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Smartphone Global Positioning System (GPS) Data Enhances Recovery Assessment After Breast Cancer Surgery

  • Breast Oncology
  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

Abstract

Purpose

We sought to determine whether smartphone GPS data uncovered differences in recovery after breast-conserving surgery (BCS) and mastectomy, and how these data aligned with self-reported quality of life (QoL).

Methods

In a prospective pilot study, adult smartphone-owners undergoing breast surgery downloaded an application that continuously collected smartphone GPS data for 1 week preoperatively and 6 months postoperatively. QoL was assessed with the Short-Form-36 (SF36) via smartphone delivery preoperatively and 4 and 12 weeks postoperatively. Endpoints were trends in daily GPS-derived distance traveled and home time, as well as SF36 Physical (PCS) and Mental Component Scores (MCS) comparing BCS and mastectomy patients.

Results

Thirty-one patients were included. Sixteen BCS and fifteen mastectomy patients were followed for a mean of 201 (SD 161) and 174 (107) days, respectively. There were no baseline differences in demographics, PCS/MCS, home time, or distance traveled. Through 12 weeks postoperatively, mastectomy patients spent more time at home [e.g., week 4: 16.7 h 95% CI (14.3, 19.6) vs. 11.0 h (9.4, 12.9), p < 0.001] and traveled shorter distances [e.g., week 4: 52.5 km 95% CI (36.1, 76.0) vs. 107.7 km (75.8–152.9), p = 0.009] compared with BCS patients. There were no significant QoL differences throughout the study as measured by the MCS [e.g., week 4 difference: 7.83 95% CI (− 9.02, 24.7), p = 0.362] or PCS [e.g., week 4 difference: 8.14 (− 6.67, 22.9), p = 0.281]. GPS and QoL trends were uncorrelated (ρ < ± 0.26, p > 0.05).

Conclusions

Differences in BCS and mastectomy recovery were successfully captured using smartphone GPS data. These data may describe currently unmeasured aspects of physical and mental recovery, which could supplement traditional and QoL outcomes to inform shared decision-making.

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Acknowledgments

This study was supported by a National Institutes of Health Research Training in Alimentary Tract Surgery grant (Grant T32 DK007754-18 [NP]), an Ariadne Labs Spark Grant funded by the Paul G. Allen Family foundation (NP, IS, JPO, ABH), and the National Institutes of Health/National Institute of Mental Health [Grant 1DP2MH103909 (JPO)].

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Author notes

  1. Nikhil Panda and Ian Solsky contributed equally as co-first authors.

  2. Jukka-Pekka Onnela and Alex B. Haynes contributed equally as co-senior authors.

Authors and Affiliations

  1. Ariadne Labs, Brigham and Women’s Hospital, Harvard T.H. School of Public Health, Boston, MA, USA

    Nikhil Panda MD, MPH, Ian Solsky MD, MPH, Becky Hawrusik MS, Stuart Lipsitz ScD, Eesha V Desai BS, Kurt W. Lowery BS, Kate Miller PhD, MPH, Jukka-Pekka Onnela DSc & Alex B. Haynes MD, MPH

  2. Department of Surgery, Massachusetts General Hospital, Boston, MA, USA

    Nikhil Panda MD, MPH, Michele A. Gadd MD, Carrie C. Lubitz MD, MPH, Barbara L. Smith MD, PhD & Michelle Specht MD

  3. Department of Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA

    Ian Solsky MD, MPH

  4. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Gang Liu MS, Harrison Reeder BA & Jukka-Pekka Onnela DSc

  5. Department of Surgery and Perioperative Care, Dell Medical School, The University of Texas at Austin, Austin, TX, USA

    Alex B. Haynes MD, MPH

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  1. Nikhil Panda MD, MPH
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Correspondence to Nikhil Panda MD, MPH.

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Disclosures

NP reports a contract agreement with Aptima, a human-centered engineering and performance assessment contractor of the DARPA/Department of Defense. JPO receives sole compensation as a member of Harvard University. His research at Harvard T.H. Chan School of Public Health is supported by research awards from the NIH, Otsuka Pharmaceutical, Boehringer Ingelheim, and Apple. He received an unrestricted gift from Mindstrong Health in 2018. He is cofounder and board member of a recently established commercial entity that operates in digital phenotyping.

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Appendix: GPS Data Sampling and Procedure Used to Process Raw GPS Data for Computing Daily Home Time and Distance Traveled

Appendix: GPS Data Sampling and Procedure Used to Process Raw GPS Data for Computing Daily Home Time and Distance Traveled

GPS data were sampled from smartphones by alternating between on-cycles (GPS data sampled) and off-cycles (no GPS data sampled). In this study the duration of the on-cycle was set to 2 min and the duration of the off-cycle to 10 min. This simple sampling scheme enables collection of enough data to infer mobility patterns while minimizing smartphone battery drainage.22 Importantly, this approach also enables quantification of the extent of missingness in data that is not due to design, but due to behavioral or other factors, such as patients turning off their phones. Patients were encouraged to use and interact with their phones as they normally would for the duration of the study.

GPS data were processed to compute daily “home time” (in hours) and “distance traveled” (in kilometers) metrics.22 Raw GPS data were collected at specific intervals according to a data collection schedule specified by the study design. Each sample recorded subjects’ latitude and longitude that were converted into a mobility trace defined by a sequence of flights and pauses. Flights were defined to be segments of linear movement and pauses were defined to be periods of time when a person did not move. Curved movement was approximated by multiple sequential flights. Also, if a missing interval was flanked by two pauses at the same location (situated within 50 m of one another), the missing interval was assumed to be a longer pause at the same location. Because the data stream has long periods of structured missingness, we next used a resampling method to estimate a complete connected path of flights and pauses. Specifically, each missing period was filled with random draws from the subjects’ empirical distributions of observed flights and pauses to create complete paths that reflected individuals’ observed mobility patterns. The resulting trajectories were summarized into fifteen daily summary statistics or metrics. Given the aims of this study, we selected two specific metrics that potentially describe aspects of postoperative recovery. The “distance traveled” metric is computed as the sum of the lengths (in kilometers) of the flights in each day (irrespective of mode of transportation). To compute “home time,” we first fixed the location of each subject’s home by identifying the set of significant locations the subject visited. In order to determine the set of all significant locations for a person, we ran a ‘k-means’ clustering algorithm on the set of all pause locations with a minimum duration of 10 min to identify a small number of locations where the subject spent the most time. The significant location with the largest total amount of time during the night hours between 9 p.m. and 6 a.m. over the course of the study period was assumed to be the location of the person’s home. The “home time” metric was the amount of time (in hours) per day spent within a 200-m radius of home.

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Panda, N., Solsky, I., Hawrusik, B. et al. Smartphone Global Positioning System (GPS) Data Enhances Recovery Assessment After Breast Cancer Surgery. Ann Surg Oncol 28, 985–994 (2021). https://doi.org/10.1245/s10434-020-09004-5

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