Evidence map›Paper›PMID 33839107›Full record

ArticleArchives of physical medicine and rehabilitation2022

Toward Improving the Prediction of Functional Ambulation After Spinal Cord Injury Through the Inclusion of Limb Accelerations During Sleep and Personal Factors.

Stephanie K Rigot, Michael L Boninger, Dan Ding, Gina McKernan, Edelle C Field-Fote, Jeanne Hoffman, Rachel Hibbs, Lynn A Worobey

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Archives of physical medicine and rehabilitation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.9field-weighted citation impact, top 28% of its field
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

4 citing papers in PubMed, 10 citations in OpenAlex.

  1. Rethinking Movement: From Capacity to Context.Kinesiology review (Champaign, Ill.) · 2026
    Article
  2. The Application of Artificial Intelligence in Spine Surgery: A Scoping Review.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2025
    Article
  3. Article
  4. 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

8 authors at 1 institution in 1 country.

Stephanie K RigotRehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA; Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA.
Michael L BoningerRehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA; Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA; Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA; Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA.
Dan DingHuman Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA; Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA.
Gina McKernanHuman Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA; Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA.
Edelle C Field-FoteCrawford Research Institute, Shepherd Center, Atlanta, GA; Division of Physical Therapy, Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA; Program in Applied Physiology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA.
Jeanne HoffmanDepartment of Rehabilitation Medicine, University of Washington School of Medicine, Seattle, WA.
Rachel HibbsPhysical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA; Physical Therapy, University of Pittsburgh, Pittsburgh, PA.
Lynn A WorobeyRehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA; Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA; Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA; Physical Therapy, University of Pittsburgh, Pittsburgh, PA. Electronic address: law93@pitt.edu.
University of Washington · US

Funding

NRSA Training CoreTL1TR001858 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI RADOMSKI, THOMAS, RUBIO, DORIS M · 2016 to 2025
$11.1M
Characterizing Activity Patterns in Functional Mobility After Spinal Cord InjuryK23HD096134 · NICHD · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI WOROBEY, LYNN A · 2019 to 2023
$625k
Monitoring Lower Limb Movement to Predict Ambulatory Ability after Spinal Cord InjuryF30HD096828 · NICHD · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI RIGOT, STEPHANIE · 2019 to 2021
$71k
NCATS NIH HHS TL1 TR001858NICHD NIH HHS F30 HD096828NICHD NIH HHS K23 HD096134
6 · The paper itself

Abstract

objectiveTo determine if functional measures of ambulation can be accurately classified using clinical measures; demographics; personal, psychosocial, and environmental factors; and limb accelerations (LAs) obtained during sleep among individuals with chronic, motor incomplete spinal cord injury (SCI) in an effort to guide future, longitudinal predictions models.

designCross-sectional, 1-5 days of data collection.

settingCommunity-based data collection.

participantsAdults with chronic (>1 year), motor incomplete SCI (N=27).

interventionsNot applicable.

main outcome measuresAmbulatory ability based on the 10-m walk test (10MWT) or 6-minute walk test (6MWT) categorized as nonambulatory, household ambulator (0.01-0.44 m/s, 1-204 m), or community ambulator (>0.44 m/s, >204 m). A random forest model classified ambulatory ability using input features including clinical measures of strength, sensation, and spasticity; demographics; personal, psychosocial, and environmental factors including pain, environmental factors, health, social support, self-efficacy, resilience, and sleep quality; and LAs measured during sleep. Machine learning methods were used explicitly to avoid overfitting and minimize the possibility of biased results.

resultsThe combination of LA, clinical, and demographic features resulted in the highest classification accuracies for both functional ambulation outcomes (10MWT=70.4%, 6MWT=81.5%). Adding LAs, personal, psychosocial, and environmental factors, or both increased the accuracy of classification compared with the clinical/demographic features alone. Clinical measures of strength and sensation (especially knee flexion strength), LA measures of movement smoothness, and presence of pain and comorbidities were among the most important features selected for the models.

conclusionsThe addition of LA and personal, psychosocial, and environmental features increased functional ambulation classification accuracy in a population with incomplete SCI for whom improved prognosis for mobility outcomes is needed. These findings provide support for future longitudinal studies that use LA; personal, psychosocial, and environmental factors; and advanced analyses to improve clinical prediction rules for functional mobility outcomes.

Indexed as

Spinal Cord InjuriesWalkingAccelerationAdultCross-Sectional StudiesHumansSleepPrognosisRecovery of functionRehabilitationSpinal cord injuriesWalkingWheelchair

Identifiers

PMID33839107
PMCPMC8596977
OpenAlexW3150768136

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.