[Show abstract][Hide abstract] ABSTRACT: Background. We examined the relationships between objective and self-reported sedentary time and health indicators among older adults residing in retirement communities.
Methods. Our cross-sectional analysis used data from 307 participants who completed baseline measurements of a physical activity trial in 11 retirement communities in San Diego County. Sedentary time was objectively measured with devices (accelerometers) and using self-reports. Outcomes assessed included emotional and cognitive health, physical function, and physical health (eg, blood pressure). Linear mixed-effects models examined associations between sedentary behavior and outcomes adjusting for demographics and accelerometer physical activity.
Results. Higher device-measured sedentary time was associated with worse objective physical function (Short Physical Performance Battery, balance task scores, 400-m walk time, chair stand time, gait speed), self-reported physical function, and fear of falling but with less sleep disturbance (all ps < .05). TV viewing was positively related to 400-m walk time (p < .05). Self-reported sedentary behavior was related to better performance on one cognitive task (trails A; p < .05).
Conclusions. Sedentary time was mostly related to poorer physical function independently of moderate-to-vigorous physical activity and may be a modifiable behavior target in interventions aiming to improve physical function in older adults. Few associations were observed with self-reported sedentary behavior measures.
The Journals of Gerontology Series A Biological Sciences and Medical Sciences 08/2015; DOI:10.1093/gerona/glv103 · 4.98 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Prevalence of walking and cycling for transport is low, varying greatly across countries. Few studies have examined neighborhood perceptions related to walking and cycling for transport in different countries. Therefore it is challenging to prioritize appropriate built environment interventions. The aim of this study was to examine the strength and shape of the relationship between adults' neighborhood perceptions and walking and cycling for transport across diverse environments.
As part of the International Physical activity and Environment Network (IPEN) adult project, self-report data were taken from 13,745 adults (18 - 65 years) living in physically and socially diverse neighborhoods in 17 cities across 12 countries. Neighborhood perceptions were measured using the Neighborhood Environment Walkability Scale, and walking and cycling for transport were measured using the International Physical Activity Questionnaire - Long Form. Generalized additive mixed models were used to model walking or cycling for transport during the last seven days with neighborhood perceptions. Interactions by city were explored.
Walking for transport outcomes were significantly associated with perceived residential density, land use mix access, street connectivity, aesthetics, and safety. Any cycling for transport was significantly related to perceived land use mix access, street connectivity, infrastructure, aesthetics, safety, and perceived distance to destinations. Between-city differences existed for some attributes in relation to walking or cycling for transport.
Many perceived environmental attributes supported both cycling and walking; however highly walkable environments may not support cycling for transport. People appear to walk for transport despite safety concerns. These findings can guide the implementation of global health strategies.
Environmental Health Perspectives 07/2015; DOI:10.1289/ehp.1409466 · 7.98 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In the past 15 years, a major research enterprise has emerged that is aimed at understanding associations between geographic and contextual features of the environment (especially the built environment) and elements of human energy balance, including diet, weight and physical activity. Here we highlight aspects of this research area with a particular focus on research and opportunities in the United States as an example. We address four main areas: (1) the importance of valid and comparable data concerning behaviour across geographies; (2) the ongoing need to identify and explore new environmental variables; (3) the challenge of identifying the causally relevant context; and (4) the pressing need for stronger study designs and analytical methods. Additionally, we discuss existing sources of geo-referenced health data which might be exploited by interdisciplinary research teams, personnel challenges and some aspects of funding for geospatial research by the US National Institutes of Health in the past decade, including funding for international collaboration and training opportunities.
Annals of GIS 03/2015; online(2). DOI:10.1080/19475683.2015.1019925
[Show abstract][Hide abstract] ABSTRACT: Objectives: To investigate relations of walking, bicycling and vehicle time to neighborhood vvalkability and total physical activity in youth. Methods: Participants (N=690) were from 380 census block groups of high/low vvalkability and income in two US regions. Home neighborhood residential density, intersection density, retail density, entertainment density and walkability were derived using GlS. Minutes/day of walking, bicycling and vehicle time were derived from processing algorithms applied to GPS. Accelerometers estimated total daily moderate-tovigorous physical activity (MVPA). Models were adjusted for nesting of days (N=2987) within participants within block groups. Results: Walking occurred on 33%, active travel on 43%, and vehicle time on 91% of the days observed. Intersection density and neighborhood walkability were positively related to walking and bicycling and negatively related to vehicle time. Residential density was positively related to walking. Conclusions: Increasing walking in youth could be effective in increasing total physical activity. Built environment findings suggest potential for increasing walking in youth through improving neighborhood walkability. (C) 2014 Elsevier Ltd. All rights reserved,
Health & Place 03/2015; 32:1-7. DOI:10.1016/j.healthplace.2014.12.008 · 2.44 Impact Factor
[Show abstract][Hide abstract] 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; DOI:10.1123/japa.2013-0208 · 1.41 Impact Factor
[Show abstract][Hide abstract] 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; 43(1). DOI:10.1249/JES.0000000000000035 · 4.82 Impact Factor
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] ABSTRACT: Purpose This study aimed to explore the relationship between objectively measured physical activity and cognitive functioning in breast cancer survivors. Methods Participants were 136 postmenopausal breast cancer survivors. Cognitive functioning was assessed using a comprehensive computerized neuropsychological test. Seven-day physical activity was assessed using hip-worn accelerometers. Linear regression models examined associations of minutes per day of physical activity at various intensities on individual cognitive functioning domains. The partially adjusted model controlled for primary confounders (model 1), and subsequent adjustments were made for chemotherapy history (model 2) and body mass index (BMI) (model 3). Interaction and stratified models examined BMI as an effect modifier. Results Moderate-to-vigorous physical activity (MVPA) was associated with information processing speed. Specifically, 10 min of MVPA was associated with a 1.35-point higher score (out of 100) on the information processing speed domain in the partially adjusted model and a 1.29-point higher score when chemotherapy was added to the model (both p p = 0.051). In models stratified by BMI (2), the favorable association between MVPA and information processing speed was stronger in the subsample of overweight and obese women (p Conclusions MVPA may have favorable effects on information processing speed in breast cancer survivors, particularly among overweight or obese women. Implications for Cancer Survivors Interventions targeting increased physical activity may enhance aspects of cognitive function among breast cancer survivors.
