Evidence map›Paper›PMID 40345393›Full record

Observational studyThe spine journal : official journal of the North American Spine Society2025

Multidomain postoperative recovery trajectories after lumbar and thoracolumbar spine surgery.

Salim Yakdan, Jingwen Zhang, Braeden Benedict, Ziqi Xu, Saad Javeed, Justin K Zhang, Benjamin A Steel, Vivek P Gupta, Kathleen Botterbush, Jay F Piccirillo and 10 more

Abstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in The spine journal : official journal of the North American Spine Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

20 authors.

Salim YakdanDepartment of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA.
Jingwen ZhangDepartment of Computer Science & Engineering, Washington University in St. Louis, St. Louis, MO, USA.
Braeden BenedictDepartment of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA.
Ziqi XuDepartment of Computer Science & Engineering, Washington University in St. Louis, St. Louis, MO, USA.
Saad JaveedDepartment of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA.
Justin K ZhangDepartment of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA.
Benjamin A SteelDepartment of Computer Science & Engineering, Washington University in St. Louis, St. Louis, MO, USA.
Vivek P GuptaDepartment of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA.
Kathleen BotterbushDepartment of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA.
Jay F PiccirilloDepartment of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, MO, USA.
Thomas L RodebaughDepartment of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Burel R GoodinDepartment of Anesthesiology, Washington University, St. Louis, MO, USA.
Jacob M BuchowskiDepartment of Orthopedic Surgery, Washington University School of Medicine, St. Louis, MO, USA.
Brian NeumanDepartment of Orthopedic Surgery, Washington University School of Medicine, St. Louis, MO, USA.
Daniel HafezDepartment of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA.
Michael KellyDepartment of Orthopedic Surgery, Rady Children's Hospital, University of California, San Diego, CA, USA.
Wilson Z RayDepartment of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA.
Chenyang LuDepartment of Computer Science & Engineering, Washington University in St. Louis, St. Louis, MO, USA; AI for Health Institute, Washington University, St. Louis, MO, USA.
Madelyn FrumkinCenter for Technology and Behavioral Health, Departments of Biomedical Data Science and Psychiatry, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
Jacob K GreenbergDepartment of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA; AI for Health Institute, Washington University, St. Louis, MO, USA. Electronic address: jacobgreenberg@wustl.edu.

Funding

Using Mobile Health Technology and Real-Time Assessments to Address Multilevel Influences on Lumbar Spine Surgery OutcomesK23AR082986 · NIAMS · WASHINGTON UNIVERSITY · PI JACOB GREENBERG · 2024 to 2026
$515k
NIAMS NIH HHS K23 AR082986
6 · The paper itself

Abstract

background contextUnderstanding early postoperative recovery is crucial for improving perioperative care and long-term outcomes. Traditional recovery assessments relying primarily on cross-sectional patient-reported measures may not fully capture the complexity of the recovery process. PURPOSE: Our study evaluates early postoperative recovery using mobile health assessment and explores the relationship between early recovery patterns, multidomain recovery, and long-term outcomes. STUDY DESIGN/

settingA prospective observational study. PATIENT SAMPLE: A total of 129 patients were included in the analysis. OUTCOME MEASURES: Patient-reported outcome measures (PROMs) of pain, physical function, and disability at 1, 6 and 12 months postoperatively.

methodsThis study recruited patients aged 21 to 85 undergoing lumbar/thoracolumbar surgery for degenerative diseases. During the first month postoperatively, patients completed daily Ecological Momentary Assessment (EMA) surveys assessing pain, depression, and physical function and were passively monitored with Fitbit. EMA and Fitbit data (steps per minute, Maximum 30-minute cadence, and activity bout number) were clustered using functional principal component analysis.

resultsA total of 129 patients were included (median age: 62; 56% female). Two dominant recovery patterns were identified across all domains. At 12 months, patients with more favorable recovery across all domains except for activity bout number and depression, showed better outcomes. Favorable recovery in pain intensity and steps per minute was associated with greater improvements in all outcomes, while favorable recovery in maximum 30-minute cadence was associated with greater improvement in disability and physical function. Patients with less favorable pain recovery had a significantly higher complication rate (23% vs 7%). A greater number of favorable recovery domains was associated with better 12-month outcomes improvement and demonstrated superior prognostic value compared to traditional patient assessment methods.

conclusionEarly postoperative recovery patterns and multidomain recovery, captured using mHealth tools, provide valuable insights into long-term outcomes. With further validation, these findings highlight the potential of integrating mHealth into clinical practice to personalize rehabilitation strategies, improve resource allocation, and enhance patient care.

Indexed as

Lumbar VertebraeRecovery of FunctionThoracic VertebraeAdultAgedAged, 80 and overFemaleHumansMaleMiddle AgedPatient Reported Outcome MeasuresPostoperative PeriodProspective StudiesYoung AdultDegenerative spine diseaseDisabilityEcological momentary assessmentFitbitLong-term outcomesPhysical functionSpine surgery

Identifiers

PMID40345393
PMCPMC13050128

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.