A translational framework mapping clinical challenges to technology solutions in upper limb neurorehabilitation

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Wang, José Zariffa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7852937/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Stroke and spinal cord injury (SCI) are leading causes of long-term upper limb (UL) impairment, imposing significant disability and economic burden. Outpatient neurorehabilitation can be critical for restoring function, yet therapists face complex challenges within the clinical process. These challenges, particularly in areas of assessment, patient engagement, and resource management, must be navigated in clinical reasoning and decision-making. While rehabilitation technologies offer potential solutions, their integration into and routine use in practice is limited by, among other reasons, a disconnect between development and clinical needs. We sought to develop a translational framework that identifies clinical process challenges in outpatient UL neurorehabilitation for stroke and SCI and maps them to technology-based solutions, guiding clinically relevant innovation. Methods Using an interpretive description methodology, we conducted a qualitative study at three sites of a large rehabilitation hospital network. Data were collected from 10 therapists (9 occupational, 1 physical) through 33 direct clinical observations (24 hours and 45 minutes), post-observation debriefs, and semi-structured interviews. Constant comparative analysis synthesized findings into challenge domains and technology solutions, validated through member checking. Results Five core challenge domains were identified: Assessment & Progress Tracking ; Knowledge Transfer ; Patient Engagement ; Home Program Implementation ; and Information & Resource Constraints . These were mapped to four technology solution categories: Remote Monitoring & Analytics , Education & Decision Support , Assistive & Therapeutic Technologies , and Gamified Interventions . The framework illustrates how these solutions address specific challenges, such as using remote monitoring to track home exercise adherence or gamified platforms to enhance patient motivation. Conclusions This study demonstrates the potential of using egocentric video to inform clinical decision-making in neurorehabilitation, particularly for hand function. The strong preference for video over metrics suggests clinical decision support systems should prioritize interpretable, observation-based information aligned with clinical reasoning processes.Despite implementation challenges, therapists across technical familiarity levels expressed trust in the system and willingness to use it regularly. These findings indicate that egocentric video systems can bridge the clinic-home divide when designed to match interest-holder priorities. outpatient neurorehabilitation spinal cord injury stroke clinical decision-making clinical process rehabilitation technology Figures Figure 1 Introduction Stroke and spinal cord injury (SCI) are leading causes of long-term disability, with annual global incidence rates of 12.2 million and 0.9 million, respectively ( 1 , 2 ). With advances in acute care and an aging population, these figures are projected to rise further ( 2 ). Upper limb (UL) impairment is often a consequence of these conditions ( 3 ), limiting an individual's ability to live independently and perform activities of daily living (ADLs) ( 4 ). Neurorehabilitation supports recovery from these nervous system injuries ( 5 ) and is the primary approach to improving UL function. Delivered by interdisciplinary teams of physicians, nurses, and therapists, it spans from intensive therapy in acute or inpatient settings and progresses to outpatient care. Practice frameworks like the Occupational Therapy Practice Framework: Domain and Process, Fourth Edition (OTPF-4) ( 6 ) guide clinicians by defining the scope of practice and clinical process across the continuum of care. The clinical process refers to the overarching structure of service delivery, encompassing the sequence of evaluation, intervention, and targeting of outcomes ( 6 ). Within this framework, therapists engage in clinical reasoning , which is a dynamic cognitive inquiry into the patient's functional life focusing on understanding the whole person, analyzing their needs, interpreting progress, and considering contextual factors to inform their actions ( 7 , 8 ). These actions or specific choices therapists make across different stages of the clinical process, such as selecting an intervention, setting a goal, or determining readiness for discharge is known as clinical decision-making ( 9 , 10 ). Outpatient care is a crucial part of this continuum of care, bridging hospital discharge and community reintegration, helping patients regain function, prevent complications, and improve quality of life ( 11 – 13 ). The clinical process in outpatient UL neurorehabilitation is complex and requires sophisticated clinical reasoning. Therapists must constantly make critical decisions as they assess progress, plan therapy, educate patients and caregivers, ensure home-based intervention adherence, and coordinate multidisciplinary care ( 14 ). Therapists must also tailor interventions to diverse patient needs (i.e., varying motor and cognitive impairments, social support systems, and home environments) while navigating systemic constraints like resource limitations ( 15 ). This can hinder therapists' ability to optimize functional recovery and highlights the need for innovative solutions that support their clinical reasoning and enhance clinical decision-making. Emerging rehabilitation technologies have the potential to address these clinical process challenges by providing more objective measurement capabilities, evidence-based treatment recommendations, and new intervention options ( 16 , 17 ). Despite this, a critical gap persists between technology development and routine integration into outpatient practice ( 18 ). For instance, Morris et al. (2019) found that while clinicians recognize the need for mobile and wearable technologies in rehabilitation, only 50% of physical therapists and 44.2% of occupational therapists feel comfortable using them in practice ( 19 ). Many technologies are developed without sufficient clinician input, resulting in solutions that are misaligned with clinical needs, difficult to use, or incompatible with existing workflows ( 18 , 20 , 21 ). This disconnect leads to underutilization, wasted resources, and missed opportunities to improve patient outcomes. To bridge this research-to-practice gap, a structured approach is needed to align technology development with the realities of outpatient neurorehabilitation. Unlike theoretical frameworks that describe phenomena ( 22 ), translational frameworks are action-oriented models that guide the application of evidence into practice ( 23 , 24 ). Current approaches often address clinical processes or technology adoption in isolation, failing to integrate the two domains effectively ( 20 ). A translational framework can overcome this limitation by grounding technology development in the lived experience of clinicians, providing a roadmap for clinical, technology development, and healthcare administration interest-holders to make informed decisions. The objective of this study was to develop a translational framework that systematically identifies clinical process challenges in outpatient UL neurorehabilitation for stroke and SCI and maps them to technology-based solutions. Through direct clinical observation and in-depth interviews, we aimed to create a clinician-centered framework that bridges the gap between clinical needs and technological capabilities. This framework offers structured guidance for developing technologies that address clinical process challenges to enhance the delivery of outpatient neurorehabilitation. Methods We employed an interpretive description methodology (25) to explore therapists' experiences and clinical process challenges in outpatient UL neurorehabilitation for stroke and SCI. Constant comparative analysis (CCA) (26) guided iterative synthesis across multiple data sources to generate applied knowledge for clinical practice. The study was approved by the Research Ethics Board of the University Health Network (UHN; Study #22-5457) and conducted in accordance with the Tri-Council Policy Statement. All participating therapists and observed patients provided written informed consent. Study Recruitment We targeted 10 therapists from the outpatient stroke and SCI programs at the Toronto Rehabilitation Institute – University Health Network across three hospital sites. The sample size was determined using the principle of information power (27), considering the study's specific aim, homogeneous clinical setting, and anticipated data richness from observations and interviews. Therapists were eligible if they were occupational or physical therapists actively treating adult stroke or SCI patients with improved UL function as a therapy goal. Patients were included if their therapy sessions, led by eligible therapists, focused on UL function. No further diagnostic criteria beyond stroke or SCI were required. Patients declining observation consent were excluded. All eligible therapists treating adult stroke or SCI patients with UL rehabilitation goals at our institution were invited to participate. A research team member contacted interested therapists to confirm eligibility, obtain consent, and explain the study's objectives and questions to guide debrief sessions. Patient consent was obtained to ensure comfort with session observations. Data Collection Data collection occurred in three phases. In the first phase, the lead investigator (AK) conducted direct observations of therapy sessions to capture therapists' clinical process and reasoning challenges. We targeted three 45-minute observations per patient to balance depth with feasibility. These observations were scheduled based on therapist and patient availability, and session focus. In particular we were interested in sessions that included UL-related activities, such as motor assessments and task-oriented training. Field notes were captured in real time to document in-session decision-making, adaptive strategies, and contextual variables influencing therapist reasoning. Each session was followed by 5–10-minute debriefs with therapists to clarify observed practices. In the second phase, in-depth semi-structured interviews (~60 minutes) explored therapists' perspectives on clinical processes, challenges, and the perceived role of technology in practice. An interview guide (see Supplementary Material) provided structure while allowing flexibility to follow emergent topics. Interviews were audio-recorded, transcribed verbatim, and anonymized prior to analysis. In the third and final phase, relevant literature was iteratively reviewed to contextualize findings and inform analytic distinctions. Literature review was treated as a supplementary data source that shaped subsequent interview questions and helped refine emerging categories. Data Analysis We used CCA to iteratively analyze findings across field notes, debriefs, interview transcripts, and existing literature. The lead investigator (AK) immersed in the data through repeated readings, developing analytic memos to capture patterns in therapists' experiences. Textual excerpts were clustered into conceptual groupings and refined through comparison, yielding clinical process challenge domains. Technology solution categories were derived by extracting technology mentions from interviews, inductively clustered, and aligned with rehabilitation technology literature. The translational framework was developed through iterative coding, thematic convergence, and senior researcher feedback, mapping challenges to technology solutions based on therapist input and clinical use cases. Ensuring Rigor The lead investigator (AK) conducted all data collection and analysis for consistency, with multiple strategies to ensure rigor despite a single coder. Prolonged engagement over eight months fostered deep understanding of the clinical context within the study setting (28). Triangulation across field notes, debriefs, interviews, and existing literature allowed for convergence of themes across different modalities and further validated our findings (29). Member checking involved emailing therapists a draft framework and results to confirm alignment with their experiences and the suitability of proposed technology solutions (30). Feedback received from therapists was used to refine and validate the final framework, thereby enhancing the credibility and clinical relevance of the findings. Reflexive analysis is characterized by transparency in documenting analytical decisions, ongoing critical self-reflection about preconceptions, and acknowledgment of how researcher positioning influences findings (31). We used interlinked digital notes to document connections between disparate observations, track evolving interpretations, and ensure data-driven themes, mitigating bias. Regular peer debriefing with senior clinical researchers (RHW, JZ) provided external validation, challenging assumptions and refining codes and themes. These strategies ensured credibility, transferability, and dependability of the findings, addressing potential limitations of a single coder. Results We recruited 10 therapists (9 occupational therapists, 1 physical therapist; all female) with a mean clinical experience of 11.7 ± 7.6 years. We observed 11 patients (6 SCI, 5 stroke) across 33 observation sessions, totaling 24 hours and 45 minutes. These sessions included a range of therapeutic activities related to UL function such as motor assessments, task-oriented training, patient education, collaborative goal-setting, and patient discharge. Challenge Domains Five core challenge domains in outpatient UL neurorehabilitation were identified: (1) Assessment & Progress Tracking , (2) Knowledge Transfer , (3) Patient Engagement , (4) Home Program Implementation , and (5) Information & Resource Constraints . These domains reflect recurring barriers that therapists face when planning, delivering, and following up on care. Member checking with participating therapists confirmed that these domains resonated with their clinical experiences and captured key challenges in current outpatient practice. Assessment & Progress Tracking Therapists struggled to monitor progress outside clinic settings, often relying on self-reports. "I don't have a good way to track how they're doing it at home exactly or if they're doing it enough... I just have been relying on their verbal feedback" (Therapist #1). Therapists must "trust that they're a good historian" when judging functional gains between clinic visits (Therapist #5). However, this reliance is problematic since self-reported data are widely recognized to suffer from various forms of bias and inaccuracy. These inaccuracies stem from several well-documented factors such as recall bias (i.e., forgetting or inaccurately recalling details about their rehabilitation activities) (32), social desirability bias (i.e., telling