Introduction
One of the major global challenges in healthcare is an aging population, as the growing number of older adults places increasing demands on healthcare systems worldwide.1 Older people are frequently hospitalised and are particularly vulnerable to hospital-related risks, including immobility and functional decline, which are both associated with longer hospital stays and increased institutionalisation.2 Low in-hospital mobility is directly linked to functional deterioration at discharge and during post-discharge follow-up.2 Studies have shown that one in five older patients who do not meet the criteria for sarcopenia at baseline develop it within one week of hospitalisation or by discharge.3 Additionally, strength loss during hospitalisation disproportionately affects the lower limbs, with an average reduction of 11% in knee-extension strength.4 Consequently, many older patients struggle to regain their pre-hospitalisation functional level and independence, making it critical to implement strategies that counteract this process.5
Despite this, most hospital settings remain poorly adapted to meet the rehabilitation needs of older patients, and efforts to minimise hospital-related inactivity are needed.3
Gerontechnology, a multidisciplinary field merging aging and technology, has gained recognition for its potential to enhance care and improve quality of life for older adults and their caregivers.6 By harnessing technological solutions, it aims to prevent, delay, or compensate for perceptual, cognitive, and physical decline associated with aging,7 offering promising innovations to counteract the challenges of inactivity during hospitalisation.
In particular, robotic exercise systems are gaining attention in the field of rehabilitation and mobility support due to their potential to facilitate early mobilisation and reduce the physical demands placed on healthcare staff.8 Compared to conventional manual exercise, robot-assisted rehabilitation may deliver greater consistency, durability, and precision, allowing for intensive and repetitive exercise sessions that are essential for effective recovery.9
However, the integration of robotic assistance systems into clinical settings remains complex, requiring a clear description of interventions to advance physical therapy research and improve the implementation of evidence-based rehabilitation.8,10
This case report is part of a large randomised controlled trial (RCT) investigating the effects of robot-assisted physical exercise on older geriatric patients during acute hospitalisation.11,12 While the RCT includes more than 400 patients, the aim of this case report is to focus on one patient receiving active robot-assisted physical exercise in order to provide in-depth insights into the intervention and patient experience within this setting.
Method
This case report provides a detailed description of the intervention, using descriptive tables to illustrate baseline characteristics, outcomes, patient admission timeline, adherence and progression.
Additionally, the case report incorporates patient perspectives from a structured interview conducted during hospitalisation, offering insight into the patient’s individual, person-centered experience with robot-assisted physical exercise.
The structured interview was transcribed verbatim, and quotes were reviewed by two researchers (ASB and LM) and categorised into themes emerging from the material.
Baseline characteristics and outcome data were collected through structured outcome assessments at baseline (within a maximum of 48 hours from admission to the Department of Geriatric Medicine) and on the day of discharge.
The case was selected as a convenience sample during hospitalisation. The patient was included because she was willing and able to participate in both the interview and the case report and because she provided complete outcome data for the hospital stay. Her age, frailty, and clinical presentation were broadly representative of the ROBUST RCT population. In this report, the term “geriatric patient” refers specifically to individuals admitted to the geriatric department, reflecting the clinical setting in which the intervention was delivered. The case report is designed with inspiration from the CARE guideline on reporting case reports13 and the study was conducted in accordance with the Declaration of Helsinki.
Outcome Measures
Baseline Measures
Demographic data was collected at baseline: age, gender, civil and living status, body mass index (BMI), use of daily medications, C-reactive protein (CRP) blood sample (mg/L) (as an indicator of acute illness severity), historic Barthel Index 100 (14 days before hospital admission), and reason for hospital admission.
Primary and Secondary Outcomes
Primary outcome was functional status defined as activities of daily living (ADL) measured by Barthel Index 10014–16 and 30-second chair stand test17 from baseline to the day of discharge. ADL characterise the capability of a person to do every day routine activities. The current study uses Barthel Index-100, which is a recognised and simple scoring instrument used to evaluate basic ADL functions, the level of physical performance, and the intensity of needed care.14,18 The Barthel Index is a sum score across ten domains of ADL and the total score ranges from 0 (completely dependent) to 100 (completely independent).16
Secondary outcomes assessed at baseline and discharge included Barthel Index 100, 30-second chair stand test, discharge destination, quality of life (EQ-5D VAS score),19 concern about falling (Short Falls Efficacy Scale International (short FES-I)),20 cognitive function (The Mini-Mental State Examination (MMSE®)),21 mood status (The Geriatric Depression Scale (GDS)),22 and length of hospital stay.
