Sequenced hybrid electromechanically assisted and conventional gait training for concurrent optimization of weight management, blood pressure regulation, and functional mobility in chronic stroke survivors: A multicenter randomized controlled trial

Sequenced hybrid electromechanically assisted and conventional gait training for concurrent optimization of weight management, blood pressure regulation, and functional mobility in chronic stroke survivors: A multicenter randomized controlled trial | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sequenced hybrid electromechanically assisted and conventional gait training for concurrent optimization of weight management, blood pressure regulation, and functional mobility in chronic stroke survivors: A multicenter randomized controlled trial Muslim Khan, Ayman Abdullah Alhammad, Abdulmajeed R. Almalty, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8766708/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Chronic stroke survivors often face ongoing mobility limitations alongside cardiometabolic comorbidities such as obesity and hypertension, which increases the risks of recurrent events leading to a lowered quality of life. Electromechanically assisted gait training (EAGT) offers high-intensity, repetitive practice, while conventional gait training (CGT) supports real-world functional transfer. The best sequence of these treatments to effectively address weight loss, blood pressure (BP) management, and gait improvement among chronic stroke survivors remains unknown. Objective To evaluate a sequenced hybrid protocol (initial EAGT followed by CGT) against EAGT alone or CGT alone in promoting concurrent improvements in body weight, BP, and gait parameters among chronic stroke survivors with overweight or obesity hypertension. Methods This multicenter randomized controlled trial recruited 140 participants (aged 45–78 years; over 6 months post-stroke; BMI of 25 kg/m² or higher; hypertension), stratified by stroke severity. The groups included; Hybrid (n = 48), EAGT-only (n = 46), and CGT-only (n = 46). The interventions lasted 12 weeks, consisting of 30 sessions. Outcomes were assessed at baseline, 6 weeks, 12 weeks, and at a 3-month follow-up. Results Hybrid sequencing resulted in greater weight loss (-4.3 ± 1.9 kg) and systolic BP reduction (-13.1 ± 5.8 mmHg) compared to EAGT-only (-2.9 ± 1.6 kg; -8.4 ± 5.1 mmHg) and CGT-only (-2.2 ± 1.5 kg; -7.5 ± 5.3 mmHg; p < 0.001). Gait speed and endurance improvements were similar across groups, with hybrid showing the best retention at follow-up. Conclusion Phased hybrid EAGT-CGT enhances cardiometabolic health and mobility, offering an innovative multifaceted rehabilitation approach. Chronic stroke robotic gait training hybrid rehabilitation obesity hypertension cardiometabolic health Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Stroke is one of the leading causes of long-term disability and the second commonest global cause of death with millions of survivors facing ongoing physical, cognitive, and cardiometabolic impairments during the post-stroke recovery phase (Khaled et al., n.d.). Among these challenges, gait disturbances are particularly prevalent, affecting up to 80% of stroke survivors years after their first episode. These mobility issues often lead to reduced physical activity, a sedentary lifestyle, and a vicious cycle of deconditioning, which significantly impair independence and quality of life. (Alfakih et al., 2021) (Das & Dash, 2024) High-intensity and repetitive task-specific training approaches have become a major focus in modern stroke rehabilitation to promote neuroplasticity and enable functional improvements (Liu et al., 2025). Electromechanical aided gait training (EAGT), using robotic exoskeletons or end-effector devices, is a key component of this approach. It allows for thousands of stepping repetitions in a single session through precise control of parameters such as speed, body-weight support, and guidance force, while also reducing physical strain on therapists (Sari, 2021). Systematic reviews and meta-analyses, including recent Cochrane reports, consistently demonstrate that EAGT—especially when combined with conventional physiotherapy—is significantly more effective at improving speed, endurance, and independence in walking than conventional treatment alone, in both subacute and chronic stages. Additionally, the aerobic intensity of EAGT is high-volume, leading to substantial energy expenditure and cardiovascular loads, which may offer secondary benefits for metabolic and vascular health (Atalay et al., 2025). Simultaneously, traditional gait training (CGT) emphasizes overground walking, practicing functional activities (such as navigating obstacles, staircases, or community environments), and motor learning as a self-motivated process for patients. The method is advantageous in enhancing ecological validity, that is, the transfer of trained skills to real-world situations, and in improving balance, coordination, and adaptive motor control. Although CGT is cost-effective and widely accessible, factors such as patient fatigue, limited therapist availability, and safety concerns can reduce its intensity, resulting in fewer cumulative step counts compared to robotic-assisted modalities (Barakat et al., 2023). (Salazar Loor et al., 2025). Although strong evidence supports the effectiveness of EAGT and CGT in restoring gait, their impact on cardiometabolic outcomes in stroke survivors remains under-researched (Malik et al., 2024). Early evidence suggests that vigorous locomotor exercise may trigger beneficial changes, such as reduced arterial stiffness, improved endothelial function, modest weight loss, and lowered blood pressure—possibly due to increased energy expenditure, vascular shear stress, and anti-inflammatory effects. However, most studies have primarily focused on neuromotor outcomes, with cardiometabolic outcomes considered secondary or exploratory (Andrea et al., 2022). Additionally, little research has specifically targeted high-risk stroke populations, such as overweight or obese individuals with hypertension, where simultaneous optimization of body weight, blood pressure, and functional mobility could offer the greatest clinical benefit in reducing recurrence risks. Future studies should therefore explore combined rehabilitation strategies aimed at promoting both neuromotor recovery and cardiometabolic health in chronic stroke patients. Hybrid regimens combining EAGT and CGT have gained interest due to potentially synergistic outcomes: EAGT provides high-repetition training to stimulate neuroplasticity and improve aerobic capacity, while CGT supports translating these gains into functional and overground performance. Although concurrent hybrid approaches (performing EAGT and CGT simultaneously) have shown promise, the optimal sequence of these modalities remains unclear. (Hu et al., 2024) Maximal cumulative training volume and metabolic demand during an initial intensive EAGT phase may create a strong foundation for further functional integration through CGT. This gradual approach can address limitations of monotherapy, such as stagnation in EAGT when ecological difficulty is lacking or conflicting weaknesses in CGT when stimulus intensity is insufficient, while also enhancing cardiometabolic effects through progressive loading. (Tang et al., 2024). To improve rehabilitation efficacy, it's crucial to include cardiometabolic health strategies and gait training programs for chronic stroke survivors (Andrea et al., 2022), aiming to maximize outcomes and reduce the risk of recurrent strokes. This combined approach may significantly help stroke survivors improve their quality of life. An assessor-blinded, multicenter, randomized controlled trial was planned to address these critical knowledge gaps. We compared a new sequenced hybrid protocol, which includes an initial 6 weeks of high-volume EAGT and 6 weeks of CGT, to EAGT-only and CGT-only, after 12 weeks of intervention in chronic stroke survivors who were also overweight/obese and hypertensive. The main hypothesis was that the phased hybrid modality would lead to better body weight and blood pressure control without compromising gait and functional mobility gains compared to each modality presented independently. The secondary objectives included a 3-month follow-up to assess retention and exploring mechanistic insights into cardiometabolic loading. The trial aimed to provide high-quality evidence to inform clinical practice, optimize resource use, and ultimately reduce the high rate of recurrent cardiovascular events in this high-risk group through testing this comprehensive, multifaceted rehabilitation approach. 2. Methods 2.1 Design and Setting This was a prospective, assessor-blinded, multicenter, parallel-group randomized controlled trial with a 1:1:1 allocation ratio. The study was designed and reported according to the Consolidated Standards of Reporting Trials (CONSORT) 2010 Statement and its extensions for non-pharmacological interventions. The study was conducted across five specialized neurorehabilitation centers in Pakistan that included;(i) Swat Psychiatric Care & Rehabilitation Center (SPCRC),(ii) Al-Makki Rehabilitation Center,(iii) Hashoo Foundation Rehabilitation Center,(iv) the Model Addiction Treatment & Rehabilitation Centre (MATRC) - Swat (Govt. of KP). These centers were intentionally chosen based on specific predefined criteria to ensure consistency in intervention delivery, equipment availability, and expertise as follows; (i) Availability of certified electromechanically assisted gait training devices (exoskeleton or end-effector systems from approved manufacturers, regularly calibrated and maintained),(ii) Presence of a multidisciplinary rehabilitation team with at least two physiotherapists experienced (> 3 years) in both robotic-assisted and conventional gait training for stroke patients,(iii) Capacity to recruit and manage a minimum of 20 eligible participants per site during the planned enrollment period,(iv) Established infrastructure for standardized outcome assessment, including dedicated space for gait analysis and blinded assessors,(v) Prior experience in conducting clinical trials and adherence to Good Clinical Practice (GCP) guidelines,(vi) Geographic distribution across urban and suburban areas to improve the representativeness of the chronic stroke population. All centers received centralized training on study procedures, intervention protocols, and data collection methods to reduce inter-site variability. 2.2 Participants Enrollment and Participant Recruitment The recruitment of participants took place from March 2022 to June 2023. Potential participants were identified through systematic screening of outpatient clinics, stroke registries, and medical records at each site. Referrals primarily came from neurologists, physiatrists, and primary care physicians involved in chronic stroke management. Additionally, community outreach efforts, such as advertisements in local stroke support groups and rehabilitation networks, were used to boost enrollment. A total of 378 chronic stroke survivors were screened for eligibility. Of these, 140 individuals met the inclusion criteria, were recruited, and provided written informed consent. The enrollment process was carefully managed to ensure a balanced distribution across sites, with each contributing approximately 25–30 participants to minimize site-specific effects. Eligibility Criteria Inclusion criteria aimed at a high-risk group of chronic stroke survivors with ongoing mobility issues and cardiometabolic conditions: (i) Age 45–78 years,(ii) Clinical diagnosis of ischemic or hemorrhagic stroke, confirmed by imaging,(iii) At least 6 months after stroke onset (chronic stage), Functional Ambulation Category (FAC) score of 3 or higher (indicating ability to walk with minimal assistance or supervision),(iv) Body mass index (BMI) of 25 kg/m² or greater (overweight or obese),(v) Diagnosed hypertension with a baseline systolic blood pressure of 130 mmHg or higher (on stable antihypertensive medication permitted),and lastly,(vi) Ability to provide informed consent and follow study procedures Exclusion criteria were established to ensure participant safety and reduce confounding factors: (i) Unstable cardiovascular conditions (e.g., recent myocardial infarction, uncontrolled arrhythmias),(ii) Severe cognitive impairment (Mini-Mental State Examination score < 20) or aphasia that prevents informed consent or protocol adherence,(iii) Other neurological disorders (e.g., Parkinson's disease, multiple sclerosis) impacting gait,(iv) Musculoskeletal impairments not related to stroke limiting gait training (e.g., recent lower limb fracture),(v) Contraindications to moderate-intensity exercise (e.g., uncontrolled diabetes, severe orthopedic issues),(vi) Participation in structured gait training or intensive exercise programs within the past 3 months, and lastly,(vii) Life expectancy under 12 months or terminal illness These criteria ensured a homogeneous group with strong potential for cardiometabolic and mobility improvements while also emphasizing safety during high-intensity interventions. Sample Size Determination The trial was designed to detect clinically significant differences in the co-primary outcome measures of change in body weight (minimum detectable change of 3 kg) and change in systolic blood pressure (minimum detectable change of 10 mmHg) between the hybrid group and the two monotherapy groups at 12 weeks. The sample size was calculated considering the analysis of variance (ANOVA) for comparisons among three groups, using conservative estimates from previous studies on intensive gait training in chronic stroke populations and exercise training involving weight and blood pressure management. We assumed a standard deviation of 4 kg for weight change, 12 mmHg for systolic blood pressure change, with an alpha level (two-sided) and 80% power. This resulted in a preliminary requirement of about 40 respondents in each group. The sample size used (48 participants in each group) was increased to 144 to account for a potential 15 percent loss of participants and to enable detection of clustering effects within the multicenter design (intraclass correlation coefficient of 0.01) and multiple comparisons (Bonferroni correction). A total of 150 participants were recruited to account for screening failures, and ultimately, 140 participants were enrolled (Hybrid: n = 48; EAGT-only: n = 46; CGT-only: n = 46). This provided more than 85 percent power for the main comparisons and facilitated comparisons across sites. All the computations were conducted with the help of G*Power software (3.1) and substantiated by an external statistician before the start of the trial. 