An Investigation Into the Use of mHealth in Musculoskeletal Physiotherapy: Scoping Review
- PMID: 35275089
- PMCID: PMC8956993
- DOI: 10.2196/33609
An Investigation Into the Use of mHealth in Musculoskeletal Physiotherapy: Scoping Review
Abstract
Background: Musculoskeletal physiotherapy provides conservative management for a range of conditions. Currently, there is a lack of engagement with exercise programs because of the lack of supervision and low self-efficacy. The use of mobile health (mHealth) interventions could be a possible solution to this problem, helping promote self-management at home. However, there is little evidence for musculoskeletal physiotherapy on the most effective forms of mHealth.
Objective: The aim of this review is to investigate the literature focusing on the use of mHealth in musculoskeletal physiotherapy and summarize the evidence.
Methods: A scoping review of 6 peer-reviewed databases was conducted in March 2021. No date limits were applied, and only articles written in the English language were selected. A reviewer screened all the articles, followed by 2 additional researchers screening a random sample before data extraction.
Results: Of the 1393 studies, 28 (2.01%) were identified. Intervention characteristics comprised stretching and strengthening exercises, primarily for degenerative joint pain and spinal conditions (5/28, 18%). The most reported use of mHealth included telephone and videoconferencing calls to provide a home exercise program or being used as an adjunct to physiotherapy musculoskeletal assessment (14/28, 50%). Although patient satisfaction with mHealth was reported to be high, reasons for disengagement included a lack of high-quality information and poor internet speeds. Barriers to clinical uptake included insufficient training with the intervention and a lack of time to become familiar.
Conclusions: mHealth has some benefits regarding treatment adherence and can potentially be as effective as normal physiotherapy care while being more cost-effective. The current use of mHealth is most effective when ongoing feedback from a health care professional is available.
Keywords: mHealth; mobile phone; musculoskeletal; physiotherapy; rehabilitation; scoping review.
©Jonathon M R Agnew, Catherine E Hanratty, Joseph G McVeigh, Chris Nugent, Daniel P Kerr. Originally published in JMIR Rehabilitation and Assistive Technology (https://rehab.jmir.org), 11.03.2022.
Conflict of interest statement
Conflicts of Interest: None declared.
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References
-
- Hawker GA. The assessment of musculoskeletal pain. Clin Exp Rheumatol. 2017;35 Suppl 107(5):8–12. http://www.clinexprheumatol.org/pubmed/find-pii.asp?pii=28967361 12202 - PubMed
-
- Amorim AB, Pappas E, Simic M, Ferreira ML, Jennings M, Tiedemann A, Carvalho-E-Silva AP, Caputo E, Kongsted A, Ferreira PH. Integrating mobile-health, health coaching, and physical activity to reduce the burden of chronic low back pain trial (IMPACT): a pilot randomised controlled trial. BMC Musculoskelet Disord. 2019 Feb 11;20(1):71. doi: 10.1186/s12891-019-2454-y. https://bmcmusculoskeletdisord.biomedcentral.com/articles/10.1186/s12891... 10.1186/s12891-019-2454-y - DOI - DOI - PMC - PubMed
-
- Chou R, Deyo R, Friedly J, Skelly A, Hashimoto R, Weimer M, Fu R, Dana T, Kraegel P, Griffin J, Grusing S, Brodt ED. Nonpharmacologic therapies for low back pain: a systematic review for an American college of physicians clinical practice guideline. Ann Intern Med. 2017 Apr 04;166(7):493–505. doi: 10.7326/M16-2459. https://www.acpjournals.org/doi/abs/10.7326/M16-2459?url_ver=Z39.88-2003... 2603230 - DOI - DOI - PubMed
-
- Gonçalves-Bradley DC, Iliffe S, Doll H, Broad J, Gladman J, Langhorne P, Richards SH, Shepperd S. Early discharge hospital at home. Cochrane Database Syst Rev. 2017 Jun 26;6:CD000356. doi: 10.1002/14651858.CD000356.pub4. http://europepmc.org/abstract/MED/28651296 - DOI - PMC - PubMed
-
- Kongsted A, Kent P, Hestbaek L, Vach W. Patients with low back pain had distinct clinical course patterns that were typically neither complete recovery nor constant pain. A latent class analysis of longitudinal data. Spine J. 2015 May 01;15(5):885–94. doi: 10.1016/j.spinee.2015.02.012. https://linkinghub.elsevier.com/retrieve/pii/S1529-9430(15)00110-2 S1529-9430(15)00110-2 - DOI - PubMed
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