{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T02:14:35Z","timestamp":1791598475787,"version":"4.3.4"},"reference-count":28,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12]]},"abstract":"\n OBJECTIVE<\/jats:title>\n Patient-reported outcome measures (PROMs) are currently the gold standard to evaluate patient physical performance and ability to recover after spine surgery. However, PROMs have significant limitations due to the qualitative and subjective nature of the information reported as well as the impossibility of using this method in a continuous manner. The smartphone global positioning system (GPS) can be used to provide continuous, quantitative, and objective information on patient mobility. The aim of this study was to use daily mobility features derived from the smartphone GPS to characterize the perioperative period of patients undergoing spine surgery and to compare these objective measurements to PROMs, the current gold standard.<\/jats:p>\n <\/jats:sec>\n \n METHODS<\/jats:title>\n Eight daily mobility features were derived from smartphone GPS data in a population of 39 patients undergoing spine surgery for a period of 2 months starting 3weeks before surgery. In parallel, three different PROMs for pain (visual analog scale [VAS]), disability (Oswestry Disability Index [ODI]) and functional status (Patient-Reported Outcomes Measurement Information System [PROMIS]) were serially measured. Segmented linear regression analysis was used to assess trends before and after surgery. The Student paired t-test was used to compare pre- and postoperative PROM scores. Pearson\u2019s correlation was calculated between the daily average of each GPS-based mobility feature and the daily average of each PROM score during the recovery period.<\/jats:p>\n <\/jats:sec>\n \n RESULTS<\/jats:title>\n Smartphone GPS features provided data documenting a reduction in mobility during the immediate postoperative period, followed by a progressive and steady increase with a return to baseline mobility values 1 month after surgery. PROMs measuring pain, physical performance, and disability were significantly different 1 month after surgery compared to the 2 immediate preoperative weeks. The GPS-based features presented moderate to strong linear correlation with pain VAS and PROMIS physical score during the recovery period (Pearson r > 0.7), whereas the ODI and PROMIS mental scores presented a weak correlation (Pearson r approximately 0.4).<\/jats:p>\n <\/jats:sec>\n \n CONCLUSIONS<\/jats:title>\n Smartphone-derived GPS features were shown to accurately characterize perioperative mobility trends in patients undergoing surgery for spine-related diseases. Features related to time (rather than distance) were better at describing patient physical and performance status. Smartphone GPS has the potential to be used for the development of accurate, noninvasive and personalized tools for patient mobility monitoring after surgery.<\/jats:p>\n <\/jats:sec>","DOI":"10.3171\/2021.2.spine202181","type":"journal-article","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T03:25:47Z","timestamp":1631244347000},"page":"796-806","source":"Crossref","is-referenced-by-count":34,"title":["Smartphone GPS signatures of patients undergoing spine surgery correlate with mobility and current gold standard outcome measures"],"prefix":"10.3171","volume":"35","author":[{"given":"Alessandro","family":"Boaro","sequence":"first","affiliation":[{"name":"Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women\u2019s Hospital, Harvard Medical School;"},{"name":"Institute of Neurosurgery, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Italy; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey","family":"Leung","sequence":"additional","affiliation":[{"name":"Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women\u2019s Hospital, Harvard Medical School;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harrison T.","family":"Reeder","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Harvard T.H. Chan School of Public Health;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesca","family":"Siddi","sequence":"additional","affiliation":[{"name":"Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women\u2019s Hospital, Harvard Medical School;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elisabetta","family":"Mezzalira","sequence":"additional","affiliation":[{"name":"Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women\u2019s Hospital, Harvard Medical School;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Harvard T.H. 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