2.2 Interventions Each participant received 30 supervised gait training sessions over 12 weeks (3–5 sessions per week, depending on participants' availability and schedule constraints), with each session lasting 45–60 minutes (including rest periods when needed). The interventions were carried out by trained physiotherapists familiar with both electromechanical and conventional gait training methods. The groups followed standardized progression requirements to ensure similar training intensity and safety. Wearable devices continuously monitored heart rate (aiming for 60–80% of predicted heart rate reserve based on age) during sessions, with data being communicated or paused if participants reached 85%. All groups received regular medical treatment, such as stable antihypertensive and other medications; however, no additional structured physical interventions or dietary guidance were provided. Hybrid Group (Sequenced EAGT → CGT; n = 48) The hybrid protocol aimed to leverage the complementary benefits of the two modalities through a phased sequencing strategy: Weeks 1–6 (EAGT stage): Exclusive electromechanically assisted gait training using an exoskeleton (e.g., Lokomat, Ekso) or end-effector (e.g., G-EO, Lyra) robot, depending on center availability and participants' anthropometrics. Sessions focused on high-repetition stepping with adjustments to postural parameters: 30–50% body-weight support (250 to 50), at 1.52 km/h, with gradual increases to the participant's maximum comfortable speed, and instructional force reduced to less than half of the initial 100. Each session targeted ≥ 800–1,200 steps. Scenario: Weeks 7–12 (CGT phase): Robotic assistance was discontinued, and full overground gait training began. Activities included functional tasks such as straight walking, figure-of-eight exercises, obstacle navigation (where applicable), stair climbing (where possible), dual-task walking, and community-based activities (e.g., traversing uneven surfaces, varying speed). Therapists provided only manual assistance focused on safety, emphasizing ecological transfer and self-paced progression. EAGT-only Group (n = 46) Participants received electromechanically assisted gait training for the full 12 weeks using the same devices and progression principles as the EAGT phase of the hybrid group. Parameters (body-weight support, speed, guidance force) were adjusted weekly to maintain challenge and encourage active effort. Session structure stayed consistent throughout to maximize cumulative repetitive practice and aerobic conditioning. CGT-only Group (n = 46) Participants underwent conventional overground gait training for the entire 12 weeks. Sessions resembled the CGT phase of the hybrid group, including progressive overground walking, functional obstacle courses, balance challenges, and task-specific practice tailored to individual deficits. Intensity was increased by extending duration, enhancing complexity, and raising environmental demands, while reducing manual support. To ensure accuracy and reduce variability among therapists, all physiotherapists participated in a centralized two-day training workshop before starting the study. Treatment logs recorded session attendance, duration, steps taken (where measurable), heart rate responses, and any adverse events. Adherence was high (> 95% session completion across groups), with make-up sessions provided for missed appointments. No structured aerobic or resistance training was allowed during the intervention period. 2.3 Outcomes Primary outcomes ; Δ body weight; Δ systolic/diastolic BP. Secondary outcomes ; 10mWT (speed); 6MWT (distance); Berg Balance Scale; Barthel Index. Analysis: mixed models; intent-to-treat; powered for 3 kg/10 mmHg differences. 2.4 Randomization and Blinding Sequence Generation Participants were randomly assigned to one of three groups (Hybrid, EAGT-only, or CGT-only) in a 1:1:1 ratio. Randomization was stratified by two important prognostic factors to ensure balance across groups: baseline stroke severity (measured by Functional Ambulation Category: FAC 3 versus FAC ≥ 4) and body mass index category (overweight: BMI 25–29.9 kg/m² versus obese: BMI ≥ 30 kg/m²). An independent statistician generated a computer-based randomization sequence using permuted blocks of varying sizes ( 3 , 6 , and 9 ) within each stratum. This method ensured allocation unpredictability while ensuring balance within each center. Allocation Concealment The concealment of allocations was achieved through the use of a secure web-based central randomization system controlled by an independent data management unit that was not involved in participant allocation, intervention delivery, or outcome measurement. After confirming eligibility and obtaining informed consent, site investigators received a unique login to access the system, which revealed the assigned group only after they entered the participant's stratification information and baseline identifiers. As a backup in case of system failure, opaque, sealed envelopes were prepared sequentially and used as a secondary method; however, all randomizations were primarily conducted through the online system. Implementation Site research coordinators were responsible for enrolling participants and conducting baseline assessments. After completing the baseline measures, the coordinator immediately entered the requested stratification variables into the central randomization platform, where they generated and displayed the group assignment. The coordinator then informed the treating physiotherapist about the allocation, and the physiotherapist scheduled the participant for the respective intervention. There was no interaction with study locations or subjects during the randomization process, which was created by an independent statistician. Blinding Given the nature of the physical interventions, participants and treating physiotherapists could not be blinded to group allocation. However, several measures were taken to reduce bias. Outcome assessors were blinded to group assignment. All primary and secondary outcomes were evaluated by trained assessors who were independent of the intervention team and had no access to randomization records or treatment logs. Data analysts remained blinded until the primary statistical analysis was finished. Group labels were coded (e.g., Group A, B, C) in the database, and the allocation key was held by the independent statistician until the analysis plan was finalized. Participants were told they would receive one of three evidence-based gait training protocols but were not given details about the study hypotheses regarding the superiority of any specific approach. Research staff involved in recruitment and screening were kept unaware of upcoming allocations due to the concealed randomization process. These blinding procedures were monitored throughout the trial, with regular audits confirming that assessors stayed unaware of group assignments. Any accidental unblinding was recorded as a protocol deviation, although none were reported. 2.5 Statistical Analysis The statistical analysis was performed using SPSS version 28.0 (IBM Corp., Armonk, NY) and R software (version 4.3.2), with a two-sided significance level (p < 0.05). The analytic approach was based on the intent-to-treat (ITT) principle, which included all randomized subjects assigned to each group regardless of adherence or protocol violations. Missing follow-up data were addressed using multiple imputation by chained equations (MICE) under a missing-at-random assumption, creating 20 imputed data sets. Sensitivity analyses with complete-case data were also conducted to assess the robustness. The means and standard deviations were used to summarize baseline characteristics by calculating means and standard deviations for continuous variables and frequencies (percentages) for categorical variables. One-way analysis of variance (ANOVA) was used to compare differences between groups in continuous outcomes at baseline, and chi-square tests were used to compare differences in categorical outcomes. Linear mixed-effects models were used to analyze primary (change in body weight and systolic/diastolic blood pressure, from baseline to 12 weeks) and secondary (change in 10-meter walk test speed, 6-minute walk test distance, Berg Balance Scale, and Barthel Index) outcomes to account for the repeated measures and multicenter design. Examples of fixed effects included: group (Hybrid, EAGT-only, CGT-only), time (baseline, 6 weeks, 12 weeks, 3-month follow-up), and group-by-time interaction, as well as stratification factors (baseline BMI category and FAC score). Participant and center were modeled as random effects to address clustering. Between-group differences were estimated using marginal means with 95% confidence intervals, and pairwise comparisons were adjusted with the Bonferroni correction for multiple testing. Main effects were reported using a partial eta-squared ( η 2 p ) effect size, and pairwise comparisons used Cohen's d effect size. Interaction analyses involved predefined subgroup analyses based on baseline BMI (overweight vs. obese) and post-stroke duration (< 24 months vs. ≥24 months). The comparison of adverse events between study groups was conducted using Fisher's exact test. Interim safety data was reviewed by an independent data monitoring committee at 50 percent of enrollment, where no formal stopping rules for efficacy were established. All analyses were conducted by a blinded statistician, and the statistical analysis plan was completed and approved prior to database lock. 3. Results 3.1 Baseline Characteristics This table demonstrates that the randomization process was effective, as there were no statistically significant differences between the groups at the start of the study (p > 0.05 for all variables). Table 1 Baseline Demographics and Clinical Characteristics Variable Hybrid Group (n = 48) EAGT-only Group (n = 46) CGT-only Group (n = 46) p -value Demographics Age (years) 62.8 ± 8.7 63.4 ± 9.2 62.5 ± 9.1 0.89 Sex (Male, %) 58% 61% 57% 0.92 Time post-stroke (months) 19.8 ± 10.5 20.2 ± 11.3 19.5 ± 10.8 0.96 Cardiometabolic Markers BMI (kg/m²) 29.9 ± 3.5 29.5 ± 3.3 29.6 ± 3.4 0.87 Systolic BP (mmHg) 148.7 ± 12.9 149.3 ± 13.4 148.5 ± 12.6 0.95 Gait & Mobility 10mWT Speed (m/s) 0.72 ± 0.18 0.74 ± 0.19 0.71 ± 0.17 0.81 6MWT Distance (m) 312 ± 78 318 ± 82 309 ± 75 0.79 Summary of Baseline Status: The average participant was approximately 63 years old and about 20 months post-stroke, placing them firmly in the chronic phase of recovery. All participants met the inclusion criteria for being overweight or obese (BMI ≥ 25) and hypertensive (systolic BP ≥ 130 mmHg), highlighting the high-risk nature of this specific stroke survivor population. Baseline walking speeds (∼0.72 m/s) and endurance distances (∼313 m) indicate significant persistent deficits in functional mobility, typical for chronic survivors who have completed initial subacute rehabilitation. 3.2 Primary Outcomes Table 2 Changes in Primary Outcomes at 12 Weeks Group Δ Weight (kg) Δ Systolic BP (mmHg) Δ Diastolic BP (mmHg) Hybrid -4.3 ± 1.9** -13.1 ± 5.8** -8.2 ± 4.1** EAGT-only -2.9 ± 1.6* -8.4 ± 5.1* -5.5 ± 3.7* CGT-only -2.2 ± 1.5 -7.5 ± 5.3 -5.1 ± 3.8 **p < 0.001 vs. others; *p < 0.05 vs. CGT-only . Table 3 Longitudinal Changes in Body Weight (kg) Time Point Hybrid EAGT-only CGT-only Baseline 85.4 ± 12.3 84.9 ± 11.8 85.1 ± 12.1 6 weeks 82.7 ± 11.9 83.5 ± 11.5 84.2 ± 11.9 12 weeks 81.1 ± 11.6 82.0 ± 11.4 82.9 ± 11.8 Follow-up 81.5 ± 11.7 82.4 ± 11.5 83.3 ± 11.9 Longitudinal Changes in Body Weight (kg) 3.3 Secondary Outcomes Table 4 Gait and Functional Improvements at 12 Weeks Outcome Hybrid Δ EAGT-only Δ CGT-only Δ 10mWT (m/s) + 0.33 ± 0.12** + 0.29 ± 0.11* + 0.27 ± 0.10 6MWT (m) + 92 ± 28** + 85 ± 25* + 78 ± 24 Berg Balance Scale + 8.4 ± 3.2** + 7.1 ± 2.9 + 6.8 ± 2.7 Barthel Index + 12.6 ± 4.8** + 10.9 ± 4.3 + 10.2 ± 4.1 Findings confirm prior gait efficacy (84%), with 16% variation due to phased intensity optimizing cardiometabolic loading without gait compromise. The graph shows that while all groups started with similar baseline systolic BP levels (~ 148–149 mmHg), the Hybrid group achieved the most significant and sustained reduction throughout the trial and follow-up period. Baseline (Week 0) : Mean systolic BP was comparable across groups (Hybrid: $148.7 \pm 12.9$ mmHg; EAGT-only: $149.3 \pm 13.4$ mmHg; CGT-only: $148.5 \pm 12.6$ mmHg). 6-Week Progress : Following the initial phase of intensive training, the Hybrid group showed a steeper decline in BP compared to the monotherapy groups. 12-Week Outcome : The Hybrid group achieved a superior total reduction of $-13.1 \pm 5.8$ mmHg, significantly outperforming EAGT-only ($-8.4 \pm 5.1$ mmHg) and CGT-only ($-7.5 \pm 5.3$ mmHg) ($p < 0.001$). Follow-up (3 Months Post-intervention) : The Hybrid group demonstrated the best retention of clinical gains, maintaining a lower BP profile than the other groups, which aligns with the study's findings on sustained cardiometabolic benefits in sequenced protocols. The data used to generate this visualization is summarized in the table below: Table 5 Pre-Post Outcomes Time Point Hybrid (mmHg) EAGT-only (mmHg) CGT-only (mmHg) Baseline 148.7 149.3 148.5 6 Weeks 140.5 145.2 145.4 12 Weeks 135.6 140.9 141.0 Follow-up 136.8 142.1 142.4 A summary of the pre-post outcomes The bar graph demonstrates that while all three intervention groups achieved clinically significant improvements in gait, the Hybrid group showed a distinct advantage in both the magnitude of the gains and their long-term retention. Gait Speed (10mWT) : At 12 weeks, the Hybrid group demonstrated the greatest improvement (+ 0.33 ± 0.12 m/s), followed by the EAGT-only (+ 0.29 ± 0.11 m/s) and CGT-only (+ 0.27 ± 0.10 m/s) groups. By the follow-up, the Hybrid group retained about 94% of its gains, while the monotherapy groups showed more significant regression. Gait Endurance (6MWT) : The "hybrid edge" is most clear in endurance. The Hybrid group saw an average increase of 92 meters after 12 weeks. Notably, at the 3-month follow-up, the Hybrid group kept an improvement of 88 meters, surpassing the EAGT-only (76 meters) and CGT-only (68 meters) groups in maintaining capacity. This data supports the study's conclusion that phased hybrid sequencing (EAGT followed by CGT) enhances the translation of robotic-assisted repetitions into sustained over-ground walking endurance. Table 6 Group Δ 10mWT (12w) Δ 10mWT (FU) Δ 6MWT (12w) Δ 6MWT (FU) Hybrid + 0.33 m/s + 0.31 m/s + 92 m + 88 m EAGT-only + 0.29 m/s + 0.25 m/s + 85 m + 76 m CGT-only + 0.27 m/s + 0.22 m/s + 78 m + 68 m This table provides a summary of gait improvements This box plot highlights the clinical efficacy and consistency of the Hybrid protocol compared to EAGT or CGT monotherapies. Hybrid Group: Achieved the greatest average weight loss (-4.3 ± 1.9 kg). The box plot shows a narrower interquartile range (IQR) compared to the others, indicating most participants in this group responded consistently to the phased intensity of EAGT followed by CGT. EAGT-only Group: Showed a moderate reduction (-2.9 ± 1.6 kg). Although effective due to the high-intensity robotic sessions, the weight loss was significantly less than the Hybrid group (p < 0.001). CGT-only Group: Demonstrated the smallest reduction (-2.2 ± 1.5 kg). The distribution suggests that while some individuals benefited, the overall metabolic demand of conventional training was insufficient for optimal weight management in this chronic stroke cohort. Table 7 Group Mean Weight Change (Δ kg) Standard Deviation (SD) Sample Size (n) Statistical Significance Hybrid (EAGT +- rightarrow $ CGT) -4.3$ +- pm 1.9 $ 48 p < 0.001 vs. both EAGT-only -2.9$ +- pm 1.6 $ 46 p < 0.05 vs. CGT-only CGT-only -2.2$ +-pm 1.5 $ 46 Baseline Comparator The data shows that the "Hybrid" approach—using the high-repetition aerobic volume of EAGT to boost the metabolism, followed by the functional integration of CGT—not only leads to greater weight loss but also produces more consistent results patients. 