Sage Journals: Discover world-class research

Abstract

Background

Robotic-assisted rehabilitation technologies (RAT) are promising tools for stroke recovery, offering repetitive, task-specific, and feedback-driven training. Yet, their integration into routine clinical practice in Saudi Arabia remains limited. This study examined healthcare professionals’ awareness, perceptions, and adoption of RAT for stroke rehabilitation in Jeddah.

Methods

A cross-sectional survey was conducted among 210 healthcare professionals (neurologists, physical therapists, occupational therapists) from major hospitals in Jeddah. Data were collected using a validated questionnaire covering demographics, awareness, perceptions, adoption, and future outlook regarding RAT. Descriptive and inferential statistics were applied, with p < 0.05 indicating significance.

Results

Overall, 59.7% of participants had heard of RAT, but only 50% reported moderate understanding. Perceptions were generally positive, with 54.5% rating RAT as effective and 54% as safe. However, clinical adoption was low (30.4%), and usage was mostly rare (59.4%). Adoption was significantly associated with age, professional role, and years of experience (p < 0.05). Key barriers included high cost (32.4%), limited availability (23.8%), insufficient training (23.3%), and lack of awareness (20.5%). More than half of respondents (53%) believed RAT would play a pivotal role in future stroke rehabilitation, citing improved accessibility, cost reduction, and enhanced training as key facilitators.

Conclusion

Awareness and perceptions of RAT among healthcare professionals in Jeddah are moderate to positive, yet clinical adoption remains limited due to cost, training, and accessibility barriers. Enhancing knowledge, providing targeted training, and developing supportive policies are essential to align RAT implementation with Saudi Arabia’s Vision 2030 objectives for advanced healthcare innovation.

Keywords

robotic-assisted therapy stroke rehabilitation awareness perceptions adoption Saudi Arabia

Introduction

Stroke is a major public health concern and a leading cause of long-term disability worldwide. Its impact on patients’ physical, psychological, and social well-being underscores the need for effective rehabilitation strategies to enhance quality of life and reduce the burden on healthcare systems.¹

Robotic-assisted rehabilitation technologies (RAT) have gained attention for their potential to improve motor recovery and independence in stroke survivors by enabling repetitive, task-specific training, providing precise feedback, and reducing the physical demands on healthcare professionals, thereby promoting neuroplasticity.^2–4 In this study, RAT refers collectively to both upper-limb systems, which target arm and hand function, and lower-limb systems, commonly called robotic-assisted gait training, which focus on mobility; both share principles of high-intensity, task-specific repetition with real-time feedback.⁵ RAT has shown meaningful improvements in functional outcomes for both upper- and lower-limb stroke recovery, offering intensive, task-specific, and feedback-driven training that supports neuroplasticity. Recent evidence also highlights the importance of personalized rehabilitation approaches that account for individual differences among stroke survivors, emphasizing the role of motivation, engagement, and patient-centered therapy in optimizing recovery.^6–8 Technologies such as robotic systems can enhance patient involvement and provide tailored interventions, supporting healthcare professionals -particularly physiotherapists-in delivering more precise, consistent, and effective treatment aligned with each patient’s needs.

Healthcare professionals’ awareness, perceptions, and adoption of emerging rehabilitation technologies have been widely examined across diverse healthcare systems. International studies consistently show that clinicians’ acceptance of robotic and digital rehabilitation technologies is influenced by factors such as perceived effectiveness, safety, usability, cost, institutional backing, and training access.^9–11 While many clinicians acknowledge the potential of robotic-assisted rehabilitation to enhance motor recovery and therapy efficiency, real-world adoption remains variable and often limited, even in well-resourced healthcare settings.^12,13 These findings highlight a persistent gap between technological advancement and clinical implementation, underscoring the need to better understand contextual and professional determinants of adoption.

