Method Article

Early Rehabilitation Interventions for Neurological Function Recovery in Patients with Acute Cerebral Infarction: A Meta-Analysis

DOI:

10.3791/70254

⸱

July 24th, 2026

 ,  , 
1Department of Neurology, Yixing People's Hospital, 2Department of Rehabilitation, Yixing People's Hospital

* These authors contributed equally

In This Article

Summary

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This protocol outlines a reproducible method for conducting a meta-analysis of predominantly East Asian studies to evaluate the efficacy of early rehabilitation interventions on neurological recovery in patients with acute cerebral infarction.

Abstract

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Acute cerebral infarction causes substantial neurological disability, and the efficacy of early rehabilitation-based interventions remains uncertain. This meta-analysis evaluated their effects on neurological recovery. PubMed, Cochrane Library, Embase, Web of Science, and China National Knowledge Infrastructure were searched for randomized controlled trials comparing early rehabilitation-based interventions with standard care. Random-effects models with restricted maximum likelihood and Hartung-Knapp adjustment were applied, and risk of bias was assessed using the Cochrane Risk of Bias 2 tool. Eight randomized controlled trials (RCTs) involving 711 patients were included in the primary analysis after exclusion of one non-RCT evaluating pharmacological reperfusion therapy without a rehabilitation component. Early rehabilitation showed a non-significant trend toward improved National Institutes of Health Stroke Scale scores (SMD: -2.75; 95% CI: -5.94 to 0.45; I2 = 95.5%; τ2 = 3.84), with a wide 95% prediction interval (-9.74 to 4.25). Cognitive outcomes also showed a non-significant trend favoring intervention (SMD: -0.89; 95% CI: -1.87 to 0.09). Although early rehabilitation may support neurological recovery, marked clinical heterogeneity, selective reporting concerns, and the predominantly East Asian evidence base limit confidence and generalizability. Larger, methodologically rigorous trials with standardized intervention reporting are needed.

Introduction

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Acute cerebral infarction, commonly known as ischemic stroke, represents a significant global health challenge, accounting for approximately 87% of all stroke cases worldwide1. This condition occurs when blood flow to a specific region of the brain is obstructed, leading to rapid neuronal death and potentially severe neurological deficits2. With an estimated 13.7 million new stroke cases occurring annually, the burden on public health and socioeconomic structures is substantial3.

Over the past few decades, the management of acute cerebral infarction has undergone considerable evolution, shifting from purely acute medical interventions toward earlier initiation of rehabilitation4. Traditionally, rehabilitation efforts were often delayed for days or weeks following the initial event, based on the assumption that the brain required a period of rest5. However, advances in the understanding of neuroplasticity have demonstrated that the brain possesses a remarkable capacity for reorganization and adaptation, including during the acute stages following injury6. Meta-analyses of animal models have confirmed that task-specific training initiated early after experimental stroke enhances functional recovery through synaptogenesis and dendritic remodelling7.

The concept of early rehabilitation intervention has emerged as a promising approach in the management of acute cerebral infarction. As used in this meta-analysis, ‘early rehabilitation’ is operationally defined as the initiation of any rehabilitation-based therapeutic program within approximately 72 h (3 days) of stroke symptom onset; a subset of studies used an ‘ultra-early’ window of <24 h. Readers should note, however, that the included studies varied in how precisely this window was defined and reported8. Early rehabilitation encompasses a diverse range of interventions, including physical therapy, occupational therapy, speech and language therapy, electroacupuncture, and cognitive rehabilitation exercises9. The underlying principle is to capitalize on the brain’s heightened plasticity during the acute phase of injury, potentially enhancing neurological recovery and improving long-term functional outcomes10.

In this study, ‘neurological function’ primarily refers to clinical measures of stroke severity as assessed by the National Institutes of Health Stroke Scale (NIHSS) and cognition as measured by standardized cognitive function tests. The NIHSS evaluates components such as level of consciousness, eye movement, visual fields, limb strength, sensory function, and language, whereas cognitive tests may include domains such as orientation, memory, and executive function. This comprehensive operationalization helps to characterize the multifaceted nature of stroke recovery.

The potential benefits of early rehabilitation intervention are multifaceted. From a neurophysiological perspective, early mobilization and targeted exercises may mitigate the detrimental effects of prolonged immobility, such as muscle atrophy and cardiopulmonary deconditioning, as demonstrated in the European Stroke Organisation consensus guidelines11. Additionally, early engagement in rehabilitation activities may prevent learned non-use of affected limbs, a phenomenon that has been operationalized and studied using the Delphi methodology and that can impede long-term recovery12. From a psychological standpoint, early rehabilitation may play a role in maintaining patient motivation and preventing post-stroke depression and anxiety, which have been shown to affect recovery trajectories significantly13.