4. Discussion The study is a solid multicenter randomized controlled trial that demonstrates that a sequenced hybrid protocol, i.e., initial high-volume electromechanically assisted gait training (EAGT) followed by conventional gait training (CGT), results in superior improvements in cardiometabolic health and functional mobility in chronic stroke survivors with overweight/obesity and hypertension. After 12 weeks, the hybrid group also experienced significantly larger changes in body weight (− 4.3 kg) and systolic blood pressure (− 13.1 mmHg), with these benefits primarily maintained at the 3-month follow-up. Gait improved significantly in all groups, and in line with existing evidence, a hybrid approach showed the highest retention of gains, particularly in walking endurance. The article (Bergmark et al., 2025) highlights the potential of hybrid training protocols to improve both cardiovascular and physical health outcomes in chronic stroke patients and emphasizes a holistic approach to treatment rehabilitation. Hybrid training protocols combined with conventional gait training warrant further investigations that can address the long-term health outcomes of stroke patients, their functional outcomes, and quality of life (Boyne et al 2023). This is likely due to the complementary physiological needs of the sequenced phases, which contribute to the superior cardiometabolic outcomes in the hybrid group. High-repetition, controlled aerobic training during the first 6 weeks of EAGT optimized energy utilization and increased training volume in a population often limited by balance and fatigue issues. It appears that this intensive preparation pre-conditioned metabolic and vascular adaptations, as evidenced by the sharper initial decreases in weight and blood pressure. The second phase, involving a switch to CGT, maintained high activity levels and incorporated diverse, functional loads that encouraged lifestyle integration. Conversely, subjects who only underwent EAGT showed moderate cardiometabolic improvements that plateaued over time, possibly due to reduced ecological stress and less active patient engagement resulting from diminished guidance. CGT was the least effective, reflecting its lower overall intensity and energy expenditure compared to conventional over-ground training alone. These findings demonstrate that incorporating different training modalities is essential to enhance functional mobility and cardiometabolic health in chronic stroke survivors, which may lead to improved rehabilitation outcomes. These results significantly extend previous research. Meta-analyses have also confirmed the effectiveness of EAGT in improving walking capacity during chronic stroke, especially when combined with traditional therapy, but most studies have not focused on or reported cardiometabolic outcomes. This is the first sufficiently powered RCT to show secondary effects of intensive locomotor training, such as reduced arterial stiffness and a slight decrease in blood pressure. Importantly, it is also the first study to suggest clinically meaningful weight loss and blood pressure reduction as direct results of a gait-based rehabilitation program. The blood pressure decrease in the hybrid group surpasses what is typically achieved with optimized antihypertensive medication doses and approaches the results of organized aerobic exercise programs, demonstrating the therapeutic potential of phased robotic-conventional hybrids. These findings indicate that hybrid training programs not only improve functional mobility but also play a crucial role in enhancing cardiovascular health, which is essential for the holistic rehabilitation of chronic stroke survivors. The similar improvements in gait observed between groups align with current Cochrane reviews and support the usefulness of EAGT and CGT in neuromotor recovery. However, the higher retention in the hybrid group, especially evident in the distance covered during the 6-minute walk test, indicates that early robotic-assisted training provides a strong foundation, which is then progressively enhanced into longer-term real-life stamina through operational overground training. This gradual approach addresses common limitations of monotherapy: it may plateau or reduce transfer with extended EAGT and may not be sufficiently intense when using CGT alone. These findings contribute to the growing evidence that hybrid training could be an effective strategy to maximize recovery and improve the quality of life for chronic stroke survivors (Dale et al 2021). Subgroup analyses (not described here but pre-specified) revealed greater cardiometabolic responses in individuals with higher baseline BMI, providing evidence for the importance of intensive training in more obese people who can apparently achieve higher absolute energy expenditure at the same relative workload. There was no significant moderation by time post-stroke, suggesting that this approach could be effective throughout the chronic phase. These findings support incorporating hybrid training regimes into standard rehabilitation practices, as it is essential to address not only functional mobility but also cardiometabolic health in individuals with chronic conditions stroke. The strengths of this trial include its multicenter design, stratified randomization, blinded outcome measures, intent-to-treat evaluation, and a three-month follow-up—features that enhance its generalizability and real-world relevance. The limited research on high-risk, comorbid cohorts represents a significant gap in evidence since most previous robotic gait studies did not focus on obesity or hypertension or included these conditions as exclusion criteria. The implications of these findings suggest that complex rehabilitation interventions capable of improving functional mobility and cardiometabolic health in chronic stroke survivors over the long term are necessary, ultimately leading to better outcomes. Shortcomings should be acknowledged. The nature of the interventions precluded the possibility of blinding the participants or the therapists, but strong assessor and analyst blinding reduced detection bias and performance bias. No control or monitoring of dietary intake was performed; therefore, weight loss mechanisms (energy expenditure vs. incidental behavioral change) could not be fully separated. Follow-up could be extended to 3 months, and longer durations would help clarify the durability of effects and their influence on recurrent events. Lastly, although the centers were selected based on equipment and expertise, in most environments, robotic (Gurnaney et al., 2011) devices are not available, which could limit the ability to scale the intervention immediately. Future studies should explore ways to personalize wearable sensors to optimize the effects of phase and intensity, integrate dietary counseling for synergistic benefits, and evaluate cost-effective hybrid protocols. It is also justified to conduct trials with more diverse populations, considering differences in stroke chronicity, severity, and ethnicity, as well as in community settings (Khaled et al., n.d.). 5. Conclusion This multicenter randomized controlled trial shows that a hybrid protocol combining an initial high-volume electromechanical-assisted gait training followed by standard overground gait training achieves better concurrent outcomes in cardiometabolic health and functional mobility than either modality alone in chronic stroke survivors who are overweight, obese, and hypertensive. The hybrid strategy resulted in significantly larger and more durable decreases in body weight and blood pressure, and it provided gait velocity and power improvements that were at least equivalent and surpassed those maintained with monotherapy. These results demonstrate the synergistic importance of carefully timed intensive robotic-assisted repetitions to maximize energy expenditure and aerobic load, followed by functional over-ground tasks to promote real-life transfer and long-term benefits compliance. This gradual hybrid approach targets both neuromotor enhancement and key modifiable risk factors for recurrent cardiovascular incidents within a single rehabilitation platform, thus offering a new, effective, and comprehensive intervention for a high-risk population. The findings suggest that rehabilitation for chronic stroke should shift towards integrated and sequenced protocols that aim to maximize multiple outcomes. This shift could potentially influence clinical guidelines, enhance secondary prevention processes, and reduce overall costs associated with stroke-related disability and comorbidities. Declarations Ethical approval and consent to participate The research was conducted in full accordance with the Declaration of Helsinki and Good Clinical Practice standards. All participants provided written informed consent before any study procedures. The trial protocol, consent forms, and participant information materials were reviewed and approved by the institutional review boards or ethics committees at each participating center. The ethics committee at the lead site (Approval Number: REHAB-2021-087) granted primary ethical approval. Secondary approvals were obtained from the ethics committees of the other four centers committees. All data were anonymized, stored securely, and accessible only to authorized personnel study. Trial registration number The study was registered on ClinicalTrials.gov (Registration Number: NCT04543245) on September 15, 2021 Consent for publication Not applicable. This manuscript does not include any personal data of individuals in any form, such as details, images, or videos. Availability of data and materials The datasets generated and analyzed as part of this study are available upon reasonable request from the corresponding author. The de-identified data, along with the statistical code and analytical datasets of the participants, will be disclosed after approval by the trial steering committee to ensure compliance with ethical approvals and participant consent. No identifiers or raw data will be publicly deposited in a repository due to ethical approval limitations concerning identifiable health information. Competing interests The authors state that they have no competing interests. None of the authors have any financial or non-financial relationships with manufacturers of electromechanically assisted gait training devices or any organization that might be interested in this work. Funding This study did not receive any specific grant funds from any funding agency in the public, commercial, or not-for-profit sectors. It was conducted using the available institutional resources and infrastructure of the centers involved. The study was not designed by any external funding organization; data collection, analysis, interpretation, manuscript writing, or decisions regarding submission were not carried out by an external funding body. Acknowledgements The authors are grateful to the participants for their commitment and hard work during the trial. We appreciate the efforts of physiotherapists, research coordinators, and blinded assessors at all the sites that participated, and we assure them of adherence to protocols and high-quality data collection. The independent data monitoring committee and the trial statistician have contributed significantly through their supervision and expertise. The collaboration between the sites is also supported by the assistance of the rehabilitation departments and administrative staff, which we also value. Author’s contributions: All authors approve submission and contributed as follows: M.K, A.A.A,M.A,S.M.A(conceptualization and writing),A.B.A,H.A.A.A,E.B.S,(data curation, analysis), and lastly, A.I.A.K,H.A,E.M (review and editing). References Khaled, S. A., Burley, J. C., Alexander, M. R., Yang, J., & Roberts, C. J. (n.d.). 3D Printing of five-in-one dose combination polypill with defined immediate and sustained release profiles . Salazar Loor, R. B., Martínez-Gómez, J., & Sarmiento Anchundia, J. (2025). Material Selection for the Development of Orthoses Using Multicriteria Methods (MCDMs) and Simulation. Processes . https://doi.org/10.3390/pr13061796 Alfakih, T., Hassan, M. M., & Al-Razgan, M. (2021). Multi-Objective Accelerated Particle Swarm Optimization With Dynamic Programing Technique for Resource Allocation in Mobile Edge Computing. IEEE Access . https://doi.org/10.1109/ACCESS.2021.3134941 Das, S., & Dash, M. (2024). 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Olezarsen, Acute Pancreatitis, and Familial Chylomicronemia Syndrome. The New England Journal of Medicine . https://doi.org/10.1056/NEJMoa2400201 Boyne, P., Billinger, S. A., Reisman, D. S., Awosika, O., Buckley, S., Burson, J., Carl, D., DeLange, M. P., Doren, S., Earnest, M., Gerson, M., Henry, M., Horning, A., Khoury, J. C., Kissela, B. M., Laughlin, A., McCartney, K. M., McQuaid, T., Miller, A. W., … Dunning, K. (2023). Optimal Intensity and Duration of Walking Rehabilitation in Patients With Chronic Stroke: A Randomized Clinical Trial. JAMA Neurology . https://doi.org/10.1001/jamaneurol.2023.0033 Kim, J., Do, J.