{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T23:37:39Z","timestamp":1792798659591,"version":"4.5.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:00:00Z","timestamp":1769731200000},"content-version":"vor","delay-in-days":23,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-34270-2","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T04:44:47Z","timestamp":1767761087000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Accurate quantification of steps from multiple smartphone positions"],"prefix":"10.1038","volume":"16","author":[{"given":"Alejandro","family":"Ena","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claudia","family":"Mazz\u00e0","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Rodr\u00edguez-Romero","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tim","family":"Woelfle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jannis","family":"Mueller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristina","family":"Granziera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ludwig","family":"Kappos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emmanuel","family":"Bartholom\u00e9","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Corrado","family":"Bernasconi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shibeshih","family":"Belachew","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00d3scar","family":"Reyes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,7]]},"reference":[{"key":"34270_CR1","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1038\/s41746-025-01572-8","volume":"8","author":"KI Taylor","year":"2025","unstructured":"Taylor, K. I. et al. Exploratory digital outcome measures of motor sign progression in parkinson\u2019s disease patients treated with prasinezumab. Npj Digit. Med. 8, 365. https:\/\/doi.org\/10.1038\/s41746-025-01572-8 (2025).","journal-title":"Npj Digit. Med."},{"key":"34270_CR2","doi-asserted-by":"publisher","first-page":"104460","DOI":"10.1016\/j.msard.2022.104460","volume":"69","author":"R Iodice","year":"2023","unstructured":"Iodice, R. et al. A review of current rehabilitation practices and their benefits in patients with multiple sclerosis. Mult Scler. Relat. Disord. 69, 104460. https:\/\/doi.org\/10.1016\/j.msard.2022.104460 (2023).","journal-title":"Mult Scler. Relat. Disord"},{"key":"34270_CR3","doi-asserted-by":"publisher","first-page":"e60673","DOI":"10.2196\/60673","volume":"8","author":"M Scaramozza","year":"2024","unstructured":"Scaramozza, M. et al. Sensor-Derived measures of motor and cognitive functions in people with multiple sclerosis using unsupervised Smartphone-Based assessments: Proof-of-Concept study. JMIR Form. Res. 8, e60673. https:\/\/doi.org\/10.2196\/60673 (2024).","journal-title":"JMIR Form. Res."},{"issue":"5","key":"34270_CR4","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1177\/13524585251316242","volume":"31","author":"A Karatsidis","year":"2025","unstructured":"Karatsidis, A. et al. Characterizing gait in people with multiple sclerosis using digital data from smartphone sensors: A proposed framework. Multiple Scler. J. 31 (5), 512\u2013528. https:\/\/doi.org\/10.1177\/13524585251316242 (2025).","journal-title":"Multiple Scler. J."},{"issue":"11","key":"34270_CR5","doi-asserted-by":"publisher","first-page":"e30394","DOI":"10.2196\/30394","volume":"23","author":"T Woelfle","year":"2021","unstructured":"Woelfle, T. et al. Practice effects of mobile tests of Cognition, Dexterity, and mobility on patients with multiple sclerosis: Data analysis of a Smartphone-Based observational study. J. Med. Internet. Res. 23 (11), e30394. https:\/\/doi.org\/10.2196\/30394 (2021).","journal-title":"J. Med. Internet. Res."