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Quantifying human movement using the Movn smartphone app: validation and field study

Maddison, Ralph, Gemming, Luke, Monedero, Javier, Bolger, Linda, Belton, Sarahjane, Issartel, Johann, Marsh, Samantha, Direito, Artur, Solenhill, Madeleine, Zhao, Jinfeng, Exeter, Daniel John, Vathsangam, Harshvardhan and Rawstorn, Jonathan Charles 2017, Quantifying human movement using the Movn smartphone app: validation and field study, JMIR Mhealth Uhealth, vol. 5, no. 8, pp. 1-15, doi: 10.2196/mhealth.7167.

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Title Quantifying human movement using the Movn smartphone app: validation and field study
Author(s) Maddison, RalphORCID iD for Maddison, Ralph orcid.org/0000-0001-8564-5518
Gemming, Luke
Monedero, Javier
Bolger, Linda
Belton, Sarahjane
Issartel, Johann
Marsh, Samantha
Direito, Artur
Solenhill, Madeleine
Zhao, Jinfeng
Exeter, Daniel John
Vathsangam, Harshvardhan
Rawstorn, Jonathan CharlesORCID iD for Rawstorn, Jonathan Charles orcid.org/0000-0002-9755-7993
Journal name JMIR Mhealth Uhealth
Volume number 5
Issue number 8
Article ID e122
Start page 1
End page 15
Total pages 15
Publisher JMIR Publications
Place of publication Toronto, Ont.
Publication date 2017-08-17
ISSN 2291-5222
Keyword(s) telemedicine
smartphone
validation studies
geographic information systems
locomotion
physical activity
humans
Science & Technology
Life Sciences & Biomedicine
Health Care Sciences & Services
Medical Informatics
PHYSICAL-ACTIVITY
ACCELEROMETRY
MORTALITY
PATTERNS
Summary 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 (F1,4=7.54, P<.001) but was relatively consistent for the convergent comparison (F1,4=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 (F3,6120=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.
Language eng
DOI 10.2196/mhealth.7167
Field of Research 110699 Human Movement and Sports Science not elsewhere classified
Socio Economic Objective 920401 Behaviour and Health
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30102362

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.