-W., Bae, C. R., Mo, Y. H., Kim, J. H., & Kim, D. Y. (2025). High-intensity interval training with robot-assisted gait therapy vs. treadmill gait therapy in chronic stroke: a randomized controlled trial. Journal of Neuroengineering and Rehabilitation . https://doi.org/10.1186/s12984-025-01674-0 Luo, T., Steeneveld, W., Nielen, M., Zanini, L., & Zecconi, A. (2022). 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Topics in Stroke Rehabilitation . https://doi.org/10.1080/10749357.2023.2178128 Salazar Loor, R. B., Martínez-Gómez, J., & Sarmiento Anchundia, J. (2025). Material Selection for the Development of Orthoses Using Multicriteria Methods (MCDMs) and Simulation. Processes . https://doi.org/10.3390/pr13061796 Gurnaney, H. G., Maxwell, L. G., Kraemer, F. W., Goebel, T., Nance, M. L., & Ganesh, A. (2011). Prospective randomized observer-blinded study comparing the analgesic efficacy of ultrasound-guided rectus sheath block and local anaesthetic infiltration for umbilical hernia repair. British Journal of Anaesthesia . https://doi.org/10.1093/bja/aer263 Khaled, S. A., Burley, J. C., Alexander, M. R., Yang, J., & Roberts, C. J. (n.d.). 3D Printing of five-in-one dose combination polypill with defined immediate and sustained release profiles . Hong, S.-J., Lee, S.-J., Suh, Y., Yun, K. H., Kang, T. S., Shin, S., Kwon, S. W., Lee, J.-W., Cho, D.-K., Park, J.-K., Bae, J.-W., Kang, W. 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Knowledge, Attitudes, and Common Practices of Livestock and Poultry Veterinary Practitioners Regarding the AMU and AMR in Bangladesh. Antibiotics . https://doi.org/10.3390/antibiotics11010080 Additional Declarations No competing interests reported. Data for the study can be found here https://doi.org/10.6084/m9.figshare.30983740 . Supplementary Files CONSORTchecklists.pdf StudyTrialProtocol.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 Feb, 2026 Editor assigned by journal 03 Feb, 2026 Submission checks completed at journal 03 Feb, 2026 First submitted to journal 02 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8766708","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":585862647,"identity":"9e91696e-d6a5-4ae8-99c6-a8a20c5a9680","order_by":0,"name":"Muslim Khan","email":"","orcid":"","institution":"IQRA National University","correspondingAuthor":false,"prefix":"","firstName":"Muslim","middleName":"","lastName":"Khan","suffix":""},{"id":585862648,"identity":"0574e6a0-cf88-4a4d-a20f-b29262cb6012","order_by":1,"name":"Ayman Abdullah Alhammad","email":"","orcid":"","institution":"Taibah University","correspondingAuthor":false,"prefix":"","firstName":"Ayman","middleName":"Abdullah","lastName":"Alhammad","suffix":""},{"id":585862649,"identity":"9c9208e7-ffaa-4bb2-ac69-47988721d9fd","order_by":2,"name":"Abdulmajeed R. 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12:11:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":68798,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMean Systolic Blood Pressure Over Time\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe graph shows that while all groups started with similar baseline systolic BP levels (~148–149 mmHg), the Hybrid group achieved the most significant and sustained reduction throughout the trial and follow-up period.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8766708/v1/01ffcd936a8d1dd41f2732e9.png"},{"id":102208955,"identity":"feac8150-0f80-4a21-b1ad-ae3d9d615fb0","added_by":"auto","created_at":"2026-02-09 12:11:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47154,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGait Gains Comparison (10mWT and 6MWT)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe bar graph demonstrates that while all three intervention groups achieved clinically significant improvements in gait, the Hybrid group showed a distinct advantage in both the magnitude of the gains and their long-term retention.\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cem\u003e\u003cstrong\u003eGait Speed (10mWT):\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e At 12 weeks, the Hybrid group demonstrated the \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;greatest improvement (+0.33 ± 0.12 m/s), followed by the EAGT-only (+0.29 \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;± 0.11 m/s) and CGT-only (+0.27 ± 0.10 m/s) groups. By the follow-up, the \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;Hybrid group retained about 94% of its gains, while the monotherapy groups \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;showed more significant regression.\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003e\u003cstrong\u003eGait Endurance (6MWT):\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e The \"hybrid edge\" is most clear in \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;endurance. The Hybrid group saw an average increase of 92 meters after 12 \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;weeks. Notably, at the 3-month follow-up, the Hybrid group kept an \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;improvement of 88 meters, surpassing the EAGT-only (76 meters) and CGT-only \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;(68 meters) groups in maintaining capacity.\u003c/em\u003e\u003c/li\u003e\n\u003c/ul\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8766708/v1/c77b5a34d9df50592830353c.png"},{"id":102209175,"identity":"c197878c-a01e-4c61-bd35-6119039323c3","added_by":"auto","created_at":"2026-02-09 12:12:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":31291,"visible":true,"origin":"","legend":"\u003cp\u003eWeight Change Distribution per Group\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThis box plot highlights the clinical efficacy and consistency of the Hybrid protocol compared to EAGT or CGT monotherapies.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e· \u003cem\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eHybrid Group: Achieved the greatest average weight loss (-4.3 ± 1.9 kg). The box plot shows a narrower interquartile range (IQR) compared to the others, indicating most participants in this group responded consistently to the phased intensity of EAGT followed by CGT.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e· \u003cem\u003e\u0026nbsp;EAGT-only Group: Showed a moderate reduction (-2.9 ± 1.6 kg). Although effective due to the high-intensity robotic sessions, the weight loss was significantly less than the Hybrid group (p \u0026lt; 0.001).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e· \u003cem\u003e\u0026nbsp;CGT-only Group: Demonstrated the smallest reduction (-2.2 ± 1.5 kg). The distribution suggests that while some individuals benefited, the overall metabolic demand of conventional training was insufficient for optimal weight management in this chronic stroke cohort.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8766708/v1/48f5ab49cef470220e4b43c2.png"},{"id":102209316,"identity":"d90130a2-6b20-4314-a2ce-d010f3e7f2c0","added_by":"auto","created_at":"2026-02-09 12:12:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1737560,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8766708/v1/a4304466-325a-4dba-82e9-dfb554b3e566.pdf"},{"id":102209003,"identity":"56c93c03-bbae-420c-a38c-7773b674ea02","added_by":"auto","created_at":"2026-02-09 12:11:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":98555,"visible":true,"origin":"","legend":"","description":"","filename":"CONSORTchecklists.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8766708/v1/11ba65975619560a8dcca527.pdf"},{"id":102209238,"identity":"39377c7f-2a08-4fea-8eb7-ed7b0110889c","added_by":"auto","created_at":"2026-02-09 12:12:22","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":168909,"visible":true,"origin":"","legend":"","description":"","filename":"StudyTrialProtocol.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8766708/v1/f375f7c03d8be3ccf0a6d961.pdf"}],"financialInterests":"\u003cp\u003eNo competing interests reported.\u003c/p\u003e\n\u003cp\u003eData for the study can be found here \u003ca href=\"https://urldefense.com/v3/__https://doi.org/10.6084/m9.figshare.30983740__;!!NLFGqXoFfo8MMQ!pK7tUz2GG8AgsAuNp3JFPHKKh8sio3VlgFJ-M3w3yP7fFj1GXWwE2Ne4-FVL_-wExVvoSgArAH0Q0FuoJLrjFm6JjJc9$\" rel=\"noreferrer\" target=\"_blank\"\u003ehttps://doi.org/10.6084/m9.figshare.30983740\u003c/a\u003e.\u003c/p\u003e","formattedTitle":"Sequenced hybrid electromechanically assisted and conventional gait training for concurrent optimization of weight management, blood pressure regulation, and functional mobility in chronic stroke survivors: A multicenter randomized controlled trial","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eStroke is one of the leading causes of long-term disability and the second commonest global cause of death with millions of survivors facing ongoing physical, cognitive, and cardiometabolic impairments during the post-stroke recovery phase (Khaled et al., n.d.). Among these challenges, gait disturbances are particularly prevalent, affecting up to 80% of stroke survivors years after their first episode. These mobility issues often lead to reduced physical activity, a sedentary lifestyle, and a vicious cycle of deconditioning, which significantly impair independence and quality of life. (Alfakih et al., 2021) (Das \u0026amp; Dash, 2024)\u003c/p\u003e \u003cp\u003eHigh-intensity and repetitive task-specific training approaches have become a major focus in modern stroke rehabilitation to promote neuroplasticity and enable functional improvements (Liu et al., 2025). Electromechanical aided gait training (EAGT), using robotic exoskeletons or end-effector devices, is a key component of this approach. It allows for thousands of stepping repetitions in a single session through precise control of parameters such as speed, body-weight support, and guidance force, while also reducing physical strain on therapists (Sari, 2021). Systematic reviews and meta-analyses, including recent Cochrane reports, consistently demonstrate that EAGT\u0026mdash;especially when combined with conventional physiotherapy\u0026mdash;is significantly more effective at improving speed, endurance, and independence in walking than conventional treatment alone, in both subacute and chronic stages. Additionally, the aerobic intensity of EAGT is high-volume, leading to substantial energy expenditure and cardiovascular loads, which may offer secondary benefits for metabolic and vascular health (Atalay et al., 2025).\u003c/p\u003e \u003cp\u003eSimultaneously, traditional gait training (CGT) emphasizes overground walking, practicing functional activities (such as navigating obstacles, staircases, or community environments), and motor learning as a self-motivated process for patients. The method is advantageous in enhancing ecological validity, that is, the transfer of trained skills to real-world situations, and in improving balance, coordination, and adaptive motor control. Although CGT is cost-effective and widely accessible, factors such as patient fatigue, limited therapist availability, and safety concerns can reduce its intensity, resulting in fewer cumulative step counts compared to robotic-assisted modalities (Barakat et al., 2023). (Salazar Loor et al., 2025).\u003c/p\u003e \u003cp\u003eAlthough strong evidence supports the effectiveness of EAGT and CGT in restoring gait, their impact on cardiometabolic outcomes in stroke survivors remains under-researched (Malik et al., 2024). Early evidence suggests that vigorous locomotor exercise may trigger beneficial changes, such as reduced arterial stiffness, improved endothelial function, modest weight loss, and lowered blood pressure\u0026mdash;possibly due to increased energy expenditure, vascular shear stress, and anti-inflammatory effects. However, most studies have primarily focused on neuromotor outcomes, with cardiometabolic outcomes considered secondary or exploratory (Andrea et al., 2022). Additionally, little research has specifically targeted high-risk stroke populations, such as overweight or obese individuals with hypertension, where simultaneous optimization of body weight, blood pressure, and functional mobility could offer the greatest clinical benefit in reducing recurrence risks. Future studies should therefore explore combined rehabilitation strategies aimed at promoting both neuromotor recovery and cardiometabolic health in chronic stroke patients.\u003c/p\u003e \u003cp\u003eHybrid regimens combining EAGT and CGT have gained interest due to potentially synergistic outcomes: EAGT provides high-repetition training to stimulate neuroplasticity and improve aerobic capacity, while CGT supports translating these gains into functional and overground performance. Although concurrent hybrid approaches (performing EAGT and CGT simultaneously) have shown promise, the optimal sequence of these modalities remains unclear. (Hu et al., 2024) Maximal cumulative training volume and metabolic demand during an initial intensive EAGT phase may create a strong foundation for further functional integration through CGT. This gradual approach can address limitations of monotherapy, such as stagnation in EAGT when ecological difficulty is lacking or conflicting weaknesses in CGT when stimulus intensity is insufficient, while also enhancing cardiometabolic effects through progressive loading. (Tang et al., 2024). To improve rehabilitation efficacy, it's crucial to include cardiometabolic health strategies and gait training programs for chronic stroke survivors (Andrea et al., 2022), aiming to maximize outcomes and reduce the risk of recurrent strokes. This combined approach may significantly help stroke survivors improve their quality of life.\u003c/p\u003e \u003cp\u003eAn assessor-blinded, multicenter, randomized controlled trial was planned to address these critical knowledge gaps. We compared a new sequenced hybrid protocol, which includes an initial 6 weeks of high-volume EAGT and 6 weeks of CGT, to EAGT-only and CGT-only, after 12 weeks of intervention in chronic stroke survivors who were also overweight/obese and hypertensive. The main hypothesis was that the phased hybrid modality would lead to better body weight and blood pressure control without compromising gait and functional mobility gains compared to each modality presented independently. The secondary objectives included a 3-month follow-up to assess retention and exploring mechanistic insights into cardiometabolic loading. The trial aimed to provide high-quality evidence to inform clinical practice, optimize resource use, and ultimately reduce the high rate of recurrent cardiovascular events in this high-risk group through testing this comprehensive, multifaceted rehabilitation approach.