Walking Objectively Measured

Kang, Bumjoon; Moudon, Anne Vernez; Hurvitz, Philip M.; Reichley, Lucas; Saelens, Brian E.

doi:10.1249/mss.0b013e318285f202

Cited by 70 publications

(53 citation statements)

References 31 publications

(40 reference statements)

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“…Kang et al (8) compared GPS-derived minutes/day of walking/running to travel diary-derived walking/running and found that GPS overestimated walking/running by 17.5% (slightly higher than the bias estimates in the present study), but other modes were not investigated. Minutes/day in each mode are frequently the final travel variables derived from GPS that are used in statistical analyses in health studies.…”

Section: Discussioncontrasting

confidence: 76%

“…PALMS allows users control over parameter settings for trip detection and mode classification algorithms. The major trip-related settings were informed from empirical testing and existing algorithms (2, 6, 7, 8, 16, 21, 23, 27) and are described in detail below. Information on the other parameter settings (of less relevance to the present study) used in the scoring protocol can be obtained by contacting the first author.…”

Section: Methodsmentioning

confidence: 99%

“…While there currently is no gold standard criterion measure for trip detection, other existing GPS algorithms were validated using self-report as the comparison measure (2, 6, 7, 8, 16, 21, 23, 25, 27). We took the novel approach of using annotated images from person worn cameras (the SenseCam) (17) which provide a more direct comparison measure (9, 10).…”

Section: Introductionmentioning

confidence: 99%

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Validity of PALMS GPS Scoring of Active and Passive Travel Compared with SenseCam

Carlson

Jankowska

Meseck

et al. 2015

Medicine &Amp; Science in Sports &Amp; Exercise

119

116

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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). 2×2 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.

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Section: Discussioncontrasting

confidence: 76%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Validity of PALMS GPS Scoring of Active and Passive Travel Compared with SenseCam

Carlson

Jankowska

Meseck

et al. 2015

Medicine &Amp; Science in Sports &Amp; Exercise

119

116

View full text Add to dashboard Cite

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“…The process by which accelerometer data were integrated with GPS and travel log information to identify walking and non-walking physical activity bouts is described elsewhere in detail (17). In summary, bouts of ≥5 minutes of accelerometer counts >500 per 30-second epoch, allowing for up to 2 minutes of counts below the 500-count threshold, were considered to be physical activity (PA) bouts.…”

Section: Methodsmentioning

confidence: 99%

Light rail leads to more walking around station areas

Huang¹,

Moudon

Zhou

et al. 2017

Journal of Transport & Health

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Areas around Light Rail Transit (LRT) stations offer ideal conditions for Transit-Oriented Development (TOD). Relatively dense, mixed-use neighborhoods can have positive impacts on mobility, health, and perceptions of neighborhood safety among nearby residents, primarily through walking activity for both transit and other purposes. To examine how station areas may attract new activity, this study analyzed changes in walking around station areas among people living close to an LRT station before and after the opening of a new transit system. This study examined walking behavior among the subset of 214 participants living within one mile of one of 13 LRT stations from among a sample of residents living close or further away from a new LRT line in Seattle. They completed a survey and a travel log and wore an accelerometer and a GPS for 7 days both before (2008) and after the opening of the Seattle area LRT (2010). Walking bouts were derived using a previously developed algorithm. The main outcome was the individual-level change in the proportion of daily walking within one quarter Euclidean mile of an LRT station. Overall walking decreased from before to after the LRT opening while station area walking did not change significantly, indicating a shift in walking activity to the station areas after the introduction of LRT. Increases in the proportion of station area walking were negatively related to participants’ distance between home and the nearest LRT station, peaking at <0.25 mile and decaying beyond >0.75 mile. Male gender, college education, normal weight status, less access to cars, and frequent LRT use were also significantly associated with greater positive changes in the proportion of station area walking. The shift in walking to station areas after the completion of light rail provides evidence that the local proximate population is attracted to station areas, which may potentially benefit both transit use and TOD area economic activity. The residential catchment area for the shift in LRT area walking was < 0.75 mile of the LRT stations.

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“…As the number of willing participants exceeded the number of monitors available, monitors were sent to a random sample of willing participants in batches as described in a previous paper [19]. In 2010 and 2011, the sub-sample who had completed a questionnaire and provided valid accelerometer data in 2009 were invited to wear a combined heart rate and movement sensor and a GPS device, and to complete a questionnaire and a detailed travel diary.…”

Section: Methodsmentioning

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

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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.

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Walking Objectively Measured

Cited by 70 publications

References 31 publications

Validity of PALMS GPS Scoring of Active and Passive Travel Compared with SenseCam

Validity of PALMS GPS Scoring of Active and Passive Travel Compared with SenseCam

Light rail leads to more walking around station areas

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

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