Identifying Walking Trips From GPS and Accelerometer Data in Adolescent Females

Rodríguez, Daniel Romero; Cho, Gi-Hyoug; Elder, John P.; Conway, Terry L.; Evenson, Kelly R.; Ghosh-Dastidar, Bonnie; Shay, Elizabeth; Cohen, Deborah A.; Veblen-Mortenson, Sara; Pickrell, Julie; Lytle, Leslie A.

doi:10.1123/jpah.9.3.421

Cited by 34 publications

(37 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rodriguez et al recorded travel data on 42 adolescent girls and found moderate to substantial agreement between GPS/accelerometry and self-reported daily (Kappa = 0.33–0.48) and weekly (Kappa = 0.41–0.64) walking trips. 35 No data were presented for trip duration. Kang et al classified accelerometer data as walking or nonwalking using GPS or travel logs.…”

Section: Discussionmentioning

confidence: 99%

Comparing GPS, Log, Survey, and Accelerometry to Measure Physical Activity

et al. 2016

View full text Add to dashboard Cite

Objectives Both self-report and objective measures have strengths and limitations for studying physical activity (PA) and travel. We explored how objectively measured global positioning system (GPS) and accelerometer data matches with travel logs and questionnaires in predicting trip duration and PA. Methods In a study of PA and travel among residents in Atlanta, GA conducted in 2008–2009, 99 participants wore GPS devices and accelerometers, and recorded all trips in a log for 5 consecutive days. Participants also completed a self-administered questionnaire on PA and travel behaviors. Results There was good agreement between GPS and log for assessment of trip duration, although log measures overestimated trip duration (concordance correlation coefficient 0.53 [0.47, 0.59]; Bland-Altman estimate 0.76 [0.16, 3.71] comparing GPS to log). Log measures underestimated light PA and overestimated moderate PA compared to accelerometry when greater than zero moderate PA was reported. Conclusions It is often not feasible to deploy accelerometry or GPS devices in population research because these devices are expensive and require technical expertise and data processing. Questionnaires and logs provide inexpensive tools to assess PA and travel with reasonable concordance with objective measures. However, they have shortcomings in evaluating the presence and amount of light and moderate PA. Future questionnaires and logs should be developed to evaluate sensitivity to light and moderate PA.

show abstract

Section: Discussionmentioning

confidence: 99%

Comparing GPS, Log, Survey, and Accelerometry to Measure Physical Activity

et al. 2016

View full text Add to dashboard Cite

show abstract

“…The majority (92%) of the studies were cross-sectional analyses, with one involving a longitudinal approach [33]. Multiple papers included in this review were focused analyses of the same, but larger, projects, including: the Sport, Physical Activity and Eating Behaviour: Environmental Determinants of Young People project (SPEEDY; UK [23,25]); the Personal and Environmental Associations with Children’s Health project (PEACH; UK [24,26,28,29]); Healthy Places; the Trial of Activity for Adolescent Girls project (TAAG 1 & 2; US [32,33]); the ICAN project (NZ [34]); and the Children’s Activity in their Local Environment study (CALE; Canada [36]). The ages of those included ranged from 5–18 years old, with the 10–12 year old age-group being the focus of five papers.…”

Section: Methodsmentioning

confidence: 99%

“…A further article [33] investigated the effects of the built environment, around each GPS point, and the odds of the GPS points being of higher intensity activity. The final study [32] used the combination of devices and technology to investigate the agreement between diary-listed locations and those objectively measured. The results of this study indicated that between 86% and 100% of all diary-reported locations matched that of the GPS identified locations.…”

Section: Methodsmentioning

confidence: 99%

Combining GPS, GIS, and accelerometry to explore the physical activity and environment relationship in children and young people - a review