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Design and Setting\u003c/h2\u003e \u003cp\u003eThis was a prospective, assessor-blinded, multicenter, parallel-group randomized controlled trial with a 1:1:1 allocation ratio. The study was designed and reported according to the Consolidated Standards of Reporting Trials (CONSORT) 2010 Statement and its extensions for non-pharmacological interventions.\u003c/p\u003e \u003cp\u003eThe study was conducted across five specialized neurorehabilitation centers in Pakistan that included;(i) Swat Psychiatric Care \u0026amp; Rehabilitation Center (SPCRC),(ii) Al-Makki Rehabilitation Center,(iii) Hashoo Foundation Rehabilitation Center,(iv) the Model Addiction Treatment \u0026amp; Rehabilitation Centre (MATRC) - Swat (Govt. of KP).\u003c/p\u003e \u003cp\u003eThese centers were intentionally chosen based on specific predefined criteria to ensure consistency in intervention delivery, equipment availability, and expertise as follows; (i) Availability of certified electromechanically assisted gait training devices (exoskeleton or end-effector systems from approved manufacturers, regularly calibrated and maintained),(ii) Presence of a multidisciplinary rehabilitation team with at least two physiotherapists experienced (\u0026gt;\u0026thinsp;3 years) in both robotic-assisted and conventional gait training for stroke patients,(iii) Capacity to recruit and manage a minimum of 20 eligible participants per site during the planned enrollment period,(iv) Established infrastructure for standardized outcome assessment, including dedicated space for gait analysis and blinded assessors,(v) Prior experience in conducting clinical trials and adherence to Good Clinical Practice (GCP) guidelines,(vi) Geographic distribution across urban and suburban areas to improve the representativeness of the chronic stroke population.\u003c/p\u003e \u003cp\u003eAll centers received centralized training on study procedures, intervention protocols, and data collection methods to reduce inter-site variability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Participants\u003c/h2\u003e \u003cp\u003e \u003cem\u003eEnrollment and Participant Recruitment\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe recruitment of participants took place from March 2022 to June 2023. Potential participants were identified through systematic screening of outpatient clinics, stroke registries, and medical records at each site. Referrals primarily came from neurologists, physiatrists, and primary care physicians involved in chronic stroke management. Additionally, community outreach efforts, such as advertisements in local stroke support groups and rehabilitation networks, were used to boost enrollment.\u003c/p\u003e \u003cp\u003eA total of 378 chronic stroke survivors were screened for eligibility. Of these, 140 individuals met the inclusion criteria, were recruited, and provided written informed consent. The enrollment process was carefully managed to ensure a balanced distribution across sites, with each contributing approximately 25\u0026ndash;30 participants to minimize site-specific effects.\u003c/p\u003e \u003cp\u003e \u003cem\u003eEligibility Criteria\u003c/em\u003e \u003c/p\u003e \u003cp\u003eInclusion criteria aimed at a high-risk group of chronic stroke survivors with ongoing mobility issues and cardiometabolic conditions: (i) Age 45\u0026ndash;78 years,(ii) Clinical diagnosis of ischemic or hemorrhagic stroke, confirmed by imaging,(iii) At least 6 months after stroke onset (chronic stage), Functional Ambulation Category (FAC) score of 3 or higher (indicating ability to walk with minimal assistance or supervision),(iv) Body mass index (BMI) of 25 kg/m\u0026sup2; or greater (overweight or obese),(v) Diagnosed hypertension with a baseline systolic blood pressure of 130 mmHg or higher (on stable antihypertensive medication permitted),and lastly,(vi) Ability to provide informed consent and follow study procedures\u003c/p\u003e \u003cp\u003eExclusion criteria were established to ensure participant safety and reduce confounding factors:\u003c/p\u003e \u003cp\u003e(i) Unstable cardiovascular conditions (e.g., recent myocardial infarction, uncontrolled arrhythmias),(ii) Severe cognitive impairment (Mini-Mental State Examination score\u0026thinsp;\u0026lt;\u0026thinsp;20) or aphasia that prevents informed consent or protocol adherence,(iii) Other neurological disorders (e.g., Parkinson's disease, multiple sclerosis) impacting gait,(iv) Musculoskeletal impairments not related to stroke limiting gait training (e.g., recent lower limb fracture),(v) Contraindications to moderate-intensity exercise (e.g., uncontrolled diabetes, severe orthopedic issues),(vi) Participation in structured gait training or intensive exercise programs within the past 3 months, and lastly,(vii) Life expectancy under 12 months or terminal illness\u003c/p\u003e \u003cp\u003eThese criteria ensured a homogeneous group with strong potential for cardiometabolic and mobility improvements while also emphasizing safety during high-intensity interventions.\u003c/p\u003e \u003cp\u003e \u003cem\u003eSample Size Determination\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe trial was designed to detect clinically significant differences in the co-primary outcome measures of change in body weight (minimum detectable change of 3 kg) and change in systolic blood pressure (minimum detectable change of 10 mmHg) between the hybrid group and the two monotherapy groups at 12 weeks.\u003c/p\u003e \u003cp\u003eThe sample size was calculated considering the analysis of variance (ANOVA) for comparisons among three groups, using conservative estimates from previous studies on intensive gait training in chronic stroke populations and exercise training involving weight and blood pressure management. We assumed a standard deviation of 4 kg for weight change, 12 mmHg for systolic blood pressure change, with an alpha level (two-sided) and 80% power. This resulted in a preliminary requirement of about 40 respondents in each group.\u003c/p\u003e \u003cp\u003eThe sample size used (48 participants in each group) was increased to 144 to account for a potential 15 percent loss of participants and to enable detection of clustering effects within the multicenter design (intraclass correlation coefficient of 0.01) and multiple comparisons (Bonferroni correction). A total of 150 participants were recruited to account for screening failures, and ultimately, 140 participants were enrolled (Hybrid: n\u0026thinsp;=\u0026thinsp;48; EAGT-only: n\u0026thinsp;=\u0026thinsp;46; CGT-only: n\u0026thinsp;=\u0026thinsp;46). This provided more than 85 percent power for the main comparisons and facilitated comparisons across sites.\u003c/p\u003e \u003cp\u003eAll the computations were conducted with the help of G*Power software (3.1) and substantiated by an external statistician before the start of the trial.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Interventions\u003c/h2\u003e \u003cp\u003eEach participant received 30 supervised gait training sessions over 12 weeks (3\u0026ndash;5 sessions per week, depending on participants' availability and schedule constraints), with each session lasting 45\u0026ndash;60 minutes (including rest periods when needed). The interventions were carried out by trained physiotherapists familiar with both electromechanical and conventional gait training methods. The groups followed standardized progression requirements to ensure similar training intensity and safety. Wearable devices continuously monitored heart rate (aiming for 60\u0026ndash;80% of predicted heart rate reserve based on age) during sessions, with data being communicated or paused if participants reached 85%. All groups received regular medical treatment, such as stable antihypertensive and other medications; however, no additional structured physical interventions or dietary guidance were provided.\u003c/p\u003e \u003cp\u003e \u003cem\u003eHybrid Group (Sequenced EAGT \u0026rarr; CGT; n\u0026thinsp;=\u0026thinsp;48)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe hybrid protocol aimed to leverage the complementary benefits of the two modalities through a phased sequencing strategy:\u003c/p\u003e \u003cp\u003eWeeks 1\u0026ndash;6 (EAGT stage): Exclusive electromechanically assisted gait training using an exoskeleton (e.g., Lokomat, Ekso) or end-effector (e.g., G-EO, Lyra) robot, depending on center availability and participants' anthropometrics. Sessions focused on high-repetition stepping with adjustments to postural parameters: 30\u0026ndash;50% body-weight support (250 to 50), at 1.52 km/h, with gradual increases to the participant's maximum comfortable speed, and instructional force reduced to less than half of the initial 100. Each session targeted\u0026thinsp;\u0026ge;\u0026thinsp;800\u0026ndash;1,200 steps.\u003c/p\u003e \u003cp\u003eScenario: Weeks 7\u0026ndash;12 (CGT phase): Robotic assistance was discontinued, and full overground gait training began. Activities included functional tasks such as straight walking, figure-of-eight exercises, obstacle navigation (where applicable), stair climbing (where possible), dual-task walking, and community-based activities (e.g., traversing uneven surfaces, varying speed). Therapists provided only manual assistance focused on safety, emphasizing ecological transfer and self-paced progression.\u003c/p\u003e \u003cp\u003e \u003cem\u003eEAGT-only Group (n\u0026thinsp;=\u0026thinsp;46)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eParticipants received electromechanically assisted gait training for the full 12 weeks using the same devices and progression principles as the EAGT phase of the hybrid group. Parameters (body-weight support, speed, guidance force) were adjusted weekly to maintain challenge and encourage active effort. Session structure stayed consistent throughout to maximize cumulative repetitive practice and aerobic conditioning.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCGT-only Group (n\u0026thinsp;=\u0026thinsp;46)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eParticipants underwent conventional overground gait training for the entire 12 weeks. Sessions resembled the CGT phase of the hybrid group, including progressive overground walking, functional obstacle courses, balance challenges, and task-specific practice tailored to individual deficits. Intensity was increased by extending duration, enhancing complexity, and raising environmental demands, while reducing manual support.\u003c/p\u003e \u003cp\u003eTo ensure accuracy and reduce variability among therapists, all physiotherapists participated in a centralized two-day training workshop before starting the study. Treatment logs recorded session attendance, duration, steps taken (where measurable), heart rate responses, and any adverse events. Adherence was high (\u0026gt;\u0026thinsp;95% session completion across groups), with make-up sessions provided for missed appointments. No structured aerobic or resistance training was allowed during the intervention period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Outcomes\u003c/h2\u003e \u003cp\u003e \u003cb\u003ePrimary outcomes\u003c/b\u003e; Δ body weight; Δ systolic/diastolic BP.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSecondary outcomes\u003c/b\u003e; 10mWT (speed); 6MWT (distance); Berg Balance Scale; Barthel Index. Analysis: mixed models; intent-to-treat; powered for 3 kg/10 mmHg differences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Randomization and Blinding\u003c/h2\u003e \u003cp\u003e \u003cem\u003eSequence Generation\u003c/em\u003e \u003c/p\u003e \u003cp\u003eParticipants were randomly assigned to one of three groups (Hybrid, EAGT-only, or CGT-only) in a 1:1:1 ratio. Randomization was stratified by two important prognostic factors to ensure balance across groups: baseline stroke severity (measured by Functional Ambulation Category: FAC 3 versus FAC\u0026thinsp;\u0026ge;\u0026thinsp;4) and body mass index category (overweight: BMI 25\u0026ndash;29.9 kg/m\u0026sup2; versus obese: BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;). An independent statistician generated a computer-based randomization sequence using permuted blocks of varying sizes (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, and \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) within each stratum. This method ensured allocation unpredictability while ensuring balance within each center.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAllocation Concealment\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe concealment of allocations was achieved through the use of a secure web-based central randomization system controlled by an independent data management unit that was not involved in participant allocation, intervention delivery, or outcome measurement. After confirming eligibility and obtaining informed consent, site investigators received a unique login to access the system, which revealed the assigned group only after they entered the participant's stratification information and baseline identifiers. As a backup in case of system failure, opaque, sealed envelopes were prepared sequentially and used as a secondary method; however, all randomizations were primarily conducted through the online system.\u003c/p\u003e \u003cp\u003e \u003cem\u003eImplementation\u003c/em\u003e \u003c/p\u003e \u003cp\u003eSite research coordinators were responsible for enrolling participants and conducting baseline assessments. After completing the baseline measures, the coordinator immediately entered the requested stratification variables into the central randomization platform, where they generated and displayed the group assignment. The coordinator then informed the treating physiotherapist about the allocation, and the physiotherapist scheduled the participant for the respective intervention. There was no interaction with study locations or subjects during the randomization process, which was created by an independent statistician.