therapists what they might want to hear) (33), reference bias (i.e., inconsistent personal benchmarks or interpretations when rating their own performance) (34), and cognitive impairments (35,36). This creates a significant challenge for their clinical reasoning, forcing them to make decisions about therapy progression based on potentially biased or incomplete data. The desire for more objective, real-world data was a consistent theme among therapists, with suggestions including wearable activity monitors and AI-supported movement analysis to supplement therapist judgment (Therapists #1, 2, 5, 6, 8, 10). Knowledge Transfer Effective communication of recovery expectations, therapeutic goals, disease or recovery progression, and exercise instructions were universally emphasized by all therapists as important. "Patient education is happening the second they walk through the door" (Therapist #9). However, the dissemination of knowledge is challenging due to cognitive, language, or cultural barriers. As one therapist explained, "The cultural aspect is difficult sometimes… it gets lost in translation" (Therapist #10). Therapists noted the frustration patients experience due to unrealistic expectations: “He thought this would just be a couple months and he's back to normal and wasn't really expecting… the variability or unpredictability of the recovery process for SCI” (Therapist #3). Caregiver education also emerged as critical, with therapists noting the need to train caregivers to support safe home exercises (Therapist #10). Proposed solutions included multimedia tools, culturally adaptive content, and interactive modules to reinforce understanding and bridge communication gaps. Patient Engagement Therapists observed that motivated patients achieve better outcomes, while disengagement due to frustration, slow recovery, or repetitive exercises hinders progress (Therapists #7 and 9). This aligns with existing literature indicating patient engagement, including motivation, active participation, and emotional buy-in to therapy, is essential for successful rehabilitation outcomes (37). While therapists found that “with this population, people are not that bored to do very simple tasks because those simple tasks are so difficult to do” (Therapist #3), maintaining engagement remains challenging, particularly when patients focus on pre-injury capabilities: “He's really focused on all the things he was able to do before the injury that he can't do now” (Therapist #3). Strategies therapists used included incorporating patient identified goals into task selection and introducing novelty. One therapist remarked, “I just always check in… and make sure like, you know, how is this going for you? Like, is there something else you want to be working on?” (Therapist #4). Another therapist described tailoring exercises to a patient's life: “I knew they had two young children… I had them on the floor, picking up toys… an activity that he does at home with his young children” (Therapist #8). Technology was seen as a promising facilitator, with therapists suggesting gamified platforms and visual biofeedback to make exercises more engaging. Home Program Implementation The home exercise program is a cornerstone of outpatient rehab and supplements the limited therapy sessions. However, several therapists estimated that patients do not do their home exercises consistently (Therapists #2, 3, 4, 5, 6, and 10), aligning with literature indicating that only 23–64% of patients fully adhere to home therapy (38,39). There is "a lot that goes into why they might not be completing the exercises, right? They might not have anyone to help them… or motivation, mental health symptoms, resources, time, support" (Therapist #2). For instance, one of the observed patients had a caregiver who was overwhelmed and lacked support, so the therapist opted not to assign a home program, recognizing it might go uncompleted and further burden the caregiver. The therapist lamented that this is “really unfortunate, but it is a challenge you have to navigate” --- that is, balancing ideal therapy with real-life feasibility (Therapist #10). Therapists usually have no insight into what patients actually do between appointments. As one therapist put it, the home program is a "black box" (Therapist #6). Suggested technological interventions included remote monitoring tools, self-guided apps, and real-time video feedback to enhance adherence and provide therapists with better oversight of home activities. Information & Resource Constraints Therapists described systemic pressures related to short appointment windows and high caseloads. One therapist remarked, "Forty-five minutes is... you don't have time. Ten minutes, 'I have to go to the bathroom,' and then... twenty minutes are gone" (Therapist #6). They felt “time is the biggest barrier” (Therapist #10) to delivering ideal therapy “because there are so many things that [they] could be doing [with patients but they] have to boil it down and pick a few” (Therapist #4). Accessing advanced technologies was also challenging due to bureaucratic and financial barriers. “There's definitely resource constraint… I would need to go through the whole bureaucracy process… Why is [it] helpful? Is there any evidence-based research already out?” (Therapist #8). While Therapist #8 did not specify a particular technology they attempted to acquire, they mentioned receiving outreach emails from promising rehabilitation technologies but was deterred by the need to justify the technology's utility to the department with evidence-based research, which is time-consuming. Therapist #2 emphasized the broader issue of "access to care and resources… Finances, money, resources, time, staff… all those barriers" means patients cannot access care all the time (Therapist #2). This highlights the need for technologies that could optimize limited resources or help therapists do more with less. Proposed solutions included technologies like remote monitoring (Therapists #2, 3, 7, 10) and decision-support or education tools for newer therapists (Therapists #2, 7, 8) (40). Technology Solution Categories Through the review of field notes, interview data, and subsequent triangulation of rehabilitation technology in the existing literature, we propose four broad, future-proof technology solution areas that cover all identified challenge domains while minimizing overlap and accommodating future advancements: (1) Remote Monitoring & Analytics , (2) Education & Decision Support , (3) Assistive & Therapeutic Technologies , and (4) Gamified Interventions . All technologies mentioned by therapists in the interviews, either explicitly or implicitly, are fully captured by these four categories. Remote Monitoring & Analytics This category encompasses technologies that collect, analyze, and visualize data on patient performance in home environments to support objective progress tracking and contextualized therapy. It includes wearable sensors (e.g., activity trackers, inertial measurement units) (41), in-home environmental sensors (42), video-based systems for capturing therapy context (43), and AI-driven analytics to process data and provide actionable insights (44–48). One therapist explained the value of these tools: "I am very visual, I just can't always get out to the person's home environment. So videos, pictures... how can I see you in this environment? Or how can I see your environment so that we can work on this, this, and this." (Therapist #5). These tools address the “black box” of home activities (Therapist #6) by combining quantitative movement data (Therapist #3) with qualitative environmental insights (Therapist #5), enabling therapists to supervise patients remotely (49), monitor adherence, assess functional gains objectively, and tailor interventions without relying on subjective self-reports. Education & Decision Support This category includes digital platforms and tools to enhance education for patients, caregivers, and therapists, as well as clinical decision-support systems to optimize treatment planning. For patients and caregivers, it encompasses dissemination of knowledge through multimedia content (e.g., videos, interactive modules), culturally adaptive resources (Therapist #10), and reminders to reinforce understanding of recovery processes and exercises (Therapist #2, 7, 8, 10) (50). Therapists frequently mentioned the difficulty of ensuring patients and caregivers retained information from a session (Therapist #10). One therapist explained the need for a "simplified program... a video they can watch a hundred times. The family can watch. I think they are lacking [understanding]." (Therapist #6). For therapists, it includes evidence-based databases, professional development platforms, dashboards (51), and tools like "ViaTherapy" (Therapist #8) (40) to streamline decision-making (52) or clinical documentation (53,54). These tools address cognitive, language, and cultural barriers for patients/caregivers, as well as the time and resource constraints for therapists. They can be especially useful as a resource for newer therapists and guide their therapy planning and decision-making. Assistive & Therapeutic Technologies These devices can range from functional electrical stimulation (FES) devices (55) to end-effector devices (56), exoskeletons (57), and even future robotic or AI-driven assistive technologies (58). As Therapist #3 stated, "Robotic arms can assist with functional and fine motor tasks that help to motivate patients in achieving their goals... It can be used as a measurement tool... [and] has a potential for home use, assisting patients in doing exercises at home." These tools can deliver high-intensity, repetitive, guided movements, and support home use, thereby addressing therapy dosage and motivation concerns (59,60). Gamified Interventions This category includes platforms that use game mechanics (61), virtual reality (VR) (62), augmented reality (AR) (63,64), and biofeedback to enhance engagement and motivation. These tools transform repetitive exercises into interactive experiences, "I feel like those have potential, because it's the same movement, but you're really enjoying it with this virtual reality, or kind of game, because it takes away the repetition feeling, I think, that kind of makes it fun." (Therapist #8). By turning a mundane task into an interactive game with scores and challenges, these tools can increase patient enthusiasm and engagement (65). Furthermore, integrated biofeedback from wearables can provide immediate rewards for correct movements, creating a powerful motivational loop that enhances compliance and reinforces effort (66,67). Translational Framework Following iterative analysis and member checking, the final translational framework (Figure 1) maps each of the five challenge domains to technology solution areas. These mappings highlight that effective solutions often span multiple domains, and no single technology operates in isolation. This mapping is intended to guide both researchers and developers in aligning innovations with clinical needs. It shows that an effective rehabilitation technology ecosystem should address each of the key domains: providing therapists with better assessment tools, educating and empowering patients, engaging them meaningfully, extending therapy into the home, and easing systemic burdens. Importantly, the domains influence each other and the solutions can synergistically overlap, thus encouraging a comprehensive approach---not simply creating a device or app in isolation, but ensuring that it fits into clinical workflows and tackles real pain points identified by therapists. By mapping these relationships, interest‐holders can identify opportunities and ensure that new rehabilitation technologies are grounded in actual clinical challenges. The ultimate goal is a coordinated set of technology solutions that enable therapists to deliver more personalized, effective, and scalable UL rehabilitation, improving outcomes for individuals recovering from stroke and spinal cord injury. Discussion This study presents a novel translational framework that systematically maps five core clinical process challenge domains in outpatient UL neurorehabilitation, identified through qualitative analysis of therapist experiences, to four broad technology solution categories. Its foundational purpose is to ground technology development in the real-world needs of clinicians, ensuring new tools are not merely solutions in search of a problem (68). Our framework achieves this by translating the implicit questions that guide clinical processes and decision-making into actionable technological interventions. For instance, a therapist's concern about tracking home performance (Therapist #10) is mapped directly to Remote Monitoring & Analytics , while challenges with patient comprehension (Therapist #6) are addressed by Education & Decision Support . Table 1 contains exemplars of real-world information needs voiced by therapists during interviews and how our framework can guide technology selection and development. Table 1: Examples of translational mapping framework use to answer exemplar therapist questions. Therapist Question Challenge Domain(s) Technology Solutions How is my patient using their hand at home? "I would love to have something like that, like a recording or like a really good summary of how they perform their activities. Just to help me make better decisions." (Therapist #10) Assessment & Progress Tracking; Home Program Implementation; Patient Engagement Remote Monitoring & Analytics; Assistive & Therapeutic Technologies Are my patients practicing exercises correctly at home? "In terms of knowing if they're doing it correctly, I will have them demonstrate back... And then aside from that I just have to rely... on their verbal feedback at the next session." (Therapist #1) Home Program Implementation; Assessment & Progress Tracking Remote Monitoring & Analytics; Immersive & Gamified Interventions How do I ensure patients understand their therapy goals? "They come here, they are so overwhelmed. I don't know if they remember everything I say." (Therapist #6) Knowledge Transfer Education & Decision Support How can I track small functional improvements? “What might seem so small to a patient, I really try to say like, 'That's a big deal, it might seem so small to you that you were able to move your thumb today, but that's a big deal'." (Therapist #9) Assessment & Progress Tracking Remote Monitoring & Analytics How can I better train caregivers to assist at home? "[I would want] educational platforms to train caregivers on how to actually take care of the person... Because as we both saw, like caregiver was maybe a little rough... I feel like education is definitely a part of that." (Therapist #10) Knowledge Transfer; Home Program Implementation Education & Decision Support By grounding development in this way, our framework is positioned to complement other established models in the literature. For instance, technology acceptance models including the original Technology Acceptance Model (TAM) (69), along with its extensions (i.e., TAM2 (70), TAM3 (71), TAM+ (72)) and related frameworks like the