Most existing evidence on robotic-assisted rehabilitation originates from high-income Western countries, with limited data from Middle Eastern healthcare systems. In Saudi Arabia, where stroke prevalence is estimated at 29.8 per 100,000 individuals and rehabilitation services remain largely reliant on conventional physiotherapy, empirical research on healthcare professionals’ awareness, perceptions, and adoption of robotic-assisted rehabilitation technologies is scarce.^14–16 Previous studies have identified barriers to RAT adoption, including high costs, limited availability, insufficient training, and low awareness among healthcare providers.¹⁷ Overcoming these barriers is essential to modernize stroke rehabilitation and supports the objectives of Saudi Vision 2030, which emphasizes healthcare innovation, technological advancement, and improved patient outcomes.¹⁸

To address these gaps, this study investigates healthcare professionals’ awareness, perceptions, and adoption of RAT for stroke rehabilitation in Jeddah, Saudi Arabia. Specifically, it assesses participants’ level of awareness, explores perceptions regarding the effectiveness, safety, and perceived cost of these technologies, and identifies barriers and facilitators that may influence their integration into clinical practice. The ultimate goal is to provide insights that can guide strategies to enhance RAT adoption and support improved rehabilitation outcomes for stroke patients.

Methods

Study design and setting

This cross-sectional study was conducted in Jeddah, Saudi Arabia, targeting major hospitals with established stroke rehabilitation programs, including Saudi German Hospital, Dr. Soliman Fakeeh Hospital, Dr. Soliman Habib Hospital, International Medical Center, and Andalusia Hospital. Data were collected at a single point in time using structured questionnaires. The study was designed and reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for observational studies.

Participants and sampling

A total of 210 healthcare professionals participated in the study. Participants were recruited using convenience sampling from the selected hospitals, based on accessibility and involvement in stroke rehabilitation programs. Eligible participants included neurologists, physical therapists, and occupational therapists currently engaged in stroke care. Healthcare professionals not directly interacting with stroke patients were excluded.

The required sample size was determined using G*Power software (v.3.1.9.7) with a priori χ² tests for goodness-of-fit. Input parameters included an effect size (w) of 0.3, alpha error probability of 0.05, power of 0.95, and degrees of freedom set to 5. The calculation yielded a required sample size of 220, ensuring adequate statistical power.¹⁹ Our study included 210 respondents, slightly below the target, which may marginally affect statistical power but remains adequate for exploratory analysis.

Data collection

Data were collected using a structured questionnaire adapted from previously validated instruments.^20,21 The questionnaire underwent expert review by professionals in rehabilitation technologies and stroke care to ensure content validity. Internal consistency was confirmed with a Cronbach’s alpha of 0.7.

The questionnaire comprised the following sections: Demographics (age, gender, education level, years of experience, and professional role); Awareness (2 questions assessing understanding of RAT and its applications); Perceptions (2 questions evaluating views on effectiveness and safety of RAT); Adoption (3 questions on clinical use and perceived barriers); and Future Outlook (2 questions on the anticipated role of RAT and factors encouraging its adoption).

The questionnaire was intentionally designed to be brief, particularly for the awareness and perception domains, to minimize respondent burden and maximize participation and completion rates among busy healthcare professionals. This concise structure was considered suitable for capturing overall assessments within a cross-sectional survey while maintaining acceptable internal consistency.

Ethical considerations

Ethical approval was granted by the Ethical Committee of Batterjee Medical College, Jeddah, Saudi Arabia (Reference No. RES-2025-0005). Written informed consent was collected from all participants. For online surveys, participants provided electronic consent prior to participation, and signed consent forms were collected when applicable.

Statistical analysis

Data were analyzed using IBM SPSS Statistics version 28. Descriptive statistics (frequencies, percentages, means, and standard deviations) summarized demographics and survey responses. Awareness and perception were treated as ordinal variables, while adoption was categorical. Associations with demographic variables were examined using Chi-square, Kruskal-Wallis, Mann-Whitney U, and Spearman’s rank correlation tests as appropriate.

The analysis focused on descriptive and bivariate statistical methods to explore associations between adoption of robotic-assisted therapy and selected demographic and professional characteristics. Multivariable analysis was not performed, as the study was exploratory in nature and the sample size of RAT users was relatively limited. Therefore, the findings should be interpreted as associations rather than independent predictors.

Statistical significance was set at p < 0.05.

