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Related Experiment Videos

Eight predictive factors associated with response patterns during physiotherapy for soft tissue shoulder disorders

Carol A Kennedy1, Ted Haines, Dorcas E Beaton

  • 1Institute for Work & Health, Mobility Program Clinical Research Unit, Martin Family Arthritis Care and Research Centre, St Michael's Hospital, and Department of Physical Therapy, University of Toronto, Toronto, Ontario, Canada.

Journal of Clinical Epidemiology
|April 25, 2006
PubMed
Summary

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Identifying predictors of physiotherapy response in soft tissue shoulder disorders is key. Age, problem duration, and worker's compensation claims influence recovery patterns, aiding clinical decision-making.

Area of Science:

  • Orthopedics
  • Physical Therapy
  • Rehabilitation Medicine

Background:

  • Soft tissue shoulder disorders present a complex recovery trajectory.
  • Understanding response patterns is crucial for effective physiotherapy.
  • Previous research identified typical response patterns.

Purpose of the Study:

  • To identify baseline factors predicting distinct response patterns to physiotherapy.
  • To inform clinical decision-making for shoulder disorder recovery.

Main Methods:

  • Prospective cohort study of 361 clients with soft tissue shoulder disorders.
  • Utilized multivariable multinomial logistic regression.
  • Assessed 28 independent predictors including demographics, clinical factors, and expectations.

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Main Results:

  • Key predictors of response patterns included age, problem duration, and worker's compensation claims.
  • Client's global rating of the problem and SF-36 scores (Physical and Mental Component Scores) were significant.
  • Therapist's prediction of return to usual activity and over-the-counter medication use also differentiated response patterns.

Conclusions:

  • Several baseline factors effectively predict physiotherapy response patterns in soft tissue shoulder disorders.
  • These predictors can assist clinicians in making informed treatment decisions.
  • A clinically sensible outcome measure was used to identify these predictive factors.