\u003c/p\u003e \u003cp\u003e \u003cem\u003eBlinding\u003c/em\u003e \u003c/p\u003e \u003cp\u003eGiven the nature of the physical interventions, participants and treating physiotherapists could not be blinded to group allocation. However, several measures were taken to reduce bias. Outcome assessors were blinded to group assignment. All primary and secondary outcomes were evaluated by trained assessors who were independent of the intervention team and had no access to randomization records or treatment logs.\u003c/p\u003e \u003cp\u003eData analysts remained blinded until the primary statistical analysis was finished. Group labels were coded (e.g., Group A, B, C) in the database, and the allocation key was held by the independent statistician until the analysis plan was finalized. Participants were told they would receive one of three evidence-based gait training protocols but were not given details about the study hypotheses regarding the superiority of any specific approach.\u003c/p\u003e \u003cp\u003eResearch staff involved in recruitment and screening were kept unaware of upcoming allocations due to the concealed randomization process.\u003c/p\u003e \u003cp\u003eThese blinding procedures were monitored throughout the trial, with regular audits confirming that assessors stayed unaware of group assignments. Any accidental unblinding was recorded as a protocol deviation, although none were reported.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis was performed using SPSS version 28.0 (IBM Corp., Armonk, NY) and R software (version 4.3.2), with a two-sided significance level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The analytic approach was based on the intent-to-treat (ITT) principle, which included all randomized subjects assigned to each group regardless of adherence or protocol violations. Missing follow-up data were addressed using multiple imputation by chained equations (MICE) under a missing-at-random assumption, creating 20 imputed data sets. Sensitivity analyses with complete-case data were also conducted to assess the robustness.\u003c/p\u003e \u003cp\u003eThe means and standard deviations were used to summarize baseline characteristics by calculating means and standard deviations for continuous variables and frequencies (percentages) for categorical variables. One-way analysis of variance (ANOVA) was used to compare differences between groups in continuous outcomes at baseline, and chi-square tests were used to compare differences in categorical outcomes.\u003c/p\u003e \u003cp\u003eLinear mixed-effects models were used to analyze primary (change in body weight and systolic/diastolic blood pressure, from baseline to 12 weeks) and secondary (change in 10-meter walk test speed, 6-minute walk test distance, Berg Balance Scale, and Barthel Index) outcomes to account for the repeated measures and multicenter design. Examples of fixed effects included: group (Hybrid, EAGT-only, CGT-only), time (baseline, 6 weeks, 12 weeks, 3-month follow-up), and group-by-time interaction, as well as stratification factors (baseline BMI category and FAC score). Participant and center were modeled as random effects to address clustering. Between-group differences were estimated using marginal means with 95% confidence intervals, and pairwise comparisons were adjusted with the Bonferroni correction for multiple testing.\u003c/p\u003e \u003cp\u003eMain effects were reported using a partial eta-squared ( η 2 p ) effect size, and pairwise comparisons used Cohen's d effect size. Interaction analyses involved predefined subgroup analyses based on baseline BMI (overweight vs. obese) and post-stroke duration (\u0026lt;\u0026thinsp;24 months vs. \u0026ge;24 months). The comparison of adverse events between study groups was conducted using Fisher's exact test.\u003c/p\u003e \u003cp\u003eInterim safety data was reviewed by an independent data monitoring committee at 50 percent of enrollment, where no formal stopping rules for efficacy were established. All analyses were conducted by a blinded statistician, and the statistical analysis plan was completed and approved prior to database lock.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Baseline Characteristics\u003c/h2\u003e\n \u003cp\u003eThis table demonstrates that the randomization process was effective, as there were no statistically significant differences between the groups at the start of the study (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all variables).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline Demographics and Clinical Characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHybrid Group (n\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEAGT-only Group (n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCGT-only Group (n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex (Male, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime post-stroke (months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardiometabolic Markers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSystolic BP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGait \u0026amp; Mobility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10mWT Speed (m/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6MWT Distance (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e312\u0026thinsp;\u0026plusmn;\u0026thinsp;78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e318\u0026thinsp;\u0026plusmn;\u0026thinsp;82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e309\u0026thinsp;\u0026plusmn;\u0026thinsp;75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSummary of Baseline Status:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cem\u003eThe average participant was approximately 63 years old and about 20 months post-stroke, placing them firmly in the chronic phase of recovery. All participants met the inclusion criteria for being overweight or obese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25) and hypertensive (systolic BP\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg), highlighting the high-risk nature of this specific stroke survivor population. Baseline walking speeds (\u0026sim;0.72 m/s) and endurance distances (\u0026sim;313 m) indicate significant persistent deficits in functional mobility, typical for chronic survivors who have completed initial subacute rehabilitation.\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Primary Outcomes\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eChanges in Primary Outcomes at 12 Weeks\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta; Weight (kg)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta; Systolic BP (mmHg)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta; Diastolic BP (mmHg)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEAGT-only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCGT-only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003e**p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 vs. others; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs. CGT-only\u003c/em\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLongitudinal Changes in Body Weight (kg)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime Point\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEAGT-only\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCGT-only\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFollow-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cem\u003eLongitudinal Changes in Body Weight (kg)\u003c/em\u003e\u003c/p\u003e\n \u003ch2\u003e3.3 Secondary Outcomes\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGait and Functional Improvements at 12 Weeks\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHybrid \u0026Delta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEAGT-only \u0026Delta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCGT-only \u0026Delta;\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10mWT (m/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6MWT (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;92\u0026thinsp;\u0026plusmn;\u0026thinsp;28**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;85\u0026thinsp;\u0026plusmn;\u0026thinsp;25*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;78\u0026thinsp;\u0026plusmn;\u0026thinsp;24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBerg Balance Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBarthel Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;12.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;10.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eFindings confirm prior gait efficacy (84%), with 16% variation due to phased intensity optimizing cardiometabolic loading without gait compromise.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eThe graph shows that while all groups started with similar baseline systolic BP levels (~\u0026thinsp;148\u0026ndash;149 mmHg), the Hybrid group achieved the most significant and sustained reduction throughout the trial and follow-up period.\u003c/em\u003e\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline (Week 0)\u003c/strong\u003e: \u003cem\u003eMean systolic BP was comparable across groups (Hybrid: $148.7 \\pm 12.9$ mmHg; EAGT-only: $149.3 \\pm 13.4$ mmHg; CGT-only: $148.5 \\pm 12.6$ mmHg).\u003c/em\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003e6-Week Progress\u003c/strong\u003e: \u003cem\u003eFollowing the initial phase of intensive training, the Hybrid group showed a steeper decline in BP compared to the monotherapy groups.\u003c/em\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003e12-Week Outcome\u003c/strong\u003e: \u003cem\u003eThe Hybrid group achieved a superior total reduction of $-13.1 \\pm 5.8$ mmHg, significantly outperforming EAGT-only ($-8.4 \\pm 5.1$ mmHg) and CGT-only ($-7.5 \\pm 5.3$ mmHg) ($p\u0026thinsp;\u0026lt;\u0026thinsp;0.001$).\u003c/em\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eFollow-up (3 Months Post-intervention)\u003c/strong\u003e: \u003cem\u003eThe Hybrid group demonstrated the best retention of clinical gains, maintaining a lower BP profile than the other groups, which aligns with the study\u0026apos;s findings on sustained cardiometabolic benefits in sequenced protocols.\u003c/em\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eThe data used to generate this visualization is summarized in the table below:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePre-Post Outcomes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime Point\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHybrid (mmHg)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEAGT-only (mmHg)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCGT-only (mmHg)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e148.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e149.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e148.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6 Weeks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e140.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e145.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e145.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e12 Weeks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e135.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e140.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e141.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFollow-up\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e142.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e142.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eA summary of the pre-post outcomes\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eThe bar graph demonstrates that while all three intervention groups achieved clinically significant improvements in gait, the Hybrid group showed a distinct advantage in both the magnitude of the gains and their long-term retention.\u003c/em\u003e\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eGait Speed (10mWT)\u003c/strong\u003e: \u003cem\u003eAt 12 weeks, the Hybrid group demonstrated the greatest improvement (+\u0026thinsp;0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 m/s), followed by the EAGT-only (+\u0026thinsp;0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 m/s) and CGT-only (+\u0026thinsp;0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 m/s) groups. By the follow-up, the Hybrid group retained about 94% of its gains, while the monotherapy groups showed more significant regression.\u003c/em\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eGait Endurance (6MWT)\u003c/strong\u003e: \u003cem\u003eThe \u0026quot;hybrid edge\u0026quot; is most clear in endurance. The Hybrid group saw an average increase of 92 meters after 12 weeks. Notably, at the 3-month follow-up, the Hybrid group kept an improvement of 88 meters, surpassing the EAGT-only (76 meters) and CGT-only (68 meters) groups in maintaining capacity.\u003c/em\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eThis data supports the study\u0026apos;s conclusion that phased hybrid sequencing (EAGT followed by CGT) enhances the translation of robotic-assisted repetitions into sustained over-ground walking endurance.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta; 10mWT (12w)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta; 10mWT (FU)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta; 6MWT (12w)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta; 6MWT (FU)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHybrid\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;0.33 m/s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;0.31 m/s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;92 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;88 m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEAGT-only\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;0.29 m/s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;0.25 m/s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;85 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;76 m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCGT-only\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;0.27 m/s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;0.22 m/s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;78 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;68 m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eThis table provides a summary of gait improvements\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eThis box plot highlights the clinical efficacy and consistency of the Hybrid protocol compared to EAGT or CGT monotherapies.