Unified Theory of Acceptance and Use of Technology (UTAUT) (73), consistently identify Perceived Usefulness as a major predictor of technology adoption. Our framework designs for usefulness from the outset by ensuring solutions are grounded in clinician-identified needs. We also align with foundational knowledge translation models like the Knowledge-to-Action (KTA) framework (74) which begin with identifying the problem and adapting knowledge to a local context. Our framework operationalizes this critical step for technology implementation in outpatient UL neurorehabilitation by first identifying the relevant challenge domains before considering any solutions. Beyond its theoretical contribution, the framework has significant practical implications for multiple interest‐holders. By translating the complex clinical process challenges therapists face into structured requirements, it provides a clear pathway for developers and a needs-driven roadmap for healthcare organizations making technology investment decisions. This mapping approach allows therapists to articulate their complex information needs, which are central to effective clinical reasoning, and encourages integrated solutions to fully address a clinical question, such as combining Remote Monitoring & Analytics with Gamified Interventions to improve home exercise adherence. Ultimately, it ensures that resources are allocated to tools that address actual clinical needs rather than perceived technological gaps. It is also crucial to recognize the interdependent nature of the identified challenge domains. While our framework illustrates direct links between challenges and solutions, the clinical reality is more complex, with challenges often compounding one another. For instance, the Information & Resource Constraints domain frequently acts as a root cause that exacerbates other issues. A therapist constrained by a short appointment window (a resource constraint) will have less time for Knowledge Transfer , which can lead to poor Home Program Implementation and diminished Patient Engagement . A key function of this translational framework is to serve as a foundation for generating testable hypotheses that can guide future intervention studies. The linkages identified in our framework can be formulated into empirical questions. For example, one could hypothesize that for the clinical goal of improving home exercise quality and consistency, a combination of Remote Monitoring & Analytics (to track performance) and Gamified Interventions (to drive engagement) will be more effective than either solution implemented in isolation. Similarly, one could test whether the effectiveness of Education & Decision Support technologies on improving patient outcomes is mediated by an increase in caregiver self-efficacy. By structuring the problem space in this way, the framework could also provide a basis for designing future clinical trials in rehabilitation technology. Limitations Several limitations should be considered when interpreting our findings. Our study was conducted at three hospital sites within the same hospital network and may not reflect the challenges present in other settings, such as community or rural clinics. Also, the hospital network is located in one city (Toronto, Canada) and may not reflect the practices, policies, and economic conditions (resource constraints) of other rehabilitation settings in different geographical locations. Our sample also had a significant demographic imbalance as a result of the study setting. The sample was predominantly occupational therapists (9 of 10 participants) and was exclusively female. This reflects both the female-dominant nature of occupational therapy profession (75) and the operational model at the recruitment sites, where OTs are primarily responsible for UL rehabilitation. Still, the OT-centric perspective may underrepresent challenges or technology needs more central to the physical therapy domain. Similarly, while representative of the profession, a single-gender perspective may omit nuances that a more diverse sample could provide. This analysis was also conducted by a single researcher. While we maintained rigor with member checking, data triangulation, and peer review with senior clinical researchers, there may still be potential bias in interpretation. Multi-researcher analysis could strengthen the framework's reliability. Furthermore, this framework focused on clinical process challenges and therefore did not capture patient perspectives, which could provide critical insights into technology acceptance, usability, or barriers (e.g., cognitive or physical limitations) (76). Ethical considerations, such as privacy risks with remote monitoring data (77–79) or equity issues (80–82) in accessing advanced tools, were not explored but could impact implementation. Patient perspectives should be considered when developing any specific technology before widespread implementation in practice. Finally, while our framework identifies challenge-solution mappings, it does not address all implementation barriers, such as cost considerations, reimbursement structures, or regulatory requirements. These practical constraints significantly impact technology adoption but fall outside our study scope. Future Directions Future directions should include developing concrete implementation tools based on this framework, such as guiding questions for technology developers, evaluation checklists for assessing whether proposed technologies align with identified clinical needs, and decision-support tools for healthcare organizations selecting technologies. Empirical studies could then evaluate whether using this framework during technology development leads to solutions with greater perceived usefulness and clinical utility compared to traditional development approaches in diverse outpatient settings. Conclusion This translational framework represents a significant advance in bridging the gap between clinical needs and technological capabilities in outpatient neurorehabilitation to improve patient care. By systematically mapping rehabilitation challenges to technology solutions, we provide practical guidance for clinicians, hospital management, and developers working to enhance rehabilitation outcomes through technology. As rehabilitation technology continues to evolve, this framework can guide development of integrated, clinically meaningful solutions that truly support the complex work of helping individuals with neurological injuries achieve their maximum functional potential. Success will require coordinated efforts among clinicians, researchers, technology developers, and healthcare systems to create rehabilitation technology ecosystems that are both innovative and practically grounded in clinical reality. Declarations Ethics approval and consent to participate This study was approved by the Research Ethics Board of the University Health Network (Study #22-5457) and conducted in accordance with the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (TCPS2). Informed consent was obtained from all participating therapists and observed patients included in the study. Consent for publication Not applicable Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available and cannot be shared due to the terms of the informed consent and privacy concerns regarding identifying information in the interviews. Competing Interests The authors declare that they have no competing interests. Funding This work was supported by the Praxis Spinal Cord Institute; the Ontario Early Researcher Award program under grant number ER16–12-013; and the Canadian Institutes of Health Research under grant number 13556838. Authors' contributions AK recruited participants, conducted and transcribed all interviews, performed data analysis, and drafted the manuscript. PE contributed to therapist recruitment and provided clinical expertise to support the claims made in the manuscript. RHW assisted in developing the framework and situating it within the existing literature. JZ contributed to the study design, guided data interpretation, and substantively revised the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable References GBD 2019 Stroke Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2021 Oct 1;20(10):795–820. GBD Spinal Cord Injuries Collaborators. Global, regional, and national burden of spinal cord injury, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2023 Nov 1;22(11):1026–47. Kwakkel G, Kollen BJ, van der Grond J, Prevo AJH. Probability of regaining dexterity in the flaccid upper limb: impact of severity of paresis and time since onset in acute stroke: Impact of severity of paresis and time since onset in acute stroke. Stroke. 2003 Sep;34(9):2181–6. Anderson KD. Targeting recovery: priorities of the spinal cord-injured population. J Neurotrauma. 2004 Oct;21(10):1371–83. Winstein CJ. Goal-oriented action: New perspectives with special emphasis on neurorehabilitation. In: Progress in Motor Control. Elsevier; 2024. p. 349–87. American Occupational Therapy Association. Occupational Therapy Practice Framework: Domain and Process—Fourth Edition. The American Journal of Occupational Therapy. 08 2020;74(Supplement_2):7412410010p1–7412410010p87. Mattingly C. What is clinical reasoning? Am J Occup Ther. 1991 Nov;45(11):979–86. Fleming MH. Clinical reasoning in medicine compared with clinical reasoning in occupational therapy. Am J Occup Ther. 1991 Nov 1;45(11):988–96. Bate L, Hutchinson A, Underhill J, Maskrey N. How clinical decisions are made: How clinical decisions are made. Br J Clin Pharmacol. 2012 Oct 28;74(4):614–20. Higgs J, Jones MA, Bds SLP, Christensen N. Clinical Reasoning in the Health Professions. Vol. 4th. Elsevier Health Sciences; 2019. 520 p. Veerbeek JM, van Wegen E, van Peppen R, van der Wees PJ, Hendriks E, Rietberg M, et al. What is the evidence for physical therapy poststroke? A systematic review and meta-analysis. PLoS One. 2014 Feb 4;9(2):e87987. Langhorne P, Baylan S, Early Supported Discharge Trialists. Early supported discharge services for people with acute stroke. Cochrane Database Syst Rev. 2017 Jul 13;7(7):CD000443. Outpatient Service Trialists. Therapy-based rehabilitation services for stroke patients at home. Cochrane Database Syst Rev. 2003;2010(1):CD002925. Shafaroodi N, Kamali M, Parvizy S, Mehraban AH, O’Toole G. Factors affecting clinical reasoning of occupational therapists: a qualitative study. Med J Islam Repub Iran. 2014 Feb 19;28:8. da Silva Araujo A, Anne Kinsella E, Thomas A, Demonari Gomes L, Quevedo Marcolino T. Clinical reasoning in occupational therapy practice: A scoping review of qualitative and conceptual peer-reviewed literature. Am J Occup Ther. 2022 May 1;76(3):7603205070. Leirós-Rodríguez R, Benítez-Andrades JA, Marqués-Sánchez MP, Calvo-Ayuso N. Automation of the assessment and diagnosis process in rehabilitation: A proposal to reduce costs and optimize the health care system. In: Global Challenges for a Sustainable Society. Cham: Springer International Publishing; 2023. p. 395–7. (Springer Proceedings in Earth and Environmental Sciences). Gower V, Aprile I, Falchini F, Fasano A, Germanotta M, Randazzo M, et al. Cost analysis of technological vs. conventional upper limb rehabilitation for patients with neurological disorders: an Italian real-world data case study. Front Public Health. 2024 Oct 14;12:1445099. Alt Murphy M, Pradhan S, Levin MF, Hancock NJ. Uptake of technology for neurorehabilitation in clinical practice: A scoping review. Phys Ther. 2024 Feb 1;104(2):zad140. Morris J, Jones M, Thompson N, Wallace T, DeRuyter F. Clinician Perspectives on mRehab Interventions and Technologies for People with Disabilities in the United States: A National Survey. Int J Environ Res Public Health [Internet]. 2019 Oct 31;16(21). Available from: http://dx.doi.org/10.3390/ijerph16214220 Greenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A’Court C, et al. Beyond adoption: A new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. J Med Internet Res. 2017 Nov 1;19(11):e367. Gagnon MP, Desmartis M, Labrecque M, Car J, Pagliari C, Pluye P, et al. Systematic review of factors influencing the adoption of information and communication technologies by healthcare professionals. J Med Syst. 2012 Feb;36(1):241–77. Jabareen Y. Building a conceptual framework: Philosophy, definitions, and procedure. Int J Qual Methods. 2009 Dec 1;8(4):49–62. White KM. Chapter Two: The Science of Translation and Major Frameworks. In: White KM, Dudley-Brown S, Terhaar MF, editors. Translation Evidence Nursing Health Care Practice : Application Nursing Health Care. 2nd ed. New York, NY: Springer Publishing; 2016. p. 35–56. Rubio DM, Schoenbaum EE, Lee LS, Schteingart DE, Marantz PR, Anderson KE, et al. Defining translational research: implications for training. Acad Med. 2010 Mar;85(3):470–5. Thorne SE. Interpretive Description: Qualitative Research for Applied Practice. Routledge; 2016. 336 p. Glaser BG, Strauss AL. The constant comparative method of qualitative analysis. In: The Discovery of Grounded Theory. 1st Edition. Routledge; 1999. p. 101–16. Malterud K, Siersma VD, Guassora AD. Sample Size in Qualitative Interview Studies: Guided by Information Power. Qual Health Res. 2016 Nov;26(13):1753–60. Creswell JW. Qualitative inquiry and research design. 3rd ed. Thousand Oaks, CA: SAGE Publications; 2013. 472 p. Patton MQ. Enhancing the quality and credibility of qualitative analysis. Health Serv Res. 1999 Dec;34(5 Pt 2):1189–208. Lincoln YS, Guba EG. Naturalistic Inquiry. Thousand Oaks, CA: SAGE Publications; 1985. 416 p. Olmos-Vega FM, Stalmeijer RE, Varpio L, Kahlke R. A practical guide to reflexivity in qualitative research: AMEE Guide No. 149. Med Teach. 2022 Apr 7;1–11. Brusco NK, Watts JJ. Empirical evidence of recall bias for primary health care visits. BMC Health Serv Res. 2015 Sep 15;15(1):381. Adams AS, Soumerai SB, Lomas J, Ross-Degnan D. Evidence of self-report bias in assessing adherence to guidelines. Int J Qual Health Care. 1999 Jun;11(3):187–92. Lira B, O’Brien JM, Peña PA, Galla BM, D’Mello S, Yeager DS, et al. Large studies reveal how reference bias limits policy applications of self-report measures. Sci Rep. 2022 Nov 10;12(1):19189. Sachdeva R, Gao F, Chan CCH, Krassioukov AV. Cognitive function after spinal cord injury: A systematic review. Neurology. 2018 Sep 25;91(13):611–21. Tatemichi TK, Desmond DW, Stern Y, Paik M, Sano M, Bagiella E. Cognitive impairment after stroke: frequency, patterns, and relationship to functional abilities. J Neurol Neurosurg Psychiatry. 1994 Feb;57(2):202–7. Loomis KJ, Roll SC, Hardison ME. The role of therapist-patient relationships in facilitating engagement and adherence in upper extremity rehabilitation. Work. 2023 May 20;76(3):1083–98. Sluijs EM, Kok GJ, van der Zee J. Correlates of exercise compliance in physical therapy. Phys Ther. 1993 Nov;73(11):771–82; discussion 783–6. Miller KK, Porter RE, DeBaun-Sprague E, Van Puymbroeck M, Schmid AA. Exercise after stroke: Patient adherence and beliefs after discharge from rehabilitation. Top Stroke Rehabil. 