OBJECTIVE: The aim of this study is to assess the effectiveness of mobile health (mHealth) exercise interventions in comparison to traditional exercise methods (exercises guided offline by a rehabilitation therapist or performed independently according to the instruction manual) for relieving pain intensity, decreasing functional disability, and improving the overall quality of life for individuals suffering from Chronic Neck Pain (CNP).
METHODS: A systematic search was performed to identify randomized controlled trials (RCTs) published from their inception until December 25, 2024, across multiple databases, such as Cochrane, Embase, Medline, and Web of Science. Data extraction was carried out independently by pairs of reviewers, who also evaluated bias using the Cochrane Risk of Bias tool.
RESULTS: In total, six studies were identified, encompassing 381 participants with an average age of 41.17±11.72 years. No statistically significant differences were observed in pain relief when comparing mHealth-based exercise to traditional exercise methods that did not incorporate mHealth (standard mean difference [SMD]=-0.31; 95% CI: -0.73 to 0.12, P=0.16). Additionally, there were no significant differences concerning functional disability (SMD=-0.33; 95% CI: -0.68 to 0.02; P=0.06) or quality of life (SMD=0.19; 95% CI: -0.19 to 0.56; P=0.34). Conversely, a significant difference was noted when comparing mHealth-supported exercise to unsupervised traditional exercise regarding pain alleviation (SMD=-0.76; 95% CI: -1.06 to -0.45; P<0.001) and functional disability (SMD=-0.66; 95% CI: -1.01 to -0.32; P<0.001).
CONCLUSION: The results indicate that exercise facilitated by mHealth is more effective than traditional unsupervised exercise in preventing pain and enhancing functional capabilities in young and middle-aged patients experiencing chronic non-specific neck pain. Traditional exercise can serve as a foundational intervention for the rehabilitation of non-specific neck pain, while mobile health-assisted exercise offers a feasible alternative in situations where offline interventions are limited. This approach enhances the accessibility and coverage of rehabilitation services.
Discipline Area | Score |
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Rehab Clinician (OT/PT) | ![]() |
Physician | ![]() |
Compared with traditional unsupervised exercise, mHealth-supported exercises provide significantly better pain relief for patients with chronic non-specific neck pain. This highlights the importance of supervision and guided intervention. mHealth-guided exercise programmes could improve access to effective pain management and rehabilitation, particularly in areas where traditional outpatient resources are limited.
This systematic review supports the incorporation of mHealth exercise interventions as an effective alternative to, or complement of, traditional rehabilitation for chronic non-specific neck pain. It emphasises the role of supervision in optimising outcomes (e.g., pain relief and functional improvement) by remotely monitoring and adjusting the interventions and improving patient adherence to the rehabilitation programme. However, the evidence is based on a limited number of RCTs of varied quality.
This is a very useful article. As a clinician with over 20 years of hands-on experience in physiotherapy, I’ve seen the strengths and limitations of traditional clinic- and community-based models. While face-to-face care can be effective during active treatment, it often falls short in supporting long-term self-management, especially for those living with chronic pain. Frequently, observed patients become dependent on regular sessions, seeking passive care rather than continuing with their home exercise programs. This cycle makes it harder to achieve lasting outcomes and true independence. Virtual approaches challenge conventional norms and help patients build sustainable habits that extend beyond the clinic setting. This article provides a strong evidence base to support this shift and can be a valuable resource for promoting meaningful cost-effective change in chronic pain care and moving us closer to models that prioritize self-efficacy and long-term recovery.