Editorial Commentary: Arthrosomnology and the Solution to Coxalgia Somnia: Arthroscopic Hip Surgeons and Patients Increasingly Appreciate the Role of Sleep as Good Medicine

It is increasingly recognized that a variety of musculoskeletal disorders significantly influence sleep. In individuals with sleep dysfunction caused by hip pain (coxalgia somnia) from osteoarthritis, total hip arthroplasty has reliably improved pain and sleep quality in most patients. In nonarthrit...

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Veröffentlicht in:Arthroscopy Jg. 37; H. 3; S. 879
1. Verfasser: Harris, Joshua D
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.03.2021
Schlagworte:
Arthroscopy
Artificial Intelligence
Femoracetabular Impingement - surgery
Hip Joint - surgery
Humans
Medicine
Sleep
Surgeons
Treatment Outcome
ISSN:1526-3231, 1526-3231
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Zusammenfassung:It is increasingly recognized that a variety of musculoskeletal disorders significantly influence sleep. In individuals with sleep dysfunction caused by hip pain (coxalgia somnia) from osteoarthritis, total hip arthroplasty has reliably improved pain and sleep quality in most patients. In nonarthritic, nondysplastic individuals with femoroacetabular impingement syndrome caused by cam and/or pincer morphology and labral tears, hip arthroscopy has similarly reliably improved pain and function in most patients. In addition, there is now early short-term evidence showing significant improvements in both sleep quantity and quality in most patients after arthroscopic hip preservation surgery. Integrating the realms of hip arthroscopy and sleep medicine, known as arthrosomnology, there are dozens of subjective patient-reported and objective clinician-measured outcomes available to analyze the impact of interventions. The Pittsburgh Sleep Quality Index is the most common subjective questionnaire used in orthopaedic surgery literature. Integrating the realms of wearable technology (fitness trackers, smart watches) and machine learning and artificial intelligence has incredible potential to collect immense volumes of accurate sleep "big data."
Bibliographie:SourceType-Scholarly Journals-1
content type line 23
ObjectType-Editorial-2
ObjectType-Commentary-1
ISSN:1526-3231
1526-3231
DOI:10.1016/j.arthro.2020.12.234