A Systematic Review of App-Based Rehabilitation for Back Pain

Introduction

Both patients with chronic back pain and those in the postoperative phase following spine surgery frequently experience rehabilitation ^[1]. These rehabilitation services have often been provided by means of in-person patient consultations. The digitalization of healthcare delivery has rapidly increased since the emergence of SARS-CoV2 ^[2]. The benefits of remote rehabilitation programs conducted via smartphone devices have been brought to light by the pandemic. In July 2022, there were more than 6 billion smartphone owners globally ^[3]. More over one-third of Americans’ media consumption in 2021 took place on mobile devices, with smartphones accounting for 72.3% of that usage ^[4]. The widespread usage of smartphones and related applications offers a chance to incorporate them into clinical procedures and lessen the obstacles that patients have while trying to obtain medical care.

Apps are being utilized more and more in the healthcare industry to collect patient outcome data, streamline communication, and occasionally measure outcome data. 81% of patients (who had never had surgery before) who participated in a poll of 146 patients in a neurosurgical waiting room said they would be interested in utilizing a postoperative communication and monitoring app ^[5].There is evidence to support telerehabilitation in general orthopaedics ^[7], but it is avoided when discussing app-based rehabilitation specifically for back pain and after spine surgery. The purpose of this systematic review is to compile the body of research and data documenting the results of app-based rehabilitation programs for back pain and after spine surgery.

MATERIALS AND TECHNIQUES On July 30, 2022, a systematic review was conducted. The Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA) standards were followed for searching the Pubmed/MEDLINE, Cochrane, and Google Scholar databases. ^[8]. The largest search phrases were believed to be [APP] AND[[Orthopaedic]OR[Neurosurgery]]. All papers analyzing the results of smartphone app-based rehabilitation for back pain patients and those recovering from spine surgery that reported their findings in English, German, or French were included. Letters to the editors, comments, and nonaccessible full articles were disqualified, as were those that did not demonstrate a functional outcome after rehabilitation. Our goal, using the PICO approach, was to compare the control group to the interventional group (O,C) in patients with low back pain (P,I).

We therefore anticipated that app-based rehabilitation is just as effective as traditional physiotherapy (C,O). Both the risk of bias and the quality of publications were evaluated (Table 1). Population demographic information was gathered, including age, gender, back pain duration, body mass index (BMI), indication, follow-up, patient-reported outcome measures (PROM), and apps used. The visual analogue scale of pain (VAS), SF-36, Likert score, PHQ-9, Korff, and current symptoms score (CSS) were employed as functional outcomes. Additionally, by contrasting the control group with the intervention group, the significances found in the various research were highlighted.

Findings Of the 7636 individuals examined in the nine prospective investigations, 466 were placed in the control group. There were 7055 patients in the interventional group after Irvine AB et al. included an alternate group (n = 199). The average age was 44.2 7.4 years, and 466 of the 7636 patients who were examined in the nine prospective investigations were placed in the control group. There were 7055 patients in the interventional group when Irvine AB et al. added an additional group (n = 199). The majority of patients were female (75.3%, n = 5638/7487), with a mean age of 44.2 ± 7.4 years. The mean, when BMI was taken into account, was 26.3 ± 2.2 kg/m2, and the reported duration of pain was 19.6 ± 11.6 months.

Table 2 presents all of the results. Back and neck discomfort ^[10], non-specific back pain ^[11], and chronic lower back pain were the topics of some authors’ reports. There were issues with the aetiology of the aforementioned pain because none of the research had precise definitions. posing queries on the cause of the previously described ache. Among the smartphone apps were FitBack ^[16], Snapcare ^[14], Fitbit ^[15], and Kaya ^[12,13]. The app utilized was not identified in the other investigations. The duration of followup ranged from four weeks ^[11] to six months ^[15]. In addition, the follow-up presentation varied throughout smartphone apps such as FitBack ^[16], Snapcare ^[14], Kaya ^[12,13], and Fitbit ^[15].

CONVERSATION According to this comprehensive study, patients who have had back pain for an average of 19.6 11.6 months do not significantly differ between application-based rehabilitation and regular physiotherapy (control group). Despite the rehabilitation technique, the majority of trials showed a significant improvement in pain. A true meta-analysis could not be carried out because of the heterogeneity of the data. Diabetes ^[19], weight loss ^[20], mental health ^[21], speech difficulties ^[22], and cardiovascular illnesses ^[23] are among the current applications in healthcare that require evaluation based on the quality of the content and comparison of the interventions with best practice recommendations.One of the main obstacles to effective app-based therapy is adherence to a postoperative rehabilitation program ^[24].

