Smartphone-based activity research: methodology and key insights
Background and objectivesObjectively studying patient outcomes following surgery has been an important aspect of evidence-based medicine. The current gold-standard—patient reported outcomes measures—provides valuable information but have subjective biases. Smartphones, which passively collect data o...
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-08-01
|
| Series: | Frontiers in Surgery |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fsurg.2025.1613915/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849768314595704832 |
|---|---|
| author | Ryan W. Turlip Daksh Chauhan Hasan S. Ahmad Mert Marcel Dagli Bonnie Y. Hu Richard J. Chung Yohannes Ghenbot Ben J. Gu Nisarg Patel Richelle J. Kim Julia Kincaid Akash Verma Jang W. Yoon |
| author_facet | Ryan W. Turlip Daksh Chauhan Hasan S. Ahmad Mert Marcel Dagli Bonnie Y. Hu Richard J. Chung Yohannes Ghenbot Ben J. Gu Nisarg Patel Richelle J. Kim Julia Kincaid Akash Verma Jang W. Yoon |
| author_sort | Ryan W. Turlip |
| collection | DOAJ |
| description | Background and objectivesObjectively studying patient outcomes following surgery has been an important aspect of evidence-based medicine. The current gold-standard—patient reported outcomes measures—provides valuable information but have subjective biases. Smartphones, which passively collect data on physical activity such as daily steps, may provide objective and valuable insight into patient recovery and functional status. This study aims to provide a methodological guide for data collection and analysis of smartphone accelerometer data to assess clinical outcomes following surgery.MethodsPatient health metrics—namely daily steps, distance travelled, and flights climbed—were extracted from patient smartphones using easy-to-download applications. These applications upload the data that smartphone accelerometers passively collect daily to a HIPAA compliant encrypted server while de-identifying the patient's personal health information. Patients were consented in multiple settings—synchronously during clinical visits or asynchronously over the phone—and could be enrolled during the initial pre-operative visit or well after the surgery. With the patient data acquired, the peri-operative window of selection is determined based on the needs to the study. The timeseries data is then statistically normalized to account for individual baselines and smoothened over a 14-day moving average to minimize noise. Mathematical analysis can be harnessed to study quantifiable recovery and decline periods, which provide continuous and nuanced insight into patient's health throughout their spine disease and treatment course. Additionally, integrating clinical variables permits computational machine models capable of predicting patient trajectories and guiding clinical decisioning.ConclusionSmartphones offer a new metric for studying patient well-being and outcomes after surgery. The research with them is in its nascent stages but further studies can potentially revolutionize our understanding of spinal disease. |
| format | Article |
| id | doaj-art-fecda7ba587443e2853478cd0bc614c8 |
| institution | DOAJ |
| issn | 2296-875X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Surgery |
| spelling | doaj-art-fecda7ba587443e2853478cd0bc614c82025-08-20T03:03:50ZengFrontiers Media S.A.Frontiers in Surgery2296-875X2025-08-011210.3389/fsurg.2025.16139151613915Smartphone-based activity research: methodology and key insightsRyan W. Turlip0Daksh Chauhan1Hasan S. Ahmad2Mert Marcel Dagli3Bonnie Y. Hu4Richard J. Chung5Yohannes Ghenbot6Ben J. Gu7Nisarg Patel8Richelle J. Kim9Julia Kincaid10Akash Verma11Jang W. Yoon12Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United StatesDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United StatesDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United StatesDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United StatesDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United StatesDepartment of Neurosurgery, School of Medicine, University of Virginia, Charlottesville, VA, United StatesDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United StatesDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United StatesDepartment of Neurosurgery, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, United StatesDepartment of Neurosurgery, Columbia College, Columbia University, New York, NY, United StatesDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United StatesDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United StatesDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United StatesBackground and objectivesObjectively studying patient outcomes following surgery has been an important aspect of evidence-based medicine. The current gold-standard—patient reported outcomes measures—provides valuable information but have subjective biases. Smartphones, which passively collect data on physical activity such as daily steps, may provide objective and valuable insight into patient recovery and functional status. This study aims to provide a methodological guide for data collection and analysis of smartphone accelerometer data to assess clinical outcomes following surgery.MethodsPatient health metrics—namely daily steps, distance travelled, and flights climbed—were extracted from patient smartphones using easy-to-download applications. These applications upload the data that smartphone accelerometers passively collect daily to a HIPAA compliant encrypted server while de-identifying the patient's personal health information. Patients were consented in multiple settings—synchronously during clinical visits or asynchronously over the phone—and could be enrolled during the initial pre-operative visit or well after the surgery. With the patient data acquired, the peri-operative window of selection is determined based on the needs to the study. The timeseries data is then statistically normalized to account for individual baselines and smoothened over a 14-day moving average to minimize noise. Mathematical analysis can be harnessed to study quantifiable recovery and decline periods, which provide continuous and nuanced insight into patient's health throughout their spine disease and treatment course. Additionally, integrating clinical variables permits computational machine models capable of predicting patient trajectories and guiding clinical decisioning.ConclusionSmartphones offer a new metric for studying patient well-being and outcomes after surgery. The research with them is in its nascent stages but further studies can potentially revolutionize our understanding of spinal disease.https://www.frontiersin.org/articles/10.3389/fsurg.2025.1613915/fullaccelerometeractivity trackingbig databiometricssmartphone |
| spellingShingle | Ryan W. Turlip Daksh Chauhan Hasan S. Ahmad Mert Marcel Dagli Bonnie Y. Hu Richard J. Chung Yohannes Ghenbot Ben J. Gu Nisarg Patel Richelle J. Kim Julia Kincaid Akash Verma Jang W. Yoon Smartphone-based activity research: methodology and key insights Frontiers in Surgery accelerometer activity tracking big data biometrics smartphone |
| title | Smartphone-based activity research: methodology and key insights |
| title_full | Smartphone-based activity research: methodology and key insights |
| title_fullStr | Smartphone-based activity research: methodology and key insights |
| title_full_unstemmed | Smartphone-based activity research: methodology and key insights |
| title_short | Smartphone-based activity research: methodology and key insights |
| title_sort | smartphone based activity research methodology and key insights |
| topic | accelerometer activity tracking big data biometrics smartphone |
| url | https://www.frontiersin.org/articles/10.3389/fsurg.2025.1613915/full |
| work_keys_str_mv | AT ryanwturlip smartphonebasedactivityresearchmethodologyandkeyinsights AT dakshchauhan smartphonebasedactivityresearchmethodologyandkeyinsights AT hasansahmad smartphonebasedactivityresearchmethodologyandkeyinsights AT mertmarceldagli smartphonebasedactivityresearchmethodologyandkeyinsights AT bonnieyhu smartphonebasedactivityresearchmethodologyandkeyinsights AT richardjchung smartphonebasedactivityresearchmethodologyandkeyinsights AT yohannesghenbot smartphonebasedactivityresearchmethodologyandkeyinsights AT benjgu smartphonebasedactivityresearchmethodologyandkeyinsights AT nisargpatel smartphonebasedactivityresearchmethodologyandkeyinsights AT richellejkim smartphonebasedactivityresearchmethodologyandkeyinsights AT juliakincaid smartphonebasedactivityresearchmethodologyandkeyinsights AT akashverma smartphonebasedactivityresearchmethodologyandkeyinsights AT jangwyoon smartphonebasedactivityresearchmethodologyandkeyinsights |