McCrorie

Fenton

Ellaway

2014

Int J Behav Nutr Phys Act

View full text Add to dashboard Cite

The environment has long been associated with physical activity engagement, and recent developments in technology have resulted in the ability to objectively quantify activity behaviours and activity context. This paper reviews studies that have combined Global Positioning Systems (GPS), Geographic Information Systems (GIS) and accelerometry to investigate the PA-environment relationship in children and young people (5–18 years old). Literature searches of the following bibliographic databases were undertaken: Sportdiscus, Medline, Embase, CINAHL, Psychinfo and Applied Social Sciences Index and Abstracts (ASSIA). Fourteen studies met the inclusion criteria, and covered topics including greenspace use, general land use, active travel, and the built environment. Studies were largely cross-sectional and took place across developed countries (UK, USA, Canada, New Zealand, and Australia). Findings suggest that roads and streets, school grounds, and the home location are important locations for total PA, and moderate to vigorous PA (MVPA). The relationship between greenspace was positive, however, multiple definitions and outcome measures add complexity to the results. MVPA was more likely in those exposed to higher levels of greenspace compared to sedentary individuals. Total MVPA time in greenspace is low, but when framed as a proportion of the total can be quite high. Domestic gardens may be an important area for higher intensity activity.Researchers are encouraged to show transparency in their methods. As a relatively new area of research, with ever-evolving technology, future work is best placed in developing novel, but robust, methods to investigate the PA and environment relationship. Further descriptive work is encouraged to build on a small but increasing knowledge base; however, longitudinal studies incorporating seasonal/weather variation would also be extremely beneficial to elicit some of the nuances associated with land use. A greater understanding of geographic variation (i.e. within and between countries), as well as urban/suburban and rural dwelling is welcomed, and future work should also include the investigation of psycho-social health as an outcome, as well as differences in socio-economic status, sex and adiposity.Electronic supplementary materialThe online version of this article (doi:10.1186/s12966-014-0093-0) contains supplementary material, which is available to authorized users.

show abstract

“…It is feasible to collect data using multiple synchronous measures, and a small number of studies have used combinations of accelerometer and GPS data to identify walking, one under controlled conditions [17] and three in free-living samples [18-20]. Nevertheless, the validation of self-reported measures against those derived from hip-worn accelerometers is problematic because these devices are known to underestimate certain activities such as cycling.…”

Section: Introductionmentioning

confidence: 99%

Development of methods to objectively identify time spent using active and motorised modes of travel to work: how do self-reported measures compare?

Panter

Costa

Dalton

et al. 2014

Int J Behav Nutr Phys Act

View full text Add to dashboard Cite

BackgroundActive commuting may make an important contribution to population health. Accurate measures of these behaviours are required, but it is unknown how self-reported estimates compare to those derived from objective measures. We sought to develop methods for objectively deriving time spent in specific travel behaviours from a combination of locational and activity data, and to assess the convergent validity of two self-reported estimates.MethodsIn 2010 and 2011, a sub-sample of participants from the Commuting and Health in Cambridge study concurrently completed objective monitoring using combined heart rate and movement sensors and global positioning system devices and reported their past-week commuting in a questionnaire (modes used, and usual time spent walking and cycling per trip) and in a day-by-day diary (all modes and durations). Automated and manual approaches were used to objectively identify total time spent using active and motorised modes. Agreement between self-reported and objectively-derived times was assessed using Lin’s concordance coefficients, Bland-Altman plots and signed-rank tests.ResultsCompared to objective assessments, day-by-day diary estimates of time spent using active modes on the commute were overestimated by a mean of 1.1 minutes/trip (95% limits of agreement (LOA): −7.7 to 9.9, p < 0.001). The magnitude of overestimation was slightly larger, but not significant (p = 0.247), when walking or cycling was used alone (mean: 2.4 minutes/trip, 95% LOA: −6.8 to 11.5). Total time spent on the commute was overestimated by a mean of 1.9 minutes/trip (95% LOA: −15.3 to 19.0, p < 0.001). The mean differences between self-reported usual time and objective estimates were −1.1 minutes/trip (95% LOA: −8.7 to 6.4) for cycling and +2.4 minutes/trip (95% LOA: −10.9 to 15.7) for walking. Mean differences between usual and daily estimates of time were <1 minute/trip for both walking and cycling.ConclusionsWe developed a novel method of combining objective data to identify time spent using active and motorised modes, and total time spent commuting. Compared to objectively-derived times, self-reported times spent active commuting were slightly overestimated with wide LOA, suggesting that they should be used with caution to infer aggregate weekly quantities of activity on the commute at the individual level.Electronic supplementary materialThe online version of this article (doi:10.1186/s12966-014-0116-x) contains supplementary material, which is available to authorized users.

show abstract

Identifying Walking Trips From GPS and Accelerometer Data in Adolescent Females

Cited by 34 publications

References 34 publications

Comparing GPS, Log, Survey, and Accelerometry to Measure Physical Activity

Comparing GPS, Log, Survey, and Accelerometry to Measure Physical Activity

Combining GPS, GIS, and accelerometry to explore the physical activity and environment relationship in children and young people - a review

Development of methods to objectively identify time spent using active and motorised modes of travel to work: how do self-reported measures compare?

Contact Info

Product

Resources

About