See the protocol paper for a complete and detailed description of primary and secondary outcomes in the ROBOST RCT.11
Overall Intervention
As the intervention is central to understanding the patient’s experience, a detailed description of the ROBUST training protocol is included here to support interpretation of the case findings.
The Hospital Setting
The study took place at the Department of Geriatric Medicine at Odense University Hospital, Svendborg, Denmark. All patients were acutely admitted and the department serves an intake area representative of other hospital intake areas in Denmark.11 The staff managing the robot were employed health-care-students entitled as “trainers” in the following section.
Timeline
Patients were enrolled within a maximum of 48 hours from admission to the Department of Geriatric Medicine. After enrolment and baseline outcome tests, patients were randomly allocated to either active or passive robot-assisted physical exercise twice daily, including weekends and public holidays, with a minimum of three hours between daily sessions until discharge.
Robot Technology
The robot-assisted physical exercise was performed using an innovative exercise robot (ROBERT®). The robot was able to hold the patient’s leg and perform extension movements of the hip and knee while the patient was lying in bed, with the exercises performed on one leg at a time. This approach enabled even very frail or bedridden patients to participate in the exercises (Figure 1). In the active intervention group, the movement was performed actively by the patient using muscular power to stretch the leg while the robot provided low/moderate resistance. In the passive control group, the robot moved the patient’s leg through hip and knee extension without requiring any active muscle engagement from the patient.
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Figure 1 Active robot-assisted extension of the Hip and knee while motivated by staff trainer.
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The Exercise Setting and Movement Programming
The robot was equipped with four wheels, and one person could easily roll the robot into the patient’s hospital room and positioned it next to the hospital bed. With the patient situated supine in bed the robot was connected to the patient’s leg with a brace on the foot and lower leg. The brace was attached to the robot, which would lift and hold the patient’s leg. Exercise was conducted on one leg at a time in random order.
The patient’s leg was positioned with the hip flexed at 70–80°, the knee at 120°, and the ankle in a neutral position (0°). The trainer initially held the patient’s leg and guided it through a hip and knee extension while programming the number of sets and repetitions on the robot’s screen. The robotic system provided weight support throughout, reducing physical strain on the trainer and eliminating the need for manual lifting. Once the movement was demonstrated by the trainer, the robotic system memorised it and carried out the remainder of the exercise session with the patient, without requiring further physical involvement from the trainer. The movement extended both the knee and hip to 0°, ensuring maximum range without hyperextension. In the active intervention group, the robot provided resistance based on adjustable levels, ranging from Level 1 (easiest) to Level 10 (maximum resistance).
Initiating Movement, Acclimatization, and Warm-up
All exercise sessions began with the robot moving the patient’s leg in the programmed motion once, allowing the patient to become accustomed to the movement. Following that, a light warm-up was initiated in the active exercise group with five repetitions of the hip and knee extension movement with lowest resistance (level 1) to help the patient engage actively in the exercise. There were no warm-up in the passive group.
The Active Robot-Assisted Physical Exercise
In the active group, each exercise session comprised three sets of active hip and knee extensions with verbal encouragement from the trainers to perform maximum numbers of repetitions. All sets were carried out on each leg to ensure symmetrical lower-limb loading. Training intensity was set at 65–100% of the patient’s maximal capacity, with a 60-second rest period between each training set.
The Borg Scale was used to assess perceived exertion, providing a subjective measure of physical effort on a scale from 0 (no exertion) to 10 (maximum exertion).23
As default, resistance exercise started at the highest intensity setting (level 10). If pre-assessment indicated that the patient could not complete at least eight repetitions with proper execution of the movement at level 10, a lower level was selected based on clinical judgment. The first set of every exercise session served as a maximum test, guiding the intensity of subsequent sets, where patients had to achieve at least 65% of their recorded maximum repetitions. The patient then performed the next two sets to fatigue while maintaining proper technique. Exercise progression was ensured by evaluating resistance at each session. If the patient completed at least eight repetitions, the resistance level was increased and maintained for reassessment in the next exercise session. However, an increase in resistance level was always contingent on the patient’s ability to perform the exercise correctly maintaining proper technique. If a patient reached level 10, further progression was achieved by increasing the number of repetitions.
Throughout the exercise sessions, both the patient and the trainer could monitor performance via a screen displaying the power output of the hip and knee extension in kilograms, along with a “power bar” indicating whether the full range of movement had been completed (Figure 1).
Passive Robot-Assisted Physical Exercise
In the passive exercise group, a passive robot-assisted physical exercise session consisted of three sets of eight repetitions, during which the robot passively moved the patient’s leg through hip and knee extensions, with a 60-second rest period between sets. The passive extension movements were performed on each leg separately, with sets repeated for both sides. Because the robot executed the movements, no active effort was required from the patient. This allowed participation even among the frailest patients, including those who occasionally fell asleep during the sessions.