Journal of Cancer Survivorship 10/2014; 9(2). DOI:10.1007/s11764-014-0404-0 · 3.29 Impact Factor
[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. Methods and Results We examined the cross‐sectional association between residential proximity to major roadways and prevalent hypertension among 5401 postmenopausal women enrolled into the San Diego cohort of the Women's Health Initiative. We used modified Poisson regression with robust error variance to estimate the association between prevalence of hypertension and residential distance to nearest major roadway, adjusting for participant demographics, medical history, indicators of individual and neighborhood socioeconomic status, and for local supermarket/grocery and fast food/convenience store density. The adjusted prevalence ratios for hypertension were 1.22 (95% CI: 1.07, 1.39), 1.13 (1.00, 1.27), and 1.05 (0.99, 1.12) for women living ≤100, >100 to 200, and >200 to 1000 versus >1000 m from a major roadway (P for trend=0.006). In a model treating the natural log of distance to major roadway as a continuous variable, a shift in distance from 1000 to 100 m from a major roadway was associated with a 9% (3%, 16%) higher prevalence of hypertension. Conclusions In this cohort of postmenopausal women, residential proximity to major roadways was positively associated with the prevalence of hypertension. If causal, these results suggest that living close to major roadways may be an important novel risk factor for hypertension.
Journal of the American Heart Association 09/2014; 3(5):e000727. DOI:10.1161/JAHA.113.000727 · 2.88 Impact Factor
[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.
[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 & Science in Sports & Exercise 08/2014; 46(8):1554-1563. DOI:10.1249/MSS.0000000000000274 · 4.46 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Purpose: 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. Methods: 40 adult cyclists wore a Qstarz BT-Q1000XT GPS data logger and SenseCam (camera worn around neck capturing multiple images every minute) for a mean of 4 days. PALMS used distance and speed between GPS points to classify whether each minute was part of a trip (yes/no), and if so, the trip mode (walking/running, bicycling, in vehicle). SenseCam images were annotated to create the same classifications (i.e., trip yes/no and mode). 2x2 contingency tables and confusion matrices were calculated at the minute-level for PALMS vs. SenseCam classifications. Mixed-effects linear regression models estimated agreement (mean differences and intraclass correlations [ICCs]) between PALMS and SenseCam with regards to minutes/day in each mode. Results: Minute-level sensitivity, specificity, and negative predictive value were >=88%, and positive predictive value was >=75% for non mode-specific trip detection. 72-80% of outdoor walking/running minutes, 73% of bicycling minutes, and 74-76% of in-vehicle minutes were correctly classified by PALMS. For minutes/day, PALMS had a mean bias (i.e., amount of over or under estimation) of 2.4-3.1 minutes (11-15%) for walking/running, 2.3-2.9 minutes (7-9%) for bicycling, and 4.3-5 minutes (15-17%) for vehicle time. ICCs were >=.80 for all modes. Conclusions: PALMS has validity for processing GPS data to objectively measure time walking/running, bicycling, and in vehicle in population studies. Assessing travel patterns is one of many valuable applications of GPS in physical activity research that can improve our understanding of the determinants and health outcomes of active transportation as well as its impact on physical activity.
Medicine & Science in Sports & Exercise 07/2014; 47(3). DOI:10.1249/MSS.0000000000000446 · 4.46 Impact Factor
[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 04/2014; 2:36. DOI:10.3389/fpubh.2014.00036
[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; 48:1634–1639. DOI:10.1136/bjsports-2013-093101 · 5.03 Impact Factor
[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 MVPA) and to investigate associations between context-specific outdoor time and MVPA.Methods: A total of 170 children had at least one weekday of nine hours combined accelerometer and GPS data and were included in the analyses. The data were processed using the Personal Activity and Location Measurement System and a purpose-built PostgreSQL database resulting in context-specific measures for outdoor time, outdoor MVPA and overall daily MVPA. In addition, four domains (leisure, school, transport and home) and 11 subdomains (e.g. urban green space, 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 outdoors minutes (pConclusion:A new methodology to assess 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 03/2014; 2:20. DOI:10.3389/fpubh.2014.00020
[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 03/2014; 2:21. DOI:10.3389/fpubh.2014.00021
[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. DOI:10.1016/j.healthplace.2014.02.003 · 2.44 Impact Factor