The Archives of Bone and Joint Surgery

The Archives of Bone and Joint Surgery

The Mobile Applications for Low Back and Neck Pain Therapy: App Review

Document Type : CURRENT CONCEPTS REVIEW

Authors
Yasaman Farjami Rad 1
Leila Shahmoradi 2
Noureddin Nakhostin Ansari 3
Scott Hasson 4
Amir Rakhshan 5
Maryam Ebrahimi 6
Meysam Rahmani 7
1 Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran - Health Professions Education Research Center, Tehran University of Medical Sciences, Tehran, Iran - Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
2 Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
3 Department of Physiotherapy, School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran - Research Center for War-affected People, Tehran University of Medical Sciences, Tehran, Iran
4 Department of Physical Therapy, Augusta University, Augusta, Georgia, USA
5 Department of Foreign Languages, Tehran University of Medical Sciences, Tehran, Iran
6 Department of Health Information Technology, Neyshabur University of Medical Sciences, Neyshabur, Iran- Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences, Mashhad University of Medical Sciences, Mashhad, Iran - Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran
7 Department of Health Information Technology, Saveh University of Medical Sciences, Saveh, Iran
10.22038/abjs.2024.81799.3724
Abstract
This study aimed to assess mobile applications (apps) designed for physiotherapy targeting low back pain (LBP) and neck pain (NP) using the Mobile Application Rating Scale (MARS). The study employed an evaluation design, in which three reviewers conducted searches in English and Persian on Google Play in October 2024 to identify apps related to LBP and NP. After initial screening, the included apps were downloaded and installed on smartphones for further evaluation. The MARS questionnaire was utilized to evaluate apps. The total score obtained from the MARS questionnaire, along with the rating on the Google Play Store, was used to assess the quality and effectiveness of the apps. Eighteen apps, consisting of eight for NP and ten for LBP, were included in this study. Among LBP apps, the application "Back Pain Relief Exercises at Home" received the highest score (3.79/5). Moreover, the app "Lia – AI Posture Trainer" achieved the highest score among NP apps at 4.25/5. The findings showed that the apps available for NP and LBP are limited and low-quality. Given the increasing number of individuals suffering from these conditions, there is a clear need for up-to-date and high-quality software to provide daily patient support. These apps must be developed based on scientific studies and incorporate user feedback.
        Level of evidence: IV
Keywords
Back pain
Digital health
Mobile applications
Neck pain
Software validation
Subjects
  1. Coe-O’Brien R, Joseph L, Kuisma R, Paungmali A, Sitilertpisan P, Pirunsan U. Outcome measures used in the smartphone applications for the management of low back pain: a systematic scoping review. Health Inf Sci Syst. 2020;8(1):1-12. doi:10.1007/s13755-019-0097-x.
  2. Kazeminasab S, Nejadghaderi SA, Amiri P, et al. Neck pain: global epidemiology, trends and risk factors. BMC Musculoskelet Disord. 2022;23(1):26. doi: 10.1186/s12891-021-04957-4.
  3. Maher C, Ferreira G. Time to reconsider what Global Burden of Disease studies really tell us about low back pain. Ann Rheum Dis. 2022;81(3):306-308. doi: 10.1136/annrheumdis-2021-221173.
  4. Marques J, Borges L, Andias R, Silva AG. Characterisation and assessment of the most popular mobile apps designed for neck pain self-management: A systematic search in app stores. Musculoskeletal Care.2022;20(1):192-199. doi: 10.1002/msc.1581.