2017 Mar;24(2):142–8. Hancock NJ, Collins K, Dorer C, Wolf SL, Bayley M, Pomeroy VM. Evidence-based practice “on-the-go”: using ViaTherapy as a tool to enhance clinical decision making in upper limb rehabilitation after stroke, a quality improvement initiative. BMJ Open Qual. 2019 Aug 19;8(3):e000592. Patel S, Park H, Bonato P, Chan L, Rodgers M. A review of wearable sensors and systems with application in rehabilitation. J Neuroeng Rehabil. 2012 Apr 20;9:21. Marwaa MN, Kristensen HK, Guidetti S, Ytterberg C. Physiotherapists’ and occupational therapists' perspectives on information and communication technology in stroke rehabilitation. PLoS One. 2020 Aug 28;15(8):e0236831. Bandini A, Dousty M, Hitzig SL, Craven BC, Kalsi-Ryan S, Zariffa J. Measuring Hand Use in the Home after Cervical Spinal Cord Injury Using Egocentric Video. J Neurotrauma. 2022 Dec;39(23-24):1697–707. Adans-Dester C, Hankov N, O’Brien A, Vergara-Diaz G, Black-Schaffer R, Zafonte R, et al. Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery. NPJ Digit Med. 2020 Sep 21;3:121. Liao Y, Vakanski A, Xian M. A deep learning framework for assessing physical rehabilitation exercises. IEEE Trans Neural Syst Rehabil Eng. 2020 Feb 13;28(2):468–77. Liao WW, Hsieh YW, Lee TH, Chen CL, Wu CY. Machine learning predicts clinically significant health related quality of life improvement after sensorimotor rehabilitation interventions in chronic stroke. Sci Rep. 2022 Jul 4;12(1):11235. Campagnini S, Arienti C, Patrini M, Liuzzi P, Mannini A, Carrozza MC. Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review. J Neuroeng Rehabil. 2022 Jun 3;19(1):54. Zhang W. Enhancing rehabilitation assessment with Artificial Intelligence: A comprehensive investigation of posture quality prediction using machine learning. ITM Web Conf. 2025;70:02025. Hjelm NM. Benefits and drawbacks of telemedicine. J Telemed Telecare. 2005;11(2):60–70. Kuwabara A, Su S, Krauss J. Utilizing digital health technologies for patient education in lifestyle medicine. Am J Lifestyle Med. 2020 Mar;14(2):137–42. Kadambi A, Manzone DM, Zariffa J. Evaluating the clinical utility of hand performance information from at-home egocentric video in outpatient neurorehabilitation [Internet]. medRxiv. 2024 [cited 2024 Sep 29]. p. 2024.09.27.24314512. Available from: https://www.medrxiv.org/content/10.1101/2024.09.27.24314512v1.abstract Aslani A, Pournik O, Abbasi SF, Arvanitis TN. Transforming healthcare: The role of artificial intelligence. Stud Health Technol Inform. 2025 May 15;327:1363–7. Haleem A, Javaid M, Singh RP, Suman R. Telemedicine for healthcare: Capabilities, features, barriers, and applications. Sens Int. 2021 Jul;2(100117):100117. Wang J, Lavender M, Hoque E, Brophy P, Kautz H. A patient-centered digital scribe for automatic medical documentation. JAMIA Open. 2021 Jan;4(1):ooab003. Anderson KD, Korupolu R, Musselman KE, Pierce J, Wilson JR, Yozbatiran N, et al. Multi-center, single-blind randomized controlled trial comparing functional electrical stimulation therapy to conventional therapy in incomplete tetraplegia. Front Rehabil Sci. 2022 Sep 9;3:995244. Mehrholz J, Pohl M, Platz T, Kugler J, Elsner B. Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke. Cochrane Database Syst Rev. 2018 Sep 3;9(9):CD006876. Yurkewich A, Ortega S, Sanchez J, Wang RH, Burdet E. Integrating hand exoskeletons into goal-oriented clinic and home stroke and spinal cord injury rehabilitation. J Rehabil Assist Technol Eng. 2022 Jan 2;9:20556683221130970. Ottoboni G, La Porta F, Piperno R, Chattat R, Bosco A, Fattori P, et al. A Multifunctional Adaptive and Interactive AI system to support people living with stroke, acquired brain or spinal cord injuries: A study protocol. PLoS One. 2022 Apr 11;17(4):e0266702. Veerbeek JM, Langbroek-Amersfoort AC, van Wegen EEH, Meskers CGM, Kwakkel G. Effects of robot-assisted therapy for the upper limb after stroke: A systematic review and meta-analysis. Neurorehabil Neural Repair. 2017 Feb;31(2):107–21. Bertani R, Melegari C, De Cola MC, Bramanti A, Bramanti P, Calabrò RS. Effects of robot-assisted upper limb rehabilitation in stroke patients: a systematic review with meta-analysis. Neurol Sci. 2017 Sep;38(9):1561–9. Lohse K, Shirzad N, Verster A, Hodges N, Van der Loos HFM. Video games and rehabilitation: using design principles to enhance engagement in physical therapy. J Neurol Phys Ther. 2013 Dec;37(4):166–75. Rodrigues P, Quaresma C, Costa M, Luz F, Fonseca MM. Virtual reality-based telerehabilitation for upper limb recovery post-stroke: A systematic review of design principles, monitoring, safety, and engagement strategies [Internet]. arXiv [cs.HC]. 2025 [cited 2025 Jul 18]. Available from: http://arxiv.org/abs/2501.06899 Assis GA de, Corrêa AGD, Martins MBR, Pedrozo WG, Lopes R de D. An augmented reality system for upper-limb post-stroke motor rehabilitation: a feasibility study. Disabil Rehabil Assist Technol. 2016 Aug;11(6):521–8. Jia C, Liu X, Ning L, Ge L. The effects of Augmented reality on rehabilitation of stroke patients: A systematic review and meta-analysis with trial sequential analysis. J Clin Nurs [Internet]. 2025 Apr 4; Available from: http://dx.doi.org/10.1111/jocn.17730 Sánchez-Gil JJ, Sáez-Manzano A, López-Luque R, Ochoa-Sepúlveda JJ, Cañete-Carmona E. Gamified devices for stroke rehabilitation: A systematic review. Comput Methods Programs Biomed. 2025 Jan;258(108476):108476. Braakhuis HEM, Bussmann JBJ, Ribbers GM, Berger MAM. Wearable activity monitoring in day-to-day stroke care: A promising tool but not widely used. Sensors (Basel). 2021 Jun 12;21(12):4066. Tosto-Mancuso J, Tabacof L, Herrera JE, Breyman E, Dewil S, Cortes M, et al. Gamified neurorehabilitation strategies for post-stroke motor recovery: Challenges and advantages. Curr Neurol Neurosci Rep. 2022 Mar 12;22(3):183–95. Soliman E, Mogefors D, Bergmann JHM. Problem-driven innovation models for emerging technologies: Graphical representation of need-led innovation methodologies in healthcare. Health Technol (Berl). 2020 Sep 23;10(5):1195–206. Davis FD. A technology acceptance model for empirically testing new end-user information systems : theory and results [Internet] [Doctoral Dissertation]. Massachusetts Institute of Technology; 1986 [cited 2025 Jul 2]. Available from: http://hdl.handle.net/1721.1/15192 Venkatesh V, Davis FD. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Manage Sci. 2000 Feb 1;46(2):186–204. Venkatesh V, Bala H. Technology acceptance model 3 and a research agenda on interventions. Decis Sci. 2008 May;39(2):273–315. Davis FD, Granić A. The technology acceptance model: 30 years of TAM. 2021st ed. Cham, Switzerland: Springer Nature; 2024. 117 p. (Human-Computer Interaction Series). Venkatesh V, Thong J, Xu X. Unified theory of acceptance and use of technology: A synthesis and the road ahead. J Assoc Inf Syst. 2016 May;17(5):328–76. Graham ID, Logan J, Harrison MB, Straus SE, Tetroe J, Caswell W, et al. Lost in knowledge translation: time for a map? J Contin Educ Health Prof. 2006 Winter;26(1):13–24. Brown GT. Male Occupational Therapists in Canada: A demographic profile. Br J Occup Ther. 1998 Dec 1;61(12):561–7. Te Boekhorst KE, Kuipers SJ, Ribbers GM, Cramm JM. Exploring rehabilitation patients’ perspectives on what matters for the adoption of home-based rehabilitation technology: Q-methodology study. JMIR Rehabil Assist Technol. 2025 Jul 9;12(v12i6e71515):e71515. Choi P, Walker R. Remote patient management: balancing patient privacy, data security, and clinical needs. Contrib Nephrol. 2019;197:35–43. Tsai MF, Atputharaj S, Zariffa J, Wang RH. Perspectives and expectations of stroke survivors using egocentric cameras for monitoring hand function at home: a mixed methods study. Disabil Rehabil Assist Technol. 2024 Apr;19(3):878–88. Bandini A, Kalsi-Ryan S, Craven BC, Zariffa J, Hitzig SL. Perspectives and recommendations of individuals with tetraplegia regarding wearable cameras for monitoring hand function at home: Insights from a community-based study. J Spinal Cord Med. 2021 May 7;1–12. Hoagland A, Kipping S. Challenges in promoting health equity and reducing disparities in access across new and established technologies. Can J Cardiol. 2024 Jun 1;40(6):1154–67. Veras M, Sigouin J, Auger LP, Auger C, Ahmed S, Boychuck Z, et al. A rapid review of ethical and equity dimensions in telerehabilitation for physiotherapy and occupational therapy. Int J Environ Res Public Health. 2025 Jul 9;22(7):1091. Durocher E, Wang RH, Bickenbach J, Schreiber D, Wilson MG. “just access”? Questions of equity in access and funding for assistive technology. Ethics Behav. 2019 Apr 3;29(3):172–91. Additional Declarations No competing interests reported. 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Arrows indicate the connections where each challenge points to one or more technology solution areas.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7852937/v1/ce8881bf239577de16817031.png"},{"id":93613832,"identity":"73e7e098-5c27-4894-aa4c-7681e8d24728","added_by":"auto","created_at":"2025-10-15 16:33:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1024682,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7852937/v1/e24a74e2-607d-4366-8cd4-ee1633fc103a.pdf"},{"id":93613293,"identity":"6102eeff-f818-45d3-b8b9-adfb3ba8f56c","added_by":"auto","created_at":"2025-10-15 16:25:45","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":219002,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary20251013.docx","url":"https://assets-eu.researchsquare.com/files/rs-7852937/v1/1d9f712168ad55a7902729e9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A translational framework mapping clinical challenges to technology solutions in upper limb neurorehabilitation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke and spinal cord injury (SCI) are leading causes of long-term disability, with annual global incidence rates of 12.2\u0026nbsp;million and 0.9\u0026nbsp;million, respectively (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). With advances in acute care and an aging population, these figures are projected to rise further (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Upper limb (UL) impairment is often a consequence of these conditions (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), limiting an individual's ability to live independently and perform activities of daily living (ADLs) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNeurorehabilitation supports recovery from these nervous system injuries (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and is the primary approach to improving UL function. Delivered by interdisciplinary teams of physicians, nurses, and therapists, it spans from intensive therapy in acute or inpatient settings and progresses to outpatient care. Practice frameworks like the Occupational Therapy Practice Framework: Domain and Process, Fourth Edition (OTPF-4) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) guide clinicians by defining the scope of practice and clinical process across the continuum of care. The \u003cb\u003eclinical process\u003c/b\u003e refers to the overarching structure of service delivery, encompassing the sequence of evaluation, intervention, and targeting of outcomes (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Within this framework, therapists engage in \u003cb\u003eclinical reasoning\u003c/b\u003e, which is a dynamic cognitive inquiry into the patient's functional life focusing on understanding the whole person, analyzing their needs, interpreting progress, and considering contextual factors to inform their actions (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). These actions or specific choices therapists make across different stages of the clinical process, such as selecting an intervention, setting a goal, or determining readiness for discharge is known as \u003cb\u003eclinical decision-making\u003c/b\u003e (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOutpatient care is a crucial part of this continuum of care, bridging hospital discharge and community reintegration, helping patients regain function, prevent complications, and improve quality of life (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The clinical process in outpatient UL neurorehabilitation is complex and requires sophisticated clinical reasoning. Therapists must constantly make critical decisions as they assess progress, plan therapy, educate patients and caregivers, ensure home-based intervention adherence, and coordinate multidisciplinary care (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Therapists must also tailor interventions to diverse patient needs (i.e., varying motor and cognitive impairments, social support systems, and home environments) while navigating systemic constraints like resource limitations (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This can hinder therapists' ability to optimize functional recovery and highlights the need for innovative solutions that support their clinical reasoning and enhance clinical decision-making.\u003c/p\u003e\u003cp\u003eEmerging rehabilitation technologies have the potential to address these clinical process challenges by providing more objective measurement capabilities, evidence-based treatment recommendations, and new intervention options (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Despite this, a critical gap persists between technology development and routine integration into outpatient practice (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). For instance, Morris et al. (2019) found that while clinicians recognize the need for mobile and wearable technologies in rehabilitation, only 50% of physical therapists and 44.2% of occupational therapists feel comfortable using them in practice (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Many technologies are developed without sufficient clinician input, resulting in solutions that are misaligned with clinical needs, difficult to use, or incompatible with existing workflows (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). This disconnect leads to underutilization, wasted resources, and missed opportunities to improve patient outcomes.\u003c/p\u003e\u003cp\u003eTo bridge this research-to-practice gap, a structured approach is needed to align technology development with the realities of outpatient neurorehabilitation. Unlike theoretical frameworks that describe phenomena (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), translational frameworks are action-oriented models that guide the application of evidence into practice (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Current approaches often address clinical processes or technology adoption in isolation, failing to integrate the two domains effectively (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). A translational framework can overcome this limitation by grounding technology development in the lived experience of clinicians, providing a roadmap for clinical, technology development, and healthcare administration interest-holders to make informed decisions.