Protein Supplements
Following each exercise session, all participants in both groups were offered nutritional drinkable protein supplements (125–250 mL) each containing a minimum of 18 to 26 gram of protein per serving.24,25
Definition of Robot-Assisted Physical Exercise
A successful robot-assisted physical exercise was defined as the completion of a minimum of three exercise sessions during their entire hospital stay at Department of Geriatric Medicine. A exercise session was complete if at least one set was performed in both legs.
Fidelity and Adherence
To ensure high fidelity, all robot trainers received comprehensive exercise under the guidance of a certified instructor, who had completed the exercise module provided by the robot manufacturer. Furthermore, workshops helped the trainers practice alongside a certified instructor to reinforce their skills and ensure consistency in exercise delivery.
Appointed research staff and trainers were present in the department every day to encourage patients to continue their exercise and to improve adherence and furthermore collect data on amount of robot exercise performed. This practice ensured daily motivation for continued participation. Participation in each session was voluntary, and patients could decline exercise at any time; all encouragement provided by staff was supportive and never coercive.
Adverse Event Procedures
To ensure patient safety, adverse event procedures were in place throughout the intervention. As part of the standard protocol, trainers were required to document each exercise session, specifically noting whether any adverse events occurred or if the session had to be interrupted. In cases where exercise was stopped, trainers were instructed to provide detailed documentation explaining the reason, ensuring transparency and enabling appropriate follow-up.
An example of when an exercise session might be paused was if the patient experienced discomfort, such as temporary numbness in the toes or skin redness from the brace being too tight. In such cases, the exercise was immediately halted, and the brace was adjusted or loosened. The session would only resume if the patient reported feeling comfortable and safe to continue.
Findings
Patient Characteristics and Outcomes
The included patient was a 98-year-old woman living alone prior to hospitalisation. She was randomised to active robot-assisted physical exercise. Her data are summarized in Table 1. Thus, she was admitted with a stable vertebral fracture due to osteoporosis and used six daily medications at admission. Her BMI was 19.3 kg/m2, and her muscle mass was 19.5 kg.
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Table 1 Patient Characteristics and Outcomes
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Upon admission, she exhibited functional decline, reflected in a Barthel Index score of 57, compared to her historical score of 100.
The patient stayed for six days in the Department of Geriatric Medicine and completed five exercise sessions, corresponding to 50% adherence. She was discharged to her private residence.
Her functional status showed slight improvements at discharge, with her Barthel Index increasing from 57 to 69. Her Geriatric Depression Scale (GDS) score improved from 5 to 4 (Scores of 0–4 are considered normal). Her Mini-Mental State Examination (MMSE®) score decreased slightly from 23 to 21 (A score of 25 or higher is classed as normal) and her Falls Efficacy Scale-International (FES-I) score increased from 9 to 15, indicating a higher concern about falling at discharge compared to admission and her chair stand test performance did not improve from baseline to discharge (3 repetitions with arm support). Patient characteristics and outcomes are shown in Table 1.
No adverse events or harms were observed.
Patient Admission timeline, Exercise Adherence, and Exercise Progression
The patient was acutely admitted to the Emergency Department at 7 pm and transferred to the Department of Geriatric Medicine shortly after noon the following day. Late in the morning, she was enrolled in the study on her second day in the department, during which she also completed her first exercise session at 18.15 pm.
As illustrated in Table 2, initial fatigue led her to decline the first scheduled session (Session Day 1, session A); however, she participated later the same day (Session Day 1, session B), completing fewer sets than outlined in the protocol (two sets on right leg and only one set on left leg). Over the following days, she showed signs of progression, increasing both the number of sets, repetitions and resistance levels. By Session Day 3, she completed more repetitions (from 8 to 9 to 10–12) at higher resistance (from 6–7 to 7–8) while reporting lower exertion on the Borg Scale (from 8–9 to 5–6), suggesting improved strength. However, she declined all exercise on Session Days 4 and 5, and was discharged on Day 6.
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Table 2 Illustration of Adherence and Progression to the Robot-Assisted Intervention
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Patient Perspective
The structured patient interview revealed nuanced insights into the patient’s experience with robot-assisted physical exercise during hospitalisation. Quotes are categorised into three themes emerging from the material: (1) hospitalisation and perceived health benefits, (2) experience with robot-assisted physical exercise, and (3) motivation and engagement.