  5. Aghazadeh A, Mansour Sohani S, Salehi R, Parnianpour M. Translation, Cross-Cultural Adaptation and Psychometric Properties of the Persian Version of Patient-Specific Functional Scale in Patients with Chronic Low Back Pain. The Archives of Bone and Joint Surgery. 2025;13(1):47–53. doi: 10.22038/abjs.2024.76731.3546
  6. Sterling M, de Zoete RMJ, Coppieters I, Farrell SF. Best Evidence Rehabilitation for Chronic Pain Part 4: Neck Pain. J Clin Med. 2019;8(8):1219. doi: 10.3390/jcm8081219.
  7. Taylor NF, Dodd KJ, Shields N, Bruder A. Therapeutic exercise in physiotherapy practice is beneficial: a summary of systematic reviews 2002–2005. Aust J Physiother. 2007;53(1):7-16. doi: 10.1016/s0004-9514(07)70057-0.
  8. van Middelkoop M, Rubinstein SM, Kuijpers T, et al. A systematic review on the effectiveness of physical and rehabilitation interventions for chronic non-specific low back pain. Eur Spine J. 2011;20(1):19-39. doi: 10.1007/s00586-010-1518-3.
  9. Wang X-Q, Zheng J-J, Yu Z-W, et al. A meta-analysis of core stability exercise versus general exercise for chronic low back pain. PLoS One. 2012;7(12):e52082. doi: 10.1371/journal.pone.0052082.
  10. Ghasemi Dehcheshmeh F, Nourbakhsh MR, Amini Farsani Z, Bazrgari B, Arab AM. Kinematic Analysis of Pelvic and Lower Limb Joints during Stand-to-sit Movement in Individuals with Chronic Low Back Pain: A cross-sectional study. The Archives of Bone and Joint Surgery. 2024;12(8):587–596. doi: 10.22038/abjs.2024.76840.3551
  11. Didyk C, Lewis LK, Lange B. Availability, content and quality of commercially available smartphone applications for the self-management of low back pain: a systematic assessment. Disabil Rehabil. 2022;44(24):7600-7609. doi: 10.1080/09638288.2021.1979664.
  12. Machado G, Pinheiro M, Lee H, et al. Smartphone apps for the self-management of low back pain: A sytematic review. Best Pract Res Clin Rheumatol. 2016;30(6):1098-1109. doi: 10.1016/j.berh.2017.04.002.
  13. Hoy D, Brooks P, Blyth F, Buchbinder R. The epidemiology of low back pain. Best Pract Res Clin Rheumatol. 2010;24(6):769-81. doi: 10.1016/j.berh.2010.10.002.
  14. Postolache G, Oliveira R, Postolache O. Designing digital tools for physiotherapy.in: Interactivity, game creation, design, learning, and innovation. 1th ed. Brooks AL, Brooks E, Vidakis E, eds. Springer; 2016.
  15. Ramey L, Osborne C, Kasitinon D, Juengst S. Apps and Mobile Health Technology in Rehabilitation: The Good, the Bad, and the Unknown. Phys Med Rehabil Clin N Am. 2019;30(2):485-497. doi: 10.1016/j.pmr.2018.12.001.
  16. L Ceci. Number of mHealth apps available in the Google Play Store from 1st quarter 2015 to 2nd quarter 2024. Available at: https://www.statista.com/statistics/779919/health-apps-available-google-play-worldwide/. Accessed June 29, 2021.
  17. Tangari G, Ikram M, Ijaz K, Kaafar MA, Berkovsky S. Mobile health and privacy: cross sectional study. BMJ. 2021:373:n1248. doi: 10.1136/bmj.n1248.
  18. Escriche-Escuder A, De-Torres I, Roldán-Jiménez C, et al. Assessment of the Quality of Mobile Applications (Apps) for Management of Low Back Pain Using the Mobile App Rating Scale (MARS). Int J Environ Res Public Health. 2020;17(24):9209. doi: 10.3390/ijerph17249209.
  19. Stoyanov S, Hides L, Kavanagh D, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth. 2015;3(1):e27. doi: 10.2196/mhealth.3422.
  20. Portelli P, Eldred C. A quality review of smartphone applications for the management of pain. Br J Pain. 2016;10(3):135-40. doi: 10.1177/2049463716638700.
  21. Dunphy E, Hamilton FL, Spasić I, Button K. Acceptability of a digital health intervention alongside physiotherapy to support patients following anterior cruciate ligament reconstruction. BMC Musculoskelet Disord. 2017;18(1):471. doi: 10.1186/s12891-017-1846-0.
  22. Frontera W, Bean J, Damiano D, et al. Rehabilitation research at the National Institutes of Health. Neurorehabil Neural

