Verena Hartung1, Mustafa Sarshar2, Viktoria Karle3, Layal Shammas4, Asarnusch Rashid4, Paul Roullier4, Caroline Eilers5, Mathias Mäurer5, Peter Flachenecker6, Klaus Pfeifer1, Alexander Tallner1. 1. Department of Sport Science and Sport, Friedrich-Alexander University Erlangen-Nürnberg, 91058 Erlangen, Germany. 2. Department of Sport Science, Division of Health and Physical Activity, Otto-von-Guericke University, 39104 Magdeburg, Germany. 3. Department of Education, University of Regensburg, 93040 Regensburg, Germany. 4. Zentrum für Telemedizin Bad Kissingen, 97688 Bad Kissingen, Germany. 5. Department of Neurology, Klinikum Würzburg Mitte gGmbH, 97070 Würzburg, Germany. 6. Neurological Rehabilitation Center Quellenhof, 75323 Bad Wildbad, Germany.
Abstract
Background: Consumer activity monitors and smartphones have gained relevance for the assessment and promotion of physical activity. The aim of this study was to determine the concurrent validity of various consumer activity monitor models and smartphone models for measuring steps. Methods: Participants completed three activity protocols: (1) overground walking with three different speeds (comfortable, slow, fast), (2) activities of daily living (ADLs) focusing on arm movements, and (3) intermittent walking. Participants wore 11 activity monitors (wrist: 8; hip: 2; ankle: 1) and four smartphones (hip: 3; calf: 1). Observed steps served as the criterion measure. The mean average percentage error (MAPE) was calculated for each device and protocol. Results: Eighteen healthy adults participated in the study (age: 28.8 ± 4.9 years). MAPEs ranged from 0.3-38.2% during overground walking, 48.2-861.2% during ADLs, and 11.2-47.3% during intermittent walking. Wrist-worn activity monitors tended to misclassify arm movements as steps. Smartphone data collected at the hip, analyzed with a separate algorithm, performed either equally or even superiorly to the research-grade ActiGraph. Conclusion: This study highlights the potential of smartphones for physical activity measurement. Measurement inaccuracies during intermittent walking and arm movements should be considered when interpreting study results and choosing activity monitors for evaluation purposes.
Background: Consumer activity monitors and smartphones have gained relevance for the assessment and promotion of physical activity. The aim of this study was to determine the concurrent validity of various consumer activity monitor models and smartphone models for measuring steps. Methods:Participants completed three activity protocols: (1) overground walking with three different speeds (comfortable, slow, fast), (2) activities of daily living (ADLs) focusing on arm movements, and (3) intermittent walking. Participants wore 11 activity monitors (wrist: 8; hip: 2; ankle: 1) and four smartphones (hip: 3; calf: 1). Observed steps served as the criterion measure. The mean average percentage error (MAPE) was calculated for each device and protocol. Results: Eighteen healthy adults participated in the study (age: 28.8 ± 4.9 years). MAPEs ranged from 0.3-38.2% during overground walking, 48.2-861.2% during ADLs, and 11.2-47.3% during intermittent walking. Wrist-worn activity monitors tended to misclassify arm movements as steps. Smartphone data collected at the hip, analyzed with a separate algorithm, performed either equally or even superiorly to the research-grade ActiGraph. Conclusion: This study highlights the potential of smartphones for physical activity measurement. Measurement inaccuracies during intermittent walking and arm movements should be considered when interpreting study results and choosing activity monitors for evaluation purposes.
Authors: C Höchsmann; R Knaier; J Eymann; J Hintermann; D Infanger; A Schmidt-Trucksäss Journal: Scand J Med Sci Sports Date: 2018-03-12 Impact factor: 4.221
Authors: André Henriksen; Martin Haugen Mikalsen; Ashenafi Zebene Woldaregay; Miroslav Muzny; Gunnar Hartvigsen; Laila Arnesdatter Hopstock; Sameline Grimsgaard Journal: J Med Internet Res Date: 2018-03-22 Impact factor: 5.428
Authors: Lynne M Feehan; Jasmina Geldman; Eric C Sayre; Chance Park; Allison M Ezzat; Ju Young Yoo; Clayon B Hamilton; Linda C Li Journal: JMIR Mhealth Uhealth Date: 2018-08-09 Impact factor: 4.773
Authors: Valerie J Silfee; Christina F Haughton; Danielle E Jake-Schoffman; Andrea Lopez-Cepero; Christine N May; Meera Sreedhara; Milagros C Rosal; Stephenie C Lemon Journal: Prev Med Rep Date: 2018-05-10
Authors: John Pk Bernstein; Madeline Dw Noland; Katherine E Dorociak; Mira I Leese; Samuel Y Lee; Adriana Hughes Journal: Neuropsychol Dev Cogn B Aging Neuropsychol Cogn Date: 2021-09-23