Treadmill gait speeds correlate with physical activity counts measured by cell phone accelerometers

Carlson, Richard H.; Huebner, Derek R.; Hoarty, Carrie A.; Whittington, Jackie; Haynatzki, Gleb; Balas, Michele C.; Schenk, A. Katrin; Goulding, Evan H.; Potter, Jane; Bonasera, Stephen J.

doi:10.1016/j.gaitpost.2012.02.025

Cited by 22 publications

(16 citation statements)

References 30 publications

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“…Eleven [66][67][68][69][70][71][89][90][91][92] of the 19 studies that evaluated mobile phones for PA assessment were carried out within the context of health enhancement/chronic disease prevention. The remaining studies concerned weight control (n ¼ 4) 72,93-95 and chronic disease management (n ¼ 4).…”

Section: Mobile Phones For Pa Assessment: Study Characteristicsmentioning

confidence: 99%

“…In the other 2 validation studies, the mobile phone PA data were evaluated against attendance records 70 and treadmill speed. 92 None of the validation studies specifically targeted overweight or obese individuals, and only one 76 91 and a questionnaire. 98 One study 71 measured whether or not participants completed an SMS-based questionnaire, and another study 72 gauged whether or not participants answered EMA questions administered through mobile phone calls.…”

Section: Mobile Phones For Pa Assessment: Study Characteristicsmentioning

confidence: 99%

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Usability of Mobile Phones in Physical Activity–Related Research: A Systematic Review

Monroe

Thompson

Bassett

et al. 2015

American Journal of Health Education

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Background: The use of mobile phones for physical activity (PA) promotion and assessment represents an attractive research area because this technology is characterized by a widespread reach and dynamic features. Purpose: The purpose of this study was to conduct a systematic review of mobile phone -based approaches for encouraging and assessing PA. Methods: Eligible studies were identified through a search of PubMed and PsycINFO and required to address the usability of mobile phones for PA promotion and/or measurement, be written in English, and be published in a peer-reviewed journal between 2010 and 2013. Results: Fiftytwo studies were included in this review. The use of mobile phones for PA promotion, assessment, and both promotion and assessment were addressed by 27, 19, and 6 studies, respectively. Out of 26 studies that reported a PA behavior change outcome, 13 observed favorable PA changes, 3 found mixed results, and 10 found no improvements. Ten studies examined the validity of mobile phones for PA assessment and 9 reported favorable outcomes. Participants found mobile phones to be highly acceptable. Discussion: The findings suggest that mobile phones have great potential as a PA promotion and assessment tool among a variety of individuals. Translation to Health Education Practice: Mobile phones may provide practitioners with efficient ways to promote PA and valid ways to assess PA.

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Section: Mobile Phones For Pa Assessment: Study Characteristicsmentioning

confidence: 99%

Section: Mobile Phones For Pa Assessment: Study Characteristicsmentioning

confidence: 99%

Usability of Mobile Phones in Physical Activity–Related Research: A Systematic Review

Monroe

Thompson

Bassett

et al. 2015

American Journal of Health Education

View full text Add to dashboard Cite

show abstract

“…21 Data analysis for cell phones can compensate for this error with personalized models, as shown in treadmill experiments with healthy subjects. 22 To compute gait speed, we need to know distance from the walktest walkway and also cadence. With a proper algorithm, 23 we can use phone sensors to accurately measure step count.…”

Section: Smartphones Versus Medical Pedometersmentioning

confidence: 99%

Health Monitors for Chronic Disease by Gait Analysis with Mobile Phones

Juen

Cheng

Prieto-Centurion

et al. 2014

Telemedicine and e-Health

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We have developed GaitTrack, a phone application to detect health status while the smartphone is carried normally. GaitTrack software monitors walking patterns, using only accelerometers embedded in phones to record spatiotemporal motion, without the need for sensors external to the phone. Our software transforms smartphones into health monitors, using eight parameters of phone motion transformed into body motion by the gait model. GaitTrack is designed to detect health status while the smartphone is carried during normal activities, namely, free-living walking. The current method for assessing free-living walking is medical accelerometers, so we present evidence that mobile phones running our software are more accurate. We then show our gait model is more accurate than medical pedometers for counting steps of patients with chronic disease. Our gait model was evaluated in a pilot study involving 30 patients with chronic lung disease. The six-minute walk test (6MWT) is a major assessment for chronic heart and lung disease, including congestive heart failure and especially chronic obstructive pulmonary disease (COPD), affecting millions of persons. The 6MWT consists of walking back and forth along a measured distance for 6 minutes. The gait model using linear regression performed with 94.13% accuracy in measuring walk distance, compared with the established standard of direct observation. We also evaluated a different statistical model using the same gait parameters to predict health status through lung function. This gait model has high accuracy when applied to demographic cohorts, for example, 89.22% accuracy testing the cohort of 12 female patients with ages 50-64 years.

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“…We build our SVM using a standard Gaussian kernel (5). Features are mapped to higher dimensional space g(x) with kernels K(x, x) defined as the distance between the non-linear hyperplanes.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

“…High quality medical devices can measure steps accurately in healthy subjects; however, they often have high error measuring older chronic patients [27]. Speed prediction has been studied with patients walking on treadmills; however, artificial treadmill walking is provably different from natural walking since the treadmill sets the walking speed for the subject [5]. A freeliving health monitor must measure a patient's natural walking.…”

Section: Introductionmentioning

confidence: 99%

Towards a natural walking monitor for pulmonary patients using simple smart phones

Juen

Cheng

Schatz

2014

Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

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Mobile devices present the opportunity to continuously collect health data including movement and walking speed. Fitness trackers have become popular which record steps taken, distance walked and caloric expenditure. While useful for fitness purposes, medical monitoring requires precise accuracy and testing on real patients with a medically valid measure. Walking speed is closely linked to morbidity in patients and is also useful to determine distance walked during sixminute walk tests, a standard assessment for both chronic obstructive pulmonary disease and congestive heart failure. Current generation smartphone hardware contains similar sensor chips as medical devices and popular fitness devices. We develop a middleware, MoveSense, to provide comparable readings to medical accelerometers using only smartphones. We evaluate six methods developed for constrained treadmill walking to obtain gait speed during natural walking with older chronic pulmonary patients and train new models to predict speed and distance. Natural walking is walking without artificial speed constraints introduced during treadmill and nurse assisted walking. We also compare our model's accuracy to popular fitness devices. Our models produce accurate 6MWT distance and higher accuracy distance estimation than dedicated fitness devices during unconstrained walking on patients using a universally trained support vector machine model.

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Treadmill gait speeds correlate with physical activity counts measured by cell phone accelerometers

Cited by 22 publications

References 30 publications

Usability of Mobile Phones in Physical Activity–Related Research: A Systematic Review

Usability of Mobile Phones in Physical Activity–Related Research: A Systematic Review

Health Monitors for Chronic Disease by Gait Analysis with Mobile Phones

Towards a natural walking monitor for pulmonary patients using simple smart phones

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