Results

Demographic characteristics of participants

A total of 210 healthcare professionals participated in the study. Table 1 presents the demographic distribution. The majority were female (56.2%), and the predominant age group was 31–45 years (36.2%), followed by 20–30 years (35.2%). Most participants held a bachelor’s degree (54.8%), and 38.2% had 6–10 years of experience. Regarding professions, physical therapists constituted the largest group (47.6%), followed by occupational therapists (23.8%) and neurologists (19%).

Table 1.

Demographic characteristics of healthcare professionals in the studied sample (n = 210).

Demographic data of healthcare professionals		N (%)
Gender	Female	118 (56.2%)
Gender	Male	92 (43.8%)
Age	20-30 years	74 (35.2%)
	31-45 years	76 (36.2%)
	46+ years	60 (28.6%)
Education Level	Diploma	20 (9.5%)
	Bachelor’s degree	115 (54.8%)
	Master’s degree	53 (25.2%)
	Doctorate or higher	22 (10.5%)
Years of Experience	0–5 years	49 (23.3%)
	6–10 years	80 (38.2%)
	11–15 years	53 (25.2%)
	16+ years	28 (13.3%)
Healthcare Profession	Neurologist	40 (19%)
	Physical therapist	100 (47.6%)
	Occupational therapist	50 (23.8%)
	Other	20 (9.5%)

Awareness and perceptions of robotic-assisted therapy

Table 2 summarizes awareness and perception levels. Overall, 59.7% of respondents had heard of robotic-assisted therapy for stroke rehabilitation, while 50% reported a moderate level of understanding. Regarding perceived effectiveness, 54.5% believed RAT is effective, 23.7% considered it moderately effective, and 21.8% perceived it as ineffective. In terms of safety, 54% considered RAT safe, 25% moderately safe, and 21% unsafe.

Table 2.

Awareness and perceptions of robotic-assisted technologies among healthcare professionals (n = 210).

Domain	Questions		N (%)	Min	Max	Mean ± SD
Awareness	Have you heard of robotic-assisted therapy for stroke rehabilitation?	Yes	125 (59.7%)	0	1	0.60 ± 0.49
		No	85 (40.3%)	0	1	0.60 ± 0.49
	How would you rate your level of understanding of robotic-assisted therapy?	High	16 (7.4%)	1	3	1.96 ± 0.76
		Moderate	105 (50%)
		Low	89 (42.3%)
Perception	What is your perception of the effectiveness of robotic-assisted therapy in improving stroke recovery?	Effective	114 (54.5%)	1	3	2.40 ± 0.78
		Moderately effective	50 (23.7%)
		Ineffective	46 (21.8%)
	Based on your experience, how safe do you perceive robotic-assisted therapy to be?	Safe	113 (54%)	1	3	2.29 ± 0.82
		Moderately safe	53 (25%)
		Unsafe	44 (21%)

The overall mean awareness score was 1.25 ± 0.49, indicating moderate awareness, while the mean perception score was 2.41 ± 0.57, reflecting generally positive perceptions toward RAT. Awareness scores were significantly associated with age, educational level, and years of experience (all p < 0.05). Similarly, perception scores, including views on effectiveness and safety, were significantly associated with profession, gender, and years of experience (all p < 0.05), with more experienced professionals and those in specific disciplines demonstrating higher perception scores.

Clinical adoption of robotic-assisted therapy

Only 30.4% (n = 64) of respondents reported incorporating RAT into their clinical practice (Table 3). Among these users, exoskeleton devices were most frequently utilized (53.1%), followed by end-effectors (35.9%). In terms of usage frequency, 59.4% reported rare use, 23.4% used RAT monthly, and 17.2% used it weekly; no participants reported daily use.

Table 3.

Clinical practice and adoption of robotic-assisted technologies among RAT users (n = 64).

Variables	N (%)
Use of robot-assisted therapy for stroke rehabilitation	64 (30.4%)
Type of robotic device used
Exoskeleton	34 (53.1%)
End-effector	23 (35.9%)
Other	7 (11%)
Frequency of robotic-assisted therapy usage
Daily	0 (0%)
Weekly	11 (17.2%)
Monthly	15 (23.4%)
Rarely	38 (59.4%)

Clinical adoption (actual use of RAT) was significantly associated with age (p < 0.01), profession (p < 0.05), and years of experience (p < 0.001), with older and more experienced professionals showing greater adoption. Major barriers to adoption included high cost (32.4%), limited availability (23.8%), insufficient training (23.3%), and lack of awareness (20.5%) (Figure 1).

