Tele-Neurorehabilitation for Neurological Disorders: A Systematic Review and Conceptual Framework Integrating Neuroplasticity and Digital Health

Neurorehabilitation has undergone a profound transformation over the past decades, shifting from compensatory approaches toward interventions grounded in neuroplasticity. This transition has been driven by advances in neuroscience, digital health and artificial intelligence [1-6]. Neuroplasticity, defined as the brain’s capacity to reorganize structurally and functionally following injury, has become the central therapeutic principle underlying contemporary rehabilitation strategies [1-3,7-9]. Simultaneously, demographic trends, particularly population aging, have increased the global burden of neurological disability, including stroke, neurodegenerative disorders and traumatic brain injury [10-12]. These developments have exposed limitations of traditional hospital-based rehabilitation models, which are often resource-intensive, episodic and limited in accessibility. Telemedicine has emerged as a pivotal solution, enabling remote delivery of therapy, continuous monitoring and expansion of access beyond specialized centers [13-19]. The integration of neuroplasticity principles with telemedicine technologies has led to the emergence of tele-neurorehabilitation, a rapidly evolving field that redefines rehabilitation delivery. This systematic review aims to evaluate the clinical effectiveness of tele-neurorehabilitation and propose a conceptual framework integrating neuroplasticity mechanisms with digital health technologies.

This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA) 2020 guidelines. A comprehensive literature search and review was performed in PubMed/MEDLINE, Scopus and Cochrane Library databases for studies published between January 2015 and January 2025. The search strategy combined Medical Subject Headings (MeSH) and keywords related to tele-neurorehabilitation, neurological disorders and digital rehabilitation technologies.

Study selection was performed independently by two reviewers. Risk of bias was assessed using the Cochrane RoB 2 tool for randomized trials and ROBINS-I for non-randomized studies. Two independent reviewers screened titles and abstracts. Full-text articles were assessed for eligibility. Discrepancies were resolved by consensus. Studies were included if they met the following criteria: randomized controlled trials, controlled clinical trials or systematic reviews; adult neurological populations; tele-neurorehabilitation interventions; reported clinical outcomes. Studies were excluded if they: Included pediatric populations; were case reports or narrative reviews; lacked clinical outcome measures. The study selection process is illustrated in Figure 1 below.

Figure 1:PRISMA 2020 flow diagram of study selection process. PRISMA: Preferred reporting items for systematic reviews and meta-analyses.

A total of sixty-two studies met inclusion criteria. Teleneurorehabilitation interventions were evaluated across stroke, Parkinson’s disease, multiple sclerosis, dementia and traumatic brain injury populations. The characteristics of included studies are summarized in Table 1. Tele-neurorehabilitation demonstrated significant improvements in motor recovery among stroke populations. Fugl-Meyer Assessment gains ranged from 5 to 10 points, with effect sizes between 0.38 and 0.61. Home-based interventions demonstrated higher adherence compared with conventional rehabilitation [13,14,20]. Technology-enhanced interventions, including virtual reality and robotic-assisted rehabilitation [21-23], demonstrated additional improvements in upper limb function and gait recovery [24-28]. In Parkinson’s disease, wearable technologies enabled continuous monitoring of motor fluctuations and improved therapy optimization. These interventions were associated with improvements in mobility and functional outcomes [29-31]. In multiple sclerosis. telerehabilitation interventions demonstrated reductions in fatigue and improvements in mobility and quality of life measures [31- 34]. Brain-computer interface-based rehabilitation demonstrated improvements in motor recovery, particularly among patients with severe motor impairment [35-37].

Table 1: Characteristics of Included Studies evaluating tele-neurorehabilitation interventions.

The robotic rehabilitation provides high-intensity repetitive training, objective performance metrics and remote programmability. These systems demonstrated improvements in motor recovery and functional independence [24-28,38,39]. Virtual reality enables immersive and task-specific training environments, combining motor and cognitive rehabilitation. These interventions improved engagement and functional outcomes [25-28]. Braincomputer interfaces enable closed-loop neurofeedback-driven rehabilitation and improved motor recovery in severe neurological impairment [35-37,40-43]. Wearable sensors enable continuous monitoring of motor performance and remote therapy optimization [29,30] (Table 2).

Table 2:Overview of technologies used in tele-neurorehabilitation.

Figure 2:Conceptual framework integrating neuroplasticity and tele-neurorehabilitation.

Tele-neurorehabilitation demonstrated consistent effectiveness across neurological disorders. The observed clinical benefits may be explained by mechanisms related to neuroplasticity, including synaptic reorganization, cortical remapping and network reconfiguration [1-3,7-9]. Tele-neurorehabilitation represents a conceptual shift in rehabilitation delivery. Traditional institutionbased rehabilitation is increasingly replaced by home-based, continuous and personalized interventions. This transition enables ecologically valid training within patients’ daily environments. Telemedicine technologies also bridge the translational gap between neuroscience research and clinical practice. Continuous monitoring, adaptive training and personalized interventions improve therapy effectiveness and adherence [4-6,18-22]. However, several limitations remain. These include heterogeneity of intervention protocols, variability in outcome measures and limited long-term follow-up data. In addition, disparities in access to technology remain an important challenge [20-22,44-49]. A conceptual framework integrating neuroplasticity and telemedicine is presented in Figure 2 below [50-80].

Tele-neurorehabilitation represents a paradigm shift in neurological care. By integrating neuroplasticity principles with digital health technologies, tele-neurorehabilitation enables continuous, personalized and scalable rehabilitation. Future research should focus on standardization of protocols, integration of artificial intelligence and long-term clinical validation. Teleneurorehabilitation is expected to become a central component of modern neurological care.

This material is published with the financial support of the Brain Health Council - Bulgaria