\u003c/p\u003e\u003cp\u003eThe objective of this study was to develop a translational framework that systematically identifies clinical process challenges in outpatient UL neurorehabilitation for stroke and SCI and maps them to technology-based solutions. Through direct clinical observation and in-depth interviews, we aimed to create a clinician-centered framework that bridges the gap between clinical needs and technological capabilities. This framework offers structured guidance for developing technologies that address clinical process challenges to enhance the delivery of outpatient neurorehabilitation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe employed an interpretive description methodology (25) to explore therapists\u0026apos; experiences and clinical process challenges in outpatient UL neurorehabilitation for stroke and SCI. Constant comparative analysis (CCA) (26) guided iterative synthesis across multiple data sources to generate applied knowledge for clinical practice. The study was approved by the Research Ethics Board of the University Health Network (UHN; Study #22-5457) and conducted in accordance with the Tri-Council Policy Statement. All participating therapists and observed patients provided written informed consent.\u003c/p\u003e\n\u003ch2\u003eStudy Recruitment\u003c/h2\u003e\n\u003cp\u003eWe targeted 10 therapists from the outpatient stroke and SCI programs at the Toronto Rehabilitation Institute \u0026ndash; University Health Network across three hospital sites. The sample size was determined using the principle of information power (27), considering the study\u0026apos;s specific aim, homogeneous clinical setting, and anticipated data richness from observations and interviews. Therapists were eligible if they were occupational or physical therapists actively treating adult stroke or SCI patients with improved UL function as a therapy goal. Patients were included if their therapy sessions, led by eligible therapists, focused on UL function. No further diagnostic criteria beyond stroke or SCI were required. Patients declining observation consent were excluded. All eligible therapists treating adult stroke or SCI patients with UL rehabilitation goals at our institution were invited to participate. A research team member contacted interested therapists to confirm eligibility, obtain consent, and explain the study\u0026apos;s objectives and questions to guide debrief sessions. Patient consent was obtained to ensure comfort with session observations.\u003c/p\u003e\n\u003ch2\u003eData Collection\u003c/h2\u003e\n\u003cp\u003eData collection occurred in three phases. In the first phase, the lead investigator (AK) conducted direct observations of therapy sessions to capture therapists\u0026apos; clinical process and reasoning challenges. We targeted three 45-minute observations per patient to balance depth with feasibility. These observations were scheduled based on therapist and patient availability, and session focus. In particular we were interested in sessions that included UL-related activities, such as motor assessments and task-oriented training. Field notes were captured in real time to document in-session decision-making, adaptive strategies, and contextual variables influencing therapist reasoning. Each session was followed by 5\u0026ndash;10-minute debriefs with therapists to clarify observed practices.\u003c/p\u003e\n\u003cp\u003eIn the second phase, in-depth semi-structured interviews (~60 minutes) explored therapists\u0026apos; perspectives on clinical processes, challenges, and the perceived role of technology in practice. An interview guide (see Supplementary Material) provided structure while allowing flexibility to follow emergent topics. Interviews were audio-recorded, transcribed verbatim, and anonymized prior to analysis. \u003c/p\u003e\n\u003cp\u003eIn the third and final phase, relevant literature was iteratively reviewed to contextualize findings and inform analytic distinctions. Literature review was treated as a supplementary data source that shaped subsequent interview questions and helped refine emerging categories.\u003c/p\u003e\n\u003ch2\u003eData Analysis\u003c/h2\u003e\n\u003cp\u003eWe used CCA to iteratively analyze findings across field notes, debriefs, interview transcripts, and existing literature. The lead investigator (AK) immersed in the data through repeated readings, developing analytic memos to capture patterns in therapists\u0026apos; experiences. Textual excerpts were clustered into conceptual groupings and refined through comparison, yielding clinical process challenge domains. Technology solution categories were derived by extracting technology mentions from interviews, inductively clustered, and aligned with rehabilitation technology literature. The translational framework was developed through iterative coding, thematic convergence, and senior researcher feedback, mapping challenges to technology solutions based on therapist input and clinical use cases.\u003c/p\u003e\n\u003ch2\u003eEnsuring Rigor\u003c/h2\u003e\n\u003cp\u003eThe lead investigator (AK) conducted all data collection and analysis for consistency, with multiple strategies to ensure rigor despite a single coder. Prolonged engagement over eight months fostered deep understanding of the clinical context within the study setting (28). Triangulation across field notes, debriefs, interviews, and existing literature allowed for convergence of themes across different modalities and further validated our findings (29). Member checking involved emailing therapists a draft framework and results to confirm alignment with their experiences and the suitability of proposed technology solutions (30). Feedback received from therapists was used to refine and validate the final framework, thereby enhancing the credibility and clinical relevance of the findings. \u003c/p\u003e\n\u003cp\u003eReflexive analysis is characterized by transparency in documenting analytical decisions, ongoing critical self-reflection about preconceptions, and acknowledgment of how researcher positioning influences findings (31). We used interlinked digital notes to document connections between disparate observations, track evolving interpretations, and ensure data-driven themes, mitigating bias. Regular peer debriefing with senior clinical researchers (RHW, JZ) provided external validation, challenging assumptions and refining codes and themes. These strategies ensured credibility, transferability, and dependability of the findings, addressing potential limitations of a single coder.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe recruited 10 therapists (9 occupational therapists, 1 physical therapist; all female) with a mean clinical experience of 11.7 \u0026plusmn; 7.6 years. We observed 11 patients (6 SCI, 5 stroke) across 33 observation sessions, totaling 24 hours and 45 minutes. These sessions included a range of therapeutic activities related to UL function such as motor assessments, task-oriented training, patient education, collaborative goal-setting, and patient discharge.\u003c/p\u003e\n\u003ch2\u003eChallenge Domains\u003c/h2\u003e\n\u003cp\u003eFive core challenge domains in outpatient UL neurorehabilitation were identified: (1) \u003cem\u003eAssessment \u0026amp; Progress Tracking\u003c/em\u003e, (2) \u003cem\u003eKnowledge Transfer\u003c/em\u003e, (3) \u003cem\u003ePatient Engagement\u003c/em\u003e, (4) \u003cem\u003eHome Program Implementation\u003c/em\u003e, and (5) \u003cem\u003eInformation \u0026amp; Resource Constraints\u003c/em\u003e. These domains reflect recurring barriers that therapists face when planning, delivering, and following up on care. Member checking with participating therapists confirmed that these domains resonated with their clinical experiences and captured key challenges in current outpatient practice.\u003c/p\u003e\n\u003ch3\u003eAssessment \u0026amp; Progress Tracking\u003c/h3\u003e\n\u003cp\u003eTherapists struggled to monitor progress outside clinic settings, often relying on self-reports. \u0026quot;I don\u0026apos;t have a good way to track how they\u0026apos;re doing it at home exactly or if they\u0026apos;re doing it enough... I just have been relying on their verbal feedback\u0026quot; (Therapist #1). Therapists must \u0026quot;trust that they\u0026apos;re a good historian\u0026quot; when judging functional gains between clinic visits (Therapist #5). However, this reliance is problematic since self-reported data are widely recognized to suffer from various forms of bias and inaccuracy. These inaccuracies stem from several well-documented factors such as recall bias (i.e., forgetting or inaccurately recalling details about their rehabilitation activities) (32), social desirability bias (i.e., telling therapists what they might want to hear) (33), reference bias (i.e., inconsistent personal benchmarks or interpretations when rating their own performance) (34), and cognitive impairments (35,36). This creates a significant challenge for their clinical reasoning, forcing them to make decisions about therapy progression based on potentially biased or incomplete data. The desire for more objective, real-world data was a consistent theme among therapists, with suggestions including wearable activity monitors and AI-supported movement analysis to supplement therapist judgment (Therapists #1, 2, 5, 6, 8, 10).\u003c/p\u003e\n\u003ch3\u003eKnowledge Transfer\u003c/h3\u003e\n\u003cp\u003eEffective communication of recovery expectations, therapeutic goals, disease or recovery progression, and exercise instructions were universally emphasized by all therapists as important. \u0026quot;Patient education is happening the second they walk through the door\u0026quot; (Therapist #9). However, the dissemination of knowledge is challenging due to cognitive, language, or cultural barriers. As one therapist explained, \u0026quot;The cultural aspect is difficult sometimes\u0026hellip; it gets lost in translation\u0026quot; (Therapist #10). Therapists noted the frustration patients experience due to unrealistic expectations: \u0026ldquo;He thought this would just be a couple months and he\u0026apos;s back to normal and wasn\u0026apos;t really expecting\u0026hellip; the variability or unpredictability of the recovery process for SCI\u0026rdquo; (Therapist #3). Caregiver education also emerged as critical, with therapists noting the need to train caregivers to support safe home exercises (Therapist #10). Proposed solutions included multimedia tools, culturally adaptive content, and interactive modules to reinforce understanding and bridge communication gaps.\u003c/p\u003e\n\u003ch3\u003ePatient Engagement\u003c/h3\u003e\n\u003cp\u003eTherapists observed that motivated patients achieve better outcomes, while disengagement due to frustration, slow recovery, or repetitive exercises hinders progress (Therapists #7 and 9). This aligns with existing literature indicating patient engagement, including motivation, active participation, and emotional buy-in to therapy, is essential for successful rehabilitation outcomes (37). While therapists found that \u0026ldquo;with this population, people are not that bored to do very simple tasks because those simple tasks are so difficult to do\u0026rdquo; (Therapist #3), maintaining engagement remains challenging, particularly when patients focus on pre-injury capabilities: \u0026ldquo;He\u0026apos;s really focused on all the things he was able to do before the injury that he can\u0026apos;t do now\u0026rdquo; (Therapist #3).\u003c/p\u003e\n\u003cp\u003eStrategies therapists used included incorporating patient identified goals into task selection and introducing novelty. One therapist remarked, \u0026ldquo;I just always check in\u0026hellip; and make sure like, you know, how is this going for you? Like, is there something else you want to be working on?\u0026rdquo; (Therapist #4). Another therapist described tailoring exercises to a patient\u0026apos;s life: \u0026ldquo;I knew they had two young children\u0026hellip; I had them on the floor, picking up toys\u0026hellip; an activity that he does at home with his young children\u0026rdquo; (Therapist #8). Technology was seen as a promising facilitator, with therapists suggesting gamified platforms and visual biofeedback to make exercises more engaging.\u003c/p\u003e\n\u003ch3\u003eHome Program Implementation\u003c/h3\u003e\n\u003cp\u003eThe home exercise program is a cornerstone of outpatient rehab and supplements the limited therapy sessions. However, several therapists estimated that patients do not do their home exercises consistently (Therapists #2, 3, 4, 5, 6, and 10), aligning with literature indicating that only 23\u0026ndash;64% of patients fully adhere to home therapy (38,39). There is \u0026quot;a lot that goes into why they might not be completing the exercises, right? They might not have anyone to help them\u0026hellip; or motivation, mental health symptoms, resources, time, support\u0026quot; (Therapist #2). For instance, one of the observed patients had a caregiver who was overwhelmed and lacked support, so the therapist opted not to assign a home program, recognizing it might go uncompleted and further burden the caregiver. The therapist lamented that this is \u0026ldquo;really unfortunate, but it is a challenge you have to navigate\u0026rdquo; --- that is, balancing ideal therapy with real-life feasibility (Therapist #10). Therapists usually have no insight into what patients actually do between appointments. As one therapist put it, the home program is a \u0026quot;black box\u0026quot; (Therapist #6). Suggested technological interventions included remote monitoring tools, self-guided apps, and real-time video feedback to enhance adherence and provide therapists with better oversight of home activities.\u003c/p\u003e\n\u003ch3\u003eInformation \u0026amp; Resource Constraints\u003c/h3\u003e\n\u003cp\u003eTherapists described systemic pressures related to short appointment windows and high caseloads. One therapist remarked, \u0026quot;Forty-five minutes is... you don\u0026apos;t have time. Ten minutes, \u0026apos;I have to go to the bathroom,\u0026apos; and then... twenty minutes are gone\u0026quot; (Therapist #6). They felt \u0026ldquo;time is the biggest barrier\u0026rdquo; (Therapist #10) to delivering ideal therapy \u0026ldquo;because there are so many things that [they] could be doing [with patients but they] have to boil it down and pick a few\u0026rdquo; (Therapist #4). \u003c/p\u003e\n\u003cp\u003eAccessing advanced technologies was also challenging due to bureaucratic and financial barriers. \u0026ldquo;There\u0026apos;s definitely resource constraint\u0026hellip; I would need to go through the whole bureaucracy process\u0026hellip; Why is [it] helpful? Is there any evidence-based research already out?\u0026rdquo; (Therapist #8). While Therapist #8 did not specify a particular technology they attempted to acquire, they mentioned receiving outreach emails from promising rehabilitation technologies but was deterred by the need to justify the technology\u0026apos;s utility to the department with evidence-based research, which is time-consuming.\u003c/p\u003e\n\u003cp\u003eTherapist #2 emphasized the broader issue of \u0026quot;access to care and resources\u0026hellip; Finances, money, resources, time, staff\u0026hellip; all those barriers\u0026quot; means patients cannot access care all the time (Therapist #2). This highlights the need for technologies that could optimize limited resources or help therapists do more with less. Proposed solutions included technologies like remote monitoring (Therapists #2, 3, 7, 10) and decision-support or education tools for newer therapists (Therapists #2, 7, 8) (40).