Validation of a smartphone-based measurement tool for the quantification of level walking

Furrer, Martina; Bichsel, Lukas; Niederer, Michael; Baur, Heiner; Schmid, Stefan

doi:10.1016/j.gaitpost.2015.06.003

Cited by 45 publications

(32 citation statements)

References 28 publications

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“…In addition, since this study focused on studies that dealt with healthy adults between the age of 18 and 70, many of the studies that observed younger or older groups were also excluded. Selected articles were divided by data assessment methods of smartphone built-in sensor based data collection method ( Table 1 ) and user entered data collection method ( Table 2 ) ( Alnasser et al, 2016 ; Duncan et al, 2014 ; Furrer et al, 2015 ; Harries et al, 2016 ; Klein et al, 2014 ; Morrison et al, 2014 ; Nolan et al, 2014 ; Seto et al, 2016 ; Shin et al, 2017 ; Vosa et al, 2016 ). Published studies included both qualitative and qualitative studies: one focus group study, four randomized control trials, one cohort study, one cross-sectional study, and two surveys.…”

Section: Resultsmentioning

confidence: 99%

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Review of researches on smartphone applications for physical activity promotion in healthy adults

Jee

2017

J Exerc Rehabil

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Physical activity is known as a preventative method for preventing life-style-related diseases. Smartphone applications for health and fitness intervention have released with rapid increase of innovative technology. Reviews of recent publications on mobile application have been conducted to observe feasibility and applicability for physical activity intervention. Bibliographic searches of PubMed and ScienceDirect were conducted with key terms, ‘physical activity,’ ‘fitness,’ ‘smart-phone,’ and ‘health’ between the years 2014 and 2017 to obtain 5,087 publications. Out of 5,087 articles, five articles on sensor-based applications and five articles on user entry-based applications were obtained through the inclusion and exclusion processes. Accuracy of the physical activity assessments were reported to be high in comparison to the conventional assessment tools. The overall subject rating on the app motivational ratings were positive with high correlation between physical activity and treats and cues. The adherence rates to the apps significantly dropped prior to 3 months. Publications that elucidate feasibility and accuracy of smartphone applications that motivates physical activity seem limited with adequately conducted study designs. Large-scaled, control-compared, long-term randomized control trials should be conducted to elucidate the effects of the app interventions.

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Section: Resultsmentioning

confidence: 99%

“… Furrer et al (2015) conducted a cross-sectional study on the quantification of level walking with 22 young healthy adults with mean age of 27.4 years. The study analyzed vertical center of mass (CoM) and step duration by comparing with conventional assessment tool of motion capture system.…”

Section: Resultsmentioning

confidence: 99%

Review of researches on smartphone applications for physical activity promotion in healthy adults

Jee

2017

J Exerc Rehabil

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“…The aim of this study was to explore the reliability of built-in step counting sensors in smartphones with a special focus on the detection of physical activity/inactivity. There are limited studies comparing the difference between gait characteristics obtained with a smartphone and those obtained with a conventional accelerometer 12) , or validating a smartphone-based measurement for quantification of level walking 13) . A few studies focusing on inter/intra smartphone reliability in actual walking tests can also be found.…”

Section: Discussionmentioning

confidence: 99%

Reliability of smartphone-based gait measurements for quantification of physical activity/inactivity levels

Ebara

Azuma

Shoji

et al. 2017

Journal of Occupational Health

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Objectives: Objective measurements using built-in smartphone sensors that can measure physical activity/inactivity in daily working life have the potential to provide a new approach to assessing workers' health effects. The aim of this study was to elucidate the characteristics and reliability of built-in step counting sensors on smartphones for development of an easy-to-use objective measurement tool that can be applied in ergonomics or epidemiological research. Methods: To evaluate the reliability of step counting sensors embedded in seven major smartphone models, the 6-minute walk test was conducted and the following analyses of sensor precision and accuracy were performed: 1) relationship between actual step count and step count detected by sensors, 2) reliability between smartphones of the same model, and 3) false detection rates when sitting during office work, while riding the subway, and driving. Results: On five of the seven models, the inter-class correlations coefficient (ICC (3,1)) showed high reliability with a range of 0.956-0.993. The other two models, however, had ranges of 0.443-0.504 and the relative error ratios of the sensor-detected step count to the actual step count were ±48.7%-49.4%. The level of agreement between the same models was ICC (3,1): 0.992-0.998. The false detection rates differed between the sitting conditions. Conclusions: These results suggest the need for appropriate regulation of step counts measured by sensors, through means such as correction or calibration with a predictive model formula, in order to obtain the highly reliable measurement results that are sought in scientific investigation.

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“…Various tools can be used to record subject movement, including video cameras or smartphones that contain integrated accelerometers and cameras. In the field of modern biomechanics there is growing demand for two-dimensional (2D) software with which to analyse subject movement, as well as for the indirect calculation of centre of mass displacement [4] and gait analysis [5]. Some examples include Ubersense, now called Hudlthecnique [6], the MarkWiiR remote from Nintendo [7], the Microsoft Xbox One Kinect T [8] and the Dartfish [9].…”

Section: Clinical and Sports Technologies For Motion Analysismentioning

confidence: 99%

Validity and Reliability of the Kinovea Program in Obtaining Angular and Distance Dimensions

Puig-Diví¹,

Riu²,

Busquets-Faciaben³

et al. 2017

Preprint

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Validation of a smartphone-based measurement tool for the quantification of level walking

Cited by 45 publications

References 28 publications

Review of researches on smartphone applications for physical activity promotion in healthy adults

Review of researches on smartphone applications for physical activity promotion in healthy adults

Reliability of smartphone-based gait measurements for quantification of physical activity/inactivity levels

Validity and Reliability of the Kinovea Program in Obtaining Angular and Distance Dimensions

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