},{"issue":"5","key":"34270_CR6","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.3233\/JND-240004","volume":"11","author":"E Arteaga-Bracho","year":"2024","unstructured":"Arteaga-Bracho, E. et al. Smartphone-Based assessment of mobility and manual dexterity in adult people with spinal muscular atrophy. J. Neuromuscul. Dis. 11 (5), 1049\u20131065. https:\/\/doi.org\/10.3233\/JND-240004 (2024).","journal-title":"J. Neuromuscul. Dis."},{"issue":"6","key":"34270_CR7","doi-asserted-by":"publisher","first-page":"822","DOI":"10.1109\/THMS.2016.2586741","volume":"46","author":"LV Nguyen","year":"2016","unstructured":"Nguyen, L. V. & La, H. M. Real-Time human foot motion localization algorithm with dynamic speed. IEEE Trans. Human-Machine Syst. 46 (6), 822\u2013833. https:\/\/doi.org\/10.1109\/THMS.2016.2586741 (2016).","journal-title":"IEEE Trans. Human-Machine Syst."},{"issue":"3","key":"34270_CR8","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1108\/SR-10-2020-0249","volume":"41","author":"S Negi","year":"2021","unstructured":"Negi, S. et al. FSR and IMU sensors-based human gait phase detection and its correlation with EMG signal for different terrain walk. Sens. Rev. 41 (3), 235\u2013245. https:\/\/doi.org\/10.1108\/SR-10-2020-0249 (2021).","journal-title":"Sens. Rev."},{"issue":"11","key":"34270_CR9","doi-asserted-by":"publisher","first-page":"4033","DOI":"10.3390\/s18114033","volume":"18","author":"L Oudre","year":"2018","unstructured":"Oudre, L. et al. Template-Based step detection with inertial measurement units. Sensors 18 (11), 4033. https:\/\/doi.org\/10.3390\/s18114033 (2018).","journal-title":"Sensors"},{"issue":"2","key":"34270_CR10","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1016\/j.gaitpost.2012.02.019","volume":"36","author":"J McCamley","year":"2012","unstructured":"McCamley, J. et al. An enhanced estimate of initial contact and final contact instants of time using lower trunk inertial sensor data. Gait Posture. 36 (2), 316\u2013318. https:\/\/doi.org\/10.1016\/j.gaitpost.2012.02.019 (2012).","journal-title":"Gait Posture"},{"key":"34270_CR11","doi-asserted-by":"publisher","first-page":"1955","DOI":"10.1109\/TNSRE.2021.3111681","volume":"29","author":"A Soltani","year":"2021","unstructured":"Soltani, A. et al. Algorithms for walking speed Estimation using a Lower-Back-Worn inertial sensor: A Cross-Validation on speed ranges. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 1955\u20131964. https:\/\/doi.org\/10.1109\/TNSRE.2021.3111681 (2021).","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"10","key":"34270_CR12","doi-asserted-by":"publisher","first-page":"3859","DOI":"10.3390\/s22103859","volume":"22","author":"R Romijnders","year":"2022","unstructured":"Romijnders, R. et al. A deep learning approach for gait event detection from a single Shank-Worn IMU: validation in healthy and neurological cohorts. Sensors 22 (10), 3859. https:\/\/doi.org\/10.3390\/s22103859 (2022).","journal-title":"Sensors"},{"issue":"1","key":"34270_CR13","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1186\/s12984-023-01198-5","volume":"20","author":"ME Mic\u00f3-Amigo","year":"2023","unstructured":"Mic\u00f3-Amigo, M. E. et al. Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium. J. Neuroeng. Rehabil. 20 (1), 78. https:\/\/doi.org\/10.1186\/s12984-023-01198-5 (2023).","journal-title":"J. Neuroeng. Rehabil."},{"key":"34270_CR14","doi-asserted-by":"publisher","first-page":"111527","DOI":"10.1016\/j.jbiomech.2023.111527","volume":"151","author":"VE Di Bacco","year":"2023","unstructured":"Di Bacco, V. E. & Gage, W. H. Evaluation of a smartphone accelerometer system for measuring nonlinear dynamics during treadmill walking: Concurrent validity and test-retest reliability. J. Biomech. 151, 111527. https:\/\/doi.org\/10.1016\/j.jbiomech.2023.111527 (2023).","journal-title":"J. Biomech."