The patient described herself as functionally limited upon admission, requiring assistance for most activities. She expressed hope that the exercise would help preserve leg strength and support post-discharge mobility. While she generally found the exercise enjoyable, accessible, and motivating, particularly appreciating the opportunity to train in bed, she also reported fatigue as a barrier to consistent participation.
Key themes and illustrative quotes are presented in Table 3
Discussion
This case report contributes to the field of gerontechnology by providing a detailed description of robot-assisted physical exercise offering insights into its use in a geriatric acute hospital setting. The study also presents baseline characteristics and outcomes including qualitative data from patient interview illustrating an individual user experience with active robot-assisted physical exercise.
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Table 3 Key Themes and Illustrative Quotes
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Robot-Assisted Physical Exercise in Geriatric Acute Care
The structured exercise protocol enabled the included patient, a frail, hospitalised 98-year-old woman, to engage in active robot-assisted physical exercise without the need for physical relocation. The bedside delivery of exercise was crucial, as the patient stated she would not have been able to participate if it required leaving the bed. While upright mobilisation is always the preferred goal, some hospitalised older adults are too ill or frail to start out with standing or walking exercises. In such cases, bed-based exercise offers a practical starting point, helping prevent further deconditioning and supporting progression toward full mobilisation.26 This supports earlier findings on the physical and logistical barriers to rehabilitation in acute care settings.8,27
Exercise was scheduled in the afternoon and evening, potentially avoiding peak clinical activity, in line with recommendations for flexible exercise timing in frail patients.27 The intervention also ran on weekends and public holidays, ensuring continuity.
Although exercise was accessible and well-integrated, the patient’s fluctuating participation due to fatigue underscores the need for individualised, patient-centred scheduling. The progression model allowed exercise at 65–100% of maximal capacity with real-time monitoring, aligning with best practices in progressive overload.2
This case report exemplifies how the field of robot technology in rehabilitation is expanding, with new devices and systems emerging each year.28 However, the applicability of such technology is influenced by several contextual factors, as access to robot-assisted rehabilitation may vary across healthcare settings due to resource availability and costs. This should be considered when interpreting the wider applicability of this technology.
While the patient in this case report was allocated to active robot-assisted physical exercise and therefore does not provide information about the passive version of the intervention, findings from a previous feasibility and pilot study of the passive ROBERT device demonstrated that also passive robot-assisted mobilisation was feasible and well accepted by patients, relatives, and staff.12
Patient Admission Timeline
The patient was included in the ROBUST RCT on her second day in the Geriatric department and completed her first exercise session the same evening, ensuring a prompt start to the intervention. However, being bedridden for two days prior to exercise may have already led to immobility-related decline, potentially impacting her readiness to engage in exercise. Research has shown that older adults can lose up to 10% of their muscle strength after just 2–3 days of bed rest, particularly in the lower limbs, with further reductions in functional capacity occurring within the first week of hospitalisation.29,30 These findings underscore the importance of initiating physical activity as early as possible during hospitalisation to prevent or mitigate functional decline.
Her fluctuating participation further underscores the challenges of maintaining engagement in frail, hospitalised older adults, emphasizing the need for flexible, patient-centered rehabilitation strategies. Therefore, integrating psychosocial approaches to enhance motivation could be a valuable supplement. Techniques such as motivational interviewing, psychoeducation, and realistic goal-setting have been shown to support adherence in older adults by strengthening coping skills, problem-solving abilities, and perceived self-efficacy.31 Combining physical exercise with tailored psychosocial support may therefore optimise outcomes for this population.
Patient Perspectives on Robot-Assisted Physical Exercise
The patient expressed both curiosity and optimism about the benefits of robot-assisted physical exercise, stating she hoped it would help maintain leg strength and facilitate a faster return to mobility at home. She described the exercise as enjoyable and mood-boosting, emphasising that the presence of “lovely young people” (trainers) increased her motivation to participate. These reflections underline the importance of social interaction in rehabilitation and support existing evidence that patient engagement is influenced by perceived enjoyment and supportive staff relationships.32 Daily encouragement, goal setting, and performance feedback were central strategies used to promote participation, approaches known to enhance physical activity levels in hospitalised patients.32
Despite this, fatigue significantly impacted exercise adherence. The patient declined several sessions, and field notes documented pauses due to exhaustion. This highlights a frequent challenge in geriatric rehabilitation, where fluctuating energy levels and medical complexity can limit consistent participation.8
The overall findings underline the importance of tailoring in-hospital exercise interventions to individual needs, energy levels, and environmental factors.
The patient described in this case report was broadly representative of the participants in the active intervention group in terms of age, frailty level, and baseline functional status, rather than a positive outlier, providing context for how this individual case fits within the wider study population.