\u003c/p\u003e\n\u003ch2\u003eTechnology Solution Categories\u003c/h2\u003e\n\u003cp\u003eThrough the review of field notes, interview data, and subsequent triangulation of rehabilitation technology in the existing literature, we propose four broad, future-proof technology solution areas that cover all identified challenge domains while minimizing overlap and accommodating future advancements: (1) \u003cem\u003eRemote Monitoring \u0026amp; Analytics\u003c/em\u003e, (2) \u003cem\u003eEducation \u0026amp; Decision Support\u003c/em\u003e, (3) \u003cem\u003eAssistive \u0026amp; Therapeutic Technologies\u003c/em\u003e, and (4) \u003cem\u003eGamified Interventions\u003c/em\u003e. All technologies mentioned by therapists in the interviews, either explicitly or implicitly, are fully captured by these four categories.\u003c/p\u003e\n\u003ch3\u003eRemote Monitoring \u0026amp; Analytics\u003c/h3\u003e\n\u003cp\u003eThis category encompasses technologies that collect, analyze, and visualize data on patient performance in home environments to support objective progress tracking and contextualized therapy. It includes wearable sensors (e.g., activity trackers, inertial measurement units) (41), in-home environmental sensors (42), video-based systems for capturing therapy context (43), and AI-driven analytics to process data and provide actionable insights (44\u0026ndash;48). One therapist explained the value of these tools: \u0026quot;I am very visual, I just can\u0026apos;t always get out to the person\u0026apos;s home environment. So videos, pictures... how can I see you in this environment? Or how can I see your environment so that we can work on this, this, and this.\u0026quot; (Therapist #5). These tools address the \u0026ldquo;black box\u0026rdquo; of home activities (Therapist #6) by combining quantitative movement data (Therapist #3) with qualitative environmental insights (Therapist #5), enabling therapists to supervise patients remotely (49), monitor adherence, assess functional gains objectively, and tailor interventions without relying on subjective self-reports. \u003c/p\u003e\n\u003ch3\u003eEducation \u0026amp; Decision Support\u003c/h3\u003e\n\u003cp\u003eThis category includes digital platforms and tools to enhance education for patients, caregivers, and therapists, as well as clinical decision-support systems to optimize treatment planning. For patients and caregivers, it encompasses dissemination of knowledge through multimedia content (e.g., videos, interactive modules), culturally adaptive resources (Therapist #10), and reminders to reinforce understanding of recovery processes and exercises (Therapist #2, 7, 8, 10) (50). Therapists frequently mentioned the difficulty of ensuring patients and caregivers retained information from a session (Therapist #10). One therapist explained the need for a \u0026quot;simplified program... a video they can watch a hundred times. The family can watch. I think they are lacking [understanding].\u0026quot; (Therapist #6). For therapists, it includes evidence-based databases, professional development platforms, dashboards (51), and tools like \u0026quot;ViaTherapy\u0026quot; (Therapist #8) (40) to streamline decision-making (52) or clinical documentation (53,54). These tools address cognitive, language, and cultural barriers for patients/caregivers, as well as the time and resource constraints for therapists. They can be especially useful as a resource for newer therapists and guide their therapy planning and decision-making.\u003c/p\u003e\n\u003ch3\u003eAssistive \u0026amp; Therapeutic Technologies\u003c/h3\u003e\n\u003cp\u003eThese devices can range from functional electrical stimulation (FES) devices (55) to end-effector devices (56), exoskeletons (57), and even future robotic or AI-driven assistive technologies (58). As Therapist #3 stated, \u0026quot;Robotic arms can assist with functional and fine motor tasks that help to motivate patients in achieving their goals... It can be used as a measurement tool... [and] has a potential for home use, assisting patients in doing exercises at home.\u0026quot; These tools can deliver high-intensity, repetitive, guided movements, and support home use, thereby addressing therapy dosage and motivation concerns (59,60). \u003c/p\u003e\n\u003ch3\u003eGamified Interventions\u003c/h3\u003e\n\u003cp\u003eThis category includes platforms that use game mechanics (61), virtual reality (VR) (62), augmented reality (AR) (63,64), and biofeedback to enhance engagement and motivation. These tools transform repetitive exercises into interactive experiences, \u0026quot;I feel like those have potential, because it\u0026apos;s the same movement, but you\u0026apos;re really enjoying it with this virtual reality, or kind of game, because it takes away the repetition feeling, I think, that kind of makes it fun.\u0026quot; (Therapist #8). By turning a mundane task into an interactive game with scores and challenges, these tools can increase patient enthusiasm and engagement (65). Furthermore, integrated biofeedback from wearables can provide immediate rewards for correct movements, creating a powerful motivational loop that enhances compliance and reinforces effort (66,67).\u003c/p\u003e\n\u003ch2\u003eTranslational Framework\u003c/h2\u003e\n\u003cp\u003eFollowing iterative analysis and member checking, the final translational framework (Figure 1) maps each of the five challenge domains to technology solution areas. These mappings highlight that effective solutions often span multiple domains, and no single technology operates in isolation.\u003c/p\u003e\n\u003cp\u003eThis mapping is intended to guide both researchers and developers in aligning innovations with clinical needs. It shows that an effective rehabilitation technology ecosystem should address each of the key domains: providing therapists with better assessment tools, educating and empowering patients, engaging them meaningfully, extending therapy into the home, and easing systemic burdens. \u003c/p\u003e\n\u003cp\u003eImportantly, the domains influence each other and the solutions can synergistically overlap, thus encouraging a comprehensive approach---not simply creating a device or app in isolation, but ensuring that it fits into clinical workflows and tackles real pain points identified by therapists.\u003c/p\u003e\n\u003cp\u003eBy mapping these relationships, interest‐holders can identify opportunities and ensure that new rehabilitation technologies are grounded in actual clinical challenges. The ultimate goal is a coordinated set of technology solutions that enable therapists to deliver more personalized, effective, and scalable UL rehabilitation, improving outcomes for individuals recovering from stroke and spinal cord injury.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study presents a novel translational framework that systematically maps five core clinical process challenge domains in outpatient UL neurorehabilitation, identified through qualitative analysis of therapist experiences, to four broad technology solution categories. Its foundational purpose is to ground technology development in the real-world needs of clinicians, ensuring new tools are not merely solutions in search of a problem (68). Our framework achieves this by translating the implicit questions that guide clinical processes and decision-making into actionable technological interventions. For instance, a therapist\u0026apos;s concern about tracking home performance (Therapist #10) is mapped directly to \u003cem\u003eRemote Monitoring \u0026amp; Analytics\u003c/em\u003e, while challenges with patient comprehension (Therapist #6) are addressed by \u003cem\u003eEducation \u0026amp; Decision Support\u003c/em\u003e. Table 1 contains exemplars of real-world information needs voiced by therapists during interviews and how our framework can guide technology selection and development.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 1: Examples of translational mapping framework use to answer exemplar therapist questions.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"681\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58.2966%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTherapist Question\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.558%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChallenge Domain(s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTechnology Solutions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58.2966%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHow is my patient using their hand at home?\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026quot;I would love to have something like that, like a recording or like a really good summary of how they perform their activities. Just to help me make better decisions.\u0026quot; (Therapist #10)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.558%;\"\u003e\n \u003cp\u003eAssessment \u0026amp; Progress Tracking; Home Program Implementation; Patient Engagement\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1454%;\"\u003e\n \u003cp\u003eRemote Monitoring\u2028 \u0026amp; Analytics; Assistive \u0026amp; Therapeutic Technologies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58.2966%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAre my patients practicing exercises correctly at home?\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026quot;In terms of knowing if they\u0026apos;re doing it correctly, I will have them demonstrate back... And then aside from that I just have to rely... on their verbal feedback at the next session.\u0026quot; (Therapist #1)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.558%;\"\u003e\n \u003cp\u003eHome Program Implementation; Assessment \u0026amp; Progress Tracking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1454%;\"\u003e\n \u003cp\u003eRemote Monitoring \u0026amp; Analytics; Immersive \u0026amp; Gamified Interventions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58.2966%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHow do I ensure patients understand their therapy goals?\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026quot;They come here, they are so overwhelmed. I don\u0026apos;t know if they remember everything I say.\u0026quot; (Therapist #6)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.558%;\"\u003e\n \u003cp\u003eKnowledge Transfer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1454%;\"\u003e\n \u003cp\u003eEducation \u0026amp; \u2028Decision Support\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58.2966%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHow can I track small functional improvements?\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;What might seem so small to a patient, I really try to say like, \u0026apos;That\u0026apos;s a big deal, it might seem so small to you that you were able to move your thumb today, but that\u0026apos;s a big deal\u0026apos;.\u0026quot; (Therapist #9)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.558%;\"\u003e\n \u003cp\u003eAssessment \u0026amp; Progress Tracking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1454%;\"\u003e\n \u003cp\u003eRemote Monitoring\u2028\u0026amp; Analytics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58.2966%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHow can I better train caregivers to assist at home?\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026quot;[I would want] educational platforms to train caregivers on how to actually take care of the person... Because as we both saw, like caregiver was maybe a little rough... I feel like education is definitely a part of that.\u0026quot; (Therapist #10)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.558%;\"\u003e\n \u003cp\u003eKnowledge Transfer; Home Program Implementation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1454%;\"\u003e\n \u003cp\u003eEducation \u0026amp; Decision Support\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBy grounding development in this way, our framework is positioned to complement other established models in the literature. For instance, technology acceptance models including the original Technology Acceptance Model (TAM) (69), along with its extensions (i.e., TAM2 (70), TAM3 (71), TAM+ (72)) and related frameworks like the Unified Theory of Acceptance and Use of Technology (UTAUT) (73), consistently identify \u003cem\u003ePerceived Usefulness\u003c/em\u003e as a major predictor of technology adoption. Our framework designs for usefulness from the outset by ensuring solutions are grounded in clinician-identified needs. We also align with foundational knowledge translation models like the Knowledge-to-Action (KTA) framework (74) which begin with identifying the problem and adapting knowledge to a local context. Our framework operationalizes this critical step for technology implementation in outpatient UL neurorehabilitation by first identifying the relevant challenge domains before considering any solutions.\u003c/p\u003e\n\u003cp\u003eBeyond its theoretical contribution, the framework has significant practical implications for multiple interest‐holders. By translating the complex clinical process challenges therapists face into structured requirements, it provides a clear pathway for developers and a needs-driven roadmap for healthcare organizations making technology investment decisions. This mapping approach allows therapists to articulate their complex information needs, which are central to effective clinical reasoning, and encourages integrated solutions to fully address a clinical question, such as combining \u003cem\u003eRemote Monitoring \u0026amp; Analytics\u003c/em\u003e with \u003cem\u003eGamified Interventions\u003c/em\u003e to improve home exercise adherence. Ultimately, it ensures that resources are allocated to tools that address actual clinical needs rather than perceived technological gaps.\u003c/p\u003e\n\u003cp\u003eIt is also crucial to recognize the interdependent nature of the identified challenge domains. While our framework illustrates direct links between challenges and solutions, the clinical reality is more complex, with challenges often compounding one another. For instance, the \u003cem\u003eInformation \u0026amp; Resource Constraints\u003c/em\u003e domain frequently acts as a root cause that exacerbates other issues. A therapist constrained by a short appointment window (a resource constraint) will have less time for \u003cem\u003eKnowledge Transfer\u003c/em\u003e, which can lead to poor \u003cem\u003eHome Program Implementation\u003c/em\u003e and diminished \u003cem\u003ePatient Engagement\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eA key function of this translational framework is to serve as a foundation for generating testable hypotheses that can guide future intervention studies. The linkages identified in our framework can be formulated into empirical questions. For example, one could hypothesize that for the clinical goal of improving home exercise quality and consistency, a combination of \u003cem\u003eRemote Monitoring\u2028\u0026amp; Analytics\u003c/em\u003e (to track performance) and \u003cem\u003eGamified Interventions\u003c/em\u003e (to drive engagement) will be more effective than either solution implemented in isolation. Similarly, one could test whether the effectiveness of \u003cem\u003eEducation \u0026amp; Decision Support\u003c/em\u003e technologies on improving patient outcomes is mediated by an increase in caregiver self-efficacy. By structuring the problem space in this way, the framework could also provide a basis for designing future clinical trials in rehabilitation technology.