},{"issue":"22","key":"34270_CR15","doi-asserted-by":"publisher","first-page":"7497","DOI":"10.3390\/s21227497","volume":"21","author":"RT Shahar","year":"2021","unstructured":"Shahar, R. T. & Agmon, M. Gait analysis using accelerometry data from a single smartphone: agreement and consistency between a smartphone application and Gold-Standard gait analysis system. Sensors 21 (22), 7497. https:\/\/doi.org\/10.3390\/s21227497 (2021).","journal-title":"Sensors"},{"key":"34270_CR16","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1016\/j.gaitpost.2017.09.030","volume":"58","author":"P Silsupadol","year":"2017","unstructured":"Silsupadol, P. et al. Reliability and validity of a smartphone-based assessment of gait parameters across walking speed and smartphone locations: Body, bag, belt, hand, and pocket. Gait Posture. 58, 516\u2013522. https:\/\/doi.org\/10.1016\/j.gaitpost.2017.09.030 (2017).","journal-title":"Gait Posture"},{"key":"34270_CR17","doi-asserted-by":"publisher","DOI":"10.23937\/2572-3243.1510121","author":"S. Shirley","year":"2023","unstructured":"Shirley, S. et al. Non-Invasive Biomechanical intervention leads to low rates of total knee replacement and reduced utilization of healthcare resources among older adults with chronic knee pain: A 5-Year Follow-Up study. J. Musculoskelet. Disorders Treat. https:\/\/doi.org\/10.23937\/2572-3243.1510121 (2023).","journal-title":"J. Musculoskelet. Disorders Treat."},{"issue":"12","key":"34270_CR18","doi-asserted-by":"publisher","first-page":"1524","DOI":"10.1016\/j.medengphy.2016.09.023","volume":"38","author":"A Godfrey","year":"2016","unstructured":"Godfrey, A. et al. Beyond the front end: investigating a thigh worn accelerometer device for step count and bout detection in parkinson\u2019s disease. Med. Eng. Phys. 38 (12), 1524\u20131529. https:\/\/doi.org\/10.1016\/j.medengphy.2016.09.023 (2016).","journal-title":"Med. Eng. Phys."},{"key":"34270_CR19","doi-asserted-by":"publisher","unstructured":"Ena, A. et al. FAIR-Q: A Frequency-Adaptive Iterative and Robust Quantification Method for Step Detection. IEEE International Conference on Digital Health (ICDH), Helsinki, Finland, pp. 196\u2013198 (2025). https:\/\/doi.org\/10.1109\/ICDH67620.2025.00035","DOI":"10.1109\/ICDH67620.2025.00035"},{"key":"34270_CR20","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1177\/1352458516657439","volume":"23","author":"M D\u2019Souza","year":"2017","unstructured":"D\u2019Souza, M. et al. Neurostatus e-Scoring improves consistency of expanded disability status scale assessments: A proof of concept study. Mult Scler. Houndmills Basingstoke Engl. 23, 597\u2013603. https:\/\/doi.org\/10.1177\/1352458516657439 (2017).","journal-title":"Mult Scler. Houndmills Basingstoke Engl."},{"key":"34270_CR21","unstructured":"Konrath, J. et al. Xsens MVN gait report: The use of inertial motion capture for cloud based reporting of gait parameters [White paper]. Xsens Technol. B V (2021)."},{"key":"34270_CR22","doi-asserted-by":"publisher","unstructured":"Ena, A. et al. A novel methodology for developing Smartphone-instrumented tests for assessing Movement, Dexterity, and balance in neurological patients: Technical verification of ground truth Datasets, and analytical and clinical validation of digital biomarkers. Neurology 102 (22_supplement_1). https:\/\/doi.org\/10.1212\/WNL.0000000000205654 (2024).","DOI":"10.1212\/WNL.0000000000205654"},{"issue":"2","key":"34270_CR23","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.acha.2010.08.002","volume":"30","author":"I Daubechies","year":"2011","unstructured":"Daubechies, I. et al. Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Appl. Comput. Harmon. Anal. 30 (2), 243\u2013261. https:\/\/doi.org\/10.1016\/j.acha.2010.08.002 (2011).","journal-title":"Appl. Comput. Harmon. Anal."