Jacqueline Kerr

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

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Publications (105)272.02 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: This study presents a novel method to assess context-specific physical activity patterns using accelerometer and GPS. The method efficiency is investigated by providing descriptive results on the use of domains and subdomains, and assessing how much of children's and adolescents' daily activity time can be classified by these domains and subdomains. Four domains and 11 subdomains were defined as important contexts for child and adolescent behaviour. During weekdays (n=367) and weekend days (n=178) the majority of children and adolescents spent time in active transport, urban green space, clubs and sports facilities. Satisfactory method efficiency was found during weekdays. Natural experiments combined with objective assessment of context-specific behaviours hold the potential to create evidence on the effects of changes to the built environment on behaviour. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.
    Health & Place 12/2014; 31C:90-99. · 2.42 Impact Factor
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    ABSTRACT: The purpose of this study was to compare estimates of sedentary time on weekdays vs. weekend days in older adults and determine if these patterns vary by measurement method. Older adults (N = 230, M = 83.5, SD = 6.5 years) living in retirement communities completed a questionnaire about sedentary behavior and wore an ActiGraph accelerometer for seven days. Participants engaged in 9.4 (SD = 1.5) hours per day of accelerometer-measured sedentary time, but self-reported engaging in 11.4 (SD = 4.9) hours per day. Men and older participants had more accelerometer-measured sedentary time than their counterparts. The difference between accelerometer-measured weekday and weekend sedentary time was non-significant. However, participants self-reported 1.1 hours per day more sedentary time on weekdays compared to weekend days. Findings suggest self-reported but not accelerometer-measured sedentary time should be investigated separately for weekdays and weekend days, and that self-reports may overestimate sedentary time in older adults.
    Journal of aging and physical activity. 11/2014;
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    ABSTRACT: Global Positioning Systems (GPS) are increasingly applied in activity studies, yet significant theoretical and methodological challenges remain. This paper presents a framework for integrating GPS data with other technologies to create dynamic representations of behaviors in context. Utilizing more accurate and sensitive measures to link behavior and environmental exposures allows for new research questions and methods to be developed.
    Exercise and sport sciences reviews. 11/2014;
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    ABSTRACT: Wrist accelerometers are being used in population level surveillance of physical activity (PA) but more research is needed to evaluate their validity for correctly classifying types of PA behavior and predicting energy expenditure (EE). In this study we compare accelerometers worn on the wrist and hip, and the added value of heart rate (HR) data, for predicting PA type and EE using machine learning. Forty adults performed locomotion and household activities in a lab setting while wearing three ActiGraph GT3X+ accelerometers (left hip, right hip, non-dominant wrist) and a HR monitor (Polar RS400). Participants also wore a portable indirect calorimeter (COSMED K4b2), from which EE and metabolic equivalents (METs) were computed for each minute. We developed two predictive models: a random forest classifier to predict activity type and a random forest of regression trees to estimate METs. Predictions were evaluated using leave-one-user-out cross-validation. The hip accelerometer obtained an average accuracy of 92.3% in predicting four activity types (household, stairs, walking, running), while the wrist accelerometer obtained an average accuracy of 87.5%. Across all 8 activities combined (laundry, window washing, dusting, dishes, sweeping, stairs, walking, running), the hip and wrist accelerometers obtained average accuracies of 70.2% and 80.2% respectively. Predicting METs using the hip or wrist devices alone obtained root mean square errors (rMSE) of 1.09 and 1.00 METs per 6 min bout, respectively. Including HR data improved MET estimation, but did not significantly improve activity type classification. These results demonstrate the validity of random forest classification and regression forests for PA type and MET prediction using accelerometers. The wrist accelerometer proved more useful in predicting activities with significant arm movement, while the hip accelerometer was superior for predicting locomotion and estimating EE.
    Physiological Measurement 10/2014; 35(11):2191. · 1.50 Impact Factor
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    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;
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    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. 09/2014;
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    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.
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    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;
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    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; 48:1634–1639. · 3.67 Impact Factor
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    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
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    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
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    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
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    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.
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    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.
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    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.
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    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.
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    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
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    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
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    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
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    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

Publication Stats

2k Citations
272.02 Total Impact Points

Institutions

  • 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