ArticleJMIR mHealth and uHealth2017
Quantifying Human Movement Using the Movn Smartphone App: Validation and Field Study.
Article in JMIR mHealth and uHealth, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- Recommendations for Determining the Validity of Consumer Wearables and Smartphones for the Estimation of Energy Expenditure: Expert Statement and Checklist of the INTERLIVE Network.Sports medicine (Auckland, N.Z.) · 2022Pooled it
- Mobile Apps to Quantify Aspects of Physical Activity: a Systematic Review on its Reliability and Validity.Journal of medical systems · 2020Pooled it
- Characterisation of Temporal Patterns in Step Count Behaviour from Smartphone App Data: An Unsupervised Machine Learning Approach.International journal of environmental research and public health · 2021Article
- Predicting Energy Expenditure During Gradient Walking With a Foot Monitoring Device: Model-Based Approach.JMIR mHealth and uHealth · 2019Article
- Electronic Patient-Generated Health Data to Facilitate Disease Prevention and Health Promotion: Scoping Review.Journal of medical Internet research · 2019Article
- The Use of Smart Technology in an Online Community of Patients With Degenerative Cervical Myelopathy.JMIR formative research · 2019Article
- Visualising Combined Time Use Patterns of Children's Activities and Their Association with Weight Status and Neighbourhood Context.International journal of environmental research and public health · 2019Article
- Comparing the Data Quality of Global Positioning System Devices and Mobile Phones for Assessing Relationships Between Place, Mobility, and Health: Field Study.JMIR mHealth and uHealth · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 5 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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, t
conclusionsThe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.