},{"issue":"2","key":"34270_CR24","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1177\/096228029900800204","volume":"8","author":"JM Bland","year":"1999","unstructured":"Bland, J. M. & Altman, D. G. Measuring agreement in method comparison studies. Stat. Methods Med. Res. 8 (2), 135\u2013160. https:\/\/doi.org\/10.1177\/096228029900800204 (1999).","journal-title":"Stat. Methods Med. Res."},{"issue":"2","key":"34270_CR25","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1037\/0033-2909.86.2.420","volume":"86","author":"PE Shrout","year":"1979","unstructured":"Shrout, P. E. & Fleiss, J. L. Intraclass correlations: uses in assessing rater reliability. Psychol. Bull. 86 (2), 420\u2013428.https:\/\/doi.org\/10.1037\/0033-2909.86.2.420 (1979).","journal-title":"Psychol. Bull."},{"issue":"5","key":"34270_CR26","doi-asserted-by":"publisher","first-page":"2523","DOI":"10.3390\/s23052523","volume":"23","author":"L Gonz\u00e1lez","year":"2023","unstructured":"Gonz\u00e1lez, L. et al. Estimation of ground contact time with inertial sensors from the upper arm and the upper back. Sensors 23 (5), 2523. https:\/\/doi.org\/10.3390\/s23052523 (2023).","journal-title":"Sensors"},{"issue":"5","key":"34270_CR27","doi-asserted-by":"publisher","first-page":"e0196463","DOI":"10.1371\/journal.pone.0196463","volume":"13","author":"F. A. Storm","year":"2018","unstructured":"Storm, F. A. et al. Free-living and laboratory gait characteristics in patients with multiple sclerosis. PloS one. 13 (5), e0196463 (2018). https:\/\/doi.org\/10.1371\/journal.pone.0196463","journal-title":"PloS One"},{"issue":"13","key":"34270_CR28","doi-asserted-by":"publisher","first-page":"13251","DOI":"10.1109\/JSEN.2022.3177951","volume":"22","author":"FA Garcia","year":"2022","unstructured":"Garcia, F. A. et al. Adaptive algorithm for gait segmentation using a single IMU in the thigh pocket. IEEE Sens. J. 22 (13), 13251\u201313261. https:\/\/doi.org\/10.1109\/JSEN.2022.3177951 (2022).","journal-title":"IEEE Sens. J."},{"key":"34270_CR29","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0171346","volume-title":"Monitoring gait in multiple sclerosis with novel wearable motion sensors","author":"Y. Moon","year":"2017","unstructured":"Moon, Y. et al. Monitoring gait in multiple sclerosis with novel wearable motion sensors. PLOS ONE 12(2), e0171346 (2017). https:\/\/doi.org\/10.1371\/journal.pone.0171346 (2017)."},{"key":"34270_CR30","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.pmcj.2013.09.006","volume":"13","author":"I Karuei","year":"2014","unstructured":"Karuei, I. et al. Robust realtime algorithm for Cadence Estimation. Pervasive Mob. Comput. 13, 52\u201366. https:\/\/doi.org\/10.1016\/j.pmcj.2013.09.006 (2014).","journal-title":"Pervasive Mob. Comput."},{"key":"34270_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.msard.2021.102943","author":"L. Abou","year":"2021","unstructured":"Abou, L. et al. Smartphone applications to assess gait and postural control in people with multiple sclerosis: A systematic review. Multiple Scler. Relat. Disorders https:\/\/doi.org\/10.1016\/j.msard.2021.102943 (2021).","journal-title":"Multiple Scler. Relat. Disorders"},{"key":"34270_CR32","doi-asserted-by":"publisher","DOI":"10.1177\/2055217316634754","author":"J. M. Balto","year":"2016","unstructured":"Balto, J. M. et al. Accuracy and precision of smartphone applications and commercially available motion sensors in multiple sclerosis. Multiple Scler. J. - Exp. Transac. Clin. https:\/\/doi.org\/10.1177\/2055217316634754 (2016).","journal-title":"Multiple Scler. J. - Exp. Transac. Clin."},{"issue":"2","key":"34270_CR33","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1159\/000525080","volume":"6","author":"C Demanuele","year":"2022","unstructured":"Demanuele, C. et al. Considerations for conducting bring your own device (BYOD) clinical studies. Digit. Biomarkers. 