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Range of studies

Current streams of research in the Musculoskeletal Health Research Group

Optimising and personalising management of musculoskeletal disease

The aim of this stream is to evaluate the most effective treatments for musculoskeletal conditions. This includes the identification of optimal modalities, as well as how these modalities are best personalised at the individual patient level. We consider systematic reviews and meta-analyses, as well as randomised controlled trials (RCTs), that can inform evidence-based clinical practice and the guidelines within these settings. Some examples of this include.

  • A network meta-analysis that identified optimal modes of exercise training for low back pain (LINK)
  • Meta-analyses that showed  pain sensitivity (LINK) and fear of movement (LINK) can be reduced via exercise training
  • Randomised control trial in back pain of showing two types of commonly performed, but different modes of exercise had a similar impact on clinical outcomes (LINK), and depressive symptoms (LINK), but may differentially impact the tissues in the spine, such as the musculature (LINK) and intervertebral discs (LINK)
  • The contribution of the placebo effect to exercise treatments for pain (systematic review and meta-analysis manuscript under review LINK)
  • Whether classification and sub-grouping approaches for back pain lead to better patient outcomes (ongoing LINK)
  • Exercise-based tele- and digital-health interventions and their efficacy (ongoing systematic review/meta-analysis LINK)

Implementing evidence-based guidelines for the management of musculoskeletal disease in clinical practice

The aim of this stream is to optimise the implementation of evidence-based guidelines in clinical practice. Although evidence-based clinical guidelines exist, the management of musculoskeletal disease often deviates from best practice. For example, in approximately 90% of back pain presentations, medical imaging is not required as findings do not influence management. Nonetheless, approximately 25% of back pain patients attending primary care physicians a referred to some form of imaging. This generates unnecessary financial costs at the healthcare and individual level and may, depending on the type of imaging done, unnecessarily expose the individual to radiation. Current examples of work in this stream include:

  • Investigating adherence to clinical guidelines in a high-volume hospital emergency department system in Melbourne, Australia (preliminary data presented at a conference; LINK)
  • Systematic review and meta-analysis of the effectiveness of interventions for implementing guidelines in clinical practice to optimise imaging use/referral (LINK), surgery use/referral (LINK), medication use/prescription (LINK) and reduce healthcare financial costs (LINK pending)

Detection of clinically  relevant sub-groups in musculoskeletal disease that can be targeted with specific intervention

This aim of this stream is to use big data and data science techniques to improve the management of musculoskeletal conditions. We recently published a series of systematic reviews (LINK) on artificial intelligence and machine learning in back pain. We also conducted a systematic review and meta-analysis of the psychosocial, brain and central nervous system and spine tissue factors that contribute to back pain (currently under review LINK). Further, the group has ongoing projects examining data from the UKBiobank (LINK) and we are collecting primary data from people with and without back pain to investigate the relative contributions of proposed causal factors (LINK). This work is critical for informing approaches to targeting treatments in the 90% of people who have back pain where no specific diagnosis can be made.

​​​​​​​Detection of clinically relevant sub-groups in musculoskeletal disease that can be targeted with specific intervention

This aim of this stream is to use big data and data science techniques to improve the management of musculoskeletal conditions. We recently published a series of systematic reviews (LINK) on artificial intelligence and machine learning in back pain. We also conducted a systematic review and meta-analysis of the psychosocial, brain and central nervous system and spine tissue factors that contribute to back pain (currently under review LINK). Further, the group has ongoing projects examining data from the UKBiobank (LINK) and we are collecting primary data from people with and without back pain to investigate the relative contributions of proposed causal factors (LINK). This work is critical for informing approaches to targeting treatments in the 90% of people who have back pain where no specific diagnosis can be made.

Awards

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