use a non-home neighborhood environment for an active transit trip or walk though the majority used their local neighborhood near home to make active trips.
    141st APHA Annual Meeting and Exposition 2013; 11/2013
  • [Show abstract] [Hide abstract]
    ABSTRACT: To explore the relationship between cognitive functioning and time spent at different intensities of physical activity (PA) in free-living older adults. Cross sectional analyses. Continuing care retirement communities. Older adults residing in seven continuing care retirement communities in San Diego County with an average age of 83; 70% were female, and 35% had a graduate-level education (N = 217). PA was measured objectively using hip worn accelerometers with data aggregated to the minute level. Three cut points were used to assess low light-intensity PA (LLPA), high light-intensity PA (HLPA), and moderate- to vigorous-intensity PA (MVPA). The Trail Making Test (TMT) Parts A and B were completed, and time for each test (seconds) and time for Part B minus time for Part A (seconds) were used as measures of cognitive function. Variables were log-transformed and entered into linear regression models adjusting for demographic factors (age, education, sex) and other PA intensity variables. LLPA was not related to any TMT test score. HLPA was significantly related to TMT A, B, and B minus A but only in unadjusted models. MVPA was related to TMT B and B minus A after adjusting for demographic variables. There may be a dose response between PA intensity and cognitive functioning in older adults. The stronger findings supporting a relationship between MVPA and cognitive functioning are consistent with previous observational and intervention studies.
    Journal of the American Geriatrics Society 11/2013; 61(11):1927-31. · 4.22 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Some attributes of neighborhood environments are associated with physical activity among older adults. This study examined whether the associations were moderated by driving status. Older adults from neighborhoods differing in walkability and income completed written surveys and wore accelerometers (n=880, mean age=75 years, 56% women). Neighborhood environments were measured by geographic information systems and validated questionnaires. Driving status was defined based on a driver's license, car ownership, and feeling comfortable to drive. Outcome variables included accelerometer-based physical activity and self-reported transport and leisure walking. Multi-level generalized linear regression was used. There was no significant neighborhood attribute × driving status interaction with objective physical activity or reported transport walking. For leisure walking almost all environmental attributes were positive and significant among driving seniors but not among non-driving seniors (5 significant interactions at p<0.05). The findings suggest that driving status is likely to moderate the association between neighborhood environments and older adults' leisure walking.
    Journal of Aging and Physical Activity 10/2013;
  • Jacqueline Kerr
    American journal of preventive medicine 10/2013; 45(4):524-5. · 4.24 Impact Factor

Publication Stats

2k Citations
259.29 Total Impact Points


  • 2014
    • National University (California)
      San Diego, California, United States
  • 2007–2014
    • University of California, San Diego
      • Department of Family and Preventive Medicine
      San Diego, California, United States
  • 2013
    • University of Sydney
      Sydney, New South Wales, Australia
    • University of Oxford
      • Department of Public Health
      Oxford, ENG, United Kingdom
  • 2008–2013
    • University of Washington Seattle
      • • Department of Health Services
      • • Department of Pediatrics
      Seattle, WA, United States
    • Palacký University of Olomouc
      • Institute of Active Lifestyle
      Olomouc, Olomoucky kraj, Czech Republic
  • 2006–2013
    • San Diego State University
      • Department of Psychology
      San Diego, California, United States
  • 2012
    • Ghent University
      • Department of Movement and Sports Sciences
      Gent, VLG, Belgium
  • 2011–2012
    • Stanford University
      • • Stanford Prevention Research Center
      • • Department of Health Research and Policy
      Stanford, CA, United States
    • CSU Mentor
      Long Beach, California, United States
    • Group Health Cooperative
      • Group Health Research Institute
      Seattle, WA, United States
  • 2008–2012
    • University of British Columbia - Vancouver
      • School of Community and Regional Planning
      Vancouver, British Columbia, Canada
  • 2009
    • University of Alabama at Birmingham
      • Department of Pediatrics
      Birmingham, AL, United States