 

        Repair. 2017;31(4):304-314. doi: 10.1177/1545968317698875.

  1. Haynes R, Wilczynski N. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: Methods of a decision-maker-researcher partnership systematic review. Implement Sci. 2010:5:12. doi: 10.1186/1748-5908-5-12.
  2. Terhorst Y, Philippi P, Sander LB, et al. Validation of the Mobile Application Rating Scale (MARS). PLoS One. 2020;15(11):e0241480. doi: 10.1371/journal.pone.0241480.
  3. Creber RMM, Maurer MS, Reading M, Hiraldo G, Hickey KT, Iribarren S. Review and analysis of existing mobile phone apps to support heart failure symptom monitoring and self-care management using the Mobile Application Rating Scale (MARS). JMIR Mhealth Uhealth. 2016;4(2):e74. doi: 10.2196/mhealth.5882.
  4. Hee Ko KK, Kim SK, Lee Y, Lee JY, Stoyanov SR. Validation of a Korean version of mobile app rating scale (MARS) for apps targeting disease management. Health Informatics J. 2022;28(0):14604582221091975. doi: 10.1177/14604582221091975.
  5. Knitza J, Tascilar K, Messner E-M, et al. German Mobile Apps in Rheumatology: Review and Analysis Using the Mobile Application Rating Scale (MARS). JMIR Mhealth Uhealth. 2019;7(8):e14991. doi: 10.2196/14991.
  6. Martin-Payo R, Fernandez-Álvarez MM, Blanco-Díaz M, Cuesta-Izquierdo M, Stoyanov SR, Llaneza Suárez E. Spanish adaptation and validation of the Mobile Application Rating Scale questionnaire. Int J Med Inform. 2019:129:95-99. doi: 10.1016/j.ijmedinf.2019.06.005.
  7. Messner E, Terhorst Y, Barke A, et al. The German version of the mobile app rating scale (MARS-G): development and validation study. JMIR Mhealth Uhealth. 2020;8(3):e14479. doi: 10.2196/14479.
  8. Roberts AE, Davenport TA, Wong T, Moon H-W, Hickie IB, LaMonica HM. Evaluating the quality and safety of health-related apps and e-tools: Adapting the Mobile App Rating Scale and developing a quality assurance protocol. Internet Interv. 2021:24:100379. doi: 10.1016/j.invent.2021.100379.
  9. Saliasi I, Martinon P, Darlington E, et al. Promoting Health via mHealth Applications Using a French Version of the Mobile App Rating Scale: Adaptation and Validation Study. JMIR Mhealth Uhealth. 2021;9(8):e30480. doi: 10.2196/30480.
  10. Barzegari S, Sharifi Kia A, Bardus M, Stoyanov SR, GhaziSaeedi M, Rafizadeh M. The Persian Version of the Mobile Application Rating Scale (MARS-Fa): Translation and Validation Study. JMIR Form Res. 2022;6(12):e42225. doi: 10.2196/42225.
  11. Machado GC, Pinheiro MB, Lee H, et al. Smartphone apps for the self-management of low back pain: a systematic review. Best Pract Res Clin Rheumatol. 2016;30(6):1098-1109. doi: 10.1016/j.berh.2017.04.002.
  12. Tinius R, Polston M, Bradshaw H, Ashley P, Greene A, Parker AN. An Assessment of Mobile Applications Designed to Address Physical Activity During Pregnancy and Postpartum. Int J Exerc Sci. 2021;14(7):382-399. doi: 10.70252/AQIG9215.
  13. Stoyanov S, Hides L, Kavanagh D, Wilson H. Development and Validation of the User Version of the Mobile Application Rating Scale (uMARS). JMIR Mhealth Uhealth. 2016;4(2):e72. doi: 10.2196/mhealth.5849.
  14. LeBeau K, Huey LG, Hart M. Assessing the quality of mobile apps used by occupational therapists: evaluation using the user version of the mobile application rating scale. JMIR Mhealth Uhealth. 2019;7(5):e13019. doi: 10.2196/13019.
  15. Salazar A, de Sola H, Failde I, Moral-Munoz JA. Measuring the Quality of Mobile Apps for the Management of Pain: Systematic Search and Evaluation Using the Mobile App Rating Scale. JMIR Mhealth Uhealth. 2018;6(10):e10718. doi: 10.2196/10718.
  16. Low back pain and sciatica in over 16s: assessment and management.Available at: https://www.nice.org.uk/guidance/ng59. National Institute for Health and Care Excellence (NICE). Accessed April 18, 2021.
  17. Blanpied PR, Gross AR, Elliott JM, et al. Neck Pain: Revision 2017. J Orthop Sports Phys Ther. 2017;47(7):A1-A83. doi: 10.2519/jospt.2017.0302.
  18. Shuren J, Patel B, Gottlieb S. FDA Regulation of Mobile Medical Apps. JAMA. 2018;320(4):337-338. doi: 10.1001/jama.2018.8832.
  19. Escriche-Escuder A, De-Torres I, Roldán-Jiménez C, et al. Assessment of the Quality of Mobile Applications (Apps) for Management of Low Back Pain Using the Mobile App Rating Scale (MARS). Int J Environ Res Public Health. 2020;17(24):9209. doi: 10.3390/ijerph17249209.
  20. Lalloo C, Shah U, Birnie KA, et al. Commercially Available Smartphone Apps to Support Postoperative Pain Self-Management: Scoping Review. JMIR Mhealth Uhealth. 2017;5(10):e162. doi: 10.2196/mhealth.8230.
  21. Psihogios AM, Stiles-Shields C, Neary M. The Needle in the Haystack: Identifying Credible Mobile Health Apps for Pediatric Populations during a Pandemic and beyond. J Pediatr Psychol. 2020;45(10):1106-1113. doi: 10.1093/jpepsy/jsaa094.
  22. Nurgalieva L, O’Callaghan D, Doherty G. Security and privacy of mHealth applications: a scoping review. IEEE Access. 2020;8:104247 68. doi: 10.1109/ACCESS.2020.2999934.
  23. Domnich A, Arata L, Amicizia D, et al. Development and validation of the Italian version of the Mobile Application Rating Scale and its generalisability to apps targeting primary prevention. BMC Med Inform Decis Mak. 2016:16:83. doi: 10.1186/s12911-016-0323-2.
The Archives of Bone and Joint Surgery
Volume 13, Issue 12
December 2025
Pages 788-799

  • Receive Date 09 August 2024
  • Revise Date 07 May 2025
  • Accept Date 28 December 2024