Is missing geographic positioning system data in accelerometry studies a problem, and is imputation the solution?

Kristin Meseck, Marta M Jankowska, Jasper Schipperijn, Loki Natarajan, Suneeta Godbole, Jordan Carlson, Michelle Takemoto, Katie Crist, Jacqueline Kerr

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

87 Downloads (Pure)

Resumé

The main purpose of the present study was to assess the impact of global positioning system (GPS) signal lapse on physical activity analyses, discover any existing associations between missing GPS data and environmental and demographics attributes, and to determine whether imputation is an accurate and viable method for correcting GPS data loss. Accelerometer and GPS data of 782 participants from 8 studies were pooled to represent a range of lifestyles and interactions with the built environment. Periods of GPS signal lapse were identified and extracted. Generalised linear mixed models were run with the number of lapses and the length of lapses as outcomes. The signal lapses were imputed using a simple ruleset, and imputation was validated against person-worn camera imagery. A final generalised linear mixed model was used to identify the difference between the amount of GPS minutes pre- and post-imputation for the activity categories of sedentary, light, and moderate-to-vigorous physical activity. Over 17% of the dataset was comprised of GPS data lapses. No strong associations were found between increasing lapse length and number of lapses and the demographic and built environment variables. A significant difference was found between the pre- and postimputation minutes for each activity category. No demographic or environmental bias was found for length or number of lapses, but imputation of GPS data may make a significant difference for inclusion of physical activity data that occurred during a lapse. Imputing GPS data lapses is a viable technique for returning spatial context to accelerometer data and improving the completeness of the dataset.

OriginalsprogEngelsk
Artikelnummer403
TidsskriftGeospatial Health
Vol/bind11
Udgave nummer2
Sider (fra-til)157-163
ISSN1827-1987
DOI
StatusUdgivet - 2016

Fingeraftryk

positioning system
Geographic Information Systems
Information Systems
GPS
physical activity
accelerometer
Linear Models
Imagery (Psychotherapy)
lifestyle
imagery
inclusion
human being

Citer dette

Meseck, Kristin ; Jankowska, Marta M ; Schipperijn, Jasper ; Natarajan, Loki ; Godbole, Suneeta ; Carlson, Jordan ; Takemoto, Michelle ; Crist, Katie ; Kerr, Jacqueline. / Is missing geographic positioning system data in accelerometry studies a problem, and is imputation the solution?. I: Geospatial Health. 2016 ; Bind 11, Nr. 2. s. 157-163.
@article{a5e308decd3546c4849a48daf57ff64f,
title = "Is missing geographic positioning system data in accelerometry studies a problem, and is imputation the solution?",
abstract = "The main purpose of the present study was to assess the impact of global positioning system (GPS) signal lapse on physical activity analyses, discover any existing associations between missing GPS data and environmental and demographics attributes, and to determine whether imputation is an accurate and viable method for correcting GPS data loss. Accelerometer and GPS data of 782 participants from 8 studies were pooled to represent a range of lifestyles and interactions with the built environment. Periods of GPS signal lapse were identified and extracted. Generalised linear mixed models were run with the number of lapses and the length of lapses as outcomes. The signal lapses were imputed using a simple ruleset, and imputation was validated against person-worn camera imagery. A final generalised linear mixed model was used to identify the difference between the amount of GPS minutes pre- and post-imputation for the activity categories of sedentary, light, and moderate-to-vigorous physical activity. Over 17{\%} of the dataset was comprised of GPS data lapses. No strong associations were found between increasing lapse length and number of lapses and the demographic and built environment variables. A significant difference was found between the pre- and postimputation minutes for each activity category. No demographic or environmental bias was found for length or number of lapses, but imputation of GPS data may make a significant difference for inclusion of physical activity data that occurred during a lapse. Imputing GPS data lapses is a viable technique for returning spatial context to accelerometer data and improving the completeness of the dataset.",
author = "Kristin Meseck and Jankowska, {Marta M} and Jasper Schipperijn and Loki Natarajan and Suneeta Godbole and Jordan Carlson and Michelle Takemoto and Katie Crist and Jacqueline Kerr",
year = "2016",
doi = "10.4081/gh.2016.403",
language = "English",
volume = "11",
pages = "157--163",
journal = "Geospatial Health",
issn = "1827-1987",
publisher = "PAGEPress",
number = "2",