Up to 30% of students miss class, and compliance is usually low ^{[25, 26]}. To get a positive result, program participation must be consistent. An efficient method for reducing pain in self-motivated patients with high compliance is app-based rehabilitation. Instead than showing patients various exercises, a sensor might be utilized to provide real-time feedback, such as a measurement of the muscular strength used. Variousappswereemployedinthestudiesunderinvestigation. According to the authors, the Kaya App uses thorough evidence-based multidisciplinary pain therapy in accordance with international disease management recommendations. In 2016, Machado et al. conducted a search and discovered 61 apps in total. Most included a mix of yoga, biomechanical exercises, and stretching and strengthening.

A mix of biomechanical exercises, such as strengthening, stretching, core stability, or McKenzie exercises, were suggested by those with the highest point totals ^[27]. Since most of the evidence-based interventions had not been put to the test in a randomized controlled trial, this was one of the weaknesses mentioned. Furthermore, the scientists noted that there was no correlation between the app’s quality and user ratings either online or within the app. As a result, they came to the conclusion that user reviews are not reliable measures of app quality. This could be because a pre-exercise questionnaire evaluating preconditions like comorbidities or prior surgeries was absent. Additionally, the users’ levels of experience could vary.This study has a number of drawbacks.

We excluded [physical therapy] from the search parameters since we thought it might yield articles about general back discomfort or diet apps. Because of the variability of the data and the poor quality of the individual studies (range of bias scores 1–3/5), a meta-analysis was not carried out. Nonetheless, these research stand as the most significant illustrations in this area. The visual analogue pain scale was the constant element across the investigations. Furthermore, the follow-up period varied from four weeks to six months. Additionally, a variety of patient-reported outcome measures were employed, such as the Korff score, PHQ-9, the Oswestry Disability Index, the SF-36, Likert, and present symptoms. FINAL THOUGHTS Application-based rehabilitation is just as effective as traditionalphysiotherapyforbackpainandafterspinesurgery.

The incorporation of application-based rehabilitation into healthcare is promising, particularly for motivated patients who frequently participate in independent rehabilitation, even if no significant differences were observed between the two groups. Furthermore, this is a great strategy that could further reduce healthcare expenses for people who are unable to see physiotherapists, such as during pandemics or because they live in rural areas.