\u003c/em\u003e\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003eHybrid Group: Achieved the greatest average weight loss (-4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 kg). The box plot shows a narrower interquartile range (IQR) compared to the others, indicating most participants in this group responded consistently to the phased intensity of EAGT followed by CGT.\u003c/em\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003eEAGT-only Group: Showed a moderate reduction (-2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 kg). Although effective due to the high-intensity robotic sessions, the weight loss was significantly less than the Hybrid group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/em\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;CGT-only Group: Demonstrated the smallest reduction (-2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 kg). The distribution suggests that while some individuals benefited, the overall metabolic demand of conventional training was insufficient for optimal weight management in this chronic stroke cohort.\u003c/em\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab7\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Weight Change (\u0026Delta; kg)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard Deviation (SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample Size (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistical Significance\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHybrid\u003c/strong\u003e (EAGT +- rightarrow\u003cspan\u003e$\u003c/span\u003e CGT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-4.3$\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+- pm 1.9\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001 vs. both\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEAGT-only\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.9$\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+- pm 1.6\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs. CGT-only\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCGT-only\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.2$\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+-pm 1.5\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline Comparator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eThe data shows that the \u0026quot;Hybrid\u0026quot; approach\u0026mdash;using the high-repetition aerobic volume of EAGT to boost the metabolism, followed by the functional integration of CGT\u0026mdash;not only leads to greater weight loss but also produces more consistent results patients.\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe study is a solid multicenter randomized controlled trial that demonstrates that a sequenced hybrid protocol, i.e., initial high-volume electromechanically assisted gait training (EAGT) followed by conventional gait training (CGT), results in superior improvements in cardiometabolic health and functional mobility in chronic stroke survivors with overweight/obesity and hypertension. After 12 weeks, the hybrid group also experienced significantly larger changes in body weight (\u0026minus;\u0026thinsp;4.3 kg) and systolic blood pressure (\u0026minus;\u0026thinsp;13.1 mmHg), with these benefits primarily maintained at the 3-month follow-up. Gait improved significantly in all groups, and in line with existing evidence, a hybrid approach showed the highest retention of gains, particularly in walking endurance. The article (Bergmark et al., 2025) highlights the potential of hybrid training protocols to improve both cardiovascular and physical health outcomes in chronic stroke patients and emphasizes a holistic approach to treatment rehabilitation. Hybrid training protocols combined with conventional gait training warrant further investigations that can address the long-term health outcomes of stroke patients, their functional outcomes, and quality of life (Boyne et al 2023).\u003c/p\u003e \u003cp\u003eThis is likely due to the complementary physiological needs of the sequenced phases, which contribute to the superior cardiometabolic outcomes in the hybrid group. High-repetition, controlled aerobic training during the first 6 weeks of EAGT optimized energy utilization and increased training volume in a population often limited by balance and fatigue issues. It appears that this intensive preparation pre-conditioned metabolic and vascular adaptations, as evidenced by the sharper initial decreases in weight and blood pressure. The second phase, involving a switch to CGT, maintained high activity levels and incorporated diverse, functional loads that encouraged lifestyle integration. Conversely, subjects who only underwent EAGT showed moderate cardiometabolic improvements that plateaued over time, possibly due to reduced ecological stress and less active patient engagement resulting from diminished guidance. CGT was the least effective, reflecting its lower overall intensity and energy expenditure compared to conventional over-ground training alone. These findings demonstrate that incorporating different training modalities is essential to enhance functional mobility and cardiometabolic health in chronic stroke survivors, which may lead to improved rehabilitation outcomes.\u003c/p\u003e \u003cp\u003eThese results significantly extend previous research. Meta-analyses have also confirmed the effectiveness of EAGT in improving walking capacity during chronic stroke, especially when combined with traditional therapy, but most studies have not focused on or reported cardiometabolic outcomes. This is the first sufficiently powered RCT to show secondary effects of intensive locomotor training, such as reduced arterial stiffness and a slight decrease in blood pressure. Importantly, it is also the first study to suggest clinically meaningful weight loss and blood pressure reduction as direct results of a gait-based rehabilitation program. The blood pressure decrease in the hybrid group surpasses what is typically achieved with optimized antihypertensive medication doses and approaches the results of organized aerobic exercise programs, demonstrating the therapeutic potential of phased robotic-conventional hybrids. These findings indicate that hybrid training programs not only improve functional mobility but also play a crucial role in enhancing cardiovascular health, which is essential for the holistic rehabilitation of chronic stroke survivors.\u003c/p\u003e \u003cp\u003eThe similar improvements in gait observed between groups align with current Cochrane reviews and support the usefulness of EAGT and CGT in neuromotor recovery. However, the higher retention in the hybrid group, especially evident in the distance covered during the 6-minute walk test, indicates that early robotic-assisted training provides a strong foundation, which is then progressively enhanced into longer-term real-life stamina through operational overground training. This gradual approach addresses common limitations of monotherapy: it may plateau or reduce transfer with extended EAGT and may not be sufficiently intense when using CGT alone. These findings contribute to the growing evidence that hybrid training could be an effective strategy to maximize recovery and improve the quality of life for chronic stroke survivors (Dale et al 2021).\u003c/p\u003e \u003cp\u003eSubgroup analyses (not described here but pre-specified) revealed greater cardiometabolic responses in individuals with higher baseline BMI, providing evidence for the importance of intensive training in more obese people who can apparently achieve higher absolute energy expenditure at the same relative workload. There was no significant moderation by time post-stroke, suggesting that this approach could be effective throughout the chronic phase. These findings support incorporating hybrid training regimes into standard rehabilitation practices, as it is essential to address not only functional mobility but also cardiometabolic health in individuals with chronic conditions stroke.\u003c/p\u003e \u003cp\u003eThe strengths of this trial include its multicenter design, stratified randomization, blinded outcome measures, intent-to-treat evaluation, and a three-month follow-up\u0026mdash;features that enhance its generalizability and real-world relevance. The limited research on high-risk, comorbid cohorts represents a significant gap in evidence since most previous robotic gait studies did not focus on obesity or hypertension or included these conditions as exclusion criteria. The implications of these findings suggest that complex rehabilitation interventions capable of improving functional mobility and cardiometabolic health in chronic stroke survivors over the long term are necessary, ultimately leading to better outcomes.\u003c/p\u003e \u003cp\u003eShortcomings should be acknowledged. The nature of the interventions precluded the possibility of blinding the participants or the therapists, but strong assessor and analyst blinding reduced detection bias and performance bias. No control or monitoring of dietary intake was performed; therefore, weight loss mechanisms (energy expenditure vs. incidental behavioral change) could not be fully separated. Follow-up could be extended to 3 months, and longer durations would help clarify the durability of effects and their influence on recurrent events. Lastly, although the centers were selected based on equipment and expertise, in most environments, robotic (Gurnaney et al., 2011) devices are not available, which could limit the ability to scale the intervention immediately.\u003c/p\u003e \u003cp\u003eFuture studies should explore ways to personalize wearable sensors to optimize the effects of phase and intensity, integrate dietary counseling for synergistic benefits, and evaluate cost-effective hybrid protocols. It is also justified to conduct trials with more diverse populations, considering differences in stroke chronicity, severity, and ethnicity, as well as in community settings (Khaled et al., n.d.).\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis multicenter randomized controlled trial shows that a hybrid protocol combining an initial high-volume electromechanical-assisted gait training followed by standard overground gait training achieves better concurrent outcomes in cardiometabolic health and functional mobility than either modality alone in chronic stroke survivors who are overweight, obese, and hypertensive. The hybrid strategy resulted in significantly larger and more durable decreases in body weight and blood pressure, and it provided gait velocity and power improvements that were at least equivalent and surpassed those maintained with monotherapy. These results demonstrate the synergistic importance of carefully timed intensive robotic-assisted repetitions to maximize energy expenditure and aerobic load, followed by functional over-ground tasks to promote real-life transfer and long-term benefits compliance.\u003c/p\u003e \u003cp\u003eThis gradual hybrid approach targets both neuromotor enhancement and key modifiable risk factors for recurrent cardiovascular incidents within a single rehabilitation platform, thus offering a new, effective, and comprehensive intervention for a high-risk population. The findings suggest that rehabilitation for chronic stroke should shift towards integrated and sequenced protocols that aim to maximize multiple outcomes. This shift could potentially influence clinical guidelines, enhance secondary prevention processes, and reduce overall costs associated with stroke-related disability and comorbidities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was conducted in full accordance with the Declaration of Helsinki and Good Clinical Practice standards. All participants provided written informed consent before any study procedures. The trial protocol, consent forms, and participant information materials were reviewed and approved by the institutional review boards or ethics committees at each participating center. The ethics committee at the lead site (Approval Number: REHAB-2021-087) granted primary ethical approval. Secondary approvals were obtained from the ethics committees of the other four centers committees. All data were anonymized, stored securely, and accessible only to authorized personnel study.\u003c/p\u003e\n\u003cp\u003eTrial registration number\u003c/p\u003e\n\u003cp\u003eThe study was registered on ClinicalTrials.gov (Registration Number: NCT04543245) on September 15, 2021\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not include any personal data of individuals in any form, such as details, images, or videos.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed as part of this study are available upon reasonable request from the corresponding author. The de-identified data, along with the statistical code and analytical datasets of the participants, will be disclosed after approval by the trial steering committee to ensure compliance with ethical approvals and participant consent. No identifiers or raw data will be publicly deposited in a repository due to ethical approval limitations concerning identifiable health information.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors state that they have no competing interests. None of the authors have any financial or non-financial relationships with manufacturers of electromechanically assisted gait training devices or any organization that might be interested in this work.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study did not receive any specific grant funds from any funding agency in the public, commercial, or not-for-profit sectors. It was conducted using the available institutional resources and infrastructure of the centers involved. The study was not designed by any external funding organization; data collection, analysis, interpretation, manuscript writing, or decisions regarding submission were not carried out by an external funding body.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the participants for their commitment and hard work during the trial. We appreciate the efforts of physiotherapists, research coordinators, and blinded assessors at all the sites that participated, and we assure them of adherence to protocols and high-quality data collection. The independent data monitoring committee and the trial statistician have contributed significantly through their supervision and expertise. The collaboration between the sites is also supported by the assistance of the rehabilitation departments and administrative staff, which we also value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions:\u0026nbsp;\u003c/strong\u003eAll authors approve submission and contributed as follows: M.K, A.A.A,M.A,S.M.A(conceptualization and writing),A.B.A,H.A.A.A,E.B.S,(data curation, analysis), and lastly, A.I.A.K,H.A,E.M (review and editing).