Shoulder adhesive capsulitis; Clinical aspects and practical algorithmic approach

Shoulder adhesive capsulitis is a relatively common shoulder problem, characterized by severe pain, progressive limitation of the active and passive movements, and significant disability. It may be primary (idiopathic) or secondary (known disorders such as diabetes, thyroid disease, cardiopulmonary diseases, stroke, cervical discopathy, after surgeries, rotator cuff tendinopathy or tears, calcific tendinitis, proximal humerus fractures, shoulder surgery, or prolonged shoulder immobilization). Adhesive capsulitis may be present in four stages, i.e. painful, freezing, frozen, and thawing. The diagnosis of adhesive capsulitis is often clinical and usually, there is no need for further imaging. There is no consensus in the management of adhesive capsulitis and the most effective treatment remains unclear. However, interventions such as patient education, analgesics, injection therapies (corticosteroids, hyaluronic acid, platelet-rich plasma, ….), exercise therapy, physiotherapy modalities, acupuncture, manual therapy and mobilization techniques, nerve blocks, hydrodilation, and surgery may help the patient to shorten the course of the longstanding disease. In this review, we aimed to provide an evidence-based algorithmic approach to this problem and an illustrated exercise protocol for proper rehabilitation of the affected patients.

Authors

Institutions

Publication Details

Journal
Current Orthopaedic Practice
Published
2025-03-06
DOI
https://doi.org/10.1097/bco.0000000000001303
Primary Topic
Shoulder Injury and Treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Shoulder adhesive capsulitis; Clinical aspects and practical algorithmic approach

Zahra Bodaghabadi, Farzin Halabchi, Reyhaneh Khazaie, Mohamadreza Guity
Current Orthopaedic Practice
Shoulder Injury and Treatment
article

Shoulder adhesive capsulitis; Clinical aspects and practical algorithmic approach

Zahra Bodaghabadi, Farzin Halabchi, Reyhaneh Khazaie, Mohamadreza Guity
article en

Abstract

Shoulder adhesive capsulitis is a relatively common shoulder problem, characterized by severe pain, progressive limitation of the active and passive movements, and significant disability. It may be primary (idiopathic) or secondary (known disorders such as diabetes, thyroid disease, cardiopulmonary diseases, stroke, cervical discopathy, after surgeries, rotator cuff tendinopathy or tears, calcific tendinitis, proximal humerus fractures, shoulder surgery, or prolonged shoulder immobilization). Adhesive capsulitis may be present in four stages, i.e. painful, freezing, frozen, and thawing. The diagnosis of adhesive capsulitis is often clinical and usually, there is no need for further imaging. There is no consensus in the management of adhesive capsulitis and the most effective treatment remains unclear. However, interventions such as patient education, analgesics, injection therapies (corticosteroids, hyaluronic acid, platelet-rich plasma, ….), exercise therapy, physiotherapy modalities, acupuncture, manual therapy and mobilization techniques, nerve blocks, hydrodilation, and surgery may help the patient to shorten the course of the longstanding disease. In this review, we aimed to provide an evidence-based algorithmic approach to this problem and an illustrated exercise protocol for proper rehabilitation of the affected patients.

Current Orthopaedic PracticeVol. 36(3)
Imam Khomeini Hospital (IR), Tehran University of Medical Sciences (IR)
Openalex Percentile: Top 93%
Shoulder Injury and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.