References

1. Madera, M.; Brady, J.; Deily, S.; McGinty, T.; Moroz, L.; Singh, D.; Tipton, G.; Truumees, E. The role of physical therapy and rehabilitation after lumbar fusion surgery for degenerative disease: A systematic review. J. Neurosurg. Spine 2017, 26, 694–704. [CrossRef] [PubMed]
2. Amankwah-Amoah,J.K.Z.; Wood, G.; Knight, G. COVID-19 and digitalization: The great acceleration. J. Bus. Res. 2021, 136, 602–611. [CrossRef] [PubMed]
3. O’Dea, S. Smartphone Subscriptions Worldwide 2016– 2027. Statista. 2022.
4. Dolan, S. How Mobile Users Spend Their Time on Their Smartphones in 2022. eMarketer. 2022.
5. Nathan, J.K.; Rodoni, B.M.; Joseph, J.R.; Smith, B.W.; Park, P. Smartphone Use and Interest in a Spine Surgery Recovery Mobile Application Among Patients in a US Academic Neurosurgery Practice. Oper. Neurosurg. 2020, 18, 98–102. [CrossRef] [PubMed]
6. Robertson, G.A.J.; Wong, S.J.; Brady, R.R.; Subramanian, A.S. Smartphone apps for spinal surgery: Is technology good or evil? Eur. Spine J. 2016, 25, 1355–1362. [CrossRef].
7. Petersen, W.; Karpinski, K.; Backhaus, L.; Bierke, S.; Haner, M. A systematic review about telemedicine in orthopedics. Arch. Orthop. Trauma Surg. 2021, 141, 1731–1739. [CrossRef] [PubMed].
8. Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G.; The PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 2009, 6, e1000097. [CrossRef].
9. Hou, J.; Yang, R.; Yang, Y.; Tang, Y.; Deng, H.; Chen, Z.; Wu, Y.; Shen, H. The Effectiveness and Safety of Utilizing Mobile Phone–Based Programs for Rehabilitation After Lumbar Spinal Surgery: Multicenter, Prospective Randomized Controlled Trial. JMIR mHealth uHealth 2019, 7, e10201. [CrossRef].
10. Bailey, J.F.; Agarwal, V.; Zheng, P.; Smuck, M.; Fredericson, M.; Kennedy, D.J.; Krauss, J. Digital Care for Chronic Musculoskeletal Pain: 10,000 Participant Longitudinal Cohort Study. J. Med. Internet Res. 2020, 22, e18250. [CrossRef].
11. Hasenöhrl, T.; Windschnurer, T.; Dorotka, R.; Ambrozy, C.; Crevenna, R. Prescription of individual therapeutic exercises via smartphone app for patients suffering from non-specific back pain: A qualitative feasibility and quantitative pilot study. Wien. Klin. Wochenschr. 2020, 132, 115–123. [CrossRef].
12. Toelle, T.R.; Utpadel-Fischler, D.A.; Haas, K.-K.; Priebe, J.A. App-based multidisciplinary back pain treatment versus combined physiotherapy plus online education: A randomized controlled trial. NPJ Digit. Med. 2019, 3, 1–9. [CrossRef].
13. Huber, S.; A Priebe, J.; Baumann, K.-M.; Plidschun, A.; Schiessl, C.; Tölle, T.R. Treatment of Low Back Pain with a Digital Multidisciplinary Pain Treatment App: Short- Term Results. JMIR Rehabil. Assist. Technol. 2017, 4, e11. [CrossRef].
14. Chhabra, H.S.; Sharma, S.; Verma, S. Smartphone app in self-management of chronic low back pain: A randomized controlled trial. Eur. Spine J. 2018, 27, 2862–2874. [CrossRef].
15. Amorim, A.B.; Pappas, E.; Simic, M.; Ferreira, M.L.; Tiedemann, A.; Jennings, M.; Ferreira, P. Integrating Mobile health and Physical Activity to reduce the burden of Chronic low back pain Trial (IMPACT): A pilot trial protocol. BMC Musculoskelet. Disord. 2016, 17, 36. [CrossRef].
16. Irvine, A.B.; Russell, H.; Manocchia, M.; Mino, D.E.; Glassen, T.C.; Morgan, R.; Gau, J.M.; Birney, A.J.; Ary, D.V.; Buhrman, M.; et al. Mobile-Web App to Self-Manage Low Back Pain: Randomized Controlled Trial. J. Med. Internet Res. 2015, 17, e1. [CrossRef].
17. Yang,J.; Wei, Q.; Ge, Y.; Meng, L.; Zhao, M.Smartphone- BasedRemoteSelf-ManagementofChronicLowBackPain: APreliminary Study. J. Healthc. Eng. 2019, 2019, 1–7. [CrossRef].
18. Shebib, R.; Bailey, J.F.; Smittenaar, P.; Perez, D.A.; Mecklenburg, G.; Hunter, S. Randomized controlled trial of a 12-week digital care program in improving low back pain. Npj Digit. Med. 2019, 2, 1–8. [CrossRef].
19. Mateo, G.F.; Granado-Font, E.; Ferré-Grau, C.; Montaña- Carreras, X. Mobile Phone Apps to Promote Weight Loss and Increase Physical Activity: A Systematic Review and Meta-Analysis. J. Med. Internet Res. 2015, 17, e253. [CrossRef].
20. Lee, H.; Sullivan, S.J.; Schneiders, A.; Ahmed, O.H.; Balasundaram, A.P.; Williams, D.; Meeuwisse, W.H.; McCrory, P. Smartphone and tablet apps for concussion road warriors (team clinicians): A systematic review for practical users. Br. J. Sports Med. 2014, 49, 499–505. [CrossRef].
21. Furlong, L.M.; Morris, M.E.; Erickson, S.; Serry, T.A.; Robles-Bykbaev, V.; Amlani, A.M. Quality of Mobile Phone and Tablet Mobile Apps for Speech Sound Disorders: Protocol for an Evidence-Based Appraisal. JMIR Res. Protoc. 2016, 5, e233. [CrossRef].
22. Santo, K.; Richtering, S.S.; Chalmers, J.; Thiagalingam, A.; Chow, C.K.; Redfern, J. Mobile Phone Apps to Improve Medication Adherence: A Systematic Stepwise Process to Identify High-Quality Apps. JMIR mHealth uHealth 2016, 4, e132. [CrossRef].
23. Argent, R.; Daly, A.; Caulfield, B. Patient Involvement With Home-Based Exercise Programs: Can Connected Health Interventions Influence Adherence? JMIR mHealth uHealth 2018, 6, e47. [CrossRef]
24. McGregor, A.H.; Henley, A.; Morris, T.P.; Doré, C.J. An Evaluation of a Postoperative Rehabilitation Program After Spinal Surgery and Its Impact on Outcome. Spine 2012, 37, E417–E422. [CrossRef]

This is a text version generated from the article. For the formatted version of record (with original tables & figures), download the PDF →