}

Meseck, K, Jankowska, MM, Schipperijn, J, Natarajan, L, Godbole, S, Carlson, J, Takemoto, M, Crist, K & Kerr, J 2016, 'Is missing geographic positioning system data in accelerometry studies a problem, and is imputation the solution?', Geospatial Health, bind 11, nr. 2, 403, s. 157-163. https://doi.org/10.4081/gh.2016.403

Is missing geographic positioning system data in accelerometry studies a problem, and is imputation the solution? / Meseck, Kristin; Jankowska, Marta M; Schipperijn, Jasper; Natarajan, Loki; Godbole, Suneeta; Carlson, Jordan; Takemoto, Michelle; Crist, Katie; Kerr, Jacqueline.

I: Geospatial Health, Bind 11, Nr. 2, 403, 2016, s. 157-163.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Is missing geographic positioning system data in accelerometry studies a problem, and is imputation the solution?

AU - Meseck, Kristin

AU - Jankowska, Marta M

AU - Schipperijn, Jasper

AU - Natarajan, Loki

AU - Godbole, Suneeta

AU - Carlson, Jordan

AU - Takemoto, Michelle

AU - Crist, Katie

AU - Kerr, Jacqueline

PY - 2016

Y1 - 2016

N2 - The main purpose of the present study was to assess the impact of global positioning system (GPS) signal lapse on physical activity analyses, discover any existing associations between missing GPS data and environmental and demographics attributes, and to determine whether imputation is an accurate and viable method for correcting GPS data loss. Accelerometer and GPS data of 782 participants from 8 studies were pooled to represent a range of lifestyles and interactions with the built environment. Periods of GPS signal lapse were identified and extracted. Generalised linear mixed models were run with the number of lapses and the length of lapses as outcomes. The signal lapses were imputed using a simple ruleset, and imputation was validated against person-worn camera imagery. A final generalised linear mixed model was used to identify the difference between the amount of GPS minutes pre- and post-imputation for the activity categories of sedentary, light, and moderate-to-vigorous physical activity. Over 17% of the dataset was comprised of GPS data lapses. No strong associations were found between increasing lapse length and number of lapses and the demographic and built environment variables. A significant difference was found between the pre- and postimputation minutes for each activity category. No demographic or environmental bias was found for length or number of lapses, but imputation of GPS data may make a significant difference for inclusion of physical activity data that occurred during a lapse. Imputing GPS data lapses is a viable technique for returning spatial context to accelerometer data and improving the completeness of the dataset.

AB - The main purpose of the present study was to assess the impact of global positioning system (GPS) signal lapse on physical activity analyses, discover any existing associations between missing GPS data and environmental and demographics attributes, and to determine whether imputation is an accurate and viable method for correcting GPS data loss. Accelerometer and GPS data of 782 participants from 8 studies were pooled to represent a range of lifestyles and interactions with the built environment. Periods of GPS signal lapse were identified and extracted. Generalised linear mixed models were run with the number of lapses and the length of lapses as outcomes. The signal lapses were imputed using a simple ruleset, and imputation was validated against person-worn camera imagery. A final generalised linear mixed model was used to identify the difference between the amount of GPS minutes pre- and post-imputation for the activity categories of sedentary, light, and moderate-to-vigorous physical activity. Over 17% of the dataset was comprised of GPS data lapses. No strong associations were found between increasing lapse length and number of lapses and the demographic and built environment variables. A significant difference was found between the pre- and postimputation minutes for each activity category. No demographic or environmental bias was found for length or number of lapses, but imputation of GPS data may make a significant difference for inclusion of physical activity data that occurred during a lapse. Imputing GPS data lapses is a viable technique for returning spatial context to accelerometer data and improving the completeness of the dataset.

U2 - 10.4081/gh.2016.403

DO - 10.4081/gh.2016.403

M3 - Journal article

VL - 11

SP - 157

EP - 163

JO - Geospatial Health

JF - Geospatial Health

SN - 1827-1987

IS - 2

M1 - 403

ER -