Evidence map›Paper›PMID 39198534›Full record

ArticleScientific reports2024

Determining critical monitoring periods for accurate wearable step counts in patients with degenerative spine disorders.

Aneysis D Gonzalez-Suarez, Nicolai Maldaner, Megan Tang, Parastou Fatemi, Chris Leung, Atman Desai, Christy Tomkins-Lane, Summer S Han, Corinna Zygourakis

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Aneysis D Gonzalez-Suarez *Department of Neurosurgery, Stanford University Hospital and Clinics, Stanford, CA, USA. adg52@stanford.edu.
Nicolai Maldaner *Department of Neurosurgery, Stanford University Hospital and Clinics, Stanford, CA, USA.
Megan TangDepartment of Neurosurgery, Stanford University Hospital and Clinics, Stanford, CA, USA.
Parastou FatemiDepartment of Neurosurgery, Stanford University Hospital and Clinics, Stanford, CA, USA.
Chris LeungDepartment of Neurosurgery, Stanford University Hospital and Clinics, Stanford, CA, USA.
Atman DesaiDepartment of Neurosurgery, Stanford University Hospital and Clinics, Stanford, CA, USA.
Christy Tomkins-LaneDepartment of Neurosurgery, Stanford University Hospital and Clinics, Stanford, CA, USA.
Summer S HanQuantitative Sciences Unit, Stanford University School of Medicine, Stanford, CA, USA.
Corinna ZygourakisDepartment of Neurosurgery, Stanford University Hospital and Clinics, Stanford, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Longitudinal physical activity monitoring is a novel and promising objective outcome measure for patients with degenerative spine disorder (DSD) that currently lacks established standards for data collection and interpretation. Here, we monitored 100 patients with DSD with the Apple Watch to establish the optimal duration and pattern of step count monitoring needed to estimate their weekly physical activity before their elective surgery. Participants were predominantly female (65.3%), had an average age of 61.5 years, and showed consistent step counts between preoperative days, as well as across weekends and weekdays. Intraclass correlations (ICC) analysis showed that a step count average over 2 days achieved an ICC of 0.92 when compared to a 7-day average before surgery, while 4 days were required for a similar agreement of 0.93 with a 14-day average. Sequential linear regression demonstrated that incorporating additional preoperative days improved the model's ability to predict 7- and 14-days step count averages. We conclude that, while daily preoperative step counts remain relatively stable, longer activity monitoring is necessary to account for the variance in step count over an increasing time frame, and the full extent of data fluctuation may only become apparent with long-term trend analysis.

Indexed as

Wearable Electronic DevicesAgedExerciseFemaleHumansMaleMiddle AgedSpinal Diseases

Identifiers

PMID39198534
PMCPMC11358281

What OpenQuestion holds

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Registered trials

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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.