Figure 1.

Main barriers to the use of robotic-assisted therapy in stroke rehabilitation (n = 210).

Future outlook of robotic-assisted therapy

More than half of the respondents (53%) believed that RAT will play a pivotal role in the future of stroke rehabilitation in Saudi Arabia. Key factors perceived to encourage adoption included improved accessibility (38%), reduced cost (27.5%), enhanced training opportunities (24.2%), and increased awareness and education (10.3%) (Figure 2).

Figure 2.

Key factors facilitating adoption of robotic-assisted therapy among healthcare professionals (n = 210).

Discussion

This study offers one of the first insights into healthcare professionals’ awareness, perceptions, and clinical adoption of robotic-assisted therapy (RAT) for stroke rehabilitation in Saudi Arabia. Findings indicate moderate awareness and generally positive perceptions, but relatively low clinical adoption, highlighting barriers that limit RAT integration into routine practice.

The study showed that only a portion of participants had heard of RAT, and merely a few reported high understanding. These findings highlight a substantial knowledge gap, consistent with prior studies in low-to middle-income regions, where awareness of advanced rehabilitation technologies remains limited.^14,21 Similar trends have been observed globally, with awareness levels rarely exceeding moderate levels among clinicians in comparable healthcare settings.^17,22 The significant associations between awareness, age, education, and years of experience in this study align with previous evidence indicating that senior professionals and those with higher educational attainment are more likely to engage with emerging technologies.^15,23,24 Targeted educational programs-integrated into continuing medical education (CME)- could enhance familiarity with RAT’s evidence base and practical applications.²⁵

Perceptions of RAT were generally positive, with many participants viewing it as effective and safe. This optimism mirrors findings from recent studies where healthcare workers acknowledged RAT’s potential to improve motor outcomes, reduce therapy workload, and enhance patient engagement.^3,16 However, the persistence of uncertainty-nearly one-quarter rated RAT as moderately effective or moderately safe-suggests lingering concerns about cost-effectiveness, training adequacy, and safety standards.²⁶ These concerns may stem from limited exposure to RAT in routine practice and a lack of national guidelines supporting its implementation. Integration of evidence-based frameworks, such as the Technology Acceptance Model (TAM), has been recommended to address these perceptions, emphasizing ease of use, perceived benefit, and institutional support.¹⁰

Despite positive perceptions, actual adoption was low, with only a portion of respondents incorporating RAT in practice-most using exoskeletons- and most reporting rare usage. This reflects a global trend where RAT remains underutilized despite promising clinical outcomes.^27,28 Barriers identified, such as high cost, limited availability, insufficient training, and lack of awareness, are consistent with previous studies highlighting financial and infrastructural constraints as primary impediments in resource-limited healthcare systems.^29,30 Cost concerns may be particularly relevant in Saudi Arabia, where advanced rehabilitation facilities remain concentrated in tertiary centers, limiting patient access and clinician exposure.^31,32 Strategies to address these barriers include government-subsidized programs, integration of RAT modules into rehabilitation curricula, and structured hands-on workshops to enhance competence and confidence in using robotic systems.³³

Over half of the respondents believed RAT will play a pivotal role in the future of stroke rehabilitation in Saudi Arabia. This finding reflects expectations rather than confirmed implementation and should be interpreted cautiously due to the cross-sectional design and reliance on self-reported data. Although these views align with national healthcare innovation strategies, they do not necessarily indicate current readiness for widespread adoption.

This forward-looking perspective is consistent with international projections anticipating increased adoption as technologies become more accessible and cost-effective.^29,34 Participants identified improved accessibility, reduced costs, enhanced training, and awareness campaigns as key facilitators, paralleling findings from recent implementation studies in which these factors significantly predicted successful integration.^29,35 Notably, the emphasis on training aligns with evidence from multiple healthcare contexts demonstrating that clinician upskilling is associated with more favorable perceptions and a greater willingness to adopt new technologies.^12,36

Although this study was conducted in Jeddah, Saudi Arabia, the identified barriers and facilitators to robotic-assisted rehabilitation adoption are consistent with those reported in international settings, supporting the broader relevance of the findings.