Despite the theoretical promise, empirical evidence supporting the efficacy of early rehabilitation has been mixed. Several studies have reported improvements in neurological function and functional independence among patients receiving early rehabilitation compared with those receiving standard care14,15. Conversely, other investigations have failed to demonstrate a substantial advantage of early intervention over traditional rehabilitation timelines16,17. These inconsistencies may be attributed to heterogeneity in study designs, differences in the timing and intensity of rehabilitation protocols, variations in outcome measures, and the inherent complexity of stroke recovery.

Despite numerous previous review articles on this subject, the present meta-analysis differs by employing robust statistical methods that are specifically suited to evidence-based research characterized by few studies and high heterogeneity. Specifically, this analysis uses restricted maximum likelihood (REML) estimation, which provides less biased variance estimates than the conventional DerSimonian-Laird (DL) method when the number of studies is small18. In addition, the Hartung-Knapp (HK) adjustment is applied to confidence intervals (CIs), which accounts for uncertainty in the heterogeneity estimate and produces wider, more conservative intervals than the standard Wald-type approach19. These methodological choices are particularly important for the current evidence base, where high between-study variability is anticipated due to diverse intervention types and populations. Furthermore, prediction intervals are reported to express the expected range of true effects in future studies, an output not routinely provided in conventional meta-analyses but essential for clinical decision-making18. The analysis also incorporates data from both English and Chinese literature, systematically investigates heterogeneity sources using multiple analytical approaches, and explicitly addresses limitations arising from the geographic concentration of the evidence.

A key limitation that readers should be aware of from the outset is that the included studies span a heterogeneous set of interventions—ranging from motor rehabilitation alone to combined pharmacological-rehabilitation programs and electroacupuncture—which creates important challenges for pooling and interpretation. The present analysis, therefore, emphasizes characterizing heterogeneity as much as estimating a single pooled effect.

In light of these considerations, this meta-analysis was conducted with the primary aim of evaluating the impact of early rehabilitation-based interventions on neurological function recovery in patients with acute cerebral infarction, employing contemporary statistical methods to account for heterogeneity appropriately and provide clinically meaningful estimates of uncertainty.

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Protocol

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This study is not subject to ethical review and participant consent.

1. Literature search strategy

  1. Access the following electronic databases: PubMed/MEDLINE, Cochrane Central Register of Controlled Trials, Embase, Web of Science Core Collection, and China National Knowledge Infrastructure (CNKI).
  2. In PubMed, construct the search strategy by combining three blocks of terms using the Boolean operator AND.
  3. For the intervention block, enter the following search string in the PubMed Advanced Search Builder: (‘Early rehabilitation’ OR ‘Early mobilization’ OR ‘Early ambulation’ OR ‘Ultra-early rehabilitation’ OR ‘Super early rehabilitation’ OR ‘Early rehabilitation intervention’ OR ‘Early rehabilitation treatment’ OR ‘Acute rehabilitation’ OR ‘Early physical therapy’ OR ‘Early occupational therapy’ OR ‘Early neurorehabilitation’).
  4. For the condition block, enter the following: (‘Cerebral Infarction’ OR ‘Brain Infarction’ OR ‘Ischemic Stroke’ OR ‘Cerebrovascular accident’ OR ‘CVA’ OR ‘Acute stroke’ OR ‘Brain Ischemia’ OR ‘Cerebral Ischemia’).
  5. Combine the intervention and condition blocks in PubMed using the following: (Intervention Block) AND (Condition Block). Execute the search and export results in Research Information Systems (RIS) format.
  6. Adapt this search syntax for each additional database by replacing Medical Subject Headings terms with the respective controlled vocabulary (e.g., Emtree for Embase) and adjusting field tags. For CNKI, translate the key terms into Chinese equivalents and use the subject and keyword fields.
  7. Apply no language or date restrictions during the initial search.
  8. Supplement the electronic search by manually screening the reference lists of all included studies and relevant systematic reviews. Open each included article, inspect the reference list, and identify any additional potentially eligible studies not captured by the database search.
  9. Perform forward citation tracking for all included studies using Google Scholar and Web of Science. In Google Scholar, locate each included study and click Cited by to screen citing articles for eligibility.
  10. Search major clinical trial registries (ClinicalTrials.gov, World Health Organization International Clinical Trials Registry Platform) for unpublished completed trials matching the search terms.
    NOTE: The current search covered five major databases. Additional databases, such as VIP, Wanfang, SinoMed, and Scopus, were not searched, which represents a potential limitation. Future updates of this review should consider expanding the database coverage to reduce the risk of missed studies.

