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Clinical validation of smartphone-based activity tracking in peripheral artery disease patients.
NPJ Digit Med 2018; 1:66ND

Abstract

Peripheral artery disease (PAD) is a vascular disease that leads to reduced blood flow to the limbs, often causing claudication symptoms that impair patients' ability to walk. The distance walked during a 6-min walk test (6MWT) correlates well with patient claudication symptoms, so we developed the VascTrac iPhone app as a platform for monitoring PAD using a digital 6MWT. In this study, we evaluate the accuracy of the built-in iPhone distance and step-counting algorithms during 6MWTs. One hundred and fourteen (114) participants with PAD performed a supervised 6MWT using the VascTrac app while simultaneously wearing an ActiGraph GT9X Activity Monitor. Steps and distance-walked during the 6MWT were manually measured and used to assess the bias in the iPhone CMPedometer algorithms. The iPhone CMPedometer step algorithm underestimated steps with a bias of -7.2% ± 13.8% (mean ± SD) and had a mean percent difference with the Actigraph (Actigraph-iPhone) of 5.7% ± 20.5%. The iPhone CMPedometer distance algorithm overestimated distance with a bias of 43% ± 42% due to overestimation in stride length. Our correction factor improved distance estimation to 8% ± 32%. The Ankle-Brachial Index (ABI) correlated poorly with steps (R = 0.365) and distance (R = 0.413). Thus, in PAD patients, the iPhone's built-in distance algorithm is unable to accurately measure distance, suggesting that custom algorithms are necessary for using iPhones as a platform for monitoring distance walked in PAD patients. Although the iPhone accurately measured steps, more research is necessary to establish step counting as a clinically meaningful metric for PAD.

Authors+Show Affiliations

1Division of Vascular & Endovascular Surgery, Department of Surgery, Stanford University, Stanford, CA 94305 USA.1Division of Vascular & Endovascular Surgery, Department of Surgery, Stanford University, Stanford, CA 94305 USA.1Division of Vascular & Endovascular Surgery, Department of Surgery, Stanford University, Stanford, CA 94305 USA.1Division of Vascular & Endovascular Surgery, Department of Surgery, Stanford University, Stanford, CA 94305 USA.1Division of Vascular & Endovascular Surgery, Department of Surgery, Stanford University, Stanford, CA 94305 USA.1Division of Vascular & Endovascular Surgery, Department of Surgery, Stanford University, Stanford, CA 94305 USA.2Division of Vascular Surgery, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304 USA.1Division of Vascular & Endovascular Surgery, Department of Surgery, Stanford University, Stanford, CA 94305 USA.1Division of Vascular & Endovascular Surgery, Department of Surgery, Stanford University, Stanford, CA 94305 USA.1Division of Vascular & Endovascular Surgery, Department of Surgery, Stanford University, Stanford, CA 94305 USA. 2Division of Vascular Surgery, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304 USA. 3Precision Health and Integrated Diagnostics Center at Stanford, Stanford University, Stanford, CA 94305 USA.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31304343

Citation

Ata, Raheel, et al. "Clinical Validation of Smartphone-based Activity Tracking in Peripheral Artery Disease Patients." NPJ Digital Medicine, vol. 1, 2018, p. 66.
Ata R, Gandhi N, Rasmussen H, et al. Clinical validation of smartphone-based activity tracking in peripheral artery disease patients. NPJ Digit Med. 2018;1:66.
Ata, R., Gandhi, N., Rasmussen, H., El-Gabalawy, O., Gutierrez, S., Ahmad, A., ... Aalami, O. (2018). Clinical validation of smartphone-based activity tracking in peripheral artery disease patients. NPJ Digital Medicine, 1, p. 66. doi:10.1038/s41746-018-0073-x.
Ata R, et al. Clinical Validation of Smartphone-based Activity Tracking in Peripheral Artery Disease Patients. NPJ Digit Med. 2018;1:66. PubMed PMID: 31304343.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Clinical validation of smartphone-based activity tracking in peripheral artery disease patients. AU - Ata,Raheel, AU - Gandhi,Neil, AU - Rasmussen,Hannah, AU - El-Gabalawy,Osama, AU - Gutierrez,Santiago, AU - Ahmad,Alizeh, AU - Suresh,Siddharth, AU - Ravi,Roshini, AU - Rothenberg,Kara, AU - Aalami,Oliver, Y1 - 2018/12/11/ PY - 2018/07/02/received PY - 2018/11/14/accepted PY - 2019/7/16/entrez PY - 2019/7/16/pubmed PY - 2019/7/16/medline KW - Diagnostic markers KW - Peripheral vascular disease SP - 66 EP - 66 JF - NPJ digital medicine JO - NPJ Digit Med VL - 1 N2 - Peripheral artery disease (PAD) is a vascular disease that leads to reduced blood flow to the limbs, often causing claudication symptoms that impair patients' ability to walk. The distance walked during a 6-min walk test (6MWT) correlates well with patient claudication symptoms, so we developed the VascTrac iPhone app as a platform for monitoring PAD using a digital 6MWT. In this study, we evaluate the accuracy of the built-in iPhone distance and step-counting algorithms during 6MWTs. One hundred and fourteen (114) participants with PAD performed a supervised 6MWT using the VascTrac app while simultaneously wearing an ActiGraph GT9X Activity Monitor. Steps and distance-walked during the 6MWT were manually measured and used to assess the bias in the iPhone CMPedometer algorithms. The iPhone CMPedometer step algorithm underestimated steps with a bias of -7.2% ± 13.8% (mean ± SD) and had a mean percent difference with the Actigraph (Actigraph-iPhone) of 5.7% ± 20.5%. The iPhone CMPedometer distance algorithm overestimated distance with a bias of 43% ± 42% due to overestimation in stride length. Our correction factor improved distance estimation to 8% ± 32%. The Ankle-Brachial Index (ABI) correlated poorly with steps (R = 0.365) and distance (R = 0.413). Thus, in PAD patients, the iPhone's built-in distance algorithm is unable to accurately measure distance, suggesting that custom algorithms are necessary for using iPhones as a platform for monitoring distance walked in PAD patients. Although the iPhone accurately measured steps, more research is necessary to establish step counting as a clinically meaningful metric for PAD. SN - 2398-6352 UR - https://www.unboundmedicine.com/medline/citation/31304343/Clinical_validation_of_smartphone-based_activity_tracking_in_peripheral_artery_disease_patients L2 - https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/31304343/ DB - PRIME DP - Unbound Medicine ER -