This study has several limitations. First, This study used convenience sampling within selected hospitals in Jeddah, which may limit representativeness and generalizability to healthcare professionals in other regions or settings with different resources, patient populations, or exposure to robotic-assisted rehabilitation technologies. Second, the cross-sectional design precludes causal inference, and longitudinal studies are needed to examine how awareness, perceptions, and adoption of RAT evolve over time. Third, reliance on self-reported data may introduce social desirability or recall bias, potentially inflating perceived awareness or favorable perceptions. Finally, the questionnaire assessed awareness and perceptions using only two items each; qualitative approaches such as interviews or focus groups could provide deeper insights into contextual and cultural factors influencing RAT adoption.

Conclusion

Awareness of robotic-assisted therapy among healthcare professionals in Jeddah is moderate, and perceptions are generally positive; however, clinical adoption remains constrained by high costs, limited availability, insufficient training, and lack of awareness. Adoption also varies with age, experience, and professional background. These findings underscore the need for targeted policy interventions, including investment in training programs, increased accessibility of RAT, and strategies to raise awareness, to support the integration of robotic-assisted therapy into routine stroke rehabilitation in Saudi Arabia.

Future directions

Future research should employ longitudinal designs to monitor changes in awareness, perceptions, and adoption following educational or infrastructural interventions. Comparative studies across regions in Saudi Arabia and the Gulf could elucidate geographical and institutional disparities. Incorporating mixed-method approaches, including qualitative interviews, can provide nuanced insights into clinician attitudes and contextual barriers. Cost-effectiveness analyses are needed to guide policy and encourage investment in RAT technologies.

Footnotes

ORCID iDs

Ahmed E. Altyar

Mai Albaik

Ethical considerations

Ethical approval was granted by the Ethical Committee of Batterjee Medical College, Jeddah, Saudi Arabia (Reference No. RES-2025-0005). Participants provided informed consent prior to beginning the online survey, in accordance with ethical guidelines. Artificial intelligence (AI) tool, Quillbot, was employed exclusively to enhance language clarity and style. It was not involved in the creation of content, data analysis, interpretation of results, or development of research ideas.

Author contributions

Ahmed E. Altyar supervised the research, provided continuous guidance throughout the study, and reviewed the final manuscript. Iffat Fatima Abdullateef conceived the research idea, carried out the practical component of the study, and contributed to drafting sections of the manuscript. Mai Albaik, as the main supervisor, oversaw the student’s work, obtained ethical approval, performed data analysis, and contributed to writing and critically revising the final version of the manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Guarantor

Dr. Mai Albaik is the guarantor of this work and takes full responsibility for the integrity of the study and the accuracy of the data analysis and reporting.

References

Feigin

Norrving

Mensah

. Global burden of stroke. Circulation research 2017; 120: 439–448. https://doi.org/10.1161/CIRCRESAHA.116.308413

Bertani

Melegari

De Cola

, et al. Effects of robot-assisted upper limb rehabilitation in stroke patients: a systematic review with meta-analysis. Neurological Sciences 2017; 38: 1561–1569. https://doi.org/10.1007/s10072-017-2995-5

Mehrholz

Pollock

Pohl

, et al. Systematic review with network meta-analysis of randomized controlled trials of robotic-assisted arm training for improving activities of daily living and upper limb function after stroke. Journal of neuroengineering and rehabilitation 2020; 17: 83. https://doi.org/10.1186/s12984-020-00715-0

Forman

Nielsen

Lorentzen

. Neuroplasticity at home: improving home-based motor learning through technological solutions. A review. Frontiers in rehabilitation sciences 2021; 2: 789165. https://doi.org/10.3389/fresc.2021.789165

