TY - JOUR
T1 - Quantifying Human Movement Using the Movn Smartphone App: Validation and Field Study.
AU - Maddison, Ralph
AU - Gemming, Luke
AU - Monedero, Javier
AU - Bolger, Linda
AU - Belton, Sarahjane
AU - Issartel, Johann
AU - Marsh, Samantha
AU - Direito, Artur
AU - Solenhill, Madeleine
AU - Zhao, Jinfeng
AU - Exeter, Daniel John
AU - Vathsangam, Harshvardhan
AU - Rawstorn, Johnathan Charles
N1 - Publisher Copyright:
©Ralph Maddison, Luke Gemming, Javier Monedero, Linda Bolger, Sarahjane Belton, Johann Issartel, Samantha Marsh, Artur Direito, Madeleine Solenhill, Jinfeng Zhao, Daniel John Exeter, Harshvardhan Vathsangam, Jonathan Charles Rawstorn.
PY - 2017/8/17
Y1 - 2017/8/17
N2 - BackgroundThe use of embedded smartphone sensors offers opportunities to measure physical activity (PA) and human movement. Big data-which includes billions of digital traces-offers scientists a new lens to examine PA in fine-grained detail and allows us to track people's geocoded movement patterns to determine their interaction with the environment.ObjectiveThe objective of this study was to examine the validity of the Movn smartphone app (Moving Analytics) for collecting PA and human movement data.MethodsThe criterion and convergent validity of the Movn smartphone app for estimating energy expenditure (EE) were assessed in both laboratory and free-living settings, compared with indirect calorimetry (criterion reference) and a stand-alone accelerometer that is commonly used in PA research (GT1m, ActiGraph Corp, convergent reference). A supporting cross-validation study assessed the consistency of activity data when collected across different smartphone devices. Global positioning system (GPS) and accelerometer data were integrated with geographical information software to demonstrate the feasibility of geospatial analysis of human movement.ResultsA total of 21 participants contributed to linear regression analysis to estimate EE from Movn activity counts (standard error of estimation [SEE]=1.94 kcal/min). The equation was cross-validated in an independent sample (N=42, SEE=1.10 kcal/min). During laboratory-based treadmill exercise, EE from Movn was comparable to calorimetry (bias=0.36 [-0.07 to 0.78] kcal/min, t82=1.66, P=.10) but overestimated as compared with the ActiGraph accelerometer (bias=0.93 [0.58-1.29] kcal/min, t89=5.27, P1,4=7.54, P1,4=1.26, P6123=101.49, P3,6120=1550.51, PConclusionsThe Movn smartphone app can provide valid passive measurement of EE and can enrich these data with contextualizing temporospatial information. Although enhanced understanding of geographic and temporal variation in human movement patterns could inform intervention development, it also presents challenges for data processing and analytics.
AB - BackgroundThe use of embedded smartphone sensors offers opportunities to measure physical activity (PA) and human movement. Big data-which includes billions of digital traces-offers scientists a new lens to examine PA in fine-grained detail and allows us to track people's geocoded movement patterns to determine their interaction with the environment.ObjectiveThe objective of this study was to examine the validity of the Movn smartphone app (Moving Analytics) for collecting PA and human movement data.MethodsThe criterion and convergent validity of the Movn smartphone app for estimating energy expenditure (EE) were assessed in both laboratory and free-living settings, compared with indirect calorimetry (criterion reference) and a stand-alone accelerometer that is commonly used in PA research (GT1m, ActiGraph Corp, convergent reference). A supporting cross-validation study assessed the consistency of activity data when collected across different smartphone devices. Global positioning system (GPS) and accelerometer data were integrated with geographical information software to demonstrate the feasibility of geospatial analysis of human movement.ResultsA total of 21 participants contributed to linear regression analysis to estimate EE from Movn activity counts (standard error of estimation [SEE]=1.94 kcal/min). The equation was cross-validated in an independent sample (N=42, SEE=1.10 kcal/min). During laboratory-based treadmill exercise, EE from Movn was comparable to calorimetry (bias=0.36 [-0.07 to 0.78] kcal/min, t82=1.66, P=.10) but overestimated as compared with the ActiGraph accelerometer (bias=0.93 [0.58-1.29] kcal/min, t89=5.27, P1,4=7.54, P1,4=1.26, P6123=101.49, P3,6120=1550.51, PConclusionsThe Movn smartphone app can provide valid passive measurement of EE and can enrich these data with contextualizing temporospatial information. Although enhanced understanding of geographic and temporal variation in human movement patterns could inform intervention development, it also presents challenges for data processing and analytics.
KW - Geographic information systems
KW - Humans
KW - Locomotion
KW - Physical activity
KW - Smartphone
KW - Telemedicine
KW - Validation studies
UR - https://www.scopus.com/pages/publications/85042355878
U2 - 10.2196/mhealth.7167
DO - 10.2196/mhealth.7167
M3 - Article
C2 - 28818819
SN - 2291-5222
VL - 5
JO - JMIR mHealth and uHealth
JF - JMIR mHealth and uHealth
IS - 8
M1 - e122
ER -