Evidence map›Paper›PMID 40874243›Full record

ArticleFrontiers in surgery2025

Smartphone-based activity research: methodology and key insights.

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 and 3 more

Abstract read
In one paragraph

Article in Frontiers in surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Ryan W TurlipDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Daksh ChauhanDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Hasan S AhmadDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Mert Marcel DagliDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Bonnie Y HuDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Richard J ChungDepartment of Neurosurgery, School of Medicine, University of Virginia, Charlottesville, VA, United States.
Yohannes GhenbotDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Ben J GuDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Nisarg PatelDepartment of Neurosurgery, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, United States.
Richelle J KimDepartment of Neurosurgery, Columbia College, Columbia University, New York, NY, United States.
Julia KincaidDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Akash VermaDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Jang W YoonDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and objectives: Objectively 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. Methods: Patient 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. Conclusion: Smartphones 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.

Indexed as

accelerometeractivity trackingbig databiometricssmartphone

Identifiers

PMID40874243
PMCPMC12378811

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.