Akıncı

Burak

, et al. Robot-Assisted Gait Training in Stroke Rehabilitation: A Multidisciplinary Overview on Lokomat Features. İstanbul Gelişim Üniversitesi Sağlık Bilimleri Dergisi 2025; 27: 1145–1155. https://doi.org/10.38079/igusabder.1644772

Dodakian

McKenzie

, et al. A home-based telerehabilitation program for patients with stroke. Neurorehabilitation and neural repair 2017; 31: 923–933. https://doi.org/10.1177/1545968317733818

Pastorino

Loreti

Giovannini

, et al. Challenges of prevention for a sustainable personalized medicine. Journal of personalized medicine 2021; 11: 311. https://doi.org/10.3390/jpm11040311

Kwakkel

Stinear

Essers

, et al. Motor rehabilitation after stroke: European Stroke Organisation (ESO) consensus-based definition and guiding framework. European stroke journal 2023; 8: 880–894. https://doi.org/10.1177/23969873231191304

Cao

H-L

Pham

Luu

, et al. Therapists’ perspective on acceptance of robot-assisted physical rehabilitation in a middle-income country: a study from Vietnam. Disability and Rehabilitation: Assistive Technology 2025; 20: 388–396. https://doi.org/10.1080/17483107.2024.2378057

10.

Klaic

Fong

Crocher

, et al. Application of the extended technology acceptance model to explore clinician likelihood to use robotics in rehabilitation. Disability and Rehabilitation: Assistive Technology 2024; 19: 52–59. https://doi.org/10.1080/17483107.2022.2060356

11.

Sørensen

Johannesen

DTS

Melkas

, et al. User acceptance of a home robotic assistant for individuals with physical disabilities: explorative qualitative study. JMIR Rehabilitation and Assistive Technologies 2025; 12: e63641. https://doi.org/10.2196/63641

12.

Fasano

Mauro

Beani

, et al. Towards the identification of patients’ needs for promoting robotics and allied digital technologies in rehabilitation: a systematic review. In: Healthcare. MDPI, 2025, p. 828.

13.

Devkar

Gupta

. The Future of Robotics in Health Care: Opportunities, Innovations, and Ethical Challenges. IJSAT-International Journal on Science and Technology 2025; 16: 1-7.

14.

Alqahtani

Alenazi

Hoover

, et al. Incidence of stroke among Saudi population: a systematic review and meta-analysis. Neurological Sciences 2020; 41: 3099–3104. https://doi.org/10.1007/s10072-020-04520-4

15.

Asirvatham

Marwan

. Stroke in Saudi Arabia: a review of the recent literature. Pan African Medical Journal 2014; 17: 1-6.

16.

Wang

, et al. Stroke rehabilitation: from diagnosis to therapy. Frontiers in neurology 2024; 15: 1402729. https://doi.org/10.3389/fneur.2024.1402729

17.

Veerbeek

Langbroek-Amersfoort

Van Wegen

, et al. Effects of robot-assisted therapy for the upper limb after stroke: a systematic review and meta-analysis. Neurorehabilitation and neural repair 2017; 31: 107–121. https://doi.org/10.1177/1545968316666957

18.

Mani

Goniewicz

. Transforming healthcare in Saudi Arabia: a comprehensive evaluation of vision 2030’s impact. Sustainability 2024; 16: 3277. https://doi.org/10.3390/su16083277

19.

Faul

Erdfelder

Lang

A-G

, et al. G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior research methods 2007; 39: 175–191. https://doi.org/10.3758/bf03193146

20.

Fundarò

Casale

Maestri

, et al. Technology Assisted Rehabilitation Patient Perception Questionnaire (TARPP-Q): development and implementation of an instrument to evaluate patients’ perception during training. Journal of NeuroEngineering and Rehabilitation 2023; 20: 35. https://doi.org/10.1186/s12984-023-01146-3

21.

Ullah

Maghazil

Qureshi

, et al. Knowledge and attitudes of rehabilitation professional toward telerehabilitation in Saudi Arabia: A cross-sectional survey. Telemedicine and e-Health 2021; 27: 587–591. https://doi.org/10.1089/tmj.2020.0016

22.

Jones

Jenkinson

Leathley

, et al. Stroke knowledge and awareness: an integrative review of the evidence. Age and ageing 2010; 39: 11–22. https://doi.org/10.1093/ageing/afp196

23.