2. Study screening and selection

  1. Import all identified records from the database searches into systematic review management software (e.g., Covidence). Navigate to the project dashboard, select Import, upload all exported RIS/BibTeX files, and confirm the import.
  2. Remove duplicate records using the software’s automated de-duplication feature. In Covidence, navigate to the Screen tab; duplicates are flagged automatically. Review flagged duplicates and confirm removal. Verify manually that no unique records have been erroneously merged.
  3. Assign two independent reviewers to screen all records at the title and abstract level. Each reviewer logs in separately to ensure a blinded assessment.
  4. Classify each record as ‘Include’, ‘Exclude’ or ‘Maybe’. Apply decisions based on whether the title and abstract suggest the study meets the population, intervention, comparison, and outcome criteria defined below.
  5. Advance any record marked as ‘Include’ or ‘Maybe’ by at least one reviewer to full-text review.
  6. Retrieve the full text of all potentially eligible studies through institutional library services, interlibrary loan, or direct author contact.
  7. Apply the following inclusion criteria during full-text review:
    1. Ensure the study is an RCT. Ensure the population consists of adult patients (≥18 years) with acute cerebral infarction confirmed by imaging.
    2. Ensure the intervention is an early rehabilitation-based program initiated within approximately 72 h post-stroke (or ultra-early if initiated within 24 h).
    3. Ensure the control group receives standard care or delayed rehabilitation. Ensure the study reports at least one of the following outcomes—NIHSS scores, overall response rate, or cognitive function scores; and the follow-up period is ≥28 days.
  8. Exclude studies in which the primary tested intervention is a pharmacological reperfusion therapy (e.g., recombinant tissue-type plasminogen activator [rtPA]) without a rehabilitation component, as such studies do not evaluate rehabilitation efficacy.
  9. Exclude any study that fails to meet any of the above inclusion criteria. Document the reason for exclusion for each full-text article in the software or a spreadsheet.
  10. Resolve disagreements between the two reviewers by scheduling a face-to-face or virtual meeting. Discuss each discrepant record item by item, referencing the full text and the inclusion criteria. Record the final consensus decision.
  11. If consensus cannot be reached on a given record, consult a third reviewer for arbitration and record the arbitrated decision.
  12. After completing screening, generate a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram documenting the number of records identified, duplicates removed, records screened, full texts assessed, and studies included or excluded, with reasons.
    NOTE: The screening phase is complete when all records have been adjudicated, and the flow diagram has been finalized.

3. Data extraction

  1. Create a standardized data extraction form in a spreadsheet application (e.g., Microsoft Excel).
  2. Include the following columns: study identifier (first author and year), sample size for intervention and control groups, participant demographics (mean age, sex distribution), detailed description of the intervention including type (e.g. motor rehabilitation, electroacupuncture, combined pharmacological–rehabilitation), timing of initiation (hours post-stroke), frequency and intensity of sessions where reported, duration of the intervention program, description of the control group treatment, outcome measures used, mean and standard deviation (or standard error) for continuous outcomes at the primary endpoint, number of events and total participants for dichotomous outcomes and follow-up duration.
  3. Assign two independent reviewers to extract data from each included study. Each reviewer fills in the extraction form separately. Upon completion, compare the two datasets cell by cell. Resolve any discrepancies by re-examining the source article together and recording the corrected value.
  4. For studies with missing or unclear data, attempt to contact the corresponding author by email. Allow at least 2 weeks for a response before proceeding with available data.
    NOTE: The data extraction phase is complete when both reviewers have reached agreement on all extracted variables for every included study.

4. Quality assessment

  1. Assess the risk of bias for all included RCTs using the Cochrane Risk of Bias 2 (RoB 2) tool, version dated August 2019 or later18.
  2. Evaluate each study across the following five domains: bias arising from the randomization process (D1), bias due to deviations from intended interventions (D2), bias due to missing outcome data (D3), bias in measurement of the outcome (D4), and bias in selection of the reported result (D5).
  3. For each domain, answer all signaling questions in the RoB 2 tool and assign a judgment of ‘Low risk of bias’, ‘Some concerns’, or ‘High risk of bias’ according to the algorithm provided in the tool.
  4. Derive an overall risk of bias judgment for each study based on the domain-level assessments following the RoB 2 algorithm: overall ‘Low risk’ if all domains are low, ‘Some concerns’ if at least one domain raises some concerns but none is high risk, and ‘High risk’ if at least one domain is high risk.
  5. Have two reviewers perform the assessment independently. Compare judgments domain by domain. Resolve disagreements by consensus discussion, referencing the original study and the RoB 2 guidance document. If consensus cannot be reached, consult a third reviewer.
  6. Generate a summary plot (showing proportions across domains) and a traffic-light plot (showing domain-level judgments per study) using the robvis R package or an equivalent visualization tool.
    NOTE: The quality assessment phase is complete when all studies have been assessed and plots generated.