\u003c/p\u003e\n\u003ch2\u003eLimitations\u003c/h2\u003e\n\u003cp\u003eSeveral limitations should be considered when interpreting our findings. Our study was conducted at three hospital sites within the same hospital network and may not reflect the challenges present in other settings, such as community or rural clinics. Also, the hospital network is located in one city (Toronto, Canada) and may not reflect the practices, policies, and economic conditions (resource constraints) of other rehabilitation settings in different geographical locations.\u003c/p\u003e\n\u003cp\u003eOur sample also had a significant demographic imbalance as a result of the study setting. The sample was predominantly occupational therapists (9 of 10 participants) and was exclusively female. This reflects both the female-dominant nature of occupational therapy profession (75) and the operational model at the recruitment sites, where OTs are primarily responsible for UL rehabilitation. Still, the OT-centric perspective may underrepresent challenges or technology needs more central to the physical therapy domain. Similarly, while representative of the profession, a single-gender perspective may omit nuances that a more diverse sample could provide.\u003c/p\u003e\n\u003cp\u003eThis analysis was also conducted by a single researcher. While we maintained rigor with member checking, data triangulation, and peer review with senior clinical researchers, there may still be potential bias in interpretation. Multi-researcher analysis could strengthen the framework\u0026apos;s reliability.\u003c/p\u003e\n\u003cp\u003eFurthermore, this framework focused on clinical process challenges and therefore did not capture patient perspectives, which could provide critical insights into technology acceptance, usability, or barriers (e.g., cognitive or physical limitations) (76). Ethical considerations, such as privacy risks with remote monitoring data (77\u0026ndash;79) or equity issues (80\u0026ndash;82) in accessing advanced tools, were not explored but could impact implementation. Patient perspectives should be considered when developing any specific technology before widespread implementation in practice.\u003c/p\u003e\n\u003cp\u003eFinally, while our framework identifies challenge-solution mappings, it does not address all implementation barriers, such as cost considerations, reimbursement structures, or regulatory requirements. These practical constraints significantly impact technology adoption but fall outside our study scope.\u003c/p\u003e\n\u003ch2\u003eFuture Directions\u003c/h2\u003e\n\u003cp\u003eFuture directions should include developing concrete implementation tools based on this framework, such as guiding questions for technology developers, evaluation checklists for assessing whether proposed technologies align with identified clinical needs, and decision-support tools for healthcare organizations selecting technologies. Empirical studies could then evaluate whether using this framework during technology development leads to solutions with greater perceived usefulness and clinical utility compared to traditional development approaches in diverse outpatient settings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis translational framework represents a significant advance in bridging the gap between clinical needs and technological capabilities in outpatient neurorehabilitation to improve patient care. By systematically mapping rehabilitation challenges to technology solutions, we provide practical guidance for clinicians, hospital management, and developers working to enhance rehabilitation outcomes through technology. As rehabilitation technology continues to evolve, this framework can guide development of integrated, clinically meaningful solutions that truly support the complex work of helping individuals with neurological injuries achieve their maximum functional potential. Success will require coordinated efforts among clinicians, researchers, technology developers, and healthcare systems to create rehabilitation technology ecosystems that are both innovative and practically grounded in clinical reality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study was approved by the Research Ethics Board of the University Health Network (Study #22-5457) and conducted in accordance with the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (TCPS2). Informed consent was obtained from all participating therapists and observed patients included in the study.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available and cannot be shared due to the terms of the informed consent and privacy concerns regarding identifying information in the interviews.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Praxis Spinal Cord Institute; the Ontario Early Researcher Award program under grant number ER16\u0026ndash;12-013; and the Canadian Institutes of Health Research under grant number 13556838.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eAK recruited participants, conducted and transcribed all interviews, performed data analysis, and drafted the manuscript. PE contributed to therapist recruitment and provided clinical expertise to support the claims made in the manuscript. RHW assisted in developing the framework and situating it within the existing literature. JZ contributed to the study design, guided data interpretation, and substantively revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGBD 2019 Stroke Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2021 Oct 1;20(10):795\u0026ndash;820.\u003c/li\u003e\n\u003cli\u003eGBD Spinal Cord Injuries Collaborators. Global, regional, and national burden of spinal cord injury, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2023 Nov 1;22(11):1026\u0026ndash;47.\u003c/li\u003e\n\u003cli\u003eKwakkel G, Kollen BJ, van der Grond J, Prevo AJH. Probability of regaining dexterity in the flaccid upper limb: impact of severity of paresis and time since onset in acute stroke: Impact of severity of paresis and time since onset in acute stroke. Stroke. 2003 Sep;34(9):2181\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eAnderson KD. Targeting recovery: priorities of the spinal cord-injured population. J Neurotrauma. 2004 Oct;21(10):1371\u0026ndash;83.\u003c/li\u003e\n\u003cli\u003eWinstein CJ. Goal-oriented action: New perspectives with special emphasis on neurorehabilitation. In: Progress in Motor Control. Elsevier; 2024. p. 349\u0026ndash;87.\u003c/li\u003e\n\u003cli\u003eAmerican Occupational Therapy Association. Occupational Therapy Practice Framework: Domain and Process\u0026mdash;Fourth Edition. The American Journal of Occupational Therapy. 08 2020;74(Supplement_2):7412410010p1\u0026ndash;7412410010p87.\u003c/li\u003e\n\u003cli\u003eMattingly C. What is clinical reasoning? Am J Occup Ther. 1991 Nov;45(11):979\u0026ndash;86.\u003c/li\u003e\n\u003cli\u003eFleming MH. Clinical reasoning in medicine compared with clinical reasoning in occupational therapy. Am J Occup Ther. 1991 Nov 1;45(11):988\u0026ndash;96.\u003c/li\u003e\n\u003cli\u003eBate L, Hutchinson A, Underhill J, Maskrey N. How clinical decisions are made: How clinical decisions are made. Br J Clin Pharmacol. 2012 Oct 28;74(4):614\u0026ndash;20.\u003c/li\u003e\n\u003cli\u003eHiggs J, Jones MA, Bds SLP, Christensen N. Clinical Reasoning in the Health Professions. Vol. 4th. Elsevier Health Sciences; 2019. 520 p.\u003c/li\u003e\n\u003cli\u003eVeerbeek JM, van Wegen E, van Peppen R, van der Wees PJ, Hendriks E, Rietberg M, et al. What is the evidence for physical therapy poststroke? A systematic review and meta-analysis. PLoS One. 2014 Feb 4;9(2):e87987.\u003c/li\u003e\n\u003cli\u003eLanghorne P, Baylan S, Early Supported Discharge Trialists. Early supported discharge services for people with acute stroke. Cochrane Database Syst Rev. 2017 Jul 13;7(7):CD000443.\u003c/li\u003e\n\u003cli\u003eOutpatient Service Trialists. Therapy-based rehabilitation services for stroke patients at home. Cochrane Database Syst Rev. 2003;2010(1):CD002925.\u003c/li\u003e\n\u003cli\u003eShafaroodi N, Kamali M, Parvizy S, Mehraban AH, O\u0026rsquo;Toole G. Factors affecting clinical reasoning of occupational therapists: a qualitative study. Med J Islam Repub Iran. 2014 Feb 19;28:8.\u003c/li\u003e\n\u003cli\u003eda Silva Araujo A, Anne Kinsella E, Thomas A, Demonari Gomes L, Quevedo Marcolino T. Clinical reasoning in occupational therapy practice: A scoping review of qualitative and conceptual peer-reviewed literature. Am J Occup Ther. 2022 May 1;76(3):7603205070.\u003c/li\u003e\n\u003cli\u003eLeir\u0026oacute;s-Rodr\u0026iacute;guez R, Ben\u0026iacute;tez-Andrades JA, Marqu\u0026eacute;s-S\u0026aacute;nchez MP, Calvo-Ayuso N. Automation of the assessment and diagnosis process in rehabilitation: A proposal to reduce costs and optimize the health care system. In: Global Challenges for a Sustainable Society. Cham: Springer International Publishing; 2023. p. 395\u0026ndash;7. (Springer Proceedings in Earth and Environmental Sciences).\u003c/li\u003e\n\u003cli\u003eGower V, Aprile I, Falchini F, Fasano A, Germanotta M, Randazzo M, et al. Cost analysis of technological vs. conventional upper limb rehabilitation for patients with neurological disorders: an Italian real-world data case study. Front Public Health. 2024 Oct 14;12:1445099.\u003c/li\u003e\n\u003cli\u003eAlt Murphy M, Pradhan S, Levin MF, Hancock NJ. Uptake of technology for neurorehabilitation in clinical practice: A scoping review. Phys Ther. 2024 Feb 1;104(2):zad140.\u003c/li\u003e\n\u003cli\u003eMorris J, Jones M, Thompson N, Wallace T, DeRuyter F. Clinician Perspectives on mRehab Interventions and Technologies for People with Disabilities in the United States: A National Survey. Int J Environ Res Public Health [Internet]. 2019 Oct 31;16(21). Available from: http://dx.doi.org/10.3390/ijerph16214220\u003c/li\u003e\n\u003cli\u003eGreenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A\u0026rsquo;Court C, et al. Beyond adoption: A new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. J Med Internet Res. 2017 Nov 1;19(11):e367.\u003c/li\u003e\n\u003cli\u003eGagnon MP, Desmartis M, Labrecque M, Car J, Pagliari C, Pluye P, et al. Systematic review of factors influencing the adoption of information and communication technologies by healthcare professionals. J Med Syst. 2012 Feb;36(1):241\u0026ndash;77.\u003c/li\u003e\n\u003cli\u003eJabareen Y. Building a conceptual framework: Philosophy, definitions, and procedure. Int J Qual Methods. 2009 Dec 1;8(4):49\u0026ndash;62.\u003c/li\u003e\n\u003cli\u003eWhite KM. Chapter Two: The Science of Translation and Major Frameworks. In: White KM, Dudley-Brown S, Terhaar MF, editors. Translation Evidence Nursing Health Care Practice : Application Nursing Health Care. 2nd ed. New York, NY: Springer Publishing; 2016. p. 35\u0026ndash;56.\u003c/li\u003e\n\u003cli\u003eRubio DM, Schoenbaum EE, Lee LS, Schteingart DE, Marantz PR, Anderson KE, et al. Defining translational research: implications for training. Acad Med. 2010 Mar;85(3):470\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eThorne SE. Interpretive Description: Qualitative Research for Applied Practice. Routledge; 2016. 336 p.\u003c/li\u003e\n\u003cli\u003eGlaser BG, Strauss AL. The constant comparative method of qualitative analysis. In: The Discovery of Grounded Theory. 1st Edition. Routledge; 1999. p. 101\u0026ndash;16.\u003c/li\u003e\n\u003cli\u003eMalterud K, Siersma VD, Guassora AD. Sample Size in Qualitative Interview Studies: Guided by Information Power. Qual Health Res. 2016 Nov;26(13):1753\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eCreswell JW. Qualitative inquiry and research design. 3rd ed. Thousand Oaks, CA: SAGE Publications; 2013. 472 p.\u003c/li\u003e\n\u003cli\u003ePatton MQ. Enhancing the quality and credibility of qualitative analysis. Health Serv Res. 1999 Dec;34(5 Pt 2):1189\u0026ndash;208.\u003c/li\u003e\n\u003cli\u003eLincoln YS, Guba EG. Naturalistic Inquiry. Thousand Oaks, CA: SAGE Publications; 1985. 416 p.\u003c/li\u003e\n\u003cli\u003eOlmos-Vega FM, Stalmeijer RE, Varpio L, Kahlke R. A practical guide to reflexivity in qualitative research: AMEE Guide No. 149. Med Teach. 2022 Apr 7;1\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eBrusco NK, Watts JJ. Empirical evidence of recall bias for primary health care visits. BMC Health Serv Res. 2015 Sep 15;15(1):381.\u003c/li\u003e\n\u003cli\u003eAdams AS, Soumerai SB, Lomas J, Ross-Degnan D. Evidence of self-report bias in assessing adherence to guidelines. Int J Qual Health Care. 1999 Jun;11(3):187\u0026ndash;92.\u003c/li\u003e\n\u003cli\u003eLira B, O\u0026rsquo;Brien JM, Pe\u0026ntilde;a PA, Galla BM, D\u0026rsquo;Mello S, Yeager DS, et al. Large studies reveal how reference bias limits policy applications of self-report measures. Sci Rep. 2022 Nov 10;12(1):19189.\u003c/li\u003e\n\u003cli\u003eSachdeva R, Gao F, Chan CCH, Krassioukov AV. Cognitive function after spinal cord injury: A systematic review. Neurology. 2018 Sep 25;91(13):611\u0026ndash;21.\u003c/li\u003e\n\u003cli\u003eTatemichi TK, Desmond DW, Stern Y, Paik M, Sano M, Bagiella E. Cognitive impairment after stroke: frequency, patterns, and relationship to functional abilities. J Neurol Neurosurg Psychiatry. 1994 Feb;57(2):202\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eLoomis KJ, Roll SC, Hardison ME. The role of therapist-patient relationships in facilitating engagement and adherence in upper extremity rehabilitation. Work. 2023 May 20;76(3):1083\u0026ndash;98.\u003c/li\u003e\n\u003cli\u003eSluijs EM, Kok GJ, van der Zee J. Correlates of exercise compliance in physical therapy. Phys Ther. 1993 Nov;73(11):771\u0026ndash;82; discussion 783\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eMiller KK, Porter RE, DeBaun-Sprague E, Van Puymbroeck M, Schmid AA. Exercise after stroke: Patient adherence and beliefs after discharge from rehabilitation. Top Stroke Rehabil. 2017 Mar;24(2):142\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eHancock NJ, Collins K, Dorer C, Wolf SL, Bayley M, Pomeroy VM. Evidence-based practice \u0026ldquo;on-the-go\u0026rdquo;: using ViaTherapy as a tool to enhance clinical decision making in upper limb rehabilitation after stroke, a quality improvement initiative. BMJ Open Qual. 2019 Aug 19;8(3):e000592.\u003c/li\u003e\n\u003cli\u003ePatel S, Park H, Bonato P, Chan L, Rodgers M. A review of wearable sensors and systems with application in rehabilitation. J Neuroeng Rehabil. 