Limitations
The patient’s cognitive function was mildly impaired (MMSE® from 23 to 21),33 which may have influenced the precision and depth of questionnaire and interview responses. This should be considered when interpreting the patient-reported outcomes. Furthermore, as findings from a single case cannot be generalised, the insights presented here should be interpreted with caution. More robust evidence from the ongoing ROBUST RCT will be required to determine the effectiveness and broader applicability of robot-assisted physical exercise.
Conclusion
This case report provides a detailed description of robot-assisted physical exercise in a hospitalised geriatric patient, offering insights into an acute care setting, and patient experience. The structured bedside intervention ensured accessibility for a frail patient, allowing participation without requiring leaving the hospital room.
The patient responded positively to the exercise, describing it as engaging, mood-boosting, and beneficial for maintaining leg strength. However, fatigue influenced her adherence, highlighting the challenges of maintaining consistent participation in rehabilitation for older patients. These findings emphasise the importance of flexible, patient-centered strategies that accommodate fluctuations in energy levels, medical conditions, and hospital routines.
By documenting the practical aspects of robot-assisted physical exercise, this case report contributes to the growing field of gerontechnology and underscores the potential of robotic in-hospital rehabilitation to support early mobilisation in hospitalised older patients. This case report illustrates that even when exercise is adapted to bedridden patients and delivered at the bedside, sustaining engagement remains difficult. Still, while full adherence may not be feasible for all hospitalised older adults, initiating even modest amounts of physical activity may yield important benefits compared to complete inactivity. Future research, including the ROBUST RCT, will provide further evidence on its effectiveness in improving functional outcomes.
Ai Statement
ChatGPT (GPT-5, OpenAI) was used for English translation and language refinement of the manuscript. All content was reviewed and verified by the authors.
Ethical Approval And Consent Statement
The ROBUST randomised controlled trial received ethical approval from The Regional Scientific Ethical Committee for Southern Denmark34 (Project-ID: S-20210029) and approval from The Danish Data Protection Agency (Journal No. 21/3398).
Written informed consent for publication was obtained from the patient. The consent covers participation in the ROBUST RCT, including data collection, interviews, and the use of anonymised information for scientific publication. The patient explicitly expressed motivation to share her experience, with the intention that her case could contribute to improved care for future older patients. In line with principles of privacy, confidentiality, and limited anonymity, all potentially identifying information has been removed. Although complete anonymity can never be guaranteed in case reports, every effort has been made to minimise the possibility of identification. Importantly, the woman depicted in Figure 1 is not the patient described in this case report but an unrelated participant who also provided written consent for publication of the photograph. The patient’s face shown in Figure 1 has been fully blurred to ensure anonymity, and signed photo consent for publication from both the patient and the trainer is kept on record.
We strongly believe that this case report fully complies with COPE guidelines and the journal’s editorial policies regarding consent, privacy, confidentiality, and anonymity. All ethical requirements have been carefully considered, and the publication is made strictly within the terms of the patient’s informed consent and with full adherence to established standards for protecting patient rights.
All sensitive participant data are stored in the REDCap database35 and on a secure SharePoint platform approved by the Danish Data Protection Agency.
Acknowledgments
The authors wish to sincerely thank the patient for participating in this case report and for sharing her valuable experiences. We also thank the dedicated research staff and robot trainers for their contributions, and the clinical staff at the Department of Geriatric Medicine, whose daily routines were affected by the conduct of the ROBUST RCT. Their flexibility and collaboration made this research possible. The robot used in the current study was leased with no funding obtained from the ROBERT® company. We would like to thank the patient and trainer who participated in the photography for Figure 1.
The Mini-Mental State Examination (MMSE®) is a registered trademark of PAR, Inc. Reproduced by special permission of the Publisher, Psychological Assessment Resources, Inc. (PAR), 16204 North Florida Avenue, Luts, Florida 33549, from the Mini Mental State Examination, by Marshal Folstein and Susan Folstein, Copyright 1975, 1998, 2001 by Mini Mental LLC, Inc. Published 2001 by PAR. Further reproduction is prohibited without permission of PAR [Copyright @parinc.com].
Funding
The study is supported by grants from the The Independent Research Fund Denmark; The Health Foundation, Denmark; the Svend Andersen Foundation, Denmark; the Gangsted Foundation, Denmark; Odense University Hospital Fund for Free Research, Denmark; The Region of Southern Denmark; Department of Geriatric Medicine, Svendborg Hospital, Denmark; Department of Geriatric Medicine, Odense, Denmark.The funding parties have no influence on the study design, study conduct, results, or dissemination.
Disclosure
The authors declare that they have no competing interests regarding the current study.
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