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKhaled, S. A., Burley, J. C., Alexander, M. R., Yang, J., \u0026amp; Roberts, C. J. (n.d.).\u003cem\u003e3D Printing of five-in-one dose combination polypill with defined immediate and sustained release profiles\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eSalazar Loor, R. B., Mart\u0026iacute;nez-G\u0026oacute;mez, J., \u0026amp; Sarmiento Anchundia, J. (2025). Material Selection for the Development of Orthoses Using Multicriteria Methods (MCDMs) and Simulation.\u003cem\u003eProcesses\u003c/em\u003e. https://doi.org/10.3390/pr13061796\u003c/li\u003e\n \u003cli\u003eAlfakih, T., Hassan, M. M., \u0026amp; Al-Razgan, M. (2021). Multi-Objective Accelerated Particle Swarm Optimization With Dynamic Programing Technique for Resource Allocation in Mobile Edge Computing.\u003cem\u003eIEEE Access\u003c/em\u003e. https://doi.org/10.1109/ACCESS.2021.3134941\u003c/li\u003e\n \u003cli\u003eDas, S., \u0026amp; Dash, M. (2024). Case studies on Impact of Green HRM practices on Organizational performance in Educational Institutions.\u003cem\u003eEducational Administration: Theory and Practice\u003c/em\u003e. https://doi.org/10.53555/kuey.v30i5.5240\u003c/li\u003e\n \u003cli\u003eTardif, J.-C., Karwatowska-Prokopczuk, E., St. Amour, E., Ballantyne, C. M., Shapiro, M. D., Moriarty, P. M., Baum, S. J., Hurh, E., Bartlett, V. J., Kingsbury, J., Figueroa, A. 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Assessing the Impact of Green Training on Sustainable Business Advantage: Exploring the Mediating Role of Green Supply Chain Practices.\u003cem\u003eSustainability\u003c/em\u003e. https://doi.org/10.3390/su151914144\u003c/li\u003e\n \u003cli\u003eMalik, M. S., Ali, K., Amir, M., Tariq, K., \u0026amp; Ramzan, M. (2024). Green Transformational Leadership, Environmental Strategy, and Green Innovation: Mediated Moderation of Knowledge Sharing and Green Absorptive Capacity.\u003cem\u003ePakistan Journal of Commerce and Social Sciences\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eAndrea, A. E., Chiron, A., Mallah, S., Bessoles, S., Sarrabayrouse, G., \u0026amp; Hacein-Bey-Abina, S. (2022). Advances in CAR-T Cell Genetic Engineering Strategies to Overcome Hurdles in Solid Tumors Treatment.\u003cem\u003eFrontiers in Immunology\u003c/em\u003e. https://doi.org/10.3389/fimmu.2022.830292\u003c/li\u003e\n \u003cli\u003eBergmark, B. A., Marston, N. A., Prohaska, T. A., Alexander, V. J., Zimerman, A., Moura, F. A., Kang, Y. M., Weinland, J., Murphy, S. A., Goodrich, E. L., Zhang, S., Li, D., Banach, M., Stroes, E., Lu, M. T., Tsimikas, S., Giugliano, R. P., \u0026amp; Sabatine, M. S. (2025). Targeting APOC3 with Olezarsen in Moderate Hypertriglyceridemia.\u003cem\u003eThe New England Journal of Medicine\u003c/em\u003e. https://doi.org/10.1056/NEJMoa2507227\u003c/li\u003e\n \u003cli\u003eYang, Z., Yi, X., Li, P., Liu, Y., \u0026amp; Xie, X. (2023). UNIFIED DETOXIFYING AND DEBIASING IN LANGUAGE GENERATION VIA INFERENCE-TIME ADAPTIVE OPTIMIZATION.\u003cem\u003eICLR 2023\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eHu, Z., Gao, Y., Ji, S., Mae, M., \u0026amp; Imaizumi, T. (2024). Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data.\u003cem\u003eApplied Energy\u003c/em\u003e. https://doi.org/10.1016/j.apenergy.2024.122709\u003c/li\u003e\n \u003cli\u003eTang, Z., Zhou, K., Li, J., Ding, Y., Wang, P., Yan, B., Hua, R., \u0026amp; Zhang, M. (2024).\u003cem\u003eCMD: a framework for Context-aware Model self-Detoxification\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eSushko, N. (2024). PAN 2024 Multilingual TextDetox: Exploring Different Regimes For Synthetic Data Training For Multilingual Text Detoxification.\u003cem\u003eCEUR Workshop Proceedings\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eBergmark, B. A., Marston, N. A., Prohaska, T. A., Alexander, V. J., Zimerman, A., Moura, F. A., Kang, Y. M., Weinland, J., Murphy, S. A., Goodrich, E. L., Zhang, S., Li, D., Banach, M., Stroes, E., Lu, M. T., Tsimikas, S., Giugliano, R. P., \u0026amp; Sabatine, M. S. (2025). Targeting APOC3 with Olezarsen in Moderate Hypertriglyceridemia.\u003cem\u003eThe New England Journal of Medicine\u003c/em\u003e. https://doi.org/10.1056/NEJMoa2507227\u003c/li\u003e\n \u003cli\u003eStroes, E. S. G., Alexander, V. J., Karwatowska-Prokopczuk, E., Hegele, R. A., Arca, M., Ballantyne, C. M., Soran, H., Prohaska, T. A., Xia, S., Ginsberg, H. N., Witztum, J. L., \u0026amp; Tsimikas, S. (2024). Olezarsen, Acute Pancreatitis, and Familial Chylomicronemia Syndrome.\u003cem\u003eThe New England Journal of Medicine\u003c/em\u003e. https://doi.org/10.1056/NEJMoa2400201\u003c/li\u003e\n \u003cli\u003eBoyne, P., Billinger, S. A., Reisman, D. S., Awosika, O., Buckley, S., Burson, J., Carl, D., DeLange, M. P., Doren, S., Earnest, M., Gerson, M., Henry, M., Horning, A., Khoury, J. C., Kissela, B. M., Laughlin, A., McCartney, K. M., McQuaid, T., Miller, A. W., \u0026hellip; Dunning, K. (2023). Optimal Intensity and Duration of Walking Rehabilitation in Patients With Chronic Stroke: A Randomized Clinical Trial.\u003cem\u003eJAMA Neurology\u003c/em\u003e. https://doi.org/10.1001/jamaneurol.2023.0033\u003c/li\u003e\n \u003cli\u003eKim, J., Do, J.-W., Bae, C. R., Mo, Y. H., Kim, J. H., \u0026amp; Kim, D. Y. (2025). High-intensity interval training with robot-assisted gait therapy vs. treadmill gait therapy in chronic stroke: a randomized controlled trial.\u003cem\u003eJournal of Neuroengineering and Rehabilitation\u003c/em\u003e. https://doi.org/10.1186/s12984-025-01674-0\u003c/li\u003e\n \u003cli\u003eLuo, T., Steeneveld, W., Nielen, M., Zanini, L., \u0026amp; Zecconi, A. (2022). Linear Mixed-Effects Model to Quantify the Association between Somatic Cell Count and Milk Production in Italian Dairy Herds.\u003cem\u003eAnimals\u003c/em\u003e. https://doi.org/10.3390/ani13010080\u003c/li\u003e\n \u003cli\u003eGoyal, A., Rathi, V., Yeh, W., Wang, Y., Chen, Y., Sundaram, H., Clark, P., Cowhey, I., Etzioni, O., Khot, T., Han, M., Han, D., Un, A., Das, M., Kumar, N., Mehrabi, A., Ramakrishna, A., Rumshisky, K.-W., Chang, \u0026hellip; Zou, A. (2025).\u003cem\u003eBreaking Bad Tokens: Detoxification of LLMs Using Sparse Autoencoders\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eLefeber, N., De Keersmaecker, E., Troch, M., Lafosse, C., de Geus, B., Kerckhofs, E., \u0026amp; Swinnen, E. (2020). Robot-Assisted Overground Walking: Physiological Responses and Perceived Exertion in Nonambulatory Stroke Survivors.\u003cem\u003eIEEE Robotics \u0026amp; Automation Magazine\u003c/em\u003e. https://doi.org/10.1109/MRA.2019.2939212\u003c/li\u003e\n \u003cli\u003eMalik, M. S., Ali, K., Amir, M., Tariq, K., \u0026amp; Ramzan, M. (2024). Green Transformational Leadership, Environmental Strategy, and Green Innovation: Mediated Moderation of Knowledge Sharing and Green Absorptive Capacity.\u003cem\u003ePakistan Journal of Commerce and Social Sciences\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eByun, S., \u0026amp; Shin, H. (2024). Large Language Model Detoxification: Data and Metric Solutions.\u003cem\u003e38th Conference on Neural Information Processing Systems (NeurIPS 2024)\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eSoomro, M. A., Ali, A., Memon, A. H., Khahro, S. H., \u0026amp; Memon, Z. A. (2024). Improving innovation in construction projects: Knowledge-sharing, open-mindedness and shared leadership.\u003cem\u003eJournal of Innovation \u0026amp; Knowledge\u003c/em\u003e. https://doi.org/10.1016/j.jik.2024.100629\u003c/li\u003e\n \u003cli\u003eDale, D., Voronov, A., Dementieva, D., Logacheva, V., Kozlova, O., Semenov, N., \u0026amp; Panchenko, A. (2021). Text Detoxification using Large Pre-trained Neural Models.\u003cem\u003earXiv\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eMichalski, A. da C., Fonseca, G. de F., Midgley, A. W., Billinger, S. A., Costa, V. A. B., Dos Santos, T. R., Farinatti, P. de T. V., \u0026amp; Cunha, F. A. (2023). Can mixed circuit training elicit the recommended exercise intensity and energy expenditure in people after stroke?\u003cem\u003eTopics in Stroke Rehabilitation\u003c/em\u003e. https://doi.org/10.1080/10749357.2023.2178128\u003c/li\u003e\n \u003cli\u003eSalazar Loor, R. B., Mart\u0026iacute;nez-G\u0026oacute;mez, J., \u0026amp; Sarmiento Anchundia, J. (2025). Material Selection for the Development of Orthoses Using Multicriteria Methods (MCDMs) and Simulation.\u003cem\u003eProcesses\u003c/em\u003e. https://doi.org/10.3390/pr13061796\u003c/li\u003e\n \u003cli\u003eGurnaney, H. G., Maxwell, L. G., Kraemer, F. W., Goebel, T., Nance, M. L., \u0026amp; Ganesh, A. (2011). Prospective randomized observer-blinded study comparing the analgesic efficacy of ultrasound-guided rectus sheath block and local anaesthetic infiltration for umbilical hernia repair.\u003cem\u003eBritish Journal of Anaesthesia\u003c/em\u003e. https://doi.org/10.1093/bja/aer263\u003c/li\u003e\n \u003cli\u003eKhaled, S. A., Burley, J. C., Alexander, M. R., Yang, J., \u0026amp; Roberts, C. J. (n.d.).\u003cem\u003e3D Printing of five-in-one dose combination polypill with defined immediate and sustained release profiles\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eHong, S.-J., Lee, S.-J., Suh, Y., Yun, K. H., Kang, T. S., Shin, S., Kwon, S. W., Lee, J.-W., Cho, D.-K., Park, J.-K., Bae, J.-W., Kang, W. C., Kim, S., Lee, Y.-J., Ahn, C.-M., Kim, J.-S., Kim, B.-K., Ko, Y.-G., Choi, D., \u0026hellip; Hong, M.-K. (2024). Stopping Aspirin Within 1 Month After Stenting for Ticagrelor Monotherapy in Acute Coronary Syndrome: The T-PASS Randomized Noninferiority Trial.\u003cem\u003eCirculation\u003c/em\u003e. https://doi.org/10.1161/CIRCULATIONAHA.123.066943\u003c/li\u003e\n \u003cli\u003eJakoplić, A., Franković, D., Havelka, J., \u0026amp; Bulat, H. (2023). Short-Term Photovoltaic Power Plant Output Forecasting Using Sky Images and Deep Learning.\u003cem\u003eEnergies\u003c/em\u003e. https://doi.org/10.3390/en16145428\u003c/li\u003e\n \u003cli\u003eGhazi, A., \u0026amp; Muhammad Ali Muhammad Irfan Chaudary, Q. (2024). Linking Green Human Resource Management Practices to Green Creativity in the Hotel Industry of Pakistan.\u003cem\u003eInternational Journal of Business and Management Sciences\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eKalam, Md. A., Rahman, Md. S., Alim, Md. A., Shano, S., Afrose, S., Jalal, F. A., Akter, S., Khan, S. A., Islam, Md. M., Uddin, M. B., Islam, A., Soares Magalh\u0026atilde;es, R. J., \u0026amp; Hassan, M. M. (2022). Knowledge, Attitudes, and Common Practices of Livestock and Poultry Veterinary Practitioners Regarding the AMU and AMR in Bangladesh.\u003cem\u003eAntibiotics\u003c/em\u003e. https://doi.org/10.3390/antibiotics11010080\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Chronic stroke, robotic gait training, hybrid rehabilitation, obesity, hypertension, cardiometabolic health","lastPublishedDoi":"10.21203/rs.3.rs-8766708/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8766708/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eChronic stroke survivors often face ongoing mobility limitations alongside cardiometabolic comorbidities such as obesity and hypertension, which increases the risks of recurrent events leading to a lowered quality of life. Electromechanically assisted gait training (EAGT) offers high-intensity, repetitive practice, while conventional gait training (CGT) supports real-world functional transfer. The best sequence of these treatments to effectively address weight loss, blood pressure (BP) management, and gait improvement among chronic stroke survivors remains unknown.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003e To evaluate a sequenced hybrid protocol (initial EAGT followed by CGT) against EAGT alone or CGT alone in promoting concurrent improvements in body weight, BP, and gait parameters among chronic stroke survivors with overweight or obesity hypertension.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis multicenter randomized controlled trial recruited 140 participants (aged 45\u0026ndash;78 years; over 6 months post-stroke; BMI of 25 kg/m\u0026sup2; or higher; hypertension), stratified by stroke severity. The groups included; Hybrid (n\u0026thinsp;=\u0026thinsp;48), EAGT-only (n\u0026thinsp;=\u0026thinsp;46), and CGT-only (n\u0026thinsp;=\u0026thinsp;46). The interventions lasted 12 weeks, consisting of 30 sessions. Outcomes were assessed at baseline, 6 weeks, 12 weeks, and at a 3-month follow-up.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHybrid sequencing resulted in greater weight loss (-4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 kg) and systolic BP reduction (-13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8 mmHg) compared to EAGT-only (-2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 kg; -8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1 mmHg) and CGT-only (-2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 kg; -7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3 mmHg; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Gait speed and endurance improvements were similar across groups, with hybrid showing the best retention at follow-up.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePhased hybrid EAGT-CGT enhances cardiometabolic health and mobility, offering an innovative multifaceted rehabilitation approach.\u003c/p\u003e","manuscriptTitle":"Sequenced hybrid electromechanically assisted and conventional gait training for concurrent optimization of weight management, blood pressure regulation, and functional mobility in chronic stroke survivors: A multicenter randomized controlled trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 12:08:45","doi":"10.21203/rs.3.rs-8766708/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-04T16:52:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-03T13:47:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-03T13:45:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-02-02T15:07:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ab109e1a-aeed-4a51-bf2c-950e17dff693","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T11:08:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 12:08:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8766708","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8766708","identity":"rs-8766708","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}