2012 Apr 20;9:21.\u003c/li\u003e\n\u003cli\u003eMarwaa MN, Kristensen HK, Guidetti S, Ytterberg C. Physiotherapists\u0026rsquo; and occupational therapists\u0026apos; perspectives on information and communication technology in stroke rehabilitation. PLoS One. 2020 Aug 28;15(8):e0236831.\u003c/li\u003e\n\u003cli\u003eBandini A, Dousty M, Hitzig SL, Craven BC, Kalsi-Ryan S, Zariffa J. Measuring Hand Use in the Home after Cervical Spinal Cord Injury Using Egocentric Video. J Neurotrauma. 2022 Dec;39(23-24):1697\u0026ndash;707.\u003c/li\u003e\n\u003cli\u003eAdans-Dester C, Hankov N, O\u0026rsquo;Brien A, Vergara-Diaz G, Black-Schaffer R, Zafonte R, et al. Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery. NPJ Digit Med. 2020 Sep 21;3:121.\u003c/li\u003e\n\u003cli\u003eLiao Y, Vakanski A, Xian M. A deep learning framework for assessing physical rehabilitation exercises. IEEE Trans Neural Syst Rehabil Eng. 2020 Feb 13;28(2):468\u0026ndash;77.\u003c/li\u003e\n\u003cli\u003eLiao WW, Hsieh YW, Lee TH, Chen CL, Wu CY. Machine learning predicts clinically significant health related quality of life improvement after sensorimotor rehabilitation interventions in chronic stroke. Sci Rep. 2022 Jul 4;12(1):11235.\u003c/li\u003e\n\u003cli\u003eCampagnini S, Arienti C, Patrini M, Liuzzi P, Mannini A, Carrozza MC. Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review. J Neuroeng Rehabil. 2022 Jun 3;19(1):54.\u003c/li\u003e\n\u003cli\u003eZhang W. Enhancing rehabilitation assessment with Artificial Intelligence: A comprehensive investigation of posture quality prediction using machine learning. ITM Web Conf. 2025;70:02025.\u003c/li\u003e\n\u003cli\u003eHjelm NM. Benefits and drawbacks of telemedicine. J Telemed Telecare. 2005;11(2):60\u0026ndash;70.\u003c/li\u003e\n\u003cli\u003eKuwabara A, Su S, Krauss J. Utilizing digital health technologies for patient education in lifestyle medicine. Am J Lifestyle Med. 2020 Mar;14(2):137\u0026ndash;42.\u003c/li\u003e\n\u003cli\u003eKadambi A, Manzone DM, Zariffa J. Evaluating the clinical utility of hand performance information from at-home egocentric video in outpatient neurorehabilitation [Internet]. medRxiv. 2024 [cited 2024 Sep 29]. p. 2024.09.27.24314512. Available from: https://www.medrxiv.org/content/10.1101/2024.09.27.24314512v1.abstract\u003c/li\u003e\n\u003cli\u003eAslani A, Pournik O, Abbasi SF, Arvanitis TN. Transforming healthcare: The role of artificial intelligence. Stud Health Technol Inform. 2025 May 15;327:1363\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eHaleem A, Javaid M, Singh RP, Suman R. Telemedicine for healthcare: Capabilities, features, barriers, and applications. Sens Int. 2021 Jul;2(100117):100117.\u003c/li\u003e\n\u003cli\u003eWang J, Lavender M, Hoque E, Brophy P, Kautz H. A patient-centered digital scribe for automatic medical documentation. JAMIA Open. 2021 Jan;4(1):ooab003.\u003c/li\u003e\n\u003cli\u003eAnderson KD, Korupolu R, Musselman KE, Pierce J, Wilson JR, Yozbatiran N, et al. Multi-center, single-blind randomized controlled trial comparing functional electrical stimulation therapy to conventional therapy in incomplete tetraplegia. Front Rehabil Sci. 2022 Sep 9;3:995244.\u003c/li\u003e\n\u003cli\u003eMehrholz J, Pohl M, Platz T, Kugler J, Elsner B. Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke. Cochrane Database Syst Rev. 2018 Sep 3;9(9):CD006876.\u003c/li\u003e\n\u003cli\u003eYurkewich A, Ortega S, Sanchez J, Wang RH, Burdet E. Integrating hand exoskeletons into goal-oriented clinic and home stroke and spinal cord injury rehabilitation. J Rehabil Assist Technol Eng. 2022 Jan 2;9:20556683221130970.\u003c/li\u003e\n\u003cli\u003eOttoboni G, La Porta F, Piperno R, Chattat R, Bosco A, Fattori P, et al. A Multifunctional Adaptive and Interactive AI system to support people living with stroke, acquired brain or spinal cord injuries: A study protocol. PLoS One. 2022 Apr 11;17(4):e0266702.\u003c/li\u003e\n\u003cli\u003eVeerbeek JM, Langbroek-Amersfoort AC, van Wegen EEH, Meskers CGM, Kwakkel G. Effects of robot-assisted therapy for the upper limb after stroke: A systematic review and meta-analysis. Neurorehabil Neural Repair. 2017 Feb;31(2):107\u0026ndash;21.\u003c/li\u003e\n\u003cli\u003eBertani R, Melegari C, De Cola MC, Bramanti A, Bramanti P, Calabr\u0026ograve; RS. Effects of robot-assisted upper limb rehabilitation in stroke patients: a systematic review with meta-analysis. Neurol Sci. 2017 Sep;38(9):1561\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eLohse K, Shirzad N, Verster A, Hodges N, Van der Loos HFM. Video games and rehabilitation: using design principles to enhance engagement in physical therapy. J Neurol Phys Ther. 2013 Dec;37(4):166\u0026ndash;75.\u003c/li\u003e\n\u003cli\u003eRodrigues P, Quaresma C, Costa M, Luz F, Fonseca MM. Virtual reality-based telerehabilitation for upper limb recovery post-stroke: A systematic review of design principles, monitoring, safety, and engagement strategies [Internet]. arXiv [cs.HC]. 2025 [cited 2025 Jul 18]. Available from: http://arxiv.org/abs/2501.06899\u003c/li\u003e\n\u003cli\u003eAssis GA de, Corr\u0026ecirc;a AGD, Martins MBR, Pedrozo WG, Lopes R de D. An augmented reality system for upper-limb post-stroke motor rehabilitation: a feasibility study. Disabil Rehabil Assist Technol. 2016 Aug;11(6):521\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eJia C, Liu X, Ning L, Ge L. The effects of Augmented reality on rehabilitation of stroke patients: A systematic review and meta-analysis with trial sequential analysis. J Clin Nurs [Internet]. 2025 Apr 4; Available from: http://dx.doi.org/10.1111/jocn.17730\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez-Gil JJ, S\u0026aacute;ez-Manzano A, L\u0026oacute;pez-Luque R, Ochoa-Sep\u0026uacute;lveda JJ, Ca\u0026ntilde;ete-Carmona E. Gamified devices for stroke rehabilitation: A systematic review. Comput Methods Programs Biomed. 2025 Jan;258(108476):108476.\u003c/li\u003e\n\u003cli\u003eBraakhuis HEM, Bussmann JBJ, Ribbers GM, Berger MAM. Wearable activity monitoring in day-to-day stroke care: A promising tool but not widely used. Sensors (Basel). 2021 Jun 12;21(12):4066.\u003c/li\u003e\n\u003cli\u003eTosto-Mancuso J, Tabacof L, Herrera JE, Breyman E, Dewil S, Cortes M, et al. Gamified neurorehabilitation strategies for post-stroke motor recovery: Challenges and advantages. Curr Neurol Neurosci Rep. 2022 Mar 12;22(3):183\u0026ndash;95.\u003c/li\u003e\n\u003cli\u003eSoliman E, Mogefors D, Bergmann JHM. Problem-driven innovation models for emerging technologies: Graphical representation of need-led innovation methodologies in healthcare. Health Technol (Berl). 2020 Sep 23;10(5):1195\u0026ndash;206.\u003c/li\u003e\n\u003cli\u003eDavis FD. A technology acceptance model for empirically testing new end-user information systems : theory and results [Internet] [Doctoral Dissertation]. Massachusetts Institute of Technology; 1986 [cited 2025 Jul 2]. Available from: http://hdl.handle.net/1721.1/15192\u003c/li\u003e\n\u003cli\u003eVenkatesh V, Davis FD. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Manage Sci. 2000 Feb 1;46(2):186\u0026ndash;204.\u003c/li\u003e\n\u003cli\u003eVenkatesh V, Bala H. Technology acceptance model 3 and a research agenda on interventions. Decis Sci. 2008 May;39(2):273\u0026ndash;315.\u003c/li\u003e\n\u003cli\u003eDavis FD, Granić A. The technology acceptance model: 30 years of TAM. 2021st ed. Cham, Switzerland: Springer Nature; 2024. 117 p. (Human-Computer Interaction Series).\u003c/li\u003e\n\u003cli\u003eVenkatesh V, Thong J, Xu X. Unified theory of acceptance and use of technology: A synthesis and the road ahead. J Assoc Inf Syst. 2016 May;17(5):328\u0026ndash;76.\u003c/li\u003e\n\u003cli\u003eGraham ID, Logan J, Harrison MB, Straus SE, Tetroe J, Caswell W, et al. Lost in knowledge translation: time for a map? J Contin Educ Health Prof. 2006 Winter;26(1):13\u0026ndash;24.\u003c/li\u003e\n\u003cli\u003eBrown GT. Male Occupational Therapists in Canada: A demographic profile. Br J Occup Ther. 1998 Dec 1;61(12):561\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eTe Boekhorst KE, Kuipers SJ, Ribbers GM, Cramm JM. Exploring rehabilitation patients\u0026rsquo; perspectives on what matters for the adoption of home-based rehabilitation technology: Q-methodology study. JMIR Rehabil Assist Technol. 2025 Jul 9;12(v12i6e71515):e71515.\u003c/li\u003e\n\u003cli\u003eChoi P, Walker R. Remote patient management: balancing patient privacy, data security, and clinical needs. Contrib Nephrol. 2019;197:35\u0026ndash;43.\u003c/li\u003e\n\u003cli\u003eTsai MF, Atputharaj S, Zariffa J, Wang RH. Perspectives and expectations of stroke survivors using egocentric cameras for monitoring hand function at home: a mixed methods study. Disabil Rehabil Assist Technol. 2024 Apr;19(3):878\u0026ndash;88.\u003c/li\u003e\n\u003cli\u003eBandini A, Kalsi-Ryan S, Craven BC, Zariffa J, Hitzig SL. Perspectives and recommendations of individuals with tetraplegia regarding wearable cameras for monitoring hand function at home: Insights from a community-based study. J Spinal Cord Med. 2021 May 7;1\u0026ndash;12.\u003c/li\u003e\n\u003cli\u003eHoagland A, Kipping S. Challenges in promoting health equity and reducing disparities in access across new and established technologies. Can J Cardiol. 2024 Jun 1;40(6):1154\u0026ndash;67.\u003c/li\u003e\n\u003cli\u003eVeras M, Sigouin J, Auger LP, Auger C, Ahmed S, Boychuck Z, et al. A rapid review of ethical and equity dimensions in telerehabilitation for physiotherapy and occupational therapy. Int J Environ Res Public Health. 2025 Jul 9;22(7):1091.\u003c/li\u003e\n\u003cli\u003eDurocher E, Wang RH, Bickenbach J, Schreiber D, Wilson MG. \u0026ldquo;just access\u0026rdquo;? Questions of equity in access and funding for assistive technology. Ethics Behav. 2019 Apr 3;29(3):172\u0026ndash;91.\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":"journal-of-neuroengineering-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jner","sideBox":"Learn more about [Journal of NeuroEngineering and Rehabilitation](http://jneuroengrehab.biomedcentral.com/)","snPcode":"12984","submissionUrl":"https://submission.nature.com/new-submission/12984/3","title":"Journal of NeuroEngineering and Rehabilitation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"outpatient neurorehabilitation, spinal cord injury, stroke, clinical decision-making, clinical process, rehabilitation technology","lastPublishedDoi":"10.21203/rs.3.rs-7852937/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7852937/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eStroke and spinal cord injury (SCI) are leading causes of long-term upper limb (UL) impairment, imposing significant disability and economic burden. Outpatient neurorehabilitation can be critical for restoring function, yet therapists face complex challenges within the clinical process. These challenges, particularly in areas of assessment, patient engagement, and resource management, must be navigated in clinical reasoning and decision-making. While rehabilitation technologies offer potential solutions, their integration into and routine use in practice is limited by, among other reasons, a disconnect between development and clinical needs. We sought to develop a translational framework that identifies clinical process challenges in outpatient UL neurorehabilitation for stroke and SCI and maps them to technology-based solutions, guiding clinically relevant innovation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eUsing an interpretive description methodology, we conducted a qualitative study at three sites of a large rehabilitation hospital network. Data were collected from 10 therapists (9 occupational, 1 physical) through 33 direct clinical observations (24 hours and 45 minutes), post-observation debriefs, and semi-structured interviews. Constant comparative analysis synthesized findings into challenge domains and technology solutions, validated through member checking.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eFive core challenge domains were identified: \u003cem\u003eAssessment \u0026amp; Progress Tracking\u003c/em\u003e; \u003cem\u003eKnowledge Transfer\u003c/em\u003e; \u003cem\u003ePatient Engagement\u003c/em\u003e; \u003cem\u003eHome Program Implementation\u003c/em\u003e; and \u003cem\u003eInformation \u0026amp; Resource Constraints\u003c/em\u003e. These were mapped to four technology solution categories: \u003cem\u003eRemote Monitoring \u0026amp; Analytics\u003c/em\u003e, \u003cem\u003eEducation \u0026amp; Decision Support\u003c/em\u003e, \u003cem\u003eAssistive \u0026amp; Therapeutic Technologies\u003c/em\u003e, and \u003cem\u003eGamified Interventions\u003c/em\u003e. The framework illustrates how these solutions address specific challenges, such as using remote monitoring to track home exercise adherence or gamified platforms to enhance patient motivation.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study demonstrates the potential of using egocentric video to inform clinical decision-making in neurorehabilitation, particularly for hand function. The strong preference for video over metrics suggests clinical decision support systems should prioritize interpretable, observation-based information aligned with clinical reasoning processes.Despite implementation challenges, therapists across technical familiarity levels expressed trust in the system and willingness to use it regularly. These findings indicate that egocentric video systems can bridge the clinic-home divide when designed to match interest-holder priorities.\u003c/p\u003e","manuscriptTitle":"A translational framework mapping clinical challenges to technology solutions in upper limb neurorehabilitation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 16:17:40","doi":"10.21203/rs.3.rs-7852937/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-01-28T12:30:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66378149107768561304945586936334978682","date":"2026-01-16T11:18:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35708001784969678426999684174036422415","date":"2026-01-09T13:22:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-21T12:42:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-15T06:52:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-15T06:45:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of NeuroEngineering and Rehabilitation","date":"2025-10-14T01:29:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroengineering-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jner","sideBox":"Learn more about [Journal of NeuroEngineering and Rehabilitation](http://jneuroengrehab.biomedcentral.com/)","snPcode":"12984","submissionUrl":"https://submission.nature.com/new-submission/12984/3","title":"Journal of NeuroEngineering and Rehabilitation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"75eb36fa-b696-4b73-8309-7d0c559b3d4f","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-21T12:53:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 16:17:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7852937","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7852937","identity":"rs-7852937","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}