6 (2), 47\u201360. https:\/\/doi.org\/10.1159\/000525080 (2022).","journal-title":"Digit. Biomarkers"},{"issue":"6","key":"34270_CR34","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1177\/2168479015609104","volume":"49","author":"C Gwaltney","year":"2015","unstructured":"Gwaltney, C. et al. Bring your own device (BYOD): the future of Field-Based Patient-Reported outcome data collection in clinical trials? Therapeutic Innov. Regul. Sci. 49 (6), 783\u2013791. https:\/\/doi.org\/10.1177\/2168479015609104 (2015).","journal-title":"Therapeutic Innov. Regul. Sci."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-34270-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-34270-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-34270-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T10:01:23Z","timestamp":1769767283000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-34270-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,7]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["34270"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-34270-2","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,7]]},"assertion":[{"value":"18 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"A. Ena, C. Mazz\u00e0, A. Rodr\u00edguez-Romero, E. Bartholom\u00e9, C. Bernasconi, S. Belachew and \u00d3. Reyes are employees of Indivi AG, which partially supported this research. T. Woelfle declares no competing interests. J. Mueller received funding from the Swiss National Science Foundation (grant no. P500PM214230 and P5R5PM225288) and the \u201ePool f\u00fcr Innere Medizin\u201c of the University Hospital Basel, outside the presented work. The University Hospital Basel (USB) and the Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB), as the employers of C. Granziera and J. Mueller, have received the following fees which were used exclusively for research support: (i) advisory board and consultancy fees from Actelion, Genzyme-Sanofi, Novartis, Merck, GeNeuro and Hoffmann La Roche; (ii) speaker fees from Genzyme-Sanofi, Novartis, Merck, Jannsen, GeNeuro and Hoffmann La Roche; (iii) research support from Siemens Healthineers, GeNeuro, Hoffmann La Roche, Sanofi-Genzyme and Novartis. L. Kappos\u2019s institution (University Hospital Basel) has received the following exclusively for research support: Steering committee, advisory board and consultancy fees (Bayer, Biogen, Biohaven, Bristol Myers Squibb, Celltrion Inc, Clene Nanomedicine Inc., Eli Lilly, EMD Serono, Genentech, GlaxoSmithKline, Galapagos NV, Janssen, Japan Tobacco, Kiniksa Pharmaceuticals, Merck Healthcare AG, Minoryx, Santhera, Neurostatus UHB AG, Novartis, Roche, Sanofi, Shionogi, Wellmera, Zai Lab); speaker fees (Bristol Myers Squibb, Janssen, Novartis, Sanofi and Roche); support for educational activities (Merck, Novartis, Roche, Sanofi); testimony for Df-mp Molina & Pohlman; and grants (European Union, Innosuisse, Merck Healthcare AG, Novartis, Neurostatus UHB AG, Sanofi, Roche). External funders did not have involvement in the study design, data collection, data analysis, interpretation, or writing of the report.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The study was conducted in accordance with the Declaration of Helsinki. Ethics approval for the healthy controls (HCs) was obtained from the University of Sheffield, UK (Approval 064650), and for people with multiple sclerosis (pwMS) from the Ethikkommission Nordwest- und Zentralschweiz, Basel, Switzerland (BASEC ID 2021 D0040, NCT05009160). All participants provided their informed consent prior to inclusion in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}}],"article-number":"4143"}}