Quantifying Human Movement Using the Movn Smartphone App: Validation and Field Study

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

doi:10.2196/mhealth.7167

Quantifying Human Movement Using the Movn Smartphone App: Validation and Field Study

JMIR Mhealth Uhealth. 2017 Aug 17;5(8):e122. doi: 10.2196/mhealth.7167.

Authors

Ralph Maddison^#^{1

2}, Luke Gemming^#², Javier Monedero^#³, Linda Bolger^#³, Sarahjane Belton^#³, Johann Issartel^#³, Samantha Marsh^#², Artur Direito^#², Madeleine Solenhill^#⁴, Jinfeng Zhao^#⁵, Daniel John Exeter^#⁵, Harshvardhan Vathsangam^#⁶, Jonathan Charles Rawstorn^#^{1

2}

Affiliations

¹ Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Burwood, Australia.
² National Institute for Health Innovation, School of Population Health, University of Auckland, Auckland, New Zealand.
³ School of Health & Human Performance, Dublin City University, Dublin, Ireland.
⁴ Center of Research on Welfare Health and Sport, School of Health and Welfare, Halmstad University, Halmstad, Sweden.
⁵ Department of Epidemiology and Biostatistics, School of Population Health, University of Auckland, Auckland, New Zealand.
⁶ Robotic Embedded Systems Laboratory, Robotics and Autonomous Systems Center, University of Southern California, Los Angeles, CA, United States.

^# Contributed equally.

Abstract

Background: The 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.

Objective: The objective of this study was to examine the validity of the Movn smartphone app (Moving Analytics) for collecting PA and human movement data.

Methods: The 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.

Results: A 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, P<.001). The absolute magnitude of criterion biases increased as a function of locomotive speed (F_1,4=7.54, P<.001) but was relatively consistent for the convergent comparison (F_1,4=1.26, P<.29). Furthermore, 95% limits of agreement were consistent for criterion and convergent biases, and EE from Movn was strongly correlated with both reference measures (criterion r=.91, convergent r=.92, both P<.001). Movn overestimated EE during free-living activities (bias=1.00 [0.98-1.02] kcal/min, t₆₁₂₃=101.49, P<.001), and biases were larger during high-intensity activities (F_3,6120=1550.51, P<.001). In addition, 95% limits of agreement for convergent biases were heterogeneous across free-living activity intensity levels, but Movn and ActiGraph measures were strongly correlated (r=.87, P<.001). Integration of GPS and accelerometer data within a geographic information system (GIS) enabled creation of individual temporospatial maps.

Conclusions: The 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.

Keywords: geographic information systems; humans; locomotion; physical activity; smartphone; telemedicine; validation studies.

©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. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 17.08.2017.