5. Statistical analysis

  1. Install R software (version 4.3.1 or later). Open the R console and install the required packages by executing the following: install.packages(c(‘meta,’ ‘metafor’)). Load the packages with library(meta) and library(metafor).
  2. For continuous outcomes (e.g., NIHSS scores, cognitive function scores), calculate the standardized mean difference (SMD) and its 95% CI for each study using the escalc() function in metafor with measure = ‘SMD’.
  3. For dichotomous outcomes (e.g., overall response rate), calculate the risk ratio (RR) and its 95% CI for each study using the escalc() function with measure = ‘RR’.
  4. Pool the effect sizes using a random-effects model with REML estimation. In R, execute the meta-analysis using the following meta package: m <- metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c, studlab, data, sm = ‘SMD’, method.tau = ‘REML’, hakn = TRUE). Alternatively, use the rma() function in metafor with method = ‘REML’ and test = ‘knha’.
    NOTE: The HK adjustment is applied automatically when hakn = TRUE (meta package) or test = ‘knha’ (metafor package). This adjustment produces wider and more conservative CIs than the standard Wald-type approach, which is particularly important when the number of studies is small18,19.
  5. Assess statistical heterogeneity by examining the I2 statistic (proportion of variance due to between-study differences), τ2 (absolute between-study variance), and Cochran’s Q test (p < 0.10 indicates significant heterogeneity). These values are reported automatically in the meta-analysis output.
  6. Calculate a 95% prediction interval to estimate the range of true effects expected in future studies. In the meta package output, the prediction interval is reported automatically when the random-effects model is fitted. Report this interval alongside the CI.
  7. Conduct pre-specified subgroup analyses by intervention type (rehabilitation only vs. drug plus rehabilitation) and by timing (ultra-early [<24 h] vs. early [24–72 h]). Use the update() function or specify subgroup arguments in metacont(). Test for subgroup differences using the Q-between statistic.
  8. Perform a sensitivity analysis by restricting the analysis to RCTs only, excluding any non-randomized studies or studies with a high risk of bias. Compare the pooled estimate and heterogeneity statistics before and after exclusion.
  9. Assess potential publication bias by generating a standard funnel plot using funnel() and a contour-enhanced funnel plot using the metafor package. Visually inspect for asymmetry.
    NOTE: Given the small number of studies, formal tests (e.g., Egger’s test) may be underpowered and should be interpreted cautiously. The statistical analysis phase is complete when all pooled estimates, subgroup analyses, sensitivity analyses, and publication bias assessments have been generated and recorded.
  10. If substantial heterogeneity persists after sensitivity and subgroup analyses, conduct exploratory meta-regression using the rma() function with study-level covariates (e.g., timing window, combination therapy status) to generate hypotheses about the sources of heterogeneity.
    NOTE: Given the typically small number of studies, these analyses are hypothesis-generating only.

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Results

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The comprehensive literature search initially identified 528 potentially relevant records across the five databases: PubMed (n = 74), Cochrane Library (n = 57), Embase (n = 12), Web of Science (n = 20), and CNKI (n = 365). After removing duplicates, 274 unique records remained for screening. Following title and abstract review, 186 records were excluded for not meeting initial screening criteria. Of the remaining 88 records screened at the abstract level, 69 were excluded for various reasons, including inappropriate stud...

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Discussion

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This meta-analysis evaluated the efficacy of early rehabilitation-based interventions on neurological function recovery in patients with acute cerebral infarction. The comprehensive analysis of 8 RCTs (primary synthesis) encompassing 711 participants provides important insights into both the potential benefits and the substantial challenges in synthesizing evidence in this field. The primary analysis using REML estimation with HK adjustment revealed a trend toward improvement in NIHSS scores with early rehabilitation-bas...

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Disclosures

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The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Materials

List of materials used in this article
NameCompanyCatalog NumberComments
CNKITsinghua Univ./CNKIhttps://www.cnki.net/
Cochrane LibraryCochrane Collaborationhttps://www.cochranelibrary.com/
Cochrane RoB 2 toolCochrane Collaborationhttps://www.riskofbias.info/
CovidenceVeritas Health Innovationhttps://www.covidence.org/
EmbaseElsevierhttps://www.embase.com/
Google ScholarGoogle LLChttps://scholar.google.com/
meta R packageCRANhttps://cran.r-project.org/package=meta
metafor R packageCRANhttps://cran.r-project.org/package=metafor
Microsoft ExcelMicrosoft Corporationhttps://www.microsoft.com/excel
PubMed/MEDLINENational Library of Medicinehttps://pubmed.ncbi.nlm.nih.gov/
R software (version 4.3.1+)R Foundationhttps://www.r-project.org/
robvis R packageCRANhttps://cran.r-project.org/package=robvis
Web of ScienceClarivate Analyticshttps://www.webofscience.com/

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Neuroscienceischemic strokeneurological recoveryNational Institutes of Health Stroke Scale

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