Vourganas

Stankovic

, et al. Factors that contribute to the use of stroke self-rehabilitation technologies: A review. JMIR Biomedical Engineering 2019; 4: e13732. https://doi.org/10.2196/13732

24.

Chen

Bode

. Factors influencing therapists' decision-making in the acceptance of new technology devices in stroke rehabilitation. American journal of physical medicine & rehabilitation 2011; 90: 415–425. https://doi.org/10.1097/PHM.0b013e318214f5d8

25.

Reddy

. Training and Education for Rehabilitation Professionals. In: Innovations in Neurocognitive Rehabilitation: Harnessing Technology for Effective Therapy. Springer, 2025, pp. 305–328.

26.

Allen

. A cost-effectiveness study of home-based stroke rehabilitation. The University of Western Ontario, 2015.

27.

Louie

Eng

. Powered robotic exoskeletons in post-stroke rehabilitation of gait: a scoping review. Journal of neuroengineering and rehabilitation 2016; 13: 53. https://doi.org/10.1186/s12984-016-0162-5

28.

Mubin

Alnajjar

Jishtu

, et al. Exoskeletons with virtual reality, augmented reality, and gamification for stroke patients’ rehabilitation: systematic review. JMIR rehabilitation and assistive technologies 2019; 6: e12010. https://doi.org/10.2196/12010

29.

Cormican

Hirani

McKeown

. Healthcare professionals’ perceived barriers and facilitators of implementing clinical practice guidelines for stroke rehabilitation: A systematic review. Clinical rehabilitation 2023; 37: 701–712. https://doi.org/10.1177/02692155221141036

30.

Munce

Graham

Salbach

, et al. Perspectives of health care professionals on the facilitators and barriers to the implementation of a stroke rehabilitation guidelines cluster randomized controlled trial. BMC health services research 2017; 17: 440. https://doi.org/10.1186/s12913-017-2389-7

31.

Feigenson

. Stroke rehabilitation: effectiveness, benefits, and cost. Some practical considerations. Stroke 1979; 10: 1–4. https://doi.org/10.1161/01.str.10.1.1

32.

Alhwoaimel

Alqahtani

Alhowimel

, et al. Barriers and Facilitators of Using Standardized Outcome Measures in Stroke Rehabilitation in Saudi Arabia: A Cross-Sectional Study of Practice Among Neurophysiotherapists. Risk Management and Healthcare Policy 2024; 17: 2319–2329. https://doi.org/10.2147/RMHP.S466602

33.

Greiner

Jr.

Improving Healthcare through Community Paramedicine. Anna Maria College, 2018.

34.

Group

CCW

Cheeran

Cohen

, et al. The future of restorative neurosciences in stroke: driving the translational research pipeline from basic science to rehabilitation of people after stroke. Neurorehabilitation and neural repair 2009; 23: 97–107. https://doi.org/10.1177/1545968308326636

35.

Nicora

Parimbelli

Mauro

, et al. Healthcare practitioners and robotic-assisted rehabilitation: understanding needs and barriers. Journal of NeuroEngineering and Rehabilitation 2025; 22: 78. https://doi.org/10.1186/s12984-025-01593-0

36.

Tyson

Weightman

. Professionals’ views and experiences of using rehabilitation robotics with stroke survivors: A mixed methods survey. Frontiers in Medical Technology 2021; 3: 780090. https://doi.org/10.3389/fmedt.2021.780090

Healthcare professionals’ awareness,perceptions,and adoption of robotic-assisted rehabilitation technologies for stroke recovery

Abstract

Background

Methods

Results

Conclusion

Keywords

Introduction

Methods

Study design and setting

Participants and sampling

Data collection

Ethical considerations

Statistical analysis

Results

Demographic characteristics of participants

Awareness and perceptions of robotic-assisted therapy

Clinical adoption of robotic-assisted therapy

Future outlook of robotic-assisted therapy

Discussion

Conclusion

Future directions

Footnotes

ORCID iDs

Ethical considerations

Author contributions

Funding

Declaration of conflicting interests

Guarantor

References